ShortRead/.Rinstignore0000644000175400017540000000003213001230551015760 0ustar00biocbuildbiocbuilddoc/images doc/simon2.bst ShortRead/DESCRIPTION0000644000175400017540000000233513001247474015206 0ustar00biocbuildbiocbuildPackage: ShortRead Type: Package Title: FASTQ input and manipulation Version: 1.32.0 Author: Martin Morgan, Michael Lawrence, Simon Anders Maintainer: Bioconductor Package Maintainer Description: This package implements sampling, iteration, and input of FASTQ files. The package includes functions for filtering and trimming reads, and for generating a quality assessment report. Data are represented as DNAStringSet-derived objects, and easily manipulated for a diversity of purposes. The package also contains legacy support for early single-end, ungapped alignment formats. License: Artistic-2.0 LazyLoad: yes Depends: BiocGenerics (>= 0.11.3), BiocParallel, Biostrings (>= 2.37.1), Rsamtools (>= 1.21.4), GenomicAlignments (>= 1.5.4) Imports: Biobase, S4Vectors (>= 0.7.1), IRanges (>= 2.3.7), GenomeInfoDb (>= 1.1.19), GenomicRanges (>= 1.21.6), hwriter, methods, zlibbioc, lattice, latticeExtra, Suggests: BiocStyle, RUnit, biomaRt, GenomicFeatures, yeastNagalakshmi LinkingTo: S4Vectors, IRanges, XVector, Biostrings biocViews: DataImport, Sequencing, QualityControl NeedsCompilation: yes Packaged: 2016-10-17 22:22:51 UTC; biocbuild ShortRead/NAMESPACE0000644000175400017540000000220513001230551014677 0ustar00biocbuildbiocbuilduseDynLib(ShortRead, .registration=TRUE) import(zlibbioc, methods, BiocGenerics, S4Vectors, IRanges, Biostrings, GenomeInfoDb, GenomicRanges, GenomicAlignments, Rsamtools, Biobase) importFrom(BiocParallel, bplapply) importFrom(grDevices, colorRampPalette, dev.off, jpeg, pdf, png) importFrom(hwriter, hwrite, hwriteImage) importFrom(lattice, dotplot, histogram, levelplot, llines, lsegments, ltext, panel.abline, panel.dotplot, panel.grid, panel.histogram, panel.levelplot, panel.xyplot, strip.custom, xyplot) importFrom(latticeExtra, xyplot.list) importFrom(stats, approxfun, setNames) importFrom(utils, capture.output, packageDescription, read.csv, read.table, Sweave) exportClassPattern("^.*$") exportMethods(show, coerce, dim, length, "[", "[<-", "[[", alphabetFrequency, alphabet, coverage, encoding, narrow, reverse, reverseComplement, strand, trimLRPatterns, width, append, rbind, "%in%", c, lapply, sapply) export(pData, phenoData, varLabels, varMetadata) exportPattern("^[^\\.]") S3method(close, ShortReadFile) S3method(open, ShortReadFile) ShortRead/NEWS0000644000175400017540000002742313001230550014167 0ustar00biocbuildbiocbuildCHANGES IN VERSION 1.27 ----------------------- SIGNIFICANT USER-VISIBLE CHANGES o fastqFilter allows several input 'files' to be written to a single 'destinations'. o readAligned() for BAM files is defunct. QA and associated methods removed. o srapply removed o 'legacy' function readInfo() renamed readIntensityInfo() CHANGES IN VERSION 1.25 ----------------------- SIGNIFICANT USER-VISIBLE CHANGES o srapply is defunct o readAligned() for BAM files is deprecated; use GenomicAlignments::readGAligned instead. BUG FIXES o close opened files when parsing old bowtie, soap, and solexa export file formats. o Don't allow R memory to be released prematurely when processing old bowtie file formats / creating external pointers. o writeFastq,FastqFile obeys 'compress' argument; mode must be specified by the caller (typically mode="a") CHANGES IN VERSION 1.23 ----------------------- NEW FEATURES o alphabetScore,PhredQuality-method implemented o reverse, reverseComplement methods for ShortReadQ objects o srlist, to access SRList data as a base R list. SIGNIFICANT USER-VISIBLE CHANGES o readFastq qualityType="Auto" chooses base-64 encoding when no characters are encoded at less than 59, and some are encoded at greater than 74. BUG FIXES o report() prints adapter contaminants correctly when user has stringsAsFactors=FALSE o qa(..., sample=FALSE) no longer tries to re-match 'pattern' argument CHANGES IN VERSION 1.21 ----------------------- NEW FEATURES o writeFastq can write (and does so by default) gz-compressed files SIGNIFICANT USER-VISIBLE CHANGES o Use BiocParallel rather than srapply, mark srapply as 'Deprecated' o qa,character-method defaults to type="fastq" o Input of 'legacy' formats marked as such o alphabetByCycle supports amino acid string sets CHANGES IN VERSION 1.19 ----------------------- SIGNIFICANT USER-VISIBLE CHANGES o qa(..., type="fastq") uses a sample of n=1000000 reads by default, rather than then entire file; use sample=FALSE to revert to previous behavior. NEW FEATURES o encoding,FastqQuality and encoding,SFastqQuality provide a convenient map between letter encodings and their corresponding integer quality values. o filterFastq transforms one fastq file to another, removing reads or nucleotides via a user-specified function. trimEnds,character-method & friends use this for an easy way to remove low-quality base. BUG FIXES o writeFastq successfully writes zero-length fastq files. o FastqStreamer / FastqSampler warn on incomplete (corrupt) files CHANGES IN VERSION 1.17 ----------------------- SIGNIFICANT USER-VISIBLE CHANGES o FastqSampler can return records in the order encountered in the sampled file. o Increase to 10000 the number of reads examined for determining Fastq quality type o as(FastqQuality, "numeric") returns a vector of quality scores concatenated end to end (previously cycle to cycle), without padding to effective equal width BUG FIXES o trimTails, successive=TRUE would return inconsistent results o FastqStreamer, FastqSampler parse fastq files created with '\r' CHANGES IN VERSION 1.15 ----------------------- NEW FEATURES o FastqStreamer accepts IRanges for selecting input records SIGNIFICANT USER-VISIBLE CHANGES o as(ShortReadQ, "matrix") now accepts ShortReadQ instances with heterogeneous widths, returning a matrix x[i, j] with NA values in when j > width()[i]. BUG FIXES o readAligned, type="BAM" correctly adds required 'what' elements o FastqSampler would only randomize first read; introduced 1.13.9 2011-12-02, fixed 1.15.4 2012-04-25 o report(qa, ...) no longer produces obviously confused base calls per cycle o FastqFileList would fail to initialize correctly from a character vector CHANGES IN VERSION 1.13 ----------------------- SIGNIFICANT USER-VISIBLE CHANGES o FastqSampler is considerably faster o FastqSampler and FastqStreamer require explicit close() to avoid warnings about closing unused connections BUG FIXES o qa reports on very large lanes would overflow alphabetFrequency o qa report scales adapaterContamination correctly o FastqSampler would rarely sample fewer than requested reads o FastqSampler supports outputs of >2^31 - 1 total nucleotides o readFastq parses records with 0 width CHANGES IN VERSION 1.11 ----------------------- NEW FEATURES o trimTails to trim low quality trailing nucleotides o trimEnds to remove arbitrary (vectors of) letters from reads or qualities o FastqStreamer to iterate over a fastq file o FastqFile, FastqFileList to represent fastq files SIGNIFICANT USER-VISIBLE CHANGES o writeFastq has argument full, default value FALSE, disabling printing of identifier a second time in '+' line o srapply requires that options(srapply_fapply="parallel") or options(srapply_fapply="Rmpi") to enable parallel processing via fapply BUG FIXES o SolexaRealign, SolexaAlign, and SolexaResult transposed strand information o FastqSampler segfaulted on some files o writeFasta had a semi-documented argument mode; it is now documented and as a consequence dis-allows argument 'append' that would previously have been passed to underlying methods. CHANGES IN VERSION 1.9 ---------------------- NEW FEATURES o Support for HiSeq tile layout o Track reads passing filters, including across logical filter operations CHANGES IN VERSION 1.7 ---------------------- BUG FIXES o qa() represented the per-cycle quality scores incorrectly; this influenced qa[["perCycle"]][["quality"]][["Score"]], but not the qa report. o qa() for type="SolexaExport" transposed the 'aligned' and 'filtered' labels on all elements of SolexaExportQA. Thanks Nicolas Delhomme for the report. o report() failed when each read was unique. Thanks Peng Yu for the report. SIGNIFICANT USER-VISIBLE CHANGES o The perCycleQuality graph in the qa report now includes boxplots for all cycles instead of just the median value. o A depthOfCoverage graph has been added to the qa report for BAM, Bowtie, SolexaExport and SolexaRealign file types. o An adapterContamination measure has been added to the qa report for BAM, Bowtie, SolexaExport, SolexaRealign and Fastq file types. o srorder is now stable (the original order of identical is preservered). NEW FEATURES o Add class BAMQA. qa() can now be called on BAM files. o The param argument in readAligned() and qa() for type="BAM" can now be a single ScanBamParam object or a list of them. o FastqSampler can be used to draw samples from a fastq file. CHANGES IN VERSION 1.5 ---------------------- SIGNIFICANT USER-VISIBLE CHANGES o levels(strand(aln)) is c("+", "-", "*") (was c("-", "+", "*")) o Add USE.NAMES argument to srapply, minimum length to (internal) function ..reduce. NEW FEATURES o Optionally retrieve multiplex bar code, paired read number, and id from SolexaExport (contribution from Nicolas Delhomme) o renew() and renewable() provide an interface to updating ShortRead instances o srapply checks for and uses multicore o readIntensities supports Illumina RTA '.cif' / '.cnf' files o readAligned type="BAM" parses BAM files, extracting simple (no indel) cigars BUG FIXES o readIntensities type="IparIntensity" correctly handles multiple tiles CHANGES IN VERSION 1.3 ---------------------- SIGNIFICANT USER-VISIBLE CHANGES o coverage,AlignedRead-method has a changed interface (shift/width rather than start/end) and default behavior (return value in genome coordinates, rather than minimal covered region). o readAligned,character-method, type="Bowtie" and readFastq return FastqQuality by default. o coverage,AlignedRead-method now returns an RleList NEW FEATURES o qa reports from _realign.txt, MAQMap files o QualityScoreDNAStringSet coercion methods o qa type="character" now accepts a filter argument with value srFilter() o alphabetByCycle supports variable-width XStringSets o qa,ShortReadQ and qa,list methods for qa on existing objects BUG FIXES o Parse .gz realign files o alphabetScore,FastqQuality-method shifted quality by +1 CHANGES IN VERSION 1.1 ---------------------- SIGNIFICANT USER-VISIBLE CHANGES o 454 quality scores are returned as FastqQuality-encoded o For functions accepting dirPath, pattern to name files, allow dirPath to be a vector of file names when pattern is character(). o width() on ShortRead and derived classes (including AlignedRead now returns a vector of widths, of length equal to the length of the object. NEW FEATURES o Add Bowtie as a 'type' value for qa and report o Add dustyScore() and dustyFilter() to identify low-complexity regions o Parse _qseq files (to ShortReadQ or XDataFrame) o Parse IPAR image intensity files _int.txt.p, _nse.txt.p, _pos.txt o Create HTML-based quality assessment reports o Add trimLRPatterns() for ShortRead and derived classes (ShortReadQ, AlignedRead). o Add narrow() for ShortRead, QualityScore, and derived classes. o Use append() to append two objects of the same ShortReadQ or QualityScore and derived classes together o writeFastq for classes derived from ShortReadQ o Input functions support .gz or text files. o readIntensity reads Solexa image intensity files into R, including information about lane, tile, x, and y coordinates of each read. o readPrb returns different types of objects, depending on the 'as' argument of the readPrb,character-method. o readXStringSet gets arguments skip, nrows; argument order changed slightly o New built-in SRFilters positionFilter, uniqueFilter to select reads aligning to particular positions, or to select only unique instances of reads aligning to each position. o readAligned gains a Solexa _results parser (_results files are listed as 'intermediate' in the Solexa manual, and not a good end-point for analysis) o readAligned gains a Bowtie output parser o readAligned gains ability to parse MAQ 0.7 version binary files BUG FIXES o readQual would fail to read 454 quality scores correctly when these spanned more than one line of input per read o coverage treated reads as 1 base longer than they were o FastqQuality got the quality encoding off by one in as(x, "matrix") o qa_solexa.Rnw incorrectly displayed read occurences when lanes were presented out-of-order (an unusual occurence) o readAligned SolexaAlign, etc., updated to parse 'chromsome' and 'position', and 'strand' information correctly o readAligned MAQMapview failed for most chromosome labels CHANGES IN VERSION 1.0 ---------------------- SIGNIFICANT USER-VISIBLE CHANGES o SRFilter allows construction of filters that can be used to subset existing data objects, or filter incoming (readAligned, at the moment) objects. o readAligned for Solexa-based alignments return 'strand' information as factor with levels "-", "+", "*" (strand not relevant), NA (no strand information available). o srorder, srsort, srrank, and srduplicated for AligendRead class now sort based on chromosome, strand, position AND sread; previous behavior can be recovers by extracting the sequences srsort(sread(aln)), etc. o Functions using SolexaPath now search all relevant directories, e.g., in analysisPath, rather than the first BUG FIXES o 'run' in eland_export files is correctly parsed as a factor (start date: 29 September, 2008) ShortRead/R/0000755000175400017540000000000013001230552013663 5ustar00biocbuildbiocbuildShortRead/R/AllClasses-Base.R0000644000175400017540000002775013001230551016716 0ustar00biocbuildbiocbuild## .STRAND_LEVELS needs to be early, to be used in class ## prototypes. C-level code retrieves this value. ## readAligned,type=MAQMap depends on this ordering. .STRAND_LEVELS <- levels(strand()) .toStrand_Solexa <- function(x) factor(.STRAND_LEVELS[match(x, c("F", "R"))], levels=.STRAND_LEVELS) .srValidity <- function(object) TRUE setGeneric(".srValidity") ## Virtual base classes setClass(".ShortReadBase") ## .SRUtil: SRError / SRList / SRVector / SRFilter setClass(".SRUtil", representation=representation("VIRTUAL")) setClass("SRError", contains=".SRUtil", representation=representation( .type="character", .message="character"), prototype=prototype( .type="Unspecified", .message="unknown error"), validity=.srValidity) setClass("SRWarn", contains=".SRUtil", representation=representation( .type="character", .message="character"), prototype=prototype( .type="Unspecified", .message="unknown warning"), validity=.srValidity) setClass("SRList", contains=".SRUtil", representation=representation( .srlist="list"), prototype=prototype( .srlist=list())) setClass("SRVector", contains="SRList", representation=representation( vclass="character"), prototype=prototype( vclass=NA_character_), validity=.srValidity) setClass("SRFilter", contains=c("function", ".SRUtil"), representation=representation( name="ScalarCharacter"), validity=.srValidity) setClass("SRFilterResult", contains=c("logical", ".SRUtil"), representation=representation( name="ScalarCharacter", stats="data.frame")) ## Intensity setClass("IntensityMeasure", contains=".ShortReadBase", representation=representation("VIRTUAL")) setClass("IntensityInfo", contains=".ShortReadBase", representation=representation("VIRTUAL")) setClass("Intensity", contains=".ShortReadBase", representation=representation( .hasMeasurementError="ScalarLogical", readInfo="IntensityInfo", intensity="IntensityMeasure", measurementError="IntensityMeasure", "VIRTUAL"), prototype=prototype( .hasMeasurementError=mkScalar(FALSE)), validity=.srValidity) ## Intensity, implementation setClass("ArrayIntensity", contains=c("array", "IntensityMeasure"), prototype=prototype(array(0, c(0, 0, 0)))) ArrayIntensity <- function(intensity=array(0, c(0, 0, 0)), ...) { new("ArrayIntensity", intensity, ...) } setClass("SolexaIntensityInfo", ## AnnotatedDataFrame as prototype does not work, r46984. ## .init is a work-around to identify user-constructed ## objects that should be valid; used in .srValidity-method contains=c("AnnotatedDataFrame", "IntensityInfo"), representation=representation(.init="ScalarLogical"), prototype=prototype(.init=mkScalar(FALSE)), validity=.srValidity) SolexaIntensityInfo <- function(lane=integer(0), tile=integer(0)[seq_along(lane)], x=integer(0)[seq_along(lane)], y=integer(0)[seq_along(lane)]) { new("SolexaIntensityInfo", data=data.frame( lane=lane, tile=tile, x=x, y=y), varMetadata=data.frame( labelDescription=c( "Solexa lane nubmer", "Solexa tile nubmer", "Tile x coordinate", "Tile y coordinate")), .init=mkScalar(TRUE)) } setClass("SolexaIntensity", contains="Intensity", prototype=prototype( readInfo=SolexaIntensityInfo(), intensity=ArrayIntensity(), measurementError=ArrayIntensity()), validity=.srValidity) setClass("RtaIntensity", contains="SolexaIntensity") ## QualityScore setClass("QualityScore", contains=".ShortReadBase", representation=representation("VIRTUAL")) setClass("NumericQuality", contains="QualityScore", representation=representation( quality="numeric")) NumericQuality <- function(quality=numeric(0)) { # used below new("NumericQuality", quality=quality) } setClass("IntegerQuality", contains="NumericQuality", representation=representation( quality="integer")) setClass("MatrixQuality", contains="QualityScore", representation=representation( quality="matrix")) setClass("FastqQuality", contains="QualityScore", representation=representation( quality="BStringSet"), prototype=prototype( quality=BStringSet(character(0)))) setClass("SFastqQuality", contains="FastqQuality") # Solexa variant ## ShortRead / ShortReadQ setClass("ShortRead", contains=".ShortReadBase", representation=representation( sread="DNAStringSet", id="BStringSet"), prototype=prototype( sread=DNAStringSet(character(0)), id=BStringSet(character(0))), validity=.srValidity) setClass("ShortReadQ", contains="ShortRead", representation=representation( quality="QualityScore"), prototype=prototype( quality=NumericQuality()), validity=.srValidity) ## ExperimentPath (base class for experimental data paths) setClass("ExperimentPath", contains = c(".ShortReadBase"), representation = representation( basePath="character"), prototype = prototype( basePath=NA_character_), validity = .srValidity) ## SRSet (base class for datasets) setClass("SRSet", contains = ".ShortReadBase", representation = representation( sourcePath="ExperimentPath", # for lazy loading readIndex="integer", # for tracking subsets and sorting readCount="integer", # counts of reads in each sample phenoData="AnnotatedDataFrame", # experimental design readData="AnnotatedDataFrame"), # arbitrary read annotations prototype = prototype( sourcePath=new("ExperimentPath"), readIndex=integer(0), readCount=integer(0), phenoData=new("AnnotatedDataFrame"), readData=new("AnnotatedDataFrame")), validity = .srValidity) ## AlignedRead: AlignedDataFrame setClass("AlignedDataFrame", contains="AnnotatedDataFrame", prototype=prototype( new("AnnotatedDataFrame", dimLabels=c("readName", "alignColumn"))), validity=.srValidity) setClass("AlignedRead", contains="ShortReadQ", representation=representation( chromosome="factor", position="integer", strand="factor", alignQuality="QualityScore", alignData="AlignedDataFrame"), prototype=prototype( strand=factor(levels=.STRAND_LEVELS), alignQuality=NumericQuality()), validity=.srValidity) ## .Solexa setClass(".Solexa", contains=".ShortReadBase", representation=representation("VIRTUAL")) setClass("SolexaPath", contains=c("ExperimentPath", ".Solexa"), representation=representation( dataPath="character", scanPath="character", imageAnalysisPath="character", baseCallPath="character", analysisPath="character"), prototype=prototype( scanPath=NA_character_, dataPath=NA_character_, imageAnalysisPath=NA_character_, baseCallPath=NA_character_, analysisPath=NA_character_), validity=.srValidity) setClass("SolexaSet", contains=".Solexa", representation=representation( solexaPath="SolexaPath", laneDescription="AnnotatedDataFrame"), prototype=prototype( solexaPath=new("SolexaPath"), laneDescription=new("AnnotatedDataFrame", data=data.frame(1:8)[,FALSE], dimLabels=c("laneNames", "laneColumns"))), validity=.srValidity) ### .Roche setClass(".Roche", contains=".ShortReadBase", representation=representation("VIRTUAL")) setClass("RochePath", contains=c("ExperimentPath", ".Roche"), representation=representation( readPath="character", qualPath="character"), prototype=prototype( readPath=NA_character_, qualPath=NA_character_), validity=.srValidity) setClass("RocheSet", contains=c("SRSet", ".Roche"), representation=representation( sourcePath="RochePath"), prototype=prototype( sourcePath=new("RochePath")), validity=.srValidity) ## QA setClass(".QA", contains=c("SRList", ".ShortReadBase"), representation=representation("VIRTUAL")) setClass("ShortReadQQA", contains=".QA") setClass("FastqQA", contains="ShortReadQQA") # synonym setClass("SolexaExportQA", contains=".QA") setClass("SolexaRealignQA", contains=".QA") setClass("MAQMapQA", contains=".QA") setClass("BowtieQA", contains=".QA") ## Snapshot setClass("SnapshotFunction", representation=representation( reader="function", viewer="function", limits="integer")) setClass("SnapshotFunctionList", "SimpleList", prototype=prototype(elementType="SnapshotFunction")) setOldClass("trellis") .SpTrellis <- setRefClass("SpTrellis", fields=list(trellis="trellis", .debug_enabled="logical")) .Snapshot <- setRefClass("Snapshot", fields=list( .debug="function", .auto_display="logical", ## ranges .range="GRanges", .orig.range="GRanges", .zin="logical", .pright="logical", ## data .data="data.frame", .data_dirty="logical", .initial_functions="SnapshotFunctionList", .current_function="character", .using_initial_functions="logical", ## more-or-less public ## annotation track files="BamFileList", functions="SnapshotFunctionList", view="SpTrellis", annTrack="ANY", ignore.strand="logical", fac="character")) ## ShortReadFile -- methods elsewhere .ShortReadFile_g <- setRefClass("ShortReadFile", fields=list(con="ANY", path="character"), methods=list( msg = function(txt) { "display 'txt' with status information as a message()" s <- status(update=TRUE) message(txt, " ", paste(names(s), s, sep="=", collapse=" ")) }, status=function(update=FALSE) {}, show = function() { cat("class:", class(.self), "\n") cat(Rsamtools:::.ppath("path", path)) cat("isOpen:", isOpen(.self), "\n") })) .FastqFile_g <- setRefClass("FastqFile", contains="ShortReadFile") .FastqFileReader_g <- setRefClass("FastqFileReader", contains="FastqFile", fields = list( reader = "function", readerBlockSize="integer", .status = "integer", sampler = "externalptr", verbose = "logical"), methods = list( yield = function(...) { stop("'yield' not implemented for class '", class(.self), "'") }, show = function() { cat("class:", class(.self), "\n") nm <- tryCatch({ basename(summary(.self$con)$description) }, error=function(err) { "closed" }) cat("file:", nm, "\n") s <- .self$status() cat("status:", paste(names(s), s, sep="=", collapse=" "), "\n") })) .FastqStreamer_g <- setRefClass("FastqStreamer", contains="FastqFileReader", fields = list( skips="integer", adds = "integer", ith = "integer", recycle = "logical")) .FastqSampler_g <- setRefClass("FastqSampler", contains="FastqFileReader", fields = list( ordered = "logical")) setClass("FastqFileList", contains="RsamtoolsFileList", prototype=prototype(elementType="FastqFile")) setClass("FastqSamplerList", contains="FastqFileList", prototype=prototype(elementType="FastqSampler")) setClass("FastqStreamerList", contains="FastqFileList", prototype=prototype(elementType="FastqStreamer")) ShortRead/R/AllClasses-QA.R0000644000175400017540000000714313001230551016337 0ustar00biocbuildbiocbuildsetClass(".QA2", representation("VIRTUAL", ".ShortReadBase")) ## data sources .QAData <- setRefClass("QAData", fields=list(seq="ShortReadQ", filter="logical"), methods=list(show=function() { cat(class(.self), " ") print(.self$seq) cat(sprintf("filter: %d of %d", sum(.self$filter), length(.self$filter)), "\n") })) setClass("QASummary", representation("VIRTUAL", ".QA2", addFilter="ScalarLogical", useFilter="ScalarLogical", values="DataFrame", flag="integer", html="ScalarCharacter"), prototype=prototype( addFilter=mkScalar(TRUE), useFilter=mkScalar(TRUE))) ## Sources setClass("QASource", representation("VIRTUAL", "QASummary", metadata="DataFrame", data="QAData", flagNSequencesRange="integer"), prototype=prototype( flagNSequencesRange=NA_integer_), validity=function(object) { msg <- NULL if (!(is.na(object@flagNSequencesRange) || 2L == length(object@flagNSequencesRange))) msg <- "'flagNSequencesRange' must be integer(2)" if (is.null(msg)) TRUE else msg }) setClass("QAFastqSource", representation("QASource", con="character", n="ScalarInteger", readerBlockSize="ScalarInteger")) ## summaries setClass("QAFlagged", representation("QASummary")) setClass("QAFiltered", representation("QASummary")) setClass("QANucleotideUse", representation("QASummary")) setClass("QAQualityUse", representation("QASummary")) setClass("QASequenceUse", representation("QASummary")) setClass("QAReadQuality", representation("QASummary", flagK="ScalarNumeric", flagA="ScalarInteger")) setClass("QAAdapterContamination", representation("QASummary", Lpattern="ScalarCharacter", Rpattern="ScalarCharacter", max.Lmismatch="ScalarNumeric", max.Rmismatch="ScalarNumeric", min.trim="ScalarInteger")) setClass("QAFrequentSequence", representation("QASummary", n="ScalarInteger", a="ScalarInteger", flagK="ScalarNumeric", reportSequences="ScalarLogical"), prototype=prototype(n=mkScalar(10L)), validity=function(object) { msg <- NULL if (is.finite(object@n) && is.finite(object@a)) msg <- c(msg, "only one of 'n' or 'a' can be defined") else if (!is.finite(object@n) && !is.finite(object@a)) msg <- c(msg, "one of 'n' or 'a' must be defined") if (is.null(msg)) TRUE else paste("\n ", msg) }) setClass("QANucleotideByCycle", representation("QASummary")) setClass("QAQualityByCycle", representation("QASummary")) ## collation setClass("QACollate", representation(".QA2", "SimpleList", src="QASource"), prototype=prototype( src=new("QAFastqSource"), elementType="QASummary")) setClass("QA", representation(".QA2", "SimpleList", src="QASource", filtered="QAFiltered", flagged="QAFlagged"), prototype=prototype( src=new("QAFastqSource"), elementType="QASummary")) ShortRead/R/AllGenerics-Base.R0000644000175400017540000002103213001230552017044 0ustar00biocbuildbiocbuild## new generics setGeneric(".throw", function(object, call=NULL, ...) standardGeneric(".throw"), signature=c("object")) setGeneric("renewable", function(x, ...) standardGeneric("renewable")) setGeneric("renew", function(x, ...) standardGeneric("renew")) countLines <- function(dirPath, pattern=character(0), ..., useFullName=FALSE) { src <- .file_names(path.expand(dirPath), pattern, ...) nLines <- .Call(.count_lines, src) names(nLines) <- if (useFullName) src else basename(src) nLines } setGeneric("countLines", signature="dirPath") alphabetByCycle <- function(stringSet, alphabet, ...) { if (missing(alphabet)) alphabet <- Biostrings::alphabet(stringSet) w <- max(0L, width(stringSet)) .Call(.alphabet_by_cycle, stringSet, w, alphabet) } setGeneric("alphabetByCycle", signature="stringSet") setGeneric("dustyScore", function(x, batchSize=NA, ...) standardGeneric("dustyScore"), signature="x") setGeneric("srorder", function(x, ...) standardGeneric("srorder")) setGeneric("srduplicated", function(x, ...) standardGeneric("srduplicated")) setGeneric("srsort", function(x, ...) standardGeneric("srsort")) setGeneric("srrank", function(x, ...) standardGeneric("srrank")) setGeneric("tables", function(x, n=50, ...) standardGeneric("tables"), signature="x") ## Intensities setGeneric("readIntensities", function(dirPath, pattern=character(0), ...) standardGeneric("readIntensities"), signature="dirPath") ## QualityScore setGeneric("FastqQuality", function(quality, ...) standardGeneric("FastqQuality")) setGeneric("SFastqQuality", function(quality, ...) standardGeneric("SFastqQuality")) setGeneric("readPrb", function(dirPath, pattern=character(0), ...) standardGeneric("readPrb"), signature="dirPath") ## ShortRead / ShortReadQ setGeneric("ShortRead", function(sread, id, ...) standardGeneric("ShortRead")) setGeneric("sread", function(object, ...) standardGeneric("sread")) setGeneric("writeFasta", function(object, file, mode="w", ...) standardGeneric("writeFasta"), signature=signature("object")) setGeneric("ShortReadQ", function(sread, quality, id, ...) standardGeneric("ShortReadQ")) setGeneric("readFastq", function(dirPath, pattern=character(0), ...) standardGeneric("readFastq"), signature="dirPath") setGeneric("writeFastq", function(object, file, mode="w", full=FALSE, compress=TRUE, ...) standardGeneric("writeFastq"), signature=c("object", "file")) setGeneric("readFasta", function(dirPath, pattern=character(0), ..., nrec=-1L, skip=0L) standardGeneric("readFasta"), signature="dirPath") setGeneric("readQual", function(dirPath, pattern=character(0), ...) standardGeneric("readQual")) setGeneric("read454", function(dirPath, ...) standardGeneric("read454")) setGeneric("readFastaQual", function(dirPath, ...) standardGeneric("readFastaQual")) setGeneric("readBaseQuality", function(dirPath, ...) standardGeneric("readBaseQuality")) setGeneric("readQseq", function(dirPath, pattern=character(0), ..., as=c("ShortReadQ", "DataFrame", "XDataFrame"), filtered=FALSE, verbose=FALSE) standardGeneric("readQseq"), signature="dirPath") setGeneric("trimTails", function(object, k, a, successive=FALSE, ..., ranges=FALSE) standardGeneric("trimTails"), signature="object") setGeneric("trimTailw", function(object, k, a, halfwidth, ..., ranges=FALSE) standardGeneric("trimTailw"), signature="object") setGeneric("trimEnds", function(object, a, left=TRUE, right=TRUE, relation=c("<=", "=="), ..., ranges=FALSE) standardGeneric("trimEnds"), signature="object") setGeneric("clean", function(object, ...) standardGeneric("clean")) setGeneric("srdistance", function(pattern, subject, ...) standardGeneric("srdistance"), signature=c("pattern", "subject")) setGeneric("alphabetScore", function(object, ...) standardGeneric("alphabetScore")) ## SRFilter setGeneric("name", function(x, ...) standardGeneric("name")) setGeneric("stats", function(x, ...) standardGeneric("stats")) setGeneric("srFilter", function(fun, name=NA_character_, ...) standardGeneric("srFilter"), signature="fun") ## AlignedRead setGeneric("readAligned", function(dirPath, pattern=character(0), ...) standardGeneric("readAligned"), signature="dirPath") setGeneric("chromosome", function(object, ...) standardGeneric("chromosome")) setGeneric("id", function(object, ...) standardGeneric("id")) setGeneric("position", function(object, ...) standardGeneric("position")) ## ExperimentPath experimentPath <- function(object, ...) { slot(object, "basePath") } setGeneric("experimentPath") ## *Set setGeneric("qa", function(dirPath, ...) standardGeneric("qa")) report <- function (x, ..., dest = tempfile(), type="html") { func <- switch(type, html=report_html, pdf=.report_pdf, .report_any) func(x, dest, type, ...) } setGeneric("report", signature="x") .report_any <- function (x, dest, type, ...) { .throw(SRError("UserArgumentMismatch", "'%s, type=\"%s\"' not implemented for class '%s'", "report", type, class(x))) } setGeneric("report_html", function(x, dest, type, ...) standardGeneric("report_html"), signature="x", useAsDefault=.report_any) setGeneric(".report_pdf", function(x, dest, type, ...) standardGeneric(".report_pdf"), signature="x", useAsDefault=.report_any) ## SolexaSet setGeneric("SolexaSet", function(path, ...) standardGeneric("SolexaSet")) setGeneric("laneNames", function(object, ...) { standardGeneric("laneNames") }) ## Roche setGeneric("RocheSet", function(path, ...) standardGeneric("RocheSet")) setGeneric("runNames", function(object, ...) standardGeneric("runNames")) ## Snapshot setGeneric("Snapshot", function(files, range, ...) standardGeneric("Snapshot")) setGeneric("SnapshotFunctionList", function(...) standardGeneric("SnapshotFunctionList")) setGeneric("files", function(x, ...) standardGeneric("files")) setGeneric("vrange", function(x, ...) standardGeneric("vrange")) setGeneric("functions", function(x, ...) standardGeneric("functions")) setGeneric("annTrack", function(x, ...) standardGeneric("annTrack")) setGeneric("ignore.strand", function(x, ...) standardGeneric("ignore.strand")) setGeneric("fac", function(x, ...) standardGeneric("fac")) setGeneric("getTrellis", function(x, ...) standardGeneric("getTrellis")) setGeneric("togglez", function(x, ...) standardGeneric("togglez")) setGeneric("togglep", function(x, ...) standardGeneric("togglep")) setGeneric("togglefun", function(x, name, ...) standardGeneric("togglefun")) setGeneric("zoom", function(x, range, ...) standardGeneric("zoom")) setGeneric("pan", function(x, ...) standardGeneric("pan")) setGeneric("view", function(x, ...) standardGeneric("view")) setGeneric("zi", function(x, ...) standardGeneric("zi")) setGeneric("zo", function(x, ...) standardGeneric("zo")) setGeneric("left", function(x, ...) standardGeneric("left")) setGeneric("right", function(x, ...) standardGeneric("right")) setGeneric("restore", function(x, ...) standardGeneric("restore")) ## ShortReadFile setGeneric(".ShortReadFile", function(g, path, ...) standardGeneric(".ShortReadFile"), signature="path") setGeneric("FastqFileList", function(..., class="FastqFile") standardGeneric("FastqFileList"), signature="...") setGeneric("FastqStreamer", function(con, n, readerBlockSize=1e8, verbose=FALSE) standardGeneric("FastqStreamer"), signature=c("con", "n")) setGeneric("FastqStreamerList", function(..., n, readerBlockSize=1e8, verbose=FALSE) standardGeneric("FastqStreamerList"), signature="...") setGeneric("FastqSamplerList", function(..., n=1e6, readerBlockSize=1e8, verbose=FALSE, ordered = FALSE) standardGeneric("FastqSamplerList"), signature="...") setGeneric("yield", function(x, ...) standardGeneric("yield")) ShortRead/R/AllGenerics-QA.R0000644000175400017540000000102613001230551016473 0ustar00biocbuildbiocbuildsetGeneric(".filter", function(object, useFilter, ...) standardGeneric(".filter"), signature="object") setGeneric(".clone", function(object, ...) standardGeneric(".clone")) setGeneric("QACollate", function(src, ...) standardGeneric("QACollate")) setGeneric("qa2", function(object, state, ..., verbose=FALSE) standardGeneric("qa2"), signature="object") setGeneric("flag", function(object, ..., verbose=FALSE) standardGeneric("flag"), signature="object") ShortRead/R/AllUtilities.R0000644000175400017540000001142513001230551016414 0ustar00biocbuildbiocbuild## public polyn <- function(nucleotides, n) { if (!is.character(nucleotides) || length(nucleotides)==0) .throw(SRError("UserArgumentMismatch", "'%s' must be '%s'", "nucleotides", "character(1) or longer")) if (!all(sapply(nucleotides, nchar) == 1)) .throw(SRError("UserArgumentMismatch", "'%s' must all have %d characters", "nucleotides", 1)) if (!is.numeric(n) || length(n) != 1) .throw(SRError("UserArgumentMismatch", "'%s' must be '%s'", "n", "numeric(1)")) sapply(nucleotides, function(elt) paste(rep(elt, n), collapse="")) } ## Errors .undefined_method_err <- function(class, method) { .throw(SRError("InternalError", "undefined method '%s' for class '%s'", method, class)) } .subset_err <- function() { .throw(SRError("UserSubset", "'[' must be called with only subscript 'i'")) } .arg_missing_err <- function(arg, method, help) { .throw(SRError("UserArgumentMismatch", "argument '%s' required for '%s'\n see %s", arg, method, help)) } .arg_mismatch_type_err <- function(arg, type) { .throw(SRError("UserArgumentMismatch", "'%s' must be '%s'", arg, type)) } .arg_mismatch_type_err2 <- function(arg, type, was) { .throw(SRError("UserArgumentMismatch", "'%s' must be '%s', was '%s'", arg, type, was)) } .arg_mismatch_value_err <- function(arg, value, possible_vals) { msg <- sprintf("arugment '%s' had value '%s'\n allowable values: '%s'", arg, value, paste(possible_vals, collapse="' '")) .throw(SRError("UserArgumentMismatch", paste(strwrap(msg, exdent=4), collapse="\n"))) } .check_type_and_length <- function(x, type, len) { name <- deparse(substitute(x)) if (!is(x, type)) .arg_mismatch_type_err2(name, type, class(x)) if (!is.na(len) && sum(length(x) == len)==0) { typelen <- paste(type, paste("(", len, ")", sep=""), sep="", collapse="' '") was <- sprintf("%s(%d)", class(x), length(x)) .arg_mismatch_type_err2(name, typelen, was) } } ## Misc .file_names <- function(dirPath, pattern, ..., full.names=TRUE) { if (!is(pattern, "character") || length(pattern)>1) .arg_mismatch_type_err("pattern", "character(0) or character(1)") if (!isTRUE(full.names)) .arg_mismatch_type_err("full.names", "TRUE") dirPath <- path.expand(dirPath) if (length(pattern) == 0 && all(file.exists(dirPath)) && all(!sapply(dirPath, function(elt) file.info(elt)$isdir))) { return(dirPath) } files <- list.files(dirPath, pattern, ..., full.names=full.names) files <- files[!file.info(files)$isdir] if (length(files)==0) { if (length(pattern)==0) pattern <- "character(0)" .throw(SRError("Input/Output", "no input files found\n dirPath: %s\n pattern: %s\n", paste(dirPath, collapse="\n "), paste(pattern, collapse="\n "))) } files } .show_some <- function(what, obj) { if (length(obj) == 0) cat(what, ": (0 total)\n", sep="") else cat(what, ": ", paste(selectSome(obj), collapse=" "), " (", length(obj), " total)\n", sep="") } ## Class- and method-related .forward_objq <- function(object, ...) callGeneric(quality(object), ...) .forward_xq <- function(x, ...) callGeneric(quality(x), ...) .forward_obj <- function(object, ...) callGeneric(sread(object), ...) .forward_x <- function(x, ...) callGeneric(sread(x), ...) .nameAll <- function(x) { ## Add names to character vector x. Elements of x without names get ## a name matching the element. if (!is.character(x)) stop("argument 'x' must be a character vector") if (length(names(x))) names(x) <- ifelse(nchar(names(x)) == 0, x, names(x)) else names(x) <- x x } .make_getter <- function(slots, where=topenv(parent.frame()), verbose=FALSE) { slots <- .nameAll(slots) nms <- names(slots) ok <- !sapply(nms, exists, where) if (verbose && !all(ok)) .throw(SRError("InternalError", "getter '%s' already exists", paste(nms[!ok], collapse=", "))) slots <- slots[ok] for (i in seq_along(slots)) { func <- eval(substitute(function(object, ...) slot(object, SLOT), list(SLOT=slots[i]))) assign(nms[i], func, where) } } ## Misc .append.factor <- function(x, values) { lvls <- unique(c(levels(x), levels(values))) factor(append(as.character(x), as.character(values)), lvls) } ShortRead/R/Snapshot.R0000644000175400017540000004143413001230551015612 0ustar00biocbuildbiocbuild.Snapshot$methods( .message = function(fmt, ...) { message(paste(strwrap(sprintf("Snapshot: %s", sprintf(fmt, ...)), exdent=2), collapse="\n")) }, .stop=function(fmt, ...) { stop(paste(strwrap(sprintf("Snapshot: %s", sprintf(fmt, ...)), exdent=2), collapse="\n")) }, .initial_range=function() { h <- scanBamHeader(.self$files[[1]])[["targets"]] if (!length(h)) .stop("header of file '%s' contains no targets", .self$files[[1]]) h <- h[1] GRanges(names(h), IRanges(1, h)) }, .update_range=function(lim) { if (lim[2] < lim[1]) .stop("The end of range must be greater than the start of the range.") if (lim[1] >= start(.self$.orig.range)) { start(.self$.range) <- lim[1] .self$.data_dirty <- TRUE } else .stop("Please make sure the range arguments define the regions within the limits of the original range.") if (lim[2] <= end(.self$.orig.range)) { end(.self$.range) <- lim[2] .self$.data_dirty <- TRUE } else .stop("Please make sure the range arguments define the regions within the limits of the original range.") invisible() }, .update_data=function() { .debug("update_data .current_function='%s'", .self$.current_function) .self$.data <- reader(.self$functions[[.self$.current_function]])(.self) .self$.data_dirty <- FALSE .self$view <- viewer(.self$functions[[.self$.current_function]])(.self) .debug("update_data view limits %.0f-%.0f", .self$view$get.x.limits()[1], .self$view$get.x.limits()[2]) .self }, .get.active_region=function() { 'get the start and end of the active region' c(start(.self$.range), end(.self$.range)) }, .is.initial_function=function() { 'check if initial reader/viwer function is currently in used:TRUE/FALSE' 'assign result to .using_initial_functions' .self$.using_initial_functions <- any(.self$.current_function %in% names(.self$.initial_functions)[1:2]) }, .check_currentFunction=function(currentFunction) { if (missing(currentFunction)) currentFunction <- .self$.current_function lms <- limits(.self$functions[[currentFunction]]) wd <- width(.self$.range) if (wd <= lms[1]) .stop("image width (%.0f) < function limit (%.0f bps)", wd, lms[1]) ## FIXME: suggest to use togglefun to change function else if (wd > lms[2]) .stop("image width (%.0f) > function limit (%.0f bps)", wd, lms[2]) invisible() }, .change_current_function=function(currentFunction) { 'Determine whether currentFunction should be change according to the size of the active region. This function is used by togglefun()' 'If yes, change .current_function and make .data_dirty TRUE' lms <- limits(.self$functions[[currentFunction]]) wd <- .self$view$get.x.limits()[2] - .self$view$get.x.limits()[1] if (wd <= lms[1]) .stop("image width (%.0f) < function limit (%.0f bps)", wd, lms[1]) ## FIXME: suggest to use togglefun to change function else if (wd > lms[2]) .stop("image width (%.0f) > function limit (%.0f bps)", wd, lms[2]) .self$.current_function=currentFunction .self$.data_dirty <- TRUE invisible() }, .zoom_in_xlim=function(){ 'get x limits for zoom in' lim <- .self$view$get.x.limits() center <- mean(lim) width <- (lim[2] - lim[1])/2 if (width > 50) xlim <- c(max(start(.self$.orig.range), center - width/2), min(end(.self$.orig.range), center + width/2)) else xlim <- lim }, .zoom_out_xlim=function() { 'get x limits for zoom out' lim <- .self$view$get.x.limits() center <- mean(lim) width <- diff(lim) xlim <- c(max(start(.self$.orig.range), center-width), min(end(.self$.orig.range), center+width)) }, .pleft_xlim=function() { 'get x limits for pan left' margin <- 50 lim <- .self$view$get.x.limits() by <- 0.8 * diff(lim) xlim <- c(max(lim[1] - by, start(.self$.orig.range)), max(lim[2] - by, start(.self$.orig.range) + margin)) ## if xlim is between the gap of the limits of .self$range ## that of the trellis object limits (.self$view$trellis$orig.x.limits xlim <- c(min(end(.self$.orig.range)-margin, xlim[1]), min(end(.self$.orig.range), xlim[2])) }, .pright_xlim=function() { 'get x limits for pan right' margin <- 50 lim <- .self$view$get.x.limits() by <- 0.8 * diff(lim) xlim <- c(min(lim[1]+by, end(.self$.orig.range) - margin), min(lim[2]+by, end(.self$.orig.range))) ## if xlim is between the gap of the limits of .self$range ## that of the trellis object limits (.self$view$trellis$orig.x.limits xlim <- c(max(start(.self$.orig.range), xlim[1]), max(start(.self$.orig.range)+margin, xlim[2])) }, .reset_active_range=function(xlim) { 'determine wether to reset active range. used by pan and zoom out' win <- .self$view$trellis$orig.x.limits f1 <- xlim[1] < min(start(.self$.range), win[1]) f2 <- xlim[2] > end(.self$.range, win[2]) any(f1,f2) }, .switch_ini_currentFunction=function(xlim) { 'determine wether to switch viewer functions (TRUE/FALSE). used only when the current function is one of default functions (fine_coverage or coarse_coverage)' sw <- FALSE win <-(xlim[2] - xlim[1]) < limits(.self$.initial_functions[["fine_coverage"]])[2] fine <- .self$.current_function == "fine_coverage" if (win) { # limits within fine_coverage limit and viewer is coarse if (!fine) sw <- TRUE } else { # limits over fine_coverage limit and viewer is fine if (fine) sw <- TRUE } return(sw) }, .initialize_currentFunction=function() { if (width(.self$.range) < limits(.self$.initial_functions[["fine_coverage"]])[2]) currentFunction <- "fine_coverage" else currentFunction <- "coarse_coverage" }, .initialize_fac=function(fac) { ## initialize fac and values(.self$fiels)[[.self$fac]] .self$fac <- fac if (is.null(values(.self$files))) .self$fac <- character(0L) if (length(.self$fac) & !is.null(values(.self$files))) { if (length(.self$fac) > 1) .self$fac <- .self$fac[1] if (!(.self$fac %in% names(values(.self$files)))) ## not sure why cannot use values(.self$files) .stop("'%s' is not a column of elementMetadata in the 'files' input arguement", .self$fac) values(.self$files)[[.self$fac]] <- factor(values(.self$files)[[.self$fac]]) } }, initialize=function(..., functions=SnapshotFunctionList(), currentFunction, ignore.strand=FALSE, fac=character(0L), annTrack=NULL, .range, .auto_display=TRUE, .debug=FALSE) { callSuper(...) .self$.debug <- if (.debug) .self$.message else function(...) {} .self$.zin <- TRUE .self$.pright <- TRUE .self$.auto_display <- .auto_display tryCatch({ for (f in as.list(.self$files)) if (!isOpen(f)) open(f) }, error=function(err) { .stop("open BamFile failed: %s", conditionMessage(err)) }) .self$.range <- if (missing(.range)) .initial_range() else .range .self$.orig.range <- .self$.range .self$.initial_functions <- SnapshotFunctionList(fine_coverage=.fine_coverage, coarse_coverage=.coarse_coverage, multifine_coverage=.multifine_coverage, multicoarse_coverage=.multicoarse_coverage) .self$functions <- c(.self$.initial_functions, functions) ## initialize current function if (!missing(currentFunction)) { if (!currentFunction %in% names(.self$functions)) .stop("'%s' is not in SnapshotFunctionList", currentFunction) .self$.check_currentFunction(currentFunction) } else { currentFunction <- .self$.initialize_currentFunction() } .self$.initialize_fac(fac) ## initialize fac and fix values(.self$fiels) .self$annTrack <- annTrack .self$ignore.strand <- ignore.strand .self$.current_function <- currentFunction .self$.is.initial_function() # assign .self$using.initial_function .self$.data_dirty <- TRUE .self$.update_data() .self$display() .self }, set_range=function(range) { 'resetting the active range, called when setting zoom(..., range=)' 'also used for determine the best fit SnapshotFunctions if the initial functions are in used.' # seqlevel must be the same if (!all(seqlevels(range) %in% seqlevels(.self$.range))) .stop("The seqlevel '%s' does not match that of the active data", seqlevels(range)) .self$.update_range(c(start(range), end(range))) .self$.is.initial_function() ## find appropriate reader/viewer if initial functions are in used if (.self$.using_initial_functions) .self$.current_function <- .self$.initialize_currentFunction() .self$.data_dirty <- TRUE .self$.update_data() }, display=function() { .debug("display") if (.data_dirty) .self$.update_data() print(.self$view$view()) }, toggle=function(zoom=FALSE, pan=FALSE, currentFunction) { .self$.debug("toggle: zoom %s; pan %s; fun %s", if (.self$.zin) "in" else "out", if (.self$.pright) "right" else "left", .self$.current_function) if (zoom) .self$.zin <- !.self$.zin if (pan) .self$.pright <- !.self$.pright if (!missing(currentFunction)) { if (!currentFunction %in% names(.self$functions)) .stop("toggle unknown function '%s'", currentFunction) if (currentFunction != .self$.current_function) { .self$.change_current_function(currentFunction) if (.self$.data_dirty) { lim <- .self$view$get.x.limits() .update_range(lim) .self$.update_data() } } } .self }, zoom=function() { .debug("zoom: %s", if (.self$.zin) "in" else "out") if (.self$.zin) { ## zoom in .self$.is.initial_function() if (.self$.using_initial_functions) { # check if need to switch viewer xlim <- .self$.zoom_in_xlim() if (.self$.switch_ini_currentFunction(xlim)) { range <- .self$.range start(range) <- xlim[1] end(range) <- xlim[2] .self$set_range(range) } else # if don't need to swith viewer .self$view$zi() } else # if not using fine_coverage or coarse_coverage .self$view$zi() } else { ## zoom out xlim <- .self$.zoom_out_xlim() if (.reset_active_range(xlim)) { ## expend the active range and .update_data() range <- .self$.range start(range) <- xlim[1] end(range) <- xlim[2] #find appropriate read/viwer funcs .self$set_range(range) } else .self$view$zo() } #.self$.update_range() .self }, pan=function() { .debug("pan: %s", if (.self$.pright) "right" else "left") if (.self$.pright) { ## shift right xlim <- .self$.pright_xlim() if (.reset_active_range(xlim)) { .update_range(xlim) .self$.update_data() } else .self$view$right() } else { ## shift left xlim <- .self$.pleft_xlim() if (.reset_active_range(xlim)) { .update_range(xlim) .self$.update_data() } else .self$view$left() } .self }, restore=function() { f1 <- start(.self$.range)==start(.self$.orig.range) f2 <- end(.self$.range)==end(.self$.orig.range) if (all(f1, f2))#original range is the same as active range .self$view$restore() else .self$set_range(.self$.orig.range) } ) ## Constructors setMethod(Snapshot, c("character", "GRanges"), function(files, range, ...) { if (is.null(names(files))) names(files) <- basename(files) files <- BamFileList(files) .Snapshot$new(files=files, .range=range, ...) }) setMethod(Snapshot, c("BamFileList", "GRanges"), function(files, range, ...) { if (is.null(names(files))) names(files) <- basename(sapply(files@listData, function(fl) path(fl))) ## duplicate names is not preferred fnames <- names(files) if (length(unique(fnames))!=length(fnames)) names(files) <- paste(1:length(fnames), fnames, sep="-") .Snapshot$new(files=files, .range=range, ...) }) setMethod(Snapshot, c("character", "missing"), function(files, range, ...) { if (is.null(names(files))) names(files) <- basename(files) files <- BamFileList(files) .Snapshot$new(files=files, ...) }) ## accessors setMethod(files, "Snapshot", function(x) x$files) setMethod(vrange, "Snapshot", function(x) x$.range ) setMethod(functions, "Snapshot", function(x) x$functions) setMethod(annTrack, "Snapshot", function(x) x$annTrack) setMethod(ignore.strand, "Snapshot", function(x) x$ignore.strand) setMethod(fac, "Snapshot", function(x) x$fac) setMethod(getTrellis, "Snapshot", function(x) x$view$trellis) ## private functions .getData <- function(x) x$.data .currentFunction <- function(x) x$.current_function setMethod(view, "Snapshot", function(x) x$view) ## interface setMethod(togglez, "Snapshot", function(x) { x$toggle(zoom=TRUE) invisible(x) }) setMethod(togglep, "Snapshot", function(x) { x$toggle(pan=TRUE) invisible(x) }) setMethod(togglefun, "Snapshot", function(x, name) { if (!missing(name)) { x$toggle(currentFunction=name) invisible(x) } }) setMethod(zoom, "Snapshot", function(x, range) { if (!missing(range)) ## FIXME: must be able to tell whether .currentFunction is appropriate x$set_range(range) else x$zoom() x$display() ## FIXME: invisible return TRUE on success, FALSE otherwise }) setMethod(pan, "Snapshot", function(x) { x$pan() x$display() ## FIXME: return TRUE on success, FALSE otherwise }) ## show setMethod(show, "Snapshot", function(object) { cat("class:", class(object), "\n") with(object, { cat("file(s):", names(files), "\n") cat("Orginal range:", sprintf("%s:%d-%d", seqlevels(.orig.range), start(.orig.range), end(.orig.range)), "\n") cat("active range:", sprintf("%s:%d-%d", seqlevels(.range), start(.range), end(.range)), "\n") cat("zoom (togglez() to change):", if (.zin) "in" else "out", "\n") cat("pan (togglep() to change):", if (.pright) "right" else "left", "\n") cat("fun (togglefun() to change):", .current_function, "\n") cat(sprintf("functions: %s\n", paste(names(functions), collapse=" "))) }) if (object$.auto_display) object$display() }) ShortRead/R/SnapshotFunction-helpers.R0000644000175400017540000002771413001230551020765 0ustar00biocbuildbiocbuild## get coverage for fine and coarse view .get_fine_coverage <- function(x) { rng <- vrange(x) wd <- width(rng) cvg <- function(aln) { if (identical(0L, length(aln))) numeric(wd) else as.numeric(unlist(coverage(aln, shift=-start(rng)+1, width=wd), use.names=FALSE)) } lst <- lapply(as.list(files(x)), function(fl, param) { aln <- readGAlignments(fl, param=param) seqlevels(aln) <- seqlevels(rng) list(`+`=cvg(aln[strand(aln)=="+"]), `-`=cvg(aln[strand(aln)=="-"])) }, param=ScanBamParam(which=rng)) } .get_coarse_coverage <- function(x) { nbins <- 5000L rng <- vrange(x) wd <- width(rng) breaks <- seq(start(rng), end(rng), length.out=nbins) lst <- lapply(as.list(files(x)), function(fl) { param <- ScanBamParam(which=rng, what=c("pos", "strand")) starts <- scanBam(fl, param=param)[[1]] bins <- lapply(split(starts$pos, starts$strand)[1:2], function(elt) { if (length(elt)) cut(elt, breaks=breaks, labels=FALSE) else integer() }) lapply(bins, tabulate, length(breaks)-1) #nbins = breaks-1 }) } .coverage_as_dataframe <- function(lst, range, ignore.strand=FALSE, resolution.fine=TRUE) { positive <- sapply(lst, "[[", "+") negative <- sapply(lst, "[[", "-") if (is.matrix(positive)) positive <- rowMeans(positive) if (is.matrix(negative)) negative <- rowMeans(negative) if (resolution.fine) pos <- seq.int(start(range), end(range), length.out=length(positive)) else { ## center the pos at the bin delta <- (end(range)-start(range))/(length(positive)) pos <- seq.int(start(range)+delta/2, by=delta, length.out=length(positive)) } if (!ignore.strand) { snames <- c("positive", "negative") group <- factor(rep(snames, each=length(positive)), levels=snames) data.frame(data=c(positive, -negative), group=group, pos=pos) } else { data.frame(data=positive+negative, pos=pos) } } .multiFile_coverage_as_dataframe <- function(lst, range, fac=NULL, ignore.strand=FALSE, resolution.fine=TRUE) { wd <- width(range) positive <- sapply(lst, "[[", "+") negative <- sapply(lst, "[[", "-") nPoints <- length(lst[[1]][[1]]) if (is.null(fac)) { ## phenolev as file names (must be unique) fnames <- names(lst) if (length(unique(fnames))!=length(fnames)) # conform the uniquenss fnames <- paste(1:length(fnames), fnames, sep="-") phenolev <- c(do.call(cbind, lapply(fnames, rep, nPoints))) } else { ## take means of the coverate on each level factor cvlst <- lapply(levels(fac), function(lev) { ps <- rowMeans(positive[, fac == lev, drop=FALSE]) ng <- rowMeans(negative[, fac == lev, drop=FALSE]) list("+"=ps, "-"=ng) }) names(cvlst) <- levels(fac) positive <- sapply(cvlst, "[[", "+") negative <- sapply(cvlst, "[[", "-") phenolev <- c(do.call(cbind, lapply(names(cvlst), rep, nPoints))) } ## define position if (resolution.fine) pos <- seq.int(start(range), end(range)) else { # position should be in the middle of the bin nIntervals <- length(lst[[1]][[1]]) delta <- (end(range)-start(range))/(nPoints) pos <- seq.int(start(range)+delta/2, by=delta, length.out=nPoints) } fnames <- names(lst) if (length(unique(fnames))!=length(fnames)) fnames <- paste(1:length(fnames), fnames, sep="-") file <- c(do.call(cbind, lapply(fnames, rep, nPoints))) if (ignore.strand) { data.frame(data=c(positive+negative), pos=pos, levels=phenolev) } else { snames <- c("positive", "negative") strand <- rep(snames, each=length(positive)) file <- rep(file, 2) data.frame(data=c(positive, -negative), pos=pos, group=strand, levels=phenolev) } } ################### readers ####################### .fine_coverage_reader <- function(x) { ## x: a Snapshot instance ## create a plot of coverage as sum of coverage of all the files lst <- .get_fine_coverage(x) rng <- vrange(x) ignore.strand <- ignore.strand(x) .coverage_as_dataframe(lst, rng, ignore.strand=ignore.strand, resolution.fine=TRUE) } .coarse_coverage_reader <- function(x) { ## x: a Snapshot instance ## create a plot of coverage as sum of coverage of all the files lst <- .get_coarse_coverage(x) rng <- vrange(x) ignore.strand <- ignore.strand(x) .coverage_as_dataframe(lst, rng, ignore.strand=ignore.strand, resolution.fine=FALSE) } .multifine_coverage_reader <- function(x) { ## x: a Sanpshot instance ## create a plot of coverage, separate lines for each file lst <- .get_fine_coverage(x) rng <- vrange(x) ignore.strand <- ignore.strand(x) if (length(fac(x)) ) gfac <- values(files(x))[[fac(x)]] else gfac <- NULL .multiFile_coverage_as_dataframe(lst, rng, ignore.strand=ignore.strand, fac=gfac, resolution.fine=TRUE) } .multicoarse_coverage_reader <- function(x) { ## x: a Sanpshot instance ## create a plot of coverage, separate lines for each file lst <- .get_coarse_coverage(x) rng <- vrange(x) ignore.strand <- ignore.strand(x) if (length(fac(x)) ) gfac <- values(files(x))[[fac(x)]] else gfac <- NULL .multiFile_coverage_as_dataframe(lst, rng, ignore.strand=ignore.strand, fac=gfac, resolution.fine=FALSE) } .update_viewer <- function(x, cv) { ## subset annTrack and validate anntrack anntrack <- annTrack(x) rng <- vrange(x) ignore.strand <- ignore.strand(x) if (any(seqnames(anntrack)@values %in% seqlevels(rng))) { gr <- anntrack seqlevels(gr, force=TRUE) <- seqlevels(vrange(x)) } else { message(paste(strwrap("SnapshotFunction-helper: seqname of 'annTrack' does not match the imported range. Annotation track will not be plotted."), collapse="\n")) return(NULL) } ## if anntrack has no elementMetada value, then return NULL if (ncol(values(anntrack)) < 1) { message(paste(strwrap("SnapshotFunction-helper: at least one column of 'annTrack' elementMetadata is required. Annotation track will not be plotted."), collapse="\n")) return(NULL) } # x: a Snapshot instance if (.currentFunction(x) %in% c("coarse_coverage", "multicoarse_coverage")) ann <- .coarse_annviewer(gr, rng, ignore.strand) if (.currentFunction(x) %in% c("fine_coverage", "multifine_coverage")) ann <- .fine_annviewer(gr, ignore.strand) strip.label <- c(dimnames(cv)$levels, "annotation track") npacket <- length(cv$packet.sizes) ann$x.limits <- cv$x.limits update(c(cv, ann), x.same=TRUE, layout=c(1,npacket+1), xlab=NULL, ylab=NULL, strip=if (!is.null(dimnames(cv)$levels)) strip.custom(factor.levels=strip.label), par.setting=list(layout.heights=list(panel=c(rep(2,npacket),1)))) } ## viewers .coverage_viewer <- function(x) { ## x: a Snapshot instance sp <- .getData(x) # x$.data lty <- rep(seq_len(length(levels(sp$group)) / 2), times=2) ## sp: data.frame with "data", "group", and "pos" column col <- c("#66C2A5", "#FC8D62") scales <- list(y=list(tck=c(1,0)), x=list(rot=45, tck=c(1,0), tick.number=20)) cv <- if (!ignore.strand(x)) xyplot(data ~ pos, data=sp, group=sp$group, col=col, scales=scales) else xyplot(data ~ pos, data=sp, col=col[1], scales=scales) cv <- update(cv, type="s", ylab="Coverage", xlab="Coordinate", panel=function(...) { panel.xyplot(...) panel.grid(h=-1, v=20) panel.abline(a=0, b=0, col="grey") }) if (!is.null(annTrack(x))) { ud <- .update_viewer(x, cv) if (!is.null(ud)) cv <- ud } SpTrellis(trellis=cv) } .multicoverage_viewer <- function(x, ...) { ## x: a Snapshot instance sp <- .getData(x) #x$.data lv <- length(levels(sp$group))/2 lty <- rep(seq_len(lv), times=2) col <- c("#FC8D62", "#66C2A5") #col <- c(rep("#FC8D62", lv) , rep("#66C2A5", lv)) scales <- list(y=list(tck=c(1,0)), x=list(rot=45, tck=c(1,0), tick.number=20)) cv <- if (ignore.strand(x)) xyplot(data ~ pos | levels, data=sp, col=col[2], scales=scales) else xyplot(data ~ pos | levels, data=sp, group=sp$group, col=col, scales=scales) cv <- update(cv, type="s", ylab="Coverage", xlab="Coordinate", layout=c(1, length(levels(factor(sp$levels)))), #key=list(space="top", column=2, cex=0.8, # lines=list(lty=lty, col=col), # text=list(levels(sp$group))), panel=function(...) { panel.xyplot(...) panel.grid(h=-1, v=20) panel.abline(a=0, b=0, col="grey") }) if (!is.null(annTrack(x))) { ud <- .update_viewer(x, cv) if (!is.null(ud)) cv <- ud } SpTrellis(trellis=cv) } ### default annotation track viewer .fine_annviewer <- function(gr, ignore.strand) { ## how to get the window x <- start(gr) x1 <- end(gr) xm <- (x+x1)/2 y <- rep(c(-1.4, -0.7, 0, 0.7, 1.4), length.out=length(x)) col <- c("#66C2A5", "#FC8D62") myCol <- if (ignore.strand) col[1] else col[as.numeric(strand(gr)@values)] mypanel <- function(x,y, genenames, x1, ...) { panel.xyplot(x,y, ..., col="transparent") ltext(x=xm, y=y, genenames, cex=0.45, pos=3) lsegments(x0=x, y0=y, x1=x1, y1=y, col=myCol, alpha=0.5) } ann <- xyplot(y ~ x, genenames=as.character(values(gr)[[1]]), x1=x1, xm=xm, panel=mypanel, xlab=NULL, ylab=NULL, scales=list(y=list(tick.number=0, labels=NULL)), par.settings= list(axis.text=list(alpha=0.5), axis.line=list(alpha=0.5)) ) ann$y.limits[2] = 2.1 ann } .coarse_annviewer <- function(gr, rng, ignore.strand) { ## gr: GRanges for tracks ## rng: range of an Snapshot instance col <- c("#66C2A5", "#FC8D62") nbins=5000L interval <- seq.int(start(rng), end(rng), length.out=nbins) l <- length(interval) ir <- IRanges(start=interval[1:(l-1)], end=interval[2:l]) scales <- list(y=list(alternating=2, tick.number=3,tck=c(0,1)), x=list(tck=c(0,0), labels=NULL)) annview <- if (!ignore.strand) { lst <- list("+" = countOverlaps(ir, ranges(gr[strand(gr)=="+"])), "-" = countOverlaps(ir, ranges(gr[strand(gr)=="-"]))) snames <- c("positive", "negative") group <- factor(rep(snames, each=length(lst[[1]])), levels=snames) cvg <- data.frame(data=c(lst[["+"]], -lst[["-"]]), group=group, pos = (start(ir)+end(ir))/2) xyplot(data ~ pos, data=cvg, groups=group, col=col, scales=scales) } else { lst <- countOverlaps(ir, ranges(gr)) cvg <- data.frame(data=lst, pos=(start(ir)+end(ir))/2) xyplot(data ~ pos, data=cvg, col=col[1], scales=scales) } update(annview, type="h", xlab=NULL, ylab=NULL, par.settings=list(axis.text=list(alpha=0.5), axis.line=list(alpha=0.5))) } ShortRead/R/SnapshotFunction.R0000644000175400017540000000373113001230552017317 0ustar00biocbuildbiocbuildSnapshotFunction <- function(reader, viewer, limits, ...) { if (missing(limits) || length(limits) != 2) stop("limits must have length 2") if ((limits[2] - limits[1]) < 50) stop("limits[2] - limits[1] must be greater than 50 bps") new("SnapshotFunction", reader=reader, viewer=viewer, limits=as.integer(limits), ...) } reader <- function(x, ...) x@reader viewer <- function(x, ...) x@viewer limits <- function(x, ...) x@limits setMethod(show, "SnapshotFunction", function(object) { cat("class:", class(object), "\n") cat("reader:\n") print(head(reader(object))) cat("...\n\n") cat("viewer:\n") print(head(viewer(object))) cat("...\n\n") cat(sprintf("limits: min. %.0f to max. %.0f bps", limits(object)[1], limits(object)[2]), "\n") }) ## SnapshotFunctionList setMethod(SnapshotFunctionList, "ANY", function(...) { if (nargs()) stop("'SnapshotFunctionList' unknown argument type: ", class(..1)) new("SnapshotFunctionList") }) setMethod(SnapshotFunctionList, "SnapshotFunction", function(...) { funs <- list(...) if (is.null(names(funs)) || any(!nzchar(names(funs)))) stop("'SnapshotFunctionList' functions must be named") new("SnapshotFunctionList", listData=funs) }) .fine_coverage <- SnapshotFunction(reader=.fine_coverage_reader, viewer=.coverage_viewer, limits=c(50L, 10000L)) .coarse_coverage <- SnapshotFunction(reader=.coarse_coverage_reader, viewer=.coverage_viewer, limits=c(10000L,.Machine$integer.max)) .multifine_coverage <- SnapshotFunction(reader=.multifine_coverage_reader, viewer=.multicoverage_viewer, limits=c(50L, 10000L)) .multicoarse_coverage <- SnapshotFunction(reader=.multicoarse_coverage_reader, viewer=.multicoverage_viewer, limits=c(10000L,.Machine$integer.max)) ShortRead/R/SpTrellis.R0000644000175400017540000001322113001230551015725 0ustar00biocbuildbiocbuild.SpTrellis$methods( initialize=function(...) { 'initialize SpTrellis' callSuper(...) if (!is.null(.self$trellis)) .self$ini.orig.x.limits(.self$get.x.limits()) .self }, get.trellis = function() { 'get trellis object' .self$trellis }, view = function(window=NULL) { 'view trellis object (in a designated window)' margin=50 if (is.null(window)) .self$trellis else { if (window[2] < window[1]) { message("Invalid window") .self$trellis } else { if ((window[2] - window[1]) < margin) window[2] <- window[1] + margin xlim <- c(max(window[1], .self$trellis$orig.x.limits[1]), min(window[2], .self$trellis$orig.x.limits[2])) .self$trellis$x.limits <- xlim .self$trellis } } }, get.x.limits = function() { 'get x.limits of a trellis object' .self$trellis$x.limits }, get.y.limits = function() { 'get y.limits of a trellis object' .self$trellis$y.limits }, ini.orig.x.limits = function(xlim) { 'set orig.x.limigs of the trellis object' .self$trellis$orig.x.limits <- xlim invisible(xlim) }, set.x.limits = function(xlim) { 'set trellis object x.limits' if (xlim[1] < xlim[2]) stop("Invalid x-axis limits") else .self$trellis$x.limits <- xlim invisible(xlim) }, set.y.limits = function(ylim) { 'set trellis object .limits' if (ylim[1] < ylim[2]) stop("Invalid x-axis limits") else .self$trellis$y.limits <- ylim invisible(ylim) }, .debug = function(fmt, ...) { if (.self$.debug_enabled) message(sprintf("'.SpTrellis' %s", sprintf(fmt, ...))) }, restore = function() { trellis$x.limits <<- trellis$orig.x.limits print(trellis) }, zo = function(by=NULL) { 'zoom out' center <- mean(.self$trellis$x.limits) width <- (.self$trellis$x.limits[2]-.self$trellis$x.limits[1]) xlim <- c(center - width, center + width) xlim[1] <- max(.self$trellis$orig.x.limits[1], xlim[1]) xlim[2] <- min(.self$trellis$orig.x.limits[2], xlim[2]) .self$trellis$x.limits <- xlim .debug("current x limits [%.0f, %.0f]", .self$get.x.limits()[1], .self$get.x.limits()[2]) invisible() }, zi = function(by) { 'zoom in 50%, change x.limits' center <- mean(.self$trellis$x.limits) width <- (.self$trellis$x.limits[2] - .self$trellis$x.limits[1])/2 if (width > 1) xlim <- c(center - width/2, center + width/2) else xlim <- .self$trellis$x.limits .self$trellis$x.limits <- xlim .debug("current x limits [%.0f, %.0f]", .self$get.x.limits()[1], .self$get.x.limits()[2]) invisible() }, left = function(by) { 'shift x.limits 80% to the left' margin <- 50 #if (is.missing(by)) ## 80% to the left by <- 0.8 * (.self$trellis$x.limits[2] - .self$trellis$x.limits[1]) .debug("shift left for %.0f bps", by) .self$trellis$x.limits[1] <- max(.self$trellis$x.limits[1] - by, .self$trellis$orig.x.limits[1]) .self$trellis$x.limits[2] <- max(.self$trellis$x.limits[2] - by, .self$trellis$orig.x.limits[1] + margin) .debug("current x limits [%.0f, %.0f]", .self$get.x.limits()[1], .self$get.x.limits()[2]) invisible() }, right = function(by=NULL) { 'shift x.limits 80% to the right' margin <- 50 #pbs #if (is.null(by)) ## 80% to the left by <- 0.8 * (.self$trellis$x.limits[2] - .self$trellis$x.limits[1]) .debug("shift right for %s bps", by) .self$trellis$x.limits[1] <- min(.self$trellis$x.limits[1] + by, .self$trellis$orig.x.limits[2] - margin) .self$trellis$x.limits[2] <- min(.self$trellis$x.limits[2] + by, .self$trellis$orig.x.limits[2]) .debug("current x limits [%.0f, %.0f]", .self$get.x.limits()[1], .self$get.x.limits()[2]) invisible() }, restore = function() { .self$trellis$x.limits <- .self$trellis$orig.x.limits invisible() }, display = function() { print(.self$trellis) } ) SpTrellis <- function(trellis, debug_enabled=FALSE) { .SpTrellis$new(trellis = trellis, .debug_enabled=debug_enabled) } setMethod(update, "SpTrellis", function(object, ...) { tr <- update(object$trellis, ...) SpTrellis(tr) }) setMethod(show, "SpTrellis", function(object) { cat("class:", class(object), "\n") with(object, { cat("region:", trellis$orig.x.limits, "\n") cat("viewing window:", get.x.limits(),"\n") }) object$display() }) setMethod(zi, "SpTrellis", function(x, by=5) { x$zi(by) x$display() }) setMethod(zo, "SpTrellis", function(x, by=5) { x$zo(by) x$display() }) setMethod(right, "SpTrellis", function(x, by=5) { x$right(by) x$display() }) setMethod(left, "SpTrellis", function(x, by=5) { x$left(by) x$display() }) setMethod(restore, "SpTrellis", function(x) { x$restore() x$display() }) ShortRead/R/filterFastq.R0000644000175400017540000000334613001230551016277 0ustar00biocbuildbiocbuild.filterFastq_check_fnames <- function(files, destinations) { if (missing('destinations')) stop("'destinations' missing") tryCatch({ S4Vectors:::V_recycle(destinations, files, "destinations", "files") }, warning=function(x) stop(conditionMessage(x), call.=FALSE)) if (any(exists <- file.exists(destinations))) stop("'destinations' exist:\n ", paste(destinations[exists], collapse="\n ")) } .filter1 <- function(filter, file, destination, ..., compress=TRUE, yieldSize) { strm <- FastqStreamer(file, yieldSize) on.exit(close(strm)) tot <- tot1 <- nNuc <- nNuc1 <- 0L while (length(fq <- yield(strm))) { tot <- tot + length(fq) nNuc <- nNuc + sum(width(fq)) fq <- if (is(filter, "FilterRules")) { subsetByFilter(fq, filter) } else filter(fq, ...) tot1 <- tot1 + length(fq) nNuc1 <- nNuc1 + sum(width(fq)) writeFastq(fq, destination, "a", compress=compress) } attr(destination, "filter") <- data.frame(Reads=tot, KeptReads=tot1, Nucl=nNuc, KeptNucl=nNuc1) destination } filterFastq <- function(files, destinations, ..., filter=FilterRules(), compress=TRUE, yieldSize=1000000L) { if (missing(filter)) warning("'filterFastq' invoked with missing 'filter'") .filterFastq_check_fnames(files, destinations) ## FIXME parallel over files, esp. random numbers x <- Map(.filter1, files, destinations, MoreArgs=list(filter=filter, ..., compress=compress, yieldSize=yieldSize)) stats <- do.call(rbind, lapply(x, attr, "filter")) rownames(stats) <- make.unique(basename(files)) attr(destinations, "filter") <- stats destinations } ShortRead/R/methods-.QA.R0000644000175400017540000000256713001230551016037 0ustar00biocbuildbiocbuildsetMethod(rbind, ".QA", function(..., deparse.level=NA) { lst <- list(...) if (length(unique(sapply(lst, class))) != 1) .throw(SRError("UserArgumentMismatch", "rbind,.QA-method '...' arguments must all be the same class")) f <- function(nm, lst) { elts <- lapply(lst, "[[", nm) if (class(elts[[1]]) == "list") { nms <- names(elts[[1]]) l <- lapply(nms, f, elts) names(l) <- nms l } else { do.call(rbind, unname(elts)) } } nms <- names(.srlist(lst[[1]])) l <- sapply(nms, f, lst, simplify=FALSE) names(l) <- nms new(class(lst[[1]]), .srlist=l) }) setMethod(show, ".QA", function(object) { callNextMethod() .dims <- function(elt) { switch(class(elt), matrix=, data.frame=paste(dim(elt), collapse=" "), length(elt)) } .names <- function(lst, depth=0) { nms <- names(lst) for (i in seq_along(lst)) { fmt <- paste("%", depth*2, "s%s: %s(%s)\n", sep="") cat(sprintf(fmt, "", nms[i], class(lst[[i]]), .dims(lst[[i]]))) if (is.list(lst[[i]]) && !is.data.frame(lst[[i]])) .names(lst[[i]], depth=depth+1) } } cat("QA elements (access with qa[[\"elt\"]]):\n") .names(.srlist(object), depth=1) }) ShortRead/R/methods-.ShortReadBase.R0000644000175400017540000000114113001230552020210 0ustar00biocbuildbiocbuildsetMethod(append, c(".ShortReadBase", ".ShortReadBase"), function(x, values, after=length(x)) { .throw(SRError("UserArgumentMismatch", "'%s' methods not defined for classes '%s', '%s'", "append", class(x), class(values))) }) setMethod(show, signature=signature(object=".ShortReadBase"), function(object) { cat("class: ", class(object), "\n", sep="") }) setMethod(detail, signature=signature(x=".ShortReadBase"), function(x, ...) { cat("class: ", class(x), "\n", sep="") }) ShortRead/R/methods-AlignedDataFrame.R0000644000175400017540000000126413001230552020562 0ustar00biocbuildbiocbuildAlignedDataFrame <- function(data, metadata, nrow=nrow(data)) { if (missing(data)) { data <- data.frame(rep(0L, nrow))[,FALSE] metadata <- data.frame(labelDescription=character(0)) } new("AlignedDataFrame", data=data, varMetadata=metadata) } setMethod(append, c("AlignedDataFrame", "AlignedDataFrame"), function(x, values, after=length(x)) { if (!identical(varMetadata(x), varMetadata(values))) { .throw(SRError("IncompatibleTypes", "'%s' and '%s' have different '%s'", "x", "values", "varMetadata")) } new(class(x), data=rbind(pData(x), pData(values)), varMetadata=varMetadata(x)) }) ShortRead/R/methods-AlignedRead.R0000644000175400017540000002775513001230552017626 0ustar00biocbuildbiocbuild### .AlignedRead_validity <- function(object) { msg <- NULL len <- length(sread(object)) slts <- c("chromosome", "position", "strand", "alignQuality") olen <- c(length(chromosome(object)), length(position(object)), length(strand(object)), length(alignQuality(object))) if (!all(olen==len)) { bad <- olen!=len msg <- c(msg, sprintf("length mismatch: expected %d, found:\n %s", len, paste(slts[bad], olen[bad], sep="=", collapse=", "))) } if (is.null(msg)) TRUE else msg } setMethod(.srValidity, "AlignedRead", .AlignedRead_validity) .AlignedRead_QualityConstructor <- function(sread) { if (length(sread) > 0) unlist(lapply(width(sread), polyn, nucleotides="!")) else character(0) } AlignedRead <- function(sread = DNAStringSet(character(0)), id = BStringSet(character(length(sread))), quality = FastqQuality( .AlignedRead_QualityConstructor(sread)), chromosome = factor(rep(NA, length(sread))), position = rep(NA_integer_, length(sread)), strand = factor(rep(NA_integer_, length(sread)), levels=.STRAND_LEVELS), alignQuality = NumericQuality( rep(NA_real_, length(sread))), alignData = AlignedDataFrame( nrow=length(sread))) { new("AlignedRead", sread=sread, id=id, quality=quality, chromosome=as.factor(chromosome), position=position, strand=strand, alignQuality=alignQuality, alignData=alignData) } .make_getter(c("alignQuality", "alignData")) setMethod(chromosome, "AlignedRead", function(object, ...) slot(object, "chromosome")) setMethod(position, "AlignedRead", function(object, ...) slot(object, "position")) setMethod(strand, "AlignedRead", function(x) slot(x, "strand")) ## coerce setAs("PairwiseAlignments", "AlignedRead", function(from, to) { pat <- pattern(from) quality <- character() if (is(pat, "QualityAlignedXStringSet")) quality <- quality(pat) new("AlignedRead", sread = unaligned(pat), id = names(pat), quality = FastqQuality(quality), position = start(Views(pat)), alignQuality = IntegerQuality(score(from))) }) setAs("AlignedRead", "RangesList", function(from) { chr <- chromosome(from) pos <- position(from) wd <- width(from) notNA <- !(is.na(chr) | is.na(pos) | is.na(wd)) split(IRanges(start=pos[notNA], width=wd[notNA]), chr[notNA]) }) setAs("AlignedRead", "RangedData", function(from) { chr <- chromosome(from) pos <- position(from) wd <- width(from) std <- strand(from) notNA <- !(is.na(chr) | is.na(pos) | is.na(wd) | is.na(std)) GRanges(IRanges(pos[notNA], width=wd[notNA]), space = chr[notNA], id = id(from)[notNA], strand = std[notNA], pData(alignData(from))[notNA,,drop=FALSE]) }) setAs("AlignedRead", "GRanges", function(from) { chr <- chromosome(from) pos <- position(from) wd <- width(from) std <- strand(from) notNA <- !(is.na(chr) | is.na(pos) | is.na(wd) | is.na(std)) GRanges(chr[notNA], IRanges(pos[notNA], width=wd[notNA]), std[notNA], id = id(from)[notNA], pData(alignData(from))[notNA,,drop=FALSE]) }) setAs("AlignedRead", "GappedReads", function(from) { if (length(from) == 0L) cigar <- character(0) else cigar <- paste(width(from), "M", sep="") GappedReads(seqnames=chromosome(from), pos=position(from), cigar=cigar, strand=strand(from), names=id(from), qseq=sread(from)) }) setAs("AlignedRead", "GAlignments", function(from) { as(as(from, "GappedReads"), "GAlignments") }) ## subset setMethod("[", c("AlignedRead", "missing", "missing"), function(x, i, j, ..., drop=NA) .subset_err()) setMethod("[", c("AlignedRead", "missing", "ANY"), function(x, i, j, ..., drop=NA) .subset_err()) setMethod("[", c("AlignedRead", "ANY", "ANY"), function(x, i, j, ..., drop=NA) .subset_err()) .AlignedRead_subset <- function(x, i, j, ..., drop=TRUE) { if (!missing(...)) .subset_err() initialize(x, sread=sread(x)[i], id=id(x)[i], quality=quality(x)[i], chromosome=factor(chromosome(x)[i]), position=position(x)[i], strand=strand(x)[i], alignQuality=alignQuality(x)[i], alignData=alignData(x)[i,]) } setMethod("[", c("AlignedRead", "ANY", "missing"), .AlignedRead_subset) setMethod(append, c("AlignedRead", "AlignedRead"), function(x, values, after=length(x)) { initialize(x, chromosome=.append.factor(chromosome(x), chromosome(values)), position=append(position(x), position(values)), strand=.append.factor(strand(x), strand(values)), alignQuality=append(alignQuality(x), alignQuality(values)), alignData=append(alignData(x), alignData(values)), quality=append(quality(x), quality(values)), sread=append(sread(x), sread(values)), id=append(id(x), id(values))) }) ## match, %in% setMethod("%in%", c("AlignedRead", "RangesList"), function(x, table) { ## could use as(x, "RangesList"), but the assumptions here (about ## the definition of notNA, and about split() preserving order) ## make this fragile enough as it is ## ## consider only sensible alignemnts chr <- chromosome(x) pos <- position(x) wd <- width(x) notNA <- !(is.na(chr) | is.na(pos) | is.na(wd)) chr <- chr[notNA] ## find overlap rl <- split(IRanges(start=pos[notNA], width=wd[notNA]), chr) olap <- rl %in% table ## map to original indicies len <- seq_len(length(x)) idx <- unlist(split(len[notNA], chr), use.names=FALSE) len %in% idx[unlist(olap)] }) ## srorder, etc; srsort picked up by generic setMethod(srorder, "AlignedRead", function(x, ..., withSread=TRUE) { if (withSread) order(chromosome(x), strand(x), position(x), srorder(sread(x))) else order(chromosome(x), strand(x), position(x)) }) setMethod(srrank, "AlignedRead", function(x, ..., withSread=TRUE) { .check_type_and_length(withSread, "logical", 1) if (is.na(withSread)) .throw(SRError("UserArgumentMismatch", "'%s' must not be NA", "withSread")) o <- srorder(x) .Call(.aligned_read_rank, x, o, withSread, environment()) }) setMethod(srduplicated, "AlignedRead", function(x, ..., withSread=TRUE) { duplicated(srrank(x, ..., withSread=withSread)) }) ## coverage setMethod(coverage, "AlignedRead", function(x, shift=0L, width=NULL, weight=1L, ..., coords=c("leftmost", "fiveprime"), extend=0L) { ## Argument checking: if(all(is.na(chromosome(x)) == TRUE)) { .throw(SRError("UserArgumentMismatch", "chromosome names are all 'NA' see %s", '?"AlignedRead-class"')) } chrlvls <- levels(chromosome(x)) if (!identical(shift, 0L)) { if (!is.numeric(shift)) { .throw(SRError("UserArgumentMismatch", "if '%s' is not 0L, then it must be a vector of integer values\n see %s", "shift", '?"AlignedRead-class"')) } if (!all(chrlvls %in% names(shift))) { .throw(SRError("UserArgumentMismatch", "'names(%s)' (or 'names(%s)') mismatch with 'levels(chromosome(x))'\n see %s", "shift", "start", '?"AlignedRead-class"')) } if (any(duplicated(names(shift)))) { .throw(SRError("UserArgumentMismatch", "'names(%s)' (or 'names(%s)') have duplicates\n see %s", "shift", "start", '?"AlignedRead-class"')) } } if (!is.null(width)) { if (!is.numeric(width)) { .throw(SRError("UserArgumentMismatch", "if '%s' is not NULL, then it must be a vector of integer values\n see %s", "width", '?"AlignedRead-class"')) } if (!all(chrlvls %in% names(width))) { .throw(SRError("UserArgumentMismatch", "'names(%s)' (or 'names(%s)') mismatch with 'levels(chromosome(x))'\n see %s", "width", "end", '?"AlignedRead-class"')) } if (any(duplicated(names(width)))) { .throw(SRError("UserArgumentMismatch", "'names(%s)' (or 'names(%s)') have duplicates\n see %s", "width", "end", '?"AlignedRead-class"')) } } if (!identical(weight, 1L)) { .throw(SRError("UserArgumentMismatch", "weighting the reads is not supported yet, sorry\n see %s", '?"AlignedRead-class"')) } tryCatch(coords <- match.arg(coords), error=function(err) { vals <- formals(sys.function(sys.parent(4)))[["cvg"]] .throw(SRError("UserArgumentMismatch", "'%s' must be one of '%s'\n see %s", "coords", paste(eval(vals), collapse="' '"), '?"AlignedRead-class"')) }) if (!is.integer(extend) || !(length(extend) == 1 || length(extend) == length(x))) { .throw(SRError("UserArgumentMismatch", "'%s' must be '%s'", "extend", "integer(n)', n == 1 or length(x)")) } ## end of argument checking. if (coords == "leftmost") { rstart <- position(x) - ifelse(strand(x) == "+", 0L, extend) rend <- position(x) + width(x) - 1L + ifelse(strand(x) == "+", extend, 0L) } else { rstart <- position(x) - ifelse(strand(x) == "+", 0L, width(x) + extend - 1L) rend <- position(x) + ifelse(strand(x) == "+", width(x) + extend - 1L, 0L) } cvg <- RleList(lapply(structure(chrlvls, names = chrlvls), function(chr, ...) { idx <- chromosome(x) == chr chr_rstart <- rstart[idx] chr_rend <- rend[idx] if (identical(shift, 0L)) chr_shift <- 0L else chr_shift <- shift[chr] if (is.null(width)) chr_width <- NULL else chr_width <- width[chr] coverage(IRanges(chr_rstart, chr_rend), shift=chr_shift, width=chr_width, ...) }, ...), compress = FALSE) metadata(cvg) <- list(method="coverage,AlignedRead-method", coords=coords, extend=extend) cvg }) ## show setMethod(show, "AlignedRead", function(object) { callNextMethod() cat("chromosome:", selectSome(chromosome(object)), "\n") cat("position:", selectSome(position(object)), "\n") cat("strand:", selectSome(strand(object)), "\n") cat("alignQuality:", class(alignQuality(object)), "\n") cat("alignData varLabels:", selectSome(varLabels(alignData(object))), "\n") }) setMethod(detail, "AlignedRead", function(x, ...) { callNextMethod() cat("\nchromosome:", selectSome(chromosome(x)), "\n") cat("position:", selectSome(position(x)), "\n") cat("strand:", selectSome(strand(x)), "\n") cat("alignQuality:\n") detail(alignQuality(x)) cat("\nalignData:\n") show(alignData(x)) }) ## summary ## perhaps summary statistics like ShortReadQ except broken down by chromosome, ## strand, and their combination ShortRead/R/methods-BowtieQA.R0000644000175400017540000001575013001230551017131 0ustar00biocbuildbiocbuild.BowtieQA <- function(x, ...) { new("BowtieQA", .srlist=x, ...) } .qa_Bowtie_lane <- function(dirPath, pattern, ..., type="Bowtie", verbose=FALSE) { if (verbose) message("qa 'Bowtie' pattern:", pattern) rpt <- readAligned(dirPath, pattern, type, ...) doc <- .qa_depthOfCoverage(rpt, pattern) ac <- .qa_adapterContamination(rpt, pattern, ...) alf <- .qa_alphabetFrequency(sread(rpt), baseOnly=TRUE, collapse=TRUE) bqtbl <- .qa_alphabetFrequency(quality(rpt), collapse=TRUE) rqs <- .qa_qdensity(quality(rpt)) freqtbl <- tables(sread(rpt)) abc <- alphabetByCycle(rpt) perCycleBaseCall <- .qa_perCycleBaseCall(abc, pattern) perCycleQuality <- .qa_perCycleQuality(abc, quality(rpt), pattern) aqtbl <- table(quality(alignQuality(rpt)), useNA="always") list(readCounts=data.frame( read=NA, filter=NA, aligned=length(rpt), row.names=pattern), baseCalls=data.frame( A=alf[["A"]], C=alf[["C"]], G=alf[["G"]], T=alf[["T"]], N=alf[["other"]], row.names=pattern), readQualityScore=data.frame( quality=rqs$x, density=rqs$y, lane=pattern, type="aligned"), baseQuality=data.frame( score=names(bqtbl), count=as.vector(bqtbl), lane=pattern), alignQuality=data.frame( score=as.numeric(names(aqtbl)), count=as.vector(aqtbl), lane=pattern, row.names=NULL), frequentSequences=data.frame( sequence=names(freqtbl$top), count=as.integer(freqtbl$top), type="aligned", lane=pattern, row.names=NULL), sequenceDistribution=cbind( freqtbl$distribution, type="aligned", lane=pattern), perCycle=list( baseCall=perCycleBaseCall, quality=perCycleQuality), perTile=list( readCounts=data.frame( count=integer(0), type=character(0), tile=integer(0), lane=character(0)), medianReadQualityScore=data.frame( score=integer(), type=character(), tile=integer(), lane=integer())), depthOfCoverage=doc, adapterContamination=ac ) } .qa_Bowtie <- function(dirPath, pattern, type="Bowtie", ..., verbose=FALSE) { fls <- .file_names(dirPath, pattern) lst <- bplapply(basename(fls), .qa_Bowtie_lane, dirPath=dirPath, type=type, ..., verbose=verbose) lst <- list(readCounts=.bind(lst, "readCounts"), baseCalls=.bind(lst, "baseCalls"), readQualityScore=.bind(lst, "readQualityScore"), baseQuality=.bind(lst, "baseQuality"), alignQuality=.bind(lst, "alignQuality"), frequentSequences=.bind(lst, "frequentSequences"), sequenceDistribution=.bind(lst, "sequenceDistribution"), perCycle=local({ lst <- subListExtract(lst, "perCycle") list(baseCall=.bind(lst, "baseCall"), quality=.bind(lst, "quality")) }), perTile=local({ lst <- subListExtract(lst, "perTile") list(readCounts=.bind(lst, "readCounts"), medianReadQualityScore=.bind( lst, "medianReadQualityScore")) }), depthOfCoverage=.bind(lst, "depthOfCoverage"), adapterContamination=.bind(lst, "adapterContamination") ) .BowtieQA(lst) } setMethod(report_html, "BowtieQA", function(x, dest, type, ...) { qa <- .qa_sampleKey(x) dir.create(dest, recursive=TRUE) fls <- c("0000-Header.html", "1000-Overview.html", "2000-RunSummary.html", "3000-ReadDistribution.html", "4000-CycleSpecific.html", "8000-DepthOfCoverage.html", "9000-AdapterContamination.html", "9999-Footer.html") sections <- system.file("template", fls, package="ShortRead") perCycle <- qa[["perCycle"]] values <- list(SAMPLE_KEY=hwrite(qa[["keyValue"]], border=0), PPN_COUNT=.html_img( dest, "readCount", .plotReadCount(qa)), PPN_COUNT_TBL=hwrite( .ppnCount(qa[["readCounts"]]), border=0), BASE_CALL_COUNT=.html_img( dest, "baseCalls", .plotNucleotideCount(qa)), READ_QUALITY_FIGURE=.htmlReadQuality( dest, "readQuality", qa, "aligned"), READ_OCCURRENCES_FIGURE=.htmlReadOccur( dest, "readOccurences", qa, "aligned"), FREQUENT_SEQUENCES_READ=.html_NA(), FREQUENT_SEQUENCES_FILTERED=.html_NA(), FREQUENT_SEQUENCES_ALIGNED=hwrite( .freqSequences(qa, "aligned"), border=0), CYCLE_BASE_CALL_FIGURE=.html_img( dest, "perCycleBaseCall", .plotCycleBaseCall(perCycle$baseCall)), CYCLE_QUALITY_FIGURE=.html_img( dest, "perCycleQuality", .plotCycleQuality(perCycle$quality)), DEPTH_OF_COVERAGE_FIGURE=.html_img( dest, "depthOfCoverage", .plotDepthOfCoverage(qa[["depthOfCoverage"]])), ADAPTER_CONTAMINATION=hwrite( .df2a(qa[["adapterContamination"]]), border=0) ) .report_html_do(dest, sections, values, ...) }) setGeneric(".bowtie_mismatches", function(object, ...) standardGeneric(".bowtie_mismatches")) setMethod(.bowtie_mismatches, "AlignedRead", function(object, ...) { adata <- alignData(object) if (!"mismatch" %in% varLabels(adata)) .throw(SRError("UserArgumentMismatch", "'%s' does not contain varLabels '%s'", "AlignedDataFrame", "mismatch")) if (any(c("nmismatch", "mismatchScore") %in% varLabels(adata))) .throw(SRError("UserArgumentMismatch", "'%s' already contains varLabels '%s'", "AlignedDataFrame", "nmismatch', 'mismatchScore'")) mmatch <- adata[["mismatch"]] idx <- which(nzchar(mmatch)) if (any(grepl(":", mmatch, fixed=TRUE))) { anuc <- lapply(strsplit(mmatch[idx], "[:,]"), "[", c(TRUE, FALSE)) cidx <- unlist(lapply(anuc, as.integer)) + 1L } else { anuc <- lapply(strsplit(mmatch[idx], ",", fixed=TRUE), as.integer) cidx <- unlist(anuc) + 1L } len <- sapply(anuc, length) ridx <- rep(idx, len) x <- as(narrow(quality(object)[ridx], cidx, cidx), "matrix") mmscore <- rep(NA_integer_, nrow(adata)) mmscore[idx] <- unlist(lapply(split(x, ridx), sum), use.names=FALSE) lngth <- integer(nrow(adata)) lngth[idx] <- len txt <- "Number of mismatches" adata[["nmismatch", labelDescription=txt]] <- lngth txt <- "Summed quality scores at mismatched nucleotides" adata[["mismatchScore", labelDescription=txt]] <- mmscore initialize(object, alignData=adata) }) ShortRead/R/methods-ExperimentPath.R0000644000175400017540000000402313001230551020402 0ustar00biocbuildbiocbuildsetMethod(.srValidity, "ExperimentPath", function(object) { msg <- NULL if (length(experimentPath(object))!=1) msg <- c(msg, "ExperimentPath 'experimentPath' must be character(1)") if (is.null(msg)) TRUE else msg }) .srPath <- function(path, pattern = character()) { path <- path.expand(path) tryCatch({ res <- list.files(path, pattern=pattern, full.names=TRUE) if (length(res)==0) NA_character_ else res }, warning=function(warn) NA_character_) } .checkPath <- function(path) { nm <- deparse(substitute(path)) if (length(path)==0) { warning(nm, " not defined") } else { for (p in path) if (!file.exists(p)) warning(nm, " '", p, "' does not exist") } } ExperimentPath <- function(experimentPath=NA_character_, ...) { new("ExperimentPath", basePath=experimentPath, ...) } basePath <- function(object, ...) { .Defunct("experimentPath") } setMethod(sampleNames, "ExperimentPath", function(object) { character(0) }) .show_additionalPathSlots <- function(object) { # for derived classes catPath <- function(nm) { vals <- do.call(nm, list(object)) vals <- substr(basename(vals), 1, 15) vals <- paste(vals, ifelse(nchar(vals)==15, "...", ""), sep="") cat(nm, ": ", paste(vals, collapse=", "), "\n", sep="") } slts <- slotNames(object) for (slt in slts[slts!="basePath"]) catPath(slt) } setMethod(show, "ExperimentPath", function(object) { callNextMethod() cat("experimentPath: ", experimentPath(object), "\n", sep="") }) .detail_additionalPathSlots <- function(object) { catPath <- function(nm) { fnms <- do.call(nm, list(object)) cat(nm, ":\n ", paste(fnms, collapse="\n "), sep="") cat("\n") } slts <- slotNames(object) for (slt in slts[slts!="basePath"]) catPath(slt) } setMethod(detail, "ExperimentPath", function(x, ...) { callNextMethod() cat("experimentPath:\n ", experimentPath(x), "\n", sep="") }) ShortRead/R/methods-FastqFile.R0000644000175400017540000000157413001230552017334 0ustar00biocbuildbiocbuildFastqFile <- function(con, ...) { .ShortReadFile(.FastqFile_g, con, ...) } setMethod(readFastq, "FastqFile", function(dirPath, pattern=character(), ...) { if (length(pattern) != 0) .throw(SRWarn("UserArgumentMismatch", "'pattern' ignored for '%s'", "readFastq,FastqFile-method")) callGeneric(path(dirPath), ...) }) setMethod(writeFastq, c("ShortReadQ", "FastqFile"), function(object, file, mode="w", full=FALSE, compress=TRUE, ...) { callGeneric(object, path(file), mode=mode, full=full, compress=compress, ...) }) setMethod(FastqFileList, "ANY", function(..., class="FastqFile") { Rsamtools:::.RsamtoolsFileList(..., class=class) }) setMethod(FastqFileList, "character", function(..., class="FastqFile") { fls <- lapply(..1, FastqFile) FastqFileList(fls, class=class) }) ShortRead/R/methods-FastqFileReader.R0000644000175400017540000000027013001230551020446 0ustar00biocbuildbiocbuild.binReader <- function(con, n) ## read 'n' bytes from 'con', returning raw() { readBin(con, raw(), n) } setMethod(yield, "FastqFileReader", function(x, ...) x$yield(...)) ShortRead/R/methods-FastqQA.R0000644000175400017540000001161213001230552016750 0ustar00biocbuildbiocbuild.ShortReadQQA <- function(x, ...) { new("ShortReadQQA", .srlist=x, ...) } .FastqQA <- function(x, ...) { new("FastqQA", .srlist=x, ...) } .qa_ShortReadQ <- function(dirPath, lane, ..., verbose=FALSE) { if (missing(lane)) .throw(SRError("UserArgumentMismatch", "'%s' must be '%s'", "lane", "character(1)")) obj <- dirPath alf <- .qa_alphabetFrequency(sread(obj), baseOnly=TRUE, collapse=TRUE) bqtbl <- .qa_alphabetFrequency(quality(obj), collapse=TRUE) rqs <- .qa_qdensity(quality(obj)) freqtbl <- tables(sread(obj)) abc <- alphabetByCycle(obj) ac <- .qa_adapterContamination(obj, lane, ...) perCycleBaseCall <- .qa_perCycleBaseCall(abc, lane) perCycleQuality <- .qa_perCycleQuality(abc, quality(obj), lane) lst <- list(readCounts=data.frame( read=length(obj), filter=NA, aligned=NA, row.names=lane), baseCalls=data.frame( A=alf[["A"]], C=alf[["C"]], G=alf[["G"]], T=alf[["T"]], N=alf[["other"]], row.names=lane), readQualityScore=data.frame( quality=rqs$x, density=rqs$y, lane=lane, type="read"), baseQuality=data.frame( score=names(bqtbl), count=as.vector(bqtbl), lane=lane), alignQuality=data.frame( score=as.numeric(NA), count=as.numeric(NA), lane=lane, row.names=NULL), frequentSequences=data.frame( sequence=names(freqtbl$top), count=as.integer(freqtbl$top), type="read", lane=lane), sequenceDistribution=cbind( freqtbl$distribution, type="read", lane=lane), perCycle=list( baseCall=perCycleBaseCall, quality=perCycleQuality), perTile=list( readCounts=data.frame( count=integer(0), type=character(0), tile=integer(0), lane=character(0)), medianReadQualityScore=data.frame( score=integer(), type=character(), tile=integer(), lane=integer(), row.names=NULL)), adapterContamination=ac ) .ShortReadQQA(lst) } setMethod(qa, "ShortReadQ", .qa_ShortReadQ) .qa_fastq_lane <- function(dirPath, ..., sample=TRUE, type="fastq", verbose=FALSE) { fl <- basename(dirPath) if (verbose) message("qa 'fastq' basename: ", sQuote(fl)) if (sample) { samp <- FastqSampler(dirPath, ...) qa <- qa(yield(samp), basename(dirPath), ..., verbose=verbose) close(samp) elts <- .srlist(qa) elts$readCounts$read <- samp$status()[["total"]] initialize(qa, .srlist=elts) } else { fq <-readFastq(dirPath, ...) qa(fq, fl, ..., verbose=verbose) } } .qa_fastq <- function(dirPath, pattern, type="fastq", ..., verbose=FALSE) { fls <- .file_names(dirPath, pattern) lst <- bplapply(fls, .qa_fastq_lane, type=type, ..., verbose=verbose) lst <- do.call(rbind, lst) .FastqQA(.srlist(lst)) # re-cast } .report_html_ShortReadQA <- # or FastqQA function(x, dest, type, ...) { qa <- .qa_sampleKey(x) dir.create(dest, recursive=TRUE) fls <- c("0000-Header.html", "1000-Overview.html", "2000-RunSummary.html", "3000-ReadDistribution.html", "4000-CycleSpecific.html", "9000-AdapterContamination.html", "9999-Footer.html") sections <- system.file("template", fls, package="ShortRead") perCycle <- qa[["perCycle"]] values <- list(SAMPLE_KEY=hwrite(qa[["keyValue"]], border=0), PPN_COUNT=.html_img( dest, "readCount", .plotReadCount(qa)), PPN_COUNT_TBL=hwrite( .ppnCount(qa[["readCounts"]]), border=0), BASE_CALL_COUNT=.html_img( dest, "baseCalls", .plotNucleotideCount(qa)), READ_QUALITY_FIGURE=.htmlReadQuality( dest, "readQuality", qa), READ_OCCURRENCES_FIGURE=.htmlReadOccur( dest, "readOccurences", qa), FREQUENT_SEQUENCES_READ=hwrite( .freqSequences(qa, "read"), border=0), FREQUENT_SEQUENCES_FILTERED=.html_NA(), FREQUENT_SEQUENCES_ALIGNED=.html_NA(), CYCLE_BASE_CALL_FIGURE=.html_img( dest, "perCycleBaseCall", .plotCycleBaseCall(perCycle$baseCall)), CYCLE_QUALITY_FIGURE=.html_img( dest, "perCycleQuality", .plotCycleQuality(perCycle$quality)), ADAPTER_CONTAMINATION=hwrite( .df2a(qa[["adapterContamination"]]), border=0) ) .report_html_do(dest, sections, values, ...) } setMethod(report_html, "ShortReadQQA", .report_html_ShortReadQA) setMethod(report_html, "FastqQA", .report_html_ShortReadQA) ShortRead/R/methods-FastqSampler.R0000644000175400017540000000545513001230552020062 0ustar00biocbuildbiocbuild.FastqSampler_g$methods( reset = function() { "reopen the connection" if (verbose) msg("FastqSampler$reset()") if (isOpen(con)) { if (verbose) msg("FastqSamper$reset() re-open") s <- summary(con) class <- s$class desc <- s$description close(con) con <<- do.call(s$class, list(desc, "rb")) } else { open(con, "rb") } .self }, status = function(update=FALSE) { "report status of FastqSampler" if (update || !length(.status)) .status <<- .Call(.sampler_status, sampler) .status }, yield = function(...) { "read and sample all records in a connection" if (verbose) msg("FastqSampler$yield()") reset() while (length(bin <- reader(con, readerBlockSize))) { if (verbose) { status(update=TRUE) msg("FastqSampler$yield() reader") } .Call(.sampler_add, sampler, bin) } if (status(update=TRUE)["buffer"]) .throw(SRWarn("IncompleteFinalRecord", "FastqSampler yield() incomplete final record:\n %s", summary(con)$description)) if (verbose) msg("FastqSampler$yield() XStringSet") elts <- .Call(.sampler_as_XStringSet, sampler, ordered) if (verbose) msg("FastqSampler$yield() ShortReadQ") ShortReadQ(elts[["sread"]], elts[["quality"]], elts[["id"]], ...) }, show = function() { callSuper() cat("ordered:", ordered, "\n") }) FastqSampler <- function(con, n = 1e6, readerBlockSize=1e8, verbose=FALSE, ordered=FALSE) { if (length(n) != 1 || !is.finite(n) || n < 0) stop("'n' must be length 1, finite and >= 0") if (is(con, "FastqFile")) con <- path(con) if (is.character(con)) { con <- file(con) open(con, "rb") } else if (is(con, "connection") && summary(con)$opened != "opened") open(con, "rb") sampler <- .Call(.sampler_new, as.integer(n)) .ShortReadFile(.FastqSampler_g, con, reader=.binReader, readerBlockSize=as.integer(readerBlockSize), sampler=sampler, verbose=verbose, ordered=ordered) } setMethod("FastqSamplerList", "ANY", function(..., n=1e6, readerBlockSize=1e8, verbose=FALSE, ordered = FALSE) { FastqFileList(..., class="FastqSampler") }) setMethod("FastqSamplerList", "character", function(..., n=1e6, readerBlockSize=1e8, verbose=FALSE, ordered = FALSE) { listData <- lapply(..1, FastqSampler, n=n, readerBlockSize=readerBlockSize, verbose=verbose, ordered=ordered) new("FastqSamplerList", listData=listData) }) ShortRead/R/methods-FastqStreamer.R0000644000175400017540000001017513001230551020233 0ustar00biocbuildbiocbuild.FastqStreamer_g$methods( add = function(bin) { if (verbose) { status(update=TRUE) msg("FastqStreamer$add()") } .Call(.streamer_add, sampler, bin, c(skips[ith], adds[ith])) status(update=TRUE) }, status = function(update=FALSE) { "report status of FastqSampler" if (update || !length(.status)) .status <<- .Call(.streamer_status, sampler) .status }, yield = function(...) { "read at most n records in a connection" if (verbose) msg("FastqStreamer$yield()") if (!recycle && ith == length(skips)) return (ShortReadQ()) ith <<- ith %% length(skips) + 1L status(update=TRUE) prevTot <- status()["total"] if (status()["current"] != adds[ith]) { ## use C scratch buffer if (verbose) msg("FastqStreamer$yield() reader") add(raw()) } while (0L != (adds[ith] - status()["current"])) { ## fill C buffer if (verbose) msg("FastqStreamer$yield() reader") bin <- reader(con, readerBlockSize) if (!length(bin)) { status(update=TRUE) if ((status()["current"] != status()["n"]) && (0L != status()["buffer"])) .throw(SRWarn("IncompleteFinalRecord", "FastqStreamer yield() incomplete final record:\n %s", summary(con)$description)) break } currTot <- status()["total"] skips[ith] <<- max(0L, skips[ith] - (currTot - prevTot)) prevTot <- currTot add(bin) } if (verbose) msg("FastqStreamer$yield() XStringSet") elts <- .Call(.streamer_as_XStringSet, sampler) if (verbose) msg("FastqStreamer$yield() ShortReadQ") ShortReadQ(elts[["sread"]], elts[["quality"]], elts[["id"]], ...) }) setMethod(FastqStreamer, c("ANY", "missing"), function(con, n, readerBlockSize=1e8, verbose=FALSE) { callGeneric(con, n=1e6, readerBlockSize=readerBlockSize, verbose=verbose) }) setMethod(FastqStreamer, c("ANY", "numeric"), function(con, n, readerBlockSize=1e8, verbose=FALSE) { n <- as.integer(n) if (length(n) != 1L || !is.finite(n) || n < 0L) stop("'n' must be length 1, finite and >= 0") if (is.character(con)) { con <- file(con) open(con, "rb") } else if (is(con, "connection") && summary(con)$opened != "opened") open(con, "rb") streamer <- .Call(.streamer_new, n) .ShortReadFile(.FastqStreamer_g, con, reader=.binReader, readerBlockSize=as.integer(readerBlockSize), skips = 0L, adds = n, ith = 0L, recycle=TRUE, sampler=streamer, verbose=verbose) }) setMethod(FastqStreamer, c("ANY", "IRanges"), function(con, n, readerBlockSize=1e8, verbose=FALSE) { if (is.character(con)) con <- file(con) open(con, "rb") skips <- start(n) - c(1L, end(n)[-length(n)] + 1L) if (any(skips < 0)) { close(con) msg <- "'n' must have all(start(n)[-1] > end(n)[-length(n)])" .throw(SRError("UserArgumentMismatch", msg)) } adds <- width(n) if (any(adds == 0)) { close(con) msg <- "'n' must have non-zero width()" .throw(SRError("UserArgumentMismatch", msg)) } streamer <- .Call(.streamer_new, max(adds)) .ShortReadFile(.FastqStreamer_g, con, reader=.binReader, readerBlockSize=as.integer(readerBlockSize), skips = skips, adds = adds, ith = 0L, recycle = FALSE, sampler=streamer, verbose=verbose) }) setMethod("FastqStreamerList", "ANY", function(..., n, readerBlockSize=1e8, verbose=FALSE) { FastqFileList(..., class="FastqStreamer") }) setMethod("FastqStreamerList", "character", function(..., n, readerBlockSize=1e8, verbose=FALSE) { listData <- lapply(..1, FastqStreamer, n=n, readerBlockSize=readerBlockSize, verbose=verbose) new("FastqStreamerList", listData=listData) }) ShortRead/R/methods-Intensity.R0000644000175400017540000000400113001230552017430 0ustar00biocbuildbiocbuild## IntensityMeasure setMethod(show, "IntensityMeasure", function(object) { callNextMethod() cat(" dim: ", dim(object), "\n") }) setMethod(get("["), c("IntensityMeasure", "ANY", "ANY", "ANY"), function(x, i, j, ..., drop=FALSE) { if (missing(i)) i <- TRUE if (missing(j)) j <- TRUE initialize(x, x@.Data[i,j,...,drop=FALSE]) }) setMethod(get("[["), c("ArrayIntensity", "ANY", "ANY"), function(x, i, j, k, ...) { if (missing(i)) i <- TRUE if (missing(j)) j <- TRUE if (missing(k)) k <- TRUE x@.Data[i,j,k] }) ## IntensityInfo ## Intensity setMethod(.srValidity, "Intensity", function(object) { msg <- NULL if (.hasMeasurementError(object) && !all(dim(intensity(object)) == dim(measurementError(object)))) { msg <- c(msg, "'intensity' and 'measurementError' dimensions differ") } if (nrow(readIntensityInfo(object)) != nrow(intensity(object))) { msg <- c(msg, "'intensity' and 'readInfo' read numbers differ") } if (is.null(msg)) TRUE else msg }) measurementError <- function(object, ...) { if (!.hasMeasurementError(object)) .throw(SRError("ValueUnavailable", "'%s' has no value '%s'", class(object), "nse")) slot(object, "measurementError") } local({ slts <- slotNames("Intensity") .make_getter(slts[!slts %in% c("readInfo", "measurementError")], verbose=TRUE) }) readIntensityInfo <- function(object, ...) { slot(object, "readInfo") } setMethod(dim, "Intensity", function(x) { dim(intensity(x)) }) setMethod(show, "Intensity", function(object) { callNextMethod() cat("dim:", dim(object), "\n") cat("readInfo:", class(readIntensityInfo(object)), "\n") cat("intensity:", class(intensity(object)), "\n") if (.hasMeasurementError(object)) { cat("measurementError:", class(measurementError(object)), "\n") } else { cat("measurementError: not available\n") } }) ShortRead/R/methods-MAQMapQA.R0000644000175400017540000001270413001230552016751 0ustar00biocbuildbiocbuild.MAQMapQA <- function(x, ...) { new("MAQMapQA", .srlist=x, ...) } .maq_reverse <- function(aln) { plus <- strand(aln) == "+" new("AlignedRead", append(aln[plus], aln[!plus]), sread=append( sread(aln)[plus], reverseComplement(sread(aln)[!plus])), quality=append( quality(aln)[plus], FastqQuality(reverse(quality(quality(aln)[!plus]))))) } .qa_MAQMap_lane <- function(dirPath, pattern, type, ..., verbose=FALSE) { if (verbose) message("qa '", type, "' pattern: ", pattern, sep="") rpt <- .maq_reverse(readAligned(dirPath, pattern, type, ...)) alf <- .qa_alphabetFrequency(sread(rpt), baseOnly=TRUE, collapse=TRUE) bqtbl <- .qa_alphabetFrequency(quality(rpt), collapse=TRUE) rqs <- .qa_qdensity(quality(rpt)) freqtbl <- tables(sread(rpt)) abc <- alphabetByCycle(rpt) perCycleBaseCall <- .qa_perCycleBaseCall(abc, pattern) perCycleQuality <- .qa_perCycleQuality(abc, quality(rpt), pattern) aqtbl <- table(quality(alignQuality(rpt))) list(readCounts=data.frame( read=NA, filter=NA, aligned=length(rpt), row.names=pattern), baseCalls=data.frame( A=alf[["A"]], C=alf[["C"]], G=alf[["G"]], T=alf[["T"]], N=alf[["other"]], row.names=pattern), readQualityScore=data.frame( quality=rqs$x, density=rqs$y, lane=pattern, type="aligned", row.names=NULL), baseQuality=data.frame( score=names(bqtbl), count=as.vector(bqtbl), lane=pattern, row.names=NULL), alignQuality=data.frame( score=as.numeric(names(aqtbl)), count=as.vector(aqtbl), lane=pattern, row.names=NULL), frequentSequences=data.frame( sequence=names(freqtbl$top), count=as.integer(freqtbl$top), type="aligned", lane=pattern, row.names=NULL), sequenceDistribution=cbind( freqtbl$distribution, type="aligned", lane=pattern, row.names=NULL), perCycle=list( baseCall=perCycleBaseCall, quality=perCycleQuality), perTile=list( readCounts=data.frame( count=integer(0), type=character(0), tile=integer(0), lane=character(0)), medianReadQualityScore=data.frame( score=integer(), type=character(), tile=integer(), lane=integer())) ) } .qa_MAQMap <- function(dirPath, pattern, type, ..., verbose=FALSE) { fls <- .file_names(dirPath, pattern) lst <- bplapply(basename(fls), .qa_MAQMap_lane, dirPath=dirPath, type=type, ..., verbose=verbose) lst <- list(readCounts=.bind(lst, "readCounts"), baseCalls=.bind(lst, "baseCalls"), readQualityScore=.bind(lst, "readQualityScore"), baseQuality=.bind(lst, "baseQuality"), alignQuality=.bind(lst, "alignQuality"), frequentSequences=.bind(lst, "frequentSequences"), sequenceDistribution=.bind(lst, "sequenceDistribution"), perCycle=local({ lst <- subListExtract(lst, "perCycle") list(baseCall=.bind(lst, "baseCall"), quality=.bind(lst, "quality")) }), perTile=local({ lst <- subListExtract(lst, "perTile") list(readCounts=.bind(lst, "readCounts"), medianReadQualityScore=.bind( lst, "medianReadQualityScore")) })) .MAQMapQA(lst) } setMethod(report_html, "MAQMapQA", function(x, dest, type, ...) { qa <- .qa_sampleKey(x) dir.create(dest, recursive=TRUE) fls <- c("0000-Header.html", "1000-Overview.html", "2000-RunSummary.html", "3000-ReadDistribution.html", "4000-CycleSpecific.html", "6000-Alignment.html", "9999-Footer.html") sections <- system.file("template", fls, package="ShortRead") perCycle <- qa[["perCycle"]] values <- list(SAMPLE_KEY=hwrite(qa[["keyValue"]], border=0), PPN_COUNT=.html_img( dest, "readCount", .plotReadCount(qa)), PPN_COUNT_TBL=hwrite( .ppnCount(qa[["readCounts"]]), border=0), BASE_CALL_COUNT=.html_img( dest, "baseCalls", .plotNucleotideCount(qa)), READ_QUALITY_FIGURE=.htmlReadQuality( dest, "readQuality", qa, "aligned"), READ_OCCURRENCES_FIGURE=.htmlReadOccur( dest, "readOccurences", qa, "aligned"), FREQUENT_SEQUENCES_READ=hwrite( .freqSequences(qa, "read"), border=0), FREQUENT_SEQUENCES_FILTERED=hwrite( .freqSequences(qa, "filtered"), border=0), FREQUENT_SEQUENCES_ALIGNED=hwrite( .freqSequences(qa, "aligned"), border=0), CYCLE_BASE_CALL_FIGURE=.html_img( dest, "perCycleBaseCall", .plotCycleBaseCall(perCycle$baseCall)), CYCLE_QUALITY_FIGURE=.html_img( dest, "perCycleQuality", .plotCycleQuality(perCycle$quality)), ALIGN_QUALITY_FIGURE=.html_img( dest, "alignmentQuality", .plotAlignQuality(qa[["alignQuality"]])) ) .report_html_do(dest, sections, values, ...) }) ShortRead/R/methods-Misc.R0000644000175400017540000002276313001230551016353 0ustar00biocbuildbiocbuild.abc_BStringSet <- function(stringSet, alphabet, ...) { if (missing(alphabet)) alphabet <- sapply(as.raw(1:255), rawToChar) callNextMethod(stringSet, alphabet=alphabet) } setMethod(clean, "DNAStringSet", function(object, ...) { object[alphabetFrequency(object, baseOnly=TRUE)[,'other']==0] }) setMethod(dustyScore, "DNAStringSet", function(x, batchSize=NA, ...) { doDusty <- function(tripletPDict, x) { tnf <- t(vcountPDict(tripletPDict, x)) - 1L tnf[tnf < 0] <- 0L rowSums(tnf * tnf) } triplets <- DNAStringSet(mkAllStrings(c("A", "C", "G", "T"), 3)) tripletPDict <- PDict(triplets) if (is.na(batchSize) || length(x) <= batchSize) return(doDusty(tripletPDict, x)) n <- as.integer(1L + length(x) / batchSize) i <- seq_len(length(x)) i <- split(i, cut(i, n, labels=FALSE)) unlist(unname(bplapply(i, function(idx, tripletPDict, x, ...) { doDusty(tripletPDict, x[idx]) }, tripletPDict, x))) }) setMethod(alphabetByCycle, "BStringSet", .abc_BStringSet) setMethod(srorder, "XStringSet", function(x, ...) { if (!missing(...)) .throw(SRError("UserArgumentMismatch", "argument '%s' not supported", names(list(...)))) .Call(.alphabet_order, x) }) setMethod(srrank, "XStringSet", function(x, ...) { if (!missing(...)) .throw(SRError("UserArgumentMismatch", "argument '%s' not supported", names(list(...)))) .Call(.alphabet_rank, x) }) setMethod(srsort, "XStringSet", function(x, ...) x[srorder(x, ...)]) setMethod(srduplicated, "XStringSet", function(x, ...) { if (!missing(...)) .throw(SRError("UserArgumentMismatch", "argument '%s' not supported", names(list(...)))) .Call(.alphabet_duplicated, x) }) setMethod(writeFasta, "DNAStringSet", function(object, file, mode="w", ...) { append = mode=="a" writeXStringSet(object, file, ..., append=append, format="fasta") }) ## srdistance .srdistance <- function(pattern, subject, distanceFunc,..., verbose=FALSE) { if (verbose) cat(".srdistance", as.character(subject), "\n") substitutionMatrix <- distanceFunc(pattern, subject) -pairwiseAlignment(pattern, subject, substitutionMatrix=substitutionMatrix, gapOpening=0, gapExtension=-1, scoreOnly=TRUE) } .srdistanceDNA <- function(pattern, subject) { m <- matrix(c(1,0,0,0,.5,.5,.5,.0,.0,.0,.3,.3,.3,.0,.25,.25,.25,.25, 0,1,0,0,.5,.0,.0,.5,.5,.0,.3,.3,.0,.3,.25,.25,.25,.25, 0,0,1,0,.0,.5,.0,.5,.0,.5,.3,.0,.3,.3,.25,.25,.25,.25, 0,0,0,1,.0,.0,.5,.0,.5,.5,.0,.3,.3,.3,.25,.25,.25,.25), nrow=4, byrow=TRUE, dimnames=list(DNA_ALPHABET[1:4], DNA_ALPHABET)) patternAlf <- alphabetFrequency(pattern, collapse=TRUE) subjectAlf <- alphabetFrequency(subject) alf <- unique(c(names(patternAlf)[patternAlf!=0], names(subjectAlf)[subjectAlf!=0])) m <- m[, alf] -(1 - t(m) %*% m) } .srdistance_DNAStringSet_character <- function(pattern, subject, ...) { strings <- lapply(subject, DNAString) res <- bplapply(strings, .srdistance, pattern=pattern, distanceFunc=.srdistanceDNA, ...) if (length(res) == length(subject)) names(res) <- subject res } setMethod(srdistance, c("DNAStringSet", "character"), .srdistance_DNAStringSet_character) .srdistance_DNAStringSet_DNAString <- function(pattern, subject, ...) { res <- list(.srdistance(pattern, subject, .srdistanceDNA, ...)) names(res) <- as.character(subject) res } setMethod(srdistance, c("DNAStringSet", "DNAString"), .srdistance_DNAStringSet_DNAString) .srdistance_DNAStringSet_DNAStringSet <- function(pattern, subject, ...) { callGeneric(pattern, as.character(subject), ...) } setMethod(srdistance, c("DNAStringSet", "DNAStringSet"), .srdistance_DNAStringSet_DNAStringSet) ## tables .stringset_tables <- function(x, n=50, ...) { if (length(x) == 0) { return(list(top=integer(0), distribution=data.frame( nOccurrences=integer(0), nReads=integer(0)))) } ## FIXME: two sorts srt <- srsort(x) r <- srrank(x) t <- tabulate(r) o <- order(t, decreasing=TRUE) ## n most common sequences n <- min(n, sum(t!=0)) # remove duplicates top <- head(t[o], n) names(top) <- as.character(head(srt[o], n)) ## overall frequency -- equivalent of table(table(sread)) tt <- tabulate(t) nOccurrences <- seq_along(tt)[tt!=0] nReads <- tt[tt!=0] ## results list(top=top, distribution=data.frame( nOccurrences=nOccurrences, nReads=nReads, row.names=NULL)) } setMethod(tables, "XStringSet", .stringset_tables) ## trimTails setMethod(trimTailw, "BStringSet", function(object, k, a, halfwidth, ..., alphabet, ranges=FALSE) { if (missing(alphabet)) alphabet <- sapply(as.raw(0:127), rawToChar) tryCatch({ k <- as.integer(k) if (1L != length(k) || k < 0L) stop("'k' must be integer(1) >= 0L") a <- as.character(a) if (1L != length(a) || 1L != nchar("A")) stop("'", a, "' must satsify 'nchar(a) == 1L'") if (!a %in% alphabet) stop("'", a, "' must be a character with encoding < 128") halfwidth <- as.integer(halfwidth) if (1L != length(halfwidth) || halfwidth <= 0) stop("'halfwidth' must be > 0") }, error=function(err) { .throw(SRError("UserArgumentMismatch", conditionMessage(err))) }) tryCatch({ a_map <- rev(cumsum(rev(alphabet==a))) # '1' if < a names(a_map) <- alphabet ends <- .Call(.trimTailw, object, k, a_map, halfwidth) }, error=function(err) { .throw(SRError("InternalError", conditionMessage(err))) }) if (ranges) IRanges(1, ends) else narrow(object, 1L, ends)[0L != ends] }) setMethod(trimTailw, "XStringQuality", function(object, k, a, halfwidth, ..., ranges=FALSE) { rng <- callGeneric(as(object, "BStringSet"), k, a, halfwidth, ..., ranges=TRUE) if (ranges) rng else narrow(object, 1L, end(rng))[0L != width(rng)] }) setMethod(trimTails, "BStringSet", function(object, k, a, successive=FALSE, ..., alphabet, ranges=FALSE) { if (missing(alphabet)) alphabet <- sapply(as.raw(0:127), rawToChar) tryCatch({ k <- as.integer(k) if (1L != length(k) || k < 0L) stop("'k' must be integer(1) >= 0L") a <- as.character(a) if (1L != length(a) || 1L != nchar("A")) stop("'", a, "' must satsify 'nchar(a) == 1L'") if (!a %in% alphabet) stop("'", a, "' must be a character with encoding < 128") successive <- as.logical(successive) if (1L != length(successive) || is.na(successive)) stop("'successive' must be logical(1), not NA") }, error=function(err) { .throw(SRError("UserArgumentMismatch", conditionMessage(err))) }) tryCatch({ a_map <- rev(cumsum(rev(alphabet==a))) # '1' if < a names(a_map) <- alphabet ends <- .Call(.trimTails, object, k, a_map, successive) }, error=function(err) { .throw(SRError("InternalError", conditionMessage(err))) }) if (ranges) IRanges(1, ends) else narrow(object, 1L, ends)[0L != ends] }) setMethod(trimTails, "XStringQuality", function(object, k, a, successive=FALSE, ..., ranges=FALSE) { rng <- callGeneric(as(object, "BStringSet"), k, a, successive, ..., ranges=TRUE) if (ranges) rng else narrow(object, 1L, end(rng))[0L != width(rng)] }) setMethod(trimEnds, "XStringSet", function(object, a, left=TRUE, right=TRUE, relation=c("<=", "=="), ..., ranges=FALSE) { relation <- match.arg(relation) alphabet <- alphabet(object) if (is.null(alphabet)) alphabet <- sapply(as.raw(0:127), rawToChar) tryCatch({ a <- as.character(a) if (!all(a %in% alphabet)) warning("some 'a' not in alphabet(object)") left <- as.logical(left)[1] right <- as.logical(right)[1] }, error=function(err) { .throw(SRError("UserArgumentMismatch", conditionMessage(err))) }) tryCatch({ a_map <- alphabet %in% a if ("<=" == relation) a_map <- as.logical(rev(cumsum(rev(a_map)))) # '1' if <= a a <- alphabet[a_map] cls <- sub("(.*)String.*", "\\1", class(object)) xs <- get_seqtype_conversion_lookup(cls, "character") if (is.null(xs)) xs <- 0:127 map <- logical(length(xs)) key <- lapply(a, function(x) as.integer(charToRaw(x))) map[match(unname(unlist(key)), xs)] <- TRUE bnds <- .Call(.trimEnds, object, map, left, right) }, error=function(err) { .throw(SRError("InternalError", conditionMessage(err))) }) if (ranges) IRanges(bnds[["start"]], bnds[["end"]]) else narrow(object, bnds[["start"]], bnds[["end"]]) }) setMethod(trimEnds, "XStringQuality", function(object, a, left=TRUE, right=TRUE, relation=c("<=", "=="), ..., ranges=FALSE) { rng <- callGeneric(as(object, "BStringSet"), a, left, right, relation, ..., ranges=TRUE) if (ranges) rng else narrow(object, 1L, end(rng))[0L != width(rng)] }) ShortRead/R/methods-QA.R0000644000175400017540000006462313001230552015763 0ustar00biocbuildbiocbuild## QAData QAData <- function(seq=ShortReadQ(), filter=logical(length(seq)), ...) { .QAData$new(seq=seq, filter=filter, ...) } setMethod(.filter, "QAData", function(object, useFilter, ...) { if (useFilter) object$seq[!object$filter] else object$seq }) .filterUpdate <- function(object, add, value) { if (add) object$filter <- object$filter | value object } ## QASummary .show_KoverA <- function(object, K=object@flagK, A=object@flagA) { cat(sprintf("flag: K over A = (%.2f x 100)%% over %d\n", K, A)) } .QASummary <- function (class, useFilter = TRUE, addFilter = TRUE, ..., html) { if (missing(html)) html <- file.path(system.file("template", package = "ShortRead"), sprintf("%s.html", class)) if (!is.na(html) && (!file.exists(html) || !nzchar(html))) .throw(SRError("UserArgumentMismatch", "'html' file does not exist:\n %s", html)) new(class, useFilter = mkScalar(as.logical(useFilter)), addFilter = mkScalar(as.logical(addFilter)), html = mkScalar(html), ...) } .QASummaryFactory <- function(summaryName) { function (useFilter = TRUE, addFilter = TRUE, ...) .QASummary(summaryName, useFilter = useFilter, addFilter = addFilter, ...) } setMethod(show, "QASummary", function(object) { callNextMethod() cat(Rsamtools:::.ppath("html template", object@html)) cat("useFilter: ", object@useFilter, "; ", "addFilter: ", object@addFilter, "\n", sep="") }) QAFlagged <- .QASummaryFactory("QAFlagged") QAFiltered <- .QASummaryFactory("QAFiltered") QANucleotideUse <- .QASummaryFactory("QANucleotideUse") QAQualityUse <- .QASummaryFactory("QAQualityUse") QASequenceUse <- .QASummaryFactory("QASequenceUse") QAReadQuality <- function(useFilter=TRUE, addFilter=TRUE, flagK=.2, flagA=30L, ...) { .QASummary("QAReadQuality", useFilter=useFilter, addFilter=addFilter, flagK=mkScalar(flagK), flagA=mkScalar(as.integer(flagA)), ...) } setMethod(show, "QAReadQuality", function(object) { callNextMethod() .show_KoverA(object) }) QAAdapterContamination <- function (useFilter=TRUE, addFilter=TRUE, Lpattern = NA_character_, Rpattern = NA_character_, max.Lmismatch = 0.1, max.Rmismatch = 0.2, min.trim = 9L, ...) { fmt <- "QAAdapterContamination not a DNA sequence\n %s=\"%s\"" if (!is.na(Lpattern)) tryCatch(DNAString(Lpattern), error = function(e) { .throw(SRError("UserArgumentMismatch", fmt, "Lpattern", Lpattern)) }) if (!is.na(Rpattern)) tryCatch(DNAString(Rpattern), error = function(e) { .throw(SRError("UserArgumentMismatch", fmt, "Rpattern", Rpattern)) }) .QASummary("QAAdapterContamination", useFilter=useFilter, addFilter=addFilter, Lpattern = mkScalar(toupper(as.character(Lpattern))), Rpattern = mkScalar(toupper(as.character(Rpattern))), max.Lmismatch = mkScalar(as.numeric(max.Lmismatch)), max.Rmismatch = mkScalar(as.numeric(max.Rmismatch)), min.trim = mkScalar(as.integer(min.trim)), ...) } setMethod(show, "QAAdapterContamination", function(object) { callNextMethod() cat("Lpattern:", object@Lpattern, "\n") cat("Rpattern:", object@Rpattern, "\n") cat("max.Lmismatch: ", object@max.Lmismatch, "; ", "max.Rmismatch: ", object@max.Rmismatch, "; ", "min.trim: ", object@min.trim, "\n", sep="") }) QAFrequentSequence <- function (useFilter = TRUE, addFilter = TRUE, n = NA_integer_, a = NA_integer_, flagK=.8, reportSequences = FALSE, ...) { .QASummary("QAFrequentSequence", addFilter = addFilter, useFilter = useFilter, n = mkScalar(as.integer(n)), a = mkScalar(as.integer(a)), flagK = mkScalar(as.numeric(flagK)), reportSequences = mkScalar(as.logical(reportSequences)), ...) } setMethod(show, "QAFrequentSequence", function(object) { callNextMethod() if (!is.na(object@n)) cat("n: ", object@n, "; ", sep="") else cat("a: ", object@a, "; ", sep="") cat("reportSequences:", object@reportSequences, "\n") .show_KoverA(object, object@flagK, object@a) }) QANucleotideByCycle <- .QASummaryFactory("QANucleotideByCycle") QAQualityByCycle <- .QASummaryFactory("QAQualityByCycle") ## QASource QAFastqSource <- function(con=character(), n=1e6, readerBlockSize=1e8, flagNSequencesRange=NA_integer_, ..., html=system.file("template", "QASources.html", package="ShortRead")) { .QASummary("QAFastqSource", con=as.character(con), n=mkScalar(as.integer(n)), readerBlockSize=mkScalar(as.integer(readerBlockSize)), flagNSequencesRange=as.integer(flagNSequencesRange), ..., html=mkScalar(html)) } setMethod(show, "QAFastqSource", function(object) { callNextMethod() cat("length:", length(object@con), "\n") cat("n: ", object@n, ";", " readerBlockSize: ", object@readerBlockSize, "\n", sep="") }) ## QACollate setMethod(QACollate, "missing", function(src, ...) { QACollate(QAFastqSource(), ...) }) setMethod(QACollate, "QAFastqSource", function(src, ...) { if (1L == length(list(...)) && is(..1, "QACollate")) renew(..1, src=src) else new("QACollate", src=src, SimpleList(...)) }) setMethod(show, "QACollate", function(object) { callNextMethod() cat("source:", class(object@src), "of length", length(object@src@con), "\n") elts <- paste(sapply(object, class), collapse = " ") txt <- paste(strwrap(sprintf("elements: %s", elts), exdent = 2), collapse = "\n ") cat(txt, "\n") }) ## QA QA <- function (src, filtered, flagged, ...) { new("QA", src = src, filtered = filtered, flagged=flagged, ...) } ## .clone setMethod(.clone, "QAData", function (object, ...) { .QAData$new(seq = object$seq, filter = object$filter, ...) }) setMethod(.clone, "QASource", function (object, ...) { object@data <- .clone(object@data) object }) ## values setMethod(values, "QASummary", function(x, ...) { x@values }) setReplaceMethod("values", c("QASummary", "DataFrame"), function (x, ..., value) { x@values <- value x }) ## rbind setMethod(rbind, "QASummary", function(..., deparse.level=1) { class <- class(..1) values <- do.call(rbind, lapply(list(...), values)) renew(..1, values = values) }) ## qa2 setMethod(qa2, "FastqSampler", function(object, state, ..., verbose=FALSE) { if (verbose) message("qa2,FastqSampler-method") state$seq <- yield(object) state$filter <- rep(FALSE, length(state$seq)) DataFrame(SourceN=object$status()[["total"]], SampleN=length(state$seq)) }) setMethod(qa2, "QAFastqSource", function(object, state, ..., verbose=FALSE) { if (verbose) message("qa2,QAFastqSource-method") if (1 != length(object@con)) .throw(SRError("InternalError", "'QAFastqSource' source length != 1")) sampler <- FastqSampler(object@con, object@n, object@readerBlockSize) on.exit(close(sampler)) df <- qa2(sampler, object@data, verbose=verbose) values <- cbind(df, DataFrame(AccessTimestamp=date(), FileName=basename(object@con))) ## Path=dirname(path(object@con)))) metadata(values) <- list(NumberOfRecords=length(object@data$seq)) renew(object, values=values) }) setMethod(qa2, "QAAdapterContamination", function(object, state, ..., verbose=FALSE) { if (verbose) message("qa2,QAAdapterContamination-method") obj <- .filter(state@data, object@useFilter) Lpattern <- if (is.na(object@Lpattern)) "" else object@Lpattern Rpattern <- if (is.na(object@Rpattern)) "" else object@Rpattern trim <- trimLRPatterns(Lpattern, Rpattern, sread(obj), object@max.Lmismatch, object@max.Rmismatch, ranges=TRUE) filt <- width(trim) < (width(obj) - object@min.trim) .filterUpdate(state@data, object@addFilter, filt) values <- DataFrame(Contaminants=sum(filt)) metadata(values) <- list(NumberOfRecords=length(filt)) renew(object, values=values) }) setMethod(qa2, "QANucleotideUse", function(object, state, ..., verbose=FALSE) { if (verbose) message("qa2,QANucleotideUse-method") obj <- .filter(state@data, object@useFilter) alf <- .qa_alphabetFrequency(sread(obj), baseOnly=TRUE, collapse=TRUE) values <- DataFrame(Nucleotide=factor(sub("other", "N", names(alf)), levels=c("A", "C", "G", "T", "N")), Count=as.vector(alf)) metadata(values) <- list(NumberOfRecords=length(obj)) renew(object, values=values) }) setMethod(qa2, "QAQualityUse", function(object, state, ..., verbose=FALSE) { if (verbose) message("qa2,QAQualityUse-method") obj <- .filter(state@data, object@useFilter) alf <- .qa_alphabetFrequency(quality(obj), collapse=TRUE) alf <- alf[alf != 0] alphabet <- alphabet(quality(obj)) quality <- factor(names(alf), levels=alphabet) q0 <- as(do.call(class(quality(obj)), list(alphabet)), "matrix") values <- DataFrame(Quality=quality, Score=as.integer(q0)[quality], Count=as.vector(alf)) metadata(values) <- list(NumberOfRecords=length(obj)) renew(object, values=values) }) setMethod(qa2, "QASequenceUse", function(object, state, ..., verbose=FALSE) { if (verbose) message("qa2,QASequenceUse-method") obj <- .filter(state@data, object@useFilter) t <- tabulate(tabulate(srrank(sread(obj)))) values <- DataFrame(Occurrences=seq_along(t)[t!=0], Reads=t[t!=0]) metadata(values) <- list(NumberOfRecords=length(obj)) renew(object, values=values) }) setMethod(qa2, "QAFrequentSequence", function(object, state, ..., verbose=FALSE) { if (verbose) message("qa2,QAFrequentSequence-method") if (is.finite(object@n)) { n <- thresh <- object@n } else { n <- 10L thresh <- object@a } obj <- .filter(state@data, object@useFilter) r <- srrank(sread(obj)) t <- tabulate(r) ttop <- head(order(t, decreasing=TRUE), n) topCount <- setNames(t[ttop], as.character(sread(obj)[match(ttop, r)])) filt <- if (is.finite(object@n)) { r %in% ttop } else r %in% which(t >= thresh) .filterUpdate(state@data, object@addFilter, filt) values <- DataFrame(Threshold=thresh, Records=length(r), Count=sum(filt), TopCount=IntegerList(topCount)) metadata(values) <- list(NumberOfRecords=length(obj)) renew(object, values=values) }) setMethod(qa2, "QAReadQuality", function(object, state, ..., verbose=FALSE) { if (verbose) message("qa2,QAReadQuality-method") obj <- .filter(state@data, object@useFilter) dens <- .qa_qdensity(quality(obj)) values <- DataFrame(Score=dens$x, Density=dens$y) metadata(values) <- list(NumberOfRecords=length(obj)) renew(object, values=values) }) setMethod(qa2, "QANucleotideByCycle", function(object, state, ..., verbose=FALSE) { if (verbose) message("qa2,QANucleotideByCycle-method") obj <- .filter(state@data, object@useFilter) abc <- alphabetByCycle(sread(obj)) values <- DataFrame(Cycle=seq_len(ncol(abc))[col(abc)], Base=factor(rownames(abc)[row(abc)]), Count=as.vector(abc), row.names=NULL) metadata(values) <- list(NumberOfRecords=length(obj)) renew(object, values=values[values$Count != 0,]) }) setMethod(qa2, "QAQualityByCycle", function(object, state, ..., verbose=FALSE) { if (verbose) message("qa2,QAQualityByCycle-method") obj <- .filter(state@data, object@useFilter) abc <- alphabetByCycle(quality(obj)) alphabet <- rownames(abc) q <- factor(rownames(abc)[row(abc)], levels = alphabet) q0 <- as(do.call(class(quality(obj)), list(alphabet)), "matrix") values <- DataFrame(Cycle=seq_len(ncol(abc))[col(abc)], Quality=q, Score=as.integer(q0)[q], Count=as.vector(abc), row.names=NULL) metadata(values) <- list(NumberOfRecords=length(obj)) renew(object, values=values[values$Count != 0,]) }) .qa2_do_collate1 <- function(object, state, ..., verbose=FALSE) { if (verbose) message("qa2,QACollate1-method") src <- .clone(object@src) srcelt <- qa2(src, verbose=verbose) # side effect -- populate seq elts <- endoapply(as(object, "SimpleList"), qa2, src, ..., verbose=verbose) names(elts) <- sapply(object, class) renew(object, elts, src=renew(object@src, values=values(srcelt))) } setMethod(qa2, "QACollate", function(object, state, ..., verbose=FALSE) { if (verbose) message("qa2,QACollate-method") qas <- bplapply(seq_along(object@src@con), function(i, object, ...) { object@src@con <- object@src@con[i] .qa2_do_collate1(object, ...) }, object, ..., verbose=verbose) ## collapse summary df <- do.call(rbind, Map(function(elt) values(elt@src), qas)) df[["Id"]] <- factor(seq_along(qas), levels=as.character(seq_along(qas))) ncol <- ncol(df) values(object@src) <- df[, c(ncol, seq_len(ncol - 1L))] ## collect NumberOfRecords filtered <- as(t(sapply(qas, function(lst) { sapply(lst, function(elt) { metadata(values(elt))[["NumberOfRecords"]] }) })), "DataFrame") filtered[["Id"]] <- values(object@src)[["Id"]] ncol <- ncol(filtered) filtered <- filtered[,c(ncol, seq_len(ncol - 1L))] ## add Id qas <- Map(function(elt, id) endoapply(elt, function(elt, id) { df <- values(elt) df[["Id"]] <- id ncol <- ncol(df) rotate <- c(ncol(df), seq_len(ncol - 1L)) values(elt) <- df[,rotate] elt }, id), qas, values(object@src)[["Id"]]) ## collapse values <- do.call(Map, c(function(...) { do.call(rbind, list(...)) }, qas)) ## flag object@src <- flag(object@src, verbose=verbose) values <- lapply(values, flag, verbose=verbose) flagged <- Reduce(rbind, Map(function(x) { f <- x@flag if (length(f)) DataFrame(Flag=f, Summary=class(x)) else DataFrame(Flag=integer(), Summary=character()) }, c(list(object@src), values))) QA(object@src, QAFiltered(values=filtered), QAFlagged(values=flagged), do.call(SimpleList, values)) }) ## flag setMethod(flag, ".QA2", function(object, ..., verbose=FALSE) { if (verbose) message("flag,ANY-method") object }) setMethod(flag, "QASource", function(object, ..., verbose=FALSE) { if (verbose) message("flag,QASource-method") rng <- object@flagNSequencesRange x <- values(object)[["SourceN"]] if (1L == length(rng) && is.na(rng)) { ## default -- outliers stats <- stats::fivenum(x, na.rm = TRUE) iqr <- diff(stats[c(2, 4)]) coef <- 1.5 object@flagNSequencesRange <- rng <- c(as.integer(floor(stats[2L] - coef * iqr)), as.integer(ceiling(stats[4L] + coef * iqr))) } object@flag <- which(!is.finite(x) | x < rng[1] | x > rng[2]) object }) setMethod(flag, "QAReadQuality", function(object, ..., verbose=FALSE) { if (verbose) message("flag,QAReadQuality-method") df <- as(values(object), "data.frame") object@flag <- which(unname(unlist(with(df, { Map(function(score, density, A, K) { dx <- diff(score) x <- score[-length(score)] + dx / 2 y <- density[-length(density)] + diff(density) / 2 k <- approxfun(x, cumsum(y * dx))(A) is.na(k) || k < K }, split(Score, Id), split(Density, Id), MoreArgs=list(A = object@flagA, K = object@flagK)) })))) object }) setMethod(flag, "QAFrequentSequence", function(object, ..., verbose=FALSE) { ppn <- values(object)[["Count"]] / values(object)[["Records"]] object@flag <- which(ppn > object@flagK ) object }) ## report .hwrite <- function(df) { hwrite(as(df, "data.frame"), border=0) } setMethod(report, "QASource", function(x, ..., dest=tempfile(), type="html") { df <- as(values(x), "data.frame") df$Id <- as.integer(as.character(df$Id)) pal <- c("#D73027", "#4575B4") # brewer.pal(9, "RdYlBu")[c(1, 9)] plt <- dotplot(Id ~ SourceN, df, type = "b", pch = 20, col = .dnaCol, rng = x@flagNSequencesRange, rngcol = pal, panel=function(x, y, ..., rng, rngcol) { panel.dotplot(x, y, ...) yy <- c(min(y), max(y)) llines(rng[1], yy, col=rngcol[1], lty=2) llines(rng[2], yy, col=rngcol[2], lty=2) }) list(SAMPLE_KEY=.hwrite(values(x)), PPN_COUNT=.html_img(dest, "readCounts", plt)) }) setMethod(report, "QAFlagged", function(x, ...., dest=tempfile(), type="html") { list(FLAGGED=.hwrite(values(x))) }) setMethod(report, "QAFiltered", function(x, ..., dest=tempfile(), type="html") { list(FILTERED=.hwrite(values(x))) }) setMethod(report, "QAAdapterContamination", function(x, ..., dest=tempfile(), type="html") { list(ADAPTER_CONTAMINATION=.hwrite(values(x))) }) setMethod(report, "QANucleotideUse", function(x, ..., dest=tempfile(), type="html") { df <- as(values(x), "data.frame") df$Id <- as.integer(as.character(df$Id)) plt <- dotplot(Id ~ Count|factor(ifelse(df$Nucleotide == "N", "N", "O")), group=Nucleotide, df, base=df$Nucleotide, type = "b", pch = 20, col = .dnaCol, key = list(space = "top", lines = list(col = .dnaCol), text = list(lab = levels(values(x)[["Nucleotide"]])), columns = 5L), strip=FALSE, scale=list(relation="free"), par.settings=list(layout.widths = list(panel = c(1, 2)))) list(BASE_CALL_COUNT=.html_img(dest, "baseCalls", plt)) }) setMethod(report, "QAQualityUse", function(x, ..., dest=tempfile(), type="html") { df <- as(values(x), "data.frame") id <- df[["Id"]] q <- df[["Quality"]] q <- factor(q, levels=levels(q)[min(as.integer(q)):max(as.integer(q))]) df[["Quality"]] <- q df <- df[order(df$Id, df$Quality),] df[["Proportion"]] <- with(df, unlist(Map("/", lapply(split(Count, Id), cumsum), lapply(split(Count, Id), sum)), use.names=FALSE)) pal <- # brewer.pal(9, "RdYlBu") c("#D73027", "#F46D43", "#FDAE61", "#FEE090", "#FFFFBF", "#E0F3F8", "#ABD9E9", "#74ADD1", "#4575B4") col <- colorRampPalette(pal)(length(levels(q))) plt <- dotplot(Id ~ Proportion, group=Quality, df, type = "b", pch = 20, col = col, xlab="Cummulative Proportion", key = list(space = "top", lines = list(col = col, size=3L), text = list(lab = levels(df[["Quality"]])), columns = min(length(col), 10L), cex=.6)) list(QUALITY_SCORE_COUNT=.html_img(dest, "qualityCalls", plt)) }) setMethod(report, "QAReadQuality", function(x, ..., dest=tempfile(), type="html") { df <- as(values(x), "data.frame") lvl <- levels(df$Id) flag <- lvl[x@flag] df$Id <- factor(df$Id, levels=c(lvl[!lvl %in% flag], flag)) xmin <- min(df$Score) ymax <- max(df$Density) pal <- # brewer.pal(8, "Set1") c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#FFFF33", "#A65628", "#F781BF") col <- c(rep("gray", length(lvl) - length(flag)), pal[1 + (seq_along(flag) - 1) %% 8]) plt <- xyplot(Density ~ Score, group=Id, df, type = "l", xlab = "Average (calibrated) base quality", nylab = "Proportion of reads", col = col, strip=FALSE, key = list(space = "top", lines = list(col=tail(col, length(flag)), size=3L, lwd=2), text = list(lab=tail(lvl, length(flag))), columns=min(length(col), 10L), cex=.6)) list(READ_QUALITY_FIGURE=.html_img(dest, "readQuality", plt)) }) setMethod(report, "QASequenceUse", function(x, ..., dest=tempfile(), type="html") { df <- with(values(x), { nOccur <- tapply(Occurrences, Id, c) cumulative <- tapply(Occurrences * Reads, Id, function(elt) { cs <- cumsum(elt) (cs - cs[1] + 1)/(diff(range(cs)) + 1L) }) id <- tapply(Id, Id, c) data.frame(Occurrences = unlist(nOccur), Cumulative = unlist(cumulative), Id = unlist(id), row.names = NULL) }) xmax <- log10(max(df$Occurrences)) plt <- xyplot(Cumulative ~ log10(Occurrences) | factor(Id), df, xlab = expression(paste("Number of occurrences of each sequence (", log[10], ")", sep = "")), ylab = "Cumulative proportion of reads", aspect = 2, panel = function(x, y, ..., subscripts, type) { lbl <- unique(df$Id[subscripts]) ltext(xmax, 0.05, lbl, adj = c(1, 0)) type <- if (1L == length(x)) "p" else "l" panel.xyplot(x, y, ..., type = type) }, strip = FALSE) list(SEQUENCE_USE=.html_img(dest, "sequenceUse", plt)) }) setMethod(report, "QAFrequentSequence", function(x, ..., dest=tempfile(), type="html") { thresholdLabel <- if (is.finite(x@n)) "n" else "a" threshold <- as.character(if (is.finite(x@n)) x@n else x@a) freqseq <- if (x@reportSequences) { seqdf <- lapply(with(values(x), { #with() gets wrong env for .hwrite lapply(split(TopCount, Id), function(elt) { data.frame(Sequence=names(elt[[1]]), Count=unname(elt[[1]])) }) }), .hwrite) paste(Map(function(id, seq) { sprintf("

Id: %s

%s", id, seq) }, names(seqdf), seqdf), collapse="\n") } else "" df <- values(x)[, c("Id", "Count")] list(THRESHOLD_LABEL=thresholdLabel, THRESHOLD=threshold, FREQUENT_SEQUENCE_COUNT=.hwrite(df), FREQUENT_SEQUENCES=freqseq) }) setMethod(report, "QANucleotideByCycle", function(x, ..., dest=tempfile(), type="html") { df <- as(values(x), "data.frame") df <- df[df$Base != "N" & df$Base != "-", ] df$Base <- factor(df$Base) xmax <- max(df$Cycle) ymax <- log10(max(df$Count)) plt <- xyplot(log10(Count) ~ as.integer(Cycle) | Id, group = factor(Base), df[order(df$Id, df$Base, df$Cycle),], panel = function(..., subscripts) { lbl <- as.character(unique(df$Id[subscripts])) ltext(xmax, ymax, lbl, adj = c(1, 1)) panel.xyplot(..., subscripts = subscripts) }, type = "l", col = .dnaCol[1:4], key = list( space = "top", lines = list(col = .dnaCol[1:4]), text = list(lab = levels(df$Base)), columns = length(levels(df$Base))), xlab = "Cycle", aspect = 2, strip = FALSE) list(CYCLE_BASE_CALL=.html_img(dest, "cycleBaseCall", plt)) }) setMethod(report, "QAQualityByCycle", function(x, ..., dest=tempfile(), type="html") { calc_means <- function(x, y, z) rowsum(y * z, x)/rowsum(z, x) calc_quantile <- function(x, y, z, q = c(0.25, 0.5, 0.75)) by(list(y, z), x, function(x) { scoreRle <- Rle(x[[1]], x[[2]]) quantile(scoreRle, q) }) df <- as(values(x), "data.frame") Id <- df$Id pal <- c("#66C2A5", "#FC8D62") lvlPal <- c("#F5F5F5", "black") rng <- range(df$Count) at <- seq(rng[1], rng[2], length.out = 512) np <- length(unique(Id)) nrow <- ceiling(np/4) layout <- c(ceiling(np/nrow), nrow) ymin <- min(df$Score) plt <- xyplot(Score ~ Cycle | Id, df, panel = function(x, y, ..., subscripts) { z <- df$Count[subscripts] mean <- calc_means(x, y, z) qtiles <- calc_quantile(x, y, z) sxi <- sort(unique(x)) panel.levelplot(x, y, z, subscripts = TRUE, at = at, col.regions = colorRampPalette(lvlPal)) llines(sxi, mean, type = "l", col = pal[[1]], lwd = 1) llines(sxi, sapply(qtiles, "[[", 1), type = "l", col = pal[[2]], lwd = 1, lty = 3) llines(sxi, sapply(qtiles, "[[", 2), type = "l", col = pal[[2]], lwd = 1) llines(sxi, sapply(qtiles, "[[", 3), type = "l", col = pal[[2]], lwd = 1, lty = 3) lbl <- as.character(unique(df$Id[subscripts])) ltext(1, ymin, lbl, adj = c(0, 0)) }, ylab = "Quality Score", layout = layout, strip = FALSE) list(CYCLE_QUALITY=.html_img(dest, "cycleQualityCall", plt)) }) setMethod(report, "QA", function(x, ..., dest=tempfile(), type="html") { if (any(type != "html")) .throw(SRError("UserArgumentMismatch", "'type' must be 'html'")) dir.create(dest, recursive=TRUE) header <- system.file("template", "QAHeader.html", package="ShortRead", mustWork=TRUE) footer <- system.file("template", "QAFooter.html", package="ShortRead", mustWork=TRUE) sections <- c(header, x@src@html, x@filtered@html, x@flagged@html, sapply(x, slot, "html"), footer) values0 <- c(list(report(x@src, dest=dest), report(x@filtered, dest=dest), report(x@flagged, dest=dest)), lapply(x, report, dest=dest)) values <- setNames(unlist(values0, recursive=FALSE, use.names=FALSE), unlist(lapply(values0, names))) .report_html_do(dest, sections, values, ...) }) ShortRead/R/methods-QualityScore.R0000644000175400017540000002427413001230551020103 0ustar00biocbuildbiocbuild## interface ## constructors, [, [[, length, width, append, show, detail ## QualityScore .QualityScore_subset <- function(x, i, j, ..., drop=TRUE) { if (!missing(...)) .subset_err() initialize(x, quality=quality(x)[i]) } setMethod("[", c("QualityScore", "ANY", "missing"), .QualityScore_subset) .QualityScore_subset2 <- function(x, i, j, ...) { if (!missing(...)) .subset_err() quality(x)[[i]] } setMethod("[[", c("QualityScore", "ANY", "missing"), .QualityScore_subset2) setMethod(length, "QualityScore", function(x) length(quality(x))) setMethod(width, "QualityScore", function(x) .undefined_method_err(class(x), "width")) setMethod(append, c("QualityScore", "QualityScore"), function(x, values, after=length(x)) { initialize(x, quality=append(quality(x), quality(values))) }) setMethod(detail, "QualityScore", function(x) { callNextMethod() cat("quality:\n") print(quality(x)) }) ## NumericQuality setMethod(width, "NumericQuality", function(x) rep(1, length(x))) setMethod(show, "NumericQuality", function(object) { callNextMethod() .show_some("quality", quality(object)) }) ## IntegerQuality IntegerQuality <- function(quality=integer(0)) { new("IntegerQuality", quality=quality) } ## Import integer qualities from 454 .qual files .readFASTA <- ## from Biostrings; legacy code but handles numeric values function(file, checkComments=TRUE, strip.descs=TRUE) { if (missing(strip.descs)) warning("use 'strip.descs=FALSE' for compatibility with old version\n", " of readFASTA(), or 'strip.descs=TRUE' to remove the \">\"\n", " at the beginning of the description lines and to get\n", " rid of this warning (see '?readFASTA' for more details)") if (is.character(file)) { file <- file(file, "r") on.exit(close(file)) } else { if (!inherits(file, "connection")) stop("'file' must be a character string or connection") if (!isOpen(file)) { open(file, "r") on.exit(close(file)) } } s1 <- scan(file=file, what="", sep="\n", quote="", allowEscapes=FALSE, quiet=TRUE) if (checkComments) { ##comments are supposedly lines beginning with semi-colons comments <- grep("^;", s1) if (length(comments) > 0) s1 <- s1[-comments] } descriptions <- which(substr(s1, 1L, 1L) == ">") numF <- length(descriptions) if (numF == 0) stop("no FASTA sequences found") dp <- descriptions + 1L dm <- descriptions - 1L end <- c(dm[-1], length(s1)) lapply(seq_len(numF), function(i) { desc <- s1[descriptions[i]] if (strip.descs) desc <- substr(desc, 2L, nchar(desc)) if (end[i] >= dp[i]) { seq <- paste(s1[dp[i]:end[i]], collapse="") } else { warning("record \"", desc, "\" contains no sequence") seq <- "" } list(desc=desc, seq=seq) } ) } .readQual <- function(file, reads = NULL) { if (!is.null(reads)) { ## a lot faster if the reads are known nums <- scan(file, integer(0), n = sum(width(reads)), comment.char = ">") inds <- seq_len(length(reads)) scores <- split(nums, factor(rep(inds, width(reads)), inds)) } else { qual <- .readFASTA(file, strip.descs=TRUE) scores <- lapply(strsplit(subListExtract(qual, "seq", TRUE), " +"), as.integer) names(scores) <- subListExtract(qual, "desc", TRUE) } scores } setMethod(readQual, "character", function(dirPath, reads = NULL, pattern=character(), sample = 1, ...) { src <- .file_names(dirPath, pattern)[sample] scores <- do.call(c, lapply(src, .readQual, reads)) FastqQuality(sapply(scores, function(elt) rawToChar(as.raw(elt+33)))) }) ## MatrixQuality MatrixQuality <- function(quality=new("matrix")) { new("MatrixQuality", quality=quality) } .MatrixQuality_subset <- function(x, i, j, ..., drop=FALSE) { if (!missing(...)) .subset_err() initialize(x, quality=quality(x)[i,, drop=FALSE]) } setMethod("[", c("MatrixQuality", "ANY", "missing"), .MatrixQuality_subset) .MatrixQuality_subset2 <- function(x, i, j, ...) { if (!missing(...)) .subset_err() quality(x)[i,] } setMethod("[[", c("MatrixQuality", "ANY", "missing"), .MatrixQuality_subset2) setMethod(dim, "MatrixQuality", function(x) dim(quality(x))) setMethod(length, "MatrixQuality", function(x) nrow(quality(x))) setMethod(width, "MatrixQuality", function(x) rep(ncol(x), nrow(x))) ## FIXME: implement this, when starts are un-equal setMethod(narrow, "MatrixQuality", function(x, start=NA, end=NA, width=NA, use.names=TRUE) { sew <- solveUserSEW(width(x), start = start, end = end, width = width) if (length(unique(width(sew))) != 1) .throw(SRError("UserArgumentMismatch", "%s of %s must be 1", "'length(unique(width()))'", "solved SEW")) if (length(unique(start(sew))) == 1) { idx <- unique(start(sew)) + seq_len(unique(width(sew))) - 1 initialize(x, quality=quality(x)[,idx]) } else { .throw(SRError("UserArgumentMismatch", "%s requires unequal 'start' positions", "'narrow,MatrixQuality-method'")) } }) setMethod(append, c("MatrixQuality", "MatrixQuality"), function(x, values, after=length(x)) { initialize(x, quality=rbind(quality(x), quality(values))) }) ## FastqQuality, SFastqQuality .FastqQuality_missing <- function(quality, ...) { callGeneric(BStringSet(character(0))) } .FastqQuality_character <- function(quality, ...) { callGeneric(BStringSet(quality), ...) } setMethod(FastqQuality, "missing", .FastqQuality_missing) setMethod(FastqQuality, "character", .FastqQuality_character) setMethod(FastqQuality, "BStringSet", function(quality, ...) { new("FastqQuality", quality=quality) }) setMethod(SFastqQuality, "missing", .FastqQuality_missing) setMethod(SFastqQuality, "character", .FastqQuality_character) setMethod(SFastqQuality, "BStringSet", function(quality, ...) { new("SFastqQuality", quality=quality) }) setAs("FastqQuality", "numeric", function(from) { v <- as.vector(t(as(from, "matrix"))) v[!is.na(v)] }) setAs("FastqQuality", "matrix", function(from) { .Call(.alphabet_as_int, quality(from), 0:255-33L) }) setAs("FastqQuality", "PhredQuality", function(from) { as(quality(from), "PhredQuality") }) setAs("SFastqQuality", "matrix", function(from) { .Call(.alphabet_as_int, quality(from), 0:255-64L) }) setAs("SFastqQuality", "SolexaQuality", function(from) { as(quality(from), "SolexaQuality") }) setMethod(width, "FastqQuality", function(x) width(quality(x))) setMethod(reverse, "FastqQuality", function(x, ...) { do.call(class(x), list(reverse(quality(x)))) }) setMethod(narrow, "FastqQuality", function(x, start=NA, end=NA, width=NA, use.names=TRUE) { initialize(x, quality=narrow(quality(x), start, end, width, use.names)) }) setMethod(alphabet, "FastqQuality", function(x, ...) rawToChar(as.raw(32:125), TRUE)) setMethod(encoding, "FastqQuality", function(x) { alf <- alphabet(x) x <- setNames(seq(-1, length.out=length(alf)), alf) x[x >= 0 & x <= 41] }) setMethod(encoding, "SFastqQuality", function(x) { alf <- alphabet(x) x <- setNames(seq(-32, length.out=length(alf)), alf) x[x >= -5 & x <= 41] }) setMethod(show, "FastqQuality", function(object) { callNextMethod() cat("quality:\n") show(quality(object)) }) .FastqQuality_af <- function(x, as.prob=FALSE, ...) { res <- callGeneric(quality(x), as.prob=as.prob, ...) if (is(res, "matrix")) { res <- res[,1+32:125, drop=FALSE] colnames(res) <- alphabet(x) } else { res <- res[1+32:125] names(res) <- alphabet(x) } res } setMethod(alphabetFrequency, "FastqQuality", .FastqQuality_af) .FastqQuality_abc <- function(stringSet, alphabet, ...) { if (missing(alphabet)) alphabet <- Biostrings::alphabet(stringSet) .abc_BStringSet(quality(stringSet), alphabet=alphabet, ...) } setMethod(alphabetByCycle, "FastqQuality", .FastqQuality_abc) .SFastqQuality_ascore <- function(object, score=0:255-64L, ...) { .Call(.alphabet_score, quality(object), as.numeric(score)) } setMethod(alphabetScore, "SFastqQuality", .SFastqQuality_ascore) .FastqQuality_ascore <- function(object, score=0:255-33L, ...) { .Call(.alphabet_score, quality(object), as.numeric(score)) } setMethod(alphabetScore, "FastqQuality", .FastqQuality_ascore) setMethod(alphabetScore, "PhredQuality", function(object, ...) { .Call(.alphabet_score, object, as.numeric(0:255 - 33L)) }) setMethod(trimTailw, "FastqQuality", function(object, k, a, halfwidth, ..., ranges=FALSE) { rng <- callGeneric(quality(object), k, a, halfwidth, ..., alphabet=alphabet(object), ranges=TRUE) if (ranges) rng else narrow(object, 1L, end(rng))[0L != width(rng)] }) setMethod(trimTails, "FastqQuality", function(object, k, a, successive=FALSE, ..., ranges=FALSE) { rng <- callGeneric(quality(object), k, a, successive, ..., alphabet=alphabet(object), ranges=TRUE) if (ranges) rng else narrow(object, 1L, end(rng))[0L != width(rng)] }) setMethod(trimEnds, "FastqQuality", function(object, a, left=TRUE, right=TRUE, relation=c("<=", "=="), ..., ranges=FALSE) { rng <- callGeneric(quality(object), a, left, right, relation, ..., alphabet=alphabet(object), ranges=TRUE) if (ranges) rng else narrow(object, 1L, end(rng))[0L != width(rng)] }) setMethod(srrank, "FastqQuality", .forward_xq) setMethod(srorder, "FastqQuality", .forward_xq) setMethod(srsort, "FastqQuality", .forward_xq) setMethod(srduplicated, "FastqQuality", .forward_xq) .FastqQuality_srduplicated<- function(x, incomparables=FALSE, ...) { callGeneric(x=quality(x), ...) } setMethod(srduplicated, "FastqQuality", .FastqQuality_srduplicated) ShortRead/R/methods-RochePath.R0000644000175400017540000000604513001230551017330 0ustar00biocbuildbiocbuildRochePath <- function(experimentPath=NA_character_, readPath=experimentPath, qualPath=readPath, ..., verbose=FALSE) { if (verbose) { .checkPath(experimentPath) .checkPath(readPath) .checkPath(qualPath) } new("RochePath", ..., basePath=experimentPath, readPath=readPath, qualPath=qualPath) } .make_getter(c("readPath", "qualPath")) .readFasta_RochePath <- function(dirPath, pattern = "\\.fna$", sample = 1, run = 1, ..., nrec=-1L, skip=0L) { dirPath <- .file_names(readPath(dirPath)[run], pattern)[sample] if (any(is.na(dirPath))) .throw(SRError("Input/Output", "'%s' is 'NA' in '%s'", "readPath", "dirPath")) callGeneric(dirPath, ..., nrec=nrec, skip=skip) } setMethod(readFasta, "RochePath", .readFasta_RochePath) .readQual_RochePath <- function(dirPath, reads = NULL, pattern = "\\.qual$", sample = 1, run = 1, ...) { dirPath <- .file_names(qualPath(dirPath)[run], pattern)[sample] if (any(is.na(dirPath))) .throw(SRError("Input/Output", "'%s' is 'NA' in '%s'", "qualPath", "dirPath")) callGeneric(dirPath, ..., reads = reads) } setMethod(readQual, "RochePath", .readQual_RochePath) .readFastaQual_RochePath <- function(dirPath, fastaPattern = "\\.fna$", qualPattern = "\\.qual$", sample = 1, run = 1) { reads <- readFasta(dirPath, fastaPattern, sample, run) quals <- readQual(dirPath, reads, qualPattern, sample, run) ## combine the two new("ShortReadQ", reads, quality=quals) } setMethod(read454, "RochePath", function(dirPath, ...) readFastaQual(dirPath, ...)) setMethod(readFastaQual, "RochePath", .readFastaQual_RochePath) setMethod(readBaseQuality, "RochePath", function(dirPath, ...) .readFastaQual_RochePath(dirPath, ...)) .readFastaQual_character <- function(dirPath, fastaPattern = "\\.fna$", qualPattern = "\\.qual$", sample = 1, run = 1) { callGeneric(RochePath(dirPath), fastaPattern, qualPattern, sample, run) } setMethod(readFastaQual, "character", .readFastaQual_character) .sampleNames_RochePath <- function(object) { path <- readPath(object) if (!is.na(path)) sub("_.*", "", basename(.file_names(path, "\\.fna"))) else callNextMethod() } setMethod(sampleNames, "RochePath", .sampleNames_RochePath) .runNames_RochePath <- function(object) { basename(readPath(object)) } setMethod(runNames, "RochePath", .runNames_RochePath) setMethod(show, "RochePath", function(object) { callNextMethod() .show_additionalPathSlots(object) }) setMethod(detail, "RochePath", function(x, ...) { callNextMethod() .detail_additionalPathSlots(x) }) ShortRead/R/methods-RocheSet.R0000644000175400017540000000174513001230551017171 0ustar00biocbuildbiocbuild.RocheSet_RochePath <- function(path, phenoData, ...) { if (missing(phenoData)) { samples <- sampleNames(path) runs <- runNames(path) df <- data.frame(samples, run=runs, row.names=1) phenoData <- new("AnnotatedDataFrame", data=df, varMetadata=data.frame( labelDescription=c("Names of sequencing runs")), dimLabels=c("sampleNames", "sampleColumns")) } else { if (!is(phenoData, "AnnotatedDataFrame")) { cls <- paste(class(phenoData), collapse=" ") .throw(SRError("UserArgumentMismatch", "expected '%s' as '%s', but got '%s'", "AnnotatedDataFrame", "phenoData", cls)) } dimLabels(phenoData) <- c("sampleNames", "sampleColumns") } new("RocheSet", ..., sourcePath=path, phenoData=phenoData) } setMethod(RocheSet, "RochePath", .RocheSet_RochePath) setMethod(RocheSet, "character", function(path, ...) { .RocheSet_RochePath(RochePath(path), ...) }) ShortRead/R/methods-RtaIntensity.R0000644000175400017540000001175713001230551020116 0ustar00biocbuildbiocbuild## RtaIntensity RtaIntensity <- function(intensity=array(0, c(0, 0, 0)), measurementError=array(0, c(0, 0, 0)), readInfo=SolexaIntensityInfo( lane=integer()[seq_len(nrow(intensity))]), ...) { .hasMeasurementError <- mkScalar(!missing(measurementError)) new("RtaIntensity", intensity=ArrayIntensity(intensity), measurementError=ArrayIntensity(measurementError), readInfo=readInfo, .hasMeasurementError=.hasMeasurementError, ...) } .readIntensities_RtaIntensity <- function(dirPath, pattern=character(0), ..., lane=integer(0), cycles=integer(0), cycleIteration=1L, tiles=integer(0), laneName=sprintf("L%.3d", lane), cycleNames=sprintf("C%d.%d", cycles, cycleIteration), tileNames=sprintf("s_%d_%d", lane, tiles), posNames=sprintf("s_%d_%.4d_pos.txt", lane, tiles), withVariability=TRUE, verbose=FALSE) { .check_type_and_length(dirPath, "character", 1) .check_type_and_length(pattern, "character", NA) .check_type_and_length(lane, "integer", 1) .check_type_and_length(cycles, "integer", NA) .check_type_and_length(cycleIteration, "integer", 1) .check_type_and_length(tiles, "integer", NA) posFilenames <- file.path(dirPath, posNames) ok <- sapply(posFilenames, file.exists) if (!all(ok)) { msg <- sprintf("%d pos files do not exist\n %s", sum(!ok), paste(selectSome(posFilenames[!ok]), collapse="\n ")) .throw(SRError("UserArgumentMismatch", msg)) } if (verbose) message("reading 'pos' files") readInfo <- do.call(rbind, mapply(function(fl, lane, tile) { cbind(lane=lane, tile=tile, read.table(fl, col.names=c("x", "y"))) }, posFilenames, laneName, tileNames, SIMPLIFY=FALSE, USE.NAMES=FALSE)) readInfo <- do.call(SolexaIntensityInfo, readInfo) laneDirname <- file.path(dirPath, laneName) if (!file.exists(laneDirname)) { msg <- sprintf("unknown lane directory\n %s", laneDirname) .throw(SRError("UserArgumentMismatch", msg)) } cycleDirnames <- file.path(laneDirname, cycleNames) ok <- sapply(cycleDirnames, file.exists) if (!all(ok)) { msg <- sprintf("%d cycle directories do not exist\n %s", sum(!ok), paste(selectSome(cycleDirnames[!ok]), collapse="\n ")) .throw(SRError("UserArgumentMismatch", msg)) } if (verbose) message("reading 'cif' files") cif <- .read_cif_or_cnf(cycleDirnames, tileNames, ".cif") if (withVariability) { if (verbose) message("reading 'cnf' files") cnf <- .read_cif_or_cnf(cycleDirnames, tileNames, ".cnf") RtaIntensity(intensity=cif, measurementError=cnf, readInfo=readInfo) } else { RtaIntensity(intensity=cif, readInfo=readInfo) } } .read_cif_or_cnf_file <- function(fileName) { conn <- file(fileName, "rb") on.exit(close(conn)) ## header id <- rawToChar(readBin(conn, "raw", 3L)) if (id != "CIF") stop("not a CIF / CNF file:\n id: ", id, "\n file: ", fileName) version <- readBin(conn, "integer", 1L, 1L, signed=FALSE) if (version != 1L) stop("unknown CIF / CNF version:\n version: ", version, "\n file: ", fileName) dataType <- readBin(conn, "integer", 1L, 1L, signed=FALSE) firstCycle <- readBin(conn, "integer", 1L, 2L, signed=FALSE, endian="little") numberOfCycles <- readBin(conn, "integer", 1L, 2L, signed=FALSE, endian="little") numberOfClusters <- readBin(conn, "integer", 1L, 4L, signed=FALSE, endian="little") ## data m <- readBin(conn, "integer", 4 * numberOfClusters, dataType, endian="little") if (length(m) != 4 * numberOfClusters) stop("incorrect number of CIF data values:", "\n expected: ", 4 * numberOfClusters, "\n found: ", length(m), "\n file: ", fileName) m } .read_cif_or_cnf <- function(cycleDirs, tileNamesRoot, ext) { res <- lapply(cycleDirs, function(dir, tileNames) { fls <- file.path(dir, tileNames) unlist(lapply(fls, .read_cif_or_cnf_file)) }, paste(tileNamesRoot, ext, sep="")) isNull <- sapply(res, is.null) if (any(isNull)) stop("no CIF or CNF files matching pattern", "\n pattern: '", paste(tileNamesRoot, ext, sep=""), "'", "\n directories:\n ", paste(cycleDirs[isNull], collapse="\n ", sep="")) nClusters <- unique(sapply(res, length)) / 4 if (length(nClusters) != 1L) stop("cluster counts differ between cycles", "\n found: ", paste(nClusters, collapse=" ")) nms <- list(NULL, c("A", "C", "G", "T"), basename(cycleDirs)) array(unlist(res), dim=c(nClusters, 4L, length(cycleDirs)), dimnames=nms) } ShortRead/R/methods-SRError.R0000644000175400017540000000401013001230552017000 0ustar00biocbuildbiocbuild.SRErrorWarning_types <- c("SRVectorClassDisagreement", "Input/Output", "UserSubset", "UserArgumentMismatch") .SRError_types <- c("UnspecifiedError", "InternalError", "RemoteError", "InvalidReadFilter", "IncompatibleTypes", "ValueUnavailable", .SRErrorWarning_types) .SRWarn_types <- c("UnspecifiedWarning", "RemoteWarning", "IncompleteFinalRecord", .SRErrorWarning_types) ## Error setMethod(.srValidity, "SRError", function(object) { msg <- NULL type <- .type(object) if (!type %in% .SRError_types) msg <- c(msg, sprintf("'%s' must be one of '%s'", '.type', paste(.SRError_types, collapse="' '"))) if (is.null(msg)) TRUE else msg }) SRError <- function(type, fmt, ...) { new("SRError", .type=type, .message=sprintf(fmt, ...)) } .make_getter(slotNames("SRError")) setMethod(.throw, "SRError", function(object, call=NULL, ...) { class <- c(.type(object), "SRError", "error", "condition") msg <- paste(.type(object), .message(object), sep="\n ") cond <- structure(list(message=msg, call=call), class=class) stop(cond) }) ## Warning setMethod(.srValidity, "SRWarn", function(object) { msg <- NULL type <- .type(object) if (!type %in% .SRWarn_types) msg <- c(msg, sprintf("'%s' must be one of '%s'", '.type', .SRWarn_types)) if (is.null(msg)) TRUE else msg }) SRWarn <- function(type, fmt, ...) { new("SRWarn", .type=type, .message=sprintf(fmt, ...)) } setMethod(.throw, "SRWarn", function(object, call=NULL, ...) { class <- c(.type(object), "SRWarn", "warning", "condition") msg <- paste(.type(object), .message(object), sep="\n ") cond <- structure(list(message=msg, call=call), class=class) warning(cond) }) ShortRead/R/methods-SRFilter.R0000644000175400017540000001664513001230551017154 0ustar00biocbuildbiocbuildsetMethod(.srValidity, "SRFilter", function (object) { msg <- NULL fmls <- formals(object) if (length(fmls) != 1 || names(fmls)[[1]] != "x") msg <- c(msg, paste("'filter' must have one argument, 'x'")) if (is.null(msg)) TRUE else msg }) setMethod(srFilter, "missing", function(fun, name, ...) { srFilter(function(x) !logical(length(x)), name=name, ...) }) setMethod(srFilter, "function", function(fun, name, ...) { name <- mkScalar(as.character(name)) fmls <- formals(fun) if (length(fmls) != 1 || names(fmls)[[1]] != "x") .throw(SRError("UserArgumentMismatch", "'filter' must have one argument, 'x'")) env <- new.env(parent=environment(fun)) env[[".stats"]] <- NULL fun <- eval(substitute(function(x) { res <- FUN(x) SRFilterResult(res, NAME) }, list(FUN=fun, NAME=name))) environment(fun) <- env new("SRFilter", fun, name=name, ...) }) setMethod(srFilter, "SRFilter", function(fun, name, ...) { slot(fun, ".Data") }) setMethod(name, "SRFilter", function(x, ...) slot(x, "name")) .getAlphabetFrequency <- function(x, ...) { if (is(x, "ShortRead")) alphabetFrequency(sread(x), ...) else alphabetFrequency(x, ...) } idFilter <- function(regex=character(0), fixed=FALSE, exclude=FALSE, .name="idFilter") { .check_type_and_length(regex, "character", 0:1) srFilter(function(x) { .idx <- logical(length(x)) .idx[grep(regex, as.character(id(x)), fixed=fixed)] <- TRUE if (exclude) .idx <- !.idx .idx }, name = .name) } chromosomeFilter <- function(regex=character(0), fixed=FALSE, exclude=FALSE, .name="ChromosomeFilter") { .check_type_and_length(regex, "character", 0:1) srFilter(function(x) { .idx <- logical(length(x)) .idx[grep(regex, chromosome(x), fixed=fixed)] <- TRUE if (exclude) .idx <- !.idx .idx }, name=.name) } positionFilter <- function(min=-Inf, max=Inf, .name="PositionFilter") { .check_type_and_length(min, "numeric", 1) .check_type_and_length(max, "numeric", 1) srFilter(function(x) { !is.na(position(x)) & position(x) >= min & position(x) <= max }, name=.name) } uniqueFilter <- function(withSread=TRUE, .name="UniqueFilter") { msg <- if (withSread) "occurrenceFilter(withSread=TRUE)" else "occurrenceFilter" .Defunct(msg, package="ShortRead") } ## withSread ## TRUE: sread, chromosome, position, strand ## FALSE: chromosome, position, strand ## NA: sread .occurrenceName <- function(min, max, withSread, duplicates) { if (!is.character(duplicates)) { duplicates <- deparse(substitute(duplicates, env=parent.frame())) if (length(duplicates) > 1) duplicates <- "custom" } sprintf("%s\n min=%d max=%d withSread='%s'\n duplicates='%s'", "OccurrenceFilter", min, max, withSread, duplicates) } occurrenceFilter <- function(min=1L, max=1L, withSread=c(NA, TRUE, FALSE), duplicates=c("head", "tail", "sample", "none"), .name=.occurrenceName(min, max, withSread, duplicates)) { .check_type_and_length(min, "numeric", 1L) .check_type_and_length(max, "numeric", 1L) if (missing(withSread)) withSread <- withSread[1] .check_type_and_length(withSread, "logical", 1L) if (is.character(duplicates)) duplicates <- match.arg(duplicates) if (max < min) .throw(SRError("UserArgumentMismatch", "'min' must be <= 'max'")) srFilter(function(x) { rnk <- if (is(x, "AlignedRead")) { if (is.na(withSread)) srrank(sread(x)) else srrank(x, withSread=withSread) } else srrank(x) t <- tabulate(rnk) result <- rnk %in% which(t >= min & t <= max) if (!(is.character(duplicates) && "none" == duplicates)) { q <- which(rnk %in% which(t > max)) if(length(q) != 0L) { x <- tapply(q, rnk[q], duplicates, max, simplify=FALSE) result[unlist(x, use.names=FALSE)] <- TRUE } } result }, name=.name) } strandFilter <- function(strandLevels=character(0), .name="StrandFilter") { .check_type_and_length(strandLevels, "character", NA) srFilter(function(x) strand(x) %in% strandLevels, name=.name) } nFilter <- function(threshold=0L, .name="CleanNFilter") { .check_type_and_length(threshold, "numeric", 1) srFilter(function(x) { .getAlphabetFrequency(x, baseOnly=TRUE)[,"other"] <= threshold }, name=.name) } polynFilter <- function(threshold=0L, nuc=c("A", "C", "T", "G", "other"), .name="PolyNFilter") { .check_type_and_length(threshold, "numeric", 1) .check_type_and_length(nuc, "character", NA) ok <- eval(formals()[["nuc"]]) if (!all(nuc %in% ok)) .arg_mismatch_value_err("nuc", paste(nuc, collapse=", "), ok) srFilter(function(x) { alf <- .getAlphabetFrequency(x, baseOnly=TRUE) apply(alf[,nuc,drop=FALSE], 1, max) <= threshold }, name=.name) } srdistanceFilter <- function(subject=character(0), threshold=0L, .name="SRDistanceFilter") { .check_type_and_length(subject, "character", NA) .check_type_and_length(threshold, "numeric", 1) srFilter(function(x) { .idx <- !logical(length(x)) dist <- srdistance(x, subject) for (i in seq_along(dist)) .idx <- .idx & dist[[i]] >= threshold .idx }, name=.name) } dustyFilter <- function(threshold=Inf, batchSize=NA, .name="DustyFilter") { .check_type_and_length(threshold, "numeric", 1) srFilter(function(x) dustyScore(x, batchSize) <= threshold, name=.name) } alignQualityFilter <- function(threshold=0L, .name="AlignQualityFilter") { .check_type_and_length(threshold, "numeric", 1) srFilter(function(x) quality(alignQuality(x)) >= threshold, name=.name) } alignDataFilter <- function(expr=expression(), .name="AlignDataFilter") { .check_type_and_length(expr, "expression", NA) srFilter(function(x) eval(expr, pData(alignData(x))), name=.name) } compose <- function(filt, ..., .name) { lst <- if (missing(filt)) list(...) else list(filt, ...) for (`filt, ...` in lst) .check_type_and_length(`filt, ...`, "SRFilter", NA) if (missing(.name)) .name <- paste(sapply(lst, name), collapse=" o ") srFilter(function(x) { .idx <- SRFilterResult(!logical(length(x))) for (elt in rev(lst)) .idx <- .idx & elt(x) .idx }, name =.name) } setMethod(show, "SRFilter", function(object) { cat("class:", class(object), "\n") cat("name:", name(object), "\n") cat("use srFilter(object) to see filter\n") }) setAs("SRFilter", "FilterRules", function(from) { exprs <- list(from) names(exprs) <- name(from) FilterRules(exprs) }) setMethod(c, "SRFilter", function (x, ..., recursive = FALSE) { if (missing(x)) args <- unname(list(...)) else args <- unname(list(x, ...)) args <- list(x, ...) rules <- lapply(args, as, "FilterRules") do.call(c, c(rules, recursive = recursive)) }) ShortRead/R/methods-SRFilterResult.R0000644000175400017540000000272213001230551020342 0ustar00biocbuildbiocbuildSRFilterResult <- function(x=logical(), name=NA_character_, input=length(x), passing=sum(x), op=NA_character_) { new("SRFilterResult", x, name=mkScalar(as.character(name)[length(name)]), stats=data.frame(Name=as.character(name), Input=input, Passing=passing, Op=op, stringsAsFactors=FALSE)) } setMethod(name, "SRFilterResult", function(x, ...) slot(x, "name")) setMethod(stats, "SRFilterResult", function(x, ...) slot(x, "stats")) setMethod("Logic", c("SRFilterResult", "SRFilterResult"), function(e1, e2) { x <- callNextMethod() s1 <- stats(e1); s2 <- stats(e2) op <- as.character(.Generic) name <- sprintf("(%s %s %s)", name(e1), op, name(e2)) s <- rbind(stats(e1), stats(e2), data.frame(Name=name, Input=length(x), Passing=sum(x), Op=op, stringsAsFactors=FALSE)) SRFilterResult(x, s$Name, s$Input, s$Passing, s$Op) }) setMethod("!", "SRFilterResult", function(x) { name <- sprintf("!(%s)", name(x)) y <- callNextMethod() s <- rbind(stats(x), data.frame(Name=name, Input=length(y), Passing=sum(y), Op="!", stringsAsFactors=FALSE)) SRFilterResult(y, s$Name, s$Input, s$Passing, s$Op) }) setMethod(show, "SRFilterResult", function(object) { cat("class:", class(object), "\n") cat("name:", name(object), "\n") cat("output:", selectSome(object), "\n") cat("stats:\n") print(stats(object)) }) ShortRead/R/methods-SRList.R0000644000175400017540000000252213001230552016630 0ustar00biocbuildbiocbuildSRList <- function(...) { args <- list(...) if (length(args)==1 && is(args[[1]], "list")) new("SRList", .srlist=args[[1]]) else new("SRList", .srlist=args) } .make_getter(".srlist") srlist <- .srlist # export setMethod(names, "SRList", function(x) names(.srlist(x))) setReplaceMethod("names", c("SRList", "character"), function(x, value) { lst <- .srlist(x) names(lst) <- value initialize(x, .srlist=lst) }) setMethod(length, "SRList", function(x) length(.srlist(x))) setMethod("[", c(x="SRList", i="ANY", j="missing"), function(x, i, j, ..., drop=FALSE) { initialize(x, .srlist=.srlist(x)[i]) }) setMethod("[[", signature(x="SRList", i="ANY", j="missing"), function(x, i, j, ...) .srlist(x)[[i]]) setMethod(sapply, "SRList", function(X, FUN, ..., simplify=TRUE, USE.NAMES=TRUE) { sapply(.srlist(X), FUN, ..., simplify=simplify, USE.NAMES=USE.NAMES) }) setMethod(lapply, "SRList", function(X, FUN, ...) { lapply(.srlist(X), FUN, ...) }) .SRList_show_class <- function(object) { cat("class: ", class(object), "(", length(object), ")\n", sep="") } setMethod(show, "SRList", .SRList_show_class) setMethod(detail, "SRList", function(x,...) { .SRList_show_class(x) .srlist(x) }) ShortRead/R/methods-SRSet.R0000644000175400017540000000504313001230551016450 0ustar00biocbuildbiocbuild.SRSet_validity <- function(object) { msg <- NULL len <- length(readIndex(object)) rlen <- c(readData = nrow(readData(object))) if (!all(rlen==len)) { bad <- rlen!=len msg <- c(msg, sprintf("read length mismatch: expected %d, found:\n %s", rlen, paste(names(rlen)[bad], rlen[bad], sep="=", collapse=", "))) } snames <- sampleNames(sourcePath(object)) slen <- length(snames) oslen <- c(phenoData = nrow(phenoData(object)), readCount = length(readCount(object))) if (!all(oslen==slen)) { bad <- oslen!=slen msg <- c(msg, sprintf("sample length mismatch: expected %d, found:\n %s", slen, paste(names(oslen)[bad], oslen[bad], sep="=", collapse=", "))) } osnames <- sampleNames(object) stest <- snames == osnames if (!all(stest)) msg <- c(msg, sprintf("sample names mismatch:\n %s", slen, paste(snames[!stest], osnames[!stest], sep = "!=", collapse = ", "))) rind <- readIndex(object) if (!all(rind > 0 & rind <= len)) msg <- c(msg, "values in 'readIndex' must be > 0 and <= number of reads") rcount <- readCount(object) if (!all(rcount >= 0)) msg <- c(msg, "values in 'readCount' must be non-negative") if (sum(rcount) != len) msg <- c(msg, sprintf("'sum(readCount)', %d, must equal the number of reads, %d", sum(rcount), len)) if (is.null(msg)) TRUE else msg } setMethod(.srValidity, "SRSet", .SRSet_validity) .make_getter(c("readData", "sourcePath", "readIndex", "readCount")) setMethod(experimentPath, "SRSet", function(object, ...) { callGeneric(sourcePath(object), ...) }) setMethod(sampleNames, "SRSet", function(object) { sampleNames(phenoData(object)) }) setMethod(show, "SRSet", function(object) { callNextMethod() cat("experimentPath(object): ", experimentPath(object), "\n", sep="") }) setMethod(detail, "SRSet", function(x, ...) { callNextMethod() cat("\nsourcePath\n") detail(sourcePath(x), ...) cat("\nphenoData\n") pd <- phenoData(x) cat("pData:\n") print(pData(pd)) cat("varMetadata:\n") print(varMetadata(pd)) }) setMethod(phenoData, "SRSet", function(object) object@phenoData) ## proposed ##setMethod(readSRQ, "SRSet", function(object) readSRQ(sourcePath(object))) ShortRead/R/methods-SRVector.R0000644000175400017540000000215413001230552017160 0ustar00biocbuildbiocbuildsetMethod(.srValidity, "SRVector", function(object) { msg <- NULL cls <- vclass(object) if (length(cls)!=1) msg <- c(msg, "'vclass' must be character(1)") if (!all(sapply(object, is, cls))) msg <- c(msg, sprintf("all elements must satisfy 'is(element, \"%s\")'", cls)) if (is.null(msg)) TRUE else msg }) SRVector <- function(..., vclass) { args <- list(...) if (length(args)>0 && missing(vclass)) vclass <- class(args[[1]]) ok <- sapply(args, is, vclass) if (!all(ok)) { classes <- paste(unique(c(sapply(args, class), vclass)), collapse="' '") .throw(SRError("SRVectorClassDisagreement", "elements and vclass: '%s'", classes), call=match.call()) } new("SRVector", .srlist=args, vclass=vclass) } .make_getter("vclass") setMethod(show, "SRVector", function(object) { callNextMethod() cat("vclass: ", vclass(object), "\n", sep="") }) setMethod(detail, "SRVector", function(x) { .SRList_show_class(x) show(unlist(.srlist(x))) }) ShortRead/R/methods-ShortRead.R0000644000175400017540000001207013001230552017342 0ustar00biocbuildbiocbuild.ShortRead_validity <- function(object) { msg <- NULL if (length(sread(object)) != length(id(object))) msg <- c(msg, sprintf("sread() and id() length mismatch: %d, %d", length(sread(object)), length(id(object)))) if (is.null(msg)) TRUE else msg } setMethod(.srValidity, "ShortRead", .ShortRead_validity) setMethod(sread, "ShortRead", function(object, ...) slot(object, "sread")) setMethod(id, "ShortRead", function(object, ...) slot(object, "id")) setMethod(ShortRead, c("DNAStringSet", "BStringSet"), function(sread, id) { new("ShortRead", sread=sread, id=id) }) setMethod(ShortRead, c("DNAStringSet", "missing"), function(sread, id) { new("ShortRead", sread=sread, id=BStringSet(rep("", length(sread)))) }) setMethod(ShortRead, c("missing", "missing"), function(sread, id) { new("ShortRead") }) setMethod(length, "ShortRead", function(x) length(sread(x))) setMethod(width, "ShortRead", function(x) width(sread(x))) ## coerce setMethod(pairwiseAlignment, "ShortRead", function(pattern, subject, ...) { pairwiseAlignment(sread(pattern), subject, ...) }) ## import / export setMethod(readFasta, "character", function(dirPath, pattern=character(0), ..., nrec=-1L, skip=0L) { src <- .file_names(dirPath, pattern) FASTAlist <- lapply(src, readDNAStringSet, nrec=nrec, skip=skip) fasta <- do.call(c, FASTAlist) new("ShortRead", ..., sread=DNAStringSet(fasta, use.names=FALSE), id=BStringSet(names(fasta))) }) setMethod(writeFasta, "ShortRead", function(object, file, mode="w", ...) { dna <- sread(object) names(dna) <- id(object) callGeneric(dna, file=file, mode=mode, ...) }) ## subset setMethod("[", c("ShortRead", "missing", "missing"), function(x, i, j, ..., drop=NA) .subset_err()) setMethod("[", c("ShortRead", "missing", "ANY"), function(x, i, j, ..., drop=NA) .subset_err()) setMethod("[", c("ShortRead", "ANY", "ANY"), function(x, i, j, ..., drop=NA) .subset_err()) .ShortRead_subset <- function(x, i, j, ..., drop=TRUE) { if (!missing(...)) .subset_err() initialize(x, sread=sread(x)[i], id=id(x)[i]) } setMethod("[", c(x="ShortRead", i="ANY", j="missing"), .ShortRead_subset) setMethod(append, c("ShortRead", "ShortRead"), function(x, values, after=length(x)) { initialize(x, id=append(id(x), id(values)), sread=append(sread(x), sread(values))) }) ## manip .abc_ShortRead <- function(stringSet, alphabet, ...) { if (!missing(alphabet)) .throw(SRWarn("UserArgumentMismatch", "'alphabet' ignored")) alphabetByCycle(sread(stringSet), ...) } setMethod(alphabetByCycle, "ShortRead", .abc_ShortRead) setMethod(clean, "ShortRead", function(object, ...) { alf <- alphabetFrequency(sread(object), baseOnly=TRUE) object[alf[,'other'] == 0] }) setMethod(dustyScore, "ShortRead", function(x, batchSize=NA, ...) { callGeneric(sread(x), batchSize=batchSize, ...) }) setMethod(srorder, "ShortRead", .forward_x) setMethod(srrank, "ShortRead", .forward_x) setMethod(srsort, "ShortRead", function(x, ...) { x[srorder(x, ...)] }) setMethod(srduplicated, "ShortRead", .forward_x) setMethod(tables, "ShortRead", function(x, n=50, ...) { callGeneric(sread(x), n=n, ...) }) .srdistance_ShortRead_ANY <- function(pattern, subject, ...) { callGeneric(sread(pattern), subject, ...) } setMethod(srdistance, c("ShortRead", "ANY"), .srdistance_ShortRead_ANY) setMethod(narrow, "ShortRead", function(x, start=NA, end=NA, width=NA, use.names=TRUE) { initialize(x, sread=narrow(sread(x), start, end, width, use.names)) }) setMethod(trimLRPatterns, c(subject="ShortRead"), function (Lpattern = "", Rpattern = "", subject, max.Lmismatch = 0, max.Rmismatch = 0, with.Lindels = FALSE, with.Rindels = FALSE, Lfixed = TRUE, Rfixed = TRUE, ranges = FALSE) { ret <- callGeneric(Lpattern, Rpattern, sread(subject), max.Lmismatch, max.Rmismatch, with.Lindels, with.Rindels, Lfixed, Rfixed, ranges=TRUE) if (ranges) ret else narrow(subject, start(ret), end(ret)) }) setMethod(trimEnds, "ShortRead", function(object, a, left=TRUE, right=TRUE, relation=c("<=", "=="), ..., ranges=FALSE) { rng <- callGeneric(sread(object), a, left, right, relation, ..., ranges=TRUE) if (ranges) rng else narrow(object, start(rng), end(rng)) }) ## show setMethod(show, "ShortRead", function(object) { callNextMethod() wd <- sort(unique(width(object))) if (length(wd)>2) wd <- paste(range(wd), collapse="..") cat("length:", length(object), "reads; width:", wd, "cycles\n") }) setMethod(detail, "ShortRead", function(x, ...) { cat("class: ", class(x), "\n") cat("\nsread:\n") show(sread(x)) cat("\nid:\n") show(id(x)) }) ## summary ## perhaps a 'summary' method with statistics on reads for each sample ShortRead/R/methods-ShortReadFile.R0000644000175400017540000000257713001230551020154 0ustar00biocbuildbiocbuildsetMethod(.ShortReadFile, "character", function(g, path, open="", ...) { path <- .file_names(path, character()) g$new(con=file(path, open, encoding="ASCII"), path=path, ...) }) setMethod(.ShortReadFile, "connection", function(g, path, ...) { descr <- summary(path)$description g$new(con=path, path=descr, ...) }) setMethod(path, "ShortReadFile", function(object, ...) { object$path }) open.ShortReadFile <- function(con, ...) { tryCatch(open(con$con), error=function(err, ...) { .throw(SRError("Input/Output", "error: %s\n%s", conditionMessage(err), Rsamtools:::.ppath(" path", con$path))) }) invisible(con) } close.ShortReadFile <- function(con, ...) { tryCatch(close(con$con), error=function(err, ...) { .throw(SRError("Input/Output", "error: %s\n%s", conditionMessage(err), Rsamtools:::.ppath(" path", con$path))) }) invisible(con) } setMethod(isOpen, "ShortReadFile", function(con, rw="") { tryCatch(isOpen(con$con), error=function(err, ...) { msg <- conditionMessage(err) if (msg != "invalid connection") .throw(SRWarn("Input/Output", "warning: %s\n%s", conditionMessage(err), Rsamtools:::.ppath(" path", con$path))) FALSE }) }) ShortRead/R/methods-ShortReadQ.R0000644000175400017540000002130413001230551017462 0ustar00biocbuildbiocbuild## validity / accessors / constructors setMethod(.srValidity, "ShortReadQ", function(object) { msg <- NULL lenq <- length(quality(object)) lens <- length(sread(object)) if (lenq != lens) { txt <- sprintf("sread and quality length mismatch: %d %d", lenq, lens) msg <- c(msg, txt) } if (!all(width(quality(object)) == width(sread(object)))) { txt <- sprintf("some sread and quality widths differ") msg <- c(msg, txt) } if (is.null(msg)) TRUE else msg }) setMethod(ShortReadQ, c("DNAStringSet", "QualityScore", "BStringSet"), function(sread, quality, id) { new("ShortReadQ", sread=sread, quality=quality, id=id) }) setMethod(ShortReadQ, c("DNAStringSet", "QualityScore", "missing"), function(sread, quality, id) { new("ShortReadQ", sread=sread, quality=quality, id=BStringSet(character(length(sread)))) }) setMethod(ShortReadQ, c("DNAStringSet", "BStringSet", "BStringSet"), function(sread, quality, id, ..., qualityType=c("Auto", "FastqQuality", "SFastqQuality"), filter=srFilter(), withIds=TRUE) { if (!missing(filter)) .check_type_and_length(filter, "SRFilter", NA) tryCatch({ qualityType <- match.arg(qualityType) }, error=function(err) { .throw(SRError("UserArgumentMismatch", conditionMessage(err))) }) tryCatch({ qualityFunc <- switch(qualityType, Auto={ alf <- alphabetFrequency(head(quality, 10000), collapse=TRUE) wch <- which(alf != 0) if (any(alf) && (min(wch) >= 58) && (max(wch) > 74)) { SFastqQuality } else FastqQuality }, SFastqQuality=SFastqQuality, FastqQuality=FastqQuality) quality <- qualityFunc(quality) srq <- if (withIds) ShortReadQ(sread, quality, id) else ShortReadQ(sread, quality) if (!missing(filter)) srq <- srq[filter(srq)] srq }, error=function(err) { .throw(SRError("IncompatibleTypes", "message: %s", conditionMessage(err))) }) }) setMethod(ShortReadQ, c("DNAStringSet", "BStringSet", "missing"), function(sread, quality, id, ...) { ShortReadQ(sread, quality, BStringSet(character(length(sread))), ...) }) setMethod(ShortReadQ, c("missing", "missing", "missing"), function(sread, quality, id) { ShortReadQ(DNAStringSet(), FastqQuality(), BStringSet()) }) setAs("ShortReadQ", "QualityScaledDNAStringSet", function(from) { q <- quality(from) q <- if (is(q, "SFastqQuality")) as(q, "SolexaQuality") else if (is(q, "FastqQuality")) as(q, "PhredQuality") else as(q, "XStringQuality") QualityScaledDNAStringSet(sread(from), q) }) setMethod(readFastq, "character", function(dirPath, pattern=character(0), ..., withIds=TRUE) { src <- .file_names(dirPath, pattern) tryCatch({ elts <- .Call(.read_solexa_fastq, src, withIds) if (withIds) ShortReadQ(elts[["sread"]], elts[["quality"]], elts[["id"]], ..., withIds=withIds) else ShortReadQ(elts[["sread"]], elts[["quality"]], ..., withIds=withIds) }, error=function(err) { .throw(SRError("Input/Output", "file(s):\n %s\n message: %s", paste(src, collapse="\n "), conditionMessage(err))) }) }) setMethod(writeFastq, c("ShortReadQ", "character"), function(object, file, mode="w", full=FALSE, compress=TRUE, ...) { if (length(file) != 1) .throw(SRError("UserArgumentMismatch", "'%s' must be '%s'", "file", "character(1)")) if (file.exists(file) && mode != "a") .throw(SRError("UserArgumentMismatch", "file '%s' exists, but mode is not 'a'", file)) file <- path.expand(file) ## FIXME: different quality types max_width <- max(0L, unique(width(id(object))), unique(width(sread(object))), unique(width(quality(object)))) if (!is(quality(quality(object)), "XStringSet")) .throw(SRError("UserArgumentMismatch", "'is(<%s>, \"%s\")' failed", "quality", "XStringSet")) .Call(.write_fastq, id(object), sread(object), quality(quality(object)), file, mode, full, compress, max_width) invisible(length(object)) }) ## coerce setMethod(pairwiseAlignment, "ShortReadQ", function(pattern, subject, ...) { mc <- as.list(match.call()) if (is.null(mc$patternQuality)) mc$patternQuality <- quality(quality(pattern)) do.call(callNextMethod, c(list(pattern, subject), mc)) }) ## subset setMethod("[", c("ShortReadQ", "missing", "missing"), function(x, i, j, ..., drop=NA) .subset_err()) setMethod("[", c("ShortReadQ", "missing", "ANY"), function(x, i, j, ..., drop=NA) .subset_err()) setMethod("[", c("ShortReadQ", "ANY", "ANY"), function(x, i, j, ..., drop=NA) .subset_err()) .ShortReadQ_subset <- function(x, i, j, ..., drop=TRUE) { if (!missing(...)) .subset_err() initialize(x, sread=sread(x)[i], id=id(x)[i], quality=quality(x)[i]) } setMethod("[", c("ShortReadQ", "ANY", "missing"), .ShortReadQ_subset) setReplaceMethod("[", c("ShortReadQ", "ANY", "missing", "ShortReadQ"), function(x, i, j, ..., value) { sread <- sread(x); sread[i] <- sread(value) quality <- quality(quality(x)); quality[i] <- quality(quality(value)) id <- id(x); id[i] <- id(value) initialize(x, sread=sread, id=id, quality=initialize(quality(x), quality=quality)) }) setMethod(append, c("ShortReadQ", "ShortReadQ"), function(x, values, after=length(x)) { initialize(x, id=append(id(x), id(values)), sread=append(sread(x), sread(values)), quality=append(quality(x), quality(values))) }) setMethod(reverse, "ShortReadQ", function(x, ...) { ShortReadQ(reverse(sread(x)), reverse(quality(x)), id(x)) }) setMethod(reverseComplement, "ShortReadQ", function(x, ...) { ShortReadQ(reverseComplement(sread(x)), reverse(quality(x)), id(x)) }) setMethod(narrow, "ShortReadQ", function(x, start=NA, end=NA, width=NA, use.names=TRUE) { initialize(x, sread=narrow(sread(x), start, end, width, use.names), quality=narrow(quality(x), start, end, width, use.names)) }) ## manip .abc_ShortReadQ <- function(stringSet, alphabet, ...) { if (!missing(alphabet)) { if (!(is.list(alphabet) && length(alphabet) == 2)) .throw(SRError("UserArgumentMismatch", "'%s' must be '%s'", "alphabet", "list(2)")) if (!all(sapply(alphabet, is, "character"))) .throw(SRError("UserArgumentMismatch", "'%s' list elements must be '%s'", "alphabet", "character()")) } sread <- sread(stringSet) quality <- quality(stringSet) if (missing(alphabet)) alphabet <- list(Biostrings::alphabet(sread), Biostrings::alphabet(quality)) w <- max(0L, width(stringSet)) res <- .Call(.alphabet_pair_by_cycle, sread, quality(quality), w, alphabet[[1]], alphabet[[2]]) dm <- dimnames(res) dm[[3]]<- seq_len(w) names(dm)[[3]] <- "cycle" dimnames(res) <- dm res } setMethod(alphabetByCycle, "ShortReadQ", .abc_ShortReadQ) setMethod(alphabetScore, "ShortReadQ", .forward_objq) setMethod(trimTailw, "ShortReadQ", function(object, k, a, halfwidth, ..., ranges=FALSE) { rng <- callGeneric(quality(object), k, a, halfwidth, ..., ranges=TRUE) if (ranges) rng else narrow(object, 1L, end(rng))[0L != width(rng)] }) setMethod(trimTails, "ShortReadQ", function(object, k, a, successive=FALSE, ..., ranges=FALSE) { rng <- callGeneric(quality(object), k, a, successive, ..., ranges=TRUE) if (ranges) rng else narrow(object, 1L, end(rng))[0L != width(rng)] }) setMethod(trimEnds, "ShortReadQ", function(object, a, left=TRUE, right=TRUE, relation=c("<=", "=="), ..., ranges=FALSE) { rng <- callGeneric(quality(object), a, left, right, relation, ..., ranges=TRUE) if (ranges) rng else narrow(object, start(rng), end(rng)) }) ## show setMethod(detail, "ShortReadQ", function(x, ...) { callNextMethod() detail(quality(x)) }) ## summary ## perhaps summary stats like ShortRead except with qualities ShortRead/R/methods-SolexaExportQA.R0000644000175400017540000002554513001230552020341 0ustar00biocbuildbiocbuild.SolexaExportQA <- function(x, ...) { new("SolexaExportQA", .srlist=x, ...) } ## qa .qa_lst_as_data_frame <- function(lst) { if (length(lst)==0) return(data.frame()) nms <- names(lst[[1]]) sublst <- sapply(nms, function(nm) { subListExtract(lst, nm, simplify=TRUE) }) names(sublst) <- nms do.call(data.frame, sublst) } .qa_Solexa_tileStats <- function(dirPath, pattern, ...) { .qa_Solexa_tileStats_tile <- function(dirPath, pattern, ...) { lane <- as.numeric(sub("s_([0-9]+)_.*", "\\1", pattern)) tile <- as.numeric(sub("s_[0-9]+_([0-9]+)_.*", "\\1", pattern)) dna <- readXStringColumns(dirPath, pattern, colClasses=list(NULL, NULL, NULL, NULL, "DNAString"))[[1]] list(lane=lane, tile=tile, slane=(lane-1)*3+trunc((tile-1)/100)+1, stile=1+pmin((tile-1)%%200, (200-tile)%%200), nReads=length(dna), nClean=sum(alphabetFrequency(dna, baseOnly=TRUE)[,"other"]==0)) } if (length(pattern)==0) pattern=".*_seq.txt$" lst <- bplapply(list.files(dirPath, pattern, full.names=TRUE), .qa_Solexa_tileStats_tile, dirPath=dirPath, ...) .qa_lst_as_data_frame(lst) } .qa_SolexaExport_lane <- function(dirPath, pattern, ..., type="SolexaExport", verbose=FALSE) { if (verbose) message("qa 'SolexaExport' pattern:", pattern) readLbls <- c("read", "filtered", "aligned") rpt <- readAligned(dirPath, pattern, type, ...) doc <- .qa_depthOfCoverage(rpt, pattern) ac <- .qa_adapterContamination(rpt, pattern, ...) df <- pData(alignData(rpt)) filterIdx <- df$filtering=="Y" mapIdx <- !is.na(position(rpt)) nbins <- max(df$tile) tiles <- seq_len(nbins) nReadByTile <- tabulate(df$tile, nbins) nFilterByTile <- tabulate(df$tile[filterIdx], nbins) nMapByTile <- tabulate(df$tile[mapIdx], nbins) qualityScore <- alphabetScore(quality(rpt)) / width(quality(rpt)) qualityDf <- function(qscore) { d <- density(qscore) data.frame(quality=d$x, density=d$y, lane=pattern) } qualityScoreRead <- qualityDf(qualityScore) qualityScoreFiltered <- qualityDf(qualityScore[filterIdx]) qualityScoreAligned <- qualityDf(qualityScore[mapIdx]) abc <- alphabetByCycle(rpt) baseQuality <- apply(abc, 2, sum) alignQuality <- table(quality(alignQuality(rpt))[mapIdx]) tablesRead <- tables(sread(rpt)) tablesFiltered <- tables(sread(rpt)[filterIdx]) tablesAligned <- tables(sread(rpt)[mapIdx]) frequentSequences <- data.frame(sequence=c( names(tablesRead$top), names(tablesFiltered$top), names(tablesAligned$top)), count=c( as.integer(tablesRead$top), as.integer(tablesFiltered$top), as.integer(tablesAligned$top)), type=rep( readLbls, c(length(tablesRead$top), length(tablesFiltered$top), length(tablesAligned$top))), lane=pattern) sequenceDistribution <- cbind(rbind(tablesRead$distribution, tablesFiltered$distribution, tablesAligned$distribution), type=rep( readLbls, c(nrow(tablesRead$distribution), nrow(tablesFiltered$distribution), nrow(tablesAligned$distribution))), lane=pattern) perCycleBaseCall <- .qa_perCycleBaseCall(abc, pattern) perCycleQuality <- .qa_perCycleQuality(abc, quality(rpt), pattern) list(readCounts=data.frame( read=sum(nReadByTile), filtered=sum(nFilterByTile), aligned=sum(nMapByTile), row.names=pattern), baseCalls=local({ n <- apply(abc, 1, sum) data.frame(A=n["A"], C=n["C"], G=n["G"], T=n["T"], N=n["N"], row.names=pattern) }), readQualityScore=cbind( rbind(qualityScoreRead, qualityScoreFiltered, qualityScoreAligned), type=rep( readLbls, c(nrow(qualityScoreRead), nrow(qualityScoreFiltered), nrow(qualityScoreAligned)))), baseQuality=data.frame( score=as.vector(names(baseQuality)), count=as.vector(baseQuality), lane=pattern, row.names=NULL), alignQuality=data.frame( score=as.numeric(names(alignQuality)), count=as.vector(alignQuality), lane=pattern, row.names=NULL), frequentSequences=frequentSequences, sequenceDistribution=sequenceDistribution, perCycle=list( baseCall=perCycleBaseCall, quality=perCycleQuality), perTile=list( readCounts=data.frame( count=c(nReadByTile, nFilterByTile, nMapByTile), type=rep( readLbls, c(length(nReadByTile), length(nFilterByTile), length(nMapByTile))), tile=rep(tiles, 3), lane=pattern, row.names=NULL), medianReadQualityScore=local({ tidx <- as.character(tiles) data.frame(score=c( tapply(qualityScore, df$tile, median)[tidx], tapply(qualityScore[filterIdx], df$tile[filterIdx], median)[tidx], tapply(qualityScore[mapIdx], df$tile[mapIdx], median)[tidx]), type=rep(readLbls, each=length(tidx)), tile=as.integer(tidx), lane=pattern, row.names=NULL) })), depthOfCoverage=doc, adapterContamination=ac) } .qa_SolexaExport <- function(dirPath, pattern, type="SolexaExport", ..., verbose=FALSE) { fls <- .file_names(dirPath, pattern) lst <- bplapply(basename(fls), .qa_SolexaExport_lane, dirPath=dirPath, type=type, ..., verbose=verbose) ## collapse into data frames lst <- list(readCounts=.bind(lst, "readCounts"), baseCalls=.bind(lst, "baseCalls"), readQualityScore=.bind(lst, "readQualityScore"), baseQuality=.bind(lst, "baseQuality"), alignQuality=.bind(lst, "alignQuality"), frequentSequences=.bind(lst, "frequentSequences"), sequenceDistribution=.bind(lst, "sequenceDistribution"), perCycle=local({ lst <- subListExtract(lst, "perCycle") list(baseCall=.bind(lst, "baseCall"), quality=.bind(lst, "quality")) }), perTile=local({ lst <- subListExtract(lst, "perTile") list(readCounts=.bind(lst, "readCounts"), medianReadQualityScore=.bind( lst, "medianReadQualityScore")) }), depthOfCoverage=.bind(lst, "depthOfCoverage"), adapterContamination=.bind(lst, "adapterContamination")) .SolexaExportQA(lst) } ## report setMethod(.report_pdf, "SolexaExportQA", function(x, dest, type, ...) { qa <- x # mnemonic alias to <- tempfile() save(qa, file=to) res <- callGeneric(to, dest, type, ...) unlink(to) res }) setMethod(report_html, "SolexaExportQA", function (x, dest, type, ...) { qa <- .qa_sampleKey(x) dir.create(dest, recursive=TRUE) fls <- c("0000-Header.html", "1000-Overview.html", "2000-RunSummary.html", "3000-ReadDistribution.html", "4000-CycleSpecific.html", "5000-PerTile.html", "6000-Alignment.html", "8000-DepthOfCoverage.html", "9000-AdapterContamination.html", "9999-Footer.html") sections <- system.file("template", fls, package="ShortRead") perCycle <- qa[["perCycle"]] perTile <- qa[["perTile"]] readCnt <- perTile[["readCounts"]] values <- list(SAMPLE_KEY=hwrite(qa[["keyValue"]], border=0), PPN_COUNT=.html_img( dest, "readCount", .plotReadCount(qa)), PPN_COUNT_TBL=hwrite( .ppnCount(qa[["readCounts"]]), border=0), BASE_CALL_COUNT=.html_img( dest, "baseCalls", .plotNucleotideCount(qa)), READ_QUALITY_FIGURE=.htmlReadQuality( dest, "readQuality", qa), READ_OCCURRENCES_FIGURE=.htmlReadOccur( dest, "readOccurences", qa), FREQUENT_SEQUENCES_READ=hwrite( .freqSequences(qa, "read"), border=0), FREQUENT_SEQUENCES_FILTERED=hwrite( .freqSequences(qa, "filtered"), border=0), FREQUENT_SEQUENCES_ALIGNED=hwrite( .freqSequences(qa, "aligned"), border=0), CYCLE_BASE_CALL_FIGURE=.html_img( dest, "perCycleBaseCall", .plotCycleBaseCall(perCycle$baseCall)), CYCLE_QUALITY_FIGURE=.html_img( dest, "perCycleQuality", .plotCycleQuality(perCycle$quality)), PER_TILE_HISTOGRAM=local({ cnts <- readCnt[readCnt$type=="read", "count"] hist <- histogram(cnts, breaks=40, xlab="Reads per tile", panel=function(x, ...) { panel.abline(v=quantile(x, .1), col="red", lty=2) panel.histogram(x, ...) }, col="white") .html_img(dest, "perTileHistogram", hist) }), PER_TILE_COUNT_FIGURE=.html_img( dest, "perTileCount", .plotTileCounts(readCnt[readCnt$type=="read",])), PER_TILE_QUALITY_FIGURE=local({ qscore <- perTile[["medianReadQualityScore"]] score <- qscore[qscore$type=="read",] .html_img(dest, "perTileQuality", .plotTileQualityScore(score)) }), ALIGN_QUALITY_FIGURE=.html_img( dest, "alignmentQuality", .plotAlignQuality(qa[["alignQuality"]])), DEPTH_OF_COVERAGE_FIGURE=.html_img( dest, "depthOfCoverage", .plotDepthOfCoverage(qa[["depthOfCoverage"]])), ADAPTER_CONTAMINATION=hwrite( .df2a(qa[["adapterContamination"]]), border=0)) .report_html_do(dest, sections, values, ...) }) ShortRead/R/methods-SolexaIntensity.R0000644000175400017540000002124113001230552020611 0ustar00biocbuildbiocbuild## SolexaIntensityInfo setMethod(.srValidity, "SolexaIntensityInfo", function(object) { msg <- NULL reqd <- c("lane", "tile", "x", "y") if (slot(object, ".init")==TRUE && !all(reqd %in% varLabels(object))) { missing <- reqd[!reqd %in% names(object)] msg <- c(msg, sprintf("'%s' must contain columns '%s'", class(object), paste(missing, collapse="' '"))) } if (is.null(msg)) TRUE else msg }) ## SolexaIntensity SolexaIntensity <- function(intensity=array(0, c(0, 0, 0)), measurementError=array(0, c(0, 0, 0)), readInfo=SolexaIntensityInfo( lane=integer(nrow(intensity))), ...) { .hasMeasurementError <- mkScalar(!missing(measurementError)) new("SolexaIntensity", intensity=ArrayIntensity(intensity), measurementError=ArrayIntensity(measurementError), readInfo=readInfo, .hasMeasurementError=.hasMeasurementError, ...) } .readIntensities_SolexaIntensity <- function(dirPath, pattern=character(0), ..., intExtension="_int.txt", nseExtension="_nse.txt", withVariability=TRUE, verbose=FALSE) { .check_type_and_length(withVariability, "logical", 1) .check_type_and_length(pattern, "character", NA) .check_type_and_length(intExtension, "character", 1) .check_type_and_length(nseExtension, "character", 1) intPattern <- paste(pattern, intExtension, sep="") nrec <- countLines(dirPath, intPattern) crec <- c(0, cumsum(nrec)) if (withVariability) { nsePattern <- paste(pattern, nseExtension, sep="") extrec <- countLines(dirPath, nsePattern) if (length(nrec) != length(extrec)) { .throw(SRError("UserArgumentMismatch", "number of files found differs between 'intensity' (%d) and 'nse' (%d)\n dirPath: '%s'\n pattern: '%s'\n intExtension: '%s'\n nseExtension: '%s'", length(nrec), length(extrec), dirPath, pattern, intExtension, nseExtension)) } if (!all(nrec == extrec)) { .throw(SRError("UserArgumentMismatch", "line counts differ between 'intensity' and 'nse'\n dirPath: '%s'\n pattern: '%s'\n intExtension: '%s'\n nseExtension: '%s'", dirPath, pattern, intExtension, nseExtension)) } } fls <- .file_names(dirPath, intPattern) gz <- gzfile(fls[[1]]); open(gz) tryCatch({ ln <- readLines(gz, 1) }, finally=close(gz)) cycles <- length(gregexpr("\t", ln, fixed=TRUE)[[1]]) - 3L reads <- sum(nrec) what <- c(rep(list(integer()), 4), rep(list(numeric()), cycles * 4L)) int <- array(numeric(), c(reads, 4L, cycles), dimnames=list(NULL, c("A", "C", "G", "T"), NULL)) df <- data.frame(lane=integer(reads), tile=integer(reads), x=integer(reads), y=integer(reads)) for (i in seq_along(fls)) { tryCatch({ gz <- gzfile(fls[[i]]); open(gz) data <- scan(gz, what, nrec[[i]],..., quiet=!verbose) idx <- (crec[i]+1):crec[i+1] int[idx,,] <- array(unlist(data[-(1:4)]), c(nrec[[i]], 4L, cycles)) df[idx,] <- data[1:4] }, error=function(err) { msg <- sprintf("parsing '%s'\n file: %s\n error: %s", "intPattern", fls[[i]], conditionMessage(err)) .throw(SRError("Input/Output", msg)) }, finally=close(gz)) } if (withVariability) { fls <- .file_names(dirPath, nsePattern) nse <- array(numeric(), c(reads, 4L, cycles), dimnames=list(NULL, c("A", "C", "G", "T"), NULL)) what <- c(rep(list(NULL), 4), what[-(1:4)]) for (i in seq_along(fls)) { tryCatch({ gz <- gzfile(fls[[i]]); open(gz) data <- scan(gz, what, nrec[[i]], ..., quiet=!verbose) idx <- (crec[i]+1):crec[i+1] nse[idx,,] <- array(unlist(data[-(1:4)]), c(nrec[[i]], 4L, cycles)) }, error=function(err) { msg <- sprintf("parsing '%s'\n file: %s\n error: %s", "nsePattern", fls[[i]], conditionMessage(err)) .throw(SRError("Input/Output", msg)) }, finally=close(gz)) } } readInfo <- SolexaIntensityInfo(df[[1]], df[[2]], df[[3]], df[[4]]) if (withVariability) SolexaIntensity(int, nse, readInfo) else SolexaIntensity(int, readInfo=readInfo) } .read_ipar_int_array <- function(fileNames, nrec, cycles, ..., verbose=FALSE) { reads <- sum(nrec) crec <- cumsum(c(0, nrec)) a <- array(numeric(), c(reads, 4L, cycles), dimnames=list(NULL, c("A", "C", "G", "T"), NULL)) for (i in seq_along(fileNames)) { tryCatch({ gz <- gzfile(fileNames[[i]]); open(gz) data <- scan(gz, nmax=nrec[[i]] * 4 * cycles, comment.char="#", ..., quiet=!verbose) idx <- (crec[i]+1):crec[i+1] a[idx,,] <- aperm(array(data, c(4L, nrec[[i]], cycles)), c(2,1,3)) }, error=function(err) { msg <- sprintf("parsing: %s\n error: %s", fileNames[[i]], conditionMessage(err)) .throw(SRError("Input/Output", msg)) }, finally=close(gz)) } a } .readIntensities_IparIntensity <- function(dirPath, pattern=character(0), ..., intExtension="_int.txt.p.gz", nseExtension="_nse.txt.p.gz", posExtension="_pos.txt", withVariability=TRUE, verbose=FALSE) { .check_type_and_length(withVariability, "logical", 1) .check_type_and_length(pattern, "character", NA) .check_type_and_length(intExtension, "character", 1) .check_type_and_length(nseExtension, "character", 1) .check_type_and_length(posExtension, "character", 1) intPattern <- paste(pattern, intExtension, sep="") intFiles <- .file_names(dirPath, intPattern) posPattern <- paste(pattern, posExtension, sep="") posFiles <- .file_names(dirPath, posPattern) dims <- .Call(.count_ipar_int_recs, intFiles) # reads, cycles nrec <- dims$reads crec <- cumsum(c(0, nrec)) cycles <- dims$cycles[[1]] if (withVariability) { nsePattern <- paste(pattern, nseExtension, sep="") nseFiles <- .file_names(dirPath, nsePattern) extrec <- .Call(.count_ipar_int_recs, nseFiles)$reads if (length(nrec) != length(extrec)) { .throw(SRError("UserArgumentMismatch", "number of files found differs between 'int' (%d) and 'nse' (%d)\n dirPath: '%s'\n pattern: '%s'\n intExtension: '%s'\n nseExtension: '%s'", length(nrec), length(extrec), dirPath, pattern, intExtension, nseExtension)) } if (!all(nrec == extrec)) { .throw(SRError("UserArgumentMismatch", "read or cycle counts differ between 'intensity' and 'nse'\n dirPath: '%s'\n pattern: '%s'\n intExtension: '%s'\n nseExtension: '%s'", dirPath, pattern, intExtension, nseExtension)) } } int <- .read_ipar_int_array(intFiles, nrec, cycles, ..., verbose=verbose) if (withVariability) nse <- .read_ipar_int_array(nseFiles, nrec, cycles, ..., verbose=verbose) ## lane, tile, x, y lanes <- sub("s_([0-9]+)_.*", "\\1", basename(posFiles)) tiles <- as.integer(sub("s_[0-9]+_([0-9]+)_.*", "\\1", basename(posFiles))) pos <- do.call(rbind, mapply(function(fl, lane, tile) { cbind(lane=lane, tile=tile, read.table(fl)) }, posFiles, lanes, tiles, SIMPLIFY=FALSE, USE.NAMES=FALSE)) readInfo <- SolexaIntensityInfo(pos[[1]], pos[[2]], pos[[3]], pos[[4]]) if (withVariability) SolexaIntensity(int, nse, readInfo) else SolexaIntensity(int, readInfo=readInfo) } setMethod(get("["), c("SolexaIntensity", "ANY", "ANY", "ANY"), function(x, i, j, k, ..., drop=TRUE) { if (missing(i)) i <- TRUE if (missing(j)) j <- TRUE if (missing(k)) k <- TRUE if (.hasMeasurementError(x)) initialize(x, intensity=intensity(x)[i,j,k], measurementError=measurementError(x)[i,j,k], readInfo=readIntensityInfo(x)[i,]) else initialize(x, intensity=intensity(x)[i,j,k], readInfo=readIntensityInfo(x)[i,]) }) ShortRead/R/methods-SolexaPath.R0000644000175400017540000001017013001230552017516 0ustar00biocbuildbiocbuildsetMethod(.srValidity, "SolexaPath", function(object) { msg <- NULL if (length(experimentPath(object))!=1) msg <- c(msg, "SolexaPath 'experimentPath' must be character(1)") if (is.null(msg)) TRUE else msg }) SolexaPath <- function(experimentPath=NA_character_, dataPath=.srPath(experimentPath, "Data"), scanPath=.srPath(dataPath, "GoldCrest"), imageAnalysisPath=.srPath(dataPath, "^(C[[:digit:]]|IPAR|Intensities)"), baseCallPath=.srPath(imageAnalysisPath, "^(Bustard|BaseCalls)"), analysisPath=.srPath(baseCallPath, "^GERALD"), ..., verbose=FALSE) { if (verbose) { .checkPath(experimentPath) .checkPath(dataPath) .checkPath(scanPath) .checkPath(imageAnalysisPath) .checkPath(baseCallPath) .checkPath(analysisPath) } new("SolexaPath", ..., basePath=experimentPath, dataPath=dataPath, scanPath=scanPath, imageAnalysisPath=imageAnalysisPath, baseCallPath=baseCallPath, analysisPath=analysisPath) } .make_getter(slotNames("SolexaPath")) .readIntensities_SolexaPath <- function(dirPath, pattern=character(0), run, ...) { callGeneric(imageAnalysisPath(dirPath)[run], pattern=pattern, ...) } setMethod(readIntensities, "SolexaPath", .readIntensities_SolexaPath) .readPrb_SolexaPath <- function(dirPath, pattern=".*_prb.txt.*", run, ...) { if (missing(pattern)) pattern <- ".*_prb.txt.*" callGeneric(baseCallPath(dirPath)[run], pattern, ...) } setMethod(readPrb, "SolexaPath", .readPrb_SolexaPath) setMethod(readFasta, "SolexaPath", function(dirPath, pattern=character(0), ..., nrec=-1L, skip=0L) { callGeneric(dirPath=analysisPath(dirPath), pattern=pattern, ..., nrec=nrec, skip=skip) }) .readFastq_SolexaPath <- function(dirPath, pattern=".*_sequence.txt$", run, ..., qualityType="SFastqQuality") { dirPath <- analysisPath(dirPath)[run] if (is.na(dirPath)) .throw(SRError("Input/Output", "'%s' is 'NA' in '%s'", "analysisPath", "dirPath")) callGeneric(dirPath, ..., pattern=pattern, qualityType=qualityType) } setMethod(readFastq, "SolexaPath", .readFastq_SolexaPath) setMethod(readQseq, "SolexaPath", function(dirPath, pattern=".*_qseq.txt.*", run, ..., as="ShortReadQ", filtered=FALSE, verbose=FALSE) { callGeneric(baseCallPath(dirPath)[run], pattern=pattern, ..., as=as, filtered=filtered, verbose=verbose) }) .readBaseQuality_SolexaPath <- function(dirPath, seqPattern=".*_seq.txt.*", prbPattern=".*_prb.txt.*", run, ...) { dirPath <- baseCallPath(dirPath)[run] .readBaseQuality_Solexa(dirPath, seqPattern=seqPattern, prbPattern=prbPattern, ...) } setMethod(readBaseQuality, "SolexaPath", .readBaseQuality_SolexaPath) .readAligned_SolexaPath <- function(dirPath, pattern=".*_export.txt.*", run, ...) { dirPath <- analysisPath(dirPath)[run] .readAligned_character(dirPath, pattern, type="SolexaExport", ...) } setMethod(readAligned, "SolexaPath", .readAligned_SolexaPath) .qa_SolexaPath <- function(dirPath, pattern=character(0), run, ...) { dirPath <- analysisPath(dirPath)[run] if (missing(pattern)) pattern <- ".*_export.txt$" callGeneric(dirPath, pattern, type="SolexaExport", ...) } setMethod(qa, "SolexaPath", .qa_SolexaPath) setMethod(report, "SolexaPath", function (x, ..., dest = tempfile(), type="html") { callGeneric(qa(x, ...), dest=dest, type=type) }) setMethod(show, "SolexaPath", function(object) { callNextMethod() .show_additionalPathSlots(object) }) setMethod(detail, "SolexaPath", function(x, ...) { callNextMethod() .detail_additionalPathSlots(x) }) ShortRead/R/methods-SolexaRealignQA.R0000644000175400017540000001524613001230551020435 0ustar00biocbuildbiocbuild.SolexaRealignQA <- function(x, ...) { new("SolexaRealignQA", .srlist=x, ...) } ## qa .qa_SolexaRealign_lane <- function(dirPath, pattern, ..., type="SolexaRealign", verbose=FALSE) { if (verbose) message("qa 'SolexaRealign' pattern:", pattern) readLbls <- c("read", "aligned") aln <- readAligned(dirPath, pattern, type=type, ...) doc <- .qa_depthOfCoverage(aln, pattern) ac <- .qa_adapterContamination(aln, pattern, ...) df <- pData(alignData(aln)) mapIdx <- alignData(aln)[["nMatch"]] == 1L alf <- .qa_alphabetFrequency(sread(aln), baseOnly=TRUE, collapse=TRUE) abc <- alphabetByCycle(aln) alignQuality <- table(quality(alignQuality(aln))[mapIdx]) tablesRead <- tables(sread(aln)) tablesAligned <- tables(sread(aln)[mapIdx]) frequentSequences <- data.frame(sequence=c( names(tablesRead$top), names(tablesAligned$top)), count=c( as.integer(tablesRead$top), as.integer(tablesAligned$top)), type=rep( readLbls, c(length(tablesRead$top), length(tablesAligned$top))), lane=pattern) sequenceDistribution <- cbind(rbind(tablesRead$distribution, tablesAligned$distribution), type=rep( readLbls, c(nrow(tablesRead$distribution), nrow(tablesAligned$distribution))), lane=pattern) perCycleBaseCall <- .qa_perCycleBaseCall(abc, pattern) perCycleQuality <- .qa_perCycleQuality() malntbl <- table(alignData(aln)[["nMatch"]]) list(readCounts=data.frame( read=length(aln), filtered=NA, aligned=sum(mapIdx), row.names=pattern), baseCalls=data.frame( A=alf[["A"]], C=alf[["C"]], G=alf[["G"]], T=alf[["T"]], N=alf[["other"]], row.names=pattern), readQualityScore=data.frame( score=numeric(0), type=factor(character(0), levels=readLbls)), baseQuality=data.frame( score=numeric(0), count=integer(0), lane=character(0), row.names=NULL), alignQuality=data.frame( score=as.numeric(names(alignQuality)), count=as.vector(alignQuality), lane=pattern, row.names=NULL), frequentSequences=frequentSequences, sequenceDistribution=sequenceDistribution, perCycle=list( baseCall=perCycleBaseCall, quality=perCycleQuality), perTile=list( readCounts=data.frame( count=integer(0), type=character(0), tile=integer(0), lane=character(0)), medianReadQualityScore=data.frame( score=integer(), type=character(), tile=integer(), lane=integer(), row.names=NULL)), multipleAlignment=data.frame( Count=as.vector(malntbl), Matches=as.integer(names(malntbl)), lane=pattern, row.names=NULL), depthOfCoverage=doc, adapterContamination=ac) } .qa_SolexaRealign <- function(dirPath, pattern, type="SolexaRealign", ..., verbose=FALSE) { fls <- .file_names(dirPath, pattern) lst <- bplapply(basename(fls), .qa_SolexaRealign_lane, dirPath=dirPath, type=type, ..., verbose=verbose) lst <- list(readCounts=.bind(lst, "readCounts"), baseCalls=.bind(lst, "baseCalls"), readQualityScore=.bind(lst, "readQualityScore"), baseQuality=.bind(lst, "baseQuality"), alignQuality=.bind(lst, "alignQuality"), frequentSequences=.bind(lst, "frequentSequences"), sequenceDistribution=.bind(lst, "sequenceDistribution"), perCycle=local({ lst <- subListExtract(lst, "perCycle") list(baseCall=.bind(lst, "baseCall"), quality=.bind(lst, "quality")) }), perTile=local({ lst <- subListExtract(lst, "perTile") list(readCounts=.bind(lst, "readCounts"), medianReadQualityScore=.bind( lst, "medianReadQualityScore")) }), multipleAlignment=.bind(lst, "multipleAlignment"), depthOfCoverage=.bind(lst, "depthOfCoverage"), adapterContamination=.bind(lst, "adapterContamination")) .SolexaRealignQA(lst) } ## report setMethod(report_html, "SolexaRealignQA", function (x, dest, type, ...) { qa <- .qa_sampleKey(x) dir.create(dest, recursive=TRUE) fls <- c("0000-Header.html", "1000-Overview.html", "1100-Overview-SolexaRealign.html", "2000-RunSummary.html", "3000-ReadDistribution.html", "4000-CycleSpecific.html", "6000-Alignment.html", "7000-MultipleAlignment.html", "8000-DepthOfCoverage.html", "9000-AdapterContamination.html", "9999-Footer.html") sections <- system.file("template", fls, package="ShortRead") perCycle <- qa[["perCycle"]] values <- list(SAMPLE_KEY=hwrite(qa[["keyValue"]], border=0), PPN_COUNT=.html_img( dest, "readCount", .plotReadCount(qa)), PPN_COUNT_TBL=hwrite( .ppnCount(qa[["readCounts"]]), border=0), BASE_CALL_COUNT=.html_img( dest, "baseCalls", .plotNucleotideCount(qa)), READ_QUALITY_FIGURE=.html_NA(), READ_OCCURRENCES_FIGURE=.htmlReadOccur( dest, "readOccurences", qa), FREQUENT_SEQUENCES_READ=hwrite( .freqSequences(qa, "read"), border=0), FREQUENT_SEQUENCES_FILTERED=.html_NA(), FREQUENT_SEQUENCES_ALIGNED=hwrite( .freqSequences(qa, "aligned"), border=0), CYCLE_BASE_CALL_FIGURE=.html_img( dest, "perCycleBaseCall", .plotCycleBaseCall(perCycle$baseCall)), CYCLE_QUALITY_FIGURE=.html_NA(), ALIGN_QUALITY_FIGURE=.html_img( dest, "alignmentQuality", .plotAlignQuality(qa[["alignQuality"]])), MULTIPLE_ALIGNMENT_COUNT_FIGURE=.html_img( dest, "multipleAlignmentCount", .plotMultipleAlignmentCount(qa[["multipleAlignment"]])), DEPTH_OF_COVERAGE_FIGURE=.html_img( dest, "depthOfCoverage", .plotDepthOfCoverage(qa[["depthOfCoverage"]])), ADAPTER_CONTAMINATION=hwrite( .df2a(qa[["adapterContamination"]]), border=0)) .report_html_do(dest, sections, values, ...) }) ShortRead/R/methods-SolexaSet.R0000644000175400017540000000550713001230552017365 0ustar00biocbuildbiocbuildsetMethod(.srValidity, "SolexaSet", function(object) { msg <- NULL nr <- nrow(laneDescription(object)) if (nr!=8) msg <- c(msg, sprintf("'laneDescription' must have 8 rows, but has %d", nr)) if (is.null(msg)) TRUE else msg }) .SolexaSet_SolexaPath <- function(path, laneDescription, ...) { if (missing(laneDescription)) { laneDescription <- new("AnnotatedDataFrame", data=data.frame(1:8)[,FALSE], varMetadata=data.frame(labelDescription=character(0)), dimLabels=c("laneNames", "laneColumns")) } else { if (!is(laneDescription, "AnnotatedDataFrame")) { cls <- paste(class(laneDescription), collapse=" ") .throw(SRError("UserArgumentMismatch", "expected '%s' as '%s', but got '%s'", "AnnotatedDataFrame", "laneDescription", cls)) } dimLabels(laneDescription) <- c("laneNames", "laneColumns") } new("SolexaSet", ..., solexaPath=path, laneDescription=laneDescription) } setMethod(SolexaSet, "SolexaPath", .SolexaSet_SolexaPath) setMethod(SolexaSet, "character", function(path, ...) { .SolexaSet_SolexaPath(SolexaPath(path), ...) }) .make_getter(slotNames("SolexaSet")) setMethod(laneNames, "SolexaSet", function(object, ...) { laneNames(laneDescription(object)) }) setMethod(laneNames, "AnnotatedDataFrame", function(object) { sampleNames(object) }) ## .qa_SolexaSet <- function(dirPath, pattern=character(0), ...) ## { ## dirPath <- analysisPath(dirPath) ## if (missing(pattern)) ## pattern <- ".*_export.txt$" ## callGeneric(dirPath, pattern, type="SolexaExport", ...) ## } ## setMethod(qa, "SolexaSet", .qa_solexa_export) ## .report_SolexaSet <- function(x, run=1, ..., qaFile=tempfile(), ## dest=tempfile(), type="pdf" ) ## { ## report(qa(x, run=run)) ## } ## alignment .readAligned_SolexaSet <- function(dirPath, pattern=".*_export.txt$", run, ...) { dirPath <- analysisPath(solexaPath(dirPath))[run] .readAligned_character(dirPath, pattern, type="SolexaExport", ...) } setMethod(readAligned, "SolexaSet", .readAligned_SolexaSet) setMethod(show, "SolexaSet", function(object) { callNextMethod() cat("experimentPath(solexaPath(object)):\n ", experimentPath(solexaPath(object)), "\n", sep="") cat("laneDescription:\n") print(laneDescription(object)) }) setMethod(detail, "SolexaSet", function(x, ...) { callNextMethod() cat("\n") detail(solexaPath(x), ...) cat("\nclass: AnnotatedDataFrame\n") ld <- laneDescription(x) cat("pData:\n") print(pData(ld)) cat("varMetadata:\n") print(varMetadata(ld)) }) ShortRead/R/qa.R0000644000175400017540000000235313001230552014412 0ustar00biocbuildbiocbuild.qa_character <- function(dirPath, pattern=character(0), type=c("fastq", "SolexaExport", "SolexaRealign", "Bowtie", "MAQMap", "MAQMapShort"), ...) { tryCatch(type <- match.arg(type), error=function(err) { .throw(SRError("UserArgumentMismatch", conditionMessage(err))) }) switch(type, SolexaExport=.qa_SolexaExport(dirPath, pattern, type=type, ...), SolexaRealign=.qa_SolexaRealign(dirPath, pattern, type=type, ...), Bowtie=.qa_Bowtie(dirPath, pattern, type=type, ...), MAQMap=.qa_MAQMap(dirPath, pattern, type=type, ...), MAQMapShort=.qa_MAQMap(dirPath, pattern, type=type, ...), fastq=.qa_fastq(dirPath, pattern, type=type, ...)) } setMethod(qa, "character", .qa_character) setMethod(qa, "list", function(dirPath, ...) { if (length(unique(sapply(dirPath, class))) != 1) .throw(SRError("UserArgumentMismatch", "qa,list-method 'dirPath' arguments must all be of same class")) l <- mapply(qa, dirPath, names(dirPath), MoreArgs=list(...), SIMPLIFY=FALSE) do.call(rbind, l) }) ShortRead/R/qa_utilities.R0000644000175400017540000003565613001230551016520 0ustar00biocbuildbiocbuild.bind <- function(lst, elt) { do.call(rbind, subListExtract(lst, elt, keep.names=FALSE)) } .qa_alphabetFrequency <- function(object, ..., collapse=FALSE, baseOnly=FALSE) { ## avoiding integer overflow in Biostrings::alphabetFrequency if (!collapse) { msg <- "'collapse' must be TRUE for '.qa_alphabetFrequency'" .throw(SRError("InternalError", msg)) } alf <- alphabetByCycle(object) mode(alf) <- "numeric" alf <- rowSums(alf) if (baseOnly && is(object, "DNAStringSet")) { bases <- names(Biostrings:::xscodes(object, baseOnly=baseOnly)) idx <- names(alf) %in% bases alf <- c(alf[idx], other=sum(alf[!idx])) } alf } ## qa summary .qa_sampleKey <- function(qa) ## use numbers to represent samples, updating all elements of { value <-rownames(qa[["readCounts"]]) kv <- data.frame(Key=factor(seq_along(value), levels=seq_along(value)), row.names=value) lst <- c(list(keyValue=kv), Map(function(elt, nm, kv) { switch(nm, readCounts=, baseCalls=, adapterContamination={ rownames(elt) <- kv[rownames(elt), "Key"] elt }, readQualityScore=, baseQuality=, alignQuality=, frequentSequences=, sequenceDistribution={ elt$lane <- kv[elt$lane, "Key"] elt }, depthOfCoverage={ elt$Lane <- kv[elt$Lane, "Key"] elt }, perCycle=, perTile={ Map(function(elt, nm, kv) { elt$lane <- kv[elt$lane, "Key"] elt }, elt, names(elt), MoreArgs=list(kv)) },{ msg <- sprintf("unhandled QA element '%s'", nm) .throw(SRError("InternalError", msg)) }) }, .srlist(qa), names(qa), MoreArgs=list(kv))) initialize(qa, .srlist=lst) } .qa_qdensity <- function(quality) { qscore <- alphabetScore(quality) / width(quality) if (length(qscore) >= 2) { density(qscore) } else { pseudo <- list(x=NA, y=NA, bw=Inf, n=0) class(pseudo) <- "density" pseudo } } .qa_perCycleBaseCall <- function(abc, lane) { if (missing(abc) || dim(abc)[[3]] == 0) { df <- data.frame(Cycle=integer(0), Base=factor(), Count=integer(0), lane=character(0)) return(df) } abc <- apply(abc, c(1, 3), sum) df <- data.frame(Cycle=as.integer(colnames(abc)[col(abc)]), Base=factor(rownames(abc)[row(abc)]), Count=as.vector(abc), lane=lane, row.names=NULL) df[df$Count != 0,] } .qa_perCycleQuality <- function(abc, quality, lane) { if (missing(abc) || dim(abc)[[3]] == 0) { df <- data.frame(Cycle=integer(0), Quality=numeric(0), Score=numeric(0), Count=integer(0), lane=character(0)) return(df) } abc <- apply(abc, 2:3, sum) q <- factor(rownames(abc)[row(abc)], levels=rownames(abc)) q0 <- as(do.call(class(quality), list(rownames(abc))), "matrix") df <- data.frame(Cycle=as.integer(colnames(abc)[col(abc)]), Quality=q, Score=as.integer(q0)[q], Count=as.vector(abc), lane=lane, row.names=NULL) df[df$Count != 0, ] } .qa_depthOfCoverage <- function(aln, lane) { idx <- !is.na(position(aln)) & occurrenceFilter(withSread=FALSE)(aln) if (0L == sum(idx)) { df <- data.frame(Coverage=character(0), Count=integer(0), CumulativePpn=integer(0), Lane=character(0), row.names=NULL) return(df) } aln <- aln[idx] cv <- coverage(aln) cvg <- Filter(function(x) length(x) > 0, cv) if (0L == length(cvg)) { df <- data.frame(Coverage=character(0), Count=integer(0), CumulativePpn=integer(0), Lane=character(0), row.names=NULL) return(df) } ## Each chromosome count <- do.call(rbind, lapply(seq_len(length(cvg)), function(i, cvg) { x <- cvg[[i]] res <- tapply(runLength(x), runValue(x), sum) data.frame(Coverage=as.numeric(names(res)), Count=as.numeric(res), Seqname=names(cvg)[[i]], row.names=NULL) }, cvg)) ## Entire lane, non-zero coverage count <- count[count$Coverage != 0,] res <- tapply(as.numeric(count$Count), count$Coverage, sum) count <- as.vector(res) data.frame(Coverage=as.numeric(names(res)), Count= count, CumulativePpn=cumsum(count) / sum(count), Lane=lane, row.names=NULL) } .qa_adapterContamination <- function(aln, lane, ..., Lpattern="", Rpattern="", max.Lmismatch=.1, max.Rmismatch=.2, min.trim=9L) { if (missing(Lpattern) && missing(Rpattern)) { df <- data.frame(contamination=NA_real_, row.names=lane) return(df) } trim <- trimLRPatterns(Lpattern, Rpattern, subject=sread(aln), max.Lmismatch=max.Lmismatch, max.Rmismatch=max.Rmismatch, ranges=TRUE) ac <- sum(width(trim) < (width(aln) - min.trim)) / length(trim) data.frame(contamination=ac, row.names=lane) } ## report-generation .dnaCol <- # brewer.pal(6, "Paired")[c(2, 4, 3, 1, 6)] c("#1F78B4", "#33A02C", "#B2DF8A", "#A6CEE3", "#E31A1C") .ppnCount <- function(m) { if(is.null(m) || is.factor(m[,-1])) { "Not available." } else { ## scale subsequent columns to be proportions of first column m[,-1] <- round(1000 * m[,-1] / ifelse(is.na(m[,1]), 1, m[,1])) / 1000 m } } .df2a <- function(df, fmt="%.3g") { FUN <- function(elt, fmt) if (is.character(elt) || is.factor(elt)) as.character(elt) else sprintf(fmt, elt) a <- if (nrow(df) == 1) as.data.frame(lapply(df, FUN, fmt=fmt)) else sapply(df, FUN, fmt=fmt) row.names(a) <- rownames(df) a } .plotReadCount <- function(qa, ...) { df <- qa[["readCounts"]] df1 <- data.frame(Count=unlist(df), Sample=factor(rownames(df), levels=rownames(df)), Census=factor(names(df)[col(df)], levels=names(df))) col <- .dnaCol[c(1, 4, 2)] dotplot(Sample~Count, group=df1$Census, df1, type="b", pch=20, col=col, key=list(space="top", lines=list(col=rev(col)), text=list(rev(names(df))), columns=ncol(df))) } .plotNucleotideCount <- function(qa, ...) { df <- qa[["baseCalls"]] alph <- df / rowSums(df) df1 <- data.frame(Frequency=unlist(alph), Sample=factor(rownames(alph), levels=rownames(alph)), Nucleotide=factor(names(alph)[col(alph)], levels=c("A", "C", "G", "T", "N"))) dotplot(Sample~Frequency, group=df1$Nucleotide, df1, type="b", pch=20, col=.dnaCol, key=list(space="top", lines=list(col=.dnaCol), text=list(lab=names(df)), columns=ncol(df))) } .plotReadQuality <- function(df, ..., strip=FALSE) { xmin <- min(df$quality) ymax <- max(df$density) xyplot(density~quality|lane, df, type="l", xlab="Average (calibrated) base quality", ylab="Proportion of reads", aspect=2, panel=function(..., subscripts) { lbl <- as.character(unique(df$lane[subscripts])) ltext(xmin, ymax, lbl, adj=c(0, 1)) panel.xyplot(...) }, strip=FALSE) } .plotReadOccurrences <- function(df, ..., strip=FALSE) { df <- local({ nOccur <- tapply(df$nOccurrences, df$lane, c) cumulative <- tapply(df$nOccurrences * df$nReads, df$lane, function(elt) { cs <- cumsum(elt) (cs-cs[1] + 1) / (diff(range(cs)) + 1L) }) lane <- tapply(df$lane, df$lane, c) data.frame(nOccurrences=unlist(nOccur), cumulative=unlist(cumulative), lane=unlist(lane), row.names=NULL) }) xmax <- log10(max(df$nOccurrences)) xyplot(cumulative~log10(nOccurrences)|factor(lane), df, xlab=expression(paste( "Number of occurrences of each sequence (", log[10], ")", sep="")), ylab="Cumulative proportion of reads", aspect=2, panel=function(x, y, ..., subscripts, type) { lbl <- unique(df$lane[subscripts]) ltext(xmax, .05, lbl, adj=c(1, 0)) type <- if (1L == length(x)) "p" else "l" panel.xyplot(x, y, ..., type=type) }, ..., strip=strip) } .freqSequences <- function(qa, read, n=20) { cnt <- qa[["readCounts"]] df <- qa[["frequentSequences"]] df1 <- df[df$type==read,] df1[["ppn"]] <- df1[["count"]] / cnt[df1[["lane"]], read] df <- head(df1[order(df1$count, decreasing=TRUE), c("sequence", "count", "lane")], n) rownames(df) <- NULL df } .plotAlignQuality <- function(df) { xyplot(count~score|lane, df, type="l", prepanel=function(x, y, ...) { list(ylim=c(0, 1)) }, panel=function(x, y, ...) { panel.xyplot(x, y/max(y), ...) }, xlab="Alignment quality score", ylab="Number of alignments, relative to lane maximum", aspect=2) } .atQuantile <- function(x, breaks) { at <- unique(quantile(x, breaks)) if (length(at)==1) at <- at * c(.9, 1.1) at } .colorkeyNames <- function(at, fmt) { paste(names(at), " (", sprintf(fmt, at), ")", sep="") } .tileGeometry <- function(tileIndicies) { n <- as.character(max(tileIndicies)) switch(n, "68"=c(8, 4), "100"=c(50, 2), "120"=c(60, 2), "300"=c(100, 3), { warning(n, " tiles; ", "assuming lane geometry with 50 tiles / row", call.=FALSE) c(50, ceiling(as.integer(n) / 50)) }) } .plotTileLocalCoords <- function(tile, nrow) { if (nrow == 8) { ## HiSeq row <- tile %% 20 col <- floor(tile / 20) %% 4 + 1L } else { row <- 1 + (tile - 1) %% nrow col <- 1 + floor((tile -1) / nrow) row[col%%2==0] <- nrow + 1 - row[col%%2==0] } list(row=as.integer(row), col=as.factor(col)) } .plotTileCounts <- function(df, nrow=.tileGeometry(df$tile)[[1]]) { df <- df[df$count != 0,] xy <- .plotTileLocalCoords(df$tile, nrow) df[,names(xy)] <- xy at <- .atQuantile(df$count, seq(0, 1, .1)) levelplot(cut(count, at)~col*row|lane, df, main="Read count (percentile rank)", xlab="Tile x-coordinate", ylab="Tile y-coordinate", cuts=length(at)-2, colorkey=list(labels=.colorkeyNames(at, "%d")), aspect=2) } .plotTileQualityScore <- function(df, nrow=.tileGeometry(df$tile)[[1]]) { df <- df[!is.na(df$score),] xy <- .plotTileLocalCoords(df$tile, nrow) df[,names(xy)] <- xy at <- .atQuantile(df$score, seq(0, 1, .1)) levelplot(cut(score, at)~col*row|lane, df, main="Read quality (percentile rank)", xlab="Tile x-coordinate", ylab="Tile y-coordinate", cuts=length(at)-2, colorkey=list(labels=.colorkeyNames(at, "%.2f")), aspect=2) } .plotCycleBaseCall <- function(df, ..., strip=FALSE) { col <- .dnaCol[1:4] df <- df[df$Base != "N" & df$Base != "-",] df$Base <- factor(df$Base) xmax <- max(df$Cycle) ymax <- log10(max(df$Count)) xyplot(log10(Count)~as.integer(Cycle)|lane, group=factor(Base), df[order(df$lane, df$Base, df$Cycle),], panel=function(..., subscripts) { lbl <- as.character(unique(df$lane[subscripts])) ltext(xmax, ymax, lbl, adj=c(1, 1)) panel.xyplot(..., subscripts=subscripts) }, type="l", col=col, key=list(space="top", lines=list(col=col, lwd=2), text=list(lab=levels(df$Base)), columns=length(levels(df$Base))), xlab="Cycle", aspect=2, strip=strip, ...) } .plotCycleQuality <- function(df, ..., strip=FALSE, strip.left=FALSE) { calc_means <- function(x, y, z) rowsum(y * z, x) / rowsum(z, x) calc_quantile <- function(x, y, z, q=c(.25, .5, .75)) by(list(y, z), x, function(x) { scoreRle <- Rle(x[[1]], x[[2]]) quantile(scoreRle, q) }) Lane <- df$lane pal <- c("#66C2A5", "#FC8D62") # brewer.pal(3, "Set2")[1:2] lvlPal <- c("#F5F5F5", "black" ) rng <- range(df$Count) at <- seq(rng[1], rng[2], length.out=512) np <- length(unique(Lane)) nrow <- ceiling(np / 4) layout <- c(ceiling(np/nrow), nrow) ymin <- min(df$Score) xyplot(Score ~ Cycle | Lane, df, panel=function(x, y, ..., subscripts) { z <- df$Count[subscripts] mean <- calc_means(x, y, z) qtiles <- calc_quantile(x, y, z) sxi <- sort(unique(x)) panel.levelplot(x, y, z, subscripts=TRUE, at=at, col.regions=colorRampPalette(lvlPal)) llines(sxi, mean, type="l", col=pal[[1]], lwd=1) llines(sxi, sapply(qtiles, "[[", 1), type="l", col=pal[[2]], lwd=1, lty=3) llines(sxi, sapply(qtiles, "[[", 2), type="l", col=pal[[2]], lwd=1) llines(sxi, sapply(qtiles, "[[", 3), type="l", col=pal[[2]], lwd=1, lty=3) lbl <- as.character(unique(df$lane[subscripts])) ltext(1, ymin, lbl, adj=c(0, 0)) }, ..., ylab="Quality Score", layout=layout, strip=strip, strip.left=strip.left) } .plotMultipleAlignmentCount <- function(df, ...) { xyplot(log10(Count)~log10(Matches + 1) | lane, df, xlab="log10(Number of matches + 1)", aspect=2, ...) } .plotDepthOfCoverage <- function(df, ..., strip=FALSE) { if (is.null(df)) return(NULL) xmin <- log(min(df$Coverage)) ymax <- max(df$CumulativePpn) xyplot(CumulativePpn~Coverage | Lane, df, type="b", pch=20, scales=list(x=list(log=TRUE)), ylab="Cumulative Proportion of Nucleotides", aspect=2, panel=function(..., subscripts) { lbl <- as.character(unique(df$Lane[subscripts])) ltext(xmin, ymax, lbl, adj=c(0, 1)) panel.xyplot(..., subscripts=subscripts) }, ..., strip=strip) } ShortRead/R/readAligned.R0000644000175400017540000003510713001230552016213 0ustar00biocbuildbiocbuild.read_csv_portion <- function(dirPath, pattern, colClasses, ...) { ## visit several files, then collapse files <- .file_names(dirPath, pattern) lsts <- lapply(files, function(fl, ...) { tryCatch({ read.csv(fl, ...) }, error=function(err) { read.csv(gzfile(fl), ...) }) }, ..., colClasses=unname(colClasses), stringsAsFactors=FALSE) cclasses <- colClasses[!sapply(colClasses, is.null)] lst <- lapply(seq_along(names(cclasses)), function(idx) unlist(lapply(lsts, "[[", idx))) names(lst) <- names(cclasses) lst } .readAligned_SolexaAlign <- function(dirPath, pattern=character(0), ..., quote="", sep="", comment.char="#", header=FALSE, Lpattern="", Rpattern="") { csvClasses <- xstringClasses <- list(sequence="DNAString", alignQuality="integer", nMatch="integer", position="character", strand="factor", refSequence=NULL, nextBestAlignQuality="integer") xstringNames <- "sequence" csvClasses[xstringNames] <- list(NULL) xstringClasses[!names(xstringClasses) %in% xstringNames] <- list(NULL) ## CSV portion lst <- .read_csv_portion(dirPath, pattern, csvClasses, ..., col.names=names(csvClasses), quote=quote, sep=sep, comment.char=comment.char, header=header) idx <- regexpr(":", lst[["position"]], fixed=TRUE) chromosome <- substr(lst[["position"]], 1, idx-1) chromosome[idx==-1] <- NA chromosome <- factor(chromosome) position <- as.integer(substr(lst[["position"]], idx+1, nchar(lst[["position"]]))) df <- data.frame(nMatch=lst$nMatch, nextBestAlignQuality=lst$nextBestAlignQuality) meta <- data.frame(labelDescription=c( "Number of matches", "Next-best alignment quality score")) alignData <- AlignedDataFrame(df, meta) ## XStringSet classes sets <- readXStringColumns(dirPath, pattern, xstringClasses, ..., sep=" \t") len <- length(sets[["sequence"]]) wd <- width(sets[["sequence"]]) q <- paste(rep(" ", max(wd)), collapse="") quality <- BStringSet(Views(BString(q), start=rep(1, len), end=wd)) AlignedRead(sread=sets[["sequence"]], id=BStringSet(character(len)), quality=SFastqQuality(quality), chromosome=chromosome, position=position, strand=.toStrand_Solexa(lst[["strand"]]), alignQuality=NumericQuality(lst[["alignQuality"]]), alignData=alignData) } .readAligned_SolexaResult <- function(dirPath, pattern=character(0), ..., sep="\t", comment.char="#", quote="", header=FALSE) { csvClasses <- xstringClasses <- list(id=NULL, sequence="DNAString", matchCode="factor", nExactMatch="integer", nOneMismatch="integer", nTwoMismatch="integer", chromosome="factor", position="integer", strand="factor", NCharacterTreatment="factor", mismatchDetailOne="character", mismatchDetailTwo="character") xstringNames <- "sequence" csvClasses[xstringNames] <- list(NULL) xstringClasses[!names(xstringClasses) %in% xstringNames] <- list(NULL) ## CSV portion lst <- .read_csv_portion(dirPath, pattern, csvClasses, ..., col.names=names(csvClasses), quote=quote, sep=sep, comment.char=comment.char, header=header) df <- data.frame(matchCode=lst[["matchCode"]], nExactMatch=lst[["nExactMatch"]], nOneMismatch=lst[["nOneMismatch"]], nTwoMismatch=lst[["nTwoMismatch"]], NCharacterTreatment=lst[["NCharacterTreatment"]], mismatchDetailOne=lst[["mismatchDetailOne"]], mismatchDetailTwo=lst[["mismatchDetailTwo"]]) meta <- data.frame(labelDescription=c( "Type of match; see ?'readAligned,character-method'", "Number of exact matches", "Number of 1-error mismatches", "Number of 2-error mismatches", "Treatment of 'N'; .: NA; D: deletion; |: insertion", "Mismatch error 1 detail; see ?'readAligned,character-method", "Mismatch error 2 detail; see ?'readAligned,character-method")) alignData <- AlignedDataFrame(df, meta) ## XStringSet classes sets <- readXStringColumns(dirPath, pattern, xstringClasses, ..., sep=sep) len <- length(sets[["sequence"]]) wd <- width(sets[["sequence"]]) q <- paste(rep(" ", max(wd)), collapse="") sfq <- BStringSet(Views(BString(q), start=rep(1, len), end=wd)) AlignedRead(sread=sets[["sequence"]], quality=SFastqQuality(sfq), chromosome=lst[["chromosome"]], position=lst[["position"]], strand=.toStrand_Solexa(lst[["strand"]]), alignQuality=NumericQuality(rep(NA_integer_, length(sfq))), alignData=alignData) } .SolexaExport_AlignedDataFrame <- function(data) { lbls <- c(run="Analysis pipeline run", lane="Flow cell lane", tile="Flow cell tile", x="Cluster x-coordinate", y="Cluster y-coordinate", filtering="Read successfully passed filtering?", contig="Contig", multiplexIndex="Multiplex index", pairedReadNumber="Paired read number")[names(data)] AlignedDataFrame(data=data, metadata=data.frame(labelDescription=lbls)) } .readAligned_SolexaExport <- function(dirPath, pattern=character(0), ..., withAll=FALSE, withId=withAll, withMultiplexIndex=withAll, withPairedReadNumber=withAll, sep="\t", commentChar="#") { files <- .file_names(dirPath, pattern) .Call(.read_solexa_export, files, sep, commentChar, c(withId, withMultiplexIndex, withPairedReadNumber)) } .readAligned_Maq_ADF <- function(lst) { df <- data.frame(nMismatchBestHit=lst$nMismatchBestHit, mismatchQuality=lst$mismatchQuality, nExactMatch24=lst$nExactMatch24, nOneMismatch24=lst$nOneMismatch24) meta <- data.frame(labelDescription=c( "Number of mismatches of the best hit", "Sum of mismatched base qualities of the best hit", "Number of 0-mismatch hits of the first 24 bases", "Number of 1-mismatch hits of the first 24 bases")) AlignedDataFrame(df, meta) } .maqmap_warning_seen <- local({ seen <- FALSE function() { if (!seen) { seen <<- TRUE FALSE } else seen } }) .maqmap_file_list_error <- function(files, type) { .throw(SRError("UserArgumentMismatch", "%s for '%s' must match 1 file, got\n %s", "'dirPath', 'pattern'", type, paste(files, collapse="\n "))) } .readAligned_MaqMapOld <- function(dirPath, pattern=character(0), records=-1L, ...) { files <- .file_names(dirPath, pattern) if (length(files) > 1) .maqmap_file_list_error(files, "MAQMapShort") lst <- .Call(.read_maq_map, files, as.integer(records), FALSE) AlignedRead(sread=lst[["readSequence"]], id=lst[["readId"]], quality=FastqQuality(lst[["fastqScores"]]), chromosome=lst[["chromosome"]], position=lst[["position"]], strand=lst[["strand"]], alignQuality=IntegerQuality(lst[["alignQuality"]]), alignData=.readAligned_Maq_ADF(lst)) } .readAligned_MaqMap <- function(dirPath, pattern=character(0), records=-1L, ...) { files <- .file_names(dirPath, pattern) if (length(files) > 1) .maqmap_file_list_error(files, "MAQMap") lst <- .Call(.read_maq_map, files, as.integer(records), TRUE) AlignedRead(sread=lst[["readSequence"]], id=lst[["readId"]], quality=FastqQuality(lst[["fastqScores"]]), chromosome=lst[["chromosome"]], position=lst[["position"]], strand=lst[["strand"]], alignQuality=IntegerQuality(lst[["alignQuality"]]), alignData=.readAligned_Maq_ADF(lst)) } .readAligned_MaqMapview <- function(dirPath, pattern=character(0), ..., sep="\t", header=FALSE, quote="") { colClasses <- list(NULL, chromosome="factor", position="integer", strand="factor", NULL, NULL, alignQuality="integer", NULL, NULL, nMismatchBestHit="integer", mismatchQuality="integer", nExactMatch24="integer", nOneMismatch24="integer", NULL, NULL, NULL) ## CSV portion csv <- .read_csv_portion(dirPath, pattern, colClasses, sep=sep, header=header, quote=quote, ...) ## XStringSet components colClasses <- list("BString", NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, "DNAString", "BString") sets <- readXStringColumns(dirPath, pattern, colClasses, sep=sep, header=header) AlignedRead(sread=sets[[2]], id=sets[[1]], quality=FastqQuality(sets[[3]]), chromosome=csv[["chromosome"]], position=csv[["position"]], strand=factor(csv[["strand"]], levels=.STRAND_LEVELS), alignQuality=IntegerQuality(csv[["alignQuality"]]), alignData=.readAligned_Maq_ADF(csv)) } .Bowtie_AlignedDataFrame <- function(similar, mismatch) { df <- data.frame(similar=similar, mismatch=mismatch, stringsAsFactors=FALSE) meta <- data.frame(labelDescription=c( "if Bowtie >= 0.9.9.3 (May 12, 2009)?: number of alignments aligning to the same reference characters; else 'Reserved'", "Comma-separated mismatch positions")) AlignedDataFrame(df, meta) } .readAligned_Bowtie <- function(dirPath, pattern=character(0), ..., qualityType=c("FastqQuality", "SFastqQuality"), sep="\t", commentChar="#") { tryCatch(qualityType <- match.arg(qualityType), error=function(err) { .throw(SRError("UserArgumentMismatch", conditionMessage(err))) }) files <- .file_names(dirPath, pattern) .Call(.read_bowtie, files, qualityType, sep, commentChar) } .SOAP_AlignedDataFrame <- function(nEquallyBestHits, pairedEnd, alignedLength, typeOfHit, hitDetail) { df <- data.frame(nEquallyBestHits=nEquallyBestHits, pairedEnd=factor(pairedEnd), alignedLength=alignedLength, typeOfHit=typeOfHit, hitDetail=hitDetail, stringsAsFactors=FALSE) meta <- data.frame(labelDescription=c( "Number of equally-best hits", "Paired end, a or b", "Length of read used in alignment", "Integer indicator of match type; 0: exact; 1-100: mismatch; 100+n: n-base insertion; 200+n: n-base deletion", "Detailed description of match")) AlignedDataFrame(df, meta) } .readAligned_SOAP <- function(dirPath, pattern=character(0), ..., qualityType="SFastqQuality", sep="\t", commentChar="#") { files <- .file_names(dirPath, pattern) .Call(.read_soap, files, qualityType, sep, commentChar) } .readAligned_bam <- function(dirPath, pattern=character(0), ..., param=ScanBamParam(simpleCigar=TRUE, reverseComplement=TRUE, what=.readAligned_bamWhat())) { .Defunct("readGAlignments", "GenomicAlignments", msg="use GenomicAlignments::readGAlignments to read BAM files") } .readAligned_character <- function(dirPath, pattern=character(0), type=c( "SolexaExport", "SolexaAlign", "SolexaPrealign", "SolexaRealign", "SolexaResult", "MAQMap", "MAQMapShort", "MAQMapview", "Bowtie", "SOAP"), ..., filter=srFilter()) { if (missing(type)) .arg_missing_err("type", "readAligned,character-method", "help(\"readAligned,character-method\")") if (!is.character(type) || length(type) != 1) .arg_mismatch_type_err("type", "character(1)") if (!missing(filter)) .check_type_and_length(filter, "SRFilter", NA) vals <- eval(formals(.readAligned_character)$type) if (!type %in% vals) .arg_mismatch_value_err("type", type, vals) aln <- tryCatch({ switch(type, SolexaExport=.readAligned_SolexaExport(dirPath, pattern=pattern, ...), SolexaPrealign=, SolexaAlign=, SolexaRealign=.readAligned_SolexaAlign(dirPath, pattern=pattern, ...), SolexaResult=.readAligned_SolexaResult(dirPath, pattern=pattern, ...), MAQMap=.readAligned_MaqMap(dirPath, pattern, ...), MAQMapShort=.readAligned_MaqMapOld(dirPath, pattern, ...), MAQMapview=.readAligned_MaqMapview( dirPath, pattern=pattern, ...), Bowtie=.readAligned_Bowtie(dirPath, pattern, ...), SOAP=.readAligned_SOAP(dirPath, pattern, ...)) }, error=function(err) { if (is(err, "SRError")) stop(err) else { pat <- paste(pattern, collapse=" ") txt <- paste("'%s' failed to parse files", "dirPath: '%s'", "pattern: '%s'", "type: '%s'", "error: %s", sep="\n ") msg <- sprintf(txt, "readAligned", paste(dirPath, collapse="'\n '"), paste(pat, collapse="'\n '"), type, conditionMessage(err)) .throw(SRError("Input/Output", msg)) } }) if (!missing(filter)) aln <- aln[filter(aln)] aln } setMethod(readAligned, "character", .readAligned_character) ShortRead/R/readBaseQuality.R0000644000175400017540000000361313001230551017067 0ustar00biocbuildbiocbuild.readBaseQuality_Solexa <- function(dirPath, seqPattern=character(0), prbPattern=character(0), ...) { prbs <- readPrb(dirPath, pattern=prbPattern, ...) ShortReadQ( sread = readXStringColumns( dirPath, pattern=seqPattern, colClasses=c(rep(list(NULL), 4), list("DNAString")))[[1]], quality=prbs, id=BStringSet(as.character(seq_len(length(prbs))))) } .readBaseQuality_character <- function(dirPath, seqPattern=character(0), prbPattern=character(0), type=c("Solexa"), ...) { if (missing(type)) .arg_missing_err("type", "readBaseQuality,character-method", "help(\"readBaseQuality,character-method\")") if (!is.character(type) || length(type) != 1) .arg_mismatch_type_err("type", "character(1)") vals <- eval(formals(.readBaseQuality_character)$type, getNamespace("ShortRead")) if (!type %in% vals) .arg_mismatch_value_err("type", type, vals) tryCatch({ switch(type, Solexa=.readBaseQuality_Solexa(dirPath, seqPattern=seqPattern, prbPattern=prbPattern,...)) }, error=function(err) { if (is(err, "SRError")) stop(err) else { seqpat <- paste(seqPattern, collapse=" ") prbpat <- paste(prbPattern, collapse=" ") txt <- paste("'%s' failed to parse files", "dirPath: '%s'", "seqPattern: '%s'", "prbPattern: '%s'", "type: '%s'", "error: %s", sep="\n ") msg <- sprintf(txt, "readBaseQuality", dirPath, seqpat, prbpat, type, conditionMessage(err)) .throw(SRError("Input/Output", msg)) } }) } setMethod(readBaseQuality, "character", .readBaseQuality_character) ShortRead/R/readBfaToc.R0000644000175400017540000000010313001230551015771 0ustar00biocbuildbiocbuildreadBfaToc <- function( bfafile ) .Call( .readBfaToc, bfafile ) ShortRead/R/readIntensities.R0000644000175400017540000000303613001230551017141 0ustar00biocbuildbiocbuild.readIntensities_character <- function(dirPath, pattern=character(0), ..., type=c("SolexaIntensity", "IparIntensity", "RtaIntensity")) { if (missing(type)) { type <- "SolexaIntensity" } else if (!is.character(type) || length(type) != 1) { .arg_mismatch_type_err("type", "character(1)") } else { vals <- eval(formals(.readIntensities_character)$type, getNamespace("ShortRead")) if (!type %in% vals) .arg_mismatch_value_err("type", type, vals) } tryCatch({ switch(type, SolexaIntensity=.readIntensities_SolexaIntensity( dirPath, pattern, ...), IparIntensity=.readIntensities_IparIntensity( dirPath, pattern, ...), RtaIntensity=.readIntensities_RtaIntensity( dirPath, pattern, ...)) }, error=function(err) { if (is(err, "SRError")) stop(err) else { pat <- paste(pattern, collapse=" ") txt <- paste("'%s' failed to parse files", "dirPath: '%s'", "pattern: '%s'", "type: '%s'", "error: %s", sep="\n ") msg <- sprintf(txt, "readIntensities", paste(dirPath, collapse="'\n '"), pat, type, conditionMessage(err)) .throw(SRError("Input/Output", msg)) } }) } setMethod(readIntensities, "character", .readIntensities_character) ShortRead/R/readPrb.R0000644000175400017540000000733513001230551015374 0ustar00biocbuildbiocbuild.readPrb <- function(file, ..., asSolexa, verbose) { if (verbose) cat(".readPrb", file, "\n") tryCatch({ .Call(.read_prb_as_character, file, asSolexa) }, error=function(err) { .throw(SRError("Input/Output", sprintf("parsing 'prb'\n file: %s\n error: %s", file, conditionMessage(err)))) }) } .readPrb_quality <- function(dirPath, pattern, qclass, ..., asSolexa, verbose) { fls <- .file_names(dirPath, pattern) qclass(unlist(bplapply(fls, .readPrb, ..., asSolexa=asSolexa, verbose=verbose))) } .readPrb_IntegerEncoding <- function(dirPath, pattern, ..., verbose) { res <- .readPrb_quality(dirPath, pattern, SFastqQuality, ..., asSolexa=TRUE, verbose=verbose) if (length(unique(width(res)))!=1) .throw(SRError("Input/Output", "reads have different widths") ) as(res, "matrix") } .readPrb_array <- function(dirPath, pattern, ..., verbose=FALSE) { nrec <- countLines(dirPath, pattern) crec <- c(0, cumsum(nrec)) fls <- .file_names(dirPath, pattern) gz <- gzfile(fls[[1]]); open(gz) tryCatch({ ln <- readLines(gz, 1) }, finally=close(gz)) cycles <- length(gregexpr("\t", ln, fixed=TRUE)[[1]]) + 1L a <- array(integer(), c(sum(nrec), 4L, cycles), dimnames=list(NULL, c("A", "C", "G", "T"), NULL)) what <- rep(list(integer()), 4L * cycles) for (i in seq_along(fls)) tryCatch({ gz <- gzfile(fls[[i]]); open(gz) data <- unlist(scan(gz, what, sum(nrec), ..., quiet=!verbose)) a[(crec[i]+1):crec[i+1],,] <- array(data, c(nrec[[i]], 4L, cycles)) }, error=function(err) { .throw(SRError("Input/Output", sprintf("parsing 'prb'\n file: %s\n error: %s", fls[[i]], conditionMessage(err)))) }, finally=close(gz)) a } .readPrb_character <- function(dirPath, pattern=character(0), as=c( "SolexaEncoding", "FastqEncoding", "IntegerEncoding", "array"), ..., verbose=FALSE) { if (missing(as)) { as <- "SolexaEncoding" } else if (!is.character(as) || length(as) != 1) { .arg_mismatch_type_err("as", "character(1)") } else { vals <- eval(formals(.readPrb_character)$as) if (!as %in% vals) .arg_mismatch_value_err("as", as, vals) } tryCatch({ switch(as, SolexaEncoding=.readPrb_quality( dirPath, pattern, SFastqQuality, ..., asSolexa=TRUE, verbose=verbose), FastqEncoding=.readPrb_quality( dirPath, pattern, FastqQuality, ..., asSolexa=FALSE, verbose=verbose), IntegerEncoding=.readPrb_IntegerEncoding( dirPath, pattern, ..., verbose=verbose), array=.readPrb_array( dirPath, pattern, ..., verbose=verbose)) }, error=function(err) { if (is(err, "SRError")) stop(err) else { pat <- paste(pattern, collapse=" ") txt <- paste("'%s' failed to parse files", "dirPath: '%s'", "pattern: '%s'", "as: '%s'", "error: %s", sep="\n ") msg <- sprintf(txt, "readPrb", paste(dirPath, collapse="'\n '"), pat, as, conditionMessage(err)) .throw(SRError("Input/Output", msg)) } }) } setMethod(readPrb, "character", .readPrb_character) ShortRead/R/readQseq.R0000644000175400017540000000716013001230551015556 0ustar00biocbuildbiocbuild.readQseq_ShortReadQ <- function(dirPath, pattern=character(0), ..., filtered=FALSE, verbose=FALSE) { colClasses <- rep(list(NULL), 11) colClasses[9:10] <- c("DNAString", "BString") elts <- readXStringColumns(dirPath, pattern, colClasses, ...) if (filtered) { what <- rep(list(NULL), 11) what[[11]] <- integer(0) filt <- sapply(.file_names(dirPath, pattern), function(fl, ...) { scan(fl, ...)[[11]] == 1 }, what=what, quiet=!verbose) filt <- as.vector(filt) elts[[1]] <- elts[[1]][filt] elts[[2]] <- elts[[2]][filt] } ShortReadQ(sread=elts[[1]], quality=SFastqQuality(elts[[2]]), id=BStringSet(rep("", length(elts[[1]])))) } .readQseq_DataFrame <- function(dirPath, pattern=character(0), ..., what=list(machine=character(0), run=integer(0), lane=integer(0), tile=integer(0), x=integer(0), y=integer(0), index=integer(0), readNumber=integer(0), sread=DNAStringSet(character(0)), quality=BStringSet(character(0)), filter=factor(levels=c("N", "Y"))), filtered=FALSE, verbose=FALSE) { if (!is.list(what) || length(what) != 11) .arg_mismatch_type_err("what", "list(1)") xWhat <- what xstrings <- which(sapply(what, class) %in% c("DNAStringSet", "BStringSet")) what[xstrings] <- list(NULL) fls <- .file_names(dirPath, pattern) elts <- lapply(fls, scan, what, ..., quiet=!verbose) data <- do.call(mapply, c(c, elts)) if (length(xstrings) != 0) { xWhat[-xstrings] <- list(NULL) xWhat[xstrings] <- lapply(xWhat[xstrings], function(elt) sub("Set$", "", class(elt))) data[xstrings] <- readXStringColumns(dirPath, pattern, xWhat) } xdf <- do.call(DataFrame, data) if (is.factor(what[[11]])) { xdf[[11]] <- factor(levels(what[[11]])[xdf[[11]] + 1], levels=levels(what[[11]])) if (filtered) xdf <- xdf[xdf[[11]] == "Y", -11] } xdf } .readQseq_character <- function(dirPath, pattern=character(0), ..., as=c("ShortReadQ", "DataFrame", "XDataFrame"), filtered=FALSE, verbose=FALSE) { if (missing(as)) { as <- "ShortReadQ" } else if (!is.character(as) || length(as) != 1) { .arg_mismatch_type_err("as", "character(1)") } else { vals <- eval(formals(.readQseq_character)$as) if (!as %in% vals) .arg_mismatch_value_err("as", as, vals) } tryCatch({ switch(as, ShortReadQ=.readQseq_ShortReadQ( dirPath, pattern, ..., filtered=filtered, verbose=verbose), DataFrame=.readQseq_DataFrame( dirPath, pattern, ..., filtered=filtered, verbose=verbose), XDataFrame={ .Defunct(msg="Use type='DataFrame' instead") }) }, error=function(err) { if (is(err, "SRError")) stop(err) else { txt <- paste("'%s' failed to parse files", "dirPath: '%s'", "pattern: '%s'", "as: '%s'", "error: %s", sep="\n ") msg <- sprintf(txt, "readQseq", paste(dirPath, collapse="'\n '"), pattern, as, conditionMessage(err)) .throw(SRError("Input/Output", msg)) } }) } setMethod(readQseq, "character", .readQseq_character) ShortRead/R/readXStringColumns.R0000644000175400017540000000313113001230552017577 0ustar00biocbuildbiocbuildreadXStringColumns <- function(dirPath, pattern=character(0), colClasses=list(NULL), nrows=-1L, skip=0L, sep="\t", header=FALSE, comment.char="#") { if (!is.list(colClasses)) .arg_mismatch_type_err("colClasses", "list()") colIndex <- which(!sapply(colClasses, is.null)) colClasses <- sub("Set$", "", colClasses[colIndex]) okClasses <- names(slot(getClass("XString"), "subclasses")) if (!all(colClasses %in% okClasses)) { bad <- colClasses[!colClasses %in% okClasses] .throw(SRError("UserArgumentMismatch", "'colClasses' contains invalid class%s '%s';\n must be one of '%s'", if (length(colClasses)>1) "es" else "", paste(bad, collapse="' '"), paste(okClasses, collapse="', '"))) } files <- .file_names(dirPath, pattern) res <- tryCatch({ .Call(.read_XStringSet_columns, files, header, sep, colIndex, colClasses, as.integer(nrows), as.integer(skip), comment.char) }, error=function(err) { .throw(SRError("Input/Output", "while reading files '%s':\n %s", paste(basename(files), collapse=", "), conditionMessage(err))) }) if (header) { gz <- gzfile(files[[1]]); open(gz) tryCatch({ ln <- readLines(gz, skip+1)[skip+1] }, finally=close(gz)) nms <- strsplit(ln, sep)[[1]] names(res) <- nms[colIndex] } else { names(res) <- names(colIndex) } res } ShortRead/R/renew.R0000644000175400017540000000222613001230551015127 0ustar00biocbuildbiocbuild.renewable_of <- function(x, ...) { cls <- names(getClass(x)@subclasses) sort(cls[!grepl("^\\.", cls)]) } setMethod(renewable, "missing", function(x, ...) { ## classes that are renew-able .renewable_of(".ShortReadBase") }) .renewable_query <- function(x) { if (1L != length(x)) .throw(SRError("UserArgumentMismatch", "'%s' must be '%s'", "x", "character(1)")) if (!x %in% names(getClass(".ShortReadBase")@subclasses)) .throw(SRError("UserArgumentMismatch", "'%s' is not a renewable class", x)) cls <- getClass(x) if (cls@virtual) { subcls <- .renewable_of(x) res <- lapply(subcls, .renewable_query) names(res) <- subcls res } else { getSlots(x) } } setMethod(renewable, ".ShortReadBase", function(x, ...) { structure(list(.renewable_query(class(x))), .Names=class(x)) }) setMethod(renewable, "character", function(x, ...) { res <- .renewable_query(x) if (!is.list(res)) res <- structure(list(res), .Names=x) res }) setMethod(renew, ".ShortReadBase", function(x, ...) { initialize(x, ...) }) ShortRead/R/report.R0000644000175400017540000000713413001230551015325 0ustar00biocbuildbiocbuild## PDF .report_pdf_do <- function(src, dest, symbolValues) { if (!file.exists(dirname(dest))) .throw(SRError("Input/Output", "'dest' directory '%s'\n does not exist", dirname(dest))) if (file.exists(dest)) .throw(SRError("Input/Output", "'dest' file '%s'\n already exists", dest)) tmpdir <- tempfile() if (!dir.create(tmpdir)) .throw(SRError("Input/Output", "failed to create temporary directory '%s'", tmpdir)) cwd <- setwd(tmpdir) on.exit(setwd(cwd)) tmpfile <- file.path(tmpdir, basename(src)) copySubstitute(src, tmpfile, symbolValues) texFile <- Sweave(tmpfile) tools::texi2dvi(texFile, pdf=TRUE) o_pdfFile <- sub(".tex$", ".pdf", texFile) ok <- file.copy(o_pdfFile, dest) if (!ok) .throw(SRError("Input/Output", "failed to copy '%s'\n to '%s'", o_pdfFile, dest)) dest } setMethod(.report_pdf, "character", function(x, dest, type, ...) { src <- system.file("template", "qa_solexa.Rnw", package="ShortRead") if (.Platform$OS.type == "windows") x <- gsub("\\\\", .Platform$file.sep, x) symbolValues <- list(QA_SAVE_FILE=x) .report_pdf_do(src, dest, symbolValues) }) ## HTML .report_html_do <- function(destDir, sections, values, cssFile=c(QA.css=system.file("template", "QA.css", package="ShortRead")), ...) { if (length(cssFile) != 1L || is.null(names(cssFile))) .throw(SRError("UserArgumentMismatch", "'%s' must be named character(1)", "cssFile")) htmlFile <- file.path(destDir, "index.html") biocFile <- "bioclogo-small.gif" values <- c(list(CSS=names(cssFile), DATE=date(), VERSION=packageDescription("ShortRead")$Version), values) toConn <- file(htmlFile, "w") for (sec in sections) { fromConn <- file(sec, open="r") copySubstitute(sec, toConn, values) close(fromConn) } close(toConn) imgDir <- file.path(destDir, "image") if (!file.exists(imgDir)) dir.create(imgDir) file.copy(cssFile, file.path(destDir, names(cssFile))) file.copy(system.file("template", "image", biocFile, package="ShortRead"), file.path(imgDir, biocFile)) htmlFile } .html_NA <- function() "
NA
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When coming back to configure, we # need to make the FD available again. if test "$no_create" != yes; then ac_cs_success=: ac_config_status_args= test "$silent" = yes && ac_config_status_args="$ac_config_status_args --quiet" exec 5>/dev/null $SHELL $CONFIG_STATUS $ac_config_status_args || ac_cs_success=false exec 5>>config.log # Use ||, not &&, to avoid exiting from the if with $? = 1, which # would make configure fail if this is the last instruction. $ac_cs_success || as_fn_exit 1 fi if test -n "$ac_unrecognized_opts" && test "$enable_option_checking" != no; then { $as_echo "$as_me:${as_lineno-$LINENO}: WARNING: unrecognized options: $ac_unrecognized_opts" >&5 $as_echo "$as_me: WARNING: unrecognized options: $ac_unrecognized_opts" >&2;} fi ShortRead/configure.ac0000644000175400017540000000021313001230552015744 0ustar00biocbuildbiocbuildAC_INIT("DESCRIPTION") AC_CHECK_LIB([z], [gzeof], , AC_ERROR([zlib not found])) AC_CHECK_SIZEOF([unsigned long]) AC_OUTPUT(src/Makevars) ShortRead/inst/0000755000175400017540000000000013001247473014451 5ustar00biocbuildbiocbuildShortRead/inst/CITATION0000644000175400017540000000220613001230551015573 0ustar00biocbuildbiocbuildcitEntry(entry="article", title = paste( "{ShortRead}: a {B}ioconductor package for input,", "quality assessment and exploration of high-throughput sequence", "data" ), author = personList( as.person("Martin Morgan" ), as.person("Simon Anders" ), as.person("Michael Lawrence" ), as.person("Patrick Aboyoun" ), as.person("Herv\\'e Pag\\`es" ), as.person("Robert Gentleman" ) ), year = 2009, journal = "Bioinformatics", volume = "25", pages = "2607-2608", doi = "10.1093/bioinformatics/btp450", url = "http://dx.doi.org10.1093/bioinformatics/btp450", textVersion = paste("M. Morgan, S. Anders, M. Lawrence, P. Aboyoun, H. Pag\\`es,", "and R. Gentleman (2009): \"ShortRead: a Bioconductor package", "for input, quality assessment and exploration of", "high-throughput sequence data\".", "Bioinformatics 25:2607-2608. " ) ) ShortRead/inst/doc/0000755000175400017540000000000013001247473015216 5ustar00biocbuildbiocbuildShortRead/inst/doc/Overview.R0000644000175400017540000001351113001247473017150 0ustar00biocbuildbiocbuild### R code from vignette source 'Overview.Rnw' ################################################### ### code chunk number 1: style ################################################### BiocStyle::latex() ################################################### ### code chunk number 2: preliminaries ################################################### library("ShortRead") ################################################### ### code chunk number 3: sample (eval = FALSE) ################################################### ## sampler <- FastqSampler('E-MTAB-1147/fastq/ERR127302_1.fastq.gz', 20000) ## set.seed(123); ERR127302_1 <- yield(sampler) ## sampler <- FastqSampler('E-MTAB-1147/fastq/ERR127302_2.fastq.gz', 20000) ## set.seed(123); ERR127302_2 <- yield(sampler) ################################################### ### code chunk number 4: stream (eval = FALSE) ################################################### ## strm <- FastqStreamer("a.fastq.gz") ## repeat { ## fq <- yield(strm) ## if (length(fq) == 0) ## break ## ## process chunk ## } ################################################### ### code chunk number 5: sampler (eval = FALSE) ################################################### ## sampler <- FastqSampler("a.fastq.gz") ## fq <- yield(sampler) ################################################### ### code chunk number 6: readFastq ################################################### fl <- system.file(package="ShortRead", "extdata", "E-MTAB-1147", "ERR127302_1_subset.fastq.gz") fq <- readFastq(fl) ################################################### ### code chunk number 7: ShortReadQ ################################################### fq fq[1:5] head(sread(fq), 3) head(quality(fq), 3) ################################################### ### code chunk number 8: encoding ################################################### encoding(quality(fq)) ################################################### ### code chunk number 9: qa-files (eval = FALSE) ################################################### ## fls <- dir("/path/to", "*fastq$", full=TRUE) ################################################### ### code chunk number 10: qa-qa (eval = FALSE) ################################################### ## qaSummary <- qa(fls, type="fastq") ################################################### ### code chunk number 11: qa-view (eval = FALSE) ################################################### ## browseURL(report(qaSummary)) ################################################### ### code chunk number 12: qa-files ################################################### load("qa_E-MTAB-1147.Rda") ################################################### ### code chunk number 13: qa-elements ################################################### qaSummary ################################################### ### code chunk number 14: qa-readCounts ################################################### head(qaSummary[["readCounts"]]) head(qaSummary[["baseCalls"]]) ################################################### ### code chunk number 15: filter-scheme ################################################### myFilterAndTrim <- function(fl, destination=sprintf("%s_subset", fl)) { ## open input stream stream <- open(FastqStreamer(fl)) on.exit(close(stream)) repeat { ## input chunk fq <- yield(stream) if (length(fq) == 0) break ## trim and filter, e.g., reads cannot contain 'N'... fq <- fq[nFilter()(fq)] # see ?srFilter for pre-defined filters ## trim as soon as 2 of 5 nucleotides has quality encoding less ## than "4" (phred score 20) fq <- trimTailw(fq, 2, "4", 2) ## drop reads that are less than 36nt fq <- fq[width(fq) >= 36] ## append to destination writeFastq(fq, destination, "a") } } ################################################### ### code chunk number 16: export ################################################### ## location of file exptPath <- system.file("extdata", package="ShortRead") sp <- SolexaPath(exptPath) pattern <- "s_2_export.txt" fl <- file.path(analysisPath(sp), pattern) strsplit(readLines(fl, n=1), "\t") length(readLines(fl)) ################################################### ### code chunk number 17: colClasses ################################################### colClasses <- rep(list(NULL), 21) colClasses[9:10] <- c("DNAString", "BString") names(colClasses)[9:10] <- c("read", "quality") ################################################### ### code chunk number 18: readXStringColumns ################################################### cols <- readXStringColumns(analysisPath(sp), pattern, colClasses) cols ################################################### ### code chunk number 19: size ################################################### object.size(cols$read) object.size(as.character(cols$read)) ################################################### ### code chunk number 20: tables ################################################### tbls <- tables(fq) names(tbls) tbls$top[1:5] head(tbls$distribution) ################################################### ### code chunk number 21: srdistance ################################################### dist <- srdistance(sread(fq), names(tbls$top)[1])[[1]] table(dist)[1:10] ################################################### ### code chunk number 22: aln-not-near ################################################### fqSubset <- fq[dist>4] ################################################### ### code chunk number 23: polya ################################################### countA <- alphabetFrequency(sread(fq))[,"A"] fqNoPolyA <- fq[countA < 30] ################################################### ### code chunk number 24: sessionInfo ################################################### toLatex(sessionInfo()) ShortRead/inst/doc/Overview.Rnw0000644000175400017540000003641313001247473017523 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{An introduction to ShortRead} %\VignetteDepends{BiocStyle} %\VignetteKeywords{Short read, I/0, quality assessment} %\VignettePackage{ShortRead} \documentclass[]{article} <>= BiocStyle::latex() @ \newcommand{\ShortRead}{\Biocpkg{ShortRead}} \title{An Introduction to \Rpackage{ShortRead}} \author{Martin Morgan} \date{Modified: 21 October, 2013. Compiled: \today} \begin{document} \maketitle <>= library("ShortRead") @ The \Rpackage{ShortRead} package provides functionality for working with FASTQ files from high throughput sequence analysis. The package also contains functions for legacy (single-end, ungapped) aligned reads; for working with BAM files, please see the \Biocpkg{Rsamtools}, \Biocpkg{GenomicRanges}, \Biocpkg{GenomicAlignments} and related packages. \section{Sample data} Sample FASTQ data are derived from ArrayExpress record \href{http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-1147/}{E-MTAB-1147}. Paired-end FASTQ files were retrieved and then sampled to 20,000 records with <>= sampler <- FastqSampler('E-MTAB-1147/fastq/ERR127302_1.fastq.gz', 20000) set.seed(123); ERR127302_1 <- yield(sampler) sampler <- FastqSampler('E-MTAB-1147/fastq/ERR127302_2.fastq.gz', 20000) set.seed(123); ERR127302_2 <- yield(sampler) @ \section{Functionality} Functionality is summarized in Table~\ref{tab:fastq}. \begin{table} \centering \begin{tabular}{lll} \hline \multicolumn{3}{l}{Input} \\ & \Rfunction{FastqStreamer} & Iterate through FASTQ files in chunks \\ & \Rfunction{FastqSampler} & Draw random samples from FASTQ files \\ & \Rfunction{readFastq} & Read an entire FASTQ file into memory \\ & \Rfunction{writeFastq} & Write FASTQ objects to a connection (file) \\ \multicolumn{3}{l}{Sequence and quality summary} \\ & \Rfunction{alphabetFrequency} & Nucleotide or quality score use per read\\ & \Rfunction{alphabetByCycle} & Nucleotide or quality score use by cycle\\ & \Rfunction{alphabetScore} & Whole-read quality summary\\ & \Rfunction{encoding} & Character / `phred' score mapping \\ \multicolumn{3}{l}{Quality assessment} \\ & \Rfunction{qa} & Visit FASTQ files to collect QA statistics \\ & \Rfunction{report} & Generate a quality assessment report \\ \multicolumn{3}{l}{Filtering and trimming} \\ & \Rfunction{srFilter} & Pre-defined and bespoke filters \\ & \Rfunction{trimTails}, etc. & Trim low-quality nucleotides \\ & \Rfunction{narrow} & Remove leading / trailing nucleotides \\ & \Rfunction{tables} & Summarize read occurrence \\ & \Rfunction{srduplicated}, etc. & Identify duplicate reads \\ & \Rfunction{filterFastq} & Filter reads from one file to another\\ \hline \end{tabular} \caption{Key functions for working with FASTQ files} \label{tab:fastq} \end{table} \paragraph{Input} FASTQ files are large so processing involves iteration in `chunks' using \Rfunction{FastqStreamer} <>= strm <- FastqStreamer("a.fastq.gz") repeat { fq <- yield(strm) if (length(fq) == 0) break ## process chunk } @ or drawing a random sample from the file <>= sampler <- FastqSampler("a.fastq.gz") fq <- yield(sampler) @ \noindent The default size for both streams and samples is 1M records; this volume of data fits easily into memory. Small FASTQ files can be read in to memory in their entirety using \Rfunction{readFastq}; we do this for our sample data <>= fl <- system.file(package="ShortRead", "extdata", "E-MTAB-1147", "ERR127302_1_subset.fastq.gz") fq <- readFastq(fl) @ The result of data input is an instance of class \Rclass{ShortReadQ} (Table~\ref{tab:classes}). \begin{table} \centering \begin{tabular}{ll} \hline \Rclass{DNAStringSet} & (\Biocpkg{Biostrings}) Short read sequences \\ \Rclass{FastqQuality}, etc. & Quality encodings \\ \Rclass{ShortReadQ} & Reads, quality scores, and ids \\ \hline \end{tabular} \caption{Primary data types in the \Biocpkg{ShortRead} package} \label{tab:classes} \end{table} This class contains reads, their quality scores, and optionally the id of the read. <>= fq fq[1:5] head(sread(fq), 3) head(quality(fq), 3) @ \noindent The reads are represented as \Rclass{DNAStringSet} instances, and can be manipulated with the rich tools defined in the \Biocpkg{Biostrings} package. The quality scores are represented by a class that represents the quality encoding inferred from the file; the encoding in use can be discovered with <>= encoding(quality(fq)) @ \noindent The primary source of documentation for these classes is \Rcode{?ShortReadQ} and \Rcode{?QualityScore}. \section{Common work flows} \subsection{Quality assessment} FASTQ files are often used for basic quality assessment, often to augment the purely technical QA that might be provided by the sequencing center with QA relevant to overall experimental design. A QA report is generated by creating a vector of paths to FASTQ files <>= fls <- dir("/path/to", "*fastq$", full=TRUE) @ \noindent collecting statistics over the files <>= qaSummary <- qa(fls, type="fastq") @ \noindent and creating and viewing a report <>= browseURL(report(qaSummary)) @ \noindent By default, the report is based on a sample of 1M reads. These QA facilities are easily augmented by writing custom functions for reads sampled from files, or by explorting the elements of the object returned from \Rcode{qa()}, e.g., for an analysis of ArrayExpress experiment E-MTAB-1147: <>= load("qa_E-MTAB-1147.Rda") @ <>= qaSummary @ %% For instance, the count of reads in each lane is summarized in the \Robject{readCounts} element, and can be displayed as <>= head(qaSummary[["readCounts"]]) head(qaSummary[["baseCalls"]]) @ %% The \Robject{readCounts} element contains a data frame with 1 row and 3 columns (these dimensions are indicated in the parenthetical annotation of \Robject{readCounts} in the output of \Rcode{qaSummary}). The rows represent different lanes. The columns indicated the number of reads, the number of reads surviving the Solexa filtering criteria, and the number of reads aligned to the reference genome for the lane. The \Robject{baseCalls} element summarizes base calls in the unfiltered reads. The functions that produce the report tables and graphics are internal to the package. They can be accessed through calling ShortRead:::functionName where functionName is one of the functions listed below, organized by report section. \begin{description} \item [] Run Summary : .ppnCount, .df2a, .laneLbl, .plotReadQuality \item [] Read Distribution : .plotReadOccurrences, .freqSequences \item [] Cycle Specific : .plotCycleBaseCall, .plotCycleQuality \item [] Tile Performance : .atQuantile, .colorkeyNames, .plotTileLocalCoords, .tileGeometry, .plotTileCounts, .plotTileQualityScore \item [] Alignment : .plotAlignQuality \item [] Multiple Alignment : .plotMultipleAlignmentCount \item [] Depth of Coverage : .plotDepthOfCoverage \item [] Adapter Contamination : .ppnCount \end{description} \subsection{Filtering and trimming} It is straight-forward to create filters to eliminate reads or to trim reads based on diverse characteristics. The basic structure is to open a FASTQ file, iterate through chunks of the file performing filtering or trimming steps, and appending the filtered data to a new file. <>= myFilterAndTrim <- function(fl, destination=sprintf("%s_subset", fl)) { ## open input stream stream <- open(FastqStreamer(fl)) on.exit(close(stream)) repeat { ## input chunk fq <- yield(stream) if (length(fq) == 0) break ## trim and filter, e.g., reads cannot contain 'N'... fq <- fq[nFilter()(fq)] # see ?srFilter for pre-defined filters ## trim as soon as 2 of 5 nucleotides has quality encoding less ## than "4" (phred score 20) fq <- trimTailw(fq, 2, "4", 2) ## drop reads that are less than 36nt fq <- fq[width(fq) >= 36] ## append to destination writeFastq(fq, destination, "a") } } @ \noindent This is memory efficient and flexible. Care must be taken to coordinate pairs of FASTQ files representing paired-end reads, to preserve order. \section{Using \Rpackage{ShortRead} for data exploration} \subsection{Data I/O} \ShortRead{} provides a variety of methods to read data into \R{}, in addition to \Rfunction{readAligned}. \subsubsection{\Rfunction{readXStringColumns}} \Rfunction{readXStringColumns} reads a column of DNA or other sequence-like data. For instance, the Solexa files \texttt{s\_N\_export.txt} contain lines with the following information: <>= ## location of file exptPath <- system.file("extdata", package="ShortRead") sp <- SolexaPath(exptPath) pattern <- "s_2_export.txt" fl <- file.path(analysisPath(sp), pattern) strsplit(readLines(fl, n=1), "\t") length(readLines(fl)) @ % Column 9 is the read, and column 10 the ASCII-encoded Solexa Fastq quality score; there are 1000 lines (i.e., 1000 reads) in this sample file. Suppose the task is to read column 9 as a \Rclass{DNAStringSet} and column 10 as a \Rclass{BStringSet}. \Rclass{DNAStringSet} is a class that contains IUPAC-encoded DNA strings (IUPAC code allows for nucleotide ambiguity); \Rclass{BStringSet} is a class that contains any character with ASCII code 0 through 255. Both of these classes are defined in the \Rpackage{Biostrings} package. \Rfunction{readXStringColumns} allows us to read in columns of text as these classes. Important arguments for \Rfunction{readXStringColumns} are the \Rcode{dirPath} in which to look for files, the \Rcode{pattern} of files to parse, and the \Rcode{colClasses} of the columns to be parsed. The \Rcode{dirPath} and \Rcode{pattern} arguments are like \Rcode{list.files}. \Rcode{colClasses} is like the corresponding argument to \Rfunction{read.table}: it is a \Rclass{list} specifying the class of each column to be read, or \Robject{NULL} if the column is to be ignored. In our case there are 21 columns, and we would like to read in columns 9 and 10. Hence <>= colClasses <- rep(list(NULL), 21) colClasses[9:10] <- c("DNAString", "BString") names(colClasses)[9:10] <- c("read", "quality") @ % We use the class of the type of sequence (e.g., \Rclass{DNAString} or \Rclass{BString}), rather than the class of the set that we will create ( e.g., \Rclass{DNAStringSet} or \Rclass{BStringSet}). Applying names to \Robject{colClasses} is not required, but makes subsequent manipulation easier. We are now ready to read our file <>= cols <- readXStringColumns(analysisPath(sp), pattern, colClasses) cols @ % The file has been parsed, and appropriate data objects were created. A feature of \Rfunction{readXStringColumns} and other input functions in the \Rpackage{ShortRead} package is that all files matching \Rcode{pattern} in the specified \Rcode{dirPath} will be read into a single object. This provides a convenient way to, for instance, parse all tiles in a Solexa lane into a single \Rclass{DNAStringSet} object. There are several advantages to reading columns as \Rclass{XStringSet} objects. These are more compact than the corresponding character representation: <>= object.size(cols$read) object.size(as.character(cols$read)) @ % They are also created much more quickly. And the \Rclass{DNAStringSet} and related classes are used extensively in \Rpackage{ShortRead}, \Rpackage{Biostrings}, \Rpackage{BSgenome} and other packages relevant to short read technology. \subsection{Sorting} Short reads can be sorted using \Rfunction{srsort}, or the permutation required to bring the short read into lexicographic order can be determined using \Rfunction{srorder}. These functions are different from \Rfunction{sort} and \Rfunction{order} because the result is independent of the locale, and they operate quickly on \Rclass{DNAStringSet} and \Rclass{BStringSet} objects. The function \Rfunction{srduplicated} identifies duplicate reads. This function returns a logical vector, similar to \Rfunction{duplicated}. The negation of the result from \Rfunction{srduplicated} is useful to create a collection of unique reads. An experimental scenario where this might be useful is when the sample preparation involved PCR. In this case, replicate reads may be due to artifacts of sample preparation, rather than differential representation of sequence in the sample prior to PCR. \subsection{Summarizing read occurrence} The \Rfunction{tables} function summarizes read occurrences, for instance, <>= tbls <- tables(fq) names(tbls) tbls$top[1:5] head(tbls$distribution) @ %% The \Robject{top} component returned by \Robject{tables} is a list tallying the most commonly occurring sequences in the short reads. Knowledgeable readers will recognize the top-occurring read as a close match to one of the manufacturer adapters. The \Robject{distribution} component returned by \Robject{tables} is a data frame that summarizes how many reads (e.g., \Sexpr{tbls[["distribution"]][1,"nReads"]}) are represented exactly \Sexpr{tbls[["distribution"]][1,"nOccurrences"]} times. \subsection{Finding near matches to short sequences} Facilities exist for finding reads that are near matches to specific sequences, e.g., manufacturer adapter or primer sequences. \Rfunction{srdistance} reports the edit distance between each read and a reference sequence. \Rfunction{srdistance} is implemented to work efficiently for reference sequences whose length is of the same order as the reads themselves (10's to 100's of bases). To find reads close to the most common read in the example above, one might say <>= dist <- srdistance(sread(fq), names(tbls$top)[1])[[1]] table(dist)[1:10] @ %% `Near' matches can be filtered, e.g., <>= fqSubset <- fq[dist>4] @ A different strategy can be used to tally or eliminate reads that consist predominantly of a single nucleotide. \Rfunction{alphabetFrequency} calculates the frequency of each nucleotide (in DNA strings) or letter (for other string sets) in each read. Thus one could identify and eliminate reads with more than 30 adenine nucleotides with <>= countA <- alphabetFrequency(sread(fq))[,"A"] fqNoPolyA <- fq[countA < 30] @ %% \Rfunction{alphabetFrequency}, which simply counts nucleotides, is much faster than \Rfunction{srdistance}, which performs full pairwise alignment of each read to the subject. Users wanting to use \R{} for whole-genome alignments or more flexible pairwise aligment are encouraged to investigate the \Rpackage{Biostrings} package, especially the \Rclass{PDict} class and \Rfunction{matchPDict} and \Rfunction{pairwiseAlignment} functions. \section{Legacy support for early file formats} The \Biocpkg{ShortRead} package contains functions and classes to support early file formats and ungapped alignments. 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[wgT]?y"8EWm4%c/s9ָ8M934 j*'oq )LSYA F]jpJz4/`/ƋZOE4jpN? _c&u~ILKS\w>~ K)}'}t]*0ݍ[( >e̐ p0o^q0~[jfm^fh6ưTBs] sVד@{:6()! @AiT1ApꃋP)KL<߾ɾ}cvF"U:O1_Ty1KhN/ t`a.Z(:6h[-K7.p jx7xe$-h TB=vND[Eơ 6djn\pVng}%cz JYCr }e?>ߠ>r|. i˴uѺ ;5FM&U2XV%,&S\׏z=yrܼ77 n;^b5 kOR GD8 +cL)-zbV$}ilK9]Cъ0u35zu7ھZQ{5r9* Y`C@)/ [CtV֨$!pzrzdЉϜYtǜ'giIeф륥~ʋhPmMgxӸ q P +V“cڱtSd4@&A>!U5yq! ,S=Ry9]֟y}ނ9GևoTjK˥ ) [{l-Gqa>cWi2 AMpR޾Q\,^zX.ܑHMulj6&UmzC4~C"ѻ1X5S|36.ڧ)ŦȘղX> endobj 138 0 obj << /Type /ObjStm /N 44 /First 367 /Length 2143 /Filter /FlateDecode >> stream xYYSܸ~_0S]*K`’CʃvOzvg{d۽$=<ͭdHg"L&QN&#b# _NcHVX…N89H"q=42b0"0b""ym#[RYA#03DZ9uEH3Pab`3S@a?`D@|?"J E& Xs i{Ne9z _t 9H=`'aˆ`0Ӝh-Yip*\ND\Fdz}FhG. \>`]_o5Z%T նFF(I==HXa13ЀF}[a{\mgFezJ5a W ~\=p2GՈֈr6aJOg @@7ZR׌9,s}~YI8bӜsnފycq>B\#7$@O*è@v o!(UtFf3n 9~@CZʔ#ѷq>% z#DmjnqXC\y}*h!2cgC̔WoГ8Na.yx ;'on="ؚppD>CKо^zYGi%}GϊxfIB]dm$xARW#zLCO=~4΋$Kï$}I Eפ0C:O3Әh>Mh' ~K2c:Β4}hJ_NR0ȓ*+CxbC]mK.;^߼ 1x8hFٗOo jCP c;`]p5ZGwxJਟGD>XF˶ofק{o7խ)EX6zqhl;e#*RaӺ:iCաlvNuгֶw[enkXTHک{w98/1PNVGc6zC(w;Ż?n<6?udxo/yD;>t[?S0Ը<%jf ·]nurW)-mJǼ|+N)LLPʛR!_e巼9ؖ+R)q+/~{};QS_^Yb>Ju<_Wdv"?ӄ endstream endobj 168 0 obj << /Type /XRef /Index [0 169] /Size 169 /W [1 3 1] /Root 166 0 R /Info 167 0 R /ID [<8CBE3520047B00DA3C5E5C59150FD845> <8CBE3520047B00DA3C5E5C59150FD845>] /Length 415 /Filter /FlateDecode >> stream xGRa{H"J((AI(9kX[lŌE9` NY p&:}vff6n|O@@ ])r)򡟮T)k Փ !Ց螒J=!A9Ւ*>=&UA5,Ր2.uڐv4['CY>)}````0KFm^@[Qx ׭V_ k- [aS-Ip>SYrn]K[J,êJhamDi`m=n`^pg†o73]]*SsOW(廷W*p?QJ~SJ_Tt%8/s m~|p=0.6)) #RUN_TIME Fri Apr 25 23:21:48 2008 #SOFTWARE_VERSION @(#) $Id: PhageAlign.cpp,v 1.7 2007/10/04 10:13:50 tc Exp $ #MAX_BLANKS 35 #SEQ_LENGTH 35 #GENOME_FILE /shared/solexa/solexa/Genomes/phi_plus_SNPs.txt #BASES_USED YYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYn #SCORE_FILE s_5_0001_score.txt #SEQ_FILE /shared/solexa/ycao/080422_HWI-EAS88_0003/Data/C1-36_Firecrest1.9.2_25-04-2008_solexa/Bustard1.9.2_25-04-2008_solexa/s_5_0001_seq.txt #SCORE_FILE s_5_0001_score.txt AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 TGAACAGCTTCTTGGGAAGTAGCGACAGCTTGGTT 3908 1 5093 R AACCAAGCTGTCGCTACTTCCCAAGAAGCTGTTCA -462 TCTACTGTAGACATTTTTACTTTTTATGTCCCTCA 3949 1 1187 F TCTACTGTAGACATTTTTACTTTTTATGTCCCTCA 340 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 TCAGATGGATACATCTGTCAACGCCGCTAATCAGG 3952 1 2574 F TCAGATGGATACATCTGTCAACGCCGCTAATCAGG -77 TGAGTCGAAAAATTATCTTGATAAAGCAGGAATTA 4119 1 52 F TGAGTCGAAAAATTATCTTGATAAAGCAGGAATTA -159 TTTCTTACCTATTAGTGGTTGAACAGCATCGGACT 4117 1 353 R AGTCCGATGCTGTTCAACCACTAATAGGTAAGAAA -34 TCTCAGGAGGAAGCGGAGCAGTCCAAATGTTTTTG 3850 1 1498 R CAAAAACATTTGGACTGCTCCGCTTCCTCCTGAGA -170 TAGTAATTCCTGCTTTATCAAGATAATTTTTCGAC 4026 1 55 R GTCGAAAAATTATCTTGATAAAGCAGGAATTACTA -29 TAATGTTTATGTTGGGTTCTTGGTTTGTTATAACT 2651 1 2808 F TAACGTTTATGTTGGTTTCATGGTTTGGTCTAACT 196 TTCAAGATTGCTGGAGGCCTCCACTATGAAATCGC 3951 1 4472 F TTCAAGATTGCTGGAGGCCTCCACTATGAAATCGC -162 TTCTGGTGATTTGCAAGAACGCGTACTTATTCGCC 3945 1 2128 F TTCTGGTGATTTGCAAGAACGCGTACTTATTCGCC -270 TTCAGACTTTTATTTCTCGCCATAATTCAAACTTT 3997 1 2399 F TTCAGACTTTTATTTCTCGCCATAATTCAAACTTT 73 TACCATGAAAAAAATGTGAGTCATGTCTAACTAAC 2250 1 273 R CTGGTTTAGATATGAGTCACATTTTGTTCATGGTA 341 TATGTTTTCATGCCTCCAAATCTTGGAGGCTTTTT 3944 1 3945 F TATGTTTTCATGCCTCCAAATCTTGGAGGCTTTTT -373 TGCTAAAGGTGAGCCGCTTAAAGCTACCAGTTATA 3947 1 4958 F TGCTAAAGGTGAGCCGCTTAAAGCTACCAGTTATA -36 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 GGTCTAACTTTACCGCTACTAAATGCCGCGGATTG 3981 1 2834 F GGTCTAACTTTACCGCTACTAAATGCCGCGGATTG -621 GTTATTTCCTAGACAAATTAGAGCCAATACCATCA 4103 1 3884 R TGATGGTATTGGCTCTAATTTGTCTAGGAAATAAC -269 TGTCAGCGTCATAAGAGGTTTTACCTCCAAATGAA 4050 1 1663 R TTCATTTGGAGGTAAAACCTCTTATGACGCTGACA 383 TGAAAAACAAAGTAGCAGCGTCGATTGTGGCAATT -186 1 4278 R CAATGCTACAATGTGCTCCCCCAACTTGATATTAA -424 TATAGTGTTATTAATATCAAGTTGGGGGAGCACAT 4106 1 4288 R ATGTGCTCCCCCAACTTGATATTAATAACACTATA -520 GCTTCTGACGTTCGTGATGAGTTTGTATCTGTTAC 3933 1 4218 F GCTTCTGACGTTCGTGATGAGTTTGTATCTGTTAC 146 CAACAAGAGAATCTCTACCATGAACAAAATGTGAC 4053 1 288 R GTCACATTTTGTTCATGGTAGAGATTCTCTTGTTG 522 GTTATATTTTGATAGTTTGACGGTTAATGCTGGTA 4113 1 2517 F GTTATATTTTGATAGTTTGACGGTTAATGCTGGTA -121 TGCCTGACCGTACCGAGGCTAACCCTAATGAGCTT 3878 1 1422 F TGCCTGACCGTACCGAGGCTAACCCTAATGAGCTT -42 TAGTTGTTATAGATATTCAAATAACCCTGAAACAA 4196 1 1370 R TTGTTTCAGGGTTATTTGAATATCTATAACAACTA 133 TTTATCAAGATAATTTTTCGACTCATCAGAAATAT 4140 1 42 R ATATTTCTGATGAGTCGAAAAATTATCTTGATAAA -34 TTGATGAAAGCAATGCGACAGGCTCATGCTGATGG 3653 1 4530 F TTGATGAATGCAATGCGACAGGCTCATGCTGATGG -471 TATGATGTTTATCCTTTGGATGGTCGCCATGATGG 3805 1 2713 F TATGATGTTTATCCTTTGAATGGTCGCCATGATGG 157 GGTTGTCAGCGTCATAAGAGGTTTTACCTCCCAAT 3714 1 1666 R ATTTGGAGGTAAAACCTCTTATGACGCTGACAACC -250 TTCGCCACCATGATTATGACCAGTGTTTCCAGTCC 3882 1 2157 F TTCGCCACCATGATTATGACCAGTGTTTCCAGTCC 463 GTTTTCTGCTTAGGAGTTTAATCATGTTTCAGACT 4012 1 2372 F GTTTTCTGCTTAGGAGTTTAATCATGTTTCAGACT -365 TTGCTATTCAGCGTTTGATGAATGCAATGCGACAG 4080 1 4516 F TTGCTATTCAGCGTTTGATGAATGCAATGCGACAG 160 GTGAGTTGTTCCATTCTTTAGCTCCTAGACCTTTA 3908 1 5054 R TAAAGGTCTAGGAGCTAAAGAATGGAACAACTCAC -237 GGATTGACACCCTCCCAATTGTATGTTTTCATGCC 3843 1 3924 F GGATTGACACCCTCCCAATTGTATGTTTTCATGCC -169 GTCATGCGCTCTAATCTCTGGGCATCTGGCTATGA 3904 1 1712 F GTCATGCGCTCTAATCTCTGGGCATCTGGCTATGA -367 TGTTCAGTAACTTGACTCATGATTTCTTACCTATT 3932 1 375 R AATAGGTAAGAAATCATGAGTCAAGTTACTGAACA 8 CACGCTGATTATTTTGACTTTGCGAGTATCGAGGC 3510 1 4011 F CACGCTGATTATTTTGACTTTGAGCGTATCGAGGC -301 GTAGTAATTCCTGCTTTATCAAGATAATTTTTCGA 4018 1 56 R TCGAAAAATTATCTTGATAAAGCAGGAATTACTAC -252 CGACCAAAATTAGGGTCAACGCTACCTGTAGGAAG 3955 1 4797 R CTTCCTACAGGTAGCGTTGACCCTAATTTTGGTCG -411 GACGCAGAAGTTAACACTTTCGGATATTTCTGATG 3877 1 19 F GACGCAGAAGTTAACACTTTCGGATATTTCTGATG -305 GTTTAATCATGTTTCAGACTTTTATTTCTCGCCAT 3995 1 2387 F GTTTAATCATGTTTCAGACTTTTATTTCTCGCCAT -394 GAGTTGTTCCATTCTTTAGCTCCTAGACCTTTAGC 3957 1 5052 R GCTAAAGGTCTAGGAGCTAAAGAATGGAACAACTC -306 GTTATCCATCTGCTTATGGAAGCCAAGCATTGGGG 4041 1 4093 R CCCCAATGCTTGGCTTCCATAAGCAGATGGATAAC -459 GTGGTCTATAGTGTTATTAATATCAAGTTGGGGGA 4129 1 4294 R TCCCCCAACTTGATATTAATAACACTATAGACCAC -396 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 GTTCATCAGCAAACGCAGAATCAGCGGTATGGCTC 3920 1 3714 F GTTCATCAGCAAACGCAGAATCAGCGGTATGGCTC 629 AAGACGGCCATTAGCTGTACCATACTCAGGCACAC 3874 1 4732 R GTGTGCCTGAGTATGGTACAGCTAATGGCCGTCTT 293 GTAGTAATTCCTGCTTTATCAAGATAATTTTTCGA 4018 1 56 R TCGAAAAATTATCTTGATAAAGCAGGAATTACTAC -252 GAGATGGCAGCAACGGAAACCATAACGACCATCAT 3609 1 1464 R ATGATGCTCGTTATGGTTTCCGTTGCTGCCATCTC 131 GAATATCCTTAAGAGGGCGCTCAGCAGCCAGCTTG 3677 1 4391 R CAAGCTGGCTGCTGAACGCCCTCTTAAGGATATTC -295 TGACTCGCAAGGTTAGTGCTGAGGTTGACTTAGTT 3907 1 3682 F TGACTCGCAAGGTTAGTGCTGAGGTTGACTTAGTT -228 GAGGAGAAGTGGCTTAATATGCTTGGCACGTTCGT 3848 1 232 F GAGGAGAAGTGGCTTAATATGCTTGGCACGTTCGT -229 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 GATTTAATTCGTAAACAAGCAGTAGTAATTCCTGC 4030 1 77 R GCAGGAATTACTACTGCTTGTTTACGAATTAAATC -318 GTTTATCGTTTTTGACACTCTCACGTTGGCTGACG 4008 1 4568 F GTTTATCGTTTTTGACACTCTCACGTTGGCTGACG -47 TTGAACACTCATCCTTAATACCTTTCTTTTTGGGG 3952 1 4442 R CCCCAAAAAGAAAGGTATTAAGGATGAGTGTTCAA -88 TGTTTTCATGCCTCCAAATCTTGGAGGCTTTTTTA 3956 1 3947 F TGTTTTCATGCCTCCAAATCTTGGAGGCTTTTTTA -42 TCCCCTTCGGGGCGGTGGTCTATAGTGTTATTTAT 3646 1 4308 R ATTAATAACACTATAGACCACCGCCCCGAAGGGGA -313 GGAGTAGTTGAAATGGTAATAAGACGACCAATCTG 4013 1 1070 R CAGATTGGTCGTCTTATTACCATTTCAACTACTCC 61 GAGAAGTTAATGGATGAATTGGCACAATGCTACAA 4009 1 4254 F GAGAAGTTAATGGATGAATTGGCACAATGCTACAA -88 GACGGTTAATGCTGGTAATGGTGGTTTTTTTCTTT 3337 1 2535 F GACGGTTAATGCTGGTAATGGTGGTTTTCTTCATT 246 GATTGGTTTCGCTGAATCAGGTTATTAAAGAGATT 4082 1 2864 F GATTGGTTTCGCTGAATCAGGTTATTAAAGAGATT -255 GATATTCAAATAACCCTGAAACAAATGCTTAGGGA 4088 1 1359 R TCCCTAAGCATTTGTTTCAGGGTTATTTGAATATC 204 GTTTATGGTACGCTGGACTTTGTAGGATACCCTCG 3613 1 564 F GTTTATGGTACGCTGGACTTTGTGGGATACCCTCG -720 GCAAAGGATATTTCTAATGTCGTCACTGATGCTGC 3934 1 3768 F GCAAAGGATATTTCTAATGTCGTCACTGATGCTGC 108 TTTTGTTAACGTATTTAGCCACATAGAAACCAACA 4198 1 4997 R TGTTGGTTTCTATGTGGCTAAATACGTTAACAAAA -219 TAAAATAGTTGTTATAGATATTCAAATAACCCTGA 4140 1 1375 R TCAGGGTTATTTGAATATCTATAACAACTATTTTA -339 GTTATTATACCGTCAAGGACTGTGTGACTATTGAC 4022 1 2750 F GTTATTATACCGTCAAGGACTGTGTGACTATTGAC -531 GTGAAATTTCTATGAAGGATGTTTTCCGTTCTGGT 3947 1 1980 F GTGAAATTTCTATGAAGGATGTTTTCCGTTCTGGT -365 ATGTAGCTTTAGGTGTCTGTAAAACAGGTGCCGAA 4063 1 2475 R TTCGGCACCTGTTTTACAGACACCTAAAGCTACAT 194 GGTGTTAATCCTGACGGTTATTTCCTAGACAAATT 3688 1 3900 R AATTTGTCTAGGAAATAACCGTCAGGATTGACACC -421 GATGGATACATCTGTCAACGCCGCTAATCAGGTTG 3896 1 2577 F GATGGATACATCTGTCAACGCCGCTAATCAGGTTG -367 GGATATTTCTAATGTCGTCACTGATGCTGCTTCTG 3907 1 3773 F GGATATTTCTAATGTCGTCACTGATGCTGCTTCTG 804 GGTTATTAAAGAGATTATTTGTCTCCTGCCACTTT 3495 1 2883 F GGTTATTAAAGAGATTATTTGTCTCCAGCCACTTA -334 GTTTGAATTATGGCGAGAAATAAAAGTCTGAAACA 4179 1 2396 R TGTTTCAGACTTTTATTTCTCGCCATAATTCAAAC 140 GATAGTAATCCACGCTCTTTTAAAATGTCAACAAG 4081 1 316 R CTTGTTGACATTTTAAAAGAGCGTGGATTACTATC 389 TCTGGTGATTCGTCTAAGAAGTTTAAGATTGCTGA 4069 1 2009 F TCTGGTGATTCGTCTAAGAAGTTTAAGATTGCTGA 165 GATAGTTTGACGGTTAATGCTGGTAATGGTGGTTT 3945 1 2527 F GATAGTTTGACGGTTAATGCTGGTAATGGTGGTTT 140 GCATTGGGATTATCATAAAACGCCTCTAATCGGTC 3948 1 4602 R GACCGATTAGAGGCGTTTTATGATAATCCCAATGC -603 TAAAATGCAACTGGACAATCAGAAAGAGATTGCCG 4022 1 3395 F TAAAATGCAACTGGACAATCAGAAAGAGATTGCCG 1242 GATAACCGCATCAAGCTCTTGGAAGAGATTCTGTC 3853 1 4122 F GATAACCGCATCAAGCTCTTGGAAGAGATTCTGTC -87 GCCGAAGAAGCTGGAGTAACAGAAGTGAGAACCAG 4001 1 2446 R CTGGTTCTCACTTCTGTTACTCCAGCTTCTTCGGC -270 GCTGCATTTCCTGAGCTTAATGCTTGGGAGCGTGC 3950 1 3297 F GCTGCATTTCCTGAGCTTAATGCTTGGGAGCGTGC -92 GATTAACCCTGATACCAACAAAATACCTAAGCATC 3280 1 1336 F GATTAACCCTGATACCAATAAAATCCCTAAGCATT 35 GGTTTCCGAGATTATGCGCCAAATGCTTACTCAAG 3995 1 3602 F GGTTTCCGAGATTATGCGCCAAATGCTTACTCAAG -315 GTACGCGTTCTTGCAAATCACCAGAAGGCGGTTCC 3910 1 2119 R GGAACCGCCTTCTGGTGATTTGCAAGAACGCGTAC -208 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 GGTTATCCATCTGCTTATGTAAGCCAAGCGTTGGG 3468 1 4094 R CCCAATGCTTGGCTTCCATAAGCAGATGGATAACC -131 GTTTAGATATGAGTCACATTTTGTTCATGGTAGAG 4101 1 276 F GTTTAGATATGAGTCACATTTTGTTCATGGTAGAG 31 GAGACTGAGCTTTCTCGCCAAATGACGACTTCTAC 3868 1 1529 F GAGACTGAGCTTTCTCGCCAAATGACGACTTCTAC -20 GATAATCACGAGTATCCTTTCCTTTATCAGCGGCA 3936 1 3257 R TGCCGCTGATAAAGGAAAGGATACTCGTGATTATC -364 GATATGAGTCACATTTTGTTCATGGTAGAGATTCT 3987 1 281 F GATATGAGTCACATTTTGTTCATGGTAGAGATTCT -234 GATATTTTTCATGGTATTGATAAAGCTGTTGCCGA 4098 1 3816 F GATATTTTTCATGGTATTGATAAAGCTGTTGCCGA 407 GGTTAATGCTGGTAATGGTGGTTTTCTTCCTTTCT 3193 1 2538 F GGTTAATGCTGGTAATGGTGGTTTTCTTCATTGCA -79 GATTGTTCAGTAACTTGACTCATGATTTCTTACCT 3915 1 378 R AGGTAAGAAATCATGAGTCAAGTTACTGAACAATC -112 GTTCAGAATCAGAATGAGCCGCAACTTCGGGATGA 3993 1 5123 F GTTCAGAATCAGAATGAGCCGCAACTTCGGGATGA -305 GGATATTTCTGATGAGTATAATTACCCCAAAAAGA 3246 1 4418 F GGATATTCGCGATGAGTATAATTACCCCAAAAAGA 614 GTGGTTTTCTTCATTGCATTCAGATGGATACATCT 4001 1 2555 F GTGGTTTTCTTCATTGCATTCAGATGGATACATCT -282 GTTTTACAGACACCTAAAGCTACATCGTCAACGTT 3984 1 2485 F GTTTTACAGACACCTAAAGCTACATCGTCAACGTT 348 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 TAATTCGTAAACAAGCAGTAGTAATTCCTGCTTTA 3965 1 73 R TAAAGCAGGAATTACTACTGCTTGTTTACGAATTA -239 GAGAACCAGCTTATCAGAAAAAAAGTTTGAATTAT 4022 1 2420 R ATAATTCAAACTTTTTTTCTGATAAGCTGGTTCTC -438 GAGGTGATTTATGTTTGGTGCTATTGCTGGCGGTT 3742 1 2921 F GAGGTGATTTATGTTTGGTGCTATTGCTGGCGGTA -107 GTGATTATCTTGCTGCTGCCTTTCCTGAGCTTAAT 3687 1 3283 F GTGATTATCTTGCTGCTGCATTTCCTGAGCTTAAT 48 GTGCCAAGAAAAGCGGCATGGTCAATATAACCAGT 3914 1 1301 R ACTGGTTATATTGACCATGCCGCTTTTCTTGGCAC -306 GTTGACGATGTAGCTTTAGGTGTCTGTAAAACAGG 4064 1 2482 R CCTGTTTTACAGACACCTAAAGCTACATCGTCAAC -270 GACACCTAAAGCTACATCGTCAACGTTATATTTTG 3896 1 2493 F GACACCTAAAGCTACATCGTCAACGTTATATTTTG 157 GTTTTACAGACACCTAAAGCTACATCGTCAACGTT 3984 1 2485 F GTTTTACAGACACCTAAAGCTACATCGTCAACGTT 348 GAAGTTAACACTTTCGGATATTTCTGATGAGTCGA 4013 1 25 F GAAGTTAACACTTTCGGATATTTCTGATGAGTCGA -194 GATGAGTATAATTACCCCAAAAAGAAAGGTATTAA 4154 1 4428 F GATGAGTATAATTACCCCAAAAAGAAAGGTATTAA -40 GTTATATGGCTGGTGGGTTTTTTTTTTTTTAATTC 1371 1 4987 F GTTATATGGCTGTTGGTTTCTATGTGGCTAAATAC -64 GGCGTTTTATGATAATCCCAATGCTTTGCGTGACT 3944 1 4613 F GGCGTTTTATGATAATCCCAATGCTTTGCGTGACT -239 GTAAGTTGGATTAAGCACTCCGTGGACAGATTTGT 3896 1 5172 R ACAAATCTGTCCACGGAGTGCTTAATCCAACTTAC -346 GAGACTGAGCTTTCTCGCCAAATGACGACTTCTAC 3868 1 1529 F GAGACTGAGCTTTCTCGCCAAATGACGACTTCTAC -20 GGTGGTTATTATACCGTCAAGGACTGTGTGACTAT 3983 1 2746 F GGTGGTTATTATACCGTCAAGGACTGTGTGACTAT -281 GTGTGGTTGATATTTTTCATGGTATTGATAAAGCT 4081 1 3808 F GTGTGGTTGATATTTTTCATGGTATTGATAAAGCT 97 GGATTTTATTGGTATCAGGGTTAATCGTGCCAAGA 4114 1 1327 R TCTTGGCACGATTAACCCTGATACCAATAAAATCC 238 TATTATGGAAAACACCAATCTTTCCAAGCAACAGC 4063 1 3566 F TATTATGGAAAACACCAATCTTTCCAAGCAACAGC -314 GTTTAGATATGAGTCACATTTTGTTCATGGTAGAG 4101 1 276 F GTTTAGATATGAGTCACATTTTGTTCATGGTAGAG 31 GTTTGGTCAGTTCCATCAACATCATAGCCAGATGC 4008 1 1733 R GCATCTGGCTATGATGTTGATGGAACTGACCAAAC -197 GAAAACATACAATTGGGAGGGTGTCAATCCTGACG 3947 1 3919 R CGTCAGGATTGACACCCTCCCAATTGTATGTTTTC -159 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 GTTTACTCTTGCGCTTGTTCGTTTTCCTCCTACTG 3644 1 1852 F GTTTACTCTTGCGCTTGTTCGTTTTCCGCCTACTG -145 GTTTGATGAATGCAATGCGACAGGCTCATGCTGAT 3957 1 4528 F GTTTGATGAATGCAATGCGACAGGCTCATGCTGAT -444 GTTTTTGACACTCTCACGTTGGCTGACGACCGATT 3961 1 4575 F GTTTTTGACACTCTCACGTTGGCTGACGACCGATT -440 GGACGCTCGACGCCATTAATAATGTTTTCCGTAAA 3919 1 696 R TTTACGGAAAACATTATTAATGGCGTCGAGCGTCC 345 GTTCAACAGACCTATAAACATTCTGTGCCGCGTTT 3885 1 1793 F GTTCAACAGACCTATAAACATTCTGTGCCGCGTTT -255 GGAGTAACAGAAGTGAGAACCAGCTTATCAGAAAA 4030 1 2434 R TTTTCTGATAAGCTGGTTCTCACTTCTGTTACTCC 150 GAGCATCATCTTGATTAAGCTCATTAGGGTTAGCC 3987 1 1438 R GGCTAACCCTAATGAGCTTAATCAAGATGATGCTC 794 GCAGGTTTAAGAGCCTCGATACGCTCAAAGTCAAA 4013 1 4023 R TTTGACTTTGAGCGTATCGAGGCTCTTAAACCTGC -429 GCACCGCATGGAAATGAAGACGGCCATTAGCTGTG 3614 1 4748 R TACAGCTAATGGCCGTCTTCATTTCCATGCGGTGC -430 GAGAACCAGCTTATCAGAAAAAAAGTTTGAATTAT 4022 1 2420 R ATAATTCAAACTTTTTTTCTGATAAGCTGGTTCTC -438 GGATTGACACCCTCCCAATTGTATGTTTTCATGCC 3843 1 3924 F GGATTGACACCCTCCCAATTGTATGTTTTCATGCC -169 GTTCCTGAGCATGGCACTATGTTTACTCTTGCGCT 3856 1 1832 F GTTCCTGAGCATGGCACTATGTTTACTCTTGCGCT -181 GATTTTCTGACGAGTAACAAAGTTTGGATTGCTAC 4031 1 492 F GATTTTCTGACGAGTAACAAAGTTTGGATTGCTAC -173 TCGTCACTGATGCTGCTTCTGGTGTGGTTGATATT 3867 1 3787 F TCGTCACTGATGCTGCTTCTGGTGTGGTTGATATT 600 GATTACTTCATGCAGCGTTACCATGATGTTATTTC 3909 1 1628 F GATTACTTCATGCAGCGTTACCATGATGTTATTTC -25 GGTGATATGTATGTTGACGGCCATAAGGCTGCTTC 3915 1 4188 F GGTGATATGTATGTTGACGGCCATAAGGCTGCTTC 288 TGCCTTTAGTACCTCGCAACGGCTGCGGACGACCA 3945 1 883 R TGGTCGTCCGCAGCCGTTGCGAGGTACTAAAGGCA -767 TTATTACCCTTCTGAATGTCACGCTGATTATTTTG 3972 1 3992 F TTATTACCCTTCTGAATGTCACGCTGATTATTTTG -294 GGAAACACTGACGTTCTTACTGACGCAGAAGAAAA 4069 1 777 F GGAAACACTGACGTTCTTACTGACGCAGAAGAAAA -109 GATTTCGATTTTCTGACGAGTAACAAAGTTTGGAT 4115 1 486 F GATTTCGATTTTCTGACGAGTAACAAAGTTTGGAT -128 GCTACATCGTCAACGTTATATTTTGATAGTTTGAC 3968 1 2503 F GCTACATCGTCAACGTTATATTTTGATAGTTTGAC -512 GCTTAGGGATTTTATTGGTATCAGGGTTAATCGTG 4030 1 1333 R CACGATTAACCCTGATACCAATAAAATCCCTAAGC -332 GGCTCATGCTGATGGTTGGTTTATCGTTTTTGACA 3917 1 4550 F GGCTCATGCTGATGGTTGGTTTATCGTTTTTGACA 111 GTAGGTTTTCTGCTTAGGAGTTTAATCATGTTTCA 4003 1 2368 F GTAGGTTTTCTGCTTAGGAGTTTAATCATGTTTCA -410 GATTAACCCTGATACCAATAAAATCCCTAAGCATT 4026 1 1336 F GATTAACCCTGATACCAATAAAATCCCTAAGCATT -396 ACGAGAGCGGTCAGTAGCAATCCAAACTTTGTTAC 3815 1 505 R GTAACAAAGTTTGGATTGCTACTGACCGCTCTCGT -186 GGGAGGGTGTCAATCCTGACGGTTATTTCCTAGAC 3822 1 3905 R GTCTAGGAAATAACCGTCAGGATTGACACCCTCCC -430 TATGTATGTTGACGGCCATAAGGCTGCTTCTTACG 3692 1 4193 F TATGTATGTTGACGGCCATAAGGCTGCTTCTGACG 445 GAGGTTGACTTAGTTCATCAGCAAACGCAGAATCA 4024 1 3702 F GAGGTTGACTTAGTTCATCAGCAAACGCAGAATCA -78 GATGAGTCGAAAAATTATCTTGATAAAGCAGGAAT 4124 1 50 F GATGAGTCGAAAAATTATCTTGATAAAGCAGGAAT 46 GCGAGTCATTTCTTTGATTTGGTCATTGGTAAAAT 4027 1 3655 R ATTTTACCAATGACCAAATCAAAGAAATGACTCGC -506 GGTGATTTGCAAGAACGCGTACTTATTCGCCACCA 3898 1 2132 F GGTGATTTGCAAGAACGCGTACTTATTCGCCACCA -64 GAAGACGGCCATTAGCTGTACCATACTCAGGCACA 3902 1 4733 R TGTGCCTGAGTATGGTACAGCTAATGGCCGTCTTC 275 GTCAAAAACTGACGCGTTGGATGAGGAGAAGTGGC 4006 1 210 F GTCAAAAACTGACGCGTTGGATGAGGAGAAGTGGC -211 GTTGTTGCAGTGGAATAGTCAGGTTAAATTTAATG 4057 1 2197 F GTTGTTGCAGTGGAATAGTCAGGTTAAATTTAATG -159 TGATGCTGGTATTAAATCTGCCATTCAAGGCTCTA 4002 1 3074 F TGATGCTGGTATTAAATCTGCCATTCAAGGCTCTA -200 GTTCAGTTGTTGCAGTGGAATAGTCAGGTTAAATT 4062 1 2192 F GTTCAGTTGTTGCAGTGGAATAGTCAGGTTAAATT -120 TGTTTGGTTCGCTTTGAGTCTTCTTCGGTTCCGAC 3917 1 2662 F TGTTTGGTTCGCTTTGAGTCTTCTTCGGTTCCGAC -328 GTTATTAATATCAAGTTGGGGGAGCACATTGTAGC 4055 1 4282 R GCTACAATGTGCTCCCCCAACTTGATATTAATAAC -317 GCAGGACGCTTTTTCACGTTCTGGTTGGTTGTGGC 3836 1 4916 F GCAGGACGCTTTTTCACGTTCTGGTTGGTTGTGGC -430 TGGTCGCCATGATGGTGGTTATTATACCGTCAAGG 3956 1 2733 F TGGTCGCCATGATGGTGGTTATTATACCGTCAAGG -248 GTTGCCGATACTTGGAACAATTTCTGGAAAGACGG 4072 1 3843 F GTTGCCGATACTTGGAACAATTTCTGGAAAGACGG -127 GTAGGTTTTCTGCTTAGGAGTTTAATCATGTTTCA 4003 1 2368 F GTAGGTTTTCTGCTTAGGAGTTTAATCATGTTTCA -410 GCGACAGCTTGGTTTTTAGTGAGTTGTTCCATTCT 3870 1 5072 R AGAATGGAACAACTCACTAAAAACCAAGCTGTCGC -308 TTAATCAAGATGATGCTCGTTATGGTTTCCGTTGC 3894 1 1455 F TTAATCAAGATGATGCTCGTTATGGTTTCCGTTGC 404 GCATTGGGGATTGAGAAAGAGTAGAAATGCCACAA 4069 1 4067 R TTGTGGCATTTCTACTCTTTCTCAATCCCCAATGC -501 TGATGGAACTGACTAAACGTCGTTAGGCCAGTTTT 3578 1 1750 F TGATGGAACTGACCAAACGTCGTTAGGCCAGTTTT 206 GGTCGCCATGATGGTGGTTATTATACCGTCAAGGA 4016 1 2734 F GGTCGCCATGATGGTGGTTATTATACCGTCAAGGA -284 TCGCTTTGAGTCTTCTTCGGTTCCGACTACCCCCA 3380 1 2670 F TCGCTTTGAGTCTTCTTCGGTTCCGACTACCCTCC -346 GGCTATGATGTTGATGGAACTGACCAAACGTCGTT 3980 1 1739 F GGCTATGATGTTGATGGAACTGACCAAACGTCGTT -393 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 GAAGTAGCGACAGCTTGGTTTTTAGTGAGTTGTTC 3849 1 5078 R GAACAACTCACTAAAAACCAAGCTGTCGCTACTTC -579 GCGAATAAGTACGCGTTCTTGCAAATCACCAGAAG 4007 1 2127 R CTTCTGGTGATTTGCAAGAACGCGTACTTATTCGC -226 GGAAGAGATTCTGTCTTTTCGTATGCAGGGCGTTG 3909 1 4142 F GGAAGAGATTCTGTCTTTTCGTATGCAGGGCGTTG -313 TTCCGTTCTGGTGATTCGTCTAAGAAGTTTAAGTT 3846 1 2003 F TTCCGTTCTGGTGATTCGTCTAAGAAGTTTAAGAT -166 GTTCGATAATGGTGATATGTATGTTGACGGCCATA 4048 1 4178 F GTTCGATAATGGTGATATGTATGTTGACGGCCATA -148 TTTTACCTTTAGACATTACATCACTCCTTCTGCAC 3756 1 829 R GTGCGGAAGGAGTGATGTAATGTCTAAAGGTAAAA -284 GAAAAAAAGTTTGAATTATGGCGAGAAATAAAAGT 4143 1 2404 R ACTTTTATTTCTCGCCATAATTCAAACTTTTTTTC -19 GACAGCTTGGTTTTTAGTGAGTTGTTCCATTCTTT 3877 1 5070 R AAAGAATGGAACAACTCACTAAAAACCAAGCTGTC -369 GTTCAGCAGCCAGCTTGCGGCAAAACTGCGTAACC 3900 1 4373 R GGTTACGCAGTTTTGCCGCAAGCTGGCTGCTGAAC -174 AACTAACGATTCTGTCAAAAACTGACGCGTTGGAT 3971 1 197 F AACTAACGATTCTGTCAAAAACTGACGCGTTGGAT -204 GAAAATAGTCACGCAAAGCATTGGGATTATCATAA 3984 1 4619 R TTATGATAATCCCAATGCTTTGCGTGACTATTTTC -277 GTTTTCCGCCTACTGCGACTAAAGAGATTCAGTAC 4015 1 1872 F GTTTTCCGCCTACTGCGACTAAAGAGATTCAGTAC -613 GAAGGGTAATAAGAACGAACCATAAAAAAGCCTCC 3978 1 3969 R GGAGGCTTTTTTATGGTTCGTTCTTATTACCCTTC -169 GTTGTTTCTGTTGGTGCTGATATTGCTTTTGATGC 3989 1 2608 F GTTGTTTCTGTTGGTGCTGATATTGCTTTTGATGC 911 TATCAGCGGCAGACTTGACACCAAGTCCAACCAAA 3677 1 3233 R TTTGGTTGGACTTGGTGGCAAGTCTGCCGCTGATA -228 TGACGACTTCTACCACATCTATTGACATTATGGGT 3936 1 1551 F TGACGACTTCTACCACATCTATTGACATTATGGGT -291 GTGGTTGAACAGCATCGGACTCTGATAGTAATCCA 3697 1 339 R TGGATTACTATCTGAGTCCGATGCTGTTCAACCAC -274 GTTGAGTTCGATAATGGTGATATGTATGTTGACGG 4040 1 4173 F GTTGAGTTCGATAATGGTGATATGTATGTTGACGG 165 GGATGAATTGGCACAATGCTACAATGTGCTCCCCC 3835 1 4265 F GGATGAATTGGCACAATGCTACAATGTGCTCCCCC 2 TAAAGAGATTCAGTACCTTAACGCTAAAGGTGCTT 3940 1 1891 F TAAAGAGATTCAGTACCTTAACGCTAAAGGTGCTT -223 GATGAGTATAATTACCCCAAAAAGAAAGGTATTAA 4154 1 4428 F GATGAGTATAATTACCCCAAAAAGAAAGGTATTAA -40 TGGTTGAACAGCATCGGACTCAGATAGTAATCCAC 3940 1 338 R GTGGATTACTATCTGAGTCCGATGCTGTTCAACCA -340 GAAGCCTGAATGAGCTTAATAGAGGCCAAAGCGGT 3983 1 426 R ACCGCTTTGGCCTCTATTAAGCTCATTCAGGCTTC -25 GCAGTCCAAATGTTTTTGAGATGGCAGCAACGGAA 4065 1 1481 R TTCCGTTGCTGCCATCTCAAAAACATTTGGACTGC 539 GTTATCCATCTGCTTATGGAAGCCAAGCATTGGGG 4041 1 4093 R CCCCAATGCTTGGCTTCCATAAGCAGATGGATAAC -459 TGTCAACGCCGCTAATCAGGTTGTTTCTGTTGGTG 3866 1 2589 F TGTCAACGCCGCTAATCAGGTTGTTTCTGTTGGTG 64 GGAGGGTGTCAATCCTGACGGTTATTTCCTAGACA 3879 1 3904 R TGTCTAGGAAATAACCGTCAGGATTGACACCCTCC -173 GATTTTCGTCTATTAATCAAAGATTTTGCTAAAGA 207 1 183 F GACCTTTCGCCATCAACTAACGATTCTGTCAAAAA -436 GAGTCAAGTTACTGAACAATCCGTACGTTTCCAGA 3948 1 392 F GAGTCAAGTTACTGAACAATCCGTACGTTTCCAGA 212 GATTTGGAGGCATGAAAACATACAATTGGGAGGGT 4059 1 3932 R ACCCTCCCAATTGTATGTTTTCATGCCTCCAAATC -611 GATAACCGCATCAAGCTCTTGGAAGAGATTCTGTC 3853 1 4122 F GATAACCGCATCAAGCTCTTGGAAGAGATTCTGTC -87 GAACTAAGTCAACCTCAGCACTAACCTTGCGAGTC 3897 1 3683 R GACTCGCAAGGTTAGTGCTGAGGTTGACTTAGTTC -21 GTGATGAGTTTGTATCTGTTACTGAGAAGTTAATG 4080 1 4231 F GTGATGAGTTTGTATCTGTTACTGAGAAGTTAATG -27 GAGCTTACTAAAATGCAACTGGACAATCAGAAAGA 4134 1 3387 F GAGCTTACTAAAATGCAACTGGACAATCAGAAAGA 1021 AAAAATTAAAATTTTTACCGCTTCGGCGTTATAAC 4006 1 2303 R GTTATAACGCCGAAGCGGTAAAAATTTTAATTTTT 1043 GTTATTAATATCAAGTTGGGGGAGCACATTGTAGC 4055 1 4282 R GCTACAATGTGCTCCCCCAACTTGATATTAATAAC -317 GGCTGTTGGTTTCTATGTGGCTAAATACGTTTACA 3792 1 4994 F GGCTGTTGGTTTCTATGTGGCTAAATACGTTAACA -256 GATTTATGTTTGGTGCTATTGCTGGCGGGTTTGCT 3425 1 2926 F GATTTATGTTTGGTGCTATTGCTGGCGGTATTGCT 34 GTTTTTGACACTCTCACGTTGGCTGACGACCGATT 3961 1 4575 F GTTTTTGACACTCTCACGTTGGCTGACGACCGATT -440 GGAGCAGTCCAAATGTTTTTGAGATGGCAGCAACG 3953 1 1484 R CGTTGCTGCCATCTCAAAAACATTTGGACTGCTCC -30 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 TGAGTGTGAGGTTATAACGCCGAAGCGGTAAAAAT 4052 1 2293 F TGAGTGTGAGGTTATAACGCCGAAGCGGTAAAAAT -293 GCTGGAGTAACAGAAGTGAGAACCAGCTTATCAGA 3956 1 2437 R TCTGATAAGCTGGTTCTCACTTCTGTTACTCCAGC -153 GGTTTTTAGTGAGTTGTTCCATTCTTTAGCTCCTA 3938 1 5062 R TAGGAGCTAAAGAATGGAACAACTCACTAAAAACC -159 GGTCTGGAAACGTACGGATTGCTCAGATACTTGAC 3139 1 394 R GTCAAGTTACTGAACAATCCGTACGTTTCCAGACC -388 GATAATTTTTCGACTCATCAGAAATATCCGAAAGT 4121 1 34 R ACTTTCGGATATTTCTGATGAGTCGAAAAATTATC -197 GCGCATAATCTCGGAAACCTGCTGTTGCTTTGTAA 3411 1 3586 R TTTCCAAGCAACAGCAGGTTTCCGAGATTATGCGC -221 ATTCGTAAACAAGCAGTAGTAATTCCTGCTTTATC 3917 1 71 R GATAAAGCAGGAATTACTACTGCTTGTTTACGAAT 109 GCTGTTGCCGATACTTGGAACAATTTCTGGAAAGA 4101 1 3840 F GCTGTTGCCGATACTTGGAACAATTTCTGGAAAGA -16 GATTAACCCTGATACCAATAAAATCCCTAAGCATT 4026 1 1336 F GATTAACCCTGATACCAATAAAATCCCTAAGCATT -396 GCAGTGGAATAGTCGGGTTAAATTTAATGTGACCG 3709 1 2203 F GCAGTGGAATAGTCAGGTTAAATTTAATGTGACCG -162 TGCCTGACCGTACCGAGGCTAACCCTAATGAGCTT 3878 1 1422 F TGCCTGACCGTACCGAGGCTAACCCTAATGAGCTT -42 TTACTGAACAATCCGTACGTTTCCAGATCGCTTTG 3668 1 400 F TTACTGAACAATCCGTACGTTTCCAGACCGCTTTG -816 GATGTTATTTCTTCATTTGGAGGTAAAACCTCTTA 4096 1 1652 F GATGTTATTTCTTCATTTGGAGGTAAAACCTCTTA 121 GTCCTTTACTTGTCATGCGCTCTAATCTCTGGGCA 3968 1 1701 F GTCCTTTACTTGTCATGCGCTCTAATCTCTGGGCA -666 GGTGTGGTTGATATTTTTCATGGTATTGATAAAGC 4093 1 3807 F GGTGTGGTTGATATTTTTCATGGTATTGATAAAGC 97 GAAATAACCGTCAGGATTGACACCCTCCCAATTGT 3837 1 3911 F GAAATAACCGTCAGGATTGACACCCTCCCAATTGT -82 GACGGCCATAAGGCTGCTTCTGACGTTCGTGATGA 3843 1 4203 F GACGGCCATAAGGCTGCTTCTGACGTTCGTGATGA 816 GCTTATCAGAAAAAAAGTTTGAATTATGGCGTGAA 3916 1 2412 R TTCTCGCCATAATTCAAACTTTTTTTCTGATAAGC 54 GTTTGTATCTGTTACTGAGAAGTTAATGGATGAAT 4166 1 4238 F GTTTGTATCTGTTACTGAGAAGTTAATGGATGAAT -409 GCAAGCATCTCATTTTGTGCATATACCTGGTCTTT 3884 1 3495 R AAAGACCAGGTATATGCACAAAATGAGATGCTTGC -471 GATGAACTAAGTCAACCTCAGCACTAACCTTGCGA 3915 1 3686 R TCGCAAGGTTAGTGCTGAGGTTGACTTAGTTCATC -140 GAAATCGCGTAGAGGCTTTGCTATTCAGCGTTTGA 3892 1 4499 F GAAATCGCGTAGAGGCTTTGCTATTCAGCGTTTGA -151 GCCATACAAAACAGGGTCGCCAGCAATATCGGTAT 3871 1 1932 R ATACCGATATTGCTGGCGACCCTGTTTTGTATGGC 649 GCGTATCCAACCTGCAGAGTTTTATCGCTTCCATG 3866 1 5371 F GCGTATCCAACCTGCAGAGTTTTATCGCTTCCATG -150 GCGAGCAGTAGACTCTTTCTGTTGATAAGCAAGCA 3687 1 3523 R TGCTTGCTTATCAACAGAAGGAGTCTACTGCTCGC 321 GTTGACTTAGTTCATCAGCAAACGCAGAATCAGCG 4064 1 3705 F GTTGACTTAGTTCATCAGCAAACGCAGAATCAGCG 27 GGACTGGTTTAGATATGAGTCACATTTTGTTCATG 3932 1 270 F GGACTGGTTTAGATATGAGTCACATTTTGTTCATG -182 GTAGTCGGAACCGAAGAAGACTCAAAGCGAACCAA 3979 1 2665 R TTGGTTCGCTTTGAGTCTTCTTCGGTTCCGACTAC 2 GGTTAAATCCAAAACGGCAGAAGCCTGAATGAGCT 3996 1 445 R AGCTCATTCAGGCTTCTGCCGTTTTGGATTTAACC -295 GCTTCTTCGGCACCTGTTTTACAGACACCTAAAGC 4021 1 2470 F GCTTCTTCGGCACCTGTTTTACAGACACCTAAAGC -22 GCCCTCTTAAGGATATTCGCGATGAGTATAATTAC 4008 1 4408 F GCCCTCTTAAGGATATTCGCGATGAGTATAATTAC 366 GTTAATGCTGGTAATGGTGGTTTTCTTCATTTCTT 3451 1 2539 F GTTAATGCTGGTAATGGTGGTTTTCTTCATTGCAT 56 GAGTTTGTATCTGTTACTGAGAAGTTAATGGATGA 4140 1 4236 F GAGTTTGTATCTGTTACTGAGAAGTTAATGGATGA -215 TATTGCTTTTGATGCCGACCCTAAATTTTTTGCCT 3918 1 2628 F TATTGCTTTTGATGCCGACCCTAAATTTTTTGCCT 352 GCTGCTTATGCTAATTTGCATACTGACCAAGAACG 4097 1 1592 F GCTGCTTATGCTAATTTGCATACTGACCAAGAACG -140 GAGGCTTGCGTTTATGGTACGCTGGACTTTGTAGG 3573 1 555 F GAGGCTTGCGTTTATGGTACGCTGGACTTTGTGGG -59 GAGTTGTTCCATTCTTTAGCTCCTAGACCTTTAGC 3957 1 5052 R GCTAAAGGTCTAGGAGCTAAAGAATGGAACAACTC -306 GAGATGCTTGCTTATCAACAGAAGGAGTCTACTGC 3973 1 3519 F GAGATGCTTGCTTATCAACAGAAGGAGTCTACTGC 374 GTAATTCCTGCTTTATCAAGATAATTTTTCGACTC 4016 1 53 R GAGTCGAAAAATTATCTTGATAAAGCAGGAATTAC -74 GGAAACACTGACGTTCTTACTGACGCAGAAGAAAA 4069 1 777 F GGAAACACTGACGTTCTTACTGACGCAGAAGAAAA -109 GTTTTTGAGATGGCAGCAACGGAAACCATAACGAG 4097 1 1470 R CTCGTTATGGTTTCCGTTGCTGCCATCTCAAAAAC 251 GTTGGTGCTGATATTGCTTTTGATGCCGACCCTAA 3978 1 2617 F GTTGGTGCTGATATTGCTTTTGATGCCGACCCTAA 362 GGTTTTCTGCTTAGGAGTTTAATCATGTTTCAGAC 4025 1 2371 F GGTTTTCTGCTTAGGAGTTTAATCATGTTTCAGAC -190 GGATTGAGAAAGAGTAGAAATGCCACAAGCCTCAA 4008 1 4060 R TTGAGGCTTGTGGCATTTCTACTCTTTCTCAATCC -224 GGATGAATTGGCACAATGCTACAATGTGCTCCCCC 3835 1 4265 F GGATGAATTGGCACAATGCTACAATGTGCTCCCCC 2 GGTTTTTAGTGAGTTGTTCCATTCTTTAGCTCCTA 3938 1 5062 R TAGGAGCTAAAGAATGGAACAACTCACTAAAAACC -159 GCTTGGTTTTTAGTGAGTTGTTCCATTCTTTAGCT 3936 1 5066 R AGCTAAAGAATGGAACAACTCACTAAAAACCAAGC -373 GTAATCCACGCTCTTTTAAAATGTCAACAAGAGAA 4169 1 312 R TTCTCTTGTTGACATTTTAAAAGAGCGTGGATTAC 271 GCATGACAAGTAAAGGACGGTTGTCAGCGTCATAA 3938 1 1684 R TTATGACGCTGACAACCGTCCTTTACTTGTCATGC -207 GGTTAATGCTGGTAATGGTGGTTTTCTTTTTTTTA 2944 1 2538 F GGTTAATGCTGGTAATGGTGGTTTTCTTCATTGCA 72 GAAATAACATCATGGTAACGCTGCATGAAGTAATC 4010 1 1628 R GATTACTTCATGCAGCGTTACCATGATGTTATTTC 211 CCGTTGGCGCTCTCCGTCTTTCTCCATTGCGTCGT 3749 1 1137 F CCGTTGGCGCTCTCCGTCTTTCTCCATTGCGTCGT -397 GGAGCAGTCCAAATGTTTTTGAGATGGCAGCAACG 3953 1 1484 R CGTTGCTGCCATCTCAAAAACATTTGGACTGCTCC -30 GTGCTGATATTGCTTTTGATGCCGACCCTAAATTT 3958 1 2621 F GTGCTGATATTGCTTTTGATGCCGACCCTAAATTT 330 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 GTCAACGTTATATTTTGATAGTTTGACGGTTAATT 3819 1 2511 F GTCAACGTTATATTTTGATAGTTTGACGGTTAATG -122 GAGGGCGTTCAGCAGCCAGCTTGCGGCAAAACTGC 3836 1 4379 R GCAGTTTTGCCGCAAGCTGGCTGCTGAACGCCCTC -3 GATTTAATTCGTAAACAAGCAGTAGTAATTCCTGC 4030 1 77 R GCAGGAATTACTACTGCTTGTTTACGAATTAAATC -318 GTTGAGTTTATTGCTGCCGTCATTGCTTATTATGT 3923 1 612 F GTTGAGTTTATTGCTGCCGTCATTGCTTATTATGT 992 GTTGAAATGGTAATAAGACGACCAATCTGACCAGC 3990 1 1064 R GCTGGTCAGATTGGTCGTCTTATTACCATTTCAAC 105 GAAGAGCCATACCGCTGATTCTGCGTTTGCTGATG 3800 1 3717 R CATCAGCAAACGCAGAATCAGCGGTATGGCTCTTC 519 GAGATGGACGCCGATGGCGCTCTCCGTCTTTCTCC 3310 1 1127 F GAGATGGACGCCGTTGGCGCTCTCCGTCTTTCTCC -130 GTTCGTGATGAGTTTGTATCTGTTACTGAGAAGTT 4048 1 4227 F GTTCGTGATGAGTTTGTATCTGTTACTGAGAAGTT 317 GTCAATCCTGACGGTTATTTCCTAGACAAATTAGA 4035 1 3897 R TCTAATTTGTCTAGGAAATAACCGTCAGGATTGAC -409 GCTGACAACCGTCCTTTACTTGTCATGCGCTCTAA 3892 1 1691 F GCTGACAACCGTCCTTTACTTGTCATGCGCTCTAA -486 GCTTGATGCGGTTATCCATCTGCTTATGGAAGCCA 3980 1 4103 R TGGCTTCCATAAGCAGATGGATAACCGCATCAAGC 101 GCTCTAATTTGTCTAGGAAATAACCGTCAGGATTG 4105 1 3895 F GCTCTAATTTGTCTAGGAAATAACCGTCAGGATTG -331 GAGTCAAGTTACTGAACAATCCGTACGTTTCCAGA 3948 1 392 F GAGTCAAGTTACTGAACAATCCGTACGTTTCCAGA 212 GTCATGCGCTCTAATCTCTGGGCATCTGGCTATGA 3904 1 1712 F GTCATGCGCTCTAATCTCTGGGCATCTGGCTATGA -367 GTAGAGATTCTCTTGTTGACATTTTAAAAGAGCGT 4063 1 305 F GTAGAGATTCTCTTGTTGACATTTTAAAAGAGCGT -54 GGTGGTTTTCTTCATTGCATTCAGATGGATACATC 4041 1 2554 F GGTGGTTTTCTTCATTGCATTCAGATGGATACATC 44 GGTTACAGTATGCCCATCGCAGTTCGCTACACGCA 3915 1 4884 F GGTTACAGTATGCCCATCGCAGTTCGCTACACGCA -648 GTTTAAGAGCCTCGATACACTCAAAGTCAAAATAA 3788 1 4019 R TTATTTTGACTTTGAGCGTATCGAGGCTCTTAAAC -441 CTGTTTTACAGACACCTAAAGCTACATCGTCAACG 4031 1 2483 F CTGTTTTACAGACACCTAAAGCTACATCGTCAACG -32 GTTGACGGGATGAACATAATAAGCAATGACGGCAG 4051 1 625 R CTGCCGTCATTGCTTATTATGTTCATCCCGTCAAC 119 GGAGGAAGCGGAGCAGTCCAAATGTTTTTGAGATG 3834 1 1493 R CATCTCAAAAACATTTGGACTGCTCCGCTTCCTCC 506 GTAGCGACAGCTTGGTTTTTAGTGAGTTGTTCCAT 3856 1 5075 R ATGGAACAACTCACTAAAAACCAAGCTGTCGCTAC -642 GAGTCAAGTTACTGAACAATCCGTACGTTTCCAGA 3948 1 392 F GAGTCAAGTTACTGAACAATCCGTACGTTTCCAGA 212 TACCGTCAAGGACTGTGTGACTATTGACGTCCTTC 3847 1 2757 F TACCGTCAAGGACTGTGTGACTATTGACGTCCTTC -401 GATTATCTTGCTGCTGCATTTCCTGAGCTTAATGC 3964 1 3285 F GATTATCTTGCTGCTGCATTTCCTGAGCTTAATGC -278 GAAGAAAACCACCATTACCAGCATTAACCGTCAAA 3968 1 2532 R TTTGACGGTTAATGCTGGTAATGGTGGTTTTCTTC -170 GACTGGTTTAGATATGAGTCACATTTTGTTCATGG 3991 1 271 F GACTGGTTTAGATATGAGTCACATTTTGTTCATGG -352 GTTGGTGCTGATATTGCTTTTGATGCCGACCCTAA 3978 1 2617 F GTTGGTGCTGATATTGCTTTTGATGCCGACCCTAA 362 GTTATAGATATTCAAATAACCCTGAAACAAATGCT 4109 1 1365 R AGCATTTGTTTCAGGGTTATTTGAATATCTATAAC -126 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 GGTATGGTTGACGCCGGATTTGAGAATCAAAAAGA 4091 1 3354 F GGTATGGTTGACGCCGGATTTGAGAATCAAAAAGA 172 GTTATCCATCTGCTTATGGAAGCCAAGCATTGGGG 4041 1 4093 R CCCCAATGCTTGGCTTCCATAAGCAGATGGATAAC -459 TTAACCGAAGATGATTTCGATTTTCTGACGAGTAA 4064 1 474 F TTAACCGAAGATGATTTCGATTTTCTGACGAGTAA -313 GAAATAACCGTCAGGATTGACACCCTCCAAATTGT 3579 1 3911 F GAAATAACCGTCAGGATTGACACCCTCCCAATTGT 157 GGTTTAGATATGAGTCACATTTTGTTCATGGTAGA 4044 1 275 F GGTTTAGATATGAGTCACATTTTGTTCATGGTAGA 11 GATACTTGGAACAATTTCTGGAAAGACGGTAAAGC 4081 1 3849 F GATACTTGGAACAATTTCTGGAAAGACGGTAAAGC -417 GGCATCAAAAGCAATATCAGCACCAACAGAAACAA 4070 1 2609 R TTGTTTCTGTTGGTGCTGATATTGCTTTTGATGCC 671 GCCAATTCATCCATTAACTTCTCAGTAACAGATAC 4028 1 4242 R GTATCTGTTACTGAGAAGTTAATGGATGAATTGGC -184 GTCATGCGGCATACGCTCGGCGCCAGTTTGTTTAT 3320 1 1008 R ATATTCAAACTGGCGCCGAGCGTATGCCGCATGAC -64 GTTTCTGATAAGTTGCTTGATTTGGTTGGACTTGG 4004 1 3213 F GTTTCTGATAAGTTGCTTGATTTGGTTGGACTTGG 253 GTTTTGTTTCTGGTGCTATGGCTAAAGCTGGTAAA 4090 1 3139 F GTTTTGTTTCTGGTGCTATGGCTAAAGCTGGTAAA 220 GGTGCTTTGACTTATACCGATATTGCTGGCGACCC 3942 1 1919 F GGTGCTTTGACTTATACCGATATTGCTGGCGACCC 29 GCTGGTATTAAATCTGCCATTCAAGGCTCTAATGT 3978 1 3078 F GCTGGTATTAAATCTGCCATTCAAGGCTCTAATGT 173 GACAAATAATCTCTTTAATAACCTGATTCAGCGAA 4062 1 2871 R TTCGCTGAATCAGGTTATTAAAGAGATTATTTGTC 195 GGTACGGTCAGGCATCCACGGCGCTTTAAAATAGT 3872 1 1401 R ACTATTTTAAAGCGCCGTGGATGCCTGACCGTACC -129 GGTTGTTTCTGTTGGTGCTGATATTGCTTTTGATG 3981 1 2607 F GGTTGTTTCTGTTGGTGCTGATATTGCTTTTGATG 931 GCCTACTGCGACTAAAGAGATTCAGTACCTTAACG 3990 1 1879 F GCCTACTGCGACTAAAGAGATTCAGTACCTTAACG -327 GTACGCCGGGCAATAATGTTTATGTTGGTTTCATG 3586 1 2795 F GTACGCCGGGCAATAACGTTTATGTTGGTTTCATG 334 GCCGAGGGTCGCAAGGCTAATGATTCACACGCCGA 3868 1 4680 F GCCGAGGGTCGCAAGGCTAATGATTCACACGCCGA 35 GGTTCGTTCTTATTACCCTTCTGAATGTCACGCTG 3929 1 3983 F GGTTCGTTCTTATTACCCTTCTGAATGTCACGCTG -329 GACAAATCTGCTCAAATTTATGCGCGCTTCGATAA 3982 1 5327 F GACAAATCTGCTCAAATTTATGCGCGCTTCGATAA -245 GCCACCATGATTATGACCAGTGTTTCCAGTCCGTT 3851 1 2160 F GCCACCATGATTATGACCAGTGTTTCCAGTCCGTT 169 TCGATTTTCTGACGAGTAACAAAGTTTGGATTGCT 4021 1 490 F TCGATTTTCTGACGAGTAACAAAGTTTGGATTGCT -313 GCGTTGGATGAGGAGAAGTGGCTTAATATGCTTGG 3943 1 223 F GCGTTGGATGAGGAGAAGTGGCTTAATATGCTTGG 27 GGTAATAAGAACGAACCATAAAAAAGCCTCCAAGA 4054 1 3965 R TCTTGGAGGCTTTTTTATGGTTCGTTCTTATTACC -312 GCTTGAGTAAGCATTTGGCGCATAATCTCGGAAAC 3995 1 3603 R GTTTCCGAGATTATGCGCCAAATGCTTACTCAAGC -158 GTGATTACTTCATGCAGCGTTACCATGATGTTATT 3975 1 1626 F GTGATTACTTCATGCAGCGTTACCATGATGTTATT -189 GTTCCATTCTTTAGCTCCTAGACCTTTAGCAGCAA 4030 1 5047 R TTGCTGCTAAAGGTCTAGGAGCTAAAGAATGGAAC 308 TAATAACCACCATCATGGCGACAATCCAAAGGATA 3508 1 2722 R TATCCTTTGAATGGTCGCCATGATGGTGGTTATTA 106 GTTTGGAGGCGGTCAAAAAGACGCCCCCGGTGGCC 3134 1 2993 F GTTTGGAGGCGGTCAAAAAGCCGCCTCCGGTGGCA -440 GCAACGGCTGAGGACGACCAGGGCGAGCGCCAGAA 3573 1 868 R TTCTGGCGCTCGCCCTGGTCGTCCGCAGCCGTTGC -474 GCTGAGGTTGACTTAGTTCATCAGCAAACGCAGAA 4060 1 3699 F GCTGAGGTTGACTTAGTTCATCAGCAAACGCAGAA -298 GCTCCTAGACCTTTAGCAGCAAGGTCCATATCTGA 3947 1 5034 R TCAGATATGGACCTTGCTGCTAAAGGTCTAGGAGC -257 GCGTTTCTTTGTTCCTGAGCATGGCACTATGTTTA 3983 1 1822 F GCGTTTCTTTGTTCCTGAGCATGGCACTATGTTTA 387 GGCCTTGCTATTGACTCTACTGTAGACATTTTTAC 3923 1 1172 F GGCCTTGCTATTGACTCTACTGTAGACATTTTTAC 723 GTCTAGGAAATAACCGTCAGGATTGACACCCTCCC 3856 1 3905 F GTCTAGGAAATAACCGTCAGGATTGACACCCTCCC -493 GAGAACGAGAAGACGGTTACGCAGTTTTGCCGCAA 3832 1 4359 F GAGAACGAGAAGACGGTTACGCAGTTTTGCCGCAA -325 GTTACTCCAGCTTCTTCGGCACCTGTTTTACAGAC 3913 1 2461 F GTTACTCCAGCTTCTTCGGCACCTGTTTTACAGAC 67 GTTTTACAGACACCTAAAGCTACATCGTCAACGTT 3984 1 2485 F GTTTTACAGACACCTAAAGCTACATCGTCAACGTT 348 GTCAAGGACTGGTTTAGATATGAGTCACATTTTGT 3944 1 265 F GTCAAGGACTGGTTTAGATATGAGTCACATTTTGT -254 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 GAGTGGTCGGCAGATTGCGATAAACGGTCACATTA 3933 1 2227 R TAATGTGACCGTTTATCGCAATCTGCCGACCACTC -524 GCTTGATTTGGTTGGACTTGGTGGCAAGTCTGCCG 3963 1 3227 F GCTTGATTTGGTTGGACTTGGTGGCAAGTCTGCCG -101 GAATTTTCTCATTTTCCGCCAGCAGTCCACTTCGA 3958 1 110 R TCGAAGTGGACTGCTGGCGGAAAATGAGAAAATTC -359 GATTATTTTGACTTTGAGCGTATCGAGGCTCTTAA 4047 1 4017 F GATTATTTTGACTTTGAGCGTATCGAGGCTCTTAA -228 GTATGTTTTCATGCCTCCAAATCTTGGAGGCTTTT 3959 1 3944 F GTATGTTTTCATGCCTCCAAATCTTGGAGGCTTTT -320 GTGATGAGTTTGTATCTGTTACTGAGAAGTTAATG 4080 1 4231 F GTGATGAGTTTGTATCTGTTACTGAGAAGTTAATG -27 GATAATGGTGATATGTATGTTGACGGCCATAAGGC 4052 1 4182 F GATAATGGTGATATGTATGTTGACGGCCATAAGGC 315 GCTTTTTCACGTTCTGGTTGGTTGTGGCCTGTTGA 3966 1 4923 F GCTTTTTCACGTTCTGGTTGGTTGTGGCCTGTTGA 157 GCAATCTCTTTTTGAGTCTCATTTTGCATCTCGGC 3961 1 3426 R GCCGAGATGCAAAATGAGACTCAAAAAGAGATTGC 1363 GGTAGGTTTTCTGCTTAGGAGTTTAATCATGTTTC 3945 1 2367 F GGTAGGTTTTCTGCTTAGGAGTTTAATCATGTTTC -280 GCATGAAGTAATCACGTTCTTGGTCAGTATGCAAA 4026 1 1606 R TTTGCATACTGACCAAGAACGTGATTACTTCATGC 86 GGTATTGATAAAGCTGTTGCCGATACTTGGAACAA 4043 1 3828 F GGTATTGATAAAGCTGTTGCCGATACTTGGAACAA -290 GAGATTCTGTCTTTTCGTATGCAGGGCGTTGAGTT 3953 1 4146 F GAGATTCTGTCTTTTCGTATGCAGGGCGTTGAGTT -161 GGTGGCATTCAAGGTGATGTGCTTGCTACCGATAA 3933 1 3021 F GGTGGCATTCAAGGTGATGTGCTTGCTACCGATAA 218 GATTGCGATAAACGGTCACATTAAATTTAACCTGA 4015 1 2215 R TCAGGTTAAATTTAATGTGACCGTTTATCGCAATC 293 GTAGACATTTTTACTTTTTATGTCCCTCATCGTCA 3970 1 1193 F GTAGACATTTTTACTTTTTATGTCCCTCATCGTCA -394 GCAGGGCGTTGAGTTCGATAATGGTGATATGTATG 3921 1 4166 F GCAGGGCGTTGAGTTCGATAATGGTGATATGTATG 89 TATTAAATCTGCCATTCAAGGCTCTAATGTTCCTA 3975 1 3083 F TATTAAATCTGCCATTCAAGGCTCTAATGTTCCTA 352 GTCTAGGAAATAACCGTCAGGATTGACACCCTCCC 3856 1 3905 F GTCTAGGAAATAACCGTCAGGATTGACACCCTCCC -493 GACTGAATGCCAGCAATCTCTTTTTGAGTCTCATT 3841 1 3438 R AATGAGACTCAAAAAGAGATTGCTGGCATTCAGTC 753 GATTTGCAAGAACGCGTACTTATTCGCCACCATGA 3962 1 2135 F GATTTGCAAGAACGCGTACTTATTCGCCACCATGA 3 GTATAATTACCCCAAAAAGAAAGGTATTAAGGATG 4129 1 4433 F GTATAATTACCCCAAAAAGAAAGGTATTAAGGATG -90 GGTGATATGTATGTTGACGGCCATAAGGCTGCTTC 3915 1 4188 F GGTGATATGTATGTTGACGGCCATAAGGCTGCTTC 288 GTCATTGTGAGCATTTTCATCCCGAAGTTGCGTCT 3643 1 5139 R AGCCGCAACTTCGGGATGAAAATGCTCACAATGAC -193 GTTTGGTCTAACTTTACCGCTACTAAATGCCGCGG 4009 1 2830 F GTTTGGTCTAACTTTACCGCTACTAAATGCCGCGG -694 GAGGAGAAGTGGCTTAATATGCTTGGCACGTTCGT 3848 1 232 F GAGGAGAAGTGGCTTAATATGCTTGGCACGTTCGT -229 GTTTACGAATTAAATCGAAGTGGACTGCTGGCGGA 4056 1 96 F GTTTACGAATTAAATCGAAGTGGACTGCTGGCGGA 112 GGTGGCATTCAAGGTGATGCGCTTGCTACCGATAC 3443 1 3021 F GGTGGCATTCAAGGTGATGTGCTTGCTACCGATAA -5 GTTGGCGCTCTCCGTCTTTCTCCATTGCGTCGTGG 3791 1 1139 F GTTGGCGCTCTCCGTCTTTCTCCATTGCGTCGTGG 102 GGTAGGTTTTCTGCTTAGGAGTTTAATCATGTTTC 3945 1 2367 F GGTAGGTTTTCTGCTTAGGAGTTTAATCATGTTTC -280 GTATATGCACAAAATGAGATGCTTGCTTATCAACA 4012 1 3504 F GTATATGCACAAAATGAGATGCTTGCTTATCAACA -69 GCTGGCACTTCTGCCGTTTCTGATAAGTTGCTTGA 3910 1 3198 F GCTGGCACTTCTGCCGTTTCTGATAAGTTGCTTGA 70 GACATTTTAAAAGAGCGTGGATTACTATCTGAGTC 3992 1 322 F GACATTTTAAAAGAGCGTGGATTACTATCTGAGTC -194 GAGCATCATCTTGATTAAGCTCATTAGGGTTAGCC 3987 1 1438 R GGCTAACCCTAATGAGCTTAATCAAGATGATGCTC 794 GCTATTGAGGCTTGTGGCATTTCTACTCTTTCTCA 3873 1 4056 F GCTATTGAGGCTTGTGGCATTTCTACTCTTTCTCA -69 GCAAGAGCAGAAGCAACACCGCCAGCAATGGCCCC 3079 1 2937 R GGTGCTATTGCTGGCGGTATTGCTTCTGCTCTTGC 208 GAGATTATGCGCCAAATGCTTACTCAAGCTCAAAC 3984 1 3609 F GAGATTATGCGCCAAATGCTTACTCAAGCTCAAAC -98 GTCGCTGCGTTGAGGCTTGCGTTTATGGTACGCTG 3837 1 544 F GTCGCTGCGTTGAGGCTTGCGTTTATGGTACGCTG -302 GGCTACAGTAACTTTTCACAGCCTCAATCTCATCT 3590 1 5266 R AGATGAGATTGAGGCTGGGAAAAGTTACTGTAGCC -569 GAGTTTAATCATGTTTCAGACTTTTATTTCTCGCC 3940 1 2385 F GAGTTTAATCATGTTTCAGACTTTTATTTCTCGCC -75 GGATTGAGAAAGAGTAGAAATGCCACAAGCCTCAA 4008 1 4060 R TTGAGGCTTGTGGCATTTCTACTCTTTCTCAATCC -224 GGTTAATGCTGGTAATGGTGGTTTTTTTCATTTCA 3432 1 2538 F GGTTAATGCTGGTAATGGTGGTTTTCTTCATTGCA 116 GCTACATCGTCAACGTTATATTTTGATAGTTTGAC 3968 1 2503 F GCTACATCGTCAACGTTATATTTTGATAGTTTGAC -512 GAAGTTGCGGCTCATTCTGATTCTGCACAGCTTCT 3618 1 5116 R AGAAGCTGTTCAGAATCAGAATGAGCCGCAACTTC -148 GACCAAGCGAAGCGCGGTAGGTTTTCTGCTTAGGA 3857 1 2352 F GACCAAGCGAAGCGCGGTAGGTTTTCTGCTTAGGA 5 ATGCAACTGGACAATCAGAAAGAGATTGCCGAGAT 4013 1 3399 F ATGCAACTGGACAATCAGAAAGAGATTGCCGAGAT 1550 GATGGTTGGTTTATCGTTTTTGACACTCTCACGTT 3917 1 4560 F GATGGTTGGTTTATCGTTTTTGACACTCTCACGTT -138 GTCTTTTCGTATGCAGGGCGTTGAGTTCGATAATG 4041 1 4154 F GTCTTTTCGTATGCAGGGCGTTGAGTTCGATAATG 17 GACGGTTAATGCTGGTAATGGTGGTTTTTTTTTTT 3101 1 2535 F GACGGTTAATGCTGGTAATGGTGGTTTTCTTCATT 260 GCACCTGTTTTACAGACACCTAAAGCTACATCGTC 3947 1 2479 F GCACCTGTTTTACAGACACCTAAAGCTACATCGTC -131 GGATGAGGAGAAGTGGCTTAATATGCTTGGCACGT 3849 1 228 F GGATGAGGAGAAGTGGCTTAATATGCTTGGCACGT -20 TGTCAGCGTCATAAGAGGTTTTACCTCCAAATGAA 4050 1 1663 R TTCATTTGGAGGTAAAACCTCTTATGACGCTGACA 383 GGTTTCATGGTTTGGTCTAACTTTACCGCTACTAA 4011 1 2821 F GGTTTCATGGTTTGGTCTAACTTTACCGCTACTAA 47 GTTTTATGATAATCCCAATGCTTTGCGTGACTATT 3990 1 4616 F GTTTTATGATAATCCCAATGCTTTGCGTGACTATT -279 GAAATTTCTATGAAGGATGTTTTCCGTTCTGGTGA 3968 1 1982 F GAAATTTCTATGAAGGATGTTTTCCGTTCTGGTGA -451 GTCTGAAACATGATTAAACTCCTAAGCAGAAAACC 4082 1 2371 R GGTTTTCTGCTTAGGAGTTTAATCATGTTTCAGAC -136 GTAAATTCAGCGCCTTCCATGATGAGACAGGCCGT 3956 1 666 R ACGGCCTGTCTCATCATGGAAGGCGCTGAATTTAC -210 GCTGGAGTAACAGAAGTGAGAACCAGCTTATCAGA 3956 1 2437 R TCTGATAAGCTGGTTCTCACTTCTGTTACTCCAGC -153 GGTAAAAAACGTTCTGGCGCTCGCCCTGGTCGTCC 3762 1 857 F GGTAAAAAACGTTCTGGCGCTCGCCCTGGTCGTCC -110 GTCTGCAAGCTGCTTATGCTAATTTGCATACTGAC 3976 1 1584 F GTCTGCAAGCTGCTTATGCTAATTTGCATACTGAC 69 GATGAGGGACATAAAAAGTAAAAATGTCTACAGTA 4053 1 1189 R TACTGTAGACATTTTTACTTTTTATGTCCCTCATC 14 GACAAGTAAAGGACGGTTGTCAGCGTCATAAGAGG 3968 1 1680 R CCTCTTATGACGCTGACAACCGTCCTTTACTTGTC 76 GTTTGCTGATGAACTAAGTCAACCTCAGCACTAAC 4024 1 3693 R GTTAGTGCTGAGGTTGACTTAGTTCATCAGCAAAC 149 GTCAGTATTTTACCAATGACCAAATCAAAGAAATG 4194 1 3649 F GTCAGTATTTTACCAATGACCAAATCAAAGAAATG -222 GTTATTAAAGAGATTATTTGTCTCCAGCCACTTAA 4036 1 2884 F GTTATTAAAGAGATTATTTGTCTCCAGCCACTTAA -199 GTTTAAGAGCCTCGATACGCTCAAAGTCAAAATAA 4091 1 4019 R TTATTTTGACTTTGAGCGTATCGAGGCTCTTAAAC -313 GATATTTTTCATGGTATTGATAAAGCTGTTGCCGA 4098 1 3816 F GATATTTTTCATGGTATTGATAAAGCTGTTGCCGA 407 GGAGCTAAAGAATGGAACAACTCACTAAAAACCAA 4045 1 5064 F GGAGCTAAAGAATGGAACAACTCACTAAAAACCAA -354 GAATGGGAAGCCTTCAAGAAGGTGATAAGCAGGAG 4048 1 2079 R CTCCTGCTTATCACCTTCTTGAAGGCTTCCCATTC -166 GTTTATGGTGAACAGTGGATTAAGTTCATGAAGGA 4172 1 1229 F GTTTATGGTGAACAGTGGATTAAGTTCATGAAGGA -51 GTCGTGTTCAACAGACCTATAAACATTCTGTGCCG 3943 1 1788 F GTCGTGTTCAACAGACCTATAAACATTCTGTGCCG -183 GATGCTGTTCAACCACTAATAGGTAAGAAATCATG 4076 1 358 F GATGCTGTTCAACCACTAATAGGTAAGAAATCATG -65 GTTGGCTGACGACCGATTAGAGGCGTTTTATGATC 3671 1 4592 F GTTGGCTGACGACCGATTAGAGGCGTTTTATGATA 33 GTTGATGGCGAAAGGTCGCAAAGTAAGAGCTTCTC 3913 1 165 R GAGAAGCTCTTACTTTGCGACCTTTCGCCATCAAC -437 GGTAAGAAATCATGAGTCAAGTTACTGAACAATCC 3997 1 379 F GGTAAGAAATCATGAGTCAAGTTACTGAACAATCC -467 GGTGCTGATATTGCTTTTGATGCCGACCCTAAATT 3926 1 2620 F GGTGCTGATATTGCTTTTGATGCCGACCCTAAATT 559 GTTGGTGCTGATATTGCTTTTGATGCCGACCCTAA 3978 1 2617 F GTTGGTGCTGATATTGCTTTTGATGCCGACCCTAA 362 GCGTTGACAGATGTATCCATCTGAATGCAATGAAG 4055 1 2563 R CTTCATTGCATTCAGATGGATACATCTGTCAACGC -419 GTTGAGTTTATTGCTGCCGTCATTGCTTATTATGT 3923 1 612 F GTTGAGTTTATTGCTGCCGTCATTGCTTATTATGT 992 GATGGTGGTTATTATACCGTCAAGGACTGTGTGAC 3984 1 2743 F GATGGTGGTTATTATACCGTCAAGGACTGTGTGAC 26 GATGAATGCAATGCGACAGGCTCATGCTGATGGTT 3860 1 4532 F GATGAATGCAATGCGACAGGCTCATGCTGATGGTT 112 GTCTGTAAAACAGGTGCCGAAGAAGCTGGAGTAAC 4005 1 2461 R GTTACTCCAGCTTCTTCGGCACCTGTTTTACAGAC 0 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 GACGGTAAAGCTGATGGTATTGGCTCTAATTTTTT 3396 1 3873 F GACGGTAAAGCTGATGGTATTGGCTCTAATTTGTC 178 GTTTCCAGACCGCTTTGGCCTCTATTAAGCTCATT 3885 1 418 F GTTTCCAGACCGCTTTGGCCTCTATTAAGCTCATT -355 GCTCCCAAGCATTAAGCTCAGGAAATGCAGCAGCA 4025 1 3293 R TGCTGCTGCATTTCCTGAGCTTAATGCTTGGGAGC -136 GGGCATCTGGCTATGATGTTGATGGAACTGACCAA 3975 1 1731 F GGGCATCTGGCTATGATGTTGATGGAACTGACCAA -224 GAGGCATGAAAACATACAATTGGGAGGGTGTCAAT 3971 1 3926 R ATTGACACCCTCCCAATTGTATGTTTTCATGCCTC -401 GATGTATCCATCTGAATGCAATGAAGAAAACCACC 4077 1 2554 R GGTGGTTTTCTTCATTGCATTCAGATGGATACATC -79 GTTTCCGTTGCTGCCATCTCAAAAACATTTGGACT 3997 1 1479 F GTTTCCGTTGCTGCCATCTCAAAAACATTTGGACT 564 GACTGCCTATGATGTTTATCCTTTGGATGGTCGCC 3646 1 2706 F GACTGCCTATGATGTTTATCCTTTGAATGGTCGCC 44 GCTGGTTCTCACTTCTGTTACTCCAGCTTCTTCGG 3851 1 2445 F GCTGGTTCTCACTTCTGTTACTCCAGCTTCTTCGG -335 GACCGTTTATCGCAATCTGCCGACCACTCGCGATT 3850 1 2233 F GACCGTTTATCGCAATCTGCCGACCACTCGCGATT -253 GAACGGACTGGAAACACTGGTCATAATCATGGTGG 3935 1 2161 R CCACCATGATTATGACCAGTGTTTCCAGTCCGTTC -30 GTACGCGCAGGAAACACTGACGTTCTTACTGACGC 3840 1 768 F GTACGCGCAGGAAACACTGACGTTCTTACTGACGC -179 GGATTGTTCAGTAACTTGACTCATGATTTCTTACC 3931 1 379 R GGTAAGAAATCATGAGTCAAGTTACTGAACAATCC -364 GCTTGTTCGTTTTCCGCCTACTGCGACTAAAGAGA 4054 1 1864 F GCTTGTTCGTTTTCCGCCTACTGCGACTAAAGAGA -370 CGTTTATGGTACGCTGGACTTTGTAGGATACCCTC 3616 1 563 F CGTTTATGGTACGCTGGACTTTGTGGGATACCCTC -615 GCCATCAACTAACGATTCTGTCAAAAACTGACGCG 4031 1 191 F GCCATCAACTAACGATTCTGTCAAAAACTGACGCG -169 GTTTTGCCGCAAGCTGGCTGCTGAACGCCCTCTTA 3828 1 4382 F GTTTTGCCGCAAGCTGGCTGCTGAACGCCCTCTTA -68 GTTGGTTGTGGCCTGTTGATGCTAAAGGTGAGCCG 3988 1 4939 F GTTGGTTGTGGCCTGTTGATGCTAAAGGTGAGCCG -492 GTAGAAGTCGTCATTTGGCGAGAAAGCTCAGTCTC 3894 1 1529 R GAGACTGAGCTTTCTCGCCAAATGACGACTTCTAC -18 GTTTTACAGACACCTAAAGCTACATCGTCAACGTT 3984 1 2485 F GTTTTACAGACACCTAAAGCTACATCGTCAACGTT 348 GACTTAGTTCATCAGCAAACGCAGAATCAGCGGTA 4036 1 3708 F GACTTAGTTCATCAGCAAACGCAGAATCAGCGGTA -161 GTTTTTAGTGAGTTGTTCCATTCTTTAGCTCCTA. 3852 1 5061 R CTAGGAGCTAAAGAATGGAACAACTCACTAAAAAC 30 GATATGTATGTTGACGGGCATAAGGCTGCTTCTGG 3407 1 4191 F GATATGTATGTTGACGGCCATAAGGCTGCTTCTGA 924 GCAGGAGAAACATACGAAGGCGCATAACGATACCA 3923 1 2051 R TGGTATCGTTATGCGCCTTCGTATGTTTCTCCTGC 184 GAAGTTAACACTTTCGGATATTTCTGATGAGTCGA 4013 1 25 F GAAGTTAACACTTTCGGATATTTCTGATGAGTCGA -194 GCTGCGTTGAGGCTTGCGTTTATGGTACGCTGGAC 3902 1 547 F GCTGCGTTGAGGCTTGCGTTTATGGTACGCTGGAC 10 TTCCTAGACAAATTAGAGCCAATACCATCAGCTTT 3969 1 3879 R AAAGCTGATGGTATTGGCTCTAATTTGTCTAGGAA -307 GGTGCTATGGCTAAAGCTGGTAAAGGACTTCTTGA 3937 1 3150 F GGTGCTATGGCTAAAGCTGGTAAAGGACTTCTTGA 43 GGTTGTTTCTGTTGGTGCTGATATTTCTTTTGATG 3710 1 2607 F GGTTGTTTCTGTTGGTGCTGATATTGCTTTTGATG 660 GTTTCGCTGAATCAGGTTATTAAAGAGATTATTCG 3834 1 2869 F GTTTCGCTGAATCAGGTTATTAAAGAGATTATTTG 52 GCGTGGATTACTATCTGAGTCCGATGCTGTTCAAC 3915 1 336 F GCGTGGATTACTATCTGAGTCCGATGCTGTTCAAC -212 GTATGCAAATTAGCATAAGCAGCTTGCAGACCCAT 3968 1 1580 R ATGGGTCTGCAAGCTGCTTATGCTAATTTGCATAC -74 GCAGATTGCGATAAACGGTCACATTAAATTTAACC 3999 1 2218 R GGTTAAATTTAATGTGACCGTTTATCGCAATCTGC 214 GGTGTGGTTGATATTTTTCATGGTATTGATAAAGC 4093 1 3807 F GGTGTGGTTGATATTTTTCATGGTATTGATAAAGC 97 GCGACAGCTTGGTTTTTAGTGAGTTGTTCCATTCT 3870 1 5072 R AGAATGGAACAACTCACTAAAAACCAAGCTGTCGC -308 GACGTTCGTGATGAGTTTGTATCTGTTACTGAGGA 3746 1 4224 F GACGTTCGTGATGAGTTTGTATCTGTTACTGAGAA 408 GGAACAATTTCTGGAAAGACGGTAAAGCTGATGGT 3997 1 3856 F GGAACAATTTCTGGAAAGACGGTAAAGCTGATGGT -321 GACGGTTAATGCTGGTAATGGTGGGTTTTTTTTTT 2848 1 2535 F GACGGTTAATGCTGGTAATGGTGGTTTTCTTCATT 7 GTGTTATTAATATCAAGTTGGGGGAGCACATTGTA 4079 1 4284 R TACAATGTGCTCCCCCAACTTGATATTAATAACAC -296 GTTATTATACCGTCAAGGACTGTGTGACTATTGAC 4022 1 2750 F GTTATTATACCGTCAAGGACTGTGTGACTATTGAC -531 GATGAGTTTGTATCTGTTACTGAGAAGTTAATGGA 4099 1 4233 F GATGAGTTTGTATCTGTTACTGAGAAGTTAATGGA -194 GGTAGAAGTCGTCATTTGGCGAGAAAGCTCAGTCT 3920 1 1530 R AGACTGAGCTTTCTCGCCAAATGACGACTTCTACC -77 GTTATTTCCTAGACAAATTAGAGCCAATACCATCA 4103 1 3884 R TGATGGTATTGGCTCTAATTTGTCTAGGAAATAAC -269 GCTTTAAAATAGTTGTTATAGATATTCAAATAACC 4176 1 1379 R GGTTATTTGAATATCTATAACAACTATTTTAAAGC -262 GGAGTTTAATCATGTTTCAGACTTTTATTTCTCGC 3920 1 2384 F GGAGTTTAATCATGTTTCAGACTTTTATTTCTCGC -335 GAAAGTGTTAACTTCTGCGTCATGGAAGCGATAAA 4052 1 5 R TTTATCGCTTCCATGACGCAGAAGTTAACACTTTC -441 GTTCTTATTACCCTTCTGAATGTCACGCTGATTAT 4020 1 3988 F GTTCTTATTACCCTTCTGAATGTCACGCTGATTAT -165 GAACGCGTACTTATTCGCCACCATGATTATGACCA 3885 1 2144 F GAACGCGTACTTATTCGCCACCATGATTATGACCA -487 TAACCATAAGGCCACGTATTTTGCAAGCTATTTAA 3964 1 4854 R TTAAATAGCTTGCAAAATACGTGGCCTTATGGTTA -180 GATGAATGCAATGCGACAGGCTCATGCTGATGGTT 3860 1 4532 F GATGAATGCAATGCGACAGGCTCATGCTGATGGTT 112 GCTTTGGCCTCTATTAAGCTCATTCAGGCTTCTGC 3920 1 429 F GCTTTGGCCTCTATTAAGCTCATTCAGGCTTCTGC -25 GGGCAATAATGTTTATGTTGGTTTCATGGTTTGGT 3609 1 2802 F GGGCAATAACGTTTATGTTGGTTTCATGGTTTGGT 120 GTCAAGGACTGGTTTAGATATGAGTCACATTTTGT 3944 1 265 F GTCAAGGACTGGTTTAGATATGAGTCACATTTTGT -254 GGTTCTCACTTCTGTTACTCCAGCTTCTTCGGCAC 3870 1 2448 F GGTTCTCACTTCTGTTACTCCAGCTTCTTCGGCAC -508 GCCTGTCTCATCATGGAAGGCGCTGAATTTACGGA 3936 1 669 F GCCTGTCTCATCATGGAAGGCGCTGAATTTACGGA -280 GTATGTTTCTCCTGCTTATCACCTTCTTGAAGGCT 3957 1 2071 F GTATGTTTCTCCTGCTTATCACCTTCTTGAAGGCT 36 GGAGAAACATACGAAGGCGCATAACGATACCACTG 3894 1 2048 R CAGTGGTATCGTTATGCGCCTTCGTATGTTTCTCC 184 GAGCAGTAGACTCCTTCTGTTGATAAGCAAGCATC 3973 1 3521 R GATGCTTGCTTATCAACAGAAGGAGTCTACTGCTC 342 GGCTGGAGACAAATAATCTCTTTAATAACCTGATT 3925 1 2878 R AATCAGGTTATTAAAGAGATTATTTGTCTCCAGCC 221 GATTAAACTCCTAAGCAGAAAACCTACCGCGCTTC 3929 1 2360 R GAAGCGCGGTAGGTTTTCTGCTTAGGAGTTTAATC -342 GCATTAACCGTCAAACTATCAAAATATAACGTTGA 4060 1 2512 R TCAACGTTATATTTTGATAGTTTGACGGTTAATGC -213 GTGCCAAGAAAAGCGGCATGGTCAATATAACCAGT 3914 1 1301 R ACTGGTTATATTGACCATGCCGCTTTTCTTGGCAC -306 GCAAACGCAGAATCAGCGGTATGGCTCCTCTCATA 3586 1 3722 F GCAAACGCAGAATCAGCGGTATGGCTCTTCTCATA 96 GATTTAACCGAAGATGATTTCGATTTTCTGACGAG 4035 1 471 F GATTTAACCGAAGATGATTTCGATTTTCTGACGAG -226 GTTGGCGCTCTCCGTCTTTCTCCATTGCGTCGTGG 3791 1 1139 F GTTGGCGCTCTCCGTCTTTCTCCATTGCGTCGTGG 102 GCCACAACCAATCAGAACGTGAAAAAGCGTCCTGC 3610 1 4916 R GCAGGACGCTTTTTCACGTTCTGGTTGGTTGTGGC -227 GCTCTTACTTTGCGACCTTTCGCCATCAACTAACG 3985 1 170 F GCTCTTACTTTGCGACCTTTCGCCATCAACTAACG -506 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 GACCTATAAACATTCTGTGCCGCGTTTCTTTGTTC 3803 1 1801 F GACCTATAAACATTCTGTGCCGCGTTTCTTTGTTC -501 GGCGTGAAGTCGCCGACTGAATGCCAGCAATCTCT 3802 1 3452 R AGAGATTGCTGGCATTCAGTCGGCGACTTCACGCC 513 GCGACAGCTTGGTTTTTAGTGAGTTGTTCCATTCT 3870 1 5072 R AGAATGGAACAACTCACTAAAAACCAAGCTGTCGC -308 GGTGTCAATCCTGACGGTTATTTCCTAGACAAATT 3985 1 3900 R AATTTGTCTAGGAAATAACCGTCAGGATTGACACC -310 GGAGCAGTCCAAATGTTTTTGAGATGGCAGCAACG 3953 1 1484 R CGTTGCTGCCATCTCAAAAACATTTGGACTGCTCC -30 GTTGGTTGTGGCCTGTTGATGCTAAAGGTGAGCCG 3988 1 4939 F GTTGGTTGTGGCCTGTTGATGCTAAAGGTGAGCCG -492 GTATGTTTTCATGCCTCCAAATCTTGGAGGCTTTT 3959 1 3944 F GTATGTTTTCATGCCTCCAAATCTTGGAGGCTTTT -320 GCTGCATGAAGTAATCACGCTCTTGGTCAGTATGC 3672 1 1609 R GCATACTGACCAAGAACGTGATTACTTCATGCAGC 79 GTCTAATATTCAAACTGGCGCCGAGCGTATGCCGC 3934 1 1003 F GTCTAATATTCAAACTGGCGCCGAGCGTATGCCGC -98 GGATTGTTCAGTAACTTGACTCATGATTTCTTACC 3931 1 379 R GGTAAGAAATCATGAGTCAAGTTACTGAACAATCC -364 GTCCTTTACCAGC.TTAGCCATAGCACCAGAAAC. 3758 1 3143 R TGTTTCTGGTGCTATGGCTAAAGCTGGTAAAGGAC 450 GTTTGAGCTTGAGTAAGCATTTGGCGCATAATCTC 3970 1 3609 R GAGATTATGCGCCAAATGCTTACTCAAGCTCAAAC -200 GGTTAATGCTGGTAATGGTGGTTTTCTTCCTTTCA 3434 1 2538 F GGTTAATGCTGGTAATGGTGGTTTTCTTCATTGCA -141 GTAAAATACTGACCAGCCGTTTGAGCTTGAGTAAG 3970 1 3627 R CTTACTCAAGCTCAAACGGCTGGTCAGTATTTTAC -129 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 GATGGACGCCGTTGGCGCTCTCCGTCTTTCTCCAT 3644 1 1129 F GATGGACGCCGTTGGCGCTCTCCGTCTTTCTCCAT 107 GGATTGTTCAGTAACTTGACTCATGATTTCTTACC 3931 1 379 R GGTAAGAAATCATGAGTCAAGTTACTGAACAATCC -364 GACAGATGTATCCATCTGAATGCAATGAAGAAAAC 4113 1 2558 R GTTTTCTTCATTGCATTCAGATGGATACATCTGTC -409 GTCGGCTACAGTAACTTTTCCCAGCCTCAATCTCA 3864 1 5269 R TGAGATTGAGGCTGGGAAAAGTTACTGTAGCCGAC -276 GACCCTAAATTTTTTGCCTGTTTGGTTCGCTTTGA 3902 1 2644 F GACCCTAAATTTTTTGCCTGTTTGGTTCGCTTTGA -305 GAATGTTTATAGGTCTGTTGAACACGACCAGAAAA 4142 1 1781 R TTTTCTGGTCGTGTTCAACAGACCTATAAACATTC 316 GGATGAATTGGCACAATGCTACAATGTGCTCCCCC 3835 1 4265 F GGATGAATTGGCACAATGCTACAATGTGCTCCCCC 2 GAAAGACGGTAAAGCTGATGGTATTGGCTCTAATT 3883 1 3869 F GAAAGACGGTAAAGCTGATGGTATTGGCTCTAATT -350 GTGATAAGCAGGAGAAACATACGAAGGCGCATAAC 4008 1 2058 R GTTATGCGCCTTCGTATGTTTCTCCTGCTTATCAC -299 GCAGTAGTAATTCCTGCTTTATCAAGATAATTTTT 3943 1 59 R AAAAATTATCTTGATAAAGCAGGAATTACTACTGC -292 AAGGCTTCCCATTCATTCAGGAACCGCCTTCTGGT 3845 1 2100 F AAGGCTTCCCATTCATTCAGGAACCGCCTTCTGGT -316 AAAGCTGTTGCCGATACTTGGAACAATTTCTGGAA 3962 1 3837 F AAAGCTGTTGCCGATACTTGGAACAATTTCTGGAA -250 GTTTCGCTGAATCAGGTTATTAAAGAGATTATTTG 4060 1 2869 F GTTTCGCTGAATCAGGTTATTAAAGAGATTATTTG -37 GTTTAGATATGAGTCACATTTTGTTCATGGTAGAG 4101 1 276 F GTTTAGATATGAGTCACATTTTGTTCATGGTAGAG 31 GAGAAACATACGAAGGCGCATAACGATACCACTGA 3930 1 2047 R TCAGTGGTATCGTTATGCGCCTTCGTATGTTTCTC -121 GATTACTTCATGCAGCGTTACCATGATGTTATTTC 3909 1 1628 F GATTACTTCATGCAGCGTTACCATGATGTTATTTC -25 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 GCTTAATATGCTTGGCACGTTCGTCAAGGACTGGT 4001 1 243 F GCTTAATATGCTTGGCACGTTCGTCAAGGACTGGT -81 GCTATCAGTATTTTTGTGTGCCTGAGTATGGTACA 4034 1 4717 F GCTATCAGTATTTTTGTGTGCCTGAGTATGGTACA -57 GCTTGGTTTTTAGTGAGTTGTTCCATTCTTTAGCT 3936 1 5066 R AGCTAAAGAATGGAACAACTCACTAAAAACCAAGC -373 GACATTATGGGTCTGCAAGCTGCTTATGCTAATTT 3906 1 1574 F GACATTATGGGTCTGCAAGCTGCTTATGCTAATTT -209 GAAAGGTATTAAGGATGAGTGTTCAAGATTGCTGG 3987 1 4451 F GAAAGGTATTAAGGATGAGTGTTCAAGATTGCTGG -119 CACATCTATTGACATTATGGGTCTGCAAGCTGCTT 3976 1 1564 F CACATCTATTGACATTATGGGTCTGCAAGCTGCTT -485 GGTATTGGCTCTAATTTGTCTAGGAAATAACCGTC 4027 1 3888 F GGTATTGGCTCTAATTTGTCTAGGAAATAACCGTC -489 GTTTTCTGCTTAGGAGTTTAATCATGTTTCAGACT 4012 1 2372 F GTTTTCTGCTTAGGAGTTTAATCATGTTTCAGACT -365 GAGCTTTCTCGCCAAATGACGACTTCTACCACATC 3925 1 1535 F GAGCTTTCTCGCCAAATGACGACTTCTACCACATC 104 GTTGAACACGACCAGAAAACTGGCCTAACGCCGCT 3455 1 1765 R AACGTCGTTAGGCCAGTTTTCTGGTCGTGTTCAAC -519 TACGTGGCCTTATGGTTACAGTATGCCCATCGCAG 3930 1 4871 F TACGTGGCCTTATGGTTACAGTATGCCCATCGCAG 191 GCAAGGTCCATATCTGACTTTTTGTTAACGTATTT 3907 1 5016 R AAATACGTTAACAAAAAGTCAGATATGGACCTTGC -326 GAGATGGCAGCAACGGAAACCATAACGAGCATCAT 3887 1 1464 R ATGATGCTCGTTATGGTTTCCGTTGCTGCCATCTC 311 GTCGGGAGAGGAGTGGCATTAACACCATCCTTCAT 3784 1 1256 R ATGAAGGATGGTGTTAATGCCACTCCTCTCCCGAC -310 GTCCACACCATTGCTTTATCTAAAAGGTTTTCACG -363 1 1558 F TTCTACCACATCTATTGACATTATGGGTCTGCAAG -529 GATGCCGACCCTAAATTTTTTGCCTGTTTGGTTCG 3826 1 2638 F GATGCCGACCCTAAATTTTTTGCCTGTTTGGTTCG -278 GATAAGTTGCTTGATTTGGTTGGACTTGGTGGCTA 3767 1 3219 F GATAAGTTGCTTGATTTGGTTGGACTTGGTGGCAA 130 GATTTGAGAATCAAAAAGAGCTTACTAAAATGCAA 4131 1 3370 F GATTTGAGAATCAAAAAGAGCTTACTAAAATGCAA 1077 GGATATTTCTGATGAGTCGAAAAATTATCTTGATA 4074 1 40 F GGATATTTCTGATGAGTCGAAAAATTATCTTGATA -17 GAAGAGATTCTGTCTTTTCGTATGCAGGGCGTTGA 3947 1 4143 F GAAGAGATTCTGTCTTTTCGTATGCAGGGCGTTGA -64 GGTAATAAGAACGAACCATAACAAAGCCTCCAAGA 3793 1 3965 R TCTTGGAGGCTTTTTTATGGTTCGTTCTTATTACC -57 GAGATTATTTGTCTCCAGCCACTTAAGTGAGGTGA 4041 1 2893 F GAGATTATTTGTCTCCAGCCACTTAAGTGAGGTGA -161 GTGATGTGCTTGCTACCGATAACAATACTGTAGGC 4004 1 3034 F GTGATGTGCTTGCTACCGATAACAATACTGTAGGC 253 GTTTTGCCGCAAGCTGGCTGCTGAACGCCCTCTTA 3828 1 4382 F GTTTTGCCGCAAGCTGGCTGCTGAACGCCCTCTTA -68 GTAATTTTTGACGCACGTTTTCTTCTGCGTCAGTA 3972 1 794 R TACTGACGCAGAAGAAAACGTGCGTCAAAAATTAC -311 GTTTTGTATGGCAACTTGCCGCCGCGTGAAATTTC 3935 1 1955 F GTTTTGTATGGCAACTTGCCGCCGCGTGAAATTTC -188 GGGTTAGGAACATTAGAGCCTTGAATGGCAGATTT 3931 1 3087 R AAATCTGCCATTCAAGGCTCTAATGTTCCTAACCC -123 GCAAAAATTAAAATTTTTACCGCTTCGGCGTTATA 3983 1 2305 R TATAACGCCGAAGCGGTAAAAATTTTAATTTTTGC 1233 TTGAATATTAGACATAATTTATCCTCAAGTAAGGG 4171 1 981 R CCCTTACTTGAGGATAAATTATGTCTAATATTCAA -446 GTAGGTTTTCTGCTTAGGAGTTTAATCATGTTTCA 4003 1 2368 F GTAGGTTTTCTGCTTAGGAGTTTAATCATGTTTCA -410 GCAGAGGAAGCATCAGCACCAGCGCGCTCCCAAGC 3510 1 3318 R GCTTGGGAGCGTGCTGGTGCTGATGCTTCCTCTGC -93 GAGATTATTTGTCTCCAGCCACTTAAGTGAGGTGA 4041 1 2893 F GAGATTATTTGTCTCCAGCCACTTAAGTGAGGTGA -161 GCCGCGTGAAATTTCTATGAAGGATGTTTTCCTTT 3617 1 1975 F GCCGCGTGAAATTTCTATGAAGGATGTTTTCCGTT -181 GGTTTAAGAGCCTCGATACGCTCAAAGTCAAAATA 4021 1 4020 R TATTTTGACTTTGAGCGTATCGAGGCTCTTAAACC -192 GAATTAAATCGAAGTGGACTGCTGGCGGAAAATGA 4056 1 102 F GAATTAAATCGAAGTGGACTGCTGGCGGAAAATGA -637 GGTGTCAATCCTGACGGTTATTTCCTAGACAAATT 3985 1 3900 R AATTTGTCTAGGAAATAACCGTCAGGATTGACACC -310 GTCCTTCCTCGTACGCCGGGCAATAATGTTTATGT 3328 1 2785 F GTCCTTCCCCGTACGCCGGGCAATAACGTTTATGT -289 GCCGAAGCGGTAAAAATTTTAATTTTTGCCGCTGA 3927 1 2311 F GCCGAAGCGGTAAAAATTTTAATTTTTGCCGCTGA 1475 GTCATGCGCTCTAATCTCTGGGCATCTGGCTATGA 3904 1 1712 F GTCATGCGCTCTAATCTCTGGGCATCTGGCTATGA -367 GTGATGTGCTTGCTACCGATAACAATACTGTAGGC 4004 1 3034 F GTGATGTGCTTGCTACCGATAACAATACTGTAGGC 253 GATGAAAATGCTCACAATGACAAATCTGTCCACGG 3991 1 5153 F GATGAAAATGCTCACAATGACAAATCTGTCCACGG -201 GACTACCCTCCCGACTGCCTATGATGTTTATCCTT 3778 1 2694 F GACTACCCTCCCGACTGCCTATGATGTTTATCCTT -140 GAACGTTTTTTACCTTTAGACATTACATCACTCCT 3972 1 836 R AGGAGTGATGTAATGTCTAAAGGTAAAAAACGTTC -175 GAAACAAATGCTTAGGGCTTTTATTGGTATCAGGG 3768 1 1342 R CCCTGATACCAATAAAATCCCTAAGCATTTGTTTC -110 GTGGTTGAACAGCATCGGACTCAGATAGTAATCCA 3977 1 339 R TGGATTACTATCTGAGTCCGATGCTGTTCAACCAC -316 ATAAAAGATTGAGTGTGAGGTTATAACGCCGAAGC 4049 1 2284 F ATAAAAGATTGAGTGTGAGGTTATAACGCCGAAGC -325 GGAAGGCGCTGAATTTACGGAAAACATTATTAATG 3955 1 683 F GGAAGGCGCTGAATTTACGGAAAACATTATTAATG -287 GTGCTCCCCCAACTTGATATTAATAACACTATAGA 4074 1 4290 F GTGCTCCCCCAACTTGATATTAATAACACTATAGA -278 GTGCCAAGAAAAGCGGCATGGTCAATATAACCAGT 3914 1 1301 R ACTGGTTATATTGACCATGCCGCTTTTCTTGGCAC -306 GCCACCATGATTATGACCAGTGTTTCCAGTCCGTT 3851 1 2160 F GCCACCATGATTATGACCAGTGTTTCCAGTCCGTT 169 GTAAACATAGTGCCATGCTCAGGAACAAAGAAACG 4098 1 1823 R CGTTTCTTTGTTCCTGAGCATGGCACTATGTTTAC 86 GAAATGACTCGCAAGGTTAGTGCTGAGGTTGACTT 3864 1 3678 F GAAATGACTCGCAAGGTTAGTGCTGAGGTTGACTT -377 GTTTTACAGACACCTAAAGCTACATCGTCAACGTT 3984 1 2485 F GTTTTACAGACACCTAAAGCTACATCGTCAACGTT 348 GCAAAGGATATTTCTAATGTCGTCACTGATGCTGC 3934 1 3768 F GCAAAGGATATTTCTAATGTCGTCACTGATGCTGC 108 GATGAGACAGGCCGTTTGAATGTTGACGGGATGAA 3989 1 646 R TTCATCCCGTCAACATTCAAACGGCCTGTCTCATC 313 GCTTAGGAGTTTAATCATGTTTCAGACTTTTATTT 3929 1 2379 F GCTTAGGAGTTTAATCATGTTTCAGACTTTTATTT -40 GTTGCTGCCATCTCAAAAACATTTGGACTGCTCCG 3910 1 1485 F GTTGCTGCCATCTCAAAAACATTTGGACTGCTCCG 25 GATGGACGCCGTTGGCGCTCTCCGTCTTTCTCCAT 3644 1 1129 F GATGGACGCCGTTGGCGCTCTCCGTCTTTCTCCAT 107 GGTATTAAGGATGAGTGTTCAAGATTGCTGGAGGC 4030 1 4455 F GGTATTAAGGATGAGTGTTCAAGATTGCTGGAGGC -196 GACCCTCAGCAATCTTAAACTTCTTAGACGAATCA 4013 1 2014 R TGATTCGTCTAAGAAGTTTAAGATTGCTGAGGGTC -202 GACGGGATGAACATAATAAGCAATGACGGCAGCAA 4015 1 622 R TTGCTGCCGTCATTGCTTATTATGTTCATCCCGTC 283 GACTGAATGCCAGCAATCTCTTTTTGAGTCTCATT 3841 1 3438 R AATGAGACTCAAAAAGAGATTGCTGGCATTCAGTC 753 GGTTTCATGGTTTGGTCTAACTTTACCGCTACTAA 4011 1 2821 F GGTTTCATGGTTTGGTCTAACTTTACCGCTACTAA 47 GATGGTATTGGCTCTAATTTGTCTAGGAAATAACC 4065 1 3885 F GATGGTATTGGCTCTAATTTGTCTAGGAAATAACC -146 GTCGTAACCCAGCTTGGTAAGTTGGATTAAGCACT 3936 1 5188 R AGTGCTTAATCCAACTTACCAAGCTGGGTTACGAC 104 GGTCGAATTTTCTCATTTTCCGCCAGCAGTCCACT 3872 1 114 R AGTGGACTGCTGGCGGAAAATGAGAAAATTCGACC -319 GTTGACGGGATGAACATAATAAGCAATGACGGCAG 4051 1 625 R CTGCCGTCATTGCTTATTATGTTCATCCCGTCAAC 119 GTTGATGGCGAAAGGTCGCAAAGTAAGAGCTTCTC 3913 1 165 R GAGAAGCTCTTACTTTGCGACCTTTCGCCATCAAC -437 GTCAGTTTTTGACAGAATCGTTAGTTGATGGCGAA 4097 1 188 R TTCGCCATCAACTAACGATTCTGTCAAAAACTGAC -602 GTGGCATTCAAGGTGATGTGCTTGCTACCGATAAC 3925 1 3022 F GTGGCATTCAAGGTGATGTGCTTGCTACCGATAAC 199 GACACCCTCCCAATTGTATGTTTTCATGCCTCCAA 3857 1 3929 F GACACCCTCCCAATTGTATGTTTTCATGCCTCCAA -648 GACACCTAAAGCTACATCGTCAACGTTATATTTTG 3896 1 2493 F GACACCTAAAGCTACATCGTCAACGTTATATTTTG 157 GTAAAGTTAGACCAAACCATGAAACCAACATAAAC 4110 1 2812 R GTTTATGTTGGTTTCATGGTTTGGTCTAACTTTAC -270 GGAAGTAGCGACAGCTTGGTTTTTAGTGAGTTGTT 3857 1 5079 R AACAACTCACTAAAAACCAAGCTGTCGCTACTTCC -534 GGCGGAAAATGAGAAAATTCGACCTATCCTTGCGC 3821 1 125 F GGCGGAAAATGAGAAAATTCGACCTATCCTTGCGC -444 GATTACTTCATGCAGCGTTACCATGATGTTATTTC 3909 1 1628 F GATTACTTCATGCAGCGTTACCATGATGTTATTTC -25 GCTCAAATTTATGCGCGCTTCGATAAAAATGATTG 4071 1 5336 F GCTCAAATTTATGCGCGCTTCGATAAAAATGATTG -195 CTCAACGCAGCGACGAGCACGAGAGCGGTCAGTAG 3903 1 523 R CTACTGACCGCTCTCGTGCTCGTCGCTGCGTTGAG 177 GTCGCTACTTCCCAAGAAGCTGTTCAGAATCAGAA 4034 1 5102 F GTCGCTACTTCCCAAGAAGCTGTTCAGAATCAGAA -81 GTCTTCTTCGGTTCCGACTACCCTCCCGACTGCCT 3844 1 2679 F GTCTTCTTCGGTTCCGACTACCCTCCCGACTGCCT -10 GATTATGCGCCAAATGCTTACTCAAGCTCAAACGG 3979 1 3611 F GATTATGCGCCAAATGCTTACTCAAGCTCAAACGG -529 GCTTTAAAATAGTTGTTATAGATATTCAAATAACC 4176 1 1379 R GGTTATTTGAATATCTATAACAACTATTTTAAAGC -262 GTTATTTCCTAGACAAATTAGAGCCAATACCATCA 4103 1 3884 R TGATGGTATTGGCTCTAATTTGTCTAGGAAATAAC -269 GACGACTTCTACCACATCTATTGACATTATGGGTC 3931 1 1552 F GACGACTTCTACCACATCTATTGACATTATGGGTC -217 GATTAGAGGCGTTTTATGATAATCCCAATGCTTTG 3988 1 4606 F GATTAGAGGCGTTTTATGATAATCCCAATGCTTTG -109 GTCGGGAGGGTAGTCGGAACCGAAGAAGACTCAAA 3952 1 2674 R TTTGAGTCTTCTTCGGTTCCGACTACCCTCCCGAC -453 GCATTTTAGTAAGCTCTTTTTGATTCTCAAATCCG 4044 1 3368 R CGGATTTGAGAATCAAAAAGAGCTTACTAAAATGC 597 GAACAATCCGTACGTTTCCAGACCGCTTTGGCCTC 3780 1 405 F GAACAATCCGTACGTTTCCAGACCGCTTTGGCCTC -277 GAAATAACCGTCAGGATTGACACCCTCCCAATTGT 3837 1 3911 F GAAATAACCGTCAGGATTGACACCCTCCCAATTGT -82 GTTATAACCTCACACTCACCCTATTATCACGTCGT 2707 1 2276 R ACTTCGTGATAAAAGATTGAGTGTGAGGTTATAAC -454 GACGGTTATTTCCTAGACAAATTAGAGCCAATACC 4013 1 3888 R GGTATTGGCTCTAATTTGTCTAGGAAATAACCGTC -285 GTTTATGGTACGCTGGACTTTGTAGGATACCCTCG 3613 1 564 F GTTTATGGTACGCTGGACTTTGTGGGATACCCTCG -720 GCAGGAATTACTACTGCTTGTTTACGAATTAAATC 3991 1 77 F GCAGGAATTACTACTGCTTGTTTACGAATTAAATC -356 GCATCCACGGCGCTTTAAAATAGTTGTTATAGATA 4029 1 1390 R TATCTATAACAACTATTTTAAAGCGCCGTGGATGC 115 GAAGAGCCATACCGCTGATTCTGCGTTTGCTGATG 3800 1 3717 R CATCAGCAAACGCAGAATCAGCGGTATGGCTCTTC 519 GGGAAAGGTCATGCGGCATACGCTCGGCGCCAGTT 3750 1 1015 R AACTGGCGCCGAGCGTATGCCGCATGACCTTTCCC -361 GGACTTGGTGGCAAGTCTGCCGCTGATAAAGGAAA 3976 1 3240 F GGACTTGGTGGCAAGTCTGCCGCTGATAAAGGAAA 108 CTCTTTCTCAATCCCCAATGCTTGGCTTCCATAAG 3991 1 4081 F CTCTTTCTCAATCCCCAATGCTTGGCTTCCATAAG -449 GAAATATCCGAAAGTGTTAACTTCTGCGTCATGGA 3965 1 14 R TCCATGACGCAGAAGTTAACACTTTCGGATATTTC -230 GGAGGCCTCCACTATGAAATCGCGTAGAGGCTTTG 3841 1 4484 F GGAGGCCTCCACTATGAAATCGCGTAGAGGCTTTG -671 GGTGCTATGGCTAAAGCTGGTAAAGGACTTCTTGA 3937 1 3150 F GGTGCTATGGCTAAAGCTGGTAAAGGACTTCTTGA 43 GCTCTTAAACCTGCTATTGAGGCTTGTGGCATTTC 3921 1 4044 F GCTCTTAAACCTGCTATTGAGGCTTGTGGCATTTC 84 GTAAAAATGTCTACAGTAGAGTCAATAGCAAGGCC 4059 1 1172 R GGCCTTGCTATTGACTCTACTGTAGACATTTTTAC 735 GCCAATATGAGAAGAGCCATACCGCTGATTCTGCG 3856 1 3727 R CGCAGAATCAGCGGTATGGCTCTTCTCATATTGGC 264 GTGGTAGAAGTCGTCATTTGGCGAGAAAGCTCAGT 3931 1 1532 R ACTGAGCTTTCTCGCCAAATGACGACTTCTACCAC 234 GTGAAGTCGCCGACTGAATGCCAGCAATCTCTTTT 3768 1 3449 R AAAAGAGATTGCTGGCATTCAGTCGGCGACTTCAC 326 GATGGCAGCAACGGAAACCCTAACGAGCATCATCT 3607 1 1462 R AGATGATGCTCGTTATGGTTTCCGTTGCTGCCATC 560 AAGTGGCTTAATATGCTTGGCACGTTCGTCAAGGA 3968 1 238 F AAGTGGCTTAATATGCTTGGCACGTTCGTCAAGGA 297 GTAAAGCTGATGGTATTGGCTCTAATTTTTCTAGG 3649 1 3877 F GTAAAGCTGATGGTATTGGCTCTAATTTGTCTAGG 425 TTTTTCGACTCATCAGAAATATCCGAAAGTGTTAA 4128 1 29 R TTAACACTTTCGGATATTTCTGATGAGTCGAAAAA 227 GATAAAGCTGTTGCCGATACTTGGAACAATTTCTG 3933 1 3834 F GATAAAGCTGTTGCCGATACTTGGAACAATTTCTG -357 GTATCAGGGTTAATCGTGCCAAGAAAAGCGGCATG 4014 1 1316 R CATGCCGCTTTTCTTGGCACGATTAACCCTGATAC -86 GCACAGAATGTTTATAGGTCTGTTGAACACGACCA 4035 1 1786 R TGGTCGTGTTCAACAGACCTATAAACATTCTGTGC -18 TTAATAACACTATAGACCACCGCCCCGAAGGGGAC 3991 1 4309 F TTAATAACACTATAGACCACCGCCCCGAAGGGGAC -389 GTCCAAATGTTTTTGAGATGGCAGCAACGGAAACC 4094 1 1478 R GGTTTCCGTTGCTGCCATCTCAAAAACATTTGGAC 445 GTGATGTGCTTGCTACCGATAACAATACTGTAGGC 4004 1 3034 F GTGATGTGCTTGCTACCGATAACAATACTGTAGGC 253 GAGTTTGTATCTGTTACTGAGAAGTTAATGGATGA 4140 1 4236 F GAGTTTGTATCTGTTACTGAGAAGTTAATGGATGA -215 GTTGCGGCTCATTCTGATTCTGAACAGCTTCTTGG 3898 1 5113 R CCAAGAAGCTGTTCAGAATCAGAATGAGCCGCAAC 129 GCTGATTATTTTGACTTTGAGCGTATCGTGGTTCT 3513 1 4014 F GCTGATTATTTTGACTTTGAGCGTATCGAGGCTCT -170 GGATAACCGCATCAAGCTCTTGGAAGAGATTCTGT 3905 1 4121 F GGATAACCGCATCAAGCTCTTGGAAGAGATTCTGT -28 GCTGACAACCGTCCTTTACTTGTCATGCGCTCTAA 3892 1 1691 F GCTGACAACCGTCCTTTACTTGTCATGCGCTCTAA -486 CATGTTTCAGACTTTTATTTCTCGCCATAATTCAA 4044 1 2394 F CATGTTTCAGACTTTTATTTCTCGCCATAATTCAA -253 GTTTTCTTCTGCGTCAGTAAGAACGTCAGTGTTTC 4003 1 778 R GAAACACTGACGTTCTTACTGACGCAGAAGAAAAC -387 GGAGCAGTCCAAATGTTTTTGAGATGGCAGCAACG 3953 1 1484 R CGTTGCTGCCATCTCAAAAACATTTGGACTGCTCC -30 GCCTACTGCGACTAAAGAGATTCAGTACCTTAACG 3990 1 1879 F GCCTACTGCGACTAAAGAGATTCAGTACCTTAACG -327 GTTACCATGATGTTATTTCTTCATTTGGAGGTAAA 4109 1 1644 F GTTACCATGATGTTATTTCTTCATTTGGAGGTAAA -217 GTGATGAGTTTGTATCTGTTACTGAGAAGTTAATG 4080 1 4231 F GTGATGAGTTTGTATCTGTTACTGAGAAGTTAATG -27 GGAAGCCTTCAAGAAGGTGATAAGCAGGAGAAACA 4076 1 2074 R TGTTTCTCCTGCTTATCACCTTCTTGAAGGCTTCC -88 GATTATGCGCCAAATGCTTACTCAAGCTCAAACGG 3979 1 3611 F GATTATGCGCCAAATGCTTACTCAAGCTCAAACGG -529 GCGATTCAATCATGACTTCGTGATAAAAGATTGAG 4095 1 2262 F GCGATTCAATCATGACTTCGTGATAAAAGATTGAG -211 GGTTGTCAGCGTCATAAGAGGTTTTACCTCCAAAT 3965 1 1666 R ATTTGGAGGTAAAACCTCTTATGACGCTGACAACC -490 GCTTGATGCGGTTATCCATCTGCTTATGGAAGCCA 3980 1 4103 R TGGCTTCCATAAGCAGATGGATAACCGCATCAAGC 101 GTAATTCCTGCTTTATCAAGATAATTTTTCGACTC 4016 1 53 R GAGTCGAAAAATTATCTTGATAAAGCAGGAATTAC -74 GGATTGACACCCTCCCAATTGTATGCTTTCATGCC 3589 1 3924 F GGATTGACACCCTCCCAATTGTATGTTTTCATGCC -167 GGTTATATTGACCATGCCGCTTTTCTTGGCACGAT 3937 1 1304 F GGTTATATTGACCATGCCGCTTTTCTTGGCACGAT -362 GAGGGGTTGACCAAGCGAAGCGCGGTAGGTTTTCT 3731 1 2344 F GAGGGGTTGACCAAGCGAAGCGCGGTAGGTTTTCT -81 GAATTTACGGAAAACATTATTAATGGCGTCGAGCG 4083 1 693 F GAATTTACGGAAAACATTATTAATGGCGTCGAGCG 776 GAAATCATCTTCGGTTAAATCCAAAACGGCAGAAG 4107 1 457 R CTTCTGCCGTTTTGGATTTAACCGAAGATGATTTC -402 TTGTATGGCAACTTGCCGCCGCGTGAAATTTCTAT 3873 1 1958 F TTGTATGGCAACTTGCCGCCGCGTGAAATTTCTAT 88 GTTAATGCTGGTAATGGTGGTTTTCTTCTTTTCCT 3185 1 2539 F GTTAATGCTGGTAATGGTGGTTTTCTTCATTGCAT 160 GCGGTGGTCTATAGTGTTATTAATATCAAGTTGTG 3809 1 4297 R CCCAACTTGATATTAATAACACTATAGACCACCGC -135 GGATGTTTTCCGTTCTGGTGATTCGTCTAAGAAGT 4014 1 1996 F GGATGTTTTCCGTTCTGGTGATTCGTCTAAGAAGT -162 GTTCAAGATTGCTGGAGGCCTCCACTATGAAATCG 3968 1 4471 F GTTCAAGATTGCTGGAGGCCTCCACTATGAAATCG -156 GCGCCAGAACGTTTTTTACCTTTAGACATTACATC 3906 1 842 R GATGTAATGTCTAAAGGTAAAAAACGTTCTGGCGC -365 GAGGCTTGCGTTTATGGTACGCTGGACTTTGTAGG 3573 1 555 F GAGGCTTGCGTTTATGGTACGCTGGACTTTGTGGG -59 GTTCGATAATGGTGATATGTATGTTGACGGCCATA 4048 1 4178 F GTTCGATAATGGTGATATGTATGTTGACGGCCATA -148 GCATTAACACCATCCTTCATGAACTTAATCCACTG 3982 1 1241 R CAGTGGATTAAGTTCATGAAGGATGGTGTTAATGC 165 GAAAACATACAATTGGGAGGGTGTCAATCCTGACG 3947 1 3919 R CGTCAGGATTGACACCCTCCCAATTGTATGTTTTC -159 GCCTATGATGTTTATCCTTTGGATGGTCGCCATGA 3754 1 2710 F GCCTATGATGTTTATCCTTTGAATGGTCGCCATGA -173 GAATGAATGGGAAGCCTTCAAGAAGGTGATAAGCA 4016 1 2083 R TGCTTATCACCTTCTTGAAGGCTTCCCATTCATTC 127 GAGTTTGTATCTGTTACTGAGAAGTTAATGGATGA 4140 1 4236 F GAGTTTGTATCTGTTACTGAGAAGTTAATGGATGA -215 GTTCAACAGACCTATAAACATTCTGTGCCGCGTTT 3885 1 1793 F GTTCAACAGACCTATAAACATTCTGTGCCGCGTTT -255 GACAGAATCGTTAGTTGGTGGAGTAAGGTGGCAAA 2944 1 178 R TTTGCGACCTTTCGCCATCAACTAACGATTCTGTC -113 GCGGCTCATTCTGATTCTGAACAGCTTCTTGGGAA 3953 1 5110 R TTCCCAAGAAGCTGTTCAGAATCAGAATGAGCCGC -226 GATTCAATCATGACTTCGTGATAAAAGATTGAGTG 4084 1 2264 F GATTCAATCATGACTTCGTGATAAAAGATTGAGTG 187 GCGTATCGAGGCTCTTAAACCTGCTATTGAGGCTT 3866 1 4034 F GCGTATCGAGGCTCTTAAACCTGCTATTGAGGCTT 280 GACGCAGAAGTTAACACTTTCGGATATTTCTGTTG 3628 1 19 F GACGCAGAAGTTAACACTTTCGGATATTTCTGATG -263 GGTTATTTCCTAGACAAATTAGAGCCAATACCATC 4058 1 3885 R GATGGTATTGGCTCTAATTTGTCTAGGAAATAACC -222 GCGCCGTGGATGCCTGACCGTACCGAGGCTAACCC 3789 1 1412 F GCGCCGTGGATGCCTGACCGTACCGAGGCTAACCC -815 GGCTTGTGGCATTTCTACTCTTTCTCAATCCCCAA 3929 1 4064 F GGCTTGTGGCATTTCTACTCTTTCTCAATCCCCAA -342 GGAAAGCGAGGGTATCCTACAAAGTCCAGCGTACC 3574 1 570 R GGTACGCTGGACTTTGTGGGATACCCTCGCTTTCC -172 GGTGAACAGTGGATTAAGTTCATGAAGGATGGTGT 3967 1 1235 F GGTGAACAGTGGATTAAGTTCATGAAGGATGGTGT -34 TGACGACATTAGAAATATCCTTTGCAGTAGCGCCA 3955 1 3758 R TGGCGCTACTGCAAAGGATATTTCTAATGTCGTCA 238 GTTTATCGTTTTTGACACTCTCACGTTGGCTGACG 4008 1 4568 F GTTTATCGTTTTTGACACTCTCACGTTGGCTGACG -47 GCGTCGTGGCCTTGCTATTGACTCTACTGTAGACA 3922 1 1165 F GCGTCGTGGCCTTGCTATTGACTCTACTGTAGACA 210 GTACGCGTTCTTGCAAATCACCAGAAGGCGGTTCC 3910 1 2119 R GGAACCGCCTTCTGGTGATTTGCAAGAACGCGTAC -208 GGCTCTTAAACCTGCTATTGAGGCTTTTGGGTTTT 3113 1 4043 F GGCTCTTAAACCTGCTATTGAGGCTTGTGGCATTT 51 GGGTTAGGAACATTAGAGCCTTGAATGGCAGATTT 3931 1 3087 R AAATCTGCCATTCAAGGCTCTAATGTTCCTAACCC -123 GCCACCATGATTATGACCAGTGTTTCCAGTCCGTT 3851 1 2160 F GCCACCATGATTATGACCAGTGTTTCCAGTCCGTT 169 GCCTAACGACGTTTGGTCAGTTCCATCAACATCAT 3912 1 1743 R ATGATGTTGATGGAACTGACCAAACGTCGTTAGGC -58 GACGACCAACATTAGGGTCAACGCTACCTGTAGGA 3675 1 4799 R TCCTACAGGTAGCGTTGACCCTAATTTTGGTCGTC -226 GCAATGACGGCAGCCATCAACTCAACAGGAGCAGG 3468 1 603 R CCTGCTCCTGTTGAGTTTATTGCTGCCGTCATTGC -74 GACCCTAAATTTTTTGCCTGTTTGGTTCGCTTTGA 3902 1 2644 F GACCCTAAATTTTTTGCCTGTTTGGTTCGCTTTGA -305 GTAAAACCTCTTATGACGCTGACAACCGTCCTTTA 3878 1 1674 F GTAAAACCTCTTATGACGCTGACAACCGTCCTTTA -576 GTTAGGCCAGTTTTCTGGTCGTGTTCAACAGACCT 3943 1 1771 F GTTAGGCCAGTTTTCTGGTCGTGTTCAACAGACCT -502 GCTGTACCATACTCAGGCACACAACAATACTGATA 3767 1 4719 R TATCAGTATTTTTGTGTGCCTGAGTATGGTACAGC 256 GTTTAATCATGTTTCAGACTTTTATTTCTCGCCAT 3995 1 2387 F GTTTAATCATGTTTCAGACTTTTATTTCTCGCCAT -394 GTTGGGGGAGCACATTGTAGCATTGTGCCAATTCA 3889 1 4268 R TGAATTGGCACAATGCTACAATGTGCTCCCCCAAC -435 GCGTAACCGTCTTCTCGTTCTCTAAAAACCATTTT 3908 1 4346 R AAAATGGTTTTTAGAGAACGAGAAGACGGTTACGC -595 GAGTCATTTCTTTGATTTGGTCATTGGTAAAATAC 4122 1 3653 R GTATTTTACCAATGACCAAATCAAAGAAATGACTC -343 GATGATGCTCGTTATGGTTTCCGTTGCTGCCATCT 3851 1 1463 F GATGATGCTCGTTATGGTTTCCGTTGCTGCCATCT 351 GACCAGAAAACTGGCCTAACGACGTTTGGTCAGTT 3835 1 1756 R AACTGACCAAACGTCGTTAGGCCAGTTTTCTGGTC 791 GCTCAAAGTCAAAATAATCAGCGTGACATTCAGAA 4059 1 4001 R TTCTGAATGTCACGCTGATTATTTTGACTTTGAGC -92 GTTTGCTGATGAACTAAGTCAACCTCAGCACTAAC 4024 1 3693 R GTTAGTGCTGAGGTTGACTTAGTTCATCAGCAAAC 149 GCCTTCAAGAAGGTGATAAGCAGGAGAAACATACG 4093 1 2070 R CGTATGTTTCTCCTGCTTATCACCTTCTTGAAGGC 53 GGCAGCAATAAACTCAACAGGAGCAGGAAAGCGAG 4068 1 595 R CTCGCTTTCCTGCTCCTGTTGAGTTTATTGCTGCC -102 GACGAGCACGAGAGCGGTCAGTAGCAATCCAAACT 3865 1 512 R AGTTTGGATTGCTACTGACCGCTCTCGTGCTCGTC -261 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 GATTAAGCACTCCGTGGACAGATTTGTCATTGTGA 3919 1 5164 R TCACAATGACAAATCTGTCCACGGAGTGCTTAATC -131 GTGGTATCGTTATGCGACTTCGTATGTTTCTCCTG 3545 1 2050 F GTGGTATCGTTATGCGCCTTCGTATGTTTCTCCTG -71 GTCAACAATTTTAATTGCAGGGGCTTCGGCCCCTT 3902 1 951 F GTCAACAATTTTAATTGCAGGGGCTTCGGCCCCTT 157 GATTATGACCAGTGTTTCCAGTCCGTTCAGTTGTT 3862 1 2168 F GATTATGACCAGTGTTTCCAGTCCGTTCAGTTGTT -32 GTGGATGCCTGACCGTACCGAGGCTAACCCTAATG 3871 1 1417 F GTGGATGCCTGACCGTACCGAGGCTAACCCTAATG -323 GAACGTCAGAAGCAGCCTTATGGCCGTCCACATAC 3615 1 4196 R GTATGTTGACGGCCATAAGGCTGCTTCTGACGTTC 543 GTAAACATAGTGCCATGCTCAGGAACAAAGAAACG 4098 1 1823 R CGTTTCTTTGTTCCTGAGCATGGCACTATGTTTAC 86 GCAGCAACGGAAACCATAACGAGCATCATCTTGAT 3879 1 1458 R ATCAAGATGATGCTCGTTATGGTTTCCGTTGCTGC 648 GAGTTTATTGCTGCCGTCATTGCTTATTATGTTCA 3946 1 615 F GAGTTTATTGCTGCCGTCATTGCTTATTATGTTCA 483 GCCGTTTGAATGTTGACGGGATGAACATAATAAGC 4083 1 636 R GCTTATTATGTTCATCCCGTCAACATTCAAACGGC -202 GTTATATTGACCATGCCGCTTTTCTTGGCACGATT 3906 1 1305 F GTTATATTGACCATGCCGCTTTTCTTGGCACGATT -216 GGCTAAAGCTGGTAAAGGACTTCTTGAAGGTACGT 3977 1 3158 F GGCTAAAGCTGGTAAAGGACTTCTTGAAGGTACGT -203 GGCTAAAGCTGGTAAAGGACTTCTTGAAGGTACGT 3977 1 3158 F GGCTAAAGCTGGTAAAGGACTTCTTGAAGGTACGT -203 GAAAATAGTCACGCAAAGCATTGGGATTATCATAA 3984 1 4619 R TTATGATAATCCCAATGCTTTGCGTGACTATTTTC -277 GTTTAGATATGAGTCACATTTTGTTCATGGTAGAG 4101 1 276 F GTTTAGATATGAGTCACATTTTGTTCATGGTAGAG 31 TTATCCTTTGGATGGTCGCCATGATGGTGGTTATT 3693 1 2721 F TTATCCTTTGAATGGTCGCCATGATGGTGGTTATT 273 GAGTTTGGCGCGATGGTGATTAAAGCGGCGAAGGA -98 1 3420 F GAGATTGCCGAGATGCAAAATGAGACTCAAAAAGA -250 GAATGCCAGCAATCTCTTTTTGAGTCTCATTTTGC 3855 1 3434 R GCAAAATGAGACTCAAAAAGAGATTGCTGGCATTC 1158 GTTGGTTTCATGGTTTGGTCTAACTTTACCGCTAC 3964 1 2818 F GTTGGTTTCATGGTTTGGTCTAACTTTACCGCTAC 76 GTGTCAAAAACGATAAACCAACCATCAGCATGAGC 3996 1 4551 R GCTCATGCTGATGGTTGGTTTATCGTTTTTGACAC 204 GATGCTGCTTCTGGTGTGGTTGATATTTTTCATTG 3686 1 3795 F GATGCTGCTTCTGGTGTGGTTGATATTTTTCATGG 143 GAAAAATATCAACCACACCAGAAGCAGCATCAGTG 3982 1 3791 R CACTGATGCTGCTTCTGGTGTGGTTGATATTTTTC 460 GAGATTCAGTACCTTAACGCTAAAGGTGCTTTGAC 3944 1 1895 F GAGATTCAGTACCTTAACGCTAAAGGTGCTTTGAC -29 GCAATCTCTTTTTGAGTCTCATTTTGCTTCTCGTT 3221 1 3426 R GCCGAGATGCAAAATGAGACTCAAAAAGAGATTGC 1071 GGTAATGGTGGGTTTCTTCATTTCATTCAGATGGG 3201 1 2548 F GGTAATGGTGGTTTTCTTCATTGCATTCAGATGGA -215 GCTGAGGGGTTGACCAAGCGAAGCGCGGTAGGTTT 3841 1 2341 F GCTGAGGGGTTGACCAAGCGAAGCGCGGTAGGTTT -223 GTTGACCAAGCGAAGCGCGGTAGGTTTTCTGCTTA 3806 1 2349 F GTTGACCAAGCGAAGCGCGGTAGGTTTTCTGCTTA -21 GATACATCTGTCAACGCCGCTAATCAGGTTGTTTC 3858 1 2581 F GATACATCTGTCAACGCCGCTAATCAGGTTGTTTC -425 GCATCAAAAGCAATATCAGCACCAACAGAAACAAC 4059 1 2608 R GTTGTTTCTGTTGGTGCTGATATTGCTTTTGATGC 939 GGTAAAGTTAGACCAAACCATGAAACCAACATAAA 4097 1 2813 R TTTATGTTGGTTTCATGGTTTGGTCTAACTTTACC -260 GTTAGGCCAGTTTTCTGGTCGTGTTCAACAGACCT 3943 1 1771 F GTTAGGCCAGTTTTCTGGTCGTGTTCAACAGACCT -502 GCTACCTGTAGGAAGTGTCCGCATAAAGTGCACCG 3955 1 4777 R CGGTGCACTTTATGCGGACACTTCCTACAGGTAGC -786 GAACAGCATCGGACTCAGATAGTAATCCACGCTCT 3853 1 333 R AGAGCGTGGATTACTATCTGAGTCCGATGCTGTTC -15 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 GTTTCCAGTCCGTTCAGTTGTTGCAGTGGAATAGT 4018 1 2181 F GTTTCCAGTCCGTTCAGTTGTTGCAGTGGAATAGT -415 GTTGTCAGCGTCATAAGAGGTTTTACCTCCAACTG 3746 1 1665 R CATTTGGAGGTAAAACCTCTTATGACGCTGACAAC 26 GTGTGGTTGATATTTTTCATGGTATTGATAAAGCT 4081 1 3808 F GTGTGGTTGATATTTTTCATGGTATTGATAAAGCT 97 GACGATGTAGCTTTAGGTGTCTGTAAAACAGGTGC 4009 1 2479 R GCACCTGTTTTACAGACACCTAAAGCTACATCGTC -194 GAAGCAATACCGCCAGCAATAGCACCAAACATAAA 3990 1 2928 R TTTATGTTTGGTGCTATTGCTGGCGGTATTGCTTC 452 GTAAAGGACGGTTGTCAGCGTCATAAGAGGTTTTA 3949 1 1675 R TAAAACCTCTTATGACGCTGACAACCGTCCTTTAC -387 GACCGACTCCAAACAATTTAGACATGGCGCCACCA 3617 1 2972 R TGGTGGCGCCATGTCTAAATTGTTTGGAGGCGGTC -20 GACAGCTTGGTTTTTAGTGAGTTGTTCCATTCTTT 3877 1 5070 R AAAGAATGGAACAACTCACTAAAAACCAAGCTGTC -369 GCTGAGGGGTTGACCAAGCGAAGCGCGGTAGGTTT 3841 1 2341 F GCTGAGGGGTTGACCAAGCGAAGCGCGGTAGGTTT -223 GAGCGTGGATTACTATCTGAGTCCGATGCTGTTCA 3829 1 334 F GAGCGTGGATTACTATCTGAGTCCGATGCTGTTCA 3 GCGACAGCTTGGTTTTTAGTGAGTTGTTCCATTCT 3870 1 5072 R AGAATGGAACAACTCACTAAAAACCAAGCTGTCGC -308 GTACGGATTGTTCAGTAACTTGACTCATGATTTCT 3939 1 383 R AGAAATCATGAGTCAAGTTACTGAACAATCCGTAC -275 TTGCTGCCATCTCAAAAACATTTGGACTGCTCCGC 3923 1 1486 F TTGCTGCCATCTCAAAAACATTTGGACTGCTCCGC 22 GACTCCTTCTGTTGATAAGCAAGCATCTCATTTTG 3973 1 3513 R CAAAATGAGATGCTTGCTTATCAACAGAAGGAGTC 53 GCAGAATCAGCGGTATGGCTCCTCTCCTATTTGCG 3086 1 3728 F GCAGAATCAGCGGTATGGCTCTTCTCATATTGGCG -346 GAGGCTCTTAAACCTGCTATTGAGGCTTTTTGCAT 3360 1 4041 F GAGGCTCTTAAACCTGCTATTGAGGCTTGTGGCAT 525 GACCCTAAATTTTTTGCCTGTTTGGTTCGCTTTGA 3902 1 2644 F GACCCTAAATTTTTTGCCTGTTTGGTTCGCTTTGA -305 GCATGAAGTAATCACGTTCTTGGTCAGTATGCAAA 4026 1 1606 R TTTGCATACTGACCAAGAACGTGATTACTTCATGC 86 GCTTCTTGGGAAGTAGCGACAGCTTGGTTTTTAGT 3890 1 5087 R ACTAAAAACCAAGCTGTCGCTACTTCCCAAGAAGC -589 GGCGTGAAGTCGCCGACTGAATGCCAGCAATCTCT 3802 1 3452 R AGAGATTGCTGGCATTCAGTCGGCGACTTCACGCC 513 GTTGAACGGCGTCGCGTCGTAACCCAGCTTGGTAA 3856 1 5203 R TTACCAAGCTGGGTTACGACGCGACGCCGTTCAAC -408 GACTTCACGCCAGAATACGAAAGACCAGGTATATG 4013 1 3476 F GACTTCACGCCAGAATACGAAAGACCAGGTATATG -233 GTATGGCAACTTGCCGCCGCGTGAAATTTCTATGA 3859 1 1960 F GTATGGCAACTTGCCGCCGCGTGAAATTTCTATGA -170 GATTTGGAGGCATGAAGACATACAATTGGGAGGGT 3774 1 3932 R ACCCTCCCAATTGTATGTTTTCATGCCTCCAAATC -426 TTGGCGCATAATCTCGGAAACCTGCTGTTGCTTGG 3866 1 3589 R CCAAGCAACAGCAGGTTTCCGAGATTATGCGCCAA 8 GATGTAGCTTTAGGTGTCTGTAAAACAGGTGCCGA 4029 1 2476 R TCGGCACCTGTTTTACAGACACCTAAAGCTACATC 162 GTACAGCTAATGGCCGTCTTCATTTCCCTTCGGTG 3334 1 4747 F GTACAGCTAATGGCCGTCTTCATTTCCATGCGGTG -302 GCGCTTCGCTTGGTCAACCCCTCAGCGGCTGCAAT 3119 1 2332 R ATTTTTGCCGCTGAGGGGTTGACCAAGCGAAGCGC -685 GAATGCAATGCGACAGGCTCATGCTGATGGTTGGT 3862 1 4535 F GAATGCAATGCGACAGGCTCATGCTGATGGTTGGT -174 GACTTTTATTTCTCGCCATAATTCAAACTTTTTTT 3963 1 2403 F GACTTTTATTTCTCGCCATAATTCAAACTTTTTTT -167 GGGGACGAAAAATGGTTTTTAGAGAACGAGAAGAC 4060 1 4338 F GGGGACGAAAAATGGTTTTTAGAGAACGAGAAGAC -28 GCGGTTATCCATCTGCTTATGGAAGCCAAGCATTG 3986 1 4096 R CAATGCTTGGCTTCCATAAGCAGATGGATAACCGC -311 GCTGAATAGCAAAGCCTCTACGCGATTTCATAGTG 3888 1 4493 R CACTATGAAATCGCGTAGAGGCTTTGCTATTCAGC -119 GCTTCCATAAGCAGATGGATAACCGCATCAAGCTC 3965 1 4105 F GCTTCCATAAGCAGATGGATAACCGCATCAAGCTC -105 GGAGGCTTTTTTATGGTTCGTTCTTATTACCCTTC 3845 1 3969 F GGAGGCTTTTTTATGGTTCGTTCTTATTACCCTTC -321 GACGCTGACAACCGTCCTTTACTTGTCATGCGCTC 3770 1 1688 F GACGCTGACAACCGTCCTTTACTTGTCATGCGCTC -601 GCGTTTATGGTACGCTGGACTTTGTAGGATACCCT 3622 1 562 F GCGTTTATGGTACGCTGGACTTTGTGGGATACCCT -622 GACTTTGTAGGATACCCTCGCTTTCCTGCTCCTGT 3476 1 579 F GACTTTGTGGGATACCCTCGCTTTCCTGCTCCTGT 104 GATTGGTTTCGCTGAATCAGGTTATTAAAGAGATT 4082 1 2864 F GATTGGTTTCGCTGAATCAGGTTATTAAAGAGATT -255 GCAAGAGCAGAAGCAATACCGCCAGCAATAGCAGC 3606 1 2937 R GGTGCTATTGCTGGCGGTATTGCTTCTGCTCTTGC 791 GAGGCTTGCGTTTATGGTACGCTGGACTTTGTAGG 3573 1 555 F GAGGCTTGCGTTTATGGTACGCTGGACTTTGTGGG -59 GCAAGCACATCACCTTGAATGCCACCGGAGGCGGC 3899 1 3012 R GCCGCCTCCGGTGGCATTCAAGGTGATGTGCTTGC -348 GAACAAGCGCAAGAGTAAACATAGTGCCATGCTCA 3898 1 1837 R TGAGCATGGCACTATGTTTACTCTTGCGCTTGTTC -204 TAATGCTTGGGAGCGTGCTGGTGCTGATGCTTCCT 3804 1 3314 F TAATGCTTGGGAGCGTGCTGGTGCTGATGCTTCCT -29 GACGACAAATCTGCTCAAATTTATGCGCGCTTCGA 3918 1 5324 F GACGACAAATCTGCTCAAATTTATGCGCGCTTCGA -440 GCTTGGAAAGATTGGTGTTTTCCCTAATAGACGCA 3793 1 3560 R TGCGTCTATTATGGAAAACACCAATCTTTCCAAGC -425 GGCAAAACTGCGTAACCGTCTTCTCGTTCTCTAAA 3834 1 4355 R TTTAGAGAACGAGAAGACGGTTACGCAGTTTTGCC -149 GTTGAACGGCGTCGCGTCGTAACCCAGCTTGTTAA 3608 1 5203 R TTACCAAGCTGGGTTACGACGCGACGCCGTTCAAC -478 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 GTTTCTATGTGGCTAAATACGTTAACAAAAAGTCA 4178 1 5002 F GTTTCTATGTGGCTAAATACGTTAACAAAAAGTCA -484 GGACACTTCCTACAGGTAGCGTTGACCCTAATTTT 3822 1 4792 F GGACACTTCCTACAGGTAGCGTTGACCCTAATTTT -172 GCCGCCAAAACGTCGGCTACAGTAACTTTTCCCAG 3794 1 5280 R CTGGGAAAAGTTACTGTAGCCGACGTTTTGGCGGC -283 GCTGAGGGTCAGTGGTATCGTTATGCGCCTTCGTA 3841 1 2039 F GCTGAGGGTCAGTGGTATCGTTATGCGCCTTCGTA 103 GATTTGTCATTGTGAGCATTTTCATCCCGAAGTTG 4011 1 5144 R CAACTTCGGGATGAAAATGCTCACAATGACAAATC -304 GTTTAGATATGAGTCACATTTTGTTCATGGTAGAG 4101 1 276 F GTTTAGATATGAGTCACATTTTGTTCATGGTAGAG 31 GAACCATAAAAAAGCCTCCAAGATTTGGAGGCATG 3986 1 3953 R CATGCCTCCAAATCTTGGAGGCTTTTTTATGGTTC -482 GACTCCTTCTGTTGATAAGCAAGCATCTCATTTTG 3973 1 3513 R CAAAATGAGATGCTTGCTTATCAACAGAAGGAGTC 53 GAGAAAGAGTAGAAATGCCACAAGCCTCAATAGCA 3960 1 4055 R TGCTATTGAGGCTTGTGGCATTTCTACTCTTTCTC 322 GAGGCTTTTTTATGGTTCGTTCTTATTACCCTTCT 3876 1 3970 F GAGGCTTTTTTATGGTTCGTTCTTATTACCCTTCT -66 GTTTTCTGCTTAGGAGTTTAATCATGTTTCAGACT 4012 1 2372 F GTTTTCTGCTTAGGAGTTTAATCATGTTTCAGACT -365 GTGCTATGGCTAAAGCTGGTAAAGGACTTCTTGAA 3971 1 3151 F GTGCTATGGCTAAAGCTGGTAAAGGACTTCTTGAA -36 GATTCTCTTGTTGACATTTTAAAAGAGCGTGGATT 4090 1 310 F GATTCTCTTGTTGACATTTTAAAAGAGCGTGGATT 500 GCGCTACTGCAAAGGATATTTCTAATGTCGTCACT 3893 1 3760 F GCGCTACTGCAAAGGATATTTCTAATGTCGTCACT -228 GATGGTATTGGCTCTAATTTGTCTAGGAAATAACC 4065 1 3885 F GATGGTATTGGCTCTAATTTGTCTAGGAAATAACC -146 GTTTTCTGCTTAGGAGTTTAATCATGTTTCAGACT 4012 1 2372 F GTTTTCTGCTTAGGAGTTTAATCATGTTTCAGACT -365 GTGTCAATCCTGACGGTTATTTCCTAGACAAATTA 3995 1 3899 R TAATTTGTCTAGGAAATAACCGTCAGGATTGACAC -426 GTTTGGTTCGCTTTGAGTCTTCTTCGGTTCCGACT 3896 1 2663 F GTTTGGTTCGCTTTGAGTCTTCTTCGGTTCCGACT -359 GTTTTTAGTGAGTTGTTCCATTCTTTAGCTCCTAG 4020 1 5061 R CTAGGAGCTAAAGAATGGAACAACTCACTAAAAAC -104 GTGAAAAAGCGTCCTGCGTGTAGCGAACTGCGATG 3908 1 4898 R CATCGCAGTTCGCTACACGCAGGACGCTTTTTCAC -447 GCTTGCAGACCCATAATGTCAATAGATGTGGTAGA 3996 1 1559 R TCTACCACATCTATTGACATTATGGGTCTGCAAGC -184 GGATTTTATTGGTATCAGGGTTAATCGTGCCAAGA 4114 1 1327 R TCTTGGCACGATTAACCCTGATACCAATAAAATCC 238 GTCATTTCTTTGATTTGGTCATTGGTAAAATACTG 4125 1 3651 R CAGTATTTTACCAATGACCAAATCAAAGAAATGAC -127 GATTCAGCGAAACCAATCCGCGGCATTTAGTAGCG 3908 1 2847 R CGCTACTAAATGCCGCGGATTGGTTTCGCTGAATC -586 GTTATAGATATTCAAATAACCCTGAAACAAATGCT 4109 1 1365 R AGCATTTGTTTCAGGGTTATTTGAATATCTATAAC -126 GCGCTTTAAAATAGTTGTTATAGATATTCAAATAA 4175 1 1381 R TTATTTGAATATCTATAACAACTATTTTAAAGCGC -321 CTCTACTGTAGACATTTTTACTTTTTATGTCCCTC 3947 1 1186 F CTCTACTGTAGACATTTTTACTTTTTATGTCCCTC 313 GATGTTTTCCGTTCTGGTGATTCGTCTAAGAAGTT 4029 1 1997 F GATGTTTTCCGTTCTGGTGATTCGTCTAAGAAGTT -141 GCTGAGGGGTTGACCAAGCGAAGCGCGGTAGGTTT 3841 1 2341 F GCTGAGGGGTTGACCAAGCGAAGCGCGGTAGGTTT -223 GTAATGTCTAAAGGTAAAAAACGTTCTGGCGCTCG 4008 1 845 F GTAATGTCTAAAGGTAAAAAACGTTCTGGCGCTCG -495 GCCTGCAACGTACCTTCAAGAAGTCCTTTACCAGC 3908 1 3165 R GCTGGTAAAGGACTTCTTGAAGGTACGTTGCAGGC -390 GTTAGCCTCGGTACGGCCAGGCATCCACGGCGCTT 3562 1 1410 R AAGCGCCGTGGATGCCTGACCGTACCGAGGCTAAC -469 GAGAAAGCTCAGTCTCAGGAGGAAGCGGAGCAGTC 3939 1 1510 R GACTGCTCCGCTTCCTCCTGAGACTGAGCTTTCTC -156 GATAAGCAAGCATCTCATTTTGTGCATATACCTGG 3921 1 3500 R CCAGGTATATGCACAAAATGAGATGCTTGCTTATC -578 GGTTTATCGTTTTTGACACTCTCACGTTGGCTGAC 3958 1 4567 F GGTTTATCGTTTTTGACACTCTCACGTTGGCTGAC -157 GACACCCTCCCACTTGTATGTTTTCATGCCTCCAA 3586 1 3929 F GACACCCTCCCAATTGTATGTTTTCATGCCTCCAA -620 GATGGTGGTTATTATACCGTCAAGGACTGTGTGAC 3984 1 2743 F GATGGTGGTTATTATACCGTCAAGGACTGTGTGAC 26 TGGTTGACGCCGGATTTGAGAATCAAAAAGAGCTT 4039 1 3358 F TGGTTGACGCCGGATTTGAGAATCAAAAAGAGCTT 462 GAGCCATACCGCTGATTCTGCGTTTGCTGATGAAC 3868 1 3714 R GTTCATCAGCAAACGCAGAATCAGCGGTATGGCTC 582 GTATCCTTTCCTTTATCAGCGGCAGGCTTGCCTCC 3409 1 3246 R GGTGGCAAGTCTGCCGCTGATAAAGGAAAGGATAC -509 CAGAACGTGAAAAAGCGTCCTGCGTGTAGCGAACT 3870 1 4904 R AGTTCGCTACACGCAGGACGCTTTTTCACGTTCTG -432 GGCTTCCATAAGCAGATGGATAACCGCATCAAGCT 4003 1 4104 F GGCTTCCATAAGCAGATGGATAACCGCATCAAGCT -128 GGGTACGCAATCGCCGCCAGTTAAATAGCTTGCAA 3890 1 4834 F GGGTACGCAATCGCCGCCAGTTAAATAGCTTGCAA -292 GATATTTTTCATGGTATTGATAAAGCTGTTGCCGA 4098 1 3816 F GATATTTTTCATGGTATTGATAAAGCTGTTGCCGA 407 GCTCGTTATGGTTTCCGTTGATGCCATCTCAAAAA 3759 1 1469 F GCTCGTTATGGTTTCCGTTGCTGCCATCTCAAAAA 88 GTGTCCGCATAAAGTGCACCGCATGGAAATGAAGA 4040 1 4763 R TCTTCATTTCCATGCGGTGCACTTTATGCGGACAC -123 GTCATACTATCAAAATATAACGTTGACGATGTAGC 3746 1 2503 R GCTACATCGTCAACGTTATATTTTGATAGTTTGAC -198 GCGTCCATCTCGAAGGAGTCGCCAGCGATAACCGG 3891 1 1103 R CCGGTTATCGCTGGCGACTCCTTCGAGATGGACGC -412 GTTCGTTTTCCGCCTACTGCGACTAAAGAGATTCA 4040 1 1868 F GTTCGTTTTCCGCCTACTGCGACTAAAGAGATTCA -558 GAAAAATGGTTTTTAGAGAACGAGAAGACGGTTAC 4070 1 4344 F GAAAAATGGTTTTTAGAGAACGAGAAGACGGTTAC -586 GATTTCATAGTGGAGGCCTCCAGCAATCTTGAACA 3951 1 4470 R TGTTCAAGATTGCTGGAGGCCTCCACTATGAAATC -562 GTCCATATCTGACTTTTTGTTAACGTATTTAGCCA 4040 1 5011 R TGGCTAAATACGTTAACAAAAAGTCAGATATGGAC -280 GGCCGTTTGAATGTTGACGGGATGAACATAATAAG 4077 1 637 R CTTATTATGTTCATCCCGTCAACATTCAAACGGCC -4 GAAGTTAACACTTTCGGATATTTCTGATGAGTCGA 4013 1 25 F GAAGTTAACACTTTCGGATATTTCTGATGAGTCGA -194 GAGAAAGCTCAGTCTCAGGAGGAAGCGGAGCAGTC 3939 1 1510 R GACTGCTCCGCTTCCTCCTGAGACTGAGCTTTCTC -156 GCTGTTCAACCACTAATAGGTAAGAAATCATGAGT 4077 1 361 F GCTGTTCAACCACTAATAGGTAAGAAATCATGAGT -145 GAATATCCTTAAGAGGGCGTTCAGCAGCCAGCTTG 3929 1 4391 R CAAGCTGGCTGCTGAACGCCCTCTTAAGGATATTC -43 GCGCCAAATGCTTACTCAAGCTCAAACGGCTGGTC 3906 1 3617 F GCGCCAAATGCTTACTCAAGCTCAAACGGCTGGTC -110 GAAGGTACGTTGCAGGCTGGCACTTCTGCCGTTTC 3731 1 3183 F GAAGGTACGTTGCAGGCTGGCACTTCTGCCGTTTC -406 GACTGCCTATGATGTTTATCCTTTGGATGGTCGCC 3646 1 2706 F GACTGCCTATGATGTTTATCCTTTGAATGGTCGCC 44 GATGCCCAGAGATTAGAGCGCATGACAAGTAAAGG 4029 1 1703 R CCTTTACTTGTCATGCGCTCTAATCTCTGGGCATC -637 GCCGTGGATGCCTGACCGTACCGAGGCTAACCCTA 3804 1 1414 F GCCGTGGATGCCTGACCGTACCGAGGCTAACCCTA -535 GTGATTATCTTGCTGCTGCATTTCCTGAGCTTAAT 3943 1 3283 F GTGATTATCTTGCTGCTGCATTTCCTGAGCTTAAT -207 GATTTCTTACCTATTAGTGGTTGAACAGCATCGGA 4122 1 355 R TCCGATGCTGTTCAACCACTAATAGGTAAGAAATC -60 GTCTGGAAACGTACGGATTGTTCAGTAACTTGACT 3910 1 393 R AGTCAAGTTACTGAACAATCCGTACGTTTCCAGAC 29 GCTGAATTGTTCGCGTTTACCTTGCGTGTACGCGC 3854 1 741 F GCTGAATTGTTCGCGTTTACCTTGCGTGTACGCGC -537 GACGCCGGATTTGAGAATCAAAAAGAGCTTACTAA 3995 1 3363 F GACGCCGGATTTGAGAATCAAAAAGAGCTTACTAA 287 GCAACCTGTGACGACAAATCTGCTCAAATTTATGC 3930 1 5315 F GCAACCTGTGACGACAAATCTGCTCAAATTTATGC -418 GGTTGGTTGTGGCCTGTTGATGCTAAAGGTGAGCT 3752 1 4938 F GGTTGGTTGTGGCCTGTTGATGCTAAAGGTGAGCC -187 GTTCTTGGTCAGTATGCAAATTAGCATAAGCAGCT 4084 1 1591 R AGCTGCTTATGCTAATTTGCATACTGACCAAGAAC -90 GGCTAAAGCTGGTAAAGGACTTCTTGAAGGTACGT 3977 1 3158 F GGCTAAAGCTGGTAAAGGACTTCTTGAAGGTACGT -203 GCAATAATGTTTATGTTGGTTTCATGGTTTGGTCT 3622 1 2804 F GCAATAACGTTTATGTTGGTTTCATGGTTTGGTCT -159 GGTAACGCTGCATGAAGTAATCACGTTCTTGGTCA 3899 1 1615 R TGACCAAGAACGTGATTACTTCATGCAGCGTTACC -221 GGTAATAAGAACGAACCATAAAAAAGCCTCCAAGA 4054 1 3965 R TCTTGGAGGCTTTTTTATGGTTCGTTCTTATTACC -312 GCTTCTGACGTTCGTGATGAGTTTGTATCTGTTAC 3933 1 4218 F GCTTCTGACGTTCGTGATGAGTTTGTATCTGTTAC 146 GTTTCAGGGTTATTTGAATATCTATAACAACTATT 4059 1 1372 F GTTTCAGGGTTATTTGAATATCTATAACAACTATT -103 GTCTAACTTTACCGCTACTAAATGCCGCGGATTGG 4007 1 2835 F GTCTAACTTTACCGCTACTAAATGCCGCGGATTGG -623 GCCTGACCGTACCGAGGCTAACCCTAATGAGCTTA 3864 1 1423 F GCCTGACCGTACCGAGGCTAACCCTAATGAGCTTA -57 GCAATAGCACCAAACATAAATCACCTCACTTAAGT 3957 1 2913 R ACTTAAGTGAGGTGATTTATGTTTGGTGCTATTGC -445 GGAGCTAAAGAATGGAACAACTCACTAAAAACCAA 4045 1 5064 F GGAGCTAAAGAATGGAACAACTCACTAAAAACCAA -354 GGAGTTTTATGATAATCCCAATGCTTTGCGACACT 3141 1 4613 F GGCGTTTTATGATAATCCCAATGCTTTGCGTGACT -496 GGAGTAGTTGAAATGGTAATAAGACGACCAATCTG 4013 1 1070 R CAGATTGGTCGTCTTATTACCATTTCAACTACTCC 61 GTTGTGGCCTGTTGATGCTAAAGGTGAGCCGCTTA 3966 1 4943 F GTTGTGGCCTGTTGATGCTAAAGGTGAGCCGCTTA -453 GATGCTGGTATTAAATCTGCCATTCAAGGCTCTAA 3979 1 3075 F GATGCTGGTATTAAATCTGCCATTCAAGGCTCTAA 146 GTTTTCTGGTCGTGTTCAACAGACCTATAAACATT 4078 1 1780 F GTTTTCTGGTCGTGTTCAACAGACCTATAAACATT 224 GTGGAGGCCTCCAGCAATCTTGAACACTCATCCTT 3780 1 4461 R AAGGATGAGTGTTCAAGATTGCTGGAGGCCTCCAC -659 GCACGATTAACCCTGATACCAATAAAATCCCTAAG 3980 1 1332 F GCACGATTAACCCTGATACCAATAAAATCCCTAAG -287 GTGAGTTGTTCCATTCTTTAGCTCCTAGACCTTTA 3908 1 5054 R TAAAGGTCTAGGAGCTAAAGAATGGAACAACTCAC -237 GCCAGTTAAATAGCTTGCAAAATACGTGGCCTTAT 3939 1 4849 F GCCAGTTAAATAGCTTGCAAAATACGTGGCCTTAT -447 GTTCATGGTAGAGATTCTCTTGTTGACATTTTAAA 4014 1 298 F GTTCATGGTAGAGATTCTCTTGTTGACATTTTAAA 538 GCACGATTAACCCTGATACCAATAAAATCCCTAAG 3980 1 1332 F GCACGATTAACCCTGATACCAATAAAATCCCTAAG -287 GACTGCTCCGCTTCCTCCTGAGACTGAGCTTTCTC 3812 1 1510 F GACTGCTCCGCTTCCTCCTGAGACTGAGCTTTCTC -213 GCTTAATAGAGGCCAAAGCGGTCTGGAAACGTACG 3999 1 413 R CGTACGTTTCCAGACCGCTTTGGCCTCTATTAAGC -557 GATTGCTGAGGGTCAGTGGTATCGTTATGCGCCTT 3845 1 2035 F GATTGCTGAGGGTCAGTGGTATCGTTATGCGCCTT -539 GCTGGTTCTCACTTCTGTTACTCCAGCTTCTTCGG 3851 1 2445 F GCTGGTTCTCACTTCTGTTACTCCAGCTTCTTCGG -335 TTGTTTGGAGGCGGTCAAAAAGCCGCCTCCGGTGG 3906 1 2991 F TTGTTTGGAGGCGGTCAAAAAGCCGCCTCCGGTGG 80 GACTGTGTGACTATTGACGTCCTTCCTCGTACGCC 3575 1 2767 F GACTGTGTGACTATTGACGTCCTTCCCCGTACGCC 302 GTTCGTCAAGGACTGGTTTAGATATGAGTCACATT 3964 1 261 F GTTCGTCAAGGACTGGTTTAGATATGAGTCACATT -390 GTTTGGTGCTATTGCTGGCGGTATTTTTTCTTCTC 3112 1 2933 F GTTTGGTGCTATTGCTGGCGGTATTGCTTCTGCTC 183 GTTTTTTACCTTTAGACATTACATCACTCCTTCTG 3782 1 832 R CGGAAGGAGTGATGTAATGTCTAAAGGTAAAAAAC -331 GCTGAATTTACGGAAAACATTATTAATGGCGTCGA 4056 1 690 F GCTGAATTTACGGAAAACATTATTAATGGCGTCGA 244 GTATTAAGGATGAGTGTTCAAGATTGCTGGAGGCC 4015 1 4456 F GTATTAAGGATGAGTGTTCAAGATTGCTGGAGGCC -433 GATTTCGATTTTCTGACGAGTAACAAAGTTTGGAT 4115 1 486 F GATTTCGATTTTCTGACGAGTAACAAAGTTTGGAT -128 GTTGATGGAACTGACCAAACGTCGTTAGGCCAGTT 3908 1 1748 F GTTGATGGAACTGACCAAACGTCGTTAGGCCAGTT 666 GCAGCATCAGTGACGACATTAGAAATATCCTTTGC 3891 1 3768 R GCAAAGGATATTTCTAATGTCGTCACTGATGCTGC 61 GTTTCTTTGTTCCTGAGCATGGCACTATGTTTACT 3957 1 1824 F GTTTCTTTGTTCCTGAGCATGGCACTATGTTTACT 115 GCCATAATTCAAACTTTTTTTCTGATAAGCTGGTT 4015 1 2417 F GCCATAATTCAAACTTTTTTTCTGATAAGCTGGTT -161 GCCGAGGGTCGCAAGGCTACTGATTTCCACGCCGT 2853 1 4680 F GCCGAGGGTCGCAAGGCTAATGATTCACACGCCGA -150 TATGGTTTCCGTTGCTGCCATCTCAAAAACATTTG 4009 1 1475 F TATGGTTTCCGTTGCTGCCATCTCAAAAACATTTG 260 GAAAACCACCATTACCAGCATTAACCGTCAAACTA 3978 1 2529 R TAGTTTGACGGTTAATGCTGGTAATGGTGGTTTTC 270 GTGCCAAGAAAAGCGGCATGGTCAATATAACCAGT 3914 1 1301 R ACTGGTTATATTGACCATGCCGCTTTTCTTGGCAC -306 GCTTAGGAGTTTAATCATGTTTCAGACTTTTATTT 3929 1 2379 F GCTTAGGAGTTTAATCATGTTTCAGACTTTTATTT -40 GTTGGCGCTCTCCGTCTTTCTCCATTGCGTCGTGG 3791 1 1139 F GTTGGCGCTCTCCGTCTTTCTCCATTGCGTCGTGG 102 GCAGTAGGCGGAAAACGAACAAGCGCAAGAGTAAA 4022 1 1853 R TTTACTCTTGCGCTTGTTCGTTTTCCGCCTACTGC -113 GATGCGGTTATCCATCTGCTTATGGAAGCCAAGCA 3989 1 4099 R TGCTTGGCTTCCATAAGCAGATGGATAACCGCATC -458 GCTTATCAGAAAAAAAGTTTGAATTATGGCGAGAA 4172 1 2412 R TTCTCGCCATAATTCAAACTTTTTTTCTGATAAGC -2 GCACCTGTTTTACAGACACCTAAAGCTACATCGTC 3947 1 2479 F GCACCTGTTTTACAGACACCTAAAGCTACATCGTC -131 GCTCATGCTGATGGTTGGTTTATCGTTTTTGACAC 3951 1 4551 F GCTCATGCTGATGGTTGGTTTATCGTTTTTGACAC 100 GCTAAATACGTTAACAAAAAGTCAGATATGGACCT 4045 1 5013 F GCTAAATACGTTAACAAAAAGTCAGATATGGACCT -2 GTTTCTGTTGGTGCTGATATTGCTTTTTATGCCGA 3746 1 2611 F GTTTCTGTTGGTGCTGATATTGCTTTTGATGCCGA 653 GGCCTCATCAGGGTTAGGAACATTAGAGCCTTGAA 3994 1 3097 R TTCAAGGCTCTAATGTTCCTAACCCTGATGAGGCC -176 GAATTGGCACAATGCTACAATGTGCTCCCCCAACT 3878 1 4269 F GAATTGGCACAATGCTACAATGTGCTCCCCCAACT -407 GAATCAGGTTATTAAAGAGATTATTTGTCTCCAGC 4020 1 2877 F GAATCAGGTTATTAAAGAGATTATTTGTCTCCAGC 140 GATTGCTGGAGGCCTCCACTATGAAATCGCGTAGA 3942 1 4477 F GATTGCTGGAGGCCTCCACTATGAAATCGCGTAGA -459 GCGACAGCTTGGTTTTTAGTGAGTTGTTCCATTCT 3870 1 5072 R AGAATGGAACAACTCACTAAAAACCAAGCTGTCGC -308 GTTATAGAGATTCAAATAACCGTGAACCACCTGCT 2799 1 1365 R AGCATTTGTTTCAGGGTTATTTGAATATCTATAAC -302 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA -138 1 2652 R ATTTTTTGCCTGTTTGGTTCGCTTTGAGTCTTCTT -194 GAGTCATTTCTTTGATTTGGTCATTGGTAAAATAC 4122 1 3653 R GTATTTTACCAATGACCAAATCAAAGAAATGACTC -343 GCAAGCATCTCATTTTGTGCATATACCTGTTCTTT 3620 1 3495 R AAAGACCAGGTATATGCACAAAATGAGATGCTTGC -595 GGGTCTGCAAGCTGCTTATGCTAATTTGCATACTG 3891 1 1582 F GGGTCTGCAAGCTGCTTATGCTAATTTGCATACTG -202 GCAGTCGGGAGGGTAGTCGGAACCGAAGAAGACTC 3895 1 2677 R GAGTCTTCTTCGGTTCCGACTACCCTCCCGACTGC -184 GAGTCTTCTTCGGTTCCGACTACCCTCCCGACTGC 3851 1 2677 F GAGTCTTCTTCGGTTCCGACTACCCTCCCGACTGC -289 GAATCGTTAGTTGATGGCGAAAGGGCGCAAAGTAA 3828 1 174 R TTACTTTGCGACCTTTCGCCATCAACTAACGATTC -561 GTAAATTCAGCGCCTTCCATGATGAGACAGGCCGT 3956 1 666 R ACGGCCTGTCTCATCATGGAAGGCGCTGAATTTAC -210 GCATACTGACCAAGAACGTGATTACTTCATGCAGC 3925 1 1609 F GCATACTGACCAAGAACGTGATTACTTCATGCAGC -60 GCGCATGACAAGTAAAGGACGGTTGTCAGCGTCAT 3901 1 1686 R ATGACGCTGACAACCGTCCTTTACTTGTCATGCGC -441 GGTAAAGGACTTCTTGAAGGTACGTTGCAGGCTGG 3936 1 3168 F GGTAAAGGACTTCTTGAAGGTACGTTGCAGGCTGG -260 GTGATTTGCAAGAACGCGTACTTATTCGCCACCAT 3904 1 2133 F GTGATTTGCAAGAACGCGTACTTATTCGCCACCAT -292 GAAATTTCTATGAAGGATGTTTTCCTTTCTGGTGA 3697 1 1982 F GAAATTTCTATGAAGGATGTTTTCCGTTCTGGTGA -394 GACGGTTGTCAGCGTCATAAGAGGTTTTTCCTCTA 3383 1 1669 R TGGAGGTAAAACCTCTTATGACGCTGACAACCGTC -171 GCAAAATGAGACTCAAAAAGAGATTGCTGGCATTC 3976 1 3434 F GCAAAATGAGACTCAAAAAGAGATTGCTGGCATTC 1198 GTTCGTTCTTATTACCCTTCTGAATGTCACGCTGA 4037 1 3984 F GTTCGTTCTTATTACCCTTCTGAATGTCACGCTGA -36 GGGTTAGGAACATTAGAGCCTTGAATGGCAGATTT 3931 1 3087 R AAATCTGCCATTCAAGGCTCTAATGTTCCTAACCC -123 GCAGTAGCGCCAATATGAGAAGAGCCATACCGCTG 3898 1 3735 R CAGCGGTATGGCTCTTCTCATATTGGCGCTACTGC -76 GCAGCAACGGAAACCATAACGAGCATCATCTTGAT 3879 1 1458 R ATCAAGATGATGCTCGTTATGGTTTCCGTTGCTGC 648 GAACGTTTTTTACCTTTAGACATTACATCACTCCT 3972 1 836 R AGGAGTGATGTAATGTCTAAAGGTAAAAAACGTTC -175 GGTTGGTTTATCGTTTTTGACACTCTCACGTTGGC 3945 1 4563 F GGTTGGTTTATCGTTTTTGACACTCTCACGTTGGC -168 GCCTCATCAGGGTTAGGAACATTAGAGCCTTGAAT 3955 1 3096 R ATTCAAGGCTCTAATGTTCCTAACCCTGATGAGGC 44 GGTCAGGCATCCACGGCGCTTTAAAATAGTTGTTA 3864 1 1396 R TAACAACTATTTTAAAGCGCCGTGGATGCCTGACC -283 GCTTTCATGCCTCCAAATCTTGGAGGCTTTTTTAT 3640 1 3948 F GTTTTCATGCCTCCAAATCTTGGAGGCTTTTTTAT -45 GCTACTGCAAAGGATATTTCTAATGTCGTCACTGA 3980 1 3762 F GCTACTGCAAAGGATATTTCTAATGTCGTCACTGA 13 GCACCAGAAACAAAACTAGGGGCGGCCTCATCAGG 3857 1 3120 R CCTGATGAGGCCGCCCCTAGTTTTGTTTCTGGTGC 470 GTTTCTATGTGGCTAAATACGTTAACAAAAAGTCA 4178 1 5002 F GTTTCTATGTGGCTAAATACGTTAACAAAAAGTCA -484 GCAGATTTAATACCAGCATCACCCATGCCTACAGT 3896 1 3060 R ACTGTAGGCATGGGTGATGCTGGTATTAAATCTGC -219 TCCATTGCGTCGTGGCCTTGCTATTGACTCTACTG 3877 1 1159 F TCCATTGCGTCGTGGCCTTGCTATTGACTCTACTG -181 TATCAGTATTTTTGTGTGCCTGAGTATGGTACAGC 4071 1 4719 F TATCAGTATTTTTGTGTGCCTGAGTATGGTACAGC -48 GTGAGCATTTTCATCCCGAAGTTGCGGCTCATTCT 3839 1 5133 R AGAATGAGCCGCAACTTCGGGATGAAAATGCTCAC -324 GTATTAAATCTGCCATTCAAGGCTCTAATGTTCCT 3946 1 3082 F GTATTAAATCTGCCATTCAAGGCTCTAATGTTCCT 73 GTTGAGTTCGATAATGGTGATATGTATGTTGACGG 4040 1 4173 F GTTGAGTTCGATAATGGTGATATGTATGTTGACGG 165 GACTCATCAGAAATATCCGAAAGTGTTAACTTCTG 3958 1 23 R CAGAAGTTAACACTTTCGGATATTTCTGATGAGTC -383 GAAGGATGTTTTCCGTTCTGGTGATTCGTCTAAGA 3955 1 1993 F GAAGGATGTTTTCCGTTCTGGTGATTCGTCTAAGA -266 GCCACTTCTCCTCATCCAACGCGTCAGTTTTTGAC 3868 1 210 R GTCAAAAACTGACGCGTTGGATGAGGAGAAGTGGC -288 TTTATGGTACGCTGGACTTTGTAGGATACCCTCGC 3570 1 565 F TTTATGGTACGCTGGACTTTGTGGGATACCCTCGC -623 GAATCTCTTCCAAGAGCTTGATGCGGTTATCCATC 3867 1 4118 R GATGGATAACCGCATCAAGCTCTTGGAAGAGATTC -198 GCTCGAGAAGCTCTTACTTTGCGACCTTTCGCCAT 3810 1 161 F GCTCGAGAAGCTCTTACTTTGCGACCTTTCGCCAT -171 GGCAGCAAGAACCATACGACCAATATCACGAAAAT 4003 1 4648 R ATTTTCGTGATATTGGTCGTATGGTTCTTGCTGCC 311 GGCGAGAAATAAAAGTCTGAGACATGATTAAACTC 3708 1 2385 R GAGTTTAATCATGTTTCAGACTTTTATTTCTCGCC -566 GGTTAAATTTAATGTGACCGTTTATCGCAATCTGC 4009 1 2218 F GGTTAAATTTAATGTGACCGTTTATCGCAATCTGC 117 GCTACACGCAGGACGCTTTTTCACGTTCTGGTTGG 3793 1 4909 F GCTACACGCAGGACGCTTTTTCACGTTCTGGTTGG -405 GGATTGGTTTCGCTGAATCAGGTTATTAAAGAGAT 4075 1 2863 F GGATTGGTTTCGCTGAATCAGGTTATTAAAGAGAT -108 GATTAAGCACTCCGTGGACAGATTTGTCATTGTGA 3919 1 5164 R TCACAATGACAAATCTGTCCACGGAGTGCTTAATC -131 GCTACTTCCCAAGAAGCTGTTCAGAATAAGAATGA 3789 1 5105 F GCTACTTCCCAAGAAGCTGTTCAGAATCAGAATGA -95 GAAGAAAACCACCATTACCAGCATTAACCGTCAAC 3730 1 2532 R TTTGACGGTTAATGCTGGTAATGGTGGTTTTCTTC -141 GCGCAAGAGTAAACATAGTGCCATGCTCAGGAACA 3970 1 1831 R TGTTCCTGAGCATGGCACTATGTTTACTCTTGCGC 58 GACAGATGTATCCATCTGAATGCAATGAAGAAAAC 4113 1 2558 R GTTTTCTTCATTGCATTCAGATGGATACATCTGTC -409 TCTTTAGCTCCTAGACCTTTAGCAGCAAGGTCCAT 3992 1 5040 R ATGGACCTTGCTGCTAAAGGTCTAGGAGCTAAAGA 853 GTTGGTTTCATGGTTTGGTCTAACTTTACCCCTAC 3704 1 2818 F GTTGGTTTCATGGTTTGGTCTAACTTTACCGCTAC 107 GCTTTAAAATAGTTGTTATAGATATTCAAATAACC 4176 1 1379 R GGTTATTTGAATATCTATAACAACTATTTTAAAGC -262 ACCTGATTAGCGGCGTTGACAGATGTATCCATCTG 3902 1 2575 R CAGATGGATACATCTGTCAACGCCGCTAATCAGGT -329 GCGCTCGCCCTGGTCGTCCGCAGCCGTTGCGAGGT 3747 1 873 F GCGCTCGCCCTGGTCGTCCGCAGCCGTTGCGAGGT -337 GAAACCAATCCGTGGCCGGTAGCAGCGGTAAAGTT 2590 1 2839 R AACTTTACCGCTACTAAATGCCGCGGATTGGTTTC -397 GGAATAGTCAGGGTAAATTTAATGTGACCGTTTAT 3696 1 2208 F GGAATAGTCAGGTTAAATTTAATGTGACCGTTTAT 211 GGTCTATAGTGTTATTAATATCAATTTGGAGGAGT 3321 1 4292 R GCTCCCCCAACTTGATATTAATAACACTATAGACC -342 GTAGACATTTTTACTTTTTATGTCCCTCATCGTCA 3970 1 1193 F GTAGACATTTTTACTTTTTATGTCCCTCATCGTCA -394 GAAAGGATACTCGTGATTATCTTGCTGCTGCATTT 3802 1 3271 F GAAAGGATACTCGTGATTATCTTGCTGCTGCATTT -170 GTGATGTAATGTCTAAAGGTAAAAAACGTTCTGGC 4066 1 840 F GTGATGTAATGTCTAAAGGTAAAAAACGTTCTGGC 219 GTAAGAAATCATGAGTCAAGTTACTGAACAATCCG 4043 1 380 F GTAAGAAATCATGAGTCAAGTTACTGAACAATCCG -427 GGACATAAAAAGTAAAAATGTCTACAGTAGAGTCA 4076 1 1183 R TGACTCTACTGTAGACATTTTTACTTTTTATGTCC 617 GTTTATCCTTTGGATGGTCGCCATGATGGTGGTTA 3728 1 2719 F GTTTATCCTTTGAATGGTCGCCATGATGGTGGTTA 70 GTCCCCTTCGGGGCGGTGGTCTTTAGTGTTATTAA 3612 1 4309 R TTAATAACACTATAGACCACCGCCCCGAAGGGGAC -383 GTTCCATCAACATCATAGCCAGATGCCCAGAGATT 3993 1 1724 R AATCTCTGGGCATCTGGCTATGATGTTGATGGAAC -205 GTTTGGTCAGTTCCATCAACATCATAGCCAGATGC 4008 1 1733 R GCATCTGGCTATGATGTTGATGGAACTGACCAAAC -197 GTTATATGGCTGGTGGGTTTTTTTTGGGTTTATTC 1637 1 4987 F GTTATATGGCTGTTGGTTTCTATGTGGCTAAATAC 129 GTTGACGATGTAGCTTTAGGTGTCTGTAAAACAGG 4064 1 2482 R CCTGTTTTACAGACACCTAAAGCTACATCGTCAAC -270 GTTCAACCACTAATAGGTAAGAAATCATGAGTCAA 4087 1 364 F GTTCAACCACTAATAGGTAAGAAATCATGAGTCAA -129 GCGGTAGGTTTTCTGCTTAGGAGTTTAATCATGTT 3951 1 2365 F GCGGTAGGTTTTCTGCTTAGGAGTTTAATCATGTT -111 GGCGTCGCGTCGTAACCCAGCTTGGTAAGTTGGAT 3848 1 5196 R ATCCAACTTACCAAGCTGGGTTACGACGCGACGCC -376 GCTTTGGCCTCTATTAAGCTCATTCAGGCTTCTGC 3920 1 429 F GCTTTGGCCTCTATTAAGCTCATTCAGGCTTCTGC -25 GCTGAATTGTTCGCGTTTACCTTGCGTGTACGCGC 3854 1 741 F GCTGAATTGTTCGCGTTTACCTTGCGTGTACGCGC -537 GAACAAAATGTGACTCATATCTAAACCAGTCCTTG 3939 1 267 R CAAGGACTGGTTTAGATATGAGTCACATTTTGTTC -6 GTCATTGTGAGCATTTTCAGCCCGAAGTTGCGGCT 3641 1 5139 R AGCCGCAACTTCGGGATGAAAATGCTCACAATGAC -518 ShortRead/inst/extdata/Data/C1-36Firecrest/Bustard/s_1_0001_prb.txt0000644000175400017540000055000013001230551025433 0ustar00biocbuildbiocbuild -40 -40 -40 40 -40 -40 -40 40 -40 -40 40 -40 -40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 40 -40 40 -40 -40 40 -40 -40 -40 -40 -40 -40 40 -40 -40 40 -40 -40 -40 -40 40 -40 -40 40 -40 40 -40 -40 -40 -40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 40 40 -40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 -40 -40 -40 40 -40 -40 40 -40 -40 -40 -40 40 40 -40 -40 -40 -40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 40 -40 -40 40 -40 -40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 40 -40 -40 40 -40 -40 -40 -40 40 -40 -40 -40 40 -40 40 -40 -40 -40 40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 -40 40 -40 -40 -40 -40 -40 40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 -40 40 40 -40 -40 -40 -40 -40 40 -40 40 -40 -40 -40 -40 40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 -40 -40 -40 40 40 -40 -40 -40 -40 40 -40 -40 40 -40 -40 -40 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40 -40 -40 40 -40 -40 40 -40 -40 -40 40 -40 -40 2 -40 -2 -40 -40 40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 -40 -7 7 -40 32 -40 -40 -32 -40 10 -10 -40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 -31 31 -40 -40 40 -40 -40 -40 -40 40 -40 -23 23 -40 -40 -40 -40 40 -40 -38 13 -13 -40 -14 -40 14 -40 -40 -40 40 40 -40 -40 -40 -40 -40 40 -40 -40 -40 -40 40 40 -40 -40 -40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 -40 40 -40 -40 40 -40 -40 -40 -40 40 -40 40 -40 -40 -40 40 -40 -40 -40 -40 -40 40 40 -40 -40 -40 -40 -40 -40 40 -40 -40 -40 40 -40 40 -40 -40 -40 -40 -40 40 -40 -40 40 -40 40 -40 -40 -40 -40 -40 -40 40 -40 -40 40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 40 -40 -40 40 -40 -40 -40 -40 -40 40 -40 -40 40 -40 -40 40 -40 -40 -40 -40 40 -40 -40 -40 -40 40 -40 -40 40 -40 -40 -40 40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 -40 -40 13 -13 -40 -40 40 -40 40 -40 -40 -40 -40 -40 40 -40 40 -40 -40 -40 -40 -40 40 -40 40 -40 -40 -40 40 -40 -40 -40 -40 40 -40 -40 -40 -40 -40 40 -40 -40 40 -40 40 -40 -40 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40 -40 -40 -40 40 -40 -40 -40 40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 -40 40 -40 -40 -18 18 -40 -40 -40 40 -40 -40 -40 -40 -40 40 -40 40 -40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 -40 -40 40 40 -40 -40 -40 40 -40 -40 -40 -40 -40 40 -40 35 -40 -40 -35 40 -40 -40 -40 -40 40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 -40 -40 -9 9 40 -40 -40 -40 -40 40 -40 -40 -40 -40 -40 40 -40 40 -40 -40 -40 40 -40 -40 -40 -40 -40 40 -40 -40 22 -22 40 -40 -40 -40 -40 5 -40 -5 -40 -40 -40 40 -40 -40 40 -40 -40 -40 -40 40 -40 40 -40 -40 40 -40 -40 -40 -40 -40 40 -40 40 -40 -40 -40 -40 -40 40 -40 40 -40 -40 -40 -40 -40 -40 40 -40 -40 40 -40 40 -40 -40 -40 -40 -40 -40 40 40 -40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 -40 -40 40 40 -40 -40 -40 40 -40 -40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 40 -40 -40 40 -40 -40 -40 -40 40 -40 -40 40 -40 -40 -40 40 -40 -40 -40 -40 -40 40 -40 40 -40 -40 -40 -40 -40 40 -40 40 -40 -40 -40 -40 40 -40 -40 -40 -40 -40 40 -40 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TTGTTTTCATGTGATTTTAAAAATGTATTTGTTTGT 1 1 105 517 TCCAAACTGGTAGACAATACAAACATTCTCAAATCT 1 1 101 522 TGCACCTGATAGGGTCTCTGCTCTGAGAGAGGAAGG 1 1 113 530 TATGAGAGTAGCCAATGCCACAAAGACGGTGTGTGT 1 1 105 511 TAGTAGGTGTCCTATTCTGATGCCCAGCACGCCAAG 1 1 121 531 GAGAGAACTGAAAATCACAGAATATGAGAAATAGAC 1 1 112 525 GCAGAGACCCACAACCCAGCCAAGCGGCTCCAGACA 1 1 121 595 GAGATATTTATTGAACACTAACACTCTGTCATGCAA 1 1 113 371 GGTGGAAATAGCAAGCATCCCCTTCTCCGCTTACAT 1 1 89 581 TCCCAACCCTCCCCCTAAGAACAATACTCCTGACTG 1 1 83 580 TCAGAGATGATAAACCTAACCCACAAGAGACTGGAG 1 1 95 513 GGAACAAAACACCCATGGAAGGAGTTACAGAGACAA 1 1 97 540 GACAATAATTTGGTATTTTTAGAGAATGTGCAGGCC 1 1 93 384 GGCCTAGCTTCAAACATGGGAGCAGGGATTCAAACT 1 1 122 247 TCTTTTTTCTTGCTATATTCCACGTCCGACAGTGGA 1 1 115 738 GTGAAAAATGAGAAATGCACACTGAAGGACCTGGAA 1 1 117 468 GGTTTGTACAACCCAGCCCCACAGGCCTTTTCTCTC 1 1 69 533 TATCAGAGTCACTCAAATCCTCACATCAACTTTCTG 1 1 121 586 AATGTGTTTTTCAGTGTAACTCACTCATCTAATATT 1 1 117 578 TGCTTCACTGTGTGCCTCTTTTGCTACTTCCACTAT 1 1 115 365 GATCGGAAGAGCTCGTATGCCGTCTTCTGCTTTGAA 1 1 119 406 GCCTGTCTACTGTGGTATCAGAGTGATTATTTCTAT 1 1 112 238 TTAGCAGGCCTTGATGGCTACTACTTCCTTTCTCTA 1 1 120 597 CATCCCACTGCAGTTAAACCTTGGCAAGCGGATAGG 1 1 119 390 TTTCCTTGCCATATTTCCCGTCCTAAAGTGTGTATT 1 1 108 606 GATGGGTCGTATTTCTCTCTTTCATTACTCATACCA 1 1 96 508 AATTTGACTTTTAGTAATAGCCTTTTTACAACTAAG 1 1 106 347 GATCGGAAGAGCTCGTATGCCGTCTTCTGCTTAGAT 1 1 92 482 TTTGTATATGTATACATGTTAAGAAGATTCTACTTT 1 1 114 415 CTGATTGCTAAATTAGTCCTGCAAATAACGTTATAA 1 1 117 462 CTCCAGTTCCACAGACTCAAGGCTCTTCTGTTCACT 1 1 88 544 ATAGGGACCAAGCAGCTTTTGAGATACTATTTTTTA 1 1 68 580 TTTCACGTTTTTCAGTATTTCGTCATTTTTCAAGTA 1 1 81 571 TTACTTTTAAAAACTTTATTACTCAATAAAGGCTGA 1 1 75 541 GATAAGTGTGGAATTGATATTACGCTGGCAAAAACT 1 1 113 775 TCATCTCTTAAACTGAGGATAATAATACTTACCACA 1 1 122 660 TATGCAGCTAGAGTCAAGAGCTCAGGGGTACTGGTT 1 1 115 329 GGGGACTGTGGGTGTAGCTCACTTGTTGCAGTGCTT 1 1 118 518 CTGAAAATCATGGAAAATGAGAAACATCCACTTGAC 1 1 120 704 GAACCAGACTCCTGGCAGAAGTTGTGTTCCACTCAC 1 1 105 427 TTCGAGACATTGCTTCTTTCTATATCTCTTTGTGTT 1 1 110 692 GCAGAGACGGTTCCAGGAGAGTTCACCAACTTGTGT 1 1 98 349 TTTCAGTTTTCTCGCCTTATTCCATGTCCTACAGTG 1 1 120 488 CAATATAGACACATTAGACTAGAGTTAATGCATCAG 1 1 120 749 TTGGATTAGAATGGAATCCAATGGAATCAACTGGAA 1 1 115 889 GAGAAAATGCCCCACAGCTGGATCTCCTGGAGGCAT 1 1 98 629 TTGCATTCAAAGGGGATGAGCAAATATTCTTCCTTG 1 1 120 446 ATCCATTCCTCTGTTGAGGGACATCTGGGTTCTTTC 1 1 115 762 ATAGCCTCTTAGGTTAGGCGGGCCTAGGCCTCTTTA 1 1 99 723 GTACATTTTCTTTTCTGTACAGGTACCAACTGCATG 1 1 80 439 TTCCTCAAATTTAATAACAAAATTAAACACATCAGC 1 1 120 897 TTAGGGTGGATTTTGTGAGACTGAGAAAGAAATTGT 1 1 113 628 TGTAACATAGACAGAGTTGAAAGTTAGAGTCTGCTA 1 1 121 143 TAACATTGTGTATCCTATGCCATTTGCCTGGAGTGG 1 1 101 353 TGAGTTTTTCTCTTAGAAATGCTTTCATTGTGTCCC 1 1 122 581 GCTAGGTTCTGCCATTCAACAACAATAAACATGTCT 1 1 97 502 TGGGATTGGTTTGGGGTCAGCTCATGTGCTTCAGGT 1 1 95 455 GCTGTGGCCAACATTCATTTGTCATGGAACCTGACA 1 1 100 681 TGAAACAGAGGCAGAAACGAGTATCGGGCGGTGCTG 1 1 122 300 TAGTAGTTGGTCGATAGTAGTCTGGGCAAACTTTTT 1 1 103 604 GAGTCGTGGATCCAGCTATGCAGGAGGCTCACAGCA 1 1 121 544 CCCTACTGCAGGAAGGAGCTGCAGCTCATTATTCAG 1 1 105 377 TGTCACCACTGTGTCAAGAGCTGTTGTACTGTACAG 1 1 109 610 CAAGAATCAAACGAATAGATGCTAAGCAAGAGCTCT 1 1 96 412 TAGCACAACATTAAATAACTCATAATTATATGACAT 1 1 88 444 ATTATCCAAGTGAACAAAGTTGACATCATCAGTATC 1 1 123 424 CTGAGAGGAGACACCCAAGAGCTACGACTTAGTGTA 1 1 98 361 GTGGGCAGGAGTTTTGTGGGTGATGGGTTCCTGAGT 1 1 119 458 TCCTTGCATAGGTGTTGGCAGCCGAGGAGAATGTGG 1 1 121 629 GCAGATATTACTTGAACTCTAGCTCAGACATTTATG 1 1 112 446 GATCCCGTGGGGAGTCCCGTGTGGGCCCTTGCGGGT 1 1 112 379 AATGATACCAATATGCTTGCCCTTGGTCAGTTCATC 1 1 113 631 GGCATAGCCTCCAACACAGGGAGGTTAGGCACCCAG 1 1 118 565 GGCTGGCTCAAGATCAGAAGCCGCTCTGGACGTTTG 1 1 89 533 GAATTCAAAGGAATCATCATCGAAGGGAATCGAATG 1 1 106 886 GAGAAACCCTCTGTGGCACAATGCAATTAGAATCCT 1 1 109 291 GGGATTACCAGTGTGAATTCACAGCAGCAGATGACA 1 1 93 629 TTCTCATTTTTCACGTTTTTTAGTGATTTCGTCATT 1 1 80 481 GATCGGAAGAGCTCGTATGCCGTCTTCTGCTTAGAT 1 1 109 568 ACAAAAGCGGCAGAGGATGGCCGTGGTGGCTATCAT 1 1 113 482 GATCGGAAGAGCTCGTATGCCGTCTTCTGCTTGAAA 1 1 79 878 GTCATTTTTCAAGTCTTCAAGTGGATGTTTCTTTTT 1 1 98 479 TTGCTCCTGATTCCTTGACCACCCCCACCCAAACAC 1 1 75 217 TTCCATCATCATCACCCTTGTTTAGGCCTTGCCCAT 1 1 100 525 AAAAAATGGATTAAGCCAGCAAGAGAAAGGAGATGA 1 1 113 326 TTCTTTTCCCTCCCTGCCCTAGCCAGGGCTGGTTAG 1 1 85 733 TTTTAGATTGGTAACTTGAAATATTATAGGGCATAG 1 1 111 862 GATCGGAAGGAGCTCGTATGCCGTCTTCTGCTTGGA 1 1 87 618 TTGTCTGTGCCAGCACAGAGAAAGCAACCTGAGCAT 1 1 83 277 TAGACTGCTGCCTAGCAAGCCTTAAGGATTCTTCTC 1 1 103 414 GGGTAGGCTACAGTGGAGGAACAGGCAAAGACCACC 1 1 93 494 GAGTGACAAAGAAGAGAGGGCAGGACAGGCCTGAAA 1 1 65 610 GTCTTATCCATAACTCCTAGAACTTCTATTGACATA 1 1 102 717 GGCCTACGTTGACTTCTGGTACTGAGGTTAAGAAAC 1 1 104 595 CCGGAGTTTCAGTGATCAGAGTACTCTCTGCAGGCA 1 1 118 675 TCCATGGGGTCCTTGGTGCTTTTAAAGCACACCCCC 1 1 107 489 ACAGACAAACTCTTTCTAAAAATAAAAACAAAACAT 1 1 72 446 TGCAAGAAGTGGAATACAAAACAAAGGCTTAGAATG 1 1 85 233 TCATGATAAAATTATTTTATAGATTAGGGATTCAAG 1 1 106 462 AATAAATCTTTAAGCAAAAGTGGCTGGTTTTTTTTT 1 1 82 588 GCTGAGGTCCGAGCTAGGTTTGGACTATCAATCAGA 1 1 80 334 TGAGTGAAAAGGTTTGTGTGTGGGTTGATGTTCTTA 1 1 117 484 TCCCACAGCAGAACAGGATGAGGGGATTTGTAAGTG 1 1 104 475 TCAACCCCCATATTCAGCTGTAAGTAGGTATGACGA 1 1 102 619 GTAAGGAAGGCATTCTTCTCAGAACAACCTAAGATA 1 1 98 190 GTGTATGCAAATGAAGAGTTTCCAGAGGTCAAGATC 1 1 115 189 GTACACACAGATTTGGGGAGACAGCTGCACGGGTCA 1 1 88 462 CATCCCTTTGTCTCCCTGAGTGTGAGCTGCTTTCTG 1 1 113 784 GACTTAGAACCAGGAAGCTGCCTGCCTTGTCATGTT 1 1 107 478 GATCGGAAGAGCTCGTATGCCGGTCTTCTGCTTAGA 1 1 102 248 TCGAGATTATGGTCAGCGTCGAAGAACGCCGCGAGG 1 1 107 310 TCCGCAAATAGCTTGTTTTCTGTGACCGTTTTGGCT 1 1 117 503 CATCTGAAAGTTCCAACCACCCCAACCACTAGACTT 1 1 465 808 GAAAAGAAAATTAAGATGAAAATTTAACTTGCAATA 1 1 87 313 TTTATAATATAATCTTTTATCACATAGATAAATAAA 1 1 90 286 TACACAATTTAGGACGTGAAATATGGCAAGGAAAAC 1 1 88 396 TGTGAGTAGAAGTAGTAGAAGAGAAGCTGTTGTAAT 1 1 81 453 TTAGTTAAAAAAAAATATTTTTTTTTAATTTTATTA 1 1 88 479 CTAGTCCTTTGCCAGTGGTAATCTAGGCATTGAGAT 1 1 120 534 AGCTGCGAAGGAAATGAGCAAATATCCCCCAAGGAT 1 1 111 505 GACTAATAATAAATAAATAAAAAAAATCTACTTAAA 1 1 120 648 GGCGTGATTCTGCTGGCCATTGCCATCAAGCTGTTT 1 1 89 450 GATCGGAAGAGCTCGTATGCCGTCTTCTGCTTGTAT 1 1 122 166 GCAAATACAGGAAGAACCAAGGGAATGGAGGAGAAG 1 1 82 533 TGTGTCTTATTTGTCTCTGCTCCCTTGGGCACCAAG 1 1 109 730 GATCGGAAGAGCTCGGTCTGCCGTCTTCTGCTTCTA 1 1 94 520 ACTGTAAAGCATTCTTGCCTAGAAAATGATAGGATA 1 1 112 852 TGAAACTAAAGCTACAATAAACTTATATAACACAAT 1 1 92 550 ACCCCTCCATCACTCTGAGGCTGACCAGAAGTATCT 1 1 104 584 GATCGGAAGAGCTCGTATGCCGTCTTCTGCTTAAAT 1 1 123 436 AAAATGAGAAACATCCACTTGACTCCTTGAAAAATG 1 1 109 580 ATCTTGGATGCCCTACCCCCCTGTCCTCATCCTCTG 1 1 102 549 CTGAGCTGCCTGGAATTAGAACAAGGAATATTTTAT 1 1 121 318 TGGGCGATGATTAGATCTCACCCACTGCGATTAAAG 1 1 119 279 TTGCGATCCTCCTGTCTCTGCCTCCTCTACAGTCCA 1 1 89 424 GCCCTCTCTGCTACTAGATGGGCCAACAAGCAGCTG 1 1 82 804 TTCTGGGTCTTCAATTCTATTCCATTGGTCTACTTT 1 1 90 304 TTCCGCCAGAGTTCCAGGAGGATGCAGGAGGATGGC 1 1 107 425 CAAATGGTTTTAACACTTGAGCCATCTCTTTGCCCT 1 1 110 408 ATGTAAACCACAGTAGAAGTTTGTGAATCTCCCATC 1 1 103 853 TTTTTCTAGGAGTTATTAAAAATACATGCTTAAGTA 1 1 115 514 CTTTCACCTTCCAAGGGCTGTGAAACCACCATTCCC 1 1 114 521 AAATGGGATTGTGTTCGTGATTTCGCTCTCAACATG 1 1 99 337 TAAGGCTGGTCTTAAACAGCAACAAAAACAACGGAA 1 1 118 632 GAGCGGAAGAGCTCGTATGCCGCAATCTCCTTCTAT 1 1 91 364 TAGGGACATGGGAGCCATCTTGACAAGTCTGCAAAG 1 1 114 400 TCCCAACGATCTTCATCTTACTCCGAAATCTAATTT 1 1 85 487 GGCCTCCCAGGAGATCTACTGCAGCCAGGGCAACAT 1 1 113 509 CTCCATTCTAGAGCCTGATTCCAGTTGCACCCACTG 1 1 104 907 GTTAGCATGAAATGTCATGGCAGCTGGATTGACAAA 1 1 123 467 CTATCTGGCACTACAAGAATTAAAATTTAAAAATAA 1 1 74 589 TGAGTTCTTTGGTATTTGGGTTGCTTCACTCAGGAT 1 1 100 564 AACAAGGCAGGAGTTCTGTAGTACTGGGCAGCGAGC 1 1 116 569 TAGCAACACCATAAAAAGTCGTCTAAAAAAGGCACA 1 1 79 401 GCCTCTAAGCACTTGAAATCCATTCAAAGGATCCTT 1 1 98 771 GGTTTTTCAAGACAGGGTTTCTCTGTCTAGCTCTGG 1 1 86 761 TGTGTGAGTTCCTTTGTGATTGTGTTACCTCACTCA 1 1 88 393 TAGGTGGACATGACGTATACACTCCAGTCTAAACAT 1 1 84 708 GATCGGAAGAGCTCGTATGCGTCTTCTGCTTAGATC 1 1 113 439 GCAGCAATCATCCGCTCTGCTGATCCGGACGCAGTC 1 1 83 506 AAATCCACTTGACGAATTGAAAAATGACGAAATCAC 1 1 107 128 GAAAATAAACACTATAGTGGAATGGCTTGATTTTTT 1 1 70 568 GGAATGCCTAAGCATTTCCAGCACTCAGACTTTACC 1 1 122 608 GATCACAACAGACAAGACAGATGGGAATCGCCACAG 1 1 97 306 TTTCACCCCAGCAGGGAGAGTGCAGGCAGGTCCTGC 1 1 121 179 GTTGTTATATGTCAGGCAGTGACTGCCCAAGCACAT 1 1 106 507 CAGATGACTGGCCACCTGCCTGCCCTGGAGAGCAGT 1 1 427 635 GAAAAACAAAGTAGGTATTGAAACAAAATTGAATAA 1 1 109 107 GTGGACATCTGATTTTCTGCTTTTACCTTCCAACTA 1 1 116 82 GAATGGGAGAGGTGGTAGGTGGAATTCTGATTAGAG 1 1 64 473 GATCGGAAGAGATCGTATGCCGTCTTCTGCTTGTAT 1 1 107 663 GTCGACTCCGCGATGCTCTCCGCGGCGATTGCCGTG 1 1 89 718 TATGGAAGGCCTGAGGAGTCTTTTGTTGCAGTCTTT 1 1 380 636 GTTAGAAAACAAAAAGGATACACATTTTTCAAGACT 1 1 88 506 TCATAGTTGCTGAGATTTACAGGACCCAAAACAACA 1 1 123 502 TGAGAAGCAACATATGGTTTAGAATAGAATTATCGA 1 1 74 510 AGGAAAAAGACCCTTTACCAAAACTACCTTGTAAAG 1 1 70 476 GTGTAAGATCTATGCAAAGTCGTGAAAACATCTCTA 1 1 118 160 GTTAATTCCCAGTTGGATAGGTAGTACACATGACGC 1 1 737 593 GATTCCTTCATTGAGAATTATCTGTTTTGCTCTGTA 1 1 84 611 CACAACACACCTCTATTAGTCAACAACTACAAATTG 1 1 114 669 CCTAAACTCACCTTAACCCTAATACTAACCTAACCC 1 1 114 570 CGTCGACTCAGTGGTTAGGGCCTACTCAAAATCTGC 1 1 366 851 GAAAAAACTGGCAAGTGTTCTTAATCTTTCCAGCCA 1 1 120 187 TTTTTATAATTGGTTAATCATTTTTTTTTAATTTTT 1 1 99 468 CATGGGAGCTGAGAGCTAAAAACACACAGATCATCA 1 1 116 556 GATCGGAAGAGCTCGTATGGCCGTCTTCTGCTTAGA 1 1 109 150 GGCTAGGAACCAGTGTGCATGCATACCATTCCCGCC 1 1 114 754 GGTCCTCTGCAAGAGCAGTAAGTGCTTTCAGCTGCC 1 1 111 432 GGCAGACAAGGGAGAGGTGAGTAGGATATGGTGGCA 1 1 373 636 GGGTCACATATATACTATCATATCATGTGCAAATAG 1 1 119 991 GTGGCTCAATCTGGCCTTGAACTTCTGATCCTCCAG 1 1 97 872 TCCTCCCTCCTGCTGCCTGTCATTTTGAGTTCATCC 1 1 117 859 CAGGACAGACTTACCATGTCCACTGTACAGACAGAT 1 1 67 557 TGTGTGTGTAGTGTTGTTGATGCTGTTCCCTTGGTT 1 1 452 555 GATGGGTGGGAGCACACAAATTGAAGCAGGGCCGGT 1 1 355 795 GTCCTACAGTGGACATTTCTAAGTATTCCACCATTT 1 1 98 511 ATTTCTGGAGACCCAGGGACAGCGTGATAGATGCCA 1 1 495 692 GACATCTATTCATATGCCTGGGTGTGTTATTTATAG 1 1 129 389 GGGAAGGAAAAATACTGGAATACTGGGTTATAGGAA 1 1 100 552 GCCCCTATGCACTCTAGACGTTGCTATGTCAGCGTG 1 1 106 452 CAGCACAGCCCGATCTCTGAATTACTTCCACTCTGT 1 1 99 487 GTTGAGTTCAGATGCAAATACCGAGTGTGTTATACA 1 1 465 826 GATAAGGAAACTAAAACACAAGTTAAATAACTTGCC 1 1 90 464 TCAGATCACCCAGGCCTATGGTGAGCTTGAAGAGTT 1 1 96 724 CATATCAATTACAGGTCTTTTAGGTTGTAGGCAATA 1 1 94 317 GGGAATCCAGGCAGGGAAGCCATCCGTGACTCTGAA 1 1 100 399 TGGCTCGGGAGCTCGCCTCCCTCGGCCTCCTCCAAG 1 1 488 607 GATCGGAACGAGCTCGCATGCCGTCTTCTTCTTTTA 1 1 73 788 TGTAGATAAGGTGAAAAGGTAAGTGGACTTTTGATT 1 1 103 490 TACACATAGCGATCAGTCACTTTACTAATGGCCTGA 1 1 123 475 CAACCAAAATACCAACAACAACAACAAAATATCCAA 1 1 443 635 GTTCCTGCTTCCAGGTTCCTGCTGTTCCATTGCTGC 1 1 117 798 GAGGTGCATTTCCTGTGTGCAGAAAAATTCTGGGTT 1 1 110 917 TCTGTCTGTCTCTTAGGAGGAAACCCGAGGCGGCAT 1 1 109 882 CCCAGAGTAGAGTCACAATGTATTAATGCCCAGAGT 1 1 81 142 GGTAATTCTTAACCAAGCTTCTATTTATAGGCAGAT 1 1 117 822 TGTACTAATCTCTGTCAAGACAAACTGTAGCATTTC 1 1 116 174 TGATGCTTCCTCCTCAGCTTGCTCCAGTAACATCAA 1 1 108 268 GATCGGAAGAGCTCGTATGCCGTCTTCTGCTTATAT 1 1 83 406 CCCATCCAGTTCCTACCTTAATGTCTGTCCCCATTC 1 1 101 484 CTGGATTTCGAAACGGAATGACAAACGTGTGCGTGC 1 1 483 628 GATCGGAAGAGCTCGTATGCCGTCTTCTGCTTAGAA 1 1 107 469 TTATGAATTATAACATCTACCTACACTTATAATATC 1 1 962 565 GGGATGTTCCAGTCAAGGGAACCAGGGTTTGCTCAA 1 1 118 572 CTATCTTGTTGATTTTCTCGAAGAACCAGCTCCTGG 1 1 121 126 GATCGGAAGAGGTCGTATGCCGTCTTCTGATTTGTT 1 1 111 537 AGAAATGCTACGGAGAAAAAGCCAGCAGGGTAAGGG 1 1 119 908 TCTGCAAGTTGAATCTTTTCCAGTTTTTCTCATATG 1 1 115 562 AAAAATGCCAATGAGGTTTAGAGATACGAGGACCCT 1 1 96 884 GACATCAGACATGGAGATGCAGAGTTTGGAGTTTGC 1 1 123 534 ACAGTCCATCGTTTCACCTGGGCTCTTCTCTATGTT 1 1 99 443 TCAGCCTCATGGAACTTTAAGGCAAACATCCCAGAA 1 1 96 906 TGAGAAGAAGATAATGAGGAAGATTTAAGGTCTGAC 1 1 117 195 GGTGGATTGCGGGTGGGTAATCCCCCCCCACCTCAC 1 1 405 806 GATGTTTCTCATTCTCCATGATTTTCAGTTTTCTTG 1 1 68 425 TACATTCCCTTGGCACAAATGCCAACCTACAAGATC 1 1 113 567 GATCGGAAGAGCTCGTATGCCGTCTTCTGCTTAGAT 1 1 69 594 CTAATGGCGATATGATGTCATGTAGATGCCCAAGCC 1 1 90 379 GACAGGCACTGGATGGCATAGCTCAACCACAGGTGT 1 1 111 977 GCTGGATGCGTCTCAGCAGGGCAACCGCATCCAAGT 1 1 85 701 GGAGCCTGTAGAGGCGGGCAGGGCTTATCGCCTGAG 1 1 116 879 CTCCAGGTGCAGCTGTCCTGAGGCTTCTCCAGGGAG 1 1 753 95 GTCTGACGTTCTTTAAGCAGAGCGATTTCAGCTTTC 1 1 104 223 AATGAGCTTTTGTGTTGAGCTGGGGGGGGTGGCGTG 1 1 91 195 TGGGAGCATGAATTTATTACTGTGGTAGAAGAGCAT 1 1 82 227 TAGGAGTTCTCTGGTGGAATTTTTAGGGTCACTTAT 1 1 78 554 TGGGGAGATGTGGGCCCTGGCTGGATATTTGAGGAG 1 1 101 329 GTGGAGGCTAGCACCTGTTTGTGGCCTTGTGAAGGA 1 1 449 243 GATTTTCAAAGTTAAGGGTAAAAATGTTATCACCCG 1 1 401 703 GAAAATGAGAAACATACAATTGACGACTTGAAAAAT 1 1 101 226 GGTATTTTCCTTTTGTTTTATTTCACTTTGGAGGGC 1 1 239 916 GATAGGAAGAGCTCGTATGCCGTCTTCTGCTTGGAT 1 1 349 63 GAAAAACGTGAAAAATGAGAAATGCACACTGTAGGT 1 1 158 505 GATTCCTTATGTGGTAATGGAAAATAATATTTCATC 1 1 793 122 GGATGAGAAGAATAGTATATTACATCTCTAGCCACA ShortRead/inst/extdata/Data/C1-36Firecrest/Bustard/s_1_1_0001_qseq.txt0000644000175400017540000005203613001230551026047 0ustar00biocbuildbiocbuildHWI-EAS88 1 1 1 972 352 0 1 CTCCTGCCTCAGCCTCCCAAGTAGCT a`aaa`a_P_\X\```a]][X[MRW_ 1 HWI-EAS88 1 1 1 973 431 0 1 TGATATTTTCCTGTCAGACTAGTCCT a`\`_aaaaa_`X_\WSQZ_ZTZ[[_ 1 HWI-EAS88 1 1 1 973 933 0 1 TGACAGCCCGGAAGCGAGGAAAATAA ab`aaa]]abbaaa`_]__X_^]]]a 1 HWI-EAS88 1 1 1 973 557 0 1 TGCTGGGACTGCAGGCATGAAACACC abaa`a`_aaa_]]`]\`Z\]`]YX\ 1 HWI-EAS88 1 1 1 973 159 0 1 CCCTAATGGCAATTTTGTTTATTCAT aaab`W`^``VUabba[_baYab[M] 1 HWI-EAS88 1 1 1 973 151 0 1 TTGTTTGGGGACAACAAAGATGGCAT abb`ba^V[`Z[]Z\UYVWQ[XFUV\ 1 HWI-EAS88 1 1 1 973 6 0 1 TA........................ BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 973 990 0 1 TATGTAAGTGTGTACACGTGCATATA abab`ba`[a`ba`aa`_]a``aaa_ 1 HWI-EAS88 1 1 1 973 540 0 1 TATAATGACTTCTTTTCCTCTGGGTA aaaaaaa_`aa`aaaa_`aa^BBBBB 1 HWI-EAS88 1 1 1 973 483 0 1 AGACCAGGGTCAGGAGGAGAGATGGA abaaaabaa\]O]]^]`XXHXNRUYX 1 HWI-EAS88 1 1 1 973 955 0 1 CTCTGACATCTGGGGCTTCTGATTCC abbbbbbbbbb`Z``a``_`a_a^Y_ 1 HWI-EAS88 1 1 1 974 417 0 1 CTTCTGCCTCAGTCTCCCGGGTCGCT H_[V_XBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 974 280 0 1 AGTCCTCTGCCTAGGAAAACCAGAGA aaaaaaaa^`aa^PUVZVZ]`BBBBB 1 HWI-EAS88 1 1 1 974 776 0 1 TTATACCAGTACCATGCTGTTTTGGT aXa`K\GI^ZW[_YGHUYOQYQSBBB 0 HWI-EAS88 1 1 1 974 744 0 1 ACCACAACCAGCTCCTCAGAGTGTGT aU\^_^_\W\^WX\WS\WYSSKUWBB 1 HWI-EAS88 1 1 1 974 801 0 1 AACTCTACATCATCATACCCATCATG BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 974 656 0 1 GCGCCAGGTTGTGCGCTGAGGCTCCC II__BBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 974 741 0 1 TGTGTCTGCAAAAATGTTTAAAAAGT aa`a_aa_[]`^__``^aa^^_^^QX 1 HWI-EAS88 1 1 1 974 961 0 1 GGGATGACAAATAAAGGAAACAAGGA a[TTKTNWBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 974 687 0 1 TGGTTTTTTTTGTTGTTTTTTTTTTT aaa]aababbaV`a_aaaabaaab]B 1 HWI-EAS88 1 1 1 975 798 0 1 AGCGATCCGTCAGGTGGCACTTTTTG aa_a^]R[`[^^^XTRZZRZ^^Z^[R 1 HWI-EAS88 1 1 1 975 737 0 1 CTGCTACCTCCACGCCCCAAGTTTAA aabaabaaabaabaaa`a``_U^_V] 1 HWI-EAS88 1 1 1 975 22 0 1 CCCTAGCGCCAAGGGTGTCCGCGGCT ^Z___H[I_IGFT_^R]GTVW]IZ\B 0 HWI-EAS88 1 1 1 975 884 0 1 TACTAATTTACATTCCTACCAACAGT ababbbaabbaabaaaaaaaaaa__W 1 HWI-EAS88 1 1 1 975 586 0 1 AACAATGGAAAGGTCATTTCAAGGTC aaaaaa^\W^^]a]`^a`\PZ\WNT[ 1 HWI-EAS88 1 1 1 975 396 0 1 CCTCAGCCTCCTGAGTAGCTGGGACT aaaa[_aaaa`_XX\]]X]\UQSO[] 1 HWI-EAS88 1 1 1 975 346 0 1 TAGTTTTTCCCTGAATTCTGAGCACC a^a_aaaa````P\[_a_`UJMVXTZ 1 HWI-EAS88 1 1 1 975 617 0 1 AGCACTTTGGGAGGCCTAGGCGGGCC aaaaaaaaa_`]TW]^\]X]XUUYBB 1 HWI-EAS88 1 1 1 975 160 0 1 CCCTAATATCCCAAATGTATCCACGT abaDTNYHWbbWBBBBBBBBBBBBBB 1 HWI-EAS88 1 1 1 975 511 0 1 TAGCCCCGCCTCCTCTAGGGTCCCGG `a_\RX^`^`_\^^Q``ZBBBBBBBB 0 HWI-EAS88 1 1 1 976 149 0 1 TGATTCTTCTCTCTTTTCTTCTTTAT aaYaa_aa`a_a_aaaa``a`aa_O` 1 HWI-EAS88 1 1 1 976 472 0 1 CCATGTTGGCCAGGATGGTCTTGATC aa`ab\```a\O\a^a_V]Y]ZGNY] 1 HWI-EAS88 1 1 1 976 490 0 1 ACACTGAAAGGGCAATTTGTCACAAC a`Yaaa^X`aX[]WZ``^X]YOVQRX 1 HWI-EAS88 1 1 1 976 969 0 1 TTCGGCCTCCCAAGGTGCTGGGATTA ababb``aaaa_ab_V`a__`]W_]_ 1 HWI-EAS88 1 1 1 976 109 0 1 GGCTCCACTACTATAGGGGCAATTAC aa`a``X`a_`[N^^```^YNNY[QY 1 HWI-EAS88 1 1 1 976 663 0 1 TTGAACCCAGGAGGTGGAGGCTGCCG aa``a^a^Y^\\`]BBBBBBBBBBBB 1 HWI-EAS88 1 1 1 976 476 0 1 ACTCCTGCTCTCTCATGGGCTTTTCT aabbbaaabbabab`_]WT_aaaaab 1 HWI-EAS88 1 1 1 976 496 0 1 AGAGAGCTTTACAAGCACTCTAGCAG aaaaaaaaaa`_[XZaa`aa]YQZXY 1 HWI-EAS88 1 1 1 976 310 0 1 CCATCTTCACTCCTCCTTTGAGTCCA ab`abaaaaaaaaaaaaa`SUQBBBB 1 HWI-EAS88 1 1 1 976 479 0 1 TGCTTCATCTGTGGGATGCTGAAGGA aaaaaa_aaba_^a[\]Z]a\RNQVR 1 HWI-EAS88 1 1 1 977 21 0 1 AAATGGCGCTTGGGGAAAGAGAGGTC [GZb_[G_`a`[ab[DXVV^BBBBBB 0 HWI-EAS88 1 1 1 977 406 0 1 GAGCGTTACCAGCTGCCTCAAAGTTA a`aab`a`a]Pa`aaaa]_X\`UL]] 1 HWI-EAS88 1 1 1 977 861 0 1 TTGCGCGCGTAGGAGTAATGGCTCGC `aaaaaaa``a```_\_]aa]]]VXB 1 HWI-EAS88 1 1 1 977 266 0 1 TGTAGGTCTCTTATCAATGAAGAACT aa`aaa_^a`aa\a[SY_WSQTSXXa 1 HWI-EAS88 1 1 1 978 312 0 1 TGAGTCTCCAAATCAATCCAATGAAG aa\a_aa_a_[]a_\_aa`YS^ZUXB 1 HWI-EAS88 1 1 1 978 647 0 1 GGTGGGGGGCTGAGAATCAGGCACGA S_T\FY_\`\QXZVEW\ZTXWZBBBB 0 HWI-EAS88 1 1 1 978 159 0 1 CTGCGAAGGCTGCCGGGAGCGGGACT `aaa_\^[`\`^`]U]\P[Y`\BBBB 1 HWI-EAS88 1 1 1 978 641 0 1 TGGTCAGGCTGGTCTCGAACTCCCAA aaa\aa`\a`]Y\^`\QHV\ZTXBBB 1 HWI-EAS88 1 1 1 978 686 0 1 CATTTTGGGAGGCTGAGGCGGGCGGT `_aaa`aaaaa`a\`\`a\a`[BBBB 1 HWI-EAS88 1 1 1 979 550 0 1 CCACGAGTGGAGACCTTCCCACGTGT aa`aaa_V_`]`]^_````]]]\R]W 1 HWI-EAS88 1 1 1 979 920 0 1 TCGCAGTACTGTTGTAATTCATTAAG a`baab]a`aa`ab\Y_b`a^aa`[[ 1 HWI-EAS88 1 1 1 979 789 0 1 CCCCTTTCTGTCACCTGCAGAGCTCC aaaaaaaaa`aa^aaa_`a``_]]`` 1 HWI-EAS88 1 1 1 979 886 0 1 TCTTTATTCTGTTCCCCCCCAACCCC aaaabaaaaa]aa``a__`a^]]_`` 1 HWI-EAS88 1 1 1 979 908 0 1 AGTTTGGAAACAATGATAGACATAAG ab_aaaa_aaa`]`a^\`a\^`]`]Y 1 HWI-EAS88 1 1 1 980 525 0 1 CGCCATTGCGCTCCAGCCTGAGCAAC aaaa`aa^_[_```_Z]\]Z[ZVYT\ 1 HWI-EAS88 1 1 1 980 828 0 1 AATTGTGTTCTGTGAACCTTATCTCA aabbb`baaaaba^`___b``a`a]_ 1 HWI-EAS88 1 1 1 980 404 0 1 CCTTCTCCTTGAGCACTTTCTCTTCT aabbbaaaaaZP]_`abaaaaaaaab 1 HWI-EAS88 1 1 1 980 867 0 1 CAGGCATGAGGCACCGTGCGCGGCCT aaaaaaaaaaaa````\aa`^`]XV\ 1 HWI-EAS88 1 1 1 980 252 0 1 GGTCGGGTGCGGCGGCTCCGGCCTGT aaZa```Z`\BBBBBBBBBBBBBBBB 1 HWI-EAS88 1 1 1 980 648 0 1 ACATGCAGGTGGAGGGGAGGGCTGCG `aa^a_V]XJXVJW]]BBBBBBBBBB 1 HWI-EAS88 1 1 1 980 284 0 1 GATCCCTAAACAGCAGCAAAACGAAA a_baaabaa`a_````a]_[]`\TZY 1 HWI-EAS88 1 1 1 980 678 0 1 AGGGAAGGGTAAAAGGCGAGGCAGGT abbaaaaaaYa``]]__`[^]XQ[BB 1 HWI-EAS88 1 1 1 980 282 0 1 TTACTTACGTAAAAACTTTAGATTAT aba`abaaa_`\]\_aab[YOL_b_b 1 HWI-EAS88 1 1 1 981 215 0 1 GTGGTTACTGGCGCCTGTTATCCCCG __R]H]_VPPX`XO\WZTUU\XBBBB 0 HWI-EAS88 1 1 1 981 351 0 1 CTATCAGAGATGCCGAGAAAGCAGGG ababa`_\]`_]_a]UYXZ[WXKWUU 1 HWI-EAS88 1 1 1 981 632 0 1 ATCCCTCAACAACTGTCCCTAATAGT \^T_a_^W_```a_V^\__VWQ^[RV 1 HWI-EAS88 1 1 1 981 855 0 1 TATTTCTGCGGCCTCTGTTCTGTTCC aaaaaaaaa`__``^a^`b`a^]`[Z 1 HWI-EAS88 1 1 1 981 123 0 1 GCTGGTCTCGAACTCCCGACCTCAGG a``a`^\a`]JO_a^\^XVZ]XPGSW 0 HWI-EAS88 1 1 1 981 551 0 1 CCTGGGAGGCATCTGGGTTCCACCAA aaaaaaa`a_\_a`_`aa_``aa`]U 1 HWI-EAS88 1 1 1 981 785 0 1 GCCAGCCCTGGAGGTAGCTGGGTTTC a^`aa`^^`a]P\]PU\U]`_XM\BB 1 HWI-EAS88 1 1 1 981 769 0 1 GGAGAGGTCTGATCGGTCCAGTGTTG aa^a^aaU^a`[^_`]XXZV]T]ZZT 1 HWI-EAS88 1 1 1 982 94 0 1 AGCAAAGGGTTAAACGAAAAAAAGAC VBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 982 426 0 1 GCAGTGAGCTATGATTGTGCCACTGC a`]a_a``aa`aZ]]_]]ZZVRW[M[ 1 HWI-EAS88 1 1 1 982 414 0 1 TAATGGAAGACGGGAGTTGCAGTGCT a`aaaa_YZ\_]`YRWSX\VOSNVNW 1 HWI-EAS88 1 1 1 982 573 0 1 CTAAATAAGATGGTTAGTACTATAAT aa`]`a```^a^^`a^SZ``^U^[V^ 1 HWI-EAS88 1 1 1 982 341 0 1 GACACATACCCATCTTTTTAGAAAGT a\aaaaaaaaa\aaaaaab[BBBBBB 0 HWI-EAS88 1 1 1 982 997 0 1 TACAGTGAACAGAAAGGTCAGAGCCC abbbbabbbbbbbbbb_T_aaaa^_] 1 HWI-EAS88 1 1 1 982 221 0 1 CAATGGAGAATGAATGGCCTTGAACT aX`b_b_`^`a_V^a_]]`]]WIW]^ 1 HWI-EAS88 1 1 1 982 655 0 1 TCAGGATTCCATGGAGAATATCACAC aaabaaaaaaaaZ]^^^^`_``^^X^ 1 HWI-EAS88 1 1 1 983 404 0 1 GGCAGTGAGCCCCAAGGCTTCCCGTT `aaaaXX\a`aaaWF[\YU_YOEYYX 0 HWI-EAS88 1 1 1 983 204 0 1 ATTAGGCTGGGCACAGTGGCTCCTGC a]\Z\ZZRPZBBBBBBBBBBBBBBBB 1 HWI-EAS88 1 1 1 983 277 0 1 CTACTCAAGCCTCAGGAATGGCGGAC ababbba^aabb`WZ^]W`a]_WYBB 1 HWI-EAS88 1 1 1 984 727 0 1 TCAGGTATATTTTCAGCAGTCTTGGG aaabb_ba`bbbaaaZa```a]\OYY 1 HWI-EAS88 1 1 1 984 703 0 1 CTGATCTATGGCACCTCTCTTCTGGC aab]aaaa`a^aaaaaaa`^`aYHU] 1 HWI-EAS88 1 1 1 984 252 0 1 CTGCAAGGCCAGGCACGGTGGCTCAT a`a`aa`a`aW^a`aa__V[a[XXSS 1 HWI-EAS88 1 1 1 984 980 0 1 CCTGAGCCACTGTGCCTGGCCAGGGG aaaaaaaa`a`a``a_]_][_aaaa` 1 HWI-EAS88 1 1 1 984 591 0 1 TCCATGTCCCTGCAAAGGACATGAAC aaaaaa_baaa^_U_T[\O]X\][[] 1 HWI-EAS88 1 1 1 984 564 0 1 ATATTTCTTGGAGGCTTTGTTCATTC ababbbbba\ZXZY]`b`S\aWD\`U 1 HWI-EAS88 1 1 1 984 379 0 1 AAAGAACTTCAGCAATTGCAGGAAGC aUWYWRZYY[VUVUUUXXBBBBBBBB 1 HWI-EAS88 1 1 1 984 824 0 1 TTGTCTATCAATTTGATTTATCTTTT abbaababa^`aab_^abbab`baaa 1 HWI-EAS88 1 1 1 984 2 0 1 A......................... BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 984 709 0 1 ATATGGAAATCTGAAGGACTTTGAAT aaaab`_aaaaa_``^a^aa``UXZ` 1 HWI-EAS88 1 1 1 984 748 0 1 ATTTTGTTTGGTAGTTAAAACCATTT abbbbbabb`]ab_aaaa`baa`aaa 1 HWI-EAS88 1 1 1 984 64 0 1 CGTGTAATATGAGAGACGCCGTACGT ababaaaa__WPZORDZZ]]`XLT]` 1 HWI-EAS88 1 1 1 985 246 0 1 CCCCACCAGGGAAATGTCTAGTGTCT aaabaaa^``_RS[``]a`ZRX\\\` 1 HWI-EAS88 1 1 1 985 431 0 1 CAATTAATCCAGCTTCTGTAATTTTT aaabbaaba`W]aaab`Q[SY`aaab 1 HWI-EAS88 1 1 1 985 743 0 1 CTGCCCCATATCTGCCACACACACTC abbbaababbab```ba_]`abaa`` 1 HWI-EAS88 1 1 1 985 857 0 1 AGGTCAGGAGTTTGAGACCAGCTTGG abbV`aaa`a^_aa_a\][V^XXRZ^ 1 HWI-EAS88 1 1 1 985 864 0 1 TTTATTACACAACTCTCTTTCACCCT ]_aa`^a]aa^[LWO]LP^ZVV]TZB 1 HWI-EAS88 1 1 1 985 827 0 1 TCCAGCCAATTTCCAGACTGCCAAAG aababab]abbbaa^b]aaaaa]]`` 1 HWI-EAS88 1 1 1 986 925 0 1 ACTTATAAATAGTATATTTGTTTGGC aaaaabaaaaab`aa`aaa`^aaZIY 1 HWI-EAS88 1 1 1 986 277 0 1 GGAGTAACCAGAGTAAGTGAGCTGGA ]a[aZ`__`_^N_WY]XZ]W\T]YRB 1 HWI-EAS88 1 1 1 986 332 0 1 GTGAGCCAGCATGCCAGCCAGCAAAC a`a``a`[a^\]Z]WDWO[Q[\BBBB 1 HWI-EAS88 1 1 1 986 655 0 1 CGGTATTTTCTCCTTACGCATCTGTG abb_bbbbbabaaba`a]a^_``b]] 1 HWI-EAS88 1 1 1 986 580 0 1 GGCATTGGGCTGGGGAAGGAGTTTGG aaa`aaaZa^`^[[SDT[WGWRX^VW 0 HWI-EAS88 1 1 1 986 956 0 1 TTTTCGGAGCTCAAATGCCATGCTGG aaaaabbabaaa``a`baa`]_\___ 1 HWI-EAS88 1 1 1 987 599 0 1 AACCTCTGACTCCGTGGTTCAAGCAA aaaaaaa_aaaa`_][`]`\Q[HZYV 1 HWI-EAS88 1 1 1 987 220 0 1 CAACATGGATCTCGCAGATACCGTCA a^`babb_[`bb`[]M]O]`a_X]\Y 1 HWI-EAS88 1 1 1 987 293 0 1 GCAGATGCTTTTGCACTGCCTGTGCT aa[aXaa_aaa`Z][_WY]`^`^Z][ 1 HWI-EAS88 1 1 1 987 485 0 1 CAACATGGTGAAACCTTATCTCTACT aaaaaaaa^][]_`aaa`aaaa_]]a 1 HWI-EAS88 1 1 1 987 808 0 1 CCCACACACACCAAGCCTGGGCAAGG a`aaaaaaaa`a``a`a````\]]_` 1 HWI-EAS88 1 1 1 987 175 0 1 AGACATAACAAAAAAAAAACAACCAA `HWBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 987 113 0 1 CTCAGCCTCCCAGAGTGCTGGGATTA aaabbaabaa]J][^Waaba`[E\_Z 1 HWI-EAS88 1 1 1 987 81 0 1 ATTCTGTGGGTTTGTTTGTTTTGTCC abbbbba\a_aba\abaX_bb_H_]X 1 HWI-EAS88 1 1 1 987 526 0 1 GCTCTGACCTTTCTGTTCACTGTAGC a`aaaa^^_aaa`aaaa_```]\]TT 1 HWI-EAS88 1 1 1 987 934 0 1 GTGCAGTGATCATAGCTCACTGCAGT a^baab_baaaaaa`_`_]^Y^^__[ 1 HWI-EAS88 1 1 1 988 201 0 1 TTTAATATTTCACAGTTGGTTACAGT aaaYT\S`U_XDXVG\MTO[`BBBBB 0 HWI-EAS88 1 1 1 988 156 0 1 TTTAGCTAGACACAGAGTGCTGATTG aba`a``SX^]X_UWO[Y]``WHX^X 1 HWI-EAS88 1 1 1 988 309 0 1 CGGCGCGCGCCTGCAATTGCAGGCAC a_`a`a`aa```XT\U_a`ZPTYWYY 1 HWI-EAS88 1 1 1 988 772 0 1 CATGTAGATTGATGTGTCATCAGCGT aabbaaa^aba^a_]Z___a[FY``` 1 HWI-EAS88 1 1 1 988 296 0 1 ATGGTAGACACAGAGGTGGACTGTTT aaaa^a_Y_\^PVUXZFWTKTYFU\] 0 HWI-EAS88 1 1 1 989 630 0 1 CCTCTATTTAACTGTACCTCCCCCAC aabbbabababaa^]aaaaaaa^[BB 1 HWI-EAS88 1 1 1 989 94 0 1 GATAAAGGGGGAGAAC.CGCCGGGCA BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 989 517 0 1 CTCCAGCTCCCCACTCAGCAGGTGTA aaaa[aaaaaa]G]`a``]\_XP\YU 1 HWI-EAS88 1 1 1 989 980 0 1 TAGCATCACTGCTTGTGGGATGGCTT abbabbbbabbbbbb`baZa`````^ 1 HWI-EAS88 1 1 1 989 292 0 1 ACTCCTGGCTCACTGCAACCTCCGCC YG]\^`_I]_OO[]]]YFWaa\]T[[ 0 HWI-EAS88 1 1 1 989 356 0 1 AGAATATACTAAAAAATGAGCAGGCA aa`abab`ab_X_`a`a`]^_^T\\\ 1 HWI-EAS88 1 1 1 989 398 0 1 TGTGGCAGAGCACAGCCTGCTTGGAT aa^__^a_V_ZLYU[]_Z]\V_QYM[ 1 HWI-EAS88 1 1 1 989 237 0 1 CCCAAAACAACCAACAAAACAACAAA \_KK_J_HJHJRW_LMSSXL__H\a^ 0 HWI-EAS88 1 1 1 990 109 0 1 TTTCAAGAGTCTCATTTCCTAGTTAA aaaa[_^Ua\aaa_a^a```Z^``\T 1 HWI-EAS88 1 1 1 990 173 0 1 AGAGATTCCTAAATACCGAGGACTCT a`Y]W_a^_`XUT]^\^ZRXSSV\\Y 1 HWI-EAS88 1 1 1 990 123 0 1 TTTTAAGCAATTTTGAAGGCGATTTT abaa__a`^`a_`^HW[T_\]V^`a` 1 HWI-EAS88 1 1 1 990 205 0 1 AGTGCAGTGGTGCGATCTTGGCTCAC a^W`W\YSPZUUUPDNVVTSNS\RRQ 1 HWI-EAS88 1 1 1 990 941 0 1 CCAACTACCTACTCCACACTCCCAAA abbbbbbbbbbbbbbabbbabaaZab 1 HWI-EAS88 1 1 1 990 812 0 1 GTGGTGCTACAGTCCATCAAGTAACA a_aa]a`a``_a\]_[_^][_TZXZ\ 1 HWI-EAS88 1 1 1 990 908 0 1 CGTCTGTACTAAAAATACAAAAATTA abaabbababbbbabbaabaa`a`__ 1 HWI-EAS88 1 1 1 990 993 0 1 TGATTCCACATCTGGAGCTGGGAAAG abaabaabababbbbabaabb^^^ab 1 HWI-EAS88 1 1 1 991 748 0 1 TCAGGGGCTTCCCTGCAGGGAGAAAG aaaaaaaaaa^``^a`]Z_XWYKTPU 1 HWI-EAS88 1 1 1 991 146 0 1 AACCAAAGACATAAATAGATTAAGAA ^RNFSDDQ]BBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 991 535 0 1 AGTTCTCTAACAGCTCTCACCCCTCC ababbbbbabbaabbaa`]``aaaaa 1 HWI-EAS88 1 1 1 991 849 0 1 TTTTTCTTTATTCTTCCTAAGGATGA aaaaaaaaaaaaaaa_aa`_ZWX[RS 1 HWI-EAS88 1 1 1 991 689 0 1 TCATGGGCCTACTCCAGTTTGGCTTT aaaabaa`aaaaa_a``]`[YU]`aa 1 HWI-EAS88 1 1 1 991 400 0 1 TCTCCATGTTGGTCAGCTCGGTCTTT aS`JY]V\[[SBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 992 775 0 1 TGGCGCGATCTCAGCTCACTGCAGCC aaa`aaa]```_]\`a`a`\XUOYY] 1 HWI-EAS88 1 1 1 992 955 0 1 AGGATATTGCCTTATTCAATTTTGGT abbaaaaabaaab`aaa``aaaa^Z_ 1 HWI-EAS88 1 1 1 992 926 0 1 GGAGGACCTGGCCCACTAGGAAGCCC a`]`a\]XV]`YKX]XWU]]\\\QYB 1 HWI-EAS88 1 1 1 992 677 0 1 CATCGATAAACTGCTGAACGTTCTGA aaaabaa``ab`Z_aa_`a_]a`_PV 1 HWI-EAS88 1 1 1 992 823 0 1 AGACTCCTCTCTGGTCCTCTGCTCTT abaaaaaaaaa`]``_`a_`^^`^\` 1 HWI-EAS88 1 1 1 992 714 0 1 GCTTTGGCTTTAATGAGGCTGAGGTG aababb^`babaaa^]ZZZ_\Z^WBB 1 HWI-EAS88 1 1 1 992 694 0 1 AGGCTACCCTGCTCTCATTCCAAAGC abaaaaaYa``^`_a[Q^`_YVX\Z] 1 HWI-EAS88 1 1 1 993 112 0 1 TCCTTACATTTCTCTTTAGTTTATGA a_`aa[_S`aa`a`aaa^^^a_O]BB 1 HWI-EAS88 1 1 1 993 93 0 1 AAGAGCAACAACTGATTTAGGCAATT a^a[`[OV`[\]a_R_^`\[LXSO\_ 1 HWI-EAS88 1 1 1 993 882 0 1 AACAAGAGTACCATGGCCTAACTATG a`^__^]YF\SMLQZYOVPX_BBBBB 1 HWI-EAS88 1 1 1 993 526 0 1 TGGGGGGATCTTGGCTCACTACAACC aaabaa`U^^^\WZ_^]VZ[^YJWX] 1 HWI-EAS88 1 1 1 993 136 0 1 CGGTGATGCGATCTCAGCTCACCGCC a``]`^a_a]Q[`a[QX\`YU\YUBB 1 HWI-EAS88 1 1 1 993 143 0 1 ATATATTGCCATCTAGTGTGTTAGAA abababa]aa]]a_R\`Y```BBBBB 0 HWI-EAS88 1 1 1 993 854 0 1 GGCGGATTAACAATTTAATCTTCAGC aa`ab_a``a`^``aa__`^^_][\] 1 HWI-EAS88 1 1 1 993 506 0 1 GGGGACACGTTGTTTGTAAAACCTAT aaa`^a_`a_aa`aaZY`\XZ]Z]W[ 1 HWI-EAS88 1 1 1 993 124 0 1 CCCCTACTGGAGGCAGCCTCATGATT ababaaaaab[`a_[`]`aYJ]KDX] 1 HWI-EAS88 1 1 1 993 149 0 1 AATTCTTCTTTATGTCCGCTTGTCAC `_bbbbbbbba`aTab`R`b_H_[Z_ 0 HWI-EAS88 1 1 1 994 321 0 1 CTGGAGGGAAACCTCAATTCAACGGA aabbaba_S_^^`a_X\_a]PZBBBB 0 HWI-EAS88 1 1 1 994 91 0 1 TAGGAGTATAATTTTAAAAGCCATTT a_aa\a\_a^U`aaaY]]VSY[Q^`] 1 HWI-EAS88 1 1 1 994 361 0 1 AGAAGTCAAATGAAGAAGGTGTTTGA aa_abaa^aab`^`_[Z^aZZ_a^BB 1 HWI-EAS88 1 1 1 994 996 0 1 CTGTCTCCTGAGCATCTTTCTGACTT abbabba``bbbbbbaa`a`a\_`a^ 1 HWI-EAS88 1 1 1 994 838 0 1 ACAGGCGCCCACCACTACGCCCGGCT a]_a`]`]X]\]^\X\ZR]TJTZRPR 1 HWI-EAS88 1 1 1 994 556 0 1 GTGGCTAAGAAAAGATGTACCACTGT aaabaa``^V^^_`\``]_Z]Z[[XV 1 HWI-EAS88 1 1 1 995 516 0 1 CATCTCCTGACCTCATGATCTGCCCG aababaaaaababb`a_W`a_]__YV 1 HWI-EAS88 1 1 1 995 410 0 1 TGGAGTGCACTGGTGCAATCTCAGCT aaa`aWa``^a`[[a\^^__`[PYU] 1 HWI-EAS88 1 1 1 995 226 0 1 ATGTTTTTATAATTCCAGAAACCGTT aaa_aaaaZ_XY[G[]VTUXXXDPX\ 0 HWI-EAS88 1 1 1 995 632 0 1 AGATACATTTTCTTGACTTGAGTCTC abaaababbbbab`Y]aba^\W_`aa 1 HWI-EAS88 1 1 1 995 1001 0 1 TCTATGGTGGATATGTGCAAGAGGCC aabbbbb`bb``a\]]]Z]`bab\I[ 1 HWI-EAS88 1 1 1 995 27 0 1 CTCGGTCCCCGTGGATGGCGGAGCAA a[JI_aa^IaZMXZX]_VQTBBBBBB 0 HWI-EAS88 1 1 1 995 605 0 1 TAGGACACGGAGCTCGTGGTGACCAG J_aXG[FW_]_S]VBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 996 872 0 1 CTGGTGCGCGGCATACGCGCTTTCTT `aaa`a`aaa`_\X_\_a_]_X_\__ 1 HWI-EAS88 1 1 1 996 450 0 1 ATGGTGCTTACTATGTAACAGGCACT aaaa_abbaZ`aXa_aaa_[TWY\_` 1 HWI-EAS88 1 1 1 996 527 0 1 TTCTTGATGTAAAATTCTTGATTGCT aaabb^_bba_TX_aa`aaZ\a`X]a 1 HWI-EAS88 1 1 1 996 102 0 1 TGGCTAATGAGTGACAGTCTATTCAA a`a`a___]Z[WZTYT\Z^\[\_BBB 1 HWI-EAS88 1 1 1 996 124 0 1 GCTGGCATATCCAGGAGCTGCGAGTC aY^a`XY^^\]YJWXO]BBBBBBBBB 0 HWI-EAS88 1 1 1 996 889 0 1 GTGGAGGGAAGGTCAGCAGATAAACA a\aa^ba_Y]`_S[^_\\]ZTYVZYV 1 HWI-EAS88 1 1 1 996 807 0 1 CCCTCCTCATCCGCATCCCGGTCCTC aaaaaaaaaaaa_aa`aaa``\]]aa 1 HWI-EAS88 1 1 1 996 976 0 1 CTAACCTGGGTATCGGCACGATTAAT aabaZ`bbbb_bbabaaa`a_^____ 1 HWI-EAS88 1 1 1 997 427 0 1 ACTCGGGCCGCGGAGGTTGCAGTGAG aaaaa`]^a`a][P\YJYVVQXMTPU 1 HWI-EAS88 1 1 1 997 172 0 1 CTCTGTCTCAAAAACAAAAACAAAAA abbb_bbba``\Y`a___]]`ZOWZS 1 HWI-EAS88 1 1 1 997 454 0 1 AGACCAGCCTAGCCAACATAGTGCAC aaaaabbabaaa`a[]_T_T_VKIS^ 1 HWI-EAS88 1 1 1 997 570 0 1 ATAGGAGTGTGAATCTTATTGTTAAC abaaa`a`a_[SZaaa`X_^OVFNXU 1 HWI-EAS88 1 1 1 997 217 0 1 GGGTTTCTTCTCAGGCTATCAGAGAT aaa]aa_aa`a]WUI[^S^[VXINV[ 1 HWI-EAS88 1 1 1 997 756 0 1 GCCTGTTATCCCAGCTACTCGGAAGG `J``WQT^a]H[OFTOV^O\BBBBBB 0 HWI-EAS88 1 1 1 997 26 0 1 CAGGGGTGGCGGTGTTCTCCTGTCGT _UTZBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 997 713 0 1 GGCACCAATCCCATTATGCCCCCATG aa`[ab^`aaaa^aaa``a_a`^U[Z 1 HWI-EAS88 1 1 1 998 902 0 1 GGGGCCCCTCTCTGGGGCTGGCTGAG a`aa``aa```\aaa`a]X_`]\`]` 1 HWI-EAS88 1 1 1 998 411 0 1 GTACATTTTTTTTTTTCTTTGTTTTG Ua]HH__XM]`a^[Z[BBBBBBBBBB 0 HWI-EAS88 1 1 1 998 747 0 1 TGTCTGTCTTACTTATACTGTTAAAA abaaab_`aaaaab`aaa_V^a_ZSY 1 HWI-EAS88 1 1 1 998 1 0 1 C......................... BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 998 606 0 1 TTGACCGTATAGCTGACAGGAGTCCC aaa`aaaaaa_]]]V[^Z[[VUXY\Y 1 HWI-EAS88 1 1 1 998 206 0 1 CACTAGCGCTCGCCGCAGCCGTGTAA _T_`aaa`aaa``_Z_a``]BBBBBB 0 HWI-EAS88 1 1 1 998 847 0 1 AAAGGCTGCAGTTTAGTAATATTAAG _X_`WNKZIGFUS]P\O^IX[H^K]S 0 HWI-EAS88 1 1 1 998 424 0 1 AGGCTGCAGTACAATAGCGTGATCTC aabaaaa`baa``_a_a``^]X^]_a 1 HWI-EAS88 1 1 1 998 418 0 1 TTTAGTGGAGACGAGGTTCCAACTTG BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 998 815 0 1 AGAAGAACCACCAAACAAACAAGACC Ka^IHGGTVbYGYKJ`V^Y_XXKO]B 0 HWI-EAS88 1 1 1 998 362 0 1 TTGGGCCATTGTATGTGAACAGATCT aaaa`^`[aa^a`aZ_XNZ][VP\Z_ 1 HWI-EAS88 1 1 1 999 750 0 1 GGCTGAGGCAGGCGCATTACTTGAGG aaaaaaaa`_a`T\TPX_\RQTMMTT 1 HWI-EAS88 1 1 1 999 695 0 1 TTACATTTCCAACTCTGGGTCCCGGA abaaabaa`_abaa``^`a]]^``TY 1 HWI-EAS88 1 1 1 999 768 0 1 TGTCTCCACAATGTCTCACCTGCACC abaabaaaaaaaaaaa___`^]]]_` 1 HWI-EAS88 1 1 1 999 405 0 1 CCACCAGAGCCTGACCTAAATTTGAA aaaabaa^aaaa`__aaa\T^`^XZZ 1 HWI-EAS88 1 1 1 999 743 0 1 AAACAATCCCAAGACATGTCAATTCT a__]V]]a_ZMS]\\TZ[V]YU[Y]^ 1 HWI-EAS88 1 1 1 1000 434 0 1 TCCTTGCCCACCCTTATCCCTAGTAC a_aaa`a\_Z]```_Y^^^_]PPYNV 1 HWI-EAS88 1 1 1 1000 335 0 1 AGAATGTCTCTGTTCTGTCAGTCCTT aa_abbaaba`V_aa]H]`]V^TZ]B 1 HWI-EAS88 1 1 1 1000 635 0 1 AAAAAATACAAAAATCATCTGGGTGT aaaa_T_aa]a^^`_a`_a`]WYV][ 1 HWI-EAS88 1 1 1 1000 149 0 1 AGCCGAGACAGAATACAGGCAACAAA BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1000 278 0 1 CAGGAGAATGGCGTCAACCAGGCGGG a_aaa```aa_a`a]X___\_YBBBB 1 HWI-EAS88 1 1 1 1001 13 0 1 CTGGAT....TGGTAATTCTTTTTTT abb`a\DDDD^\]]W\aa_abbabbb 0 HWI-EAS88 1 1 1 1001 779 0 1 GGGTCTATCTTCAATTCAGAATCATT aaa``bababa]Z]aaaYY\\aaYaa 1 HWI-EAS88 1 1 1 1001 852 0 1 ATGCATTCGGGATTCAAACATTGCCT aaaa`aaaaa`[_a`^_``^`[^[]_ 1 HWI-EAS88 1 1 1 1001 62 0 1 CGAAGAGGTTACACCTTAGACCGACT BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1001 196 0 1 CGCAAACATTCCACATTCGTCGGTTA `GbGFXaGJ\FYM\R_H_L]\INI]^ 0 HWI-EAS88 1 1 1 1001 373 0 1 CAAAGATGCAGAGCATCCGATCGATA BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1001 474 0 1 CAATGGAGCCGAGGAATCGCTCAACA aGbBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1001 817 0 1 TGACTCTCACCCACTCCCCAAAAGCT abaabbbaaa`aaa]aaa`]_`Y`_a 1 HWI-EAS88 1 1 1 1001 26 0 1 AACCA.GAACCGAAACACAACAACCA BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1001 694 0 1 TCACTCTGGGGGGAGTGTGTGTGGCA aaaaaaaa__`^^X\WZ\YPTXYBBB 1 HWI-EAS88 1 1 1 1001 427 0 1 CAAGTGATCCTCCTGCCTCAGCCTCT aabbb`bbbbbbb_X^bb_X\aaabb 1 HWI-EAS88 1 1 1 1001 160 0 1 GATGCCATTTGGTGCTCTTTCTTTTT a_aaa_T`ab_Q]^`baaabaaaabb 1 HWI-EAS88 1 1 1 1002 931 0 1 TTTATGTAGTTGGCCAATAGGTATTT abbbbbabbaa\\aaa`a_\\\_bab 1 HWI-EAS88 1 1 1 1002 9 0 1 .AG..........AC.....AGATCA BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1002 180 0 1 .TTCTGGCTTGTAAGGTTTCTGCTTT DPZZWSSWZWBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1002 735 0 1 .TTATCTCAGCTGGGAGCGGTGGCTC DNUTUTUTTTTSBBBBBBBBBBBBBB 1 HWI-EAS88 1 1 1 1002 172 0 1 .GGCAATTGCATAAGCTTCATCTGGT DNZYYYYWUVWWUUQTVYBBBBBBBB 0 HWI-EAS88 1 1 1 1002 354 0 1 .ACCGCACCCAACGAAAAAAAAGACA BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1002 389 0 1 .TCAACTACTCCTGTTATAGCTTGCG DOYYZYVVWYWQFJTUWVWBBBBBBB 0 HWI-EAS88 1 1 1 1002 498 0 1 .AAACCCAAAAACAAACAAGACAACC BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1002 684 0 1 .CCCTTTCCCCCTTTAGAGGTACCTT DNXXXWXWWWWVXXWVSNJOSRSNTR 1 HWI-EAS88 1 1 1 1002 799 0 1 .CAAGGAAAGCAATAAACAAAAGAAC BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1002 969 0 1 .GTACTGAGCGGGATAATTCGTCGTG BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1003 630 0 1 .ATCCCCTGACCCCTTGCACTTCCCA DNUUTUTSSSSTTSSSSSUSSSSSBB 1 HWI-EAS88 1 1 1 1003 973 0 1 .TCTCCCTATTCCCTCTGGAACTGGA DNVVWXWWWXXXWUXXWVVXWWWWWU 1 HWI-EAS88 1 1 1 1003 149 0 1 .GCGGAGGTTGTAGTGAGCCAAGATC DNVUBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1003 224 0 1 .AATCCTGGGTGAGAAATGTTCTCCA DLUWZZYVPTWQWVUUUVYYYYWWQQ 1 HWI-EAS88 1 1 1 1003 232 0 1 .AAAAAGACAAAAAAAAAAAAAAAAA BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1003 236 0 1 .ATTATCTTCAACTGAAAGCCCCTCA BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1003 269 0 1 .CAAAAACAACCCAGCCAACAAACAA BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1003 341 0 1 .CACGCAAAAAACAGTCTAAAACACA BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1003 400 0 1 .ATAAGGCGGAAA.AGAAGCA.AC.A BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1003 487 0 1 .CACAACACAAAC.TACAAACTAAAA BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1003 806 0 1 .GCAGGCTACAACAACAGCAGCAACA BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1003 196 0 1 .TCACACCTTCCCTTTGCTGCGTTTC DPWXWUWWWWXWWWWRLTWUWSTWUU 1 HWI-EAS88 1 1 1 1003 446 0 1 .CACAGAGATGGACAGCCTTGATACT DPYWVWWVWWVTUTUUVWWUPQSVWW 1 HWI-EAS88 1 1 1 1003 101 0 1 .CAGCTACAAGGCTACCGTGCTCATA BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1003 133 0 1 .CGAAGTAACAACAACACACGACCCA BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1003 321 0 1 .AAACCACAAAGTACACAAGAACACA BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1003 394 0 1 .CAACAACAGAAAACC.CCACAAAAA BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1003 456 0 1 .AGCGAGACTCCGTCTGCAATCCCGG DMSTTTSSSTTUBBBBBBBBBBBBBB 1 HWI-EAS88 1 1 1 1003 906 0 1 .TATGTCTTTGACCGTTGGGTCGTAT BBBBBBBBBBBBBBBBBBBBBBBBBB 0 HWI-EAS88 1 1 1 1003 61 0 1 .TACGCGCTTTCTTCTGATCCAGCCT DPSUWWWWWXWWWWWUNSTWBBBBBB 0 HWI-EAS88 1 1 1 1003 417 0 1 .CCTCCCCTAAAGACCTCTGACCGTG DMUTTTTTTSSRORTTTURNRTBBBB 1 HWI-EAS88 1 1 1 1003 475 0 1 .CATGTTGCCCAGGCTGGTTTCCAAT DPWXVXWWXWTRRTWWWTWWWTRRRS 1 HWI-EAS88 1 1 1 1003 373 0 1 .CAAGTTGCTGATCATCCTTTTTTTT DPXXZZZZZXPNRVVVZYYYYYYXZ[ 1 ShortRead/inst/extdata/Data/C1-36Firecrest/s_1_0001_int.txt0000644000175400017540000077614013001230551024055 0ustar00biocbuildbiocbuild1 1 109 548 409.0 504.5 475.0 11120.8 880.8 3231.2 464.8 7933.4 951.9 3059.5 5077.9 6208.9 497.6 944.8 323.7 6858.3 1009.3 3135.8 279.1 7079.8 2822.2 3229.6 242.2 7284.7 1120.4 3011.4 297.4 6954.9 2745.9 7237.6 307.2 723.4 4052.1 6018.4 333.0 1091.4 2261.5 2687.3 709.2 8650.3 988.6 2745.4 4628.5 7704.0 1037.3 3195.5 763.3 5544.4 1340.8 3376.9 3672.5 6658.9 4058.9 5211.3 607.3 2015.4 1002.3 1519.9 1899.4 7716.5 810.8 1475.7 603.3 8588.1 1867.8 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#&.Xncq•[G?;ir(ʼIx1F͗M|ǐCUv! )BNuNg\LznrEو[*M;*DuT.aB]}80kJ4Nv)W?В,~j,m)Yu#CLY(3{܁q˃F5wǥoIɶ?,òZZKU4mUՐ;`|)-iuNriN;x\1FONNnꝯZpINy3c, .qJMCP"M֐cu ;kt:2 1R^',9ȑk^mU,&tyX&xq8Fq'3L`Q'gLTdGIU?5,WNT=6JDŽKKAeeg/& Ld6M" 3)8 02sn5/2]CS44v|YQ5U[/KߡF*']G'~R `a^ eLw"-0x7<3ʥ '>a^?}I"ZѰ"m dPyϿv^g&@+(9M~W\~~op4MhOcgV`*:$J}EUpEO%Yusse toIwpȗψ QsK@{-uvrWK7?}^ƗOf0f]ƊHSY`>T# ~< BPP]I6D\Y7~|Wpf̖1ǹnk6γcRT^3>ShortRead/inst/extdata/bowtie/0000755000175400017540000000000013001230551017361 5ustar00biocbuildbiocbuildShortRead/inst/extdata/bowtie/s_1_aligned_bowtie.txt0000644000175400017540000034441413001230551023652 0ustar00biocbuildbiocbuildHWI-EAS88_1:1:1:109:548 - chr5 151311501 CAAACAAATACATTTTTAAAATCACATGAAAACAA *777*:::::::::::::::::5:::::::::::: 0 HWI-EAS88_1:1:1:101:522 - chr10 35505988 CTTCCTCTCTCAGAGCAGAGACCCTATCAGGTGCA ,7777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:121:595 - chr16 55311504 TGCATGACAGAGTGTTAGTGTTCAATAAATATCTC 55555::::::::::::::::::::::::::1::: 0 HWI-EAS88_1:1:1:83:580 - chr3 42422486 TCCAGTCTCTTGTGGGTTAGGTTTATCATCTCTGA 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:95:513 + chr4 94552390 GGAACAAAACACCCATGGAAGGAGTTACAGAGACA :::::::::::::::::::::::::::3::41777 7759 HWI-EAS88_1:1:1:97:540 - chr15 77506543 GCCTGCACATTCTCTAAAAATACCAAATTATTGTC 24477.:::::7:7::::::::::::::::::::: 0 HWI-EAS88_1:1:1:115:738 + chr2 98506779 GTGAAAAATGAGAAATGCACACTGAAGGACCTGGA :::::::::::::::::::::::::::1::72/77 1 HWI-EAS88_1:1:1:117:578 - chr18 29610052 TAGTGGAAGTAGCAAAAGAGGCACACAGTGAAGCA 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:112:238 + chr8 29528229 TTAGCAGGCCTTGATGGCTACTACTTCCTTTCTCT ::::::::::::::::::::::::::::::74747 0 HWI-EAS88_1:1:1:96:508 - chrX 117886178 TTAGTTGTAAAAAGGCTATTACTAAAAGTCAAATT 77777::::::::::7::::::::::::::::::: 0 HWI-EAS88_1:1:1:114:415 + chr2 99695065 CTGATTGCTAAATTAGTCCTGCAAATAACGTTATA ::::::::::::::::::::4::::::::177777 0 HWI-EAS88_1:1:1:117:462 - chr13 94473217 GTGAACAGAAGAGCCTTGAGTCTGTGGAACTGGAG 774776::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:81:571 + chr1 196527698 TTACTTTTAAAAACTTTATTACTCAATAAAGGCTG ::::::::::::::::::::::::::::::,2774 0 HWI-EAS88_1:1:1:122:660 + chr9 87840595 TATGCAGCTAGAGTCAAGAGCTCAGGGGTACTGGT ::::::::::::::::::::::::::::::77747 1948 HWI-EAS88_1:1:1:115:329 + chrX 8641018 GGGGACTGTGGGTGTAGCTCACTTGTTGCAGTGCT :::::::::::::::::::::::::::.::&7477 1 HWI-EAS88_1:1:1:118:518 + chr2 98507197 CTGAAAATCATGGAAAATGAGAAACATCCACTTGA ::::::::::::::::::::::::::::::77777 5 HWI-EAS88_1:1:1:120:704 + chr15 29527390 GAACCAGACTCCTGGCAGAAGTTGTGTTCCACTCA ::::::::::::::::::::::::::::::77777 1352 HWI-EAS88_1:1:1:110:692 - chr2 30903181 CACAAGTTGGTGAACTCTCCTGGAACCGTCTCTGC ,7477:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:98:349 - chrUn_random 5889635 ACTGTAGGACATGGAATAAGGCGAGAAAACTGAAA 7,777::::6:::::::::::7::::::::::::: 2 16,24 HWI-EAS88_1:1:1:120:488 - chr13 27398425 TGATGCATTAACTCTAGTCTAATGTGTCTATATTG 1)1-)+:11::,,&:::-::::::11::::::::: 0 22 HWI-EAS88_1:1:1:115:889 + chr11 27330701 GAGAAAATGCCCCACAGCTGGATCTCCTGGAGGCA ::::::::::::::::::::::::::::::77777 1 HWI-EAS88_1:1:1:120:446 + chr3 149312234 ATCCATTCCTCTGTTGAGGGACATCTGGGTTCTTT ::::::::::::::::::7/::::::::3:74777 2234 HWI-EAS88_1:1:1:80:439 - chrX 95193511 CTGATGTGTTTAATTTTGTTATTAAATTTGAGGAA ,4677:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:113:628 + chr5 81696161 TGTAACATAGACAGAGTTGAAAGTTAGAGTCTGCT :::::::::::1::::::1:::1:::11+:55)15 0 HWI-EAS88_1:1:1:121:143 - chr3 138145990 CACTCCAGGCAAATGGCATAGGATACACAATGTTA 27272:::::::::::::::::::::::::::::: 0 21 HWI-EAS88_1:1:1:101:353 + chr2 55907066 TGAGTTTTTCTCTTAGAAATGCTTTCATTGTGTCC ::::::::::::::::::::::::::::::72777 33 HWI-EAS88_1:1:1:97:502 - chr8 19623646 CCTGAAGCACATGAGCTGACCCCAAACCAATCCCA +2747::6::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:95:455 - chr5 137991226 GTCAGGTTCCATGACAAATGAATGTTGGCCACAGC 77171::::3::::/:::::::::::::::::::: 0 HWI-EAS88_1:1:1:123:424 + chr15 80876301 CTGAGAGGAGACACCCAAGAGCTACGACTTAGTGT :::::::::::::7::::::::::::::::77777 0 HWI-EAS88_1:1:1:98:361 + chr13 27275679 GTGGGCAGGAGTTTTGTGGGTGATGGGTTCCTGAG :::::::::::::::::::::::::6::::4727, 0 HWI-EAS88_1:1:1:119:458 - chr11 33034359 CACATTCTCCTCGGCTGCCAACACCTATGCAAGGA 27477:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:121:629 - chr6 19201874 ATAAATGTCTGAGCTAGAGTTCAAGTAATATCTGC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:112:446 + chr16 45467769 GATCCCGTGGGGAGTCCCGTGTGGGCCCTTGCGGG ::::::::::::::::::::::::::::::77777 455 HWI-EAS88_1:1:1:118:565 + chr2 126799738 GGCTGGCTCAAGATCAGAAGCCGCTCTGGACGTTT ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:106:886 - chr12 108091440 GGATTCTAATTGCATTGTGCCACAGAGGGTTTCTC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:93:629 + chr2 98502407 TTCTCATTTTTCACGTTTTTTAGTGATTTCGTCAT ::::::::::::::::::::::7:::::::,7777 0 HWI-EAS88_1:1:1:79:878 - chr9 35112968 AAAAGAAACATCCACTTGAAGACTTGAAAAATGAC 7%%7%:::::::::::::::::::::::::::::: 0 15,32,33 HWI-EAS88_1:1:1:113:326 - chr16 93329139 TAACCAGCCCTGGCTAGGGCAGGGAGGGAAAAGAA 67777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:87:618 + chr14 28417016 TTGTCTGTGCCAGCACAGAGAAAGCAACCTGAGCA ::::::::::::::::::::::::::::::47477 0 HWI-EAS88_1:1:1:83:277 - chr1 163068612 AGAAGAATCCTTAAGGCTTGCTAGGCAGCAGTCTA 77777:::::::::::::::::::::::::::::: 0 23 HWI-EAS88_1:1:1:103:414 + chrX 30017731 GGGTAGGCTACAGTGGAGGAACAGGCAAAGACCAC :::::::::::::::::::::::7::::::7+717 1 HWI-EAS88_1:1:1:93:494 - chr11 16451552 TTCAGGCCTGTCCTGCCCTCTCTTCTTTGTCACTC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:65:610 - chr4 6913304 ATGTCAATAGAAGTTCTAGGAGTTATGGATAAGAC 55-51::::::::::-:::-:1::1:::::::::: 0 HWI-EAS88_1:1:1:104:595 + chr9 8241476 CCGGAGTTTCAGTGATCAGAGTACTCTCTGCAGGC :::::::::::::5:::::::::::::::177/27 0 HWI-EAS88_1:1:1:107:489 + chr14 25109201 ACAGACAAACTCTTTCTAAAAATAAAAACAAAACA ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:117:484 - chr17 45306513 ACTTACAAATCCCCTCATCCTGTTCTGCTGTGGGA 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:102:619 - chr3 98925241 ATCTTAGGTTGTTCTGAGAAGAATGCCTTCCTTAC 77777:::::::::::::::::::::::::::::: 1 17,34 HWI-EAS88_1:1:1:98:190 - chr13 49151162 ATCTTGACCTCTGGAAACTCTTCATTTGCATACAC 77447:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:115:189 + chr11 49097805 GTACACACAGATTTGGGGAGACAGCTGCACGGGTC ::::::::::::::::::::::::::::::77774 0 HWI-EAS88_1:1:1:88:462 + chr16 92402871 CATCCCTTTGTCTCCCTGAGTGTGAGCTGCTTTCT :::::::::::::::::::::::::3::::77777 0 HWI-EAS88_1:1:1:113:784 - chr3 27564436 ACATGACAAGGCAGGCAGCTTCCTGGTTCTAAGTC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:117:503 + chr12 113816601 CATCTGAAAGTTCCAACCACCCCAACCACTAGACT ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:465:808 - chr4 72904399 ATTGCAAGTTAAATTTTCATCTTAATTTTCTTTTC 74774:::::::::::::::::::::::::::::: 0 25 HWI-EAS88_1:1:1:88:396 + chr3 64607170 TGTGAGTAGAAGTAGTAGAAGAGAAGCTGTTGTAA ::::::::::::::::::::::::::::::7+777 1 HWI-EAS88_1:1:1:88:479 - chr10 54088176 TCTCAATGCCTAGATTACCACTGGCAAAGGACTAG 7%1+7:::,::::::::7::::::::::::::::: 0 HWI-EAS88_1:1:1:122:166 + chr1 154575629 GCAAATACAGGAAGAACCAAGGGAATGGAGGAGAA ::::::::::::::::::::::::::::::74777 0 HWI-EAS88_1:1:1:82:533 + chr17 81150594 TGTGTCTTATTTGTCTCTGCTCCCTTGGGCACCAA ::::::::::::7:::::7:::::::6.::76676 0 HWI-EAS88_1:1:1:94:520 + chr10 129847826 ACTGTAAAGCATTCTTGCCTAGAAAATGATAGGAT :::::::::::::2:::::::7::::::3:71777 0 HWI-EAS88_1:1:1:92:550 + chr5 52477413 ACCCCTCCATCACTCTGAGGCTGACCAGAAGTATC ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:123:436 - chr9 3025862 ATTTTTCAAGGAGTCAAGTGGATGTTTCTCATTTT 77777:::::::::::::::::::::::::::::: 0 23 HWI-EAS88_1:1:1:109:580 + chr9 119934090 ATCTTGGATGCCCTACCCCCCTGTCCTCATCCTCT ::::::::::::::::::::::::::::6:77777 0 HWI-EAS88_1:1:1:102:549 + chr4 103786365 CTGAGCTGCCTGGAATTAGAACAAGGAATATTTTA ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:121:318 + chr7 148749053 TGGGCGATGATTAGATCTCACCCACTGCGATTAAA ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:119:279 - chr1 93630657 GGACTGTAGAGGAGGCAGAGACAGGAGGATCGCAA 77747:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:89:424 - chr12 21341127 AGCTGCTTGTTGGCCCATCTAGTAGCAGAGAGGGC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:82:804 + chr3 45654027 TTCTGGGTCTTCAATTCTATTCCATTGGTCTACTT :::::::::::::::::::::::::::,::77477 5099 HWI-EAS88_1:1:1:90:304 + chr13 113023027 TTCCGCCAGAGTTCCAGGAGGATGCAGGAGGATGG ::::::::::::::::::::::::::::::77772 0 HWI-EAS88_1:1:1:107:425 - chr1 148635275 GGGCAAAGAGATGGCTCAAGTGTTAAAACCATTTG 7*7*7:::::::::4:5:::::::::::::::::: 0 HWI-EAS88_1:1:1:103:853 + chr12 26445976 TTTTTCTAGGAGTTATTAAAAATACATGCTTAAGT ::::::::::::::::::::::::::::::7722/ 0 HWI-EAS88_1:1:1:115:514 - chr9 72652180 GGAATGGTGGTTTCACAGCCCTTGGAAGGTGAAAG 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:99:337 - chr18 66702023 TCCGTTGTTTTTGTTGCTGTTTAAGACCAGCCTTA 77777:::::::::::::::::::::::::::::: 0 16 HWI-EAS88_1:1:1:91:364 + chr8 121963969 TAGGGACATGGGAGCCATCTTGACAAGTCTGCAAA ::::::::::::::::::::::::::6:::27777 0 HWI-EAS88_1:1:1:85:487 + chr17 94350740 GGCCTCCCAGGAGATCTACTGCAGCCAGGGCAACA ::::::::::::::::::::::::::::::77777 12 HWI-EAS88_1:1:1:113:509 - chr2 26341042 AGTGGGTGCAACTGGAATCAGGCTCTAGAATGGAG 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:123:467 - chr13 95699951 TATTTTTAAATTTTAATTCTTGTAGTGCCAGATAG 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:74:589 + chrX 106950434 TGAGTTCTTTGGTATTTGGGTTGCTTCACTCAGGA ::::::::::::::::::::::::::::::77727 0 HWI-EAS88_1:1:1:100:564 - chr4 129888766 CTCGCTGCCCAGTACTACAGAACTCCTGCCTTGTT 24774::6::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:98:771 + chr2 50906132 GGTTTTTCAAGACAGGGTTTCTCTGTCTAGCTCTG :::::::::::::::::::::::::::::677774 0 HWI-EAS88_1:1:1:88:393 + chr4 128917171 TAGGTGGACATGACGTATACACTCCAGTCTAAACA ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:70:568 + chr3 151947827 GGAATGCCTAAGCATTTCCAGCACTCAGACTTTAC ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:122:608 + chr15 73005135 GATCACAACAGACAAGACAGATGGGAATCGCCACA ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:97:306 - chr11 116868860 CAGGACCTGCCTGCACTCTCCCTGCTGGGGTGAAA 1)-5-+::+1::0:&:::::1:::::::1:::::: 0 20,29 HWI-EAS88_1:1:1:121:179 - chr11 119472789 TGTGCTTGGGCAGTCACTGCCTGACATATAACAAC 55351:::::::::::::::::::1:::::::::: 0 HWI-EAS88_1:1:1:106:507 + chr13 103686267 CAGATGACTGGCCACCTGCCTGCCCTGGAGAGCAG ::::::::::::::7:::::::::::::::77427 0 HWI-EAS88_1:1:1:109:107 + chr7 77575507 GTGGACATCTGATTTTCTGCTTTTACCTTCCAACT ::::::::::::::::::::::::::::::47747 0 HWI-EAS88_1:1:1:380:636 + chr6 88467025 GTTAGAAAACAAAAAGGATACACATTTTTCAAGAC ::::::::::::::::::::::::::::::74477 0 HWI-EAS88_1:1:1:88:506 + chr11 60987562 TCATAGTTGCTGAGATTTACAGGACCCAAAACAAC ::::::::::::::::::::::::::::::67474 0 HWI-EAS88_1:1:1:70:476 + chr11 63555010 GTGTAAGATCTATGCAAAGTCGTGAAAACATCTCT ::::::::::::::::::::::::::::::77747 0 HWI-EAS88_1:1:1:118:160 - chr16 53463641 CGTCATGTGTACTACCTATCCAACTGGGAATTAAC 26727:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:737:593 + chr14 87984710 GATTCCTTCATTGAGAATTATCTGTTTTGCTCTGT ::::::::::::::::::::::::::::::777,7 0 HWI-EAS88_1:1:1:84:611 + chr3 134638627 CACAACACACCTCTATTAGTCAACAACTACAAATT ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:114:669 - chr2 178250635 GGTTAGGTTAGTATTAGGGTTAAGGTGAGTTTAGG 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:114:570 + chr11 36889387 CGTCGACTCAGTGGTTAGGGCCTACTCAAAATCTG ::::::::::::::::::::::::::::::77772 0 HWI-EAS88_1:1:1:366:851 - chr17 60066142 GGCTGGAAAGATTAAGAACACTTGCCAGTTTTTTC 35-45:::::::::::::::::::::1:::::::: 0 HWI-EAS88_1:1:1:109:150 + chr13 31083932 GGCTAGGAACCAGTGTGCATGCATACCATTCCCGC ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:114:754 - chr9 65931645 GCAGCTGAAAGCACTTACTGCTCTTGCAGAGGACC 72772:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:373:636 - chr15 33268025 TATTTGCACATGATATGATAGTATATATGTGACCC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:119:991 + chr8 119776480 GTGGCTCAATCTGGCCTTGAACTTCTGATCCTCCA ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:97:872 - chr15 102018926 GATGAACTCAAAATGACAGGCAGCAGGAGGGAGGA 77777:6:::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:67:557 - chr19 55278807 ACCAAGGGAACAGCATCAACAACACTACACACACA 555)5::::::::::::::::::::::::::::1: 0 31 HWI-EAS88_1:1:1:355:795 - chr2 98507377 AATGGTGGAATACTTAGAAATGTCCACTGTAGGAC 77477:::::::::::::::::::::::::::::: 2 22,32 HWI-EAS88_1:1:1:495:692 + chr7 70657636 GACATCTATTCATATGCCTGGGTGTGTTATTTATA ::::::::::::::::::::::::::::::77677 0 HWI-EAS88_1:1:1:129:389 + chr6 16290023 GGGAAGGAAAAATACTGGAATACTGGGTTATAGGA ::::::1:::1:::::1::::::::11:::5-115 0 HWI-EAS88_1:1:1:100:552 + chr11 100308857 GCCCCTATGCACTCTAGACGTTGCTATGTCAGCGT ::::::::::::::::::::::::::::::74747 0 HWI-EAS88_1:1:1:100:399 - chr12 112645017 TTGGAGGAGGCCGAGGGAGGCGAGCTCCCGAGCCA 11555::::':::::::::::::::1::::::::: 0 25 HWI-EAS88_1:1:1:117:798 + chr9 3553983 GAGGTGCATTTCCTGTGTGCAGAAAAATTCTGGGT ::::::::::::::::::::::::::::::77247 0 HWI-EAS88_1:1:1:110:917 + chr1 95060791 TCTGTCTGTCTCTTAGGAGGAAACCCGAGGCGGCA ::::::::::::::::::::::::::::::477+7 0 HWI-EAS88_1:1:1:81:142 + chr1 70616476 GGTAATTCTTAACCAAGCTTCTATTTATAGGCAGA :::::::::::::::::::::::::::::627747 0 HWI-EAS88_1:1:1:117:822 + chr13 38591529 TGTACTAATCTCTGTCAAGACAAACTGTAGCATTT :::::::::::::::::::6::::::::::22777 0 HWI-EAS88_1:1:1:83:406 - chr3 51397238 AATGGGGACAGACATTAAGGTAGGAACTGGATGGG 77277:::6:::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:101:484 - chr5 72595028 CACGCACACGTTTGTCATTCCGTTTCGAAATCCAG 27424:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:962:565 - chr5 137548339 TGAGCAAACCCTGGTTCCCTTGACTGGAACATCCC 67777:::::::::::::::::::::::::::::: 0 25 HWI-EAS88_1:1:1:118:572 + chr2 14309097 CTATCTTGTTGATTTTCTCGAAGAACCAGCTCCTG ::::::::::::::::::::::::::::::77774 0 HWI-EAS88_1:1:1:115:562 + chr15 67132730 AAAAATGCCAATGAGGTTTAGAGATACGAGGACCC :::::::::::::::::::::::::::::677777 0 HWI-EAS88_1:1:1:96:884 + chr8 59431019 GACATCAGACATGGAGATGCAGAGTTTGGAGTTTG :::::::::::::::7::::7:::::::6:2,,7, 31 HWI-EAS88_1:1:1:99:443 - chr3 101340562 TCTGGGATGTTTGCCTTAAAGTTCCATGAGGCTGA 72772::::::::4::::::::::::::::::::: 0 HWI-EAS88_1:1:1:90:379 - chr17 67983210 CACCTGTGGTTGAGCTATGCCATCCAGTGCCTGTC 74777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:111:977 - chrX 7207297 CTTGGATGCGGTTGCCCTGCTGAGACGCATCCAGC 77777:::::::::::::::::::::::::::::: 0 26 HWI-EAS88_1:1:1:116:879 - chr17 25793251 TCCCTGGAGAAGCCTCAGGACAGCTGCACCTGGAG 27477:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:82:227 + chr18 39915116 TAGGAGTTCTCTGGTGGAATTTTTAGGGTCACTTA ::::::::::::::::,::::::::'&&:+62772 3811 HWI-EAS88_1:1:1:78:554 + chr11 115120689 TGGGGAGATGTGGGCCCTGGCTGGATATTTGAGGA ::::::::::::::::::::::::::::::47772 0 HWI-EAS88_1:1:1:101:329 + chr13 9319191 GTGGAGGCTAGCACCTGTTTGTGGCCTTGTGAAGG ::::::::::::::::::::::7:::::6:,4744 0 HWI-EAS88_1:1:1:401:703 - chr9 3028438 TTTTTCAAGTCGTCAATTGTATGTTTCTCATTTTC 74747:::::::::::::::::::::::::::::: 1 18 HWI-EAS88_1:1:1:158:505 - chr4 33828440 ATGAAATATTATTTTCCATTACCACATAAGGAATC 76777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:793:122 - chr1 103384115 GTGGCTAGAGATGTAATATACTATTCTTCTCATCC 777/7:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:446:886 + chrX 132203087 GTTTACTACTTTGCTTGACTACTTTGTCTTTGATC ::::::::::::::::::::::::::::::62274 0 HWI-EAS88_1:1:1:92:979 - chr9 55980151 GCGAGATGACAAGGGAGCTGAAAAATTCCCATCAC 74277:::::::.:::::::::::::::::::::: 0 HWI-EAS88_1:1:1:743:806 - chr8 84429869 AATAGCAACATGTAAAGAACAACAACAACAACAAC %7+72'::.:::7::::::0::::::::::::::: 0 18,26,29,32 HWI-EAS88_1:1:1:644:944 - chr7 82681418 GGTCAATGAGAGAGACTATCTCAACACACACACAC //2/7:&+:+:3:4::::4:::::::::::::::: 0 HWI-EAS88_1:1:1:80:638 - chr10 40361614 CAGTGCCCTGAGCCAATACAGCTCCCTTTTCCTCA 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:292:800 + chr2 98502984 GGAAAATTTAGAAATGTCCACTGTAGGACGTGGAA ::::::::::::::::::::::::::::::77467 0 HWI-EAS88_1:1:1:120:758 + chr15 8508695 GCCCAAGAGAAGATGCAGTTGAAGAGACTTCTGAT ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:84:882 + chr8 19737105 TAGGCTCCTGTCTACAAGCATAGCAGAACATCATT ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:96:553 - chr17 27699048 GCCCCATGGCTCTCCCATTCTTGTCTCTTGCCCTT 5-1-1::1+::::::::::::::::1:::1::::: 0 HWI-EAS88_1:1:1:114:254 + chrX 8945578 CCACCCTTTGAAGGGTAAGCAATGCTTCAGACGTT ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:189:938 + chr9 3024457 GTCATTTTTCAAGTCGTCAAGTGGATGTTTCTCAT ::::::::::::::::::::::::::::::+7627 10 HWI-EAS88_1:1:1:680:764 + chr1 117093106 GCCATCTTCTGATACATATGCAGCTAGAGACACGA ::::::::::::::::::::::::::::::77776 1214 HWI-EAS88_1:1:1:932:573 + chr10 129861130 GAGGGACACTCCTCCATTGTTGGTGGGATTGCAAG ::::::::::::::::::::::::::::::2644+ 39 HWI-EAS88_1:1:1:104:920 + chr16 8038662 TCCCTGGTATTATCAGGGAGGAGCAGCAGCTGAGC ::::::::::::::::::::::::::::::77772 665 HWI-EAS88_1:1:1:361:885 - chr16 66264786 TCATATTATTTATGAGTGTCATGTATGAGTTTTAC 77767:::::::::::::::::::::::::::::: 0 31 HWI-EAS88_1:1:1:101:99 - chr1 156153246 CTTTAAATGAGTCTAAAATTAAATTTCCCTGAACC ,7777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:84:601 + chr10 23218476 CTAACTGTCAAAAATCAAAACAAAACAAAACCCAC ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:121:653 - chr1 93431116 CTGGCGTCCTTGGAGACTGCATGCATATGGCATCT 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:108:748 + chr5 144905571 CAGAAGACTGCTCCTACTTAGCACGCTGTAGCTGC :::::::::::::::::::::::::::1::*77/7 0 HWI-EAS88_1:1:1:658:576 + chr10 65452571 GTGGAAAAAAAGATACATTTTCAACAAAAGGTGCT ::::::::::::::::::::::::::::::74777 0 HWI-EAS88_1:1:1:110:96 - chr7 139610865 ACACCCTTCTGCTGATTCAGCATGGCAGGTGCTCC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:761:681 - chr13 38472224 TCAGATAAGTGGCCTGCTTCATTTCCAGCTCTTCC 4-31-+01:+::++::1:0::+:::1::11::::: 0 HWI-EAS88_1:1:1:114:969 + chr5 38091139 TGGGCTGACGTCATGCCTGAGCTGTCACGAGCAGA ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:91:488 - chr1 79737830 GCCGGGCTGGCCCCGCCTGCCTCAGAGGACCTCTA 447%7::::6::::::::::::::::::::::::: 0 31 HWI-EAS88_1:1:1:98:496 + chr10 82607222 AATGGAGGGCCAACTCCATGCTGAAAGCTGCAGGC :::::::::::::::::::::::::::::677777 0 HWI-EAS88_1:1:1:229:504 + chr2 169471813 GCATTAGGGAAGTTCTGGAGCTACAGCCTTGAAGC ::::::::::::::::::::::::::::::72476 0 HWI-EAS88_1:1:1:343:187 + chr9 35112883 GCGAGGAAAACTGAAAAAGGTGGAAAATTTAGAAA ::::::::::::::::::::::::::::::77777 2 HWI-EAS88_1:1:1:326:884 - chr9 89471099 TTTCATTTTGACCTTTTGGTAAACTCTTGTTCATC 66744:::::::::::::::::.:::::::::::: 0 HWI-EAS88_1:1:1:259:198 - chr7 106443757 GGGAAGCTAATGTTACAGATGTTCTGATATCCCTC 77677:::::::,:::::::::::::::::::::: 0 30 HWI-EAS88_1:1:1:905:603 + chrUn_random 5474428 GAAAAATGAGAAATGCACACTGTAGGACCTGGAAT ::::::::::::::::::::::::::::::77267 2 HWI-EAS88_1:1:1:441:786 - chr5 130297134 CAACTCTCCAGCCCTGAAAACATAAAGTGATAGCC /2)/)122:2::..::::::::::::::::::::: 0 32 HWI-EAS88_1:1:1:438:607 - chr15 30113482 ACAATCTCTGTCAGTATCTTTCATGTTTACACAAC 44724:3:::5:::::::5:::::::::::::::: 0 HWI-EAS88_1:1:1:122:325 - chr5 114938473 ATTTGGGGTTTTTATTTGTTTGGTTTTGGTTTTTT 74747:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:694:318 + chrX 146627765 GGTGGGTGGATATGGGGGACTTTTGGTATAGCATT :::::::::::::::::::::::::6::::76677 2686 HWI-EAS88_1:1:1:67:631 + chr13 30117647 TCTAGCCTCAACTCTAGCCCAGAGTCAGTCAGACT ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:96:390 - chr12 103429255 CAGCCTGAGTCACTTTTGCTTGTGGCCATCTGTGA 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:123:354 + chr11 62783324 GATTCTCGATCTTACAGCACAAGCCATTGCTGTTC ::::::::::::::::2:::::::::::::74777 1549 HWI-EAS88_1:1:1:66:327 + chr16 10776630 TCATCGAAGGTCGGGTCTTCAAGCTCAAGTTTCAG ::::::::::::::::::::::::::::::77674 0 HWI-EAS88_1:1:1:90:639 + chr11 53341749 GGAGTCTCTCCAAACTCTCCTGTCCCTGTGCATGC ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:462:687 + chr5 64913204 GCACTTTGTAAATGAATCCCTGACCCTGTGAGAGG :::::::::::::::2::::::::::::0:2,32/ 0 HWI-EAS88_1:1:1:627:243 + chr15 102534496 GATAGATAGATAGATAGATAGATAGGGTAGATAGG ::::::::::::::::::::::::::::::67777 0 HWI-EAS88_1:1:1:213:730 - chr11 46888161 ATTTTTCTCTCAAACTTTTATCCTTAAGAGTTGTC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:107:890 + chr16 56137956 GTGTAGGGGAATGCCAGGGCAAGAAGGTGGGAGTG ::::::::::::::::::::::::::::::77747 0 HWI-EAS88_1:1:1:298:233 - chr4 83752135 TCCATATGGCTTTCTGTATCTGCCAAAAGCCTCGC 47777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:722:589 - chr7 141500020 ACCAGGAATCTCCTTGTGTTTAGTACAACATTCAC 47446:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:92:111 + chrX 139333123 TGTACTGGTGCACTGTCTGTAACCCCAGCATTTGA ::::::::::::::::::::::::::::::77724 0 HWI-EAS88_1:1:1:228:416 - chr15 60585510 ATGAGCTCAGTGAGTGATGAAGTCAGTACAGAACC 76777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:321:882 - chr7 49256021 TACTTCATTACTCCCTAGAATATGGAACAAAATAC 76746:::::::::::::::::::::::::::::: 1 25 HWI-EAS88_1:1:1:86:590 - chr12 103461832 GGTGGGCTGTGTGCCGTGACTCTGTGTACTACACG 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:872:90 + chr9 3008120 GCCATATTCCAGGTCCTACAGTGTGCATTTCTCAT ::::::::::::::::::::::::::::::77777 78 HWI-EAS88_1:1:1:381:813 - chrUn_random 5249472 GATTTTCAGTTTTCTTGCCATATTCCACGTCCTAC 77776:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:331:120 - chr6 135096315 CGGACCTTCACACCCTCTTCATGGCATACCCAGAC 6+464:::::::::::::::::::::::::::::: 0 33 HWI-EAS88_1:1:1:109:436 + chr15 37955304 TGGGGCAGTTATGGGGCGGGCGGGCAGCTGGCCTC ::::::::::::77::::::::::::6:::77+%+ 0 HWI-EAS88_1:1:1:323:771 - chr6 108256045 CGAGTTGTTTCTTCTATAAGGAGAGTTCCTAAAAC 27776:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:170:530 + chr19 30155442 GGTGATTTTGATTTTGAGCATTCTGGAGTCAACCC ::::::::::::::::::::::::::::::74674 0 HWI-EAS88_1:1:1:570:691 - chr12 33045808 CAAACAAACAAACAAAACCCAATGTTCTAACTGTC 27774:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:724:766 + chr2 84672060 GGAAGAGGAGCCACTGTGAGGAAAAGAGAAACTGT ::::::::::::::::::::::::::::::76776 0 HWI-EAS88_1:1:1:64:556 + chr15 93713928 GCCCACGCCCACTTCTCACTTTTTACTCCTGGTTC ::::::::::::::::::::::::::::::42774 0 HWI-EAS88_1:1:1:214:833 - chr2 51250763 ATGGCAAGAGAGAGTGATTGCACATATGTTCTTAC 64476:::::::::::::::::::::::::::::: 0 16 HWI-EAS88_1:1:1:85:155 - chr2 102194880 CCCCACCTTCTGAGTGTAGGTTTGTGAGCCAACAC 47777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:244:874 + chr1 158726947 GTGTGTGTGTGTGTGTGTGTATGTGTGTGTGCGTT ::::::::::::::::7:::(:7:::6::+4&+%7 1 HWI-EAS88_1:1:1:117:832 - chr16 17222480 TAACGCAGGTGTCCTAAGGCGAGCTCAGGGAGGAC 777271::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:917:573 - chr6 29460745 GCTTGCTCAGCCTGCTTTCTTACAGAACCTAAGAC 67466:::::::::::::::::7:::::::::::: 0 HWI-EAS88_1:1:1:78:609 + chr16 39722924 AGACACAACTCCTTCAAGCAGCACATAGAGTTATT :::::::::::::::::7::::::::::::77777 0 HWI-EAS88_1:1:1:113:666 - chr18 68539317 TTTAAACTTGCTGCCCATCCAACCTGGCCAAAAAA 77477:::::::::::::::::::::::::::::: 0 32 HWI-EAS88_1:1:1:737:698 + chr14 29380898 GGTTTATGAAAATGTGTTACCTGCACAGATGCAAG ::::::::::::::::::::::::::::::47242 0 HWI-EAS88_1:1:1:266:112 + chr18 21276366 GCATGTTTACGTATGCATGTGTGTGCGTGTGCATT ::::::::::::::::::::::::::::::46277 0 HWI-EAS88_1:1:1:362:99 + chr4 81221337 TACATATTTATATCCCTTTACTTAGGTTTTGGAAA ::::::::::::::::::::::::::::::4+717 0 HWI-EAS88_1:1:1:194:817 + chr8 100334502 GTGATGCCATAGAGAGAAATGCACCAAAAATCTTC ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:74:916 - chrUn_random 4261776 ATGAGAAATGCGCACTGAAGGAACTGGAATATGGC 77777:::::::::::::::::::::::::::::: 1 17,23 HWI-EAS88_1:1:1:467:640 + chrX 110412423 GCACTGTAGAGCAAGTTCGAATCCTCCGTGGGCTG ::::::1::::::1:&::::::::::::(+)-44) 0 HWI-EAS88_1:1:1:594:462 + chr17 21515395 GTGCTGTCAATAAAGCAGCAATGAACATGATGGGC :::::::::::::::::::::::::::::,67446 0 HWI-EAS88_1:1:1:644:329 - chr12 116644041 AAACACAATCAAATTCCCTTTATATGTAAGGAATC 77747:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:98:85 - chr5 91942433 TGCATCTAGCATTGGGCATCCATCACACCACCTAA 77777:::2:::::::5:::::::::::::::::: 0 HWI-EAS88_1:1:1:465:739 - chr9 123086445 CTTCTATGCTTCCTTTGGTGTCTTGTGCATTTCTC -)4141:::1+::::&::0::::::::11:::::1 0 HWI-EAS88_1:1:1:189:394 - chr13 4027997 AATGACTATAATGAGGTCGATTTTGTTTCTACAGC 77277::::::::4::::::::::::::::::::: 0 HWI-EAS88_1:1:1:105:680 + chr7 126820203 TTCTCTGACGTGTTCAAATTATGGCTCATAGATAA ::::::::::::::::::::::::::::::24777 0 HWI-EAS88_1:1:1:395:208 - chr2 159270803 TGGATGCTCATCACCAATAGAAAATGATTCCCTCC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:730:590 + chr4 99048180 GGGTTAGGGTTAGGGTTAGGGTTAGGGCTAGGGTT ::::::::::::::::::::::::::::::77746 2 HWI-EAS88_1:1:1:467:751 - chr9 105192522 GGGCGAGGAAATGCAGTCCAAGATGGCTGCCTTTC 77667:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:532:767 + chr18 71682834 GTTCATTATGGAGTATAAAGTCACTAAGACTCATT ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:762:272 - chr15 10869845 GAATATGTTCATAATTTACTATGACATACAGATTC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:920:541 - chr5 9512873 TTTAAAGCTTTATAACAGGTATTAGGCAGAACTTC %4074:::3:::::::::7:::::::::::::::: 0 HWI-EAS88_1:1:1:70:512 + chr13 17647469 TCCCTTCTGGCCTTCATCTTCAGCCAGGAGGCAGA ::::::::::::::::::::::::::::::47647 0 HWI-EAS88_1:1:1:393:151 - chr4 79650067 CAATAACAGCCTTTTCATCAGTAACACACATCTGC &7717::::::::::7::::::::::::::::::: 0 HWI-EAS88_1:1:1:120:109 - chr2 122497729 GCAAATGCTGGAAACTGAGGTAGCCCAAGTTACCA 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:682:585 - chr14 34552069 GATACACAGTTGTGCTAACATCTATCTTTACCTGC 66446:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:942:226 + chr4 56283219 GTGTAAATGCGTGTGTAGTTGTGTGTGTGTCTGTG ::::::::::::::::::::::::::::::1777+ 0 HWI-EAS88_1:1:1:112:742 + chrX 149366751 CCATATGCCAAGTGTTCTCGTGTTATATGTTTTAT ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:94:501 + chr14 104357152 AAAACTTGTTATCGCTGGACTTGGTCCATCAGACT ::::::::::::::::::::::::::::::74777 0 HWI-EAS88_1:1:1:406:898 - chr2 70261703 ACATACTAATAATTAAATTTTAAATAAAAAAATAC 727776::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:168:417 + chr4 146990452 GTACATTTGATGTCAAGATGCAATAATGAATATTT ::::::::::::::::::::::::::::::76777 0 HWI-EAS88_1:1:1:648:464 + chr9 3002760 GTGTATTTCTCATTTTCCGTGATTTTCAGTTTTCT ::::::::::::::::::7:7:::::::6:77777 62 HWI-EAS88_1:1:1:352:812 - chr9 42299005 GAACTCTGTGAATATGTCACTTTAAAAGATGGTAC 67644:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:158:516 - chr12 109489439 GGTGATGACTCGTCCGCAGTTGCTCCTCCACAGTC 77776:::::::::::::::::::::::::::::: 0 27 HWI-EAS88_1:1:1:82:318 - chr1 195475068 ATGAACCTGTAAGCCAGCCCCAATTAAATGCTGTC %2772&::1+:::44:::.5::::::::::::::: 62 34 HWI-EAS88_1:1:1:198:821 + chr4 123390148 GATATCATGCATTCGCCTCATCTGTCAGGGATAAA ::::::::::::::::::::::::::::::67477 0 HWI-EAS88_1:1:1:992:610 - chr5 41152931 CATCACTGTGACATGCAGAGGGATATGCTGAAGTC 15114::::::::::::::::::::::::::1::: 0 HWI-EAS88_1:1:1:216:192 + chr16 69531389 GCATACACTAGCAAGATTTTATCGAAAGGTCCCAG ::::::::::::::::::::::::::::::77764 4 HWI-EAS88_1:1:1:494:879 - chr8 49100547 CACTCTGGAAACCAGCAGAAGTGTAAGACTTCATC 76767:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:497:682 + chr1 120679178 GCTCCCATCTCAAGCATGAAGACCTGAGTTTGAAG ::::::::::::4:::::::::::::::::63452 0 HWI-EAS88_1:1:1:90:332 - chr3 25759645 CACTGAATCTTTGAAGTATATGAAATGCTCTCTGC 47277:::6:::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:79:820 - chr16 57787398 TCTGTATGAGAGTGCTTCCCCACCCACCCACACTC /**//:::::4::::::::::4::::::::::::: 1 HWI-EAS88_1:1:1:463:880 + chr8 97621192 GTCTGAGCAGAGAGGGCACCCTGTGGGTCTGAGCC ::::::::::::::::::::::::::::::6+644 0 HWI-EAS88_1:1:1:307:819 - chr6 13457763 CCAGAGACAGCTTGAAAATTAACACCAGAGATTAC 11777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:216:412 - chr1 133707056 ATTTTTGTCATGCCTCTTCCCCATCTGTTTCCACC 716+4:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:94:412 + chr16 44147156 ACTTTAAAAATAAAAATAATGTTCTTGGAAAAAAT ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:112:685 + chr7 116508643 AAAGAAAACTCAAACTGAATTGAGCAGGATTTTAA ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:297:112 - chr12 101924695 GTTGCAGAAAAGTTTACAAACTCCTAGTGTCGGTC 77674:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:377:824 + chr10 93331652 GTATGTCATATTTGGGATTAAAAGTAACAGTAAAA ::::::::::::::::::::::::4:::::77276 0 HWI-EAS88_1:1:1:643:340 + chr19 56312821 GCCTTTCCAAGGGGACAAAAGAATTTCTTCTTGAA ::::::::::::::::::::::::::::::77744 0 HWI-EAS88_1:1:1:486:689 - chr11 102964183 CAGCCCGCCTCGGGCTGTCGCGCGGTGCGGAGTGC 74742:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:98:895 + chr12 6025965 GATGTAGCTGTCTCTTGTGAGACTATGCCGGGGCC ::::::::::::::::::::::::::::,:&4611 7257 HWI-EAS88_1:1:1:206:113 - chr9 76486175 CCAACTCCTTCAACTTAAATGCTGTCCTGTCTTTC 47777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:155:543 - chr14 45182408 CTGCCTTGGTCTGGCCTCCCTTGATTGCCATTCTC 44647::::5::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:103:621 - chr11 109511761 GGCGGAGTCGCCCACCTGTCACCGAATCGTCCCAC 77777:::++::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:618:814 + chr1 62348806 GTGGGTAAGGGGGACTTTTGGTATAGCATTGGAAA ::::::::::::::::::::::::::::::77666 1691 HWI-EAS88_1:1:1:115:476 + chr5 34556924 ACGAAGCTGGGGCTGCCTCCAAGAAAAGGCTTTAT ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:221:561 - chr8 92773704 CAGAAACCTGAACCACTAGAAAATGTCAAAACTTC 27777::6:::::7::::::::::::::::::::: 0 HWI-EAS88_1:1:1:192:535 + chrUn_random 5813213 GACATGGAATATGGCAAGAAAACTGAAAATCATGG ::::::::::::::::::::::::::::::76767 2 HWI-EAS88_1:1:1:90:606 + chr14 100491335 ACTGAGAAAAACTGGATGAAGCTCAAGGCCGCCGA ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:200:903 - chr9 3007123 TCGTCATTTTTCAAATCGTCAAGTGGATGTTTCTC 77777:::::::::::::::::::::::::::::: 9 20 HWI-EAS88_1:1:1:106:815 + chr13 65755374 GTTGTGAGCCACCATGTGGTTGCTGGGATTTGAAC ::::::::::::::::::::::::::::::77774 11329 HWI-EAS88_1:1:1:95:584 + chr8 5300717 ACATGCTGCCCTCCCATCCCCCTTTCCCTCCCAGG ::::::::::1:::::::::::::::1:::5551- 0 HWI-EAS88_1:1:1:432:157 - chr5 122971608 CCTACCTACCTACCTACCTGACCTACCTACCTACC 17644:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:104:642 + chr14 82150006 ATCATATTTAAAGAACTCTGAATGAGAATATTACA ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:389:185 + chr8 6265632 GGTTTCTGAAGTTTCTGGGTTGATGGGGCTATAAT ::::::1:::::::::::::::,::+:::1--5%1 0 HWI-EAS88_1:1:1:226:124 - chr2 134165388 CAAGTGAACACTCAGGGGCATCCTCCGAATTGGTC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:651:916 + chr15 31137094 GATTTCTCTTTACCATCCCGGTCTCCATCTAGACC ::::::::::::::::::::::::::::::47677 0 HWI-EAS88_1:1:1:101:878 + chr2 80879385 AAATCTGGAATCATAGATGCAAGCATCAGCAGCAG ::::::::::::::::::::::::::::::77777 2 HWI-EAS88_1:1:1:233:952 - chr9 60532291 TGTGGCCCTGTGAACCCACATCCACAAGCACCATC 31%55:+&0::::::&:::::::::::::1::::: 0 19,28 HWI-EAS88_1:1:1:86:206 - chr7 89906681 GTGAAGGGAGAGTATAGGGAACTTTTGGGATAGCA 74677:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:60:425 - chr11 34907051 CTTTTACAGGGCACATTTGCAAAGAGAATGGGAGC 47777::::3::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:374:151 + chr8 3118444 GTATATATAAGTGACCCTAAAAATTCCACCAGAGA :1::::::::::1:::&::::::::::1::51111 3938 HWI-EAS88_1:1:1:356:618 - chr5 129140045 TCCAGATCCAGGCTTGGTGGCACACATCTTTAATC 44777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:69:586 + chr13 49537380 GTAAGAGTCCCCTCTCATTTGTTGTTTTGAGGGTG ::::1:::::::::::::::::::1:1:::%-&%- 0 HWI-EAS88_1:1:1:83:547 + chr11 56855742 ATCCAGTAATCTCAGTCCACCCACACCGGGTGTGT ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:97:928 - chr17 34762939 GAGTGCACTGTCCCTGTCTTCAGACACACACACCA )725*::&:3::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:107:731 + chr13 65942529 TCTTCATGGGATTGTGACCCTGAGAGTAGCAGGGC ::::::::::::::::::::::::::3:::77777 1 HWI-EAS88_1:1:1:553:911 - chr6 77405836 AAAAGCGAGGCTTGAGGGGTGTTCTCAGCTAGGAC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:471:581 + chr4 154722117 GGAACTCGCATTTGTACACCTGATCACCAGCTGGC ::::::::::::::::::::::::::::::66767 0 HWI-EAS88_1:1:1:132:526 - chr3 119416677 GAGGAGGTGCACGTGGGCATTTGTGTATTCCTTAC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:404:557 + chr6 65383682 GACAGTGCAGCACCATCAGAATGGTTTGAAGGTCC ::::::::::::::::::::::::::::::76677 0 HWI-EAS88_1:1:1:558:961 - chr9 35113003 GAAATCACTAAAAAACGTGAAAAATGAGAAATGCC 2777/::3::::::::::::::::::::::::::: 0 27 HWI-EAS88_1:1:1:421:643 + chr5 3103723 GTCAAATGGAAAAATGTCCTTGATAATATATGGTA ::::::::::::::::3::::::::::,::64126 0 HWI-EAS88_1:1:1:352:91 - chr13 27613660 AAAAGATCTAATACCAAAACCCTTCAAACTATCCC 77776::::::::7::::::::::::::::::::: 0 HWI-EAS88_1:1:1:160:579 - chrUn_random 4739859 ATTTTCCACCTTTTTCAGTTTTCTTGCCATATTCC 77277:::::::::::::::::::::::::::::: 1 HWI-EAS88_1:1:1:119:920 + chr15 75776541 TCAGCACTGGGGAGGCAGAAGTAGGTGGATCTTCG ::::::::::::::::::::::::::::::47772 0 HWI-EAS88_1:1:1:924:101 + chr12 6776262 GTGTGTTTAAATGTAGCCAGTAGATACTACTCATA ::::::::::::::::::::::::::::::77+76 0 HWI-EAS88_1:1:1:667:780 + chr14 21969132 GTCACCTGCTGGAATGCCCTGCTCAGGTGTCACCT :::::::::::::::::::::::::::3::76777 0 HWI-EAS88_1:1:1:447:667 - chr9 59387868 GTGGCCTCTGCACAGCCCAGTCAGTCACCTCCCAC &%644:::,::5::::::::::::::::::::::: 0 34 HWI-EAS88_1:1:1:704:338 + chrX 94504587 GATGTTTGCTGGACCTTTGAGTTGAAAATCTTCAT ::::::::::::::::::::::::::::::77777 948 HWI-EAS88_1:1:1:480:517 + chr5 112962939 GCTGGGCTCCAGGATTCAGCTGTCCTGGCTGCTCT ::::::::::::::::::::::::::::::66646 0 HWI-EAS88_1:1:1:494:137 + chr5 20173292 GGTGTATCAGGCTCTTCCTGATGCTCGGCTTGTGA ::::::::::::::::::::::::::6:::7+740 0 HWI-EAS88_1:1:1:708:269 + chr5 61137181 GAAACTTAATAATTCATATTGCTGACAGTTGTGTC ::::::::::::::::::::::::::::::47477 0 HWI-EAS88_1:1:1:384:121 + chr1 75376681 GGAACACAAGATCCTGCCAGTCACTCGTCAGGCCC :::::::::::::::::::::::::::::376677 0 HWI-EAS88_1:1:1:962:572 - chr8 26314087 TGGGCAACATGTCTACTTTTGGACCATAAAATTCC 46466:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:670:445 - chr5 102897419 AGGTAAGGGAAGCCTATTTTTCAGGCCCCATTCTC 667/7:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:608:501 + chr18 26162501 GGGCAAGGGGAAGGAGTACCAAGTAAGTCTGTGCC ::::::::::::::::::::::::::::::74676 20 HWI-EAS88_1:1:1:86:257 + chr7 17160629 GAGAGCCCTGCTGGCTGCTGCCTCTCATACAGGCT ::::::::::::::::::::::::::::6:72277 0 HWI-EAS88_1:1:1:247:371 - chr15 95156470 GAGATGTGTGGAGCATGAGGCAAGCGTGGGTACGC 7677+:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:652:94 - chr8 10070379 CTTCCTTACCTGCTCCTCTACCCTAAACTTCTTCC 76447:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:94:396 + chr15 99736506 AAACTCAGAAATCCGCCTGCCTCTGCCTCCCGAGT ::::::::::::::::::::::::::::::77777 446 HWI-EAS88_1:1:1:115:492 + chr16 56873826 CCAGGTCCAATAACCCTAAGGGTACAGTACTGGTA ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:771:633 + chr9_random 204671 GATGACTGTGACGTGAATAGGCTTTACCACATTGG ::::::::::::::::::::::::::::::%644+ 0 HWI-EAS88_1:1:1:324:316 + chr18 69778792 GAGGTGGGAACGGGAGCGCTCTTTTAGAGAGTGCA :::::::::::::::::::::::::3::::47764 0 HWI-EAS88_1:1:1:450:874 + chr9 35112970 GAGAAACATCCACTTGATGACTTGAAAAATGACGA ::::::::::::::::::::::::::::::76776 0 HWI-EAS88_1:1:1:700:595 - chr4 32657771 GCAGGTTCGCCCTCCTGCTGGTCAGTGTAAGTTCC ))3351:&&+:1++::::::::::0:::::::::: 0 22,26,28 HWI-EAS88_1:1:1:331:688 + chr17 8309936 GCAGAATGTGAAAATCCCCTTTAGGGTGGGACTAT ::::::::::::::::::::::::::::::67777 0 HWI-EAS88_1:1:1:997:526 - chr16 16115576 CCACATGGTTCTAAAAAAAAGTCTGTAATTACTAC %2627:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:951:242 + chr15 56522981 GTTACATATTTGGACTTTATATGCTTATGACTTCA ::::::::::::::::::::::::::::6:77774 0 HWI-EAS88_1:1:1:274:814 + chrX 35013066 TCCTGGATCAGCCACACACATATGGCATGAACGTG ::::::::::::::::::::::::::::3:47774 0 HWI-EAS88_1:1:1:71:613 - chr14 102072477 TAGTGTTTATCAAATTGGGCTGATGAATTAATTGA 76467:::::7:::::5:::::::::::::::::: 0 HWI-EAS88_1:1:1:85:378 + chr6 96710226 TGGTTGGTGTGTGTGTGTGTGTGTGTGTGTGTGTG ::::::::::::::::::::.:::.:,:,:&7+7+ 21 HWI-EAS88_1:1:1:428:867 - chr14 68491034 GATGTGTGACTTACAGAGTGTTCCACACAAGCACC 46444:::3:::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:550:111 + chr7 110634524 GGGTTTATCTATCTTGTTGATTTTCTCAAAGAACC ::::::::::::::::::::::::::::::47777 6638 HWI-EAS88_1:1:1:85:640 - chr15 88558386 AACTCCATAGTGGAATCAATCACCGCATGCTAGCA 77272:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:95:618 + chr11 34603374 TGCAAAGTTGCTGAGACTCATTTCTGTGTGTGTGT ::::::::::::::::::::::::::::::727/7 0 HWI-EAS88_1:1:1:831:272 + chr1 91061776 GATGGCTAATGTCGCTACGCTACTGAAGAGTAATT ::::::::::::::::::::::::::::::66777 0 HWI-EAS88_1:1:1:220:378 - chr15 7182647 TTCTGGGAAATGTTATTTGGTGCCTTACCTCTTTC 64++7:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:107:783 - chr15 41748710 TACAAATGCCAACACCCACGGGCCAGCACAGGGGT 77477:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:96:133 - chrX 116706790 AAATCAGATATCTGTTCACCTTCTCTGCAAGAGGA 77774:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:347:245 + chr3 88253630 GGAAACAGAGGGAGAATCGGCGGGCAGCAGGACCG :::::::::::::::::::::::::::5::74446 0 HWI-EAS88_1:1:1:73:379 + chr3 51001490 TCTGTAACTGGAAATGATCCCGGAAAACCAGGAAC ::::::::::::::::::::3:::::::::77772 0 HWI-EAS88_1:1:1:88:982 - chr2 143395483 TCCAAGGAGGCTTCAAATCCTGTCATTTGCACTAC 44777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:889:108 - chr4 109507423 TAGGTTAGGATTTTCACGAACTCTCGCTTTTCACC 4767*:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:116:380 + chr4 42605827 TGGCCTCGAACTCAGAAATCCGCCTGCCTCTGCCT ::::::::::::::::::::::::::::::77777 5955 HWI-EAS88_1:1:1:110:618 + chr12 112017964 GGGGGGGGTGTAAAGTGATACTTTTGATTTTCAGC ::::::::::::::::::::::::::::::77444 0 HWI-EAS88_1:1:1:109:646 + chr18 61567798 ACTGTGGATGTCAGGATGTTACGTTCTAGGCCAAG :::::::::::::::::::::::::6::.:77472 0 HWI-EAS88_1:1:1:152:655 + chr11 76453129 GAGTTGGCAGAGGGAGCCTTCTGTTCTTTCAGGAT ::::::::::::::::::::::::::::::7,246 0 HWI-EAS88_1:1:1:107:251 + chr10 75034546 GGACTTTCTTGGTGGACCTCTATTCCTCCTCCTCC ::::::::::1:::1:::::::::::::::55551 0 HWI-EAS88_1:1:1:359:247 + chr8 25541687 GTCTCCCTGGTACTCTGCTGTCCTCCCTGTAGAGT ::::::::::::::::::::::::::::::77476 0 HWI-EAS88_1:1:1:206:922 + chr10 15518089 GCCTCTTTATTTTTCAGGTTCTGGCAGAGCCTCTC ::::::::::::::::::::::::::::6:67777 0 HWI-EAS88_1:1:1:117:902 - chr17 66135895 CACTAGGATAGTTCACTTATCACTGTTATGCCTCT 47777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:344:204 - chr3 153531530 ACTGGAAGAGGTCATGCAGCCTGGAATTTCACCTC 44477:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:960:238 + chr14 37045628 GGTTTCTGTTAGTAAGATTGTTAGGTTTGCCTTTC ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:111:832 - chr1 47701520 CAAAGGGTGTGCACATGTCTGTGACAGTGGAAAGG 27777::::::::::7::::::::::::::::::: 0 HWI-EAS88_1:1:1:210:414 - chr1 159360031 CTGACAACCACAACCTGAGTTCTATCTCTAAGACC 2%267:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:113:423 + chr9 73109791 ATGGGGGAGGAAATCAGGTTGGTCGTGTTTTGTAA ::::::::::::::::::::::::::::::72777 0 HWI-EAS88_1:1:1:179:609 - chr12 112743285 CACCAGCACTCAGAGATCACAGCCAGGTTCGCCAC 63/27::::::::::4::::::::::::::::::: 0 HWI-EAS88_1:1:1:100:481 - chr11 119564002 GAGGTTTCTGTGGCTGATAGAGATGCTGTTTGGAG 7+777::,::::::::::::::::::::::::::: 291 29 HWI-EAS88_1:1:1:412:613 + chr4 123271108 GGAATGTGCCTGGTAAAGATGAGGGAAGTCTGGGC ::::::::::::::::::::::::::::::44764 0 HWI-EAS88_1:1:1:199:268 - chr2 73892592 GAACTGATGGACTGTGAAAATCCAGAGAGTGTACC 77627:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:106:79 - chr10 14480275 ACATCATGCTGGTGAAGATATTCCTCACCAACAGC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:496:630 - chr10 75295195 GGAAGGCCCTGTCCCTGAGGAACTTGTCCTCCAGC 77776:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:360:608 - chr3 153774512 ATTCCCCTCTATTGTGCCACTTGTGCTGGGACCTC 66177:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:61:311 + chr10 20188508 TGCTGTTACTAGGAAAATTTCTAATGCCAAGATTG ::::::::::::::::::::::::::::::71777 0 HWI-EAS88_1:1:1:84:831 - chr15 23300174 TGAAGTTGATCCAATTAATATTTCTATGATTGCTT /77*7:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:178:149 - chrUn_random 5495981 TTTTGCCATATTCCACGTCCTACAGTGGACATTTC 772466::::::::::::::::::::::::::::: 0 19,30,33 HWI-EAS88_1:1:1:277:916 + chr10 108813503 GGACTGTGAGATGAGAATGAGATTAAACGTAAGGT ::::::::::::::::::::::::::::::76776 0 HWI-EAS88_1:1:1:711:412 + chr5 103942573 GACTTTAATGCCTTTGAATGAAATAGTTCTCCTTT ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:99:466 + chr1 92919260 TAGAGCCATTTGTCACCTCAGCCTCATGCGTTTGG ::::::::::::::::::::::::::::::77774 0 HWI-EAS88_1:1:1:91:328 + chr4 84203001 GCATGGCTCACCACTGCCTGTAATTCCAACCCAGG ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:185:969 + chr7 147561908 GACTCAATAAATGAATCTCTTGATTTAAAAGCTGA ::::::::::::::::::::::::::::::76776 0 HWI-EAS88_1:1:1:82:493 + chr13 13268471 CAGGACTGGTGACTCCCAACAGCTGCAGCTGGCCG ::::::::::::::::::::::::::::::74774 0 HWI-EAS88_1:1:1:410:656 + chr2 163439071 GCTAAGGAAACCTATTTGACTGTGTGGGTCACCAC ::::::::::::::::::+:::::::::::267/7 0 HWI-EAS88_1:1:1:850:732 + chr1 177499321 GAAAAGCCATGGAACTTCATACAAGTGTACCAAGG ::::::::::::::::::::::::::::::44242 0 HWI-EAS88_1:1:1:333:226 + chr13 12898740 GACCAGAAACATGAGTATTACCAAGTGCCTGTGTG :::::::::::::::::::::::::3::::7%744 1 HWI-EAS88_1:1:1:341:723 - chr2 98506739 CCACTTGACGACTTGAAAAATGACGAAATCACTAA 14744:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:885:752 - chr11 54560619 GGTATGACTTATCAATACAGTTCATTCTTCGCAGC 46477:::::::::::::::77::::::::::::: 0 HWI-EAS88_1:1:1:370:865 + chr9 103721462 GGCAGGTGGAAGTCATATTGAGAGCCTAAGGGCCT ::::::::::::::::::::::::::::::76567 0 HWI-EAS88_1:1:1:195:373 + chr6 31379504 GATATGTTCTACTAGCTGGGCTGCCTTGTCTGGCC ::::::::::::::::::::::::::::::76477 14 HWI-EAS88_1:1:1:691:713 - chrX 127271312 GAAATCTTAAAGCCCCTTCTTCCCCTGTAACACAC 27666::::::::4::::::::::::::::::::: 0 HWI-EAS88_1:1:1:200:783 - chr14 67312024 TTATCTACTTTCCCATCGATTTCTGTCTTGAGTTC 67767:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:608:471 + chr4 24833044 GGAGTTGAGTGCCTGGAGCTGAGACTGGGACGTGG ::::::::::::::::&::::+::::3:::77572 0 HWI-EAS88_1:1:1:907:589 + chr8 106537334 GACATGCCAGACAATCTGAGGTCCTAAAAGGTCAT ::::::::::::::::::::::::::::::74767 0 HWI-EAS88_1:1:1:231:898 + chr6 45680501 GGGGGTGTTACTTTTGCCTACTCATAAAACTCATA :::::::::::::::7::::::::::::::74276 0 HWI-EAS88_1:1:1:91:411 + chr16 39759874 CGTCTTATACATCAAAATGGAGTAAAAAAAATTGC ::::::::::::::::::::::::::::::77747 0 HWI-EAS88_1:1:1:187:290 - chr12 35349962 TGACCTGATGCATTCCTCTTGAGAAGAACATATTC 44664:::::::::6:::::::::::::::::::: 0 19 HWI-EAS88_1:1:1:652:723 + chr4 71689007 GCTGATTCTGCCAAGCCCATTCCCTTGGCTGTGGT ::::::::::::::::::::::::::::::71444 0 HWI-EAS88_1:1:1:65:447 - chr7 53532789 GAGTGCTTCTGTGCCAGTGTAGCTGCCTATGCCCA 77776:::::::::7:::::::::::::::::::: 0 HWI-EAS88_1:1:1:958:555 - chr2 56965751 TTGGCCCCTTTGCTGTAGATGGAAGGGCCTTTGTC 24444:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:376:236 + chr16 96817004 GACCCTCTGGTGGAGTGAAATGACACAGTTTTACT ::::::::::::::::::::::::::::::77677 0 HWI-EAS88_1:1:1:946:509 - chr12 30277069 TGTGTCCATGGGAATGACTAACTGGAGTTAGACAC 52677:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:337:645 - chr1 116566389 CCTTTTAGCTCCTTGGTTACTTTCTTTAGCTCTTC 47777::::5::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:128:119 + chr10 32770726 TTTTGTCTTCCTTCTTCCTGAGTTTCATGTGTTTT :::::::::::::::::::::0::::6:.:&7777 1 HWI-EAS88_1:1:1:565:485 + chr5 28346557 GTGGGAAAAGCAGTGCACCACTGTGGATGACAGCT ::::::::::::::::::::::::::3:::76677 0 HWI-EAS88_1:1:1:405:164 + chr1 17147751 GCAGCATGGTTGGCTGTAGCTAGGAAGGACAGAGG ::::::::::::::::5:::,:::::::::2726/ 0 HWI-EAS88_1:1:1:212:578 - chr13 6479104 CTGACCACACCACAGATGTTTATCCCACAACTTCC 6476+:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:961:620 + chr12 79819294 GTATAGATGTTTTCCTTACATGGTGTTTAGACACC :::::::::::::::::::::-7::::::346464 0 HWI-EAS88_1:1:1:741:570 - chr11 62987228 GTCCAGTCCTTCCTGTTACTGTTGGCTGCAATCTC 62644:&:::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:344:553 + chr10 55010320 GCTGTGCACCTAGATTGGGCAGATCTACTGCTACA ::::::::::::::::::::4:::::::::7727/ 10 HWI-EAS88_1:1:1:241:314 - chr3 105762738 TGGCCCTGCCCCAGCTCAGCTCCACAAATCCGATC 27677:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:864:838 + chr13 103825003 GAAAGTTAGCATCCATTATGACAAACCCCCTTTCC ::::::::::::::::::::::::::::::77776 0 HWI-EAS88_1:1:1:925:551 - chr11 50912572 CTATGTTGGCCTTCACAATGCTGACAGAAAGCCTC 647+6:::::::5:::::::::::::::::::::: 0 HWI-EAS88_1:1:1:262:800 - chr3 88434327 AAGTCTGCAGATTCAAGGACAGGCTGGGATTATCC 77747:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:458:520 - chr3 16974505 TAACCAGGCCACTCAGTGCTCCCAGAGACTAAATC 27444::::::::::::::::7::::::::::::: 0 HWI-EAS88_1:1:1:715:921 - chr19 34158722 GTGACACACCTCCTCCAACAAGGCTACACTTTTGC +6774::::,::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:675:808 - chr12 22821024 CAGCACATCTGATGCATCCCATCTAACTACATCCC +7777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:396:103 + chr16 16580857 GAATATTATATTGAGTGAGGAAACCCAGACCTAGA ::::::::::::::::::::::::::::::77572 0 HWI-EAS88_1:1:1:797:353 + chrX 58967089 GAGAAGAAGGTATATACTTTTGTTCTAGGATAAAA ::::::::::::::::::::::5:::::::74662 1 HWI-EAS88_1:1:1:406:214 - chr13 98751823 CTGAATGCCTTGTAAAATCCTCAAGCAGTCTGCTC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:219:829 + chr8 74418650 GACAGCTAACTGAATCTGGAATGTGTGTCTCTTAT ::::::::::::::::::,::::::::2::75767 0 HWI-EAS88_1:1:1:115:37 + chr11 30912580 TGTCTAAGGAACCGCCAGACTGATTTCCAGAGTAG ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:948:531 + chr3 22833876 GCTCAAAGCACATATTGCACCTCACACAATAATAG ::::::::::::::::::::::::::::,:76744 152 HWI-EAS88_1:1:1:889:553 + chr15 88661253 GAGGTCACTTACTCCAGCACGCTGGCCCCACAGAG ::::::::::::::::::::::::::::::76646 0 HWI-EAS88_1:1:1:106:213 + chr9 67031467 CTGTAACAGCAGCAAACTGGCTGGGCTTAAACACA ::::::::::::::::::::::7::6::::77+74 0 HWI-EAS88_1:1:1:210:690 + chr16 88433300 TCTTTTAATTAACTGTTGAGCCTTTACATTCAGAG :::::::::::::::::7::::::::::::7727, 0 HWI-EAS88_1:1:1:859:708 - chr3 153443662 ACAATGACAGATGCTAATTTAGGTCACAGATGTTC 7+774:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:781:695 + chr12 112560834 GAAACAATTTTAAATATAGACAACAGGCTACGATT ::::::::::::::::::::::::::::::77677 0 HWI-EAS88_1:1:1:779:278 + chr8 93370018 GTAGCCATTGATGGCTTGCCAGGAACTCACTGTGT :::::::::::::::::::::::3::::::44426 0 HWI-EAS88_1:1:1:479:587 - chr2 98505313 TAGTGAGTTACACTGAAAAACACATTCGTTGGAAA *7727:::::4:::::::::::::::::::::::: 0 34 HWI-EAS88_1:1:1:489:529 - chr11 109193641 GTTGGAGCCAGGAAGGACAGGGACTGAAAACCAAC 7+477:::::::::::::::::::::::::::::: 0 27 HWI-EAS88_1:1:1:166:121 - chr11 50624595 CCTCAATTTTTATTACATTTAAACATAGGTACAGA 24727:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:999:490 - chr5 21763989 ATTTACACCCCAGCTAACTAACCATCTACACCATC 74466::::::::::::7::::::::::::::::: 0 HWI-EAS88_1:1:1:605:618 - chr5 20481976 TTTCAGTAGATGGGATACCTAACATTAAAATGAAC 66777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:354:822 + chr4 104744944 TACAACAATAAAAACTAAATATTTCAAAGCCTATA ::::::::::::::::::::::::::::::44674 0 HWI-EAS88_1:1:1:493:197 + chr3 98355078 GTCTCTGTGTTAATGAGAAACAAAGTGGTCTTCCT ::::::::::::::::::::::::::::::77777 3 HWI-EAS88_1:1:1:242:720 + chr2 98507280 GCACACTGAAGGACCTGGAATATGGCGAGAAAACT ::::::::::::::::::::::::4::+++77777 0 HWI-EAS88_1:1:1:174:678 - chr12 26465257 TGACCTCTGCCTCTGCATGGAAGTTAGAGACCACC 67777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:612:761 - chr17 60066142 GGCTGGAAAGATTAAGAACACTTGCCAGTTTTTTC 55)55:::::::::::1:::::::1:::::::::: 0 HWI-EAS88_1:1:1:223:904 - chr11 75102617 AACCTGGAAATGAAACGTCTTTCCCTTTCTTCAGA 76474:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:84:350 + chr6 138276272 AATGCACAAACAATTAAAGATTTTGATACTAAGCA ::::::::::::::::::::::::7:::::77277 0 HWI-EAS88_1:1:1:790:347 + chr9 31978094 GACACCATAGTTGAAGCCCAGAGCTGAACAAAATC ::::::::::::::::::::::::::::::04277 0 HWI-EAS88_1:1:1:465:814 - chrM 3064 TTTAGCAACATCTAGCCTATCAGTTTACTCCATTC 02476:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:93:236 - chrX 34090612 ATGAATATCCTCTCTCTTTTTAATTTTTAATTTTT 777%7&::::::4-::&:::::::::::::::::: 0 HWI-EAS88_1:1:1:328:60 + chr1 39806605 GTGGCGCCTCCTCTGCTTGGCCTACTGAGAGGGAG :::::::::::::::::::::::::::::3424%7 0 HWI-EAS88_1:1:1:166:932 - chr18 64418758 GGAGTTGTGAATGAATAGAAGTTACAACTTCCAGC 74767&::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:498:217 + chr19 33402276 GGAAGTCAAGGCAGGAACTGAAGGAAAGTACTTAG ::::::::::::::::::::::::::::::77667 0 HWI-EAS88_1:1:1:540:820 + chr6 132316409 GATTATCTCAGAAAAAATTACCTAAATTGTCCTTT ::::::::::::::::::::::::::::::77767 0 HWI-EAS88_1:1:1:688:509 + chr3 91291942 GAAGCCATCAGCTTTTGGACTCACATGAGCAAAAG ::::::::::::::::::::::::::::::46447 0 HWI-EAS88_1:1:1:237:519 - chr9 42158436 TGTTGAAGGATTGGGTATCAGGTACCAGGCTGGGC 47+27:3::::::::::,::::::::::::::::: 0 HWI-EAS88_1:1:1:594:787 - chr11 49763362 TAATTGTCAACATGACTGAGGATGGATAACGGAGC 77707:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:842:367 + chr3 85433973 GCTTACAGAGCAGGATATGCTGTGAGCTCTGGTGC ::::::::::::::::::::::::::::::66666 0 HWI-EAS88_1:1:1:541:512 - chr15 21841648 TAGCAGTTTGACAACACACATAAAAGCTCTAGAAC 277/7::::::4::::::::::::::::::::::: 1 HWI-EAS88_1:1:1:107:630 - chr10 65700439 ATTCTCCCTCAAACAGACTCACTGGATGCCTTGTG 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:386:339 - chr15 9805112 TGTTTATCCTTCTTAATTCTATGGCTCTTTAGTAC 77777::::::::::::5::::::::::::::::: 0 HWI-EAS88_1:1:1:800:671 + chrX 17987304 GTCAAGAGCTCCGGGGTACTGGTTAGTTCATATTG ::::::::::::::::4:::,:::::::::7/762 460 HWI-EAS88_1:1:1:307:339 - chr13 52192648 CGGCTGTCCACACACGTGTACCCCTCCCCCAACAC 1))550&::::::0::+:+1::::1::::::::1: 0 29,33 HWI-EAS88_1:1:1:334:635 - chr9 95659328 TTACAAGTAAGGCCACACTCTCAAGTCGTGTATTC 26/)2::::::::44:+:::::4:::::::::::: 0 HWI-EAS88_1:1:1:194:116 + chr3 19490690 GAGAGGCTATTGGTGAAGCCTAGTTGCAGAGGAAG :::::::::::::::::::::::::,3::,47+61 0 HWI-EAS88_1:1:1:765:305 - chr6 109227670 AAGACTAGATGTAGATTATGTAGATTAGGTTTTTC 77744:::::::::::::::::::::::::::::: 0 27 HWI-EAS88_1:1:1:453:472 + chr10 57002395 GGATGACAAAACTCATAAGCTCTTTAGGACCAATT :::::::::::::::::4::::::::::::72277 0 HWI-EAS88_1:1:1:194:868 + chr17 80210336 GGAGTTATCACAGAGAAAGGAGCTTCAGTTGGGGA ::::::::::::::::::::::::::::::74474 675 HWI-EAS88_1:1:1:585:958 - chr14 69835948 AATTCTTTTAAACTGGCATCTTCTTACCCATTATC 77467:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:878:124 - chr4 35616910 ACTCATATCAGAGATAGCATTCAGGAAGTGATATC 74247::::::::::::::5::::::::::::::: 0 HWI-EAS88_1:1:1:880:559 - chr7 29167745 TGTCTTTGAAGGGACTTCATGAGAACTGTCACTTC 24244:3:::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:484:959 - chr18 61539992 TCTATCATCCCTTGGTCACCTAATCCACTGGCTTC 74476:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:631:798 + chr5 34543335 GTAAGACACAAAAGTGGAGGCACAGGAACTCGAGA ::::::::::::::::::::::::::::::76+74 0 HWI-EAS88_1:1:1:959:143 + chr2 15314287 GGTTTCATATGGTCTGTGAGTTGTATCATGGGTAT :::::::::::::::7:.::::7:::::::%4407 5 HWI-EAS88_1:1:1:97:817 + chr5 33971991 GTGCTGAGACGCCGGCTGGCCCTAGAGGGTGTTCC ::::::::::::::::::::5:::::::::44726 0 HWI-EAS88_1:1:1:513:570 + chr18 88174086 GGTATCAGGGTAATTGTGGCTTCATAGAATGAATT :::::::::::::::::::::::5:33:::+4677 8 HWI-EAS88_1:1:1:114:913 + chr11 73822624 TGAAACTCCAAAGTTTCTGCAAGGCAAAAGACACC ::::::::::::::::::::::::::::::77777 4331 HWI-EAS88_1:1:1:475:879 + chr4 108507641 GGGCGCTCTTAATTCTTGGGATTGAGAAGATATGG ::::::::::::::::::::::::::::::76772 0 HWI-EAS88_1:1:1:74:894 - chr13 98771228 GGCTTGGTGCGACCTAGCATGAAGCTAGCCACCTC 40777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:275:788 - chr17 80577649 TAGGCCTGTCACAAGGTAAGACTGTAAGTCAAGTA 676726::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:244:885 + chr1 60473612 TCTTGAATAAATTTAAGTGAATGGCTTCTTCCATC :::::::::::::::::::::::4,:::::2/577 0 HWI-EAS88_1:1:1:705:123 + chr16 34326682 GATATGTTATTTGAGGGTAGATATTCATACGTGTG ::::::::::::::::::::::::::::::2747, 0 HWI-EAS88_1:1:1:868:349 + chr15 59731032 GAACTGAACAAAGAATTCTCACCAGAGGAATACCG ::::::::::::::::::::::::::::::76477 18 HWI-EAS88_1:1:1:796:716 - chr19 19915124 CCCTTCCCTCACCAGCTGCAGATTTCCATTCATTC 47744:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:420:151 + chrX 141389154 GCAATGGTGTCAGCCTTTGGATGCTGATTATGGGG ::::::::::::::::::::::::::::::72747 10 HWI-EAS88_1:1:1:489:844 - chr5 104932314 CTGGCTCAGAATTGTTATCGTTTACTGTTCCCATC 74777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:112:809 - chr8 86264088 ATCTTGGGGTAACTCCAGCTGGTGCCCCGTTCCAT 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:705:854 + chr11 10071950 GACCTAAAGCTGTACTACAGAGCAATTGTGATAAA ::::::::::::::::::::::::::::::77777 1344 HWI-EAS88_1:1:1:232:230 - chr16 38316488 CTCTCCAAGGCTAGAACACAGAAGGCCATAGCGCC 747475::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:500:289 + chr10 101898003 GGCTTTTGCCCAACCCCTGGGGGCTTTAACCTATC ::1::::::::::1::::::::::::::+:-3151 25 HWI-EAS88_1:1:1:495:528 - chr18 4077005 AAACCAGGTCAAGCACACACTACTTTTATTCTTGC 77677:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:924:391 + chr2 98507129 GGTGGAAAATTTAGAAATGTCCACTGTAGGACGTG ::::::::::::::::::::::::::::6:47774 0 HWI-EAS88_1:1:1:366:70 - chr4 136210793 GGGTGGCTCATCTCTTCCCTTCATGTTAATGTTCC 44776:::::::::::::::::::::::::::::: 0 24 HWI-EAS88_1:1:1:112:318 - chr6 88946188 AAACACACCACCACCACCACCACCATCATCATCAG 4,&%&66...::.77.:7::::::::::::::::: 0 30,31,32,33 HWI-EAS88_1:1:1:608:778 - chr16 39740194 GGGAAGGCTCCAAGACCTGACACTATTACTGATGC 77667:::::7:::::::::::::::::::::::: 2 HWI-EAS88_1:1:1:140:226 - chr5 93792972 TGTCTAGAAAAACCAAAAAACCAAACCAAACCAAC 161+1&::::::/'::::::::::::::::::::: 1 29 HWI-EAS88_1:1:1:872:710 + chr1 105820669 GTAGATATTCAGAATTGAGAGTTCCTCTTGGAGGA :::::::::::::::::::::5::::::::70262 2356 HWI-EAS88_1:1:1:467:252 + chr10 27119787 GTTTATCCTCTGTATATAGGAAGGCTAGTGATTTG :::::::::::::::::::7::::::::::47777 0 HWI-EAS88_1:1:1:342:885 + chr1 40495567 GGAGCATTCCAGCCCCTATAAGGAGCTGGCTCAAG ::::::::::::::::::::::::::::::76647 0 HWI-EAS88_1:1:1:360:519 - chrX 17785061 GCTCTTCCTCCCTCATTAAAGCAACATTTCCTATC %7411:::::::::::::::::::::::::::::: 0 34 HWI-EAS88_1:1:1:860:372 + chr10 85765967 GCAGGCCCTGTACCCTATTAAAGTGTTTAGAAGAA :::::::::::::::::::::5:::::::.46264 0 HWI-EAS88_1:1:1:352:887 - chr17 13923541 AGAATTCCAAAGTCATACTTGCAGAAACTCTCTGC 76461:::::::::::5::::7::::::::::::: 0 HWI-EAS88_1:1:1:579:944 + chr6 19274392 GTCCACGATCAGGCAGGGGTCCCTGCCTGAACGTC :::::::::::::::::::-::::::2:::6)724 26 HWI-EAS88_1:1:1:369:87 - chr19 13934860 TAAGTATAATTTCTTTGAGCAATTGGTACATAACA 77777:::::::::::::::::::::::::::::: 1 HWI-EAS88_1:1:1:318:362 + chrUn_random 5665648 GGAGAGAGGTTGGAGACCGTACTGTTGGAAGGGCA ::::::::::::::::::::::::::::::77760 6 HWI-EAS88_1:1:1:310:838 - chr4 154696239 GAGGAAGCTTCTAGGCCACACTCCATCTAAGCGAC 13114::::+:::::::::::::::1::::::::: 0 HWI-EAS88_1:1:1:238:910 + chr16 37853307 TACATTTCAGATAGGTATTACTCGACATTTGTGAC ::::::::::::::::::::::::::::::272** 0 HWI-EAS88_1:1:1:735:691 - chr8 33529618 ACTATGTTTCATTCATTTACTTCCATCTTCTCTAC 64446:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:335:499 + chrUn_random 5889997 GAATATGTCAAGAAAATTGAAAATCATGGAAAATG ::::::::::::::::::::::::::::::66477 1 HWI-EAS88_1:1:1:261:898 + chr13 23390543 GAACCCCATGATAACACACATATACAAAGAACGTC ::::::::::::::::::::::::::::::47777 0 HWI-EAS88_1:1:1:660:515 + chr7 138344976 GTCTCTACAGGGTGCCAAGGAGGAAAGCATTTCGG ::::::::::::::::::::::::::::::77744 0 HWI-EAS88_1:1:1:73:531 + chr8 68114923 AAGCCACAGCAGCAGCGGTCGCCATCTTGGTCCAG ::::::::::::::::::::::::6:.6::27774 49 HWI-EAS88_1:1:1:284:865 - chr15 36378939 CACACACACACAGCATGCTGTGCCAGGCCCTATAC 44447:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:116:169 - chr3 142008386 GCAGGCAGAGCTGAGGCACAGTCTTTTGCTGGGCC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:855:888 + chr2 160565376 GGTTGTCCTGCTTTCTGCTCTGAGAACTGGCCTGC ::::::::::::::::::::::::::::::44747 0 HWI-EAS88_1:1:1:263:912 + chr5 43866456 GAAAAGGGGTCACAGTGTGCCTGTCAATTGACATG ::::::::::::::::::::::::::::::47764 0 HWI-EAS88_1:1:1:72:318 + chr10 104922468 CAAAAAAGAAAAGGCAGTTTGAAGTACTCTAAAAT ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:709:742 - chr10 74849829 GTTGTGGGGGTAGACTTTGAGACCCTCCTCCTAGC 74476:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:76:580 + chrX 4829958 CATCATGACCAAGAAGCAAGGTGGGGAGGAATGGG ::::::::::::::::::::::::::::::77774 1 HWI-EAS88_1:1:1:503:545 - chrX 34429223 CAGCCGTCATTCGGTTAAACTGAGCCGTATCATTC 17+46:::::::::::::::::::::::::::::: 0 32 HWI-EAS88_1:1:1:262:193 + chr5 30383649 GGTAATCATAAAGACTCCTGGCCTATCAGCTCAAT ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:70:747 - chr10 100579133 CCAGCCCATGTATGCTTTTTGGGTGGTCCCCAAGA 1155-::::::::::::::::1::::::::1:1:: 0 30 HWI-EAS88_1:1:1:731:628 + chr17 26821445 GCATGTAGGCTGCACCTTCCTTGGAAAGTATTGAG ::::::::::::::::::::::::::::::77626 0 HWI-EAS88_1:1:1:479:713 + chr6 140361067 GGATTAAAAGTGTGAACAATCATGCCTGGCTGAAT ::::::::::::::::::::::::::::::66247 0 HWI-EAS88_1:1:1:460:241 + chr8 60320208 GAACTACAGCTGAAAAATGTCACAAGCCGTTGTAA ::::::::::::::::::::::::,:::::67444 0 HWI-EAS88_1:1:1:501:821 + chr15 65547625 GGACCCTAAGACCTCTGGTGAGTGGATCACAGTGC ::::::::::::::::::::::::::::::62444 1046 HWI-EAS88_1:1:1:1001:577 - chr9 20930823 TGTGGTGGTTTGGCCATGTTCCAGCGTTCACAAGC 44466:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:103:406 + chr19 30999499 AACAAGACAGAAACCTGGAGGTAAGAGCTGATGCA ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:895:881 - chr15 23154304 ATGCATGCTTTATCCTATGCAATTTAGAACAATTC 77677:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:602:870 - chr1 98872753 CATGTGCTGATAGAGTACACAATGGTACATTATTC 77276:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:643:875 + chr1 101716116 GTTTTATGTGAAGTTCCTTGATCCACTTAGATTTG ::::::::::::::::::::::::::::::77774 5333 HWI-EAS88_1:1:1:463:194 + chr2 71402859 GTGTGCCTACAGGACAAGAACCTGGCTTGTGGGCA ::::::::::::::::::::::::::::::77744 0 HWI-EAS88_1:1:1:217:848 - chr2 4025352 CCCTGGTCCAAGGATATACAAACCATCACAAAGGC 44447:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:283:435 + chr3 32496509 GAAGGTGGGCTCAGGGCAACGAGTCATAAAAACCA :::::::::::::::::::::::::::::344746 0 HWI-EAS88_1:1:1:236:529 + chr12 117544647 GCTCTGCTTTCTCTGTCTCATGCCGCAGTTTGCAG ::::::::::::::7::::::7::-:3,::7174+ 0 HWI-EAS88_1:1:1:99:940 + chr5 112789336 TATTAACAGGTGTGTTTGGTGGCGGGGTAGTGTGG ::::::::::::::::::::::::::::5:77777 0 HWI-EAS88_1:1:1:441:580 + chr13 46029797 GGGCATTCTTCTTGACTTTATAAAGCAGAGCTAAC ::::::::::::::::::::::::6:::::77677 0 HWI-EAS88_1:1:1:77:597 + chr15 98518820 ACTGTGATAAAACACCCTCCCCAGGGCAGCTCATG ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:835:882 + chr5 36641713 GCGTCAGTGTCACTTAGGTGTCTGATGGTGACTCC ::::::::::::::::::::::::::::::24776 0 HWI-EAS88_1:1:1:186:522 + chrX 73361585 GCAGACCCTCCCATCTCTGGCGTGCATCACAACAG ::::::::::::::::::::::::::::,:6%742 0 HWI-EAS88_1:1:1:779:887 + chr4 42477453 GGCAAGGATGGTATAGGCTCCACTGGCACCGGCTG :::::::::::::::::::::,:::::+::+4417 394 HWI-EAS88_1:1:1:394:247 + chr15 67840867 GTAGCAACCAGAAAGTCCCAGATGCCAGGAAAGGA ::::::::::::::::::::::::::::::44667 0 HWI-EAS88_1:1:1:596:935 - chr6 89346371 GCTCCACTCCTGGAACAGAGGGAGATCAAGACCAC 47474:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:609:86 + chr8 116595772 GATTAGCACAATATACACAACATGTGTGCATTCAT ::::::::::::::::::::::::::::::77767 0 HWI-EAS88_1:1:1:910:778 - chr6 30557002 CCTTCTGAGCACATGGATTAAAGGTGTTTGCCACC 77674:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:860:108 - chr16 84677794 TGAGACTTAAAAATCAGGTGCTGTTTAAAAGGCCC 2777763::::::5::::::::::::::::::::: 0 HWI-EAS88_1:1:1:123:569 + chr4 134255529 GGGGGTATTTCAGATCAGCAACCTACCATCACGAG ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:823:660 + chr14 63528461 GAATAAATGAATGAATGTAAGGTCTGTTGAGCTGA ::::::::::::::::::::::::::::::26764 0 HWI-EAS88_1:1:1:782:417 + chr15 62737092 GTTTTACAAGGAAAGACACTGATCCTACACATATA ::::::::::::::::::::::::::::::77774 0 HWI-EAS88_1:1:1:937:791 + chr5 59705827 GGAAGTCAAAATATCACTCTTTGCAGATGATATGA ::::::::::::::::::::::::::::::77774 21 HWI-EAS88_1:1:1:78:296 - chr4 36873656 AGGCACAGGGGAGGATGCTTGAATCTCACTGAGTA 77677:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:458:217 + chr14 66500513 GTTAGATCACGATAAATGGCTATCAGGAAACTCAG ::::::::::::::::::::::::::::::76644 0 HWI-EAS88_1:1:1:633:789 + chr13 17661991 GAAAGTCAACTAAAGTATTATTAAAGTGACAATGA :::::::::::::::::::::::::6::::60746 0 HWI-EAS88_1:1:1:946:587 - chr11 3055333 TCTTGGACAACTGTGCATCAAACTAACCAGGTGTC 22442:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:941:392 - chr12 10027785 AATATTAGGAAGCAACAATTACTTTTTTTAATATC 77476:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:68:266 + chr10 111470315 GCCAGTAGCAGGAGAGATCGCTAAGACCACTGCAG ::::::::::::::::::::::::::::::74462 0 HWI-EAS88_1:1:1:366:310 + chr8 18166847 GATATTCTAAGAAAAATGATTTGAAAATTCATCTA :::::::::::::::::::5::::5:::::67471 0 HWI-EAS88_1:1:1:340:800 + chr15 101762732 GTCAGACACTGACAAAGTCCCAATGGCTTCCGACA ::::::::::::::::::::::::::::::7%274 0 HWI-EAS88_1:1:1:229:861 + chr3 104316044 GAAAAAGCGAAACCCGAAACGCCAAATCCTCACTG ::::::::::::::::::::::::::::::74676 0 HWI-EAS88_1:1:1:216:775 - chr13 112407111 AATAGAAGCGTGAGTTGAAAGTCAGCCAGGGCTAC 66777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:698:761 - chrX 85643415 TTTAATCTTCAAAAATTGCCATACATTACAAATCC 46677:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:123:566 + chrY_random 7783568 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA ::::::::::::::::::::::::::::::77777 21373 HWI-EAS88_1:1:1:59:664 + chr1 188599577 GAAAGAGTCAGATGTAGTTATTTGCACCCAACCAA :::::::::::::::::::::::7::::::77467 2 HWI-EAS88_1:1:1:309:144 - chrX 163243477 TAGACAGAAGCACATGAAGGAAACCCAGGTGTCTC 67767:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:284:523 + chr13 30160744 GTCCCAGGGTCACACCTAGTCAGCATGGTGAGTTT ::::::::::::::::::::::::::::::47777 0 HWI-EAS88_1:1:1:882:88 - chr13 105351782 ATTTATATCTTTCTACAATGTCAGAATTCATTGAC 76467:::::::::::::::::::::::::::::: 0 28 HWI-EAS88_1:1:1:61:241 + chr13 91813031 GCAGGAGGAGCACTAGGCAGGCAGCTGAGTGATGC ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:130:654 + chr1 101301523 TTTAAAACTAATTATCAATATAGGAAAATCTCATA 1:::::::::::1:::::::::,11::1::55511 0 HWI-EAS88_1:1:1:738:180 + chr18 43912807 GATACTTGCACCCAAACAGTGGATAGAAGCAGCTG ::::::::::::::::::::::5:::::::67774 0 HWI-EAS88_1:1:1:635:515 + chr8 110888284 GAAAATTAGTGAGATTAGGAATCACGGGCATATTG ::::::::::::::::::::::::::::::7474+ 0 HWI-EAS88_1:1:1:684:593 - chr15 38380001 TCAGCTTACACGGGCTGTCCCATCACTCGTCAGCC 33566::0::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:513:963 - chr2 177504442 AGGAATGTCCCCCAAATACATAAGCGAACACATAC 77677::::::.::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:470:603 + chr12 82923288 GAGCTCTTCTGGGGCTGTAAAAGGAGAACACTGAG ::::::::::::::::::::::::::::::44724 0 HWI-EAS88_1:1:1:247:269 - chr6 46635037 TTCAAGGCAAAATATACCTTGCAAAGCCATTTTCC 44%76::6&:::5::5:::::.::::::::::::: 20 26 HWI-EAS88_1:1:1:554:883 + chr4 31000498 GGTCTGAGACTGTAGAACTTAAGGAGAAAGTGTGG ::::::::::::::::+::3+:442::::+6//*5 0 HWI-EAS88_1:1:1:721:400 - chr9 106846628 TTTGCTCAGCAGTCGTCAGAGAAGCTTTCTCCTAC 424773::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:356:92 - chr8 97105889 AGGAACATCTGCAGCAGATAGAGGATGTGCCAAAC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:95:479 + chr1 69270515 CCATATAATATGAAGCAAATAGAAGTATTATCAGC ::::::::::::::::::::::::::::::76/22 12 HWI-EAS88_1:1:1:89:735 - chr11 105144080 GCTAAAAGTGTTCCTAAGGACACCTTTTCTAAACC 67777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:448:969 - chr8 129053334 GAATCTACCCATGTACAGATTTGGCACCCTTTTCC 47777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:100:747 - chr4 107414153 AACCAGAATTGATTGCACTCTCTGGAGGTCGACAT 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:942:550 - chr18 4364056 TGGGCAGGGCTGAATAAGATGCATCTTTCCGGCTC 26462:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:92:897 + chr16 49863664 TCAGGCTTCCTGGGAGGACTCAATGTGAGTAGGGA ::::::::::::::::::::::::::::::71744 0 HWI-EAS88_1:1:1:243:857 - chr11 64298823 CCATTCAGGATTGGGAGGACAAGGTAGGAGGGAAC 77744:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:875:549 + chr17 46548277 GTCATCATCAGATCTTCTCTTGCCTCCAGATATCA ::::::::::::::::::::::::::::::42774 0 HWI-EAS88_1:1:1:357:398 + chr3 136870147 GATTGATTTAATGCAGCTTCTTTTTACACTGCCCT ::::::::::::::::::::::::::::::,7467 0 HWI-EAS88_1:1:1:87:816 - chr14 76025505 ACTGGAACATTCTACTGTGGCTTATCTAACATTCA 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:167:649 + chr9 7683485 TTCTTCTTCTGTAAGATCACCAGCAATCCAGCTTG ::::::::::::::::::::::::::::::44624 0 HWI-EAS88_1:1:1:138:903 + chr3 11961887 GGTCCTTTCGATAAAATCTTGCTAGTGTATGCGAT ::::::::::::::::::::::::::::::47477 18 HWI-EAS88_1:1:1:440:836 + chr2 19120116 GAACAGAGAGCAGGTGGGACAGGAGTCTGCAAAGT ::::::::::::::::::::::::::::::46474 0 HWI-EAS88_1:1:1:169:429 + chr4 23687605 GGCCAAGTAACATATAAAGTCAGACCTATTAGAAT ::::::::::::::::::::::::::::::67767 0 HWI-EAS88_1:1:1:438:149 + chr2 150613231 GCCGGCGGATTTTCCGGCTGTTTTCCAGCTGTGGA ::::::::::::::::::::.:::5:+6::%2%2% 0 HWI-EAS88_1:1:1:207:932 - chr16 36726435 ATCTTCCAGTAAAGCTCAGAGAAGAATCAGGCCAC 76774:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:79:164 + chr6 68516258 TTTATAACCTGGAGAGAGTTTGCCTGTTGACATTT ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:692:733 + chr6 29281330 GTCTCTACTCCAGCCACATCTGTTGCAGTCAGATT ::::::::::::::::::::::::::::::74467 0 HWI-EAS88_1:1:1:411:235 - chr11 6381887 CCAGGCACACTATAGGTCACTTGTGGGTCTTCTGC 46774:::::::::::::::::5:::::::::::: 0 HWI-EAS88_1:1:1:589:804 + chr1 179800979 GGACTAGGTGGGGTGGGAGTGCTGCGTTCTGATGA ::::::::::::::::::::::::::::::42672 1754 HWI-EAS88_1:1:1:74:426 + chr19 55214035 TCTTTGCCAGGCCCTGAGGCTCCCTGCTGACTCAC ::::::::::::::::::::::::::::::67777 0 HWI-EAS88_1:1:1:108:901 - chr4 120221957 CTAGGCAGTCTAGTCATTAAAGACATTTTTACAGG 11771::::&::7:::::::::::::::::::::: 0 HWI-EAS88_1:1:1:276:401 + chr6 12678887 GCCATTAAATGGTTAGAATTTAAGTGCATTTATTA ::::::::::::::::3:::::::::::::75756 0 HWI-EAS88_1:1:1:671:730 + chr9 3014062 GTCGTCAAGTGGATGTTTCTCATTTTCCATGATTT :::::::::::::::::::::,::::::::24677 19 HWI-EAS88_1:1:1:442:703 - chr2 157046318 TCCCATTTCTTTTCCCTTCCTCTCTCCCTCCCTCC -4)4,::::0:::::::0::::::0:::::::::: 0 34 HWI-EAS88_1:1:1:836:281 + chrX 145728394 GTTAGCTTCTGTATTCCACACTCTAACCTGTGCAG :::::::::::::::::4::::::+::::+4/22% 6 HWI-EAS88_1:1:1:375:655 - chr13 43492985 GGCATGCACCACCACCTACAGGCCTTGTTTTGATC 77244:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:697:106 - chr6 138612338 CACCTCTTCTTCCTCCTCTCCTTCTCCTCCTCCTC 7/772:::::+4:::54::::44:::::::::::: 0 19,33,34 HWI-EAS88_1:1:1:207:915 + chr17 31199817 TATAGGTGTTCATTAGCAGAGTGGGTTAGCACAGT ::::::::::::::::::::::::::::::47477 1 HWI-EAS88_1:1:1:656:776 + chr19 61137687 GTGTCAAGTCCACCATTTCCTGAATATTTGGTCTC ::::::::::::::::::::::::::::::/5775 0 HWI-EAS88_1:1:1:371:249 - chr9 63628230 GCTCATGCTCCTCAGGAAGGATAGGGACCCTCTCC 45415::::::::::::::::::::1::::::::: 0 HWI-EAS88_1:1:1:192:836 - chrX 149420568 AGCAGAAAGGAAAAACAGGAAGACTGAGAAAATAC 76277:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:820:240 - chr16 47623082 TAATCCAAATACCCAATAGAACAAGACAGGAAAAC +77)+,,::::.,7::::::::::::::::::::: 0 28 HWI-EAS88_1:1:1:83:857 - chr19 61113994 GCTTAGCCAATGCTATCCTCAGAATTCTTACCTCC 74777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:444:237 + chr6 35409343 GCCTGCAAGTTGCACAGGGAGCCCGAGGGATGTAG :::::::::::::::::::::::::,::::67647 0 HWI-EAS88_1:1:1:77:278 + chr8 52136633 TACAGGATTAGCTCTTAGGCTCACAGGCTTTTACC ::::::::::::::::::::::::::::::77774 766 HWI-EAS88_1:1:1:324:830 - chr18 74967801 CGGCCGTGCACCCAAGCATCACGAACAAACAGACC 74647:::::7:::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:160:887 - chr1 20909434 AGAGACATTTTTGTGATAGCATGCTTGCATAGCAA 74777::::::::::::::7::::::::::::::: 0 HWI-EAS88_1:1:1:883:579 - chr10 120154582 CTCACAGGGCTTTATTAAGTGATGTCAAGCTCCCC 44424:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:219:729 - chr7 81767050 AGGCACAAATAAACACACTTAGAGATCCTTAGCAT 727*7+:::::::.-::5::::::::::::::::: 0 HWI-EAS88_1:1:1:718:906 + chr3 109294286 GGAATTCATAGAGGGGATTTGAGGATCTGGCATTG ::::::::::::::::::::::::::::::66774 0 HWI-EAS88_1:1:1:81:887 - chr8 78210594 ATCTGATAGGATGCTTGCAATAATTTTAATATTTT 74777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:669:891 - chrX 111281872 GGGATCACATCAAGAATTAACAATATTTATTTTCC 664773::::::::::::::::::::::::::::: 0 17 HWI-EAS88_1:1:1:578:500 + chr4 84169733 GAATTTTAACACAATGCCATTTCCCTAGTTTAACC ::::::::::::::::::::::::::::::74777 0 HWI-EAS88_1:1:1:190:55 + chr4 48169879 GTTAGGTTAATGTTCTAAGCAACTTAAATCAAGCT ::::::::::::::::::::::::::::::77767 0 HWI-EAS88_1:1:1:357:281 + chr16 35319124 GAGGAGTAACTGGAGATCTGATTCAGCAGCTAAAA ::::::::::::::::::::::::::::::74447 0 HWI-EAS88_1:1:1:740:881 - chr11 96691439 GAGAGAAGTCCAATGAGTGTGCAGGCCCGCCCCCC 77464:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:379:531 - chr18 55610334 GACACCTTTAAACTCTTGAGTCAAAATACACATGC 67772:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:98:807 - chr10 59347466 TCTCAGACACTAGGATTAGAGGTGTGCTGTGTGCC 67777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:923:93 + chr19 58478181 GTTAGTTTTCCTCAACTTGACACAGAGCCAGGGAT ::::::::::::::::::::::::::::::27447 0 HWI-EAS88_1:1:1:692:765 + chr4 89942917 GGTTAAGGGAAATGCAATTGCTGATTCAGCCACCA :::::::::::::::::::::::::1::::75777 54 HWI-EAS88_1:1:1:579:797 + chr18 21737313 GTGCAATGAAGATGTGGAAATCAAGGTTTGTGTGA ::::::::::::::::::::::::::::::77644 0 HWI-EAS88_1:1:1:905:418 - chr2 17937214 CACGCACCTGCAGATGCAGCCAGCAGAATTTTCTC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:341:479 + chr10 61318033 GGGTTGTTGGCATCATGAAGGCACACAACAGGTGA ::::::::::::::::::::::5::::::376474 0 HWI-EAS88_1:1:1:490:922 + chr11 68375151 GACAGATCTTAGCAGGTCCAGGTGTGAGCAGGCAA ::::::::::::::::::::::::::::::77674 0 HWI-EAS88_1:1:1:804:56 - chr3 104733674 CCTGGCTAAGATGAAAACCTCATCTGACATTATGC 76264:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:371:614 + chr12 39991191 GTCAACATCCTTAGTCATCAGGGAAATGTAAATCA ::::::::::::::::::::::::::::::26774 0 HWI-EAS88_1:1:1:365:281 + chr4 63329328 GCCAGTGGCAAGGTGGTCAGCGGCAATCCCCATCC :::::::::::::::::::::::::5::::72467 0 HWI-EAS88_1:1:1:90:437 - chr10 110564558 AAAGACAGGTGGTGACCACTGGATGGGTCCAGTCT 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:519:801 - chr12 8600932 CGTCTTAACTGAACTCCTGGGAGAGAACCTTATTA 27727:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:55:174 + chr7 57926732 TTGAAATTTCCAAGTAAAACGAAAACTCCTGGCAC :::::::::::::7::::::::::::::::6624+ 0 HWI-EAS88_1:1:1:926:152 - chr3 92634964 TCAAAAATAAGTCATCAAATTCTGAAAAGATCTTC 62777::::::::::::::::7::::::::::::: 0 HWI-EAS88_1:1:1:594:65 + chr7 110235696 GTGTCATGGCATGTGACAAGAATCATAGCAACAGG :::::::::::::::::::::::::::6::7777% 0 HWI-EAS88_1:1:1:969:461 + chr10 91601940 GAGAGCTTCTAAGTACATAGACCCTCTATGACTGC ::::::::::::::::::::::::4::::267777 0 HWI-EAS88_1:1:1:982:414 + chr18 84509585 GTCCAGCAGGGAGAGCGAGGGCACCCGTGACATTC ::::::::::::::::7:::::::::::::74+44 0 HWI-EAS88_1:1:1:590:459 + chr2 98507409 GACGTGAAATATGGCAAGAAAACTGAAAATCATGG ::::::::::::::::::::::::::::::74774 0 HWI-EAS88_1:1:1:189:354 + chr8 95860503 GACAAAGACCTTTGACAGCAGCTTCCATTCTGCCT ::::::::::::::::::::::::::3:::74777 0 HWI-EAS88_1:1:1:392:197 - chr18 84010337 CATTTGAGCCCTCCCTGTCTTCACCCTGCCAGGGC 644+6:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:980:597 + chr15 36224994 GAATGAAAGGGCAACTGCTGTCCCAGGCAAGATGG ::::::::::::::::::::::::::::::76472 0 HWI-EAS88_1:1:1:309:892 - chr9 63046001 CACTGCAGAGTACTTCAACAGTCATCTCATTAGGC 26767:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:476:232 - chr1 149329348 ATAAACTGAACACTCTTACACACTGTGGGGTATCC %4777::::::5::::::::::::::::::::::: 0 34 HWI-EAS88_1:1:1:617:931 - chr2 49745917 CGTTTCTAATCCCACGTTCACACCAGTTCTCTCTC 54451::::::::::::::::::::::::::::1: 0 HWI-EAS88_1:1:1:100:764 + chr14 40499982 AAAATGAGAAAAAACTTTGATTCTGATATGTGGCT ::::::::::::::::::::::::::::::77277 0 HWI-EAS88_1:1:1:680:770 - chr14 100005090 TTTCAAAAGTGAACATTGTACTTTGCTCTGGCAGC 72677:::::::::7:::::::::::::::::::: 0 HWI-EAS88_1:1:1:934:89 + chr12 88133136 GTAGGAGTAACCAAGGTCTTGTTGGAGGAAGTATG ::::::::::::::::::::::::::::::77477 0 HWI-EAS88_1:1:1:180:810 - chr2 72892471 CATGCAAGCCAGCCTCCTTGACCTCACCTTCACCC 2622*:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:145:681 - chr11 65274313 GCTCTCATATAGGTGAGTGACGGATTACCTAGTAC 322)6:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:154:732 - chr2 88483322 TTATGGACCGGGAGAGATGCAATAAGATGTTGCTC 44646:3:::::::::::::::::::::::::::: 0 33 HWI-EAS88_1:1:1:659:673 - chr18 7254708 ACTATAACCAGCTATACACTCAAACTCAGGTCCTC 77476:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:464:138 + chr6 21900193 GCTGTGACAAGGAGGCGGCGGGCACACCGTACCGT ::::::::::::::::::::::::::::::26775 0 HWI-EAS88_1:1:1:865:304 - chr1 156941051 GGCATTCCACGAGGCATGTATATTTTACACTACGC 44466:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:607:65 + chr18 32510769 GGCAATGAGCAGCTGATGGAATGACATCTTCACTG :::::::::::::::6::::::::::::::64677 0 HWI-EAS88_1:1:1:606:750 - chr9 6521376 GCCAGAGTTCAGAGATAAGGTGCCTATCCTTGGAC 64747:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:276:410 - chr5 53971040 GCTCACCAATATGGCCAATCTCCTAGCCAGCTTGC 24*24:.:::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:179:375 + chr5 47782368 TCTTGCTCTGGATCATTTGTCCTGAGTCCCCTTGT ::::::::::::::::::::::::::::::77767 0 HWI-EAS88_1:1:1:611:79 - chr4 109292772 AGATTACCTCCATAACAATCGTGCCACTATTGCTC 13)%5&::&&:::::::&:::::::1::::::::: 0 14,17,32,34 HWI-EAS88_1:1:1:930:164 + chr12 25866440 GATAAAACCAATCTATTCATAGTGCATAATGGCTA ::::::::::::::::::::::::::::::77476 0 HWI-EAS88_1:1:1:531:966 - chr4 152871828 TGCAGGTGCTCCCTGCATACAGGACACTGCACTCC )67+7::::::::3:::,::::::::::::::::: 0 HWI-EAS88_1:1:1:108:402 - chr4 40651208 CCTGAGAGCAAGGGAAAGCCGTGCTTTTGTTGACA +2+7733:.:::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:80:642 + chr1 35962822 CAAGGGCAGGAGCCATCTGCACTAGAGAAATGTTG ::::::::::::::::::::::::::::::77774 0 HWI-EAS88_1:1:1:205:696 - chr4 10995327 AGTGCAGGACCACCTGCTTAGGAATGGGACTGCCC 77774:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:988:578 - chr8 11615345 TTAGTTCCCAGCATTTCCGTGAGCTAGCTGTTCAC 44444:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:210:508 - chr9 101639144 CCTCTCTGCCACTTTCAATACTGAGACTAGACTGC 24444:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:515:830 - chr9 26992010 TTCCAGAGCCCTGAAAAATGCAGTCAACCTGTAAC 76777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:904:104 + chr5 85102840 TTATAACCCGTCTTTGTCTCTTTCTTATACTGATT :::::1:::::::::::::::::0::::+:5-155 0 HWI-EAS88_1:1:1:346:321 + chr1 32024086 GGAGACAGAAAGAAAGAGTGGGAGAGTGGCTTTAA ::::::::::::::::::::::::::::::777*2 0 HWI-EAS88_1:1:1:832:50 + chr11 26556578 GCAGAGAATGGCCTTGTTGGGCATCAATGGAAGAA :::::::::::::::::::::::::3::::2+4%1 0 HWI-EAS88_1:1:1:90:716 + chr14 52360866 CTGCGACCTGAAAAATGGTGGCTGCTTCCTGACTC :::::::::::::::::::::::'::::::++777 0 HWI-EAS88_1:1:1:980:484 - chr14 106807648 AGCCTTGGGTCTGACAGTGGAACTCAGGAAACAGC 64444:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:832:334 + chr1 169599474 GGCAAAGTTGGGATCTCGAAGATTAGACTCAGGAA ::::::::::::::::::::::::::::::47744 0 HWI-EAS88_1:1:1:520:851 - chr7 149527166 AACTGCTGAGCCATCTCTCTAGCCCTCAGATGTCC 77727:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:488:376 + chr9 53047723 AAAACAAAACAAAACAAAGATTGTTAGTTTGTTAT ::::::::::::::::5:::::::::::::27767 0 HWI-EAS88_1:1:1:842:277 - chr12 30624107 CAGCTGCACTTCTGCATTCCAAAAAGAGTTTTCAC 46666:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:404:453 + chr13 7748634 ATCCACTTATCAGTGAGTACATACCATGTGTGTTC ::::::::::::::::::::::::::::::77777 83 HWI-EAS88_1:1:1:483:934 - chr17 15041865 AATCAGTATCACCAGAGCACACTGGCCAGAAAGCC 77477:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:205:928 - chrX 91998074 GAAGAGTTTGTTTTCCCATTGAGATTCCCTTGGAA 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:963:539 - chr17 65985558 ACAGCGCCAGGTAGTAAGGCTGTCCCACTCTCTGC 44444:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:479:312 - chr14 10418861 CGGACAGGTGTGAGGATGGCAATGTGCTCTGTCAC 47766:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:914:587 - chr7 16776484 AGAAGCCAAACAGGCAAGGAGGAACACTCGGTCTC 74776:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:107:471 + chr15 25306210 AAGGGCCTTATTCATTCGAACCCCTATTCTCCTTT ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:559:113 - chr8 50069390 CACAAACAAGGGGGACTCCAAAGCACAAGCTATCC ,7477:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:95:652 - chr14 79582034 TTCTCCCATGTAGTCAGGCTCCATGGCAAGTTCCT 77474:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:346:208 - chr2 126229406 TAAAAGGGTAAGCACAGAAAAGACCATGTTTAGGC 67777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:59:586 + chr5 123262186 CAGGTATCTCCTGCATGACTTCTTCCTTTGTGTTC ::::::::::::::::::::::::::::::72777 0 HWI-EAS88_1:1:1:950:465 - chr9 3035817 CCTACAGTGGACATTTCTAAATTTTCCACCTTTTC 67767:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:37:511 - chr12 112569389 CAGTGGCGTCCAATGGCACCAGTGTCGGGAGCGCA 77446:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:842:298 + chr13 51449553 GCACGGAACAGAAGCAGAGGTGTGACTGGGGTGGT ::::::::::::::::::::::::::::::7*774 0 HWI-EAS88_1:1:1:728:418 - chr9 3024084 TTCCGCCTTTTTCAGTTTTCCTCGCCATATTTCAC 40766:::::::::::::::::::::::::::::: 29 30 HWI-EAS88_1:1:1:766:289 - chr14 65592611 TGTACACTGCATGCCTGTGCTTCGCTAGTTTCATC 24444::3::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:442:335 + chr8 99916411 GCTGACAGAAGGGGTTTGTTTGTTTGAGTGCACAA :::::::::::::::::::::::::3:,:364740 0 HWI-EAS88_1:1:1:741:333 - chr6 84142154 GCTTGGCTCAAAAGAACAGTGACCCACTAATAGCC -)3-4:+::::::1:::::::::1:1::::::::: 0 HWI-EAS88_1:1:1:471:619 - chr6 48996704 CTGCAGAGGGAAACGGAATGACTCAGATGTGTTCC 44726:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:432:389 - chrX 124926192 CCTCATGGATTCTTCTCCAAAATGGATGATACAGT 47777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:429:771 + chr4 112242027 GTTAAAGTAAGGGCTCTAATTGGCAAAGAATGGGA :::::1::::::::::::::::::::::+:5553) 326 HWI-EAS88_1:1:1:97:74 - chr13 53589422 AAGGAAGTTCATATCAGGAAGTGCCCTTTGTTAGA 77667:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:566:896 - chr6 29467360 AACCACAGGCCAATAGTTCTGCATACAGCTGTCTC 76777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:819:674 + chr14 65248061 GTAACTTTGAACTTCTAATTCTCCTACCTTCACAG ::::::::::::::::::::::::::::::72762 0 HWI-EAS88_1:1:1:172:671 + chrX 33576656 AATTTCATGAACCAACTGATATCTCTGTACAACTG ::::::::1:::::::::::::::::+::+33-5) 0 HWI-EAS88_1:1:1:70:991 - chrUn_random 5872863 ACTGTAGGACGTGGAATATGACAAGAAAACTGAAA 74777::::6::::::::::::::::::::::::: 0 24,33,34 HWI-EAS88_1:1:1:945:213 - chr2 98502417 TCATGTTTTTTAGTGATTTCGTCATTTTTCAAGTC 67777:::::::::::::::::::::::::::::: 0 31 HWI-EAS88_1:1:1:450:742 + chr11 99841702 GCCTGGTTCCTGCTAAGACTTGCGATGGAATGGAA ::::::::::::::::::::::::::::::77704 0 HWI-EAS88_1:1:1:206:572 - chr8 74045910 TGCTTAACTTAAAATATACATGACTTTATATCTTC 76177:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:876:554 - chr6 10913668 AAGACCTCTCCTGAGCATATATCCAGAAGATGCCC 45%3100:11:,:::::::::::1::::::::::: 22 32 HWI-EAS88_1:1:1:197:728 - chr2 76084622 GTGATTTCCAAACCATTTTGTCTGGAAGAGAATTA 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:273:941 + chr9 3018047 GTGGATGTTTCTCATTTTCCATGATTTTCAGTTTT ::::::::::::::::::::::::::::::27777 27 HWI-EAS88_1:1:1:453:342 + chr7 119991870 GACCTGAGTTTAGATCTTAGACCATAGCCCCAACA ::::::::::::::::::::::::::::::76171 0 HWI-EAS88_1:1:1:383:476 - chr5 60669036 TAAGTGATACATGATAGAATCTAGTGTTTTAGCAT 37776::::1:::::::::4::::::::::::::: 0 HWI-EAS88_1:1:1:348:47 + chr5 36813946 GAGGGGAAAGAACCAGGCACTGTGGCACTAGTTAG ::::::::::::::::::::::::::::::74766 0 HWI-EAS88_1:1:1:366:313 + chrX 3402464 TAAAAATAGCTATCTTGCCAAAAGCAATCTACAGA ::::::::::::::::::::::::::::::67772 68 HWI-EAS88_1:1:1:235:340 - chr12 55632262 TAGCACAGCTAACTTGCTTGCTCGGGAATCCTACC 04444::::::::,5:::::::::::::::::::: 0 HWI-EAS88_1:1:1:107:687 - chr18 8078052 ATATTCTCTCCACTGTGAGGGACCAAATTCTCCAA 76767:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:230:889 + chr15 88906807 GCTTCTTTAGAAGTGGGGACAGTCTCCAAGCTTTC ::::::::::::::::::::::::::::::46774 0 HWI-EAS88_1:1:1:282:781 - chr12 3109994 AATTCATGGAAAATGAGAAACATCCACTTGACGAC 77644:::::::::::::::::::::::::::::: 3 32 HWI-EAS88_1:1:1:150:685 - chr4 25004887 CTTTTCTTGATCATGTTATTTGTCTATGCTTTTAC 236232:::::4::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:904:762 - chr11 93969163 GACTTCCAAGTGCTAGGATTACAGGTGTCAGTCAC 67764:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:101:33 - chr6 67242125 TTGTCTTCACTGTCACAGACCTTTGGAGTAGGAAC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:226:671 - chr17 23672319 CCACGACCCTGAGATTAAGTGTCTCATGCTCTACC 47777:::::::::::::::::::::::::::::: 3 15 HWI-EAS88_1:1:1:89:837 - chr1 8056457 AAGCAGCCTCAGTGCAGCTTATGTGGCACTTCTTA 77777:::::::::::::::::::::::::::::: 0 14,19 HWI-EAS88_1:1:1:565:768 + chr6 93504853 GCAAATCCACAGCTACCTCAGGATTGACCTTGAAG ::::::::::::::::::::::::::::::77647 0 HWI-EAS88_1:1:1:661:630 - chr13 52038780 TTCTCCGTTCCTGCCTCCTGATCTACTTGCTACCC 44646:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:366:305 - chr13 17623312 CTGGAAATCCCAGTGACCTGGTTCCTGACTCTGGC 72477:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:694:750 - chr13 67492772 GCTACCATGATTCAAGTGCTGGTATTAATGGCAGC 66446:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:242:264 + chrX 147541949 GGTTTCCGTAAGGGCTTTGGGAGTGTTGCACAATT ::::::::::::::::::::::::::::::74677 0 HWI-EAS88_1:1:1:649:958 + chr5 76399207 GTGTGTCTGTGTGTCTGTGTGTCTGTGTGTGTCTG ::::::::::::::::::::::::::.:5:27+7% 44 HWI-EAS88_1:1:1:799:87 - chr1 29511051 AGTGGGGAAAGGGCAGGGAAAGGAGGCACTGTGTC 76067:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:251:341 + chr13 29019191 GAGGAGTTTGTTTTGGGTTTGTGAGTTTGGGATCC ::::::::::::::::::::::::7:::6.,4777 0 HWI-EAS88_1:1:1:984:477 - chr12 3109948 TTTAGAAATGTCCACTGTAGCACATGGAATATGGC 14174:::::::::::::::::::::::::::::: 0 14 HWI-EAS88_1:1:1:176:102 + chr12 102340086 TAAAAATAAAAATTAATATGACTCAACATTGGTAC ::::::::::::::::::::::::::::::47777 0 HWI-EAS88_1:1:1:349:688 - chr8 11235884 TATACAGCGGGCACACATCTGGGTACACTGAGTTC 47)44::3:::::::::5::::::::::::::::: 0 HWI-EAS88_1:1:1:430:379 - chr8 51378714 ACAGAGTGAAGATTATTATGAACTAAGAATGAATT 72777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:699:539 - chr17 27592681 ACCACACACACACCACACACACAAAGCACACATAC 62**/:::22,::::::::5:4::::::::::::: 0 HWI-EAS88_1:1:1:496:845 + chr4 53077909 GCTGCCTTGGCCATGGTGTTTTATCACAGCAATAG ::::::::::::::::::::::::::::::76742 0 HWI-EAS88_1:1:1:998:430 - chr7 25384942 TTTTTCTCTTGCAGTGCTGGGGACGGAAACTAGGC 66464:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:116:700 + chr1 126449933 AGGGACTGCTGAGCTGGCTTGGGGGTCACTGAGCC ::::::::::::::::::::::::::::::47777 0 HWI-EAS88_1:1:1:353:866 - chr12 88581933 ACTTAAAAGCGTGTACCACCACTGCCCGGCTTTAC 1,%%3:::+:&:0:(:0:0:::::1:::::::::: 0 33,34 HWI-EAS88_1:1:1:224:84 + chr11 106362802 GTCCTGTACTGTCCTTTGTCCCTGCCCCAAGCGTG :1:1::::1::11111:::1:11:::01::131%1 0 HWI-EAS88_1:1:1:640:539 + chr10 75421961 GATGCTTTCCACACCCACGGTCTTCCTGCTTTAAT :::::::::::::::::::5,:5::::::,64%44 0 HWI-EAS88_1:1:1:850:348 - chr14 41621594 TAAAACGGCAAAGCTTCTGTAAGGCAAAAGACACC ,5555:+::::::,::::::::::::::::::::1 53 28 HWI-EAS88_1:1:1:99:702 + chr8 64280802 CCAGTCCCTGTACATACTTTTCAGAGCACTAGGGA ::::::::::::::::::::::::::::::77477 0 HWI-EAS88_1:1:1:773:384 - chr14 24868884 GCACACTTATCCCGTTCCCATTGGTTGGTTTATTC 67777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:855:104 + chr10 125377747 GTGACAGGCCTGGTCAGAAGGGATAACAGAAAAGT ::::::::::::::::::::::::::22::64476 0 HWI-EAS88_1:1:1:99:333 - chr4 149261220 CATAAAAATAAGCTGGCAGCTGACACAGAGCTCAG +7777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:203:550 - chr18 47329829 GAAGCAGGTGGTGGTGACCGCGGAGGGTAGCCTGC 64+7+3::3:::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:105:785 - chr11 96686230 ATTTTAGCTTTTTAAAGAACTTGGGGGAGGGGCTG 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:166:853 - chr14 13866161 CAGTCATGCTTCATTGTGTTTTGTGAGAGAAATAC 47447:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:381:132 - chr10 119810465 TTCAACAAACACAAATCCTATGCTTTTATATATAC 64%775:::6::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:714:823 - chr2 60399038 AATTAATAAATGAGCTTTTTTACCATAGTCCAAAC 672*6:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:308:860 + chr8 129981915 GTGCCAGTAACCAACCCGTGACTTGAAACCTTTTT ::::::::::::::::::::::::::::::77477 0 HWI-EAS88_1:1:1:92:878 + chr11 64835392 ATCAGTTTTCCCAATACTATATACTATATAGAAGG ::::::::::::::::::::::::::::::4772, 0 HWI-EAS88_1:1:1:349:167 - chr10 107707487 TCAGATTTTTGTGCCTAGAAACTTGGAATTAAGCC 31555:::+:::::::::,1&:::::::::::::1 0 HWI-EAS88_1:1:1:860:554 + chr7 87010633 GGAAGACAATGTCAGATTCCCTGGAGCTGGACATA ::::::::::::::::::::::::::::::46440 0 HWI-EAS88_1:1:1:296:103 - chr12 79870259 GTGAGTCATTGCAATGATAACAGATGCTGTCAGAA 7)677:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:196:721 - chr16 21171610 GAGTTCTTTAGAGCTTTGCTGTCTACGAAGTCACA 67327::::::::::3::::::::::::::::::: 0 HWI-EAS88_1:1:1:221:93 + chr16 9908456 GCTGAATGCATGATCACAGCAATGTGGGCTTCCTG ::::::::::::::::::::::::::::::76774 0 HWI-EAS88_1:1:1:67:439 - chr11 50571021 TTCAAGGACCTCTTGCTGTGTAGTCTTAGTAAGAG 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:959:753 + chr6 116145663 GACTGTTGGGCAGAAGCTCTGGTTCCTCTTCAAGT ::::::::::::::::::::::::::::::62266 2 HWI-EAS88_1:1:1:723:636 + chr4 42059385 GCTACCTGCTTTACAGATGGGAGCAGTTTTCTCCA :::::::::::1::+:0::::0::0:::::4-443 1 HWI-EAS88_1:1:1:123:215 - chr17 41961429 AGCAGGACCTATGCATCCTGCCAGAGGCACATATT 55555:::::::::::::::::::::::::::::1 0 HWI-EAS88_1:1:1:95:737 - chr4 144484919 GGACAGCTGTTTAACCTTGCCTTTGGAAGGGGAGT 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:813:830 + chr15 84624366 GTGGAGAAGATGTTAGTGGGGGTGAGAGGAGCATG ::::::::::::::::::::::::0::::040444 0 HWI-EAS88_1:1:1:611:34 - chr10 54966350 CACTAACTTTCATTATAGAGACTGTTCAGTAAGCC 27467:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:337:384 + chr6 92896494 GGGAGGCAGAGGCAGGCAGATCTCTGAATTTGAGA :::::::::::::::::::5::::::::::7+442 1 HWI-EAS88_1:1:1:117:908 + chr5 58245291 AGTCACTTTAGATGTAAAAACTATGTAACTATCAG ::::::::::::::::::::::::::::::77774 0 HWI-EAS88_1:1:1:584:274 + chr19 33060475 TTATATGAATGTTTGCTGACTAATGAGTCTTTTCT :::1:::::1::1:&0:1:::,:1+:+0::)51,3 0 HWI-EAS88_1:1:1:241:287 + chr13 20403450 GACATTTCAGTGTGCACTCTATTTCCCACCAGAGG ::::::::::::::::::::::::::::::40444 0 HWI-EAS88_1:1:1:787:93 + chr5 40411697 GTCTTCCACATAGCTGAGTGCAAGAGCACAAGTCA :::::::::::::::::4::::::::3:::27262 0 HWI-EAS88_1:1:1:730:549 + chrX 163243386 GTCTGCTCTAAATCTTGGCCACATTGTCATCTGGA ::::::::::::::::::::::::::::::76260 0 HWI-EAS88_1:1:1:317:242 + chr4 129099097 GCACACCTTTAATCCCAGCACTTGGGAGGCAGAAG ::::::::::::::::::::::::::::::67477 61 HWI-EAS88_1:1:1:121:979 + chr2 141245719 CTTTGTGGGTGGAGTCAGTTGTCTCCTATGGTGCA ::::::::::::::::::::::::::::::+7477 0 HWI-EAS88_1:1:1:850:422 - chr15 16896739 TATAAGGCTCAAAATTTTGGGTGGATGTTCATCTC 77/67:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:65:863 + chr3 63617222 TTTAAAAAAAGAGAAAGATGCTGATGACAAGCAGT ::::::::::::::::::::::::::::::76777 0 HWI-EAS88_1:1:1:375:550 - chr15 12720207 GCCCGTAACTCCAGGGGATCTGACATCCTCTTCTC 76647::::3::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:126:412 + chr14 107589395 GCATGTGCCATCATAAAAATATAGAGATGAGAGGA ::::::::::::::::::::::::::::::47777 0 HWI-EAS88_1:1:1:432:525 - chr10 83081942 ACAAAACGCCTCTCCCCAGTCTTCTGCCACAGCAC 47666,::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:700:650 - chrUn_random 5249479 ACTTTTCTTGCCATATTCCACGTCCTACAGTGGAC 264263::::::::::::::::::::::::::::: 0 33 HWI-EAS88_1:1:1:241:563 + chr5 104173660 GTCATACTGACTCATACATAGCTCCTACACTCAGA ::::::::::::::::::::::::::::5:77724 0 HWI-EAS88_1:1:1:917:891 - chr1 55510902 ACCTGGCATTGATAGGAAAGGTAAACACGGGACAC 72724:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:694:592 - chr10 63629278 CTTCAACTAAACTTGCAAACTCTGAAATGTTCTTA 46677:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:93:768 - chr12 48115673 AGTTCGGGGTATACTGGTTAGTTTATATTGTTCCT 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:867:516 + chr3 113878743 GACCATAAGTGTGTGGTTTTATTTCTGGGTCTTTA ::::::::::::::::::::::::::66.:77776 0 HWI-EAS88_1:1:1:548:480 - chr13 20070663 GTTCTTTGGTGCATTTGTCTCTACGAATCCATGAC 64442::::3::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:475:646 - chr15 21907106 GGCCTACCTGACCAGCATCAGCCTAGTGGGTGGTC 574/20::+:::34::::::::5:::::::::::: 0 HWI-EAS88_1:1:1:185:417 - chr5 3816829 TTAGCTCACCCTACCTCATAATTTGTTGTGACAAC 2,62%33333:::55:::::::::::::::::::: 0 27 HWI-EAS88_1:1:1:695:799 + chr5 114319271 GTGCAATGAGCGCAACAGTAACCTGCGATCCCTGG ::::::::::::::::::::::::::::::77674 0 HWI-EAS88_1:1:1:59:368 + chr2 72531796 GGCACGTCAGCCTCTTGACGTCAGTCTTCCCAGCC ::::::::::::::::::::::::::::::77477 0 HWI-EAS88_1:1:1:115:192 - chr4 154460838 ACAGGCGTGGTCCAGGCTGCAGGCTCACGGCCAGC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:980:503 - chr5 136478377 CCCCTCACTGAAGGATTCTAGGCAGGGGCTCTACC 24664:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:333:616 + chrUn_random 5746062 GCTGCATCCTTGGTTCAATGACTTGCTCAGCCATC ::::::::::::::::4:::::::::::22776*6 0 HWI-EAS88_1:1:1:589:491 - chr2 168770559 GATTTTGGAGATGCCAGTGTCACGGAATGACCACC 66404+::::::::::::::::::::::::::::: 0 15 HWI-EAS88_1:1:1:106:818 + chr4 126937828 ACATCAAAAGGAACACTGGTCATACACATCCTGCC ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:964:410 + chr10 7570859 GGGTTATGGCAAGCAAGAGAGTTTTCTGGAGGTGG ::::::::::::::::::::::::::::::42424 0 HWI-EAS88_1:1:1:86:888 - chr15 81660428 CCACTACACCTTCCAGGATCCTAGCATTTATATTC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:427:493 + chr10 28251298 AGCATTTTCAACAATTGGTGCTGGCACAACTGGTT ::::::::::::::::::::::::::::::77477 5262 HWI-EAS88_1:1:1:176:639 + chr2 154677559 TAGGATCTGATGCAGTGAAGGAATGGACTTGCTAG ::::::::::::::::::::::::::::::77762 0 HWI-EAS88_1:1:1:590:246 - chr18 27280792 TGACTTAAGTTTAATGGTTGGGTGTAAGTATTTGC 77777:::::5:::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:260:261 + chr2 65418677 GTTCAATGACTGCAAGTGCTTGAGCAATCACAACC ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:731:275 + chr2 36686396 GAACATGAAGAACAGAACAACAAAAAATCCACTAA ::::::::::::::::::::::::::::::77742 0 HWI-EAS88_1:1:1:849:567 - chr14 99329260 GAAGCGCAGAGATATGCCAACAGCTGTAGCAAACC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:162:973 - chr1 91442049 GAAGTGACCAGCCATGGGGTTGTGACTTCTTCTTC 04644:3:::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:410:451 - chr4 25781534 CACTTCTCTGCATCTCTGGCTTCACCTCATTGCAC 717+6::::::65:::::::::::::::::::::: 0 HWI-EAS88_1:1:1:179:383 - chr7 142774930 ATGGTATAGCACATATACCATGAGGGACAGCTTAC 74774::::::7::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:188:185 - chr16 33332847 TAGGTACTAGGGACTGATCGGAGGTCCTTGATAAC 2+424:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:433:441 - chr11 108240003 GGACCACAACCATGTACCACCAAACCTAGCTTTTT 7722*:3:::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:206:725 - chr12 113513201 CTCTGCTGCCTGACAGGCAGGGCTCCTGGCTAGAC 76667:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:808:657 - chr18 62755963 TGAAGCTGAATGTGCCATGTACTCAGCGAGGGGTC 26447:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:554:244 + chrX 101858868 GAAGGTAGGTGCATGGGGAGGGAAGAAGGGATCTA ::::::::::::::::::::::::::::::47774 0 HWI-EAS88_1:1:1:711:428 - chr8 116844688 ACACGCCTCCTCTATCAAGACCACACCTCCTAATC 66762::::::::::::::,::::::::::::::: 0 HWI-EAS88_1:1:1:924:770 - chr6 92193078 CTCCCTTTTGCCCGGATGCTGGCGTTTTGTGATCC 74777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:741:535 - chr9 3034640 CCACATCCTACAGTAGACATTTCTAAATTTTCCAC 6+667:::::::::::::::::::::::::::::: 31 20,30,31 HWI-EAS88_1:1:1:369:453 - chr4 24570663 TGGCATTTTGGTGTAGTCTGCTTCTTGCATCTTCC 26764:::&:::::::::::::::::::::::::: 1 HWI-EAS88_1:1:1:885:138 - chr5 110955887 TGCTAGGGAAGACATACCAGAAACCCCTTACCCAC %/)-3::::::::::::::::::::::1::::::: 0 HWI-EAS88_1:1:1:108:420 + chrX 111064812 ACACTTCCACATTGCTGTTCATCACTGAAGGAAGT ::::::::::::::::::::::::::::::77777 23 HWI-EAS88_1:1:1:866:613 - chr6 104498999 ATGTTCAAAATCAAACTCAAGGAGAAAGTGTTCAC 74766:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:499:872 - chr2 154522915 CTCCTGCTGCACCATGCCTGGATGCTGCTACGCTC 72774:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:532:575 + chr16 54097763 GTTCTCTCTGAAGAATTACACAATGGTATTTTTAC ::::::::::::::::::::::::::::::77767 0 HWI-EAS88_1:1:1:880:866 - chr2 98502386 TCCAGGTCTTTCAGTGTGCATTTCTCATTTTTCAC 46774:::::::::::::::::::::::::::::: 0 26,30,33 HWI-EAS88_1:1:1:756:863 + chr1 69944995 GTCTTTAAATTCCACCACTCTTTCGTGCCTCATTA ::::::::::::::::::::::::::::::44774 1 HWI-EAS88_1:1:1:764:28 + chr15 97026683 GATACCGACTATGGCTCTCCAGTTCCACCTTCTGT ::::::::::::::::::::::::::3:::74624 0 HWI-EAS88_1:1:1:241:338 - chr2 59840331 ACTTCACATCGCTCATTACATCGCCTTTCAGTAAA )%&%/:&:&1&4:-:3::::::::::::::::::: 0 24,34 HWI-EAS88_1:1:1:769:714 + chr10 80879669 GTATGGAGCTTCCCTCTAGCACTCGCACGCTCACA ::::::::::::::::::::::::::::::76274 0 HWI-EAS88_1:1:1:240:932 - chr11 77968403 ACCCCAGCCCCACCGGTCAACGGCCTACCAGCCCC ++777:&::::::::::::5::::::::::::::: 0 28 HWI-EAS88_1:1:1:705:777 - chr3 62599070 TAAATACTTTGTTTATTCTAGACCCAGACTACTAA 47727::::::::::::4:::-::::::::::::: 0 HWI-EAS88_1:1:1:562:597 - chr12 49782535 TTACAGAGTACTATGGTTAAGTGTTTGCATGAGAC 46777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:676:156 + chr14 92039519 GAATACTCTTTCATTTTAGGGTGAAATATTCTGTA ::::::::::::::::::::::::::::::77774 0 HWI-EAS88_1:1:1:431:544 + chrX 42960859 GAAGAACATAGTGTCGTCGTTCTTTTCTGCTGGTC ::::::::1:::::::::::::::::::::41335 0 HWI-EAS88_1:1:1:945:150 + chr14 85932619 GTGGGTGTTAATCCTCGCAGATTGCGACCCCTAGA :::::::::::::::::::::::6:6::::47274 6 HWI-EAS88_1:1:1:191:747 - chr5 27920815 AGAGAGCCACTGAGCCTGGCTTGAGTTTCTGAAAC 46477:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:799:612 + chr17 58281063 GAGAGTGGGATAGATAGCTAGGGCGGGGGTGGGAG ::::::::::::::::::::::::::::::77644 0 HWI-EAS88_1:1:1:163:627 + chr11 5018235 TCAGTGGGGGAGGGGGAGGTTAGAGAGATGGCTCA ::::::::::::::::::::::::::::::64766 0 HWI-EAS88_1:1:1:245:700 - chr1 135274060 TTGAGGCACCCCAGTTGAGGACTGTACTCTACGTA 67776:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:376:244 + chr11 90214041 GTCAGTAGCGTGTGTGTCATTGCCATAACGCGTTT :::::::::::::5::::::::::::::::6/777 0 HWI-EAS88_1:1:1:123:267 - chr11 52653462 GAGACAGGAGGATGCAACTAGGCTGCCTAATGAGT 77774:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:268:917 + chr2 130598775 GTATTCAAATCCCCAGCACCCACATACAAAGCTGG ::::::::::::::::::::::::::::::76664 0 HWI-EAS88_1:1:1:908:230 - chr9 71945140 TTACCACTTGGCCACTTCCTGCTCATCGTTGATAC 66677:::5:::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:197:419 + chr1 130307049 GGCAATATGTTATTTACCTCTCAAATGAGCGCACG :::::::::::::::::::::::::::3::77274 0 HWI-EAS88_1:1:1:866:327 - chr1 4202455 ATAAACAGAACTCCAATGACTCAGGCTCTAAGATC %4746:::::::::::::::::::::::::::::: 0 23,34 HWI-EAS88_1:1:1:57:637 + chr15 17631444 GCTGACATGCGTCAGTCCATCCATCTGTCCACTGA ::::::::::::::::::::::::::::::77777 1 HWI-EAS88_1:1:1:681:291 + chr7 18025669 GATCCATTCCTATCTCCTTGTACTAAGGTCAAATC ::::::::::::::::::::::::::::::74677 1838 HWI-EAS88_1:1:1:673:673 + chr8 7896236 GTTGTACAAGCCTGCAATCCCACCAACAATGGAGG ::::::::::::::::5::::::::3::::42426 4826 HWI-EAS88_1:1:1:968:531 + chr19 53623034 GGCCTGGGCTGCCCCTCCTGTGGCACCTCCTTAGG ::::::::::::::::::::::::::::::77066 0 HWI-EAS88_1:1:1:188:499 - chrX 160376728 CTGAGAGCACCCAGTGTACAACTAAACTGATCCAC +2777::::6::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:83:813 + chr3 80617174 AACCTACCCATAAAACATTTAGATAGAAAGGGAGA ::::::::::::::::::::::::::::::27777 0 HWI-EAS88_1:1:1:88:491 - chr15 38510553 GAATGTGTCCTCCCACTCCTGATTCTCCTGCCTCT 77776:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:194:801 + chr19 16016181 GTCACTGAATGCACAGCCTTGCTTGGTGGAATTTT ::::::::::::::::::::::::7:5:::67777 0 HWI-EAS88_1:1:1:689:169 - chr8 69772488 CATATCTCTTTTAGAATTTTTTCTAACAAGATTTC 77464::::3::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:581:905 + chr16 23078140 GATGCTAAGACTTGAAGTGCTCTGAGGCTCCAGAG ::::::::::::::::::::::::::::::74747 0 HWI-EAS88_1:1:1:227:975 - chr8 26384959 ATCTGCCTGGAGTCTGAACAAGATGAGTAAACACC 77225:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:886:158 - chr8 99512766 CAGACTTCTCCACTCTCCTTCCTTGGGTTTGTTCC 77667:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:243:676 + chrX 159385213 GAGATCCTGGGTGTGTCAGAGCTCCTGGGAGTCAA ::::::::::::::::::::::::::::::74764 27 HWI-EAS88_1:1:1:123:548 - chr10 126521221 TTGCCTATAGGCGTTCACCTCTCATTGTTCTCCCT 67%1+,3::::&::::::::::::::::::::::: 0 32 HWI-EAS88_1:1:1:216:402 + chr8 72039647 GGACCGCTTCACACTGTTGTCTGCATTTGGATTCC :::::::::::::::::::::::::::::626767 6 HWI-EAS88_1:1:1:118:696 - chr1 137517853 ACAGAATCTATTGGATGGAACACAGGGCCCTCAAT 7/7/&::1:::::::.::::::::::::::::::: 0 30 HWI-EAS88_1:1:1:268:211 + chr16 27977293 GGCAGGAAGAAGTCGCACTGAACTTCATATGAGAT ::::::::::::::::::::::::::::0:75256 0 HWI-EAS88_1:1:1:174:184 + chr2 27880843 GGTCTTTGTGCCATCCACCCATCAAGGTCAGCAGA ::::::::::::::::::::::::::::::77277 0 HWI-EAS88_1:1:1:80:660 + chr12 74577404 GAAACCAGGGCCCCTGCACCTGTTCCTTGCATTGC :::::::::::::::::::5::::::::::47777 0 HWI-EAS88_1:1:1:810:735 - chr4 34086152 CTTTAGACTGGTTTACAGAGGCTGATATGTTTCTC 3/327:22::::::+:::::::::::::::::::: 0 HWI-EAS88_1:1:1:474:263 + chr1 71336215 GTATTTCAGAAGTCATGTTTGCATCTTTGTCTCTT ::::::::::::::::::::::::::::::77766 0 HWI-EAS88_1:1:1:478:393 + chr19 24849407 ACCTGGAGGGTATCATCCTGAGTGAAGTAACCCAA ::::::::::::::::::::::::::::::77746 627 HWI-EAS88_1:1:1:163:211 - chr1 62953437 TGAGTATGTAGCCCAAAGATAGAGTGTATACCTAC 46644::::::5,5::::::::::::::::::::: 0 23,30 HWI-EAS88_1:1:1:988:555 + chr2 133667668 GACTAGCTTCTAATACTATAATTACTAAGAAATGG ::::::::::::::::::::::::::3:3:&4420 0 HWI-EAS88_1:1:1:854:533 - chr7 95344460 TGGAAGTAAAAACGACCCCTGCCCTAAGAAAATGC 27467::::::::::::7:::7::::::::::::: 0 HWI-EAS88_1:1:1:435:632 + chr5 50715185 GCCCTACTTAAAGCATTCCACCCCTTTCCAAATCC ::::::::::::::::::::::::::::::22677 0 HWI-EAS88_1:1:1:525:489 + chrUn_random 5263558 GCTAGGTGTGGGACTATGGCCTGGTCAATTAGAAC ::::::::::::::::::::::::::,+::41044 0 HWI-EAS88_1:1:1:200:836 - chr16 10994521 TGCTTTTCCCTGGGTGAGGCTTATCCATTGTATTC 236672::&:::+:::::::::::::::::::::: 0 HWI-EAS88_1:1:1:242:728 - chr1 187053498 TCAGGGGCTACAGAGCTTTGCCATCTTTGCTCTTT +1477::::::::::6::::::::::::::::::: 0 HWI-EAS88_1:1:1:621:412 + chr14 113068828 GATTGGACTAGAGGGAATGACATAGGTGGCCAAAC ::::::::::::::::::::::::::::::4%0)4 0 HWI-EAS88_1:1:1:101:860 - chr4 138564087 TGCACGTGGCAGCCTGAATCACTGCAGCCTTGACC 67777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:777:729 + chr11 64869257 GTATCCTGACAGCTGTTCCTCGCTGACTAAGTGTG ::::::::::::::::::::::::::::::74744 0 HWI-EAS88_1:1:1:864:751 + chr16 73629325 GTAAGGTAGAAATCAAGTTTGAGTCCTTGATTGGG ::::::::::::::::::::::::::::::77277 0 HWI-EAS88_1:1:1:786:152 - chr12 109230419 CTTACAGCCTTTGATAAAAATAATAAACCCACATC 44477:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:318:334 - chrX 53407449 ACAAGTTCCCTCGCCCCACTGCACGGCATTTCATC 4677633:3:3:&:::::::::::::::::::::: 0 22 HWI-EAS88_1:1:1:357:844 - chr18 83636587 TCAAAGGCTGTGATGGCTCAGGCCATCTGGTGTCC 47767:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:54:518 - chr12 33337199 CAGAGTCTTTCCTCTTCTGTATCCGTGTGGTTTGA 46427:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:406:321 + chr2 98506541 GTTTCCAACGAATGTGTTTTTCAGTGTAACTCACT ::::::::::::::::::::::::::::::77467 2 HWI-EAS88_1:1:1:701:112 + chr7 68581094 GACTATTAATAACTGCTTCTATTCCTTTAGGGGAT ::::::::::::::::::::::::::::+34+107 0 HWI-EAS88_1:1:1:81:830 + chr1 105564744 TCCACCTATAGGGTTGCAGATCCCTTTAGCTCCTT :::::::::::::::::::::4::::1:+:77277 10694 HWI-EAS88_1:1:1:590:419 + chr14 106558713 GAGCACCTCTTTTTGACTTAAAGGAATGGGAAGCA ::::::::::::::::::::::::::::::74776 0 HWI-EAS88_1:1:1:366:406 - chr14 53070370 TGACAATTGCATTGCACAGCTCTTCTTCCTACATC 46477:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:342:363 - chr5 87228828 AAGCAGAAGTAAGTGTGGTATATGCACCCAGAGCC 77647:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:351:700 - chr13 62810951 GTCAATTCTTACAAAAGTTCTTCTTTATTTTTAAT 77477:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:603:503 + chr18 41053558 GAAAGAGAGGCCCATTGGACTTGCAAAATTTATAT :::::::::::::::::::::::::,::::74767 10 HWI-EAS88_1:1:1:366:923 - chr7 24982474 AACTAGTATGTGCATGCATCTTTCATTTATGAATC -55-511:1:::::::::::::::1:::::::1:: 0 HWI-EAS88_1:1:1:929:238 + chr3 107063886 GCACCAGGATAGAGAACTCAGGCAGGGAGACTGCA ::::::::::::::::::::::::::::::46766 0 HWI-EAS88_1:1:1:106:67 + chr7 148104030 TAATGGAGCCATAATGTTGGGGACAGCAGGTAGCA :::::::::::::::::::::::::::::,77144 0 HWI-EAS88_1:1:1:369:644 - chr6 93356241 ATCCTCGAGGCTTTTACCTCTGGATGTAGTCAGAA 4464+:::::::::::,:::::::::::::::::: 0 HWI-EAS88_1:1:1:368:437 - chr6 14104807 CATTAATGTATTTATTCACTTTATTTCCAGATCAC 47777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:106:319 + chr4 53819113 GCCCTTGCCTGCGTCTGGCAGGAGTCCTGCCACAA ::::::::::::::::::::::::::::::74777 66 HWI-EAS88_1:1:1:557:185 - chr12 119197313 TTGTTTTACAGATTTATAATTTCCACAAATCATAC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:71:183 + chr19 46396168 TAGAGCAGGGTGAGACTCAGCCTCTTGGAGGAGGG ::::::::::::::::::::::::::::::72777 0 HWI-EAS88_1:1:1:647:592 + chr17 69295642 GACAATCCATAACTCCAGGGTGGACTACTAAGCCC :::::::::::::::::::::::::::::052663 636 HWI-EAS88_1:1:1:126:215 - chr2 126229406 TAAAAGGGTAAGCACAGAAAAGACCATGTTTAGGC 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:184:372 - chr9 99510895 TAGTAGAAATGTTGTCTACTCATCCCTGGAAGTTC %4)46::3&:::::::::5:::::::::::::::: 0 32,34 HWI-EAS88_1:1:1:309:188 + chr3 103241354 GAGGAGCCCCTGGGAGGTACAGGGAAGGAGATAGC ::::::::::::::::::::::::::::::66774 0 HWI-EAS88_1:1:1:124:392 - chr11 52110335 GACAAGCCTTCTCCCTCAGCCTCCCCCAAAACACT 77777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:692:620 + chrY_random 28859341 GGTAATTGAGAGTTTGGCCGGGTATAGTAGCCTGG ::::::::::::::::::::::::::::::64444 1917 HWI-EAS88_1:1:1:767:338 + chr13 112850398 GGAGGACGGCTTCAAATTTGAGTGTAGTCTAGGTT :::::::::::::::5::::::::::::::24227 0 HWI-EAS88_1:1:1:83:121 + chr2 174234880 TGAGAGTACCTGAGAGGCAGAAAGATCTCCGTGAG ::::::1::::::::::&::::::::+:::15515 0 HWI-EAS88_1:1:1:98:275 + chr1 116089801 AGTAAAAGAATGAAGCTTGATATGTTCATGTGCCA :::::::::::::::::::::::::::::.72774 0 HWI-EAS88_1:1:1:278:728 + chrX 55214153 GTGTACATGCTGATCTAGCAATTCTGTTCTAAGTA ::::::::::::::::::::::::::::::74277 0 HWI-EAS88_1:1:1:218:362 - chr16 4790563 TAAACAAATAAGCCACAAAATGACCAGCATCTCAA 27+7+::::::::.::::::::::::::::::::: 0 32 HWI-EAS88_1:1:1:563:297 - chr11 50865423 CTTGTACTAGTCAGGGTTCTCTGGAGTCACAGAAC 44477:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:259:940 + chr15 25799668 GAGGCTGCCTTTGAACTTCATCTTCCCACTTCTAC ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:140:679 - chr15 85400829 AGCCCCTGGCAAGCACTCTCTCTACACCACCTGCC 60447:3:::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:109:344 + chr11 57893963 ACCAGGCTGGCCTCAAACTCAGAAATTTGCCTGCC ::::::::::::::::::::::::::::::77477 85 HWI-EAS88_1:1:1:574:454 - chr2 135728931 GTGTCTCCAGAAGCCCTGAGTGGCACGCTGCTTTC 66764:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:594:454 - chr14 94065098 AAGCTTTTCAAAGCTCCAAGCCCATCCCCAGCGAC 77466:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:750:305 - chr1 146036887 ATATGGAATTGCATAACAGTTACTAGAACCAGCTC 74426::::::6::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:361:156 + chr9 75264392 GTGAATGGGAGTCCAAGGATCTAGCAGTTGTTCAG :::::::::::::::::::::::::::+::4444+ 0 HWI-EAS88_1:1:1:596:20 + chr13 117979300 GAATATGCCAATACAAATGGGACTCAATTCCTTCT ::::::::::::::::::::::::::::::67777 0 HWI-EAS88_1:1:1:499:747 - chr18 22028144 GGGTTACATGGAATCTTTTTGCTACTTCCAGTTTT 77647::::::::::5::::::::::::::::::: 0 HWI-EAS88_1:1:1:620:438 + chr14 52864319 GAGAGGCCTATCCGGTTTCTAGGGCCAAGACCAAG :::::::::::::::4:::::::4:2::2:57%%% 0 HWI-EAS88_1:1:1:577:505 + chr12 24388280 GATGTCTTAAAAGTAGTAGTCTTAGTAAGAAGACA ::::::::::::::::::::::::4::::26236/ 7 HWI-EAS88_1:1:1:113:638 + chr17 43292958 CTTGGGCAGACTTCAAGAAAGGGTCTGAGGAGACA ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:615:446 - chr5 136138360 CTTAACCTTACCCTAAACCTAACCTCCACCCCACC 4*264:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:991:529 - chr10 12548378 GCAGATGAATTTTACCTTGTGCATTTAAACTCATC 2/6%60:::+::2:::3+::::::::::::::::: 0 31 HWI-EAS88_1:1:1:138:949 + chr17 16087054 GGGGCCAAGTTGACCTGCTATGGTGGGAAAGATGG :::::::::::::::::::::::::::.:54,774 0 HWI-EAS88_1:1:1:161:583 - chr11 116671085 AATGAAATGAACTCAGGCCCTGCTGGGACCTCAAC 44777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:853:122 - chr11 6039485 TCTTAGCTGGGCTCCCATGAGTGTATAGTGGTTTC 67647:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:593:709 + chr9 3037227 TGTATTTCTCATTTTCCGTGATTTTCAGTTTTCTC ::::::::::::::::::::5::::::.::77777 30 HWI-EAS88_1:1:1:980:577 + chr11 101001436 GTGTGGCACAGGTCTTTAATCACAACACTTGGGAT ::::::::::::::::::::::::::::::76664 0 HWI-EAS88_1:1:1:607:959 + chr15 40412980 GTATATTAGCAGAAACAGGAAATCAATGATAATGG ::::::::::::::::::::::::::2:::66677 0 HWI-EAS88_1:1:1:731:502 + chr16 23351281 GAACTCAGCCTGGCCCTCCCCCCTTCACACATATC ::::::::::::::::::::::::::2:::22/67 0 HWI-EAS88_1:1:1:408:607 + chr3 8850850 GCACTGACTAACAGAGCAGCTGTCAGTGCAGAGTG ::::::::::::::::::::::::,::::3426,2 0 HWI-EAS88_1:1:1:600:700 + chr2 29998828 GGCTAGTGATTCTGCTGCAGAGACAAAAGCTCTCT ::::::::::::::::::::::::::::::77477 0 HWI-EAS88_1:1:1:921:580 + chr17 14461130 GCCTTAAGGCCAGAACTTGTGGTACAGCTTTGTCC :::::11::::::+:01::1::+::+::1:33411 0 HWI-EAS88_1:1:1:842:893 - chr5 36767520 TCAGGCTGTCAGGCTTGCACAGTTAATACTTAACC 52776::02:::::::::::::::::::::::::: 0 26 HWI-EAS88_1:1:1:335:454 + chr8 19929673 GCCACTTCCATGAAGATAACACTGAAGATAAAGGA ::::::::::::::::::::::::::::::41444 5 HWI-EAS88_1:1:1:898:402 - chr14 55818025 GATATGGAGATGCCCCATGGAGAAGACCTAGATGC 77777:::::::6:::::::::::::::::::::: 0 HWI-EAS88_1:1:1:122:985 + chr15 81057375 AGCAAGTCATGATGAGAAGGGGCAGGTGATCTGAT ::::::::::::::::::::::::::::::67777 0 HWI-EAS88_1:1:1:87:44 + chr17 17141877 TTTCGCACACTCTCACCCTGAGAGAGTTGCTCCTG ::::::::::::::::::::::::::::::77772 0 HWI-EAS88_1:1:1:632:537 + chr12 3109884 GAAAATGAGAAATACACACTTTAGGACGTGAAATA ::::::::::::::::::::::::::::::66474 4 HWI-EAS88_1:1:1:85:771 - chr9 63777305 ACAGGAAATGCTAAGCCTTTTCTCTGTGAGGCAGT 77777:::::::::::::::::::::::::::::: 0 27 HWI-EAS88_1:1:1:479:384 + chrX 69921640 ATATATTGTTTGAAAAAGAAAAGCTAAGGAGACAG :::::::::::::::::::::::::::1:&/2757 0 HWI-EAS88_1:1:1:325:919 - chr12 39399093 CTGTGGAGAAACTGTTCTCTTCCAGTTGGTCCCTC 66767:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:548:713 + chr1 171502972 GGATGGCCTTGTGGGACATCAATGAGAGGAGAGGC ::::::::::::::::::::::::+:::::72644 50 HWI-EAS88_1:1:1:709:757 - chr6 56837978 ACTGATGAGCATAGCCAACACCAGAGGCCTTGTCC 44076:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:421:463 - chr3 7874832 CACCAAGTAAGTCCCTTTTCCCCAAAGCATTTCTC 77677:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:189:305 + chr1 172550695 AAAACAAGAGGAGAATGTCAGGCAGTGGATGCAGA ::::::::::::::::::::::::::::::44272 0 HWI-EAS88_1:1:1:121:559 + chr5 150295494 ACCATGGTGTCCTTTTATGCAGACTGTCAGTTTGA ::::::::::::::::::::::::::::::777,4 0 HWI-EAS88_1:1:1:224:985 - chr5 149715546 TCCGTTGAAGAGTCACATGTAAAATCTTAGGTTGC 46444:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:333:730 + chr9 77349179 TGGACAGAATCAGCAAAGCTCAGAAAGGCAAAATG ::::::::::::::::::::::::::::::76677 0 HWI-EAS88_1:1:1:295:481 - chr18 88971116 CATGTTAATTTGGTTCCCAAAATTGCACAAAGATC 67777:::::::::::6:::::::::::::::::: 0 HWI-EAS88_1:1:1:102:695 - chr19 43764369 GGGACGAGCGGGCCTCGGGATTCCCGTGAGTGGGA 66477:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:208:782 - chr12 101714235 GCTTCCTGCGAGGGCTCTGGAAAGCTACAACTTCC 44646:::::::::::::::::::::::::::::: 0 30 HWI-EAS88_1:1:1:283:124 - chr13 106216589 AACAATAAAAATTTCCCCCCAACATTTAAAGATGC 74/67:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:348:926 - chr3 99538672 GTTTTGTAGTTTGTCAAGCTAATTACTAATTACTC 76767:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:534:649 + chr9 35112888 GAAAACTGAAAAAGGTGGAAAATTTAGAAATGTCC ::::::::::::::::::::::::::::::77477 2 HWI-EAS88_1:1:1:57:247 + chr12 95443584 AAAATCTGAAAGTAATTTTTTTTTTCTACTTTGTT ::::::::::::::::::::::::::::::77+77 0 HWI-EAS88_1:1:1:371:264 + chr9 73120278 GTGAATGCAGTGCATGTCAAGGCCACAATAGGTCA :::::::::::::::::::::7::::::::77664 0 HWI-EAS88_1:1:1:119:447 + chr7 130527814 ATCCATTCCTCTGTTGAGGGACATCTGGGTTCTTT ::::1:::::::::::::1,::::::::1:51555 2234 HWI-EAS88_1:1:1:452:633 - chr8 109751349 CAGCAGCTGCTTTCACCTGCTGTGAAAGAGGGGAC 2/346:::::::::2:::::::::::::::::::: 0 HWI-EAS88_1:1:1:708:636 + chr13 72865562 GGGGAAGGGAACTCTGTACAGTACCTGATCCCAGG ::::::::::::::::::::::::::::::66244 0 HWI-EAS88_1:1:1:924:783 - chr6 86696490 TGCCCAGCAAGCACTCTTCCCTGGTGAGCCATCTC 24777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:955:62 + chr19 54431703 GCAGTGATAAAAAAGCACAGAGGACAATGGCAGAA ::::::::::::::::::::::::::::::77766 0 HWI-EAS88_1:1:1:831:864 - chr19 8651438 CCCAGAAGGACACAAGCATGCAAACACATGTAAAC +4672:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:230:664 + chr7 28363630 GCAGTGTCATCCCTCCTTGGCATCTGCTCTGTGGT ::::::::::::::::::::::::::::::47624 0 HWI-EAS88_1:1:1:67:666 - chr16 30187400 AGTGAGGGTCTGCCTGGGATGCCAAACAGCCTCGC 70467:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:71:122 + chr8 115811635 TCCCTACTATCTCTGCTATCACGTCCAGTATATCT :::::::::::1:11:::::::::::::::54515 0 HWI-EAS88_1:1:1:729:594 + chr9 3012271 TTTCACGTTTTTCAGTGATTTCGTCATTTTTCAAG ::::::::::::::::::::::::::::::7776+ 44 HWI-EAS88_1:1:1:763:790 + chr2 131371697 GGAGGTTGCTGAGGTCGCCCGGCTCATAAGCCTGG ::::::::::::::::::::::::::::::76477 0 HWI-EAS88_1:1:1:747:163 + chr14 79581708 GTCTTATTTCTATGTCATTGCCATGAGTCAACAGC :::::::::::::7::5::7::3::::::,76472 0 HWI-EAS88_1:1:1:171:940 - chr12 112902860 GGGGAGTGAGTGGTCAGAGCATCTAACCGCTAGGC 67777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:427:673 - chr7 19324026 GGCCACTGTGGGCAGCACCATCCCTAGCCAGATTC 2420*&::+::::::5::::::::::::::::::: 0 HWI-EAS88_1:1:1:100:652 + chrUn_random 3903666 GCACACTGTAGGACCTGGAATATGGCGAGAAAACT ::::::::::::::::::::::::::::::47727 4 HWI-EAS88_1:1:1:82:416 + chr4 127997225 AAGCCCAAAACGTGTCAGTCACCCAGGATTTCCCT ::::::::::::::::::::::::::::::77777 0 HWI-EAS88_1:1:1:212:420 - chr14 75952284 CGGAACTCTCCCCAGCACTCGGTACTCTCCCCTCC 434330&::::::::::::::::1::::::::::: 0 HWI-EAS88_1:1:1:424:699 + chr11 34153610 GAATGGCATCTACACAGCACAGTGGCCCTGTCCTC ::::::::::::::::::::::::::::::27357 0 HWI-EAS88_1:1:1:68:587 - chr4 107296611 GGCTCTCTGGGCCTGACTCTGAAGGCCCATTTGGG 772/7::::+::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:620:296 + chr3 146911739 GTTTCTACTGAGGAGTCAGTGGTAGATATGGGAGT ::::::::::::::::::::::::::::::46264 0 HWI-EAS88_1:1:1:433:476 - chr6 129450714 TTCTTTATGGATGTGTCCCTTGTGAGATTACCTAT 46662:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:321:757 - chr10 45031807 GGAAGCACAGTTCCTCTTAAGACCCTCAGGACCAC 67677:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:858:880 - chr1 100174508 GAGAAGAGGATGAAAAGTGAGAATAAGGTGCCTAC 67467:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:100:104 + chr12 74353200 GACCATATGTCATTTTGTAGGGGAAATGAGTACTG ::::::::::::::::::::::7::::3::4777) 0 HWI-EAS88_1:1:1:698:245 - chr6 98564914 CAAGATGGAAACCAAGCCTTTGCCCTCCCTGGCCC 27777:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:607:839 + chr13 67521655 GCACTCTGCTTTGTAGTCTTTCCCTCCATGTGTTG :::::1:::1::::+:::::::::::::::51-4) 0 HWI-EAS88_1:1:1:672:737 + chr11 88364685 GAAGTCTCATTAGTCAGCACCGACAGCTGCTGCCC :::::::::::::::::::::::::+::::64666 0 HWI-EAS88_1:1:1:157:844 + chr1 173614620 GTCCCCAGAGTCCATTTCGCTCTGACATGTGTTCC ::::::::::::::::::::::::::::::42444 0 HWI-EAS88_1:1:1:756:357 - chr16 6420503 TGCAGTCAGTGCCAGGCTCCTTCTTGCCTGCAACC 17667:::::::::::::::::::::::::::::: 0 20 HWI-EAS88_1:1:1:363:132 - chr2 98507116 TAAAACTGAAAAAGCTGGAAAATTTAGAAATGTCC 722771::::4:::4:::::::4:::::::::::: 2 20,34 HWI-EAS88_1:1:1:460:575 + chr3 135434599 TGAGTTAGAATGGCTGGTCACACTGAAGAAAAGAG ::::::::::::::::::::3:::&+0:::/3)22 24 HWI-EAS88_1:1:1:139:586 + chr3 99468196 GCTTGCTAGGTACCAAGAGGTCTCACATAAGGGCT ::::::::::::::::::::::::::::::42676 0 HWI-EAS88_1:1:1:85:75 - chr17 11013366 TCCCCCTGCCTCTGCCTCCCAAGTACTGGAACTAA 2407)::::::::::::7::::::::::::::::: 0 HWI-EAS88_1:1:1:330:853 - chr15 87231619 CTAAATTAATGCAAAGTTCACACAGTGTGTTTCCA 277773::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:470:334 + chr16 62513434 GCTGAAGGGGTTTGCAATCCATAGGAAGAACAAGA ::::::::::::::::::::::::::::::62674 0 HWI-EAS88_1:1:1:410:160 + chr10 128257163 GGGAAGAGGGAAAGGGACAGTATAAGGGGTAGGAA ::::::::::::::::::::::::::::::55633 0 HWI-EAS88_1:1:1:841:671 + chr9 120654897 GTCAACAAGTCCAGAAAAATGTGATTGACACTGAG ::::::::::::::::::::::::::::::76644 0 HWI-EAS88_1:1:1:95:489 - chr1 57261469 TAATAGAAATTGAGGAAAGCAAGAGAAGGTATCTG 77777:.:::::::::::::::::::::::::::: 0 28 HWI-EAS88_1:1:1:972:553 + chr1 91352070 GTCCACCCCAACATCTACTCCATCTACGAGCTGCT ::::::::::::::::::::::::::::::66766 0 HWI-EAS88_1:1:1:693:177 - chr3 10279015 CTACCCAAGAGCTTTCCTTCACTGTGTGAGCGAGC 44477:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:744:238 + chr19 31521175 GGTAAGACACGTACTCTATTCCCTCAATTTTAGGG ::::::::::::::::::::::::::&:::7442+ 0 HWI-EAS88_1:1:1:727:555 + chr1 186549400 GTACCTTTGGGCTGTTTGGATTCCAGGTTTTTAAT ::::::::::::::::::::::::::::::64246 0 HWI-EAS88_1:1:1:271:146 - chr3 69993825 TATTAAATAAAATTACTATTGCAAGATCCAAAACC 4%677:::::::::::::::::::::::::::::: 0 33 HWI-EAS88_1:1:1:950:165 - chr10 6956507 AGGTTGTGTCACTAAGATCTCAGGAGGTCTGTTAC 76446:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:377:667 + chr2 152283384 GTGGCCTTGTGTGGCCTTAGTACCGGGTAGGCTGT :1::::::::::::::::::::::::::::5545) 0 HWI-EAS88_1:1:1:371:378 + chr12 15750846 GAGGGAGAGAGAGATTGAGGCTGTGGTGTACACAT :::::::::::::::::::::::,5:,::340404 0 HWI-EAS88_1:1:1:264:394 + chr2 154470423 GACATGGGAGAGAGTAGATCAAGACCTTTGACATC ::::::::::::::::::::::::::::::06)44 0 HWI-EAS88_1:1:1:624:792 + chr6 72605380 GGATGCTCCCTAGGGGTTAGAACCCGCTTTCCTGC ::::::::::::::::::::::::::::::77244 0 HWI-EAS88_1:1:1:853:327 - chr17 28644977 TCAAAAAATTCTACAACCACCATCATCTTACTGTC *+277::::::::6::::::::::::::::::::: 0 32 HWI-EAS88_1:1:1:265:361 - chr1 127557882 AAATAAATCCACTTCTCCTCCTGACAGTTGTCTTC 77747:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:243:37 + chrX 64309731 GGTGTAGCATTCAATATACCATCTCTAGTTGACTG ::::::::::::::::::::::::::::::77774 0 HWI-EAS88_1:1:1:700:410 - chr2 155914257 GGAATAGCTGGGGTCATAGAACCATAGACACTTAC 74762:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:723:817 + chr11 69624706 GGTGCCTCTTGACGGAAGCGTCTGGAACAGAGAGC ::::::::::::::::::::::::::::::61446 0 HWI-EAS88_1:1:1:334:777 - chr16 39693205 TACTGGGCAAGACACCTCTCCTTGGAGGAGAGTTC 04467:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:163:362 - chr4 138614747 TCCAGATCTTCCATCAACTCTCCCAGGCTGCCCAC 46664:3::::::::.::::::::::::::::::: 0 HWI-EAS88_1:1:1:206:774 + chr3 136314154 GACAAACAGATCTCACATCAGACACATATGGTGAC ::::::::::::::::::::::::::::::74746 0 HWI-EAS88_1:1:1:197:150 - chr17 36270084 ACACTGGTAGAGAAGCGCTGAGGGTTTGGGACAAC 77644:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:353:78 - chr1 117296243 AGAGGAGTGGAAACAGGATAGTTGAGCCTGAACTC 76746:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:656:761 + chr9 110033571 GGAGGGTTTCCTGTAGCCCTGGTTGTCCTGGCTTT ::::::::::::::::::::::::::::::44460 0 HWI-EAS88_1:1:1:208:965 + chr5 7361379 GATATTCCTCCCAAAACATAATAATAGAACAACAA ::::::::::::::::::::::::::::::67774 0 HWI-EAS88_1:1:1:712:710 - chr2 129230917 GAGATCGCCTCTAGCAGCTGCTTTTGAATTGTGGC 40444::::::5::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:98:392 + chr9 9709749 CCAGAAAAGAAATTCCTCCAGACACATAATAATCA :::::::::::::::::::::,::::::::77767 186 HWI-EAS88_1:1:1:922:547 - chr16 76633237 TCTGATTGTTCATTCCCTTTGAACGCTTGTCAGCC 24466:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:349:906 + chr13 52437522 GCCAAGTGCCAGCTAGCCCAGCATGCATTGCAGGC ::::::::::::::::::4::::::::::24/%7) 0 HWI-EAS88_1:1:1:261:689 + chr9 77370003 TCTGTAACGAAGGGAATGTCAGAAGTAAAATGGAA ::::::::::::::::::::::::::::::77764 0 HWI-EAS88_1:1:1:779:699 - chr14 62468136 TTCTCTCCCCACCACCTCTGTCATCTATGAATCAT 22204:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:511:111 + chr7 53005167 GGAGGCTCAGTCCCAGCATTTCTGTGACGCATGGC ::::::::::::::::::::::::5:::::16177 0 HWI-EAS88_1:1:1:168:791 + chrX 8031885 GAAGGTCTCCAAACTACCCAGCACTACAGTAACAG ::::::::::::::::::::::::::::::76464 2 HWI-EAS88_1:1:1:540:235 + chr16 36613167 GGAGAAACTCTCCATGCTCACAGGCAGGCTGGAAA ::::::::::::::::::::::::::::::77276 0 HWI-EAS88_1:1:1:879:434 + chr8 55694925 GCTTTCTGAAGCAATATCTAAAAATACATGAGTCA ::::::::::::::::::::::2::::+++%,)52 0 HWI-EAS88_1:1:1:927:439 + chr17 16974040 GGAAATCTTAAGAGTCCCTTACCTGAACCCCATGA 1:::::::::::::::::::::::::::::43454 0 HWI-EAS88_1:1:1:454:839 + chr15 21415873 GAAAGTGGGGAAAAGCCTTGAAGATATGGCCACAG ::::::::::::::::::::::::::::::60222 6 HWI-EAS88_1:1:1:131:356 + chr18 5462322 TTGTATATGAGCATTGTGCTAATCATCTTCTATTG ::::::::::::::::::::::::::::::7777* 0 HWI-EAS88_1:1:1:410:806 - chr4 95519928 CTGCCATTAGCTCCCACCAGACTCCTGCTTCTCCC 7*466:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:173:649 + chr14 32569656 GAGAGGCTCATAGTAGGAGTGTGCTGCGAGCAGAA ::::::::::::::::::::::::::::::77764 0 HWI-EAS88_1:1:1:765:577 - chr17 16509278 TTGTTCACAACCCCTCTCAGGAGAATGGAATTAAC 34736:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:232:747 - chr14 63416748 CCACAGAAGCAACTTAAGGCAGAAAGTAGAAAGGT ,2727:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:200:860 + chr5 143550277 GCCAAACAACGGAGGAGGGTCTCCACCACGGCCTG ::::::::::::::::::::::::::::::46244 0 HWI-EAS88_1:1:1:226:301 - chr1 193909090 GCCCCTGTTTTCAGTACACCGACCCTCACCAAAGC 64647::::::5::::5:::::::::::::::::: 0 HWI-EAS88_1:1:1:720:593 - chr5 117721479 TTTGTCACGACAGCTACACCGGCCTGGTTGTCACC 22022:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:179:435 + chr14 54958717 GGATCTCAATGTAAAACCAGATACACTGAACCTAA ::::::::::::::::::::::::::::::47726 0 HWI-EAS88_1:1:1:334:739 - chr11 100841423 CGTTGGCGGAGTGACTCACGTTTCCCTCAAGACAC 47446:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:527:570 - chr15 10799644 TCTGTCTCTTTATCGGCAACACCAACTTAACCGTA 5257/2:+:+:::4::5::.::::::::::::::: 0 HWI-EAS88_1:1:1:788:926 + chrX 42404451 GTATTAGAAAACAAACATTGTTCCTATTTGTCCTA ::::::::::::::::::::::::::::::77774 0 HWI-EAS88_1:1:1:307:465 + chr12 40752491 GCACGCAGCAGCTGGTTTCTAATGCCACTCTCCTC ::::::::::::::::::::::::::2:::76757 0 HWI-EAS88_1:1:1:800:265 - chr12 106258463 CCACCTTCGTCAGAGCAGCTGTGACTGCCTGCAGC 44242&+,:0:::3::,:::::::::::::::::: 0 HWI-EAS88_1:1:1:69:120 + chr17 20667690 GTAGAAGAATTGGAATGTACTGGTAAAGGTACTGG ::::::::::::::::::::::::::::::77774 23 HWI-EAS88_1:1:1:567:667 - chr14 43324811 GAAGAATGGAAAGTCAGGGACACGGCATGACATTC 42673:::::::::::::::::::::::::::::: 0 HWI-EAS88_1:1:1:164:144 + chr2 57416770 GTAAATTGAAAAACATTTTCTTGATGAACATTTTG :::::::::::0::::::::::::::::::5555) 0 HWI-EAS88_1:1:1:101:736 - chr15 7183990 TTATAGAATCACTCTGTATTTAACAGAAAGCCAGG 27777::::::4:-::::::::::::::::::::: 0 HWI-EAS88_1:1:1:715:680 + chr9 92383679 GTGAGTCAGTCAGGTACTAAGAAGAAAGAATTACT :::::::::::::::::::::::::2::::76257 0 HWI-EAS88_1:1:1:873:386 + chr15 25552486 GAAGAATGTGCCACCAGCTGTTCTCCTCCCGGGAG ::::::::::::::::::::::::::::::44404 0 HWI-EAS88_1:1:1:273:800 + chr16 33204791 GAGTACTTTGTAAATGAACAGTCTAGAAATCAGAA ::::::::::::::::::::::::::::::44744 0 ShortRead/inst/extdata/maq/0000755000175400017540000000000013001230551016646 5ustar00biocbuildbiocbuildShortRead/inst/extdata/maq/out.aln.1.txt0000644000175400017540000040065513001230551021140 0ustar00biocbuildbiocbuildHWI-EAS88_4_1_6_505_934 ChrA 1 + 0 0 15 15 15 1 12 0 1 35 aaagttagagaagtttgacttttgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_7_163_963 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_9_175_236 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_9_733_962 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_12_440_508 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_12_293_339 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_15_389_86 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_16_316_478 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_18_347_964 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_20_796_561 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_20_402_454 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_25_407_867 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_26_687_534 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_26_338_107 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_36_257_639 ChrA 1 + 0 0 23 23 23 4 16 0 0 35 aaagttagagaagtttnacttntgtagtcaacatc ----------------!----!---)))))))### HWI-EAS88_4_1_36_638_853 ChrA 1 + 0 0 23 23 23 3 12 0 0 35 aaagttagagaagtttnatttntgtaggcaccatc ----------------!----!---)))))))### HWI-EAS88_4_1_50_569_725 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_51_124_197 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_57_575_605 ChrA 1 + 0 0 15 15 15 1 12 0 1 35 aaagttagagaagtttgactcctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_59_934_643 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_60_449_118 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_61_671_133 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_61_164_18 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_62_851_658 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_67_837_274 ChrA 1 + 0 0 15 15 15 1 12 0 1 35 aaagttagagaagtctgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_69_804_185 ChrA 1 + 0 0 23 23 23 3 36 0 0 35 aaagttagagaagtttggactctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_69_116_866 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_70_954_879 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_72_316_172 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_74_908_747 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_77_324_548 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_78_998_596 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_80_381_562 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_85_636_154 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_87_864_224 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_88_211_199 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_89_908_419 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_92_816_658 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_93_566_937 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_95_845_393 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_102_351_841 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_103_997_210 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_107_217_826 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_107_71_977 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_112_750_464 ChrA 1 + 0 0 15 15 15 1 12 0 1 35 aaagctagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_114_317_567 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_118_207_399 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_121_642_529 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_124_905_423 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_126_425_312 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_128_605_529 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_129_714_139 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_129_879_918 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_135_394_765 ChrA 1 + 0 0 15 15 15 1 12 0 1 35 aaagttaaagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_138_523_739 ChrA 1 + 0 0 15 15 15 1 12 0 1 35 aaacttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_140_624_38 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_142_221_50 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_146_555_781 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_157_348_728 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_165_817_549 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_175_101_218 ChrA 1 + 0 0 12 12 12 2 20 0 0 35 aaagttagagaagattgacttctgtacgcaccatc -------------------------)))))))### HWI-EAS88_4_1_180_437_690 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_183_965_413 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_184_998_567 ChrA 1 + 0 0 15 15 15 1 12 0 1 35 aaagttagagaagtttgacctctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_186_241_804 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_190_96_602 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_191_401_219 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_197_362_263 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_198_21_166 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_199_377_859 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_199_550_924 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_207_336_676 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_208_329_98 ChrA 1 + 0 0 15 15 15 1 12 0 1 35 aaagttagagaagtttgacttctttaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_214_380_887 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_219_171_610 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_220_901_680 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_220_659_985 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_223_393_46 ChrA 1 + 0 0 15 15 15 3 0 0 1 35 aaagttagagaagtttgacttcngtaggcnccanc ----------------------!--))))!))#!# HWI-EAS88_4_1_226_94_519 ChrA 1 + 0 0 12 12 12 3 0 0 0 35 aaagttagagaagttngacttctgtnggcaccntc ---------------!---------!))))))!## HWI-EAS88_4_1_226_516_529 ChrA 1 + 0 0 12 12 12 3 0 0 0 35 aaagttagagaagttngacttctgtnggcaccntc ---------------!---------!))))))!## HWI-EAS88_4_1_228_627_50 ChrA 1 + 0 0 12 12 12 4 2 0 0 35 aaagttagagaagtttgacttcngtnggcaccaan ----------------------!--!))))))##! HWI-EAS88_4_1_236_499_460 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_240_681_913 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_241_643_582 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_242_252_257 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_242_65_952 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_245_177_198 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_245_806_819 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_246_701_623 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_247_169_893 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_247_812_955 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_249_875_416 ChrA 1 + 0 0 15 15 15 1 12 0 1 35 aaggttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_252_774_765 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_253_702_960 ChrA 1 + 0 0 23 23 23 3 20 0 0 35 aaagttagagaagtttgncttatgtagtcaccatc -----------------!-------)))))))### HWI-EAS88_4_1_260_109_511 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_260_32_588 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_262_351_961 ChrA 1 + 0 0 15 15 15 1 12 0 1 35 aaagttagagaagtttgacttccgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_267_546_883 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_268_935_456 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_269_122_97 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_272_825_213 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_274_636_98 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_280_310_986 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_280_702_969 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_282_135_970 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_284_923_377 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_284_244_113 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_285_573_113 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_288_361_945 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_293_200_268 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_296_571_373 ChrA 1 + 0 0 22 22 22 0 0 1 0 35 aaagttagagaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_298_436_249 ChrA 1 + 0 0 15 15 15 1 12 0 1 35 aaagttagggaagtttgacttctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_99_379_546 ChrA 2 + 0 0 22 22 22 0 0 1 0 35 aagttagagaagtttgacttctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_168_686_853 ChrA 2 + 0 0 15 15 15 2 12 0 1 35 cagttagagaagtttgacttctgtaggcaccatcn +------------------------)))))))##! HWI-EAS88_4_1_205_353_54 ChrA 2 + 0 0 23 23 23 3 36 0 0 35 aaagttgagaagtttgacttctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_289_504_719 ChrA 2 + 0 0 23 23 23 3 36 0 0 35 aaagttgagaagtttgacttctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_1_554_516 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagcttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_1_670_579 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_1_766_705 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_2_939_240 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_2_666_202 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_2_74_588 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_3_224_196 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_3_822_378 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_3_428_152 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_3_281_271 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_4_302_84 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_4_967_989 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_5_933_109 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_5_215_27 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_5_476_235 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_5_154_433 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_5_576_223 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagttgctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_5_931_657 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_5_683_969 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_6_767_356 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_6_735_96 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_6_353_316 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagtttctgctgagccgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_6_780_324 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_6_164_380 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_6_929_924 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_6_843_460 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_7_900_265 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_7_441_710 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_7_911_942 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_7_274_184 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagtttcttctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_8_587_220 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_8_475_773 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_8_156_449 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_8_941_976 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_9_105_638 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_9_260_582 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_9_275_157 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_9_598_626 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_9_281_484 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_10_336_522 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_10_231_590 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_10_909_844 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_10_518_601 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_10_468_329 ChrA 51 + 0 0 22 22 22 1 2 1 0 35 tacccgtataagtttctgctgagctgtaggcaccc -------------------------)))))))### HWI-EAS88_4_1_11_876_81 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_11_483_84 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_11_549_515 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_11_686_794 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_12_687_64 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_12_399_681 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_12_177_488 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_12_724_741 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_12_333_34 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_13_365_664 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_13_765_808 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_14_443_868 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_14_983_739 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_14_598_919 ChrA 51 + 0 0 22 22 22 1 2 1 0 35 tacccgtataagtttctgctgagctgtaggcaaca -------------------------)))))))### HWI-EAS88_4_1_15_100_670 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_15_698_937 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagtctctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_16_168_610 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_17_782_381 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_17_275_365 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_17_429_856 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_17_309_358 ChrA 51 + 0 0 22 22 22 4 14 1 0 35 tacccgtataagtttctgctgagctgtacgcaaac -------------------------)))))))### HWI-EAS88_4_1_18_142_762 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_18_388_993 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_19_578_127 ChrA 51 + 0 0 22 22 22 1 2 1 0 35 tacccgtataagtttctgctgagctgtaggcacct -------------------------)))))))### HWI-EAS88_4_1_19_687_139 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_19_611_543 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 taaccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_19_779_638 ChrA 51 + 0 0 15 15 15 3 28 0 1 35 tagccgtataagtttctgctgagctgtagagacca -------------------------)))))))### HWI-EAS88_4_1_20_211_45 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_20_357_830 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_20_230_817 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_21_672_50 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_21_870_905 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccatataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_21_937_43 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_21_785_946 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_22_348_187 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_22_344_199 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagtctctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_25_776_318 ChrA 51 + 0 0 22 22 22 2 4 1 0 35 tacccgtataagtttctgctgagctgtaggcacat -------------------------)))))))### HWI-EAS88_4_1_25_152_91 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_25_968_507 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_26_994_219 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_26_425_640 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagttactgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_26_179_814 ChrA 51 + 0 0 22 22 22 1 2 1 0 35 tacccgtataagtttctgctgagctgtaggcaccc -------------------------)))))))### HWI-EAS88_4_1_27_255_646 ChrA 51 + 0 0 23 23 23 3 0 0 0 35 tacccgtataagtttctnctnngctgtaggcacca -----------------!--!!---)))))))### HWI-EAS88_4_1_27_437_166 ChrA 51 + 0 0 23 23 23 3 0 0 0 35 tacccgtataagtttctnctnngctgtaggcacca -----------------!--!!---)))))))### HWI-EAS88_4_1_27_222_982 ChrA 51 + 0 0 23 23 23 3 0 0 0 35 tacccgtataagtttctnctnngctgtaggcacca -----------------!--!!---)))))))### HWI-EAS88_4_1_30_71_568 ChrA 51 + 0 0 15 15 15 2 14 0 1 35 tacccgtataagttcctgctgagctgtaggcacta -------------------------)))))))### HWI-EAS88_4_1_30_469_602 ChrA 51 + 0 0 15 15 15 4 24 0 1 35 tacccgtataagttactgctgagctgtagggacat -------------------------)))))))### HWI-EAS88_4_1_35_794_339 ChrA 51 + 0 0 22 22 22 1 0 1 0 35 tacccgtataagtttctgctgagctgtaggcanca -------------------------)))))))!## HWI-EAS88_4_1_35_763_713 ChrA 51 + 0 0 22 22 22 1 0 1 0 35 tacccgtataagtttctgctgagctgtaggcanca -------------------------)))))))!## HWI-EAS88_4_1_35_696_246 ChrA 51 + 0 0 22 22 22 1 0 1 0 35 tacccgtataagtttctgctgagctgtaggcanca -------------------------)))))))!## HWI-EAS88_4_1_35_286_341 ChrA 51 + 0 0 22 22 22 1 0 1 0 35 tacccgtataagtttctgctgagctgtaggcanca -------------------------)))))))!## HWI-EAS88_4_1_35_247_331 ChrA 51 + 0 0 15 15 15 2 12 0 1 35 tccccgtataagtttctgctgagctgtaggcanca -------------------------)))))))!## HWI-EAS88_4_1_36_468_589 ChrA 51 + 0 0 12 12 12 2 0 0 0 35 tacccgtataagtttcngctgngctgtaggcacca ----------------!----!---)))))))### HWI-EAS88_4_1_36_438_771 ChrA 51 + 0 0 23 23 23 3 12 0 0 35 tacccgtataagtctcngctgngctgtaggcacca ----------------!----!---)))))))### HWI-EAS88_4_1_40_359_742 ChrA 51 + 0 0 22 22 22 1 2 1 0 35 tacccgtataagtttctgctgagctgtaggcacta -------------------------)))))))### HWI-EAS88_4_1_40_456_979 ChrA 51 + 0 0 12 12 12 3 26 0 0 35 tacccgtataagcttctgctaagctgtaggcacta -------------------------)))))))### HWI-EAS88_4_1_42_765_439 ChrA 51 + 0 0 22 22 22 1 0 1 0 35 tacccgtataagtttctgctgagctgtaggcaccn -------------------------)))))))##! HWI-EAS88_4_1_42_988_704 ChrA 51 + 0 0 22 22 22 1 0 1 0 35 tacccgtataagtttctgctgagctgtaggcaccn -------------------------)))))))##! HWI-EAS88_4_1_42_328_130 ChrA 51 + 0 0 22 22 22 1 0 1 0 35 tacccgtataagtttctgctgagctgtaggcaccn -------------------------)))))))##! HWI-EAS88_4_1_42_974_932 ChrA 51 + 0 0 22 22 22 1 0 1 0 35 tacccgtataagtttctgctgagctgtaggcaccn -------------------------)))))))##! HWI-EAS88_4_1_43_727_345 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_43_897_716 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_43_494_109 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagtttctgctgaactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_44_656_493 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_44_448_163 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_44_751_711 ChrA 51 + 0 0 22 22 22 1 2 1 0 35 tacccgtataagtttctgctgagctgtaggcaccc -------------------------)))))))### HWI-EAS88_4_1_44_195_474 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_44_676_1000 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_46_537_419 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_46_699_851 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_46_567_703 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_47_820_48 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagtttctgctgaactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_47_511_496 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_47_327_894 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagttactgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_48_831_163 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_48_101_426 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_48_461_518 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_48_367_291 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_49_749_341 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_49_297_670 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_50_519_35 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_50_911_397 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_50_265_657 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_50_471_607 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_51_722_659 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_51_166_596 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_51_987_611 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_52_343_550 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_52_613_232 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_53_906_222 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_53_302_895 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_53_251_886 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_54_740_970 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_55_478_507 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_55_529_406 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_55_980_989 ChrA 51 + 0 0 22 22 22 1 8 1 0 35 tacccgtataagtttctgctgagctgtagggacca -------------------------)))))))### HWI-EAS88_4_1_56_888_533 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagtctctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_56_624_541 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_56_760_759 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_57_761_415 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_57_101_792 ChrA 51 + 0 0 12 12 12 2 24 0 0 35 tacccgtataagttactgcagagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_57_244_795 ChrA 51 + 0 0 12 12 12 2 24 0 0 35 tacccgtataagtttctacagagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_58_493_276 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_59_458_44 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_59_360_636 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_59_888_699 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_60_428_629 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_60_260_435 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_60_638_563 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_60_425_164 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_60_209_541 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_60_843_870 ChrA 51 + 0 0 22 22 22 1 8 1 0 35 tacccgtataagtttctgctgagctgtagggacca -------------------------)))))))### HWI-EAS88_4_1_61_345_328 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_61_393_363 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_61_516_693 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_62_706_506 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagtttctgctgagccgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_62_399_960 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_64_953_13 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagcttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_64_195_783 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_64_824_567 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_64_287_482 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_64_476_213 ChrA 51 + 0 0 15 15 15 1 8 0 1 35 tacccgtataagtttctgctgagctgtcggcacca -------------------------)))))))### HWI-EAS88_4_1_65_713_451 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_65_300_711 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_65_55_182 ChrA 51 + 0 0 22 22 22 1 2 1 0 35 tacccgtataagtttctgctgagctgtaggcaccc -------------------------)))))))### HWI-EAS88_4_1_67_602_630 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_68_975_227 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_69_777_463 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_69_849_46 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_70_665_574 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_70_660_219 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_71_709_518 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_73_726_882 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_73_983_991 ChrA 51 + 0 0 22 22 22 1 2 1 0 35 tacccgtataagtttctgctgagctgtaggcacaa -------------------------)))))))### HWI-EAS88_4_1_74_165_616 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_74_304_558 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_75_866_136 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_75_823_428 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_75_473_257 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_75_91_37 ChrA 51 + 0 0 12 12 12 3 32 0 0 35 tacccgtataagtttctgctgcgatgtaggaacca -------------------------)))))))### HWI-EAS88_4_1_76_638_869 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_76_599_911 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_77_149_181 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_77_759_942 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_78_510_65 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_79_225_387 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_79_586_578 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_80_491_462 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_80_922_101 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_80_321_54 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_80_824_610 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_80_476_339 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_81_868_661 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagtctctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_81_401_32 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_82_894_629 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_83_245_634 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_83_904_85 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_83_463_804 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_83_878_496 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_83_89_343 ChrA 51 + 0 0 12 12 12 4 30 0 0 35 tacccgtataagtttctgctgagatgaagccacaa -------------------------)))))))### HWI-EAS88_4_1_84_576_335 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_84_644_336 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_84_835_702 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_84_938_141 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_84_919_792 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_85_349_392 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_85_862_309 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataggtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_86_581_64 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_86_567_651 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_86_618_340 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_86_290_785 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_86_664_283 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_86_346_704 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_87_607_484 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_87_481_131 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_87_409_756 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_88_600_97 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_88_959_372 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_88_170_563 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_88_711_941 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 aacccgtataagtttctgctgagctgtaggcacca +------------------------)))))))### HWI-EAS88_4_1_89_886_292 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_89_951_934 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_90_352_641 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_90_770_508 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_90_856_151 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_90_315_646 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_90_521_224 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_90_775_936 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_91_217_492 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_91_328_427 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_91_445_463 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_92_547_262 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_92_197_929 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_92_584_39 ChrA 51 + 0 0 22 22 22 1 8 1 0 35 tacccgtataagtttctgctgagctgtagggacca -------------------------)))))))### HWI-EAS88_4_1_93_898_338 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_94_784_401 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_94_579_347 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_94_319_41 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_94_961_639 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_94_629_109 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_95_312_344 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_96_962_132 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_96_753_420 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_96_866_167 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_97_451_304 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_97_615_686 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_98_622_163 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_98_571_422 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_98_466_903 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_98_265_987 ChrA 51 + 0 0 12 12 12 3 32 0 0 35 taaccgtataagtttatgctgagctgtagtcacca -------------------------)))))))### HWI-EAS88_4_1_99_899_239 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_99_502_692 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_99_646_668 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_99_974_785 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_99_88_981 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_100_614_308 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_100_982_17 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_101_874_781 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_101_146_787 ChrA 51 + 0 0 12 12 12 2 24 0 0 35 tacccgtataagtttctgctggactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_102_966_655 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_102_318_343 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_103_651_423 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_103_218_297 ChrA 51 + 0 0 22 22 22 1 2 1 0 35 tacccgtataagtttctgctgagctgtaggcaccc -------------------------)))))))### HWI-EAS88_4_1_104_489_438 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_105_840_542 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_105_86_783 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_105_958_843 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_105_199_942 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_106_84_432 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_106_479_834 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_106_545_370 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_106_999_282 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_107_617_88 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_107_204_373 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_107_913_344 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_109_658_693 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_109_624_975 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_110_762_557 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_111_685_225 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_111_594_661 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_111_981_447 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_111_431_747 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_111_191_957 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_111_329_865 ChrA 51 + 0 0 15 15 15 3 22 0 1 35 tacccgtataagtttctgcttagctgtaggaacaa -------------------------)))))))### HWI-EAS88_4_1_112_611_472 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_112_934_899 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_114_653_144 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_114_298_235 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_114_494_118 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_114_774_468 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_114_538_653 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_115_904_185 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_115_175_228 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_115_504_833 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_115_265_756 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_115_538_847 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_116_547_634 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_116_809_128 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_116_116_773 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_116_265_952 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_117_631_566 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_117_430_508 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_117_654_456 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_117_484_632 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_117_727_367 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_118_31_314 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_118_67_453 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_118_118_442 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_118_407_511 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_118_571_378 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_119_550_357 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_119_446_122 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_119_637_173 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_119_823_82 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_119_389_698 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_119_851_942 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_120_732_162 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_120_290_298 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_120_971_602 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_120_900_960 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagtctctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_120_40_964 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_121_567_532 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_121_639_276 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_121_123_944 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_122_843_437 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_122_830_593 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_122_247_567 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_123_150_346 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_123_871_789 ChrA 51 + 0 0 22 22 22 1 2 1 0 35 tacccgtataagtttctgctgagctgtaggcacta -------------------------)))))))### HWI-EAS88_4_1_124_402_502 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_124_938_389 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_124_943_753 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_125_614_325 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_125_866_777 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_126_85_230 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_126_369_824 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_126_891_922 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_126_932_503 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_128_589_455 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagttcctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_128_983_394 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_128_111_735 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_128_129_875 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_128_479_403 ChrA 51 + 0 0 22 22 22 5 28 1 0 35 tacccgtataagtttctgctgagctgtagcaccat -------------------------)))))))### HWI-EAS88_4_1_129_706_587 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_129_290_700 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_129_96_166 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_130_245_420 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_130_103_617 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_130_901_669 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagtttcttctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_130_860_473 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_131_703_156 ChrA 51 + 0 0 12 12 12 2 24 0 0 35 tacccgtataagtttcttcttagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_131_400_258 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_131_946_716 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_131_382_850 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_132_450_689 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_132_822_417 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_132_104_435 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_132_367_708 ChrA 51 + 0 0 22 22 22 1 8 1 0 35 tacccgtataagtttctgctgagctgtaggcccca -------------------------)))))))### HWI-EAS88_4_1_134_612_503 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_134_330_308 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_134_948_795 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_135_715_131 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_135_532_187 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_135_130_623 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_135_332_142 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_135_234_201 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_136_565_94 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_136_576_470 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_137_910_193 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_138_819_224 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_138_385_648 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_138_154_598 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_138_441_142 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_138_288_543 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_139_352_379 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_139_395_912 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_140_491_178 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_140_95_332 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_140_349_303 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_140_715_771 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_140_976_655 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_140_675_898 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_141_588_224 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_141_627_829 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_141_106_475 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_142_304_50 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_142_871_41 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_142_727_500 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_142_186_136 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_142_92_152 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_142_358_898 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_142_869_967 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_145_297_37 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_146_276_538 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_146_110_179 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_146_853_746 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_146_113_708 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_148_325_78 ChrA 51 + 0 0 15 15 15 1 0 0 1 35 tacccgtataagtttctgcngagctgtaggcacca -------------------!-----)))))))### HWI-EAS88_4_1_148_157_257 ChrA 51 + 0 0 15 15 15 1 0 0 1 35 tacccgtataagtttctgcngagctgtaggcacca -------------------!-----)))))))### HWI-EAS88_4_1_148_271_700 ChrA 51 + 0 0 15 15 15 1 0 0 1 35 tacccgtataagtttctgcngagctgtaggcacca -------------------!-----)))))))### HWI-EAS88_4_1_149_794_567 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_149_919_742 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagtttctgctgaactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_151_190_313 ChrA 51 + 0 0 23 23 23 3 32 0 0 35 tacccgtataagtttctgctaagatgaaggcacca -------------------------)))))))### HWI-EAS88_4_1_152_949_525 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_153_852_370 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_153_705_680 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_154_649_428 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_154_488_570 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_157_819_905 ChrA 51 + 0 0 12 12 12 3 26 0 0 35 tatccgtataagtttctgctgagatgtaggcacaa -------------------------)))))))### HWI-EAS88_4_1_160_88_593 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_160_889_791 ChrA 51 + 0 0 22 22 22 1 8 1 0 35 tacccgtataagtttctgctgagctgtaggaacca -------------------------)))))))### HWI-EAS88_4_1_160_674_142 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_165_368_62 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_165_29_522 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_165_259_416 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_165_906_773 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_165_995_791 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_166_809_486 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_166_458_803 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tatccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_166_537_760 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_168_961_770 ChrA 51 + 0 0 22 22 22 1 0 1 0 35 tacccgtataagtttctgctgagctgtaggcaccn -------------------------)))))))##! HWI-EAS88_4_1_168_490_159 ChrA 51 + 0 0 22 22 22 1 0 1 0 35 tacccgtataagtttctgctgagctgtaggcaccn -------------------------)))))))##! HWI-EAS88_4_1_168_534_317 ChrA 51 + 0 0 22 22 22 1 0 1 0 35 tacccgtataagtttctgctgagctgtaggcaccn -------------------------)))))))##! HWI-EAS88_4_1_175_112_698 ChrA 51 + 0 0 22 22 22 1 2 1 0 35 tacccgtataagtttctgctgagctgtaggcagca -------------------------)))))))### HWI-EAS88_4_1_175_605_154 ChrA 51 + 0 0 15 15 15 1 8 0 1 35 tacccgtataagtttctgctgagctgtgggcacca -------------------------)))))))### HWI-EAS88_4_1_175_972_297 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_175_171_599 ChrA 51 + 0 0 15 15 15 1 8 0 1 35 tacccgtataagtttctgctgagctgaaggcacca -------------------------)))))))### HWI-EAS88_4_1_175_93_66 ChrA 51 + 0 0 12 12 12 5 38 0 0 35 tacccgtataagtctctgctgagctgtcgcccccc -------------------------)))))))### HWI-EAS88_4_1_180_530_275 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_180_452_358 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_180_480_750 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_180_842_313 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_180_178_795 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_181_305_574 ChrA 51 + 0 0 22 22 22 1 0 1 0 35 tacccgtataagtttctgctgagctgtagncacca -------------------------))))!))### HWI-EAS88_4_1_181_257_310 ChrA 51 + 0 0 22 22 22 1 0 1 0 35 tacccgtataagtttctgctgagctgtagncacca -------------------------))))!))### HWI-EAS88_4_1_181_962_646 ChrA 51 + 0 0 22 22 22 1 0 1 0 35 tacccgtataagtttctgctgagctgtagncacca -------------------------))))!))### HWI-EAS88_4_1_183_203_839 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_183_970_713 ChrA 51 + 0 0 22 22 22 1 2 1 0 35 tacccgtataagtttctgctgagctgtaggcaccc -------------------------)))))))### HWI-EAS88_4_1_184_766_249 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_184_882_385 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_184_636_896 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_184_846_810 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_184_162_545 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_184_231_438 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_184_436_990 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_185_1000_559 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_186_123_514 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagtttctgctgaactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_186_455_449 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_186_650_292 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_187_126_568 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacctgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_187_491_774 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_187_686_663 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_187_730_402 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_187_117_491 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_187_24_291 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_187_581_802 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_188_177_124 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacctgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_188_538_465 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_188_421_175 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_188_142_642 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_188_168_855 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_189_520_222 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_189_951_674 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_190_655_308 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_190_880_30 ChrA 51 + 0 0 15 15 15 3 14 0 1 35 tactcgtataagtttctgctgagctgtaggcacan -------------------------)))))))##! HWI-EAS88_4_1_190_234_928 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_190_687_550 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_191_458_206 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_191_649_507 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_191_491_673 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_191_21_963 ChrA 51 + 0 0 22 22 22 1 2 1 0 35 tacccgtataagtttctgctgagctgtaggcacaa -------------------------)))))))### HWI-EAS88_4_1_192_794_557 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_192_544_257 ChrA 51 + 0 0 22 22 22 1 8 1 0 35 tacccgtataagtttctgctgagctgtatgcacca -------------------------)))))))### HWI-EAS88_4_1_193_883_105 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_193_685_566 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_193_795_382 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_194_359_775 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_194_656_781 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtacaagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_194_809_458 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_194_747_835 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_195_686_739 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtgtaagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_195_767_178 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_196_915_465 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_196_519_286 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_196_42_290 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_196_202_931 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_197_807_639 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_198_770_415 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_198_305_805 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_198_524_957 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_199_868_422 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_199_400_423 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_199_644_838 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_200_928_418 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_200_900_389 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_200_923_376 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_200_236_768 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_201_919_682 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_201_839_348 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_202_510_101 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_202_397_622 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_202_274_564 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_203_213_153 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_203_408_525 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_203_227_880 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_203_181_936 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_204_53_101 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagtttctgctgagccgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_204_769_806 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_205_876_683 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_205_181_450 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_206_240_716 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_206_609_930 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_207_509_47 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_207_85_106 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_207_855_686 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_208_930_317 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_208_199_170 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_208_285_457 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_208_652_463 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_208_780_225 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_209_360_419 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_209_414_637 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_209_723_472 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_209_977_221 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_209_28_938 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_210_154_160 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_210_792_53 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_210_456_474 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_210_452_805 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_210_275_949 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_210_604_930 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_210_461_463 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 cacccgtataagtttctgctgagctgtaggcacca +------------------------)))))))### HWI-EAS88_4_1_211_795_381 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_211_855_34 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_213_152_620 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_213_749_56 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_213_327_378 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_213_60_389 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_213_927_698 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_214_837_360 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_214_832_577 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagtttctgctgaactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_214_424_551 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_214_593_370 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_214_506_253 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_214_373_107 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_215_21_252 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_215_293_558 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_215_746_879 ChrA 51 + 0 0 22 22 22 2 4 1 0 35 tacccgtataagtttctgctgagctgtaggcactc -------------------------)))))))### HWI-EAS88_4_1_215_163_822 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_215_587_831 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_216_920_421 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagcttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_216_438_512 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_216_362_66 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_216_668_844 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_217_439_468 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_217_192_858 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_218_49_622 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_218_457_857 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_218_711_988 ChrA 51 + 0 0 22 22 22 1 2 1 0 35 tacccgtataagtttctgctgagctgtaggcacaa -------------------------)))))))### HWI-EAS88_4_1_219_649_268 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_219_701_534 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_219_116_141 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_219_159_658 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_219_673_440 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagcttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_220_412_283 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_220_494_612 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_220_378_674 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_220_373_619 ChrA 51 + 0 0 22 22 22 1 0 1 0 35 tacccgtataagtttctgctgagctgtaggnacca -------------------------)))))!)### HWI-EAS88_4_1_221_887_101 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_221_56_28 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_221_432_887 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_221_549_866 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_221_454_243 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_221_123_508 ChrA 51 + 0 0 22 22 22 1 2 1 0 35 tacccgtataagtttctgctgagctgtaggcaccc -------------------------)))))))### HWI-EAS88_4_1_223_528_444 ChrA 51 + 0 0 15 15 15 4 2 0 1 35 tacccgtataagtttctgctganctgtagncacnc ----------------------!--))))!))#!# HWI-EAS88_4_1_226_940_30 ChrA 51 + 0 0 12 12 12 3 0 0 0 35 tacccgtataagtttntgctgagctntaggcanca ---------------!---------!))))))!## HWI-EAS88_4_1_226_623_199 ChrA 51 + 0 0 12 12 12 3 0 0 0 35 tacccgtataagtttntgctgagctntaggcanca ---------------!---------!))))))!## HWI-EAS88_4_1_226_576_620 ChrA 51 + 0 0 12 12 12 3 0 0 0 35 tacccgtataagtttntgctgagctntaggcanca ---------------!---------!))))))!## HWI-EAS88_4_1_226_824_208 ChrA 51 + 0 0 12 12 12 3 0 0 0 35 tacccgtataagtttntgctgagctntaggcanca ---------------!---------!))))))!## HWI-EAS88_4_1_226_976_394 ChrA 51 + 0 0 12 12 12 3 0 0 0 35 tacccgtataagtttntgctgagctntaggcanca ---------------!---------!))))))!## HWI-EAS88_4_1_226_855_159 ChrA 51 + 0 0 12 12 12 3 0 0 0 35 tacccgtataagtttntgctgagctntaggcanca ---------------!---------!))))))!## HWI-EAS88_4_1_226_745_497 ChrA 51 + 0 0 12 12 12 3 0 0 0 35 tacccgtataagtttntgctgagctntaggcanca ---------------!---------!))))))!## HWI-EAS88_4_1_226_88_624 ChrA 51 + 0 0 12 12 12 3 0 0 0 35 tacccgtataagtttntgctgagctntaggcanca ---------------!---------!))))))!## HWI-EAS88_4_1_228_610_567 ChrA 51 + 0 0 12 12 12 3 0 0 0 35 tacccgtataagtttctgctganctntaggcaccn ----------------------!--!))))))##! HWI-EAS88_4_1_228_220_314 ChrA 51 + 0 0 12 12 12 3 0 0 0 35 tacccgtataagtttctgctganctntaggcaccn ----------------------!--!))))))##! HWI-EAS88_4_1_229_292_106 ChrA 51 + 0 0 12 12 12 4 10 0 0 35 tacccgtataagtttctgctnagctatagncagca --------------------!----))))!))### HWI-EAS88_4_1_230_884_573 ChrA 51 + 0 0 12 12 12 6 30 0 0 35 tacccgtataagtttctgctgatctttacncnaca -------------------------))))!)!### HWI-EAS88_4_1_230_773_680 ChrA 51 + 0 0 15 15 15 4 10 0 1 35 tacccgtataagtttctgctgagctttagncntca -------------------------))))!)!### HWI-EAS88_4_1_232_647_877 ChrA 51 + 0 0 12 12 12 3 0 0 0 35 tacccgtataagtttctgctganctgtnggcaccn ----------------------!--))!))))##! HWI-EAS88_4_1_233_647_357 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtattagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_233_217_188 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_233_396_890 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_233_435_208 ChrA 51 + 0 0 12 12 12 2 24 0 0 35 tacccgtatcagcttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_235_405_177 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_235_129_332 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_235_602_269 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_235_679_774 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagcttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_235_944_888 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_235_668_861 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_235_758_997 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagcttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_237_81_345 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_237_78_399 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_237_394_64 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_237_480_461 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_239_235_449 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_239_715_556 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_239_409_731 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_241_801_104 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_241_279_677 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_241_617_980 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_242_194_140 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_244_416_239 ChrA 51 + 0 0 22 22 22 1 0 1 0 35 tacccgtataagtttctgctgagctgtaggcncca -------------------------))))))!### HWI-EAS88_4_1_245_587_286 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_245_316_916 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagtttctgctgagccgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_246_681_146 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_246_321_576 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_246_699_834 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_246_19_940 ChrA 51 + 0 0 12 12 12 7 54 0 0 35 tacccgtataagtttctgctgagcactaacacaca -------------------------)))))))### HWI-EAS88_4_1_247_476_209 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_247_831_394 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_247_792_484 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_247_791_485 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_248_768_671 ChrA 51 + 0 0 12 12 12 2 20 0 0 35 tacctgtataagtttctgctgagctggaggcacca -------------------------)))))))### HWI-EAS88_4_1_248_383_85 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_249_645_322 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_249_269_106 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_249_171_764 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_249_737_188 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_250_187_371 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_250_646_255 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_250_493_799 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_251_381_501 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_251_573_456 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_251_458_651 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_251_734_378 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_251_62_933 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_252_563_112 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_252_91_843 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_253_551_206 ChrA 51 + 0 0 23 23 23 3 16 0 0 35 tacccgtataagtttctnctgagcttcaggcacca -----------------!-------)))))))### HWI-EAS88_4_1_253_772_502 ChrA 51 + 0 0 12 12 12 2 12 0 0 35 tacccgtataagtttctnctgatctgtaggcacca -----------------!-------)))))))### HWI-EAS88_4_1_253_598_84 ChrA 51 + 0 0 12 12 12 3 8 0 0 35 tacccgtataagtttctnctgagctgnagtcacca -----------------!-------)!)))))### HWI-EAS88_4_1_253_582_583 ChrA 51 + 0 0 15 15 15 1 0 0 1 35 tacccgtataagtttctnctgagctgtaggcacca -----------------!-------)))))))### HWI-EAS88_4_1_253_764_489 ChrA 51 + 0 0 15 15 15 1 0 0 1 35 tacccgtataagtttctnctgagctgtaggcacca -----------------!-------)))))))### HWI-EAS88_4_1_254_738_845 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_256_329_354 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_256_680_573 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_256_26_645 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_256_564_258 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_257_626_621 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_257_78_698 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_258_523_534 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_258_772_515 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_259_678_203 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_260_717_779 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_260_743_816 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagttactgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_261_378_694 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_261_161_512 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_261_755_432 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_262_302_459 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_262_622_21 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_262_361_595 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_263_383_1001 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_264_162_313 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccatataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_264_566_672 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_264_710_87 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_264_514_924 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_264_840_944 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_265_542_74 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_265_322_178 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_266_367_782 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_266_649_368 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_266_173_53 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_266_122_857 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_267_487_312 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_267_144_434 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_267_650_739 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_268_368_498 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_268_26_470 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_269_281_83 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_269_690_345 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_269_857_463 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_269_157_365 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_269_587_772 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_269_290_871 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_270_892_277 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_270_117_279 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_270_215_575 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_271_652_721 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_271_272_399 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_272_798_25 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_272_673_977 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_273_329_277 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_273_781_314 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_275_36_507 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_275_109_486 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_275_973_783 ChrA 51 + 0 0 22 22 22 1 2 1 0 35 tacccgtataagtttctgctgagctgtaggcacaa -------------------------)))))))### HWI-EAS88_4_1_276_770_460 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_276_232_401 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_276_735_947 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_276_855_971 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_276_704_988 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_277_655_19 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_278_376_780 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_279_236_280 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_279_179_482 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_279_34_424 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_279_685_385 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_280_888_993 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_281_357_202 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_281_692_791 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_282_622_237 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_282_138_998 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_283_242_727 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_283_763_94 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_283_861_930 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_284_31_151 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_284_358_886 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_284_147_374 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_284_453_23 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_284_808_820 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_284_395_566 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_285_51_364 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_285_493_98 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_285_870_341 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_285_568_167 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_285_435_444 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_285_732_285 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtatgagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_286_165_226 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_286_339_308 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_287_768_702 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_287_375_624 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_287_372_265 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_287_155_546 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagtttctgctgaactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_287_465_487 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_288_163_484 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_288_49_940 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_290_315_185 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_290_234_120 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_290_763_806 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_290_212_729 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_290_566_850 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_291_625_644 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_291_129_698 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_291_921_735 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_291_474_325 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_293_798_461 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_293_241_105 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_293_615_527 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_293_742_429 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_293_331_414 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_293_97_519 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_294_452_305 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_294_583_685 ChrA 51 + 0 0 15 15 15 1 12 0 1 35 tacccgtataagcttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_294_485_195 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_296_47_478 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_296_568_871 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_297_736_36 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_297_870_795 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_298_548_618 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_298_351_875 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_298_206_863 ChrA 51 + 0 0 22 22 22 1 2 1 0 35 tacccgtataagtttctgctgagctgtaggcaccg -------------------------)))))))### HWI-EAS88_4_1_298_796_993 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_299_828_126 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_299_381_298 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_299_650_704 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_299_611_763 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_300_782_305 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_300_91_707 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_300_661_19 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_300_118_894 ChrA 51 + 0 0 22 22 22 0 0 1 0 35 tacccgtataagtttctgctgagctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_30_360_204 ChrA 52 + 0 0 23 23 23 3 36 0 0 35 taccgtataagtttttgctgagctgtaggcaccat ++-----------------------)))))))### HWI-EAS88_4_1_42_567_90 ChrA 52 + 0 0 12 12 12 4 26 0 0 35 taccgtataagtttctgctgagctgtaggcacccn ++-----------------------)))))))##! HWI-EAS88_4_1_84_647_783 ChrA 52 + 0 0 15 15 15 1 12 0 1 35 tcccgtataagtttctgctgagctgtaggcaccat +------------------------)))))))### HWI-EAS88_4_1_85_852_68 ChrA 52 + 0 0 12 12 12 2 24 0 0 35 taccgtataagtttctgctgagctgtaggcaccat ++-----------------------)))))))### HWI-EAS88_4_1_89_556_255 ChrA 52 + 0 0 12 12 12 2 24 0 0 35 taccgtataagtttctgctgagctgtaggcaccat ++-----------------------)))))))### HWI-EAS88_4_1_110_383_762 ChrA 52 + 0 0 15 15 15 1 12 0 1 35 tcccgtataagtttctgctgagctgtaggcaccat +------------------------)))))))### HWI-EAS88_4_1_138_964_202 ChrA 52 + 0 0 22 22 22 0 0 1 0 35 acccgtataagtttctgctgagctgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_180_572_279 ChrA 52 + 0 0 15 15 15 1 12 0 1 35 tcccgtataagtttctgctgagctgtaggcaccat +------------------------)))))))### HWI-EAS88_4_1_196_371_63 ChrA 52 + 0 0 22 22 22 0 0 1 0 35 acccgtataagtttctgctgagctgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_209_27_122 ChrA 52 + 0 0 15 15 15 1 12 0 1 35 tcccgtataagtttctgctgagctgtaggcaccat +------------------------)))))))### HWI-EAS88_4_1_241_321_218 ChrA 52 + 0 0 12 12 12 2 24 0 0 35 taccgtataagtttctgctgagctgtaggcaccat ++-----------------------)))))))### HWI-EAS88_4_1_263_418_901 ChrA 52 + 0 0 12 12 12 2 24 0 0 35 taccgtataagtttctgctgagctgtaggcaccat ++-----------------------)))))))### HWI-EAS88_4_1_276_104_117 ChrA 52 + 0 0 23 23 23 3 36 0 0 35 taccctataagtttctgctgagctgtaggcaccat ++-----------------------)))))))### HWI-EAS88_4_1_11_791_180 ChrA 101 + 0 0 15 15 15 1 12 0 1 35 cagctttttagttttcacgctgtaggcaccatcaa +------------------------)))))))### HWI-EAS88_4_1_13_514_596 ChrA 101 + 0 0 15 15 15 1 12 0 1 35 tagcttcttagttttcacgctgtaggcaccatcaa -------------------------)))))))### HWI-EAS88_4_1_36_648_772 ChrA 101 + 0 0 12 12 12 2 0 0 0 35 tagctttttagttttcncgctntaggcaccatcaa ----------------!----!---)))))))### HWI-EAS88_4_1_51_638_881 ChrA 101 + 0 0 22 22 22 0 0 1 0 35 tagctttttagttttcacgctgtaggcaccatcaa -------------------------)))))))### HWI-EAS88_4_1_56_611_301 ChrA 101 + 0 0 22 22 22 0 0 1 0 35 tagctttttagttttcacgctgtaggcaccatcaa -------------------------)))))))### HWI-EAS88_4_1_67_684_559 ChrA 101 + 0 0 22 22 22 0 0 1 0 35 tagctttttagttttcacgctgtaggcaccatcaa -------------------------)))))))### HWI-EAS88_4_1_69_801_661 ChrA 101 + 0 0 22 22 22 0 0 1 0 35 tagctttttagttttcacgctgtaggcaccatcaa -------------------------)))))))### HWI-EAS88_4_1_71_987_730 ChrA 101 + 0 0 22 22 22 0 0 1 0 35 tagctttttagttttcacgctgtaggcaccatcaa -------------------------)))))))### HWI-EAS88_4_1_73_690_22 ChrA 101 + 0 0 22 22 22 0 0 1 0 35 tagctttttagttttcacgctgtaggcaccatcaa -------------------------)))))))### HWI-EAS88_4_1_76_889_789 ChrA 101 + 0 0 22 22 22 0 0 1 0 35 tagctttttagttttcacgctgtaggcaccatcaa -------------------------)))))))### HWI-EAS88_4_1_91_981_885 ChrA 101 + 0 0 22 22 22 0 0 1 0 35 tagctttttagttttcacgctgtaggcaccatcaa -------------------------)))))))### HWI-EAS88_4_1_94_394_46 ChrA 101 + 0 0 22 22 22 0 0 1 0 35 tagctttttagttttcacgctgtaggcaccatcaa -------------------------)))))))### HWI-EAS88_4_1_108_560_901 ChrA 101 + 0 0 22 22 22 0 0 1 0 35 tagctttttagttttcacgctgtaggcaccatcaa -------------------------)))))))### HWI-EAS88_4_1_138_625_488 ChrA 101 + 0 0 22 22 22 0 0 1 0 35 tagctttttagttttcacgctgtaggcaccatcaa -------------------------)))))))### HWI-EAS88_4_1_165_469_971 ChrA 101 + 0 0 15 15 15 2 16 0 1 35 tagctttttagttttcacgctgtagtcagcatcaa -------------------------)))))))### HWI-EAS88_4_1_193_212_968 ChrA 101 + 0 0 22 22 22 0 0 1 0 35 tagctttttagttttcacgctgtaggcaccatcaa -------------------------)))))))### HWI-EAS88_4_1_215_444_185 ChrA 101 + 0 0 22 22 22 0 0 1 0 35 tagctttttagttttcacgctgtaggcaccatcaa -------------------------)))))))### HWI-EAS88_4_1_232_416_788 ChrA 101 + 0 0 12 12 12 3 0 0 0 35 tagctttttagttttcacgctgnaggcnccatcan ----------------------!--))!))))##! HWI-EAS88_4_1_241_75_622 ChrA 101 + 0 0 22 22 22 0 0 1 0 35 tagctttttagttttcacgctgtaggcaccatcaa -------------------------)))))))### HWI-EAS88_4_1_248_642_940 ChrA 101 + 0 0 22 22 22 0 0 1 0 35 tagctttttagttttcacgctgtaggcaccatcaa -------------------------)))))))### HWI-EAS88_4_1_250_372_60 ChrA 101 + 0 0 22 22 22 0 0 1 0 35 tagctttttagttttcacgctgtaggcaccatcaa -------------------------)))))))### HWI-EAS88_4_1_252_729_227 ChrA 101 + 0 0 22 22 22 0 0 1 0 35 tagctttttagttttcacgctgtaggcaccatcaa -------------------------)))))))### HWI-EAS88_4_1_254_716_653 ChrA 101 + 0 0 22 22 22 0 0 1 0 35 tagctttttagttttcacgctgtaggcaccatcaa -------------------------)))))))### HWI-EAS88_4_1_274_40_854 ChrA 101 + 0 0 15 15 15 1 12 0 1 35 tagctttttagttttaacgctgtaggcaccatcaa -------------------------)))))))### HWI-EAS88_4_1_276_802_411 ChrA 101 + 0 0 22 22 22 0 0 1 0 35 tagctttttagttttcacgctgtaggcaccatcaa -------------------------)))))))### HWI-EAS88_4_1_298_887_457 ChrA 101 + 0 0 22 22 22 0 0 1 0 35 tagctttttagttttcacgctgtaggcaccatcaa -------------------------)))))))### HWI-EAS88_4_1_12_1000_484 ChrA 151 + 0 0 22 22 22 0 0 1 0 35 ttcgaggcctattaaacctctgctgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_14_688_438 ChrA 151 + 0 0 15 15 15 1 12 0 1 35 ttcgaggcctattaaacccctgctgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_25_975_715 ChrA 151 + 0 0 12 12 12 2 24 0 0 35 ttcgaggcctattaaaactcggctgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_43_764_665 ChrA 151 + 0 0 22 22 22 0 0 1 0 35 ttcgaggcctattaaacctctgctgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_79_384_542 ChrA 151 + 0 0 22 22 22 0 0 1 0 35 ttcgaggcctattaaacctctgctgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_85_692_526 ChrA 151 + 0 0 15 15 15 1 12 0 1 35 tccgaggcctattaaacctctgctgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_97_428_606 ChrA 151 + 0 0 22 22 22 0 0 1 0 35 ttcgaggcctattaaacctctgctgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_202_266_568 ChrA 151 + 0 0 22 22 22 0 0 1 0 35 ttcgaggcctattaaacctctgctgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_207_525_148 ChrA 151 + 0 0 22 22 22 0 0 1 0 35 ttcgaggcctattaaacctctgctgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_214_811_380 ChrA 151 + 0 0 22 22 22 0 0 1 0 35 ttcgaggcctattaaacctctgctgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_272_840_571 ChrA 151 + 0 0 22 22 22 0 0 1 0 35 ttcgaggcctattaaacctctgctgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_273_260_580 ChrA 151 + 0 0 22 22 22 0 0 1 0 35 ttcgaggcctattaaacctctgctgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_278_470_89 ChrA 151 + 0 0 22 22 22 0 0 1 0 35 ttcgaggcctattaaacctctgctgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_14_446_215 ChrA 152 + 0 0 23 23 23 3 36 0 0 35 ttcgggcctattaaacctctgctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_25_311_196 ChrA 152 + 0 0 23 23 23 3 36 0 0 35 ttcgggcctattaaacctctgctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_70_948_414 ChrA 152 + 0 0 23 23 23 3 36 0 0 35 ttcgggcctattaaacctctgctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_154_938_263 ChrA 152 + 0 0 23 23 23 3 36 0 0 35 ttcgggcctattaaacctctgctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_217_324_105 ChrA 152 + 0 0 23 23 23 3 36 0 0 35 ttcgggcctattaaacctctgctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_296_335_731 ChrA 152 + 0 0 12 12 12 2 24 0 0 35 ttcaggcctattaaacctctgctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_1_689_272 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_1_247_616 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_1_765_530 ChrA 201 + 0 0 15 15 15 1 12 0 1 35 ttttctcgatgttttctgatctgtaggcaccatca +------------------------)))))))### HWI-EAS88_4_1_1_408_74 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_1_566_624 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_1_474_948 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_2_644_906 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_3_475_521 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_3_298_368 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_3_459_421 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_3_619_218 ChrA 201 + 0 0 15 15 15 1 12 0 1 35 gattctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_4_882_358 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_4_837_148 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_4_343_249 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_4_668_527 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_4_188_105 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_4_399_240 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_4_91_408 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_4_433_144 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_4_370_657 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_4_347_731 ChrA 201 + 0 0 23 23 23 7 62 0 0 35 tttcctcgatgttttctgatatgtaggcaaactcg +------------------------)))))))### HWI-EAS88_4_1_5_750_634 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_5_175_355 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_5_154_980 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_6_485_227 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_6_291_284 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_6_629_956 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_6_216_836 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_6_408_351 ChrA 201 + 0 0 15 15 15 1 8 0 1 35 gtttctcgatgttttctgatctgtagtcaccatca -------------------------)))))))### HWI-EAS88_4_1_7_274_72 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_7_975_642 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_7_246_382 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_7_941_56 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_7_340_449 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_7_369_42 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_8_486_24 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_8_889_617 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_8_790_848 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_9_500_208 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_9_532_773 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_9_863_353 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_9_452_339 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_10_863_88 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_10_635_779 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_10_514_210 ChrA 201 + 0 0 22 22 22 1 2 1 0 35 gtttctcgatgttttctgatctgtaggcaccatcg -------------------------)))))))### HWI-EAS88_4_1_10_125_708 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_10_599_930 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_10_199_964 ChrA 201 + 0 0 22 22 22 1 8 1 0 35 gtttctcgatgttttctgatctgtaggcacaatca -------------------------)))))))### HWI-EAS88_4_1_10_221_964 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_10_685_144 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_11_942_199 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### HWI-EAS88_4_1_11_875_373 ChrA 201 + 0 0 22 22 22 0 0 1 0 35 gtttctcgatgttttctgatctgtaggcaccatca -------------------------)))))))### ShortRead/inst/extdata/maq/out.aln.2.txt0000644000175400017540000040444513001230551021142 0ustar00biocbuildbiocbuildHWI-EAS88_4_1_175_165_901 ChrA 1151 + 0 0 15 15 15 1 0 0 1 35 tttgtactccgatnccattcagactgtaggcacca -------------!-----------)))))))### HWI-EAS88_4_1_180_452_137 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_180_422_486 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_180_723_162 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_180_722_562 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_180_726_812 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_180_499_849 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_180_792_179 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_180_626_889 ChrA 1151 + 0 0 22 22 22 1 2 1 0 35 tttgtactccgatgccattcagactgtaggcaaca -------------------------)))))))### HWI-EAS88_4_1_180_673_993 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_180_757_364 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_180_709_540 ChrA 1151 + 0 0 23 23 23 5 40 0 0 35 tttgtactccgatgcacttccgactgtaggcacac -------------------------)))))))### HWI-EAS88_4_1_181_663_285 ChrA 1151 + 0 0 22 22 22 1 0 1 0 35 tttgtactccgatgccattcagactgtagncacca -------------------------))))!))### HWI-EAS88_4_1_181_109_366 ChrA 1151 + 0 0 22 22 22 1 0 1 0 35 tttgtactccgatgccattcagactgtagncacca -------------------------))))!))### HWI-EAS88_4_1_181_636_677 ChrA 1151 + 0 0 22 22 22 1 0 1 0 35 tttgtactccgatgccattcagactgtagncacca -------------------------))))!))### HWI-EAS88_4_1_181_604_736 ChrA 1151 + 0 0 22 22 22 2 8 1 0 35 tttgtactccgatgccattcagactgtagntacca -------------------------))))!))### HWI-EAS88_4_1_181_982_96 ChrA 1151 + 0 0 15 15 15 2 12 0 1 35 tttgtactccaatgccattcagactgtagncacca -------------------------))))!))### HWI-EAS88_4_1_181_241_252 ChrA 1151 + 0 0 22 22 22 1 0 1 0 35 tttgtactccgatgccattcagactgtagncacca -------------------------))))!))### HWI-EAS88_4_1_181_579_856 ChrA 1151 + 0 0 22 22 22 1 0 1 0 35 tttgtactccgatgccattcagactgtagncacca -------------------------))))!))### HWI-EAS88_4_1_181_434_740 ChrA 1151 + 0 0 22 22 22 1 0 1 0 35 tttgtactccgatgccattcagactgtagncacca -------------------------))))!))### HWI-EAS88_4_1_181_301_554 ChrA 1151 + 0 0 22 22 22 1 0 1 0 35 tttgtactccgatgccattcagactgtagncacca -------------------------))))!))### HWI-EAS88_4_1_183_907_317 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_183_25_353 ChrA 1151 + 0 0 22 22 22 1 2 1 0 35 tttgtactccgatgccattcagactgtaggcacct -------------------------)))))))### HWI-EAS88_4_1_183_808_466 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_183_782_572 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_183_627_938 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_183_264_682 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_183_816_584 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_183_525_937 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_184_662_198 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_184_762_462 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_184_66_222 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_184_572_704 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_184_703_412 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_184_221_982 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_185_158_88 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_185_760_348 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_185_649_674 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_185_720_835 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_185_316_853 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_185_570_386 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_186_868_139 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_186_174_298 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_186_328_987 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_187_162_164 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_187_230_544 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_187_363_241 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_187_229_963 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgccattcaggctgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_187_675_370 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_188_816_98 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_188_944_223 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_188_450_457 ChrA 1151 + 0 0 22 22 22 1 2 1 0 35 tttgtactccgatgccattcagactgtaggcaccc -------------------------)))))))### HWI-EAS88_4_1_189_212_165 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_189_501_600 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_189_446_498 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_189_354_354 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttggactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_189_128_449 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_189_237_655 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_190_957_344 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_190_69_42 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_190_404_161 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_190_976_311 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_190_624_67 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_190_491_383 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_190_469_732 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_190_262_523 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_190_715_653 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_190_604_894 ChrA 1151 + 0 0 22 22 22 1 2 1 0 35 tttgtactccgatgccattcagactgtaggcatca -------------------------)))))))### HWI-EAS88_4_1_191_925_314 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_191_390_512 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_191_304_487 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_191_585_557 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_191_859_346 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_191_513_411 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_191_670_155 ChrA 1151 + 0 0 22 22 22 2 4 1 0 35 tttgtactccgatgccattcagactgtaggcacat -------------------------)))))))### HWI-EAS88_4_1_191_196_457 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_191_646_906 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_191_204_328 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_191_967_701 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_191_486_952 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_191_85_654 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_191_948_870 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_191_475_957 ChrA 1151 + 0 0 23 23 23 6 50 0 0 35 tttgtactccgatgccatacagacaggagcaaaca -------------------------)))))))### HWI-EAS88_4_1_192_764_321 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_192_718_255 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_192_61_89 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_192_951_760 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_192_483_826 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_192_108_546 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_192_592_956 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_192_872_977 ChrA 1151 + 0 0 12 12 12 2 24 0 0 35 cttgtactccgaagccattcagactgtaggcacca +------------------------)))))))### HWI-EAS88_4_1_193_795_502 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_193_888_420 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_193_48_358 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_193_294_393 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_193_613_321 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_193_422_868 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_193_898_223 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_193_921_867 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_193_726_726 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_193_136_757 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_193_24_904 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_193_511_886 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_193_383_330 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_194_365_385 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_194_295_402 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_194_387_262 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_194_363_153 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_194_89_374 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_194_402_193 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_194_766_716 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_194_88_633 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_194_729_394 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_195_500_309 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_195_152_287 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_195_984_244 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_195_748_809 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_195_894_534 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_195_955_862 ChrA 1151 + 0 0 15 15 15 2 14 0 1 35 tttgtactccgatgccattaagactgtaggcaaca -------------------------)))))))### HWI-EAS88_4_1_196_227_537 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_196_492_199 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_196_453_633 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgattccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_196_504_531 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_196_200_602 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgccaatcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_197_805_63 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_197_386_302 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_197_401_229 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_197_411_59 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_197_704_865 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_197_252_540 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_197_548_344 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_197_948_809 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_197_402_230 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_198_205_99 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_198_239_418 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_198_568_92 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_198_614_761 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_198_702_704 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_198_668_817 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_198_383_262 ChrA 1151 + 0 0 12 12 12 2 24 0 0 35 tttgtactccgatgcccatcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_199_202_326 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_199_598_501 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgccattcggactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_199_122_507 ChrA 1151 + 0 0 22 22 22 1 8 1 0 35 tttgtactccgatgccattcagactgtagtcacca -------------------------)))))))### HWI-EAS88_4_1_199_156_219 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_199_129_619 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_199_848_436 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_199_946_702 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_199_746_868 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_199_332_767 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_199_280_808 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_199_965_133 ChrA 1151 + 0 0 12 12 12 5 44 0 0 35 tttgtactccgatgccatacagactgcatgaccca -------------------------)))))))### HWI-EAS88_4_1_199_932_407 ChrA 1151 + 0 0 12 12 12 3 22 0 0 35 tttgtactccgatgccattcagattttaggcacaa -------------------------)))))))### HWI-EAS88_4_1_200_501_535 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_200_441_220 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_200_793_808 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtacttcgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_200_345_959 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_200_910_938 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_200_61_984 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_201_697_398 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_201_289_647 ChrA 1151 + 0 0 22 22 22 1 2 1 0 35 tttgtactccgatgccattcagactgtaggcacaa -------------------------)))))))### HWI-EAS88_4_1_201_687_657 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_201_448_120 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_201_187_98 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_201_944_542 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_201_269_895 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_201_287_679 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_201_698_889 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_201_286_781 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_201_578_490 ChrA 1151 + 0 0 15 15 15 5 32 0 1 35 tttgtactccgatgccattaagactgtagtccaaa -------------------------)))))))### HWI-EAS88_4_1_202_306_167 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_202_596_525 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_202_172_252 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_202_740_685 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_202_86_811 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_202_523_851 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_202_799_461 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_202_589_37 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_202_284_351 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_203_590_565 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_203_775_150 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_203_67_465 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_203_534_760 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_203_547_548 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_203_567_928 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_203_642_954 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_204_934_247 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_204_481_495 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_204_551_300 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_204_485_353 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_204_205_687 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_204_45_121 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_204_425_585 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_204_536_511 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_204_821_621 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_204_768_704 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_204_869_840 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_204_284_48 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_204_704_886 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_204_914_845 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_204_324_976 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_204_942_964 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgccactcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_205_110_329 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_205_865_454 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_205_511_456 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_205_123_134 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_205_629_890 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactctgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_205_228_807 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgccatccagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_205_444_472 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_205_938_952 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_206_736_217 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_206_130_178 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_206_875_606 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgccactcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_206_714_674 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_206_124_786 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_206_78_765 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_206_467_288 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_206_476_395 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_206_707_723 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_206_286_809 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_206_784_138 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_206_854_733 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_207_792_159 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_207_938_212 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_207_288_238 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_207_242_789 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_207_626_750 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_208_144_322 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_208_433_337 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_208_455_683 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_208_521_558 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_208_45_220 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_208_152_283 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_208_555_38 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_208_841_578 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgccatttagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_208_660_603 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_209_238_44 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_209_141_379 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_209_29_734 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_209_118_650 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_209_329_419 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_209_543_413 ChrA 1151 + 0 0 22 22 22 1 2 1 0 35 tttgtactccgatgccattcagactgtaggcacaa -------------------------)))))))### HWI-EAS88_4_1_209_381_729 ChrA 1151 + 0 0 22 22 22 1 2 1 0 35 tttgtactccgatgccattcagactgtaggcaccg -------------------------)))))))### HWI-EAS88_4_1_210_658_411 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_210_639_202 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgccatccagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_210_455_814 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_210_779_584 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_210_700_705 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_210_831_968 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_210_663_135 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_210_863_949 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactcagatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_210_471_855 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_211_397_560 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_211_649_461 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_211_690_87 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_211_841_298 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_211_664_727 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_211_226_604 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_211_383_129 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_211_292_717 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_213_507_347 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_213_560_543 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_213_346_601 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_213_755_756 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_213_729_796 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_214_458_730 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_214_112_485 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_214_669_785 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_214_725_492 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_214_308_685 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_214_783_751 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_214_278_863 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_215_428_812 ChrA 1151 + 0 0 22 22 22 2 4 1 0 35 tttgtactccgatgccattcagactgtaggcacat -------------------------)))))))### HWI-EAS88_4_1_215_586_28 ChrA 1151 + 0 0 22 22 22 3 4 1 0 35 tttgtactccgatgccattcagactgtaggcaaan -------------------------)))))))##! HWI-EAS88_4_1_215_738_687 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_215_284_622 ChrA 1151 + 0 0 22 22 22 1 2 1 0 35 tttgtactccgatgccattcagactgtaggcaccc -------------------------)))))))### HWI-EAS88_4_1_215_649_713 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_215_909_104 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgccattcatactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_215_185_296 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_215_431_785 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_215_329_666 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_215_572_189 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_215_732_956 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_216_131_408 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_216_240_470 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_216_576_360 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgccattcagaccgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_216_468_766 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_216_643_705 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccggtgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_216_716_897 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_216_937_848 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_216_423_906 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_217_329_459 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_217_536_111 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_217_375_329 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_217_83_62 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_217_684_786 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_217_24_703 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_217_863_573 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_217_744_775 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_218_531_678 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_218_872_54 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_218_951_46 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_218_793_398 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgattccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_218_416_343 ChrA 1151 + 0 0 22 22 22 1 2 1 0 35 tttgtactccgatgccattcagactgtaggcaccc -------------------------)))))))### HWI-EAS88_4_1_218_719_783 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_218_831_860 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_218_84_993 ChrA 1151 + 0 0 22 22 22 1 2 1 0 35 tttgtactccgatgccattcagactgtaggcacaa -------------------------)))))))### HWI-EAS88_4_1_219_704_121 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_219_929_571 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_219_182_101 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_219_862_326 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_219_612_196 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_219_758_618 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_219_680_694 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_219_631_602 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccaatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_219_645_277 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_219_630_888 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_219_767_148 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_219_307_994 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_220_645_733 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_220_216_92 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgccactcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_220_579_660 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_220_795_176 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_220_348_979 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_220_673_364 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_221_709_595 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_221_214_397 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_221_391_609 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_221_521_573 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_221_316_845 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_221_533_924 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_221_901_694 ChrA 1151 + 0 0 23 23 23 4 34 0 0 35 tttgtactccgatgccattacgactataggcacaa -------------------------)))))))### HWI-EAS88_4_1_223_611_65 ChrA 1151 + 0 0 15 15 15 4 2 0 1 35 tttgtactccgatgccattcagnctgtagncacnc ----------------------!--))))!))#!# HWI-EAS88_4_1_223_954_583 ChrA 1151 + 0 0 15 15 15 3 0 0 1 35 tttgtactccgatgccattcagnctgtagncacna ----------------------!--))))!))#!# HWI-EAS88_4_1_223_51_74 ChrA 1151 + 0 0 15 15 15 3 0 0 1 35 tttgtactccgatgccattcagnctgtagncacna ----------------------!--))))!))#!# HWI-EAS88_4_1_223_680_332 ChrA 1151 + 0 0 15 15 15 3 0 0 1 35 tttgtactccgatgccattcagnctgtagncacna ----------------------!--))))!))#!# HWI-EAS88_4_1_223_963_676 ChrA 1151 + 0 0 15 15 15 3 0 0 1 35 tttgtactccgatgccattcagnctgtagncacna ----------------------!--))))!))#!# HWI-EAS88_4_1_223_276_869 ChrA 1151 + 0 0 15 15 15 3 0 0 1 35 tttgtactccgatgccattcagnctgtagncacna ----------------------!--))))!))#!# HWI-EAS88_4_1_226_143_133 ChrA 1151 + 0 0 12 12 12 3 0 0 0 35 tttgtactccgatgcnattcagactntaggcanca ---------------!---------!))))))!## HWI-EAS88_4_1_226_501_380 ChrA 1151 + 0 0 12 12 12 3 0 0 0 35 tttgtactccgatgcnattcagactntaggcanca ---------------!---------!))))))!## HWI-EAS88_4_1_226_37_674 ChrA 1151 + 0 0 12 12 12 3 0 0 0 35 tttgtactccgatgcnattcagactntaggcanca ---------------!---------!))))))!## HWI-EAS88_4_1_226_179_898 ChrA 1151 + 0 0 12 12 12 3 0 0 0 35 tttgtactccgatgcnattcagactntaggcanca ---------------!---------!))))))!## HWI-EAS88_4_1_226_83_791 ChrA 1151 + 0 0 12 12 12 3 0 0 0 35 tttgtactccgatgcnattcagactntaggcanca ---------------!---------!))))))!## HWI-EAS88_4_1_228_74_73 ChrA 1151 + 0 0 12 12 12 3 0 0 0 35 tttgtactccgatgccattcagnctntaggcaccn ----------------------!--!))))))##! HWI-EAS88_4_1_228_411_222 ChrA 1151 + 0 0 12 12 12 3 0 0 0 35 tttgtactccgatgccattcagnctntaggcaccn ----------------------!--!))))))##! HWI-EAS88_4_1_228_619_698 ChrA 1151 + 0 0 23 23 23 4 12 0 0 35 tttgtacgccgatgccattcagnctntaggcaccn ----------------------!--!))))))##! HWI-EAS88_4_1_228_801_195 ChrA 1151 + 0 0 12 12 12 3 0 0 0 35 tttgtactccgatgccattcagnctntaggcaccn ----------------------!--!))))))##! HWI-EAS88_4_1_228_281_830 ChrA 1151 + 0 0 12 12 12 3 0 0 0 35 tttgtactccgatgccattcagnctntaggcaccn ----------------------!--!))))))##! HWI-EAS88_4_1_228_784_140 ChrA 1151 + 0 0 12 12 12 3 0 0 0 35 tttgtactccgatgccattcagnctntaggcaccn ----------------------!--!))))))##! HWI-EAS88_4_1_228_155_803 ChrA 1151 + 0 0 12 12 12 3 0 0 0 35 tttgtactccgatgccattcagnctntaggcaccn ----------------------!--!))))))##! HWI-EAS88_4_1_228_454_603 ChrA 1151 + 0 0 12 12 12 3 0 0 0 35 tttgtactccgatgccattcagnctntaggcaccn ----------------------!--!))))))##! HWI-EAS88_4_1_228_83_127 ChrA 1151 + 0 0 23 23 23 6 22 0 0 35 tttgtactccgatgccatccagnctntatgcacan ----------------------!--!))))))##! HWI-EAS88_4_1_229_826_72 ChrA 1151 + 0 0 12 12 12 4 14 0 0 35 tttgaactccgatgccattcngactgtagncaaca --------------------!----))))!))### HWI-EAS88_4_1_229_306_87 ChrA 1151 + 0 0 12 12 12 4 14 0 0 35 tttgaactccgatgccattcngactgtagncaaca --------------------!----))))!))### HWI-EAS88_4_1_229_705_139 ChrA 1151 + 0 0 23 23 23 5 26 0 0 35 tttgaactccgatgccatttngactgtagncaaca --------------------!----))))!))### HWI-EAS88_4_1_230_717_323 ChrA 1151 + 0 0 12 12 12 4 20 0 0 35 tttgtactccgatgccattcagtctgtcgncncca -------------------------))))!)!### HWI-EAS88_4_1_230_629_347 ChrA 1151 + 0 0 22 22 22 3 2 1 0 35 tttgtactccgatgccattcagactgtagncntca -------------------------))))!)!### HWI-EAS88_4_1_230_172_790 ChrA 1151 + 0 0 15 15 15 5 14 0 1 35 tttgtactccgatgccattcagcctgtanncnccc -------------------------)))!!)!### HWI-EAS88_4_1_230_234_525 ChrA 1151 + 0 0 22 22 22 4 10 1 0 35 tttgtactccgatgccattcagactgtagngncaa -------------------------))))!)!### HWI-EAS88_4_1_230_846_946 ChrA 1151 + 0 0 12 12 12 8 26 0 0 35 tttgtactccgatgccattcaggctgtgnncnagt -------------------------)))!!)!### HWI-EAS88_4_1_230_37_920 ChrA 1151 + 0 0 23 23 23 9 38 0 0 35 tttgtactccgatgccatagagactctanncnaac -------------------------)))!!)!### HWI-EAS88_4_1_232_627_724 ChrA 1151 + 0 0 12 12 12 3 0 0 0 35 tttgtactccgatgccattcagnctgtnggcaccn ----------------------!--))!))))##! HWI-EAS88_4_1_232_280_802 ChrA 1151 + 0 0 23 23 23 4 8 0 0 35 tttgtactccgatgccattcagnctganggcaccn ----------------------!--))!))))##! HWI-EAS88_4_1_232_565_423 ChrA 1151 + 0 0 23 23 23 4 8 0 0 35 tttgtactccgatgccattcagnctganggcaccn ----------------------!--))!))))##! HWI-EAS88_4_1_232_775_759 ChrA 1151 + 0 0 23 23 23 5 16 0 0 35 tttgtactccgatgccattcagnctggngccaccn ----------------------!--))!))))##! HWI-EAS88_4_1_232_159_270 ChrA 1151 + 0 0 23 23 23 5 16 0 0 35 tttgtactccgatgccattcagnctgcngccaccn ----------------------!--))!))))##! HWI-EAS88_4_1_233_148_640 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactcagatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_233_749_849 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_233_23_710 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_233_476_708 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactcggatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_233_865_423 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_233_61_619 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactcagatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_235_121_248 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_235_519_656 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_235_683_317 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_235_140_884 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_235_292_455 ChrA 1151 + 0 0 22 22 22 1 2 1 0 35 tttgtactccgatgccattcagactgtaggcaccg -------------------------)))))))### HWI-EAS88_4_1_235_614_28 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_235_504_947 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_235_377_826 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_235_464_957 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_235_861_506 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_235_264_167 ChrA 1151 + 0 0 22 22 22 2 10 1 0 35 tttgtactccgatgccattcagactgtaggaaaca -------------------------)))))))### HWI-EAS88_4_1_236_565_56 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_236_939_820 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_236_670_101 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_236_235_926 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_236_362_977 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_236_722_747 ChrA 1151 + 0 0 22 22 22 1 2 1 0 35 tttgtactccgatgccattcagactgtaggcacga -------------------------)))))))### HWI-EAS88_4_1_236_879_125 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_237_805_196 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_237_162_681 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_237_901_284 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_237_485_181 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_237_622_630 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_237_705_635 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_237_704_636 ChrA 1151 + 0 0 22 22 22 2 10 1 0 35 tttgtactccgatgccattcagactgtaggcccct -------------------------)))))))### HWI-EAS88_4_1_239_365_347 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_239_490_25 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_239_743_665 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_239_276_118 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_239_771_591 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_239_456_232 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_239_305_278 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_239_676_844 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_239_639_902 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_240_823_476 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_240_817_511 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_240_375_507 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_240_511_674 ChrA 1151 + 0 0 12 12 12 2 24 0 0 35 ttcgtactccgatgccatccagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_240_799_989 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_241_60_411 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_241_97_266 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_241_407_48 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_241_436_709 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_241_715_792 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_241_559_649 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_242_182_182 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_242_454_313 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_244_429_934 ChrA 1151 + 0 0 22 22 22 1 0 1 0 35 tttgtactccgatgccattcagactgtaggcncca -------------------------))))))!### HWI-EAS88_4_1_244_419_937 ChrA 1151 + 0 0 22 22 22 1 0 1 0 35 tttgtactccgatgccattcagactgtaggcncca -------------------------))))))!### HWI-EAS88_4_1_244_376_478 ChrA 1151 + 0 0 15 15 15 2 12 0 1 35 tttttactccgatgccattcagactgtaggcncca -------------------------))))))!### HWI-EAS88_4_1_245_671_21 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_245_429_404 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_245_648_851 ChrA 1151 + 0 0 22 22 22 1 2 1 0 35 tttgtactccgatgccattcagactgtaggcaccc -------------------------)))))))### HWI-EAS88_4_1_245_289_568 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_246_524_611 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_246_279_391 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_246_406_842 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_246_131_381 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_246_267_961 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_246_671_581 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_246_433_582 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_246_495_330 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_247_224_270 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_247_291_130 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_247_122_362 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_247_960_474 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_247_463_152 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_247_543_637 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_248_488_62 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_248_635_87 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_248_506_558 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_248_302_699 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgccattcggactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_248_424_213 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_248_816_813 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_248_438_740 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_248_449_942 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_249_262_630 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_249_377_103 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_249_226_199 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_249_554_791 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_249_239_867 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_250_269_84 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_250_609_681 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_250_492_90 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_250_690_144 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_250_695_711 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_250_223_897 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_250_219_530 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_251_376_644 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_251_376_123 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_251_378_728 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_251_218_819 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_251_222_829 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_251_190_862 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_252_174_596 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_252_193_669 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_252_562_930 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_252_31_167 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_253_156_265 ChrA 1151 + 0 0 12 12 12 2 12 0 0 35 tttgtactccgatgccantcacactgtaggcacca -----------------!-------)))))))### HWI-EAS88_4_1_253_62_468 ChrA 1151 + 0 0 12 12 12 2 12 0 0 35 tttgtactccgatgccantcaaactgtaggcacca -----------------!-------)))))))### HWI-EAS88_4_1_253_299_386 ChrA 1151 + 0 0 12 12 12 2 12 0 0 35 ttcgtactccgatgccantcagactgtaggcacca -----------------!-------)))))))### HWI-EAS88_4_1_253_526_275 ChrA 1151 + 0 0 12 12 12 2 8 0 0 35 tttgtactccgatgccantcagactgcaggcacca -----------------!-------)))))))### HWI-EAS88_4_1_253_871_51 ChrA 1151 + 0 0 23 23 23 3 20 0 0 35 tttgtactccgatgccantcagtctgttggcacca -----------------!-------)))))))### HWI-EAS88_4_1_254_555_684 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_254_560_804 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_254_668_587 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_254_90_36 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_254_859_668 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_254_131_144 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_254_489_951 ChrA 1151 + 0 0 15 15 15 3 18 0 1 35 tttgtactccgatgccattcagactgcagtcacaa -------------------------)))))))### HWI-EAS88_4_1_254_365_375 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_256_659_53 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_256_677_567 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_256_861_395 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_256_228_591 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgccgttcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_256_95_580 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_256_520_209 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgccactcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_256_636_363 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_256_55_652 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_256_377_862 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_256_256_860 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_256_305_746 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_256_355_981 ChrA 1151 + 0 0 12 12 12 3 26 0 0 35 tttgtactccgatgccattcagcatgtaggcaaca -------------------------)))))))### HWI-EAS88_4_1_257_227_567 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_257_48_630 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tctgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_257_99_350 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_257_138_641 ChrA 1151 + 0 0 22 22 22 1 2 1 0 35 tttgtactccgatgccattcagactgtaggcatca -------------------------)))))))### HWI-EAS88_4_1_257_836_193 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_257_492_907 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_257_847_309 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtaccccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_257_600_909 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_258_123_93 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_258_462_188 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_258_651_460 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_258_631_196 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_258_640_790 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_259_161_245 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_259_249_535 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_259_772_419 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtaccccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_259_210_179 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_259_780_422 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_259_251_43 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_259_300_641 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_259_103_851 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_259_584_475 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_259_742_943 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_260_778_689 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_260_307_677 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgtcattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_260_461_679 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgacattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_260_203_461 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgtcattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_260_534_855 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgacattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_260_372_111 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgacattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_260_109_662 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_260_242_863 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_260_759_444 ChrA 1151 + 0 0 22 22 22 1 8 1 0 35 tttgtactccgatgccattcagactgtaggaacca -------------------------)))))))### HWI-EAS88_4_1_261_783_239 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_261_572_54 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_261_346_117 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_261_566_127 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_261_633_524 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_261_527_211 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_261_26_555 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_261_670_787 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_262_774_604 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_262_92_602 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_262_939_593 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_262_454_572 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_262_35_20 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_262_630_825 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgccatacagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_262_653_923 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_263_889_131 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_263_305_350 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_263_388_57 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_263_421_442 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_263_534_834 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 cttgtactccgatgccattcagactgtaggcacca +------------------------)))))))### HWI-EAS88_4_1_264_86_487 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_264_575_727 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_264_607_708 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_264_250_815 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_264_418_844 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_264_307_758 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_264_34_470 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_264_113_362 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_265_906_202 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_265_577_32 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_265_114_221 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_265_165_830 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_265_955_331 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_266_856_608 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_266_128_883 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_266_485_372 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgccactcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_266_519_713 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_266_726_875 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_266_696_915 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_266_365_996 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_267_580_633 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_267_251_151 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_267_247_688 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_267_438_902 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtaccccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_267_231_895 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgccactcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_267_775_910 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_268_68_166 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_268_703_412 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_268_909_179 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_268_700_926 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_268_768_802 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_268_669_995 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_269_369_28 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_269_760_652 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_269_52_525 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_269_245_269 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_269_55_463 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_269_398_212 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtaccccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_269_659_763 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_269_569_917 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_269_500_976 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_269_88_947 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_269_678_986 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_270_695_235 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_270_133_319 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_270_316_613 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_270_744_618 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 cttgtactccgatgccattcagactgtaggcacca +------------------------)))))))### HWI-EAS88_4_1_270_491_800 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_270_625_862 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_270_142_871 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_270_828_662 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttatactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_271_812_449 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_271_445_347 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_271_650_393 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_271_61_99 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_271_64_84 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_271_750_504 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_271_885_134 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_271_190_651 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_271_715_908 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_271_835_643 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_271_662_425 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_272_927_106 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_272_452_89 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_272_836_200 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_272_331_594 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgccattcatactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_272_758_505 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_272_332_708 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_272_194_75 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_272_243_56 ChrA 1151 + 0 0 23 23 23 3 28 0 0 35 tttgtactccgatgcaattcagactgagggcacca -------------------------)))))))### HWI-EAS88_4_1_273_268_204 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_273_280_637 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_274_113_302 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_274_530_96 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccaatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_274_491_230 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgccactcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_274_840_76 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_274_24_896 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_275_86_67 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_275_583_27 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_275_167_731 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_275_19_998 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgccattgagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_276_202_544 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_276_186_582 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_276_242_68 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_276_814_742 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_276_148_674 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_276_636_991 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_276_175_50 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_277_272_633 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 ttcgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_277_841_184 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_277_611_21 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_277_50_240 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_277_311_459 ChrA 1151 + 0 0 22 22 22 1 2 1 0 35 tttgtactccgatgccattcagactgtaggcaccc -------------------------)))))))### HWI-EAS88_4_1_277_47_571 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_277_243_652 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_277_995_903 ChrA 1151 + 0 0 15 15 15 2 14 0 1 35 tttgtactcagatgccattcagactgtaggcagca -------------------------)))))))### HWI-EAS88_4_1_278_521_371 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_278_348_22 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_278_972_510 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_278_522_952 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_278_269_874 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_278_817_979 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_279_756_616 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_279_916_650 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_279_200_818 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgccatccagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_279_188_200 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_279_444_758 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_279_142_224 ChrA 1151 + 0 0 12 12 12 4 34 0 0 35 tttgtactccgatgaccttcagactgtatgcaaca -------------------------)))))))### HWI-EAS88_4_1_280_780_448 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_280_279_586 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_280_43_20 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_280_343_99 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_280_562_767 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_280_41_201 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_280_382_630 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_280_203_463 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_280_93_931 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_281_215_676 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_281_708_682 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_281_466_953 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_281_638_893 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_281_433_367 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_282_231_535 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_282_769_401 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtaccccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_282_779_760 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_282_571_748 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_282_145_740 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_282_146_904 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_282_738_104 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_283_849_723 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_283_557_515 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_283_127_630 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_283_677_637 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_283_470_288 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_283_255_384 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_284_560_188 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_284_266_16 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_284_89_201 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_284_414_285 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_284_724_602 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_284_367_632 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_284_420_900 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_284_480_968 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_284_206_107 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_284_337_990 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_285_524_332 ChrA 1151 + 0 0 22 22 22 1 2 1 0 35 tttgtactccgatgccattcagactgtaggcaccc -------------------------)))))))### HWI-EAS88_4_1_285_24_266 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_285_498_157 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_285_717_792 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_285_672_959 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_286_184_155 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_286_463_64 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_286_804_653 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_286_810_843 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_286_313_114 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_286_702_981 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_287_205_813 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_287_768_689 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_287_424_171 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_288_461_26 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_288_565_627 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_288_495_759 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_288_617_198 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_288_206_907 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgacgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_288_62_584 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_288_90_552 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_288_244_843 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgatgccgttcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_288_469_29 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_289_588_342 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_289_930_44 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_289_269_516 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_289_765_764 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_290_316_662 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_290_690_340 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_290_394_930 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_290_743_950 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_290_479_562 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_290_784_976 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_291_112_430 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_291_379_256 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_291_94_199 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_291_269_779 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactccgataccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_291_501_504 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_291_432_866 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_291_645_155 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_292_743_72 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_292_121_392 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_292_416_184 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_292_522_49 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_292_444_89 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_292_39_887 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_292_54_182 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_292_95_409 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_293_681_414 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_293_796_77 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_293_350_464 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_293_31_595 ChrA 1151 + 0 0 15 15 15 1 12 0 1 35 tttgtactctgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_293_399_239 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_293_46_837 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_293_375_957 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_294_84_634 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_294_302_780 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_294_277_796 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_294_471_916 ChrA 1151 + 0 0 22 22 22 1 8 1 0 35 tttgtactccgatgccattcagactgtaagcacca -------------------------)))))))### HWI-EAS88_4_1_294_219_989 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_295_490_25 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_295_754_331 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_295_922_652 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_295_834_524 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_295_469_952 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_295_743_866 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_295_287_669 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_296_893_387 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_296_52_248 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_296_739_545 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_296_300_245 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_296_885_305 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_296_596_585 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_296_224_530 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_296_585_659 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_296_168_375 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_296_963_212 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_296_263_918 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_296_290_998 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_296_987_353 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_296_901_962 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_297_208_101 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_297_787_83 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_298_593_178 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_298_553_520 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_298_138_270 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_298_305_736 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_298_965_694 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_299_133_495 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_299_170_731 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_299_85_542 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_299_76_258 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_300_198_699 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_300_211_191 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_300_708_625 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_300_275_637 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_300_671_638 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_300_806_610 ChrA 1151 + 0 0 22 22 22 0 0 1 0 35 tttgtactccgatgccattcagactgtaggcacca -------------------------)))))))### HWI-EAS88_4_1_2_680_125 ChrA 1152 + 0 0 15 15 15 1 12 0 1 35 ttttactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_6_435_358 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_8_939_640 ChrA 1152 + 0 0 15 15 15 1 12 0 1 35 ttgtactccgatgtcattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_13_574_465 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_14_52_750 ChrA 1152 + 0 0 15 15 15 1 12 0 1 35 ttgtactccgatgccactcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_46_344_473 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_49_475_700 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_53_584_821 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_61_965_204 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_62_499_699 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_71_276_62 ChrA 1152 + 0 0 23 23 23 3 36 0 0 35 tttgtctccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_74_955_763 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_81_386_22 ChrA 1152 + 0 0 15 15 15 1 12 0 1 35 atgtactccgatgccattcagactgtaggcaccat +------------------------)))))))### HWI-EAS88_4_1_88_482_346 ChrA 1152 + 0 0 22 22 22 1 8 1 0 35 ttgtactccgatgccattcagactgtaggaaccat -------------------------)))))))### HWI-EAS88_4_1_91_138_131 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_101_869_302 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_102_456_658 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_110_906_286 ChrA 1152 + 0 0 15 15 15 1 12 0 1 35 ttgtgctccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_116_436_387 ChrA 1152 + 0 0 15 15 15 1 12 0 1 35 ttgtactccgatgacattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_117_777_68 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_117_169_803 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_118_153_193 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_122_964_545 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_129_238_144 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_135_368_679 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_139_56_433 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_180_511_114 ChrA 1152 + 0 0 15 15 15 1 12 0 1 35 ttttactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_186_917_788 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_195_715_187 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_196_513_290 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_200_300_85 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_202_139_721 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_204_201_831 ChrA 1152 + 0 0 15 15 15 1 12 0 1 35 ttgtactccgatgctattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_207_828_128 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_207_591_367 ChrA 1152 + 0 0 15 15 15 1 12 0 1 35 ttgtactccgatgccattcagaccgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_215_650_870 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_223_789_953 ChrA 1152 + 0 0 12 12 12 4 12 0 0 35 ttttactccgatgccattcagantgtaggnaccnt ----------------------!--))))!))#!# HWI-EAS88_4_1_226_633_216 ChrA 1152 + 0 0 12 12 12 3 0 0 0 35 ttgtactccgatgccnttcagactgnaggcacnat ---------------!---------!))))))!## HWI-EAS88_4_1_235_583_31 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_247_846_234 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_250_144_475 ChrA 1152 + 0 0 23 23 23 3 36 0 0 35 tttgtctccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_260_16_377 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_269_870_651 ChrA 1152 + 0 0 15 15 15 1 12 0 1 35 ttttactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_278_654_839 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_284_774_594 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_285_241_207 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_285_112_835 ChrA 1152 + 0 0 12 12 12 3 28 0 0 35 ttttactccgatgccattcagactgaaggcaacat -------------------------)))))))### HWI-EAS88_4_1_292_606_842 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_293_736_486 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_294_165_735 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_294_545_165 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_298_421_350 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_300_817_405 ChrA 1152 + 0 0 22 22 22 0 0 1 0 35 ttgtactccgatgccattcagactgtaggcaccat -------------------------)))))))### HWI-EAS88_4_1_2_541_123 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_2_141_608 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_2_452_372 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_2_196_900 ChrA 1201 + 0 0 0 0 0 1 12 0 1 35 ctagtttcactcgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_3_861_833 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_6_809_802 ChrA 1201 + 0 0 15 15 15 7 38 0 1 35 ctagtttcacttgttctgcacctgtagcaccatca -------------------------)))))))### HWI-EAS88_4_1_8_349_546 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_8_833_26 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_8_828_779 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_8_839_911 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_9_840_645 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_9_328_905 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_11_989_685 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_12_139_691 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_14_265_793 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_14_515_927 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_14_832_934 ChrA 1201 + 0 0 15 15 15 1 12 0 1 35 ctagtttcacttgtcctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_15_300_277 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_16_639_553 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_16_123_317 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_16_567_237 ChrA 1201 + 0 0 12 12 12 5 36 0 0 35 ctagtttcacttgttctgaccctgtaggcacaggc -------------------------)))))))### HWI-EAS88_4_1_17_239_394 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_17_878_855 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_18_670_256 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_18_446_37 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_20_383_488 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_25_590_787 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_25_943_892 ChrA 1201 + 0 0 12 12 12 2 24 0 0 35 ctagtttcacttgttctgcaaatgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_26_184_540 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_26_186_778 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_30_552_455 ChrA 1201 + 0 0 15 15 15 1 12 0 1 35 ctagtttcacttgtgctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_40_714_263 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_42_463_581 ChrA 1201 + 0 0 0 0 0 1 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatn -------------------------)))))))##! HWI-EAS88_4_1_43_187_265 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_47_405_391 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_47_248_134 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_47_793_62 ChrA 1201 + 0 0 12 12 12 2 24 0 0 35 ctagtttcacttgttcttcacctttaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_48_944_51 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_48_322_885 ChrA 1201 + 0 0 0 0 0 1 12 0 1 35 ctagtttcacttgctctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_48_463_968 ChrA 1201 + 0 0 15 15 15 1 12 0 1 35 ctagtttcacttgttctgcacatgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_50_800_346 ChrA 1201 + 0 0 0 0 0 2 4 1 0 35 ctagtttcacttgttctgcacctgtaggcaccaca -------------------------)))))))### HWI-EAS88_4_1_50_985_871 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_51_327_554 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_52_254_414 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_52_536_603 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_53_394_61 ChrA 1201 + 0 0 15 15 15 5 28 0 1 35 ctagtttcacttgttctgcacctgtagttaacccc -------------------------)))))))### HWI-EAS88_4_1_54_759_374 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_58_458_728 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_59_491_90 ChrA 1201 + 0 0 0 0 0 1 12 0 2 35 atagtttcacttgttctgcacctgtaggcaccatc +------------------------)))))))### HWI-EAS88_4_1_59_760_172 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_60_520_814 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_60_292_1001 ChrA 1201 + 0 0 0 0 0 1 8 1 0 35 ctagtttcacttgttctgcacctgtaggcacaatc -------------------------)))))))### HWI-EAS88_4_1_64_462_862 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_65_740_444 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_67_873_488 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_68_272_231 ChrA 1201 + 0 0 0 0 0 1 12 0 1 35 ctagtttcactcgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_69_416_586 ChrA 1201 + 0 0 15 15 15 1 12 0 1 35 ctagtttcacttgttctgctcctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_70_939_523 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_70_891_102 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_73_392_321 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_75_446_14 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_77_109_905 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_78_453_750 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_79_241_957 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_80_315_498 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_83_663_556 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_83_542_829 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_84_260_557 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_84_271_350 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_84_662_532 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_86_547_815 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_87_115_733 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_88_135_297 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_92_984_53 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_92_777_595 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_93_187_232 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_93_117_190 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_96_714_280 ChrA 1201 + 0 0 0 0 0 1 8 1 0 35 ctagtttcacttgttctgcacctgtaggcgccatc -------------------------)))))))### HWI-EAS88_4_1_98_840_599 ChrA 1201 + 0 0 0 0 0 1 12 0 1 35 ctagtttcacttgctctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_99_270_317 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_99_587_955 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_101_982_420 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_103_484_175 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_103_108_418 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_103_48_799 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_104_282_695 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_105_210_69 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_106_687_477 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_106_439_789 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_107_397_800 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_107_834_997 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_108_397_545 ChrA 1201 + 0 0 15 15 15 1 12 0 1 35 ctagtttcacttgttctgcatctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_112_714_473 ChrA 1201 + 0 0 15 15 15 1 12 0 1 35 ctagtttcacttgttcggcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_112_172_435 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_112_120_621 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_113_139_234 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_116_144_710 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_118_822_149 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_119_299_372 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_119_870_999 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_121_976_81 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_121_742_985 ChrA 1201 + 0 0 0 0 0 1 12 0 1 35 ctattttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_122_701_420 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_123_368_852 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_124_402_605 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_125_195_242 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_129_318_557 ChrA 1201 + 0 0 0 0 0 1 8 1 0 35 ctagtttcacttgttctgcacctgtaggcatcatc -------------------------)))))))### HWI-EAS88_4_1_129_898_772 ChrA 1201 + 0 0 15 15 15 1 12 0 1 35 ctagtttcacttgttctgcacctctaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_129_159_154 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_131_785_699 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_131_657_864 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_134_384_841 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_135_969_278 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_135_173_767 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_135_346_334 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_136_611_212 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_136_446_909 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_137_984_320 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_138_326_218 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_139_910_611 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_139_872_706 ChrA 1201 + 0 0 23 23 23 3 32 0 0 35 ctagtttcacttgttctgcttctgttggcaccatc -------------------------)))))))### HWI-EAS88_4_1_139_397_897 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_140_649_246 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_140_805_315 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_141_854_46 ChrA 1201 + 0 0 15 15 15 1 12 0 1 35 ctagtttcacttgttctgcgcctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_142_664_405 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_145_423_43 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_146_787_49 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_148_48_981 ChrA 1201 + 0 0 0 0 0 1 0 0 1 35 ctagtttcacttgttctgcncctgtaggcaccatc -------------------!-----)))))))### HWI-EAS88_4_1_149_838_74 ChrA 1201 + 0 0 0 0 0 1 12 0 1 35 ctagtttcactcgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_149_70_730 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_150_353_284 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_150_95_920 ChrA 1201 + 0 0 15 15 15 1 12 0 1 35 ctagtttcacttgttctgcatctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_151_451_377 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_151_155_350 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_151_48_804 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_153_770_83 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_154_492_642 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_157_207_300 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_165_27_872 ChrA 1201 + 0 0 0 0 0 1 8 1 0 35 ctagtttcacttgttctgcacctgtaggtaccatc -------------------------)))))))### HWI-EAS88_4_1_166_174_214 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_166_391_34 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_180_915_682 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_181_818_141 ChrA 1201 + 0 0 0 0 0 1 0 1 0 35 ctagtttcacttgttctgcacctgtaggcnccatc -------------------------))))!))### HWI-EAS88_4_1_183_269_287 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_184_509_632 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_189_293_972 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_191_891_642 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_191_340_220 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_192_513_745 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_192_995_674 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_193_683_625 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_193_901_395 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_193_33_145 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_195_902_772 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_197_473_569 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### HWI-EAS88_4_1_197_249_528 ChrA 1201 + 0 0 0 0 0 2 4 1 0 35 ctagtttcacttgttctgcacctgtaggcaccaca -------------------------)))))))### HWI-EAS88_4_1_197_561_501 ChrA 1201 + 0 0 0 0 0 0 0 1 0 35 ctagtttcacttgttctgcacctgtaggcaccatc -------------------------)))))))### ShortRead/inst/script/0000755000175400017540000000000013001230551015742 5ustar00biocbuildbiocbuildShortRead/inst/script/qa-test.R0000644000175400017540000000046413001230551017447 0ustar00biocbuildbiocbuild## fastq nLanes <- 50 library(ShortRead) sp <- SolexaPath(system.file('extdata', package='ShortRead')) rfq <- readFastq(analysisPath(sp), pattern="s_1_sequence.txt") nms <- sprintf("file_%d.fastq", seq_len(nLanes)) qas <- lapply(nms, qa, dirPath=rfq) qa <- do.call(rbind, qas) res <- browseURL(report(qa)) ShortRead/inst/template/0000755000175400017540000000000013001230551016251 5ustar00biocbuildbiocbuildShortRead/inst/template/0000-Header.html0000644000175400017540000000123713001230551020707 0ustar00biocbuildbiocbuild ShortRead Quality Assessment

ShortRead/inst/template/1000-Overview.html0000644000175400017540000000110213001230551021315 0ustar00biocbuildbiocbuild

Overview

This document provides a quality assessment of Genome Analyzer results. The assessment is meant to complement, rather than replace, quality assessment available from the Genome Analyzer and its documentation. The narrative interpretation is based on experience of the package maintainer. It is applicable to results from the 'Genome Analyzer' hardware single-end module, configured to scan 300 tiles per lane. The 'control' results refered to below are from analysis of PhiX-174 sequence provided by Illumina.

ShortRead/inst/template/1100-Overview-SolexaRealign.html0000644000175400017540000000021413001230551024054 0ustar00biocbuildbiocbuild

The 'aligned' reads referenced in most sections of the report are the reads reported to align uniquely to the reference genome

ShortRead/inst/template/2000-RunSummary.html0000644000175400017540000000223113001230551021636 0ustar00biocbuildbiocbuild

Run Summary

Subsequent sections of the report use the following to identify figures and other information.

@SAMPLE_KEY@

Read counts. Filtered and aligned read counts are reported relative to the total number of reads (clusters; if only filtered or aligned reads are available, total read count is reported). Consult Genome Analyzer documentation for official guidelines. From experience, very good runs of the Genome Analyzer 'control' lane result in 25-30 million reads, with up to 95% passing pre-defined filters. 

  ShortRead:::.ppnCount(qa[["readCounts"]])
@PPN_COUNT_TBL@ 
  ShortRead:::.plotReadCount(qa)
@PPN_COUNT@

Base call frequency over all reads. Base frequencies should accurately reflect the frequencies of the regions sequenced. 

  ShortRead:::.plotNucleotideCount(qa)
@BASE_CALL_COUNT@

Overall read quality. Lanes with consistently good quality reads have strong peaks at the right of the panel. 

  df <- qa[["readQualityScore"]]
  ShortRead:::.plotReadQuality(df[df$type=="read",])
@READ_QUALITY_FIGURE@ ShortRead/inst/template/3000-ReadDistribution.html0000644000175400017540000000371613001230551023001 0ustar00biocbuildbiocbuild

Read Distribution

These curves show how coverage is distributed amongst reads. Ideally, the cumulative proportion of reads will transition sharply from low to high.

Portions to the left of the transition might correspond roughly to sequencing or sample processing errors, and correspond to reads that are represented relatively infrequently. 10-15%; of reads in a typical Genome Analyzer 'control' lane fall in this category.

Portions to the right of the transition represent reads that are over-represented compared to expectation. These might include inadvertently sequenced primer or adapter sequences, sequencing or base calling artifacts (e.g., poly-A reads), or features of the sample DNA (highly repeated regions) not adequately removed during sample preparation. About 5% of Genome Analyzer 'control' lane reads fall in this category.

Broad transitions from low to high cumulative proportion of reads may reflect sequencing bias or (perhaps intentional) features of sample preparation resulting in non-uniform coverage. the transition is about 5 times as wide as expected from uniform sampling across the Genome Analyzer 'control' lane. 

  df <- qa[["sequenceDistribution"]]
  ShortRead:::.plotReadOccurrences(df[df$type=="read",], cex=.5)
@READ_OCCURRENCES_FIGURE@

Common duplicate reads might provide clues to the source of over-represented sequences. Some of these reads are filtered by the alignment algorithms; other duplicate reads might point to sample preparation issues. 

  ShortRead:::.freqSequences(qa, "read")
@FREQUENT_SEQUENCES_READ@

Common duplicate reads after filtering 

  ShortRead:::.freqSequences(qa, "filtered")
@FREQUENT_SEQUENCES_FILTERED@

Common aligned duplicate reads are 

  ShortRead:::.freqSequences(qa, "aligned")
@FREQUENT_SEQUENCES_ALIGNED@ ShortRead/inst/template/4000-CycleSpecific.html0000644000175400017540000000227113001230551022227 0ustar00biocbuildbiocbuild

Cycle-Specific Base Calls and Read Quality

Per-cycle base call should usually be approximately uniform across cycles. Genome Analyzer `control' lane results often show a deline in A and increase in T as cycles progress. This is likely an artifact of the underlying technology. 

  perCycle <- qa[["perCycle"]]
  ShortRead:::.plotCycleBaseCall(perCycle$baseCall)
@CYCLE_BASE_CALL_FIGURE@

Per-cycle quality score. Reported quality scores are `calibrated', i.e., incorporating phred-like adjustments following sequence alignment. These typically decline with cycle, in an accelerating manner. Abrupt transitions in quality between cycles toward the end of the read might result when only some of the cycles are used for alignment: the cycles included in the alignment are calibrated more effectively than the reads excluded from the alignment.

The reddish lines are quartiles (solid: median, dotted: 25, 75), the green line is the mean. Shading is proporitional to number of reads. 

  perCycle <- qa[["perCycle"]]
  ShortRead:::.plotCycleQuality(perCycle$quality)
@CYCLE_QUALITY_FIGURE@ ShortRead/inst/template/5000-PerTile.html0000644000175400017540000000460713001230551021074 0ustar00biocbuildbiocbuild

Tile Performance

Counts per tile. Dashed red line indicates the 10% of tiles with fewest reads. An approximately uniform distribution suggests consistent read representation in each tile. Distinct separation of 'good' versus poor quality tiles might suggest systematic failure, e.g., of many tiles in a lane, or excessive variability (e.g., due to unintended differences in sample DNA concentration) in read number per lane. 

  perTile <- qa[["perTile"]]
  readCnt <- perTile[["readCounts"]]
  cnts <- readCnt[readCnt$type=="read", "count"]
  histogram(cnts, breaks=40, xlab="Reads per tile",
            panel=function(x, ...) {
            panel.abline(v=quantile(x, .1),
                col="red", lty=2)
                panel.histogram(x, ...)
            }, col="white"))
@PER_TILE_HISTOGRAM@

Spatial counts per tile. Divisions on the color scale are quantized, so that the range of counts per tile is divided into 10 equal increments. Parenthetic numbers on the scale represent the break points of the quantized values. Because the scale is quantized, some tiles will necessarily have `few' reads and other necessarily `many' reads.

Consistent differences in read number per lane will result in some lanes being primarily one color, other lanes primarily another color. Genome Analyzer data typically have greatest read counts in the center column of each lane. There are usually consistent gradients from `top' to `bottom' of each column.

Low count numbers in the same tile across runs of the same flow cell may indicate instrumentation issues. HiSeq: columns are upper swaths 1 and 2, and lower swaths 1 and 2, respectively. 

  ShortRead:::.plotTileCounts(readCnt[readCnt$type=="read",])
@PER_TILE_COUNT_FIGURE@

Median read quality score per tile. Divisions on the color scale are quantized, so that the range of average quality scores per tile is divided into 10 equal increments. Parenthetic numbers on the scale represent the break points of the quantized values.

Often, quality and count show an inverse relation. HiSeq: columns are upper swaths 1 and 2, and lower swaths 1 and 2, respectively. 

  qscore <- perTile[["medianReadQualityScore"]]
  ShortRead:::.plotTileQualityScore(qscore[qscore$type=="read",])
@PER_TILE_QUALITY_FIGURE@ ShortRead/inst/template/6000-Alignment.html0000644000175400017540000000052613001230551021443 0ustar00biocbuildbiocbuild

Alignment

Mapped alignment score. Counts measured relative to counts in score category with maximum representation. Successful alignments will be reflected in a strong peak to the right of each panel. 

  ShortRead:::.plotAlignQuality(qa[["alignQuality"]])
@ALIGN_QUALITY_FIGURE@ ShortRead/inst/template/7000-MultipleAlignment.html0000644000175400017540000000040113001230551023150 0ustar00biocbuildbiocbuild

Multiple Alignment

Number of reads matching 0, 1, ... times to the reference genome. 

  ShortRead:::.plotMultipleAlignmentCount(qa[["multipleAlignment"]])
@MULTIPLE_ALIGNMENT_COUNT_FIGURE@ ShortRead/inst/template/8000-DepthOfCoverage.html0000644000175400017540000000037113001230551022532 0ustar00biocbuildbiocbuild

Depth Of Coverage

The number of times the aligned reads overlap a given sequence position. 

  ShortRead:::.plotDepthOfCoverage(qa[["depthOfCoverage"]])
@DEPTH_OF_COVERAGE_FIGURE@ ShortRead/inst/template/9000-AdapterContamination.html0000644000175400017540000000101013001230551023621 0ustar00biocbuildbiocbuild

Adapter Contamination

Adapter contamination is defined here as non-genetic sequences attached at either or both ends of the reads. The 'contamination' measure is the number of reads with a right or left match to the adapter sequence over the total number of reads. Mismatch rates are 10% on the left and 20% on the right with a minimum overlap of 10 nt. 

  ShortRead:::.ppnCount(qa[["adapterContamination"]])
@ADAPTER_CONTAMINATION@ ShortRead/inst/template/9999-Footer.html0000644000175400017540000000015413001230551021016 0ustar00biocbuildbiocbuild

@DATE@; ShortRead v. @VERSION@
Report template: Martin Morgan

ShortRead/inst/template/QA.css0000644000175400017540000000107713001230551017271 0ustar00biocbuildbiocbuildbody{ width: 350pt } table { font-family: monospace } td { padding: 0px 10px } th { text-align: right; font-family: auto } img { display: block; margin-left: auto; margin-right: auto } #header { margin: 1px; padding: 1px; line-height: 82px; } #logo { margin: 0px; padding: 0; } #logo img { margin-left: 0px; vertical-align: middle; } #locationline { clear: right; padding: 0px; } #pagelocation { display: inline; margin: 0px 10px; padding: 0px; font-size: 1.5em; } #pagelocation li { display: inline; margin: 0; } ShortRead/inst/template/QAAdapterContamination.html0000644000175400017540000000062213001230551023465 0ustar00biocbuildbiocbuild

Adapter Contamination

Adapter contamination are at either or both ends of the reads. The 'contamination' measure is the number of reads with a right or left match to the adapter sequence over the total number of reads. Mismatch rates are 10% on the left and 20% on the right with a minimum overlap of 10 nt.

@ADAPTER_CONTAMINATION@ ShortRead/inst/template/QAFiltered.html0000644000175400017540000000022213001230551021113 0ustar00biocbuildbiocbuild

Filtering

Reads avaialable at the start of each processing step are summarized below.

@FILTERED@ ShortRead/inst/template/QAFlagged.html0000644000175400017540000000013213001230551020706 0ustar00biocbuildbiocbuild

Flagged Samples

Not yet implemented.

ShortRead/inst/template/QAFooter.html0000644000175400017540000000015413001230551020617 0ustar00biocbuildbiocbuild

@DATE@; ShortRead v. @VERSION@
Report template: Martin Morgan

ShortRead/inst/template/QAFrequentSequence.html0000644000175400017540000000023313001230551022641 0ustar00biocbuildbiocbuild

Frequent Sequences

Threshold @THRESHOLD_LABEL@ = @THRESHOLD@. @FREQUENT_SEQUENCE_COUNT@ @FREQUENT_SEQUENCES@ ShortRead/inst/template/QAHeader.html0000644000175400017540000000123613001230551020553 0ustar00biocbuildbiocbuild ShortRead Quality Assessment ShortRead/inst/template/QANucleotideByCycle.html0000644000175400017540000000052013001230551022724 0ustar00biocbuildbiocbuild

Nucleotide Use by Cycle

Per-cycle base calls should usually be approximately uniform across cycles. Genome Analyzer `control' lane results often show a deline in A and increase in T as cycles progress. This is likely an artifact of the underlying technology.

@CYCLE_BASE_CALL@ ShortRead/inst/template/QANucleotideUse.html0000644000175400017540000000031713001230551022132 0ustar00biocbuildbiocbuild

Nucleotide Use

Base call frequency over all reads. Base frequencies should accurately reflect the frequencies of the regions sequenced.

@BASE_CALL_COUNT@ ShortRead/inst/template/QAQualityByCycle.html0000644000175400017540000000121613001230551022264 0ustar00biocbuildbiocbuild

Quality by Cycle

Per-cycle quality score. Reported quality scores are `calibrated', i.e., incorporating phred-like adjustments following sequence alignment. These typically decline with cycle, in an accelerating manner. Abrupt transitions in quality between cycles toward the end of the read might result when only some of the cycles are used for alignment: the cycles included in the alignment are calibrated more effectively than the reads excluded from the alignment.

The reddish lines are quartiles (solid: median, dotted: 25, 75), the green line is the mean. Shading is proporitional to number of reads.

@CYCLE_QUALITY@ ShortRead/inst/template/QAQualityUse.html0000644000175400017540000000033013001230551021462 0ustar00biocbuildbiocbuild

Quality Score Use

Quality scores over all reads. High quality scores are blue, low quality scores red. Lines are cummulative frequencies.

@QUALITY_SCORE_COUNT@ ShortRead/inst/template/QAReadQuality.html0000644000175400017540000000042713001230551021610 0ustar00biocbuildbiocbuild

Read Quality

Overall read quality. Lanes with consistently good quality reads have strong peaks at the right of the panel. Flagged samples are non-gray and appear in the legend at the top of the figure.

@READ_QUALITY_FIGURE@ ShortRead/inst/template/QASequenceUse.html0000644000175400017540000000237413001230551021614 0ustar00biocbuildbiocbuild

Sequence Use

These curves show how coverage is distributed amongst reads. Ideally, the cumulative proportion of reads will transition sharply from low to high.

Portions to the left of the transition might correspond roughly to sequencing or sample processing errors, and correspond to reads that are represented relatively infrequently. 10-15%; of reads in a typical Genome Analyzer 'control' lane fall in this category.

Portions to the right of the transition represent reads that are over-represented compared to expectation. These might include inadvertently sequenced primer or adapter sequences, sequencing or base calling artifacts (e.g., poly-A reads), or features of the sample DNA (highly repeated regions) not adequately removed during sample preparation. About 5% of Genome Analyzer 'control' lane reads fall in this category.

Broad transitions from low to high cumulative proportion of reads may reflect sequencing bias or (perhaps intentional) features of sample preparation resulting in non-uniform coverage. the transition is about 5 times as wide as expected from uniform sampling across the Genome Analyzer 'control' lane.

@SEQUENCE_USE@ ShortRead/inst/template/QASources.html0000644000175400017540000000031213001230551021000 0ustar00biocbuildbiocbuild

Data Sources

Subsequent sections of the report use the Ids in the following table to identify figures and other information.

@SAMPLE_KEY@ @PPN_COUNT@ ShortRead/inst/template/image/0000755000175400017540000000000013001230551017333 5ustar00biocbuildbiocbuildShortRead/inst/template/image/bioclogo-small.gif0000644000175400017540000001701413001230551022730 0ustar00biocbuildbiocbuildGIF89aN,{|~ghjffhźpps飯qrt刊⪤Q}’x׮[\^ɛF[[]B}͏kꐑ{S]岲b̚\]_轴aq߶ݙܪօݯiڟoprRքԂMMOוԋһΛAABA@BBAC񝟢rtvrsvNNP<=OOQ񓕘NOQ񴶹䜟pqsrsustw󭮰rtw=~򵷹ģ㰱⣣gik<ɴujNNQڏm⸿ٖǿ躺MMPMNOAAC!,N H*\ȰÇ#JHŋFHI  dIɓ(S2OH!jɳϝ lHt&%? -[uGϯ`Ê5^Q3`} \}i78fÈKК0k)¥An"tBe  qW5 dRװc3V͋~XE R/k7ߣ5tb\M_7dkߎ؁Q~mҏӿEm=A0e*:(2@ p$PB@P"qB!p!lH?А4a./; h?BmS BG;p-C &d`jآX@ Bv MlB4<5`wB!Br -ANq &Xa8q-Tb @6xC$01,!@A n;;Pd@rBsI!VXEl yC XKC< KH1!r"AE-Z <!1́ |6 2Y`"<}iQ!Av ,< ?uD-rF\ q@# HvCFx Dj:!pK"D`KlH-J ;ĢYB 1*R"8p v,qR"G ?!4,H!|qQz(AhQBĀDa>i, l-09"X²LjnHq`[XOgء$F`N&"q'd! a[@/d ?t]!@C ?  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The assessment is meant to complement, rather than replace, quality assessment available from the Genome Analyzer and its documentation. The narrative interpretation is based on experience of the package maintainer. It is applicable to results from the `Genome Analyzer' hardware single-end module, configured to scan 300 tiles per lane. The `control' results refered to below are from analysis of $\varphi$X-174 sequence provided by Illumina. An R script containing the code used in this document can be created with <>= fl <- system.file("template", "qa_solexa.Rnw", package="ShortRead") Stangle(fl) @ \section{Run summary} Read counts. Filtered and aligned read counts are reported relative to the total number of reads (clusters). Consult Genome Analyzer documentation for official guidelines. From experience, very good runs of the Genome Analyzer `control' lane result in 6-8 million reads, with up to 80\% passing pre-defined filters. <>= ShortRead:::.ppnCount(qa[["readCounts"]]) @ Base call frequency over all reads. Base frequencies should accurately reflect the frequencies of the regions sequenced. <>= qa[["baseCalls"]] / rowSums(qa[["baseCalls"]]) @ Overall read quality. Lanes with consistently good quality reads have strong peaks at the right of the panel. <>= df <- qa[["readQualityScore"]] print(ShortRead:::.plotReadQuality(df[df$type=="read",])) @ \section{Read distribution} These curves show how coverage is distributed amongst reads. Ideally, the cumulative proportion of reads will transition sharply from low to high. Portions to the left of the transition might correspond roughly to sequencing or sample processing errors, and correspond to reads that are represented relatively infrequently. 10-15\% of reads in a typical Genome Analyzer `control' lane fall in this category. Portions to the right of the transition represent reads that are over-represented compared to expectation. These might include inadvertently sequenced primer or adapter sequences, sequencing or base calling artifacts (e.g., poly-A reads), or features of the sample DNA (highly repeated regions) not adequately removed during sample preparation. About 5\% of Genome Analyzer `control' lane reads fall in this category. Broad transitions from low to high cumulative proportion of reads may reflect sequencing bias or (perhaps intentional) features of sample preparation resulting in non-uniform coverage. Typically, the transition is about 5 times as wide as expected from uniform sampling across the Genome Analyzer `control' lane. <>= df <- qa[["sequenceDistribution"]] print(ShortRead:::.plotReadOccurrences(df[df$type=="read",], cex=.5)) @ Common duplicate reads might provide clues to the source of over-represented sequences. Some of these reads are filtered by the alignment algorithms; other duplicate reads migth point to sample preparation issues. <>= ShortRead:::.freqSequences(qa, "read") @ Common duplicate reads after filtering <>= ShortRead:::.freqSequences(qa, "filtered") @ \section{Cycle-specific base calls and read quality} Per-cycle base call should usually be approximately uniform across cycles. Genome Analyzer `control' lane results often show a deline in A and increase in T as cycles progress. This is likely an artifact of the underlying technology. <>= perCycle <- qa[["perCycle"]] print(ShortRead:::.plotCycleBaseCall(perCycle$baseCall)) @ Per-cycle quality score. Reported quality scores are `calibrated', i.e., incorporating phred-like adjustments following sequence alignment. These typically decline with cycle, in an accelerating manner. Abrupt transitions in quality between cycles toward the end of the read might result when only some of the cycles are used for alignment: the cycles included in the alignment are calibrated more effectively than the reads excluded from the alignment. <>= print(ShortRead:::.plotCycleQuality(perCycle$quality)) @ \section{Tile performance} Counts per tile. The dashed red line in the following plot indicates the 10\% of tiles with fewest reads. An approximately uniform % FIXME (wh 6 June 2009): do you mean uni-modal? distribution suggests consistent read representation in each tile. Distinct separation of 'good' versus poor quality tiles might suggest systematic failure, e.g., of many tiles in a lane, or excessive variability (e.g., due to unintended differences in sample DNA concentration) in read number per lane. <>= perTile <- qa[["perTile"]] readCnt <- perTile[["readCounts"]] cnts <- readCnt[readCnt$type=="read", "count"] print(histogram(cnts, breaks=40, xlab="Reads per tile", panel=function(x, ...) { panel.abline(v=quantile(x, .1), col="red", lty=2) panel.histogram(x, ...) }, col="white")) @ Spatial counts per tile. Divisions on the color scale are quantized, so that the range of counts per tile is divided into 10 equal increments. Parenthetic numbers on the scale represent the break points of the quantized values. Because the scale is quantized, some tiles will necessarily have `few' reads and other necessarily `many' reads. Consistent differences in read number per lane will result in some lanes being primarily one color, other lanes primarily another color. Genome Analyzer data typically have greatest read counts in the center column of each lane. There are usually consistent gradients from `top' to `bottom' of each column. Low count numbers in the same tile across runs of the same flow cell may indicate instrumentation issues. <>= print(ShortRead:::.plotTileCounts(readCnt[readCnt$type=="read",])) @ Median read quality score per tile. Divisions on the color scale are quantized, so that the range of average quality scores per tile is divided into 10 equal increments. Parenthetic numbers on the scale represent the break points of the quantized values. Often, quality and count show an inverse relation. <>= qscore <- perTile[["medianReadQualityScore"]] print(ShortRead:::.plotTileQualityScore(qscore[qscore$type=="read",])) @ \section{Alignment} Mapped alignment score. Counts measured relative to counts in score category with maximum representation. Successful alignments will be reflected in a strong peak to the right of each panel. <>= print(ShortRead:::.plotAlignQuality(qa[["alignQuality"]])) @ \end{document} ShortRead/inst/unitTests/0000755000175400017540000000000013001230551016440 5ustar00biocbuildbiocbuildShortRead/inst/unitTests/cases/0000755000175400017540000000000013001230551017536 5ustar00biocbuildbiocbuildShortRead/inst/unitTests/cases/PE_export.txt.gz0000644000175400017540000004530013001230551022625 0ustar00biocbuildbiocbuildK\˖ƒ\sEsP[b(ȅ@nE,$"##4~ 'c=UYU7(UUYU~9y^k'~}s~黮n;םjWiߧbD=};2+l|dO?= (<+\)UXT%^;πO\qT>'6&<{p/},*Zʕ, xo,x.-UJA^ :}aMpDEuI/ Kr 2O6`ڗ>?]U}6vdydlGT"ŷ {T\6F2 DnKVe5\D2iaS`_b!l}aj6:A'nH :AfD돶 qd`LAd2v?bH|"4X X蝿Zxv&1XD6,I2Iqqu\V`\nLS(ޚnY^ ,; "xuIy>]M X'B 3b/u;s\ݝl@+Jͽ.<|B6ˈW!u840ҽcl2)XӅXba=uX邛<]|龵8W 70 Ƒp)5ɻ 1®qyڙ۸; haǾA' ,Fr˷)&zO$y4*t?UOZabx+7 x 4+7Ĝǵ$ ͧ!(9 SyD2\[t9WiK f 8-'y"ۍ.I$s@r8UHzR fY1Tٶ"[6'DT}ni؜|&evֈ}_e~0iґFF ؞# H(#ڞyzfت՝s4=9WEtG]"@A PpBni&"2`[D ,tPb^ӽ;EMƉH\Yn#߱>)=*yTdA2냕VaKs~橒a|`^nļDRE0՗j>}*Dsjxw(9+r Z6{9Pm&WQVslKxME wpJ i]qj*~bsٖ,3YZ_JS?[iݧuJcXHfVH<|ҫ%u,z;Zó [NFV,5qvk@okHKf[OC9֘(9*="eUN| b0{?osbĤ/6tuָ1̝x-kӖ(OQ٠|XT}9߇~"^ঁUjݴxЊ'_y5ku, g c|]z:R U5ЃPVj!(Q vLOSUY>RnXANfLZ*%RclYPyg¦l@eQ^+޼^\Q, .MyE+"~A<&(aFA%b߄g.{y}oe7]$L$=ҟ盅! 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)~ҿRz3ԋbwW'."( / Oӛlm}Iޔb mϫ7C? >U;vKAZ( @r'LYڒ6~Fi%ixY* )u߽EEa`~y/Әr߸Qo\d]$S4UФ X "S9`[y0 JwV4"0zijؒ˼8h, LylkdRJP}+X&Qn;jY3!fڎ6K}ڞ7\=Ot4U0o_t1\꾬PviJt^vb7ehlNn~  =wg5=h۲_ DL=|o;&N{TU0;>\+ _׭L{V@+vI<[Hb+,ا_D(*ڊ3Gқ' T sE[om3}U~5+ňKɳB@J̒`ST\N B"Xx ڕF{k\QZ`[$'t^ixT8JͷI7`n,@dPMvP:AOe"z:Zbgh༓37Q2gfSWy50TӁ3ЊajOE09\'gR/9CȖ+vpuր}hXF)YbMsb7  sDqlTGȎ=-uYcXdUQ\ =ȖJ”ǖi`W81=#Wj@*M;^ /O#_d_ShortRead/inst/unitTests/cases/s_1_0001_int_head.txt.p0000644000175400017540000000073313001230551023515 0ustar00biocbuildbiocbuild#CH4:OBJ152577 11.7 14.3 -1.1 86.2 49.4 110.6 3.2 5.9 53.0 54.6 49.2 46.7 20.8 32.1 0.4 54.7 6.1 16.7 3.9 7.9 #END CYCLE 1 18.8 21.1 96.2 177.4 120.2 218.8 2.7 9.1 74.8 63.5 103.1 203.4 33.9 36.2 94.0 77.2 63.3 39.2 15.8 -1.8 #END CYCLE 2 55.9 112.0 0.8 15.7 340.9 164.8 2.6 4.6 129.2 76.1 77.2 168.4 19.1 11.8 10.9 80.2 2.6 6.9 8.9 31.0 #END CYCLE 3 ShortRead/inst/unitTests/cases/s_1_0001_nse_head.txt.p0000644000175400017540000000073313001230551023510 0ustar00biocbuildbiocbuild#CH4:OBJ152577 8.5 9.0 3.0 6.6 6.2 9.2 3.1 6.4 9.8 10.4 3.0 6.1 8.0 9.0 2.8 5.2 8.2 8.5 3.1 4.3 #END CYCLE 1 12.6 12.5 3.8 6.6 11.9 11.0 3.6 5.8 13.0 14.0 3.3 6.5 13.1 13.5 3.3 7.3 12.5 13.4 3.6 5.7 #END CYCLE 2 13.6 12.4 4.7 7.3 10.5 12.7 4.2 6.4 13.7 12.5 4.6 9.7 12.6 11.7 4.1 6.7 11.3 12.4 4.6 7.8 #END CYCLE 3 ShortRead/inst/unitTests/cases/s_1_0001_pos_head.txt0000644000175400017540000000012013001230551023254 0ustar00biocbuildbiocbuild -0.47 1073.78 -0.45 1558.67 -0.45 1157.37 -0.44 144.35 -0.43 1497.99 ShortRead/inst/unitTests/cases/s_1_results_head.txt0000644000175400017540000023255313001230551023535 0ustar00biocbuildbiocbuild>CMLIVERKIDNEY_7:1:1:112:735 GTGGTGGGGTTGGTATTTGGTTTCTCGTTTTA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:114:564 GGATACTCAGGCTGGCCCAATTTCTGGGCGTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:109:558 GTAGAATTAGAATTGTGAAGATGATAAGTGTA R0 2 0 0 >CMLIVERKIDNEY_7:1:1:94:531 GTGTTTGTGTGTGTGTTTGCTTGTTTATGTAA U1 0 1 0 chrX.fa 93540767 R .. 3G >CMLIVERKIDNEY_7:1:1:107:680 GTTTAGGTGTAGTGAACTAATATAATTGGAGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:122:531 GCACCTTCCATTTCCAGGTCCTTACTATGTCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:101:539 GCGAGGTTAGCGAGGCTTGCTAGAAGTCATCA U0 1 0 2 chrM.fa 11832 R .. >CMLIVERKIDNEY_7:1:1:105:577 GGGGTGAGGGTCCGGGCCAGGTGGTGGTATTA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:109:458 GTTGTTAATGTGGTGGGTGAGTGAGGCCCATT U1 0 1 0 chrM.fa 12004 R .. 7G >CMLIVERKIDNEY_7:1:1:120:589 GAAAAACTTGAGAATCATGTTGAAGCTAAACC R0 3 2 0 >CMLIVERKIDNEY_7:1:1:97:605 GTTTTTTCAATTTACCATCCTCTTCTTCTTCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:114:594 GTTGATTTGGTTGACATATAGTAGAGTTTTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:117:581 GTTTGGATGAGAATGGCTGTTTTTACTTTGGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:122:569 GGGGTGGTGAAGGTCTCAGACATAAGCTCAGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:120:414 GTCCAGGAGATTCTCAATGGCTTCTTCTTTTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:113:396 GAAGACGTAGGTTTGGCTCTGGTCGTCTCAGG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:118:586 GCTTAAGCAAAAAATAGGTTACATTAAGCAGA U2 0 0 1 chr12.fa 122311748 F .. 15G 17A >CMLIVERKIDNEY_7:1:1:108:689 GGTGAGGAGGGGAGGAGTGTAATCCAAGTGCT U2 0 0 1 chr16.fa 56243177 F .. 1T 2C >CMLIVERKIDNEY_7:1:1:105:591 AATCAATACTCATCATTAATAATCATAATGGC R0 2 0 0 >CMLIVERKIDNEY_7:1:1:113:483 GTTTTCCCGTGCGATTGTGGGCTTGTTTATAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:121:520 AAAACTCTGAACCGTCAATTAAATGCCCATTA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:118:534 TGTTAATTGTCAGTTCAGTGTTTTAATCTGAC U1 0 1 0 chrM.fa 2347 R .. 8C >CMLIVERKIDNEY_7:1:1:113:657 GTGGAACTTGTGGTCGATGTTGATCTCCACAT U0 1 0 0 chr2.fa 241841076 F .. >CMLIVERKIDNEY_7:1:1:117:511 AAAACACAGTGTCATGCCCATAGAACTAAAAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:111:884 GTTGGGAGTAGGGTCTTGGTGACTATGTTTGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:101:711 GGAAGGTCTTAGCGCTGTACATCAAGTCACAA U1 0 1 0 chr2.fa 36629450 R .. 31A >CMLIVERKIDNEY_7:1:1:103:475 GGTGTATGAACATGAGGGTGTTTTCTCGTGTG U0 1 1 0 chrM.fa 12050 R .. >CMLIVERKIDNEY_7:1:1:97:862 GGGGGGGGGGGATGTATGAAGGCACTTTGTAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:92:597 GTGACTGTAATTTGCTGAGAGAGCAGTGAACA U1 0 1 0 chr7.fa 138140031 R .. 3G >CMLIVERKIDNEY_7:1:1:124:544 AGACGCCAAACGCATTAACTGGCGAACAGTGC U0 1 0 0 chr19.fa 2222858 F .. >CMLIVERKIDNEY_7:1:1:102:591 GATTTCTGGTGCTGTTGAGTCCGTAGCATTTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:75:582 GTTTAGGGGATACATCGCCAAGCGCAGGCTAC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:78:544 GTCGGTGTCCTCGTTGGTTCGGTACCATTGGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:108:522 GCTCCGGGAGCTCGAGGGCATCTTCTCGTTGA U2 0 0 1 chr4.fa 2925125 F .. 1T 7T >CMLIVERKIDNEY_7:1:1:110:602 GTCAGAGGTTGCAGTGAGCTAGGTGTGGTTGG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:101:597 GCTTCCTCTCCTGTCAATTCCAGGCTCTTTCC R0 2 0 0 >CMLIVERKIDNEY_7:1:1:118:503 GGATGAACGAGATTCCCACTGTCCCTACCTAC R0 4 1 1 >CMLIVERKIDNEY_7:1:1:96:606 GGGCCTCCGCGCTACCCTCCTCTTCTTATTCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:100:545 GTGAAATATGCTCGTGTGTCTACGTCTATTCC R0 2 0 2 >CMLIVERKIDNEY_7:1:1:98:583 GCTAGGGAGAGAGGTAGGAAGTTTTTTCATAG R2 0 0 2 >CMLIVERKIDNEY_7:1:1:110:854 GGGCGGGCTATGGAAGAATTAATGATAGTATA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:106:930 GGTTTTAATTGTGAGGGATGAGAAGCCATCGA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:101:608 GCGAGACGGATCAGATCAAGCAGGGCTCCTGA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:100:619 GGTTTACTCACAGTCATTATCAAACCATCTCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:78:572 GACAGCAGCTAGGATTGGGAAAGAATGGAGAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:87:586 GCAGAAAATACAATGAGGACCTGGACTTCAAA U0 1 0 0 chr20.fa 39574989 R .. >CMLIVERKIDNEY_7:1:1:113:773 GCGAACATTACTGGAGGCGCCCTACTCATGCA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:93:419 GGAATGATGGTTGTCTTTGGATATACTACAGC U0 1 1 0 chrM.fa 14460 R .. >CMLIVERKIDNEY_7:1:1:102:793 GTTCCATTGTTGTCAAATGCCCACTCTCCATC U0 1 0 0 chr5.fa 179090403 F .. >CMLIVERKIDNEY_7:1:1:119:677 GGCCGGCCCACTCTTTTGCCAAGTATTAGTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:119:741 GAAATAGAATGATCAGTACTGCGGCGGGTAGG R0 3 0 1 >CMLIVERKIDNEY_7:1:1:111:717 GCAAGACCCTGTCTCAAAAAAAAAAAAAAACA R0 10 255 255 >CMLIVERKIDNEY_7:1:1:91:562 GGAAGCAGAAACAAAGAATTGGTCTTCTAGGA U0 1 0 0 chr3.fa 4862836 F .. >CMLIVERKIDNEY_7:1:1:123:493 GCCATTTGAAACGGAGAGGTGAGGAGACTGGA U0 1 0 0 chr17.fa 39451455 R .. >CMLIVERKIDNEY_7:1:1:92:638 GGCCGAGGAGGGTAGCTGTGGCAATAAAAATG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:114:520 GCCTTACCCCCCACTATTAACCTACTGGGAGA U0 1 1 0 chrM.fa 11862 F .. >CMLIVERKIDNEY_7:1:1:91:623 GCATATGCTTGTCTCAAAGATTAAGCCATGCA U0 1 0 2 chr21_random.fa 1678073 R .. >CMLIVERKIDNEY_7:1:1:123:632 AATTGTACTACCGGGTCACACTGGTCATCGTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:114:588 GGGATCCTCCCACCTCAGCCTCCTAAGTAGCT R0 21 255 255 >CMLIVERKIDNEY_7:1:1:117:572 GCGGTTGGGCCCCTCTCTTATGGAGACTGAAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:96:877 TAACTGTATTTTGTCAGGTGCAATAAAAACAA U0 1 0 0 chr3.fa 12600155 F .. >CMLIVERKIDNEY_7:1:1:118:477 AATAGTTATGTCATCCCTCTTATTAATCATCA U0 1 0 0 chrM.fa 10317 F .. >CMLIVERKIDNEY_7:1:1:102:228 GGGATGTATTTTTTCTAATCATCATTCTTGCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:75:558 GGCGGTTTCGATGATGTGGTCTTTGGAGTAGA R1 0 2 0 >CMLIVERKIDNEY_7:1:1:100:276 CCTGCTCTTCGAAGACAGCAATGAGGAGTTTT U2 0 0 1 chr11.fa 128213498 R .. 1C 2C >CMLIVERKIDNEY_7:1:1:112:136 GGGTTGGGTTCCACTGTGGCCCTCCTGTTATT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:112:214 GCCAGAAGAAAGTACCACTGCAAGACATCGTG U2 0 0 1 chr4.fa 114523116 R .. 5C 12A >CMLIVERKIDNEY_7:1:1:110:591 GTCATAGGTAACTACTCGAGGCTCTGCCAGCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:122:477 GACAATTGCTTACTCATTAAAAATAATAGAGC U0 1 0 0 chr1.fa 110080721 R .. >CMLIVERKIDNEY_7:1:1:74:587 GTATCTTCTGTTTTAATTTTCTTAGGTTTATA U0 1 0 0 chr20.fa 39143226 R .. >CMLIVERKIDNEY_7:1:1:109:598 GCTACCACAGCCTTTTTCGGCTGATTCTCATT U1 0 1 0 chr10.fa 8046596 R .. 2G >CMLIVERKIDNEY_7:1:1:99:604 GTTTTTTATAGGGACCTTGCTTTTCTTCTTCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:98:804 TGTATTCTCTACTCTTGACATTACTATCGCAT U0 1 0 0 chr9.fa 82103831 R .. >CMLIVERKIDNEY_7:1:1:123:186 CGCAAGTCCCATCGGACCATCTAGAAGCTTGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:120:855 GCAAGATGAAGTGTAAGGTAAAGATTCCTCTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:100:552 GGGAAATCAAGAGTTGGTTTAAGAGACTGTTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:100:815 GTTTTTTTCTTAGGAGGGTTTTTCTGAGCCTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:114:598 CTCTACACAGGGCTTGCTTTCCTAAGAAAAGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:105:508 GGGTACATCACAGTTGACTGGCAACGAGTGGA R1 0 2 2 >CMLIVERKIDNEY_7:1:1:87:686 GGTTGGGGGTAAGGTCATAGATCAACAGGATC R0 175 255 255 >CMLIVERKIDNEY_7:1:1:117:596 GGACGTGTACATGCCGGGGAAAGGCTACGCTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:85:690 GCTTTTTTGAGTTTAGTCTTAGGGTTACTCAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:104:901 GTAGCTGTTGAGTTGTGGTAGTTAAAATGTAA U2 0 0 1 chrM.fa 10118 R .. 10G 27G >CMLIVERKIDNEY_7:1:1:99:614 GAATGTTATAATTAAGGAGATTTGTAGGGAGA U0 1 2 0 chrM.fa 11059 R .. >CMLIVERKIDNEY_7:1:1:119:865 GAGGTTGGGGGGTCAAGTAATAAGAAGGTAAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:82:814 GACAACTTTGCCTTTAAGGTTTTTTTTCTTTT U2 0 0 1 chr2.fa 108438354 F .. 2C 10A >CMLIVERKIDNEY_7:1:1:91:769 GGTATTTGTGGCGAGGCTAGGCAACATTGCGA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:116:681 CAGGAAACAAGTTGAAAATAGCATAGATCATG U0 1 0 0 chr2.fa 160497720 F .. >CMLIVERKIDNEY_7:1:1:112:779 GTCTACCTAGCGCAGAGGAGAGGCTGTGTTAC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:90:619 GGGGTTAGTCCTTGCTATATTATGCTTGGTTA R0 2 1 0 >CMLIVERKIDNEY_7:1:1:119:838 GGCATTTTCCTCCTTTTTTTTTTTTTTTTTAG R2 0 0 3 >CMLIVERKIDNEY_7:1:1:113:746 GATTTTTAACCAACTTCCACTTCTAGCTTCAA U1 0 1 0 chr11.fa 65024168 R .. 14G >CMLIVERKIDNEY_7:1:1:97:685 GCTGTTCACTCTCGTGTGCTGCAGCCTCTACA R0 2 0 0 >CMLIVERKIDNEY_7:1:1:98:430 GGCATTTTGGAAGCTTTAGGCTCTGTAAGCAT U0 1 0 0 chr13.fa 21174264 F .. >CMLIVERKIDNEY_7:1:1:88:520 GCGAGGAGTCCTGTCAGTTCCGGAAGGAGTAT U0 1 0 0 chr8.fa 2679443 F .. >CMLIVERKIDNEY_7:1:1:112:900 TGGTGACAGACATTGAAAACATAAGTTACATG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:75:705 GGTGGGTAGGCCTAGGATTGTGGGGGCTATGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:100:399 GAGAGGATTATGATACGACTGTGAGTGCGTTC U1 0 1 0 chrM.fa 11763 R .. 18C >CMLIVERKIDNEY_7:1:1:120:494 GTTCCTTCTCCCTCTTAATAATCTGCCACTCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:117:697 TCTAAACTGACTCGCAGGACAACATTACCCTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:73:593 GATGACTTTTTTTGCTTAAGGTAGAGGGCCAG U2 0 0 1 chr1.fa 151882971 F .. 27A 30A >CMLIVERKIDNEY_7:1:1:105:842 GTCGTGTAGCGGTGTTAGTGGTTTTGTTTATA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:102:457 GTCAGAACTAGGGTCAGGGCTAGTCCAGTGCC U0 1 0 0 chr11.fa 118474037 R .. >CMLIVERKIDNEY_7:1:1:114:328 ATTTAACATTTTAATATAAAAAACAGGAAGCT U0 1 0 0 chr17.fa 16066158 F .. >CMLIVERKIDNEY_7:1:1:110:631 GCTTACTAGAAGTGTTAGAACGTAGGCTTGTA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:92:456 GGCACCCTGGCGTCCTCGCCTAAAGGAAAGTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:111:381 GCTTGTTATAATTATGCCTCATAGGGATAGTA U0 1 1 2 chrM.fa 11548 R .. >CMLIVERKIDNEY_7:1:1:123:331 CGTTCTTTCATCCTCATCCCTATTCTTCCTCG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:110:806 GTTGGCAGCTTGGAAGCAGGGGGATGGGACCT U0 1 0 0 chr16.fa 84172414 F .. >CMLIVERKIDNEY_7:1:1:116:529 GGAGATGGTGATACCAGAAGTCAAGGGCTGGG U1 0 1 0 chr17.fa 45800376 R .. 5A >CMLIVERKIDNEY_7:1:1:112:429 GTAAAATCCCCCTGCTAGGGTTTGCTTTATCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:109:576 GTTTTGAGTAGTCCTCCTATTTTTCGAATATC R0 2 1 0 >CMLIVERKIDNEY_7:1:1:115:250 GTTCAGCTCTTCAGTCATTCAGCTGTGACTCA U0 1 0 0 chr1.fa 2967533 F .. >CMLIVERKIDNEY_7:1:1:121:793 GAGAAATTAGAACCCATAACCATACACAGGCT U0 1 0 0 chrX.fa 37893628 R .. >CMLIVERKIDNEY_7:1:1:104:804 GAAATATTTGCAGACAAAAAGATCCAGAAAAA U0 1 0 0 chr2.fa 231021536 F .. >CMLIVERKIDNEY_7:1:1:101:660 GCAGAAGGTAGGTGAGGGGCTCCCCGCCCCCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:83:211 GGGCAATTTGCCCGGGACTTTGATTTCATTAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:118:569 GCGTCATATGTTGTTCCTAGGAAGATTGTAGT U0 1 1 0 chrM.fa 4022 R .. >CMLIVERKIDNEY_7:1:1:118:754 TTTATGTTGTTAATGTGGTGGGTGAGTGAGTC U1 0 1 0 chrM.fa 12009 R .. 2G >CMLIVERKIDNEY_7:1:1:59:555 GCGCAATCCTATTCTAGAGTCCATATCAACAA R0 3 2 2 >CMLIVERKIDNEY_7:1:1:71:502 GTGGGAAAAAAAATCATATGGCTGGCCCAGAC U2 0 0 1 chr14.fa 74618883 F .. 25T 31G >CMLIVERKIDNEY_7:1:1:119:880 GACAGACTCGTGACTGAAAAACAGCAGGAAGG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:111:895 GAAGAACTCAAGAATGTTACTATCAATCTGAT R2 0 0 2 >CMLIVERKIDNEY_7:1:1:87:511 GGATAGTAATAGGGCAAGGACGCCTCCTAGCT U1 0 1 0 chrM.fa 15607 R .. 2A >CMLIVERKIDNEY_7:1:1:115:725 GGGGGGTATAGGGGTAGGTGCTTGCTATATTA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:100:572 TTGTTATTATTATGTCCTACAAGCATTAATTA U0 1 0 0 chrM.fa 213 R .. >CMLIVERKIDNEY_7:1:1:111:439 GTGTCATTTCCTTCCTTGTCAAACACCCGAAG U1 0 1 1 chr12.fa 54840127 R .. 29G >CMLIVERKIDNEY_7:1:1:104:740 GCCGCCGCAGGTGCAGATCTTGGTGGTAGTAG U1 0 1 0 chr1.fa 107915036 R .. 14C >CMLIVERKIDNEY_7:1:1:117:482 AGGAGCTGTATTTGCCATCATAGGAGGCTTCA R0 2 0 0 >CMLIVERKIDNEY_7:1:1:113:535 GGAAGTCAATATAATGTATGGCTTAATTTATC U0 1 0 0 chr15.fa 50735804 R .. >CMLIVERKIDNEY_7:1:1:110:670 GTTTGAGTTTGCTAGGCAGAATAGTAATGAGG R0 2 0 0 >CMLIVERKIDNEY_7:1:1:88:601 GAGGAGGAGGAGGAATATGCCCTTTTACTTGG U0 1 0 0 chr19.fa 19038852 R .. >CMLIVERKIDNEY_7:1:1:120:898 GTTTGCTTTGTTTTTCTTTTGGTCTGGGTTGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:97:496 GCCAAAGCCTCCGTTTATGATGGGTATTACTA R1 0 3 1 >CMLIVERKIDNEY_7:1:1:122:644 CTAAATCTGTGTGTGAGAAATGGCAGGTCTAG U0 1 0 0 chr11.fa 64948725 F .. >CMLIVERKIDNEY_7:1:1:94:529 GTGTTTGTGGGTGTGTTTGCTTGTTTATGCAA U1 0 1 0 chrX.fa 93540767 R .. 23A >CMLIVERKIDNEY_7:1:1:76:631 GAGGATATGAGGTGTGAGCGATATACTAGTAT U0 1 1 0 chrM.fa 10525 R .. >CMLIVERKIDNEY_7:1:1:108:570 GTGCTGCTAGGGCTGCAATAATGAAGGGCAAG U1 0 1 0 chrM.fa 15299 R .. 4A >CMLIVERKIDNEY_7:1:1:85:553 GGCCTGGCACGAACGCGGCTGCACTTGGGCGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:99:317 AAAAAATGCAAATTCAAAAACAAATACACATA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:119:767 GCTGTTCCTTCTGCCTGGTGCCACGATTATTT U0 1 0 0 chr12.fa 112250797 F .. >CMLIVERKIDNEY_7:1:1:98:502 GGGCGGAGGACGGCGAGCTCTTTTGAAAGTGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:112:919 TGAGTCTGTGAAAATTGTATTACAGGAGAAAG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:117:226 GCACGTCCTGGAAGCTTGGGTAGCGCTAGTAG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:119:528 CGACATGATACTATTAAGTGTCTCTATCCACC U1 0 1 0 chr10.fa 72309597 R .. 6G >CMLIVERKIDNEY_7:1:1:106:874 GAGATATTGTCTGACTACCTTCTATGGCATGG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:107:288 AACCAATAGCCCTGGCCGTACGCCTAACCGCT R0 2 0 0 >CMLIVERKIDNEY_7:1:1:117:220 TTGAAATGTCTGCACTGGCAATAACGCCTGCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:118:799 TGAGGAGGCAGTAAATGAAGTTACAGGCTAAC U1 0 1 0 chr15.fa 75564644 F .. 31G >CMLIVERKIDNEY_7:1:1:123:391 CCAGACGTCTGCAGGTTAAACCCATTCAATAG U2 0 0 1 chr12.fa 40767570 F .. 1A 6A >CMLIVERKIDNEY_7:1:1:91:381 GAAGAAATCTTCTTTGGAAGAATTACAACTGG U0 1 0 0 chr18.fa 12330246 R .. >CMLIVERKIDNEY_7:1:1:86:700 GTTTCTCTCATTGTGTAGAGTCAGTGCTAGTG U0 1 0 0 chr3.fa 150339328 F .. >CMLIVERKIDNEY_7:1:1:96:713 ATTCTCCAATTATTATATGAGCTTCTCCTCTC U1 0 1 0 chr15.fa 58291887 F .. 28A >CMLIVERKIDNEY_7:1:1:53:543 GTAATTTTATTTTGAAGTCTCATGCAAGTTGT R0 3 0 0 >CMLIVERKIDNEY_7:1:1:121:229 AAAAGAATGAGTTGAATTTACAAAATCACCAG U0 1 0 0 chr15.fa 55517962 F .. >CMLIVERKIDNEY_7:1:1:99:826 TGATCAGGATTATTGCATCGAGCTTTTAGTGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:116:467 TGGATACTAGTATAAGAGATCAGGTTCGTCCT U1 0 1 2 chrM.fa 8719 R .. 32T >CMLIVERKIDNEY_7:1:1:104:215 AGTCTCGGTAGGGTTGTCTCATTTTTTTTTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:83:545 GGAAAGACCATGGTCAAAGCAGCGAGATCGCA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:122:472 GCGAGTCCTTGACGTTGACAATCGAGTAGTAC R1 0 2 1 >CMLIVERKIDNEY_7:1:1:118:541 CACCATGTTAAGAATGAACTGAAAGTAAGACC U0 1 0 0 chr10.fa 38396171 R .. >CMLIVERKIDNEY_7:1:1:118:775 TTCAAACTGTCATTTTATTTTTACGTTGTTAG R0 2 0 0 >CMLIVERKIDNEY_7:1:1:118:184 GAACTCTCCTGGCTAGGCCCGCGTTCATTAGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:107:158 GTGACTCAACGTCTGTAATTTCAGCAATTTGT U1 0 1 0 chr1.fa 233570139 F .. 32G >CMLIVERKIDNEY_7:1:1:94:706 GTGACTTCATATGAGATTGTTTGGGCTACTGC U0 1 0 1 chrM.fa 3710 R .. >CMLIVERKIDNEY_7:1:1:119:335 ATTCAAGAAGTGAAAGAGCCCGAAAATCAGCA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:90:442 GGTCATGCATCTCTACCTACCCACGGCCGCGG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:98:756 GGAGCACACCTAGTCTCAGCTACTCGGGAGGC U0 1 0 3 chr4.fa 84723339 F .. >CMLIVERKIDNEY_7:1:1:90:493 GTAGGCTATGTGTTTTGTCAGGGGGTTGTGAA U1 0 1 1 chrM.fa 11506 R .. 4T >CMLIVERKIDNEY_7:1:1:98:761 GGCAGAGCAAGCATTCCCCTTTAAGAGCTTCC U0 1 0 0 chr10.fa 102790158 F .. >CMLIVERKIDNEY_7:1:1:75:607 GGAGGAGCAGTTTAACGTGTGAGATGGTGGAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:105:504 GGATCCACAAACTTCCTGACACTATTTCCATA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:96:638 AATGTGTCAGTATGCCGTCATTGAGCAAAGAC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:94:571 TTATGTTCTTTCTTGTCCTTTTGTTTGACCTT U0 1 0 0 chrX.fa 102826849 R .. >CMLIVERKIDNEY_7:1:1:122:913 GGTATATCGGGGTAACGCGTGCTTTTAAGATC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:95:626 AGCCTCGCTAACCTCGCCTTACCCCCCACTAT U0 1 0 1 chrM.fa 11847 F .. >CMLIVERKIDNEY_7:1:1:95:847 GGATACTGGCATTTTGTAGATGTGGTTTGGAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:109:384 GTTTTTGGGACAGAGTCTCGCTACGCCACCCA U2 0 0 1 chr1.fa 16114545 R .. 9A 10G >CMLIVERKIDNEY_7:1:1:98:489 AATTGAGTGATCGTCGTTTTGGCAAAAATTTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:92:690 GCCGGATTCAATAAACTGGGGTTAATGCGCGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:87:447 GTAAGTCAGGAAGTAGCCTAGAATGAATCGTG U1 0 1 0 chr1.fa 26996843 R .. 7A >CMLIVERKIDNEY_7:1:1:121:953 GAATGGTAGCACTGGATTTGCTGATGGCCCAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:107:73 TTCCCCCCCTCCCTCCCCCCACCAGCTTCCTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:107:638 AAACAATCTCATATGAAGTCACCCTAGCCATC U0 1 0 2 chrM.fa 3720 F .. >CMLIVERKIDNEY_7:1:1:105:786 GTGGGAAGGTCACCGGCGTGTAGTTGGTAGCG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:80:21 GAAATAATTTTTATACTTNTTTACACCTNNTC QC >CMLIVERKIDNEY_7:1:1:93:486 GGCCACTTTGCATTTATTATAGATTTGTTTCG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:116:397 GGCGGTTTTAATTAGTCAATAAACACCTTAAG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:111:874 TTGAAACCCTGTATCCCTCTGAAACACTGGAA U0 1 0 0 chrX.fa 99771371 R .. >CMLIVERKIDNEY_7:1:1:117:434 GGCCGCGGTTGCGATCCGTTCCATTGCGCTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:110:657 GTCATCATCTCCTCCTGAACAGTTATCCGACT U0 1 0 0 chr5.fa 179193203 R .. >CMLIVERKIDNEY_7:1:1:122:731 TTTTAGAAGAAAAAAGATAAATTTAAACCTGA U0 1 0 0 chr11.fa 65023785 F .. >CMLIVERKIDNEY_7:1:1:85:791 GGCGACTAGTGTGAGCGTATGAACGAGGGTAC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:73:472 GTAAATTTAACTGTTAGTCCAAAGAGGAACAG R0 4 0 3 >CMLIVERKIDNEY_7:1:1:119:663 GATATTTATAACAAGCTCCATCTGCCTACGTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:123:197 GAAGACCCTGTGGAGCTTTCACTTATTAATGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:99:56 TAAGATGACAACTGCCCTAGACCACTTCTTCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:117:558 CTAGCATTAAGACATTCATGGAAAGCCATTTG U0 1 0 0 chr15.fa 43443453 F .. >CMLIVERKIDNEY_7:1:1:79:463 GGACATCCCGGTATGAGCAGACCTAAACCAGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:96:841 GGTTAATAGTGGGGGGTAAGGCGAGGTTAGAG U1 0 1 1 chrM.fa 11852 R .. 2G >CMLIVERKIDNEY_7:1:1:94:669 GAGATAGGTAGGAGTAGCGTGGTAAGGGCGAT U0 1 0 1 chrM.fa 5458 R .. >CMLIVERKIDNEY_7:1:1:121:114 GGGGTGCAGTTCCTTCACAGGGTCTGTTTGTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:119:274 ATCCCTCTTATTAATCATCATCCTAGCCCTAA U0 1 1 1 chrM.fa 10329 F .. >CMLIVERKIDNEY_7:1:1:108:920 GCCTTGGGAGGGCCCTTCAGGAAGTAGGAGTG U0 1 0 0 chr5.fa 150388408 R .. >CMLIVERKIDNEY_7:1:1:123:237 GTCATTTTGCACCAGGTAATAGGGGAAAATTG R0 3 2 1 >CMLIVERKIDNEY_7:1:1:114:859 TGGTATTGCTCGGGGGGTGCTTTCCATTAGGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:104:478 GCAAAGGTGCCCTTGAGGTTGTCCAGGTGAGC U0 1 0 1 chr11.fa 5204438 F .. >CMLIVERKIDNEY_7:1:1:76:566 GATTGGATTTTTGACGGGGCACGCACGGTTCA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:105:415 GGTCAATGCTCTGAAATCTGTGGAGCAAACCA U0 1 1 1 chrM.fa 8166 F .. >CMLIVERKIDNEY_7:1:1:97:196 GCAGGTTAGTTGTTTGTAGGGCTCATGGTAGT U1 0 1 0 chrM.fa 10281 R .. 1C >CMLIVERKIDNEY_7:1:1:114:608 GCGAGTACAAGGGTCGACCCCCAAAGGCAGGG U0 1 0 0 chr3.fa 187944204 F .. >CMLIVERKIDNEY_7:1:1:119:136 GGATGTTCCTGCCCTGGCCAGGTACTGGGCAC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:106:439 GTTATGAGACCGGCACTGAGGCCGAAAACGAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:120:193 TCTGCAGCCACCCTAGCCCTCCTCCTTCTCTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:102:620 GACCTGGCCGCACTCATACTAGCCTGTCGGTA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:117:437 CGAGATTTCTTTGACTTTCCTTAAATATTACA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:100:716 AATTCCCCTAAAAATCTTTGAAATAGGGCTCG R1 0 2 0 >CMLIVERKIDNEY_7:1:1:109:665 GTGGAAGTAAAATTGTTGGTCAGTGGGGAAAG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:103:658 CTAAAGCCTAAAAAGAAAGACTCTCCCTTGTT U2 0 0 1 chr1.fa 177786935 R .. 5T 27A >CMLIVERKIDNEY_7:1:1:113:755 GGGTGATGCCTGTTGGGGGCCAGCGCCCTCCT U1 0 1 1 chrM.fa 9543 R .. 9A >CMLIVERKIDNEY_7:1:1:66:504 GGGGCTTTGATGTATTATTTTGATAAGTAGTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:67:406 GGAAAATTTAAATACAGAAACCCAAGTACTGC U2 0 0 1 chr21.fa 34388496 R .. 12T 32T >CMLIVERKIDNEY_7:1:1:113:387 AAGGGAGCAGGCAAGACTGTCTGAGTGATGAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:122:421 CTGATACTGGCATTTTGTAGATGTGGTTTGTC U1 0 1 0 chrM.fa 9924 F .. 31A >CMLIVERKIDNEY_7:1:1:118:661 AAAGCTGTGGACAAGAAGGATGCTGCTGCTGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:97:793 GGGAAGTGTGGGGGGGCCTGGGTGAGAGCAGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:120:418 GGAAATTAAAGCACAGAACCTTGTGAGATAAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:116:449 CGAGGAGATATGATAGCATGTGTGTGTCTAGG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:116:526 ATTCGAATCACCCTAACAAGCCGCAACGTAAA U0 1 3 0 chr8.fa 57149187 R .. >CMLIVERKIDNEY_7:1:1:121:291 CTTCTCCCCGGCTGCTGGTAGCCACGGTGATC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:102:449 GTTCTTCCCTCCTGCCACGCTCCTGAAACCAG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:116:908 TGCAGGGCTGGTTACCACAAACTCAGTAGGAG U0 1 0 0 chr3.fa 50312384 F .. >CMLIVERKIDNEY_7:1:1:80:802 GTTAAAAGTAAGAGACAGCTGAACCCTCGTGG U0 1 4 3 chrM.fa 2641 R .. >CMLIVERKIDNEY_7:1:1:116:566 GAGAAGGGATCCATGTGAACAGCAGTGGAACA U2 0 0 1 chr2.fa 132755102 R .. 6A 24A >CMLIVERKIDNEY_7:1:1:97:468 GGATTTTCAAGGGCCAGCGAGAGCTCACCGGA U0 1 0 1 chr5.fa 71182646 R .. >CMLIVERKIDNEY_7:1:1:107:880 GCCGTGATGATTTTATAGCATCCTGGGCATTT U1 0 1 5 chr11.fa 116411841 F .. 1A >CMLIVERKIDNEY_7:1:1:84:436 GGGTTTTGTATGTAATATTTTCTTTTTTGTAG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:61:511 GGGAATTTGCCGCTGGTATCTCCACGACTGGG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:119:525 GGAAGGCTCCATGGTTGTCCTACTTTAAGCAG U2 0 0 1 chr11.fa 65029257 R .. 1A 2G >CMLIVERKIDNEY_7:1:1:107:702 TTTCGATAATAACTAGTATGGGGATAAGGGGT U0 1 1 0 chrM.fa 8932 R .. >CMLIVERKIDNEY_7:1:1:65:532 GAAACGATCTACATCATTGTCATCTCCCAGAA U0 1 0 0 chr9.fa 32532806 R .. >CMLIVERKIDNEY_7:1:1:105:767 GTTAGGGATTGTCCTCCTCTGACATCGCTGTA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:102:464 GCCCACTCGAGCCGCAGCCATGTCTGGGGACG R0 2 1 0 >CMLIVERKIDNEY_7:1:1:91:763 GGGAGATTTCTCTCGTTTATCCATTGCTGTGA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:84:858 GGGAGAATTCTCTGTGCTAGTAACCACGTCCT U2 0 0 1 chrM.fa 11888 F .. 8C 30T >CMLIVERKIDNEY_7:1:1:59:579 GGCAAGATGAAGTGAAAGGTAAAGAATCGTGT U1 0 1 1 chrM.fa 15273 R .. 30A >CMLIVERKIDNEY_7:1:1:116:695 CTAGGATATAAAATGCGGTTTTTTTAAATGTA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:75:622 GCCATTCTCATCCACCTTATAATATTTCAGGA R0 3 1 3 >CMLIVERKIDNEY_7:1:1:95:696 GGTTGATTATTGAGTTTCACGGCTGGCGTAGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:98:651 GGGGATAGGGGTATGAATATGACGGTGTTTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:75:611 GTCATTAAGGAGAGAATGAAGAGAAGTAAGCC U1 0 1 0 chrM.fa 15438 R .. 16C >CMLIVERKIDNEY_7:1:1:99:62 GTTTGGTGCTCTTCCTTTTTCTACTGTTAAAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:115:763 TGTTAAGCTTGTTTTCCTGCAACTGGATTTTT R2 0 0 9 >CMLIVERKIDNEY_7:1:1:96:221 GCTGAATCAGCGCGGCCCCACGACCCCGCTCA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:761:671 GTGATAGCGTGGGGCGGCGTCCGTTTGATTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:110:320 AAAAAAAAAAACAAAAAAAAAGAAGAAGAAAG R1 0 68 255 >CMLIVERKIDNEY_7:1:1:103:198 GTTTGGCTAAGGTTGTCTGGTAGTAAGGTGGA U0 1 0 1 chrM.fa 1696 R .. >CMLIVERKIDNEY_7:1:1:113:914 GAAAATCTTCAAAGCCAACCACCCCATGGACG U0 1 0 0 chr19.fa 55000959 F .. >CMLIVERKIDNEY_7:1:1:113:243 GACGTCCTCATAGTACTCACAAAGGGCTAGCC U0 1 0 0 chr2.fa 121812768 R .. >CMLIVERKIDNEY_7:1:1:115:965 GGATACCGCAGCTAGGAATAATGGAATAGGAA U1 0 1 0 chr16.fa 33870842 F .. 32C >CMLIVERKIDNEY_7:1:1:53:599 GGAAAAGGTTGGGGAACAGCTAAATAGGTTGT U0 1 0 0 chrM.fa 10900 R .. >CMLIVERKIDNEY_7:1:1:115:901 GGATAATGGGTTTGCTGCGGTCAGCCACATAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:90:555 TCTTGGTTTGCTCTCAGCCCCAATTTTAAAAG U0 1 0 0 chr20.fa 35282756 R .. >CMLIVERKIDNEY_7:1:1:99:808 GCCTGGTTCTAGGAATAATGGGGGAAGTATGT R0 2 0 0 >CMLIVERKIDNEY_7:1:1:115:625 AAAATCCACCCCTTACGAGTGCGGCTTCGACC U0 1 1 0 chrM.fa 10155 F .. >CMLIVERKIDNEY_7:1:1:115:154 GGACTATACATATTCTGTATCCGCAGCTTCCG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:122:106 GCCCCCTCCTCTTTCTCGCTTTTTACTATTAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:86:465 GGGGTCCACATATGTTCTTACACCTGAGTGAT U0 1 0 0 chr2.fa 222016548 F .. >CMLIVERKIDNEY_7:1:1:120:35 GCAGTTTTTCGCTCTCGGGCCTCCTCTCTCTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:102:548 AAACGCTGTCTCTACTAAAAATACAAAATGGC U0 1 1 38 chr12.fa 100209286 F .. >CMLIVERKIDNEY_7:1:1:71:615 GTTTTCATTTACTAAGAATTGAAAGCAATTAA U0 1 0 0 chr22.fa 22601942 R .. >CMLIVERKIDNEY_7:1:1:117:588 CTTTATCCGACGTGTATTCTACGTTCAGCTGA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:118:616 CTGGTTAAAAGTAAGAGACAGCTGAACCCTCG U0 1 5 7 chrM.fa 2644 R .. >CMLIVERKIDNEY_7:1:1:78:262 AAGAAAACAAAGGCAAAATGTCCATTGCTTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:81:420 GCTAGGGTGAGTGGTAGGAAGTTTTTTCATAG R0 2 0 0 >CMLIVERKIDNEY_7:1:1:79:550 GCTTACTAGAAGTGAGAAAACGTAGGCTTCGA U2 0 0 1 chrM.fa 9153 R .. 3C 18A >CMLIVERKIDNEY_7:1:1:120:851 TCGGATCCTCAAATGGGGGAGATCACGATGTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:116:595 GGTGGTTTGGTTAAAAAATAGTAAAGGGATGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:119:387 GGCCTCTTGGAGACAGATGATGACTGGCAAGG U0 1 0 0 chr7.fa 130837333 R .. >CMLIVERKIDNEY_7:1:1:106:402 CCTCCATCACCCCTTCATACATTAGCTTCACC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:120:727 CTCTTTTCTGTACACTCAGGGAGCTAAAAAAC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:122:224 GGCGGCCCAGCGTTCCTAGCGCCGTCGCTTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:79:733 GGACAATTTATGTCTATTCATTAGATTACAAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:96:462 TATGTATCCAAATGGCTCTTTTTTTCCGGAGT U1 0 1 1 chrM.fa 6686 R .. 17A >CMLIVERKIDNEY_7:1:1:110:674 GAGGGGAAGGTGCTTTCCTTACCTCTAAACAG U0 1 0 0 chr19.fa 50013642 F .. >CMLIVERKIDNEY_7:1:1:101:38 TATGCACATTCCACTTCTGCTCCTTACCTGTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:79:875 GGATACAATAGGAGAGTCATGGTTATTTCCTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:113:847 GCCTTGGAGCTTTTGATACTGACGATTGCGCT U0 1 0 0 chr14.fa 19851570 F .. >CMLIVERKIDNEY_7:1:1:122:244 ACGGACTACACCTATCACTCCCTAAACCCCCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:90:225 GGGATAGAAACCTCGCCACCTTCCATTCCTCT U2 0 0 1 chr1.fa 38184430 F .. 9C 16A >CMLIVERKIDNEY_7:1:1:122:219 GCTCTTTTAGCTGTTCTTAGGTAGCTCGTCTG R0 4 1 0 >CMLIVERKIDNEY_7:1:1:81:779 GGACTTCATGTCATTATTGGCTCAACTTTCCT U1 0 1 0 chrM.fa 9820 F .. 9C >CMLIVERKIDNEY_7:1:1:109:533 TGGAGACATGTCATATAAGTAATGCTAGGGTG R1 0 2 0 >CMLIVERKIDNEY_7:1:1:122:182 TGGGATTATAGGTGTGAGTGAGCTACTACGCC R0 3 0 0 >CMLIVERKIDNEY_7:1:1:115:219 CTTCGAGTCTCCCTTCACCATTTCCGACTGCC U2 0 0 1 chrM.fa 9750 F .. 29G 32A >CMLIVERKIDNEY_7:1:1:108:978 GTTTGGATTAGAAGTCAGGGAGGTGTTTCTGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:112:949 GTTGATTTTAGGTTTAGGATAGATATGAAGTA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:118:70 GTCGCGCTCCCCGGATGCTCGCCTGCTCCTCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:99:215 GAAATACTTGTTGGCTGCTTCTGTGGAACTAG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:60:692 GGGGATTTTTCTATGTAGCTGTTGAGTTGTGG U2 0 0 1 chrM.fa 10132 R .. 13G 31A >CMLIVERKIDNEY_7:1:1:104:671 GTGTTGGGAGCGCTTTGGTGACCGCGTGTTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:106:683 CCCATCTATATTTTCCACTATAGACTTCAAAA U0 1 0 0 chr7.fa 30164378 R .. >CMLIVERKIDNEY_7:1:1:106:877 GCCACCAATTAAGAAAGCGTTCAAGCTCAACA R0 6 7 2 >CMLIVERKIDNEY_7:1:1:98:183 GCTGGCTGGTCCCCCCCGCCCTTCTTCTTGTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:100:189 GTCCAATTGGGCGTGAGGCGTTCAGTTATATG U2 0 0 1 chrM.fa 2242 R .. 14T 21A >CMLIVERKIDNEY_7:1:1:118:259 AGAGGTTTAGATGATGTGGTCTTTGGTGTAAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:102:956 TGATACTGGCATTTTGTAGATGTGGTTTGACT U0 1 0 0 chrM.fa 9925 F .. >CMLIVERKIDNEY_7:1:1:102:347 AAAAAAAGTAAAAGGAACTCGGCAAATCTTAC R0 4 4 2 >CMLIVERKIDNEY_7:1:1:106:177 GGAGAGGGACCCTATCCTGTACCTCGAAGCTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:143:877 GTTGGCCTCATTATGCCAGTATGGCTGCCATT U2 0 0 1 chrX.fa 2834019 R .. 1T 2G >CMLIVERKIDNEY_7:1:1:107:787 TTTCACCGCTACACGACCGGGGGTATATTACG U1 0 1 0 chrM.fa 8135 F .. 28C >CMLIVERKIDNEY_7:1:1:86:608 ACACATTTGTCCTCAACGCAACGGTCATCTCC U2 0 0 1 chr16.fa 19778851 F .. 1C 3C >CMLIVERKIDNEY_7:1:1:119:150 GTGAGCCCCATTGTGTAGTGGTAAATATGTAA U2 0 0 1 chrM.fa 11984 R .. 1C 16A >CMLIVERKIDNEY_7:1:1:95:198 GAAGTAGCGGTTAAGGAGGGTGATGGTGGCTA U1 0 1 1 chrM.fa 5315 R .. 25T >CMLIVERKIDNEY_7:1:1:120:130 GTGGGTTCCGCACCCGCCTCGGCTCCCGCTGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:79:497 GGTGTGCAAGGGATGCGGGAGATGTGTGCAGA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:80:716 GTGGAAGGGAGAGTGCTGGCCCGAAGCCTTAG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:98:715 GCCCTGACGTGCAAATCGGCCGTCAGACTTGG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:76:389 GAGGGAGAAAGTGGCAGGAGGTGCCTTAAAGA U0 1 0 0 chr5.fa 133946269 R .. >CMLIVERKIDNEY_7:1:1:108:464 GGGGGCACCATCTCCTACCGTTCCCCTCTCCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:119:378 CGTTTTCCTACTACTCAACTTAATCGCCTTCA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:69:565 GCCGGGGTGACATTCGCTTTGGGGGGGTAGCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:71:625 ATTTCCTCATGATGTTAATACACTTCTTACTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:100:158 GTTGTATACTTCAAAAGACTTTTTCTCTTGGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:118:876 GTGGGGTGGAAGGGAAAGCAAAAGGCTTGGGA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:115:497 TTGGGGTTGAGGGAGAAGGATAGAGGGGTGTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:90:978 GTGGATGTGGACAATTGATATCAATGTTCAGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:122:702 CTCAACCTAGGCCTCCTATTTATTCTAGCCAC U0 1 0 0 chrM.fa 3599 F .. >CMLIVERKIDNEY_7:1:1:112:221 GTCAGTCGGTCCTGAGAGTTGGGCTCGTGCGG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:121:929 TGCTTGTATGGCTGCTGTGTTGGTATCTTCTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:84:642 GTCCTCTCGTATTGTTGTGACGGCTGCGCTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:90:451 GTCTAAATCAGCTGGGACAACACCCTTCCTGG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:110:735 GGAGATGGCTGTGTATGTGTTTTCTCGTGCTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:96:555 AGAGAAGCCAGGTTCCAAGCTTTAGGCACCCA U0 1 0 0 chr20.fa 44425279 R .. >CMLIVERKIDNEY_7:1:1:123:435 CCAAGAAAAACCTAAAAAACACCAAAACCAGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:121:550 TGCCGGGCAGGCCACCTACGGTGAAAAGAAAG U2 0 0 1 chrM.fa 6939 R .. 26A 28T >CMLIVERKIDNEY_7:1:1:116:401 AGCTTTTGAAAGTAAAAGAGAAGCTACTTCTG U1 0 1 0 chr15.fa 99643570 R .. 8T >CMLIVERKIDNEY_7:1:1:111:620 TACTGCCTTCTCCTGCGGTATGTTCTTACGCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:116:787 GTGGTCTCGTGGTCCTGAGTTCAGGTGATCCG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:100:168 GCCGTTCTTCCTGGCTCCTTTCACTTTTTTTA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:107:271 CTCCACACCCACTCCCACTCAGCCAATATTGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:49:558 GGAAAACTGGAAGACAGAAGTACGGGAAGGCG U0 1 0 0 chr11.fa 65023707 F .. >CMLIVERKIDNEY_7:1:1:91:652 GAAGCGTTAGGAATGCCATTGCGCTTAGAATG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:109:399 GGACAGATCACGAGGTCAGCAGATCGAGGCCA U0 1 3 219 chr4.fa 166216071 R .. >CMLIVERKIDNEY_7:1:1:116:737 CGCTCTTCAACTTGGGAATTTACGTAACCTTC U0 1 0 0 chr11.fa 66434990 F .. >CMLIVERKIDNEY_7:1:1:78:443 GCAAAATAGTGGGATGATTTATAGGTAGAGCC R2 0 0 2 >CMLIVERKIDNEY_7:1:1:82:180 GCAGGTAGAGGCTTACTAGAAGTGTGAAAACG U0 1 1 0 chrM.fa 9163 R .. >CMLIVERKIDNEY_7:1:1:119:688 GGTAGCAGTGGGGCGGGTCTTTTTCTGTTTTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:57:739 GGGTTGTTTTCTGGGTATCTAGGTCATCGTAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:102:891 GTGAGTTTGGAGATTTCTTACTATTGCTTGAC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:115:44 GTTGTCGTCCGCGTCGGGGCTCTGTGCAGCTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:101:946 GGAGTATTTTGGTTCGTTTTGATTTGTTTTGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:92:178 GTTGGACCTGAGGGTAAGTTAACAAGGATGAA U0 1 0 0 chr10.fa 93713900 R .. >CMLIVERKIDNEY_7:1:1:88:815 TAAAGTGGGAGAAACTGAGTCCTTTTTTGGTG U0 1 0 0 chr10.fa 28380978 R .. >CMLIVERKIDNEY_7:1:1:109:497 GTTGAATGTGAAATATAAAGAATTAAGCAGCT U0 1 0 0 chr11.fa 95618811 R .. >CMLIVERKIDNEY_7:1:1:89:833 GCGGGGGTTGTATCGGACTGGGTGGTGGTTGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:107:947 GCCATAAGGAGGACAAGGAAGATCTTAGGGAA R2 0 0 2 >CMLIVERKIDNEY_7:1:1:113:567 CTAAGCTTCAAACTAGACTACTTCTCCATAAT U0 1 3 11 chrM.fa 12569 F .. >CMLIVERKIDNEY_7:1:1:123:398 GAGAAAACACCCTCATGTTCATACACCTATCC U0 1 1 1 chrM.fa 12055 F .. >CMLIVERKIDNEY_7:1:1:107:627 TGGAGATGGCGACTAGTGGACATAAGAACTAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:85:799 GGAAATGTTTGCAGTTCATTCTGTCAACTTGC U0 1 0 0 chr15.fa 59958150 R .. >CMLIVERKIDNEY_7:1:1:103:176 GTTCCATCAGATTTCGCCCTCTACCAACCACC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:60:401 GTTTTAATGAGCTTATGCGTGATCCTGTTAAC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:109:818 TGCAGCCTAGCCTTCTTGTTTTTATTCTCTGT U1 0 1 0 chr7.fa 111616996 F .. 1A >CMLIVERKIDNEY_7:1:1:81:189 GGTGGGTGGAGCAGATTGTTTGGTTTGTTTGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:100:693 GTAAGTAGGAGATTGATCTTTGATCAGGAGAG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:104:435 TGAATGAGGGATTTATGTTGTTAATGTGATGG R2 0 0 2 >CMLIVERKIDNEY_7:1:1:114:712 CCTGTGTAGATTATTCAGTGCCACAAATTGAA U0 1 0 0 chr20.fa 17535783 F .. >CMLIVERKIDNEY_7:1:1:120:206 AAAGCACATACCAAGGCCACCACACACCACCT R0 2 0 1 >CMLIVERKIDNEY_7:1:1:63:608 GTTTCAGGGGGTTTGGATGAGTATGGCTGTGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:99:915 GCCAAGCACATACACCAAATGTCTGAACCTGC R1 0 2 3 >CMLIVERKIDNEY_7:1:1:118:520 GGCCCATCCATGAGTCAGGAAAGGAGCACTGC U1 0 1 0 chr16.fa 55499051 R .. 5G >CMLIVERKIDNEY_7:1:1:81:243 GTTTTAGCCGGTTATTGTCATTTAATTTTTAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:84:516 AAAGGACACGATGGATGTACATACAAATTTAC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:77:642 ATAAATACTACCGTATGGGCCACCATATTTAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:69:499 GATTGGGTTAGATGTCCGGCAATTGCATCTGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:119:77 ATAATTTCTCCCTCTCACCAGTTAACTCTCTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:85:666 GGTTAGGAGTGGGACTTCTAGGGGTTTTAGTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:86:604 GTGAGGCCCCGCTTTCACGGTCTGTATTCGAG U2 0 0 1 chr1.fa 91625562 F .. 31T 32A >CMLIVERKIDNEY_7:1:1:97:764 GTTGGTATAGAATGGGGTCTCCTCCTCCGGCG U0 1 1 0 chrM.fa 6570 R .. >CMLIVERKIDNEY_7:1:1:92:559 GTAGAAGTAGAGGTTAAGGAGGGTGATGGTGG U0 1 1 0 chrM.fa 5318 R .. >CMLIVERKIDNEY_7:1:1:69:159 GGGTGAATCACTTTATAACAGTGGCTGATTCA R2 0 0 2 >CMLIVERKIDNEY_7:1:1:79:757 GAAAGATTGACTAACGAGAGATTCTAGATAGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:117:23 CCTTCGTCCTCTAGGAGCATCATCACCTATTG U2 0 0 1 chr18.fa 72184599 F .. 5A 11A >CMLIVERKIDNEY_7:1:1:95:932 TTTGCTTTTTAATATACAAACCATGGTTTTTT U0 1 0 0 chr2.fa 111638510 F .. >CMLIVERKIDNEY_7:1:1:84:209 GTGATTATGTGTTGTCGTGCAGGTAGCGGCTT R2 0 0 2 >CMLIVERKIDNEY_7:1:1:123:111 TTGTATTCCAAGAGAGTTGTTCTCCACTTACC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:92:233 GATCTTCTTAAAGTACGACCCACATTTGTTTT U0 1 0 0 chr11.fa 77089589 F .. >CMLIVERKIDNEY_7:1:1:83:457 GAAGCTCGAGGCGAAGACAGGAAAAAAGACAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:122:368 CTGCTAAGAACGTTACAACGACCCCCAAAGCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:55:715 GGAAGGCCACCTTTGGTGGTGGTTTCATTCTA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:74:738 GCAAGAACAACATTCCCATGGTCAGTGACTTC U0 1 0 0 chr11.fa 30315626 R .. >CMLIVERKIDNEY_7:1:1:94:782 GTTTTATGCCTCTGTGCCTTCGTTCATGCTCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:110:579 CAATTTTCTTCTCCACGTTCTTCTCGGCCTGT R0 6 1 3 >CMLIVERKIDNEY_7:1:1:109:750 GCGGCTGCTGGCACCAGACTTGCCCTCCAATG U1 0 1 2 chrY.fa 10645457 R .. 31A >CMLIVERKIDNEY_7:1:1:106:729 GTTCCATGTGAAAAGCAGTTGAACTTGGGACA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:80:554 GTCGGGTCTGCGAGAGCGCCAGCTATCCTGAG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:74:374 ATGAAGCGAACAGATTTTCGTTCATTTTTGTT U1 0 1 4 chrM.fa 8521 R .. 4C >CMLIVERKIDNEY_7:1:1:99:523 GTATAGCCAAGCAAGCGTAGCATGATTTGCCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:64:542 GACATACAGACCAATGAAATAGAATTGGGTGC U0 1 0 0 chr14.fa 63907003 F .. >CMLIVERKIDNEY_7:1:1:96:394 AAAATATTTCACCACGACCAAGAAAGGGGAGA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:93:800 GTCTGCACGAACTTATAGATTTAAGATTTGTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:73:424 GCATTTTGGACTCTTATTCTTCATCTCAGAGA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:121:489 CCCCGATGCATACACCACATGAAACATCCTCT U1 0 1 2 chrM.fa 7233 F .. 31A >CMLIVERKIDNEY_7:1:1:122:285 CTGTGTTGAGCACTGGTCACATTGACGGCGCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:110:443 TAGAAAACAACCGAAACCAAATAATTCAAGCA R0 2 0 0 >CMLIVERKIDNEY_7:1:1:124:759 CTTAGGTTCCTTGTTTAGGGCATTTGTTTTCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:112:255 CGCCTTATGGCGCGGGGTTGACGGGGCCCCCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:106:756 GATTGGTCCAATTGGGTGTGAGGAGTTCAGTT U0 1 0 1 chrM.fa 2247 R .. >CMLIVERKIDNEY_7:1:1:120:669 AAATTTTTTACTCTCTCTACAAGGTTTTTTCC U0 1 4 0 chrM.fa 2130 R .. >CMLIVERKIDNEY_7:1:1:83:467 AACTCTTTTAAGTTATGGGAAAAAAAATCTAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:121:713 GGTGTATGCCGTTTTCCTAACACTCACAACAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:96:490 ACATGATTGTTCGACGTTCTTGCAAAAATCTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:95:145 TCGTTCTTTAGTGTTGTGTATGGTTATTTTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:48:459 GGTTTTGTTTTTCTTTTCTCTGTTAGCCACTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:108:224 GGCGGTGGAGGGGATGGTTTTGCCGCCTCATA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:107:470 GAAGAGGCAGGACACGTCATTGCTACCCTATT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:103:526 ACGAACTGGAGTTCAGCTCTTTTGTCAGAAAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:100:27 GGCCAATTGAGTGTTGTAGTTCGCTTCGATCG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:76:879 GTCCAATTATGCATCAGAAACAATAGATAGGT U0 1 0 0 chr7.fa 11828110 F .. >CMLIVERKIDNEY_7:1:1:72:138 GTAAATGTAAAGTGTTGTTTTTAGTGACAGGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:116:474 GGCCCATTAAGTCCCTACTAAGAGGGCGTGTC U0 1 0 0 chr19.fa 3927315 F .. >CMLIVERKIDNEY_7:1:1:120:300 ATTTCCTATTCGCCTACACAATTCTCCGATCC U0 1 1 0 chrM.fa 15565 F .. >CMLIVERKIDNEY_7:1:1:113:164 TGCAAATCGGTCGTCCGACCTGGGTCTCGGTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:116:927 GTGACTACAAAAAGGATTAGACTGAACCGAAT U1 0 1 0 chrM.fa 10374 F .. 26G >CMLIVERKIDNEY_7:1:1:47:178 GTACACTATCAATCACTGAAGCAATTATGCTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:107:908 GAACACGGAAACCTGAAGCGTGGATAGAGATG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:122:944 TGGGAGATTATTCCGAAGCCTGGTAGGATAAG U0 1 3 7 chrM.fa 6640 R .. >CMLIVERKIDNEY_7:1:1:115:150 GTATTGGAGACTTTCATTGCAAAGCACTTACA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:72:128 GGCTTGCCTTATGAGCATGCCTGTGTTGGGTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:97:243 CAAATTATCCTCCACATTCTGCTGCTTGTTTT R1 0 7 0 >CMLIVERKIDNEY_7:1:1:116:179 AAACAACCGAAACCACATAATTCAAGCACTGC R1 0 2 0 >CMLIVERKIDNEY_7:1:1:106:5 TNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN QC >CMLIVERKIDNEY_7:1:1:107:153 GTGGGCTGCGATCATGCCTGGGCCTCTGAATA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:106:220 GCCTGACTGGGGCAAGAAAACAGAGTTTCATC U0 1 0 0 chr20.fa 32979352 R .. >CMLIVERKIDNEY_7:1:1:94:671 AAGTGGCCTTCCCGACGTTCTGCTTCGATGAG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:105:894 TGGGACTACAGGAGAAGATAACCACACTAGGA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:80:741 GGGATCTCCCAAGGAGTCATGTTCTGAGGGAT U1 0 1 0 chr10.fa 102248463 F .. 32C >CMLIVERKIDNEY_7:1:1:122:17 TGCCATCANNNANGACTNNNNNTGTACANNNT QC >CMLIVERKIDNEY_7:1:1:79:659 AAACCGCCTGGGTACTATCTCGCATGTGATTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:57:517 GCTTCAAGATGAAGCTGAACATCTCCTTCTCA R1 0 3 2 >CMLIVERKIDNEY_7:1:1:112:176 GTCGGGTTGCTTGGGAATGCAGCCCCAAGCGG U1 0 1 0 chr11.fa 84872677 F .. 26A >CMLIVERKIDNEY_7:1:1:96:831 GAAAAATCCACGCCTTACGAGTGCGGCTTCTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:90:174 GTTGAGGTTGACCAGGGGGTTGGGTATGGGAG U2 0 0 1 chrM.fa 3573 R .. 1T 2C >CMLIVERKIDNEY_7:1:1:101:510 ATTCGGTTCAGTCTAATCCTTTTTGTAGTCAC U1 0 1 0 chrM.fa 10374 R .. 26G >CMLIVERKIDNEY_7:1:1:115:146 GGAATTTGAAGTAGATAGAAACCGACCTGGAT U0 1 0 0 chrM.fa 3087 R .. >CMLIVERKIDNEY_7:1:1:106:752 GCACAATATTGGCTAAGAGGGAGTGGGTGTTG U0 1 2 0 chrM.fa 10617 R .. >CMLIVERKIDNEY_7:1:1:120:444 ATTGCCCACACACAGATGCGCCTGCTTCCTCT U1 0 1 2 chr6.fa 108432948 R .. 21G >CMLIVERKIDNEY_7:1:1:92:938 GGGGGGGTGATAGGGGGTAGGGGGAAATGGTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:108:261 CAAGGGTATTCTAGCCTGTACCATCTCGCTTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:82:844 TCCGCTTTTGCCATATCTTCAAATTTTCCTTT U0 1 4 7 chr13.fa 29935440 F .. >CMLIVERKIDNEY_7:1:1:108:704 ATTTAGTTGACTCGCCACTCTGCACGGTGGGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:112:549 ATGAGGGAATAACTAGGATTATCTCGTATAGG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:92:215 GTCAAACCAGCCACTGCTTCCCATATCACATT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:48:621 GGTGTGCCTTGTGGTGAGTAGTGGGTTAGGGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:113:92 CTGCATTGGTTTTTTTTCCATTACACCCGTCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:123:234 ATCCACCAGCCTTTGCATTCTCCTCGGAGCAC U0 1 0 0 chr11.fa 2866195 F .. >CMLIVERKIDNEY_7:1:1:68:846 GGTGTGGAAAGTCATGCATGTGGAAGGATCAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:86:889 GTTGGTGAGGTATTGGGGGGAGGGGGTGAATA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:92:445 GACCACATCACAGTGAGCCTGGGAAGTAAATA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:107:540 CTGGAATTACCGCGGCTGCTTGCTTCACCTGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:101:517 ATTCCACCTACTTCAACTCATTATTGACTTAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:67:568 GCGATTTCTAGGATAGTCAGTAGAATTAGAAT R0 2 0 0 >CMLIVERKIDNEY_7:1:1:120:700 GTTGAATTATTTGGTTTCGGTTGTTTTGTATT R2 0 0 2 >CMLIVERKIDNEY_7:1:1:121:342 CCTGCCCCATGGTATCTACGATGAGATCCAGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:797:649 GTTTTGTATGTTCAAACTGTCATTTTATTTTT U0 1 1 0 chrM.fa 5403 R .. >CMLIVERKIDNEY_7:1:1:204:901 GTTTAGTAGTAATCAGTGTGGCTAGAGATTAC U0 1 0 0 chr12.fa 10234229 R .. >CMLIVERKIDNEY_7:1:1:108:514 TGGGGCTTAGAGCACGGGTAAGAGCACGATCA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:112:1000 GTTAACGTAATTGAAGATTCTGGCTCTGGTGG U0 1 0 0 chr21.fa 33847161 R .. >CMLIVERKIDNEY_7:1:1:97:369 CCTTCTTAGGCTATTTCCTTCTGTCCGGACAC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:45:436 GTTAGAGTCAGCTTTTTTTTTTTTTTTTTAAC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:87:307 CCTCAATACTGTCAAGTGCACCTACTTAATAA R0 2 1 1 >CMLIVERKIDNEY_7:1:1:103:629 ATGATGGATTGGCGACACTGTTGAACAACATC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:121:640 GACCAAGCTGGGGCTCAACCAGAACAAGAAGG U0 1 0 0 chr5.fa 529355 R .. >CMLIVERKIDNEY_7:1:1:100:581 GGGGGGTGGCTTCAAACCCGCTTTGGGGGGTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:102:162 TGACGATAACGTTGTAGATGTGGTCGTTACCT R0 2 1 1 >CMLIVERKIDNEY_7:1:1:71:411 GGACAGTGTGTGCTGGTGATTCTTATTAAAAC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:51:560 ATGAACCATAACCAATACTACCAATCAATACT U0 1 1 0 chrM.fa 4719 F .. >CMLIVERKIDNEY_7:1:1:92:129 GTTTATTTCTCTTCCTCCTCCCGTACTAGATC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:122:451 GTTACACCCCCCTCCCCCCATCACGCACGCCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:98:981 GTTTTATGTTGTTAATGTGGTGGGAGGGTGAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:93:211 GTTGGAATTCCACGGGTTATTCTTCCTGCACT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:73:689 AAACTTTTGGCCAAGAGAATGTAGGAGGTTTA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:123:811 CCCCAACCGAAATTTTTAATGCAGGTTTGGTA U0 1 2 0 chrM.fa 2788 R .. >CMLIVERKIDNEY_7:1:1:121:506 GTTTTTGTATTCTGTTTTATTCTGTTTTTGGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:104:69 GAAAAGTACAAATCACAGGCATCAAGAAAAAG U0 1 0 0 chr13.fa 97848101 R .. >CMLIVERKIDNEY_7:1:1:82:882 TGTGTAAGCTAGTCGTATTAAGTTGTTGGCTC U1 0 1 0 chrM.fa 11329 R .. 18T >CMLIVERKIDNEY_7:1:1:112:393 CCTTCCCAACCATCCCACAAAGTCTTAGCTCA U0 1 0 13 chr6.fa 146153153 F .. >CMLIVERKIDNEY_7:1:1:93:154 GTCAGTTGTGGTCCTTAAACCTCTTGGCACCT R0 2 0 0 >CMLIVERKIDNEY_7:1:1:122:250 CTCCAATGGTGATGCCTGGGTTGAGGCTCATG R0 2 0 0 >CMLIVERKIDNEY_7:1:1:98:69 GTTTGGATGAGAATGGCTGTTTCTTCTTCGTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:105:692 CTTGGCGGAATCAGCGGGGGAAGCAGACCGTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:86:627 GCAAGTGGAGGTATCTGGGTCTCCCAAGGCTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:77:694 GCCAAACCCCAAAAACAAAGAACCCTAACACC U0 1 0 1 chrM.fa 349 F .. >CMLIVERKIDNEY_7:1:1:59:595 GAGAAGGCCTTAAAGTACGTCCGCGGGTTGCT U0 1 0 0 chr7.fa 100670211 R .. >CMLIVERKIDNEY_7:1:1:96:135 GTTTTTGTATTCCCTCCTAGGCCAGGCTTAGG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:107:669 CACCTTACTACCAGACAACCTTAGCCAAACCA U0 1 0 1 chrM.fa 1698 F .. >CMLIVERKIDNEY_7:1:1:117:428 CCTAGAAGTCCCACTCCTAAACACATCCGTAT R0 2 1 1 >CMLIVERKIDNEY_7:1:1:237:883 GTTGTTCAGTAACAATTGCTGATACTGCAGCT U0 1 0 0 chr4.fa 111136003 F .. >CMLIVERKIDNEY_7:1:1:121:140 ACAGCTCTTTGGTCATTAGGTACATATCTTGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:97:472 TGCCCACCTCAAGGTTAATAAATAAGGTTGTA U1 0 1 0 chr11.fa 63283050 F .. 14T >CMLIVERKIDNEY_7:1:1:97:514 CCCCGCGGATTCATTGAACTAGGACTGTCCCG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:95:284 CGACTGTGAGTGCGTTCGTAGTTTGAGTTTGC U0 1 0 1 chrM.fa 11748 R .. >CMLIVERKIDNEY_7:1:1:110:46 GTTCTTGCGGCGGGTCTTGGCTGTATTTTCAC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:104:277 ATAACAGCCCCGGCCCCAAATACCCCCACTCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:61:438 TATGGTTGATATTGCTAGGGTGGCGCTTCCAA U1 0 1 0 chrM.fa 9048 R .. 32T >CMLIVERKIDNEY_7:1:1:98:561 CCTGAGAATAGGGGAAATCAGTGAATGATGCC R1 0 3 0 >CMLIVERKIDNEY_7:1:1:112:932 GCAAAATCTTAGCATACTCCTCAATTACCCAC R0 2 0 0 >CMLIVERKIDNEY_7:1:1:422:223 AGAACTTCTCCGCTCAGCGCTGGATGAGTTGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:94:814 TGTAATTCCAGCTACTAGGGAGGCAGCGATAG U2 0 0 1 chr3.fa 171498044 R .. 3G 6T >CMLIVERKIDNEY_7:1:1:81:431 GCCTTGTACTGTTCCCTCTACCCCCTTCAGTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:111:924 GGACAGCTCATGAGTGCAAGACGTCTTGTGAT U0 1 2 1 chrM.fa 8051 R .. >CMLIVERKIDNEY_7:1:1:99:735 GCCGTTTCTCAGGCTCCCTCTCCGGAATCGAA U0 1 0 1 chr21_random.fa 1677674 F .. >CMLIVERKIDNEY_7:1:1:100:952 GCCTCTTGGTTCATCACAACTGCAGCAACTGA U1 0 1 0 chr16.fa 20269487 R .. 6G >CMLIVERKIDNEY_7:1:1:72:744 GTTGTTTTCTATTAGACTATGGTGAGCTCAGG R0 2 0 0 >CMLIVERKIDNEY_7:1:1:106:174 CATCACCCTCCTTAACCTCTACTTCTACCTAC U0 1 1 0 chrM.fa 5322 F .. >CMLIVERKIDNEY_7:1:1:110:581 TCGATTCCTTCCTTTTTTGTCTAGATTTTATG R0 3 0 0 >CMLIVERKIDNEY_7:1:1:121:457 CTTCATTTGCATAGGAGTATAACTTTGTAACT R0 11 33 31 >CMLIVERKIDNEY_7:1:1:73:245 GTGACTTTTATTTTTTAACCAAGAATAATCTA R0 2 0 0 >CMLIVERKIDNEY_7:1:1:106:42 AGGGAGCCGGGGAGGCCGCGGTTCCGGGTGTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:116:611 CGCGTACTTCGTTGTAGCTCACTTCCACTATT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:62:806 GAAAAACGAGCTTTGCATCTTCAGGCCAGTAA U0 1 0 0 chr7.fa 29646071 R .. >CMLIVERKIDNEY_7:1:1:73:183 GTGGCTGGGTCCTGGCCCGCAGCCCACCCCCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:123:118 CCCGGCTTGAGCCGCCCACTTCAGGCTCTTCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:65:596 GGAATGAGAAGCGAAAGAAAGAAAGAACACGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:60:664 GGGTTTTTTCGGTAGTGGGGGTTGAAATTGAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:84:493 GGGTTCGATTCCTTCCTTTTTTGTCTAGATTT R0 3 0 0 >CMLIVERKIDNEY_7:1:1:123:321 CCTTTGACGCGTCCTGGTTTCTTACCCTCCTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:85:260 CTTTTTCAGGTTAGTACGCGTTCTTCTGTCAG U1 0 1 0 chr18.fa 12020678 F .. 19T >CMLIVERKIDNEY_7:1:1:77:494 GGCCAGTACTTCTCATTTGTATTCTGCAAAAA U0 1 0 0 chr11.fa 117850740 R .. >CMLIVERKIDNEY_7:1:1:124:449 ATTTATTCTAGGACGCTGGGCATGAAATTGTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:121:787 GGCGAGTCAGCTAAATACTTTGACGCCGGTGG R0 2 2 0 >CMLIVERKIDNEY_7:1:1:116:96 GAACTCTTTGGACACTAGGATTAAAACTGTCA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:86:421 CAGGGCACATTCACTCCCTGGCGCAGTCTTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:93:732 GTAAAGACTCAAAATAAAAACCTAGACCATTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:89:86 GTGGCTGTTGTTGTTTTGTTTTTATAGATTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:114:905 GAAGCAGGCCGGATGTCAGCGGGGTGCGTTGG U2 0 0 1 chrM.fa 4827 R .. 5G 13T >CMLIVERKIDNEY_7:1:1:115:110 AAGATTGAGAGAGTGCGGAGCAGGCAGTTGAG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:776:651 GGGGGGTGTTCAGTTATCTTTTTGGGGTTTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:162:481 GCGTCATATGCTTATTTCAATCTACCACTATC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:69:519 GTCATCTTGAACTATCTCTTAAAAGTTCTTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:95:444 GTCACATCCCCGACGCTCCGGCCCGTGACCCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:118:723 TTAATTGTCAGTTCAGTGTTTTAATCTGACGC U1 0 1 0 chrM.fa 2345 R .. 10C >CMLIVERKIDNEY_7:1:1:217:543 GGAAGAGATAGGATGAGGAAAAATGTGTATAA U0 1 0 0 chr21.fa 41513196 F .. >CMLIVERKIDNEY_7:1:1:65:545 GTCGTCTTAGCGAGTCAGTGAGCACCTCTAAG U0 1 0 0 chr5.fa 175706486 F .. >CMLIVERKIDNEY_7:1:1:122:511 GTCATCAACAACCGCCTACTCACCACCCACCA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:72:927 GCTAGGATGATGATTAATAAGAGGGATGACAT U0 1 0 0 chrM.fa 10324 R .. >CMLIVERKIDNEY_7:1:1:113:622 AAAGTGTAGCCTTGGGACTGGATTTTTGCTTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:97:940 TTTCAATCGATGGGAGCGGGGTAGCTGGTTTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:109:27 GTTTGCATGAGGATGCCTGGGCCTTCGCGGGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:94:445 GACAACATCCCTGGGACCCTGGAAACCAAATA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:99:413 AAGCGGTTTGGTTTAGACGTCCGGGTTTTACA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:152:500 GTGGCCTTGGTATGTGCTTTCTCTTGTTACAT R1 0 2 0 >CMLIVERKIDNEY_7:1:1:122:279 CTGTGGAGGTAGGAGATGGGGTAGGGGGCGGG U2 0 0 1 chr22.fa 40632456 R .. 1T 4T >CMLIVERKIDNEY_7:1:1:122:625 CTGAACAGTTCCTTTTTCAGAGACATAGATAC R0 8 3 3 >CMLIVERKIDNEY_7:1:1:52:661 GCTTTGCTCTATAACTGTCGACTTGGGCTTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:98:362 ATTTCTCTTCCCCGAGGAAAGGGTATCCGCCT R0 4 5 2 >CMLIVERKIDNEY_7:1:1:123:520 CAGACTACCACACCTCACAAAAATGGTCAGTA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:81:599 GGGAAGGTGGCTCGGGGGAGTTGAAAAGAACT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:105:540 GGGGGATTAGCGCTGTGGGTGTCAGCAGGCTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:112:665 ATTTAGTCAGTGAATACGGAGTGCCTGGGCCC U2 0 0 1 chr11.fa 8783965 R .. 1T 9A >CMLIVERKIDNEY_7:1:1:89:596 CTACTACTCACTCTCACTGCCCAAGAACTATC U0 1 0 1 chrM.fa 11289 F .. >CMLIVERKIDNEY_7:1:1:81:425 AAATAAATGAATAGGCCAAGAAGATCTACCAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:116:122 CTGTGTTCAGACCGGCGTAATCCAGGTCGGTT U2 0 0 1 chrM.fa 3068 F .. 4A 16A >CMLIVERKIDNEY_7:1:1:38:596 GTTTTCATCTTCGGTTTACAAGACTGGTGTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:102:882 ATCTGCTTCAGAGGAAAATGCACACTATTCGG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:100:130 GCCAAAGCCTCCGATTATGATGGGTATTACTA R0 3 1 1 >CMLIVERKIDNEY_7:1:1:80:502 GCAAATAACAGCCACCAAGGAAATACAGGTGG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:122:904 GGCTGGGACTGAGGCGATCCTGCGATCCCCTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:106:961 TTTGAAGTTCTTGTCAAGTCACAGTGAGAAGT U0 1 0 0 chr16.fa 11701351 R .. >CMLIVERKIDNEY_7:1:1:92:811 TACTAATCTCCCTACAAATCTCCTTAATTATA U0 1 1 1 chrM.fa 11053 F .. >CMLIVERKIDNEY_7:1:1:73:648 GTTGGGTTGACAGTGAGGGTAATAATGACTTG U0 1 0 1 chrM.fa 2402 R .. >CMLIVERKIDNEY_7:1:1:93:10 GNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN QC >CMLIVERKIDNEY_7:1:1:81:927 TGTAACTTTAAGGCAGGAAAGACAAATTTTAT U0 1 0 0 chr11.fa 65027835 F .. >CMLIVERKIDNEY_7:1:1:115:462 ATTGGCTGTACACGCGATAATCTATCTTTCAG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:112:488 CGGGGAGAGGCTTACTAGCAGCGTGCAAACGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:92:498 GCGGCACTCCGCACGCCATGCCGCTTTACCCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:96:387 GATTTTTTTACCGATGGCACACACTTCAGGGG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:114:788 TAGATGGAGACATACAGAAATAGTCAAACCAC U0 1 0 0 chrM.fa 9946 R .. >CMLIVERKIDNEY_7:1:1:100:115 GTGGCTGTCTACACGCCCAGCATTGGGCAGCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:836:667 GTTTGTATTATGTAATCAAAATAAGGAGCTTG U0 1 0 0 chr14.fa 30178778 F .. >CMLIVERKIDNEY_7:1:1:408:176 AGGCTCAGAAAAATCCTGCGAAGAAAAAAACT R0 2 0 0 >CMLIVERKIDNEY_7:1:1:118:447 ACGAAATAGTGCACCGCATGTTCATTGCTATG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:91:465 GCCGGCGTAAAGAGTGTTTTAGATCACCCCCT U0 1 0 0 chrM.fa 932 F .. >CMLIVERKIDNEY_7:1:1:60:677 GGCCGACTCAGGTAAAAAATGAGTGCGTGCTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:107:562 GCGGCGCCGCCGCATTGGTTTTTTTCTCCTTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:105:567 CCTCCTGCTAACTCCTAGCTGACTCAGCATAG U0 1 0 0 chr11.fa 33687265 R .. >CMLIVERKIDNEY_7:1:1:114:152 CTATTTACCATATCATGTACCCGCAGCCACCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:95:791 TTTCTGTTCTTGACCTCGTCCTCTTGCTGTCA U0 1 0 0 chr3.fa 65409523 F .. >CMLIVERKIDNEY_7:1:1:122:35 CGTTTCTGTCTCTCTCACTTTGTCTTTTCTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:52:612 GGTCATTCCTGGCTGTTTACTGACCAGGGGCA U1 0 1 0 chr10.fa 6340817 R .. 2T >CMLIVERKIDNEY_7:1:1:87:437 GTTTAGTAATGAGGTTTGAGGGGCTTTCTTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:97:947 TAAAAGTATAAAGTTCACAATATTCTATGTTA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:91:314 ACCTCGCCATGGCCATACACTTATATGCTGGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:203:882 AAAAGAACCATTTGGATACATAGGCATGGTCT R1 0 2 2 >CMLIVERKIDNEY_7:1:1:77:396 GCTTTGTGATGAGTGCAGGGATTTCTACTTTT U0 1 0 0 chr6.fa 160558371 F .. >CMLIVERKIDNEY_7:1:1:123:801 AAACATACCGGCTGGGCAAAAAGGCCTTCGAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:109:658 GTCGTCATCTCCTCCTGTACAGTTGGCCTATT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:279:788 GGAATATTGAAGTGGAAATGATGAGCTGAGAT R2 0 0 15 >CMLIVERKIDNEY_7:1:1:75:515 AATTACCCCCATACTCCTTACACTATTCCTCA R0 2 0 1 >CMLIVERKIDNEY_7:1:1:51:507 GAGTTGGGGGGGGCACGGCCATAGGGGCATTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:100:701 GGGGTGTTAGTGATCTTTGCTTTTGTTGTTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:85:590 ATTAAGGTGAAGATAATTACTACTCCCCCGCG U1 0 1 0 chr16.fa 82661071 R .. 14G >CMLIVERKIDNEY_7:1:1:96:374 CAGCAACATGCTTTAACCCCATTGTATGTTTG U0 1 0 1 chr1.fa 25042349 F .. >CMLIVERKIDNEY_7:1:1:94:424 GCTCACTGCAACCTCCACCTCCCAGGTTCAAG R0 255 255 255 >CMLIVERKIDNEY_7:1:1:88:918 GGGGAAGCTTGTTGTTATTTTGGATTCGAATT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:85:410 TTCGATAATAACTAGTATGGGGACAAGCCGTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:114:514 AGCATGTCCAGTATGAGTGGTGGATGGGGAAT U0 1 1 0 chr5.fa 178974452 R .. >CMLIVERKIDNEY_7:1:1:100:897 GTTTCACCATGTTAGCCAGGATGGTCTCGAGA R1 0 127 255 >CMLIVERKIDNEY_7:1:1:68:439 GTTTGGTGAATTCTGCTTCACAATGATAGGAA U1 0 1 1 chr1.fa 91625409 F .. 2C >CMLIVERKIDNEY_7:1:1:93:573 CTTTGATGAAAAATCTAAGGAGGGTAAAGCCA U0 1 0 0 chr8.fa 91706305 F .. >CMLIVERKIDNEY_7:1:1:90:384 GGACTCTTTCGTTGGTGTACAGCTTTATGAGT U1 0 1 0 chr17.fa 23551581 R .. 32A >CMLIVERKIDNEY_7:1:1:80:617 GGGCCCAAGATGCGTGACAAGGTGAAGAGAGG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:109:945 TAAAAAAAAAAACTTAAAAAAAAACATTAATT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:123:818 CACAGTTCTAATTCTACTGACTATCCTAGAAA R1 0 2 0 >CMLIVERKIDNEY_7:1:1:106:303 CTTGGTTACAATTACTCGTTATTAACTCCAAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:78:627 GGTGAAGATGGTTAGGTATACGGAGGGGCCTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:123:242 GAATAGGATTGCGCTGTTATCCAAAGGGTAAC R2 0 0 4 >CMLIVERKIDNEY_7:1:1:105:66 CCGCAGTCGTGCGCTGCGGTAAGCCTCTGTGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:106:454 CAGGAATTCAGGTCTCTGCTATACATATCTGG U0 1 0 0 chr12.fa 121729590 F .. >CMLIVERKIDNEY_7:1:1:796:641 GAAGTGTTTGGGCCTAATATGGGCGTTGAATT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:121:306 CAGGCTGTTCACAGTTAACAGGAGTTGCACTT U0 1 0 0 chr3.fa 69179321 F .. >CMLIVERKIDNEY_7:1:1:94:139 CTGGGGGCCCAGAGGAAGTAACTGATGGCCTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:66:445 TGGGCTAGAACCGGCCTCACAGCCGCCTCCTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:112:127 GGACTGCTCTTGCGTCCTCTCTTTCTCGTCAC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:232:878 GGCTGCATGAGCTCTCTGTCATATTTCTGTAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:762:686 GTTCCTCTTTGGCTTGCATCTCATTGCTAAGA U0 1 0 0 chr2.fa 11274683 F .. >CMLIVERKIDNEY_7:1:1:116:453 TGATGGATAAGATTGAGAGAGTGAGGAGAAGG U0 1 1 0 chrM.fa 4926 R .. >CMLIVERKIDNEY_7:1:1:96:936 TAGCGATGAGAGTAATAGATAGGGCTCAGGCG U0 1 1 3 chrM.fa 13552 R .. >CMLIVERKIDNEY_7:1:1:88:530 CTGGTAATCTATCCACTAGGATGAATGCAAGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:66:839 GAAAGGATAAGGATGCTAAATTGCGTCTGATT R2 0 0 2 >CMLIVERKIDNEY_7:1:1:86:219 TAAAAACTAAGTTATCCAGTTAAGACTTAAAA U0 1 0 0 chr17.fa 24607565 R .. >CMLIVERKIDNEY_7:1:1:88:748 GTTTGATTTTGTTTTTTGTTTTGTTGTGTGTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:87:842 TTCCACCAATCACATGCCTATCATATAGTAAA R2 0 0 2 >CMLIVERKIDNEY_7:1:1:85:633 AAGAGGCGGAGGGGGAGCTCAGTGATGGTGAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:103:608 ACGCAACTGAAAAGACAAAAAATCTCTCTCGA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:151:415 GGAGATCCTGGCGGCCACCGCCTCCCAATGGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:108:751 GCACATGATTGGATAAGAGGGAGCGGGCGATG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:277:763 GGTTTAGAGCCGAATCCGATTGAAGAGTCATT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:69:854 GAAAACCTCATTTGATATTGACACAATCATTA R2 0 0 2 >CMLIVERKIDNEY_7:1:1:44:538 GTTTTTTAAAAATGTTCGTATATTTTTTAGCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:92:217 GTGTAGGATTTCATCGCAAGAAACCTACTCTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:92:35 GTTGGGTTGACAGTGCGGGTAATAATGCCTTG U2 0 0 1 chrM.fa 2402 R .. 5T 17T >CMLIVERKIDNEY_7:1:1:365:762 GAAGGTTTATGGATGCGGTTGATTGGGTGAGG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:72:576 GGTTAAGGGTCACAGTTGGGGCAGGTTTCTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:150:377 GTTAAAATAGCCACTTTAAAATCTTTGTTAAT U0 1 0 0 chr3.fa 60778920 F .. >CMLIVERKIDNEY_7:1:1:101:286 ATGAGACGTGGCGATCCCTCTGGCCTTTTTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:111:88 GCTATCTTCATCACTTCTATCATTACCTAAAC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:116:9 GNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN QC >CMLIVERKIDNEY_7:1:1:109:869 TTGGTACTCAGGGCTGATGTCGTCAAGTGAGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:79:787 GCTCTGCACACCTCCTTTCTACCCCCAAAGTA U0 1 0 0 chr9.fa 80112847 R .. >CMLIVERKIDNEY_7:1:1:102:751 GTCATCCCTGATTGATGCTATCTGAATATAGT U1 0 1 0 chr2.fa 44401490 R .. 4T >CMLIVERKIDNEY_7:1:1:76:217 GCTTAATTGGTGGCTGCGTTTAGGCCTTCCAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:799:656 GAGAGATTGGCAGTATATAAGTGGGTTGAAGA U0 1 0 0 chr2.fa 160105166 F .. >CMLIVERKIDNEY_7:1:1:337:423 ATCCATACATTGGGACAGACCTAGTTCAATGA U2 0 0 1 chrM.fa 15205 F .. 1T 2C >CMLIVERKIDNEY_7:1:1:111:368 GGGGGGTTAATGGATGGTCTCGCTAGAGTGGG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:39:589 GACAGAAATCAGGTATTGGCAGGTTTTTCTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:133:580 ACCACCCCAGAAGTGGATGAGACCGAACATCT U0 1 0 0 chr1.fa 36565199 F .. >CMLIVERKIDNEY_7:1:1:116:659 GTCGGTTGTTGATGAGATATTTGGAGGGTGGG R2 0 0 3 >CMLIVERKIDNEY_7:1:1:428:884 GTTACATCGCGTCATCATTGGTATATGGTTAG U1 0 1 1 chr1.fa 559772 R .. 21G >CMLIVERKIDNEY_7:1:1:107:622 CTTAACCTCTACTTCTACCTACGCCTAATCTA U0 1 1 0 chrM.fa 5332 F .. >CMLIVERKIDNEY_7:1:1:85:358 AATCTCATCCCCCAAACAGGGTCTCAATCCAG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:81:889 GAGCAAACCACAGTTTCATGCCCATCGTCCTA U0 1 1 0 chrM.fa 8188 F .. >CMLIVERKIDNEY_7:1:1:121:915 GGCATCTCTAGGCTAATTGCGCTTTCAAGAGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:87:140 GTAGCTTACTGGGTGTGCGCCGGTTGAGGTTA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:72:172 GTCTTTCGCCCCTATACCCAGGTCGGACGACC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:122:595 TGAGCCCAGAGTCTTCTACCAGCTCTCATCTT U0 1 0 0 chr6.fa 49529348 F .. >CMLIVERKIDNEY_7:1:1:454:173 ATAACACTGGACCAGCTGTAAAAGTAAACAGT U0 1 0 0 chr10.fa 13220221 F .. >CMLIVERKIDNEY_7:1:1:120:154 TTGTCACCTGGCGCAATAGATATAGGACACCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:105:244 GCATCAGTGAGTGGAAGCGGCGGTCCGCACCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:116:834 TTTGTTTTTACTTGTTGTTTACTTGAATTTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:89:547 AACTTCCAGGAATTGACTTATTTAATTAAGAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:83:923 TTTGGCATCCTTGGGGGTTTATGAACTAATCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:90:407 ATGAACACAGACAGTTAACTCTTTCATAACTG U0 1 0 0 chr5.fa 74703763 R .. >CMLIVERKIDNEY_7:1:1:115:68 ATTTCCTGGCCATTCTAACTCGGCGGCGCCCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:62:240 GGTGATATAAAATCTAGACTATGTTGGTTCAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:107:188 GGGGTTCTCACTCTGGAGGTCCACGGAGAGCA U1 0 1 0 chr11.fa 64655563 F .. 29T >CMLIVERKIDNEY_7:1:1:124:745 AAACAAGGCTCATCTCCACCACCTCCACAGTC U0 1 0 0 chr5.fa 10486752 F .. >CMLIVERKIDNEY_7:1:1:101:160 CATGTATTTGTCAAGAAACTTCTTATCTTCAT U0 1 0 0 chr11.fa 113821923 R .. >CMLIVERKIDNEY_7:1:1:97:768 GGCCAGGGCCCTCCTAATTCGGGGGCAGGGGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:90:130 AAGACACTCAGGATCGCACTGCCAGATTACTT U2 0 0 1 chr10.fa 120822498 R .. 11T 16A >CMLIVERKIDNEY_7:1:1:852:650 GCTTGTTTCAGGTGCGTGTTAGTGGGAGGGGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:60:445 GGATTATACCGTATCGAAGGCCTTTTTGGATA U2 0 0 1 chrM.fa 9429 R .. 2G 25G >CMLIVERKIDNEY_7:1:1:121:846 TCTCAAATGAGTTTTCATTTTTCTTGTCTTTT U0 1 0 0 chr12.fa 44628527 F .. >CMLIVERKIDNEY_7:1:1:83:748 ATTGCAGCCCTAGTAGCACTCCTCCTCATATT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:95:779 ATCGTCTACATTCATGCTGGAATGGAAACGAC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:92:951 TCTCAGGGTTTGTTATAATTTTTTATTTTTAT U0 1 1 0 chrM.fa 8490 R .. >CMLIVERKIDNEY_7:1:1:95:824 GCAGGATCCTGCGACTCAACGTGCAAGATCGG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:84:204 CCTCATTCTTACCTGAATCGCCGGACATACAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:115:592 GTCTGTGTCCTGGACCTTTTAGAATTTTTTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:104:701 AGAAGAACTAATGTTAGTATAAGTAACATGAA R0 3 1 3 >CMLIVERKIDNEY_7:1:1:48:798 GTTGTTTGGTGTGTGTGGGTGTAATGCTACGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:95:89 GCTTAGCATCGAGTTCCTATACATCCAATGAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:813:705 GGAGGAAGTGGTTGTGAGGGGGAGTGCTGTCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:74:959 GTTGATTTTATGTGATTTTTTTGCGTCGTAAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:121:823 ACAAGCCCTAGTGATACTCATAGCACCTGTCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:105:121 TAGCATCATCCCTCTACTATTTTTTAACCAAC U2 0 0 1 chrM.fa 10862 F .. 13C 32A >CMLIVERKIDNEY_7:1:1:123:43 AATAAGGCCTACTTCACAAAGCGCCTTCCCCC U0 1 0 0 chrM.fa 3142 F .. >CMLIVERKIDNEY_7:1:1:66:173 GGGTTTTCTTCCTCCTTAACCAAGTTGAGAAC U0 1 0 0 chrX.fa 19287739 R .. >CMLIVERKIDNEY_7:1:1:72:946 TAAAAACATACTTTTAGAAGAAAAAAGATAAA U0 1 0 0 chr11.fa 65023774 F .. >CMLIVERKIDNEY_7:1:1:124:410 ACGCCCTCCTACTCATCTTCCTTATCTGCTTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:92:113 GAAAGACAAACTCGAATTATGGATTCAGCTCA U0 1 0 0 chr6.fa 45037506 R .. >CMLIVERKIDNEY_7:1:1:105:583 CCATCTAAGGCTAAATACCGGCACGAGACCGA U1 0 1 0 chr11.fa 84872719 F .. 5T >CMLIVERKIDNEY_7:1:1:96:179 TCTATTCCTACTGTAAATATATGGTGTGCTCA R0 2 5 1 >CMLIVERKIDNEY_7:1:1:105:194 GCCCCTACTCCACCCCCTGCCCTTTCTAACCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:109:962 GGAGAAGGGGCGGAGCGGCAGTGGGCGGTTGG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:788:646 GGGTTAGCGATGGAGGTAGGATTGGTGCTGTG U0 1 0 0 chrM.fa 14361 R .. >CMLIVERKIDNEY_7:1:1:67:799 GCCAAGCGTTCATAGCGAAGTCGCTTTTTGAT U1 0 1 0 chr1.fa 91625457 R .. 14G >CMLIVERKIDNEY_7:1:1:90:183 CGGGTTCACACTTGCCACCTGCAGGCATCGCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:114:541 TTACAGTAGGAATAGACGTAGACACACGAGCA R0 2 1 2 >CMLIVERKIDNEY_7:1:1:73:92 TATGATTCCCTGGATTATGCAAAGACAAATAC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:294:703 GAGGAGGTTAGTTGTGGCAATAAAAATGATTA R0 2 0 1 >CMLIVERKIDNEY_7:1:1:115:194 GGGTGGGTAGCCGACGTCGCCGCCGACCCCGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:100:841 TGGGAGTTTGAGACCATCCTGACCAACATGGA R0 11 255 255 >CMLIVERKIDNEY_7:1:1:155:680 AAAAATTAGCCGGGCGAGGTCGCAGGAGCATG R2 0 0 2 >CMLIVERKIDNEY_7:1:1:117:399 AGCTTAGCATATTTTGAGTTGCTATGCTACAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:98:323 ATGGCTGCTACGTCATCAGCCATTTCTCACCG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:255:764 GGAGTCATAAGTGGAGTCCGTAAAGAGGTATC U0 1 0 0 chrM.fa 11378 R .. >CMLIVERKIDNEY_7:1:1:82:909 GAAGTTTGTCGCTGATGGCATCTTCAAAGCTG R2 0 0 3 >CMLIVERKIDNEY_7:1:1:202:405 GTGCCCACTTCCTATAAAACATATCAAGCCGA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:700:423 AAGACTTCCAACAGCCATTGCTGCTGACCAGA U0 1 0 0 chr17.fa 34659015 R .. >CMLIVERKIDNEY_7:1:1:192:890 GTTCTGTGATTTAATCTGACGCAGGTTTATTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:237:899 GTAGAATTAGAATTGTGAAGATGATAAGTGTA R0 2 0 0 >CMLIVERKIDNEY_7:1:1:122:911 CCAACAATGACTAATCAAACTAACCTCAAAAC U0 1 2 0 chrM.fa 8662 F .. >CMLIVERKIDNEY_7:1:1:60:452 GGGGGCTCGAATGGTTCGTGAACTCTTTGTAA U1 0 1 0 chr7.fa 102793758 F .. 30A >CMLIVERKIDNEY_7:1:1:519:403 AACCAGACTCAGCGAGAAGTCTTTTTTGAGAG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:90:532 CACCTGAGCTCACCATAGTCTAATAGAAAACA R0 2 0 0 >CMLIVERKIDNEY_7:1:1:99:579 CCGCCTTTTCATCAATCGCCCACATCACTCGA U0 1 0 0 chrM.fa 14929 F .. >CMLIVERKIDNEY_7:1:1:115:402 GTCTTTTGCCCTTAACAGCGAAGCAACTTCTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:98:855 AAGAATGATCATCTTCCCAGGGTGTTCTCTTA U0 1 1 0 chr2.fa 8741416 F .. >CMLIVERKIDNEY_7:1:1:54:669 GTTGGGTCTTTGAGTGACAATGAGGTTGACGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:649:257 AGCCTACAGTGACTATTCACACTGTAACAAGA U0 1 0 0 chr1.fa 10133197 R .. >CMLIVERKIDNEY_7:1:1:73:211 GTTGATTTGGTTAAAAAATAGTAGAGGGATGA U1 0 1 1 chrM.fa 10867 R .. 8C >CMLIVERKIDNEY_7:1:1:107:88 TAGCATGGCTTTTGCAAGAGTCTTCTGTACTA U1 0 1 2 chr17.fa 20326034 F .. 12G >CMLIVERKIDNEY_7:1:1:102:188 AACCATTTACCCCAATAAAGTATAGGCGATAG R1 0 2 3 >CMLIVERKIDNEY_7:1:1:121:401 CAGGCTGGGACCAGCCCCAACTTTGCCTTGGT U0 1 0 0 chr8.fa 143815005 R .. >CMLIVERKIDNEY_7:1:1:254:820 GCCTACTGTAAATATATGGTGTGCTCACACGA R1 0 2 3 >CMLIVERKIDNEY_7:1:1:320:837 GGGGTCTTAGCTTTGGTTCTCCTTGCAAAGTT R1 0 3 1 >CMLIVERKIDNEY_7:1:1:358:642 GTTGGGGATAGGGGTAGGGGGGGTATGTTTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:88:430 GGAGATGCAGAAAGACAAGCATGCGAGCAGCT U0 1 0 0 chr1.fa 182164181 R .. >CMLIVERKIDNEY_7:1:1:89:111 TGATAGCTCTTTCTCGATTCCGTGGGTGGTGG U0 1 0 3 chr12.fa 20595644 F .. >CMLIVERKIDNEY_7:1:1:105:717 TGAAAATGAGGATGAGGAAGATTCCTATTGCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:801:419 AAAAAAAACAGCATGTGCAAACCTGACAGATG U0 1 0 0 chr21.fa 46433697 F .. >CMLIVERKIDNEY_7:1:1:120:791 CATGGGGTTGGCTTGAAACCAGCTTTGGGGGG R0 2 0 0 >CMLIVERKIDNEY_7:1:1:183:589 ACTTTCCAAAAAAACACCTAATTTGAATCACC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:117:835 CTTCTTTTTACTGAGGGGTTACTTGAATGTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:299:842 GTACAATGAGGAGTAGGAGGTTGGCCATGGGT U0 1 0 0 chrM.fa 3310 R .. >CMLIVERKIDNEY_7:1:1:68:617 ACGAGAACTTTGAAGGCCGAAGTGGAGAAGGG R1 0 3 0 >CMLIVERKIDNEY_7:1:1:103:853 GATTGTTGTTTGGAAGGGGGATGCGGGGGAAA U0 1 0 0 chrM.fa 13752 R .. >CMLIVERKIDNEY_7:1:1:76:147 GGTGTTGAGCTTGAACGCTTTCTTAATTGGTG R0 4 4 5 >CMLIVERKIDNEY_7:1:1:120:591 CTACATCGCAAGAAATATGTGAGGGTTATTCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:67:517 GGGAATGCTTTCCAGAGGCTACCTAGCAAGCA U0 1 0 0 chr1.fa 202659542 F .. >CMLIVERKIDNEY_7:1:1:718:654 GGGAGGGGGGCCCTGCTAAGGGAGGGCAGGCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:115:493 TGGCACTTTCTCTATGTCCTCTAGAATTAAGA U1 0 1 0 chr14.fa 34314957 F .. 1A >CMLIVERKIDNEY_7:1:1:71:547 TTGATTAGTCATTGTTGGGTGGTGATTAGTCG U0 1 0 2 chrM.fa 8648 R .. >CMLIVERKIDNEY_7:1:1:81:391 GTAAGAATGGTTGGTGTCAGCAGGGACGGGGA U1 0 1 0 chr20.fa 17891399 R .. 1G >CMLIVERKIDNEY_7:1:1:99:391 CAGGGGGTAGGGGCATGCGATGTTGGTTTTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:222:886 GGCAGGTATTAGGGATAATATTCATTTAGCCT R0 16 9 4 >CMLIVERKIDNEY_7:1:1:42:602 GTGAGCTCAGGTGATTGTTACTCCTGCTGCTA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:90:797 GACGGAAAGCGGGGCCTGTGGGGACTTGTGTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:84:475 AGCCGGAAGCCTATTCGCAGGGTTTCTCATTA U1 0 1 0 chrM.fa 13711 F .. 22A >CMLIVERKIDNEY_7:1:1:83:917 GCTAAATCCACCTTCGACCCTTAAGTTTCATA U0 1 2 1 chrM.fa 1400 R .. >CMLIVERKIDNEY_7:1:1:175:437 ATTTAACACCCACCCACCATTTCTCCCTTTTA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:255:781 GATTGTGTGAGTGCATGTGTTTTTTTTTTTTT U1 0 1 0 chr3.fa 129821553 R .. 32A >CMLIVERKIDNEY_7:1:1:123:736 AGATAACGTTGTAGATGTGGAAGTTTCCATAG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:286:823 GAGATGAAACATGAAAGGTAAGGTCTGACATA U0 1 0 0 chr13.fa 32133657 F .. >CMLIVERKIDNEY_7:1:1:99:894 GCCGAATAAAGGGGCCCTGTGGGTTTACTGTT U1 0 1 0 chr1.fa 2486775 F .. 27C >CMLIVERKIDNEY_7:1:1:757:546 AAAAAAAAAAAAAAAAATTCCCCCGGAATTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:227:885 GAGTTTTAAGCTGTGGCTCGTAGTGTTTTTGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:736:450 ACACGATAAACCCTAGGAAGCCAATTGATATC R0 2 1 0 >CMLIVERKIDNEY_7:1:1:84:216 GTTCTTCAATCAGCCACATAGCCCTCGTAGTA R2 0 0 2 >CMLIVERKIDNEY_7:1:1:230:488 GAAAAATTGAGCCTTGGGACGTGCCCATTTTT U0 1 0 1 chr1.fa 167367634 F .. >CMLIVERKIDNEY_7:1:1:229:454 GCATCAGCGGGGTATCTGTGTCCTACATCAAG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:111:16 CAATAGGNNNNNNGTCGNNNNNNGTCACNNNA QC >CMLIVERKIDNEY_7:1:1:92:630 TGGAAATACTGCTGTTATCGTTTAGCCAACAA U0 1 0 0 chr7.fa 30164850 F .. >CMLIVERKIDNEY_7:1:1:108:53 GGTGGAGTAGATTAGGCGTAGGTAGAAGTAGA U0 1 1 0 chrM.fa 5339 R .. >CMLIVERKIDNEY_7:1:1:784:642 GAAATGCTCAGCCGAGTAAAACCTTTGCGTCA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:67:561 GTTTATCGTGTGAGCACACCATATATTTAAAG R1 0 2 3 >CMLIVERKIDNEY_7:1:1:116:785 CTAGAAATCGATGTCGCCTTAATCGAGATCTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:957:807 ACTGGATGAACTGAGGATCAGCCGGAAGAGAA U0 1 0 0 chr22.fa 37399151 F .. >CMLIVERKIDNEY_7:1:1:78:809 GGGGCTTTAAGGAGTTGGAGCTCTTGGGAGTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:73:732 AGTCCGGACTGGCAGTTGGGCTGGGGGTGTCG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:112:28 CCGCTCACTAAGCTGGGGGTGGCCGACTGCCA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:81:750 TTACACTATTCCTCCTCACCCAACTAAAAATA R1 0 2 0 >CMLIVERKIDNEY_7:1:1:120:246 GCTACATCGTATACTCCACCCCTTACGAGTGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:183:222 AGAAGCTCCCCTCAAGTTTTTCCTCACCCAAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:824:640 GGGCTGGAAACCGGGGGGGAAGGAGGGGTGGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:117:234 CGTGCTCTTGGCTCACTGCAACCTCAGGAGCA U0 1 0 0 chr6.fa 18374777 F .. >CMLIVERKIDNEY_7:1:1:44:472 GCCAGTCATACCTTTCTAGAGGACGATGAAGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:118:984 TACCACTTGCTCATGTCTGTTCAAGAGAGTTT U1 0 1 0 chrX.fa 120010609 F .. 13C >CMLIVERKIDNEY_7:1:1:65:387 GGTGCGACTTGTTTTTATGCCAGAAGAAATGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:169:606 GGAGCATCCGTACTATACTTCACAACAATCCT U1 0 1 0 chrM.fa 15804 F .. 2T >CMLIVERKIDNEY_7:1:1:90:482 GAAAAATCCACCCCTTACGAGTGAGGCAAAGA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:118:997 GGCCTACTCAGGTAAAAAATCAGTGCGAGCTT R0 2 0 0 >CMLIVERKIDNEY_7:1:1:116:457 TCCGAACTAGTCTCAGGCTTCAACATCGAATA R0 2 0 0 >CMLIVERKIDNEY_7:1:1:113:119 GCTGAAGCAGATAGTGAGGAGAGTATAGCCAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:88:504 GCTCCCTGTGGTTCCTGCCATCGACGCCTCAG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:770:478 GTTCAGTCTAATCCTTTTTGTAGTCACTCATA U1 0 1 0 chrM.fa 10369 R .. 31G >CMLIVERKIDNEY_7:1:1:115:757 GGATTTCATCGATGGGCGTGACCGAGTGGATT U0 1 0 0 chr4.fa 5509446 R .. >CMLIVERKIDNEY_7:1:1:723:454 ATGGCAAAGTGCTTCCCCTCATGGCCCCACTC U1 0 1 0 chr7.fa 66461545 F .. 27T >CMLIVERKIDNEY_7:1:1:75:669 GCCGCAACCACTTGCTAGTGATACACTGTATC U0 1 0 0 chr8.fa 91141537 R .. >CMLIVERKIDNEY_7:1:1:71:785 GGGGAGAATGCGTGTTAATGTAGTAAAATAAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:220:571 ATACTTCTGAACACAAGACACACCAAACACAC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:123:557 CCTCAAACTCCTGAGTAGCTCGCATTAAAGCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:79:230 GTGAATCTTCTGGGTGTTATGGCCTTCAAATG U0 1 0 0 chr17.fa 25099472 R .. >CMLIVERKIDNEY_7:1:1:91:385 GCAGTTTGTCGTGGAGTTAGAGAATTAGTACA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:96:156 GCTTGCTAGAAGTCATCAAAAAGCTATTAGTG U0 1 1 1 chrM.fa 11818 R .. >CMLIVERKIDNEY_7:1:1:237:488 GTACACAACACTAGCAAGCATGTAATATTCAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:127:907 GGAGCTTCAATTTTGTCTCTGTGCATACAAAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:422:816 GTAATAAGCCGGGCTTGAATTGTTTGGTTTCG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:86:370 ACACGACACAGTATTGCTCATTGGGTTAGGGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:101:831 TATTATCGAAACCATCAGCCTACTCATTCAAC R0 3 0 0 >CMLIVERKIDNEY_7:1:1:303:819 GGGAGTTTTTGTGGGTGACTGAAATGATCTAC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:96:86 GCTGGGCTTTGGCCTAAAGGTCCTACACCAAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:103:240 CTTTTTAAGTCTGGTTTTGTCTTGTGATAATG U0 1 0 0 chr1.fa 112103657 R .. >CMLIVERKIDNEY_7:1:1:123:637 GGGCGCCTGTAATCCCAGCTACTTGGGAGGCT R0 255 255 255 >CMLIVERKIDNEY_7:1:1:743:406 ACTAGGCCTACTATCCTTCACTCTCACCATAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:255:746 GTTGAAGATTAGTCCGCCGTAGTCGGTGTACT U0 1 1 1 chrM.fa 7912 R .. >CMLIVERKIDNEY_7:1:1:100:996 GAAATTGATATCTTATAATAGTATCCTTAATC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:121:693 TGCCAGGAACCATATCAACAATGGCAGCATCA R0 7 5 4 >CMLIVERKIDNEY_7:1:1:76:617 GTATGTTTGGTGGCATTAAATTGGTTTCTTTA U1 0 1 0 chr8.fa 120326783 F .. 1T >CMLIVERKIDNEY_7:1:1:113:474 CTGAGCCCTTCAGTTTCATAATTGTTTTATCC U0 1 0 0 chr9.fa 2647392 F .. >CMLIVERKIDNEY_7:1:1:90:132 GGAAGAAGTGTGGTTCAGTGGGATGCAAGGAT U0 1 0 0 chr5.fa 179240915 R .. >CMLIVERKIDNEY_7:1:1:116:982 GGTTGAGCTAGGCTTCTACATATTGTATACTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:98:220 CCTTAACAACCTAAAACCCTCATTCACACGAC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:475:902 GTTTAATCAGAGGGCCATTTTTTTTTTTTTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:440:615 ATAAAGTTGAAAAGCTTTTTAAATTTTTAAAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:685:498 AAAAAAATTAAAAAACAGTAGATGTTGGTGTG R0 4 10 35 >CMLIVERKIDNEY_7:1:1:115:421 CTTCACCATTTCCGACGGCATCTACGGCTCAA U0 1 0 0 chrM.fa 9762 F .. >CMLIVERKIDNEY_7:1:1:87:103 ATGCCTTGGGGAGTTGCGCCTGCATGATCCTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:87:796 TTCGGTTCAGTCTAATCCTTTTTGTAGTCACT U1 0 1 0 chrM.fa 10373 R .. 27G >CMLIVERKIDNEY_7:1:1:50:488 GTGAGACCATCGTAAATGGGAAGGCCAAGATC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:106:991 GTGCAGTTGCCATGGTGATTAGAGAAAGGCCG U0 1 0 0 chr1.fa 63761780 F .. >CMLIVERKIDNEY_7:1:1:494:796 GGTGCAGCCGCTATTAAAGGTTCGTTTGTTCA R0 2 2 0 >CMLIVERKIDNEY_7:1:1:121:774 AAAAAATGTATTTAAAAGAAAATTGAGAGAAA U0 1 0 0 chr11.fa 65023379 F .. >CMLIVERKIDNEY_7:1:1:64:458 GTTTTAAGCAGGAGGTGTCAGAAAAGTTACCA U0 1 1 0 chr1.fa 91625511 R .. >CMLIVERKIDNEY_7:1:1:104:112 GTATTGGAGAAGTATAGAAGATCGAAACATAT U2 0 0 1 chr11.fa 65023971 F .. 23A 28A >CMLIVERKIDNEY_7:1:1:243:517 GTAGTATTTAACCTCTCTGAGCTTTAGGTTCC U0 1 0 0 chr1.fa 116799815 R .. >CMLIVERKIDNEY_7:1:1:238:476 GTTTTGTCAGGGGGTTGTGAATGAGTGTGATC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:108:493 CATTTTTGTCCCTTTGGAAGAAGAAATAGGAC U0 1 0 0 chr21.fa 29637790 F .. >CMLIVERKIDNEY_7:1:1:108:447 GGAGGAACAGGCACGCGCCAAAGAGAGAGAAC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:105:764 AGCTGTTCAATCCTCCAACATGCTCTTTCTTA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:445:893 GGGGAGTTGGCCTCTATGAGGGTTTGTCTTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:102:234 TAATGATGTCGGGGCTGTGGGCTCGGAGGAGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:437:778 GGAGAAGAGGAGGTAGTTATTTGATAGTAGAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:487:775 GCTTTCTTTGCAAAGCTCCTTGTTCTGCTGCT U2 0 0 1 chr17.fa 44281632 R .. 8C 31A >CMLIVERKIDNEY_7:1:1:101:75 GGGAAAGTTGAAAAGAACTTTGAAGAGCGAGT U1 0 1 0 chr11.fa 84872770 F .. 28A >CMLIVERKIDNEY_7:1:1:218:892 GGGGAGAAGGGTCTGTGGTAAACGTTAAGAGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:70:410 GCACAGCGAGTTATCTTTATTTTTAATAAAAC U2 0 0 1 chr10.fa 134029388 R .. 20G 26A >CMLIVERKIDNEY_7:1:1:169:494 GTTTTATATATACACACACAAACACACACACA U0 1 3 36 chr6.fa 154801193 F .. >CMLIVERKIDNEY_7:1:1:62:23 TTACAACTCCGCCTCTCGACTCTACCTCACCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:58:462 GGCCAAGGCAGGCGGATCATAGGGACAGGAGG R2 0 0 2 >CMLIVERKIDNEY_7:1:1:239:554 GTTTTTTTCTTCGCAGGATTTTTCGGAGCCTT R1 0 2 0 >CMLIVERKIDNEY_7:1:1:374:847 GTTAGAAGGTAGATCTTGAGATGATGATGTTA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:57:405 GCATGAGTAGGTGGCCTGCAGTAATGTTAGCG R0 2 0 0 >CMLIVERKIDNEY_7:1:1:123:838 GCCAACTATCATTCTGAGGGGCCACAGTGCTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:174:881 GGAAGTTAAATATGAGCCACTGGGTGTACCAG U0 1 0 0 chr11.fa 65026108 F .. >CMLIVERKIDNEY_7:1:1:104:519 CTTAACAGGGAAGAGAGAGGGTGGGGGAGAAA U0 1 0 0 chr11.fa 65025582 F .. >CMLIVERKIDNEY_7:1:1:119:4 GNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN QC >CMLIVERKIDNEY_7:1:1:64:349 ACGTAACGATTCACATGCCAACTAGGATCATA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:117:12 GNNNNTNNNNNNNNNNNNNNNNNNNNNNNNNN QC >CMLIVERKIDNEY_7:1:1:105:429 TTTCTTCCCACAACACTTTCTCGGCCTATCCG R0 2 0 2 >CMLIVERKIDNEY_7:1:1:652:652 GAATTCTGGTAGCCAGTGTTCGGATTTCTCTG U0 1 0 0 chr12.fa 100679059 F .. >CMLIVERKIDNEY_7:1:1:123:824 ACTCACCCTAGCGTTACTTATATGACATGTGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:103:775 GCTGGTTATATTCTTTTCCTCGGTTGTGTATT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:113:957 TTGGTTCTTCTCCAATGTCTCCTTTTGGAGTT R0 8 14 7 >CMLIVERKIDNEY_7:1:1:87:946 GGAAAAGGGCATACAGGACTAGGAAGCAGATA U0 1 1 0 chrM.fa 7685 R .. >CMLIVERKIDNEY_7:1:1:95:449 ATTTTCTAAGAAAGATTGGTATGATGCGAAAT R2 0 0 8 >CMLIVERKIDNEY_7:1:1:117:423 TGTATATATGTTAGGAAATGGAGAGGTATAGA R0 2 0 0 >CMLIVERKIDNEY_7:1:1:73:490 GCCTTCCTGGGGTTTGGAAATAAAACTTCTGG U0 1 0 0 chr1.fa 149235942 R .. >CMLIVERKIDNEY_7:1:1:35:376 GTTGTATTACTTTTTCTCTTCCTTATTTTTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:788:687 GTAAAATGTATGAAGAACATCTGAAAAGAATG U0 1 0 0 chr14.fa 68923516 R .. >CMLIVERKIDNEY_7:1:1:215:876 ATTAAATGGAAGTAATGGAGAAAGGGGAGAGA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:131:591 GTCATAAGTGGAGTCCGTAAAGAGGTTTCTTT U1 0 1 0 chrM.fa 11375 R .. 6T >CMLIVERKIDNEY_7:1:1:698:696 GGAAAATGACACGGCAAATAAATTAGACCTAT U0 1 0 2 chr12.fa 68281517 R .. >CMLIVERKIDNEY_7:1:1:85:156 TTCGTTTGTTCAACGATTAAAGTCCTACGTGA U0 1 2 7 chrM.fa 3035 F .. >CMLIVERKIDNEY_7:1:1:244:114 GTAGTAATATAATTGTTGGGACGATTAGTTTT U0 1 1 1 chrM.fa 10766 R .. >CMLIVERKIDNEY_7:1:1:158:572 GTCGCTCAGCGGCATCCAGCTGTTTGAACGCA U2 0 0 1 chr17.fa 39684369 R .. 5G 11G >CMLIVERKIDNEY_7:1:1:79:472 GTGATGCGGGCTTAGCGATGGGCTCTACTCAC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:173:271 ATATTATTCCGCAGACCTGAAATCACTTAATT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:62:472 GTTATGCCCTTCTCCCATCTCCCGCTCGCCCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:152:483 GCTCTTTCTTCCCCTCTTCATAGTCAGCCTGG U1 0 1 0 chr7.fa 101619836 R .. 21T >CMLIVERKIDNEY_7:1:1:233:521 AATTCATTGAGTTTGAAGATGCTCTGGAACAA U0 1 0 0 chr16.fa 1714560 F .. >CMLIVERKIDNEY_7:1:1:84:631 AAAACTAGGAATAGCCCCCTTTCACTTCTGAG R0 7 3 3 >CMLIVERKIDNEY_7:1:1:396:755 GAAGGAGTTGTAGGGAAGGAACAGGGTGAGTA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:325:752 GTTGGATCATCCCTGTCTTCATCATGCACATT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:122:721 GTTGGCGGGACTGTACGGATGCCCCCTCGCCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:335:638 GAGATTAGAACAGGAGAGACTAGCATTGGAAG U0 1 0 0 chr15.fa 46364605 F .. >CMLIVERKIDNEY_7:1:1:765:629 GGAATTTGAAGTAGATAGAAACCGACCTGGAT U0 1 0 0 chrM.fa 3087 R .. >CMLIVERKIDNEY_7:1:1:430:919 GCATAAGTAGTCTTACAACTTAGGAAGATCAT U0 1 0 0 chr17.fa 24178972 F .. >CMLIVERKIDNEY_7:1:1:123:13 GNNNNTNNNNNNNNAGNNNNNNNNNCTTNNNN QC >CMLIVERKIDNEY_7:1:1:115:340 CCCACACCGAGTGACTTCATGTAGTCATCGAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:186:623 GGTTTCTGCTTTGTCTCATACCTGGCCCACTT U1 0 1 0 chr1.fa 200237171 F .. 15T >CMLIVERKIDNEY_7:1:1:123:844 CTCGGCGTGGTGGCAAGCGGCCGGGACGGCGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:109:238 CTAAGATAGAGGAGACACCTGCTAGGTGTAAG U0 1 1 0 chrM.fa 6351 R .. >CMLIVERKIDNEY_7:1:1:774:643 GTTTGTCGTAGGCAGATGGAGCTTGTTATCAT R1 0 2 1 >CMLIVERKIDNEY_7:1:1:113:409 AAACGGGTGGGGTCCGCGCAGTCCGCCCAGAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:86:461 GTTAGGGTGCCTTGAATAAGAGGGGTAGGTGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:108:19 GTTTGGCTNNACAGAATTNNNATCCCTTNNNA QC >CMLIVERKIDNEY_7:1:1:70:863 GGCGATTTTAGGTCTGTTTGTCGTAGGCAGTT U2 0 0 1 chrM.fa 11583 R .. 2T 32T >CMLIVERKIDNEY_7:1:1:568:782 AAGGATACTAGTATAAGAGATCAGGTTCGTCC R0 2 1 1 >CMLIVERKIDNEY_7:1:1:63:844 GTGATGGCTGGGGGGGGTTGCTTTTAGTAAGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:89:3 TNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN QC >CMLIVERKIDNEY_7:1:1:830:647 GAACGAAAAATTCTAGGCTATATACAACTACG U0 1 0 0 chrM.fa 3377 F .. >CMLIVERKIDNEY_7:1:1:169:878 GTTGGATTGTTCACCCACTAATAGGGAACGTG U0 1 0 0 chr1.fa 91625377 R .. >CMLIVERKIDNEY_7:1:1:54:694 GAAGAAGCCAGACGACAGTCAGGAGCTCAGTA R0 5 6 3 >CMLIVERKIDNEY_7:1:1:74:659 GCTCAGAGCACTGCAGCAGATCATTTCATATT R0 2 2 0 >CMLIVERKIDNEY_7:1:1:121:897 AATTTCTGTTTTTTTGGTGTGGTCTGGGTTGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:78:218 GCATAATATTAAGCAATGTTAAAGGCTTCCAG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:65:216 GCTAAATCCAGAACTGGAAGAAAAGCTGCTGA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:90:726 CTTTGGAAACCTAAGGCATGAAGGGGAATTTA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:89:515 AAAAAACTCTACCTCTCTATACTAATCTCCCT R0 2 1 0 >CMLIVERKIDNEY_7:1:1:674:648 AGCAGATTCACCTACACTGAGCAATTAAGAAT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:79:467 GCAGGCACGCCTCCGTTCCTCGGGTAGATCTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:423:207 AAATGATCAGAACCGACCCAGTGAGAAAGGAA U1 0 1 1 chr17.fa 25536335 F .. 32G >CMLIVERKIDNEY_7:1:1:751:745 GTATTTTCCCTTGAGCACCCCCTCTACCCCCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:440:900 GGGGGATGGGGGTCTGTGAGGTTGTGGGTAGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:218:388 GCAGTGTAGACGTAGGTTGATGTAGCTGTTAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:108:163 GGGGAATTAATTCTAGGACGATGGGCATGACA U1 0 1 2 chrM.fa 8201 R .. 2T >CMLIVERKIDNEY_7:1:1:256:721 GTAAAGTTTTAAGTTTTATGCGATTACCGGGC U0 1 0 0 chrM.fa 3245 R .. >CMLIVERKIDNEY_7:1:1:264:314 AAAAGCTAGCATGTTTATTTCTAGGCCTACTC R0 2 0 0 >CMLIVERKIDNEY_7:1:1:114:384 CCTCCTTCAAATATCCAGGCAGTGTTCAAATT U1 0 1 0 chr17.fa 70361220 R .. 32T >CMLIVERKIDNEY_7:1:1:851:665 GCCATAGGGGTATCATTGCCTTTATTTTCTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:102:29 CGTAGCCCAAACGTTGACCCAAATCTAGTATT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:79:954 GGTTATTTGAGCATGGGGGGACAAAAGTGTAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:275:655 GTGGTTTTTCACTGAAAAGGGGGGTACACCTA U0 1 0 0 chr22.fa 40633150 F .. >CMLIVERKIDNEY_7:1:1:129:388 GTTCTTCTATGGGGTGCTAGATGGGCCCATTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:123:721 ATTGGAGGGACTGTACGGATGCCGCCCCGCCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:77:250 GGGCACTCTCTGCCAGCTCCACAGGGTGTGAT U1 0 1 0 chr2.fa 95414281 F .. 29G >CMLIVERKIDNEY_7:1:1:38:580 GTTACTGGAGGGCTGGGTGCTGGGCGAGGCGG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:280:693 GTAGGTTAGGACCTGTGGGTTTGTTCGGTACT U2 0 0 1 chrM.fa 2759 R .. 7T 28A >CMLIVERKIDNEY_7:1:1:103:761 GGCTAGGGCATTTTTAATCTTAGAGCGAAAGA R1 0 2 0 >CMLIVERKIDNEY_7:1:1:240:467 GATGAGACATGGGGGATGATGTAACCCTTTTC U2 0 0 1 chr12.fa 54894075 R .. 5C 18A >CMLIVERKIDNEY_7:1:1:116:603 TGGGGGCTTTGTATGATTATGGGCGTTGTTTA R2 0 0 2 >CMLIVERKIDNEY_7:1:1:111:512 CGGCTCCAATCTTTTGTTCATTTCTTATTGCT R1 0 4 3 >CMLIVERKIDNEY_7:1:1:68:717 GGGTGGGGGATGTCGGGTGGGTGCACGATACT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:466:836 AAGCGCCTTCCCCCGTAAATGATATCATCTCG U1 0 1 0 chrM.fa 3160 F .. 32A >CMLIVERKIDNEY_7:1:1:86:904 GGAAAATCAGAATAGGTGTTGGTATAGAATGG R1 0 2 0 >CMLIVERKIDNEY_7:1:1:105:295 CCTTTTAACCCCTAGTCAGAGCATTTCAGCCG U0 1 2 0 chr3.fa 50338075 R .. >CMLIVERKIDNEY_7:1:1:95:254 CGGACAGAGACGTAAACAACAGCATAGTGTGC U0 1 0 0 chr4.fa 109759652 F .. >CMLIVERKIDNEY_7:1:1:297:877 GGAAAATTCTCAATGGTTTACAAAAGTGCACA U2 0 0 1 chr10.fa 43201192 F .. 11G 24C >CMLIVERKIDNEY_7:1:1:189:553 GATATTTGATCAGGAGAACGTGGTTACTAGCA U0 1 1 0 chrM.fa 11902 R .. >CMLIVERKIDNEY_7:1:1:476:758 GCACATATGACTTGGTCGTTAACATCCCTATT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:110:918 GGAGATTGTGAGAATTGCTTTACAGGAGAAAG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:63:657 AAGATATTTAGGCTTTATTCAGAAGGCTTTAG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:90:497 GCTACCGCCCGGCGGCGGCTCTACACCATCCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:69:75 TGTATCCGGACCATTGACCTTCCGGCTCTTGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:77:444 GCTACACTTACTTTTTTTCTCCTGTGTTTTCA U2 0 0 1 chr3.fa 192592311 F .. 1T 30G >CMLIVERKIDNEY_7:1:1:104:171 TGTGAGGGGTAGGAGTCAGGTAGTTAGTATTA U0 1 2 0 chrM.fa 10957 R .. >CMLIVERKIDNEY_7:1:1:77:871 TGATAGTCTAACTACTGAGTAAGATCCTCATC U2 0 0 1 chr8.fa 120325808 F .. 13A 20C >CMLIVERKIDNEY_7:1:1:112:504 CTTGAGTGATAGGAAAGGGGTCTGTGATGGGT U0 1 0 0 chr5.fa 150387968 R .. >CMLIVERKIDNEY_7:1:1:111:772 AAGGAATGGGGGAGTGGTGGGGAGGAGAGGTG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:101:152 AGACCCCAAAGTGGATCACACCCGTGTTGCTC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:206:420 GCGACAGCGATTTCTAGGATAGTCAGTAGAAT R0 2 0 0 >CMLIVERKIDNEY_7:1:1:229:422 GTTTATATATCACAGTGAGAATTCTATGATTG U1 0 1 0 chrM.fa 12633 R .. 2C >CMLIVERKIDNEY_7:1:1:270:682 GGGAAGTCCTGTGTCCTTTCAACAGGGGAAAA U0 1 0 0 chr17.fa 362681 R .. >CMLIVERKIDNEY_7:1:1:124:597 CACGCCTGTAATCCCGGCACTTTGGGAGGCCG R0 255 255 255 >CMLIVERKIDNEY_7:1:1:79:50 TTGAGAAGCCGTTTCCTGCGCTGAAGGTTCCC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:71:202 GTGAAATTGACCTGCCCGTGAAGAGGCGGGAC R2 0 0 2 >CMLIVERKIDNEY_7:1:1:75:24 GTTTTTGCTACTGTCTCTGTGCCCTGGGTCTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:151:434 GCTCAGGCTACTGCTAAGGCCTTAGCGTTTTT U2 0 0 1 chr12.fa 51979373 F .. 23G 26A >CMLIVERKIDNEY_7:1:1:57:729 GCAGGGTTGAGTAGTTTTGACAGAGATTTATG U0 1 0 0 chr15.fa 27869625 F .. >CMLIVERKIDNEY_7:1:1:219:427 GTCTGTTAGTAGTATAGTGATGCCAGCAGCTA R0 2 1 0 >CMLIVERKIDNEY_7:1:1:110:282 CCTCTGCCTACTTGTCCCTCCAGATTCAGTTA U0 1 0 0 chr12.fa 2776421 R .. >CMLIVERKIDNEY_7:1:1:79:291 CGGCACCGTGTCCACGTTTTTAGAACCCTTGT U1 0 1 0 chr4.fa 9685390 R .. 18T >CMLIVERKIDNEY_7:1:1:104:495 CTCGCATCAGGAGTATCAATCACCTGAGCTCA U0 1 2 0 chrM.fa 9619 F .. >CMLIVERKIDNEY_7:1:1:190:604 GTCAGGTCTAATCTCACTCAGTCTAGGATAAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:111:428 ATAAAATCCCCCAGTTAGTGTTTGCTTTATCT U2 0 0 1 chr17.fa 71285518 F .. 26A 32A >CMLIVERKIDNEY_7:1:1:108:726 CGCAAGGATGCGCTTTCAAGCACAGAGTAGAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:79:505 TGACTTTGGAAGTCCGTAGTGTCTCATTGCTG U1 0 1 0 chr7.fa 10946281 R .. 2T >CMLIVERKIDNEY_7:1:1:85:810 TTTTTTGCTTCCTTAGCAGCCCTGATAGATTG R1 0 4 3 >CMLIVERKIDNEY_7:1:1:109:227 AATCACATGAGTTCTTCAACATTCAAGACTTT U0 1 0 0 chrX.fa 43490430 R .. >CMLIVERKIDNEY_7:1:1:98:678 CTTGGATTAAGGCGACAGCGATTTCTAGGATA R0 2 0 0 >CMLIVERKIDNEY_7:1:1:105:208 TAATGATGTCGGGGTTGAGGGATAGGAGGTGA U1 0 1 0 chrM.fa 12092 R .. 3T >CMLIVERKIDNEY_7:1:1:81:82 GTTGATAACGCGTTGTGATCTCCTTCTGAAGT U0 1 0 0 chr2.fa 47457660 R .. >CMLIVERKIDNEY_7:1:1:686:588 AACCGACTAATCACCACCCAACAATGACTAAT U0 1 2 2 chrM.fa 8645 F .. >CMLIVERKIDNEY_7:1:1:711:177 GTTTGGATGAGAATGGCTGTTTCGCCAACCCA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:298:731 GTTTGTTTTAATTATGCCTCTTAGGGTGAGAA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:89:880 GAATGTTCCTGTTTACCTTCTTACAAGAGTCT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:122:790 GGTTGGCTTGTAACTAGCTTTGGGTGGTCTTT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:77:900 TGAGAATGTCAAGGCAAAGATCCAAGACAAGG R0 4 1 0 >CMLIVERKIDNEY_7:1:1:86:36 GAAAAATTCTCGGCTATATACCACTCCGCCAC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:409:763 GTTCATACACCTATCCCCCATTCTCCTCCTAT U0 1 0 2 chrM.fa 12071 F .. >CMLIVERKIDNEY_7:1:1:74:448 GCCTCAGAGTACTTCGAGTCTCCCTTCACCAT U0 1 0 0 chrM.fa 9739 F .. >CMLIVERKIDNEY_7:1:1:724:492 AATGCATCACATCTCTTTGGGTACCCTGGCTA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:116:743 ATTCCCCTAAAAATCTTTGAAATAGGGCCCGT R0 2 0 1 >CMLIVERKIDNEY_7:1:1:524:815 AAAAAGGCCTTCGATACGGGATAATCCTATTT U0 1 2 0 chrM.fa 9434 F .. >CMLIVERKIDNEY_7:1:1:412:769 GTTGGATGAGTAGGCTGATGGTTTCGATAATA R1 0 3 0 >CMLIVERKIDNEY_7:1:1:104:866 TGCAGTTAGGTGAGTAAAAAGCAAGGAAGTGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:299:739 GCGTAAACTAGGGTGATGAGTAGTTGGGTGGT NM 0 0 0 >CMLIVERKIDNEY_7:1:1:116:617 CTTGTTTAAAGTAAGTGAACGCTGAACTGCCG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:75:711 GTTTGTGTCCTATGTCACCACCTTCTTTGTGC NM 0 0 0 >CMLIVERKIDNEY_7:1:1:744:491 AGAGGTTCTTCAGCGGAGAGGGTCTCCAGGCA NM 0 0 0 >CMLIVERKIDNEY_7:1:1:82:459 AAGCCTTTAGTCTTTTCCAGATGTAACCTTAA U1 0 1 0 chr11.fa 65028685 F .. 24C >CMLIVERKIDNEY_7:1:1:83:697 TCTCTGTGCAAAAATATCTCTTCCAGCTCGAA U0 1 0 0 chr15.fa 43445867 F .. >CMLIVERKIDNEY_7:1:1:82:193 GGTTTGTTAAGATGGCAGAGCCCGGAGATCGG NM 0 0 0 >CMLIVERKIDNEY_7:1:1:237:454 GTAGACAGAGGTCTGATAAATCCCTAAAAATG U0 1 0 0 chr2.fa 108277990 F .. >CMLIVERKIDNEY_7:1:1:103:189 AACCATTTACCCAAATAAAGTATAGGAGATAG R1 0 2 4 >CMLIVERKIDNEY_7:1:1:218:888 GTTATTATTTGTTTTGAGGTTAGTTTGATTAG U2 0 0 1 chrM.fa 8672 R .. 27G 31G >CMLIVERKIDNEY_7:1:1:571:370 GCGAATATACCTTGTTTGGTCAATGACTTTAC U2 0 0 1 chr17.fa 34211225 R .. 27T 31A ShortRead/inst/unitTests/cases/s_1_sequence.8_lines_illegal_nucleotide.txt0000644000175400017540000000037213001230551030117 0ustar00biocbuildbiocbuild@HWI-EAS88_1_1_1_1001_499 GGACTTTGTAGGATACCXTCGCTTTCCTTCTCCTGT +HWI-EAS88_1_1_1_1001_499 ]]]]]]]]]]]]Y]Y]]]]]]]]]]]]VCHVMPLAS @HWI-EAS88_1_1_1_898_392 GATTTCTTACCTATTAGTGGYTGAACAGCATCGGAC +HWI-EAS88_1_1_1_898_392 ]]]]]]]]]]]]Y]]]]]]]]]YPV]T][PZPICCK ShortRead/inst/unitTests/cases/s_1_sequence.8_lines_read_mismatch.txt0000644000175400017540000000036013001230551027070 0ustar00biocbuildbiocbuild@HWI-EAS88_1_1_1_1001_499 GGACTTTGTAGGATACCCTCGCTTTCCTTCTCCTGT +HWI-EAS88_1_1_1_1001_499 ]]]]]]]]]]]]Y]Y]]]]]]]]]]]]VCHVMPLAS @HWI-EAS88_1_1_1_898_392 GATTTCTTACCTATTAGTGGTTGAACAGCAT +HWI-EAS88_1_1_1_898_392 ]]]]]]]]]]]]Y]]]]]]]]]YPV]T][PZ ShortRead/inst/unitTests/cases/s_1_sequence.8_lines_read_quality_mismatch.txt0000644000175400017540000000036213001230551030642 0ustar00biocbuildbiocbuild@HWI-EAS88_1_1_1_1001_499 GGACTTTGTAGGATACCCTCGCTTTCCTTCTCCTGT +HWI-EAS88_1_1_1_1001_499 ]]]]]]]]]]]]Y]Y]]]]]]]]]]]]VCHVM @HWI-EAS88_1_1_1_898_392 GATTTCTTACCTATTAGTGGTTGAACAGCATCGGAC +HWI-EAS88_1_1_1_898_392 ]]]]]]]]]]]]Y]]]]]]]]]YPV]T][PZP ShortRead/inst/unitTests/cases/s_1_sequence.9_lines.txt0000644000175400017540000000042313001230551024211 0ustar00biocbuildbiocbuild@HWI-EAS88_1_1_1_1001_499 GGACTTTGTAGGATACCCTCGCTTTCCTTCTCCTGT +HWI-EAS88_1_1_1_1001_499 ]]]]]]]]]]]]Y]Y]]]]]]]]]]]]VCHVMPLAS @HWI-EAS88_1_1_1_898_392 GATTTCTTACCTATTAGTGGTTGAACAGCATCGGAC +HWI-EAS88_1_1_1_898_392 ]]]]]]]]]]]]Y]]]]]]]]]YPV]T][PZPICCK @HWI-EAS88_1_1_1_922_465 ShortRead/inst/unitTests/cases/s_1_sequence.txt0000644000175400017540000007577413001230551022675 0ustar00biocbuildbiocbuild@HWI-EAS88_1_1_1_1001_499 GGACTTTGTAGGATACCCTCGCTTTCCTTCTCCTGT +HWI-EAS88_1_1_1_1001_499 ]]]]]]]]]]]]Y]Y]]]]]]]]]]]]VCHVMPLAS @HWI-EAS88_1_1_1_898_392 GATTTCTTACCTATTAGTGGTTGAACAGCATCGGAC +HWI-EAS88_1_1_1_898_392 ]]]]]]]]]]]]Y]]]]]]]]]YPV]T][PZPICCK @HWI-EAS88_1_1_1_922_465 GCGGTGGTCTATAGTGTTATTAATATCAATTTGGGT +HWI-EAS88_1_1_1_922_465 ]]]]Y]]]]]V]T]]]]]T]]]]]V]TMJEUXEFLA @HWI-EAS88_1_1_1_895_493 GTTACCATGATGTTATTTCTTCATTTGGAGGTAAAA +HWI-EAS88_1_1_1_895_493 ]]]]]]]]]]]]]]]]]]]]]]T]]]]RJRZTQLOA @HWI-EAS88_1_1_1_953_493 GTATGTTTCTCCTGCTTATCACCTTCTTGAAGGCTT +HWI-EAS88_1_1_1_953_493 ]]]]]]]]]]]]]]]]]T]]]]]]]]]]MJUJVLSS @HWI-EAS88_1_1_1_868_763 GTTCTCTAAAAACCATTTTTCGTCCCCTTCGGGGCG +HWI-EAS88_1_1_1_868_763 ]]]]]]]]]]]Y]]T]]]O]]]]VO]W]VZMXVOLS @HWI-EAS88_1_1_1_819_788 GTACGCTGGACTTTGTAGGATACCCTCGCTTTCCTT +HWI-EAS88_1_1_1_819_788 ]]]]]]]]]]]]]]]]Y]]P]RRTYYV][VZXHFSO @HWI-EAS88_1_1_1_801_123 GAACAGCATCTGACTCAGATAGTAATCCACGCTCTT +HWI-EAS88_1_1_1_801_123 ]]]]]]]]]]]]]]]]Y]]]R]]]]]]]UZZXVSSS @HWI-EAS88_1_1_1_885_419 GCTTGGTAAGTTGGATTAAGCACTCCGTGGGCAGTT +HWI-EAS88_1_1_1_885_419 ]]]]]]]]]]]]]]C]]VYY]R]]V]]TRVHPAJAM @HWI-EAS88_1_1_1_941_477 GAGAAGTTAATGGATGAATTGGCACAATGCTACAAT +HWI-EAS88_1_1_1_941_477 ]]]]]]]]]]]]]]]]]]]]]]]R]TPVVVZCSFLO @HWI-EAS88_1_1_1_984_473 GTTGGTTTCTATGTGGCTTAATACGTTAATTAAAAT +HWI-EAS88_1_1_1_984_473 ]]]]]]]]]]]]]]]]]]ETY]VJ]]]HTOMEQAHC @HWI-EAS88_1_1_1_570_435 GTCTATAGTGTTATTAATATCAAGTTGGGGGAGCAT +HWI-EAS88_1_1_1_570_435 ]]]]Y]]]]]]]R]]]]]]]]]R]]]HVTREEVHAA @HWI-EAS88_1_1_1_649_729 GATATTTCTGATGAGTCGAAAAATTATCTTGATAAA +HWI-EAS88_1_1_1_649_729 ]]]]]]]]]]]]]V]]]]VYV]]]]T]][ZVRVSSL @HWI-EAS88_1_1_1_867_781 GAGTTTGTATCTGTTACTGAGAAGTTAATGGATGAA +HWI-EAS88_1_1_1_867_781 ]]]]]]]]]]]]]]]Y]]]T]OV]]]]T[PZJVSFF @HWI-EAS88_1_1_1_722_426 GGACTTTGTAGGATACCCTCGCTTTCCTGCTCCTGT +HWI-EAS88_1_1_1_722_426 ]]]]]]]]]]]]R]]]]]YYY]VT]RY]VVZPQMOO @HWI-EAS88_1_1_1_789_111 GGTTTCATGGTTTGGTCTAACTTTACCGCTACTAAA +HWI-EAS88_1_1_1_789_111 ]]]]]]]]]]]]]]T]]]]]]]]]P]]][ZZXVASM @HWI-EAS88_1_1_1_945_812 GTATTTTACCAATGACCAAATCAAAGAAATGACTCG +HWI-EAS88_1_1_1_945_812 ]]]]]]]]]]]]]]]Y]YY]]]YV]]]][ZZUQSSS @HWI-EAS88_1_1_1_974_468 GTGTACGCGCAGGAAACTCTGACGTTCTTTCTGTCG +HWI-EAS88_1_1_1_974_468 ]]]]]]]]]]T]]OYYHP]R]T]]Y]HHREEXIAMH @HWI-EAS88_1_1_1_321_368 GTCCCCTTCGGGGCGGTGGTCTTTTGTGTTTTTAAT +HWI-EAS88_1_1_1_321_368 ]]]]]]]]]]]]]]]]Y]]R]]C]M]Y][ZMXVAJS @HWI-EAS88_1_1_1_974_763 GACTGAATGCCAGCAATCTCTTTTTTTGTCTCATTT +HWI-EAS88_1_1_1_974_763 ]]]]]]]]]]]P]]VY]]]Y]]]]]EH][ZZXHSSS @HWI-EAS88_1_1_1_923_392 GCAATGGAGAAAGACGGAGAGCGCCAACGGCGTCCC +HWI-EAS88_1_1_1_923_392 ]]]]]]]]]]]]]]]]]T]R]RTRYECVVVSPEAHA @HWI-EAS88_1_1_1_331_887 GCCACCATGATTATGACCAGTGTTTCCAGTCCGTTC +HWI-EAS88_1_1_1_331_887 ]]]]]]V]]]]]]]]YV]]]T]]]]]]YRVQXVOSK @HWI-EAS88_1_1_1_681_650 GGATTACTATCTGAGTCCGATGCTGTTCAACCACTA +HWI-EAS88_1_1_1_681_650 ]]]]]]]]]]]]]]H]]]]R]]]]]]]PMVSMLOSH @HWI-EAS88_1_1_1_1001_376 GCTACCGATAACAATACTGTAGGCCTGGGTGGTGCT +HWI-EAS88_1_1_1_1001_376 ]]]]]]]Y]]]]YY]]]]]VTYY]CY]][QZMVFFJ @HWI-EAS88_1_1_1_812_666 GGTGGTTATTATACCGTCAAGGACTGTGTGACTATT +HWI-EAS88_1_1_1_812_666 ]]]]]]]]]]]]]O]YT]MV]]J]]]R]JVCMSCSS @HWI-EAS88_1_1_1_879_409 GTGACTATTGACGTCCTTCCTCGTACGCCGGGCCAT +HWI-EAS88_1_1_1_879_409 ]Y]]Y]]Y]]Y]]Y]]Y]]]]]YJJ]EVXVZXOHAJ @HWI-EAS88_1_1_1_874_833 GAGGCTTGCGTTTATGGTACGCTGGTCTTTGTATGT +HWI-EAS88_1_1_1_874_833 ]Y]]]]]]]]]]]]]]]]]YT]]T]HJVTZOXIFNF @HWI-EAS88_1_1_1_671_184 GGATATTTCTAATGTCGTCACTGATGCTGCTTCTGT +HWI-EAS88_1_1_1_671_184 ]]]]]]]]]]Y]]]]]Y]]Y]]VP]]V][ZZXQSSF @HWI-EAS88_1_1_1_770_657 GATAGTTTGACGGTTAATGCTGGTAATGGTGGGTTT +HWI-EAS88_1_1_1_770_657 ]]]]]]]]]]]]]]]]]]]]]]]YRY]][XZXASSS @HWI-EAS88_1_1_1_864_736 GCCTCATCAGGGTTAGGAACATTAGAGCCTTGAATG +HWI-EAS88_1_1_1_864_736 ]]]]]]]]]]]]]]]]]YT]Y]]YYYY]VZUXAOSS @HWI-EAS88_1_1_1_885_763 GTTAGGCCAGTTTTCTGGTCGTGTTCAACAGACCTC +HWI-EAS88_1_1_1_885_763 ]]]]]]]]Y]]]]]]]]]Y]]Y]]]]TRWOVJQOSA @HWI-EAS88_1_1_1_246_659 GTTTTTTACCTTTAGACATTACATCACTCCTTCTGC +HWI-EAS88_1_1_1_246_659 ]]]]]]]]]]]]]]]]]Y]]]]]]]T]][ZZXVSNS @HWI-EAS88_1_1_1_977_481 GTTGATAAGCAAGCATCTCATTTTGTGCATATACCT +HWI-EAS88_1_1_1_977_481 ]]]]]]]]]]]]]]T]]]]M]]]]R]EYTZOXLHOS @HWI-EAS88_1_1_1_844_119 GGCATTTAGTAGCGGTAAAGTTAGGCCAAACCCTGG +HWI-EAS88_1_1_1_844_119 ]]]]]]]]]]]]]]]NYVY]]]R]JP]CRJOXEOLL @HWI-EAS88_1_1_1_947_483 GAGGATAAATTATGTCTAATATTCAAACTTGCGCCG +HWI-EAS88_1_1_1_947_483 ]]]]]]]]]]]Y]]P]]YY]VY]]PTVYMCMPLOLH @HWI-EAS88_1_1_1_878_486 GAGAAATAAAAGTCTGAAACATGATTAAACTCCTAA +HWI-EAS88_1_1_1_878_486 ]]]]]]]]]]Y]]]]]]R]]T]]OV]VTMXZRQSNA @HWI-EAS88_1_1_1_966_456 GCTTGTTTACGAATTAAATCGAAGTGGACTTCTTGT +HWI-EAS88_1_1_1_966_456 ]]]]]]]]]]]Y]]]]]V]]]]P]]]YMPZEEVAKC @HWI-EAS88_1_1_1_786_629 GAGATTATTTGTCTCCAGCCACTTAAGTGAGGTGAT +HWI-EAS88_1_1_1_786_629 ]Y]]]]]]]]]]]]]]Y]Y]Y]]]Y]YYXQVXLMAS @HWI-EAS88_1_1_1_817_744 GTATAAGTCAAAGCACCTTTAGCGTTAAGGTACTGA +HWI-EAS88_1_1_1_817_744 ]]]]]]]]]]Y]]]Y]]]Y]YY]]]]RO[ZTRQSNH @HWI-EAS88_1_1_1_726_628 GGATTGGTTTCGCTGAATCAGGTTATTAAAGAGATT +HWI-EAS88_1_1_1_726_628 ]]]]]]]]]]]]]]]V]]]R]]]]]]]R[ZZHVLSS @HWI-EAS88_1_1_1_985_406 GATTATTTGTCTCCAGCCACTTAAGTGAGGTGATTT +HWI-EAS88_1_1_1_985_406 ]]]]]]]]]]]]]]V]]]T]]]HMT]JRWZZJASSS @HWI-EAS88_1_1_1_717_240 GACTTAGTTCATCAGCAAACGCAGAATCAGCGGTAT +HWI-EAS88_1_1_1_717_240 ]]]]]]]]]]Y]]]]]V]Y]]]T]PM]]UZZOVSHO @HWI-EAS88_1_1_1_346_566 GTTCCGACTACCCTCCCGACTGCCTATGATGTTTAT +HWI-EAS88_1_1_1_346_566 ]]]]]]]]]]]]]]]]]]O]]]]]YH]]MZVUVSHS @HWI-EAS88_1_1_1_930_759 GGCTTTTTTATGGTTCGTTCTTGTTACCCTTCTGTT +HWI-EAS88_1_1_1_930_759 ]]]T]]]]]]]]]]]]]Y]P]]C]]VMVXZZHVCAA @HWI-EAS88_1_1_1_441_780 GGTTTATCGTTTTTGACACTCTCACGTTGGCTGACG +HWI-EAS88_1_1_1_441_780 ]]]]]]]]]]]]]]]]]]]]]]]]]]R]VVZXVASC @HWI-EAS88_1_1_1_893_385 GTTAACACTACTGGTTATATTGACCATGCCGCTTTT +HWI-EAS88_1_1_1_893_385 ]]]]]]]]]]]]]]]]Y]TV]YJRVRVTOMHEOSLN @HWI-EAS88_1_1_1_860_742 GTCCCCTTCGGGGCGGTGGTCTATAGTGTTATTAAT +HWI-EAS88_1_1_1_860_742 ]]]]]]]]]]]]]]]]T]]RHYC]H]OVVZCRVFNS @HWI-EAS88_1_1_1_646_490 GTAACCGTCTTCTCGTTCTCTAAAAACCATTTTTCT +HWI-EAS88_1_1_1_646_490 ]]]]]]]]]]]]]]]]]]]]]PER]V]]PVZXQOOC @HWI-EAS88_1_1_1_484_791 GCTGATGAACTAAGTCAACCTCAGCACTAACCTTGC +HWI-EAS88_1_1_1_484_791 ]]]]]]]]]]]VY]]]T]]]V]M]YC]]TZZMOJSL @HWI-EAS88_1_1_1_698_397 GTTTTCATGCCTCCCAATCTTGGAGGCTTTTTTATG +HWI-EAS88_1_1_1_698_397 ]]]]]]]]]]]]]]HT]]]]]]]C]]V][ZZXVASS @HWI-EAS88_1_1_1_955_440 GGAAAACGAACAAGCGCAAGAGTAAACATAGTGCCA +HWI-EAS88_1_1_1_955_440 ]]]]]]]]]]]Y]]]]]TR]H]VPVVTOOHPMQLOH @HWI-EAS88_1_1_1_976_442 GTATTAAGGATGAGTGTTCAAGATTGCTGGAGGCCT +HWI-EAS88_1_1_1_976_442 ]]]]]]]]]]]]]]]]]]]TV]R]]]YYOPCPQHKO @HWI-EAS88_1_1_1_366_209 GAGCAGAAGCAATACCGCCAGCAATAGCACCAAACA +HWI-EAS88_1_1_1_366_209 ]]]]]]]Y]]]]]]]]]]]VY]JH]TY]TTZMLFSH @HWI-EAS88_1_1_1_872_762 GTTTATAGGTCTGGTGAACACGACCAGAAAACTGGC +HWI-EAS88_1_1_1_872_762 PPPPPPPPPPPPPEPPPPPOPPMOOPPOMMMPOOOJ @HWI-EAS88_1_1_1_361_357 GAAGAAATAACATCATGGTAACGCTGCATGAAGTAA +HWI-EAS88_1_1_1_361_357 ]]]]]]]]]]]]]]]]]]V]]]]]]]]H[ZEMVSOA @HWI-EAS88_1_1_1_804_628 GTCAAAAATTACGTGCAGAAGGAGTGATGTAATGTC +HWI-EAS88_1_1_1_804_628 ]]]]]]]]]]]]]]]]]]TY]]R]R]R]WXVRVLMJ @HWI-EAS88_1_1_1_864_773 GGGAGGGTGTCAATCCTGACGGTTATTTCCTAGACA +HWI-EAS88_1_1_1_864_773 ]]]]]]]]]]]VY]]]]]R]]]T]Y]Y]RVVMKHLJ @HWI-EAS88_1_1_1_561_780 GTTATTAATATCAAGTTGGGGGAGCACATTGTAGCA +HWI-EAS88_1_1_1_561_780 ]]]]]]]]]]]]]V]]]]]]]EE]YTRRVZVTOSKJ @HWI-EAS88_1_1_1_415_588 GTAGGATACCCTCGCTTTCCTGCTCCTGTTGAGTTT +HWI-EAS88_1_1_1_415_588 ]]]]]Y]]]]]]]]]]]]]]]]V]]]]]PZOCVSSS @HWI-EAS88_1_1_1_862_123 GTCACATTAAATTTAACCTGACTATTCCACTGCAAC +HWI-EAS88_1_1_1_862_123 ]]]]]]]]]]]]]]]]]]]TT]]P]]]]UZZXVCOS @HWI-EAS88_1_1_1_686_617 GTTTCCGAGATTATGCGCCAAATGCTTACTCAAGCT +HWI-EAS88_1_1_1_686_617 ]]]]]]]]]]]]]]]]]]]MH]]]]]]M[ZZJNSSL @HWI-EAS88_1_1_1_299_507 GTTTTCTGGTCGTGTTCAACAGACCTATAAACATTC +HWI-EAS88_1_1_1_299_507 ]]]]]]]]]]]]]]]]]J]]T]P]Y]O]RJVXOSLO @HWI-EAS88_1_1_1_433_756 GTTAACTTCTGCGTCATGGAAGCGATAAAACTCTGC +HWI-EAS88_1_1_1_433_756 PPPPPPPPPPPPPPPPPPPPHPPPOPPMOPPPNKMA @HWI-EAS88_1_1_1_604_463 GATTTATGTTTGGTGCTATTGCTGGCGGGTTTTTTT +HWI-EAS88_1_1_1_604_463 ]]]]]Y]]]]]]]]]]]R]]]]]RY]OYEHVTQHKS @HWI-EAS88_1_1_1_366_254 GCATTCAAGGTGATGTGCTTGCTACCGATAACCATA +HWI-EAS88_1_1_1_366_254 ]]]]]]]]]]]]Y]]]]]]]]]]V]]]V[RZXAJSO @HWI-EAS88_1_1_1_861_780 GTTGGTTTCTATGTGGCTAAATACGTTAACAAAAAG +HWI-EAS88_1_1_1_861_780 ]]]]]]]]]]]]]]]]]]Y]Y]]]]]]R[XOXQLOS @HWI-EAS88_1_1_1_51_508 GGGGGAGCACATTGTAGCATTGTGCCAATTCATCCA +HWI-EAS88_1_1_1_51_508 ]]]]]Y]]]]]]V]]]]]]]]YTJ]]OM[ZZRPAJH @HWI-EAS88_1_1_1_918_394 GCAAGCCTCAACGCAGCGACGAGCACGAGAGCGGTC +HWI-EAS88_1_1_1_918_394 ]]]]]]]]]]]]]]Y]]]R]]VY]M]]OVJZPQKAF @HWI-EAS88_1_1_1_873_770 GAATTTACGGAAAACATTATTAATGGCGTCGAGCGT +HWI-EAS88_1_1_1_873_770 ]]]]]]]]]]V]V]]P]]YY]VVV]]R]PUOCOCMH @HWI-EAS88_1_1_1_712_190 GCCGTTTTGGATTTAACCGAAGATGATTTCGATTTT +HWI-EAS88_1_1_1_712_190 ]]]]]]]]]]O]]]V]]]]OC]O]]O]][QTCVSSS @HWI-EAS88_1_1_1_411_573 GAGTTTATTGCTGCCGTCATTGCTTATTATGTTCAT +HWI-EAS88_1_1_1_411_573 ]]]]]]]]]]]]]]]]T]H]]]Y]]R]]RZEXVAJS @HWI-EAS88_1_1_1_228_633 GATTTTATTGGTATCAGGGTTAATCGTGCCAAGAAA +HWI-EAS88_1_1_1_228_633 ]]]]]]]]]]]]]]]]]]]]]VV]]Y]P[ZOUVFNH @HWI-EAS88_1_1_1_359_604 GGTGTCTGTAAAACAGGTGCCGAAGAAGCTGGAGTA +HWI-EAS88_1_1_1_359_604 ]]]]]]]]]]]]]]]]]]]]]]EV]VV][ZZXMSMH @HWI-EAS88_1_1_1_303_791 GGATTAAGTTCATGAAGGATGGTGTTAATGCCACTC +HWI-EAS88_1_1_1_303_791 ]]]]]]]]]]]]]]]]]]]]]]]]]]TVVVZXOSSS @HWI-EAS88_1_1_1_998_450 GTTCAGTTGTTGCAGTGGAATAGTCAGGTTAAATTT +HWI-EAS88_1_1_1_998_450 ]]]]]]]]]]]]]R]]]]CT]O]]YR]][ZEEKSSS @HWI-EAS88_1_1_1_697_640 GTGTGAGGTTATAACGCCGAAGCGGGAAAAATTTTA +HWI-EAS88_1_1_1_697_640 ]]]]]]]]]]]]]]]]]]]ET]]]]EPJTXQXVSSL @HWI-EAS88_1_1_1_961_516 GAAGCCTGAATGAGCTTAATAGAGGCCAAAGCGGTC +HWI-EAS88_1_1_1_961_516 ]]]]]]]]]]]]T]]]]Y]]V]TT]]YPTHVOQKLH @HWI-EAS88_1_1_1_676_167 GAATCAGCGGTATGGCTCCTCTCCTATTTGCTCTTT +HWI-EAS88_1_1_1_676_167 ]]]]]]]]]]V]]]]]]]EYV]TOTMV]JEHCENFA @HWI-EAS88_1_1_1_908_493 GATTCAGTACCTTAACGCTAAAGGTGCTTTGACTTA +HWI-EAS88_1_1_1_908_493 ]]]]]]]]]]]]]Y]]]]VR]H]]V]YV[ZECQSSM @HWI-EAS88_1_1_1_335_282 GACATTATGGGTCTGCAAGCTGCTTATGCTACTTTG +HWI-EAS88_1_1_1_335_282 ]]]]]]]]]]]]]]]]YR]]]]]]]T]VWZSEVSSJ @HWI-EAS88_1_1_1_706_512 GTTGAAATGGTAATAAGACGACCAATCTGACCAGCC +HWI-EAS88_1_1_1_706_512 ]]]]]]]]]]]]]]]Y]R]]JVYHRV]]WMZOKLHA @HWI-EAS88_1_1_1_927_495 GTAAGCATTTGGCGCATAATCTCGGAAACCTGCTGT +HWI-EAS88_1_1_1_927_495 ]]]]]]]]]]]]]]]T]]T]]]]T]HOOTTMMKNLH @HWI-EAS88_1_1_1_370_877 GTGAGAGTGTCAAAAACGATAAACCAACCATCAGCA +HWI-EAS88_1_1_1_370_877 ]]]]]]]]]]]]]]TVT]]]]]]]]JVPTQMRQJLJ @HWI-EAS88_1_1_1_223_238 GTTAACAGTCGGGAGAGGAGTGGCATTAACACCATC +HWI-EAS88_1_1_1_223_238 ]]]]]]Y]]]]]]N]R]]Y]]Y]]P]]VPSCTVHHS @HWI-EAS88_1_1_1_324_781 GTATGTTGACGGCCATAAGGCTGCTTCTGACGTTCG +HWI-EAS88_1_1_1_324_781 ]]]]]]]]]]]]]YR]P]]]Y]T]E]WTRJVMHKAF @HWI-EAS88_1_1_1_833_311 GGGGGAGCACATTGTAGCATTGTGCCAATTCATCCA +HWI-EAS88_1_1_1_833_311 ]]]]]Y]]Y]Y]Y]HTRVVT]MRY]VCEVVZJQKHF @HWI-EAS88_1_1_1_364_260 GGTTATCCATCTGCTTATGGAAGCCAAGCATTGGGG +HWI-EAS88_1_1_1_364_260 ]]]]]]]]Y]]]]]]]]]]]PY]V]HM]WMZXIMHS @HWI-EAS88_1_1_1_900_770 GGTCGCAAAGTAAGAGCTTCTCGAGCTGCGCAAGGG +HWI-EAS88_1_1_1_900_770 ]]]]]]]]Y]]]V]J]]]]YY]VO]Y]]TZOCLMOA @HWI-EAS88_1_1_1_674_661 GATATGGACCTTGCTGCTAAAGGTCTAGGAGCTAAA +HWI-EAS88_1_1_1_674_661 ]]]]]]]]]]]]]]]]]]OPY]TY]YE]UQZTQJSM @HWI-EAS88_1_1_1_524_466 GGTAAAGCTGATGGTATTGGCTCTAATTTGTCTAGG +HWI-EAS88_1_1_1_524_466 ]]]]]]]]]]]]]]]Y]]]]]]]YHOV][MTPVHHK @HWI-EAS88_1_1_1_960_818 GGTTTAGATATGAGTCACATTTTGTTCATGGTAGAG +HWI-EAS88_1_1_1_960_818 ]]]]]]]]]]]]]]]]]]V]]]]T]]]][VZXMSLN @HWI-EAS88_1_1_1_227_700 GTTGACATTTTAAAAGAGCGTGGATTACTATCTGAG +HWI-EAS88_1_1_1_227_700 ]]]]]]]]]]]]]]]]]]]]]]]]]]]][VZXVOLH @HWI-EAS88_1_1_1_662_208 GTCTAAAGGTAAAAAACGTTCTGGCGCTCGCCCTGG +HWI-EAS88_1_1_1_662_208 ]]]]]]]]]]V]P]TR]]]]]]M]]]TV[PREKMLF @HWI-EAS88_1_1_1_635_393 GTTTCTGTTGGTGCTGATATTGCTTTTGATGCTTAA +HWI-EAS88_1_1_1_635_393 ]]]]]]]]]]]]]]]]]]]]]]]]]]]OJZHUVAFM @HWI-EAS88_1_1_1_403_780 GCCTCCAAATCTTGGAGGCTTTTTTATGGTTCGTTC +HWI-EAS88_1_1_1_403_780 ]]]]]]]]Y]]]]]]T]]]]]]]]]Y]RRZZRMOSO @HWI-EAS88_1_1_1_468_756 GCAGAAGCAATACCGCCAGCAATAGCACCAAACATA +HWI-EAS88_1_1_1_468_756 ]]]]]]]]]]]]V]Y]]Y]]]HV]OTRVVJMONHFF @HWI-EAS88_1_1_1_484_755 GGTGCTATTGCTGGCGGTATTGGTTCTTCTCTTTCT +HWI-EAS88_1_1_1_484_755 ]]]]]]]]]]]]]]]]]HOT]HCTOEYCCMHMKHAH @HWI-EAS88_1_1_1_973_421 GTTTCCGTTGCTGCCATCTCCAAAACATTTTGACTG +HWI-EAS88_1_1_1_973_421 ]]]]]]]]]]]]]]]]]]]]M]MM]]V][ZEEINSF @HWI-EAS88_1_1_1_497_908 GGTTATAACGCCGAAGCGGTAAAAATTTTAATTTTT +HWI-EAS88_1_1_1_497_908 ]]]]]]]]]]]]]]Y]]]]]R]R]YY]][VTXVSSS @HWI-EAS88_1_1_1_991_521 GAGCTTCTCGAGCTGCGCAAGGATAGGTCGGATTTT +HWI-EAS88_1_1_1_991_521 ]]]]]]]]]]V]]]]]]]RR]]P]T]]YTQOEVSSS @HWI-EAS88_1_1_1_495_814 GCAGTAGACTCCTTCTGTTGATAAGCAAGCATCTCA +HWI-EAS88_1_1_1_495_814 ]]]]]]]]]]]]]]]]]]]]V]Y]]]Y][ZVXVSSA @HWI-EAS88_1_1_1_703_438 GATTATTTTGACTTTGAGCGTATCGAGGCTCTTTAA +HWI-EAS88_1_1_1_703_438 ]Y]]]]]]]]]]]]]]]]]]V]]]]ORWVVZUQCHF @HWI-EAS88_1_1_1_730_507 GTCATTGTGAGCATTTTCATCCCGAAGTTGCGGCTC +HWI-EAS88_1_1_1_730_507 ]]]]]]]]]]]]]]]]]]H]YYV]HOYT[HMOQALH @HWI-EAS88_1_1_1_866_100 GCCATTGCTCATATTGAAGTTCAGGCTGTTATTTTT +HWI-EAS88_1_1_1_866_100 ]]]]]]]]]]Y]]]]]]]]]]]]R]]]]CZTXVSSS @HWI-EAS88_1_1_1_949_458 GGTATGTAGGTGGCCAACAATTTTTATTGCTTGGGT +HWI-EAS88_1_1_1_949_458 ]]]]]]]]]]]]]H]Y]]RRY]]]RH]VEMCMHCJF @HWI-EAS88_1_1_1_320_300 GACACCTAAAGCTACATCGTCAACGTTATATTTTGT +HWI-EAS88_1_1_1_320_300 ]]]]]]]]]]]]]]]YY]]R]EJ]]]YT[JZXVSAH @HWI-EAS88_1_1_1_996_317 GCTTATCACCTTCTTGAAGGCTTCCCATTCATTCAG +HWI-EAS88_1_1_1_996_317 ]]]]]]]]]]]]]]]]]VYR]]]]]]O][ZSXVSHJ @HWI-EAS88_1_1_1_843_780 GGCTTCCATAAGCAGATGGATAACCGCATCAAGCTC +HWI-EAS88_1_1_1_843_780 ]]]]]]]Y]]Y]]Y]Y]]]V]V]]]]YPWTENVNLN @HWI-EAS88_1_1_1_337_794 GTCTCCAGCCACTTAAGTGAGGTGATTTATGTTTGG +HWI-EAS88_1_1_1_337_794 ]]]]]]T]]]Y]]]]Y]Y]]]]Y]R]]]MZVXVSJF @HWI-EAS88_1_1_1_599_542 GATAATGGTGATATGTATGTTGACGGCCCTAAGGCT +HWI-EAS88_1_1_1_599_542 ]Y]]]]]]]]V]V]]YY]]Y]JCYTRRVEJENLLAF @HWI-EAS88_1_1_1_636_218 GTTTGTATCTGTTACTGAGAAGTTAATGGTTGGATT +HWI-EAS88_1_1_1_636_218 ]]]]]]]]]]]]]]]]]J]JRY]]VYVH[CVCAAKS @HWI-EAS88_1_1_1_801_780 GTTGCAGTGGAATAGTCAGGTTAAATTTAATGTGAC +HWI-EAS88_1_1_1_801_780 ]]]]]]]]]]Y]Y]]T]Y]]O]EYT]]]TPZTVSCM @HWI-EAS88_1_1_1_753_627 GGATTAAGCACTCCGTGGACAGATTTGTCATTGTGA +HWI-EAS88_1_1_1_753_627 ]]]]]]Y]]]]]]]]]]]R]]]T]]]YV[ZZXQSSC @HWI-EAS88_1_1_1_234_684 GGTAAAAATTTTAATTTTTGCCGCTGAGGGGTTGAC +HWI-EAS88_1_1_1_234_684 ]]]]]]]]]]]]]]]]]]]Y]]]]]]V]RTZUVOAS @HWI-EAS88_1_1_1_915_728 GTTATTATACCGTCAAGGACTGTGTGACTATTGACG +HWI-EAS88_1_1_1_915_728 ]]]]]]]]]]]]]]V]]]]]]]]]Y]O][XZXOALK @HWI-EAS88_1_1_1_970_579 GCTTACTCAAGCTCAAACGGCTGGTCCGTTTTTTTT +HWI-EAS88_1_1_1_970_579 ]]]]]]Y]YY]]Y]]]]]RYR]]]O]RR[PTXVSFL @HWI-EAS88_1_1_1_706_163 GTTGCTGCCATCTCAAAAACATTTGGACTGCTCCGC +HWI-EAS88_1_1_1_706_163 ]]]]]]]]]]]]]]NTVT]]C]Y]VVH][ZTPLSOH @HWI-EAS88_1_1_1_851_764 GAAAATGCTCACAATGACAAATCTGTCCACGGAGTG +HWI-EAS88_1_1_1_851_764 ]]]]]]]]]]T]R]]]R]OTRY]Y]]VVJUSRESHM @HWI-EAS88_1_1_1_963_398 GGGTGATAAGCAGGAGAAACATACGAAGGCGCATAA +HWI-EAS88_1_1_1_963_398 ]]]]]]]]]]]Y]]V]YVTYHYJ]VJTVWPZOHJHF @HWI-EAS88_1_1_1_706_182 GCTTTGAGTCTTCTTCGGTTCCGACTACCCTCCCGA +HWI-EAS88_1_1_1_706_182 ]]]]]]]]]]]]]]]]T]]]Y]VTY]RVOSZHHLJA @HWI-EAS88_1_1_1_886_399 GATGTTATTTCTTCATTTGGAGGTAAAACCTCTTAT +HWI-EAS88_1_1_1_886_399 ]]]]]]]]]]]]]]T]]]]PYYYTVRMRWPVRQLAO @HWI-EAS88_1_1_1_975_702 GTAACCCAGCTTGGTAAGTTGGATTAAGCACTCCGT +HWI-EAS88_1_1_1_975_702 ]]]]]]]]]]]]]]]]Y]Y]]]P]]TRYVZVXQSNH @HWI-EAS88_1_1_1_634_538 GGTTAATGCTGGTAATGGTGGGTTTTTTTCTTTTTT +HWI-EAS88_1_1_1_634_538 ]]]]]Y]]]]]]]YO]O]YY]RR]]]E]MVVXEHHS @HWI-EAS88_1_1_1_803_696 GTTTAGATATGAGTCACATTTTGTTCATGGTAGAGA +HWI-EAS88_1_1_1_803_696 ]]]]]]]]]]]]]]]O]N]]]]]]]]R][ZTOPKOF @HWI-EAS88_1_1_1_878_417 GAACGAACCATAAAAAAGCCTCCAAGATTTGGAGGC +HWI-EAS88_1_1_1_878_417 ]]]]]]]]]]]]]Y]]]]]]Y]]EJ]JTRVVRKHOM @HWI-EAS88_1_1_1_926_442 GACGCGTTGGATGAGGAGAAGTGGCTTAATATGCTT +HWI-EAS88_1_1_1_926_442 ]]]]]]]]]]Y]]Y]]]]JP]R]]PY]HPVOXOAMO @HWI-EAS88_1_1_1_371_757 GCAGAAGTTAACACTTTCGGATATTTCTGATTAGTC +HWI-EAS88_1_1_1_371_757 ]]]]]]]]]]]]]]]]Y]]YRYT]Y]Y]VOPEIHJJ @HWI-EAS88_1_1_1_986_397 GCAATAGCACCAAACATAAATCCCCTCACTTAAGTG +HWI-EAS88_1_1_1_986_397 ]]]]]]]]]]]YY]]PYVRMVYE]YT]JTOZMHHCF @HWI-EAS88_1_1_1_553_75 GTTGAGTTTATTGCTGCCGTCATTGCTTATTATTTT +HWI-EAS88_1_1_1_553_75 ]]]]]]]]]]]]]Y]]]]]]]CY]CH]]MZZCVFOS @HWI-EAS88_1_1_1_692_494 GGCTGCGGACGACCAGGGCGAGCGCCAGAACGTTTT +HWI-EAS88_1_1_1_692_494 ]]]]]]]]R]]V]]N]]]]]HYRYRRC]CJORAJLO @HWI-EAS88_1_1_1_971_538 GGTTTAAGAGCCTCGATACGCTCAAAGTCAAAATAA +HWI-EAS88_1_1_1_971_538 ]]]]]]]]]]]]]]]V]]]]]]]PY]YTTPZMHNNN @HWI-EAS88_1_1_1_867_629 GTAAAGGCGCTCGTCTTTGGTATGTAGGTGGTCTAC +HWI-EAS88_1_1_1_867_629 ]]]]]]]]]]]]]]]]]]Y]VO]RVTJTPVQREAAC @HWI-EAS88_1_1_1_326_303 GAAGTGGCTTAATATGCTTGGCACGTTCGTCAAGGA +HWI-EAS88_1_1_1_326_303 ]]]]]]]]]]]]]]]]]]T]]]V]]]T][VZCESHF @HWI-EAS88_1_1_1_680_172 GTTCGTTTTCCGCCTACTGCGACTAAAGAGATTCTG +HWI-EAS88_1_1_1_680_172 ]]]]]]]]]]]Y]]]T]]]]YR]]VVVTRVHRVFAJ @HWI-EAS88_1_1_1_997_339 GTGAACAGTGGATTAAGTTCATGAAGGATGGTGTTA +HWI-EAS88_1_1_1_997_339 ]]]]]]]]]]]]]]]Y]]]YH]TJV]]M[ZZRVOOH @HWI-EAS88_1_1_1_415_754 GCATGACCTTTCCCATCTTGGCTTCCTTGCTGGTCA +HWI-EAS88_1_1_1_415_754 ]]Y]]]]]]]Y]]]H]]]]]]]]]]Y]]WPXJQSNC @HWI-EAS88_1_1_1_629_192 GTTCTCACTTCTGTTACTCCAGCTTCTTCGGCACCT +HWI-EAS88_1_1_1_629_192 ]]]]]]]]]]]]]]]V]]]]PY]]]Y]YVEOMHJOO @HWI-EAS88_1_1_1_160_207 GTGATGTGCTTGCTACCGATAACAATACTGTAGGCA +HWI-EAS88_1_1_1_160_207 ]]]]]]]]]]]]]]]]]]P]TY]V]]V]UZPRVSJC @HWI-EAS88_1_1_1_705_461 GTTTAAGAGCCTCGATACGCTCCAAGTCAAAATAAT +HWI-EAS88_1_1_1_705_461 ]]]]]]]]]]]]]]V]Y]]]]]CYY]R][TPRQHKH @HWI-EAS88_1_1_1_584_460 GAGTTGTTCCATTCTTTAGCTCCTAGACCTTTATCA +HWI-EAS88_1_1_1_584_460 ]]]]]]]]]]Y]]]]]]Y]]]]]]TYJ]VZZXKAMJ @HWI-EAS88_1_1_1_434_845 GTTCTGCTTCAATATCTGGTTGAACGGCGTCGCGTC +HWI-EAS88_1_1_1_434_845 ]]]]]]]]]]VV]]]]]]]]]]V]]]]][ZVTQNSF @HWI-EAS88_1_1_1_319_700 GATACCCTCGCTTTCCTGCTCCTGATGCGTTTATTG +HWI-EAS88_1_1_1_319_700 ]]]]T]]]V]]]YERYRYPYYPEYM]MCOHTUIKHJ @HWI-EAS88_1_1_1_882_462 GCATTCATCAAACGCTGAATAGCAAAGCCTCTACGC +HWI-EAS88_1_1_1_882_462 ]]]]]]]]]]]]]]]]]P]]Y]]HTRWV[PVPHMJH @HWI-EAS88_1_1_1_346_126 GTTCTCACTTCTGTTACTCCAGCTTCTTCGGCACCT +HWI-EAS88_1_1_1_346_126 ]]]]]]]]]]]]]]]]]]]]OY]]]]]][JVXQOSS @HWI-EAS88_1_1_1_285_738 GGTCTATAGTGTTATTAATATCAAGTTGGTGGTGCC +HWI-EAS88_1_1_1_285_738 ]]]]]]]]]Y]Y]YV]VV]P]]POORVRVCPCAMCA @HWI-EAS88_1_1_1_885_551 GTATTAAATCTGCCATTCAAGGCTCTAATGTTCCTA +HWI-EAS88_1_1_1_885_551 ]]Y]]]]]Y]]]]]]]]]Y]Y]]]]VPWUZRXIJLK @HWI-EAS88_1_1_1_211_313 GATGGAACTGACCAAACGTCGTTAGGCCAGTTTTCT +HWI-EAS88_1_1_1_211_313 ]]]]]]]]]]]]]]]T]]]]]]]Y]]]]TZZXVSSS @HWI-EAS88_1_1_1_905_706 GTAAAGGCGCTCGTCTTTGGTATGTAGGTGGTCAAC +HWI-EAS88_1_1_1_905_706 ]]]]]]]]]]]]]]]]]]]Y]]]]]T]]VZUPMFLL @HWI-EAS88_1_1_1_364_133 GTTGATATTTTTCATGGTATTGATAAAGCTGTTTCC +HWI-EAS88_1_1_1_364_133 ]]]]]]]]]]]]]T]]]]]]]]Y]]O]H[ZVXVFJH @HWI-EAS88_1_1_1_984_424 GTATGCCGCATGACCTTTCCCATCTTGGCTTTCTTG +HWI-EAS88_1_1_1_984_424 ]Y]]]]]]]]]]T]]]]]]Y]R]Y]RTTPZZEOSSF @HWI-EAS88_1_1_1_885_432 GGCTCATTCTGATTCTGAACAGCTTCTTGGGAAGTA +HWI-EAS88_1_1_1_885_432 ]]]]]Y]]]]]Y]]]]]RY]V]YVY]]YORVJNOMA @HWI-EAS88_1_1_1_730_651 GCAGAAGCCTGAATGAGCTTAATAGAGGCCAAAGCG +HWI-EAS88_1_1_1_730_651 ]]]]]]]]]]]YR]]]]]]]]]]]VTW]XZZPOSMS @HWI-EAS88_1_1_1_571_420 GGTTATTAAAGAGATTATTTGTCTCCAGCCACTTAA +HWI-EAS88_1_1_1_571_420 ]]]]]]]]]]]]]R]]]]]]]]P]]]OVPOHCILFN @HWI-EAS88_1_1_1_721_668 GTAGGTTTTCTGCTTAGGAGTTTAATCATGTTTCAG +HWI-EAS88_1_1_1_721_668 ]]]]]]]]]]]]]]]Y]]V]Y]]YO]]R[ZZXVLLL @HWI-EAS88_1_1_1_863_648 GCCTTCTGGTGATTTGCAAGAACGCGTACTTTTTCG +HWI-EAS88_1_1_1_863_648 ]]]]]]]]]]]T]]]]]TJ]YRV]T]YHMZZEPOFC @HWI-EAS88_1_1_1_714_518 GCATGGGTGATGCTGGTATTAAATCTGCCATTCAAG +HWI-EAS88_1_1_1_714_518 ]]]]]]]]]]]]]]]]]P]]R]V]]]]V[PZXOHHO @HWI-EAS88_1_1_1_832_717 GTTCTTATTACCCTTCTGAATGTCACGCTGATTATT +HWI-EAS88_1_1_1_832_717 ]]]]]]]]]]]]]]]]]]TT]]Y]V]]][ZJRVNSS @HWI-EAS88_1_1_1_345_593 GGGATGAACATAATAAGCAATGACGGCAGCAATAAA +HWI-EAS88_1_1_1_345_593 ]]]]]]]]]]]]Y]]]]]]]]]]]]]]][PCPMJLF @HWI-EAS88_1_1_1_833_651 GTAAAGCTGATGGTATTGGCTCTAATTTTTCTATGA +HWI-EAS88_1_1_1_833_651 ]]]]]]]]]]]]]]V]]]]]]]]CRY]]EZMXIALA @HWI-EAS88_1_1_1_794_763 GTGAAAAAGCGTCCTGCGTGTAGCGAACTGCGGTGG +HWI-EAS88_1_1_1_794_763 ]]]]]]]]]]]]Y]Y]Y]PYRRVR]OMRMSCMAJJL @HWI-EAS88_1_1_1_570_882 GTTTTGGATTTAACCGAAGATGATTTCGATTTTCTT +HWI-EAS88_1_1_1_570_882 ]]]]]]]Y]]]]Y]]]EV]HYYC]]RVVCZVXVOLA @HWI-EAS88_1_1_1_677_183 GAATGCAATGAAGAAAACCACCATTACCAGCATTAA +HWI-EAS88_1_1_1_677_183 ]Y]]]]]Y]]T]]]RVV]]]]]R]]]]][ZZMVSJH @HWI-EAS88_1_1_1_894_262 GAGCGTATGCCGCATGACCTTTCCCATCTTGGCTTC +HWI-EAS88_1_1_1_894_262 ]]]]]]]]]]]]]]]]T]]]]]]]]HV][ZVROSSO @HWI-EAS88_1_1_1_109_416 GTCGCAGTAGGCGGAAAACGCACCAGCGCAAGAGTC +HWI-EAS88_1_1_1_109_416 ]]]]]]]]]]]]]]N]V]]]H]TCYR]V[JPMHHAA @HWI-EAS88_1_1_1_168_329 GGATGAAAATGCTCACAATGACAAATCTGTCCACGG +HWI-EAS88_1_1_1_168_329 ]]]]]]]]]]]]]]]]]]]]O]MO]Y]RRVZXOSSO @HWI-EAS88_1_1_1_955_838 GGTGATGCTGGTATTAAATCTGCCATTCAAGGCTCT +HWI-EAS88_1_1_1_955_838 ]]T]Y]]]]]]]]]]V]]]]]V]]J]]]RQZXVSSJ @HWI-EAS88_1_1_1_451_882 GTGTTCAAGATTGCTGGAGGCCTCCACTATGAAATC +HWI-EAS88_1_1_1_451_882 ]]]]]]]]]]]]]]]]]]]]V]P]]RYJ[ZXCSKSS @HWI-EAS88_1_1_1_210_485 GTTATATTTTGATAGTTTGACGGTTAATGCTTGTAA +HWI-EAS88_1_1_1_210_485 ]]]]]]]]]]]]]]]]]]RT]]P]]CVYOHTCKSLA @HWI-EAS88_1_1_1_858_816 GTTGACAGATGTATCCATCTGAATGCAATGAAGAAA +HWI-EAS88_1_1_1_858_816 ]]]]]]]]]]]]]]]]]]]]]]Y]]]]Y[ZZXVKOO @HWI-EAS88_1_1_1_111_677 GGGCGGTGGTCTATAGTGTTATTAATATCAAGTTGG +HWI-EAS88_1_1_1_111_677 ]]]]]]]]]]]]Y]]]]]]]]]]]R]Y][ZPXQSLS @HWI-EAS88_1_1_1_669_439 GCTGACAACCGTCCTTTACTTGTCATGCGCTCTAAT +HWI-EAS88_1_1_1_669_439 ]]]]]]]]]]]]]]]]]Y]]]YO]T]]]WZZXQCJM @HWI-EAS88_1_1_1_176_181 GATTAGAGGCGTTTTATGATAATCCCCATGCTTTGC +HWI-EAS88_1_1_1_176_181 ]]]]]]]]]]]]]]]]]]]]MY]]Y]CV[RZXVSFS @HWI-EAS88_1_1_1_681_526 GAAATATCCTTTGCAGTAGCGCCCATATGAGAAGAG +HWI-EAS88_1_1_1_681_526 ]]]V]]]]]]]]]]]]TY]Y]T]C]]R][JZHHFFN @HWI-EAS88_1_1_1_734_219 GGTAAAGGACTTCTTGACGGTACGTTGCATGCTTGG +HWI-EAS88_1_1_1_734_219 ]]]]]]]]]]T]]]]]RC]Y]R]]]]WPCTVCQCMA @HWI-EAS88_1_1_1_643_478 GTGAGTTGTTCCATTCTTTAGCTCCTAGACCTTTAG +HWI-EAS88_1_1_1_643_478 ]]]]]]]]]]]]Y]]]]]]J]]V]]]RJJZORVSAH @HWI-EAS88_1_1_1_152_301 GAAGTAGCGACAGCTTGGTTTTTAGTGAGTTTTTCC +HWI-EAS88_1_1_1_152_301 ]]]]]]]]]Y]V]]]]]]]]]]]V]]JJTZZHVSJL @HWI-EAS88_1_1_1_864_228 GTATTGCTTCTGCTCTTGCTGGTGGCGCCCTTTCTA +HWI-EAS88_1_1_1_864_228 ]]]]]]]]]]]]]]]]]]]]RH]VJVROTCXCPHMF @HWI-EAS88_1_1_1_623_542 GATAATCCCAATGCTTTGCGTGACTATTTTCGTGCT +HWI-EAS88_1_1_1_623_542 ]P]]]]]]]YVT]]]]]]]]O]C]]]]][VOJPCAS @HWI-EAS88_1_1_1_851_725 GTTTTTGAGATGGCAGCAACGGAAACCATAACGGGC +HWI-EAS88_1_1_1_851_725 ]]]]]]]]]T]]]]Y]]TJT]YCOVOVCHQJJOCLF @HWI-EAS88_1_1_1_643_262 GTTGGTTTCTATGTGGCTAAATACGTTAACAAAAAG +HWI-EAS88_1_1_1_643_262 ]]]]]]]]]]]]Y]]]V]]RVVVYV]]RPVTMNFHN @HWI-EAS88_1_1_1_664_726 GTTAATGCTGGTAATGGTGGTTTTCTTCTTTTCCTT +HWI-EAS88_1_1_1_664_726 ]]]]]]]]]]]Y]]]]]]]]J]]]OR]MCUZEKAOO @HWI-EAS88_1_1_1_736_517 GAAGTCATGATTGAATCGCGAGTGGTCGGCGGGTTG +HWI-EAS88_1_1_1_736_517 ]]]]]]]]]]]]]V]]]]]]O]M]]YR]WEHRMMML @HWI-EAS88_1_1_1_99_173 GTATAATTACCCCCAAAAGAAAGGTATTAAGGATGA +HWI-EAS88_1_1_1_99_173 ]]]]]]]]]]]]]CTTOT]REP]]OO]]RVSOMOSF @HWI-EAS88_1_1_1_664_501 GAGTATCCTTTCCTTTATCAGCGGCAGACTTGCCCC +HWI-EAS88_1_1_1_664_501 ]V]]]Y]]]]]]]]]]V]]TV]YVVHVOWMZEHJAK @HWI-EAS88_1_1_1_339_626 GTTATATGGCTGTTTGGTTTTTTTTTTGTTTATTTT +HWI-EAS88_1_1_1_339_626 ]]]]]]]]]]]]E]ERR]]P]P]V]YOCUEJCLFCA @HWI-EAS88_1_1_1_740_733 GGTGTGGTTGATATTTTTCATGGTATTGATAAAGCT +HWI-EAS88_1_1_1_740_733 ]]]]]]]]]]P]V]]]]]]Y]TT]Y]]PJZVXOOMO @HWI-EAS88_1_1_1_878_404 GCCTGTCTCATCATGGAAGGCGCTGAATTTACGGGA +HWI-EAS88_1_1_1_878_404 ]]]]]]]]]V]]]]]]P]]]]]]VYMP][ZCMQHCC @HWI-EAS88_1_1_1_822_121 GTTTATCCTTTGGATGGTCGCCATGATGGTGTTTTT +HWI-EAS88_1_1_1_822_121 ]]]]]]]]]]]]]]]]]]Y]]PM]]C]PVZVCVSFS @HWI-EAS88_1_1_1_708_703 GAGGAAGCATCAGCACCAGCACGCTCCCAAGCATTA +HWI-EAS88_1_1_1_708_703 ]]]]]]]]]]]]]]Y]]P]]R]]]PYY]MPZXAMMJ @HWI-EAS88_1_1_1_362_553 GTCTCATTTTGCATCTCGGCAATCTCTTTCTGATTT +HWI-EAS88_1_1_1_362_553 ]]]]]]]]]]]]Y]]]]]]]TT]]]]]][ZZMHSSF @HWI-EAS88_1_1_1_960_757 GGTATTAAATCTGCCATTCAAGGCTCTAATGTTCCT +HWI-EAS88_1_1_1_960_757 ]]]]]]]]]]]]]]]Y]]]RT]]]]]YPVZOXVLKS @HWI-EAS88_1_1_1_752_651 GCACGTTCGTCAAGGACTGGTTTAGATATGAGTCAC +HWI-EAS88_1_1_1_752_651 ]]]]]]]]]]]]Y]]Y]]]]]]]YYV]T[ZRUONFK @HWI-EAS88_1_1_1_754_734 GTAAGAAATCATGAGTCAAGTTACTGAACAATCCGT +HWI-EAS88_1_1_1_754_734 ]]]]]]]Y]]Y]]]]R]OY]TY]]]YMPTJPRQKJJ @HWI-EAS88_1_1_1_825_711 GATGGATACATCTGTCAACGCCGCTAATCAGGTTGT +HWI-EAS88_1_1_1_825_711 ]]]]]]]]]]]]]]Y]NTYYYVVV]RCVRPRRHSNO @HWI-EAS88_1_1_1_308_236 GTGCTCGTCGCTGCGTTGAGGCTTTCGTTTTTTTTT +HWI-EAS88_1_1_1_308_236 PPPPPPPPPPPPPPPPPOPPPPPPEPMPPPMPOFAF @HWI-EAS88_1_1_1_937_329 GGACGCTCGACGCCATTAATAATGTTTTCCGTAAAT +HWI-EAS88_1_1_1_937_329 ]]]]]]]]]O]]]]O]]R]]V]]]Y]]]WZVXVJOS @HWI-EAS88_1_1_1_838_878 GATTACTTCATGCAGCGTTACCATGATGTTATTTCT +HWI-EAS88_1_1_1_838_878 ]]]]]]]]]]]]]]]]]]]]]]J]]M]]TZQXVSNS @HWI-EAS88_1_1_1_414_792 GATTTTATTGGTATCAGGGTTAATCGTGCCAAGAAA +HWI-EAS88_1_1_1_414_792 ]]]]]]]]]]]YY]]V]]]]]V]]]HRRTVCOQJOM @HWI-EAS88_1_1_1_730_497 GTTGCTGCCATCTCAAAAACATTTGGACTGCTCCGC +HWI-EAS88_1_1_1_730_497 ]]]]]]]]]V]]]]NYPNRYR]]]]YCY[VXXQFMF @HWI-EAS88_1_1_1_969_419 GAGTGGTCGGCAGATTGCGCTAAACGGTCACATTAA +HWI-EAS88_1_1_1_969_419 ]]]]]]]]]]]Y]T]]CRYE]PET]]VEJJQCOMLN @HWI-EAS88_1_1_1_104_533 GTCATGATTGAATCGCGAGTGGTCGGCAGATTTTGC +HWI-EAS88_1_1_1_104_533 ]]]]]]]]]]Y]]]]]RTVV]M]O]TVCWPZXAALA @HWI-EAS88_1_1_1_836_628 GGACGCCGTTGGCGCTCTCCGTCTTTCTCCCTTGCG +HWI-EAS88_1_1_1_836_628 ]]]]]]]]]]]]]]]]]Y]]]T]]]]YYXZEXSFFM @HWI-EAS88_1_1_1_596_390 GCAAGCTGCTTATGCTAATTTGCATACTGACCAAGA +HWI-EAS88_1_1_1_596_390 ]]]]]]]]]]]]]]]]]V]]]]]O]]]]MQZXEAOK @HWI-EAS88_1_1_1_987_447 GTCTGGAAACGTACGGATTGTTCAGTAACTTTACTC +HWI-EAS88_1_1_1_987_447 ]]]]]]]]]]]]]]]]T]]]]]VTT]ERRVZMAHSJ @HWI-EAS88_1_1_1_370_352 GGCCTTGCTATTGACTCTACTGTAGACATTTTTACT +HWI-EAS88_1_1_1_370_352 ]]]]]]]]]]]]YY]]]]R]]]]RRP]O[ZZXVFLS @HWI-EAS88_1_1_1_843_797 GCAGTGGAATAGTCAGGTTAAATTTAATGTGACCGT +HWI-EAS88_1_1_1_843_797 ]]]]]]]]]]]]]]R]]O]CVY]]]T]Y[RMOOSSS @HWI-EAS88_1_1_1_720_664 GTTTACGAATTAAATCGAAGTGGACTGCTGGGGGGA +HWI-EAS88_1_1_1_720_664 ]]]]]]]T]]]]Y]]]]RR]R]]VTRVERVZEQSFF @HWI-EAS88_1_1_1_892_748 GTTGGATTAAGCACTCCGTGGGCAGATTTGTCATTG +HWI-EAS88_1_1_1_892_748 ]]]]]Y]]]]Y]Y]V]]]R]]CJM]JV]WZTEINOS @HWI-EAS88_1_1_1_569_417 GAAATGCAGCAGCAAGATAATCACGAGTATCCTTTC +HWI-EAS88_1_1_1_569_417 ]]]]]]]]]]]]]YV]N]]]]]Y]]O]VVVPNOSNS @HWI-EAS88_1_1_1_231_669 GACTACCCTCCCGACTGCCTATGATGTTTATCCTTC +HWI-EAS88_1_1_1_231_669 ]]]]]]]]]]]]]O]]]R]TOYYR]]Y]EPZXOOSA @HWI-EAS88_1_1_1_990_296 GGCTCTTCTCATATTGGCGCTACTGCAAAGGATATT +HWI-EAS88_1_1_1_990_296 ]]]]]]]]]]T]Y]]]T]]]]T]]O]JPRZTCVASS @HWI-EAS88_1_1_1_113_590 GCATGGGTGATGCTGGTATTAAATCTGCCATTCAAG +HWI-EAS88_1_1_1_113_590 ]]]]]]]]]]]]]]]]YV]]]]R]]]RV[QZXVCNO @HWI-EAS88_1_1_1_355_102 GATAAACCAACCATCAGCATGAGCCTGTCGCCTTGC +HWI-EAS88_1_1_1_355_102 ]]]]]]]]T]]]T]]Y]]MY]T]Y]YVMUSVEVNOF @HWI-EAS88_1_1_1_658_670 GTTTTCCGTAAATTCAGCGCCTTCCATGATGAGACA +HWI-EAS88_1_1_1_658_670 ]]]]]]]]]]YV]]]]]Y]]]]Y]]MTTHSZMQFFF @HWI-EAS88_1_1_1_699_385 GCAATGGAGAAAGACGGAGAGCGCCAACGGCGGCCA +HWI-EAS88_1_1_1_699_385 ]]]]]]]]]V]]]R]]]]]P]Y]Y]REV[ZRXAFHF @HWI-EAS88_1_1_1_943_855 GTTAACAAAAAGTCAGATATGGACCTTGCTGCTAAA +HWI-EAS88_1_1_1_943_855 ]]]]]]]]]]]]]]]]]]]]]]VT]]]][VZXVLKM @HWI-EAS88_1_1_1_465_881 GGTTTCCGTTGCTGCCATCTCAAAAACATTTGGACT +HWI-EAS88_1_1_1_465_881 ]]]]]]]]]]]]]]]]E]]]]CHT]V]M[ZZHVFOS @HWI-EAS88_1_1_1_110_475 GAACAGCATCGGACTCAGATAGTAATCCACGCTCTT +HWI-EAS88_1_1_1_110_475 ]]]]]]]]]]]]Y]]]]]T]Y]TY]YR]PZZUNOLO @HWI-EAS88_1_1_1_334_219 GACGCAATGGAGAAAGACGGAGAGCGCCAACGGCGT +HWI-EAS88_1_1_1_334_219 ]]]]]]]]]]]]OYY]Y]]]C]PYY]]VCOXXVHOA @HWI-EAS88_1_1_1_313_372 GACGCTGACAACCGGCCTTTACTTGTCATGCGCTCT +HWI-EAS88_1_1_1_313_372 ]]]]]Y]]]]]]]]YV]TVO]]]]YY]R[ZZXVSLS @HWI-EAS88_1_1_1_423_931 GGAGCACATTGTAGCATTGTGCCAATTCATCCATTA +HWI-EAS88_1_1_1_423_931 ]]]]]]]]]]]]]]]]]]]V]]]OO]]]RPZTQOOO @HWI-EAS88_1_1_1_511_536 GTATGGCTCTTCTCATATTGGCGCTACTGCAAAGGG +HWI-EAS88_1_1_1_511_536 ]]]]]]]]]]]]]]V]Y]]O]]]]]T]VPZOJIHJC @HWI-EAS88_1_1_1_233_304 GGTTATTAAAGAGATTATTTGTCTCCAGCCACTTAA +HWI-EAS88_1_1_1_233_304 ]]]]]]]]]]]]]]]]]]]]]]]]]]M][ZTXVSJL @HWI-EAS88_1_1_1_239_243 GCCCTCTTAAGGATATTCGCGATGAGTATAATTACC +HWI-EAS88_1_1_1_239_243 ]]]]]]]]]]]]]]]]]]Y]Y]]]Y]Y][UZXVJSM @HWI-EAS88_1_1_1_705_445 GCTGATGCTTCCTCTGCTGGTATGGTTGACGCCGGG +HWI-EAS88_1_1_1_705_445 ]]]]]]]]]]]]]]]]]]]]R]]]]Y]YHPPJQLNH @HWI-EAS88_1_1_1_371_846 GCCATCAACTAACGATTCTGTCAAAAACTGACGCGT +HWI-EAS88_1_1_1_371_846 ]]]]]]]]]]]]]]RP]]]]Y]]]Y]R]TTRXSSSO @HWI-EAS88_1_1_1_921_496 GCAATGGAGAAAGACGGAGAGCGCCCACAGCGGCCC +HWI-EAS88_1_1_1_921_496 ]]]]]]]Y]YYY]Y]]]HYO]TYPYHCMEPOJAFAF @HWI-EAS88_1_1_1_322_845 GTCACATTTTGTTCATGGTAGAGATTCTCTTGTTGA +HWI-EAS88_1_1_1_322_845 ]]]]]]]]]]]]]]Y]]]]]]]]Y]]]]WZZOVSMA @HWI-EAS88_1_1_1_243_812 GCTGCTAAAGGTCTAGGAGCTAAAGAATGGAACAAC +HWI-EAS88_1_1_1_243_812 ]]]]]]]]]]]Y]]]]]V]]]V]PYTR][ZZNQJLH @HWI-EAS88_1_1_1_370_333 GGGATGAACATAATAAGCAATGACGGCAGCAATAAA +HWI-EAS88_1_1_1_370_333 ]]]]]]]]]]]]]]TY]]YY]]]]]]]W[JHMOJSM @HWI-EAS88_1_1_1_859_299 GAGTTGTTCCATTCTTTAGCTCCTAGACCTTTAGCA +HWI-EAS88_1_1_1_859_299 ]]]]]]]]]]R]]]]]]]]]]]]]P]R]XZZXMHOJ @HWI-EAS88_1_1_1_797_117 GAAGTGTCCGCATAAAGTGCACCGCATGGAAATGAA +HWI-EAS88_1_1_1_797_117 ]]]]]]]]]]]Y]]Y]]]]]M]]]]M]]OHHNSSAF @HWI-EAS88_1_1_1_748_430 GCGCTACTGCAAAGGATATTTCTAATGTCGTCACTT +HWI-EAS88_1_1_1_748_430 ]]]]]]]]]]]]Y]]V]Y]]]]]R]]M][RTRMLOH @HWI-EAS88_1_1_1_356_375 GACATTATGGGTCTGCAAGCTGCTTATGCTAATTTT +HWI-EAS88_1_1_1_356_375 ]]]]]]]]]]]]]]]]]Y]]]]]]]O]]WVZPQSSH @HWI-EAS88_1_1_1_655_181 GTTCTGGCGCTCGCCCTGGTCGTCCGCAGCCGTTGG +HWI-EAS88_1_1_1_655_181 ]]]]]]]]]]]]]]Y]]]]]]]T]]]TJ[PMPLSJA @HWI-EAS88_1_1_1_801_48 GAAAGGTATTAAGGATGAGTGTTCAAGATTGCTGGG +HWI-EAS88_1_1_1_801_48 ]]]]]]]]]]]]]]Y]]R]]]Y]]OR]T[ZZRVNSH @HWI-EAS88_1_1_1_802_724 GCGTACTTATTCGCCACCATGATTATGACCTGTGTT +HWI-EAS88_1_1_1_802_724 ]]]Y]]]]]]]Y]]]VV]EV]C]P]RREHSCRHHHL @HWI-EAS88_1_1_1_705_499 GTCAACCTCAGCACTAACCTTGCGAGTCATTTCTTT +HWI-EAS88_1_1_1_705_499 ]]]]]]]]]]]]]]]TY]]]]]YYT]V]PVZXQSOS @HWI-EAS88_1_1_1_186_694 GCGTTTGATGAATGCAATGCGACAGGCTCATGCTGT +HWI-EAS88_1_1_1_186_694 ]]]]]]]]]]]]]]]]Y]]]]E]T]]]]WCZXVSSH @HWI-EAS88_1_1_1_354_371 GTTAGGAACATTAGAGCCTTGAATGGCAGATTTAAT +HWI-EAS88_1_1_1_354_371 ]]]]]]]]]]]]]]]]]]]]]V]]]]]V[OZXVHKS @HWI-EAS88_1_1_1_967_272 GGAAAACACCAATCTTTCCAAGCAACAGCAGGTTTC +HWI-EAS88_1_1_1_967_272 ]]]]]]]]]]]Y]]]]]]]J]]]PP]H][HZXOSSK @HWI-EAS88_1_1_1_668_200 GAACTGACCAAACGTCGTTAGGCCAGTTTTCTGTTC +HWI-EAS88_1_1_1_668_200 ]Y]]]]]]]]]]]]Y]]]]]T]]]P]V][XZXIASL @HWI-EAS88_1_1_1_306_556 GTTTTACCTCCAAATGAAGAAATAACATCATGGTAA +HWI-EAS88_1_1_1_306_556 ]]]]]]]]]]]]H]]]]Y]Y]]]VV]]]XVZTVMMM @HWI-EAS88_1_1_1_881_561 GTCGTCACTGATGCTGCTTCTGGTGTGGTTGGTATT +HWI-EAS88_1_1_1_881_561 ]]]]]]]]]]V]]]]]]]]]]]]VYY]]VZZCVFSS @HWI-EAS88_1_1_1_238_692 GTGGTCAACAATTTTAATTGCAGGGGCTTCGGCCCC +HWI-EAS88_1_1_1_238_692 ]]]]]]]]]]]]]]]]]]]Y]]]]]]]][VZXVSSS @HWI-EAS88_1_1_1_443_888 GCTCAAAGTCAAAATAATCAGCGTGACATTCAGAAG +HWI-EAS88_1_1_1_443_888 ]]]]]]]]Y]]]]]]]]Y]]]]YRPV]]XZVUVHSS @HWI-EAS88_1_1_1_167_340 GTCTTTCGTATTCTGGCGTGTAGTCGCCTTCTGTTT +HWI-EAS88_1_1_1_167_340 ]]]]]]]]]]]]]]]]]Y]EMVM]YHJVECZPLCHS @HWI-EAS88_1_1_1_603_569 GTTCTCACTTCTGTTACTCCAGCTTCTTCGGCACCT +HWI-EAS88_1_1_1_603_569 PPPPPPPPPPPPPPPPPPPPOPPPPPPPPPOPOOHK @HWI-EAS88_1_1_1_718_225 GTCAACGTTATATTTTGATAGTTTGACGGTTTATGT +HWI-EAS88_1_1_1_718_225 ]]]]]]]]]]]]]]]]NT]V]]]]MY]H[ZZEKSOF @HWI-EAS88_1_1_1_406_412 GGAAAGATTGGTGTTTTCCATAATAGACGCCACGCG +HWI-EAS88_1_1_1_406_412 ]]]]]]]]]]]]]]]]]]]T]YY]T]J][VCMVFMS @HWI-EAS88_1_1_1_549_119 GGAAAGACGGTAAAGCTGATGGTATTGGCTCTAATT +HWI-EAS88_1_1_1_549_119 ]]]]]]]]]]]]]]]]]]R]]]TYY]]]VVOPAAKS @HWI-EAS88_1_1_1_693_898 GTTTAGATATGAGTCACATTTTGTTCATGGTAGAGT +HWI-EAS88_1_1_1_693_898 ]]]]]]]]]]]Y]]]]]NY]]]]Y]VR]MJQNSAOC @HWI-EAS88_1_1_1_183_559 GTTTTACAGACACCTAAAGCTACATCGTCAACGTTA +HWI-EAS88_1_1_1_183_559 ]]]]]]]]]]]]]]]]]]]]]]]Y]]]VTVVRVMSM @HWI-EAS88_1_1_1_314_891 GATGAACTAAGTCAACCTCAGCACTAACCTTGCGAG +HWI-EAS88_1_1_1_314_891 ]]]]Y]Y]]]]]]]OYY]]]Y]]]YYVVTSZUOOHH @HWI-EAS88_1_1_1_884_867 GTTTGGTTCGCTTTGAGTCTTCTTCGGTTCCGACTA +HWI-EAS88_1_1_1_884_867 ]]]]]]]]]]]]]]]T]]]]]]]]]V]T[OVXEJSJ @HWI-EAS88_1_1_1_878_444 GCAATCTGCCGACCACTCGCGATTCAATCATGACTT +HWI-EAS88_1_1_1_878_444 ]]]]]]]]]]]Y]]T]T]]]]TRYVMEVVRSRHHNH ShortRead/inst/unitTests/cases/s_2_0001_realign_3col_head.txt0000644000175400017540000000260013001230551025022 0ustar00biocbuildbiocbuild#RUN_TIME Thu Aug 28 00:51:44 2008 #SOFTWARE_VERSION @(#) $Id: qualityFilter.pl,v 1.8 2007/11/26 14:42:26 tc Exp $ #FILTER_CRITERION ((CHASTITY>=0.6)) TTAGAAATGTCCACTGCAGGACGTGG 11953 5 GAAAACTTAAAAAGGTGTTAAATTTT 0 0 GTTTTTTAGTGATTTCGTCATTTTTC 13000 3 TACATATACACATACACATACATATA 13000 56 GTGTTTTTCAGTGTAACTCACTCATC 13000 10 TAGATGCTAAATATCCCCCTCAAAGT 0 0 TGAGGGGTATGACTGAATATCTTCAG 13000 14 GGAAAATTTAGAAATGTCCACTGTAG 13000 50 GACTTGAAGTTATTATCATATAGATC 13000 4 GTATTTCACTTCCTTAAGTGTGTATA 0 0 GAGAAATAACAAAACTAACAGACATT 13000 2 GTAGGACATGGAATATGCCAAGAAAA 11953 71 GCCTCAACAGAAACAAAAATATTGAA 13000 37 GAAAATTTAGAAATGTCCAATGTAGG 11953 66 TGTGCATTTCTCATTTTTCACGTTTT 13000 93 TTGACTTCTTCCTTTCAAATTGTATC 13000 3 GATTTTCAGTTTTCTCGCCATATTTT 10906 57 GAAGGACTTAAATGACTCACTTAAAG 13000 4 GCAAAGTGAGTCCCAGGACAGCCAGG 13000 3 GGTATCAAGAGTTAAAGTTTTATGTA 13000 5 GGACCTGGAATATGGCGAGAAAACTG 13000 48 GTCCTACAGGGGACATTTCTAAATTT 11953 62 TGTATGACATGAAATATGGCAAGAAA 11953 10 TAGTAAAGTTTCTTTTATGAATGTGG 13000 255 TACAATGGTCACTAATCATTCTTAAC 0 0 GTGTATATCAATGAGTTACAATGAAA 13000 2 GAGAAATACACACTTTAGGACGTGAA 13000 49 GTGATTTTCAGTTCTCTCGCCATATT 11953 44 GTGGTTTTTATCATTTTCCATGTTTC 13000 5 GGACTAATAGCCACTTATCAGTGAGT 13000 8 GACACCTGGACAGATTAGATATTATT 0 0 GTGGAAAATTTAGAAATGTCCACTGT 13000 50 GTATGATAAAAACTTCAAATCTCTGA 13000 255 GTGATTTTCAGTTTTCTCGCCATATT 13000 44 GAATCCGGTTAAAGTTGGCAGTTGGT 0 0 GTGGATAATTTAGAAATGTCCACTGT 11953 51 ShortRead/inst/unitTests/cases/s_2_0001_realign_head.txt0000644000175400017540000001472113001230551024111 0ustar00biocbuildbiocbuild#RUN_TIME Thu Aug 28 00:51:44 2008 #SOFTWARE_VERSION @(#) $Id: qualityFilter.pl,v 1.8 2007/11/26 14:42:26 tc Exp $ #FILTER_CRITERION ((CHASTITY>=0.6)) TTAGAAATGTCCACTGCAGGACGTGG 11953 5 GAAAACTTAAAAAGGTGTTAAATTTT 0 0 GTTTTTTAGTGATTTCGTCATTTTTC 13000 3 TGACTGTTGGATTTATATTATTTCTT 13000 1 mmu_ref_1_37:179764953 F TGACTGTTGGATTTATATTATTTCTT 10906 TACATATACACATACACATACATATA 13000 56 GTGTTTTTCAGTGTAACTCACTCATC 13000 10 TAGATGCTAAATATCCCCCTCAAAGT 0 0 GATAAAAGTTTGATACACTTTAGACA 13000 1 mmu_ref_5_37:88334169 R TGTCTAAAGTGTATCAAACTTTTATC 9859 TGAGGGGTATGACTGAATATCTTCAG 13000 14 AATGGGTTCACAGGCAGATCGAGCCG 13000 1 mmu_ref_4_37:41300066 F AATGGGTTCACAGGCAGATCGAGCCG 9859 GGAAAATTTAGAAATGTCCACTGTAG 13000 50 GACTTGAAGTTATTATCATATAGATC 13000 4 GTATTTCACTTCCTTAAGTGTGTATA 0 0 GAGAAATAACAAAACTAACAGACATT 13000 2 GCTGTAACTCCCTCTATTAAGAATCT 13000 1 mmu_ref_6_37:12143858 R AGATTCTTAATAGAGGGAGTTACAGC 9859 GTCAGTTCTCTCCTTCCTCCATACAG 13000 1 mmu_ref_7_37:17322027 F GTCAGTTCTCTCCTTCCTCCATACAG 11953 GTGGCACACAGAACCTATAACCCTCA 13000 1 mmu_ref_16_37:41376291 R TGAGGGTTATAGGTTCTGTGTGCCAC 9859 TTTCTCCAATAGGGTCATACCTCATA 13000 1 mmu_ref_12_37:27398329 R TATGAGGTATGACCCTATTGGAGAAA 10906 TTCCTACAAAGTTTTAATTTTAAATT 13000 1 mmu_ref_3_37:14092109 R AATTTAAAATTAAAACTTTGTAGGAA 9859 GAATGATTGGTGTGATAAATAATGTT 13000 1 mmu_ref_1_37:116730648 R AACATTATTTATCACACCAATCATTC 9859 GAAGCAGACTTTTTAATTTTTTACTA 13000 1 mmu_ref_1_37:97868632 F GAAGCAGACTTTTTAATTTTTTACTA 10906 GTAGGACATGGAATATGCCAAGAAAA 11953 71 GGTAATTCACAGTTTGAAATGAAGCA 13000 1 mmu_ref_X_37:66984760 F GGTAATTCACAGTTTGAAATGAAGCA 9859 GCCTCAACAGAAACAAAAATATTGAA 13000 37 TGCTAACAGGTACTCATGGCACAGAT 11953 1 mmu_ref_3_37:22679356 F TGCTAACAGGTACTCATGGCACAGAA 9859 GAAAATTTAGAAATGTCCAATGTAGG 11953 66 GGGGTTGGTCTGGTGTTGCTCTGTAT 13000 1 mmu_ref_18_37:57492171 F GGGGTTGGTCTGGTGTTGCTCTGTAT 11953 GAGAATGCTCGCCTTTTATATAATGT 13000 1 mmu_ref_4_37:116267591 F GAGAATGCTCGCCTTTTATATAATGT 9859 TGTGCATTTCTCATTTTTCACGTTTT 13000 93 TTGACTTCTTCCTTTCAAATTGTATC 13000 3 GAGGGAATTAGTGAAAGGAATGAATA 13000 1 mmu_ref_1_37:154720925 F GAGGGAATTAGTGAAAGGAATGAATA 9859 GATTTTCAGTTTTCTCGCCATATTTT 10906 57 GTTTTCTCGCCATATTCCAGGTCCTT 13000 1 mmu_ref_2_37:98507289 R AAGGACCTGGAATATGGCGAGAAAAC 11953 GTATATAATTAGTATGCCATGTTTTT 13000 1 mmu_ref_12_37:62103751 F GTATATAATTAGTATGCCATGTTTTT 9859 GTCAAATAAATGCAAAGTCCTTCAAG 13000 1 mmu_ref_12_37:94072062 F GTCAAATAAATGCAAAGTCCTTCAAG 9859 GAAGGACTTAAATGACTCACTTAAAG 13000 4 GCAAAGTGAGTCCCAGGACAGCCAGG 13000 3 GGAAGGGTGAAAGAGGAAGAGAAGGA 13000 1 mmu_ref_1_37:184288690 R TCCTTCTCTTCCTCTTTCACCCTTCC 10906 GTCAGATATTCTATATAATGAAAGAA 13000 1 mmu_ref_13_37:40107677 R TTCTTTCATTATATAGAATATCTGAC 9859 GGTATCAAGAGTTAAAGTTTTATGTA 13000 5 TTTCTCTCTCTCTCTCCACTTCTCTG 13000 1 mmu_ref_13_37:118278073 R CAGAGAAGTGGAGAGAGAGAGAGAAA 10906 GGACCTGGAATATGGCGAGAAAACTG 13000 48 GTCCTACAGGGGACATTTCTAAATTT 11953 62 TGTATGACATGAAATATGGCAAGAAA 11953 10 TAGTAAAGTTTCTTTTATGAATGTGG 13000 255 GTTCCTTTGTACATTAATGGTCATAG 13000 1 mmu_ref_3_37:25517235 R CTATGACCATTAATGTACAAAGGAAC 9859 TACAATGGTCACTAATCATTCTTAAC 0 0 TCAATAAGTAGTAACGGACAGGTGGA 13000 1 mmu_ref_11_37:88900847 F TCAATAAGTAGTAACGGACAGGTGGA 9859 GGAGAAAGTTTATGTTGAGACATTTT 13000 1 mmu_ref_14_37:60444247 F GGAGAAAGTTTATGTTGAGACATTTT 9859 GTGTATATCAATGAGTTACAATGAAA 13000 2 GTGTACTAGAACATTAGCATTTCAAG 13000 1 mmu_ref_9_37:39813035 F GTGTACTAGAACATTAGCATTTCAAG 9859 GAGAAATACACACTTTAGGACGTGAA 13000 49 GTGATTTTCAGTTCTCTCGCCATATT 11953 44 GTGGTTTTTATCATTTTCCATGTTTC 13000 5 GGACTAATAGCCACTTATCAGTGAGT 13000 8 GACACCTGGACAGATTAGATATTATT 0 0 TAAAATTATTGTTCTGGAACGAAAAG 13000 1 mmu_ref_8_37:35218943 F TAAAATTATTGTTCTGGAACGAAAAG 9859 TGGAGAAGTTCTATGTTCCTAAAGGA 13000 1 mmu_ref_2_37:144191295 R TCCTTTAGGAACATAGAACTTCTCCA 10906 GTATATGAACATGTCTACATGTTTGA 13000 1 mmu_ref_1_37:173724022 F GTATATGAACATGTCTACATGTTTGA 9859 GGTGTGGGTGGGTTGTTGTTGGTGGG 13000 1 mmu_ref_12_37:8688329 F GGTGTGGGTGGGTTGTTGTTGGTGGG 10906 GAAACACTGGCAGGTTAACCACAGTC 11953 1 mmu_ref_12_37:119479603 R GACTGTGGTTAAACTGCCAGTGTTTC 10906 GTGGAAAATTTAGAAATGTCCACTGT 13000 50 GTCTCATTCATTTATCCTCAGAGAAG 13000 1 mmu_ref_13_37:89183794 R CTTCTCTGAGGATAAATGAATGAGAC 9859 GATAGCTAAAGCTGAAGTTAGTTAAG 13000 1 mmu_ref_6_37:54554544 F GATAGCTAAAGCTGAAGTTAGTTAAG 9859 GTAAGCAGTATCTGGGTAGCAGTGAT 13000 1 mmu_ref_4_37:30409546 R ATCACTGCTACCCAGATACTGCTTAC 9859 GTTTTGTCGATTTTATTCTTTCCAGG 13000 1 mmu_ref_3_37:112008324 F GTTTTGTCGATTTTATTCTTTCCAGG 9859 TTAATTCCTCTTACTCAGGAAGCAAA 13000 1 mmu_ref_8_37:108643109 F TTAATTCCTCTTACTCAGGAAGCAAA 9859 GTTTGGGAAATTGGATTTTGTTTCGT 10906 1 mmu_ref_7_37:146810559 F GTTTGGGAAATTGGATTTTGTTTGGC 9859 GATAATTCCACATGCATATGTCACAT 13000 1 mmu_ref_9_37:17363912 F GATAATTCCACATGCATATGTCACAT 9859 GAAAAATGAAATCACTTGAATGATGG 13000 1 mmu_ref_4_37:19271012 R CCATCATTCAAGTGATTTCATTTTTC 9859 GTCTCCATCTAGGATACTGTAGGGAT 13000 1 mmu_ref_14_37:47151876 R ATCCCTACAGTATCCTAGATGGAGAC 9859 GGAAAACACGGAGCTAAGGACGGAAT 11953 1 mmu_ref_3_37:145805803 F GGAAAACACGGAGCTAAGGACGGTAT 9859 GTATGATAAAAACTTCAAATCTCTGA 13000 255 GAGGGATGGATGCTATGCTCACTTAT 13000 1 mmu_ref_2_37:136005373 F GAGGGATGGATGCTATGCTCACTTAT 9859 GTGATTTTCAGTTTTCTCGCCATATT 13000 44 GAATCCGGTTAAAGTTGGCAGTTGGT 0 0 GTGGATAATTTAGAAATGTCCACTGT 11953 51 GTCTGCTATCCCTTCCTTCTCTTCCC 13000 1 mmu_ref_1_37:22573779 R GGGAAGAGAAGGAAGGGATAGCAGAC 9859 GAAAAAATATCAAGTGATCAAGTACA 13000 1 mmu_ref_2_37:86868605 F GAAAAAATATCAAGTGATCAAGTACA 9859 GATGTTTCTCATTTTCCATGATTTTC 13000 71 TTCAACATAGTTGTTGAATTGGTGGT 13000 1 mmu_ref_5_37:148339512 F TTCAACATAGTTGTTGAATTGGTGGT 9859 GATATGCGCTTACCCTGCTAGAAGGG 13000 1 mmu_ref_5_37:76436477 R CCCTTCTAGCAGGGTAAGCGCATATC 9859 GTAGAATTTCTGCTCAGAATCCATTT 13000 1 mmu_ref_18_37:19741659 R AAATGGATTCTGAGCAGAAATTCTAC 9859 GTTAAAGCGCCGCAAGTGTTGATTTG 13000 1 mmu_ref_2_37:48411382 R CAAATCAACACTTGCGGCGCTTTAAC 9859 GTGCATTTTTCCTTCCTTCCATCACA 13000 1 mmu_ref_2_37:54382378 F GTGCATTTTTCCTTCCTTCCATCACA 9859 GTGGTTTTTATCATTTTCCATGTTTC 13000 5 GTGAAAAATGAGAAATGCACACTCTA 11953 90 TTTGAATGATTACTAAGGGAAGATTT 13000 1 mmu_ref_6_37:6065828 R AAATCTTCCCTTAGTAATCATTCAAA 9859 GTGTGTAAGGGTGTGGGGGGGATTGT 13000 1 mmu_ref_7_37:60884160 F GTGTGTAAGGGTGTGGGGGGGATTGT 10906 TCTCCAACTATAAATTCATGTTCCAG 11953 1 mmu_ref_3_37:92135783 F TCTCCAACTATAAATTAATGTTCCAG 9859 GGTTGCCAAAAATTATGCCTACAATT 13000 1 mmu_ref_X_37:22779882 R AATTGTAGGCATAATTTTTGGCAACC 10906 GTTTTCTCGCCATATTCCAGGTCCTT 13000 1 mmu_ref_2_37:98507289 R AAGGACCTGGAATATGGCGAGAAAAC 11953 GGAAAATGAGAAACATCCACTTGACG 13000 23 TTCCAAAGTGTGCTTGCAATATGACC 13000 1 mmu_ref_6_37:18221380 R GGTCATATTGCAAGCACACTTTGGAA 10906 GGAAGTTTCTCATATTCTTCGATTTT 0 0 AATCATGGAAAATGAGAAACATCCAC 13000 67 GAATATTTAGAAATGTCCACTGTAGG 13000 3 ShortRead/inst/unitTests/cases/s_2_export_run_as_factor.txt0000644000175400017540000000233213001230551025270 0ustar00biocbuildbiocbuildHWI-EAS88 genome 2 1 451 945 CCAGAGCCCCCCGCTCACTCCTGAACCAGTCTCTC YQMIMIMMLMMIGIGMFICMFFFIMMHIIHAAGAH NM N HWI-EAS88 genome 2 1 409 991 AGCCTCCCTCTTTCTGAATATACGGCAGAGCTGTT ZXZUYXZQYYXUZXYZYYZZXXZZIMFHXQSUPPO NM Y HWI-EAS88 genome 2 1 451 939 ACCAAAAACACCACATACACGAGCAACACACGTAC LGDHLILLLLLLLIGFLLALDIFDILLHFIAECAE NM N HWI-EAS88 genome 2 1 447 961 AATCGGAAGAGCTCGTATGCCGGCTTCTGCTTGGA JJYYIYVSYYYYYYYYSDYYWVUYYNNVSVQQELQ NM N HWI-EAS88 genome 2 1 450 960 AAAGATAAACTCTAGGCCACCTCCTCCTTCTTCTA LLLILIIIDLLHLLLLLLLLLLLALLLLHLLLLEL NM N HWI-EAS88 genome 2 1 467 922 AAAAAAAAAAAGGACACACCATGAGATCACAGGGA YYYYYYYWVVMGGUHQHQMUFMICDMCDHQHEDDD NM N HWI-EAS88 genome 2 1 874 313 TAAAAAATTAGCAAAAAACCAAAAATGTAATTGAT ZZZZZZZZZZYZZZZZYZZZZYYZZZZZZZUUUUU chr17.fa 69345321 R A30A3 14 Y HWI-EAS88 genome 2 1 907 256 TAAATCGTGCTGTAACCTTTCCCAACATCTCTGTG ZZZZZZZZUZZUZZZZZZZZZZZZYZYZZZUUHUH chr18.fa 54982866 F 35 67 Y HWI-EAS88 genome 2 1 889 547 AATGACCGATAATTAAAAATAAAATCTTTGCATAT ZZZZZZZYZZYZZZZYZZZZZZZZZZZZZXUNUUU NM Y HWI-EAS88 genome 2 1 892 426 TCATCATTTTTCTAAGTGTTATGAAGAAAATATAT ZZZZUUZYZYZZZZZLZSZZYYUUZDUJIYUUULU chr12.fa 80537786 R 25T9 18 Y ShortRead/inst/unitTests/cases/s_2_export_toIUPAC.txt0000644000175400017540000000166113001230551023653 0ustar00biocbuildbiocbuildHWI-EAS88 3 2 1 451 945 CCAG.GCCA-CCGTTCACTCCTGAACCAGTCTCTC YQMI.IMML-MIGIGMFICMFFFIMMHIIHAAGAH NM N HWI-EAS88 3 2 1 409 991 AGCCTCCCTCTTTCTGAATATACGGCAGAGCTGTT ZXZUYXZQYYXUZXYZYYZZXXZZIMFHXQSUPPO NM Y HWI-EAS88 3 2 1 451 939 ACCAAAAACACCACATACACGAGCAACACACGTAC LGDHLILLLLLLLIGFLLALDIFDILLHFIAECAE NM N HWI-EAS88 3 2 1 447 961 AATCGGAAGAGCTCGTATGCCGGCTTCTGCTTGGA JJYYIYVSYYYYYYYYSDYYWVUYYNNVSVQQELQ NM N HWI-EAS88 3 2 1 450 960 AAAGATAAACTCTAGGCCACCTCCTCCTTCTTCTA LLLILIIIDLLHLLLLLLLLLLLALLLLHLLLLEL NM N HWI-EAS88 3 2 1 467 922 AAAAAAAAAAAGGACACACCATGAGATCACAGGGA YYYYYYYWVVMGGUHQHQMUFMICDMCDHQHEDDD NM N HWI-EAS88 3 2 1 874 313 TAAAAAATTAGCAAAAAACCAAAAATGTAATTGAT ZZZZZZZZZZYZZZZZYZZZZYYZZZZZZZUUUUU chr17.fa 69345321 R A30A3 14 Y HWI-EAS88 3 2 1 907 256 TAAATCGTGCTGTAACCTTTCCCAACATCTCTGTG ZZZZZZZZUZZUZZZZZZZZZZZZYZYZZZUUHUH chr18.fa 54982866 F 35 67 Y ShortRead/inst/unitTests/cases/sanger.fastq0000644000175400017540000000022613001230551022055 0ustar00biocbuildbiocbuild@sanger ASCII 33-126; < 59 marks as not solexa GCGGACCGCTTGATATCCATGCCCCAG +sanger ASCII 33-126; < 59 marks as not solexa !\"#$%&'()*+,-./0123456789: ShortRead/inst/unitTests/cases/soap.txt0000644000175400017540000000475713001230551021256 0ustar00biocbuildbiocbuildSIMU_0001_00000081/1 TGTACAGTATGTGAAGAGATTTGTTCTGAACCAAA hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh 1 a 35 + refseq 2210 0 SIMU_0001_00000081/2 CATGCCCATGACACTACCTCAGGAGGTCCTGACAG hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh 1 b 35 - refseq 2708 1 G->15A40 SIMU_0001_00000082/1 GTATAAAAAGAAAAATGTTTATTAAACTTCTATAG hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh 1 a 35 + refseq 50653 2 C->2A40 C->9G40 SIMU_0001_00000082/2 GACATTCTTTAAAGTTTCACTTAAAAGATCTGCAA hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh 1 b 35 - refseq 51126 0 SIMU_0001_00000083/1 ATAGAGGGAGTGGAGTAGAGGAAAAGCCAATGATT hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh 1 a 35 + refseq 20925 0 SIMU_0001_00000083/2 TCTGCGCTCACTATTAAATAAGAATGACCAGGAGA hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh 1 b 35 - refseq 21371 0 SIMU_0001_00000084/1 AGACCCCAGGTGGATGCATTGGTCCTAGGTAAACA hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh 1 a 35 + refseq 45408 0 SIMU_0001_00000084/2 GCCGCAGGTGCCTCACCTCTACTCCCTGAAACCTC hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh 1 b 35 - refseq 45856 1 A->3G40 SIMU_0001_00000085/1 TAAATTTAAAAATGAGAGAGAAATAAAGAAGGAGA hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh 1 a 35 + refseq 57096 0 SIMU_0001_00000085/2 ATATTTCAGACATCCAAAGAGAGAAAGAAAAGTCA hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh 1 b 35 - refseq 57516 1 A->6C40 SIMU_0001_00000086/1 TAGGCTTAAGGACAGTGGCAAACATGGCCTCTGCC hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh 1 a 35 + refseq 6075 1 T->7A40 SIMU_0001_00000086/2 AGCTGTCCAAACACCTTATCTTTTCATCTCTGACC hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh 1 b 35 - refseq 6526 0 SIMU_0001_00000087/1 ACCACTGAGGCCCGCTTTCCCTGCTGAGGGGGGGG hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh 1 a 35 + refseq 6827 0 SIMU_0001_00000087/2 ATCACATCACCAGGAGGGTGTAGAAATCTGTAGTT hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh 1 b 35 - refseq 7278 0 SIMU_0001_00000088/1 GACTATCTGAGTAACTCTTTCAAAAAGAATTGTCC hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh 1 a 35 + refseq 88610 1 G->9A40 SIMU_0001_00000088/2 TTTCAATGATGTTCCAATTTTCTAAAATATAATAT hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh 1 b 35 - refseq 89058 2 G->21C40 A->19T40 SIMU_0001_00000089/1 GAACTACAAAATTAGAGATCATCATAGCTATATTG hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh 1 a 35 + refseq 91226 0 SIMU_0001_00000089/2 AGGCTATGAAGTGTTGCACGTGATAATCCAAGCAT hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh 1 b 35 - refseq 91679 1 A->7G40 SIMU_0001_00000091/1 CTCGCCAAGGCAACACAATGTAGCAGTCTCTCTTG hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh 1 a 35 + refseq 31544 0 SIMU_0001_00000091/2 CAGGCAGACAGCTTCCCTGAGAACCAGTCTCTTAC hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh 1 b 35 - refseq 32056 0 SIMU_0001_00000092/1 TCTGAAACAACCTTTATTCTCTTGAGAGTTAATAT hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh 1 a 35 + refseq 63491 1 C->34T40 ShortRead/inst/unitTests/cases/solexa.fastq0000644000175400017540000000025613001230551022074 0ustar00biocbuildbiocbuild@solexa ASCII 59-126; 59-104 realistic GCGCGGATCTTTAGCATTGTAGTACCGGACATAACAACAATTTTGCC +solexa ASCII 59-126; 59-104 realistic ;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefgh ShortRead/inst/unitTests/test_AlignedRead.R0000644000175400017540000002633113001230551021766 0ustar00biocbuildbiocbuildsp <- SolexaPath(system.file("extdata", package="ShortRead")) aln <- readAligned(sp, "s_2_export.txt") .checkAlignedRead_identical<- function(obs, exp) ## can't compare external pointers { checkIdentical(as.character(sread(obs)), as.character(sread(exp))) checkIdentical(as.character(quality(quality(obs))), as.character(quality(quality(exp)))) checkIdentical(as.character(id(obs)), as.character(id(exp))) checkIdentical(chromosome(obs), chromosome(exp)) checkIdentical(strand(obs), strand(exp)) checkIdentical(alignQuality(obs), alignQuality(exp)) checkIdentical(alignData(obs), alignData(exp)) } test_AlignedRead_Bowtie <- function() { src <- system.file("extdata", "bowtie", package="ShortRead") df <- read.table(file.path(src, "s_1_aligned_bowtie.txt"), fill=TRUE, quote="", sep="\t") aln <- readAligned(src, "^s_1_aligned_bowtie.txt$", "Bowtie") checkIdentical(nrow(df), length(aln)) checkIdentical(as.character(df[[2]]), as.character(strand(aln))) checkIdentical(as.character(df[[3]]), as.character(chromosome(aln))) checkIdentical(df[[4]]+1L, position(aln)) idx <- strand(aln)=="-" s1 <- as.character(df[[5]]) s1[idx] <- as.character(reverseComplement(DNAStringSet(s1[idx]))) checkIdentical(s1, as.character(sread(aln))) q1 <- as.character(df[[6]]) q1[idx] <- as.character(reverse(BStringSet(q1[idx]))) checkIdentical(q1, as.character(quality(quality(aln)))) checkIdentical(as.character(df[[8]]), as.character(alignData(aln)[["mismatch"]])) } test_AlignedRead_SOAP <- function() { fl <- "soap.txt" src <- system.file("unitTests", "cases", package="ShortRead") tbl <- read.table(file.path(src, fl), fill=TRUE) aln <- readAligned(src, fl, "SOAP") checkTrue(validObject(aln)) checkIdentical(as.character(tbl[[1]]), as.character(id(aln))) strand <- as.character(strand(aln)) checkIdentical(as.character(tbl[[7]]), strand) sread <- as.character(sread(aln)) sread[strand=="-"] <- as.character(reverseComplement(sread(aln)))[strand=="-"] checkIdentical(as.character(tbl[[2]]), sread) qual <- as.character(quality(quality(aln))) qual[strand=="-"] <- as.character(reverse(quality(quality(aln)))[strand=="-"]) checkIdentical(as.character(tbl[[3]]), qual) checkIdentical(as.character(tbl[[8]]), as.character(chromosome(aln))) checkIdentical(tbl[[9]], position(aln)) checkTrue(all(is.na(quality(alignQuality(aln))))) with(pData(alignData(aln)), { checkIdentical(tbl[[4]], nEquallyBestHits) checkIdentical(as.character(tbl[[5]]), as.character(pairedEnd)) checkIdentical(tbl[[6]], alignedLength) checkIdentical(tbl[[10]], typeOfHit) checkIdentical(c("", "G->15A40", "C->2A40\tC->9G40", "", "", "", "", "A->3G40", "", "A->6C40", "T->7A40", "", "", "", "G->9A40", "G->21C40\tA->19T40", "", "A->7G40", "", "", "C->34T40"), hitDetail) }) } test_AlignedRead_readAligned_SolexaExport <- function() { obj <- readAligned(analysisPath(sp), pattern="s_2_export.txt", type="SolexaExport") checkTrue(validObject(obj)) checkTrue(is(quality(obj), "SFastqQuality")) checkTrue(is(alignQuality(obj), "NumericQuality")) checkIdentical(varLabels(alignData(obj)), c("run", "lane", "tile", "x", "y", "filtering", "contig")) } test_AlignedRead_readAligned_SolexaExport_filter <- function() { chr <- "chr5.fa" filt <- chromosomeFilter(chr) obs <- readAligned(sp, "s_2_export.txt", filter=filt) exp <- aln[grep(chr, chromosome(aln))] .checkAlignedRead_identical(obs, exp) obs <- readAligned(analysisPath(sp), "s_2_export.txt", "SolexaExport", filter=filt) .checkAlignedRead_identical(obs, exp) } test_AlignedRead_readAligned_SolexaExport_withWhat <- function() { src <- system.file("unitTests", "cases", package="ShortRead") aln <- readAligned(src, "PE_export.txt.gz", type="SolexaExport", withAll=TRUE) checkIdentical(400L, length(aln)) e0 <- c("HWUSI-EAS618_1:1:1:0:1122#AGCACGA/1", "HWUSI-EAS618_1:1:1:0:843#ACCACGA/1", "HWUSI-EAS618_1:1:1:4:873#ATCACGA/1", "HWUSI-EAS618_1:1:1:4:480#ACCACGA/1") checkIdentical(e0, as.character(id(aln)[c(1,2, 399, 400)])) e1 <- structure(c(41L, 2L, 72L, 4L, 17L, 17L, 5L, 2L, 8L, 70L, 1L, 2L, 1L, 1L, 1L, 37L, 1L, 3L, 2L, 1L, 1L, 70L, 1L, 1L, 1L, 4L, 31L, 1L, 1L, 1L), .Dim = 30L, .Dimnames = structure(list(c("AACACGA", "AACCCGA", "ACCACGA", "ACCCCGA", "AGACCAA", "AGCACGA", "AGCCCAA", "AGCCCCA", "AGCCCGA", "ATCACGA", "ATCCCCA", "ATCCCGA", "CACCCTC", "CACGACC", "CCCCCGA", "CGACCAA", "CGACCAC", "CGCCCAA", "CGCCCCA", "CTCCCTT", "GCGCCCA", "GGACCAA", "GGACCCA", "GGACNAA", "GGCCCAA", "NNNNNNN", "TGACCAA", "TGCCCTA", "TTCCCTG", "TTCCCTT")), .Names = ""), class = "table") checkIdentical(e1, table(alignData(aln)[["multiplexIndex"]])) aln0 <- readAligned(src, "PE_export.txt.gz", type="SolexaExport", withId=TRUE, withMultiplexIndex=TRUE) checkIdentical(sub("/1$", "", as.character(id(aln))), as.character(id(aln0))) colidx <- varLabels(alignData(aln)) != "pairedReadNumber" checkIdentical(alignData(aln)[, colidx], alignData(aln0)) } test_AlignedRead_readAligned_MAQMapview <- function() { fl <- system.file("extdata", "maq", package="ShortRead") obj <- readAligned(fl, pattern=".*aln.*", type="MAQMapview") checkTrue(validObject(obj)) checkTrue(is(quality(obj), "FastqQuality")) checkTrue(is(alignQuality(obj), "NumericQuality")) checkIdentical(varLabels(alignData(obj)), c("nMismatchBestHit", "mismatchQuality", "nExactMatch24", "nOneMismatch24")) checkIdentical(levels(chromosome(obj)), "ChrA") checkTrue(!any(is.na(chromosome(obj))) && !any(is.null(chromosome(obj)))) } readAligned_maq_consistent <- function() { ## FIXME: find adequate data to store in ShortRead pkg if (!file.exists("/home/jdavison/sharedrsrc/proj/ycao/data/binary_maps/s_5.map")) return(TRUE) x <- readAligned("/home/jdavison/sharedrsrc/proj/ycao/data/binary_maps", "s_5.map", "MAQMap") y <- readAligned("/home/jdavison/sharedrsrc/proj/ycao/data/text_maps", "s_5.txt", "MAQMapview") checkIdentical(length(x), length(y)) checkIdentical(width(x), width(y)) checkIdentical(as.character(chromosome(x)), as.character(chromosome(y))) ## FIXME: we'd really like chromosome to have identical levels, ## but info on levels with no mapped reads is not available in the ## text version idx <- match(levels(chromosome(y)), levels(chromosome(x))) checkTrue(all(!is.na(idx)) && all(diff(idx) > 0)) checkIdentical(position(x), position(y)) checkIdentical(strand(x), strand(y)) checkIdentical(alignQuality(x), alignQuality(y)) checkIdentical(alignData(x), alignData(y)) .checkXString <- function(x, y) { checkIdentical(as.character(x), as.character(y)) } .checkXString(sread(x), sread(y)) .checkXString(sread(x), sread(y)) .checkXString(quality(quality(x)), quality(quality(y))) .checkXString(id(x), id(y)) } test_AlignedRead_readAligned_run_as_factor <- function() { src <- system.file("unitTests", "cases", package="ShortRead") aln <- readAligned(src, "^s_2_export_run_as_factor.txt$", "SolexaExport") checkIdentical(alignData(aln)[["run"]], factor(rep("genome", length(aln)))) } test_AlignedRead_readAligned_realign_targetpos <- function() { ## column 4 can be target:pos fl <- "s_2_0001_realign_head.txt" src <- system.file("unitTests", "cases", package="ShortRead") tbl <- read.table(file.path(src, fl), fill=TRUE) aln <- readAligned(src, fl, "SolexaRealign") checkIdentical(as.character(tbl[[1]]), as.character(sread(aln))) checkIdentical(tbl[[2]], quality(alignQuality(aln))) checkIdentical(tbl[[3]], alignData(aln)[["nMatch"]]) checkIdentical(table(tbl[[5]])[["F"]], table(strand(aln))[["+"]]) chr <- sub(":.*", "", tbl[[4]]) chr[nchar(chr)==0] <- NA checkIdentical(factor(chr), chromosome(aln)) checkIdentical(as.integer(sub(".*:", "", tbl[[4]])), position(aln)) checkIdentical(ShortRead:::.toStrand_Solexa(tbl[[5]]), strand(aln)) checkIdentical(tbl[[7]], alignData(aln)[["nextBestAlignQuality"]]) } test_AlignedRead_readAligned_realign_threecol <- function() { ## column 4 can be target:pos fl <- "s_2_0001_realign_3col_head.txt" src <- system.file("unitTests", "cases", package="ShortRead") tbl <- read.table(file.path(src, fl), fill=TRUE) aln <- readAligned(src, fl, "SolexaRealign") checkIdentical(as.character(tbl[[1]]), as.character(sread(aln))) checkIdentical(tbl[[2]], quality(alignQuality(aln))) checkIdentical(tbl[[3]], alignData(aln)[["nMatch"]]) checkIdentical(factor(rep(NA_character_, nrow(tbl))), chromosome(aln)) checkIdentical(rep(NA_integer_, nrow(tbl)), position(aln)) checkIdentical(ShortRead:::.toStrand_Solexa(rep("", nrow(tbl))), strand(aln)) checkIdentical(rep(NA_integer_, nrow(tbl)), alignData(aln)[["nextBestAlignQuality"]]) } test_AlignedRead_readAligned_SolexaResult <- function() { fl <- "s_1_results_head.txt" src <- system.file("unitTests", "cases", package="ShortRead") tbl <- read.table(file.path(src, fl), fill=TRUE, col.names=paste("V", 1:12, sep="")) aln <- readAligned(src, fl, "SolexaResult") checkIdentical(as.character(tbl[[2]]), as.character(sread(aln))) chr <- tbl[[7]] checkIdentical(factor(chr), chromosome(aln)) checkIdentical(tbl[[8]], position(aln)) checkIdentical(ShortRead:::.toStrand_Solexa(tbl[[9]]), strand(aln)) ad <- alignData(aln) checkIdentical(tbl[[3]], ad[[1]]) checkIdentical(tbl[[4]], ad[[2]]) checkIdentical(tbl[[5]], ad[[3]]) checkIdentical(tbl[[6]], ad[[4]]) checkIdentical(tbl[[10]], ad[[5]]) checkIdentical(tbl[[11]], ad[[6]]) checkIdentical(tbl[[12]], ad[[7]]) } test_AlignedRead_constructor <- function() { aln <- AlignedRead() checkTrue(validObject(aln)) aln <- AlignedRead(sread=DNAStringSet(polyn("A", 5))) checkTrue(validObject(aln)) aln <- AlignedRead(sread=DNAStringSet( c(polyn("A", 5), polyn("A", 10)))) checkTrue(validObject(aln)) checkIdentical(c(5L, 10L), width(aln)) } test_AlignedRead_compact <- function() { exp <- aln[1:100] obs <- compact(exp) checkIdentical(as.character(sread(exp)), as.character(sread(obs))) checkIdentical(as.character(quality(quality(exp))), as.character(quality(quality(obs)))) checkIdentical(as.character(id(exp)), as.character(id(obs))) checkIdentical(alignData(exp), alignData(obs)) } ShortRead/inst/unitTests/test_AllClasses.R0000644000175400017540000000101113001230551021641 0ustar00biocbuildbiocbuildtest_AllClasses_STRAND_LEVELS <- function() { checkIdentical(ShortRead:::.STRAND_LEVELS, levels(strand())) } test_AllClasses_new <- function() { nmspace <- getNamespace("ShortRead") nms <- names(slot(getClass(".ShortReadBase", where=nmspace), "subclasses")) ## 'new' with no additional arguments ok <- Map(function(x) validObject(new(x)), Filter(function(x) !slot(getClass(x, where=nmspace), "virtual"), nms)) checkTrue(all(unlist(ok))) } ShortRead/inst/unitTests/test_FastqFile.R0000644000175400017540000000136613001230550021505 0ustar00biocbuildbiocbuildsp <- SolexaPath(system.file('extdata', package='ShortRead')) fl <- file.path(analysisPath(sp), "s_1_sequence.txt") test_FastqFile <- function() { fq <- FastqFile(fl) checkTrue(validObject(fq)) checkIdentical(path(fq), fl) checkTrue(!isOpen(fq)) close(fq) } test_FastqFileList <- function() { fql0 <- FastqFileList(c(fl, fl)) checkTrue(validObject(fql0)) checkIdentical(2L, length(fql0)) fql1 <- FastqFileList(FastqFile(fl), FastqFile(fl)) checkIdentical(sapply(fql0, path), sapply(fql1, path)) checkIdentical(sapply(fql0, isOpen), sapply(fql1, isOpen)) open(fql0) checkTrue(all(sapply(fql0, isOpen))) close(fql0) checkTrue(all(!sapply(fql0, isOpen))) checkTrue(all(!sapply(fql1, isOpen))) } ShortRead/inst/unitTests/test_Intensity.R0000644000175400017540000000225213001230551021611 0ustar00biocbuildbiocbuildtest_IntensityMeasure_subset <- function() { a <- array(1:1000, c(10, 10, 10)) x <- ArrayIntensity(a) checkTrue(all(a==x)) checkTrue(all(a[,,]==x[,,])) checkTrue(all(a[1:5,,]==x[1:5,,])) checkTrue(all(a[,1:5,]==x[,1:5,])) checkTrue(all(a[1:5,1:5,]==x[1:5,1:5,])) checkTrue(all(a[,,1:5]==x[,,1:5])) checkTrue(all(a[1:5,,1:5]==x[1:5,,1:5])) checkTrue(all(a[,1:5,1:5]==x[,1:5,1:5])) checkTrue(all(a[1:5,1:5,1:5]==x[1:5,1:5,1:5])) } test_Intensity_subset <- function() { check <- function(obj, m, adf) { checkTrue(all(m==intensity(obj))) checkTrue(all(m==measurementError(obj))) checkIdentical(adf, readIntensityInfo(obj)) } m <- array(1:1000, c(10, 10, 10)) adf <- SolexaIntensityInfo(1:10) si <- SolexaIntensity(intensity=m, measurementError=m, readInfo=adf) ridx <- sample(nrow(m), 5) cidx <- sample(ncol(m), 5) x <- ArrayIntensity(m) checkTrue(all(intensity(si)==x)) checkTrue(all(measurementError(si)==x)) check(si[,,], m[,,], adf) check(si[ridx,,], m[ridx,,], adf[ridx,]) check(si[,cidx,], m[,cidx,], adf) check(si[ridx, cidx,], m[ridx, cidx,], adf[ridx,]) } ShortRead/inst/unitTests/test_SRError.R0000644000175400017540000000317513001230551021166 0ustar00biocbuildbiocbuildthrow <- ShortRead:::.throw ## SRError test_SRError_construction <- function() { checkTrue(validObject(SRError("UnspecifiedError", "message"))) checkException(SRError(), silent=TRUE) checkException(SBError("UnspecifiedError"), silent=TRUE) # must have message checkException(SRError("Bad error class", "Message"), silent=TRUE) # must have valid class } test_SRError_throw <- function() { err <- SRError("UnspecifiedError", "error message") checkException(throw(err), silent=TRUE) } test_SRError_throw <- function() { err <- SRError("UnspecifiedError", "error message") checkTrue(tryCatch(throw(err), SRError=function(err) TRUE)) checkTrue(tryCatch(throw(err), UnspecifiedError=function(err) TRUE)) } ## SRWarn test_SRWarn_construction <- function() { checkTrue(validObject(SRWarn("UnspecifiedWarning", "message"))) checkException(SRWarn(), silent=TRUE) checkException(SRWarn("UnspecifiedWarning"), silent=TRUE) checkException(SRWarn("Bad Warn Class"), silent=TRUE) } test_SRWarn_throw <- function() { old.opt <- options(warn=2) on.exit(options(old.opt)) warn <- SRWarn("UnspecifiedWarning", "warning message") checkException(throw(warn), silent=TRUE) } test_SRWarn_catch <- function() { old.opt <- options(warn=2) on.exit(options(old.opt)) warn <- SRWarn("UnspecifiedWarning", "warning message") checkTrue(tryCatch(throw(warn), SRWarn=function(warn) TRUE)) checkTrue(tryCatch(throw(warn), UnspecifiedWarning=function(warn) TRUE)) } ShortRead/inst/unitTests/test_SRFilter.R0000644000175400017540000001627413001230551021326 0ustar00biocbuildbiocbuildaln <- local({ sp <- SolexaPath(system.file("extdata", package="ShortRead")) readAligned(sp, "s_2_export.txt") }) test_srFilter <- function() { checkTrue(validObject(srFilter())) checkTrue(validObject(srFilter(name="Filter"))) checkIdentical(name(srFilter(name="Filter")), Biobase::mkScalar("Filter")) checkTrue(validObject(srFilter(function(x) {}))) checkException(srFilter(function(){}), silent=TRUE) checkException(srFilter(function(x, ...) {}), silent=TRUE) } test_occurrenceFilter <- function() { checkTrue(validObject(occurrenceFilter())) aln <- AlignedRead(DNAStringSet(character(2)), chromosome=c("chr1", "chr1"), position=c(1L, 1L), strand=rep(strand("+"), 2)) checkTrue(all(c(TRUE, FALSE) == occurrenceFilter(withSread=TRUE)(aln))) checkTrue(all(c(TRUE, FALSE) == occurrenceFilter(withSread=FALSE)(aln))) checkTrue(all(c(TRUE, FALSE) == occurrenceFilter(withSread=NA)(aln))) aln <- AlignedRead(DNAStringSet(c("A", "T")), chromosome=c("chr1", "chr1"), position=c(1L, 1L), strand=rep(strand("+"), 2)) checkTrue(all(c(TRUE, TRUE) == occurrenceFilter(withSread=TRUE)(aln))) checkTrue(all(c(TRUE, FALSE) == occurrenceFilter(withSread=FALSE)(aln))) checkTrue(all(c(TRUE, TRUE) == occurrenceFilter(withSread=NA)(aln))) aln <- AlignedRead(DNAStringSet(character(4)), chromosome=rep(c("chr1", "chr2"), each=2), position=rep(1:2, 2), strand=rep(strand("+"), 4)) checkTrue(all(occurrenceFilter(withSread=FALSE)(aln))) checkTrue(all(occurrenceFilter(withSread=TRUE)(aln))) checkTrue(all(c(TRUE, FALSE, FALSE, FALSE) == occurrenceFilter(withSread=NA)(aln))) sp <- SolexaPath(system.file("extdata", package="ShortRead")) aln <- readAligned(analysisPath(sp), "s_2_export.txt", "SolexaExport") checkIdentical(980L, sum(occurrenceFilter(withSread=NA)(aln))) checkIdentical(996L, sum(occurrenceFilter(withSread=TRUE)(aln))) df <- data.frame(chromosome(aln), position(aln), strand(aln)) checkIdentical(sum(!duplicated(df)), sum(occurrenceFilter(withSread=FALSE)(aln))) checkIdentical(15L, sum(occurrenceFilter(min=5, max=10, withSread=NA)(aln))) checkIdentical(13L, sum(occurrenceFilter(min=3, max=5, withSread=NA)(aln))) checkIdentical(8L, sum(occurrenceFilter(min=3, max=5, duplicates="none", withSread=NA)(aln))) } test_chromosomeFilter <- function() { checkTrue(validObject(chromosomeFilter())) checkException(chromosomeFilter(c("foo", "bar")), silent=TRUE) chr <- "chr5.fa" obj <- chromosomeFilter(chr) checkIdentical(aln[obj(aln)], aln[grep(chr, chromosome(aln))]) } test_strandFilter <- function() { checkTrue(validObject(strandFilter())) checkException(strandFilter(1), silent=TRUE) str <- character(0) obj <- strandFilter(str) checkIdentical(aln[obj(aln)], aln[FALSE]) str <- "+" obj <- strandFilter(str) checkIdentical(aln[obj(aln)], aln[strand(aln)=="+" & !is.na(strand(aln))]) str <- c("+", "-") obj <- strandFilter(str) checkIdentical(aln[obj(aln)], aln[(strand(aln)=="+" |strand(aln)=="-") & !is.na(strand(aln))]) str <- c("+", NA) obj <- strandFilter(str) checkIdentical(aln[obj(aln)], aln[strand(aln)=="+" | is.na(strand(aln))]) obj <- strandFilter(NA_character_) checkIdentical(aln[obj(aln)], aln[is.na(strand(aln))]) } test_nFilter <- function() { checkTrue(validObject(nFilter())) checkTrue(validObject(nFilter(20))) checkException(nFilter("alf"), silent=TRUE) checkException(nFilter(1:2), silent=TRUE) n <- 0 checkIdentical(aln[nFilter(n)(aln)], clean(aln)) n <- 30 alf <- alphabetFrequency(sread(aln), baseOnly=TRUE) checkIdentical(aln[nFilter(n)(aln)], aln[alf[,"other"] <= n]) } test_polynFilter <- function() { checkTrue(validObject(polynFilter())) checkTrue(validObject(polynFilter(20))) checkTrue(validObject(polynFilter(nuc=c("A", "other")))) checkException(polynFilter(1:2), silent=TRUE) checkException(polynFilter("x"), silent=TRUE) checkException(polynFilter(nuc="Z"), silent=TRUE) alf <- alphabetFrequency(sread(aln), baseOnly=TRUE) n <- 30 obj <- polynFilter(n) checkIdentical(aln[obj(aln)], aln[apply(alf, 1, max) <= n]) n <- 30 obj <- polynFilter(n, c("A", "C", "T", "G")) checkIdentical(aln[obj(aln)], aln[apply(alf[,1:4], 1, max) <= n]) } test_dustyFilter <- function() { checkTrue(validObject(dustyFilter())) checkTrue(validObject(dustyFilter(20))) checkTrue(validObject(lgl0 <- dustyFilter(10L)(aln))) checkTrue(validObject(lgl1 <- dustyFilter(10L)(sread(aln)))) checkIdentical(lgl0, lgl1) checkIdentical(lgl0, dustyFilter(10L, 100L)(aln)) checkIdentical(lgl0, dustyFilter(10L, 100L)(sread(aln))) } test_srdistanceFilter <- function() { checkTrue(validObject(srdistanceFilter())) checkTrue(validObject(srdistanceFilter("sdf", 1))) checkException(srdistanceFilter(123), silent=TRUE) checkException(srdistanceFilter("sdfs", 1:2), silent=TRUE) obj <- srdistanceFilter() checkIdentical(aln[obj(aln)], aln) nr <- c("GATCGGAAGAGCTCGTATGCCGTCTTCTGCTTAGA", "GATCGGAAGAGCTCGTATGCCGTCTTCTGCTTGAA") obj <- srdistanceFilter(nr[[1]], 1L) checkIdentical(aln[obj(aln)], aln[as.character(sread(aln))!=nr[[1]]]) obj <- srdistanceFilter(nr, 1L) checkIdentical(aln[obj(aln)], aln[as.character(sread(aln))!=nr[[1]] & as.character(sread(aln))!=nr[[2]] ]) } test_alignQualityFilter <- function() { checkTrue(validObject(alignQualityFilter())) checkTrue(validObject(alignQualityFilter(70))) checkException(alignQualityFilter("foo"), silent=TRUE) checkException(alignQualityFilter(threshold=1:2), silent=TRUE) checkIdentical(aln[alignQualityFilter()(aln)], aln) n <- 70 obj <- alignQualityFilter(n) checkIdentical(aln[obj(aln)], aln[quality(alignQuality(aln))>=70]) } test_alignDataFilter <- function() { checkTrue(validObject(alignDataFilter())) ex <- expression(x>200 & y<600) checkTrue(validObject(alignDataFilter(ex))) ad <- pData(alignData(aln)) checkIdentical(aln[alignDataFilter(ex)(aln)], aln[eval(ex, ad)]) } test_compose <- function() { f1 <- chromosomeFilter("chr5.fa") f2 <- polynFilter(12) checkTrue(validObject(compose())) checkTrue(validObject(compose(f1))) checkTrue(validObject(compose(f1, f2))) obj <- compose(f1, f2) checkTrue(validObject(obj)) checkIdentical(name(obj), Biobase::mkScalar(paste(name(f1), name(f2), sep=" o "))) checkException(compose("foo"), silent=TRUE) checkException(compose(f1, "foo"), silent=TRUE) checkIdentical(aln[compose(f1)(aln)], aln[f1(aln)]) checkIdentical(aln[obj(aln)], aln[f1(aln) & f2(aln)]) } ShortRead/inst/unitTests/test_SRFilterResult.R0000644000175400017540000000606613001230551022523 0ustar00biocbuildbiocbuildmkScalar <- Biobase::mkScalar test_SRFilterResult_constructor <- function() { checkTrue(validObject(SRFilterResult())) fr <- SRFilterResult(TRUE) checkTrue(validObject(fr)) checkIdentical(mkScalar(NA_character_), name(fr)) df <- data.frame(Name=NA_character_, Input=1L, Passing=1L, Op=NA_character_, stringsAsFactors=FALSE) checkIdentical(df, stats(fr)) fr <- SRFilterResult(c(TRUE, FALSE)) df <- data.frame(Name=NA_character_, Input=2L, Passing=1L, Op=NA_character_, stringsAsFactors=FALSE) checkIdentical(df, stats(fr)) fr <- SRFilterResult(c(TRUE, FALSE),"A") df <- data.frame(Name="A", Input=2L, Passing=1L, Op=NA_character_, stringsAsFactors=FALSE) checkIdentical(df, stats(fr)) } test_SRFilterResult_logic <- function() { a <- SRFilterResult(c(TRUE, FALSE), "A") b <- SRFilterResult(c(FALSE, TRUE), "B") checkTrue(all(a|b)) exp <- structure(list(Name = c("A", "B", "(A | B)"), Input = c(2L, 2L, 2L), Passing = c(1L, 1L, 2L), Op = c(NA, NA, "|")), .Names = c("Name", "Input", "Passing", "Op"), row.names = c(NA, -3L), class = "data.frame") checkIdentical(exp, stats(a|b)) checkTrue(all(!b == !(b@.Data))) exp <- structure(list(Name = c("B", "!(B)"), Input = c(2L, 2L), Passing = c(1L, 1L), Op = c(NA, "!")), .Names = c("Name", "Input", "Passing", "Op"), row.names = c(NA, -2L), class = "data.frame") checkIdentical(exp, stats(!b)) } test_SRFilterResult_SRFilter <- function() { fa <- srFilter(function(x) logical(length(x)), "A") x <- fa(1:10) checkIdentical(logical(10L), x@.Data) checkIdentical(mkScalar("A"), name(x)) exp <- structure(list(Name = "A", Input = 10L, Passing = 0L, Op = NA_character_), .Names = c("Name", "Input", "Passing", "Op"), row.names = c(NA, -1L), class = "data.frame") checkIdentical(exp, stats(x)) x <- fa(1:10) & fa(1:10) checkIdentical(logical(10L), x@.Data) checkIdentical(mkScalar("(A & A)"), name(x)) exp <- structure(list(Name = c("A", "A", "(A & A)"), Input = c(10L, 10L, 10L), Passing = c(0L, 0L, 0L), Op = c(NA, NA, "&")), .Names = c("Name", "Input", "Passing", "Op"), row.names = c(NA, -3L), class = "data.frame") checkIdentical(exp, stats(x)) fb <- srFilter(function(x) !logical(length(x)), "B") x <- fa(1:10) | fb(1:10) checkIdentical(!logical(10L), x@.Data) checkIdentical(mkScalar("(A | B)"), name(x)) exp <- structure(list(Name = c("A", "B", "(A | B)"), Input = c(10L, 10L, 10L), Passing = c(0L, 10L, 10L), Op = c(NA, NA, "|")), .Names = c("Name", "Input", "Passing", "Op"), row.names = c(NA, -3L), class = "data.frame") checkIdentical(exp, stats(x)) } ShortRead/inst/unitTests/test_SRList.R0000644000175400017540000000107013001230550020777 0ustar00biocbuildbiocbuildtest_SRList_construction <- function() { srl <- SRList() checkTrue(validObject(srl)) checkEquals(0, length(srl)) srl <- SRList(list()) checkTrue(validObject(srl)) checkEquals(0, length(srl)) srl <- SRList(list(1)) checkTrue(validObject(srl)) checkEquals(1, length(srl)) srl <- SRList(list(1, 2)) checkTrue(validObject(srl)) checkEquals(2, length(srl)) checkEquals(1, length(srl[[1]])) srl <- SRList(1, 2) checkTrue(validObject(srl)) checkEquals(2, length(srl)) checkEquals(1, length(srl[[1]])) } ShortRead/inst/unitTests/test_SRVector.R0000644000175400017540000000071313001230551021332 0ustar00biocbuildbiocbuildtest_SRVector_construction <- function() { check <- function(srv, cls, len) { checkTrue(validObject(srv)) checkEquals(cls, vclass(srv)) checkEquals(len, length(srv)) } check(SRVector(vclass="numeric"), "numeric", 0) check(SRVector(1), "numeric", 1) checkException(SRVector(),silent=TRUE) checkException(SRVector("a", vclass="numeric"), silent=TRUE) checkException(SRVector(1, "a"), silent=TRUE) } ShortRead/inst/unitTests/test_ShortRead.R0000644000175400017540000000217613001230551021523 0ustar00biocbuildbiocbuildsp <- SolexaPath(system.file("extdata", package="ShortRead")) sr <- as(readFastq(sp, "s_1_sequence.txt"), "ShortRead") .equals <- function(x, y) { checkIdentical(as.character(sread(x)), as.character(sread(y))) checkIdentical(as.character(id(x)), as.character(id(y))) } test_ShortRead_construction <- function() { obj <- ShortRead() checkTrue(class(obj) == "ShortRead") checkTrue(validObject(obj)) obj <- ShortRead(sread(sr)) checkTrue(class(obj) == "ShortRead") checkTrue(validObject(obj)) .equals(new("ShortRead", sread=DNAStringSet(sread(sr)), id=BStringSet(rep("", length(sr)))), obj) obj <- ShortRead(sread(sr), id(sr)) checkTrue(class(obj) == "ShortRead") checkTrue(validObject(obj)) .equals(sr, obj) } test_ShortRead_narrow <- function() { obj <- narrow(sr, start=1, end=10) checkTrue(class(obj) == "ShortRead") checkTrue(length(obj) == length(sr)) checkTrue(unique(width(obj)) == 10) checkIdentical(as.character(sread(obj)), substr(as.character(sread(sr)), 1, 10)) checkIdentical(narrow(sr, start=start(sread(sr))), sr) } ShortRead/inst/unitTests/test_ShortReadQ.R0000644000175400017540000002077213001230551021646 0ustar00biocbuildbiocbuildsp <- SolexaPath(system.file('extdata', package='ShortRead')) fl <- file.path(analysisPath(sp), "s_1_sequence.txt") checkShortReadQ <- function(obj, len, wd) { checkStringSet <- function(obj, type, len, wd) { checkTrue(is(obj, type)) checkEquals(len, length(obj)) checkEquals(wd, unique(width(obj))) } checkStringSet(obj, "ShortReadQ", len, wd[[1]]) checkStringSet(sread(obj), "DNAStringSet", len, wd[[2]]) checkStringSet(id(obj), "BStringSet", len, wd[[3]]) # ids w/ diff lengths checkStringSet(quality(obj), "QualityScore", len, wd[[4]]) } .equals <- function(x, y) { checkIdentical(as.character(sread(x)), as.character(sread(y))) checkIdentical(as.character(quality(quality(x))), as.character(quality(quality(y)))) checkIdentical(as.character(id(x)), as.character(id(y))) } test_ShortReadQ_constructors <- function() { sp <- SolexaPath(system.file('extdata', package='ShortRead')) sr <- obj <- readFastq(sp) checkTrue(validObject(obj)) checkShortReadQ(obj, 256, list(36, 36, 24:22, 36)) obj <- ShortReadQ() checkTrue(class(obj) == "ShortReadQ") checkTrue(validObject(obj)) obj <- ShortReadQ(sread(sr), quality(sr)) checkTrue(class(obj) == "ShortReadQ") checkTrue(validObject(obj)) .equals(new("ShortReadQ", sread=sread(sr), id=BStringSet(rep("", length(sr))), quality=quality(sr)), obj) obj <- ShortReadQ(sread(sr), quality(sr), id(sr)) checkTrue(class(obj) == "ShortReadQ") checkTrue(validObject(obj)) .equals(sr, obj) } test_FastqSampler <- function() { sr <- readFastq(fl) ## here to re-use equality checker obj <- yield(f <- FastqSampler(fl)) close(f) .equals(sr, obj) yld <- yield(f <- FastqSampler(fl, readerBlockSize=1000)) close(f) checkTrue(validObject(yld)) ## regression yld <- yield(f <- FastqSampler(fl, readerBlockSize=256)) close(f) checkIdentical(256L, length(yld)) } test_FastqSampler_rand <- function() { ## two samples with the same random number seed are identical samp <- FastqSampler(fl, 50) set.seed(123L); obs <- yield(samp) set.seed(123L); exp <- yield(samp) close(samp) .equals(obs, exp) ## different samples set.seed(123L) samp <- FastqSampler(fl, 50) obs <- length(Reduce(intersect, replicate(2, id(yield(samp))))) checkIdentical(7L, obs) obs <- length(Reduce(intersect, replicate(3, id(yield(samp))))) checkIdentical(0L, obs) close(samp) } test_FastqStreamer <- function() { sr <- readFastq(fl) f <- FastqStreamer(fl, n=50) i <- 0L; len <- 0L while (length(y <- yield(f))) { len <- len + length(y) i <- i + 1L } close(f) checkIdentical(6L, i) checkIdentical(256L, len) ## values equal? f <- FastqStreamer(fl, n=50) .equals(sr[1:50], yield(f)) .equals(sr[50+1:50], yield(f)) close(f) ## whole file f <- FastqStreamer(fl, n=500) i <- 0L; len <- 0L while (length(y <- yield(f))) { .equals(sr, y) len <- len + length(y) i <- i + 1L } close(f) checkIdentical(1L, i) checkIdentical(256L, len) ## small reader block size f <- FastqStreamer(fl, n=50, readerBlockSize=100) i <- 0L; len <- 0L while (length(y <- yield(f))) { len <- len + length(y) i <- i + 1L } close(f) checkIdentical(6L, i) checkIdentical(256L, len) } test_FastqStreamer_roundtrip <- function() { out <- tempfile() writeFastq(v1 <- readFastq(fl), out) s <- FastqStreamer(out) .equals(v1, yield(s)) } test_FastqStreamer_IRanges <- function() { sr <- readFastq(fl) ## basics rng <- IRanges(c(50, 100, 200), width=c(5, 4, 3)) f <- FastqStreamer(fl, rng) .equals(sr[50:54], yield(f)) .equals(sr[100:103], yield(f)) .equals(sr[200:202], yield(f)) .equals(ShortReadQ(), yield(f)) close(f) ## successive rng <- IRanges(c(50, 60), width=10) f <- FastqStreamer(fl, rng) .equals(sr[50:59], yield(f)) .equals(sr[60:69], yield(f)) .equals(ShortReadQ(), yield(f)) close(f) ## off-the-end rng <- IRanges(250, width=100) f <- FastqStreamer(fl, rng) .equals(sr[250:256], yield(f)) .equals(ShortReadQ(), yield(f)) close(f) ## too-short buffer to skip all reads in one binary input rng <- IRanges(250, width=5) f <- FastqStreamer(fl, rng, readerBlockSize=10000) .equals(sr[250:254], yield(f)) .equals(ShortReadQ(), yield(f)) close(f) rng <- IRanges(241, width=5) f <- FastqStreamer(fl, rng, readerBlockSize=10000) .equals(sr[241:245], yield(f)) .equals(ShortReadQ(), yield(f)) close(f) ## exceptions rng <- IRanges(50, 49) # non-zero checkException(FastqStreamer(fl, rng), silent=TRUE) rng <- IRanges(c(50, 59), c(60, 70)) # strictly increasing checkException(FastqStreamer(fl, rng), silent=TRUE) } test_ShortReadQ_coerce_QualityScaledDNAStringSet <- function() { sp <- SolexaPath(system.file('extdata', package='ShortRead')) obj <- readFastq(sp, qualityType="SFastqQuality") res <- as(obj, "QualityScaledDNAStringSet") checkIdentical(as.character(sread(obj)), as.character(as(res, "DNAStringSet"))) checkIdentical(as.character(quality(quality(obj))), as.character(quality(res))) checkTrue(is(quality(res), "SolexaQuality")) obj <- initialize(obj, quality=FastqQuality(quality(quality(obj)))) res <- as(obj, "QualityScaledDNAStringSet") checkIdentical(as.character(sread(obj)), as.character(as(res, "DNAStringSet"))) checkIdentical(as.character(quality(quality(obj))), as.character(quality(res))) checkTrue(is(quality(res), "PhredQuality")) q <- MatrixQuality(as(quality(obj), "matrix")) obj <- initialize(obj, quality=q) checkException(as(obj, "QualityScaledDNAStringSet"), silent=TRUE) } test_ShortReadQ_coerce_matrix <- function() { ## 0-length fq <- FastqQuality() exp <- matrix(NA_integer_, 0, 0) checkIdentical(exp, as(fq, "matrix")) ## ragged matrix fq <- FastqQuality(BStringSet(c("]]X", "]]]X"))) exp <- matrix(c(rep(60L, 4), 55L, 60L, NA_integer_, 55L), 2) checkIdentical(exp, as(fq, "matrix")) } test_ShortReadQ_subset <- function() { sp <- SolexaPath(system.file('extdata', package='ShortRead')) obj <- readFastq(sp) obj1 <- obj[c(3, 5:7, 9)] checkShortReadQ(obj1, 5, list(36, 36, 23, 36)) checkException(obj[,1], silent=TRUE) checkException(obj[1,1], silent=TRUE) checkIdentical(2L, length(obj[1:2,])) checkIdentical(2L, length(obj[1:2,drop=TRUE])) checkIdentical(2L, length(obj[1:2,,drop=TRUE])) } test_ShortReadQ_subset_gets <- function() { sp <- SolexaPath(system.file('extdata', package='ShortRead')) sr <- obj <- readFastq(sp) i <- sample(length(obj)) sr[i] <- obj .equals(sr[i], obj) } test_ShortReadQ_narrow <- function() { sp <- SolexaPath(system.file('extdata', package='ShortRead')) sr <- readFastq(sp) obj <- narrow(sr, start=1, end=10) checkTrue(class(obj) == "ShortReadQ") checkTrue(length(obj) == length(sr)) checkTrue(unique(width(obj)) == 10) checkIdentical(as.character(sread(obj)), substr(as.character(sread(sr)), 1, 10)) checkIdentical(as.character(quality(quality(obj))), substr(as.character(quality(quality(sr))), 1, 10)) checkIdentical(as.character(id(obj)), as.character(id(sr))) checkIdentical(narrow(sr, start=start(sread(sr))), sr) } test_ShortReadQ_compact <- function() { sp <- SolexaPath(system.file('extdata', package='ShortRead')) sr <- readFastq(sp)[1:10] res <- compact(sr) checkIdentical(as.character(sread(sr)), as.character(sread(res))) checkIdentical(as.character(quality(quality(sr))), as.character(quality(quality(res)))) } test_ShortReadQ_clean <- function() { sp <- SolexaPath(system.file('extdata', package='ShortRead')) obj <- readFastq(sp) cln <- clean(obj) checkIdentical(class(obj), class(cln)) ## FIXME: need a stronger test checkEquals(length(obj), length(clean(obj))) } test_ShortReadQ_srsort <- function() { sp <- SolexaPath(system.file('extdata', package='ShortRead')) obj <- readFastq(sp) srt <- srsort(obj) checkIdentical(class(obj), class(srt)) checkIdentical(length(obj), length(srt)) checkIdentical(srsort(sread(obj)), sread(srt)) checkIdentical(quality(obj)[srorder(obj)], quality(srt)) } ShortRead/inst/unitTests/test_SolexaIntensity.R0000644000175400017540000000625413001230551022773 0ustar00biocbuildbiocbuildtest_SolexaIntensity_construction <- function() { checkTrue(validObject(SolexaIntensityInfo())) checkTrue(validObject(SolexaIntensityInfo(lane=rep(1, 10)))) checkTrue(validObject(SolexaIntensity())) checkTrue(validObject(SolexaIntensity(intensity=array(0,c(1,2,3))))) checkException(SolexaIntensity(measurementError=array(0,c(1,2,3))), silent=TRUE) checkTrue(validObject(SolexaIntensity(intensity=array(0,c(1,2,3)), measurementError=array(0,c(1,2,3))))) checkException(SolexaIntensityInfo()[,"lane"], silent=TRUE) } test_SolexaIntensity_access <- function() { checkException(measurementError(SolexaIntensity()), silent=TRUE) } test_SolexaIntensity_io <- function() { sp <- SolexaPath(system.file("extdata", package="ShortRead")) int <- readIntensities(sp) checkIdentical(c(256L, 4L, 36L), dim(intensity(int))) checkIdentical(c(256L, 4L, 36L), dim(measurementError(int))) checkIdentical(256L, nrow(pData(readIntensityInfo(int)))) int <- readIntensities(sp, withVariability=FALSE) checkIdentical(c(256L, 4L, 36L), dim(intensity(int))) checkIdentical(256L, nrow(pData(readIntensityInfo(int)))) checkException(measurementError(int), silent=TRUE) } test_IparIntensity_io <- function() { src <- system.file("unitTests","cases",package="ShortRead") int <- readIntensities(src, type="IparIntensity", intExtension="_int_head.txt.p", nseExtension="_nse_head.txt.p", posExtension="_pos_head.txt") checkIdentical(c(5L, 4L, 3L), dim(int)) checkIdentical(structure(c(11.7, 49.4, 14.3, 110.6, -1.1, 3.2, 86.2, 5.9, 18.8, 120.2, 21.1, 218.8, 96.2, 2.7, 177.4, 9.1, 55.9, 340.9, 112, 164.8, 0.8, 2.6, 15.7, 4.6), .Dim = c(2L, 4L, 3L), .Dimnames = list( NULL, c("A", "C", "G", "T"), NULL)), as(intensity(int), "array")[1:2,,]) checkIdentical(structure(c(8.5, 6.2, 9, 9.2, 3, 3.1, 6.6, 6.4, 12.6, 11.9, 12.5, 11, 3.8, 3.6, 6.6, 5.8, 13.6, 10.5, 12.4, 12.7, 4.7, 4.2, 7.3, 6.4), .Dim = c(2L, 4L, 3L), .Dimnames = list(NULL, c("A", "C", "G", "T"), NULL)), as(measurementError(int), "array")[1:2,,]) checkIdentical(structure(list(lane = structure(c(1L, 1L, 1L, 1L, 1L), class = "factor", .Label = "1"), tile = c(1L, 1L, 1L, 1L, 1L), x = c(-0.47, -0.45, -0.45, -0.44, -0.43), y = c(1073.78, 1558.67, 1157.37, 144.35, 1497.99 )), .Names = c("lane", "tile", "x", "y"), row.names = c(NA, -5L), class = "data.frame"), pData(readIntensityInfo(int))) } ShortRead/inst/unitTests/test_append.R0000644000175400017540000000317313001230551021075 0ustar00biocbuildbiocbuild## Function: append (package base) ## x="AlignedDataFrame", values="AlignedDataFrame", after="missing" ## x="AlignedRead", values="AlignedRead", after="missing" ## x="QualityScore", values="QualityScore", after="missing" ## x="ShortRead", values="ShortRead", after="missing" ## x="ShortReadQ", values="ShortReadQ", after="missing" sp <- SolexaPath(system.file("extdata", package="ShortRead")) .equal <- function(x, y) { checkIdentical(class(x), class(y)) checkIdentical(length(x), length(y)) checkIdentical(as.character(id(x)), as.character(id(y))) checkIdentical(as.character(sread(x)), as.character(sread(y))) if (is(x, "ShortReadQ")) checkIdentical(as.character(quality(quality(x))), as.character(quality(quality(y)))) if (is(x, "AlignedRead")) { checkIdentical(strand(x), strand(y)) checkIdentical(chromosome(x), chromosome(y)) checkIdentical(position(x), position(y)) checkIdentical(dim(alignData(x)), dim(alignData(y))) adx <- alignData(x); ady <- alignData(y) checkIdentical(varMetadata(adx), varMetadata(ady)) pdx <- pData(adx); pdy <- pData(ady) row.names(pdx) <- row.names(pdy) <- NULL checkIdentical(pdx, pdy) } } test_append <- function() { aln <- readAligned(sp, "s_2_export.txt") aaln <- append(aln, aln) .equal(aln, aaln[seq_len(length(aln))]) .equal(aln, aaln[length(aln) + seq_len(length(aln))]) } test_append_exception <- function() { checkException(append(sp, sp), silent=TRUE) aln <- readAligned(sp, "s_2_export.txt") checkException(append(quality(aln), aln), silent=TRUE) } ShortRead/inst/unitTests/test_coverage.R0000644000175400017540000000407413001230551021422 0ustar00biocbuildbiocbuild.width <- 10 .mkAln <- function(position, width, strand) { n <- length(position) AlignedRead(sread=DNAStringSet(rep(polyn("A", width), n)), chromosome=rep("ChrA", n), position=as.integer(position), strand=strand) } test_coverage_leftmost_plus <- function() { ## 'leftmost' ## 1 2 ## 8 7 2 ## ++++++++++----- ## ....|....|....|....|....| aln <- .mkAln(8L, .width, strand("+")) cvg <- coverage(aln, width=c(ChrA=25L), extend=5L) checkIdentical(c(7L, 15L, 3L), runLength(cvg[[1]])) } test_coverage_leftmost_minus <- function() { ## ....|....|....|....| ## -----++++++++++ ## 3 8 1 ## 7 aln <- .mkAln(8L, .width, strand("-")) cvg <- coverage(aln, width=c(ChrA=20L), extend=5L) checkIdentical(c(2L,15L,3L), runLength(cvg[[1]])) } test_coverage_fiveprime_plus <- function() { ## 5' ## 1 2 ## 8 7 2 ## ++++++++++----- ## ....|....|....|....|....| aln <- .mkAln(8L, .width, strand("+")) cvg <- coverage(aln, width=c(ChrA=25L), coords="fiveprime", extend=5L) checkIdentical(c(7L, 15L, 3L), runLength(cvg[[1]])) } test_coverage_fiveprime_minus <- function() { ## ....|....|....|....|....| ## -----++++++++++ ## 3 8 1 ## 7 aln <- .mkAln(17L, .width, strand("-")) cvg <- coverage(aln, width=c(ChrA=25L), coords="fiveprime", extend=5L) checkIdentical(c(2L, 15L, 8L), runLength(cvg[[1]])) } test_coverage_width_names <- function() { aln <- .mkAln(1, 10, strand("+")) checkTrue(validObject(coverage(aln))) checkTrue(validObject(coverage(aln, width=c(ChrA=20L)))) ## no names on width checkException(coverage(aln, width=100), silent=TRUE) ## wrong name on width checkException(coverage(aln, width=c(ChrB=100)), silent=TRUE) ## extra width element -- ok checkTrue(validObject(coverage(aln, width=c(ChrA=20L, ChrB=20L)))) } ShortRead/inst/unitTests/test_filterFastq.R0000644000175400017540000000220013001230550022077 0ustar00biocbuildbiocbuildsp <- SolexaPath(system.file('extdata', package='ShortRead')) fl <- file.path(analysisPath(sp), "s_1_sequence.txt") .all_equal <- function(target, current, ...) { ac <- as.character all.equal(ac(sread(target)), ac(sread(current))) && all.equal(ac(quality(quality(target))), ac(quality(quality(current)))) && all.equal(ac(id(target)), ac(id(current))) } test_filterFastq <- function() { tf <- c(TRUE, FALSE) exp <- readFastq(fl)[tf] filt <- function(x) x[tf] dest <- filterFastq(fl, tempfile(), filter=filt) checkTrue(.all_equal(exp, readFastq(dest))) dest <- filterFastq(fl, tempfile(), filter=filt, yieldSize=100) checkTrue(.all_equal(exp,readFastq(dest))) filt <- function(x) tf rule <- FilterRules(list(filt=filt)) dest <- filterFastq(fl, tempfile(), filter=rule) checkTrue(.all_equal(exp,readFastq(dest))) dest <- tempfile() file.create(dest) obs <- tryCatch(filterFastq(fl, dest, filter=filt), error=conditionMessage) checkIdentical(sprintf("'destinations' exist:\n %s", dest), obs) } ShortRead/inst/unitTests/test_functions.R0000644000175400017540000001147113001230551021636 0ustar00biocbuildbiocbuild## readFastq test_readFastq_autoDetectType <- function() { src <- system.file("unitTests","cases", package="ShortRead") srq <- readFastq(file.path(src, "sanger.fastq")) checkTrue(class(quality(srq)) == "FastqQuality") srq <- readFastq(file.path(src, "solexa.fastq")) checkTrue(class(quality(srq)) == "SFastqQuality") srq <- readFastq(file.path(src, "solexa.fastq"), qualityType="FastqQuality") checkTrue(class(quality(srq)) == "FastqQuality") } test_readFastq_withids <- function() { sp <- SolexaPath(system.file('extdata', package='ShortRead')) rfq <- readFastq(analysisPath(sp), pattern="s_1_sequence.txt") rfq1 <- readFastq(analysisPath(sp), pattern="s_1_sequence.txt", withIds=FALSE) checkIdentical(as.character(sread(rfq)), as.character(sread(rfq1))) checkIdentical(as.character(quality(quality(rfq))), as.character(quality(quality(rfq1)))) checkIdentical(as.character(id(rfq1)), character(length(rfq1))) } test_readFastq_zerowidth <- function() { fl <- tempfile(); writeLines("@ \n\n+\n", fl) fq <- readFastq(fl) checkTrue(validObject(fq)) checkIdentical(0L, width(fq)) } ## alphabetByCycle checkAlphabetByCycle <- function(obj) { abc <- alphabetByCycle(obj) validObject(abc) checkEquals(length(obj)*unique(width(obj)), sum(abc)) } test_alphabetByCycle <- function() { sp <- SolexaPath(system.file('extdata', package="ShortRead")) sq <- readFastq(sp) checkAlphabetByCycle(sread(sq)) checkAlphabetByCycle(quality(sq)) obj <- alphabetByCycle(sq) validObject(obj) checkEquals(c(18, 94, 36), dim(obj)) checkEqualsNumeric(alphabetByCycle(sread(sq)), apply(obj, c(1, 3), sum)) checkEqualsNumeric(alphabetByCycle(quality(sq)), apply(obj, 2:3, sum)) obj <- rowSums(alphabetByCycle(id(sq))) obj <- obj[obj != 0] exp <- table(unlist(strsplit(as.character(id(sq)), ""), use.names=FALSE)) checkTrue(setequal(names(obj), names(exp))) checkIdentical(as.numeric(exp[names(obj)]), as.vector(obj)) srq <- ShortReadQ(DNAStringSet(), FastqQuality()) abc <- alphabetByCycle(srq) alf <- alphabet(sread(srq)) qalf <- alphabet(quality(srq)) checkIdentical(matrix(0L, nrow=length(alf), ncol=0, dimnames=list(alphabet=alf, cycle=character(0))), alphabetByCycle(sread(srq))) checkIdentical(array(0L, dim=c(18, 94, 0), dimnames=list(base=alf, quality=qalf, cycle=character(0))), alphabetByCycle(srq)) } ## countLines test_countLines <- function() { sp <- SolexaPath(system.file('extdata', package="ShortRead")) nlines <- countLines(analysisPath(sp), "s_1_sequence.txt") exp <- 1024; names(exp) <- "s_1_sequence.txt" checkEquals(exp, nlines) dir <- tempfile() dir.create(dir) checkException(countLines(dir), silent=TRUE) } ## sort / order test_order_stats <- function() { checkIdentical(integer(0), srrank(AlignedRead())) checkIdentical(integer(0), srorder(AlignedRead())) checkIdentical(logical(0), srduplicated(AlignedRead())) } test_alphabetOrder <- function() { ## setup oldc <- Sys.getlocale("LC_COLLATE") on.exit(Sys.setlocale("LC_COLLATE", oldc)) Sys.setlocale("LC_COLLATE", "C") sp <- SolexaPath(system.file('extdata', package='ShortRead')) rfq <- readFastq(analysisPath(sp), pattern="s_1_sequence.txt") checkEquals(srorder(sread(rfq)), order(as.character(sread(rfq)))) checkEquals(srorder(quality(rfq)), order(as.character(quality(quality(rfq))))) checkEquals(srduplicated(sread(rfq)), duplicated(as.character(sread(rfq)))) checkEquals(srduplicated(quality(rfq)), duplicated(as.character(quality(quality(rfq))))) } ## _mark_field (C code) test_mark_field <- function() { fl <- tempfile() do <- function(s, fl) { doexp(s, strsplit(unlist(strsplit(s, "\n")), "\t"), fl) } doexp <- function(s, exp, fl) { writeChar(s, fl) res <- .Call("_mark_field_test", fl, "\t", c(2L, 3L), PACKAGE="ShortRead") checkIdentical(exp, res) } do("a\tb\tc\nd\te\tf\n", fl) do("a\t\tc\nd\te\tf\n", fl) do("\tb\tc\nd\te\tf\n", fl) do("\t\tc\nd\te\tf\n", fl) ## trailing \t are problematic for strsplit doexp("a\tb\t\nd\te\tf\n", list(c("a","b",""), c("d","e","f")), fl) doexp("a\t\t\nd\te\tf\n", list(c("a","",""), c("d","e","f")), fl) writeChar("\n", fl) res <- .Call("_mark_field_test", fl, "\t", c(1L,1L), PACKAGE="ShortRead") checkIdentical(list(""), res) } ShortRead/inst/unitTests/test_qa.R0000644000175400017540000000225313001230551020225 0ustar00biocbuildbiocbuildtest_missingLaneName <- function() { caught <- FALSE tryCatch(qa(AlignedRead()), error=function(err) { caught <<- conditionMessage(err) == "UserArgumentMismatch\n 'lane' must be 'character(1)'" }) checkTrue(caught) } test_no_replicate_reads <- function() { df <- data.frame(nOccurrences=1, nReads=10, lane=1) x <- ShortRead:::.plotReadOccurrences(df) checkTrue(is(x, "trellis")) } test_qa_alphabetFrequency <- function() { FUN <- ShortRead:::.qa_alphabetFrequency checkException(FUN(DNAStringSet()), silent=TRUE) exp <- alphabetFrequency(DNAStringSet(), collapse=TRUE, baseOnly=TRUE) checkEquals(exp, FUN(DNAStringSet(), collapse=TRUE, baseOnly=TRUE)) exp <- alphabetFrequency(DNAStringSet(), collapse=TRUE) checkEquals(exp, FUN(DNAStringSet(), collapse=TRUE)) dna <- DNAStringSet(c("ACTG", "GTCANM")) checkEquals(alphabetFrequency(dna, collapse=TRUE), FUN(dna, collapse=TRUE)) checkEquals(alphabetFrequency(dna, collapse=TRUE, baseOnly=TRUE), FUN(dna, collapse=TRUE, baseOnly=TRUE)) } ShortRead/inst/unitTests/test_readPrb.R0000644000175400017540000000216413001230550021203 0ustar00biocbuildbiocbuildsp <- SolexaPath(system.file("extdata", package="ShortRead")) test_readPrb_input <- function() { check <- function(obj) { checkTrue(validObject(obj)) checkIdentical(256L, length(obj)) checkIdentical(36L, unique(width(obj))) } icheck <- function(obj) { checkTrue(validObject(obj)) checkIdentical(c(256L, 36L), dim(obj)) } acheck <- function(obj, width) { checkIdentical("array", class(obj)) checkIdentical("integer", typeof(obj)) checkIdentical(c(256L, 4L, width), dim(obj)) } check(readPrb(sp, ".*prb.txt", as="SolexaEncoding")) check(readPrb(sp, ".*prb.txt", as="FastqEncoding")) icheck(readPrb(sp, ".*prb.txt", as="IntegerEncoding")) acheck(readPrb(sp, ".*prb.txt", as="array"), 36L) } test_readPrb_consistent <- function() { exp <- readPrb(sp, ".*prb.txt", as="IntegerEncoding") checkIdentical(exp, as(readPrb(sp, ".*prb.txt", as="SolexaEncoding"), "matrix")) checkIdentical(exp, as(readPrb(sp, ".*prb.txt", as="FastqEncoding"), "matrix")) } test_readPrb_errors <- function() { } ShortRead/inst/unitTests/test_readQseq.R0000644000175400017540000000250213001230551021366 0ustar00biocbuildbiocbuildsp <- SolexaPath(system.file("extdata", package="ShortRead")) checkBstring <- function(obs, exp) { checkEquals(as.character(obs), as.character(exp)) } test_readQseq_ShortReadQ<- function() { res <- readQseq(sp) checkEquals("ShortReadQ", as.vector(class(res))) checkEquals(256L, length(res)) checkEquals(158223L, sum(alphabetScore(res))) alf <- alphabetFrequency(sread(res), collapse=TRUE, baseOnly=TRUE) checkEquals(structure(c(1697L, 1639L, 1481L, 1706L, 133L), .Names = c("A", "C", "G", "T", "other")), alf) } test_readQseq_ShortReadQ_filtered <- function() { res <- readQseq(sp, filtered=TRUE) checkEquals(187L, length(res)) } test_readQseq_DataFrame <- function() { res <- readQseq(sp) xdf <- readQseq(sp, as="DataFrame") checkEquals("DataFrame", as.vector(class(xdf))) checkEquals(c(256L, 11L), dim(xdf)) checkBstring(sread(res), xdf[[9]]) checkBstring(quality(quality(res)), xdf[[10]]) } test_readQseq_DataFrame_filtered <- function() { xdf0 <- readQseq(sp, as="DataFrame") xdf0 <- xdf0[xdf0[[11]]=="Y", -11] xdf <- readQseq(sp, as="DataFrame", filtered=TRUE) checkEquals(dim(xdf0), dim(xdf)) for (i in 1:8) checkEquals(xdf0[[i]], xdf[[i]]) for (i in 9:10) checkBstring(xdf0[[i]], xdf[[i]]) } ShortRead/inst/unitTests/test_readXStringColumns.R0000644000175400017540000000523213001230551023417 0ustar00biocbuildbiocbuildtest_readXStringColumns_toIUPAC <- function() { src <- system.file("unitTests", "cases", package="ShortRead") fl <- file.path(src, "s_2_export_toIUPAC.txt") colClasses <- rep(list(NULL), 22) colClasses[9:10] <- c(sread="DNAString", quality="BString") names(colClasses)[9:10] <- c("sread", "quality") res <- readXStringColumns(dirname(fl), basename(fl), colClasses=colClasses) ## '.' converted to "-" in DNAString, but not BString checkTrue(all(gregexpr("-", as.character(res$sread[1]))[[1]] == c(5, 10))) checkTrue(all(gregexpr("\\.", as.character(res$quality[1]))[[1]] == 5)) checkTrue(all(gregexpr("-", as.character(res$quality[1]))[[1]] == 10)) } test_readXStringColumns_skip_nrows <- function() { what <- vector("list", 22) what[[2]] <- character() colClasses <- what colClasses[[2]] <- "DNAString" ## single file dir <- system.file("unitTests", "cases", package="ShortRead"); fl <- "s_1_results_head.txt" check <- function(dir, fl, skip, nrows) { pth <- file.path(dir, fl) exp <- DNAStringSet(scan(pth, what, nmax=nrows, skip=skip, fill=TRUE, quiet=TRUE)[[2]]) obs <- readXStringColumns(dir, fl, colClasses=colClasses, nrows=nrows, skip=skip)[[1]] checkEquals(as.character(exp), as.character(obs)) } check(dir, fl, 0L,-1L) check(dir, fl, 100L, -1L) check(dir, fl, 0L, 100L) check(dir, fl, 100L, 100L) ## multiple files dir <- system.file("unitTests", "cases", package="ShortRead"); pattern <- "s_1_results_head.*txt" mcheck <- function(dir, pattern, skip=0L, nrows=-1L) { fls <- list.files(dir, pattern, full=TRUE) exp <- vector("list",length(fls)) nread <- 0 for (i in seq_along(fls)) { if (nrows > 0 && nread >= nrows) break exp[[i]] <- scan(fls[i], what=what, fill=TRUE, skip=skip, nmax=nrows-nread, quiet=TRUE) nread <- nread + length(exp[[i]][[2]]) } exp <- DNAStringSet(unlist(exp)) obs <- readXStringColumns(dir, pattern, colClasses=colClasses, skip=skip, nrows=nrows)[[1]] checkTrue(validObject(obs)) checkEquals(as.character(exp), as.character(obs)) } mcheck(dir, pattern, 0L) mcheck(dir, pattern, 100L) mcheck(dir, pattern, 0L, 500L) mcheck(dir, pattern, 0L, 1500L) mcheck(dir, pattern, 0L, 15000L) mcheck(dir, pattern, 100L, 500L) mcheck(dir, pattern, 100L, 1500L) mcheck(dir, pattern, 100L, 15000L) } ShortRead/inst/unitTests/test_renew.R0000644000175400017540000000203613001230551020743 0ustar00biocbuildbiocbuildsp <- SolexaPath(system.file("extdata", package="ShortRead")) ap <- analysisPath(sp) filt <- chromosomeFilter("chr[[:digit:]+].fa") aln <- readAligned(ap, "s_2_export.txt", "SolexaExport", filter=filt) test_renewable0 <- function() { cls <- renewable() for (cl in cls) { def <- getClass(cl, where=getNamespace("ShortRead")) checkTrue(validObject(df)) } } test_renewable_non_virtual<- function() { cls <- renewable() for (cl in cls) { if (!getClass(cl)@virtual) checkIdentical(getSlots(cl), renewable(cl)[[1]]) } } test_renew <- function() { checkIdentical(aln, renew(aln)) labels <- sub("\\.fa", "", levels(chromosome(aln))) updt <- factor(chromosome(aln), labels=labels) checkIdentical(updt, chromosome(renew(aln, chromosome=updt))) obs <- renew(aln, chromosome=updt, position=1L+position(aln)) checkIdentical(updt, chromosome(obs)) checkIdentical(1L+position(aln), position(obs)) checkException(renew(aln, position=1L), silent=TRUE) } ShortRead/inst/unitTests/test_trimEnds.R0000644000175400017540000000336713001230551021420 0ustar00biocbuildbiocbuildsp <- SolexaPath(system.file('extdata', package='ShortRead')) fl <- file.path(analysisPath(sp), "s_1_sequence.txt") rfq <- readFastq(fl) test_trimEnds <- function() { exp <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 10, 16, 72, 152) checkIdentical(as.integer(exp), tabulate(width(trimEnds(rfq, "I")))) rng <- trimEnds(sread(rfq), "G", relation="==", ranges=TRUE) checkTrue(!all(1L == start(rng))) checkTrue(!all(end(rfq) == end(rng))) checkTrue(all(1L == start(trimEnds(sread(rfq), "G", left=FALSE, relation="==", ranges=TRUE)))) checkTrue(all(width(rfq) == end(trimEnds(sread(rfq), "G", right=FALSE, relation="==", ranges=TRUE)))) exp <- c(1L, 1L, 3L, 3L, 8L, 8L, 12L, 10L, 41L, 41L, 38L, 43L, 47L) obs <- trimEnds(sread(rfq), c("G", "T"), relation="==") checkIdentical(exp, as.vector(table(width(obs)))) } test_trimEnds_unknown_a <- function() { checkIdentical(as.character(sread(rfq)), suppressWarnings(as.character(trimEnds(sread(rfq), "Z")))) obs <- tryCatch(trimEnds(sread(rfq), "Z"), warning=conditionMessage) checkIdentical("some 'a' not in alphabet(object)", obs) } test_trimEnds_classes <- function() { rng <- trimEnds(quality(rfq), "I", ranges=TRUE) checkIdentical(as.character(quality(narrow(quality(rfq), start(rng), end(rng)))), as.character(quality(trimEnds(quality(rfq), "I")))) ## FIXME: additional, e.g., PhredQuality } test_trimEnds_file <- function() { dest <- trimEnds(fl, "I", destinations=tempfile()) checkIdentical(width(trimEnds(rfq, "I")), width(readFastq(dest))) } ShortRead/inst/unitTests/test_trimTails.R0000644000175400017540000000472713001230551021604 0ustar00biocbuildbiocbuildsp <- SolexaPath(system.file('extdata', package='ShortRead')) fl <- file.path(analysisPath(sp), "s_1_sequence.txt") rfq <- readFastq(fl) .check <- function(xexp, xobs) checkIdentical(as.character(xexp), as.character(xobs)) test_trimTails_BStringSet <- function() { .check(BStringSet("CCCBBB"), trimTails(BStringSet("CCCBBBAAA"), 1, "A")) .check(BStringSet("CCCABBB"), trimTails(BStringSet("CCCABBBAAA"), 2, "A")) .check(BStringSet("CCCABBBAB"), trimTails(BStringSet("CCCABBBABAA"), 2, "A", successive=TRUE)) .check(BStringSet("CCC"), trimTails(BStringSet("CCCABBBABAA"), 2, "B", successive=TRUE)) .check(BStringSet(), trimTails(BStringSet("CCCABBBABAA"), 1, "C")) } test_trimTails_QualityScore <- function() { .qq <- function(x) quality(quality(x)) checkTrue(validObject(trimTails(rfq, 1, "H"))) .check(.qq(rfq), .qq(trimTails(rfq, 1, " "))) .check(BStringSet(), .qq(trimTails(rfq, 1, "]"))) } test_trimTails_XStringQuality <- function() { .qq <- function(x) quality(quality(x)) .qb <- function(x) as(x, "BStringSet") qual <- as(quality(rfq), "PhredQuality") checkTrue(validObject(trimTails(qual, 1, "H"))) .check(.qq(rfq), .qb(trimTails(qual, 1, "!"))) .check(BStringSet(), .qb(trimTails(qual, 1, "]"))) } test_trimTails_file <- function() { exp <- width(trimTails(rfq, 1, "H")) dest <- trimTails(fl, 1, "H", destinations=tempfile()) checkIdentical(exp, width(readFastq(dest))) } test_trimTailw <- function() { b <- BStringSet("BBBBBB") checkIdentical(BStringSet(), trimTailw(b, 1L, "C", 3L)) checkIdentical(BStringSet(), trimTailw(b, 1L, "B", 3L)) checkIdentical(b, trimTailw(b, 1L, "A", 3L)) checkIdentical(BStringSet(), trimTailw(b, 3L, "C", 1L)) checkIdentical(b, trimTailw(b, 4L, "C", 1L)) b <- BStringSet("DDDBBBBB") checkIdentical(BStringSet("DDD"), trimTailw(b, 2L, "C", 1L)) checkIdentical(BStringSet("DD"), trimTailw(b, 1L, "C", 1L)) checkIdentical(BStringSet("D"), trimTailw(b, 1L, "C", 2L)) checkIdentical(BStringSet(), trimTailw(b, 1L, "C", 3L)) b <- BStringSet("DDDBDBBBB") checkIdentical(BStringSet("DDDBD"), trimTailw(b, 2L, "C", 1L)) checkIdentical(BStringSet("DDDBDB"), trimTailw(b, 4L, "C", 2L)) } test_trimTailw_file <- function() { exp <- width(trimTailw(rfq, 2L, "C", 1L)) dest <- trimTailw(fl, 2L, "C", 1L, destinations=tempfile()) checkIdentical(exp, width(readFastq(dest))) } ShortRead/inst/unitTests/test_writeFastq.R0000644000175400017540000000224213001230551021753 0ustar00biocbuildbiocbuildsp <- SolexaPath(system.file("extdata", package="ShortRead")) test_writeFastq_roundtrip <- function() { ## potential coercion from '.' to 'N' rfq <- readFastq(analysisPath(sp), pattern="s_1_sequence.txt") file <- tempfile() writeFastq(rfq, file) fq <- readFastq(dirname(file), basename(file)) checkIdentical(as.character(id(fq)), as.character(id(rfq))) checkIdentical(as.character(sread(fq)), as.character(sread(rfq))) checkIdentical(as.character(quality(quality(fq))), as.character(quality(quality(rfq)))) } test_writeFastq_writeError <- function() { object <- readFastq(analysisPath(sp), pattern="s_1_sequence.txt") file <- tempfile() mode <- "w" max_width <- 10L .write_fastq <- ShortRead:::.write_fastq checkException(.Call(.write_fastq, id(object), sread(object), quality(quality(object)), file, mode, max_width), silent=TRUE) } test_writeFastq_roundtrip0length <- function() { dest <- tempfile() file.create(dest) exp <- readFastq(dest) writeFastq(exp, dest <- tempfile()) checkIdentical(exp, readFastq(dest)) } ShortRead/man/0000755000175400017540000000000013001230551014234 5ustar00biocbuildbiocbuildShortRead/man/AlignedDataFrame-class.Rd0000644000175400017540000000463213001230550020742 0ustar00biocbuildbiocbuild\name{AlignedDataFrame-class} \docType{class} \alias{AlignedDataFrame-class} \alias{append,AlignedDataFrame,AlignedDataFrame-method} \title{ (Legacy) "AlignedDataFrame" representing alignment annotations as a data frame } \description{ This class extends \code{\link[Biobase:class.AnnotatedDataFrame]{AnnotatedDataFrame}}. It is a data frame and associated metadata (describing the columns of the data frame). The main purpose of this class is to contain alignment data in addition to the central information of \code{\linkS4class{AlignedRead}}. } \section{Objects from the Class}{ Objects from the class are created by calls to the \code{\link{AlignedDataFrame}} function. } \section{Slots}{ \describe{ \item{\code{data}:}{Object of class \code{"data.frame"} containing the data. See \code{\link[Biobase:class.AnnotatedDataFrame]{AnnotatedDataFrame}} for details.} \item{\code{varMetadata}:}{Object of class \code{"data.frame"} describing columns of \code{data}. See \code{\link[Biobase:class.AnnotatedDataFrame]{AnnotatedDataFrame}} for details.} \item{\code{dimLabels}:}{Object of class \code{character} describing the dimensions of the AnnotatedDataFrame. Used internally; see \code{\link[Biobase:class.AnnotatedDataFrame]{AnnotatedDataFrame}} for details.} \item{\code{.__classVersion__}:}{Object of class \code{"Versions"} describing the version of this object. Used internally; see \code{\link[Biobase:class.AnnotatedDataFrame]{AnnotatedDataFrame}} for details.} } } \section{Extends}{ Class \code{"\link[Biobase:class.AnnotatedDataFrame]{AnnotatedDataFrame}"}, directly. Class \code{"\link[Biobase:class.Versioned]{Versioned}"}, by class "AnnotatedDataFrame", distance 2. } \section{Methods}{ This class inherits methods \code{pData} (to retrieve the underlying data frame) and \code{varMetadata} (to retrieve the metadata) from \code{AnnotatedDataFrame}. Additional methods include: \describe{ \item{append}{\code{signature(x = "AlignedDataFrame", values = "AlignedDataFrame")}: append \code{values} after \code{x}. \code{varMetadata} of \code{x} and \code{y} must be identical; \code{pData} and \code{varMetadata} are appended using \code{rbind}.} } } \author{Martin Morgan } \seealso{ \code{\link[Biobase:class.AnnotatedDataFrame]{AnnotatedDataFrame}} } \keyword{classes} ShortRead/man/AlignedDataFrame.Rd0000644000175400017540000000163613001230550017640 0ustar00biocbuildbiocbuild\name{AlignedDataFrame} \alias{AlignedDataFrame} \title{(Legacy) AlignedDataFrame constructor} \description{ Construct an \code{AlignedDataFrame} from a data frame and its metadata } \usage{ AlignedDataFrame(data, metadata, nrow = nrow(data)) } \arguments{ \item{data}{A data frame containing alignment information.} \item{metadata}{A data frame describing the columns of \code{data}, and with number of rows of \code{metadata} corresponding to number of columns of \code{data}. . The data frame must contain a column \code{labelDescription} providing a verbose description of each column of \code{data}.} \item{nrow}{An optional argument, to be used when \code{data} is not provided, to construct an AlignedDataFrame with the specified number of rows.} } \value{ An object of \code{\linkS4class{AlignedDataFrame}}. } \author{Martin Morgan } \keyword{manip} ShortRead/man/AlignedRead-class.Rd0000644000175400017540000002175513001230550017776 0ustar00biocbuildbiocbuild\name{AlignedRead-class} \docType{class} \alias{AlignedRead-class} \alias{[,AlignedRead,ANY,ANY-method} \alias{[,AlignedRead,ANY,missing-method} \alias{[,AlignedRead,missing,ANY-method} \alias{[,AlignedRead,missing,missing-method} \alias{[,AlignedRead,missing,missing,ANY-method} \alias{[,AlignedRead,missing,ANY,ANY-method} \alias{[,AlignedRead,ANY,ANY,ANY-method} \alias{[,AlignedRead,ANY,missing,ANY-method} \alias{append,AlignedRead,AlignedRead-method} \alias{coerce,PairwiseAlignments,AlignedRead-method} \alias{coerce,AlignedRead,RangesList-method} \alias{coerce,AlignedRead,RangedData-method} \alias{coerce,AlignedRead,GRanges-method} \alias{coerce,AlignedRead,GAlignments-method} \alias{coerce,AlignedRead,GappedReads-method} \alias{chromosome,AlignedRead-method} \alias{position,AlignedRead-method} \alias{strand,AlignedRead-method} \alias{coverage,AlignedRead-method} \alias{srrank,AlignedRead-method} \alias{srorder,AlignedRead-method} \alias{srduplicated,AlignedRead-method} \alias{\%in\%,AlignedRead,RangesList-method} \alias{detail,AlignedRead-method} \alias{show,AlignedRead-method} \title{(Legacy) "AlignedRead" class for aligned short reads} \description{ This class represents and manipulates reads and their genomic alignments. Alignment information includes genomic position, strand, quality, and other data. } \section{Objects from the Class}{ Objects of this class can be created from a call to the \code{\link{AlignedRead}} constructor, or more typically by parsing appropriate files (e.g., \code{\link{readAligned}}). } \section{Slots}{ \describe{ \item{\code{chromosome}}{Object of class \code{"factor"} the particular sequence within a set of target sequences (e.g. chromosomes in a genome assembly) to which each short read aligns.} \item{\code{position}}{Object of class \code{"integer"} the (base-pair) position in the genome to which the read is aligned. AlignedRead objects created by readAligned use 1-based indexing, with alignemnts reported in \sQuote{left-most} coordinates, as described in the vignette.} \item{\code{strand}}{Object of class \code{"factor"} the strand of the alignment.} \item{\code{alignQuality}}{Object of class \code{"numeric"} representing an alignment quality score.} \item{\code{alignData}}{Object of class \code{"AlignedDataFrame"} additional alignment information.} \item{\code{quality}}{Object of class \code{"BStringSet"} representing base-call read quality scores.} \item{\code{sread}}{Object of class \code{"DNAStringSet"} DNA sequence of the read.} \item{\code{id}}{Object of class \code{"BStringSet"} read identifier.} } } \section{Extends}{ Class \code{"\linkS4class{ShortReadQ}"}, directly. Class \code{"\linkS4class{ShortRead}"}, by class "ShortReadQ", distance 2. Class \code{"\linkS4class{.ShortReadBase}"}, by class "ShortReadQ", distance 3. } \section{Methods}{ See \code{\link{accessors}} for additional functions to access slot content, and \code{\linkS4class{ShortReadQ}}, \code{\linkS4class{ShortRead}} for inherited methods. Additional methods include: \describe{ \item{[}{\code{signature(x = "AlignedRead", i = "ANY", j = "missing")}: This method creates a new \code{AlignedRead} object containing only those reads indexed by \code{i}. \code{chromosome} is recoded to contain only those levels in the new subset.} \item{append}{\code{signature(x = "AlignedRead", values = "AlignedRead")}: append \code{values} after \code{x}. \code{chromosome} and \code{strand} must be factors with the same levels. See methods for \code{ShortReadQ}, \code{AlignedDataFrame} for details of how these components of \code{x} and \code{y} are appended.} \item{coerce}{ \code{signature(from = "PairwiseAlignments", to = "AlignedRead")}: \code{signature(from = "AlignedRead", to = "RangesList")}: \code{signature(from = "AlignedRead", to = "RangedData")}: \code{signature(from = "AlignedRead", to = "GRanges")}: \code{signature(from = "AlignedRead", to = "GAlignments")}: \code{signature(from = "AlignedRead", to = "GappedReads")}: Invoke these methods with, e.g., \code{as(from, "AlignedRead")} to coerce objects of class \code{from} to class \code{"AlignedRead"}. Coercion from AlignedRead to \link[IRanges]{RangesList}, \link[IRanges]{RangedData} or \link[GenomicRanges]{GRanges} assumes that \code{position(from)} uses a \sQuote{leftmost} (see \code{coverage} on this page) coordinate system. Since \link[IRanges]{Ranges} objects cannot store \code{NA} values, reads with \code{NA} in the \code{position}, \code{width}, \code{chromosome} or (in the case of GRanges) \code{strand} vectors are dropped. } \item{chromosome}{\code{signature(object = "AlignedRead")}: access the chromosome slot of \code{object}.} \item{position}{\code{signature(object = "AlignedRead")}: access the position slot of \code{object}.} \item{strand}{\code{signature(object = "AlignedRead")}: access the strand slot of \code{object}.} \item{coverage}{ \code{signature(x = "AlignedRead", shift = 0L, width = NULL, weight = 1L, ..., coords = c("leftmost", "fiveprime"), extend=0L)}: Calculate coverage across reads present in \code{x}. \code{shift} must be either \code{0L} or a named integer vector with names including all \code{levels(chromosome(x))}. It specifies how the reads in \code{x} should be (horizontally) shifted \emph{before} the coverage is computed. \code{width} must be either \code{NULL} or a named vector of non-negative integers with names including all \code{levels(chromosome(x))}. In the latter case, it specifies for each chromosome the end of the chromosome region over which coverage is to be calculated \emph{after} the reads have been shifted. Note that this region always starts at chromosome position 1. If \code{width} is \code{NULL}, it ends at the rightmost chromosome position covered by at least one read. \code{weight} must be \code{1L} for now (weighting the reads is not supported yet, sorry). \code{coords} specifies the coordinate system used to record position. Both systems number base pairs from left to right on the 5' strand. \code{leftmost} indicates the eland convention, where \code{position(x)} is the left-most (minimum) base pair, regardless of strand. \code{fiveprime} is the MAQ convention, where \code{position(x)} is the coordinate of the 5' end of the aligned read. \code{extend} indicates the number of base pairs to extend the read. Extension is in the 3' direction, measured from the 3' end of the aligned read. The return value of \code{coverage} is a \code{SimpleRleList} object. } \item{\%in\%}{ \code{signature(x = "AlignedRead", table = "RangesList")}: Return a length(x) logical vector indicating whether the chromosome, position, and width of \code{x} overlap (see IRanges \code{\link[IRanges:IntervalTree-class]{overlap}}) with ranges in \code{table}. Reads for which \code{chromosome()}, \code{position()}, or \code{width()} return \code{NA} \emph{never} overlap with \code{table}. This function assumes that positions are in \sQuote{leftmost} coordinates, as defined in \code{coverage}. } \item{srorder}{\code{signature(x = "AlignedRead", ..., withSread=TRUE)}:} \item{srrank}{\code{signature(x = "AlignedRead", ..., withSread=TRUE)}:} \item{srsort}{\code{signature(x = "AlignedRead", ..., withSread=TRUE)}:} \item{srduplicated}{\code{signature(x = "AlignedRead", ..., withSread=TRUE)}: Order, rank, sort, and find duplicates in \code{AlignedRead} objects. Reads are sorted by \code{chromosome}, \code{strand}, \code{position}, and then (if \code{withSread=TRUE}) \code{sread}; less fine-grained sorting can be accomplished with, e.g., \code{x[srorder(sread(x))]}. \code{srduplicated} behaves like \code{duplicated}, i.e., the first copy of a duplicate is \code{FALSE} while the remaining copies are \code{TRUE}.} \item{show}{\code{signature(object = "AlignedRead")}: provide a compact display of the \code{AlignedRead} content.} \item{detail}{\code{signature(x = "AlignedRead")}: display \code{alignData} in more detail.} } } \author{Martin Morgan } \seealso{ \code{\link{readAligned}} } \examples{ showMethods(class="AlignedRead", where=getNamespace("ShortRead")) dirPath <- system.file('extdata', 'maq', package='ShortRead') (aln <- readAligned(dirPath, 'out.aln.1.txt', type="MAQMapview")) coverage(aln)[[1]] cvg <- coverage(aln, shift=c(ChrA=10L)) ## remove 0 coverage on left ends ltrim0 <- function(x) { i <- !cumprod(runValue(x) == 0) Rle(runValue(x)[i], runLength(x)[i]) } endoapply(cvg, ltrim0) ## demonstration of show() and detail() methods show(aln) detail(aln) } \keyword{classes} ShortRead/man/AlignedRead.Rd0000644000175400017540000000334213001230550016663 0ustar00biocbuildbiocbuild\name{AlignedRead} \alias{AlignedRead} \title{(Legacy) Construct objects of class "AlignedRead"} \description{ This function constructs objects of \code{\linkS4class{AlignedRead}}. It will often be more convenient to create \code{AlignedRead} objects using parsers such as \code{\link{readAligned}}. } \usage{ AlignedRead(sread, id, quality, chromosome, position, strand, alignQuality, alignData = AlignedDataFrame(nrow = length(sread))) } \arguments{ \item{sread}{An object of class \code{DNAStringSet}, containing the DNA sequences of the short reads.} \item{id}{An object of class \code{BStringSet}, containing the identifiers of the short reads. This object is the same length as \code{sread}.} \item{quality}{An object of class \code{BStringSet}, containing the ASCII-encoded quality scores of the short reads. This object is the same length as \code{sread}.} \item{chromosome}{A \code{factor} describing the particular sequence within a set of target sequences (e.g. chromosomes in a genome assembly) to which each short read aligns.} \item{position}{A \code{integer} vector describing the (base pair) position at which each short read begins its alignment.} \item{strand}{A \code{factor} describing the strand to which the short read aligns.} \item{alignQuality}{A \code{numeric} vector describing the alignment quality.} \item{alignData}{An \code{AlignedDataFrame} with number of rows equal to the length of \code{sread}, containing additional information about alignments.} } \value{ An object of class \code{\linkS4class{AlignedRead}}. } \author{Martin Morgan } \seealso{\code{\linkS4class{AlignedRead}}.} \keyword{manip} ShortRead/man/BowtieQA-class.Rd0000644000175400017540000000246013001230550017302 0ustar00biocbuildbiocbuild\name{BowtieQA-class} \docType{class} \alias{BowtieQA-class} \alias{report,BowtieQA-method} \alias{report_html,BowtieQA-method} \title{(Legacy) Quality assessment summaries from Bowtie files} \description{ This class contains a list-like structure with summary descriptions derived from visiting one or more Bowtie files. } \section{Objects from the Class}{ Objects of the class are usually produced by a \code{\link{qa}} method, with the argument \code{type="Bowtie"}. } \section{Slots}{ \describe{ \item{\code{.srlist}:}{Object of class \code{"list"}, containing data frames or lists of data frames summarizing the results of qa.} } } \section{Extends}{ Class \code{"\linkS4class{SRList}"}, directly. Class \code{"\linkS4class{.QA}"}, directly. Class \code{"\linkS4class{.SRUtil}"}, by class "SRList", distance 2. Class \code{"\linkS4class{.ShortReadBase}"}, by class ".QA", distance 2. } \section{Methods}{ Accessor methods are inherited from the \code{\linkS4class{SRList}} class. \describe{ \item{report}{\code{signature(x="BowtieQA", ..., dest=tempfile(), type="html")}: produces an html file summarizing the QA results.} } } \author{Martin Morgan } \seealso{ \code{\link{qa}}. } \examples{ showClass("BowtieQA") } \keyword{classes} ShortRead/man/ExperimentPath-class.Rd0000644000175400017540000000332113001230550020561 0ustar00biocbuildbiocbuild\name{ExperimentPath-class} \docType{class} \alias{ExperimentPath-class} % constructors \alias{ExperimentPath} % etc \alias{show,ExperimentPath-method} \alias{detail,ExperimentPath-method} \title{(Legacy) "ExperimentPath" class representing a file hierarchy of data files} \description{ Short read technologies often produce a hierarchy of output files. The content of the hierarchy varies. This class represents the root of the file hierarchy. Specific classes (e.g., \code{\linkS4class{SolexaPath}}) represent different technologies. } \section{Objects from the Class}{ Objects from the class are created by calls to the constructor: \code{ExperimentPath(experimentPath)} \describe{ \item{experimentPath}{\code{character(1)} object pointing to the top-level directory of the experiment; see specific technology classes for additional detail.} \item{verbose=FALSE}{(optional) logical vector which, when \code{TRUE} results in warnings if paths do not exist.} } All paths must be fully-specified. } \section{Slots}{ \code{ExperimentPath} has one slot, containing a fully specified path to the corresponding directory (described above). \describe{ \item{\code{basePath}}{See above.} } The slot is accessed with \code{experimentPath}. } \section{Extends}{ Class \code{"\linkS4class{.ShortReadBase}"}, directly. } \section{Methods}{ Methods include: \describe{ \item{show}{\code{signature(object = "ExperimentPath")}: briefly summarize the file paths of \code{object}.} \item{detail}{\code{signature(x = "ExperimentPath")}: summarize file paths of \code{x}.} } } \author{Michael Lawrence} \examples{ showClass("ExperimentPath") } \keyword{classes} ShortRead/man/FastqQA-class.Rd0000644000175400017540000000320613001230550017126 0ustar00biocbuildbiocbuild\name{ShortReadQA-class} \docType{class} \alias{FastqQA} \alias{FastqQA-class} \alias{ShortReadQQA-class} \alias{report,FastqQA-method} \alias{report_html,ShortReadQQA-method} \alias{report_html,FastqQA-method} \title{Quality assessment of fastq files and ShortReadQ objects} \description{ These classes contains a list-like structure with summary descriptions derived from visiting one or more fastq files, or from a \code{\linkS4class{ShortReadQ}} object. } \section{Objects from the Class}{ Objects of the class are usually produced by a \code{\link{qa}} method. } \section{Slots}{ \describe{ \item{\code{.srlist}:}{Object of class \code{"list"}, containing data frames or lists of data frames summarizing the results of qa.} } } \section{Extends}{ Class \code{"\linkS4class{SRList}"}, directly. Class \code{"\linkS4class{.QA}"}, directly. Class \code{"\linkS4class{.SRUtil}"}, by class "SRList", distance 2. Class \code{"\linkS4class{.ShortReadBase}"}, by class ".QA", distance 2. } \section{Methods}{ Accessor methods are inherited from the \code{\linkS4class{SRList}} class. Additional methods defined on this class are: \describe{ \item{report}{\code{signature(x="FastqQA", ..., dest=tempfile(), type="html")}: produces HTML files summarizing QA results. \code{dest} should be a directory.} \item{report}{\code{signature(x="ShortReadQA", ..., dest=tempfile(), type="html")}: produces HTML files summarizing QA results. \code{dest} should be a directory.} } } \author{Martin Morgan } \seealso{ \code{\link{qa}}. } \examples{ showClass("FastqQA") } \keyword{classes} ShortRead/man/Intensity-class.Rd0000644000175400017540000000774513001230550017630 0ustar00biocbuildbiocbuild\name{Intensity-class} \docType{class} \alias{Intensity-class} \alias{IntensityMeasure-class} \alias{IntensityInfo-class} \alias{ArrayIntensity-class} \alias{ArrayIntensity} % accessors \alias{readIntensityInfo} \alias{intensity} \alias{measurementError} % subset \alias{[,IntensityMeasure,ANY,ANY-method} \alias{[,IntensityMeasure,ANY,ANY,ANY-method} \alias{[,IntensityMeasure,ANY,missing,ANY-method} \alias{[,IntensityMeasure,missing,ANY,ANY-method} \alias{[,IntensityMeasure,missing,missing,ANY-method} \alias{[[,ArrayIntensity,ANY,ANY-method} % etc. \alias{dim,Intensity-method} \alias{show,Intensity-method} \alias{show,IntensityMeasure-method} \title{(Legacy) "Intensity", "IntensityInfo", and "IntensityMeasure" base classes for short read image intensities} \description{ The \code{Intensity}, \code{IntensityMeasure}, and \code{IntensityInfo} classes represent and manipulate image intensity measures. Instances from the class may also contain information about measurement errors, and additional information about the reads from which the intensities are derived. \code{Intensity}, and \code{IntensityMeasure}, are virtual classes, and cannot be created directly. Classes derived from \code{IntensityMeasure} (e.g., \code{ArrayIntensity}) and \code{Intensity} (e.g., \code{\linkS4class{SolexaIntensity}}) are used to represent specific technologies. } \section{Objects from the Class}{ \code{ArrayIntensity} objects can be created with calls of the form \code{ArrayIntensity(array(0, c(1,2,3)))}. Objects of derived classes can be created from calls such as the \code{\link{SolexaIntensity}} constructor, or more typically by parsing appropriate files (e.g., \code{\link{readIntensities}}). } \section{Slots}{ Class \code{Intensity} has slots: \describe{ \item{\code{readInfo}:}{Object of class \code{"IntensityInfo"} containing columns for the lane, tile, x, and y coordinates of the read.} \item{\code{intensity}:}{Object of class \code{"IntensityMeasure"} containing image intensity data for each read and cycle.} \item{\code{measurementError}:}{Object of class \code{"IntensityMeasure"} containing measures of image intensity uncertainty for each read and cycle.} \item{\code{.hasMeasurementError}:}{Length 1 logical variable indicating whether intensity standard errors are included (internal use only).} } Classes \code{IntensityInfo} and \code{IntensityMeasure} are virtual classes, and have no slots. } \section{Extends}{ These classes extend \code{"\linkS4class{.ShortReadBase}"}, directly. } \section{Methods}{ Methods and accessor functions for \code{Intensity} include: \describe{ \item{readIntensityInfo}{\code{signature(object = "Intensity")}: access the \code{readInfo} slot of \code{object}.} \item{intensity}{\code{signature(object = "Intensity")}: access the \code{intensity} slot of \code{object}.} \item{measurementError}{\code{signature(object = "Intensity")}: access the \code{nse} slot of \code{object}, or signal an error if no standard errors are available.} \item{dim}{\code{signature(object = "Intensity")}: return the dimensions (e.g., number of reads by number of cycles) represented by \code{object}.} \item{show}{\code{signature(object = "Intensity")}: provide a compact representation of the object.} } Subsetting \code{"["} is available for the \code{IntensityMeasure} class; the \code{drop} argument to \code{"["} is ignored. Subsetting with \code{"[["} is available for the \code{ArrayIntensity} class. The method accepts three arguments, corresponding to the read, base, and cycle(s) to be selected. The return value is the array (i.e., underlying data values) corresponding to the selected indices. } \author{Martin Morgan } \seealso{ \code{\link{readIntensities}} } \examples{ showMethods(class="Intensity", where=getNamespace("ShortRead")) example(readIntensities) } \keyword{classes} ShortRead/man/MAQMapQA-class.Rd0000644000175400017540000000243213001230550017124 0ustar00biocbuildbiocbuild\name{MAQMapQA-class} \docType{class} \alias{MAQMapQA} \alias{MAQMapQA-class} \alias{report,MAQMapQA-method} \alias{report_html,MAQMapQA-method} \title{(Legacy) Quality assessment summaries from MAQ map files} \description{ This class contains a list-like structure with summary descriptions derived from visiting one or more MAQMap files. } \section{Objects from the Class}{ Objects of the class are usually produced by a \code{\link{qa}} method. } \section{Slots}{ \describe{ \item{\code{.srlist}:}{Object of class \code{"list"}, containing data frames or lists of data frames summarizing the results of qa.} } } \section{Extends}{ Class \code{"\linkS4class{SRList}"}, directly. Class \code{"\linkS4class{.QA}"}, directly. Class \code{"\linkS4class{.SRUtil}"}, by class "SRList", distance 2. Class \code{"\linkS4class{.ShortReadBase}"}, by class ".QA", distance 2. } \section{Methods}{ Accessor methods are inherited from the \code{\linkS4class{SRList}} class. \describe{ \item{report}{\code{signature(x="MAQMapQA", ..., dest=tempfile(), type="html")}: produces an html file summarizing the QA results.} } } \author{Martin Morgan } \seealso{ \code{\link{qa}}. } \examples{ showClass("MAQMapQA") } \keyword{classes} ShortRead/man/QA-class.Rd0000644000175400017540000000703513001230550016133 0ustar00biocbuildbiocbuild\name{QA-class} \docType{class} \alias{QA-class} \alias{.QA2-class} \alias{QA-class} \alias{QAAdapterContamination-class} \alias{QAFrequentSequence-class} \alias{QANucleotideByCycle-class} \alias{QANucleotideUse-class} \alias{QAQualityByCycle-class} \alias{QAQualityUse-class} \alias{QAReadQuality-class} \alias{QASequenceUse-class} \alias{QASource-class} \alias{QAFastqSource-class} \alias{QAData-class} \alias{QACollate-class} \alias{QAFiltered-class} \alias{QAFlagged-class} \alias{QASummary-class} \title{(Updated) classes for representing quality assessment results} \description{ Classes derived from \code{.QA-class} represent results of quality assurance analyses. } \section{Objects from the Class}{ Users create instances of many of these classes by calling the corresponding constructors, as documented on the help page for \code{\link{qa2}}. Classes constructed in this way include \code{\link{QACollate}}, \code{\link{QAFastqSource}}, \code{\link{QAAdapterContamination}}, \code{\link{QAFrequentSequence}}, \code{\link{QANucleotideByCycle}}, \code{\link{QANucleotideUse}}, \code{\link{QAQualityByCycle}}, \code{\link{QAQualityUse}}, \code{\link{QAReadQuality}}, and \code{\link{QASequenceUse}}. The classes \code{QASource}, \code{QAFiltered}, \code{QAFlagged} and \code{QASummary} are generated internally, not by users. } \section{Extends}{ \code{.QA2} extends class \code{"\linkS4class{.ShortReadBase}"}, directly. \code{QASummary} is a virtual class extending \code{.QA2}; all user-creatable classes extend \code{QASummary}. \code{QASource} extends \code{QASummary}. All classes used to represent raw data input (\code{QAFastqSource}) extend \code{QASource}. \code{QAData} is a reference class, used to contain a single instance of the fastq used in all QA Summary steps. \code{QACollate} extends \code{.QA2}. It contains a \code{SimpleList} instance with zero or more \code{QASummary} elements. \code{QA} extends \code{.QA2}, and contains a \code{SimpleList} of zero or more \code{QASummary} elements. This class represents the results of the \code{qa2} analysis. } \section{Methods}{ Methods defined on this class include: \describe{ \item{qa2}{\code{signature(object="QACollate", state, ..., verbose=FALSE)} creates a QA report from the elements of \code{QACollate}. Methods on \code{qa2} for objects extending class \code{QASummary} summarize QA statistics for that class, e.g., \code{qa2,QAFrequentSequences-method} implements the calculations required to summarize frequently used sequences, using data in \code{state}.} \item{report}{\code{signature(x="QA", ...)} creates an HTML report. Methods on \code{report} for objects extending class \code{QASummary} are responsible for creating the html snippet for that QA component.} \item{flag}{\code{signature(object=".QA2", ..., verbose=FALSE)} implements criteria to flag individual lanes as failing quality assessment. NOTE: flag is not fully implemented.} \item{rbind}{\code{signature(...="QASummary")}: rbind multiple summary elements of the same class, as when these have been created by separately calculating statistics on a number of fastq files.} \item{show}{\code{signature(object = "SolexaExportQA")}: Display an overview of the object contents.} } } \seealso{ Specific classes derived from \code{.QA2} } \author{Martin Morgan } \examples{ getClass(".QA2", where=getNamespace("ShortRead")) } \keyword{classes} ShortRead/man/QualityScore-class.Rd0000644000175400017540000002411013001230550020247 0ustar00biocbuildbiocbuild\name{QualityScore-class} \docType{class} \alias{QualityScore-class} \alias{NumericQuality-class} \alias{IntegerQuality-class} \alias{MatrixQuality-class} \alias{FastqQuality-class} \alias{SFastqQuality-class} % QualityScore \alias{[,QualityScore,ANY,missing-method} \alias{[,QualityScore,ANY,missing,ANY-method} \alias{[[,QualityScore,ANY,missing-method} \alias{append,QualityScore,QualityScore-method} \alias{length,QualityScore-method} \alias{width,QualityScore-method} \alias{detail,QualityScore-method} % NumericQuality \alias{width,NumericQuality-method} \alias{show,NumericQuality-method} % MatrixQuality \alias{[,MatrixQuality,ANY,missing-method} \alias{[,MatrixQuality,ANY,missing,ANY-method} \alias{[[,MatrixQuality,ANY,missing-method} \alias{dim,MatrixQuality-method} \alias{length,MatrixQuality-method} \alias{narrow,MatrixQuality-method} \alias{width,MatrixQuality-method} \alias{append,MatrixQuality,MatrixQuality-method} % FastqQuality \alias{width,FastqQuality-method} \alias{show,FastqQuality-method} \alias{alphabet,FastqQuality-method} \alias{encoding,FastqQuality-method} \alias{alphabetFrequency,FastqQuality-method} \alias{alphabetByCycle,FastqQuality-method} \alias{alphabetScore,FastqQuality-method} \alias{coerce,FastqQuality,numeric-method} \alias{coerce,FastqQuality,matrix-method} \alias{coerce,FastqQuality,PhredQuality-method} \alias{reverse,FastqQuality-method} \alias{narrow,FastqQuality-method} \alias{trimTailw,FastqQuality-method} \alias{trimTails,FastqQuality-method} \alias{srsort,FastqQuality-method} \alias{srorder,FastqQuality-method} \alias{srrank,FastqQuality-method} \alias{srduplicated,FastqQuality-method} % SFastqQuality \alias{encoding,SFastqQuality-method} \alias{alphabetScore,SFastqQuality-method} \alias{coerce,SFastqQuality,matrix-method} \alias{coerce,SFastqQuality,SolexaQuality-method} %% Biostrings::PhredQuality \alias{alphabetScore,PhredQuality-method} \title{Quality scores for short reads and their alignments} \description{ This class hierarchy represents quality scores for short reads. \code{QualityScore} is a virtual base class, with derived classes offering different ways of representing qualities. Methods defined on \code{QualityScore} are implemented in all derived classes. } \section{Objects from the Class}{ Objects from the class are created using constructors (e.g., \code{\link{NumericQuality}}) named after the class name. Defined classes are as follows: \describe{ \item{QualityScore}{Virtual base class; instances cannot be instantiated.} \item{NumericQuality}{A single numeric vector, where values represent quality scores on an arbitrary scale.} \item{IntegerQuality}{A integer numeric vector, where values represent quality scores on an arbitrary scale.} \item{MatrixQuality}{A rectangular matrix of quality scores, with rows representing reads and columns cycles. The content and interpretation of row and column entries is arbitrary; the rectangular nature implies quality scores from equal-length reads.} \item{FastqQuality}{\sQuote{fastq} encoded quality scores stored in a \code{BStringSet} instance. Base qualities of a single read are represented as an ASCII character string. The integer-valued quality score of a single base is encoded as its ASCII equivalent plus 33. The precise definition of the integer-valued quality score is unspecified, but is usually a Phred score; the meaning can be determined from the source of the quality scores. Multiple reads are stored as a \code{BStringSet}, and so can be of varying lengths.} \item{SolexaQuality}{As with \code{FastqQuality}, but with integer qualities encoded as ASCII equivalent plus 64.} } } \section{Extends}{ Class \code{"\linkS4class{.ShortReadBase}"}, directly. } \section{Methods}{ The following methods are defined on all \code{QualityScore} and derived classes: \describe{ \item{[}{\code{signature(x = "QualityScore", i = "ANY", j = "missing")}} \item{[}{\code{signature(x = "MatrixQuality", i = "ANY", j = "missing")}: Subset the object, with index \code{i} indicating the reads for which quality scores are to be extracted. The class of the result is the same as the class of \code{x}. It is an error to provide any argument other than \code{i}.} \item{[[}{\code{signature(x = "QualityScore", i = "ANY", j = "ANY")}: Subset the object, returning the quality score (e.g., numeric value) of the \code{i}th read. } \item{[[}{\code{signature(x = "MatrixQuality", i = "ANY", j = "ANY")}: Returns the vector of quality scores associated with the \code{i}th read.} \item{dim}{\code{signature(x = "MatrixQuality")}: The integer(2) dimension (e.g., number of reads, read width) represented by the quality score.} \item{length}{\code{signature(x = "QualityScore")}:} \item{length}{\code{signature(x = "MatrixQuality")}: The integer(1) length (e.g., number of reads) represented by the quality score. Note that \code{length} of \code{MatrixQuailty} is the number of rows of the corresponding matrix, and not the length of the corresponding numeric vector.} \item{append}{\code{signature(x = "QualityScore", values = "QualityScore")}: append \code{values} after \code{x}.} \item{width}{\code{signature(x = "QualityScore")}:} \item{width}{\code{signature(x = "NumericQuality")}:} \item{width}{\code{signature(x = "MatrixQuality")}:} \item{width}{\code{signature(x = "FastqQuality")}: A numeric vector with length equal to the number of quality scores, and value equal to the number of quality scores for each read. For instance, a \code{\link{FastqQuality}} will have widths equal to the number of nucleotides in the underlying short read. } \item{show}{\code{signature(object = "QualityScore")}:} \item{show}{\code{signature(object = "NumericQuality")}:} \item{show}{\code{signature(object = "FastqQuality")}: provide a brief summary of the object content. } \item{detail}{\code{signature(x = "QualityScore")}: provide a more detailed view of object content. } } The following methods are defined on specific classes: \describe{ \item{alphabet}{\code{signature(x = "FastqQuality", ...)}: Return a character vector of valid quality characters. } \item{encoding}{\code{signature(x = "FastqQuality", ...)}, \code{signature(x = "SFastqQuality", ...)}: Returns a named character vector of integer encodings. } \item{alphabetFrequency}{\code{signature(stringSet = "FastqQuality")}: Apply \code{\link[Biostrings:letterFrequency]{alphabetFrequency}} to quality scores, returning a matrix as described in \code{\link[Biostrings:letterFrequency]{alphabetFrequency}}.} \item{alphabetByCycle}{\code{signature(stringSet = "FastqQuality")}: Apply \code{\link{alphabetByCycle}} to quality scores, returning a matrix as described in \code{\link{alphabetByCycle}}.} \item{alphabetScore}{\code{signature(object = "FastqQuality")}:} \item{alphabetScore}{\code{signature(object = "SFastqQuality")}:} \item{alphabetScore}{\code{signature(object = "PhredQuality")}: Apply \code{\link{alphabetScore}} (i.e., summed base quality, per read) to \code{object}.} \item{coerce}{\code{signature(from = "FastqQuality", to = "numeric")}:} \item{coerce}{\code{signature(from = "FastqQuality", to = "matrix")}:} \item{coerce}{\code{signature(from = "FastqQuality", to = "PhredQuality")}:} \item{coerce}{\code{signature(from = "SFastqQuality", to = "matrix")}:} \item{coerce}{\code{signature(from = "SFastqQuality", to = "SolexaQuality")}: Use \code{as(from, "matrix")}) and similar to coerce objects of class \code{from} to class \code{to}, using the quality encoding implied by the class. When \code{to} is \dQuote{matrix}, the result is a matrix of type \code{integer} with number of columns equal to the maximum width of \code{from}; elements \code{i, j} with \code{j > width(from)[i]} have value \code{NA_integer_}. The result always represents the integer encoding of the corresponding quality string.} \item{reverse}{\code{signature(x = "FastqQuality", ...}: reverse the quality sequence.} \item{narrow}{\code{signature(x = "FastqQuality", start = NA, end = NA, width = NA, use.names = TRUE)}: \sQuote{narrow} \code{quality} so that scores are between \code{start} and \code{end} bases, according to \code{\link[IRanges:intra-range-methods]{narrow}} in the \code{IRanges} package.} \item{trimTailw}{\code{signature(object="FastqQuality", k="integer", a="character", halfwidth="integer", ..., ranges=FALSE)}: trim trailing nucleotides when a window of width 2 * halfwidth + 1 contains \code{k} or more quality scores falling at or below \code{a}.} \item{trimTails}{\code{signature(object="FastqQuality", k="integer", a="character", successive=FALSE, ..., ranges=FALSE)}: trim trailing scores if \code{k} scores fall below the quality encoded by \code{a}. If \code{successive=FALSE}, the k'th failing score and all subseqent scores are trimmed. If \code{successive=TRUE}, failing scores must occur successively; the sequence is trimmed from the first of the successive failing score.} \item{srorder}{\code{signature(x = "FastqQuality")}:} \item{srrank}{\code{signature(x = "FastqQuality")}:} \item{srduplicated}{\code{signature(x = "FastqQuality")}: Apply \code{\link{srsort}}, \code{srorder}, \code{srrank}, and \code{srduplicated} to quality scores, returning objects as described on the appropriate help page.} } Integer representations of \code{SFastqQuality} and \code{FastqQuality} can be obtained with \code{as(x, "matrix")}. } \seealso{ \code{\link{NumericQuality}} and other constructors. } \author{Martin Morgan } \examples{ names(slot(getClass("QualityScore"), "subclasses")) encoding(FastqQuality()) encoding(SFastqQuality()) } \keyword{classes} ShortRead/man/QualityScore.Rd0000644000175400017540000000310613001230550017146 0ustar00biocbuildbiocbuild\name{QualityScore} \alias{NumericQuality} \alias{IntegerQuality} \alias{MatrixQuality} \alias{FastqQuality} \alias{FastqQuality,BStringSet-method} \alias{FastqQuality,character-method} \alias{FastqQuality,missing-method} \alias{SFastqQuality} \alias{SFastqQuality,BStringSet-method} \alias{SFastqQuality,character-method} \alias{SFastqQuality,missing-method} \title{Construct objects indicating read or alignment quality} \description{ Use these functions to construct quality indicators for reads or alignments. See \code{\linkS4class{QualityScore}} for details of object content and methods available for manipulating them. } \usage{ NumericQuality(quality = numeric(0)) IntegerQuality(quality = integer(0)) MatrixQuality(quality = new("matrix")) FastqQuality(quality, ...) SFastqQuality(quality, ...) } \arguments{ \item{quality}{An object used to initialize the data structure. Appropriate objects are indicated in the constructors above for Numeric, Integer, and Matrix qualities. For \code{FastqQuality} and \code{SFastqQuality}, methods are defined for \code{\link[Biostrings:XStringSet-class]{BStringSet}}, \code{character}, and \code{missing}.} \item{...}{Additional arguments, currently unused.} } \value{ Constructors return objects of the corresponding class derived from \code{\linkS4class{QualityScore}}. } \author{Martin Morgan } \seealso{ \code{\linkS4class{QualityScore}}, \code{\link{readFastq}}, \code{\link{readAligned}} } \examples{ nq <- NumericQuality(rnorm(20)) nq quality(nq) quality(nq[10:1]) } \keyword{ manip } ShortRead/man/RochePath-class.Rd0000644000175400017540000001173713001230551017514 0ustar00biocbuildbiocbuild\name{RochePath-class} \docType{class} \alias{RochePath-class} \alias{detail,RochePath-method} \alias{read454,RochePath-method} \alias{readFastaQual,RochePath-method} \alias{readFastaQual,character-method} \alias{readFasta,RochePath-method} \alias{readPath} \alias{readQual} \alias{readQual,character-method} \alias{readQual,RochePath-method} \alias{readBaseQuality,RochePath-method} \alias{read454} \alias{readFastaQual} \alias{runNames} \alias{qualPath} \alias{runNames,RochePath-method} \alias{RocheSet,character-method} \alias{RocheSet,RochePath-method} \alias{runNames,RochePath-method} \alias{show,RochePath-method} % \alias{RochePath} \title{(Legacy) "RochePath" class representing a Roche (454) experiment location} \description{ This class represents the directory location where Roche (454) result files (fasta sequences and qualities) can be found. } \section{Objects from the Class}{ Objects from the class are created with the \code{RochePath} constructor: \code{RochePath(experimentPath = NA_character_, readPath = experimentPath, qualPath = readPath, ..., verbose = FALSE) } \describe{ \item{experimentPath}{\code{character(1)} or \code{\linkS4class{RochePath}} pointing to the top-level directory of a Roche experiment.} \item{readPath}{\code{character()} of directories (typically in \code{experimentPath}) containing sequence (read) information. The default selects all directories matching \code{list.files(experimentPath, "run")}.} \item{qualPath}{\code{character()} of directories (typically in \code{experimentPath}) containing quality information. The default selects all directories matching \code{list.files(experimentPath, "run")}.} \item{verbose}{\code{logical(1)} indicating whether invalid paths should be reported interactively.} } } \section{Slots}{ \code{RocheSet} has the following slots: \describe{ \item{\code{readPath}:}{Object of class \code{"character"}, as described in the constructor, above.} \item{\code{qualPath}:}{Object of class \code{"character"}, as described in the constructor, above.} \item{\code{basePath}:}{Object of class \code{"character"}, containing the \code{experimentPath}.} } } \section{Extends}{ Class \code{"\linkS4class{ExperimentPath}"}, directly. Class \code{"\linkS4class{.Roche}"}, directly. Class \code{"\linkS4class{.ShortReadBase}"}, by class "ExperimentPath", distance 2. Class \code{"\linkS4class{.ShortReadBase}"}, by class ".Roche", distance 2. } \section{Methods}{ \code{RochePath} has the following methods or functions defined: \describe{ \item{readFasta}{\code{signature(dirPath = "RochePath", pattern=".\\.fna$", sample = 1, run = 1, ...)}: Read sequences from files matching \code{list.files(dirPath, pattern)} (when \code{dirPath="character"}) or \code{list.files(readPath(dir)[run], pattern)[sample]}. The result is a \code{DNAStringSet}.} \item{readQual}{\code{signature(dirPath = "RochePath", reads=NULL, pattern="\\.qual$", sample=1, run=1, ...)}: Read quality scores from files matching \code{list.files(qualPath(dirPath)[run])[sample]}. Non-null \code{reads} is used as an (optional) template for parsing quality scores. } \item{readFastaQual}{\code{signature(dirPath = "RochePath", fastaPattern = "\\.fna$", qualPattern = "\\.qual$", sample = 1, run = 1)}: read sequences and quality scores into a \code{\linkS4class{ShortReadQ}} instance.} \item{readFastaQual}{\code{signature(dirPath = "character", fastaPattern = "\\.fna$", qualPattern = "\\.qual$", sample = 1, run = 1)}: wrapper for method above, coercing \code{dirPath} to a \code{RochePath} via \code{RochePath(dirPath)}.} \item{readBaseQuality}{\code{signature(dirPath = "RochePath", ...)}: Reads in base and quality information. Currently delegates to \code{readFastaQual}, above, but will do more after \code{RochePath} supports more file types. } \item{read454}{ \code{signature(dirPath = "RochePath", ...)}: Pass arguments on to \code{readFastaQual}, documented above. } \item{readPath}{\code{signature(object = "RochePath")}: return the contents of the \code{readPath} slot.} \item{runNames}{\code{signature(object = "RochePath")}: return the \code{basename}s of \code{readPath(object)}.} \item{RocheSet}{\code{signature(path = "RochePath")}: create a \code{\linkS4class{RocheSet}} from \code{path}.} } Additional methods include: \describe{ \item{show}{\code{signature(object = "RochePath")}: Briefly summarize the experiment path locations.} \item{detail}{\code{signature(x = "RochePath")}: Provide additional detail on the Roche path. All file paths are presented in full.} } } \author{Michael Lawrence } \seealso{ \code{\linkS4class{ExperimentPath}}. } \examples{ showClass("RochePath") } \keyword{classes} ShortRead/man/RocheSet-class.Rd0000644000175400017540000000360113001230550017341 0ustar00biocbuildbiocbuild\name{RocheSet-class} \docType{class} \alias{RocheSet-class} \alias{RocheSet} \title{(Legacy) Roche (454) experiment-wide data container} \description{ This class is meant to coordinate all data in a Roche (454) experiment. See \code{\linkS4class{SRSet}} for additional details. } \section{Objects from the Class}{ Create objects from this class using one of the \code{RocheSet} methods documented below } \section{Slots}{ \describe{ \item{\code{sourcePath}:}{Object of class \code{"RochePath"} The file system location of the data used in this experiment.} \item{\code{readIndex}:}{Object of class \code{"integer"} indexing reads included in the experiment; see \code{\linkS4class{SRSet}} for details on data representation in this class.} \item{\code{readCount}:}{Object of class \code{"integer"} containing the number of reads associated with each sample; see \code{\linkS4class{SRSet}} for details on data representation in this class.} \item{\code{phenoData}:}{Object of class \code{"AnnotatedDataFrame"} with as many rows as there are samples, containing information on experimental design.} \item{\code{readData}:}{Object of class \code{"AnnotatedDataFrame"} containing as many rows as there are reads, containing information on each read in the experiment.} } } \section{Extends}{ Class \code{"\linkS4class{SRSet}"}, directly. Class \code{"\linkS4class{.Roche}"}, directly. Class \code{"\linkS4class{.ShortReadBase}"}, by class "SRSet", distance 2. Class \code{"\linkS4class{.ShortReadBase}"}, by class ".Roche", distance 2. } \section{Methods}{ No methods defined with class "RocheSet" in the signature; see \code{\linkS4class{SRSet}} for inherited methods. } \author{Michael Lawrence } \seealso{ \code{\linkS4class{SRSet}} } \examples{ showClass("RocheSet") } \keyword{classes} ShortRead/man/RtaIntensity-class.Rd0000644000175400017540000000342013001230551020262 0ustar00biocbuildbiocbuild\name{RtaIntensity-class} \docType{class} \alias{RtaIntensity-class} \title{(Legacy) Class "RtaIntensity"} \description{ Subclass of \code{\linkS4class{Intensity}} for representing image intensity data from the Illumina RTA pipeline. } \section{Objects from the Class}{ Objects can be created by calls to \code{RtaIntensity} or more usually \code{readIntensities}. } \section{Slots}{ Object of \code{RtaIntensity} have slots: \describe{ \item{\code{readInfo}:}{Object of class \code{"RtaIntensityInfo"} representing information about each read.} \item{\code{intensity}:}{Object of class \code{"ArrayIntensity"} containing an array of intensities with dimensions read, base, and cycle. Nucleotide are A, C, G, T for each cycle.} \item{\code{measurementError}:}{Object of class \code{"ArrayIntensity"} containing measurement errors for each read, cycle, and base, with dimensions like that for \code{intensity}. } \item{\code{.hasMeasurementError}:}{Object of class \code{"ScalarLogical"} used internally to indicate whether measurement error information is included.} } } \section{Extends}{ Class \code{"\linkS4class{SolexaIntensity}"}, directly. Class \code{"\linkS4class{Intensity}"}, by class "SolexaIntensity", distance 2. Class \code{"\linkS4class{.ShortReadBase}"}, by class "SolexaIntensity", distance 3. } \section{Methods}{ Class "RtaIntensity" inherits accessor, subsetting, and display methods from class \code{\linkS4class{SolexaIntensity}}. } \author{Martin Morgan } \seealso{\code{\linkS4class{SolexaIntensity}}, \code{\link{readIntensities}}} \examples{ showClass("RtaIntensity") showMethods(class="RtaIntensity", where=getNamespace("ShortRead")) } \keyword{classes} ShortRead/man/RtaIntensity.Rd0000644000175400017540000000267513001230551017172 0ustar00biocbuildbiocbuild\name{RtaIntensity} \alias{RtaIntensity} \title{(Legacy) Construct objects of class "RtaIntensity"} \description{ \code{\linkS4class{RtaIntensity}} objects contain Illumina image intensity measures created by the RTA pipeline. It will often be more convenient to create this object using \code{\link{readIntensities}}. } \usage{ RtaIntensity(intensity=array(0, c(0, 0, 0)), measurementError=array(0, c(0, 0, 0)), readInfo=SolexaIntensityInfo( lane=integer()[seq_len(nrow(intensity))]), ...) } \arguments{ \item{intensity}{A matrix of image intensity values. Successive columns correspond to nucleotides A, C, G, T; four successive columns correspond to each cycle. Typically, derived from \code{"_int.txt"} files.} \item{measurementError}{As \code{intensity}, but measuring standard error. Usually derived from \code{"_nse.txt"} files.} \item{readInfo}{An object of class \code{AnnotatedDataFrame}, containing information described by \code{RtaIntensityInfo}.} \item{...}{Additional arguments, not currently used.} } \value{ An object of class \code{\linkS4class{RtaIntensity}}. } \author{Martin Morgan } \seealso{\code{\linkS4class{RtaIntensity}}, \code{\link{readIntensities}}.} \examples{ rta <- RtaIntensity(array(runif(60), c(5,4,3))) intensity(rta) ## subsetting, access, and coercion as(intensity(rta)[1:2,,], "array") } \keyword{manip} ShortRead/man/SRFilter-class.Rd0000644000175400017540000000547413001230550017331 0ustar00biocbuildbiocbuild\name{SRFilter-class} \docType{class} \alias{SRFilter-class} \alias{srFilter,SRFilter-method} \alias{name} \alias{name,SRFilter-method} \alias{show,SRFilter-method} \alias{c,SRFilter-method} \alias{coerce,SRFilter,FilterRules-method} \title{"SRFilter" for representing functions operating on ShortRead objects} \description{ Objects of this class are functions that, when provided an appropriate object from the ShortRead package, return logical vectors indicating which parts of the object satisfy the filter criterion. A number of filters are built-in (described below); users are free to create their own filters, using the \code{srFilter} function. } \section{Objects from the Class}{ Objects can be created through \code{\link{srFilter}} (to create a user-defined filter) or through calls to constructors for predefined filters, as described on the \code{\link{srFilter}} page. } \section{Slots}{ \describe{ \item{\code{.Data}:}{Object of class \code{"function"} taking a single named argument \code{x} corresponding to the ShortRead object that the filter will be applied to. The return value of the filter function is expected to be a logical vector that can be used to subset \code{x} to include those elements of \code{x} satisfying the filter. } \item{\code{name}:}{Object of class \code{"ScalarCharacter"} representing the name of the filter. The name is useful for suggesting the purpose of the filter, and for debugging failed filters. } } } \section{Extends}{ Class \code{"\linkS4class{function}"}, from data part. Class \code{"\linkS4class{.SRUtil}"}, directly. Class \code{"\linkS4class{OptionalFunction}"}, by class "function", distance 2. Class \code{"\linkS4class{PossibleMethod}"}, by class "function", distance 2. } \section{Methods}{ \describe{ \item{srFilter}{\code{signature(fun = "SRFilter")}: Return the function representing the underlying filter; this is primarily for interactive use to understanding filter function; usually the filter is invoked as a normal function call, as illustrated below } \item{name}{\code{signature(x = "SRFilter")}: Return, as a \code{ScalarCharacter}, the name of the function. } \item{show}{\code{signature(object = "SRFilter")}: display a brief summary of the filter } \item{coerce}{\code{signature(from = "SRFilter", to = "FilterRules")}: Coerce a filter to a \code{\link[IRanges]{FilterRules}} object of length one. } \item{c}{\code{signature(x = "SRFilter", ...)}: Combine filters into a single \code{\link[IRanges]{FilterRules}} object. } } } \author{Martin Morgan } \seealso{ \code{\link{srFilter}} for predefined and user-defined filters. } \examples{ ## see ?srFilter } \keyword{classes} ShortRead/man/SRFilterResult-class.Rd0000644000175400017540000000775113001230550020530 0ustar00biocbuildbiocbuild\name{SRFilterResult-class} \Rdversion{1.1} \docType{class} \alias{SRFilterResult-class} \alias{SRFilterResult} \alias{Logic,SRFilterResult,SRFilterResult-method} \alias{!,SRFilterResult-method} \alias{name,SRFilterResult-method} \alias{show,SRFilterResult-method} \alias{stats} \alias{stats,SRFilterResult-method} \title{"SRFilterResult" for SRFilter output and statistics} \description{ Objects of this class are logical vectors indicating records passing the applied filter, with an associated data frame summarizing the name, input number of records, records passing filter, and logical operation used for all filters in which the result participated. } \usage{ SRFilterResult(x = logical(), name = NA_character_, input = length(x), passing = sum(x), op = NA_character_) \S4method{Logic}{SRFilterResult,SRFilterResult}(e1, e2) \S4method{name}{SRFilterResult}(x, ...) stats(x, ...) \S4method{show}{SRFilterResult}(object) } \arguments{ \item{x, object, e1, e2}{For \code{SRFilterResult}, \code{logical()} indicating records that passed filter or, for others, an instance of \code{SRFilterResult} class.} \item{name}{\code{character()} indicating the name by which the filter is to be referred. Internally, \code{name}, \code{input}, \code{passing}, and \code{op} may all be vectors representing columns of a \code{data.frame} summarizing the application of successive filters.} \item{input}{\code{integer()} indicating the length of the original input.} \item{passing}{\code{integer()} indicating the number of records passing the filter.} \item{op}{\code{character()} indicating the logical operation, if any, associated with this filter.} \item{...}{Additional arguments, unused in methods documented on this page.} } \section{Objects from the Class}{ Objects can be created through \code{\link{SRFilterResult}}, but these are automatically created by the application of \code{\link{srFilter}} instances. } \section{Slots}{ \describe{ \item{\code{.Data}:}{Object of class \code{"logical"} indicating records that passed the filter. } \item{\code{name}:}{Object of class \code{"ScalarCharacter"} representing the name of the filter whose results are summarized. The name is either the actual name of the filter, or a combination of filter names and logical operations when the outcome results from application of several filters in a single logical expression. } \item{\code{stats}:}{Object of class \code{"data.frame"} summarizing the name, input number of records, records passing filter, and logical operation used for all filters in which the result participated. The \code{data.frame} rows correspond either to single filters, or to logical combinations of filters.} } } \section{Extends}{ Class \code{"\linkS4class{logical}"}, from data part. Class \code{"\linkS4class{.SRUtil}"}, directly. Class \code{"\linkS4class{vector}"}, by class "logical", distance 2. Class \code{"\link{atomic}"}, by class "logical", distance 2. Class \code{"vectorORfactor"}, by class "logical", distance 3. } \section{Methods}{ \describe{ \item{Logic}{\code{signature(e1 = "SRFilterResult", e2 = "SRFilterResult")}: logic operations on filters.} \item{!}{\code{signature(x = "SRFilterResult")}: Negate the outcome of the current filter results } \item{name}{\code{signature(x = "SRFilterResult")}: The name of the filter that the results are based on.} \item{stats}{\code{signature(x = "SRFilterResult")}: a \code{data.frame} as described in the \sQuote{Slots} section of this page.} \item{show}{\code{signature(object = "SRFilterResult")}: summary of filter results.} } } \author{Martin Morgan \url{mailto:mtmorgan@fhcrc.org}} \seealso{\code{\link{srFilter}}} \examples{ fa <- srFilter(function(x) x \%\% 2 == 0, "Even") fb <- srFilter(function(x) x \%\% 2 == 1, "Odd") x <- 1:10 fa(x) | fb(x) fa(x) & fb(x) !(fa(x) & fb(x)) } \keyword{classes} ShortRead/man/SRSet-class.Rd0000644000175400017540000000547513001230550016640 0ustar00biocbuildbiocbuild\name{SRSet-class} \docType{class} \alias{SRSet-class} \alias{readCount} \alias{readData} \alias{readIndex} \alias{sourcePath} \alias{phenoData,SRSet-method} \alias{experimentPath,SRSet-method} \alias{show,SRSet-method} \alias{detail,SRSet-method} \title{(Legacy) A base class for Roche experiment-wide data} \description{ This class coordinates phenotype (sample) and sequence data, primarily as used on the Roche platform. Conceptually, this class has reads from a single experiment represented as a long vector, ordered by sample. The \code{readCount} slot indicates the number of reads in each sample, so that the sum of \code{readCount} is the total number of reads in the experiment. The \code{readIndex} field is a light-weight indicator of which reads from all those available that are currently referenced by the \code{SRSet}. } \section{Objects from the Class}{ Objects of this class are not usually created directly, but instead are created by a derived class, e.g., \code{\linkS4class{RocheSet}}. } \section{Slots}{ \describe{ \item{\code{sourcePath}:}{Object of class \code{"ExperimentPath"}, containing the directory path where sequence files can be found.} \item{\code{readIndex}:}{Object of class \code{"integer"} indicating specific sequences included in the experiment.} \item{\code{readCount}:}{Object of class \code{"integer"} containing the number of reads in each sample included in the experiment. The sum of this vector is the total number of reads.} \item{\code{phenoData}:}{Object of class \code{"AnnotatedDataFrame"} describing each sample in the experiment. The number of rows of \code{phenoData} equals the number of elements in \code{readCount}.} \item{\code{readData}:}{Object of class \code{"AnnotatedDataFrame"} containing annotations on all reads.} } } \section{Extends}{ Class \code{"\linkS4class{.ShortReadBase}"}, directly. } \section{Methods}{ \describe{ \item{experimentPath}{\code{signature(object = "SRSet")}: return the \code{\linkS4class{ExperimentPath}} associated with this object.} \item{phenoData}{\code{signature(object = "SRSet")}: return the \code{\linkS4class{phenoData}} associated with this object.} \item{readCount}{\code{signature(object="SRSet")}:} \item{readIndex}{\code{signature(object="SRSet")}:} \item{readData}{\code{signature(object="SRSet")}:} \item{sourcePath}{\code{signature(object="SRSet")}: Retrieve the corresponding slot from \code{object}.} \item{show}{\code{signature(object = "SRSet")}: display the contents of this object.} \item{detail}{\code{signature(x = "SRSet")}: provide more extensive information on the object.} } } \author{Michael Lawrence } \examples{ showClass("SRSet") } \keyword{classes} ShortRead/man/SRUtil-class.Rd0000644000175400017540000001512013001230550017006 0ustar00biocbuildbiocbuild\name{SRUtil-class} \docType{class} \alias{.SRUtil-class} \alias{SRError-class} \alias{SRWarn-class} \alias{SRList-class} \alias{SRVector-class} % constructors \alias{SRError} \alias{SRWarn} \alias{SRList} \alias{SRVector} % methods \alias{detail,.ShortReadBase-method} % internal method, not documented \alias{length,SRList-method} \alias{names,SRList-method} \alias{names<-,SRList,character-method} \alias{lapply,SRList-method} \alias{lapply,SRList,ANY-method} \alias{sapply,SRList-method} \alias{srlist} \alias{[,SRList,ANY,missing-method} \alias{[,SRList,ANY,missing,ANY-method} \alias{[[,SRList,ANY,missing-method} \alias{detail,SRList-method} \alias{show,SRList-method} \alias{detail,SRVector-method} \alias{show,SRVector-method} \title{".SRUtil" and related classes} \description{ These classes provide important utility functions in the \pkg{ShortRead} package, but may occasionally be seen by the user and are documented here for that reason. } \section{Objects from the Class}{ Utility classes include: \itemize{ \item \code{.SRUtil-class} a virtual base class from which all utility classes are derived. \item \code{SRError-class} created when errors occur in \pkg{ShortRead} package code. \item \code{SRWarn-class} created when warnings occur in \pkg{ShortRead} package code \item \code{SRList-class} representing a list (heterogeneous collection) of objects. The S4Vectors::SimpleList class is a better choice for a list-like container. \item \code{SRVector-class} representing a vector (homogeneous collection, i.e., all elements of the same class) of objects. } Objects from these classes are not normally constructed by the user. However, constructors are available, as follows. \code{SRError(type, fmt, ...)}, \code{SRWarn(type, fmt, ...)}: \describe{ \item{type}{\code{character(1)} vector describing the type of the error. \code{type} must come from a pre-defined list of types.} \item{fmt}{a \code{\link{sprintf}}-style format string for the message to be reported with the error.} \item{...}{additional arguments to be interpolated into \code{fmt}.} } \code{SRList(...)} \describe{ \item{...}{elements of any type or length to be placed into the \code{SRList}. If the length of \code{...} is 1 and the argument is a list, then the list itself is placed into \code{SRList}.} } \code{SRVector(..., vclass)} \describe{ \item{...}{elements all satisfying an \code{\link{is}} relationship with \code{vclass}, to be placed in \code{SRVector}.} \item{vclass}{the class to which all elements in \code{...} belong. If \code{vclass} is missing and \code{length(list(...))} is greater than zero, then \code{vclass} is taken to be the class of the first argument of \code{...}.} } \code{SRVector} errors: \describe{ \item{SRVectorClassDisagreement}{this error occurs when not all arguments \code{...} satisfy an \sQuote{is} relationship with \code{vclass}.} } } \section{Slots}{ \code{SRError} and \code{SRWarn} have the following slots defined: \describe{ \item{\code{.type}:}{Object of class \code{"character"} containing the type of error or warning. \code{.type} must come from a pre-defined list of types, see, e.g., \code{ShortRead:::.SRError_types}.} \item{\code{.message}:}{Object of class \code{"character"} containing a detailed message describing the error or warning.} } \code{SRList} has the following slot defined: \describe{ \item{\code{.srlist}:}{Object of class \code{"list"} containing the elements in the list.} } \code{SRVector} extends \code{SRList}, with the following additional slot: \describe{ \item{\code{vclass}:}{Object of class \code{"character"} naming the type of object all elements of \code{SRVector} must be.} } } \section{Methods}{ Accessors are available for all slots, and have the same name as the slot, e.g., \code{vclass} to access the \code{vclass} slot of \code{SRVector}. Internal slots (those starting with \sQuote{.} also have accessors, but these are not exported e.g., \code{ShortRead:::.type}. \code{SRList} has the following methods: \describe{ \item{length}{\code{signature(x = "SRList")}: return the (\code{integer(1)}) length of the \code{SRList}.} \item{names}{\code{signature(x = "SRList")}: return a character vector of list element names. The length of the returned vector is the same as the length of \code{x}.} \item{names<-}{\code{signature(x = "SRList", value = "character")}: assign \code{value} as names for members of \code{x}.} \item{[}{\code{signature(x = "SRList", i = "ANY", j = "missing")}: subset the list using standard R list subset paradigms.} \item{[[}{\code{signature(x = "SRList", i = "ANY", j = "missing")}: select element \sQuote{i} from the list, using standard R list selection paradigms.} \item{lapply}{\code{signature(X = "SRList", FUN="ANY")}: apply a function to all elements of \code{X}, with additional arguments interpreted as with \code{\link{lapply}}.} \item{sapply}{\code{signature(X = "SRList")}: apply a function to all elements of \code{X}, simplifying the result if possible. Additional arguments interpreted as with \code{\link{sapply}}.} \item{srlist}{\code{signature(object="SRList")}: coerce the SRList object to a list.} \item{show}{\code{signature(object = "SRList")}: display an informative summary of the object content, including the length of the list represented by \code{object}.} \item{detail}{\code{signature(x = "SRList")}: display a more extensive version of the object, as one might expect from printing a standard list in R.} } \code{SRVector} inherits all methods from \code{SRList}, and has the following additional methods: \describe{ \item{show}{\code{signature(object = "SRVector")}: display an informative summary of the object content, e.g., the vector class (\code{vclass}) and length.} \item{detail}{\code{signature(x = "SRVector")}: display a more extensive version of the object, as one might expect from a printing a standard R list.} } } \author{Martin Morgan} \examples{ getClass(".SRUtil", where=getNamespace("ShortRead")) ShortRead:::.SRError_types ShortRead:::.SRWarn_types detail(SRList(1:5, letters[1:5])) tryCatch(SRVector(1:5, letters[1:5]), SRVectorClassDisagreement=function(err) { cat("caught:", conditionMessage(err), "\n") }) } \keyword{classes} ShortRead/man/Sampler-class.Rd0000644000175400017540000001453113001230550017234 0ustar00biocbuildbiocbuild\name{FastqFile-class} \docType{class} % Classes \alias{ShortReadFile-class} \alias{FastqFile-class} \alias{FastqFileReader-class} \alias{FastqSampler-class} \alias{FastqSamplerList-class} \alias{FastqStreamer-class} \alias{FastqStreamerList-class} \alias{FastqFileList-class} % ShortReadFile / FastqFile / FastqFileList \alias{FastqFile} \alias{FastqFileList} \alias{open.ShortReadFile} \alias{close.ShortReadFile} \alias{readFastq,FastqFile-method} % FastqFileList \alias{FastqFileList,ANY-method} \alias{FastqFileList,character-method} % FastqFileReader \alias{yield,FastqFileReader-method} % FastqSampler \alias{FastqSampler} \alias{FastqSamplerList} \alias{FastqSamplerList,ANY-method} \alias{FastqSamplerList,character-method} \alias{yield} \alias{yield,FastqSampler-method} % FastqStreamer \alias{FastqStreamer} \alias{FastqStreamer,ANY,missing-method} \alias{FastqStreamer,ANY,numeric-method} \alias{FastqStreamer,ANY,IRanges-method} \alias{FastqStreamerList} \alias{FastqStreamerList,ANY-method} \alias{FastqStreamerList,character-method} \alias{yield,FastqStreamer-method} \title{Sampling and streaming records from fastq files} \description{ \code{FastqFile} represents a path and connection to a fastq file. \code{FastqFileList} is a list of such connections. \code{FastqSampler} draws a subsample from a fastq file. \code{yield} is the method used to extract the sample from the \code{FastqSampler} instance; a short illustration is in the example below. \code{FastqSamplerList} is a list of \code{FastqSampler} elements. \code{FastqStreamer} draws successive subsets from a fastq file, a short illustration is in the example below. \code{FastqStreamerList} is a list of \code{FastqStreamer} elements. } \usage{ ## FastqFile and FastqFileList FastqFile(con, ...) FastqFileList(..., class="FastqFile") \S3method{open}{ShortReadFile}(con, ...) \S3method{close}{ShortReadFile}(con, ...) \S4method{readFastq}{FastqFile}(dirPath, pattern=character(), ...) ## FastqSampler and FastqStreamer FastqSampler(con, n=1e6, readerBlockSize=1e8, verbose=FALSE, ordered = FALSE) FastqSamplerList(..., n=1e6, readerBlockSize=1e8, verbose=FALSE, ordered = FALSE) FastqStreamer(con, n, readerBlockSize=1e8, verbose=FALSE) FastqStreamerList(..., n, readerBlockSize=1e8, verbose=FALSE) yield(x, ...) } \arguments{ \item{con, dirPath}{A character string naming a connection, or (for \code{con}) an R connection (e.g., \code{file}, \code{gzfile}).} \item{n}{For \code{FastqSampler}, the size of the sample (number of records) to be drawn. For \code{FastqStreamer} a \code{numeric(1)} (set to 1e6 when \code{n} is missing) providing the number of successive records to be returned on each yield, or an \code{\linkS4class{IRanges}}-class delimiting the (1-based) indicies of records returned by each yield; entries in \code{n} must have non-zero width and must not overlap.} \item{readerBlockSize}{The number of bytes or characters to be read at one time; smaller \code{readerBlockSize} reduces memory requirements but is less efficient.} \item{verbose}{Display progress.} \item{ordered}{logical(1) indicating whether sampled reads should be returned in the same order as they were encountered in the file.} \item{x}{An instance from the \code{FastqSampler} or \code{FastqStreamer} class.} \item{...}{Additional arguments. For \code{FastqFileList}, \code{FastqSamplerList}, or \code{FastqStreamerList}, this can either be a single character vector of paths to fastq files, or several instances of the corresponding \code{FastqFile}, \code{FastqSampler}, or \code{FastqStreamer} objects.} \item{pattern}{Ignored.} \item{class}{For developer use, to specify the underlying class contained in the \code{FastqFileList}.} } \section{Objects from the class}{ Available classes include: \describe{ \item{\code{FastqFile}}{A file path and connection to a fastq file.} \item{\code{FastqFileList}}{A list of \code{FastqFile} instances.} \item{\code{FastqSampler}}{Uniformly sample records from a fastq file.} \item{\code{FastqStreamer}}{Iterate over a fastq file, returning successive parts of the file.} } } \section{Methods}{ The following methods are available to users: \describe{ \item{\code{readFastq,FastqFile-method}:}{see also \code{?\link{readFastq}}.} \item{\code{writeFastq,ShortReadQ,FastqFile-method}:}{see also \code{?\link{writeFastq}}, \code{?"writeFastq,ShortReadQ,FastqFile-method"}.} \item{\code{yield}:}{Draw a single sample from the instance. Operationally this requires that the underlying data (e.g., file) represented by the \code{Sampler} instance be visited; this may be time consuming.} } } \note{ \code{FastqSampler} and \code{FastqStreamer} use OpenMP threads (when available) during creation of the return value. This may sometimes create problems when a process is already running on multiple threads, e.g., with an error message like \preformatted{ libgomp: Thread creation failed: Resource temporarily unavailable } A solution is to precede problematic code with the following code snippet, to disable threading \preformatted{ nthreads <- .Call(ShortRead:::.set_omp_threads, 1L) on.exit(.Call(ShortRead:::.set_omp_threads, nthreads)) } } \seealso{ \code{\link{readFastq}}, \code{\link{writeFastq}}, \code{\link{yield}}. } \examples{ sp <- SolexaPath(system.file('extdata', package='ShortRead')) fl <- file.path(analysisPath(sp), "s_1_sequence.txt") f <- FastqFile(fl) rfq <- readFastq(f) close(f) f <- FastqSampler(fl, 50) yield(f) # sample of size n=50 yield(f) # independent sample of size 50 close(f) ## Return sample as ordered in original file f <- FastqSampler(fl, 50, ordered=TRUE) yield(f) close(f) f <- FastqStreamer(fl, 50) yield(f) # records 1 to 50 yield(f) # records 51 to 100 close(f) ## iterating over an entire file f <- FastqStreamer(fl, 50) while (length(fq <- yield(f))) { ## do work here print(length(fq)) } close(f) ## iterating over IRanges rng <- IRanges(c(50, 100, 200), width=10:8) f <- FastqStreamer(fl, rng) while (length(fq <- yield(f))) { print(length(fq)) } close(f) ## Internal fields, methods, and help; for developers ShortRead:::.FastqSampler_g$methods() ShortRead:::.FastqSampler_g$fields() ShortRead:::.FastqSampler_g$help("yield") } ShortRead/man/ShortRead-class.Rd0000644000175400017540000001501113001230550017516 0ustar00biocbuildbiocbuild\name{ShortRead-class} \docType{class} \alias{ShortRead-class} \alias{ShortRead} \alias{ShortRead,DNAStringSet,BStringSet-method} \alias{ShortRead,DNAStringSet,missing-method} \alias{ShortRead,missing,missing-method} \alias{srrank,ShortRead-method} \alias{srorder,ShortRead-method} \alias{srsort,ShortRead-method} \alias{srduplicated,ShortRead-method} \alias{srdistance,ShortRead,ANY-method} \alias{trimLRPatterns,ShortRead-method} \alias{alphabetByCycle,ShortRead-method} \alias{tables,ShortRead-method} \alias{clean,ShortRead-method} \alias{[,ShortRead,ANY,ANY-method} \alias{[,ShortRead,ANY,missing-method} \alias{[,ShortRead,missing,ANY-method} \alias{[,ShortRead,missing,missing-method} \alias{[,ShortRead,missing,missing,ANY-method} \alias{[,ShortRead,missing,ANY,ANY-method} \alias{[,ShortRead,ANY,ANY,ANY-method} \alias{[,ShortRead,ANY,missing,ANY-method} \alias{append,ShortRead,ShortRead-method} \alias{narrow,ShortRead-method} \alias{detail,ShortRead-method} \alias{id,ShortRead-method} \alias{width,ShortRead-method} \alias{length,ShortRead-method} \alias{writeFasta,ShortRead-method} \alias{show,ShortRead-method} \title{"ShortRead" class for short reads} \description{ This class provides a way to store and manipulate, in a coordinated fashion, uniform-length short reads and their identifiers. } \section{Objects from the Class}{ Objects from this class are created by \code{readFasta}, or by calls to the constructor \code{ShortRead}, as outlined below. } \section{Slots}{ \describe{ \item{\code{sread}:}{Object of class \code{"DNAStringSet"} containing IUPAC-standard, uniform-length DNA strings represent short sequence reads.} \item{\code{id}:}{Object of class \code{"BStringSet"} containing identifiers, one for each short read.} } } \section{Extends}{ Class \code{"\linkS4class{.ShortReadBase}"}, directly. } \section{Methods}{ Constructors include: \describe{ \item{ShortRead}{\code{signature(sread = "DNAStringSet", id = "BStringSet")}: Create a \code{ShortRead} object from reads and their identifiers. The length of \code{id} must match that of \code{sread}.} \item{ShortRead}{\code{signature(sread = "DNAStringSet", id = "missing")}: Create a \code{ShortRead} object from reads, creating empty identifiers.} \item{ShortRead}{\code{signature(sread = "missing", id = "missing")}: Create an empty \code{ShortRead} object.} } Methods include: \describe{ \item{sread}{\code{signature(object = "AlignedRead")}: access the sread slot of \code{object}.} \item{id}{\code{signature(object = "AlignedRead")}: access the id slot of \code{object}.} \item{[}{\code{signature(x = "ShortRead", i = "ANY", j = "missing")}: This method creates a new \code{ShortRead} object containing only those reads indexed by \code{i}. Additional methods on \sQuote{[,ShortRead} do not provide additional functionality, but are present to limit inappropriate use.} \item{append}{\code{signature(x = "ShortRead", values = "ShortRead")}: append the \code{sread} and \code{id} slots of \code{values} after the corresponding fields of \code{x}.} \item{narrow}{\code{signature(x = "ShortRead", start = NA, end = NA, width = NA, use.names = TRUE)}: \sQuote{narrow} \code{sread} so that sequences are between \code{start} and \code{end} bases, according to \code{\link[IRanges:intra-range-methods]{narrow}} in the \code{IRanges} package. } \item{length}{\code{signature(x = "ShortRead")}: returns a \code{integer(1)} vector describing the number of reads in this object.} \item{width}{\code{signature(x = "ShortRead")}: returns an \code{integer()} vector of the widths of each read in this object.} \item{srorder}{\code{signature(x = "ShortRead")}:} \item{srrank}{\code{signature(x = "ShortRead")}:} \item{srsort}{\code{signature(x = "ShortRead")}:} \item{srduplicated}{\code{signature(x = "ShortRead")}: Order, rank, sort, and find duplicates in \code{ShortRead} objects based on \code{sread(x)}, analogous to the corresponding functions \code{order}, \code{rank}, \code{sort}, and \code{duplicated}, ordering nucleotides in the order \code{ACGT}.} \item{srdistance}{\code{signature(pattern="ShortRead", subject="ANY")}: Find the edit distance between each read in \code{pattern} and the (short) sequences in \code{subject}. See \code{\link{srdistance}} for allowable values for \code{subject}, and for additional details.} \item{trimLRPatterns}{\code{signature(Lpattern = "", Rpattern = "", subject = "ShortRead", max.Lmismatch = 0, max.Rmismatch = 0, with.Lindels = FALSE, with.Rindels = FALSE, Lfixed = TRUE, Rfixed = TRUE, ranges = FALSE)}: Remove left and / or right flanking patterns from \code{sread(subject)}, as described in \code{\link[Biostrings:trimLRPatterns]{trimLRPatterns}}. Classes derived from \code{ShortRead} (e.g., \code{\link{ShortReadQ}}, \code{\link{AlignedRead}}) have corresponding base quality scores trimmed, too. The class of the return object is the same as the class of \code{subject}, except when \code{ranges=TRUE} when the return value is the ranges to use to trim 'subject'.} \item{alphabetByCycle}{\code{signature(stringSet = "ShortRead")}: Apply \code{\link{alphabetByCycle}} to the \code{sread} component of \code{stringSet}, returning a matrix as described in \code{\link{alphabetByCycle}}.} \item{tables}{\code{signature(x= "ShortRead", n = 50)}: Apply \code{\link{tables}} to the \code{sread} component of \code{x}, returning a list summarizing frequency of reads in \code{x}.} \item{clean}{\code{signature(object="ShortRead")}: Remove all reads containing non-nucleotide (\code{"N", "-"}) symbols.} \item{show}{\code{signature(object = "ShortRead")}: provides a brief summary of the object, including its class, length and width.} \item{detail}{\code{signature(x = "ShortRead")}: provides a more extensive summary of this object, displaying the first and last entries of \code{sread} and \code{id}.} \item{writeFasta}{\code{signature(object, file, ...)}: write \code{object} to \code{file} in fasta format. See \code{\link{writeXStringSet}} for \code{...} argument values.} } } \author{Martin Morgan} \seealso{ \code{\linkS4class{ShortReadQ}} } \examples{ showClass("ShortRead") showMethods(class="ShortRead", where=getNamespace("ShortRead")) } \keyword{classes} ShortRead/man/ShortRead-deprecated.Rd0000644000175400017540000000206713001230550020520 0ustar00biocbuildbiocbuild\name{ShortRead-deprecated} \alias{uniqueFilter} \title{Deprecated functions from the ShortRead package} \description{ These functions are deprecated, and will become defunct. } \usage{ uniqueFilter(withSread=TRUE, .name="UniqueFilter") } \arguments{ \item{withSread}{A \code{logical(1)} indicating whether uniqueness includes the read sequence (\code{withSread=TRUE}) or is based only on chromosome, position, and strand (\code{withSread=FALSE})} \item{.name}{An optional \code{character(1)} object used to over-ride the name applied to default filters.} } \details{ See \code{\link{srFilter}} for details of ShortRead filters. \code{uniqueFilter} selects elements satisfying \code{!srduplicated(x)} when \code{withSread=TRUE}, and \code{!(duplicated(chromosome(x)) & duplicated(position(x)) & duplicated(strand(x)))} when \code{withSread=FALSE}. The behavior when \code{withSread=TRUE} can be obtained with \code{occurrenceFilter(withSread=TRUE)}. The behavior when \code{withSread=FALSE} can be obtained using a custom filter }ShortRead/man/ShortRead-package.Rd0000644000175400017540000000147513001230551020016 0ustar00biocbuildbiocbuild\name{ShortReadBase-package} \alias{ShortReadBase-package} \alias{.ShortReadBase-class} \alias{show,.ShortReadBase-method} \alias{append,.ShortReadBase,.ShortReadBase-method} \alias{.Solexa-class} \alias{.Roche-class} \docType{package} \title{ FASTQ input and manipulation. } \description{ This package implements sampling, iteration, and input of FASTQ files. The package includes functions for filtering and trimming reads, and for generating a quality assessment report. Data are represented as DNAStringSet-derived objects, and easily manipulated for a diversity of purposes. The package also contains legacy support for early single-end, ungapped alignment formats. } \details{ See \code{packageDescription('ShortRead')} } \author{ Maintainer: Martin Morgan } \keyword{package} ShortRead/man/ShortReadQ-class.Rd0000644000175400017540000002000313001230550017634 0ustar00biocbuildbiocbuild\name{ShortReadQ-class} \docType{class} \alias{ShortReadQ-class} \alias{ShortReadQ} \alias{ShortReadQ,DNAStringSet,QualityScore,BStringSet-method} \alias{ShortReadQ,DNAStringSet,QualityScore,missing-method} \alias{ShortReadQ,DNAStringSet,BStringSet,BStringSet-method} \alias{ShortReadQ,DNAStringSet,BStringSet,missing-method} \alias{ShortReadQ,missing,missing,missing-method} \alias{coerce,ShortReadQ,QualityScaledDNAStringSet-method} \alias{writeFastq,ShortReadQ,character-method} \alias{writeFastq,ShortReadQ,FastqFile-method} \alias{alphabetByCycle,ShortReadQ-method} \alias{alphabetScore,ShortReadQ-method} \alias{[,ShortReadQ,ANY,ANY-method} \alias{[,ShortReadQ,ANY,missing-method} \alias{[,ShortReadQ,missing,ANY-method} \alias{[,ShortReadQ,missing,missing-method} \alias{[,ShortReadQ,missing,missing,ANY-method} \alias{[,ShortReadQ,missing,ANY,ANY-method} \alias{[,ShortReadQ,ANY,ANY,ANY-method} \alias{[,ShortReadQ,ANY,missing,ANY-method} \alias{[<-,ShortReadQ,ANY,missing,ShortReadQ-method} \alias{append,ShortReadQ,ShortReadQ-method} \alias{reverse,ShortReadQ-method} \alias{reverseComplement,ShortReadQ-method} \alias{narrow,ShortReadQ-method} \alias{trimTailw,ShortReadQ-method} \alias{trimTails,ShortReadQ-method} \alias{qa,ShortReadQ-method} \alias{detail,ShortReadQ-method} \title{"ShortReadQ" class for short reads and their quality scores} \description{ This class provides a way to store and manipulate, in a coordinated fashion, the reads, identifiers, and quality scores of uniform-length short reads. } \section{Objects from the Class}{ Objects from this class are the result of \code{\link{readFastq}}, or can be constructed from \code{DNAStringSet}, \code{QualityScore}, and \code{BStringSet} objects, as described below. } \section{Slots}{ Slots \code{sread} and \code{id} are inherited from \code{\linkS4class{ShortRead}}. An additional slot defined in this class is: \describe{ \item{\code{quality}:}{Object of class \code{"BStringSet"} representing a quality score (see \code{\link{readFastq}} for some discussion of quality score).} } } \section{Extends}{ Class \code{"\linkS4class{ShortRead}"}, directly. Class \code{"\linkS4class{.ShortReadBase}"}, by class "ShortRead", distance 2. } \section{Methods}{ Constructors include: \describe{ \item{ShortReadQ}{\code{signature(sread = "DNAStringSet", quality = "QualityScore", id = "BStringSet")}:} \item{ShortReadQ}{\code{signature(sread = "DNAStringSet", quality = "BStringSet", id = "BStringSet")}: Create a \code{ShortReadQ} object from reads, their quality scores, and identifiers. When \code{quality} is of class \code{BStringSet}, the type of encoded quality score is inferred from the letters used in the scores. The length of \code{id} and \code{quality} must match that of \code{sread}.} \item{ShortReadQ}{\code{signature(sread = "DNAStringSet", quality = "QualityScore", id = "missing")}:} \item{ShortReadQ}{\code{signature(sread = "DNAStringSet", quality = "BStringSet", id = "missing")}: Create a \code{ShortReadQ} object from reads and their quality scores, creating empty identifiers. When \code{quality} is of class \code{BStringSet}, the type of encoded quality score is inferred from the letters used in the scores. } \item{ShortReadQ}{\code{signature(sread = "missing", quality = "missing", id = "missing")}: Create an empty \code{ShortReadQ} object.} } See \code{\link{accessors}} for additional functions to access slot content, and \code{\linkS4class{ShortRead}} for inherited methods. Additional methods include: \describe{ \item{quality}{inherited from \code{signature(object = "ANY")}: access the quality slot of \code{object}.} \item{coerce}{\code{signature(from = "SFastqQuality", to = "QualityScaledDNAStringSet")}: (Use \code{as(from, "QualityScaledDNAStringSet")}) coerce objects of class \code{from} to class \code{to}, using the quality encoding implied by \code{quality(from)}. See \code{\linkS4class{QualityScore}} for supported quality classes and their coerced counterparts.} \item{writeFastq}{\code{signature(object = "ShortReadQ", file = "character", ...)}:} \item{writeFastq}{\code{signature(object = "ShortReadQ", file = "FastqFile", ...)}: Write \code{object} to \code{file} in fastq format. See \code{?\link{writeFastq}} for additional arguments \code{mode} and \code{full}.} \item{[}{\code{signature(x = "ShortReadQ", i = "ANY", j = "missing")}: This method creates a new \code{ShortReadQ} object containing only those reads indexed by \code{i}. Additional methods on \sQuote{[,ShortRead} do not provide additional functionality, but are present to limit inappropriate use.} \item{[<-}{\code{signature(x = "ShortReadQ", i = "ANY", j = "missing", ..., y="ShortReadQ")}: This method updates \code{x} so that records indexed by \code{i} are replaced by corresponding records in \code{value}.} \item{append}{\code{signature(x = "ShortReadQ", values = "ShortRead")}: append the \code{sread}, \code{quality} and \code{id} slots of \code{values} after the corresponding fields of \code{x}.} \item{reverse, reverseComplement}{\code{signature(x = "ShortReadQ", ...}: reverse or reverse complement the DNA sequence, and reverse the quality sequence.} \item{narrow}{\code{signature(x = "ShortReadQ", start = NA, end = NA, width = NA, use.names = TRUE)}: narrow \code{sread} and \code{quality} so that sequences are between \code{start} and \code{end} bases, according to \code{\link[IRanges:intra-range-methods]{narrow}} in the \code{IRanges} package.} \item{trimTailw}{\code{signature(object="ShortReadQ", k="integer", a="character", halfwidth="integer", ..., ranges=FALSE)}: trim trailing nucleotides when a window of width 2 * halfwidth + 1 contains \code{k} or more quality scores falling at or below \code{a}.} \item{trimTails}{\code{signature(object="ShortReadQ", k="integer", a="character", successive=FALSE, ..., ranges=FALSE)}: trim trailing nucleotides if \code{k} nucleotides fall below the quality encoded by \code{a}. If \code{successive=FALSE}, the k'th failing nucleotide and all subseqent nucleotides are trimmed. If \code{successive=TRUE}, failing nucleotides must occur successively; the sequence is trimmed from the first of the successive failing nucleotides.} \item{alphabetByCycle}{\code{signature(stringSet = "ShortReadQ")}: Apply \code{\link{alphabetByCycle}} to the \code{sread} component, the \code{quality} component, and the combination of these two components of \code{stringSet}, returning a list of matrices with three elements: \code{"sread"}, \code{"quality"}, and \code{"both"}.} \item{alphabetScore}{\code{signature(object = "ShortReadQ")}: See \code{\link{alphabetScore}} for details.} \item{qa}{\code{signature(dirPath = "ShortReadQ", lane="character", ..., verbose=FALSE)}: Perform quality assessment on the \code{ShortReadQ} object using \code{lane} to identify the object and returning an instance of \code{\linkS4class{ShortReadQQA}}. See \code{\link{qa}}} \item{detail}{\code{signature(x = "ShortReadQ")}: display the first and last entries of each of \code{sread}, \code{id}, and \code{quality} entries of \code{object}.} } } \author{Martin Morgan} \seealso{ \code{\link{readFastq}} for creation of objects of this class from fastq-format files. } \examples{ showClass("ShortReadQ") showMethods(class="ShortReadQ", where=getNamespace("ShortRead"), inherit=FALSE) showMethods(class="ShortRead", where=getNamespace("ShortRead"), inherit=FALSE) sp <- SolexaPath(system.file('extdata', package='ShortRead')) rfq <- readFastq(analysisPath(sp), pattern="s_1_sequence.txt") quality(rfq) sread(reverseComplement(rfq)) quality(reverseComplement(rfq)) quality(trimTails(rfq, 2, "H", successive=TRUE)) } \keyword{classes} ShortRead/man/Snapshot-class.Rd0000644000175400017540000002232713001230550017432 0ustar00biocbuildbiocbuild\name{Snapshot-class} \Rdversion{1.1} \docType{class} \alias{Snapshot-class} \alias{trellis-class} % Constructor: \alias{Snapshot} \alias{Snapshot,character,GRanges-method} \alias{Snapshot,character,missing-method} \alias{Snapshot,BamFileList,GRanges-method} % Accessors: \alias{files} \alias{files,Snapshot-method} \alias{functions} \alias{functions,Snapshot-method} \alias{show,Snapshot-method} \alias{view} \alias{view,Snapshot-method} \alias{vrange} \alias{vrange,Snapshot-method} \alias{annTrack} \alias{annTrack,Snapshot-method} \alias{fac} \alias{fac,Snapshot-method} \alias{getTrellis} \alias{getTrellis,Snapshot-method} \alias{ignore.strand} \alias{ignore.strand,Snapshot-method} % methods: \alias{pan} \alias{pan,Snapshot-method} \alias{togglefun} \alias{togglefun,Snapshot-method} \alias{togglep} \alias{togglep,Snapshot-method} \alias{togglez} \alias{togglez,Snapshot-method} \alias{zoom} \alias{zoom,Snapshot-method} \title{Class \code{"Snapshot"}} \description{ A \code{\linkS4class{Snapshot}}-class to visualize genomic data from BAM files with zoom and pan functionality. } \usage{ Snapshot(files, range, ...) } \arguments{ \item{files}{A character() or \code{BamFileList} specifying the file(s) to be visualized.} \item{range}{A \code{\link{GRanges}} object specifying the range to be visualized.} \item{...}{Additional, optional, arguments to be passed to the Snapshot \code{initialize} function. Arguments include: \describe{ \item{functions:}{A \code{\link{SnapshotFunctionList}} of functions, in addition to built-in \sQuote{fine_coverage}, \sQuote{coarse_coverage}, \sQuote{multifine_coverage}, to be used for visualization.} \item{currentFunction:}{character(1) naming the function, from \code{functions} to be used for data input and visualization. The default chooses a function based on the scale at which the data is being visualized.} \item{annTrack:}{Annotation track. If built-in visualization functions are to be used, \code{annTrack} should be a \code{GRanges} instance and the first column of its elementMeatdata would be used to annotate the range.} \item{fac:}{Character(1) indicating which factor used for grouping the sample files. The factor should be included in the elementMetadata of \code{files}, otherwise ignored. Used only to visualize multiple files. } \item{.auto_display:}{logical(1) indicating whether the visualization is to be updated when \code{show} is invoked.} \item{.debug}{logical(1) indicating whether debug messages are to be printed.} }} } \section{Methods}{ \describe{ \item{zoom}{\code{signature(x = "Snapshot")}: Zoom (in or out) the current plot. } \item{pan}{\code{signature(x = "Snapshot")}: Pan (right or left) the current plot. } \item{togglefun}{\code{signature(x = "Snapshot")}: Toggle the current functions which imported records are to be immediately evaluated. Note that the active range will be changed to the current active window.} \item{togglep}{\code{signature(x = "Snapshot")}: Toggle the panning effects.} \item{togglez}{\code{signature(x = "Snapshot")}: Toggle the zooming effects.} } } \section{Accessors}{ \describe{ \item{show}{\code{signature(object = "Snapshot")}: Display a \code{Snapshot} object. } \item{files}{\code{signature(x = "Snapshot")}: Get the \code{files} field (object of class \code{BamFileList}) of a \code{Snapshot} object.} \item{functions}{\code{signature(x = "Snapshot")}: Get the \code{functions} field (object of \code{SnapshotFunctionList}) of a \code{Snapshot} object.} \item{view}{\code{signature(x = "Snapshot")}: Get the \code{view} field (object of \code{SpTrellis}) of a \code{Snapshot} object.} \item{vrange}{\code{signature(x = "Snapshot")}: Get the \code{.range} field (object of \code{GRanges}) of a \code{Snapshot} object. } \item{getTrellis}{\code{signature(x = "Snapshot")}: Get the \code{trellis} object, a field of the \code{SpTrellis} object.} } } \section{Fields}{ \describe{ \item{\code{.debug}:}{Object of class \code{function} to display messages while in debug mode } \item{\code{.auto_display}:}{Object of class \code{logical} to automatically display the coverage plot. } \item{\code{.range}:}{Object of class \code{GRanges} indicating which ranges of records to be imported from BAM fields. } \item{\code{.zin}:}{Object of class \code{logical} indicating whether the current zooming effect is zoom in. } \item{\code{.pright}:}{Object of class \code{logical} indicating whether the current panning effect is right. } \item{\code{.data}:}{Object of class \code{data.frame} containing coverage a position is represented for each strand and BAM file.} \item{\code{.data_dirty}:}{Object of class \code{logical} indicating whether to re-evaluate the imported records. } \item{\code{.initial_functions}:}{Object of class \code{SnapshotFunctionList} available by the \code{Snapshot} object. } \item{\code{.current_function}:}{Object of class \code{character} of the function the imported recorded are currently evaluated and visualized.} \item{\code{annTrack}:}{Default to \code{NULL} if not intended to visualize the annotation track. If default visualization function(s) is intended to be used to plot the annotation, \code{annTrack} has to be a \code{GRanges} instance.} \item{\code{functions}:}{Object of class \code{SnapshotFunctionList} of customized functions to evaluate and visualize the imported records. } \item{\code{files}:}{Object of class \code{BamFileList} to be imported. } \item{\code{view}:}{Object of class \code{SpTrellis} that is essentially a reference class wrapper of \code{Trellis} objects. } } } \section{Class-Based Methods}{ \describe{ \item{\code{display()}:}{Display the current \code{Snapshot} object. } \item{\code{pan()}:}{Pan (right or left) the current plot. } \item{\code{zoom()}:}{Zoom (in or out) the current plot. } % \item{\code{set_range(range)}:}{ ~~ } \item{\code{toggle(zoom, pan, currentFunction)}:}{Toggle zooming, panning effects or the currentFuction in which the imported records are to be evaluated and visualized.} % \item{\code{initialize(..., functions, currentFunction, .range, .auto_display, .debug)}:}{ ~~ } } } \author{Martin Morgan and Chao-Jen Wong \email{cwon2@fhcrc.org}} \seealso{\code{\link{SpTrellis}}} \examples{ ## example 1: Importing specific ranges of records file <- system.file("extdata", "SRR002051.chrI-V.bam", package="yeastNagalakshmi") which <- GRanges("chrI", IRanges(1, 2e5)) s <- Snapshot(file, range=which) ## methods zoom(s) # zoom in ## zoom in to a specific region zoom(s, range=GRanges("chrI", IRanges(7e4, 7e4+8000))) pan(s) # pan right togglez(s) # change effect of zooming zoom(s) # zoom out togglep(s) # change effect of panning pan(s) ## accessors functions(s) vrange(s) show(s) ignore.strand(s) view(s) ## extract the spTrellis object getTrellis(s) ## extract the trellis object ## example 2: ignore strand s <- Snapshot(file, range=which, ignore.strand=TRUE) ## ## example 3: visualizing annotation track ## library(GenomicFeatures) getAnnGR <- function(txdb, which) { ex <- exonsBy(txdb, by="gene") seqlevels(ex, force=TRUE) <- seqlevels(which) r <- range(ex) gr <- unlist(r) values(gr)[["gene_id"]] <- rep.int(names(r), times=lengths(r)) gr } txdbFile <- system.file("extdata", "sacCer2_sgdGene.sqlite", package="yeastNagalakshmi") # txdb <- makeTxDbFromUCSC(genome="sacCer2", tablename="sgdGene") txdb <- loadDb(txdbFile) which <- GRanges("chrI", IRanges(1, 2e5)) gr <- getAnnGR(txdb, which) ## note that the first column of the elementMetadata annotates of the ## range of the elements. gr s <- Snapshot(file, range=which, annTrack=gr) annTrack(s) ## zoom in to an interesting region zoom(s, range=GRanges("chrI", IRanges(7e4, 7e4+8000))) togglez(s) ## zoom out zoom(s) pan(s) ## example 4, 5, 6: multiple BAM files with 'multicoarse_covarage' ## and 'multifine_coverage' view. ## Resolution does not automatically switch for views of multiple ## files. It is important to note if width(which) < 10,000, use ## multifine_coverage. Otherwise use multicoarse_coverage file <- system.file("extdata", "SRR002051.chrI-V.bam", package="yeastNagalakshmi") which <- GRanges("chrI", IRanges(1, 2e5)) s <- Snapshot(c(file, file), range=which, currentFunction="multicoarse_coverage") ## grouping files and view by 'multicoarse_coverage' bfiles <- BamFileList(c(a=file, b=file)) values(bfiles) <- DataFrame(sampleGroup=factor(c("normal", "tumor"))) values(bfiles) s <- Snapshot(bfiles, range=which, currentFunction="multicoarse_coverage", fac="sampleGroup") ## grouping files and view by 'multifine_coverage' which <- GRanges("chrI", IRanges(7e4, 7e4+8000)) s <- Snapshot(bfiles, range=which, currentFunction="multifine_coverage", fac="sampleGroup") } \keyword{classes} ShortRead/man/SnapshotFunction-class.Rd0000644000175400017540000000404213001230550021132 0ustar00biocbuildbiocbuild\name{SnapshotFunction-class} \Rdversion{1.1} \docType{class} \alias{SnapshotFunction-class} \alias{SnapshotFunction} \alias{show,SnapshotFunction-method} \alias{SnapshotFunctionList-class} \alias{SnapshotFunctionList} \alias{SnapshotFunctionList,SnapshotFunction-method} \alias{SnapshotFunctionList,ANY-method} \alias{reader} \alias{viewer} \alias{limits} \title{Class "SnapshotFunction"} \description{ A class to store custom reader and viewer functions for the \code{\link{Snapshot}} class. } \usage{ SnapshotFunction(reader, viewer, limits, ...) reader(x, ...) viewer(x, ...) limits(x, ...) } \arguments{ \item{reader}{A function for reading data. The function must take a single argument (a \code{\link{Snapshot}} instance) and return a \code{data.frame} summarizing the file.} \item{viewer}{A function for visualizing the data. The function must accept the \code{data.frame} created by \code{reader}, and return an \code{\link{SpTrellis}} object representing the view.} \item{limits}{An integer(2) indicating the minimum and maximum number of nucleotides the \code{SnapshotFunction} is intended to visualize. For instance, a \sQuote{fine-scale} viewer displaying a pileup might be appropriate at between 1000 and 50000 nucleotides.} \item{x}{An instance of \code{SnapshotFunction}} \item{...}{Additional arguments, currently unused.} } \section{Fields}{ \describe{ \item{\code{reader}:}{Object of class \code{\link{function}} for reading data from BAM files and returning a \code{\link{data.frame}}.} \item{\code{viewer}:}{Object of class \code{\link{function}} for visualization that returns an \code{\link{SpTrellis}} object.} \item{\code{limits}:}{Object of class \code{integer} for the limits of ranges to be visualized.} } } \author{Martin Morgan and Chao-Jen Wong} \seealso{\code{\link{Snapshot}}} \examples{ ## internally defined function reader(ShortRead:::.fine_coverage) viewer(ShortRead:::.fine_coverage) limits(ShortRead:::.fine_coverage) } \keyword{classes} ShortRead/man/SolexaExportQA-class.Rd0000644000175400017540000000367313001230550020515 0ustar00biocbuildbiocbuild\name{SolexaExportQA-class} \docType{class} \alias{SolexaExportQA} \alias{SolexaExportQA-class} \alias{SolexaRealignQA-class} \alias{report,SolexaExportQA-method} \alias{report_html,SolexaExportQA-method} \alias{report_html,SolexaRealignQA-method} \alias{show,SolexaExportQA-method} \title{(Legacy) Quality assessment summaries from Solexa export and realign files} \description{ This class contains a list-like structure with summary descriptions derived from visiting one or more Solexa \sQuote{export} or \sQuote{realign} files. } \section{Objects from the Class}{ Objects of the class are usually produced by a \code{\link{qa}} method. } \section{Slots}{ \describe{ \item{\code{.srlist}:}{Object of class \code{"list"}, containing data frames or lists of data frames summarizing the results of qa.} } } \section{Extends}{ Class \code{"\linkS4class{SRList}"}, directly. Class \code{"\linkS4class{.QA}"}, directly. Class \code{"\linkS4class{.SRUtil}"}, by class "SRList", distance 2. Class \code{"\linkS4class{.ShortReadBase}"}, by class ".QA", distance 2. } \section{Methods}{ Accessor methods are inherited from the \code{\linkS4class{SRList}} class. Additional methods defined on this class are: \describe{ \item{report}{\code{signature(x="SolexaExportQA", ..., dest=tempfile(), type="html")}: produces HTML files summarizing QA results. \code{dest} should be a directory.} \item{report}{\code{signature(x="SolexaExportQA", ..., dest=tempfile(), type="pdf")}: (deprecated; use \code{type="html"} instead) produces a pdf file summarizing QA results. \code{dest} should be a file.} \item{report}{\code{signature(x="SolexaRealignQA", ..., dest=tempfile(), type="html")}: produces HTML files summarizing QA results. \code{dest} should be a directory.} } } \author{Martin Morgan } \seealso{ \code{\link{qa}}. } \examples{ showClass("SolexaExportQA") } \keyword{classes} ShortRead/man/SolexaIntensity-class.Rd0000644000175400017540000000725213001230551020776 0ustar00biocbuildbiocbuild\name{SolexaIntensity-class} \docType{class} \alias{SolexaIntensity-class} \alias{SolexaIntensityInfo-class} % subset \alias{[,SolexaIntensity,ANY,ANY-method} \alias{[,SolexaIntensity,ANY,ANY,ANY-method} \alias{[,SolexaIntensity,ANY,missing,ANY-method} \alias{[,SolexaIntensity,missing,ANY,ANY-method} \alias{[,SolexaIntensity,missing,missing,ANY-method} \title{Classes "SolexaIntensity" and "SolexaIntensityInfo"} \description{ Instances of \code{\linkS4class{Intensity}} and \code{\linkS4class{IntensityInfo}} for representing image intensity data from Solexa experiments. } \section{Objects from the Class}{ Objects can be created by calls to \code{SolexaIntensityInfo} or \code{SolexaIntensity}, or more usually \code{readIntensities}. } \section{Slots}{ Object of \code{SolexaIntensity} have slots: \describe{ \item{\code{readInfo}:}{Object of class \code{"SolexaIntensityInfo"} representing information about each read.} \item{\code{intensity}:}{Object of class \code{"ArrayIntensity"} containing an array of intensities with dimensions read, base, and cycle. Nucleotide are A, C, G, T for each cycle.} \item{\code{measurementError}:}{Object of class \code{"ArrayIntensity"} containing measurement errors for each read, cycle, and base, with dimensions like that for \code{intensity}. } \item{\code{.hasMeasurementError}:}{Object of class \code{"ScalarLogical"} used internally to indicate whether measurement error information is included.} } Object of \code{SolexaIntensityInfo} \describe{ \item{data}{Object of class \code{"data.frame"}, inherited from \code{AnnotatedDataFrame}.} \item{varMetadata}{Object of class \code{"data.frame"}, inherited from \code{AnnotatedDataFrame}.} \item{dimLabels}{Object of class \code{"character"}, inherited from \code{AnnotatedDataFrame}.} \item{\code{.__classVersion__}}{Object of class \code{"Versions"}, inherited from \code{AnnotatedDataFrame}.} \item{.init}{Object of class \code{"ScalarLogical"}, used internally to indicate whether the user initialized this object.} } } \section{Extends}{ Class \code{SolexaIntensity}: Class \code{"\linkS4class{Intensity}"}, directly. Class \code{"\linkS4class{.ShortReadBase}"}, by class "Intensity", distance 2. Class \code{SolexaIntensityInfo}: Class \code{"\link[Biobase:class.AnnotatedDataFrame]{AnnotatedDataFrame}"}, directly Class \code{"\linkS4class{IntensityInfo}"}, directly Class \code{"\link[Biobase:class.Versioned]{Versioned}"}, by class "AnnotatedDataFrame", distance 2 Class \code{"\linkS4class{.ShortReadBase}"}, by class "IntensityInfo", distance 2 Class \code{"\linkS4class{IntensityInfo}"}, directly. } \section{Methods}{ Class "SolexaIntensity" inherits accessor and display methods from class \code{\linkS4class{Intensity}}. Additional methods include: \describe{ \item{\code{[}}{\code{signature(x = "SolexaIntensity", i="ANY", j="ANY", k="ANY")}: Selects the ith read, jth nucleotide, and kth cycle. Selection is coordinated across intensity, measurement error, and read information.} } Class "SolexaIntensityInfo" inherits accessor, subsetting, and display methods from class \code{\linkS4class{IntensityInfo}} and \code{\link[Biobase:class.AnnotatedDataFrame]{AnnotatedDataFrame}}. } \author{Martin Morgan } \seealso{\code{\link{readIntensities}}} \examples{ showClass("SolexaIntensity") sp <- SolexaPath(system.file('extdata', package='ShortRead')) int <- readIntensities(sp) int # SolexaIntensity readIntensityInfo(int) # SolexaIntensityInfo int[1:5,,] # read 1:5 } \keyword{classes} ShortRead/man/SolexaIntensity.Rd0000644000175400017540000000374713001230551017700 0ustar00biocbuildbiocbuild\name{SolexaIntensity} \alias{SolexaIntensity} \alias{SolexaIntensityInfo} \title{(Legacy) Construct objects of class "SolexaIntensity" and "SolexaIntensityInfo"} \description{ These function constructs objects of \code{\linkS4class{SolexaIntensity}} and \code{\linkS4class{SolexaIntensityInfo}}. It will often be more convenient to create these objects using parsers such as \code{\link{readIntensities}}. } \usage{ SolexaIntensity(intensity=array(0, c(0, 0, 0)), measurementError=array(0, c(0, 0, 0)), readInfo=SolexaIntensityInfo( lane=integer(nrow(intensity))), ...) SolexaIntensityInfo(lane=integer(0), tile=integer(0)[seq_along(lane)], x=integer(0)[seq_along(lane)], y=integer(0)[seq_along(lane)]) } \arguments{ \item{intensity}{A matrix of image intensity values. Successive columns correspond to nucleotides A, C, G, T; four successive columns correspond to each cycle. Typically, derived from \code{"_int.txt"} files.} \item{measurementError}{As \code{intensity}, but measuring standard error. Usually derived from \code{"_nse.txt"} files.} \item{readInfo}{An object of class \code{AnnotatedDataFrame}, containing information described by \code{SolexaIntensityInfo}.} \item{lane}{An integer vector giving the lane from which each read is derived.} \item{tile}{An integer vector giving the tile from which each read is derived.} \item{x}{An integer vector giving the tile-local x coordinate of the read from which each read is derived.} \item{y}{An integer vector giving the tile-local y coordinate of the read from which each read is derived.} \item{...}{Additional arguments, not currently used.} } \value{ An object of class \code{\linkS4class{SolexaIntensity}}, or \code{SolexaIntensityInfo}. } \author{Martin Morgan } \seealso{\code{\linkS4class{SolexaIntensity}}.} \keyword{manip} ShortRead/man/SolexaPath-class.Rd0000644000175400017540000002026313001230550017700 0ustar00biocbuildbiocbuild\name{SolexaPath-class} \docType{class} \alias{SolexaPath-class} % constructors \alias{SolexaPath} % methods \alias{SolexaSet,SolexaPath-method} \alias{qa,SolexaPath-method} \alias{report,SolexaPath-method} \alias{show,SolexaPath-method} \alias{detail,SolexaPath-method} % transforming methods \alias{readIntensities,SolexaPath-method} \alias{readPrb,SolexaPath-method} \alias{readQseq,SolexaPath-method} \alias{readFasta,SolexaPath-method} \alias{readFastq,SolexaPath-method} \alias{readBaseQuality,SolexaPath-method} \alias{readAligned,SolexaPath-method} \title{(Legacy) "SolexaPath" class representing a standard output file hierarchy} \description{ Solexa produces a hierarchy of output files. The content of the hierarchy varies depending on analysis options. This class represents a standard class hierarchy, constructed by searching a file hierarchy for appropriately named directories. } \section{Objects from the Class}{ Objects from the class are created by calls to the constructor: \code{SolexaPath(experimentPath, dataPath=.solexaPath(experimentPath, "Data"), scanPath=.solexaPath(dataPath, "GoldCrest"), imageAnalysisPath=.solexaPath(dataPath, "^(C|IPAR)"), baseCallPath=.solexaPath(imageAnalysisPath, "^Bustard"), analysisPath=.solexaPath(baseCallPath, "^GERALD"), ..., verbose=FALSE) } \describe{ \item{experimentPath}{\code{character(1)} object pointing to the top-level directory of a Solexa run, e.g., \code{/home/solexa/user/080220_HWI-EAS88_0004}. This is the only required argument} \item{dataPath}{(optional) Solexa \sQuote{Data} folder .} \item{scanPath}{(optional) Solexa GoldCrest image scan path.} \item{imageAnalysisPath}{(optional) Firecrest image analysis path.} \item{baseCallPath}{(optional) Bustard base call path.} \item{analysisPath}{(optional) Gerald analysis pipeline path.} \item{...}{Additional arguments, unused by currently implemented methods.} \item{verbose=FALSE}{(optional) logical vector which, when \code{TRUE} results in warnings if paths do not exist.} } All paths must be fully-specified. } \section{Slots}{ \code{SolexaPath} has the following slots, containing either a fully specified path to the corresponding directory (described above) or \code{NA} if no appropriate directory was discovered. \describe{ \item{\code{basePath}}{See \code{experimentPath}, above.} \item{\code{dataPath}}{See above.} \item{\code{scanPath}}{See above.} \item{\code{imageAnalysisPath}}{See above.} \item{\code{baseCallPath}}{See above.} \item{\code{analysisPath}}{See above.} } } \section{Extends}{ Class \code{"\linkS4class{.Solexa}"}, directly. Class \code{"\linkS4class{.ShortReadBase}"}, by class ".Solexa", distance 2. } \section{Methods}{ Transforming methods include: \describe{ \item{readIntensities}{ \code{signature(dirPath = "SolexaPath", pattern=character(0), run, ...)}: Use \code{imageAnalysisPath(sp)[run]} as the directory path(s) and \code{pattern=character(0)} as the pattern for discovering Solexa intensity files. See \code{\link{readIntensities,character-method}} for additional parameters.} \item{readPrb}{ \code{signature(dirPath = "SolexaPath", pattern=character(0), run, ...)}: Use \code{baseCallPath(dirPath)[run]} as the directory path(s) and \code{pattern=character(0)} as the pattern for discovering Solexa \sQuote{prb} files, returning a \code{\linkS4class{SFastqQuality}} object containing the maximum qualities found for each base of each cycle. The \code{...} argument may include the named argument \code{as}. This influences the return value, as explained on the \code{\link{readPrb,character-method}} page. } \item{readFasta}{ \code{signature(dirPath, pattern = character(0), ..., nrec=-1L, skip=0L)}: Use \code{analysisPath(dirPath)[run]} as the directory path(s) for discovering fasta-formatted files, returning a \code{\linkS4class{ShortRead}} object. The default method reads \emph{all} files into a single object.} \item{readFastq}{ \code{signature(dirPath = "SolexaPath", pattern = ".*_sequence.txt", run, ..., qualityType="SFastqQuality")}: Use \code{analysisPath(dirPath)[run]} as the directory path(s) and \code{pattern=".*_sequence.txt"} as the pattern for discovering fastq-formatted files, returning a \code{\linkS4class{ShortReadQ}} object. The default method reads \emph{all} sequence files into a single object.} \item{readBaseQuality}{ \code{signature(dirPath = "SolexaPath", seqPattern = ".*_seq.txt", prbPattern = "s_[1-8]_prb.txt", run, ...)}: Use \code{baseCallPath(dirPath)[run]} as the directory path(s) and \code{seqPattern=".*_seq.txt"} as the pattern for discovering base calls and \code{prbPattern=".*_prb.txt"} as the pattern for discovering quality scores. Note that the default method reads \emph{all} base call and quality score files into a single object; often one will want to specify a pattern for each lane.} \item{readQseq}{ \code{signature(directory="SolexaPath", pattern=".*_qseq.txt.*", run, ...., filtered=FALSE)}: Use \code{analysisPath(dirPath)[run]} as the directory path and \code{pattern=".*_qseq.txt.*"} as the pattern for discovering read and quality scores in Solexa 'qseq' files. Data from \emph{all} files are read into a single object; often one will want to specify a pattern for each lane. Details are as for \code{\link{readQseq,character-method}}.} \item{readAligned}{ \code{signature(dirPath = "SolexaPath", pattern = ".*_export.txt.*", run, ..., filter=srFilter())}: Use \code{analysisPath(dirPath)[run]} as the directory path and \code{pattern=".*_export.txt"} as the pattern for discovering Eland-aligned reads in the Solexa 'export' file format. Note that the default method reads \emph{all} aligned read files into a single object; often one will want to specify a pattern for each lane. Use an object of \code{\linkS4class{SRFilter}} to select specific chromosomes, strands, etc.} \item{qa}{ \code{signature(dirPath="SolexaPath", pattern="character(0)", run, ...)}: Use \code{analysisPath(dirPath)[run]} as the directory path(s) and \code{pattern=".*_export.txt"} as the pattern for discovering Solexa \code{export}-formatted fileds, returning a \code{\linkS4class{SolexaExportQA}} object summarizing quality assessment. If \code{Rmpi} or \code{parallel} has been initiated, quality assessment calculations are distributed across available nodes or cores (one node per export file.)} \item{report}{ \code{signature(x, ..., dest=tempfile(), type="pdf")}: Use \code{qa(x, ...)} to generate quality assessment measures, and use these to generate a quality assessment report at location \code{dest} of type \code{type} (e.g., \sQuote{pdf}). } \item{SolexaSet}{\code{signature(path = "SolexaPath")}: create a \code{\linkS4class{SolexaSet}} object based on \code{path}.} } Additional methods include: \describe{ \item{show}{\code{signature(object = "SolexaPath")}: briefly summarize the file paths of \code{object}. The \code{experimentPath} is given in full; the remaining paths are identified by their leading characters.} \item{detail}{\code{signature(x = "SolexaPath")}: summarize file paths of \code{x}. All file paths are presented in full.} } } \author{Martin Morgan} \examples{ showClass("SolexaPath") showMethods(class="SolexaPath", where=getNamespace("ShortRead")) sf <- system.file("extdata", package="ShortRead") sp <- SolexaPath(sf) sp readFastq(sp, pattern="s_1_sequence.txt") \dontrun{ nfiles <- length(list.files(analysisPath(sp), "s_[1-8]_export.txt")) library(Rmpi) mpi.spawn.Rslaves(nslaves=nfiles) report(qa(sp)) } \dontrun{ nfiles <- length(list.files(analysisPath(sp), "s_[1-8]_export.txt")) report(qa(sp)) } } \keyword{classes} ShortRead/man/SolexaSet-class.Rd0000644000175400017540000000754113001230551017544 0ustar00biocbuildbiocbuild\name{SolexaSet-class} \docType{class} \alias{SolexaSet-class} % constructors \alias{SolexaSet} \alias{SolexaSet,character-method} % methods \alias{detail,SolexaSet-method} \alias{laneNames,SolexaSet-method} \alias{laneNames,AnnotatedDataFrame-method} \alias{show,SolexaSet-method} % transforming methods \alias{readAligned,SolexaSet-method} \title{(Legacy) "SolexaSet" coordinating Solexa output locations with sample annotations} \description{ This class coordinates the file hierarchy produced by the Solexa `pipeline' with annotation data contained in an \code{\link[Biobase:class.AnnotatedDataFrame]{AnnotatedDataFrame}} (defined in the \pkg{Biobase} package). } \section{Objects from the Class}{ Objects can be created from the constructor: \code{SolexaSet(path, ...)}. \describe{ \item{path}{A \code{character(1)} vector giving the fully-qualified path to the root of the directory hierarchy associated with each Solexa flow cell, or an object of class \code{SolexaPath} (see \code{\linkS4class{SolexaPath}} for this method).} \item{...}{Additional arguments, especially \code{laneDescription}, an \code{\link[Biobase:class.AnnotatedDataFrame]{AnnotatedDataFrame}} describing the content of each of the 8 lanes in the Solexa flow cell.} } } \section{Slots}{ \code{SolexaSet} has the following slots: \describe{ \item{\code{solexaPath}:}{Object of class \code{"SolexaPath"}.} \item{\code{laneDescription}:}{Object of class \code{"AnnotatedDataFrame"}, containing information about the samples in each lane of the flow cell.} } } \section{Extends}{ Class \code{"\linkS4class{.Solexa}"}, directly. Class \code{"\linkS4class{.ShortReadBase}"}, by class ".Solexa", distance 2. } \section{Methods}{ \describe{ \item{solexaPath}{\code{signature(object = "SolexaSet")}: Return the directory paths present when this object was created as a \code{\linkS4class{SolexaPath}}.} \item{laneNames}{\code{signature(object = "SolexaSet")}: Return the names of each lane in the flow cell, currently names are simply \code{1:8}. } \item{show}{\code{signature(object = "SolexaSet")}: Briefly summarize the experiment path and lane description of the Solexa set. } \item{detail}{\code{signature(x = "SolexaSet")}: Provide additional detail on the Solexa set, including the content of \code{solexaPath} and the \code{pData} and \code{varMetadata} of \code{laneDescription}.} } Methods transforming \code{SolexaSet} objects include: \describe{ \item{readAligned}{ \code{signature(dirPath = "SolexaSet", pattern = ".*_export.txt", run, ..., filter=srFilter())}: Use \code{analysisPath(solexaPath(dirPath))[run]} as the directory path(s) and \code{pattern=".*_export.txt"} as the pattern for discovering Eland-aligned reads in the Solexa 'export' file format. Note that the default method reads \emph{all} aligned read files into a single object; often one will want to specify a pattern for each lane. Use an object of \code{\linkS4class{SRFilter}} to select specific chromosomes, strands, etc.} } } \author{Martin Morgan} \examples{ showClass("SolexaSet") showMethods(class="SolexaSet", where=getNamespace("ShortRead")) ## construct a SolexaSet sf <- system.file("extdata", package="ShortRead") df <- data.frame(Sample=c("Sample 1", "Sample 2", "Sample 3", "Sample 4", "Center-wide control", "Sample 6", "Sample 7", "Sample 8"), Genome=c(rep("hg18", 4), "phi_plus_SNPs.txt", rep("hg18", 3))) dfMeta <- data.frame(labelDescription=c("Type of sample", "Alignment genome")) adf <- new("AnnotatedDataFrame", data=df, varMetadata=dfMeta) SolexaSet(sf, adf) } \keyword{classes} ShortRead/man/SpTrellis-class.Rd0000644000175400017540000000470313001230551017553 0ustar00biocbuildbiocbuild\name{SpTrellis-class} \Rdversion{1.1} \docType{class} % constructor: \alias{SpTrellis-class} \alias{SpTrellis} % methods: \alias{zi} \alias{zi,SpTrellis-method} \alias{zo} \alias{zo,SpTrellis-method} \alias{right,SpTrellis-method} \alias{left,SpTrellis-method} \alias{restore} \alias{restore,SpTrellis-method} \alias{show,SpTrellis-method} \title{Class "SpTrellis"} \description{ A reference class to manage the trellis graphics related component of the \code{\link{Snapshot}} functionality for visualization of genomic data. } \usage{ SpTrellis(trellis, debug_enabled=FALSE) } \arguments{ \item{trellis}{A trellis object for storing the plot of the genome area being visualized.} \item{debug_enabled}{\code{logical(1)} indicating whether class methods should report debugging information to the user.} } \section{Fields}{ \describe{ \item{\code{trellis}:}{Object of class \code{trellis} for storing the plot information.} \item{debug_enabled}{\code{logical(1)} indicating whether class methods should report debugging information to the user.} } } \section{Methods}{ \describe{ \item{zi}{\code{signature(x="SpTrellis")}: zoom in} \item{zo}{\code{signature(x="SpTrellis")}: zoom out} \item{right}{\code{signature(x="SpTrellis")}: shift to the right} \item{left}{\code{signature(x="SpTrellis")}: shift to the left} \item{restore}{\code{signature(x="SpTrellis")}: restore to the original plot} \item{show}{\code{signature(x="SpTrellis")}: show the current plot} \item{update}{\code{signature(x="SpTrellis")}: update the trellis parameters of the \code{SpTrellis} object.} } } \author{Chao-Jen \url{cwon2@fhcrc.org}} \seealso{\code{\link{Snapshot}}} \examples{ col <- c("#66C2A5", "#FC8D62") x = numeric(1000) x[sample(1000, 100)] <- abs(rnorm(100)) df <- data.frame(x = c(x, -x), pos = seq(1, 1e5, length.out=1000), group = rep(c("positive", "negative"), each=1000)) cv <- lattice::xyplot(x ~ pos, df, group=group, type="s", col=col, main="yeast chrI:1 - 2e5", ylab="Coverage", xlab="Coordinate", scales=list(y=list(tck=c(1,0)), x=list(rot=45, tck=c(1,0), tick.number=20)), panel=function(...) { lattice::panel.xyplot(...) lattice::panel.grid(h=-1, v=20) lattice::panel.abline(a=0, b=0, col="grey") }) s <- SpTrellis(cv) s zi(s) zi(s) left(s) right(s) zo(s) restore(s) } \keyword{classes} ShortRead/man/accessors.Rd0000644000175400017540000000352613001230550016515 0ustar00biocbuildbiocbuild\name{accessors} \alias{accessors} % SRVector \alias{vclass} % ShortRead / ShortReadQ \alias{sread} \alias{sread,ShortRead-method} \alias{id} % AlignedRead \alias{chromosome} \alias{position} \alias{alignQuality} \alias{alignData} % Solexa \alias{experimentPath} \alias{dataPath} \alias{scanPath} \alias{imageAnalysisPath} \alias{baseCallPath} \alias{analysisPath} % SolexaSet \alias{solexaPath} \alias{laneDescription} \alias{laneNames} \title{(Legacy) Accessors for ShortRead classes} \description{ These functions and generics define `accessors' (to get and set values) for objects in the \pkg{ShortRead} package; methods defined in other packages may have additional meaning. } \usage{ ## SRVector vclass(object, ...) ## ShortRead / ShortReadQ sread(object, ...) id(object, ...) ## AlignedRead chromosome(object, ...) position(object, ...) alignQuality(object, ...) alignData(object, ...) ## Solexa experimentPath(object, ...) dataPath(object, ...) scanPath(object, ...) imageAnalysisPath(object, ...) baseCallPath(object, ...) analysisPath(object, ...) ## SolexaSet solexaPath(object, ...) laneDescription(object, ...) laneNames(object, ...) } \arguments{ \item{object}{An object derived from class \code{ShortRead}. See help pages for individual objects, e.g., \code{\linkS4class{ShortReadQ}}. The default is to extract the contents of a slot of the corresponding name (e.g., slot \code{sread}) from \code{object}.} \item{...}{Additional arguments passed to the accessor. The default definitions do not make use of additional arguments.} } \value{ Usually, the value of the corresponding slot, or other simple content described on the help page of \code{object}. } \author{Martin Morgan} \examples{ sp <- SolexaPath(system.file('extdata', package='ShortRead')) experimentPath(sp) basename(analysisPath(sp)) } \keyword{manip} ShortRead/man/alphabetByCycle.Rd0000644000175400017540000000441613001230550017562 0ustar00biocbuildbiocbuild\name{alphabetByCycle} \alias{alphabetByCycle} \alias{alphabetByCycle,BStringSet-method} \title{Summarize nucleotide, amino acid, or quality scores by cycle} \description{ \code{alphabetByCycle} summarizes nucleotides, amino acid, or qualities by cycle, e.g., returning the number of occurrences of each nucleotide \code{A, T, G, C} across all reads from 36 cycles of a Solexa lane. } \usage{ alphabetByCycle(stringSet, alphabet, ...) } \arguments{ \item{stringSet}{A R object representing the collection of reads, amino acid sequences, or quality scores, to be summarized.} \item{alphabet}{The alphabet (character vector of length 1 strings) from which the sequences in \code{stringSet} are composed. Methods often define an appropriate alphabet, so that the user does not have to provide one.} \item{...}{Additional arguments, perhaps used by methods defined on this generic.} } \details{ The default method requires that \code{stringSet} extends the \code{\link[Biostrings:XStringSet-class]{XStringSet}} class of \pkg{Biostrings}. The following method is defined, in addition to methods described in class-specific documentation: \describe{ \item{alphabetByCycle}{\code{signature(stringSet = "BStringSet")}: this method uses an alphabet spanning all ASCII characters, codes \code{1:255}. } } } \value{ A matrix with number of rows equal to the length of \code{alphabet} and columns equal to the maximum width of reads or quality scores in the string set. Entries in the matrix are the number of times, over all reads of the set, that the corresponding letter of the alphabet (row) appeared at the specified cycle (column). } \seealso{ The IUPAC alphabet in Biostrings. \url{http://www.bioperl.org/wiki/FASTQ_sequence_format} for the BioPerl definition of fastq. Solexa documentation `Data analysis - documentation : Pipeline output and visualisation'. } \author{Martin Morgan} \examples{ showMethods("alphabetByCycle") sp <- SolexaPath(system.file('extdata', package='ShortRead')) rfq <- readFastq(analysisPath(sp), pattern="s_1_sequence.txt") alphabetByCycle(sread(rfq)) abcq <- alphabetByCycle(quality(rfq)) dim(abcq) ## 'high' scores, first and last cycles abcq[64:94,c(1:5, 32:36)] } \keyword{manip} ShortRead/man/alphabetScore.Rd0000644000175400017540000000117013001230550017275 0ustar00biocbuildbiocbuild\name{alphabetScore} \alias{alphabetScore} \title{Efficiently calculate the sum of quality scores across bases} \description{ This generic takes a \code{\linkS4class{QualityScore}} or \code{PhredQuality} object and calculates, for each read, the sum of the encoded nucleotide probabilities. } \usage{ alphabetScore(object, ...) } \arguments{ \item{object}{An object of class \code{\linkS4class{QualityScore}}.} \item{\dots}{Additional arguments, currently unused.} } \value{ A vector of numeric values of length equal to the length of \code{object}. } \author{Martin Morgan } \keyword{manip} ShortRead/man/clean.Rd0000644000175400017540000000200013001230550015574 0ustar00biocbuildbiocbuild\name{clean} \alias{clean} \alias{clean,DNAStringSet-method} \title{Remove sequences with ambiguous nucleotides from short read classes} \description{ Short reads may contain ambiguous base calls (i.e., IUPAC symbols different from A, T, G, C). This generic removes all sequences containing 1 or more ambiguous bases. } \usage{ clean(object, ...) } \arguments{ \item{object}{An object for which \code{clean} methods exist; see below to discover these methods.} \item{\dots}{Additional arguments, perhaps used by methods.} } \details{ The following method is defined, in addition to methods described in class-specific documentation: \describe{ \item{clean}{\code{signature(x = "DNAStringSet")}: Remove all sequences containing non-base (A, C, G, T) IUPAC symbols.} } } \value{ An instance of \code{class(object)}, containing only sequences with non-redundant nucleotides. } \author{Martin Morgan } \examples{ showMethods('clean') } \keyword{manip} ShortRead/man/countLines.Rd0000644000175400017540000000303613001230550016647 0ustar00biocbuildbiocbuild\name{countLines} \alias{countLines} \title{Count lines in all (text) files in a directory whose file name matches a pattern} \description{ \code{countLines} visits all files in a directory path \code{dirPath} whose base (i.e., file) name matches \code{pattern}. Lines in the file are counted as the number of new line characters. } \usage{ countLines(dirPath, pattern=character(0), ..., useFullName=FALSE) } \arguments{ \item{dirPath}{A character vector (or other object; see methods defined on this generic) giving the directory path (relative or absolute) of files whose lines are to be counted.} \item{pattern}{The (\code{\link{grep}}-style) pattern describing files whose lines are to be counted. The default (\code{character(0)}) results in line counts for all files in the directory.} \item{...}{Additional arguments, passed internally to list.files. See \code{\link{list.files}}.} \item{useFullName}{A \code{logical(1)} indicating whether elements of the returned vector should be named with the base (file) name (default; \code{useFullName=FALSE}) or the full path name (\code{useFullName=TRUE}).} } \value{ A named integer vector of line counts. Names are paths to the files whose lines have been counted, excluding \code{dirPath}. } \author{Martin Morgan} \examples{ sp <- SolexaPath(system.file('extdata', package='ShortRead')) countLines(analysisPath(sp)) countLines(experimentPath(sp), recursive=TRUE) countLines(experimentPath(sp), recursive=TRUE, useFullName=TRUE) } \keyword{manip} ShortRead/man/deprecated.Rd0000644000175400017540000000100613001230551016620 0ustar00biocbuildbiocbuild\name{deprecated} \alias{deprecated} \alias{defunct} \alias{basePath} \alias{srapply} \alias{readAligned,BamFile-method} \title{Deprecated and defunct functions} \description{ These functions were introduced but are now deprecated or defunct. } \details{ Defunct functions: \itemize{ \item{\code{srapply}}{. Use the BiocParallel package instead.} \item{\code{readAligned,BamFile-method}}{. Use the GenomicAlignments package instead.} \item{\code{basePath()}}{} } } \keyword{manip} ShortRead/man/dotQA-class.Rd0000644000175400017540000000220713001230550016636 0ustar00biocbuildbiocbuild\name{.QA-class} \docType{class} \alias{.QA-class} \alias{rbind,.QA-method} \alias{show,.QA-method} \title{Virtual class for representing quality assessment results} \description{ Classes derived from \code{.QA-class} represent results of quality assurance analyses. Details of derived class structure are found on the help pages of the derived classes. } \section{Objects from the Class}{ Objects from the class are created by ShortRead functions, in particular \code{\link{qa}}. } \section{Extends}{ Class \code{"\linkS4class{.ShortReadBase}"}, directly. } \section{Methods}{ Methods defined on this class include: \describe{ \item{rbind}{\code{signature(...="list")}: rbind data frame objects in \code{...}. All objects of \code{...} must be of the same class; the return value is an instance of that class.} \item{show}{\code{signature(object = "SolexaExportQA")}: Display an overview of the object contents.} } } \seealso{ Specific classes derived from \code{.QA} } \author{Martin Morgan } \examples{ getClass(".QA", where=getNamespace("ShortRead")) } \keyword{classes} ShortRead/man/dustyScore.Rd0000644000175400017540000000421513001230551016671 0ustar00biocbuildbiocbuild\name{dustyScore} \alias{dustyScore} \alias{dustyScore,DNAStringSet-method} \alias{dustyScore,ShortRead-method} \title{Summarize low-complexity sequences} \description{ \code{dustyScore} identifies low-complexity sequences, in a manner inspired by the \code{dust} implementation in \code{BLAST}. } \usage{ dustyScore(x, batchSize=NA, ...) } \arguments{ \item{x}{A \code{DNAStringSet} object, or object derived from \code{ShortRead}, containing a collection of reads to be summarized.} \item{batchSize}{\code{NA} or an \code{integer(1)} vector indicating the maximum number of reads to be processed at any one time.} \item{...}{Additional arguments, not currently used.} } \details{ The following methods are defined: \describe{ \item{dustyScore}{\code{signature(x = "DNAStringSet")}: operating on an object derived from class \code{DNAStringSet}.} \item{dustyScore}{\code{signature(x = "ShortRead")}: operating on the \code{sread} of an object derived from class \code{ShortRead}.} } The dust-like calculations used here are as implemented at \url{https://stat.ethz.ch/pipermail/bioc-sig-sequencing/2009-February/000170.html}. Scores range from 0 (all triplets unique) to the square of the width of the longest sequence (poly-A, -C, -G, or -T). The \code{batchSize} argument can be used to reduce the memory requirements of the algorithm by processing the \code{x} argument in batches of the specified size. Smaller batch sizes use less memory, but are computationally less efficient. } \value{ A vector of numeric scores, with length equal to the length of \code{x}. } \references{ Morgulis, Getz, Schaffer and Agarwala, 2006. WindowMasker: window-based masker for sequenced genomes, Bioinformatics 22: 134-141. } \seealso{ The WindowMasker supplement defining \code{dust} \url{ftp://ftp.ncbi.nlm.nih.gov/pub/agarwala/windowmasker/windowmasker_suppl.pdf} } \author{Herve Pages (code); Martin Morgan} \examples{ sp <- SolexaPath(system.file('extdata', package='ShortRead')) rfq <- readFastq(analysisPath(sp), pattern="s_1_sequence.txt") range(dustyScore(rfq)) } \keyword{manip} ShortRead/man/filterFastq.Rd0000644000175400017540000000352213001230550017010 0ustar00biocbuildbiocbuild\name{filterFastq} \alias{filterFastq} \title{Filter fastq from one file to another} \description{ \code{filterFastq} filters reads from source to destination file(s) applying a filter to reads in each file. The filter can be a function or FilterRules instance; operations are done in a memory-efficient manner. } \usage{ filterFastq(files, destinations, ..., filter = FilterRules(), compress=TRUE, yieldSize = 1000000L) } \arguments{ \item{files}{a character vector of valid file paths.} \item{destinations}{a character vector of destinations, recycled to be the same length as \code{files}. \code{destinations} must not already exist.} \item{...}{Additional arguments, perhaps used by a \code{filter} function.} \item{filter}{A simple function taking as it's first argument a \code{ShortReadQ} instance and returning a modified \code{ShortReadQ} instance (e.g., with records or nucleotides removed), or a \code{FilterRules} instance specifying which records are to be removed.} \item{compress}{A logical(1) indicating whether the file should be gz-compressed. The default is \code{TRUE}.} \item{yieldSize}{Number of fastq records processed in each call to \code{filter}; increase this for (marginally) more efficient I/O at the expense of increased memory use.} } \author{Martin Morgan \url{mtmorgan@fhcrc.org}} \examples{ ## path to a convenient fastq file sp <- SolexaPath(system.file('extdata', package='ShortRead')) fl <- file.path(analysisPath(sp), "s_1_sequence.txt") ## filter reads to keep those with GC < 0.7 fun <- function(x) { gc <- alphabetFrequency(sread(x), baseOnly=TRUE)[,c("G", "C")] x[rowSums(gc) / width(x) < .7] } filterFastq(fl, tempfile(), filter=fun) ## trimEnds,character-method uses filterFastq internally trimEnds(fl, "V", destinations=tempfile()) } ShortRead/man/polyn.Rd0000644000175400017540000000145413001230550015667 0ustar00biocbuildbiocbuild\name{Utilites} \alias{polyn} \title{Utilities for common, simple operations} \description{ These functions perform a variety of simple operations. } \usage{ polyn(nucleotides, n) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{nucleotides}{A character vector with all elements having exactly 1 character, typically from the IUPAC alphabet.} \item{n}{An \code{integer(1)} vector.} } \details{ \code{polyn} returns a character vector with each element having \code{n} characters. Each element contains a single nucleotide. Thus \code{polyn("A", 5)} returns \code{AAAAA}. } \value{ \code{polyn} returns a character vector of length \code{length(nucleotide)} } \author{Martin Morgan } \examples{ polyn(c("A", "N"), 35) } \keyword{manip} ShortRead/man/qa.Rd0000644000175400017540000001022113001230550015117 0ustar00biocbuildbiocbuild\name{qa} \alias{qa} \alias{qa,character-method} \alias{qa,list-method} \title{Perform quality assessment on short reads} \description{ This function is a common interface to quality assessment functions available in \code{ShortRead}. Results from this function may be displayed in brief, or integrated into reports using, e.g., \code{\link{report}}. } \usage{ qa(dirPath, ...) \S4method{qa}{character}(dirPath, pattern=character(0), type=c("fastq", "SolexaExport", "SolexaRealign", "Bowtie", "MAQMap", "MAQMapShort"), ...) \S4method{qa}{list}(dirPath, ...) } \arguments{ \item{dirPath}{A character vector or other object (e.g., \code{\link{SolexaPath}}; see \code{showMethods}, below) locating the data for which quality assessment is to be performed. See help pages for defined methods (by evaluating the example code, below) for details of available methods.} \item{pattern}{A character vector limiting the files in \code{dirPath} to be processed, as with \code{\link{list.files}}. Care should be taken to specify pattern to avoid reading unintended files.} \item{type}{The type of file being parsed; must be a character vector of length 1, selected from one of the types enumerated in the parameter.} \item{\dots}{Additional arguments used by methods. \describe{ \item{\code{sample=TRUE}:}{Logical(1) indicating whether QA should be performed on a sample (default size 1000000) drawn from each FASTQ file, or from the entire file.} \item{\code{n}:}{The number of reads to sample when processing FASTQ files.} \item{\code{Lpattern}, \code{Rpattern}:}{A character vector or XString object to be matched to the left end of a sequence. If either \code{Lpattern} or \code{Rpattern} are provided, \code{trimLRPatterns} is invoked to produce a measure of adapter contamination. Mismatch rates are 0.1 on the left and 0.2 on the right, with a minimum overlap of 10 nt.} \item{\code{BPPARAM}:}{How parallel evalutation will be performed. see \code{\link{BiocParallelParam}}; the default is \code{BiocParallel::registered()[1]}.} } } } \details{ The most common use of this function provides a directory path and pattern identifying FASTQ files for quality assessment. The default is then to create a quality assessment report based on a random sample of n=1000000 reads from each file. The following methods are defined, in addition to those on S4 formal classes documented elsewhere: \describe{ \item{\code{qa,character-method}}{ Quality assessment is performed on all files in directory \code{dirPath} whose file name matches \code{pattern}. The type of analysis performed is based on the \code{type} argument. Use \code{SolexaExport} when all files matching \code{pattern} are Solexa \code{_export.txt} files. Use \code{SolexaRealign} for Solexa \code{_realign.txt} files. Use \code{Bowtie} for Bowtie files. Use \code{MAQMapShort} for MAQ \code{map} files produced by MAQ versions below 0.70 and \code{MAQMap} for more recent output. Use \code{fastq} for collections of fastq-format files. Quality assessment details vary depending on data source. } \item{\code{qa,list-method}}{ \code{dirPath} is a list of objects, all of the same class and typically derived from \code{ShortReadQ}, on which quality assessment is performed. All elements of the list must have names, and these should be unique. } } } \value{ An object derived from class \code{\linkS4class{.QA}}. Values contained in this object are meant for use by \code{\link{report}} } \author{Martin Morgan } \seealso{ \code{\linkS4class{.QA}}, \code{\linkS4class{SolexaExportQA}} \code{\linkS4class{MAQMapQA}} \code{\linkS4class{FastqQA}} } \examples{ dirPath <- system.file(package="ShortRead", "extdata", "E-MTAB-1147") ## sample 1M reads / file qa <- qa(dirPath, "fastq.gz", BPPARAM=SerialParam()) if (interactive()) browseURL(report(qa)) showMethods("qa", where=getNamespace("ShortRead")) } \keyword{manip} ShortRead/man/qa2.Rd0000644000175400017540000002207613001230550015214 0ustar00biocbuildbiocbuild\name{qa2} \alias{QAFastqSource} \alias{QACollate} \alias{QA} \alias{QAFlagged} \alias{QAFiltered} \alias{QAAdapterContamination} \alias{QAData} \alias{QAFrequentSequence} \alias{QANucleotideByCycle} \alias{QANucleotideUse} \alias{QAQualityByCycle} \alias{QAQualityUse} \alias{QAReadQuality} \alias{QASequenceUse} \alias{QACollate,QAFastqSource-method} \alias{QACollate,missing-method} \alias{qa2} \alias{qa2,FastqSampler-method} \alias{qa2,QAAdapterContamination-method} \alias{qa2,QACollate-method} \alias{qa2,QAFastqSource-method} \alias{qa2,QAFrequentSequence-method} \alias{qa2,QANucleotideByCycle-method} \alias{qa2,QANucleotideUse-method} \alias{qa2,QAQualityByCycle-method} \alias{qa2,QAQualityUse-method} \alias{qa2,QAReadQuality-method} \alias{qa2,QASequenceUse-method} \alias{flag} \alias{flag,.QA2-method} \alias{flag,QAFrequentSequence-method} \alias{flag,QAReadQuality-method} \alias{flag,QASource-method} \alias{report,QA-method} \alias{report,QAAdapterContamination-method} \alias{report,QAFiltered-method} \alias{report,QAFlagged-method} \alias{report,QAFrequentSequence-method} \alias{report,QANucleotideByCycle-method} \alias{report,QANucleotideUse-method} \alias{report,QAQualityByCycle-method} \alias{report,QAQualityUse-method} \alias{report,QAReadQuality-method} \alias{report,QASequenceUse-method} \alias{report,QASource-method} \alias{rbind,QASummary-method} \alias{show,QAAdapterContamination-method} \alias{show,QACollate-method} \alias{show,QAFastqSource-method} \alias{show,QAFrequentSequence-method} \alias{show,QAReadQuality-method} \alias{show,QASummary-method} \title{(Updated) quality assessment reports on short reads} \description{ This page summarizes an updated approach to quality assessment reports in \code{ShortRead}. } \usage{ ## Input source for short reads QAFastqSource(con = character(), n = 1e+06, readerBlockSize = 1e+08, flagNSequencesRange = NA_integer_, ..., html = system.file("template", "QASources.html", package="ShortRead")) QAData(seq = ShortReadQ(), filter = logical(length(seq)), ...) ## Possible QA elements QAFrequentSequence(useFilter = TRUE, addFilter = TRUE, n = NA_integer_, a = NA_integer_, flagK=.8, reportSequences = FALSE, ...) QANucleotideByCycle(useFilter = TRUE, addFilter = TRUE, ...) QANucleotideUse(useFilter = TRUE, addFilter = TRUE, ...) QAQualityByCycle(useFilter = TRUE, addFilter = TRUE, ...) QAQualityUse(useFilter = TRUE, addFilter = TRUE, ...) QAReadQuality(useFilter = TRUE, addFilter = TRUE, flagK = 0.2, flagA = 30L, ...) QASequenceUse(useFilter = TRUE, addFilter = TRUE, ...) QAAdapterContamination(useFilter = TRUE, addFilter = TRUE, Lpattern = NA_character_, Rpattern = NA_character_, max.Lmismatch = 0.1, max.Rmismatch = 0.2, min.trim = 9L, ...) ## Order QA report elements QACollate(src, ...) ## perform analysis qa2(object, state, ..., verbose=FALSE) ## Outputs from qa2 QA(src, filtered, flagged, ...) QAFiltered(useFilter = TRUE, addFilter = TRUE, ...) QAFlagged(useFilter = TRUE, addFilter = TRUE, ...) ## Summarize results as html report \S4method{report}{QA}(x, ..., dest = tempfile(), type = "html") ## additional methods; 'flag' is not fully implemented flag(object, ..., verbose=FALSE) \S4method{rbind}{QASummary}(..., deparse.level = 1) } \arguments{ \item{con}{\code{character(1)} file location of fastq input, as used by \code{FastqSampler}.} \item{n}{\code{integer(1)} number of records to input, as used by \code{FastqStreamer} (\code{QAFastqSource}). \code{integer(1)} number of sequences to tag as \sQuote{frequent} (\code{QAFrequentSequence}). } \item{readerBlockSize}{integer(1) number of bytes to input, as used by \code{FastqStreamer}.} \item{flagNSequencesRange}{\code{integer(2)} minimum and maximum reads above which source files will be flagged as outliers.} \item{html}{\code{character(1)} location of the HTML template for summarizing this report element.} \item{seq}{\code{\link{ShortReadQ}} representation of fastq data.} \item{filter}{\code{logical()} vector with length equal to \code{seq}, indicating whether elements of \code{seq} are filtered (\code{TRUE}) or not.} \item{useFilter, addFilter}{\code{logical(1)} indicating whether the QA element should be calculating using the filtered (\code{useFilter=TRUE}) or all reads, and whether reads failing the QA element should be added to the filter used by subsequent steps (\code{addFilter = TRUE}) or not.} \item{a}{\code{integer(1)} count of number of sequences above which a read will be considered \sQuote{frequent} (\code{QAFrequentSequence}).} \item{flagK, flagA}{\code{flagK} \code{numeric(1)} between 0 and 1 indicating the fraction of frequent sequences greater than or equal to \code{n} or \code{a} above which a fastq file will be flagged (\code{QAFrequentSequence}). \code{flagK} \code{numeric{1}} between 0 and 1 and \code{flagA} \code{integer(1)} indicating that a run should be flagged when the fraction of reads with quality greater than or equal to \code{flagA} falls below threshold \code{flagK}.} \item{reportSequences}{\code{logical(1)} indicating whether frequent sequences are to be reported.} \item{Lpattern, Rpattern, max.Lmismatch, max.Rmismatch, min.trim}{Parameters influencing adapter identification, see \code{\link{matchPattern}}.} \item{src}{The source, e.g., \code{QAFastqSource}, on which the quality assessment report will be based.} \item{object}{An instance of class derived from \code{QA} on which quality metrics will be derived; for end users, this is usually the result of \code{QACollate}.}. \item{state}{The data on which quality assessment will be performed; this is not usually necessary for end-users.} \item{verbose}{\code{logical(1)} indicating whether progress reports should be reported.} \item{filtered, flagged}{Primarily for internal use, instances of \code{QAFiltered} and \code{QAFlagged}.} \item{x}{An instance of \code{QA} on which a report is to be generated.} \item{dest}{\code{character(1)} providing the directory in which the report is to be generated.} \item{type}{\code{character(1)} indicating the type of report to be generated; only \dQuote{html} is supported.} \item{deparse.level}{see \code{\link{rbind}}.} \item{...}{Additional arguments, e.g., \code{html} to specify the location of the html source to use as a template for the report.} } \details{ Use \code{QACollate} to specify an order in which components of a QA report are to be assembled. The first argument is the data source (e.g., \code{QAFastqSource}). Functions related to data input include: \describe{ \item{\code{QAFastqSource}}{defines the location of fastq files to be included in the report. \code{con} is used to construct a \code{\link{FastqSampler}} instance, and records are processed using \code{qa2,QAFastqSource-method}.} \item{\code{QAData}}{is a class for representing the data during the QA report generation pass; it is primarily for internal use.} } Possible elements in a QA report are: \describe{ \item{\code{QAFrequentSequence}}{identifies the most-commonly occuring sequences. One of \code{n} or \code{a} can be non-NA, and determine the number of frequent sequences reported. \code{n} specifies the number of most-frequent sequences to filter, e.g., \code{n=10} would filter the top 10 most commonly occurring sequences; \code{a} provides a threshold frequency (count) above which reads are filtered. The sample is flagged when a fraction \code{flagK} of the reads are filtered. \code{reportSequences} determines whether the most commonly occuring sequences, as determined by \code{n} or \code{a}, are printed in the html report. } \item{\code{QANucleotideByCycle}}{reports nucleotide frequency as a function of cycle.} \item{\code{QAQualityByCycle}}{reports average quality score as a function of cycle.} \item{\code{QAQualityUse}}{summarizes overall nucleotide qualities.} \item{\code{QAReadQuality}}{summarizes the distribution of read qualities.} \item{\code{QASequenceUse}}{summarizes the cumulative distribution of reads occurring 1, 2, \dots times.} \item{\code{QAAdapterContamination}}{reports the occurrence of \sQuote{adapter} sequences on the left and / or right end of each read.} } } \value{ An object derived from class \code{\linkS4class{.QA}}. Values contained in this object are meant for use by \code{\link{report}} } \author{Martin Morgan } \seealso{\code{\linkS4class{QA}}.} \examples{ dirPath <- system.file(package="ShortRead", "extdata", "E-MTAB-1147") fls <- dir(dirPath, "fastq.gz", full=TRUE) coll <- QACollate(QAFastqSource(fls), QAReadQuality(), QAAdapterContamination(), QANucleotideUse(), QAQualityUse(), QASequenceUse(), QAFrequentSequence(n=10), QANucleotideByCycle(), QAQualityByCycle()) x <- qa2(coll, BPPARAM=SerialParam(), verbose=TRUE) res <- report(x) if (interactive()) browseURL(res) } \keyword{manip} ShortRead/man/readAligned.Rd0000644000175400017540000003540213001230550016725 0ustar00biocbuildbiocbuild\name{readAligned} \alias{readAligned} \alias{readAligned,character-method} \title{(Legacy) Read aligned reads and their quality scores into R representations} \description{ Import files containing aligned reads into an internal representation of the alignments, sequences, and quality scores. Most methods (see \sQuote{details} for exceptions) read all files into a single R object. } \usage{ readAligned(dirPath, pattern=character(0), ...) } \arguments{ \item{dirPath}{A character vector (or other object; see methods defined on this generic) giving the directory path (relative or absolute; some methods also accept a character vector of file names) of aligned read files to be input.} \item{pattern}{The (\code{\link{grep}}-style) pattern describing file names to be read. The default (\code{character(0)}) results in (attempted) input of all files in the directory.} \item{...}{Additional arguments, used by methods. When \code{dirPath} is a character vector, the argument \code{type} must be provided. Possible values for \code{type} and their meaning are described below. Most methods implement \code{filter=srFilter()}, allowing objects of \code{\linkS4class{SRFilter}} to selectively returns aligned reads.} } \details{ There is no standard aligned read file format; methods parse particular file types. The \code{readAligned,character-method} interprets file types based on an additional \code{type} argument. Supported types are: \describe{ \item{\code{type="SolexaExport"}}{ This type parses \code{.*_export.txt} files following the documentation in the Solexa Genome Alignment software manual, version 0.3.0. These files consist of the following columns; consult Solexa documentation for precise descriptions. If parsed, values can be retrieved from \code{\linkS4class{AlignedRead}} as follows: \describe{ \item{Machine}{see below} \item{Run number}{stored in \code{alignData}} \item{Lane}{stored in \code{alignData}} \item{Tile}{stored in \code{alignData}} \item{X}{stored in \code{alignData}} \item{Y}{stored in \code{alignData}} \item{Multiplex index}{see below} \item{Paired read number}{see below} \item{Read}{\code{sread}} \item{Quality}{\code{quality}} \item{Match chromosome}{\code{chromosome}} \item{Match contig}{\code{alignData}} \item{Match position}{\code{position}} \item{Match strand}{\code{strand}} \item{Match description}{Ignored} \item{Single-read alignment score}{\code{alignQuality}} \item{Paired-read alignment score}{Ignored} \item{Partner chromosome}{Ignored} \item{Partner contig}{Ignored} \item{Partner offset}{Ignored} \item{Partner strand}{Ignored} \item{Filtering}{\code{alignData}} } The following optional arguments, set to \code{FALSE} by default, influence data input \describe{ \item{withMultiplexIndex}{When \code{TRUE}, include the multiplex index as a column \code{multiplexIndex} in \code{alignData}.} \item{withPairedReadNumber}{When \code{TRUE}, include the paired read number as a column \code{pairedReadNumber} in \code{alignData}.} \item{withId}{When \code{TRUE}, construct an identifier string as \sQuote{Machine_Run:Lane:Tile:X:Y#multiplexIndex/pairedReadNumber}. The substrings \sQuote{#multiplexIndex} and \sQuote{/pairedReadNumber} are not present if \code{withMultiplexIndex=FALSE} or \code{withPairedReadNumber=FALSE}.} \item{withAll}{A convencience which, when \code{TRUE}, sets all \code{with*} values to \code{TRUE}.} } Note that not all paired read columns are interpreted. Different interfaces to reading alignment files are described in \code{\linkS4class{SolexaPath}} and \code{\linkS4class{SolexaSet}}. } \item{\code{type="SolexaPrealign"}}{See SolexaRealign} \item{\code{type="SolexaAlign"}}{See SolexaRealign} \item{\code{type="SolexaRealign"}}{ These types parse \code{s_L_TTTT_prealign.txt}, \code{s_L_TTTT_align.txt} or \code{s_L_TTTT_realign.txt} files produced by default and eland analyses. From the Solexa documentation, \code{align} corresponds to unfiltered first-pass alignments, \code{prealign} adjusts alignments for error rates (when available), \code{realign} filters alignments to exclude clusters failing to pass quality criteria. Because base quality scores are not stored with alignments, the object returned by \code{readAligned} scores all base qualities as \code{-32}. If parsed, values can be retrieved from \code{\linkS4class{AlignedRead}} as follows: \describe{ \item{Sequence}{stored in \code{sread}} \item{Best score}{stored in \code{alignQuality}} \item{Number of hits}{stored in \code{alignData}} \item{Target position}{stored in \code{position}} \item{Strand}{stored in \code{strand}} \item{Target sequence}{Ignored; parse using \code{\link{readXStringColumns}}} \item{Next best score}{stored in \code{alignData}} } } \item{\code{type="SolexaResult"}}{ This parses \code{s_L_eland_results.txt} files, an intermediate format that does not contain read or alignment quality scores. Because base quality scores are not stored with alignments, the object returned by \code{readAligned} scores all base qualities as \code{-32}. Columns of this file type can be retrieved from \code{\linkS4class{AlignedRead}} as follows (description of columns is from Table 19, Genome Analyzer Pipeline Software User Guide, Revision A, January 2008): \describe{ \item{Id}{Not parsed} \item{Sequence}{stored in \code{sread}} \item{Type of match code}{Stored in \code{alignData} as \code{matchCode}. Codes are (from the Eland manual): NM (no match); QC (no match due to quality control failure); RM (no match due to repeat masking); U0 (best match was unique and exact); U1 (best match was unique, with 1 mismatch); U2 (best match was unique, with 2 mismatches); R0 (multiple exact matches found); R1 (multiple 1 mismatch matches found, no exact matches); R2 (multiple 2 mismatch matches found, no exact or 1-mismatch matches).} \item{Number of exact matches}{stored in \code{alignData} as \code{nExactMatch}} \item{Number of 1-error mismatches}{stored in \code{alignData} as \code{nOneMismatch}} \item{Number of 2-error mismatches}{stored in \code{alignData} as \code{nTwoMismatch}} \item{Genome file of match}{stored in \code{chromosome}} \item{Position}{stored in \code{position}} \item{Strand}{(direction of match) stored in \code{strand}} \item{\sQuote{N} treatment}{stored in \code{alignData}, as \code{NCharacterTreatment}. \sQuote{.} indicates treatment of \sQuote{N} was not applicable; \sQuote{D} indicates treatment as deletion; \sQuote{|} indicates treatment as insertion} \item{Substitution error}{stored in \code{alignData} as \code{mismatchDetailOne} and \code{mismatchDetailTwo}. Present only for unique inexact matches at one or two positions. Position and type of first substitution error, e.g., 11A represents 11 matches with 12th base an A in reference but not read. The reference manual cited below lists only one field (\code{mismatchDetailOne}), but two are present in files seen in the wild.} } } \item{\code{type="MAQMap", records=-1L}}{Parse binary \code{map} files produced by MAQ. See details in the next section. The \code{records} option determines how many lines are read; \code{-1L} (the default) means that all records are input. For \code{type="MAQMap"}, \code{dir} and \code{pattern} must match a single file.} \item{\code{type="MAQMapShort", records=-1L}}{The same as \code{type="MAQMap"} but for map files made with Maq prior to version 0.7.0. (These files use a different maximum read length [64 instead of 128], and are hence incompatible with newer Maq map files.). For \code{type="MAQMapShort"}, \code{dir} and \code{pattern} must match a single file.} \item{\code{type="MAQMapview"}}{ Parse alignment files created by MAQ's \sQuote{mapiew} command. Interpretation of columns is based on the description in the MAQ manual, specifically \preformatted{ ...each line consists of read name, chromosome, position, strand, insert size from the outer coordinates of a pair, paired flag, mapping quality, single-end mapping quality, alternative mapping quality, number of mismatches of the best hit, sum of qualities of mismatched bases of the best hit, number of 0-mismatch hits of the first 24bp, number of 1-mismatch hits of the first 24bp on the reference, length of the read, read sequence and its quality. } The read name, read sequence, and quality are read as \code{XStringSet} objects. Chromosome and strand are read as \code{factor}s. Position is \code{numeric}, while mapping quality is \code{numeric}. These fields are mapped to their corresponding representation in \code{AlignedRead} objects. Number of mismatches of the best hit, sum of qualities of mismatched bases of the best hit, number of 0-mismatch hits of the first 24bp, number of 1-mismatch hits of the first 24bp are represented in the \code{AlignedRead} object as components of \code{alignData}. Remaining fields are currently ignored. } \item{\code{type="Bowtie"}}{ Parse alignment files created with the Bowtie alignment algorithm. Parsed columns can be retrieved from \code{\linkS4class{AlignedRead}} as follows: \describe{ \item{Identifier}{\code{id}} \item{Strand}{\code{strand}} \item{Chromosome}{\code{chromosome}} \item{Position}{\code{position}; see comment below} \item{Read}{\code{sread}; see comment below} \item{Read quality}{\code{quality}; see comments below} \item{Similar alignments}{\code{alignData}, \sQuote{similar} column; Bowtie v. 0.9.9.3 (12 May, 2009) documents this as the number of other instances where the same read aligns against the same reference characters as were aligned against in this alignment. Previous versions marked this as \sQuote{Reserved}} \item{Alignment mismatch locations}{\code{alignData} \sQuote{mismatch}, column} } NOTE: the default quality encoding changes to \code{FastqQuality} with \pkg{ShortRead} version 1.3.24. This method includes the argument \code{qualityType} to specify how quality scores are encoded. Bowtie quality scores are \sQuote{Phred}-like by default, with \code{qualityType='FastqQuality'}, but can be specified as \sQuote{Solexa}-like, with \code{qualityType='SFastqQuality'}. Bowtie outputs positions that are 0-offset from the left-most end of the \code{+} strand. \code{ShortRead} parses position information to be 1-offset from the left-most end of the \code{+} strand. Bowtie outputs reads aligned to the \code{-} strand as their reverse complement, and reverses the quality score string of these reads. \code{ShortRead} parses these to their original sequence and orientation. } \item{\code{type="SOAP"}}{ Parse alignment files created with the SOAP alignment algorithm. Parsed columns can be retrieved from \code{\linkS4class{AlignedRead}} as follows: \describe{ \item{id}{\code{id}} \item{seq}{\code{sread}; see comment below} \item{qual}{\code{quality}; see comment below} \item{number of hits}{\code{alignData}} \item{a/b}{\code{alignData} (\code{pairedEnd})} \item{length}{\code{alignData} (\code{alignedLength})} \item{+/-}{\code{strand}} \item{chr}{\code{chromosome}} \item{location}{\code{position}; see comment below} \item{types}{\code{alignData} (\code{typeOfHit}: integer portion; \code{hitDetail}: text portion)} } This method includes the argument \code{qualityType} to specify how quality scores are encoded. It is unclear from SOAP documentation what the quality score is; the default is \sQuote{Solexa}-like, with \code{qualityType='SFastqQuality'}, but can be specified as \sQuote{Phred}-like, with \code{qualityType='FastqQuality'}. SOAP outputs positions that are 1-offset from the left-most end of the \code{+} strand. \code{ShortRead} preserves this representation. SOAP reads aligned to the \code{-} strand are reported by SOAP as their reverse complement, with the quality string of these reads reversed. \code{ShortRead} parses these to their original sequence and orientation. } } } \value{ A single R object (e.g., \code{\linkS4class{AlignedRead}}) containing alignments, sequences and qualities of all files in \code{dirPath} matching \code{pattern}. There is no guarantee of order in which files are read. } \seealso{ The \code{\linkS4class{AlignedRead}} class. Genome Analyzer Pipeline Software User Guide, Revision A, January 2008. The MAQ reference manual, \url{http://maq.sourceforge.net/maq-manpage.shtml#5}, 3 May, 2008. The Bowtie reference manual, \url{http://bowtie-bio.sourceforge.net}, 28 October, 2008. The SOAP reference manual, \url{http://soap.genomics.org.cn/soap1}, 16 December, 2008. } \author{ Martin Morgan , Simon Anders (MAQ map)} \examples{ sp <- SolexaPath(system.file("extdata", package="ShortRead")) ap <- analysisPath(sp) ## ELAND_EXTENDED (aln0 <- readAligned(ap, "s_2_export.txt", "SolexaExport")) ## PhageAlign (aln1 <- readAligned(ap, "s_5_.*_realign.txt", "SolexaRealign")) ## MAQ dirPath <- system.file('extdata', 'maq', package='ShortRead') list.files(dirPath) ## First line readLines(list.files(dirPath, full.names=TRUE)[[1]], 1) countLines(dirPath) ## two files collapse into one (aln2 <- readAligned(dirPath, type="MAQMapview")) ## select only chr1-5.fa, '+' strand filt <- compose(chromosomeFilter("chr[1-5].fa"), strandFilter("+")) (aln3 <- readAligned(sp, "s_2_export.txt", filter=filt)) } \keyword{manip} ShortRead/man/readBaseQuality.Rd0000644000175400017540000000415413001230551017606 0ustar00biocbuildbiocbuild\name{readBaseQuality} \alias{readBaseQuality} \alias{readBaseQuality,character-method} \title{(Legacy) Read short reads and their quality scores into R representations} \description{ \code{readBaseQuality} reads all base call files in a directory \code{dirPath} whose file name matches \code{seqPattern} and all quality score files whose name matches \code{prbPattern}, returning a compact internal representation of the sequences, and quality scores in the files. Methods read all files into a single R object. } \usage{ readBaseQuality(dirPath, ...) \S4method{readBaseQuality}{character}(dirPath, seqPattern=character(0), prbPattern=character(0), type=c("Solexa"), ...) } \arguments{ \item{dirPath}{A character vector (or other object; see methods defined on this generic) giving the directory path (relative or absolute) of files to be input.} \item{seqPattern}{The (\code{\link{grep}}-style) pattern describing base call file names to be read. The default (\code{character(0)}) results in (attempted) input of all files in the directory.} \item{prbPattern}{The (\code{\link{grep}}-style) pattern describing quality score file names to be read. The default (\code{character(0)}) results in (attempted) input of all files in the directory.} \item{type}{The type of file to be parsed. Supported types include: \code{Solexa}: parse reads and their qualities from \code{_seq.txt} and \code{_prb.txt}-formatted files, respectively.} \item{...}{Additional arguments, perhaps used by methods.} } \value{ A single R object (e.g., \code{\linkS4class{ShortReadQ}}) containing sequences and qualities of all files in \code{dirPath} matching \code{seqPattern} and \code{prbPattern} respectively. There is no guarantee of order in which files are read. } \seealso{ A \code{\linkS4class{ShortReadQ}} object. \code{\link{readXStringColumns}}, \code{\link{readPrb}} } \author{ Patrick Aboyoun } \examples{ sp <- SolexaPath(system.file("extdata", package="ShortRead")) readBaseQuality(sp, seqPattern="s_1.*_seq.txt", prbPattern="s_1.*_prb.txt") } \keyword{manip} ShortRead/man/readBfaToc.Rd0000644000175400017540000000140413001230551016514 0ustar00biocbuildbiocbuild\name{readBfaToc} \alias{readBfaToc} \title{(Legacy) Get a list of the sequences in a Maq .bfa file} \description{ As \code{\link{coverage}} needs to know the lengths of the reference sequences, this function is provided which extracts this information from a .bfa file (Maq's "binary FASTA" format). } \usage{ readBfaToc( bfafile ) } \arguments{ \item{bfafile}{The file name of the .bfa file.} } \value{An integer vector with one element per reference sequence found in the .bfa file, each vector element named with the sequence name and having the sequence length as value.} \author{Simon Anders, EMBL-EBI, \email{sanders@fs.tum.de} (Note: The C code for this function incorporates code from Li Heng's MAQ software, (c) Li Heng and released by him under GPL 2.} ShortRead/man/readFasta.Rd0000644000175400017540000000563113001230551016422 0ustar00biocbuildbiocbuild\name{readFasta} \alias{readFasta} \alias{readFasta,character-method} \alias{writeFasta} \alias{writeFasta,DNAStringSet-method} \title{Read and write FASTA files to or from ShortRead objects} \description{ \code{readFasta} reads all FASTA-formated files in a directory \code{dirPath} whose file name matches pattern \code{pattern}, returning a compact internal representation of the sequences and quality scores in the files. Methods read all files into a single R object; a typical use is to restrict input to a single FASTA file. \code{writeFasta} writes an object to a single \code{file}, using \code{mode="w"} (the default) to create a new file or \code{mode="a"} append to an existing file. Attempting to write to an existing file with \code{mode="w"} results in an error. } \usage{ readFasta(dirPath, pattern = character(0), ..., nrec=-1L, skip=0L) \S4method{readFasta}{character}(dirPath, pattern = character(0), ..., nrec=-1L, skip=0L) writeFasta(object, file, mode="w", ...) \S4method{writeFasta}{DNAStringSet}(object, file, mode="w", ...) } \arguments{ \item{dirPath}{A character vector giving the directory path (relative or absolute) or single file name of FASTA files to be read.} \item{pattern}{The (\code{\link{grep}}-style) pattern describing file names to be read. The default (\code{character(0)}) results in (attempted) input of all files in the directory.} \item{object}{An object to be output in \code{fasta} format.} \item{file}{A length 1 character vector providing a path to a file to the object is to be written to.} \item{mode}{A length 1 character vector equal to either \sQuote{w} or \sQuote{a} to write to a new file or append to an existing file, respectively.} \item{...}{Additional arguments used by methods or, for \code{writeFasta}, \code{\link{writeXStringSet}}.} \item{nrec}{See \code{?readDNAStringSet}.} \item{skip}{See \code{?readDNAStringSet}.} } \value{ \code{readFasta} returns a \code{\linkS4class{DNAStringSet}}. containing sequences and qualities contained in all files in \code{dirPath} matching \code{pattern}. There is no guarantee of order in which files are read. \code{writeFasta} is invoked primarily for its side effect, creating or appending to file \code{file}. The function returns, invisibly, the length of \code{object}, and hence the number of records written. There is a \code{writeFasta} method for any class derived from \code{\linkS4class{ShortRead}}. } \author{Martin Morgan} \examples{ showMethods("readFasta") showMethods("writeFasta") f1 <- system.file("extdata", "someORF.fa", package="Biostrings") rfa <- readFasta(f1) sread(rfa) id(rfa) sp <- SolexaPath(system.file('extdata', package='ShortRead')) rfq <- readFastq(analysisPath(sp), pattern="s_1_sequence.txt") file <- tempfile() writeFasta(rfq, file) readLines(file, 8) writeFasta(sread(rfq), file) # no 'id's } \keyword{manip} ShortRead/man/readFastq.Rd0000644000175400017540000001266613001230550016447 0ustar00biocbuildbiocbuild\name{readFastq} \alias{readFastq} \alias{writeFastq} \alias{readFastq,character-method} \title{Read and write FASTQ-formatted files} \description{ \code{readFastq} reads all FASTQ-formated files in a directory \code{dirPath} whose file name matches pattern \code{pattern}, returning a compact internal representation of the sequences and quality scores in the files. Methods read all files into a single R object; a typical use is to restrict input to a single FASTQ file. \code{writeFastq} writes an object to a single \code{file}, using \code{mode="w"} (the default) to create a new file or \code{mode="a"} append to an existing file. Attempting to write to an existing file with \code{mode="w"} results in an error. } \usage{ readFastq(dirPath, pattern=character(0), ...) \S4method{readFastq}{character}(dirPath, pattern=character(0), ..., withIds=TRUE) writeFastq(object, file, mode="w", full=FALSE, compress=TRUE, ...) } \arguments{ \item{dirPath}{A character vector (or other object; see methods defined on this generic) giving the directory path (relative or absolute) or single file name of FASTQ files to be read.} \item{pattern}{The (\code{\link{grep}}-style) pattern describing file names to be read. The default (\code{character(0)}) results in (attempted) input of all files in the directory.} \item{object}{An object to be output in \code{fastq} format. For methods, use \code{showMethods(object, where=getNamespace("ShortRead"))}.} \item{file}{A length 1 character vector providing a path to a file to the object is to be written to.} \item{mode}{A length 1 character vector equal to either \sQuote{w} or \sQuote{a} to write to a new file or append to an existing file, respectively.} \item{full}{A logical(1) indicating whether the identifier line should be repeated \code{full=TRUE} or omitted \code{full=FALSE} on the third line of the fastq record.} \item{compress}{A logical(1) indicating whether the file should be gz-compressed. The default is \code{TRUE}.} \item{...}{Additional arguments. In particular, \code{qualityType} and \code{filter}: \describe{ \item{qualityType:}{Representation to be used for quality scores, must be one of \code{Auto} (choose Illumina base 64 encoding \code{SFastqQuality} if all characters are ASCII-encoded as greater than 58 \code{:} and some characters are greater than 74 \code{J}), \code{FastqQuality} (Phred-like base 33 encoding), \code{SFastqQuality} (Illumina base 64 encoding).} \item{filter:}{An object of class \code{\link{srFilter}}, used to filter objects of class \code{\linkS4class{ShortReadQ}} at input.} } } \item{withIds}{\code{logical(1)} indicating whether identifiers should be read from the fastq file.} } \details{ The fastq format is not quite precisely defined. The basic definition used here parses the following four lines as a single record: \preformatted{ @HWI-EAS88_1_1_1_1001_499 GGACTTTGTAGGATACCCTCGCTTTCCTTCTCCTGT +HWI-EAS88_1_1_1_1001_499 ]]]]]]]]]]]]Y]Y]]]]]]]]]]]]VCHVMPLAS } The first and third lines are identifiers preceded by a specific character (the identifiers are identical, in the case of Solexa). The second line is an upper-case sequence of nucleotides. The parser recognizes IUPAC-standard alphabet (hence ambiguous nucleotides), coercing \code{.} to \code{-} to represent missing values. The final line is an ASCII-encoded representation of quality scores, with one ASCII character per nucleotide. The encoding implicit in Solexa-derived fastq files is that each character code corresponds to a score equal to the ASCII character value minus 64 (e.g., ASCII \code{@} is decimal 64, and corresponds to a Solexa quality score of 0). This is different from BioPerl, for instance, which recovers quality scores by subtracting 33 from the ASCII character value (so that, for instance, \code{!}, with decimal value 33, encodes value 0). The BioPerl description of fastq asserts that the first character of line 4 is a \code{!}, but the current parser does not support this convention. \code{writeFastq} creates files following the specification outlined above, using the IUPAC-standard alphabet (hence, sequences containing \sQuote{.} when read will be represented by \sQuote{-} when written). } \value{ \code{readFastq} returns a single R object (e.g., \code{\linkS4class{ShortReadQ}}) containing sequences and qualities contained in all files in \code{dirPath} matching \code{pattern}. There is no guarantee of order in which files are read. \code{writeFastq} is invoked primarily for its side effect, creating or appending to file \code{file}. The function returns, invisibly, the length of \code{object}, and hence the number of records written. } \seealso{ The IUPAC alphabet in Biostrings. \url{http://www.bioperl.org/wiki/FASTQ_sequence_format} for the BioPerl definition of fastq. Solexa documentation `Data analysis - documentation : Pipeline output and visualisation'. } \author{Martin Morgan} \examples{ showMethods(readFastq) showMethods(writeFastq) sp <- SolexaPath(system.file('extdata', package='ShortRead')) rfq <- readFastq(analysisPath(sp), pattern="s_1_sequence.txt") sread(rfq) id(rfq) quality(rfq) ## SolexaPath method 'knows' where FASTQ files are placed rfq1 <- readFastq(sp, pattern="s_1_sequence.txt") rfq1 file <- tempfile() writeFastq(rfq, file) readLines(file, 8) } \keyword{manip} ShortRead/man/readIntensities.Rd0000644000175400017540000001124413001230550017656 0ustar00biocbuildbiocbuild\name{readIntensities} \alias{readIntensities} \alias{readIntensities,character-method} \title{(Legacy) Read Illumina image intensity files} \description{ \code{readIntensities} reads image `intensity' files (such as Illumina's \code{_int.txt} and (optionally) \code{_nse.txt}) into a single object. } \usage{ readIntensities(dirPath, pattern=character(0), ...) } \arguments{ \item{dirPath}{Directory path or other object (e.g., \code{\linkS4class{SolexaPath}}) for which methods are defined.} \item{pattern}{A length 1 character vector representing a regular expression to be combined with \code{dirPath}, as described below, to match files to be summarized.} \item{\dots}{Additional arguments used by methods.} } \details{ Additional methods are defined on specific classes, see, e.g., \code{\linkS4class{SolexaPath}}. The \code{readIntensities,character-method} contains an argument \code{type} that determines how intensities are parsed. Use the \code{type} argument to \code{readIntensities,character-method}, as described below. All \code{readIntensities,character} methods accepts the folling arguments: \describe{ \item{withVariability:}{Include estimates of variability (i.e., from parsing \code{_nse} files).} \item{verbose:}{Report on progress when starting to read each file.} } The supported types and their signatures are: \describe{ \item{\code{type="RtaIntensity"}}{ Intensities are read from Illumina \code{_cif.txt} and \code{_cnf.txt}-style files. The signature for this method is \code{dirPath, pattern=character(0), ..., type="RtaIntensity", lane=integer(0), cycles=integer(0), cycleIteration=1L, tiles=integer(0), laneName=sprintf("L%.3d", lane), cycleNames=sprintf("C%d.%d", cycles, cycleIteration), tileNames=sprintf("s_%d_%d", lane, tiles), posNames=sprintf("s_%d_%.4d_pos.txt", lane, tiles), withVariability=TRUE, verbose=FALSE} \describe{ \item{lane:}{\code{integer(1)} identifying the lane in which cycles and tiles are to be processed.} \item{cycles:}{\code{integer()} enumerating cycles to be processed.} \item{cycleIteration:}{\code{integer(1)} identifying the iteration of the base caller to be summarized} \item{tiles:}{\code{integer()} enumerating tile numbers to be summarized.} \item{laneName, cycleNames, tileNames, posNames:}{\code{character()} vectors identifying the lane and cycle directories, and the \sQuote{pos} and tile file names (excluding the \sQuote{.cif} or \sQuote{.cnf} extension) to be processed.} } The \code{dirPath} and \code{pattern} arguments are combined as \code{list.files(dirPath, pattern)}, and must identify a single directory. Most uses of this function will focus on a single tile (specified with, e.g., \code{tiles=1L}); the \code{laneName}, \code{cycleNames}, \code{tileNames}, and \code{posNames} parameters are designed to work with the default Illumina pipeline and do not normally need to be specified. } \item{\code{type="IparIntensity"}}{ Intensities are read from Solexa \code{_pos.txt}, \code{_int.txt.p}, \code{_nse.txt.p}-style file triplets. The signature for this method is \code{dirPath, pattern=character(0), ..., type="IparIntensity", intExtension="_int.txt.p.gz", nseExtension="_nse.txt.p.gz", posExtension="_pos.txt", withVariability=TRUE, verbose=FALSE} Files to be parsed are determined as, e.g., \code{paste(pattern, intExtension, sep="")}. } \item{\code{type="SolexaIntensity"}}{ Intensities are read from Solexa \code{_int.txt} and \code{_nse.txt}-style files. The signature for this method is \code{dirPath, pattern=character(0), ..., type="SolexaIntensity", intExtension="_int.txt", nseExtension="_nse.txt", withVariability=TRUE, verbose=FALSE} Files to be parsed are determined as, e.g., \code{paste(pattern, intExtension, sep="")}. } } } \value{ An object derived from class \code{\linkS4class{Intensity}}. } \author{ Martin Morgan , Michael Muratet (RTA). } \examples{ fl <- system.file("extdata", package="ShortRead") sp <- SolexaPath(fl) int <- readIntensities(sp) int intensity(int)[1,,] # one read intensity(int)[[1:2,,]] # two reads, as 'array' head(rowMeans(intensity(int))) # treated as 'array' head(pData(readIntensityInfo(int))) \dontrun{## RTA Lane 2, cycles 1:80, cycle iteration 1, tile 3 int <- readIntensities("Data/Intensities", type="RtaIntensity", lane=2, cycles=1:80, tiles=3) } } \keyword{manip} ShortRead/man/readPrb.Rd0000644000175400017540000000400313001230551016077 0ustar00biocbuildbiocbuild\name{readPrb} \alias{readPrb} \alias{readPrb,character-method} \title{(Legacy) Read Solexa prb files as fastq-style quality scores} \description{ \code{readPrb} reads all \code{_prb.txt} files in a directory into a single object. Most methods (see details) do this by identifying the maximum base call quality for each cycle and read, and representing this as an ASCII-encoded character string. } \usage{ readPrb(dirPath, pattern = character(0), ...) } \arguments{ \item{dirPath}{Directory path or other object (e.g., \code{\linkS4class{SolexaPath}} for which methods are defined.} \item{pattern}{Regular expression matching names of \code{_prb} files to be summarized.} \item{\dots}{Additional arguments, unused.} } \details{ The \code{readPrb,character-method} contains an argument \code{as} that determines the value of the returned object, as follows. \describe{ \item{\code{as="SolexaEncoding"}}{ The ASCII encoding of the maximum per cycle and read quality score is encoded using Solexa conventions. } \item{\code{as="FastqEncoding"}}{ The ASCII encoding of the maximum per cycle and read quality score is encoded using Fastq conventions, i.e., \code{!} has value 0. } \item{\code{as="IntegerEncoding"}}{ The maximum per cycle and read quality score is returned as a in integer value. Values are collated into a matrix with number of rows equal to number of reads, and number of columns equal to number of cycles. } \item{\code{as="array"}}{ The quality scores are \emph{not} summarized; the return value is an integer array with dimensions corresponding to reads, nucleotides, and cycles. } } } \value{ An object of class \code{\linkS4class{QualityScore}}, or an integer matrix. } \author{Martin Morgan } \examples{ fl <- system.file("extdata", package="ShortRead") sp <- SolexaPath(fl) readPrb(sp, "s_1.*_prb.txt") # all tiles to a single file } \keyword{manip} ShortRead/man/readQseq.Rd0000644000175400017540000000303013001230550016263 0ustar00biocbuildbiocbuild\name{readQseq} \alias{readQseq} \alias{readQseq,character-method} \title{(Legacy) Read Solexa qseq files as fastq-style quality scores} \description{ \code{readQseq} reads all files matching \code{pattern} in a directory into a single \code{\linkS4class{ShortReadQ}}-class object. Information on machine, lane, tile, x, and y coordinates, filtering status, and read number are not returned (although filtering status can be used to selectively include reads as described below). } \usage{ readQseq(dirPath, pattern = character(0), ..., as=c("ShortReadQ", "DataFrame", "XDataFrame"), filtered=FALSE, verbose=FALSE) } \arguments{ \item{dirPath}{Directory path or other object (e.g., \code{\linkS4class{SolexaPath}}) for which methods are defined.} \item{pattern}{Regular expression matching names of \code{_qseq} files to be summarized.} \item{\dots}{Additional argument, passed to I/O functions.} \item{as}{\code{character(1)} indicating the class of the return type. \dQuote{XDataFrame} is included for backward compatibility, but is no longer supported.} \item{filtered}{\code{logical(1)} indicating whether to include only those reads passing Solexa filtering?} \item{verbose}{\code{logical(1)} indicating whether to report on progress during evaluation.} } \value{ An object of class \code{\linkS4class{ShortReadQ}}. } \author{Martin Morgan } \examples{ fl <- system.file("extdata", package="ShortRead") sp <- SolexaPath(fl) readQseq(sp) } \keyword{manip} ShortRead/man/readXStringColumns.Rd0000644000175400017540000000601413001230550020316 0ustar00biocbuildbiocbuild\name{readXStringColumns} \alias{readXStringColumns} \title{ Read one or more columns into XStringSet (e.g., DNAStringSet) objects } \description{ This function allows short read data components such as DNA sequence, quality scores, and read names to be read in to \code{XStringSet} (e.g., \code{DNAStringSet}, \code{BStringSet}) objects. One or several files of identical layout can be specified. } \usage{ readXStringColumns(dirPath, pattern=character(0), colClasses=list(NULL), nrows=-1L, skip=0L, sep = "\t", header = FALSE, comment.char="#") } \arguments{ \item{dirPath}{A character vector giving the directory path (relative or absolute) of files to be read.} \item{pattern}{The (\code{\link{grep}}-style) pattern describing file names to be read. The default (\code{character(0)}) reads all files in \code{dirPath}. All files are expected to have identical numbers of columns.} \item{colClasses}{A list of length equal to the number of columns in a file. Columns with corresponding \code{colClasses} equal to \code{NULL} are ignored. Other entries in \code{colClasses} are expected to be character strings describing the base class for the \code{XStringSet}. For instance a column of DNA sequences would be specified as \code{"DNAString"}. The column would be parsed into a \code{DNAStringSet} object.} \item{nrows}{A length 1 integer vector describing the maximum number of \code{XString} objects to read into the set. Reads may come from more than one file when \code{dirPath} and \code{pattern} parse several files and \code{nrow} is greater than the number of reads in the first file.} \item{skip}{A length 1 integer vector describing how many lines to skip at the start of each file.} \item{sep}{A length 1 character vector describing the column separator.} \item{header}{A length 1 logical vector indicating whether files include a header line identifying columns. If present, the header of the first file is used to name the returned values.} \item{comment.char}{A length 1 character vector, with a single character that, when appearing at the start of a line, indicates that the entire line should be ignored. Currently there is no way to use comment characters in other than the first position of a line.} } \value{ A list, with each element containing an \code{XStringSet} object of the type corresponding to the non-NULL elements of \code{colClasses}. } \author{Martin Morgan } \examples{ ## valid character strings for colClasses names(slot(getClass("XString"), "subclasses")) dirPath <- system.file('extdata', 'maq', package='ShortRead') colClasses <- rep(list(NULL), 16) colClasses[c(1, 15, 16)] <- c("BString", "DNAString", "BString") ## read one file readXStringColumns(dirPath, "out.aln.1.txt", colClasses=colClasses) ## read all files into a single object for each column res <- readXStringColumns(dirPath, colClasses=colClasses) } \keyword{IO} ShortRead/man/renew.Rd0000644000175400017540000000576413001230550015656 0ustar00biocbuildbiocbuild\name{renewable} \alias{renewable} \alias{renewable,missing-method} \alias{renewable,character-method} \alias{renewable,.ShortReadBase-method} \alias{renew} \alias{renew,.ShortReadBase-method} \title{Renew (update) a ShortRead object with new values} \description{ Use \code{renew} to update an object defined in \pkg{ShortRead} with new values. Discover update-able classes and values with \code{renewable}. } \usage{ renewable(x, \dots) renew(x, \dots) } \arguments{ \item{x}{For \code{renewable}: \code{missing}, \code{character(1)}, or a class defined in the \pkg{ShortRead} package. For \code{renew}: an instance of a class defined in the \pkg{ShortRead} package.} \item{\dots}{For \code{renewable}, ignored. For \code{renew}, named arguments identifying which parts of \code{x} are to be renewed.} } \details{ When invoked with no arguments \code{renewable} returns a character vector naming classes that can be renewed. When invoked with a \code{character(1)} or an instance of a \pkg{ShortRead} class, a list of the names and values of the elements that can be renewed. When \code{x} is a character vector naming a virtual class, then each element of the returned list is a non-virtual descendant of that class that can be used in renewal. This is not fully recursive. \code{renew} is always invoked with the \code{x} argument being an instance of a class identified by \code{renewable()}. Remaining arguments are name-value pairs identifying the components of \code{x} that are to be renewed (updated). The name-value pairs must be consistent with \code{renewable(x)}. The resulting object is checked for validity. Multiple components of the object can be updated in a single call to \code{renew}, allowing comparatively efficient complex transformations. } \value{ \code{renewable()} returns a character vector of renewable classes. \code{renewable(x)} returns a named list. The names correspond to renewable classes, and the elements of the list correspond to renewable components of the class. \code{renew(x, \dots)} returns an object of the same class as \code{x}, but with components of \code{x} replaced by the named values of \code{\dots}. } \author{Martin Morgan } \examples{ ## discovery renewable() renewable("AlignedRead") renewable("QualityScore") ## instantiable classes ## example data sp <- SolexaPath(system.file("extdata", package="ShortRead")) ap <- analysisPath(sp) filt <- chromosomeFilter("chr[[:digit:]+].fa") aln <- readAligned(ap, "s_2_export.txt", "SolexaExport", filter=filt) ## renew chromosomes from 'chr1.fa' to 'chr1', etc labels <- sub("\\\\.fa", "", levels(chromosome(aln))) renew(aln, chromosome=factor(chromosome(aln), labels=labels)) ## multiple changes -- update chromosome, offset position renew(aln, chromosome=factor(chromosome(aln), labels=labels), position=1L+position(aln)) ## oops! invalid instances cannot be constructed try(renew(aln, position=1:10)) } \keyword{manip} ShortRead/man/report.Rd0000644000175400017540000000671113001230550016042 0ustar00biocbuildbiocbuild\name{report} \alias{report} \alias{report,ANY-method} \alias{report_html} \title{Summarize quality assessment results into a report} \description{ This generic function summarizes results from evaluation of \code{\link{qa}} into a report. Available report formats vary depending on the data analysed. } \usage{ report(x, ..., dest=tempfile(), type="html") report_html(x, dest, type, ...) } \arguments{ \item{x}{An object returned by \code{\link{qa}}, usually derived from class \code{\linkS4class{.QA}}} \item{\dots}{Additional arguments used by specific methods. All methods with \code{type="html"} support the argument \code{cssFile}, which is a named, length 1 character vector. The value is a path to a CSS file to be incorporated into the report (e.g., \code{system.file("template", "QA.css", package="ShortRead")}). The name of \code{cssFile} is the name of the CSS file as seen by the html report (e.g., \dQuote{QA.css}). See specific methods for details on additional \code{\dots} arguments.} \item{dest}{The output destination for the final report. For \code{type="html"} this is a directory; for (deprecated) \code{type="pdf"} this is a file.} \item{type}{A text string defining the type of report; available report types depend on the type of object \code{x}; usually this is \dQuote{html}.} } \details{ \code{report_html} is meant for use by package authors wishing to add methods for creating HTML reports; users should always invoke \code{report}. The following methods are defined: \describe{ \item{\code{x="BowtieQA", ..., dest=tempfile(), type="html"}}{ Produce an HTML-based report from an object of class \code{\linkS4class{BowtieQA}}.} \item{\code{x="FastqQA", ..., dest=tempfile(), type="html"}}{ Produce an HTML-based report from an object of class \code{\linkS4class{FastqQA}}.} \item{\code{x="MAQMapQA", ..., dest=tempfile(), type="html"}}{ Produce an HTML-based report from an object of class \code{\linkS4class{MAQMapQA}}.} \item{\code{x="SolexaExportQA", ..., dest=tempfile(), type="html"}}{ Produce an HTML-based report from an object of class \code{\linkS4class{SolexaExportQA}}.} \item{\code{x="SolexaExportQA", ..., dest=tempfile(), type="pdf"}}{ (Deprecated) Produce an PDF report from an object of class \code{\linkS4class{SolexaExportQA}}.} \item{\code{x="SolexaPath", ..., dest=tempfile(), type="html"}}{ Produce an HTML report by first visiting all \code{_export.txt} files in the \code{analysisPath} directory of \code{x} to create a \code{SolexaExportQA} instance.} \item{\code{x="SolexaPath", ..., dest=tempfile(), type="pdf"}}{ (Deprecated) Produce an PDF report by first visiting all \code{_export.txt} files in the \code{analysisPath} directory of \code{x} to create a \code{SolexaExportQA} instance.} \item{ \code{x="ANY", ..., dest=tempfile(), type="ANY"} }{This method is used internally} } } \value{ This function is invoked for its side effect; the return value is the name of the directory or file where the report was created. } \author{Martin Morgan } \seealso{ \code{\linkS4class{SolexaExportQA}} } \examples{ showMethods("report") ## default CSS file cssFile <- c(QA.css=system.file("template", "QA.css", package="ShortRead")) noquote(readLines(cssFile)) } \keyword{manip} ShortRead/man/spViewPerFeature.Rd0000644000175400017540000000456513001230550017774 0ustar00biocbuildbiocbuild\name{spViewPerFeature} \alias{spViewPerFeature} \title{ Tools to visualize genomic data } \description{ Use \code{Snapshot}-class to visualize a specific region of genomic data } \usage{ spViewPerFeature(GRL, name, files, ignore.strand=FALSE, multi.levels = FALSE, fac=character(0L), ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{GRL}{Object \code{GRangeList} containing annotation of genomic data. It can be generated by applying \code{exonsBy()} or \code{transcriptsBy()} to a \code{TxDb} instance. See examples below.} \item{name}{Character(1) specifying which element in \code{GRL} to be visualized.} \item{files}{Charactor() or \code{BamFileList} specifying the file(s) to be visualized. If multiple files, local metadata of the files can be hold by setting a \code{DataFrame} (values(files) <- DataFrame(...)). See examples below.} \item{ignore.strand}{Logical(1) indicating whether to ignore the strand of the genomic data.} \item{multi.levels}{Logical(1) indicating whether to plot the coverage of multiple files on different panels. If \code{FALSE}, the mean coverage of multiple files would be plotted. } \item{fac}{Character(1) indicating which column of local metadata (\code{elementMetatdata()}) should be used to group the samples. Ignore} \item{\dots}{Arguments used for creating a \code{\link{Snapshot}} object.} } \value{A \code{Snapshot} instance} \author{Chao-Jen Wong \email{cwon2@fhcrc.org}} \seealso{ \code{\link{Snapshot}} } \examples{ ## Example 1 library(GenomicFeatures) txdbFile <- system.file("extdata", "sacCer2_sgdGene.sqlite", package="yeastNagalakshmi") ## either use a txdb file quaried from UCSC or use existing TxDb packages. txdb <- loadDb(txdbFile) grl <- exonsBy(txdb, by="gene") file <- system.file("extdata", "SRR002051.chrI-V.bam", package="yeastNagalakshmi") s <- spViewPerFeature(GRL=grl, name="YAL001C", files=file) ## Example 2 ## multi-files: using 'BamFileList' and setting up the 'DataFrame' ## holding the phenotype data bfiles <- BamFileList(c(a=file, b=file)) values(bfiles) <- DataFrame(sampleGroup=factor(c("normal", "tumor"))) values(bfiles) s <- spViewPerFeature(GRL=grl, name="YAL001C", files=bfiles, multi.levels=TRUE, fac="sampleGroup") } ShortRead/man/srFilter.Rd0000644000175400017540000002412013001230550016313 0ustar00biocbuildbiocbuild\name{srFilter} \alias{srFilter} \alias{srFilter,missing-method} \alias{srFilter,function-method} \alias{idFilter} \alias{chromosomeFilter} \alias{positionFilter} \alias{strandFilter} \alias{occurrenceFilter} \alias{nFilter} \alias{polynFilter} \alias{dustyFilter} \alias{srdistanceFilter} \alias{alignQualityFilter} \alias{alignDataFilter} \alias{compose} \title{Functions for user-created and built-in ShortRead filters} \description{ These functions create user-defined (\code{srFitler}) or built-in instances of \code{\linkS4class{SRFilter}} objects. Filters can be applied to objects from \code{ShortRead}, returning a logical vector to be used to subset the objects to include only those components satisfying the filter. } \usage{ srFilter(fun, name = NA_character_, ...) \S4method{srFilter}{missing}(fun, name=NA_character_, ...) \S4method{srFilter}{function}(fun, name=NA_character_, ...) compose(filt, ..., .name) idFilter(regex=character(0), fixed=FALSE, exclude=FALSE, .name="idFilter") occurrenceFilter(min=1L, max=1L, withSread=c(NA, TRUE, FALSE), duplicates=c("head", "tail", "sample", "none"), .name=.occurrenceName(min, max, withSread, duplicates)) nFilter(threshold=0L, .name="CleanNFilter") polynFilter(threshold=0L, nuc=c("A", "C", "T", "G", "other"), .name="PolyNFilter") dustyFilter(threshold=Inf, batchSize=NA, .name="DustyFilter") srdistanceFilter(subject=character(0), threshold=0L, .name="SRDistanceFilter") ## ## legacy filters for ungapped alignments ## chromosomeFilter(regex=character(0), fixed=FALSE, exclude=FALSE, .name="ChromosomeFilter") positionFilter(min=-Inf, max=Inf, .name="PositionFilter") strandFilter(strandLevels=character(0), .name="StrandFilter") alignQualityFilter(threshold=0L, .name="AlignQualityFilter") alignDataFilter(expr=expression(), .name="AlignDataFilter") } \arguments{ \item{fun}{An object of class \code{function} to be used as a filter. \code{fun} must accept a single named argument \code{x}, and is expected to return a logical vector such that \code{x[fun(x)]} selects only those elements of \code{x} satisfying the conditions of \code{fun} } \item{name}{A \code{character(1)} object to be used as the name of the filter. The \code{name} is useful for debugging and reference.} \item{filt}{A \code{\linkS4class{SRFilter}} object, to be used with additional arguments to create a composite filter.} \item{.name}{An optional \code{character(1)} object used to over-ride the name applied to default filters.} \item{regex}{Either \code{character(0)} or a \code{character(1)} regular expression used as \code{grep(regex, chromosome(x))} to filter based on chromosome. The default (\code{character(0)}) performs no filtering} \item{fixed}{\code{logical(1)} passed to \code{\link{grep}}, influencing how pattern matching occurs.} \item{exclude}{\code{logical(1)} which, when \code{TRUE}, uses \code{regex} to exclude, rather than include, reads.} \item{min}{\code{numeric(1)}} \item{max}{\code{numeric(1)}. For \code{positionFilter}, \code{min} and \code{max} define the closed interval in which position must be found \code{min <= position <= max}. For \code{occurrenceFilter}, \code{min} and \code{max} define the minimum and maximum number of times a read occurs after the filter.} \item{strandLevels}{Either \code{character(0)} or \code{character(1)} containing strand levels to be selected. \code{ShortRead} objects have standard strand levels \code{NA, "+", "-", "*"}, with \code{NA} meaning strand information not available and \code{"*"} meaning strand information not relevant.} \item{withSread}{A \code{logical(1)} indicating whether uniqueness includes the read sequence (\code{withSread=TRUE}), is based only on chromosome, position, and strand (\code{withSread=FALSE}), or only the read sequence (\code{withSread=NA}), as described for \code{occurrenceFilter} below..} \item{duplicates}{Either \code{character{1}}, a function \code{name}, or a function taking a single argument. Influence how duplicates are handled, as described for \code{occurrenceFilter} below.} \item{threshold}{A \code{numeric(1)} value representing a minimum (\code{srdistanceFilter}, \code{alignQualityFilter}) or maximum (\code{nFilter}, \code{polynFilter}, \code{dustyFilter}) criterion for the filter. The minima and maxima are closed-interval (i.e., \code{x >= threshold}, \code{x <= threshold} for some property \code{x} of the object being filtered).} \item{nuc}{A \code{character} vector containing IUPAC symbols for nucleotides or the value \code{"other"} corresponding to all non-nucleotide symbols, e.g., \code{N}.} \item{batchSize}{\code{NA} or an \code{integer(1)} vector indicating the number of DNA sequences to be processed simultaneously by \code{dustyFilter}. By default, all reads are processed simultaneously. Smaller values use less memory but are computationally less efficient.} \item{subject}{A \code{character()} of any length, to be used as the corresponding argument to \code{\link{srdistance}}.} \item{expr}{A \code{expression} to be evaluated with \code{pData(alignData(x))}.} \item{\dots}{Additional arguments for subsequent methods; these arguments are not currently used.} } \details{ \code{srFilter} allows users to construct their own filters. The \code{fun} argument to \code{srFilter} must be a function accepting a single argument \code{x} and returning a logical vector that can be used to select elements of \code{x} satisfying the filter with \code{x[fun(x)]} The \code{signature(fun="missing")} method creates a default filter that returns a vector of \code{TRUE} values with length equal to \code{length(x)}. \code{compose} constructs a new filter from one or more existing filter. The result is a filter that returns a logical vector with indices corresponding to components of \code{x} that pass all filters. If not provided, the name of the filter consists of the names of all component filters, each separated by \code{" o "}. The remaining functions documented on this page are built-in filters that accept an argument \code{x} and return a logical vector of \code{length(x)} indicating which components of \code{x} satisfy the filter. \code{idFilter} selects elements satisfying \code{grep(regex, id(x), fixed=fixed)}. \code{chromosomeFilter} selects elements satisfying \code{grep(regex, chromosome(x), fixed=fixed)}. \code{positionFilter} selects elements satisfying \code{min <= position(x) <= max}. \code{strandFilter} selects elements satisfying \code{match(strand(x), strand, nomatch=0) > 0}. \code{occurrenceFilter} selects elements that occur \code{>=min} and \code{<=max} times. \code{withSread} determines how reads will be treated: \code{TRUE} to include the sread, chromosome, strand, and position when determining occurrence, \code{FALSE} to include chromosome, strand, and position, and \code{NA} to include only sread. The default is \code{withSread=NA}. \code{duplicates} determines how reads with more than \code{max} reads are treated. \code{head} selects the first \code{max} reads of each set of duplicates, \code{tail} the last \code{max} reads, and \code{sample} a random sample of \code{max} reads. \code{none} removes all reads represented more than \code{max} times. The user can also provide a function (as used by \code{\link{tapply}}) of a single argument to select amongst reads. \code{nFilter} selects elements with fewer than \code{threshold} \code{'N'} symbols in each element of \code{sread(x)}. \code{polynFilter} selects elements with fewer than \code{threshold} copies of any nucleotide indicated by \code{nuc}. \code{dustyFilter} selects elements with high sequence complexity, as characterized by their \code{\link{dustyScore}}. This emulates the \code{dust} command from \code{WindowMaker} software. Calculations can be memory intensive; use \code{batchSize} to process the argument to \code{dustyFilter} in batches of the specified size. \code{srdistanceFilter} selects elements at an edit distance greater than \code{threshold} from all sequences in \code{subject}. \code{alignQualityFilter} selects elements with \code{alignQuality(x)} greater than \code{threshold}. \code{alignDataFilter} selects elements with \code{pData(alignData(x))} satisfying \code{expr}. \code{expr} should be formulated as though it were to be evaluated as \code{eval(expr, pData(alignData(x)))}. } \value{ \code{srFilter} returns an object of \code{\linkS4class{SRFilter}}. Built-in filters return a logical vector of \code{length(x)}, with \code{TRUE} indicating components that pass the filter. } \author{Martin Morgan } \seealso{\code{\linkS4class{SRFilter}}.} \examples{ sp <- SolexaPath(system.file("extdata", package="ShortRead")) aln <- readAligned(sp, "s_2_export.txt") # Solexa export file, as example # a 'chromosome 5' filter filt <- chromosomeFilter("chr5.fa") aln[filt(aln)] # filter during input readAligned(sp, "s_2_export.txt", filter=filt) # x- and y- coordinates stored in alignData, when source is SolexaExport xy <- alignDataFilter(expression(abs(x-500) > 200 & abs(y-500) > 200)) aln[xy(aln)] # both filters as a single filter chr5xy <- compose(filt, xy) aln[chr5xy(aln)] # both filters as a collection filters <- c(filt, xy) subsetByFilter(aln, filters) summary(filters, aln) # read, chromosome, strand, position tuples occurring exactly once aln[occurrenceFilter(withSread=TRUE, duplicates="none")(aln)] # reads occurring exactly once aln[occurrenceFilter(withSread=NA, duplicates="none")(aln)] # chromosome, strand, position tuples occurring exactly once aln[occurrenceFilter(withSread=FALSE, duplicates="none")(aln)] # custom filter: minimum calibrated base call quality >20 goodq <- srFilter(function(x) { apply(as(quality(x), "matrix"), 1, min, na.rm=TRUE) > 20 }, name="GoodQualityBases") goodq aln[goodq(aln)] } \keyword{manip} ShortRead/man/srdistance.Rd0000644000175400017540000000443113001230550016663 0ustar00biocbuildbiocbuild\name{srdistance} \alias{srdistance} % \alias{srdistance,DNAStringSet,character-method} \alias{srdistance,DNAStringSet,DNAString-method} \alias{srdistance,DNAStringSet,DNAStringSet-method} \title{Edit distances between reads and a small number of short references} \description{ \code{srdistance} calculates the edit distance from each read in \code{pattern} to each read in \code{subject}. The underlying algorithm \code{\link[Biostrings]{pairwiseAlignment}} is only efficient when both reads are short, and when the number of \code{subject} reads is small. } \usage{ srdistance(pattern, subject, ...) } \arguments{ \item{pattern}{An object of class \code{DNAStringSet} containing reads whose edit distance is desired.} \item{subject}{A short \code{character} vector, \code{DNAString} or (small) \code{DNAStringSet} to serve as reference.} \item{\dots}{additional arguments, unused.} } \details{ The underlying algorithm performs pairwise alignment from each read in \code{pattern} to each sequence in \code{subject}. The return value is a list of numeric vectors of distances, one list element for each sequence in \code{subject}. The vector in each list element contains for each read in \code{pattern} the edit distance from the read to the corresponding subject. The weight matrix and gap penalties used to calculate the distance are structured to weight base substitutions and single base insert/deletions equally. Edit distance between known and ambiguous (e.g., N) nucleotides, or between ambiguous nucleotides, are weighted as though each possible nucleotide in the ambiguity were equally likely. } \value{ A list of length equal to that of \code{subject}. Each element is a numeric vector equal to the length of \code{pattern}, with values corresponding to the minimum distance between between the corresponding pattern and subject sequences. } \author{Martin Morgan } \seealso{\code{\link[Biostrings]{pairwiseAlignment}}} \examples{ sp <- SolexaPath(system.file("extdata", package="ShortRead")) aln <- readAligned(sp, "s_2_export.txt") polyA <- polyn("A", 35) polyT <- polyn("T", 35) d1 <- srdistance(clean(sread(aln)), polyA) d2 <- srdistance(sread(aln), polyA) d3 <- srdistance(sread(aln), c(polyA, polyT)) } \keyword{manip} ShortRead/man/srduplicated.Rd0000644000175400017540000000650513001230550017213 0ustar00biocbuildbiocbuild\name{srduplicated} \alias{srorder} \alias{srrank} \alias{srsort} \alias{srduplicated} % XStringSet-methods \alias{srorder,XStringSet-method} \alias{srrank,XStringSet-method} \alias{srsort,XStringSet-method} \alias{srduplicated,XStringSet-method} \title{Order, sort, and find duplicates in XStringSet objects} \description{ These generics order, rank, sort, and find duplicates in short read objects, including fastq-encoded qualities. \code{srorder}, \code{srrank} and \code{srsort} differ from the default functions \code{rank}, \code{order} and \code{sort} in that sorting is based on an internally-defined order rather than, e.g., the order implied by \code{LC_COLLATE}. } \usage{ srorder(x, ...) srrank(x, ...) srsort(x, ...) srduplicated(x, ...) } \arguments{ \item{x}{The object to be sorted, ranked, ordered, or to have duplicates identified; see the examples below for objects for which methods are defined.} \item{\dots}{Additional arguments available for use by methods; usually ignored.} } \details{ Unlike \code{sort} and friends, the implementation does not preserve order of duplicated elements. Like \code{duplicated}, one element in each set of duplicates is marked as \code{FALSE}. \code{srrank} settles ties using the \dQuote{min} criterion described in \code{\link{rank}}, i.e., identical elements are ranked equal to the rank of the first occurrence of the sorted element. The following methods are defined, in addition to methods described in class-specific documentation: \describe{ \item{srsort}{\code{signature(x = "XStringSet")}:} \item{srorder}{\code{signature(x = "XStringSet")}:} \item{srduplicated}{\code{signature(x = "XStringSet")}: Apply \code{srorder}, \code{srrank}, \code{srsort}, \code{srduplicated} to \code{\link[Biostrings:XStringSet-class]{XStringSet}} objects such as those returned by \code{\link{sread}}.} \item{srsort}{\code{signature(x = "ShortRead")}:} \item{srorder}{\code{signature(x = "ShortRead")}:} \item{srduplicated}{\code{signature(x = "ShortRead")}: Apply \code{srorder}, \code{srrank}, \code{srsort}, \code{srduplicated} to \code{\link[Biostrings:XStringSet-class]{XStringSet}} objects to the \code{sread} component of \code{\linkS4class{ShortRead}} and derived objects.} } } \value{ The functions return the following values: \item{srorder}{An integer vector the same length as \code{x}, containing the indices that will bring \code{x} into sorted order.} \item{srrank}{An integer vector the same length as \code{x}, containing the rank of each seqeunce when sorted.} \item{srsort}{An instance of \code{x} in sorted order.} \item{srduplicated}{A logical vector the same length as \code{x} indicating whether the indexed element is already present. Note that, like \code{duplicated}, subsetting \code{x} using the result returned by \code{!srduplicated(x)} includes one representative from each set of duplicates.} } \author{Martin Morgan } \examples{ showMethods("srsort") showMethods("srorder") showMethods("srduplicated") sp <- SolexaPath(system.file('extdata', package='ShortRead')) rfq <- readFastq(analysisPath(sp), pattern="s_1_sequence.txt") sum(srduplicated(sread(rfq))) srsort(sread(rfq)) srsort(quality(rfq)) } \keyword{manip} ShortRead/man/tables.Rd0000644000175400017540000000404113001230550015773 0ustar00biocbuildbiocbuild\name{tables} \alias{tables} \alias{tables,XStringSet-method} \title{Summarize XStringSet read frequencies} \description{ This generic summarizes the number of times each sequence occurs in an \code{\link[Biostrings:XStringSet-class]{XStringSet}} instance. } \usage{ tables(x, n=50, ...) } \arguments{ \item{x}{An object for which a \code{tables} method is defined.} \item{n}{An \code{integer(1)} value determining how many named sequences will be present in the \code{top} portion of the return value.} \item{\dots}{Additional arguments available to methods} } \details{ Methods of this generic summarize the frequency with which each read occurs, There are two components to the summary. The reads are reported from most common to least common; typically a method parameter controls how many reads to report. Methods also return a pair of vectors describing how many reads were represented 1, 2, ... times. The following methods are defined, in addition to methods described in class-specific documentation: \describe{ \item{tables}{\code{signature(x= "XStringSet", n = 50)}: Apply \code{tables} to the \code{XStringSet} \code{x}.} } } \value{ A list of length two. \item{top}{A named integer vector. Names correspond to sequences. Values are the number of times the corresponding sequence occurs in the \code{XStringSet}. The vector is sorted in decreasing order; methods typically include a parameter specifying the number of sequences to return.} \item{distribution}{a \code{data.frame} with two columns. \code{nOccurrences} is the number of times any particular sequence is represented in the set (1, 2, ...). \code{nReads} is the number of reads with the corresponding occurrence.} } \author{Martin Morgan } \examples{ showMethods("tables") sp <- SolexaPath(system.file("extdata", package="ShortRead")) aln <- readAligned(sp) tables(sread(aln), n=6) lattice::xyplot(log10(nReads)~log10(nOccurrences), tables(sread(aln))$distribution) } \keyword{manip} ShortRead/man/trimTails.Rd0000644000175400017540000001455513001230551016505 0ustar00biocbuildbiocbuild\name{trimTails} \alias{trimTailw} \alias{trimTailw,character-method} \alias{trimTailw,BStringSet-method} \alias{trimTailw,XStringQuality-method} \alias{trimTails} \alias{trimTails,character-method} \alias{trimTails,BStringSet-method} \alias{trimTails,XStringQuality-method} \alias{trimEnds} \alias{trimEnds,character-method} \alias{trimEnds,XStringSet-method} \alias{trimEnds,XStringQuality-method} \alias{trimEnds,FastqQuality-method} \alias{trimEnds,ShortRead-method} \alias{trimEnds,ShortReadQ-method} \title{Trim ends of reads based on nucleotides or qualities} \description{ These generic functions remove leading or trailing nucleotides or qualities. \code{trimTails} and \code{trimTailw} remove low-quality reads from the right end using a sliding window (\code{trimTailw}) or a tally of (successive) nucleotides falling at or below a quality threshold (\code{trimTails}). \code{trimEnds} takes an alphabet of characters to remove from either left or right end. } \usage{ ## S4 methods for 'ShortReadQ', 'FastqQuality', or 'SFastqQuality' trimTailw(object, k, a, halfwidth, ..., ranges=FALSE) trimTails(object, k, a, successive=FALSE, ..., ranges=FALSE) trimEnds(object, a, left=TRUE, right=TRUE, relation=c("<=", "=="), ..., ranges=FALSE) \S4method{trimTailw}{BStringSet}(object, k, a, halfwidth, ..., alphabet, ranges=FALSE) \S4method{trimTails}{BStringSet}(object, k, a, successive=FALSE, ..., alphabet, ranges=FALSE) \S4method{trimTailw}{character}(object, k, a, halfwidth, ..., destinations, ranges=FALSE) \S4method{trimTails}{character}(object, k, a, successive=FALSE, ..., destinations, ranges=FALSE) \S4method{trimEnds}{character}(object, a, left=TRUE, right=TRUE, relation=c("<=", "=="), ..., destinations, ranges=FALSE) } \arguments{ \item{object}{An object (e.g., \code{\linkS4class{ShortReadQ}} and derived classes; see below to discover these methods) or character vector of fastq file(s) to be trimmed. } \item{k}{\code{integer(1)} describing the number of failing letters required to trigger trimming.} \item{a}{For \code{trimTails} and \code{trimTailw}, a \code{character(1)} with \code{nchar(a) == 1L} giving the letter at or below which a nucleotide is marked as failing. For \code{trimEnds} a \code{character()} with all \code{nchar() == 1L} giving the letter at or below which a nucleotide or quality scores marked for removal.} \item{halfwidth}{The half width (cycles before or after the current; e.g., a half-width of 5 would span 5 + 1 + 5 cycles) in which qualities are assessed.} \item{successive}{\code{logical(1)} indicating whether failures can occur anywhere in the sequence, or must be successive. If \code{successive=FALSE}, then the k'th failed letter and subsequent are removed. If \code{successive=TRUE}, the first succession of k failed and subsequent letters are removed.} \item{left, right}{\code{logical(1)} indicating whether trimming is from the left or right ends.} \item{relation}{\code{character(1)} selected from the argument values, i.e., \dQuote{<=} or \dQuote{==} indicating whether all letters at or below the \code{alphabet(object)} are to be removed, or only exact matches.} \item{\dots}{Additional arguments, perhaps used by methods.} \item{destinations}{For \code{object} of type \code{character()}, an equal-length vector of destination files. Files must not already exist.} \item{alphabet}{\code{character()} (ordered low to high) letters on which quality scale is measured. Usually supplied internally (user does not need to specify). If missing, then set to ASCII characters 0-127.} \item{ranges}{\code{logical(1)} indicating whether the trimmed object, or only the ranges satisfying the trimming condition, be returned.} } \details{ \code{trimTailw} starts at the left-most nucleotide, tabulating the number of cycles in a window of \code{2 * halfwidth + 1} surrounding the current nucleotide with quality scores that fall at or below \code{a}. The read is trimmed at the first nucleotide for which this number \code{>= k}. The quality of the first or last nucleotide is used to represent portions of the window that extend beyond the sequence. \code{trimTails} starts at the left-most nucleotide and accumulates cycles for which the quality score is at or below \code{a}. The read is trimmed at the first location where this number \code{>= k}. With \code{successive=TRUE}, failing qualities must occur in strict succession. \code{trimEnds} examines the \code{left}, \code{right}, or both ends of \code{object}, marking for removal letters that correspond to \code{a} and \code{relation}. The \code{trimEnds,ShortReadQ-method} trims based on quality. \code{ShortReadQ} methods operate on quality scores; use \code{sread()} and the \code{ranges} argument to trim based on nucleotide (see examples). \code{character} methods transform one or several fastq files to new fastq files, applying trim operations based on quality scores; use \code{filterFastq} with your own \code{filter} argument to filter on nucleotides. } \value{ An instance of \code{class(object)} trimmed to contain only those nucleotides satisfying the trim criterion or, if \code{ranges=TRUE} an \code{IRanges} instance defining the ranges that would trim \code{object}. } \author{Martin Morgan } \note{ The \code{trim*} functions use OpenMP threads (when available) during creation of the return value. This may sometimes create problems when a process is already running on multiple threads, e.g., with an error message like \preformatted{ libgomp: Thread creation failed: Resource temporarily unavailable } A solution is to precede problematic code with the following code snippet, to disable threading \preformatted{ nthreads <- .Call(ShortRead:::.set_omp_threads, 1L) on.exit(.Call(ShortRead:::.set_omp_threads, nthreads)) } } \examples{ showMethods(trimTails) sp <- SolexaPath(system.file('extdata', package='ShortRead')) rfq <- readFastq(analysisPath(sp), pattern="s_1_sequence.txt") ## remove leading / trailing quality scores <= 'I' trimEnds(rfq, "I") ## remove leading / trailing 'N's rng <- trimEnds(sread(rfq), "N", relation="==", ranges=TRUE) narrow(rfq, start(rng), end(rng)) ## remove leading / trailing 'G's or 'C's trimEnds(rfq, c("G", "C"), relation="==") } \keyword{manip} ShortRead/src/0000755000175400017540000000000013001247473014263 5ustar00biocbuildbiocbuildShortRead/src/Biostrings_stubs.c0000644000175400017540000000003713001247474017773 0ustar00biocbuildbiocbuild#include "_Biostrings_stubs.c" ShortRead/src/IRanges_stubs.c0000644000175400017540000000003413001247474017175 0ustar00biocbuildbiocbuild#include "_IRanges_stubs.c" ShortRead/src/Makevars.in0000644000175400017540000000013513001247474016364 0ustar00biocbuildbiocbuildPKG_CXXFLAGS=@DEFS@ PKG_CFLAGS=$(SHLIB_OPENMP_CFLAGS) PKG_LIBS=@LIBS@ $(SHLIB_OPENMP_CFLAGS) ShortRead/src/Makevars.win0000644000175400017540000000055313001247474016557 0ustar00biocbuildbiocbuildZLIB_CFLAGS+=$(shell echo 'zlibbioc::pkgconfig("PKG_CFLAGS")'|\ "${R_HOME}/bin/R" --vanilla --slave) PKG_LIBS+=$(shell echo 'zlibbioc::pkgconfig("PKG_LIBS_shared")' |\ "${R_HOME}/bin/R" --vanilla --slave) %.o: %.c $(CC) $(ZLIB_CFLAGS) $(ALL_CPPFLAGS) $(ALL_CFLAGS) -c $< -o $@ %.o: %.cc $(CXX) $(ZLIB_CFLAGS) $(ALL_CPPFLAGS) $(ALL_CXXFLAGS) -c $< -o $@ ShortRead/src/R_init_ShortRead.c0000644000175400017540000000465713001247474017643 0ustar00biocbuildbiocbuild#include "ShortRead.h" #include "trim.h" #ifdef SUPPORT_OPENMP #include #endif SEXP set_omp_threads(SEXP nthreads) { int n = 1; #ifdef SUPPORT_OPENMP n = omp_get_max_threads(); if (!IS_INTEGER(nthreads) || 1L != LENGTH(nthreads)) Rf_error("'nthreads' must be integer(1)"); omp_set_num_threads(INTEGER(nthreads)[0]); #endif return ScalarInteger(n); } static const R_CallMethodDef callMethods[] = { /* util.c */ {".set_omp_threads", (DL_FUNC) & set_omp_threads, 1}, {".count_lines", (DL_FUNC) & count_lines, 1}, /* trim.c */ {".trimTails", (DL_FUNC) & trim_tails, 4}, {".trimTailw", (DL_FUNC) & trim_tailw, 4}, {".trimEnds", (DL_FUNC) & trim_ends, 4}, /* io.c */ {".read_prb_as_character", (DL_FUNC) & read_prb_as_character, 2}, {".read_solexa_fastq", (DL_FUNC) & read_solexa_fastq, 2}, {".read_XStringSet_columns", (DL_FUNC) & read_XStringSet_columns, 8}, {".read_solexa_export", (DL_FUNC) & read_solexa_export, 4}, {".write_fastq", (DL_FUNC) & write_fastq, 8}, {".count_ipar_int_recs", (DL_FUNC) & count_ipar_int_recs, 1}, /* io_bowtie.c, io_soap.c */ {".read_bowtie", (DL_FUNC) & read_bowtie, 4}, {".read_soap", (DL_FUNC) & read_soap, 4}, /* alphabet */ {".alphabet_by_cycle", (DL_FUNC) & alphabet_by_cycle, 3}, {".alphabet_pair_by_cycle", (DL_FUNC) & alphabet_pair_by_cycle, 5}, {".alphabet_score", (DL_FUNC) & alphabet_score, 2}, {".alphabet_as_int", (DL_FUNC) & alphabet_as_int, 2}, {".alphabet_order", (DL_FUNC) & alphabet_order, 1}, {".alphabet_duplicated", (DL_FUNC) & alphabet_duplicated, 1}, {".alphabet_rank", (DL_FUNC) & alphabet_rank, 1}, {".aligned_read_rank", (DL_FUNC) & aligned_read_rank, 4}, {".read_maq_map", (DL_FUNC) & read_maq_map, 3}, /* readBfaToc */ {".readBfaToc", (DL_FUNC) & readBfaToc, 1}, /* sampler */ {".sampler_new", (DL_FUNC) & sampler_new, 1}, {".sampler_add", (DL_FUNC) & sampler_add, 2}, {".sampler_status", (DL_FUNC) & sampler_status, 1}, {".sampler_as_XStringSet", (DL_FUNC) & sampler_as_XStringSet, 2}, {".streamer_new", (DL_FUNC) & streamer_new, 1}, {".streamer_add", (DL_FUNC) & streamer_add, 3}, {".streamer_status", (DL_FUNC) & streamer_status, 1}, {".streamer_as_XStringSet", (DL_FUNC) & streamer_as_XStringSet, 1}, {NULL, NULL, 0} }; void R_init_ShortRead(DllInfo * info) { R_registerRoutines(info, NULL, callMethods, NULL, NULL); } ShortRead/src/S4Vectors_stubs.c0000644000175400017540000000003613001247474017503 0ustar00biocbuildbiocbuild#include "_S4Vectors_stubs.c" ShortRead/src/ShortRead.h0000644000175400017540000000702413001247474016333 0ustar00biocbuildbiocbuild#ifndef _SHORTREAD_H_ #define _SHORTREAD_H_ #ifdef __cplusplus extern "C" { #endif #include #include #include "S4Vectors_interface.h" #include "IRanges_interface.h" #include "XVector_interface.h" #include "Biostrings_interface.h" /* util.c */ typedef unsigned char (*DECODE_FUNC) (char); /* DNAdecode, RNAdecode */ typedef char (*ENCODE_FUNC) (char); /* DNAdecode, RNAdecode */ DECODE_FUNC decoder(const char *); ENCODE_FUNC encoder(const char *); void _reverse(char *); void _reverseComplement(char *); SEXP _get_namespace(const char *pkg); SEXP _get_strand_levels(); int _char_as_strand_int(const char c, const char *fname, const int lineno); typedef char *(MARK_FIELD_FUNC) (char *, const char *); MARK_FIELD_FUNC _mark_field_1; /* nchar(delim) == 1 */ MARK_FIELD_FUNC _mark_field_n; /* nchar(delim) != 1 */ int _mark_field_0(char *, char **, const int); extern const int LINEBUF_SIZE; gzFile _fopen(const char *, const char *); int _linebuf_skip_p(char *, gzFile, const char *, int, const char *); int _rtrim(char *linebuf); void _solexa_to_IUPAC(char *linebuf); void _as_factor_SEXP(SEXP vec, SEXP lvls); void _as_factor(SEXP vec, const char **levels, const int n_lvls); int _count_lines_sum(SEXP files); SEXP count_lines(SEXP files); SEXP count_ipar_int_recs(SEXP files); SEXP _get_SEXP(SEXP from, SEXP rho, const char *with); /* xstring_util.c */ typedef SEXP _XSnap; _XSnap _NEW_XSNAP(int nelt, const char *baseclass); void _APPEND_XSNAP(_XSnap snap, const char *str); void _XSNAP_ELT(SEXP x, int elt); /* io.c */ SEXP write_fastq(SEXP id, SEXP sread, SEXP quality, SEXP fname, SEXP fmode, SEXP full, SEXP compress, SEXP max_width); SEXP read_prb_as_character(SEXP file, SEXP asSolexa); SEXP read_solexa_fastq(SEXP files, SEXP withIds); SEXP read_XStringSet_columns(SEXP files, SEXP header, SEXP sep, SEXP colIndex, SEXP colClasses, SEXP nrows, SEXP skip, SEXP commentChar); SEXP read_solexa_export(SEXP files, SEXP sep, SEXP commentChar, SEXP withFlags); /* io_bowtie.c, io_soap.c */ SEXP read_bowtie(SEXP files, SEXP qualityType, SEXP sep, SEXP commentChar); SEXP read_soap(SEXP files, SEXP qualityType, SEXP sep, SEXP commentChar); /* alphabet.c */ SEXP alphabet_by_cycle(SEXP stringSet, SEXP width, SEXP alphabet); SEXP alphabet_pair_by_cycle(SEXP stringSet1, SEXP stringSet2, SEXP width, SEXP alphabet1, SEXP alphabet2); SEXP alphabet_score(SEXP stringSet, SEXP score); SEXP alphabet_as_int(SEXP stringSet, SEXP score); SEXP alphabet_order(SEXP stringSet); SEXP alphabet_duplicated(SEXP stringSet); SEXP alphabet_rank(SEXP stringSet); SEXP aligned_read_rank(SEXP stringSet, SEXP order, SEXP withSread, SEXP rho); /* read_maq_map.c */ SEXP read_maq_map(SEXP filename, SEXP maxreads, SEXP maq_longread); /* readBfaToc.c */ SEXP readBfaToc(SEXP bfa_filename); /* sampler */ SEXP sampler_new(SEXP n); SEXP sampler_add(SEXP s, SEXP bin); SEXP sampler_status(SEXP s); SEXP sampler_as_XStringSet(SEXP s, SEXP ordered); SEXP streamer_new(SEXP n); SEXP streamer_add(SEXP s, SEXP bin, SEXP skipadd); SEXP streamer_status(SEXP s); SEXP streamer_as_XStringSet(SEXP s); #ifdef __cplusplus } #endif #endif /* _SHORTREAD_H_ */ ShortRead/src/XVector_stubs.c0000644000175400017540000000003413001247474017237 0ustar00biocbuildbiocbuild#include "_XVector_stubs.c" ShortRead/src/alphabet.c0000644000175400017540000003044013001247474016211 0ustar00biocbuildbiocbuild#include "ShortRead.h" #include /* * visit all sequences in a set, tallying character frequency as a * function of nucleotide position in the read. */ SEXP alphabet_by_cycle(SEXP stringSet, SEXP width, SEXP alphabet) { const int MAX_MAP = 256; /* FIXME: check types of incoming arguments */ if (!IS_INTEGER(width) || LENGTH(width) != 1) Rf_error("'%s' must be '%s'", "width", "integer(1)"); if (!IS_CHARACTER(alphabet)) Rf_error("'%s' must be '%s'", "alphabet", "character()"); /* allocate and initialize the answer matrix */ const int nrow = LENGTH(alphabet), ncol = INTEGER(width)[0]; SEXP ans, dimnms, nms; PROTECT(ans = allocMatrix(INTSXP, nrow, ncol)); PROTECT(dimnms = NEW_LIST(2)); SET_VECTOR_ELT(dimnms, 0, alphabet); /* FIXME: Cycle dimnames? */ PROTECT(nms = NEW_STRING(2)); SET_STRING_ELT(nms, 0, mkChar("alphabet")); SET_STRING_ELT(nms, 1, mkChar("cycle")); SET_NAMES(dimnms, nms); SET_DIMNAMES(ans, dimnms); UNPROTECT(2); int *ansp = INTEGER(ans); /* convenient pointer to data */ memset(ansp, 0, LENGTH(ans) * sizeof(int)); /* initialize to 0 */ /* set up a decoder for the string */ const char *base = get_XStringSet_xsbaseclassname(stringSet); DECODE_FUNC decode = decoder(base); /* map between decoded character and offset into 'ans' */ int i, j; int *map = (int *) R_alloc(MAX_MAP, sizeof(int)); memset(map, -1, MAX_MAP * sizeof(int)); /* default; ignore */ for (i = 0; i < LENGTH(alphabet); ++i) { unsigned char c = (unsigned char) *CHAR(STRING_ELT(alphabet, i)); map[c] = i; } /* The main loop. Cache the string set for fast access, then * iterate over all strings, and over all characters in the * string. For each character, decode and map into the answer * matrix. * */ XStringSet_holder holder = hold_XStringSet(stringSet); const int len = get_XStringSet_length(stringSet); for (i = 0; i < len; ++i) { Chars_holder seq = get_elt_from_XStringSet_holder(&holder, i); for (j = 0; j < seq.length; ++j) { int idx = map[decode(seq.ptr[j])]; if (idx >= 0) ansp[j * nrow + idx] += 1; } } UNPROTECT(1); return ans; } SEXP alphabet_pair_by_cycle(SEXP stringSet1, SEXP stringSet2, SEXP width, SEXP alphabet1, SEXP alphabet2) { const int MAX_MAP = 256; /* FIXME: check types of incoming arguments */ if (get_XStringSet_length(stringSet1) != get_XStringSet_length(stringSet2)) Rf_error("'stringSet1' and 'stringSet2' must have the same length"); if (!IS_CHARACTER(alphabet1) || !IS_CHARACTER(alphabet2)) Rf_error("'alphabet' must be list of character vectors"); /* allocate and initialize the answer matrix */ const int dim1 = LENGTH(alphabet1), dim2 = LENGTH(alphabet2), dim3 = INTEGER(width)[0]; const int dim1xdim2 = dim1 * dim2; SEXP ans, dimnms, nms; PROTECT(ans = alloc3DArray(INTSXP, dim1, dim2, dim3)); PROTECT(dimnms = NEW_LIST(3)); SET_VECTOR_ELT(dimnms, 0, alphabet1); SET_VECTOR_ELT(dimnms, 1, alphabet2); /* FIXME: Cycle dimnames? */ PROTECT(nms = NEW_STRING(3)); SET_STRING_ELT(nms, 0, mkChar("base")); SET_STRING_ELT(nms, 1, mkChar("quality")); SET_STRING_ELT(nms, 2, mkChar("cycle")); SET_NAMES(dimnms, nms); SET_DIMNAMES(ans, dimnms); UNPROTECT(2); int *ansp = INTEGER(ans); /* convenient pointer to data */ memset(ansp, 0, LENGTH(ans) * sizeof(int)); /* initialize to 0 */ /* set up a decoder for string1 and string2 */ const char *base1 = get_XStringSet_xsbaseclassname(stringSet1); const char *base2 = get_XStringSet_xsbaseclassname(stringSet2); DECODE_FUNC decode1 = decoder(base1); DECODE_FUNC decode2 = decoder(base2); /* map between decoded character and offset into 'ans' */ int i, j; int *map1 = (int *) R_alloc(MAX_MAP, sizeof(int)), *map2 = (int *) R_alloc(MAX_MAP, sizeof(int)); memset(map1, -1, MAX_MAP * sizeof(int)); /* default; ignore */ memset(map2, -1, MAX_MAP * sizeof(int)); /* default; ignore */ for (i = 0; i < LENGTH(alphabet1); ++i) { unsigned char c = (unsigned char) *CHAR(STRING_ELT(alphabet1, i)); map1[c] = i; } for (i = 0; i < LENGTH(alphabet2); ++i) { unsigned char c = (unsigned char) *CHAR(STRING_ELT(alphabet2, i)); map2[c] = i; } /* The main loop. Cache the string set for fast access, then * iterate over all strings, and over all characters in the * string. For each character, decode and map into the answer * matrix. * */ XStringSet_holder holder1 = hold_XStringSet(stringSet1); XStringSet_holder holder2 = hold_XStringSet(stringSet2); const int len = get_XStringSet_length(stringSet1); for (i = 0; i < len; ++i) { Chars_holder seq1 = get_elt_from_XStringSet_holder(&holder1, i); Chars_holder seq2 = get_elt_from_XStringSet_holder(&holder2, i); for (j = 0; j < seq1.length; ++j) { int idx1 = map1[decode1(seq1.ptr[j])]; int idx2 = map2[decode2(seq2.ptr[j])]; if (idx1 >= 0 && idx2 >= 0) ansp[j * dim1xdim2 + idx2 * dim1 + idx1] += 1; } } UNPROTECT(1); return ans; } SEXP alphabet_score(SEXP stringSet, SEXP score) { /* FIXME: stringSet is XStringSet */ const char *base = get_XStringSet_xsbaseclassname(stringSet); if (strcmp(base, "BString") != 0) Rf_error("'stringSet' must contain BString elements"); if (!IS_NUMERIC(score) || LENGTH(score) != 256) Rf_error("'%s' must be '%s'", "score", "integer(256)"); DECODE_FUNC decode = decoder(base); const int len = get_XStringSet_length(stringSet); int i, j; const double *dscore = REAL(score); SEXP ans; PROTECT(ans = NEW_NUMERIC(len)); double *dans = REAL(ans); XStringSet_holder holder = hold_XStringSet(stringSet); for (i = 0; i < len; ++i) { Chars_holder seq = get_elt_from_XStringSet_holder(&holder, i); dans[i] = 0; for (j = 0; j < seq.length; ++j) dans[i] += dscore[decode(seq.ptr[j])]; } UNPROTECT(1); return ans; } SEXP alphabet_as_int(SEXP stringSet, SEXP score) { /* FIXME: stringSet is XStrinSet(1) or longer? */ const char *base = get_XStringSet_xsbaseclassname(stringSet); if (strcmp(base, "BString") != 0) Rf_error("'stringSet' must contain BString elements"); if (!IS_INTEGER(score) || LENGTH(score) != 256) Rf_error("'%s' must be '%s'", "score", "integer(256)"); DECODE_FUNC decode = decoder(base); const int len = get_XStringSet_length(stringSet); if (len == 0) return allocMatrix(INTSXP, 0, 0); XStringSet_holder holder = hold_XStringSet(stringSet); int i; Chars_holder seq = get_elt_from_XStringSet_holder(&holder, 0); int width = seq.length; int *ians = NULL; SEXP ans; for (i = 1; i < len && width > 0; ++i) { seq = get_elt_from_XStringSet_holder(&holder, i); if (seq.length > width) width = seq.length; } ans = PROTECT(allocMatrix(INTSXP, len, width)); ians = INTEGER(ans); for (i = 0; i < LENGTH(ans); ++i) ians[i] = NA_INTEGER; const int *iscore = INTEGER(score); int j; for (i = 0; i < len; ++i) { seq = get_elt_from_XStringSet_holder(&holder, i); for (j = 0; j < seq.length; ++j) ians[len * j + i] = iscore[decode(seq.ptr[j])]; } UNPROTECT(1); return ans; } /* rank / order / sort / duplicated */ typedef struct { int offset; Chars_holder ref; } XSort; typedef int XSEQ_SORT_FUN(const void *, const void *); XSEQ_SORT_FUN compare_Chars_holder; XSEQ_SORT_FUN stable_compare_Chars_holder; int compare_Chars_holder(const void *a, const void *b) { const Chars_holder ra = ((const XSort *) a)->ref; const Chars_holder rb = ((const XSort *) b)->ref; const int diff = ra.length - rb.length; size_t len = diff < 0 ? ra.length : rb.length; int res = memcmp(ra.ptr, rb.ptr, len); return res == 0 ? diff : res; } int stable_compare_Chars_holder(const void *a, const void *b) { const Chars_holder ra = ((const XSort *) a)->ref; const Chars_holder rb = ((const XSort *) b)->ref; const int diff = ra.length - rb.length; size_t len = diff < 0 ? ra.length : rb.length; int res = memcmp(ra.ptr, rb.ptr, len); if ((0 == res) && (0 == diff)) res = ((const XSort *) a)->offset - ((const XSort *) b)->offset; return res == 0 ? diff : res; } void _alphabet_order(XStringSet_holder holder, XSort * xptr, const int len) { int i; for (i = 0; i < len; ++i) { xptr[i].offset = i; xptr[i].ref = get_elt_from_XStringSet_holder(&holder, i); } qsort(xptr, len, sizeof(XSort), stable_compare_Chars_holder); } SEXP alphabet_order(SEXP stringSet) { /* FIXME: stringSet is XStringSet; non-zero len? */ const int len = get_XStringSet_length(stringSet); if (len == 0) return NEW_INTEGER(0); XStringSet_holder holder = hold_XStringSet(stringSet); XSort *xptr = (XSort *) R_alloc(len, sizeof(XSort)); _alphabet_order(holder, xptr, len); SEXP ans; PROTECT(ans = NEW_INTEGER(len)); int *ians = INTEGER(ans); int i; for (i = 0; i < len; ++i) ians[i] = xptr[i].offset + 1; UNPROTECT(1); return ans; } SEXP alphabet_duplicated(SEXP stringSet) { /* FIXME: stringSet is XStringSet; non-zero len? */ const int len = get_XStringSet_length(stringSet); if (len == 0) return NEW_LOGICAL(0); XStringSet_holder holder = hold_XStringSet(stringSet); XSort *xptr = (XSort *) R_alloc(len, sizeof(XSort)); _alphabet_order(holder, xptr, len); SEXP ans; PROTECT(ans = NEW_LOGICAL(len)); int *ians = INTEGER(ans); ians[xptr[0].offset] = 0; int i; for (i = 1; i < len; ++i) ians[xptr[i].offset] = compare_Chars_holder(xptr + i - 1, xptr + i) == 0; UNPROTECT(1); return ans; } SEXP alphabet_rank(SEXP stringSet) { /* integer vector of unique indices into sorted set */ const int len = get_XStringSet_length(stringSet); if (len == 0) return NEW_INTEGER(0); XStringSet_holder holder = hold_XStringSet(stringSet); XSort *xptr = (XSort *) R_alloc(len, sizeof(XSort)); _alphabet_order(holder, xptr, len); SEXP rank = PROTECT(NEW_INTEGER(len)); int *irank = INTEGER(rank), i; irank[xptr[0].offset] = 1; for (i = 1; i < len; ++i) { if (compare_Chars_holder(&xptr[i - 1], &xptr[i]) == 0) { irank[xptr[i].offset] = irank[xptr[i - 1].offset]; } else { irank[xptr[i].offset] = i + 1; } } UNPROTECT(1); return rank; } SEXP aligned_read_rank(SEXP alignedRead, SEXP order, SEXP withSread, SEXP rho) { if (LENGTH(order) == 0) return NEW_INTEGER(0); SEXP chr, str, pos; PROTECT(chr = _get_SEXP(alignedRead, rho, "chromosome")); PROTECT(str = _get_SEXP(alignedRead, rho, "strand")); PROTECT(pos = _get_SEXP(alignedRead, rho, "position")); int *c = INTEGER(chr), *s = INTEGER(str), *p = INTEGER(pos), *o = INTEGER(order), len = LENGTH(order); SEXP rank; PROTECT(rank = NEW_INTEGER(len)); int *r = INTEGER(rank), i; if (LOGICAL(withSread)[0]) { SEXP sread; PROTECT(sread = _get_SEXP(alignedRead, rho, "sread")); XStringSet_holder holder = hold_XStringSet(sread); XSort *xptr = (XSort *) R_alloc(2, sizeof(XSort)); xptr[0].ref = get_elt_from_XStringSet_holder(&holder, 0); r[o[0] - 1] = 1; for (i = 1; i < len; ++i) { const int this = o[i] - 1, prev = o[i - 1] - 1; xptr[i % 2].ref = get_elt_from_XStringSet_holder(&holder, this); if (c[this] != c[prev] || s[this] != s[prev] || p[this] != p[prev] || compare_Chars_holder(xptr, xptr + 1) != 0) r[this] = i + 1; else r[this] = r[prev]; } UNPROTECT(1); } else { r[o[0] - 1] = 1; for (i = 1; i < len; ++i) { const int this = o[i] - 1, prev = o[i - 1] - 1; if (c[this] != c[prev] || s[this] != s[prev] || p[this] != p[prev]) r[this] = i + 1; else r[this] = r[prev]; } } UNPROTECT(4); return rank; } ShortRead/src/call.h0000644000175400017540000000123113001247474015345 0ustar00biocbuildbiocbuild#ifndef _SHORTREAD_CALL_H_ #define _SHORTREAD_CALL_H_ #ifdef __cplusplus extern "C" { #endif #define NEW_CALL(S, T, NAME, ENV, N) \ PROTECT((S) = (T) = allocList((N))); \ SET_TYPEOF((T), LANGSXP); \ SETCAR((T), findFun(install((NAME)), (ENV))); \ (T) = CDR((T)) #define CSET_CDR(T, NAME, VALUE) \ SETCAR((T), (VALUE)); \ SET_TAG((T), install((NAME))); \ (T) = CDR((T)) #define CEVAL_TO(S, ENV, GETS) \ (GETS) = eval((S), (ENV)); \ UNPROTECT(1) #ifdef __cplusplus } #endif #endif /* _SHORTREAD_CALL_H_ */ ShortRead/src/const.h0000644000175400017540000000135013001247474015562 0ustar00biocbuildbiocbuild/* Note: This file has been copied from the source code of Maq, version 0.6.5, and is hence copyright (c) Li Hang, who has released Maq under GPL 2. The test for SIZEOF_UNSIGNED_LONG == 8 has been added, and depends on ../configure.ac */ #ifndef NST_CONST_H #define NST_CONST_H #define MAX_ULL 0xffffffffffffffffull #if SIZEOF_UNSIGNED_LONG == 8 typedef unsigned long bit64_t; #else typedef unsigned long long bit64_t; #endif typedef unsigned bit32_t; typedef unsigned short bit16_t; typedef unsigned char bit8_t; extern bit8_t nst_nt4_table[]; extern bit8_t nst_nt16_table[]; extern char *nst_nt4_rev_table; extern char *nst_nt16_rev_table; extern bit8_t nst_nt16_nt4_table[]; extern int nst_nt16_count_table[]; #endif ShortRead/src/io.c0000644000175400017540000006674513001247474015061 0ustar00biocbuildbiocbuild#include #include /* atoi */ #include #include "ShortRead.h" #include "call.h" static const int SOLEXA_QBASE = 64; static const int PHRED_QBASE = 33; static const int LINES_PER_FASTQ_REC = 4; /* * Solexa 'fastq' files consist of records, each 4 lines long. Here is * an example: @HWI-EAS88_1_1_1_1001_499 GGACTTTGTAGGATACCCTCGCTTTCCTTCTCCTGT +HWI-EAS88_1_1_1_1001_499 ]]]]]]]]]]]]Y]Y]]]]]]]]]]]]VCHVMPLAS * inst/extdata/s_1_sequences.txt contains 256 records */ void _write_err(int i) { Rf_error("failed to write record %d", i + 1); } char *_holder_to_char(XStringSet_holder * holder, const int i, char *buf, const int width, DECODE_FUNC decode) { Chars_holder chars_holder = get_elt_from_XStringSet_holder(holder, i); if (chars_holder.length > width) return NULL; if (decode != NULL) { int j; for (j = 0; j < chars_holder.length; ++j) buf[j] = decode(chars_holder.ptr[j]); } else strncpy(buf, chars_holder.ptr, chars_holder.length); buf[chars_holder.length] = '\0'; return buf; } SEXP write_fastq(SEXP id, SEXP sread, SEXP quality, SEXP fname, SEXP fmode, SEXP full, SEXP compress, SEXP max_width) { if (!(IS_S4_OBJECT(id) && strcmp(get_classname(id), "BStringSet") == 0)) Rf_error("'%s' must be '%s'", "id", "BStringSet"); if (!(IS_S4_OBJECT(sread) && strcmp(get_classname(sread), "DNAStringSet") == 0)) Rf_error("'%s' must be '%s'", "sread", "DNAStringSet"); /* check in R -- C-level R_check_super... is not adequate */ /* if (R_check_class_etc(quality, qualityClasses) < 0) */ /* Rf_error("'is(<%s>, \"%s\")' failed", "quality", qualityClasses[0]); */ const int len = get_XStringSet_length(id); if ((len != get_XStringSet_length(sread)) || (len != get_XStringSet_length(quality))) Rf_error("length() of %s must all be equal", "'id', 'sread', 'quality'"); if (!(IS_CHARACTER(fname) && LENGTH(fname) == 1)) /* FIXME: nzchar */ Rf_error("'%s' must be '%s'", "file", "character(1)"); if (!(IS_CHARACTER(fmode) && LENGTH(fmode) == 1)) /* FIXME nchar()<3 */ Rf_error("'%s' must be '%s'", "mode", "character(1)"); if (!(IS_LOGICAL(full) && LENGTH(full) == 1)) Rf_error("'%s' must be '%s'", "full", "logical(1)"); if (!(IS_LOGICAL(compress) && LENGTH(compress) == 1 && LOGICAL(compress)[0] != NA_LOGICAL)) Rf_error("'%s' must be '%s'", "compress", "logical(1) (TRUE or FALSE)"); const int compress1 = LOGICAL(compress)[0]; if (!(IS_INTEGER(max_width) && LENGTH(max_width) == 1 && INTEGER(max_width)[0] >= 0)) Rf_error("'%s' must be %s", "max_width", "'integer(1)', >=0"); const int width = INTEGER(max_width)[0]; DECODE_FUNC dnaDecoder = decoder(get_XStringSet_xsbaseclassname(sread)); XStringSet_holder xid = hold_XStringSet(id), xsread = hold_XStringSet(sread), xquality = hold_XStringSet(quality); char *idbuf0 = (char *) R_alloc(sizeof(char), width + 1), *idbuf1, *readbuf = (char *) R_alloc(sizeof(char), width + 1), *qualbuf = (char *) R_alloc(sizeof(char), width + 1), *gzbuf = NULL; int i, gzbuf_n = 0; idbuf1 = TRUE == LOGICAL(full)[0] ? idbuf0 : ""; FILE *fout = NULL; gzFile gzout = NULL; if (compress1 == FALSE) fout = fopen(CHAR(STRING_ELT(fname, 0)), CHAR(STRING_ELT(fmode, 0))); else { gzout = gzopen(CHAR(STRING_ELT(fname, 0)), CHAR(STRING_ELT(fmode, 0))); gzbuf_n = 4 * width + 8; /* liberal */ gzbuf = (char *) R_alloc(sizeof(char), gzbuf_n); } if ((gzout == NULL) && (fout == NULL)) Rf_error("failed to open file '%s'", CHAR(STRING_ELT(fname, 0))); const char *fmt = "@%s\n%s\n+%s\n%s\n"; int err = 0; for (i = 0; i < len; ++i) { idbuf0 = _holder_to_char(&xid, i, idbuf0, width, NULL); if (idbuf0 == NULL) { err = 1; break; } readbuf = _holder_to_char(&xsread, i, readbuf, width, dnaDecoder); if (readbuf == NULL) { err = 1; break; } qualbuf = _holder_to_char(&xquality, i, qualbuf, width, NULL); if (qualbuf == NULL) { err = 1; break; } if (compress1) { int n_out = snprintf(gzbuf, gzbuf_n, fmt, idbuf0, readbuf, idbuf1, qualbuf); if (n_out > gzbuf_n) { /* happens rarely, e.g., identifiers longer than sequence */ gzbuf_n = n_out + 1; gzbuf = (char *) R_alloc(sizeof(char), gzbuf_n); snprintf(gzbuf, gzbuf_n, fmt, idbuf0, readbuf, idbuf1, qualbuf); } if (gzputs(gzout, gzbuf) == -1) { err = 1; break; } } else if (fprintf(fout, fmt, idbuf0, readbuf, idbuf1, qualbuf) < 0) { err = 1; break; }; } if (compress1) gzclose(gzout); else fclose(fout); if (err != 0) _write_err(i); return R_NilValue; } /* * solexa/IPAR .*_int.txt.p.gz file */ void _count_ipar_int_recs(gzFile file, int *n_recs, int *n_cycles) { const char CYCLE_END = '#'; const int LINEBUF_SIZE = 200001; size_t bytes_read = 0; char *buf = Calloc(LINEBUF_SIZE + 1, char); *n_recs = *n_cycles = 0; char *p = 0; /* records and cycles */ while (*n_cycles == 0 && (bytes_read = gzread(file, buf, LINEBUF_SIZE)) > 0) { p = buf; while ((p = memchr(p, '\n', (buf + bytes_read) - p))) { ++p; if (*p == CYCLE_END) { ++p; *n_cycles += 1; break; } else *n_recs += 1; } } /* just cycles */ while ((p = memchr(p, CYCLE_END, (buf + bytes_read) - p))) { ++p; *n_cycles += 1; } while ((bytes_read = gzread(file, buf, LINEBUF_SIZE)) > 0) { p = buf; while ((p = memchr(p, CYCLE_END, (buf + bytes_read) - p))) { ++p; *n_cycles += 1; } } Free(buf); } SEXP count_ipar_int_recs(SEXP fnames) { int i, nfile; const char *filepath; gzFile file; SEXP ans = R_NilValue, nms = R_NilValue; if (!IS_CHARACTER(fnames)) error("'fnames' must be character()"); nfile = LENGTH(fnames); PROTECT(ans = NEW_LIST(2)); SET_VECTOR_ELT(ans, 0, NEW_INTEGER(nfile)); SET_VECTOR_ELT(ans, 1, NEW_INTEGER(nfile)); PROTECT(nms = NEW_CHARACTER(2)); SET_STRING_ELT(nms, 0, mkChar("reads")); SET_STRING_ELT(nms, 1, mkChar("cycles")); setAttrib(ans, R_NamesSymbol, nms); for (i = 0; i < nfile; ++i) { R_CheckUserInterrupt(); filepath = translateChar(STRING_ELT(fnames, i)); file = _fopen(filepath, "rb"); _count_ipar_int_recs(file, INTEGER(VECTOR_ELT(ans, 0)) + i, INTEGER(VECTOR_ELT(ans, 1)) + i); gzclose(file); } UNPROTECT(2); return ans; } /* * Read a solexa .*_prb.txt file into STRING_VEC */ SEXP read_prb_as_character(SEXP fname, SEXP asSolexa) { const int NUC_PER_CYCLE = 4; if (!IS_CHARACTER(fname) || LENGTH(fname) != 1) error("'fname' must be 'character(1)'"); if (!IS_LOGICAL(asSolexa) || LENGTH(asSolexa) != 1) error("'asSolexa' must be 'logical(1)'"); const int n_reads = INTEGER(count_lines(fname))[0]; const int qbase = LOGICAL(asSolexa)[0] ? SOLEXA_QBASE : PHRED_QBASE; SEXP ans = PROTECT(NEW_CHARACTER(n_reads)); gzFile file = _fopen(translateChar(STRING_ELT(fname, 0)), "rb"); char buf[LINEBUF_SIZE + 1]; int read = 0; if (gzgets(file, buf, LINEBUF_SIZE) == Z_NULL) { gzclose(file); error("could not read file '%f'", translateChar(STRING_ELT(fname, 0))); } int n_cycles = 0; char *quad = strtok(buf, "\t"); while (quad != NULL) { n_cycles++; quad = strtok(NULL, "\t"); } gzrewind(file); char *score = R_alloc(sizeof(char), n_cycles + 1); score[n_cycles] = '\0'; while (gzgets(file, buf, LINEBUF_SIZE) != Z_NULL) { if (read >= n_reads) { gzclose(file); error("too many reads, %d expected", n_reads); } quad = strtok(buf, "\t"); int cycle = 0; while (quad != NULL && cycle < n_cycles) { int v[4]; int bases = sscanf(quad, " %d %d %d %d", &v[0], &v[1], &v[2], &v[3]); if (bases != NUC_PER_CYCLE) { gzclose(file); error("%d bases observed, %d expected", bases, NUC_PER_CYCLE); } v[0] = v[0] > v[1] ? v[0] : v[1]; v[2] = v[2] > v[3] ? v[2] : v[3]; score[cycle++] = qbase + ((char) v[0] > v[2] ? v[0] : v[2]); quad = strtok(NULL, "\t"); } if (cycle != n_cycles) { gzclose(file); error("%d cycles observed, %d expected", cycle, n_cycles); } SET_STRING_ELT(ans, read++, mkChar(score)); } UNPROTECT(1); gzclose(file); return ans; } /* * Read a solexa 's__sequence.txt' file into CharAEAE objects. */ static void _read_solexa_fastq_file(const char *fname, SEXP ans) { gzFile file; char linebuf[LINEBUF_SIZE]; int lineno, reclineno, nchar_in_buf; _XSnap seq = VECTOR_ELT(ans, 0), id = VECTOR_ELT(ans, 1), qualities = VECTOR_ELT(ans, 2); file = _fopen(fname, "rb"); lineno = 0; while (gzgets(file, linebuf, LINEBUF_SIZE) != NULL) { if ((reclineno = lineno % LINES_PER_FASTQ_REC) == 2) { lineno++; continue; } nchar_in_buf = _rtrim(linebuf); if (nchar_in_buf >= LINEBUF_SIZE - 1) { // should never be gzclose(file); error("line too long %s:%d", fname, lineno); } else if ((0 == reclineno) && (0 == nchar_in_buf)) { gzclose(file); error("unexpected empty line %s:%d", fname, lineno); } switch (reclineno) { case 0: /* add linebuf to CharAEAE; start at char +1 to skip the * fastq annotation. */ if (id != R_NilValue) _APPEND_XSNAP(id, linebuf + 1); break; case 1: _solexa_to_IUPAC(linebuf); _APPEND_XSNAP(seq, linebuf); break; case 3: _APPEND_XSNAP(qualities, linebuf); break; default: error("unexpected 'reclineno'; consult maintainer"); break; } lineno++; } gzclose(file); if ((lineno % LINES_PER_FASTQ_REC) != 0) error("unexpected number of lines in file '%s'", fname); } SEXP read_solexa_fastq(SEXP files, SEXP withId) { int i, nfiles, nrec = 0; const char *fname; SEXP ans = R_NilValue, nms = R_NilValue; if (!IS_CHARACTER(files)) Rf_error("'%s' must be '%s'", "files", "character"); if (!IS_LOGICAL(withId) || LENGTH(withId) != 1) Rf_error("'%s' must be '%s'", "withId", "logical(1)"); nfiles = LENGTH(files); nrec = _count_lines_sum(files) / LINES_PER_FASTQ_REC; PROTECT(ans = NEW_LIST(3)); SET_VECTOR_ELT(ans, 0, _NEW_XSNAP(nrec, "DNAString")); /* sread */ if (LOGICAL(withId)[0] == TRUE) /* id */ SET_VECTOR_ELT(ans, 1, _NEW_XSNAP(nrec, "BString")); else SET_VECTOR_ELT(ans, 1, R_NilValue); SET_VECTOR_ELT(ans, 2, _NEW_XSNAP(nrec, "BString")); /* quality */ PROTECT(nms = NEW_CHARACTER(3)); SET_STRING_ELT(nms, 0, mkChar("sread")); SET_STRING_ELT(nms, 1, mkChar("id")); SET_STRING_ELT(nms, 2, mkChar("quality")); setAttrib(ans, R_NamesSymbol, nms); UNPROTECT(1); for (i = 0; i < nfiles; ++i) { R_CheckUserInterrupt(); fname = translateChar(STRING_ELT(files, i)); _read_solexa_fastq_file(fname, ans); } _XSNAP_ELT(ans, 0); if (VECTOR_ELT(ans, 1) != R_NilValue) _XSNAP_ELT(ans, 1); _XSNAP_ELT(ans, 2); UNPROTECT(1); return ans; } int _io_XStringSet_columns(const char *fname, int header, const char *sep, MARK_FIELD_FUNC * mark_field, const int *colidx, int ncol, int nrow, int skip, const char *commentChar, SEXP sets, const int *toIUPAC) { gzFile file; char *linebuf; int lineno = 0, recno = 0; file = _fopen(fname, "rb"); linebuf = S_alloc(LINEBUF_SIZE, sizeof(char)); /* auto free'd */ while (skip-- > 0) gzgets(file, linebuf, LINEBUF_SIZE); if (header == TRUE) gzgets(file, linebuf, LINEBUF_SIZE); while (recno < nrow && gzgets(file, linebuf, LINEBUF_SIZE) != NULL) { if (_linebuf_skip_p(linebuf, file, fname, lineno, commentChar)) { lineno++; continue; } int j = 0, cidx = 0; char *curr = linebuf, *next; for (j = 0; cidx < ncol && curr != NULL; ++j) { next = (*mark_field) (curr, sep); if (j == colidx[cidx]) { if (toIUPAC[cidx]) _solexa_to_IUPAC(curr); _APPEND_XSNAP(VECTOR_ELT(sets, cidx), curr); cidx++; } curr = next; } lineno++; recno++; } gzclose(file); return recno; } SEXP read_XStringSet_columns(SEXP files, SEXP header, SEXP sep, SEXP colIndex, SEXP colClasses, SEXP nrows, SEXP skip, SEXP commentChar) { if (!IS_CHARACTER(files)) Rf_error("'%s' must be '%s'", "files", "character(1)"); if (!IS_LOGICAL(header) || LENGTH(header) != 1) Rf_error("'%s' must be '%s'", "header", "logical(1)"); if (!IS_CHARACTER(sep) || LENGTH(sep) != 1) Rf_error("'%s' must be '%s'", "sep", "character(1)"); /* FIXME: !nzchar(sep[1]) */ if (!IS_INTEGER(colIndex) || LENGTH(colIndex) == 0) Rf_error("'colIndex' must be 'integer' with length > 0"); if (!IS_CHARACTER(colClasses) || LENGTH(colClasses) != LENGTH(colIndex)) Rf_error("'%s' must be '%s', length(colClasses) == length(colIndex)", "colClasses", "character()"); if (!IS_INTEGER(nrows) || LENGTH(nrows) != 1) Rf_error("'%s' must be '%s'", "nrows", "integer(1)"); if (!IS_INTEGER(skip) || LENGTH(skip) != 1) Rf_error("'%s' must be '%s'", "skiip", "integer(1)"); if (!IS_CHARACTER(commentChar) || LENGTH(commentChar) != 1) Rf_error("'%s' must be '%s'", "commentChar", "character(1)"); if (LENGTH(STRING_ELT(commentChar, 0)) != 1) Rf_error("'nchar(commentChar[[1]])' must be 1 but is %d", LENGTH(STRING_ELT(commentChar, 0))); int i, j; /* Count lines and pre-allocate space */ const char *csep = translateChar(STRING_ELT(sep, 0)); const int nfiles = LENGTH(files); MARK_FIELD_FUNC *sep_func; /* how to parse fields; minor efficiency */ if (csep[0] != '\0' && csep[1] == '\0') sep_func = _mark_field_1; else sep_func = _mark_field_n; int nrow = INTEGER(nrows)[0]; if (nrow < 0) { nrow = _count_lines_sum(files); nrow -= nfiles * (LOGICAL(header)[0] + INTEGER(skip)[0]); } int ncol = LENGTH(colIndex); SEXP ans = PROTECT(NEW_LIST(ncol)); int *colidx = (int *) R_alloc(sizeof(int), ncol); int *toIUPAC = (int *) R_alloc(sizeof(int), ncol); for (j = 0; j < ncol; ++j) { const char *baseclass = CHAR(STRING_ELT(colClasses, j)); SET_VECTOR_ELT(ans, j, _NEW_XSNAP(nrow, baseclass)); colidx[j] = INTEGER(colIndex)[j] - 1; toIUPAC[j] = !strcmp(baseclass, "DNAString"); } /* read columns */ int nreads = 0; for (i = 0; i < nfiles; ++i) { R_CheckUserInterrupt(); if (nreads >= nrow) break; const char *fname = translateChar(STRING_ELT(files, i)); nreads += _io_XStringSet_columns(fname, LOGICAL(header)[0], csep, sep_func, colidx, ncol, nrow - nreads, INTEGER(skip)[0], CHAR(STRING_ELT(commentChar, 0)), ans, toIUPAC); } /* formulate return value */ for (j = 0; j < ncol; ++j) _XSNAP_ELT(ans, j); UNPROTECT(1); return ans; } /* * _export parser */ enum { /* fields from the _export spec */ SLX_MACHINE = 0, SLX_RUN, SLX_LANE, SLX_TILE, SLX_X, SLX_Y, SLX_MULTIPLEX, SLX_PAIRID, SLX_SREAD, SLX_QUAL, SLX_CHR, SLX_CONTIG, SLX_POS, SLX_STRAND, SLX_ALIGNQUAL, SLX_FILT, /* ID, when calculated */ SLX_ID, /* length of ENUM */ SLX_ELEMENT_END }; SEXP _AlignedRead_Solexa_make(SEXP fields) { const char *FILTER_LEVELS[] = { "Y", "N" }; SEXP s, t, nmspc = PROTECT(_get_namespace("ShortRead")); const Rboolean withMultiplexIndex = R_NilValue != VECTOR_ELT(fields, SLX_MULTIPLEX), withPairedReadNumber = R_NilValue != VECTOR_ELT(fields, SLX_PAIRID), withIds = R_NilValue != VECTOR_ELT(fields, SLX_MACHINE); SEXP sfq; /* SFastqQuality */ NEW_CALL(s, t, "SFastqQuality", nmspc, 2); CSET_CDR(t, "quality", VECTOR_ELT(fields, SLX_QUAL)); CEVAL_TO(s, nmspc, sfq); PROTECT(sfq); SEXP alnq; /* NumericQuality() */ NEW_CALL(s, t, "NumericQuality", nmspc, 2); CSET_CDR(t, "quality", VECTOR_ELT(fields, SLX_ALIGNQUAL)); CEVAL_TO(s, nmspc, alnq); PROTECT(alnq); /* .SolexaExport_AlignedDataFrame(...) */ _as_factor(VECTOR_ELT(fields, SLX_FILT), FILTER_LEVELS, sizeof(FILTER_LEVELS) / sizeof(const char *)); SEXP run; NEW_CALL(s, t, "factor", nmspc, 2); CSET_CDR(t, "x", VECTOR_ELT(fields, SLX_RUN)); CEVAL_TO(s, nmspc, run); PROTECT(run); SEXP dataframe; NEW_CALL(s, t, "data.frame", nmspc, 8 + withMultiplexIndex + withPairedReadNumber); CSET_CDR(t, "run", run); CSET_CDR(t, "lane", VECTOR_ELT(fields, SLX_LANE)); CSET_CDR(t, "tile", VECTOR_ELT(fields, SLX_TILE)); CSET_CDR(t, "x", VECTOR_ELT(fields, SLX_X)); CSET_CDR(t, "y", VECTOR_ELT(fields, SLX_Y)); CSET_CDR(t, "filtering", VECTOR_ELT(fields, SLX_FILT)); CSET_CDR(t, "contig", VECTOR_ELT(fields, SLX_CONTIG)); if (withMultiplexIndex) { CSET_CDR(t, "multiplexIndex", VECTOR_ELT(fields, SLX_MULTIPLEX)); } if (withPairedReadNumber) { CSET_CDR(t, "pairedReadNumber", VECTOR_ELT(fields, SLX_PAIRID)); } CEVAL_TO(s, nmspc, dataframe); PROTECT(dataframe); SEXP adf; NEW_CALL(s, t, ".SolexaExport_AlignedDataFrame", nmspc, 2); CSET_CDR(t, "data", dataframe); CEVAL_TO(s, nmspc, adf); PROTECT(adf); SEXP aln; NEW_CALL(s, t, "AlignedRead", nmspc, 8 + withIds); CSET_CDR(t, "sread", VECTOR_ELT(fields, SLX_SREAD)); CSET_CDR(t, "quality", sfq); if (withIds) { CSET_CDR(t, "id", VECTOR_ELT(fields, SLX_ID)); } CSET_CDR(t, "chromosome", VECTOR_ELT(fields, SLX_CHR)); CSET_CDR(t, "position", VECTOR_ELT(fields, SLX_POS)); CSET_CDR(t, "strand", VECTOR_ELT(fields, SLX_STRAND)); CSET_CDR(t, "alignQuality", alnq); CSET_CDR(t, "alignData", adf); CEVAL_TO(s, nmspc, aln); UNPROTECT(6); return aln; } int _read_solexa_export_file(const char *fname, const char *commentChar, int offset, SEXP result) { const int N_FIELDS = 22; Rboolean withMultiplexIndex = R_NilValue != VECTOR_ELT(result, SLX_MULTIPLEX), withPairedReadNumber = R_NilValue != VECTOR_ELT(result, SLX_PAIRID), withId = R_NilValue != VECTOR_ELT(result, SLX_MACHINE); gzFile file; char linebuf[LINEBUF_SIZE], **elt = (char **) R_alloc(N_FIELDS, sizeof(char*)); int lineno = 0, irec = offset; SEXP machine = NULL, run = VECTOR_ELT(result, SLX_RUN); int *lane = INTEGER(VECTOR_ELT(result, SLX_LANE)), *tile = INTEGER(VECTOR_ELT(result, SLX_TILE)), *x = INTEGER(VECTOR_ELT(result, SLX_X)), *y = INTEGER(VECTOR_ELT(result, SLX_Y)); _XSnap sread = VECTOR_ELT(result, SLX_SREAD), quality = VECTOR_ELT(result, SLX_QUAL); SEXP chromosome = VECTOR_ELT(result, SLX_CHR); int *position = INTEGER(VECTOR_ELT(result, SLX_POS)), *strand = INTEGER(VECTOR_ELT(result, SLX_STRAND)), *alignQuality = INTEGER(VECTOR_ELT(result, SLX_ALIGNQUAL)), *filtering = INTEGER(VECTOR_ELT(result, SLX_FILT)); SEXP contig = VECTOR_ELT(result, SLX_CONTIG), multiplexIndex = NULL; int *pairedReadNumber = NULL; if (withMultiplexIndex) multiplexIndex = VECTOR_ELT(result, SLX_MULTIPLEX); if (withPairedReadNumber) pairedReadNumber = INTEGER(VECTOR_ELT(result, SLX_PAIRID)); if (withId) machine = VECTOR_ELT(result, SLX_MACHINE); file = _fopen(fname, "rb"); while (gzgets(file, linebuf, LINEBUF_SIZE) != NULL) { if (*linebuf == *commentChar) { lineno++; continue; } /* field-ify */ int n_fields = _mark_field_0(linebuf, elt, N_FIELDS); if (n_fields != N_FIELDS) { gzclose(file); error("incorrect number of fields (%d) %s:%d", n_fields, fname, lineno); } if (withId) SET_STRING_ELT(machine, irec, mkChar(elt[0])); SET_STRING_ELT(run, irec, mkChar(elt[1])); lane[irec] = atoi(elt[2]); tile[irec] = atoi(elt[3]); x[irec] = atoi(elt[4]); y[irec] = atoi(elt[5]); if (withMultiplexIndex) { SEXP idxString = *elt[6] == '\0' ? R_BlankString : mkChar(elt[6]); SET_STRING_ELT(multiplexIndex, irec, idxString); } if (withPairedReadNumber) pairedReadNumber[irec] = atoi(elt[7]); _APPEND_XSNAP(sread, elt[8]); _APPEND_XSNAP(quality, elt[9]); SET_STRING_ELT(chromosome, irec, mkChar(elt[10])); SET_STRING_ELT(contig, irec, mkChar(elt[11])); if (*elt[12] == '\0') position[irec] = NA_INTEGER; else position[irec] = atoi(elt[12]); if (*elt[13] == '\0') strand[irec] = NA_INTEGER; else { switch (*elt[13]) { case 'F': strand[irec] = 1; break; case 'R': strand[irec] = 2; break; default: gzclose(file); error("invalid 'strand' field '%s', %s:%d", *elt[13], fname, lineno); break; } } /* 14: descriptor */ alignQuality[irec] = atoi(elt[15]); /* 16: pairedScore, 17: partnerCzome, 18: partnerContig 19: partnerOffset, 20: partnerStrand */ switch (*elt[21]) { case 'Y': filtering[irec] = 1; break; case 'N': filtering[irec] = 2; break; default: gzclose(file); error("invalid 'filtering' field '%s', %s:%d", *elt[21], fname, lineno); break; } lineno++; irec++; } gzclose(file); return irec - offset; } int _solexa_export_make_id(SEXP result) { const int *lane = INTEGER(VECTOR_ELT(result, SLX_LANE)), *tile = INTEGER(VECTOR_ELT(result, SLX_TILE)), *x = INTEGER(VECTOR_ELT(result, SLX_X)), *y = INTEGER(VECTOR_ELT(result, SLX_Y)), *pairedReadNumber = NULL; const SEXP * run = STRING_PTR(VECTOR_ELT(result, SLX_RUN)), *multiplexIndex = NULL, *machine = STRING_PTR(VECTOR_ELT(result, SLX_MACHINE)); const Rboolean withMultiplexIndex = R_NilValue != VECTOR_ELT(result, SLX_MULTIPLEX), withPairedReadNumber = R_NilValue != VECTOR_ELT(result, SLX_PAIRID); if (withMultiplexIndex) multiplexIndex = STRING_PTR(VECTOR_ELT(result, SLX_MULTIPLEX)); if (withPairedReadNumber) pairedReadNumber = INTEGER(VECTOR_ELT(result, SLX_PAIRID)); const int nrec = LENGTH(VECTOR_ELT(result, SLX_RUN)); char buf[LINEBUF_SIZE]; SET_VECTOR_ELT(result, SLX_ID, _NEW_XSNAP(nrec, "BString")); _XSnap id = VECTOR_ELT(result, SLX_ID); /* FIXME: machine */ int n = 0; for (int i = 0; i < nrec; ++i) { n = snprintf(buf, LINEBUF_SIZE, "%s_%s:%d:%d:%d:%d", CHAR(machine[i]), CHAR(run[i]), lane[i], tile[i], x[i], y[i]); if (withMultiplexIndex) n += snprintf(buf + n, LINEBUF_SIZE - n, "#%s", CHAR(multiplexIndex[i])); if (withPairedReadNumber) n += snprintf(buf + n, LINEBUF_SIZE - n, "/%d", pairedReadNumber[i]); if (n > LINEBUF_SIZE) return -1; _APPEND_XSNAP(id, buf); } _XSNAP_ELT(result, SLX_ID); return 1; } SEXP read_solexa_export(SEXP files, SEXP sep, SEXP commentChar, SEXP withFlags) { const int N_ELTS = SLX_ELEMENT_END; if (!IS_CHARACTER(files)) Rf_error("'%s' must be '%s'", "files", "character()"); if (!IS_CHARACTER(sep) || LENGTH(sep) != 1 || *(CHAR(STRING_ELT(sep, 0))) != '\t') Rf_error("'%s' must be '%s'", "sep", "\t"); /* FIXME: !nzchar(sep[1]) */ if (!IS_CHARACTER(commentChar) || LENGTH(commentChar) != 1) Rf_error("'%s' must be '%s'", "commentChar", "character(1)"); if (LENGTH(STRING_ELT(commentChar, 0)) != 1) Rf_error("'nchar(commentChar[[1]])' must be 1 but is %d", LENGTH(STRING_ELT(commentChar, 0))); if (!IS_LOGICAL(withFlags) || LENGTH(withFlags) != 3) Rf_error("'%s' must be '%s'", "withFlags", "logical(3)"); Rboolean withId = LOGICAL(withFlags)[0], withMultiplexIndex = LOGICAL(withFlags)[1], withPairedReadNumber = LOGICAL(withFlags)[2]; int nrec = _count_lines_sum(files); SEXP result = PROTECT(NEW_LIST(N_ELTS));; if (withId) SET_VECTOR_ELT(result, SLX_MACHINE, NEW_STRING(nrec)); SET_VECTOR_ELT(result, SLX_RUN, NEW_STRING(nrec)); SET_VECTOR_ELT(result, SLX_LANE, NEW_INTEGER(nrec)); SET_VECTOR_ELT(result, SLX_TILE, NEW_INTEGER(nrec)); SET_VECTOR_ELT(result, SLX_X, NEW_INTEGER(nrec)); SET_VECTOR_ELT(result, SLX_Y, NEW_INTEGER(nrec)); if (withMultiplexIndex) SET_VECTOR_ELT(result, SLX_MULTIPLEX, NEW_STRING(nrec)); if (withPairedReadNumber) SET_VECTOR_ELT(result, SLX_PAIRID, NEW_INTEGER(nrec)); SET_VECTOR_ELT(result, SLX_SREAD, _NEW_XSNAP(nrec, "DNAString")); SET_VECTOR_ELT(result, SLX_QUAL, _NEW_XSNAP(nrec, "BString")); SET_VECTOR_ELT(result, SLX_CHR, NEW_STRING(nrec)); SET_VECTOR_ELT(result, SLX_POS, NEW_INTEGER(nrec)); SET_VECTOR_ELT(result, SLX_STRAND, NEW_INTEGER(nrec)); SET_VECTOR_ELT(result, SLX_ALIGNQUAL, NEW_INTEGER(nrec)); SET_VECTOR_ELT(result, SLX_FILT, NEW_INTEGER(nrec)); SET_VECTOR_ELT(result, SLX_CONTIG, NEW_STRING(nrec)); nrec = 0; for (int i = 0; i < LENGTH(files); ++i) { R_CheckUserInterrupt(); nrec += _read_solexa_export_file(CHAR(STRING_ELT(files, i)), CHAR(STRING_ELT(commentChar, 0)), nrec, result); } _XSNAP_ELT(result, SLX_SREAD); _XSNAP_ELT(result, SLX_QUAL); SEXP strand_lvls = PROTECT(_get_strand_levels()); _as_factor_SEXP(VECTOR_ELT(result, SLX_STRAND), strand_lvls); if (withId) { int ok = _solexa_export_make_id(result); if (ok <= 0) { UNPROTECT(2); Rf_error("internal error: could not make id"); } } SEXP aln = _AlignedRead_Solexa_make(result); UNPROTECT(2); return aln; } ShortRead/src/io_bowtie.c0000644000175400017540000001241313001247474016411 0ustar00biocbuildbiocbuild#include #include "ShortRead.h" #include "call.h" /* HWI-EAS88_1:1:1:83:277 - chr1 163068612 AGAAGAATCCTTAAGGCTTGCTAGGCAGCAGTCTA 77777:::::::::::::::::::::::::::::: 0 23 */ static const char *ELT_NMS[] = { "id", "strand", "chromosome", "position", "sread", "quality", "similar", "mismatch" }; static const int N_ELTS = sizeof(ELT_NMS) / sizeof(const char *); int _read_bowtie(const char *fname, const char *commentChar, SEXP ref, int offset) { const int N_FIELDS = 8; gzFile file; char linebuf[LINEBUF_SIZE], **elt = (char **) R_alloc(N_FIELDS, sizeof(char*)); int lineno = 0, irec = offset; file = _fopen(fname, "rb"); _XSnap id = VECTOR_ELT(ref, 0), sread = VECTOR_ELT(ref, 4), quality = VECTOR_ELT(ref, 5); SEXP chromosome = VECTOR_ELT(ref, 2), mismatch = VECTOR_ELT(ref, 7); int *strand = INTEGER(VECTOR_ELT(ref, 1)), *position = INTEGER(VECTOR_ELT(ref, 3)), *similar = INTEGER(VECTOR_ELT(ref, 6)); while (gzgets(file, linebuf, LINEBUF_SIZE) != NULL) { if (*linebuf == *commentChar) { lineno++; continue; } lineno++; int n_fields = _mark_field_0(linebuf, elt, N_FIELDS); if (n_fields != N_FIELDS) { gzclose(file); error("incorrect number of fields (%d) %s:%d", n_fields, fname, lineno); } _APPEND_XSNAP(id, elt[0]); strand[irec] = _char_as_strand_int(*elt[1], fname, lineno); SET_STRING_ELT(chromosome, irec, mkChar(elt[2])); position[irec] = atoi(elt[3]) + 1; /* leftmost-aligned, 0-based */ if (strand[irec] == 2) { _reverseComplement(elt[4]); _reverse(elt[5]); } _APPEND_XSNAP(sread, elt[4]); _APPEND_XSNAP(quality, elt[5]); similar[irec] = atoi(elt[6]); /* previous: 'reserved' */ SET_STRING_ELT(mismatch, irec, mkChar(elt[7])); irec++; } gzclose(file); return irec - offset; } SEXP _AlignedRead_Bowtie_make(SEXP ref, const char *qtype) { SEXP s, t, nmspc = PROTECT(_get_namespace("ShortRead")); SEXP sfq; /* SFastqQuality by default */ NEW_CALL(s, t, qtype, nmspc, 2); CSET_CDR(t, "quality", VECTOR_ELT(ref, 5)); CEVAL_TO(s, nmspc, sfq); PROTECT(sfq); SEXP adf; NEW_CALL(s, t, ".Bowtie_AlignedDataFrame", nmspc, 3); CSET_CDR(t, "similar", VECTOR_ELT(ref, 6)); CSET_CDR(t, "mismatch", VECTOR_ELT(ref, 7)); CEVAL_TO(s, nmspc, adf); PROTECT(adf); SEXP aln; NEW_CALL(s, t, "AlignedRead", nmspc, 8); CSET_CDR(t, "id", VECTOR_ELT(ref, 0)); CSET_CDR(t, "sread", VECTOR_ELT(ref, 4)); CSET_CDR(t, "quality", sfq); CSET_CDR(t, "chromosome", VECTOR_ELT(ref, 2)); CSET_CDR(t, "position", VECTOR_ELT(ref, 3)); CSET_CDR(t, "strand", VECTOR_ELT(ref, 1)); /* alignQuality */ CSET_CDR(t, "alignData", adf); CEVAL_TO(s, nmspc, aln); UNPROTECT(3); return aln; } SEXP read_bowtie(SEXP files, SEXP qualityType, SEXP sep, SEXP commentChar) { if (!IS_CHARACTER(files)) Rf_error("'%s' must be '%s'", "files", "character()"); if (!IS_CHARACTER(sep) || LENGTH(sep) != 1 || *CHAR(STRING_ELT(sep, 0)) != '\t') Rf_error("'%s' must be '%s'", "sep", "\t"); if (!IS_CHARACTER(commentChar) || LENGTH(commentChar) != 1) Rf_error("'%s' must be '%s'", "commentChar", "character(1)"); if (LENGTH(STRING_ELT(commentChar, 0)) != 1) Rf_error("'nchar(commentChar[[1]])' must be 1 but is %d", LENGTH(STRING_ELT(commentChar, 0))); if (!IS_CHARACTER(qualityType) || LENGTH(qualityType) != 1) Rf_error("'%s' must be '%s'", "qualityType", "character(1)"); const char *qtype = CHAR(STRING_ELT(qualityType, 0)); if (strcmp(qtype, "SFastqQuality") != 0 && strcmp(qtype, "FastqQuality") != 0) Rf_error("'%s' must be '%s'", "qualityType", "SFastqQuality' or 'FastqQuality"); int nrec = _count_lines_sum(files); SEXP ref = PROTECT(NEW_LIST(N_ELTS)); SET_VECTOR_ELT(ref, 0, _NEW_XSNAP(nrec, "BString")); /* id */ SET_VECTOR_ELT(ref, 1, NEW_INTEGER(nrec)); /* strand */ SET_VECTOR_ELT(ref, 2, NEW_STRING(nrec)); /* chromosome */ SET_VECTOR_ELT(ref, 3, NEW_INTEGER(nrec)); /* position */ SET_VECTOR_ELT(ref, 4, _NEW_XSNAP(nrec, "DNAString")); /* sread */ SET_VECTOR_ELT(ref, 5, _NEW_XSNAP(nrec, "BString")); /* quality */ SET_VECTOR_ELT(ref, 6, NEW_INTEGER(nrec)); /* similar */ SET_VECTOR_ELT(ref, 7, NEW_STRING(nrec)); /* mismatch encoding */ SEXP names = PROTECT(NEW_CHARACTER(N_ELTS)); for (int i = 0; i < N_ELTS; ++i) SET_STRING_ELT(names, i, mkChar(ELT_NMS[i])); SET_ATTR(ref, R_NamesSymbol, names); UNPROTECT(1); nrec = 0; for (int i = 0; i < LENGTH(files); ++i) { R_CheckUserInterrupt(); nrec += _read_bowtie(CHAR(STRING_ELT(files, i)), CHAR(STRING_ELT(commentChar, 0)), ref, nrec); } _XSNAP_ELT(ref, 0); _XSNAP_ELT(ref, 4); _XSNAP_ELT(ref, 5); SEXP strand_lvls = PROTECT(_get_strand_levels()); _as_factor_SEXP(VECTOR_ELT(ref, 1), strand_lvls); UNPROTECT(1); SEXP aln = _AlignedRead_Bowtie_make(ref, qtype); UNPROTECT(1); return aln; } ShortRead/src/io_soap.c0000644000175400017540000001434313001247474016066 0ustar00biocbuildbiocbuild#include #include "ShortRead.h" #include "call.h" /* SIMU_0001_00000081/1 TGTACAGTATGTGAAGAGATTTGTTCTGAACCAAA hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh 1 a 35 + refseq 2210 0 */ static const char *ELT_NMS[] = { "id", "sread", "quality", "nEquallyBestHits", "pairedEnd", "alignedLength", "strand", "chromosome", "position", "typeOfHit", "hitDetail" }; static const int N_ELTS = sizeof(ELT_NMS) / sizeof(const char *); int _read_soap(const char *fname, const char *csep, const char *commentChar, MARK_FIELD_FUNC * mark_func, SEXP ref, int offset) { const int N_FIELDS = N_ELTS; gzFile file; char linebuf[LINEBUF_SIZE], **elt = (char **) R_alloc(N_FIELDS, sizeof(char*)); int lineno = 0; file = _fopen(fname, "rb"); _XSnap id = VECTOR_ELT(ref, 0), sread = VECTOR_ELT(ref, 1), quality = VECTOR_ELT(ref, 2); SEXP pairedEnd = VECTOR_ELT(ref, 4), chromosome = VECTOR_ELT(ref, 7), hitDetail = VECTOR_ELT(ref, 10); int *nEquallyBestHits = INTEGER(VECTOR_ELT(ref, 3)), *alignedLength = INTEGER(VECTOR_ELT(ref, 5)), *strand = INTEGER(VECTOR_ELT(ref, 6)), *position = INTEGER(VECTOR_ELT(ref, 8)), *typeOfHit = INTEGER(VECTOR_ELT(ref, 9)); while (gzgets(file, linebuf, LINEBUF_SIZE) != NULL) { if (_linebuf_skip_p(linebuf, file, fname, lineno, commentChar)) { lineno++; continue; } /* field-ify */ elt[0] = linebuf; for (int i = 1; i < N_FIELDS; ++i) { elt[i] = (*mark_func) (elt[i - 1], csep); if (elt[i] == elt[i - 1]) error("too few fields, %s:%d", fname, lineno); } nEquallyBestHits[offset] = atoi(elt[3]); SET_STRING_ELT(pairedEnd, offset, mkChar(elt[4])); alignedLength[offset] = atoi(elt[5]); strand[offset] = _char_as_strand_int(*elt[6], fname, lineno); SET_STRING_ELT(chromosome, offset, mkChar(elt[7])); position[offset] = atoi(elt[8]); /* leftmost-aligned, 1-based */ typeOfHit[offset] = atoi(elt[9]); SET_STRING_ELT(hitDetail, offset, mkChar(elt[10])); /* 1-3: id, strand, quality */ _APPEND_XSNAP(id, elt[0]); if (strand[offset] == 2) { _reverseComplement(elt[1]); _reverse(elt[2]); } _APPEND_XSNAP(sread, elt[1]); _APPEND_XSNAP(quality, elt[2]); lineno++; offset++; } gzclose(file); return offset; } SEXP _AlignedRead_SOAP_make(SEXP ref, const char *qtype) { SEXP s, t, nmspc = PROTECT(_get_namespace("ShortRead")); SEXP sfq; NEW_CALL(s, t, qtype, nmspc, 2); CSET_CDR(t, "quality", VECTOR_ELT(ref, 2)); CEVAL_TO(s, nmspc, sfq); PROTECT(sfq); SEXP adf; NEW_CALL(s, t, ".SOAP_AlignedDataFrame", nmspc, 6); CSET_CDR(t, "nEquallyBestHits", VECTOR_ELT(ref, 3)); CSET_CDR(t, "pairedEnd", VECTOR_ELT(ref, 4)); CSET_CDR(t, "alignedLength", VECTOR_ELT(ref, 5)); CSET_CDR(t, "typeOfHit", VECTOR_ELT(ref, 9)); CSET_CDR(t, "hitDetail", VECTOR_ELT(ref, 10)); CEVAL_TO(s, nmspc, adf); PROTECT(adf); SEXP aln; NEW_CALL(s, t, "AlignedRead", nmspc, 8); CSET_CDR(t, "sread", VECTOR_ELT(ref, 1)); CSET_CDR(t, "id", VECTOR_ELT(ref, 0)); CSET_CDR(t, "quality", sfq); CSET_CDR(t, "chromosome", VECTOR_ELT(ref, 7)); CSET_CDR(t, "position", VECTOR_ELT(ref, 8)); CSET_CDR(t, "strand", VECTOR_ELT(ref, 6)); /* alignQuality */ CSET_CDR(t, "alignData", adf); CEVAL_TO(s, nmspc, aln); UNPROTECT(3); return aln; } SEXP read_soap(SEXP files, SEXP qualityType, SEXP sep, SEXP commentChar) { if (!IS_CHARACTER(files)) Rf_error("'%s' must be '%s'", "files", "character()"); if (!IS_CHARACTER(sep) || LENGTH(sep) != 1) Rf_error("'%s' must be '%s'", "sep", "character(1)"); /* FIXME: !nzchar(sep[1]) */ if (!IS_CHARACTER(commentChar) || LENGTH(commentChar) != 1) Rf_error("'%s' must be '%s'", "commentChar", "character(1)"); if (LENGTH(STRING_ELT(commentChar, 0)) != 1) Rf_error("'nchar(commentChar[[1]])' must be 1 but is %d", LENGTH(STRING_ELT(commentChar, 0))); if (!IS_CHARACTER(qualityType) || LENGTH(qualityType) != 1) Rf_error("'%s' must be '%s'", "qualityType", "character(1)"); const char *qtype = CHAR(STRING_ELT(qualityType, 0)); if (strcmp(qtype, "SFastqQuality") != 0 && strcmp(qtype, "FastqQuality") != 0) Rf_error("'%s' must be '%s'", "qualityType", "SFastqQuality' or 'FastqQuality"); int nrec = _count_lines_sum(files); SEXP ref = PROTECT(NEW_LIST(N_ELTS)); SET_VECTOR_ELT(ref, 0, _NEW_XSNAP(nrec, "BString")); SET_VECTOR_ELT(ref, 1, _NEW_XSNAP(nrec, "DNAString")); SET_VECTOR_ELT(ref, 2, _NEW_XSNAP(nrec, "BString")); SET_VECTOR_ELT(ref, 3, NEW_INTEGER(nrec)); /* nEquallyBestHits */ SET_VECTOR_ELT(ref, 4, NEW_STRING(nrec)); /* pairedEnd */ SET_VECTOR_ELT(ref, 5, NEW_INTEGER(nrec)); /* alignedLength */ SET_VECTOR_ELT(ref, 6, NEW_INTEGER(nrec)); /* strand */ SET_VECTOR_ELT(ref, 7, NEW_STRING(nrec)); /* chromosome */ SET_VECTOR_ELT(ref, 8, NEW_INTEGER(nrec)); /* position */ SET_VECTOR_ELT(ref, 9, NEW_INTEGER(nrec)); /* typeOfHit */ SET_VECTOR_ELT(ref, 10, NEW_STRING(nrec)); /* hitDetail */ SEXP names = PROTECT(NEW_CHARACTER(N_ELTS)); for (int i = 0; i < N_ELTS; ++i) SET_STRING_ELT(names, i, mkChar(ELT_NMS[i])); SET_ATTR(ref, R_NamesSymbol, names); UNPROTECT(1); const char *csep = translateChar(STRING_ELT(sep, 0)); MARK_FIELD_FUNC *sep_func; /* how to parse fields; minor efficiency */ if (csep[0] != '\0' && csep[1] == '\0') sep_func = _mark_field_1; else sep_func = _mark_field_n; nrec = 0; for (int i = 0; i < LENGTH(files); ++i) { R_CheckUserInterrupt(); nrec += _read_soap(CHAR(STRING_ELT(files, i)), csep, CHAR(STRING_ELT(commentChar, 0)), sep_func, ref, nrec); } _XSNAP_ELT(ref, 0); _XSNAP_ELT(ref, 1); _XSNAP_ELT(ref, 2); SEXP strand_lvls = PROTECT(_get_strand_levels()); _as_factor_SEXP(VECTOR_ELT(ref, 6), strand_lvls); SEXP aln = _AlignedRead_SOAP_make(ref, qtype); UNPROTECT(2); return aln; } ShortRead/src/maqmap_m.h0000644000175400017540000000703613001247474016233 0ustar00biocbuildbiocbuild/* Note: This file is based on the file maqmap.h of the source code of Maq, version 0.7.2, which is copyright (c) Li Hang, who has released Maq under GPL 2. The changes to the original allow to switch the value of MAX_READLEN, which is a preprocessor macro in heng's code, at run-time, because Maq used 64 until 0.6.x, then (unless the macro MAQ_LONGREADS is not set) the value 128. */ #ifndef MAQMAP_M_H_ #define MAQMAP_M_H_ #define MAX_READLEN_OLD 64 #define MAX_READLEN_NEW 128 #define MAX_NAMELEN 36 #define MAQMAP_FORMAT_OLD 0 #define MAQMAP_FORMAT_NEW -1 #define PAIRFLAG_FF 0x01 #define PAIRFLAG_FR 0x02 #define PAIRFLAG_RF 0x04 #define PAIRFLAG_RR 0x08 #define PAIRFLAG_PAIRED 0x10 #define PAIRFLAG_DIFFCHR 0x20 #define PAIRFLAG_NOMATCH 0x40 #define PAIRFLAG_SW 0x80 #include #include #include "const.h" /* name: read name size: the length of the read seq: read sequence (see also below) seq[MAX_READLEN-1]: single end mapping quality (equal to map_qual if !paired) map_qual: the final mapping quality alt_qual: the lower quality of the two ends (equals to map_qual if not paired) flag: status of the pair dist: offset of the mate (zero if not paired) info1: mismatches in the 24bp (higher 4 bits) and mismatches (lower 4 bits) info2: sum of errors of the best hit c[2]: count of all 0- and 1-mismatch hits on the reference */ template < int max_readlen > struct maqmap1_T { bit8_t seq[max_readlen]; /* last base is single-end mapping quality. */ bit8_t size, map_qual, info1, info2, c[2], flag, alt_qual; bit32_t seqid, pos; int dist; char name[MAX_NAMELEN]; }; template < int max_readlen > struct maqmap_T { int format, n_ref; char **ref_name; bit64_t n_mapped_reads; maqmap1_T < max_readlen > *mapped_reads; }; template < int max_readlen > inline int maqmap_read1(gzFile fp, maqmap1_T < max_readlen > *m1) { return gzread(fp, m1, sizeof(maqmap1_T < max_readlen >)); } template < int max_readlen > maqmap_T < max_readlen > *maq_new_maqmap() { maqmap_T < max_readlen > *mm = (maqmap_T < max_readlen > *)std::calloc(1, sizeof(maqmap_T < max_readlen >)); mm->format = MAQMAP_FORMAT_NEW; return mm; } template < int max_readlen > void maq_delete_maqmap(maqmap_T < max_readlen > *mm) { int i; if (mm == 0) return; for (i = 0; i < mm->n_ref; ++i) std::free(mm->ref_name[i]); std::free(mm->ref_name); std::free(mm->mapped_reads); std::free(mm); } template < int max_readlen > maqmap_T < max_readlen > *maqmap_read_header(gzFile fp) { maqmap_T < max_readlen > *mm; int k, len; mm = maq_new_maqmap < max_readlen > (); gzread(fp, &mm->format, sizeof(int)); if (mm->format != MAQMAP_FORMAT_NEW) { if (mm->format > 0) { maq_delete_maqmap(mm); error ("obsolete map format; use MAQ 'mapass2maq' command to convert"); } if (mm->format != MAQMAP_FORMAT_NEW) { maq_delete_maqmap(mm); error("MAQ format '%d' not supported", mm->format); } } gzread(fp, &mm->n_ref, sizeof(int)); mm->ref_name = (char **) std::calloc(mm->n_ref, sizeof(char *)); for (k = 0; k != mm->n_ref; ++k) { gzread(fp, &len, sizeof(int)); mm->ref_name[k] = (char *) std::malloc(len * sizeof(char)); gzread(fp, mm->ref_name[k], len); } /* read number of mapped reads */ gzread(fp, &mm->n_mapped_reads, sizeof(bit64_t)); return mm; } #endif ShortRead/src/readBfaToc.cc0000644000175400017540000000361713001247474016574 0ustar00biocbuildbiocbuild#include #include #include #include #include #include #include "maqmap_m.h" struct seq_meta_info { seq_meta_info( int len_, char * name_ ) : len(len_), name(name_) {}; int len; std::string name; }; extern "C" SEXP readBfaToc( SEXP bfa_filename ) { FILE * fp; int name_len, seq_ori_len, seq_len, status; char seq_name[201]; std::deque< seq_meta_info > seqs; if( (! isString(bfa_filename) ) || ( length(bfa_filename) != 1 ) ) error( "First argument invalid: should be the filename." ); fp = fopen( CHAR(STRING_ELT(bfa_filename,0)), "r" ); if( !fp ) { char buf[300]; snprintf( buf, 300, "Failed to open file '%s': %s (errno=%d)", CHAR(STRING_ELT(bfa_filename,0)), strerror(errno), errno ); error( buf ); } while( fread( &name_len, sizeof(int), 1, fp) ) { if( name_len > 200 ) Rf_error( "sequence name >200 characters; invalid BFA file?" ); status = fread( seq_name, sizeof(char), name_len, fp ); status = fread( &seq_ori_len, sizeof(int), 1, fp ); status = fread( &seq_len, sizeof(int), 1, fp ); if( ( seq_ori_len >> 5 != seq_len ) && ( seq_ori_len >> 5 != seq_len - 1) ) Rf_error( "Fields bfa.len and bfa_ori_len do not agree. This is not a " "valid BFA file." ); fseek( fp, 2 * sizeof(bit64_t) * seq_len, SEEK_CUR); seqs.push_back( seq_meta_info( seq_ori_len, seq_name ) ); } fclose( fp ); SEXP res, names; PROTECT( res = allocVector( INTSXP, seqs.size() ) ); PROTECT( names = allocVector( STRSXP, seqs.size() ) ); int i = 0; for( std::deque< seq_meta_info >::iterator a = seqs.begin(); a != seqs.end(); a++, i++ ) { INTEGER(res)[i] = a->len; SET_STRING_ELT( names, i, mkChar( a->name.c_str() ) ); } namesgets( res, names); UNPROTECT(2); return res; } ShortRead/src/read_maq_map.cc0000644000175400017540000001547613001247474017216 0ustar00biocbuildbiocbuild/* Code to read in a .map file produced by the alignment program Maq. Authr: Simon Anders, EBI, sanders@fs.tum.de */ #include #include #include #include #include #include #include "ShortRead.h" #include "maqmap_m.h" #if INT_MAX < 0x7fffffffL #error This package needs an int type with at least 32 bit. #endif template< int max_readlen > SEXP read_maq_map_B( SEXP filename, SEXP maxreads ) /* Reads in the Maq map file with the given filename. If maxreads == -1, the whole file is read, otherwise at most the specified number of reads. The function returns a list (i.e., a VECSXP) with the elements listed below in eltnames, which correspond to the columns of maq mapview. */ { gzFile mapfile; maqmap_T * mapheader; SEXP seqnames, seq, start, dir, aq, mm, mm24, errsum, nhits0, nhits1, eltnm, df, klass; char readseqbuf[ max_readlen ], fastqbuf[ max_readlen ]; CharAEAE *readid, *readseq, *fastq; int i, actnreads, j; maqmap1_T read; char enc[] = { DNAencode('A'), DNAencode('C'), DNAencode('G'), DNAencode('T'), DNAencode('N') }; static const char *eltnames[] = { "chromosome", "position", "strand", "alignQuality", "nMismatchBestHit", "nMismatchBestHit24", "mismatchQuality", "nExactMatch24", "nOneMismatch24", "readId", "readSequence", "fastqScores" }; /* Check arguments */ if( !isString(filename) || length(filename) != 1 ) error( "First argument invalid: should be the filename." ); if( !isInteger(maxreads) || length(maxreads) != 1 ) error( "Second argument invalid: should be the maximum number" "of reads, provided as integer(1)." ); /* Check that file can be opened and is a Maq map file */ mapfile = gzopen( CHAR(STRING_ELT(filename,0)), "rb" ); if( !mapfile ) { if( errno ) { error( "Failed to open file '%s': %s (errno=%d)", CHAR(STRING_ELT(filename,0)), strerror(errno), errno ); } else { error( "Failed to open file '%s':" " zlib out of memory", CHAR(STRING_ELT(filename,0))); } } gzread( mapfile, &i, sizeof(int) ); if( i != MAQMAP_FORMAT_NEW ) { gzclose( mapfile ); error( "File '%s' is not a MAQ map file", CHAR(STRING_ELT(filename,0))); } i = gzrewind( mapfile ); if (i) error("internal error: gzrewind: '%d'", i); /* Read in header and map maqfile sequence indices to veclist indices */ mapheader = maqmap_read_header( mapfile ); PROTECT( seqnames = allocVector( STRSXP, mapheader->n_ref ) ); for( i = 0; i < mapheader->n_ref; i++ ) { SET_STRING_ELT( seqnames, i, mkChar( mapheader->ref_name[i] ) ); } if( INTEGER(maxreads)[0] < 0 || INTEGER(maxreads)[0] >= (int) mapheader->n_mapped_reads ) actnreads = mapheader->n_mapped_reads; else actnreads = INTEGER(maxreads)[0]; maq_delete_maqmap(mapheader); /* Allocate memory */ PROTECT( seq = allocVector( INTSXP, actnreads ) ); PROTECT( start = allocVector( INTSXP, actnreads ) ); PROTECT( dir = allocVector( INTSXP, actnreads ) ); PROTECT( aq = allocVector( INTSXP, actnreads ) ); PROTECT( mm = allocVector( INTSXP, actnreads ) ); PROTECT( mm24 = allocVector( INTSXP, actnreads ) ); PROTECT( errsum = allocVector( INTSXP, actnreads ) ); PROTECT( nhits0 = allocVector( INTSXP, actnreads ) ); PROTECT( nhits1 = allocVector( INTSXP, actnreads ) ); readid = new_CharAEAE( actnreads, 0 ); readseq = new_CharAEAE( actnreads, 0 ); fastq = new_CharAEAE( actnreads, 0 ); for( i = 0; i < actnreads; i++ ) { /* Various checks */ if( gzeof(mapfile) ) { error( "Unexpected end of file." ); gzclose(mapfile); } maqmap_read1( mapfile, &read ); if( read.flag || read.dist ) { error( "Paired read found. This function cannot deal with paired reads (yet)." ); gzclose(mapfile); } /* Build the read sequence and the FASTQ quality string */ if( read.size > max_readlen ) error( "Read with illegal size encountered." ); for (j = 0; j < read.size; j++) { if (read.seq[j] == 0) readseqbuf[j] = enc[ 4 ]; else readseqbuf[j] = enc[ read.seq[j] >> 6 & 0x03 ]; fastqbuf[j] = ( read.seq[j] & 0x3f ) + 33; } readseqbuf[ read.size ] = 0; fastqbuf [ read.size ] = 0; /* Copy the data */ INTEGER(start)[i] = ( read.pos >> 1 ) + 1; INTEGER(dir )[i] = ( read.pos & 0x01 ) + 1; /* '+': 1, '-': 2 */ INTEGER(seq )[i] = read.seqid + 1; INTEGER(aq )[i] = read.map_qual; INTEGER(mm )[i] = read.info1 & 0x0f; INTEGER(mm24 )[i] = read.info1 >> 4; INTEGER(errsum)[i] = read.info2; INTEGER(nhits0)[i] = read.c[0]; INTEGER(nhits1)[i] = read.c[1]; append_string_to_CharAEAE( readid, read.name ); append_string_to_CharAEAE( readseq, readseqbuf ); append_string_to_CharAEAE( fastq, fastqbuf ); } /* Build the data frame */ PROTECT( df = allocVector( VECSXP, 12 ) ); SET_VECTOR_ELT( df, 0, seq ); SET_VECTOR_ELT( df, 1, start ); SET_VECTOR_ELT( df, 2, dir ); SET_VECTOR_ELT( df, 3, aq ); SET_VECTOR_ELT( df, 4, mm ); SET_VECTOR_ELT( df, 5, mm24 ); SET_VECTOR_ELT( df, 6, errsum ); SET_VECTOR_ELT( df, 7, nhits0 ); SET_VECTOR_ELT( df, 8, nhits1 ); SET_VECTOR_ELT( df, 9, new_XRawList_from_CharAEAE( "BStringSet", "BString", readid, R_NilValue ) ); SET_VECTOR_ELT( df, 10, new_XRawList_from_CharAEAE( "DNAStringSet", "DNAString", readseq, R_NilValue ) ); SET_VECTOR_ELT( df, 11, new_XRawList_from_CharAEAE( "BStringSet", "BString", fastq, R_NilValue ) ); setAttrib( seq, install( "levels" ), seqnames ); PROTECT( klass = allocVector( STRSXP, 1 ) ); SET_STRING_ELT( klass, 0, mkChar( "factor" ) ); setAttrib( seq, install( "class" ), klass ); UNPROTECT( 1 ); SEXP strand_levels = PROTECT(_get_strand_levels()); _as_factor_SEXP(dir, strand_levels); UNPROTECT( 1 ); PROTECT( eltnm = allocVector( STRSXP, 12 ) ); for( i = 0; i < 12; i++ ) SET_STRING_ELT( eltnm, i, mkChar( eltnames[i] ) ); namesgets( df, eltnm ); UNPROTECT( 12 ); return df; } extern "C" SEXP read_maq_map( SEXP filename, SEXP maxreads, SEXP maq_longreads ) { if( LOGICAL(maq_longreads)[0] ) return read_maq_map_B< MAX_READLEN_NEW >( filename, maxreads ); else return read_maq_map_B< MAX_READLEN_OLD >( filename, maxreads ); } ShortRead/src/sampler.c0000644000175400017540000004020413001247474016073 0ustar00biocbuildbiocbuild#include #include #include #include "ShortRead.h" struct bufnode { int len; Rbyte *bytes; struct bufnode *next; }; struct record { int order, length; const Rbyte *record; }; struct records { int n, n_curr, n_tot, n_added; struct record *records; }; SEXP _records_status(struct records *records, struct bufnode *bufnode) { SEXP result = PROTECT(NEW_INTEGER(5)); INTEGER(result)[0] = records->n; INTEGER(result)[1] = records->n_curr; INTEGER(result)[2] = records->n_added; INTEGER(result)[3] = records->n_tot; INTEGER(result)[4] = (NULL != bufnode) ? bufnode->len : 0; SEXP nms = PROTECT(NEW_CHARACTER(5)); SET_STRING_ELT(nms, 0, mkChar("n")); SET_STRING_ELT(nms, 1, mkChar("current")); SET_STRING_ELT(nms, 2, mkChar("added")); SET_STRING_ELT(nms, 3, mkChar("total")); SET_STRING_ELT(nms, 4, mkChar("buffer")); SET_NAMES(result, nms); UNPROTECT(2); return result; } static int _records_compare_order(const void *a, const void *b) { return ((struct record * const) a)->order - ((struct record * const) b)->order; } /* fastq */ const Rbyte *_fastq_record_end(const Rbyte * buf, const Rbyte * bufend) { int id = 1, nchr = 0; if (*buf++ != '@') Rf_error("record does not start with '@'"); while (buf != bufend && *buf++ != '\n') ; /* id 1 */ while (buf != bufend && *buf != '+') /* read */ if (*buf++ != '\n') ++nchr; if (buf != bufend && *buf == '+') id -= 1; while (buf != bufend && *buf++ != '\n') ; /* id 2 */ while (buf != bufend && nchr) /* qual */ if (*buf++ != '\n') --nchr; if (0 != id || 0 != nchr) buf = NULL; if (buf && buf != bufend && *buf++ != '\n') Rf_error("internal: buf != "); return buf; } SEXP _fastq_as_XStringSet(struct records *fastq) { static int init = 0; SEXP widths = PROTECT(NEW_LIST(2)); SET_VECTOR_ELT(widths, 0, NEW_INTEGER(fastq->n_curr)); SET_VECTOR_ELT(widths, 1, NEW_INTEGER(fastq->n_curr)); int *sread_w = INTEGER(VECTOR_ELT(widths, 0)), *id_w = INTEGER(VECTOR_ELT(widths, 1)); /* geometry */ #ifdef SUPPORT_OPENMP #pragma omp parallel for #endif for (int i = 0; i < fastq->n_curr; ++i) { const Rbyte *buf = fastq->records[i].record; const Rbyte *start; start = ++buf; /* id; skip '@' */ while (!((*buf == '\n') || (*buf == '\r'))) ++buf; id_w[i] = buf - start; while ((*buf == '\n') || (*buf == '\r')) ++buf; sread_w[i] = 0; /* read */ while (*buf != '+') { while (!((*buf == '\n') || (*buf == '\r'))) { /* strip '\n' */ sread_w[i] += 1; ++buf; } ++buf; } } /* results */ SEXP ans = PROTECT(NEW_LIST(3)); SET_VECTOR_ELT(ans, 0, alloc_XRawList("DNAStringSet", "DNAString", VECTOR_ELT(widths, 0))); SET_VECTOR_ELT(ans, 1, alloc_XRawList("BStringSet", "BString", VECTOR_ELT(widths, 0))); SET_VECTOR_ELT(ans, 2, alloc_XRawList("BStringSet", "BString", VECTOR_ELT(widths, 1))); XVectorList_holder sread = hold_XVectorList(VECTOR_ELT(ans, 0)), qual = hold_XVectorList(VECTOR_ELT(ans, 1)), id = hold_XVectorList(VECTOR_ELT(ans, 2)); if ((!init) && fastq->n_curr) { /* hack -- install symbols to avoid stack imbalance */ (void) get_elt_from_XRawList_holder(&id, 0); (void) DNAencode('A'); init = 1; } #ifdef SUPPORT_OPENMP #pragma omp parallel for #endif for (int i = 0; i < fastq->n_curr; ++i) { Chars_holder x; const Rbyte *buf = fastq->records[i].record, *bufend = buf + fastq->records[i].length, *start; char *curr; /* id */ start = ++buf; /* skip '@' */ while (!((*buf == '\n') || (*buf == '\r'))) ++buf; x = get_elt_from_XRawList_holder(&id, i); memcpy((char *) x.ptr, start, (buf - start) * sizeof(Rbyte)); /* read */ while ((*buf == '\n') || (*buf == '\r')) ++buf; curr = (char *) get_elt_from_XRawList_holder(&sread, i).ptr; while (*buf != '+') { while (!((*buf == '\n') || (*buf == '\r'))) /* strip '\n' */ *curr++ = DNAencode(*buf++); buf++; } /* second id tag */ while (!((*buf == '\n') || (*buf == '\r'))) ++buf; /* quality */ while ((*buf == '\n') || (*buf == '\r')) ++buf; /* leading '\n' */ start = buf; x = get_elt_from_XRawList_holder(&qual, i); curr = (char *) x.ptr; while (buf != bufend && curr - x.ptr != x.length) { if ((*buf != '\n') && (*buf != '\r')) *curr++ = *buf++; else buf++; } } SEXP nms = PROTECT(NEW_CHARACTER(3)); SET_STRING_ELT(nms, 0, mkChar("sread")); SET_STRING_ELT(nms, 1, mkChar("quality")); SET_STRING_ELT(nms, 2, mkChar("id")); SET_NAMES(ans, nms); UNPROTECT(3); return ans; } /* Sampler */ struct sampler { struct records *sample; struct { struct record *records; int n, n_curr; } current; struct bufnode *bufnode; /* tail end of binary stream */ }; struct sampler * _sampler_new(int n) { struct sampler *sampler = Calloc(1, struct sampler); sampler->sample = Calloc(1, struct records); sampler->sample->records = Calloc(n, struct record); sampler->sample->n = n; sampler->current.records = Calloc(n, struct record); sampler->current.n = n; sampler->bufnode = Calloc(1, struct bufnode); return sampler; } void _sampler_reset(struct sampler *sampler) { struct records *sample = sampler->sample; for (int i = 0; i < sample->n_curr; ++i) Free(sample->records[i].record); if (NULL != sampler->bufnode->bytes) Free(sampler->bufnode->bytes); sample->n_curr = sample->n_added = sample->n_tot = 0; sampler->current.n_curr = 0; } void _sampler_free(struct sampler *sampler) { struct records *sample = sampler->sample; for (int i = 0; i < sample->n_curr; ++i) Free(sample->records[i].record); if (NULL != sampler->bufnode->bytes) Free(sampler->bufnode->bytes); Free(sampler->sample->records); Free(sampler->sample); Free(sampler->current.records); Free(sampler->bufnode); Free(sampler); } void _sampler_add1(struct records *sample, const Rbyte *record, int len, int order, int idx) { /* add record to sample */ if (sample->n_curr == sample->n) Free(sample->records[idx].record); sample->records[idx].length = len; sample->records[idx].order = order; Rbyte *intern_record = Calloc(len, Rbyte); memcpy(intern_record, record, len * sizeof(Rbyte)); sample->records[idx].record = intern_record; sample->n_added += 1; sample->n_tot += 1; } int * _sampler_wout_replacement(int n, int k) { /* sample k of n without replacement */ int *idx = Calloc(n, int); for (int i = 0; i < n; ++i) idx[i] = i; for (int i = 0; i < k; ++i) { int j = (n - i) * unif_rand(); int tmp = idx[i]; idx[i] = idx[i + j]; idx[i + j] = tmp; } return idx; } void _sampler_dosample(struct sampler *sampler) { int n_curr = sampler->current.n_curr; int n_tot = n_curr + sampler->sample->n_tot; double n_choose = n_tot < sampler->sample->n ? n_tot : sampler->sample->n; int n_samp = rbinom(n_curr, n_choose / n_tot); if (0 != n_samp) { int sn_curr = sampler->sample->n_curr; int *keep = _sampler_wout_replacement(n_curr, n_samp); int *drop = _sampler_wout_replacement(sn_curr, n_samp); /* save selected reads */ for (int i = 0; i < n_samp; ++i) { struct record *r = sampler->current.records + keep[i]; _sampler_add1(sampler->sample, r->record, r->length, r->order, drop[i]); } Free(keep); Free(drop); } sampler->sample->n_tot = n_tot; sampler->current.n_curr = 0; } void _sampler_add(struct sampler *sampler, const Rbyte *record, int len) { struct records *sample = sampler->sample; if (sample->n_curr < sample->n) { /* sampling not yet needed */ _sampler_add1(sample, record, len, sample->n_curr, sample->n_curr); sample->n_curr++; } else { /* sample */ struct record *r = sampler->current.records + sampler->current.n_curr; r->record = record; r->length = len; r->order = sample->n_tot + sampler->current.n_curr; if (sampler->current.n == ++sampler->current.n_curr) _sampler_dosample(sampler); } } void _sampler_order(struct records *sample) { qsort(sample->records, sample->n_curr, sizeof(struct record), _records_compare_order); } void _sampler_scratch_set(struct sampler *sampler, const Rbyte *record, int len) { if (NULL != sampler->bufnode->bytes) Free(sampler->bufnode->bytes); if (NULL != record) { Rbyte *bytes = Calloc(len, Rbyte); memcpy(bytes, record, len * sizeof(Rbyte)); sampler->bufnode->bytes = bytes; } sampler->bufnode->len = len; } /* R implementation -- FastqSampler */ #define SAMPLER(s) ((struct sampler *) R_ExternalPtrAddr(s)) void _sampler_finalize(SEXP s) { struct sampler *sampler = SAMPLER(s); if (!sampler) return; _sampler_free(sampler); R_ClearExternalPtr(s); } SEXP sampler_new(SEXP n) { struct sampler *sampler = _sampler_new(INTEGER(n)[0]); SEXP s = PROTECT(R_MakeExternalPtr(sampler, PROTECT(mkString("sampler")), R_NilValue)); R_RegisterCFinalizerEx(s, _sampler_finalize, TRUE); UNPROTECT(2); return s; } SEXP sampler_add(SEXP s, SEXP bin) { /* create a buffer with both scratch and new data */ struct sampler *sampler = SAMPLER(s); struct bufnode *scratch = sampler->bufnode; if (scratch->bytes) { int len = Rf_length(bin), buflen = scratch->len + len; Rbyte *buf = Calloc(buflen, Rbyte), *obuf = scratch->bytes; memcpy(buf, scratch->bytes, scratch->len * sizeof(Rbyte)); Free(obuf); memcpy(buf + scratch->len, RAW(bin), len * sizeof(Rbyte)); scratch->bytes = buf; scratch->len = buflen; } else { int buflen = Rf_length(bin); Rbyte *buf = Calloc(buflen, Rbyte); memcpy(buf, RAW(bin), buflen * sizeof(Rbyte)); scratch->bytes = buf; scratch->len = buflen; } /* parse the buffer */ const Rbyte *buf = scratch->bytes, *bufend = buf + scratch->len; GetRNGstate(); while (buf < bufend) { while (buf < bufend && *buf == '\n') ++buf; const Rbyte *prev = buf; if (NULL == (buf = _fastq_record_end(buf, bufend))) { buf = prev; break; } _sampler_add(sampler, prev, buf - prev); } _sampler_dosample(sampler); PutRNGstate(); if (bufend - buf) { int len = bufend - buf; Rbyte *tail = Calloc(len, Rbyte); memcpy(tail, buf, len * sizeof(Rbyte)); Free(scratch->bytes); scratch->bytes = tail; scratch->len = len; } else { scratch->len = 0; Free(scratch->bytes); } return s; } SEXP sampler_status(SEXP s) { struct sampler *sampler = SAMPLER(s); return _records_status(sampler->sample, sampler->bufnode); } SEXP sampler_as_XStringSet(SEXP s, SEXP ordered) { struct sampler *sampler = SAMPLER(s); if (TRUE == LOGICAL(ordered)[0]) _sampler_order(sampler->sample); SEXP result = _fastq_as_XStringSet(sampler->sample); _sampler_scratch_set(sampler, NULL, 0); _sampler_reset(sampler); return result; } /* Streamer */ struct streamer { struct records *stream; struct bufnode *bufnode; }; struct streamer * _streamer_new(int n) { struct streamer *streamer = Calloc(1, struct streamer); streamer->stream = Calloc(1, struct records); streamer->stream->records = Calloc(n, struct record); streamer->stream->n = n; return streamer; } void _streamer_reset(struct streamer *streamer) { streamer->stream->n_curr = 0; struct bufnode *bufnode = streamer->bufnode, *prev; if (NULL != bufnode) { bufnode = bufnode->next; while (NULL != bufnode) { prev = bufnode; bufnode = prev->next; Free(prev->bytes); Free(prev); } streamer->bufnode->next = NULL; } } void _streamer_free(struct streamer *streamer) { struct bufnode *curr, *next = streamer->bufnode; while (next) { curr = next; next = curr->next; Free(curr->bytes); Free(curr); } Free(streamer->stream->records); Free(streamer->stream); Free(streamer); } void _streamer_add(struct records *stream, const Rbyte *record, int len) { stream->records[stream->n_curr].length = len; stream->records[stream->n_curr].record = record; stream->n_curr += 1; stream->n_added += 1; } #define STREAMER(s) ((struct streamer *) R_ExternalPtrAddr(s)) void _streamer_finalize(SEXP s) { struct streamer *streamer = STREAMER(s); if (!streamer) return; _streamer_free(streamer); R_ClearExternalPtr(s); } SEXP streamer_new(SEXP n) { struct streamer *streamer = _streamer_new(INTEGER(n)[0]); SEXP s = PROTECT(R_MakeExternalPtr(streamer, PROTECT(mkString("streamer")), R_NilValue)); R_RegisterCFinalizerEx(s, _streamer_finalize, TRUE); UNPROTECT(2); return s; } SEXP streamer_add(SEXP s, SEXP bin, SEXP skipadd) { struct streamer *streamer = STREAMER(s); int len = Rf_length(bin); int skip = INTEGER(skipadd)[0], add = INTEGER(skipadd)[1]; /* start with tail of previous bin */ struct bufnode *scratch = streamer->bufnode; if (NULL == scratch) { /* first record */ scratch = streamer->bufnode = Calloc(1, struct bufnode); } if (NULL == scratch->bytes) { /* nothing 'extra' from previous bin */ scratch->bytes = Calloc(len, Rbyte); scratch->len = len; memcpy(scratch->bytes, RAW(bin), len * sizeof(Rbyte)); } else { /* scratch contains tail of prev. bin */ int buflen = scratch->len; Rbyte *bytes = Calloc(buflen + len, Rbyte); memcpy(bytes, scratch->bytes, buflen * sizeof(Rbyte)); memcpy(bytes + buflen, RAW(bin), len * sizeof(Rbyte)); Free(scratch->bytes); scratch->bytes = bytes; scratch->len = buflen + len; } /* find record starts and lengths */ const Rbyte *buf = scratch->bytes, *bufend = buf + scratch->len; struct records *stream = streamer->stream; while (add > stream->n_curr && buf < bufend) { while (buf < bufend && *buf == '\n') ++buf; const Rbyte *prev = buf; if (NULL == (buf = _fastq_record_end(buf, bufend))) { buf = prev; break; } stream->n_tot += 1; if (skip == 0) _streamer_add(stream, prev, buf - prev); else skip -= 1; } /* capture tail of bin */ if (NULL != scratch->bytes) { struct bufnode *next = scratch; scratch = streamer->bufnode = Calloc(1, struct bufnode); scratch->next = next; } if (bufend - buf) { int len = bufend - buf; Rbyte *tail = Calloc(len, Rbyte); memcpy(tail, buf, len * sizeof(Rbyte)); scratch->bytes = tail; scratch->len = len; } return s; } SEXP streamer_status(SEXP s) { struct streamer *streamer = STREAMER(s); return _records_status(streamer->stream, streamer->bufnode); } SEXP streamer_as_XStringSet(SEXP s) { struct streamer *streamer = STREAMER(s); struct records *stream = streamer->stream; SEXP result = _fastq_as_XStringSet(stream); _streamer_reset(streamer); return result; } ShortRead/src/trim.c0000644000175400017540000001257713001247474015417 0ustar00biocbuildbiocbuild#include "trim.h" #include "IRanges_interface.h" #include "Biostrings_interface.h" #define MIN(a,b) ((a) > (b) ? (b) : (a)) #define MAX(a,b) ((a) > (b) ? (a) : (b)) SEXP trim_tailw(SEXP quality, SEXP k, SEXP a_map, SEXP width) { int map[256]; const XStringSet_holder holder = hold_XStringSet(quality); const int len = get_XStringSet_length(quality); const int kmax = INTEGER(k)[0], wd = INTEGER(width)[0]; SEXP end = PROTECT(NEW_INTEGER(len)); int *endp = INTEGER(end); int i, j; for (j = 0; j < Rf_length(a_map); ++j) { const char c = CHAR(STRING_ELT(GET_NAMES(a_map), j))[0]; map[(int) c] = INTEGER(a_map)[j]; } for (i = 0; i < len; ++i) { const Chars_holder seq = get_elt_from_XStringSet_holder(&holder, i); if (0 == seq.length) { endp[i] = 0; continue; } int n = (wd + 1) * map[(int) seq.ptr[0]]; for (j = 1; j <= wd; ++j) n += map[(int) seq.ptr[MIN(seq.length - 1, j)]]; for (j = 0; j < seq.length; ++j) { const int wstart = MAX(0, j - wd); const int wend = MIN(seq.length - 1, j + wd); n += map[(int) seq.ptr[wend]] - map[(int) seq.ptr[wstart]]; if (kmax <= n) break; } endp[i] = j; } UNPROTECT(1); return end; } SEXP trim_tails(SEXP quality, SEXP k, SEXP a_map, SEXP successive) { static int init = 0; SEXP end; int map[256]; const XStringSet_holder holder = hold_XStringSet(quality); const int len = get_XStringSet_length(quality); int i, j, *endp; end = PROTECT(NEW_INTEGER(len)); endp = INTEGER(end); for (j = 0; j < Rf_length(a_map); ++j) { const char c = CHAR(STRING_ELT(GET_NAMES(a_map), j))[0]; map[(int) c] = INTEGER(a_map)[j]; } if ((!init) && len) { /* hack -- install symbols to avoid stack imbalance */ (void) get_elt_from_XStringSet_holder(&holder, 0); init = 1; } const int kmax = INTEGER(k)[0]; if (!LOGICAL(successive)[0]) { #ifdef SUPPORT_OPENMP #pragma omp parallel for private(j) #endif for (i = 0; i < len; ++i) { const Chars_holder seq = get_elt_from_XStringSet_holder(&holder, i); int n = 0; for (j = 0; j < seq.length; ++j) { n += map[(int) seq.ptr[j]]; if (kmax <= n) break; } endp[i] = j; } } else { const int nbuf = INTEGER(k)[0]; int *kbuf = (int *) R_alloc(sizeof(int), nbuf), ibuf; for (i = 0; i < len; ++i) { const Chars_holder seq = get_elt_from_XStringSet_holder(&holder, i); int n = 0; for (ibuf = 0; ibuf < nbuf; ++ibuf) kbuf[ibuf] = 0; int m; for (j = 0; j < seq.length; ++j) { m = map[(int) seq.ptr[j]]; n += m - kbuf[j % nbuf]; if (kmax <= n) break; kbuf[j % nbuf] = m; } endp[i] = j == seq.length ? j : j - nbuf + 1L; } } UNPROTECT(1); return end; } SEXP trim_ends(SEXP quality, SEXP a_map, SEXP left, SEXP right) { static int init = 0; SEXP bounds; const int *const map = LOGICAL(a_map); const XStringSet_holder holder = hold_XStringSet(quality); const int len = get_XStringSet_length(quality); int i, j, *startp, *endp; bounds = PROTECT(NEW_LIST(2)); SET_VECTOR_ELT(bounds, 0, NEW_INTEGER(len)); SET_VECTOR_ELT(bounds, 1, NEW_INTEGER(len)); SEXP nm = PROTECT(NEW_CHARACTER(2)); SET_STRING_ELT(nm, 0, mkChar("start")); SET_STRING_ELT(nm, 1, mkChar("end")); SET_NAMES(bounds, nm); UNPROTECT(1); startp = INTEGER(VECTOR_ELT(bounds, 0)); endp = INTEGER(VECTOR_ELT(bounds, 1)); if ((!init) && len) { /* hack -- install symbols to avoid stack imbalance */ (void) get_elt_from_XStringSet_holder(&holder, 0); init = 1; } if (LOGICAL(left)[0]) { #ifdef SUPPORT_OPENMP #pragma omp parallel for private(j) #endif for (i = 0; i < len; ++i) { const Chars_holder seq = get_elt_from_XStringSet_holder(&holder, i); for (j = 0; j < seq.length; ++j) { if (0 == map[(int) seq.ptr[j]]) break; } startp[i] = j + 1L; } } else { for (i = 0; i < len; ++i) startp[i] = 1; } if (LOGICAL(right)[0]) { #ifdef SUPPORT_OPENMP #pragma omp parallel for private(j) #endif for (i = 0; i < len; ++i) { const Chars_holder seq = get_elt_from_XStringSet_holder(&holder, i); for (j = seq.length - 1; j >= 0; --j) { if (0 == map[(int) seq.ptr[j]]) break; } endp[i] = j + 1L; } } else { for (i = 0; i < len; ++i) { const Chars_holder seq = get_elt_from_XStringSet_holder(&holder, i); endp[i] = seq.length; } } #ifdef SUPPORT_OPENMP #pragma omp parallel for private(j) #endif for (i = 0; i < len; ++i) { const Chars_holder seq = get_elt_from_XStringSet_holder(&holder, i); if (seq.length + 1 == startp[i]) { endp[i] = 0; startp[i] = 1; } else if (0 == endp[i]) { startp[i] = 1; } } UNPROTECT(1); return bounds; } ShortRead/src/trim.h0000644000175400017540000000040413001247474015406 0ustar00biocbuildbiocbuild#ifndef TRIM_H #define TRIM_H #include SEXP trim_tails(SEXP quality, SEXP k, SEXP a_map, SEXP successive); SEXP trim_tailw(SEXP quality, SEXP k, SEXP a_map, SEXP winsize); SEXP trim_ends(SEXP quality, SEXP a_map, SEXP left, SEXP right); #endif ShortRead/src/util.c0000644000175400017540000002237413001247474015415 0ustar00biocbuildbiocbuild#include /* R_CheckUserInterrupt */ #include /* isspace */ #include "ShortRead.h" unsigned char _bDecode(char); unsigned char _dnaDecode(char); unsigned char _rnaDecode(char); /* * Encode / decode XString wrappers */ char _bEncode(char c) { return c; } #define _dnaEncode DNAencode; #define _rnaEncode RNAencode; unsigned char _bDecode(char c) { return (unsigned char) c; } unsigned char _dnaDecode(char c) { return (unsigned char) DNAdecode(c); } unsigned char _rnaDecode(char c) { return (unsigned char) RNAdecode(c); } DECODE_FUNC decoder(const char *base) { DECODE_FUNC decode = NULL; if (strcmp(base, "DNAString") == 0) { decode = _dnaDecode; } else if (strcmp(base, "RNAString") == 0) { decode = _rnaDecode; } else if (strcmp(base, "BString") == 0) { decode = _bDecode; } else if (strcmp(base, "AAString") == 0) { decode = _bDecode; } else { Rf_error("unknown class '%s'", base); } return decode; } ENCODE_FUNC encoder(const char *base) { ENCODE_FUNC encode = NULL; if (strcmp(base, "DNAString") == 0) { encode = _dnaEncode; } else if (strcmp(base, "RNAString") == 0) { encode = _rnaEncode; } else if (strcmp(base, "BString") == 0) { encode = _bEncode; } else if (strcmp(base, "AAString") == 0) { encode = _bEncode; } else { Rf_error("unknown class '%s'", base); } return encode; } SEXP _get_namespace(const char *pkg) { SEXP fun = PROTECT(findFun(install("getNamespace"), R_GlobalEnv)); SEXP nmspc = PROTECT(mkString(pkg)); SEXP lng2 = PROTECT(lang2(fun, nmspc)); nmspc = eval(lng2, R_GlobalEnv); UNPROTECT(3); return nmspc; } SEXP _get_strand_levels() { SEXP nmspc = PROTECT(_get_namespace("ShortRead")); SEXP ans = eval(findVar(install(".STRAND_LEVELS"), nmspc), nmspc); UNPROTECT(1); return ans; } int _char_as_strand_int(const char c, const char *fname, const int lineno) { int strand = 0; if (c == '\0') strand = NA_INTEGER; else { switch (c) { case '+': strand = 1; break; case '-': strand = 2; break; default: error("invalid 'strand' field '%s', %s:%d", &c, fname, lineno); break; } } return strand; } /* * apply function 'with' to object 'from' in environment 'rho', e.g., * becuase 'from' is an object and 'with' an accessor. */ SEXP _get_SEXP(SEXP from, SEXP rho, const char *with) { SEXP fun = PROTECT(findFun(install(with), rho)); SEXP lng2 = PROTECT(lang2(fun, from)); SEXP res = eval(lng2, rho); UNPROTECT(2); return res; } /* * populate elt with pointers into tab-delimited strings in ptr */ int _mark_field_0(char *ptr, char **elt, const int n_fields) { elt[0] = ptr; int i = 0; for (; *ptr != '\0'; ++ptr) if (*ptr == '\t') { if (++i == n_fields) break; elt[i] = ptr + 1; *ptr = '\0'; } if (*(ptr - 1) == '\n') /* trailing newline? */ *(ptr - 1) = '\0'; return i + 1; } /* * parse lines into fields. * * string is parsed until a character in delim is found, or end of * string reached. * * return value is pointer to the start of the next field, or NULL if * no more fields. */ char *_mark_field_1(char *curr, const char *delim) { char *c = curr; while (*c != '\0' && *c != *delim) c++; if (*c != '\0') /* i.e., delim */ *c++ = '\0'; return c; } char *_mark_field_n(char *curr, const char *delim) { const char *d = NULL; while (*curr != '\0' && *curr != '\n') { d = delim; while (*d != '\0' && *d != *curr) ++d; if (*d != '\0') *curr = '\0'; else ++curr; } if (*curr == '\n') { *curr = '\0'; return NULL; } return ((d == NULL) || (*d == '\0')) ? NULL : curr + 1; } SEXP _mark_field_test(SEXP filename, SEXP delimiters, SEXP dim) { if (!IS_CHARACTER(filename) || LENGTH(filename) != 1) error("'%s' must be '%s'", "filename", "character(1)"); if (!IS_CHARACTER(delimiters) || LENGTH(delimiters) != 1) error("'%s' must be '%s'", "delimiters", "character(1)"); if (!IS_INTEGER(dim) || LENGTH(dim) != 2) error("'%s' must be '%s'", "dim", "integer(2)"); SEXP ans = PROTECT(NEW_LIST(INTEGER(dim)[0])); int i, j; for (i = 0; i < INTEGER(dim)[0]; ++i) SET_VECTOR_ELT(ans, i, NEW_CHARACTER(INTEGER(dim)[1])); #define LINEBUF_SIZE 1024 FILE *file; char linebuf[LINEBUF_SIZE]; if ((file = fopen(CHAR(STRING_ELT(filename, 0)), "rb")) == NULL) error("cannot open file '%s'", CHAR(STRING_ELT(filename, 0))); const char *delim = CHAR(STRING_ELT(delimiters, 0)); for (i = 0; i < INTEGER(dim)[0]; ++i) { if (fgets(linebuf, LINEBUF_SIZE, file) == NULL) error("unexpected end-of-file"); j = 0; char *curr = linebuf, *next; while (curr != NULL) { if (j >= INTEGER(dim)[1]) error("too many fields"); next = _mark_field_n(curr, delim); SET_STRING_ELT(VECTOR_ELT(ans, i), j, mkChar(curr)); j++; curr = next; } } fclose(file); #undef LINEBUF_SIZE UNPROTECT(1); return ans; } const int LINEBUF_SIZE = 200001; /* * open and check file; signal error */ gzFile _fopen(const char *fname, const char *mode) { gzFile file = gzopen(fname, mode); if (file == NULL) error("cannot open file %s", fname); return file; } /* * trim & check linebuf, return 0 if processing should continue */ int _linebuf_skip_p(char *linebuf, gzFile file, const char *fname, int lineno, const char *commentChar) { int nchar_in_buf; nchar_in_buf = _rtrim(linebuf); if (nchar_in_buf >= LINEBUF_SIZE - 1) { // should never be > gzclose(file); error("line too long %s:%d", fname, lineno); } else if (nchar_in_buf == 0) { gzclose(file); error("unexpected empty line %s:%d", fname, lineno); } return *linebuf == *commentChar; } /* * Return the number of chars that remain in the buffer after we've removed * the right spaces ('\n', '\r', '\t', ' ', etc...) */ int _rtrim(char *linebuf) { int i; i = strlen(linebuf) - 1; while (i >= 0 && isspace(linebuf[i])) i--; linebuf[++i] = 0; return i; } /* * Solexa sometimes encodes an uncalled base as '.', but the * Biostrings standard is '-'. Convert a null-terminated character * string in-place. */ void _solexa_to_IUPAC(char *p) { while ((p = strchr(p, '.')) != NULL) *p = '-'; } void _reverse(char *linebuf) { size_t len = strlen(linebuf); int i; char tmp; for (i = 0; i < floor(len / 2); ++i) { tmp = linebuf[len - i - 1]; linebuf[len - i - 1] = linebuf[i]; linebuf[i] = tmp; } } void _reverseComplement(char *linebuf) { static const int MAX_MAP = 256; static char map[256]; static int init = 0; if (init == 0) { init = 1; for (int i = 0; i < MAX_MAP; ++i) map[i] = (char) i; map['A'] = 'T'; map['C'] = 'G'; map['G'] = 'C'; map['T'] = 'A'; map['a'] = 't'; map['c'] = 'g'; map['g'] = 'c'; map['t'] = 'a'; } _reverse(linebuf); for (unsigned int i = 0; i < strlen(linebuf); ++i) linebuf[i] = map[(int) linebuf[i]]; } /* * Chenge vector class and attribute to represent factor */ void _as_factor_SEXP(SEXP vec, SEXP lvls) { SEXP cls = PROTECT(NEW_CHARACTER(1)); SET_STRING_ELT(cls, 0, mkChar("factor")); SET_CLASS(vec, cls); SET_ATTR(vec, install("levels"), lvls); UNPROTECT(1); } void _as_factor(SEXP vec, const char **levels, const int n_lvls) { SEXP lvls = PROTECT(NEW_CHARACTER(n_lvls)); int i; for (i = 0; i < n_lvls; ++i) SET_STRING_ELT(lvls, i, mkChar(levels[i])); _as_factor_SEXP(vec, lvls); UNPROTECT(1); } /* * Count the number of lines ('\n') in a file. * * file: an open file stream at position 0 * */ static int _count_lines(gzFile file) { const int LINEBUF_SIZE = 20001; size_t bytes_read; char *buf = (char *) R_alloc(LINEBUF_SIZE + 1, sizeof(char)); int lines = 0; while ((bytes_read = gzread(file, buf, LINEBUF_SIZE)) > 0) { char *p = buf; while ((p = memchr(p, '\n', (buf + bytes_read) - p))) { ++p; ++lines; } } return lines; } int _count_lines_sum(SEXP files) { SEXP nlines = count_lines(files); int i, nrec = 0; for (i = 0; i < LENGTH(files); ++i) nrec += INTEGER(nlines)[i]; return nrec; } SEXP count_lines(SEXP files) { int i, nfile; const char *filepath; gzFile file; SEXP ans = R_NilValue; if (!IS_CHARACTER(files)) error("'files' must be character()"); nfile = LENGTH(files); PROTECT(ans = NEW_INTEGER(nfile)); for (i = 0; i < nfile; ++i) { R_CheckUserInterrupt(); filepath = translateChar(STRING_ELT(files, i)); file = _fopen(filepath, "rb"); INTEGER(ans)[i] = _count_lines(file); gzclose(file); } UNPROTECT(1); return ans; } ShortRead/src/xsnap.c0000644000175400017540000002067113001247474015567 0ustar00biocbuildbiocbuild/* * An _XSNAP is a SEXP that contains sufficient information to create * (`snap') an XStringSet object from its content. It is allocated * once to an initial size, and grows as needed. Any `extra' * allocation is recovered. * * Basic usage is * * SEXP lst = PROTECT(NEW_LIST(4)); * SET_VECTOR_ELT(lst, 0, _NEW_XSNAP(final_length, "DNAString")); * _APPEND_XSNAP(VECTOR_ELT(lst, 0), "ACTAGAC"); * SEXP xStringSet = PROTECT(_XSNAP_ELT(lst, 0)); * UNPROTECT(2); * */ #include "ShortRead.h" SEXP _to_XStringSet(SEXP seq, SEXP start, SEXP width, const char *baseclass); const char *_get_lookup(const char *baseclass); static const int _BUFFERNODE_SIZE = 250000000; /* _Buffer, _BufferNode: linked list of XString data chunks */ struct _Buffer { char *baseclass; int *offset, i_offset; struct _BufferNode *root, *curr; }; struct _BufferNode { int n; /* number of entries */ int buf_size; char *buf, *curr; /* linked list */ struct _BufferNode *next; }; /* _BufferNode implementation */ struct _BufferNode *_BufferNode_new() { struct _BufferNode *node = Calloc(1, struct _BufferNode); node->curr = node->buf = Calloc(_BUFFERNODE_SIZE, char); node->n = 0; node->buf_size = _BUFFERNODE_SIZE; node->next = NULL; return node; } void _BufferNode_free(struct _BufferNode *node) { Free(node->buf); Free(node); } void _BufferNode_encode(struct _BufferNode *node, const char *lkup) { for (char *buf = node->buf; buf < node->curr; ++buf) { const char c = lkup[(int) *buf]; if (c == 0) Rf_error("invalid character '%c'", c); *buf = c; } } int _BufferNode_append(struct _BufferNode *node, const char *s, int w) { int offset = node->curr - node->buf; if (offset + w >= node->buf_size) return -1; memcpy(node->curr, s, w); node->curr += w; node->n += 1; return offset; } SEXP _BufferNode_snap(struct _BufferNode * node, const int *offset, const char *baseclass) { const int n_raw = node->curr - node->buf; SEXP seq = PROTECT(NEW_RAW(n_raw)), start = PROTECT(NEW_INTEGER(node->n)), width = PROTECT(NEW_INTEGER(node->n)); memcpy(RAW(seq), node->buf, n_raw); for (int i = 0; i < node->n; ++i) INTEGER(start)[i] = offset[i] + 1; for (int i = 0; i < node->n - 1; ++i) INTEGER(width)[i] = offset[i + 1] - offset[i]; if (node->n > 0) INTEGER(width)[node->n - 1] = node->curr - (node->buf + offset[node->n - 1]); SEXP xstringset = _to_XStringSet(seq, start, width, baseclass); UNPROTECT(3); return xstringset; } /* _Buffer implementation */ struct _Buffer *_Buffer_new(int n_offsets, const char *baseclass) { struct _Buffer *buffer = Calloc(1, struct _Buffer); buffer->baseclass = Calloc(strlen(baseclass) + 1, char); buffer->offset = Calloc(n_offsets, int); buffer->i_offset = 0; strcpy(buffer->baseclass, baseclass); buffer->root = buffer->curr = _BufferNode_new(); return buffer; } void _Buffer_free(struct _Buffer *buf) { struct _BufferNode *curr = buf->root; while (curr != NULL) { struct _BufferNode *tmp = curr; curr = curr->next; _BufferNode_free(tmp); } Free(buf->offset); Free(buf->baseclass); Free(buf); } void _Buffer_append(struct _Buffer *buf, const char *s) { int w = strlen(s); struct _BufferNode *curr = buf->curr; int i; if ((i = _BufferNode_append(curr, s, w)) < 0) { curr = buf->curr = curr->next = _BufferNode_new(); i = _BufferNode_append(curr, s, w); if (i < 0) Rf_error("ShortRead internal: _BufferNode too small"); } buf->offset[buf->i_offset++] = i; } void _Buffer_encode(struct _Buffer *buf) { const char *lkup = _get_lookup(buf->baseclass); struct _BufferNode *curr; for (curr = buf->root; curr != NULL; curr = curr->next) _BufferNode_encode(curr, lkup); } SEXP _Buffer_snap(struct _Buffer *buf) { int n_buf = 0, n_off = 0; struct _BufferNode *curr, *tmp; for (curr = buf->root; curr != NULL; curr = curr->next) ++n_buf; SEXP xstringsets = PROTECT(NEW_LIST(n_buf)); curr = buf->root; for (int i = 0; i < n_buf; ++i) { SEXP xs = _BufferNode_snap(curr, buf->offset + n_off, buf->baseclass); SET_VECTOR_ELT(xstringsets, i, xs); n_off += curr->n; tmp = curr; curr = curr->next; _BufferNode_free(tmp); } buf->curr = buf->root = NULL; UNPROTECT(1); return xstringsets; } /* Wrap _Buffer in external pointer */ void _xsnap_finalizer(SEXP xsnap) { struct _Buffer *buffer = R_ExternalPtrAddr(xsnap); if (!buffer) return; _Buffer_free(buffer); R_ClearExternalPtr(xsnap); } _XSnap _NEW_XSNAP(int n_elt, const char *baseclass) { struct _Buffer *buffer = _Buffer_new(n_elt, baseclass); SEXP xsnap = PROTECT(R_MakeExternalPtr(buffer, PROTECT(mkString("XSnap")), R_NilValue)); R_RegisterCFinalizerEx(xsnap, _xsnap_finalizer, TRUE); UNPROTECT(2); return xsnap; } void _APPEND_XSNAP(_XSnap snap, const char *str) { _Buffer_append(R_ExternalPtrAddr(snap), str); } SEXP _to_XStringSet(SEXP seq, SEXP start, SEXP width, const char *baseclass) { char classname[40]; /* longest string should be "DNAStringSet" */ int res = snprintf(classname, sizeof(classname), "%sSet", baseclass); if (res < 0 || ((unsigned int) res) >= sizeof(classname)) error("ShortRead internal error in _to_XStringSet(): " "'classname' buffer too small or other error"); SEXP irange = PROTECT(new_IRanges("IRanges", start, width, R_NilValue)); SEXP xstringset = new_XRawList_from_tag(classname, baseclass, seq, irange); UNPROTECT(1); return xstringset; } const char *_get_lookup(const char *baseclass) { ENCODE_FUNC encode = encoder(baseclass); SEXP nmspc = PROTECT(_get_namespace("ShortRead")); SEXP lng1 = PROTECT(lang1(install(baseclass))); SEXP cls = PROTECT(eval(lng1, nmspc)); SEXP lng2 = PROTECT(lang2(install("alphabet"), cls)); SEXP alf = PROTECT(eval(lng2, nmspc)); char *lkup = (char *) R_alloc(256, sizeof(char)); int i; if (alf == R_NilValue) { for (i = 0; i < 256; ++i) lkup[i] = (char) i; } else { for (i = 0; i < 256; ++i) lkup[i] = 0; for (i = 0; i < LENGTH(alf); ++i) { char c = CHAR(STRING_ELT(alf, i))[0]; lkup[(int) c] = encode(c); } lng2 = PROTECT(lang2(install("tolower"), alf)); alf = PROTECT(eval(lng2, nmspc)); for (i = 0; i < LENGTH(alf); ++i) { char c = CHAR(STRING_ELT(alf, i))[0]; lkup[(int) c] = encode(c); } UNPROTECT(2); } UNPROTECT(5); return lkup; } SEXP _get_appender(const char *baseclass) { char *class = (char *) R_alloc(strlen(baseclass) + 4, sizeof(char)); sprintf(class, "%sSet", baseclass); SEXP cls = PROTECT(mkString(class)); SEXP lng3 = PROTECT(lang3(install("selectMethod"), install("c"), cls)); SEXP nmspc = PROTECT(_get_namespace("ShortRead")); SEXP appender = eval(lng3, nmspc); UNPROTECT(3); return appender; } SEXP _XSnap_to_XStringSet(_XSnap snap) { struct _Buffer *buffer = (struct _Buffer *) R_ExternalPtrAddr(snap); _Buffer_encode(buffer); SEXP xstringset = PROTECT(_Buffer_snap(buffer)); /* concatenate */ SEXP appender = PROTECT(_get_appender(buffer->baseclass)); SEXP nmspc = PROTECT(_get_namespace("ShortRead")); int n = LENGTH(xstringset); while (n > 1) { SEXP res; int i; for (i = 0; i < n; i += 2) { if (i != n - 1) { SEXP lng3 = PROTECT(lang3(appender, VECTOR_ELT(xstringset, i), VECTOR_ELT(xstringset, i + 1))); res = eval(lng3, nmspc); SET_VECTOR_ELT(xstringset, i + 1, R_NilValue); UNPROTECT(1); } else { res = VECTOR_ELT(xstringset, i); } SET_VECTOR_ELT(xstringset, i, R_NilValue); SET_VECTOR_ELT(xstringset, i / 2, res); } n = i / 2; } UNPROTECT(3); return VECTOR_ELT(xstringset, 0); } void _XSNAP_ELT(SEXP x, int elt) { SEXP xstringset = PROTECT(_XSnap_to_XStringSet(VECTOR_ELT(x, elt))); SET_VECTOR_ELT(x, elt, xstringset); UNPROTECT(1); } ShortRead/tests/0000755000175400017540000000000013001230552014624 5ustar00biocbuildbiocbuildShortRead/tests/ShortRead_unit_tests.R0000644000175400017540000000005013001230552021116 0ustar00biocbuildbiocbuildBiocGenerics:::testPackage("ShortRead") ShortRead/vignettes/0000755000175400017540000000000013001247473015504 5ustar00biocbuildbiocbuildShortRead/vignettes/Overview.Rnw0000644000175400017540000003641313001230552017777 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{An introduction to ShortRead} %\VignetteDepends{BiocStyle} %\VignetteKeywords{Short read, I/0, quality assessment} %\VignettePackage{ShortRead} \documentclass[]{article} <>= BiocStyle::latex() @ \newcommand{\ShortRead}{\Biocpkg{ShortRead}} \title{An Introduction to \Rpackage{ShortRead}} \author{Martin Morgan} \date{Modified: 21 October, 2013. Compiled: \today} \begin{document} \maketitle <>= library("ShortRead") @ The \Rpackage{ShortRead} package provides functionality for working with FASTQ files from high throughput sequence analysis. The package also contains functions for legacy (single-end, ungapped) aligned reads; for working with BAM files, please see the \Biocpkg{Rsamtools}, \Biocpkg{GenomicRanges}, \Biocpkg{GenomicAlignments} and related packages. \section{Sample data} Sample FASTQ data are derived from ArrayExpress record \href{http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-1147/}{E-MTAB-1147}. Paired-end FASTQ files were retrieved and then sampled to 20,000 records with <>= sampler <- FastqSampler('E-MTAB-1147/fastq/ERR127302_1.fastq.gz', 20000) set.seed(123); ERR127302_1 <- yield(sampler) sampler <- FastqSampler('E-MTAB-1147/fastq/ERR127302_2.fastq.gz', 20000) set.seed(123); ERR127302_2 <- yield(sampler) @ \section{Functionality} Functionality is summarized in Table~\ref{tab:fastq}. \begin{table} \centering \begin{tabular}{lll} \hline \multicolumn{3}{l}{Input} \\ & \Rfunction{FastqStreamer} & Iterate through FASTQ files in chunks \\ & \Rfunction{FastqSampler} & Draw random samples from FASTQ files \\ & \Rfunction{readFastq} & Read an entire FASTQ file into memory \\ & \Rfunction{writeFastq} & Write FASTQ objects to a connection (file) \\ \multicolumn{3}{l}{Sequence and quality summary} \\ & \Rfunction{alphabetFrequency} & Nucleotide or quality score use per read\\ & \Rfunction{alphabetByCycle} & Nucleotide or quality score use by cycle\\ & \Rfunction{alphabetScore} & Whole-read quality summary\\ & \Rfunction{encoding} & Character / `phred' score mapping \\ \multicolumn{3}{l}{Quality assessment} \\ & \Rfunction{qa} & Visit FASTQ files to collect QA statistics \\ & \Rfunction{report} & Generate a quality assessment report \\ \multicolumn{3}{l}{Filtering and trimming} \\ & \Rfunction{srFilter} & Pre-defined and bespoke filters \\ & \Rfunction{trimTails}, etc. & Trim low-quality nucleotides \\ & \Rfunction{narrow} & Remove leading / trailing nucleotides \\ & \Rfunction{tables} & Summarize read occurrence \\ & \Rfunction{srduplicated}, etc. & Identify duplicate reads \\ & \Rfunction{filterFastq} & Filter reads from one file to another\\ \hline \end{tabular} \caption{Key functions for working with FASTQ files} \label{tab:fastq} \end{table} \paragraph{Input} FASTQ files are large so processing involves iteration in `chunks' using \Rfunction{FastqStreamer} <>= strm <- FastqStreamer("a.fastq.gz") repeat { fq <- yield(strm) if (length(fq) == 0) break ## process chunk } @ or drawing a random sample from the file <>= sampler <- FastqSampler("a.fastq.gz") fq <- yield(sampler) @ \noindent The default size for both streams and samples is 1M records; this volume of data fits easily into memory. Small FASTQ files can be read in to memory in their entirety using \Rfunction{readFastq}; we do this for our sample data <>= fl <- system.file(package="ShortRead", "extdata", "E-MTAB-1147", "ERR127302_1_subset.fastq.gz") fq <- readFastq(fl) @ The result of data input is an instance of class \Rclass{ShortReadQ} (Table~\ref{tab:classes}). \begin{table} \centering \begin{tabular}{ll} \hline \Rclass{DNAStringSet} & (\Biocpkg{Biostrings}) Short read sequences \\ \Rclass{FastqQuality}, etc. & Quality encodings \\ \Rclass{ShortReadQ} & Reads, quality scores, and ids \\ \hline \end{tabular} \caption{Primary data types in the \Biocpkg{ShortRead} package} \label{tab:classes} \end{table} This class contains reads, their quality scores, and optionally the id of the read. <>= fq fq[1:5] head(sread(fq), 3) head(quality(fq), 3) @ \noindent The reads are represented as \Rclass{DNAStringSet} instances, and can be manipulated with the rich tools defined in the \Biocpkg{Biostrings} package. The quality scores are represented by a class that represents the quality encoding inferred from the file; the encoding in use can be discovered with <>= encoding(quality(fq)) @ \noindent The primary source of documentation for these classes is \Rcode{?ShortReadQ} and \Rcode{?QualityScore}. \section{Common work flows} \subsection{Quality assessment} FASTQ files are often used for basic quality assessment, often to augment the purely technical QA that might be provided by the sequencing center with QA relevant to overall experimental design. A QA report is generated by creating a vector of paths to FASTQ files <>= fls <- dir("/path/to", "*fastq$", full=TRUE) @ \noindent collecting statistics over the files <>= qaSummary <- qa(fls, type="fastq") @ \noindent and creating and viewing a report <>= browseURL(report(qaSummary)) @ \noindent By default, the report is based on a sample of 1M reads. These QA facilities are easily augmented by writing custom functions for reads sampled from files, or by explorting the elements of the object returned from \Rcode{qa()}, e.g., for an analysis of ArrayExpress experiment E-MTAB-1147: <>= load("qa_E-MTAB-1147.Rda") @ <>= qaSummary @ %% For instance, the count of reads in each lane is summarized in the \Robject{readCounts} element, and can be displayed as <>= head(qaSummary[["readCounts"]]) head(qaSummary[["baseCalls"]]) @ %% The \Robject{readCounts} element contains a data frame with 1 row and 3 columns (these dimensions are indicated in the parenthetical annotation of \Robject{readCounts} in the output of \Rcode{qaSummary}). The rows represent different lanes. The columns indicated the number of reads, the number of reads surviving the Solexa filtering criteria, and the number of reads aligned to the reference genome for the lane. The \Robject{baseCalls} element summarizes base calls in the unfiltered reads. The functions that produce the report tables and graphics are internal to the package. They can be accessed through calling ShortRead:::functionName where functionName is one of the functions listed below, organized by report section. \begin{description} \item [] Run Summary : .ppnCount, .df2a, .laneLbl, .plotReadQuality \item [] Read Distribution : .plotReadOccurrences, .freqSequences \item [] Cycle Specific : .plotCycleBaseCall, .plotCycleQuality \item [] Tile Performance : .atQuantile, .colorkeyNames, .plotTileLocalCoords, .tileGeometry, .plotTileCounts, .plotTileQualityScore \item [] Alignment : .plotAlignQuality \item [] Multiple Alignment : .plotMultipleAlignmentCount \item [] Depth of Coverage : .plotDepthOfCoverage \item [] Adapter Contamination : .ppnCount \end{description} \subsection{Filtering and trimming} It is straight-forward to create filters to eliminate reads or to trim reads based on diverse characteristics. The basic structure is to open a FASTQ file, iterate through chunks of the file performing filtering or trimming steps, and appending the filtered data to a new file. <>= myFilterAndTrim <- function(fl, destination=sprintf("%s_subset", fl)) { ## open input stream stream <- open(FastqStreamer(fl)) on.exit(close(stream)) repeat { ## input chunk fq <- yield(stream) if (length(fq) == 0) break ## trim and filter, e.g., reads cannot contain 'N'... fq <- fq[nFilter()(fq)] # see ?srFilter for pre-defined filters ## trim as soon as 2 of 5 nucleotides has quality encoding less ## than "4" (phred score 20) fq <- trimTailw(fq, 2, "4", 2) ## drop reads that are less than 36nt fq <- fq[width(fq) >= 36] ## append to destination writeFastq(fq, destination, "a") } } @ \noindent This is memory efficient and flexible. Care must be taken to coordinate pairs of FASTQ files representing paired-end reads, to preserve order. \section{Using \Rpackage{ShortRead} for data exploration} \subsection{Data I/O} \ShortRead{} provides a variety of methods to read data into \R{}, in addition to \Rfunction{readAligned}. \subsubsection{\Rfunction{readXStringColumns}} \Rfunction{readXStringColumns} reads a column of DNA or other sequence-like data. For instance, the Solexa files \texttt{s\_N\_export.txt} contain lines with the following information: <>= ## location of file exptPath <- system.file("extdata", package="ShortRead") sp <- SolexaPath(exptPath) pattern <- "s_2_export.txt" fl <- file.path(analysisPath(sp), pattern) strsplit(readLines(fl, n=1), "\t") length(readLines(fl)) @ % Column 9 is the read, and column 10 the ASCII-encoded Solexa Fastq quality score; there are 1000 lines (i.e., 1000 reads) in this sample file. Suppose the task is to read column 9 as a \Rclass{DNAStringSet} and column 10 as a \Rclass{BStringSet}. \Rclass{DNAStringSet} is a class that contains IUPAC-encoded DNA strings (IUPAC code allows for nucleotide ambiguity); \Rclass{BStringSet} is a class that contains any character with ASCII code 0 through 255. Both of these classes are defined in the \Rpackage{Biostrings} package. \Rfunction{readXStringColumns} allows us to read in columns of text as these classes. Important arguments for \Rfunction{readXStringColumns} are the \Rcode{dirPath} in which to look for files, the \Rcode{pattern} of files to parse, and the \Rcode{colClasses} of the columns to be parsed. The \Rcode{dirPath} and \Rcode{pattern} arguments are like \Rcode{list.files}. \Rcode{colClasses} is like the corresponding argument to \Rfunction{read.table}: it is a \Rclass{list} specifying the class of each column to be read, or \Robject{NULL} if the column is to be ignored. In our case there are 21 columns, and we would like to read in columns 9 and 10. Hence <>= colClasses <- rep(list(NULL), 21) colClasses[9:10] <- c("DNAString", "BString") names(colClasses)[9:10] <- c("read", "quality") @ % We use the class of the type of sequence (e.g., \Rclass{DNAString} or \Rclass{BString}), rather than the class of the set that we will create ( e.g., \Rclass{DNAStringSet} or \Rclass{BStringSet}). Applying names to \Robject{colClasses} is not required, but makes subsequent manipulation easier. We are now ready to read our file <>= cols <- readXStringColumns(analysisPath(sp), pattern, colClasses) cols @ % The file has been parsed, and appropriate data objects were created. A feature of \Rfunction{readXStringColumns} and other input functions in the \Rpackage{ShortRead} package is that all files matching \Rcode{pattern} in the specified \Rcode{dirPath} will be read into a single object. This provides a convenient way to, for instance, parse all tiles in a Solexa lane into a single \Rclass{DNAStringSet} object. There are several advantages to reading columns as \Rclass{XStringSet} objects. These are more compact than the corresponding character representation: <>= object.size(cols$read) object.size(as.character(cols$read)) @ % They are also created much more quickly. And the \Rclass{DNAStringSet} and related classes are used extensively in \Rpackage{ShortRead}, \Rpackage{Biostrings}, \Rpackage{BSgenome} and other packages relevant to short read technology. \subsection{Sorting} Short reads can be sorted using \Rfunction{srsort}, or the permutation required to bring the short read into lexicographic order can be determined using \Rfunction{srorder}. These functions are different from \Rfunction{sort} and \Rfunction{order} because the result is independent of the locale, and they operate quickly on \Rclass{DNAStringSet} and \Rclass{BStringSet} objects. The function \Rfunction{srduplicated} identifies duplicate reads. This function returns a logical vector, similar to \Rfunction{duplicated}. The negation of the result from \Rfunction{srduplicated} is useful to create a collection of unique reads. An experimental scenario where this might be useful is when the sample preparation involved PCR. In this case, replicate reads may be due to artifacts of sample preparation, rather than differential representation of sequence in the sample prior to PCR. \subsection{Summarizing read occurrence} The \Rfunction{tables} function summarizes read occurrences, for instance, <>= tbls <- tables(fq) names(tbls) tbls$top[1:5] head(tbls$distribution) @ %% The \Robject{top} component returned by \Robject{tables} is a list tallying the most commonly occurring sequences in the short reads. Knowledgeable readers will recognize the top-occurring read as a close match to one of the manufacturer adapters. The \Robject{distribution} component returned by \Robject{tables} is a data frame that summarizes how many reads (e.g., \Sexpr{tbls[["distribution"]][1,"nReads"]}) are represented exactly \Sexpr{tbls[["distribution"]][1,"nOccurrences"]} times. \subsection{Finding near matches to short sequences} Facilities exist for finding reads that are near matches to specific sequences, e.g., manufacturer adapter or primer sequences. \Rfunction{srdistance} reports the edit distance between each read and a reference sequence. \Rfunction{srdistance} is implemented to work efficiently for reference sequences whose length is of the same order as the reads themselves (10's to 100's of bases). To find reads close to the most common read in the example above, one might say <>= dist <- srdistance(sread(fq), names(tbls$top)[1])[[1]] table(dist)[1:10] @ %% `Near' matches can be filtered, e.g., <>= fqSubset <- fq[dist>4] @ A different strategy can be used to tally or eliminate reads that consist predominantly of a single nucleotide. \Rfunction{alphabetFrequency} calculates the frequency of each nucleotide (in DNA strings) or letter (for other string sets) in each read. Thus one could identify and eliminate reads with more than 30 adenine nucleotides with <>= countA <- alphabetFrequency(sread(fq))[,"A"] fqNoPolyA <- fq[countA < 30] @ %% \Rfunction{alphabetFrequency}, which simply counts nucleotides, is much faster than \Rfunction{srdistance}, which performs full pairwise alignment of each read to the subject. Users wanting to use \R{} for whole-genome alignments or more flexible pairwise aligment are encouraged to investigate the \Rpackage{Biostrings} package, especially the \Rclass{PDict} class and \Rfunction{matchPDict} and \Rfunction{pairwiseAlignment} functions. \section{Legacy support for early file formats} The \Biocpkg{ShortRead} package contains functions and classes to support early file formats and ungapped alignments. Help pages are flagged as `legacy'; versions of \Biocpkg{ShortRead} prior to 1.21 (\Bioconductor{} version 2.13) contain a vignette illustrating common work flows with these file formats. %--------------------------------------------------------- % SessionInfo %--------------------------------------------------------- \section{sessionInfo} <>= toLatex(sessionInfo()) @ \end{document} ShortRead/vignettes/README0000644000175400017540000000121213001230552016346 0ustar00biocbuildbiocbuildI have added an unserscore to the extention of the vignette file, i.e., it is called ShortRead_and_HilbertCurveDisplay.Rnw_, so that it does not get processed automatically. This is because it takes quite long to get build the vignette and you have to download some example data before building it. If you do want to rebuild the vignette: In the same directory that contains the Rnw file, make two subdirecties, called H3K4me1 and H3K4me3. Go to http://www.ebi.ac.uk/~anders/ShortReadExampleData/, download the content of the two directories H3K4me1 and H3K4me3 there and put these files in the directories just created. Then run Sweave. ShortRead/vignettes/ShortRead_and_HilbertVis.Rnw_0000644000175400017540000011645613001230552023206 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{Processing and Visualisation of High-Throughput Sequencing with ShortRead and HilbertVis} %\VignettePackage{ShortRead} \documentclass{article} \usepackage[a4paper]{geometry} \usepackage{hyperref,graphicx} \SweaveOpts{keep.source=TRUE,eps=FALSE,include=FALSE,width=4,height=4.5} \newcommand{\Robject}[1]{\texttt{#1}} \newcommand{\Rpackage}[1]{\textit{#1}} \newcommand{\Rclass}[1]{\textit{#1}} \newcommand{\Rfunction}[1]{{\small\texttt{#1}}} \author{Simon Anders\\[1em]European Bioinformatics Institute,\\ Hinxton, Cambridge, UK\\[1em] \texttt{sanders@fs.tum.de}} \title{\textbf{Processing and Visualisation of High-Throughput Sequencing Data with \texttt{ShortRead} and \texttt{HilbertVis}}} \date{version 2: 2009-06-30} \begin{document} \maketitle \begin{abstract} This document serves a double purpose: (i) It explains the use of the Bioconductor packages \Rpackage{HilbertVis} and \Rpackage{HilbertVisGUI}. This pair of packages offers a tool to visualise very long one-dimensional data vectors (with up to billions of entries) in an efficient fashion that allows to get a quick impression of the spatial distribution and rough shape of the features present in the data. This is especially useful in the initial exploration of high-resolution position-dependent genomic data, such as tiling array or ChIP-Seq data. (ii) It provides a specific example by walking the reader through the task of processing ChIP-Seq data using the stand-alone alignment tool Maq and the Bioconductor packages Biostrings, ShortRead and HilbertVis/HilbertVisGUI. \end{abstract} \medskip \noindent{\small\textbf{Note:} If you are only interested in the use of the \texttt{HilbertVis}/\texttt{HilbertVisGUI} packages, you can skip the first section and start reading at Section \ref{secHilbert}.} \medskip \noindent{\small\textbf{Note:} If you have trouble installing the package \texttt{HilbertVisGUI}, read the file \texttt{INSTALL} in the package.} \section{Introduction} Bioconductor offers substantial support for genomic experiments, which, for the case of microarray platforms, including tiling arrays, has reached maturity already a while ago. For data from high-throughput sequencing experiments, development of new tools is currently (mid 2008) ongoing. In this document, I would like to show what can already been done by re-doing step for step the analysis of an already published Solexa ChIP-Seq experiment. I use this to demonstrate some aspects of the ShortRead package (by M.~Morgan, \cite{ShortRead}) and the use of my packages ``HilbertVis'' and ``HilbertVisGUI''. ShortRead introduces data structures to represent aligned short sequence reads and offers functions to read in such data from files output by the SolexaPipeline (the software that Illumina provides with its GenomeAnalyzer machine) or by Maq (a stand-alone alignment program, \cite{Maq}). ShortRead's data structures are based on the infrastructure provided by Biostrings. As an example, we use data from a published study, Ref. \cite{HistMeth_ChipSeq}, on histone methylations in the human genome. Although this was not the main focus of that study, we re-analyse the data for histone methylation patterns H3K4me1 and H3K4me3, as these are data sets of manageable size. We first re-do the alignment with Maq, then use ShortRead to read the result into R and then visualise the data with HilbertVis. \section{The example data} The authors of our example have deposited their raw data in the NCBI's Provisional Short Read Archive (SRA, \url{http://www.ncbi.nlm.nih.gov/Traces/sra/sra.cgi}) under accession number SRA000206. Use the ``Submissions'' tab in the archive's ``Download Facility'' to find the submission (under ``SRA000''). You will get to a directory that contains all the data as output by Bustard, the base-calling program in the SolexaPipeline, as well as a number of fairly self-explanatory XML files with meta-data.\footnote{When I first wrote this vignette in June 2008, the SRA was still in a provisional state, and the presentation of the data has changed since then. You can still find the old files in the subdirectory ``provisional''.} There are 3 lanes for H3K4me1 and 7 lanes for H3K4me3. I have used Maq to align the reads from these lanes against the human reference genome as provided on Ensembl. Doing so requires converting the \texttt{\_seq.txt} and \texttt{\_prb.txt} files for the lanes to the Sanger Institute's FASTQ format and on to Maq's BFQ (binary FASTQ format). Likewise, the reference genome is converted to one large BFA (binary FASTA) file. Then, the \texttt{maq map} command may be used to perform the alignment. As these steps are described in the documentation on the Maq web site, I do not go into detail here.\footnote{However, feel free to contact me if you want to know details.} In the end, we have, as output from Maq, a mapping file for each lane. I have put these files onto my web page. So, if you want to try out the following steps for yourself, please download them from \url{http://www.ebi.ac.uk/~anders/ShortReadExampleData/}. Note, however, that you should use a machine with at least 4 GB of RAM to perform the examples. Of course, Maq is not the only choice to align the reads to the genome. You may as well use Eland (the alignment program that comes with the SolexaPipeline), which can be read in as well by ShortRead, so that the following steps apply to this case as well. Within certain limits, the matching functionality of the Biostrings package allows you to even do everything within R. Finally, there are other alignment tools specialised for high-throughput sequences. Recently, the \texttt{ShortRead} package's \texttt{readAligned} function was extended and it can now parse the output formats of several popular tools, including Eland, Maq, SOAP, Bowtie, and the SAM format used e.g. by BWA. \section{Reading in the alignment} Assume that the current working directory contains two sub-directories, names \texttt{H3K4me1} and \texttt{H3K4me3}. Then we can read in all the files of pattern \texttt{run\textit{xx}lane\textit{x}.map} with the following commands: <<>>= library("ShortRead") maps.me1 <- sapply( list.files( "H3K4me1", "run.*lane.\\.map" ), function(filename) readAligned( "H3K4me1", filename, type="MAQMapShort" ) ) maps.me3 <- sapply( list.files( "H3K4me3", "run.*lane.\\.map" ), function(filename) readAligned( "H3K4me3", filename, type="MAQMapShort" ) ) @ Here, \texttt{readAligned} takes three arguments: the directory that contains the map file, the name of the map file, and the type of data to be read, for Maq alignment data \texttt{MAQMap}. (Our example data has been aligned with an older version of Maq, prior to the recent change in binary format in Maq version 0.7. Using the type \texttt{MAQMapShort} allows to read the old format.) You may also use the type \texttt{SolexaExport} to read in mappings produced by Eland (see help page for \texttt{readAligned} for details on the supported formats). In any case, the function \texttt{readAligned} returns an S4 object of class \texttt{AlignedRead}. \section{The class \texttt{AlignedRead}} An \texttt{AlignedRead} object is conceptionally quite similar to a data frame. It contains as many ``rows'' as there are mapped reads: <<>>= length( maps.me1$run4_lane8.map ) @ %$ For each read, all the data parsed from the map file are stored. Think of these types of data as of columns of a data frame, even though you do not access them with the \texttt{\$} operator but with accessor functions. The ``columns'' \texttt{chromosome}, \texttt{position} and \texttt{width} show where in the genome the reads were mapped: <<>>= head( chromosome( maps.me1$run4_lane8.map ) ) head( position( maps.me1$run4_lane8.map ) ) head( width( maps.me1$run4_lane8.map ) ) @ %$ As we see, the first 6 of the 3.4 mio reads in lane 8 of run 4 were all mapped to chromosome 10, to the given positions, and extending from there all by 25 bp.\footnote{Note that Maq stores the aligned reads in order of their alignment. Hence, we start with very low base-pair indices, which then increase. Maq has also started with chromosome 10, as that one happened to be the first one in the BFA file.} The actual reads are stored as well, <<>>= head( sread( maps.me1$run4_lane8.map ) ) @ %$ as are the reads' identifiers (which here encode their position on the lane): <<>>= head( id( maps.me1$run4_lane8.map ) ) @ %$ These last two objects are not ordinary R \texttt{character} vectors but \texttt{DNAStringSet} and \texttt{BStringSet} objects. These are specialised data structures provided by the \texttt{Biostrings} package designed to handle large amounts of character (or sequence) data. They are not elementary-type vectors but S4 objects. (See the Biostrings vignette for details.) As they only mimic a vector they cannot be columns of a data frame. This is the reason why \texttt{AlignedRead} is not a data frame although its structure is reminiscent of one. Other information stored in the \texttt{AlignedRead} object is the base-call quality as reported by Bustard, here given in FASTQ quality string representation. (See the Maq web site for an explanation of the format.) <<>>= head( quality( maps.me1$run4_lane8.map ) ) @ %$ Each of the letters codes for for the quality of a base call, i.e., which stands for the probability that the base call is incorrect. To see the actual quality scores, coerce the quality BStringSet to a matrix: <<>>= quals <- as( head( quality( maps.me1$run4_lane8.map ) ), "matrix" ) quals @ %$ For an explanation how the letters are converted to scores, look up the FASTQ standard. (Wikipedia has a good explanation.) The numbers are Phred scores, i.e. the probability for a base being wrong is given by $10^{-Q/10}$: <<>>= 10 ^ ( -quals / 10 ) @ %$ Maq calculates from this information and from the uniqueness and perfectness of the alignment an alignment score, which is stored in an \texttt{alignQuality} object: <<>>= alignQuality( maps.me1$run4_lane8.map ) @ %$ The actual integer vector of qualities (one number per read) can be obtained with the accessor function \texttt{quality} <<>>= head( quality( alignQuality( maps.me1$run4_lane8.map ) ) ) @ %$ An alignment quality score of 0 may mean that the read could not be uniquely aligned and has been put by Maq at one of the possible positions, chosen at random. As before, the probability for the alignment being wrong is $10^{-Q/10}$, where $Q$ is the quality score. Bear in mind that all these probabilities are estimates derived partly by heuristics. See the SolexaPipeline manual and the Maq paper for details before relying on them. \begin{figure} \centering \includegraphics[height=5cm]{images/Strand_and_Dir} \caption{The \texttt{strand} information shows how the read is aligned against the genome. If \texttt{strand} is \texttt{"+"}, the \texttt{position} indicates the start of the DNA read (dark green) as well as the start of the whole fragment. The part of the fragment that was not read (light green) extends to the right (i.e. towards larger chromosome coordinates). If \texttt{strand} is \texttt{"-"}, then the fragment extends to the left. As \texttt{position} always indicates the left edge of the read (but not necessarily an edge of the whole fragment) it now points to a position within the whole fragment. } \label{strand} \end{figure} The accessor function \texttt{strand} reports whether the read was mapped to the ``+'' or to the ``--'' strand of the chromosome. It returns a factor with three levels: <<>>= head( strand( maps.me1$run4_lane8.map ) ) @ %$ You should never see the level ``*'' in an AlignedData object. It is used in other contexts to indicate that a strand information is not just unavailable (this would be an \texttt{NA}) but does not have any meaning. Remember that Solexa sequencing is not strand-specific (unless you use one of the new strand-specific RNA-Seq protocols). Hence, it is better to think of this factor not as information on the strand but rather on the \textit{direction} of the fragment. Have a look at Fig.\ \ref{strand} for an illustration. The fields described so far are available for all \texttt{AlignedRead} objects. Depending on the alignment software that was used additional information may be available. The slot \texttt{alignData} is meant to hold such information. The fields that you can see here are explained in the manual to Maq. <<>>= alignData( maps.me1$run4_lane8.map ) @ %$ \texttt{AlignedDataFrame} is a subclass of \texttt{AnnotatedDataFrame}. Hence, we can see the meaning of the columns from the meta information displayed above and access the underlying data frame with \texttt{pData}: <<>>= head( pData( alignData( maps.me1$run4_lane8.map ) ) ) @ %$ \section{Coverage} In ChIP-Seq, one is usually interested in the number of precipitated DNA fragments in the sample that were mapped to each genomic locus. This is best represented by what is often called a ``coverage vector'' (or sometimes a ``pile-up vector''). This is a very long \texttt{integer} vector with as many elements as there are base pairs in the chromosome under consideration. Each vector element counts the number of fragments that were mapped such that they cover this base pair. The function \texttt{coverage} in the \texttt{ShortRead} package calculates such a vector from alignment information. In order to allocate a vector of the right size, \texttt{coverage} needs to know the length of the chromosome. \texttt{readBfaToc} obtains the lengths of all sequences in a BFA file (binary FASTA, the compressed FASTA format used by Maq). As a BFA file has a table of content at the beginning, \texttt{readBfaToc} only has to read the header of the BFA file and is hence quite fast. \begin{verbatim} > seqlens <- readBfaToc( "Homo_sapiens.NCBI36.48.dna.all.bfa" ) > seqlens 10 11 12 13 14 15 16 17 135374737 134452384 132349534 114142980 106368585 100338915 88827254 78774742 18 19 1 20 21 22 2 3 76117153 63811651 247249719 62435964 46944323 49691432 242951149 199501827 4 5 6 7 8 9 MT X 191273063 180857866 170899992 158821424 146274826 140273252 16571 154913754 Y NT_113887 NT_113947 NT_113903 NT_113908 NT_113940 NT_113917 NT_113963 57772954 3994 4262 12854 13036 19187 19840 24360 NT_113876 NT_113950 NT_113946 NT_113920 NT_113911 NT_113907 NT_113937 NT_113941 25994 28709 31181 35155 36148 37175 37443 37498 NT_113909 NT_113921 NT_113919 NT_113960 NT_113945 NT_113879 NT_113938 NT_113928 38914 39615 40524 40752 41001 42503 44580 44888 NT_113906 NT_113904 NT_113873 NT_113966 NT_113943 NT_113914 NT_113948 NT_113886 46082 50950 51825 68003 81310 90085 92689 96249 NT_113932 NT_113929 NT_113878 NT_113927 NT_113900 NT_113918 NT_113875 NT_113942 104388 105485 106433 111864 112804 113275 114056 117663 NT_113926 NT_113934 NT_113954 NT_113953 NT_113874 NT_113883 NT_113924 NT_113933 119514 120350 129889 131056 136815 137703 139260 142595 NT_113884 NT_113890 NT_113870 NT_113881 NT_113939 NT_113956 NT_113951 NT_113902 143068 143687 145186 146010 147354 150002 152296 153959 NT_113913 NT_113958 NT_113949 NT_113889 NT_113936 NT_113957 NT_113961 NT_113925 154740 158069 159169 161147 163628 166452 166566 168820 NT_113882 NT_113916 NT_113930 NT_113955 NT_113944 NT_113901 NT_113905 NT_113872 172475 173443 174588 178865 182567 182896 183161 183763 NT_113952 NT_113912 NT_113935 NT_113880 NT_113931 NT_113923 NT_113915 NT_113885 184355 185143 185449 185571 186078 186858 187035 189789 NT_113888 NT_113871 NT_113964 NT_113877 NT_113910 NT_113962 NT_113899 NT_113965 191469 197748 204131 208942 211638 217385 520332 1005289 NT_113898 1305230 \end{verbatim} If you try to reproduce this example, you may not have the BFA file\footnote{I have not put it on my web page as it is very big and easily created from the Ensembl files.}. So, you can obtain the object \texttt{seqlens} manually with the following command (which omits the \texttt{NT\_}xxxxx contigs): <<>>= seqlens <- c( `10`=135374737, `11`=134452384, `12`=132349534, `13`=114142980, `14`=106368585, `15`=100338915, `16`=88827254, `17`=78774742, `18`=76117153, `19`=63811651, `1`=247249719, `20`=62435964, `21`=46944323, `22`=49691432, `2`=242951149, `3`=199501827, `4`=191273063, `5`=180857866, `6`=170899992, `7`=158821424, `8`=146274826, `9`=140273252, MT=16571, X=154913754, Y=57772954 ) @ In order to get coverage vectors for all chromosomes, using only mappings in \texttt{maps.me3\$run13\_lane4.map} with a mapping quality of at least 10, we first create a new \texttt{AlignedRead} object containing only these reads (note that we also filter out reads that map to chromosomes or contigs for which we do not have sequence lengths) <<>>= filteredReads <- maps.me3$run13_lane4.map[ chromosome( maps.me3$run13_lane4.map ) %in% names(seqlens) & quality(alignQuality( maps.me3$run13_lane4.map )) >= 10 ] @ %$ and then run the \texttt{coverage} function\footnote{texttt{coverage} is a generic method defined in the \texttt{IRanges} object. Here, we use its specialization for \texttt{ReadAligned} objects, defined in the \texttt{ShortRead} package. For the help pages, see both \texttt{?coverage} and \texttt{class?AlignedRead}.} on these: <<>>= coverage.me3.lane4 <- coverage( filteredReads, width=seqlens ) coverage.me3.lane4 @ The object \texttt{coverage.me3} is a \texttt{SimpleRleList} object, essentially a list of coverage vectors, one for each chromosome. Here is the coverage vector for chromsome 10: <<>>= coverage.me3.lane4$`10` @ %$ This is a vector with 135 mio elements, i.e., one number for each base pair on chromosome 10. If we stored this as an ordinary vector in RAM, it would need 135 MB. However, it contains long stretches of constant values, and hence, \texttt{coverage} returns its result as run-length encoded (\texttt{Rle}) vectors. As you can see, the coverage vector contains a few ten thousands of ``runs'', i.e., of repeats of the same value, and stores this information in the form of the lengths and the values of these runs. \begin{figure} \centering \includegraphics{ShortRead_and_HilbertVis-me3_p10} \caption{Output of \texttt{plotLongVector( me3.p10 )}.} \label{me3_p10} \end{figure} In principle, we could now plot this vector by converting it to an ordinary vector and using the standard ``plot'' function: <>= plot( as.vector( coverage.me3.lane4$`10` ), type='h' ) @ $% However, this command takes very long, as it plots one needle for each vector element, spending most of its time plotting over and over at the same spot. The function \texttt{plotLongVector} (in \texttt{HilbertVis}) produces the same plot with a decent speed: <>= library("HilbertVis") plotLongVector( coverage.me3.lane4$`10` ) @ $% [Output: See Fig.\ \ref{me3_p10}.] It does so by first partitioning the vector in 4,000 segments of equal length and the gets the maxima and minima of each segments (with the \texttt{shrinkVector} funcion). It the draws vertcial lines from the minima to the maxima. In case you want to write your own plotting function (because \texttt{plotLongVector} is rather rudimentary), you can use the function \texttt{shrinkVector} (in \texttt{HilbertVis}) to accomplish this. In the form used above, the function \texttt{coverage} counts only which base pairs the actual read covers. Typically, the read length (here: 25 nt) is much shorter than the length of the DNA fragments. In the present data, the length of the fragments after sonication, adaptor ligation and gel-electrophoretic size selection was about 220 bp including adaptors, i.\,e., approx.\ 185 bp without adaptors. Given that the immuno-precipitated histone can be anywhere on the fragment, not necessarily within the part at the end that is actually sequenced (the ``read'') we get a less biased picture by incorporating this information into the calculation of the pile-up vector. The \texttt{coverage} can be called with an \texttt{extend} argument to extend each fragment by a certain size. It uses the strand information to know which direction to extend to (see Fig.\ \ref{strand}). <<>>= coverage.me3.lane4.ext <- coverage( filteredReads, width=seqlens, extend=185L-width(filteredReads) ) @ Our coverage vector incorporated only the information from one lane. We get better count statistics by getting such a vector for each lane from the H3K4me3 sample and then simply summing them all up: <<>>= sumUpCoverage <- function( lanes, seqLens, minAQual, fragmentLength ) { res <- NULL for( i in 1:length(lanes) ) { filteredLane <- lanes[[i]][ quality(alignQuality( lanes[[i]] )) >= minAQual & chromosome(lanes[[i]]) %in% names(seqlens) ] cvg <- coverage( filteredLane, width = seqLens, extend = as.integer(fragmentLength) - width(filteredLane) ) if( is.null( res ) ) res <- cvg else { stopifnot( all( names(res) == names(cvg) ) ) for( seq in names(res) ) res[[seq]] <- res[[seq]] + cvg[[seq]] } } res } coverage.me3 <- sumUpCoverage( maps.me3, seqlens, 10, 185 ) @ Note that \texttt{for} loops are used here instead of \texttt{sapply}. The latter may look more natural in R but it builds up a two-dimensional array of all the intermediate coverage vectors, which is wasteful. Even with the \texttt{for} loop the operation takes a while. Let's do the same for ``me1'': <<>>= coverage.me1 <- sumUpCoverage( maps.me1, seqlens, 10, 185 ) @ As ``me1'' has only 3 lanes as opposed to ``me3'''s 7 lanes, we cannot compare them directly. A simple way of normalizing is to divide by the ``library size'', i.e., the total number of reads. <<>>= nreads.me1 <- sum( sapply( maps.me1, length ) ) coverage.me1.n <- GenomeData( lapply( coverage.me1, function(r) r / nreads.me1 ) ) nreads.me3 <- sum( sapply( maps.me3, length ) ) coverage.me3.n <- GenomeData( lapply( coverage.me3, function(r) r / nreads.me3 ) ) @ \section{Visualisation with Hilbert curve plots} \label{secHilbert} \subsection{The Hilbert curve} \noindent{\small \textbf{Note:} If you have skipped the previous sections as you only want to read about \texttt{HilbertVis}, here is what you need to know in order to start reading here: We have re-analysed part of the ChIP-Seq experiments done in Ref.\ \cite{HistMeth_ChipSeq}, namely the data regarding histone methylation patterns H3K4me1 and H3K4me3. We have constructed two sets of very long ``coverage'' vectors in \texttt{IRanges}'s \texttt{Rle} form, \texttt{coverage.me1} and \texttt{coverage.me3}, which represent the human chromosomes and have a length corresponding to the number of base pairs of each chromosome. Each element corresponds to a base pair and counts how many precipitated and sequenced DNA fragments within the respective sample (H3K4me1 or H3K4me3) cover this position. The vectors \texttt{coverage.me1.n} and \texttt{coverage.me3.n} have been normalized by dividing by the total number of reads. To do your own experiments with these vectors, you can download these vectors (truncated to only contain chromosome 10, to save space) as R data file from \url{http://www.ebi.ac.uk/~anders/ShortReadExampleData/meX.chr10.rda}.} \bigskip \noindent{\small \textbf{Note 2:} Since I have written this vignette, I have restructured the package and split it into two parts, called ``HilbertVis'' and ``HilbertVisGUI''. This text focuses on the functionality of ``HilbertVisGUI'', which provides an interactive tool to explore data using teh visualisation technique desribed in the following. ``HilbertVis'' contains further functions to produce the same kind of images but without interactive tools, i.e. solely from the R command line. If you want to know more about these functions, which are not mentioned in the present text, see the vignette ``Visualising very long data vectors with the Hilbert curve'', which is included in the ``HilbertVis'' package.} \bigskip \begin{figure} \centering \includegraphics{ShortRead_and_HilbertVis-pileup1D} \caption{Pile-up representation of the ChIP-Seq data for H3K4me1 and H3K4me3, depicting the whole of chromosome 10.} \label{pileup1D} \end{figure} \noindent A first approach to visualising the two vectors is plotting them with the \texttt{plotLongVector} function described above: <>= library( ShortRead ) library( HilbertVis ) library( HilbertVisGUI ) par( mfrow = c(2,1) ) plotLongVector( coverage.me1.n$`10`, main="Chr 10, H3K3me1" ) plotLongVector( coverage.me3.n$`10`, main="Chr 10, H3K3me3" ) @ [Output: Fig.\ \ref{pileup1D}.] \begin{figure} \centering \includegraphics{ShortRead_and_HilbertVis-pileup1Dzoom} \caption{Zoom into a small portion of Fig.\ \ref{pileup1D}.} \label{pileup1Dzoom} \end{figure} The two vectors do look different but it is hard to make out what gives rise to the difference. Is the number of peaks different, or their distribution, or their typical width? Given that each pixel on the x axis corresponds to more than 100 kp, each of the needle can as well be a small peak, only a few fragment lengths wide, a wide peak with a base of tens of kb, or even a cluster of several peaks. We might zoom in somewhere but this is not too illuminating: <>= par( mfrow = c(2,1) ) plotLongVector( coverage.me1.n$`10`[100000000:101000000], main="Chr 10, H3K3me1", offset=100000000 ) plotLongVector( coverage.me1.n$`10`[100000000:101000000], main="Chr 10, H3K3me3", offset=100000000 ) @ [Output: Fig.\ \ref{pileup1Dzoom}.] The standard approach would be to export the pile-up vectors into a genome track format such as BED\footnote{A function to do that might be added soon to \texttt{ShortRead}.} and then use a genome browser such as those on the UCSC or Ensembl web sites, or IGB, to zoom in at many places to get a feeling for the data. \begin{figure} \centering \includegraphics[width=.4\textwidth]{images/HilbertPlot_H3K4me1}\qquad \includegraphics[width=.4\textwidth]{images/HilbertPlot_H3K4me3} \caption{Hilbert curve plot of pile-ups for H3K4me1 (left) and H3K4me3 (right) on chromosome 10.} \label{twoHilbertPlots} \end{figure} The Hilbert curve plot is an approach to display an as detailed picture of the whole chromosome as possible by letting each pixel of a large square represent a quite short part of the chromosome, coding with its colour for the maximum count in this short stretch, where the pixels are arranged such that neighbouring parts of the chromosome appear next to each other in the square. Furthermore, parts which are not directly neighbouring but are ion close distance should not be separated much in the square either. Fig.\ \ref{twoHilbertPlots} shows the two pile-up vectors in this so-called Hilbert curve plot. \begin{figure} \centering \includegraphics{ShortRead_and_HilbertVis-HilbertCurves} \caption{The first four levels of the Hilbert curve fractal.} \label{HilbertCurves} \end{figure} In order to understand this plot you need to know how the pixels are arranged to fulfil the requirements just outlined as well as possible. To my knowledge, the first to study this problem in detail and to come up with the solution also used here was D.\ A.\ Keim in Ref.\ \cite{HilbertVisualization_first} (where he used the data to visualise long time-series data of stock-market prices). He went back to an old idea of Peano \cite{PeanoCurve_first} and Hilbert \cite{HilbertCurve_first}, space-filling curves. Peano astonished the mathematics community at the end of the 19th century by presenting a continuous mapping of a line to a square, i.e., showed that a line can be folded up such that it passes through every point within a square, thus blurring the seemingly clear-cut distinction between one- and two-dimensional objects. Such a space-filling curve is a fractal, i.e., it has infinitely many corners and repeats its overall form in all levels of its details. Fig.\ \ref{HilbertCurves} shows the first six level of the construction of Hilbert's variant of Peano's curve. Observe how at level $k$ a line of length $2^{2k}$ passes through each ``pixel'' of a square of dimension $2^k \times 2^k$, and how this curve is produced connecting four copies (in different orientations) of the curve at the previous level, $k-1$. Figure \ref{HilbertCurves} has been produced with the function \texttt{plotHilbertCurve} which is provided just for demonstration purposes. <>= library( grid ) pushViewport( viewport( layout=grid.layout( 2, 2 ) ) ) for( i in 1:4 ) { pushViewport( viewport( layout.pos.row=1+(i-1)%/%2, layout.pos.col=1+(i-1)%%2 ) ) plotHilbertCurve( i, new.page=FALSE ) popViewport( ) } @ Going back to Fig.\ \ref{twoHilbertPlots}, we can now see clear difference between the two samples. The following observations my be made just from comparing these two plots: The peaks of H3K4me3 are tall, narrow, and well defined, while those for H3K4me1 are rather washed out. In both cases the peaks spread out over the whole chromosome, but some areas have nearly no signal. These empty parts are the same in both cases. These points were not clear only from Fig.\ {}. Exploring the plot interactively as described in the following allows to get considerable more insights. \subsection{The \texttt{HilbertVis} GUI} In order to study the pile-up vectors, you can now simply call \begin{verbatim} hilbertDisplay( coverage.me1.n$`10`, coverage.me3.n$`10` ) \end{verbatim} \begin{figure} \centering \includegraphics[width=.6\textwidth]{images/HilbertDisplay_GUI} \caption{The graphical user interface (GUI) provided by \texttt{HilbertVisGUI}.} \label{GUI} \end{figure} A GUI, as depicted in Fig.\ \ref{GUI} will pop up that allows you to interactively explore your data in the Hilbert curve plot representation. First, press the ``Darker'' button two or three times to get better contrast. Then, move the mouse over the coloured square and observe how the small red line in the right-hand gauge (labelled ``Displayed part of sequence'') indicates where within the chromosome you are pointing. Playing with this feature allows you to quickly orient yourself on how the chromosome is folded into the square. You can also read off the exact position from the field ``Bin under mouse cursor''\footnote{The display of the bin's value is not yet functional.} Use the left mouse button to zoom in by clicking on one of the four quarters of the image. You can only zoom into a quarter, not into any part of the image, because this ensures that the displayed part is always a single consecutive stretch of the chromosome. The left-hand gauge (labelled ``Full sequence'') indicates which part is displayed: the full width of the gauge represents the whole chromosome, the portion highlighted in red the part that is currently displayed in the square. The coordinates of the first and last displayed base are printed in the edges of the right-hand gauge. With the radio buttons labelled ``Effect of left mouse button'' you my switch from zooming into a quarter to zooming into a 1/64 part, i.e. into one of the small squares in a though $8\times 8$ grid. Use the buttons at the bottom to zoom out. If you have passed several vectors when calling \texttt{hilbertDisplay}, you may switch back and forth between them with the buttons ``Next'' and ``Previous'' (or by pressing Alt-N and Alt-P) in order to compare the displayed parts. The two buttons ``Coarser'' and ``Finer'' allow to adjust the pixel size. Initially, each bin is represented by one pixel at your monitor's resolution, and there are $512\times 512$ pixels in the image. Pressing ``Coarser'' once blows up each image pixel to a $2\times 2$ square of monitor pixels, which allows for easier viewing but reduces the number of displayed image pixels to $256\times 256$, i.e., each pixel now represents four times as many base pairs. There are a number of optional parameters to \texttt{hilbertDisplay} that come in handy, e.g., if you have vectors of differing length, if you want to customise colours or change a few other points. Refer to the help page (displayed with \texttt{?hilbertDisplay}) for details. \subsection{The callback interface} If you select the mode ``Linear plot'' as ``Effect of left mouse button'' and click somewhere in the plot, a windows pops up with a linear plot that displays the part of the chromosome represented by 256 pixels around the pixel on which you have clicked. (256 pixels correspond quite roughly to the size of the cross-hair mouse cursor). This is useful to get a detailed view of the shape of peaks. To do the linear plot, HilbertDisplay calls the R function \texttt{simpleLinPlot} defined in the \texttt{HilbertVisGUI} package. This is a simple wrapper around the function \texttt{plotLongVector} discussed earlier. Here is the definition of simpleLinPlot: <<>>= simpleLinPlot @ You can replace this function by supplying your own plotting function as the argument \texttt{plotFun} to \texttt{hilbertDisplay}. Your function must take two arguments that should be called \texttt{data} and \texttt{info}, as above, and will be filled in by \texttt{hilbertDisplay} with the displayed vector and information about where the user clicked and which part of the vector is being displayed. Try the following example to see the format of this data: \begin{verbatim} dumpDataInsteadOfPlotting <- function( data, info ) { str( data ) print( info ) } hilbertDisplay( me1.p10, me3.p10, plotFunc=dumpDataInsteadOfPlotting ) \end{verbatim} Zoom in a bit, then switch to "linear plot" and click somewhere. \texttt{dumpDataInsteadOfPlotting} will be called and output such as the following appears on your R console: \begin{verbatim} num [1:135374737] 0 0 0 0 0 0 0 0 0 0 ... $binLo [1] 22950198 $bin [1] 22950262 $binHi [1] 22950327 $dispLo [1] 16921843 $dispHi [1] 25382764 $seqIdx [1] 1 $seqName [1] "me1.p10" \end{verbatim} See \texttt{?hilbertDisplay} for an explanation of those fields that are not self-explanatory. This feature is meant to allow for customised linear plots (maybe using the \texttt{GenomeGraph} package to add annotation) but can also be used for other things than plotting, e.g., calculating some statistics about a peak clicked on. \subsection{Three-channel display} In order to look for spatial correlations in different data vectors, it may be useful to overlay the corresponding Hilbert curve plots in different colours. The function \texttt{hilbertDisplayThreeChannel} allows to display three data vectors simultaneously, using the red, green, and blue channel of the displayed image for the first, second, and third, vector. We may want to see whether the areas with strong H3K4me1 occurance are at the same chromosome regions as the majority of the H3K4me3 peaks. Furthermore, we may use the third channel to indicate the presence of exons. We first obtain a list of all exons on chromosome 10 from EnsEMBL via BioMart: <<>>= library( biomaRt ) ensembl <- useMart("ensembl", dataset = "hsapiens_gene_ensembl") exons <- getBM( attributes=c( "exon_chrom_start", "exon_chrom_end" ), filters="chromosome_name", values="10", mart=ensembl ) @ This is a set of intervals and hence best represented as an \texttt{IRanges} object: <<>>= exon.chr10.ranges <- IRanges( start=exons$exon_chrom_start, end=exons$exon_chrom_end ) @ %$ Them, we construct a vector that indicates for each base pair on chromosome 10, whether it is exonic or not (bty means of the values 1 and 0). <<>>= exons.chr10 <- coverage( exon.chr10.ranges, width = seqlens[["10"]] ) @ \begin{figure} \centering \includegraphics[width=.7\textwidth]{images/hilbert_3col} \caption{A three-color overlay (obtained with the function \texttt{hilbertDisplayThreeChannel}) of Hilbert curves for H3K4me1 (red), H3K4me3 (green) and an exon indication (blue). The image shows a zoom into the first quarter of chromosome 10 (i.e., the top left quarter of the images in Fig.\ \ref{twoHilbertPlots}).} \label{threeColor} \end{figure} With the following command, we get a 3-color representation of the three vectors in the HilbertDisplay GUI: <>= hilbertDisplayThreeChannel( coverage.me1.n$`10` * 5e5, coverage.me3.n$`10` * 5e5, exons.chr10 * .5 ) @ See Fig.\ ref{threeColor} for the image that the GUI shows. While the function \texttt{hilbertDisplay} adjusts to the value range of the data (or can be manually adjusted with optional the \texttt{paletteSteps} argument), the function \texttt{hilbertDisplayThreeChannel} expects all three vectors to be in the value range between 0 and 1. This range is transformed to colours from black to a saturated red, green, and blue. Values below 0 or above 1 are cut and displayed as if they were 0 or 1. To get the pile-up vectors down to this range, an obvious step would be to divide by their maximum value. However, this gives a too dark value, and hence, I have chosen for Fig.\ \ref{threeColor} larger scaling factors, allowing extremely high peaks to become saturated. \section{Correlation with transcription start} A common plot to do with histone modification ChIP-Seq data is to see how the pile-up correlates with transcription start sites (TSS). This is done quite easily. First, we get a list of known TSSs on chromosome 10 from EnsEMBL (again via BioMart). <<>>= tss <- getBM( attributes=c( "transcript_start", "transcript_end", "strand" ), filters="chromosome_name", values="10", mart=ensembl ) @ <<>>= head(tss) @ Note that \texttt{transcript\_start} is always smaller than \texttt{transcript\_end}, even when the transcript is on the ``--'' strand. Hence, we have to use either the start or the end coordinate of the transcript, depending on the strand, to get the actual transcription start sites, i.e., the 5' ends of the transcripts. Then, we go through all TSS, cutting out a window from 2000 bp upstreams to 2000 bp downstreams of the TSS and sum these up these vectors of length 4001 (reversing them whenever they are from the ``--'' strand): <<>>= tme1 <- rep( 0, 4001 ) tme3 <- rep( 0, 4001 ) for( i in 1:nrow(tss) ) { if( tss$strand[i] == 1 ) { tme1 <- tme1 + as.vector( coverage.me1.n$`10`[ IRanges( tss$transcript_start[i] - 2000, tss$transcript_start[i] + 2000 ) ] ) tme3 <- tme3 + as.vector( coverage.me3.n$`10`[ IRanges( tss$transcript_start[i] - 2000, tss$transcript_start[i] + 2000 ) ] ) } else { tme1 <- tme1 + rev( as.vector( coverage.me1.n$`10`[ IRanges( tss$transcript_end[i] - 2000, tss$transcript_end[i] + 2000 ) ] ) ) tme3 <- tme3 + rev( as.vector( coverage.me3.n$`10`[ IRanges( tss$transcript_end[i] - 2000, tss$transcript_end[i] + 2000 ) ] ) ) } } @ $% Note the use of \texttt{as.vector}, which transforms the \texttt{Rle} vector into an ordinary one. Without it, we would sum up many short \texttt{Rle} vectors which is very slow. \begin{figure} \centering \includegraphics{ShortRead_and_HilbertVis-tssPlot} \caption{Correlation against transcription start sites for H3K4me1 (red) and H3K4me3 (green).} \label{tssPlot} \end{figure} Normally, one would add all the other chromosomes, as well. For this vignette, we simply plot what we have so far: <>= matplot( -2000:2000, cbind( tme1, tme3 ), type="l", col=c("red","green"), lty="solid", xlab="distance to TSS", ylab="" ) abline( v=0, col="gray" ) @ [Output: Fig.\ \ref{tssPlot}.] \section*{Session info} <<>>= sessionInfo() @ \section*{Version history} \begin{itemize} \item v1: 2008-07-21 \item v2: 2009-06-30 \end{itemize} \bibliographystyle{simon2} \bibliography{hilbert} \end{document} ShortRead/vignettes/ShortRead_and_HilbertVis.pdf0000644000175400017540000477267613001230552023072 0ustar00biocbuildbiocbuild%PDF-1.4 5 0 obj << /S /GoTo /D (section.1) >> endobj 8 0 obj (Introduction) endobj 9 0 obj << /S /GoTo /D (section.2) >> endobj 12 0 obj (The example data) endobj 13 0 obj << /S /GoTo /D (section.3) >> endobj 16 0 obj (Reading in the alignment) endobj 17 0 obj << /S /GoTo /D (section.4) >> endobj 20 0 obj (The class AlignedRead) endobj 21 0 obj << /S /GoTo /D (section.5) >> endobj 24 0 obj (Coverage) endobj 25 0 obj << /S /GoTo /D (section.6) >> endobj 28 0 obj (Visualisation with Hilbert curve plots) endobj 29 0 obj << /S /GoTo /D (subsection.6.1) >> endobj 32 0 obj (The Hilbert curve) endobj 33 0 obj << /S /GoTo /D (subsection.6.2) >> endobj 36 0 obj (The HilbertVis GUI) endobj 37 0 obj << /S /GoTo /D (subsection.6.3) >> endobj 40 0 obj (The callback interface) endobj 41 0 obj << /S /GoTo /D (subsection.6.4) >> endobj 44 0 obj (Three-channel display) endobj 45 0 obj << /S /GoTo /D (section.7) >> endobj 48 0 obj (Correlation with transcription start) endobj 49 0 obj << /S /GoTo /D [50 0 R /Fit ] >> endobj 52 0 obj << /Length 2610 /Filter /FlateDecode >> stream xڭrF`` aWNk'lr%r.I ,_ @ʒ@bfzq&ffW73gq峼WE&iow j_8wsw2oqg*-+Sd-֋u'??{\lvdAKk$yƹ@ ~A PȁHeh,!"SbiJZ z%7tf,4v˸g᷁mSdgq^&6z@ZGFݩhzܑGټ$.-LWNV/j"hKV'PA^di%$8qd<2g3qbsxXXao7cX~?_4a" ~ 8HiTʸs#Ziy̙+"d+venص0w'Alj-mኊohm(Sɖ=K1! 3B 'Y",0K:Qk>Y> #!1B1z qiNKNcPmE O+yVЌlQ6 UVĥ+g0qPqIƖ!$ ȞEDo?) 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68.15 238.59 l S 68.15 236.53 m 68.19 240.64 l S 68.19 236.53 m 68.24 238.18 l S 68.24 236.53 m 68.29 237.15 l S 68.29 236.53 m 68.33 238.18 l S 68.33 236.53 m 68.38 237.15 l S 68.38 236.53 m 68.42 237.56 l S 68.42 236.53 m 68.47 237.56 l S 68.47 236.53 m 68.52 237.15 l S 68.52 236.53 m 68.56 237.15 l S 68.56 236.53 m 68.61 236.94 l S 68.61 236.53 m 68.65 237.15 l S 68.65 236.53 m 68.70 237.56 l S 68.70 236.53 m 68.75 237.56 l S 68.75 236.53 m 68.79 237.97 l S 68.79 236.53 m 68.84 237.36 l S 68.84 236.53 m 68.88 237.15 l S 68.88 236.53 m 68.93 240.03 l S 68.93 236.53 m 68.98 237.15 l S 68.98 236.53 m 69.02 237.36 l S 69.02 236.53 m 69.07 237.36 l S 69.07 236.53 m 69.11 239.00 l S 69.11 236.53 m 69.16 237.15 l S 69.16 236.53 m 69.21 237.15 l S 69.21 236.53 m 69.25 236.94 l S 69.25 236.53 m 69.30 237.15 l S 69.30 236.53 m 69.34 237.15 l S 69.34 236.53 m 69.39 236.94 l S 69.39 236.53 m 69.44 237.36 l S 69.44 236.53 m 69.48 237.97 l S 69.48 236.53 m 69.53 236.94 l S 69.53 236.53 m 69.57 237.36 l S 69.57 236.53 m 69.62 237.36 l S 69.62 236.53 m 69.67 236.94 l S 69.67 236.53 m 69.71 237.36 l S 69.71 236.53 m 69.76 236.94 l S 69.76 236.53 m 69.80 237.15 l S 69.80 236.53 m 69.85 238.18 l S 69.85 236.53 m 69.90 237.56 l S 69.90 236.53 m 69.94 237.15 l S 69.94 236.53 m 69.99 236.94 l S 69.99 236.53 m 70.03 237.15 l S 70.03 236.53 m 70.08 236.94 l S 70.08 236.53 m 70.13 237.15 l S 70.13 236.53 m 70.17 236.94 l S 70.17 236.53 m 70.22 237.15 l S 70.22 236.53 m 70.26 237.56 l S 70.26 236.53 m 70.31 237.15 l S 70.31 236.53 m 70.36 237.15 l S 70.36 236.53 m 70.40 237.77 l S 70.40 236.53 m 70.45 238.59 l S 70.45 236.53 m 70.50 237.77 l S 70.50 236.53 m 70.54 237.15 l S 70.54 236.53 m 70.59 237.15 l S 70.59 236.53 m 70.63 238.59 l S 70.63 236.53 m 70.68 241.47 l S 70.68 236.53 m 70.73 237.15 l S 70.73 236.53 m 70.77 238.79 l S 70.77 236.53 m 70.82 237.36 l S 70.82 236.53 m 70.86 240.23 l S 70.86 236.53 m 70.91 237.15 l S 70.91 236.53 m 70.96 237.36 l S 70.96 236.53 m 71.00 237.36 l S 71.00 236.53 m 71.05 236.94 l S 71.05 236.53 m 71.09 237.36 l S 71.09 236.53 m 71.14 240.85 l S 71.14 236.53 m 71.19 239.62 l S 71.19 236.53 m 71.23 237.77 l S 71.23 236.53 m 71.28 237.97 l S 71.28 236.53 m 71.32 238.38 l S 71.32 236.53 m 71.37 237.36 l S 71.37 236.53 m 71.42 237.97 l S 71.42 236.53 m 71.46 242.49 l S 71.46 236.53 m 71.51 238.38 l S 71.51 236.53 m 71.55 240.23 l S 71.55 236.53 m 71.60 240.85 l S 71.60 236.53 m 71.65 243.93 l S 71.65 236.53 m 71.69 243.52 l S 71.69 236.53 m 71.74 240.85 l S 71.74 236.53 m 71.78 237.77 l S 71.78 236.53 m 71.83 240.85 l S 71.83 236.53 m 71.88 241.05 l S 71.88 236.53 m 71.92 237.97 l S 71.92 236.53 m 71.97 240.03 l S 71.97 236.53 m 72.01 241.05 l S 72.01 236.53 m 72.06 239.21 l S 72.06 236.53 m 72.11 253.39 l S 72.11 236.53 m 72.15 237.97 l S 72.15 236.53 m 72.20 237.15 l S 72.20 236.53 m 72.24 237.77 l S 72.24 236.53 m 72.29 239.82 l S 72.29 236.53 m 72.34 237.15 l S 72.34 236.53 m 72.38 236.94 l S 72.38 236.53 m 72.43 236.94 l S 72.43 236.53 m 72.47 237.15 l S 72.47 236.53 m 72.52 237.56 l S 72.52 236.53 m 72.57 237.56 l S 72.57 236.53 m 72.61 236.94 l S 72.61 236.53 m 72.66 237.56 l S 72.66 236.53 m 72.70 236.74 l S 72.70 236.53 m 72.75 237.15 l S 72.75 236.53 m 72.80 238.38 l S 72.80 236.53 m 72.84 237.36 l S 72.84 236.53 m 72.89 236.94 l S 72.89 236.53 m 72.93 236.94 l S 72.93 236.53 m 72.98 236.94 l S 72.98 236.53 m 73.03 244.34 l S 73.03 236.53 m 73.07 240.44 l S 73.07 236.53 m 73.12 237.36 l S 73.12 236.53 m 73.16 237.15 l S 73.16 236.53 m 73.21 236.74 l S 73.21 236.53 m 73.26 236.94 l S 73.26 236.53 m 73.30 236.94 l S 73.30 236.53 m 73.35 236.94 l S 73.35 236.53 m 73.39 237.36 l S 73.39 236.53 m 73.44 237.15 l S 73.44 236.53 m 73.49 236.74 l S 73.49 236.53 m 73.53 237.36 l S 73.53 236.53 m 73.58 236.94 l S 73.58 236.53 m 73.62 237.97 l S 73.62 236.53 m 73.67 247.63 l S 73.67 236.53 m 73.72 248.04 l S 73.72 236.53 m 73.76 237.15 l S 73.76 236.53 m 73.81 238.59 l S 73.81 236.53 m 73.85 240.23 l S 73.85 236.53 m 73.90 237.56 l S 73.90 236.53 m 73.95 239.21 l S 73.95 236.53 m 73.99 241.88 l S 73.99 236.53 m 74.04 239.21 l S 74.04 236.53 m 74.08 237.36 l S 74.08 236.53 m 74.13 236.94 l S 74.13 236.53 m 74.18 237.77 l S 74.18 236.53 m 74.22 245.58 l S 74.22 236.53 m 74.27 239.00 l S 74.27 236.53 m 74.31 239.82 l S 74.31 236.53 m 74.36 239.62 l S 74.36 236.53 m 74.41 239.21 l S 74.41 236.53 m 74.45 239.82 l S 74.45 236.53 m 74.50 237.56 l S 74.50 236.53 m 74.54 244.75 l S 74.54 236.53 m 74.59 237.56 l S 74.59 236.53 m 74.64 240.64 l S 74.64 236.53 m 74.68 239.21 l S 74.68 236.53 m 74.73 240.64 l S 74.73 236.53 m 74.77 243.52 l S 74.77 236.53 m 74.82 242.49 l S 74.82 236.53 m 74.87 240.64 l S 74.87 236.53 m 74.91 248.45 l S 74.91 236.53 m 74.96 241.05 l S 74.96 236.53 m 75.00 239.62 l S 75.00 236.53 m 75.05 240.64 l S 75.05 236.53 m 75.10 241.05 l S 75.10 236.53 m 75.14 241.67 l S 75.14 236.53 m 75.19 238.79 l S 75.19 236.53 m 75.23 241.88 l S 75.23 236.53 m 75.28 238.18 l S 75.28 236.53 m 75.33 242.08 l S 75.33 236.53 m 75.37 242.29 l S 75.37 236.53 m 75.42 244.75 l S 75.42 236.53 m 75.46 246.40 l S 75.46 236.53 m 75.51 242.08 l S 75.51 236.53 m 75.56 243.93 l S 75.56 236.53 m 75.60 238.59 l S 75.60 236.53 m 75.65 237.56 l S 75.65 236.53 m 75.69 237.56 l S 75.69 236.53 m 75.74 237.36 l S 75.74 236.53 m 75.79 236.74 l S 75.79 236.53 m 75.83 236.94 l S 75.83 236.53 m 75.88 237.56 l S 75.88 236.53 m 75.92 238.38 l S 75.92 236.53 m 75.97 241.26 l S 75.97 236.53 m 76.02 238.18 l S 76.02 236.53 m 76.06 238.59 l S 76.06 236.53 m 76.11 239.00 l S 76.11 236.53 m 76.15 236.94 l S 76.15 236.53 m 76.20 238.79 l S 76.20 236.53 m 76.25 239.82 l S 76.25 236.53 m 76.29 247.01 l S 76.29 236.53 m 76.34 241.26 l S 76.34 236.53 m 76.38 245.37 l S 76.38 236.53 m 76.43 240.44 l S 76.43 236.53 m 76.48 241.05 l S 76.48 236.53 m 76.52 238.59 l S 76.52 236.53 m 76.57 243.52 l S 76.57 236.53 m 76.61 239.21 l S 76.61 236.53 m 76.66 237.15 l S 76.66 236.53 m 76.71 242.08 l S 76.71 236.53 m 76.75 240.85 l S 76.75 236.53 m 76.80 239.82 l S 76.80 236.53 m 76.84 237.36 l S 76.84 236.53 m 76.89 237.56 l S 76.89 236.53 m 76.94 237.77 l S 76.94 236.53 m 76.98 237.36 l S 76.98 236.53 m 77.03 237.15 l S 77.03 236.53 m 77.07 236.94 l S 77.07 236.53 m 77.12 243.52 l S 77.12 236.53 m 77.17 242.08 l S 77.17 236.53 m 77.21 240.03 l S 77.21 236.53 m 77.26 239.41 l S 77.26 236.53 m 77.30 237.77 l S 77.30 236.53 m 77.35 238.18 l S 77.35 236.53 m 77.40 237.36 l S 77.40 236.53 m 77.44 238.59 l S 77.44 236.53 m 77.49 240.44 l S 77.49 236.53 m 77.53 239.00 l S 77.53 236.53 m 77.58 241.88 l S 77.58 236.53 m 77.63 237.15 l S 77.63 236.53 m 77.67 239.00 l S 77.67 236.53 m 77.72 241.26 l S 77.72 236.53 m 77.76 242.29 l S 77.76 236.53 m 77.81 237.36 l S 77.81 236.53 m 77.86 240.23 l S 77.86 236.53 m 77.90 239.21 l S 77.90 236.53 m 77.95 240.03 l S 77.95 236.53 m 77.99 240.85 l S 77.99 236.53 m 78.04 237.15 l S 78.04 236.53 m 78.09 237.15 l S 78.09 236.53 m 78.13 237.15 l S 78.13 236.53 m 78.18 236.94 l S 78.18 236.53 m 78.22 236.94 l S 78.22 236.53 m 78.27 237.15 l S 78.27 236.53 m 78.32 236.94 l S 78.32 236.53 m 78.36 237.36 l S 78.36 236.53 m 78.41 237.15 l S 78.41 236.53 m 78.46 237.77 l S 78.46 236.53 m 78.50 236.94 l S 78.50 236.53 m 78.55 236.94 l S 78.55 236.53 m 78.59 237.15 l S 78.59 236.53 m 78.64 237.15 l S 78.64 236.53 m 78.69 237.15 l S 78.69 236.53 m 78.73 237.15 l S 78.73 236.53 m 78.78 238.79 l S 78.78 236.53 m 78.82 237.15 l S 78.82 236.53 m 78.87 237.36 l S 78.87 236.53 m 78.92 238.79 l S 78.92 236.53 m 78.96 236.94 l S 78.96 236.53 m 79.01 237.56 l S 79.01 236.53 m 79.05 238.18 l S 79.05 236.53 m 79.10 236.94 l S 79.10 236.53 m 79.15 236.94 l S 79.15 236.53 m 79.19 238.18 l S 79.19 236.53 m 79.24 236.94 l S 79.24 236.53 m 79.28 237.56 l S 79.28 236.53 m 79.33 236.94 l S 79.33 236.53 m 79.38 236.94 l S 79.38 236.53 m 79.42 236.74 l S 79.42 236.53 m 79.47 236.94 l S 79.47 236.53 m 79.51 236.74 l S 79.51 236.53 m 79.56 236.74 l S 79.56 236.53 m 79.61 236.74 l S 79.61 236.53 m 79.65 236.94 l S 79.65 236.53 m 79.70 236.94 l S 79.70 236.53 m 79.74 237.15 l S 79.74 236.53 m 79.79 236.94 l S 79.79 236.53 m 79.84 237.15 l S 79.84 236.53 m 79.88 237.36 l S 79.88 236.53 m 79.93 236.94 l S 79.93 236.53 m 79.97 236.94 l S 79.97 236.53 m 80.02 236.94 l S 80.02 236.53 m 80.07 236.94 l S 80.07 236.53 m 80.11 236.74 l S 80.11 236.53 m 80.16 236.94 l S 80.16 236.53 m 80.20 236.94 l S 80.20 236.53 m 80.25 237.15 l S 80.25 236.53 m 80.30 236.74 l S 80.30 236.53 m 80.34 236.94 l S 80.34 236.53 m 80.39 236.94 l S 80.39 236.53 m 80.43 237.15 l S 80.43 236.53 m 80.48 237.15 l S 80.48 236.53 m 80.53 236.94 l S 80.53 236.53 m 80.57 236.94 l S 80.57 236.53 m 80.62 236.94 l S 80.62 236.53 m 80.66 237.15 l S 80.66 236.53 m 80.71 237.77 l S 80.71 236.53 m 80.76 236.94 l S 80.76 236.53 m 80.80 237.36 l S 80.80 236.53 m 80.85 237.56 l S 80.85 236.53 m 80.89 236.94 l S 80.89 236.53 m 80.94 237.15 l S 80.94 236.53 m 80.99 236.94 l S 80.99 236.53 m 81.03 237.36 l S 81.03 236.53 m 81.08 236.94 l S 81.08 236.53 m 81.12 236.94 l S 81.12 236.53 m 81.17 236.94 l S 81.17 236.53 m 81.22 236.94 l S 81.22 236.53 m 81.26 238.38 l S 81.26 236.53 m 81.31 238.79 l S 81.31 236.53 m 81.35 236.94 l S 81.35 236.53 m 81.40 236.94 l S 81.40 236.53 m 81.45 236.94 l S 81.45 236.53 m 81.49 237.77 l S 81.49 236.53 m 81.54 237.56 l S 81.54 236.53 m 81.58 237.97 l S 81.58 236.53 m 81.63 240.85 l S 81.63 236.53 m 81.68 240.44 l S 81.68 236.53 m 81.72 247.84 l S 81.72 236.53 m 81.77 245.99 l S 81.77 236.53 m 81.81 247.22 l S 81.81 236.53 m 81.86 240.64 l S 81.86 236.53 m 81.91 244.14 l S 81.91 236.53 m 81.95 239.00 l S 81.95 236.53 m 82.00 237.97 l S 82.00 236.53 m 82.04 244.75 l S 82.04 236.53 m 82.09 238.79 l S 82.09 236.53 m 82.14 238.18 l S 82.14 236.53 m 82.18 236.94 l S 82.18 236.53 m 82.23 237.56 l S 82.23 236.53 m 82.27 237.36 l S 82.27 236.53 m 82.32 238.59 l S 82.32 236.53 m 82.37 237.36 l S 82.37 236.53 m 82.41 240.23 l S 82.41 236.53 m 82.46 241.47 l S 82.46 236.53 m 82.50 237.77 l S 82.50 236.53 m 82.55 237.15 l S 82.55 236.53 m 82.60 237.15 l S 82.60 236.53 m 82.64 239.21 l S 82.64 236.53 m 82.69 239.21 l S 82.69 236.53 m 82.73 237.36 l S 82.73 236.53 m 82.78 237.15 l S 82.78 236.53 m 82.83 238.18 l S 82.83 236.53 m 82.87 238.38 l S 82.87 236.53 m 82.92 241.05 l S 82.92 236.53 m 82.96 239.00 l S 82.96 236.53 m 83.01 238.38 l S 83.01 236.53 m 83.06 237.56 l S 83.06 236.53 m 83.10 238.79 l S 83.10 236.53 m 83.15 239.21 l S 83.15 236.53 m 83.19 238.59 l S 83.19 236.53 m 83.24 237.15 l S 83.24 236.53 m 83.29 237.97 l S 83.29 236.53 m 83.33 244.75 l S 83.33 236.53 m 83.38 239.41 l S 83.38 236.53 m 83.42 239.00 l S 83.42 236.53 m 83.47 242.49 l S 83.47 236.53 m 83.52 239.41 l S 83.52 236.53 m 83.56 241.47 l S 83.56 236.53 m 83.61 240.85 l S 83.61 236.53 m 83.65 241.05 l S 83.65 236.53 m 83.70 239.00 l S 83.70 236.53 m 83.75 238.18 l S 83.75 236.53 m 83.79 237.36 l S 83.79 236.53 m 83.84 237.97 l S 83.84 236.53 m 83.88 237.56 l S 83.88 236.53 m 83.93 237.36 l S 83.93 236.53 m 83.98 237.15 l S 83.98 236.53 m 84.02 237.15 l S 84.02 236.53 m 84.07 236.94 l S 84.07 236.53 m 84.11 237.36 l S 84.11 236.53 m 84.16 237.15 l S 84.16 236.53 m 84.21 236.94 l S 84.21 236.53 m 84.25 236.94 l S 84.25 236.53 m 84.30 237.15 l S 84.30 236.53 m 84.34 245.37 l S 84.34 236.53 m 84.39 237.56 l S 84.39 236.53 m 84.44 237.97 l S 84.44 236.53 m 84.48 237.15 l S 84.48 236.53 m 84.53 237.36 l S 84.53 236.53 m 84.57 237.77 l S 84.57 236.53 m 84.62 237.97 l S 84.62 236.53 m 84.67 242.29 l S 84.67 236.53 m 84.71 237.56 l S 84.71 236.53 m 84.76 237.15 l S 84.76 236.53 m 84.80 237.36 l S 84.80 236.53 m 84.85 237.15 l S 84.85 236.53 m 84.90 237.36 l S 84.90 236.53 m 84.94 237.36 l S 84.94 236.53 m 84.99 240.64 l S 84.99 236.53 m 85.03 237.97 l S 85.03 236.53 m 85.08 238.79 l S 85.08 236.53 m 85.13 237.97 l S 85.13 236.53 m 85.17 238.79 l S 85.17 236.53 m 85.22 237.56 l S 85.22 236.53 m 85.26 241.05 l S 85.26 236.53 m 85.31 242.90 l S 85.31 236.53 m 85.36 238.18 l S 85.36 236.53 m 85.40 239.00 l S 85.40 236.53 m 85.45 237.15 l S 85.45 236.53 m 85.49 238.18 l S 85.49 236.53 m 85.54 237.36 l S 85.54 236.53 m 85.59 242.08 l S 85.59 236.53 m 85.63 237.56 l S 85.63 236.53 m 85.68 237.56 l S 85.68 236.53 m 85.72 236.94 l S 85.72 236.53 m 85.77 237.77 l S 85.77 236.53 m 85.82 240.03 l S 85.82 236.53 m 85.86 237.15 l S 85.86 236.53 m 85.91 237.15 l S 85.91 236.53 m 85.95 236.94 l S 85.95 236.53 m 86.00 237.36 l S 86.00 236.53 m 86.05 237.15 l S 86.05 236.53 m 86.09 239.62 l S 86.09 236.53 m 86.14 237.97 l S 86.14 236.53 m 86.18 237.15 l S 86.18 236.53 m 86.23 237.77 l S 86.23 236.53 m 86.28 243.93 l S 86.28 236.53 m 86.32 251.54 l S 86.32 236.53 m 86.37 245.37 l S 86.37 236.53 m 86.42 239.21 l S 86.42 236.53 m 86.46 240.64 l S 86.46 236.53 m 86.51 237.56 l S 86.51 236.53 m 86.55 240.23 l S 86.55 236.53 m 86.60 237.56 l S 86.60 236.53 m 86.65 238.79 l S 86.65 236.53 m 86.69 238.18 l S 86.69 236.53 m 86.74 237.97 l S 86.74 236.53 m 86.78 238.38 l S 86.78 236.53 m 86.83 237.77 l S 86.83 236.53 m 86.88 242.90 l S 86.88 236.53 m 86.92 236.94 l S 86.92 236.53 m 86.97 236.94 l S 86.97 236.53 m 87.01 237.15 l S 87.01 236.53 m 87.06 239.41 l S 87.06 236.53 m 87.11 241.88 l S 87.11 236.53 m 87.15 238.59 l S 87.15 236.53 m 87.20 242.90 l S 87.20 236.53 m 87.24 238.79 l S 87.24 236.53 m 87.29 239.62 l S 87.29 236.53 m 87.34 237.56 l S 87.34 236.53 m 87.38 237.36 l S 87.38 236.53 m 87.43 238.18 l S 87.43 236.53 m 87.47 236.94 l S 87.47 236.53 m 87.52 237.56 l S 87.52 236.53 m 87.57 237.15 l S 87.57 236.53 m 87.61 237.15 l S 87.61 236.53 m 87.66 236.94 l S 87.66 236.53 m 87.70 237.77 l S 87.70 236.53 m 87.75 236.94 l S 87.75 236.53 m 87.80 237.15 l S 87.80 236.53 m 87.84 237.15 l S 87.84 236.53 m 87.89 237.15 l S 87.89 236.53 m 87.93 237.36 l S 87.93 236.53 m 87.98 237.36 l S 87.98 236.53 m 88.03 237.36 l S 88.03 236.53 m 88.07 238.79 l S 88.07 236.53 m 88.12 237.56 l S 88.12 236.53 m 88.16 236.94 l S 88.16 236.53 m 88.21 236.94 l S 88.21 236.53 m 88.26 236.94 l 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236.74 l S 145.68 236.53 m 145.72 237.15 l S 145.72 236.53 m 145.77 236.94 l S 145.77 236.53 m 145.82 236.74 l S 145.82 236.53 m 145.86 237.36 l S 145.86 236.53 m 145.91 236.94 l S 145.91 236.53 m 145.95 236.74 l S 145.95 236.53 m 146.00 237.15 l S 146.00 236.53 m 146.05 237.15 l S 146.05 236.53 m 146.09 236.74 l S 146.09 236.53 m 146.14 236.74 l S 146.14 236.53 m 146.18 236.94 l S 146.18 236.53 m 146.23 236.74 l S 146.23 236.53 m 146.28 236.94 l S 146.28 236.53 m 146.32 236.74 l S 146.32 236.53 m 146.37 236.94 l S 146.37 236.53 m 146.41 236.94 l S 146.41 236.53 m 146.46 236.94 l S 146.46 236.53 m 146.51 236.94 l S 146.51 236.53 m 146.55 237.15 l S 146.55 236.53 m 146.60 236.74 l S 146.60 236.53 m 146.64 236.94 l S 146.64 236.53 m 146.69 236.94 l S 146.69 236.53 m 146.74 237.15 l S 146.74 236.53 m 146.78 236.74 l S 146.78 236.53 m 146.83 236.74 l S 146.83 236.53 m 146.87 236.74 l S 146.87 236.53 m 146.92 236.94 l S 146.92 236.53 m 146.97 237.15 l S 146.97 236.53 m 147.01 236.94 l S 147.01 236.53 m 147.06 236.94 l S 147.06 236.53 m 147.10 236.94 l S 147.10 236.53 m 147.15 236.94 l S 147.15 236.53 m 147.20 236.94 l S 147.20 236.53 m 147.24 237.15 l S 147.24 236.53 m 147.29 236.74 l S 147.29 236.53 m 147.33 236.94 l S 147.33 236.53 m 147.38 237.15 l S 147.38 236.53 m 147.43 236.94 l S 147.43 236.53 m 147.47 236.94 l S 147.47 236.53 m 147.52 238.18 l S 147.52 236.53 m 147.56 238.79 l S 147.56 236.53 m 147.61 238.59 l S 147.61 236.53 m 147.66 238.38 l S 147.66 236.53 m 147.70 239.41 l S 147.70 236.53 m 147.75 238.79 l S 147.75 236.53 m 147.79 237.56 l S 147.79 236.53 m 147.84 237.15 l S 147.84 236.53 m 147.89 236.94 l S 147.89 236.53 m 147.93 237.36 l S 147.93 236.53 m 147.98 236.94 l S 147.98 236.53 m 148.02 236.74 l S 148.02 236.53 m 148.07 237.36 l S 148.07 236.53 m 148.12 236.94 l S 148.12 236.53 m 148.16 236.94 l S 148.16 236.53 m 148.21 236.94 l S 148.21 236.53 m 148.25 236.94 l S 148.25 236.53 m 148.30 237.15 l S 148.30 236.53 m 148.35 236.94 l S 148.35 236.53 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151.06 236.53 m 151.11 237.15 l S 151.11 236.53 m 151.15 237.36 l S 151.15 236.53 m 151.20 238.38 l S 151.20 236.53 m 151.25 236.94 l S 151.25 236.53 m 151.29 237.36 l S 151.29 236.53 m 151.34 237.15 l S 151.34 236.53 m 151.38 237.36 l S 151.38 236.53 m 151.43 236.94 l S 151.43 236.53 m 151.48 236.94 l S 151.48 236.53 m 151.52 236.94 l S 151.52 236.53 m 151.57 237.15 l S 151.57 236.53 m 151.61 236.74 l S 151.61 236.53 m 151.66 236.94 l S 151.66 236.53 m 151.71 238.59 l S 151.71 236.53 m 151.75 237.15 l S 151.75 236.53 m 151.80 236.94 l S 151.80 236.53 m 151.84 237.15 l S 151.84 236.53 m 151.89 236.94 l S 151.89 236.53 m 151.94 237.15 l S 151.94 236.53 m 151.98 237.15 l S 151.98 236.53 m 152.03 236.94 l S 152.03 236.53 m 152.07 236.94 l S 152.07 236.53 m 152.12 236.94 l S 152.12 236.53 m 152.17 236.94 l S 152.17 236.53 m 152.21 236.74 l S 152.21 236.53 m 152.26 236.74 l S 152.26 236.53 m 152.30 240.85 l S 152.30 236.53 m 152.35 238.18 l S 152.35 236.53 m 152.40 237.36 l S 152.40 236.53 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163.21 236.53 m 163.25 237.15 l S 163.25 236.53 m 163.30 237.36 l S 163.30 236.53 m 163.35 237.97 l S 163.35 236.53 m 163.39 237.56 l S 163.39 236.53 m 163.44 237.15 l S 163.44 236.53 m 163.48 237.97 l S 163.48 236.53 m 163.53 236.94 l S 163.53 236.53 m 163.58 237.36 l S 163.58 236.53 m 163.62 237.97 l S 163.62 236.53 m 163.67 239.41 l S 163.67 236.53 m 163.71 243.52 l S 163.71 236.53 m 163.76 241.88 l S 163.76 236.53 m 163.81 239.82 l S 163.81 236.53 m 163.85 239.00 l S 163.85 236.53 m 163.90 238.38 l S 163.90 236.53 m 163.94 237.56 l S 163.94 236.53 m 163.99 243.32 l S 163.99 236.53 m 164.04 239.62 l S 164.04 236.53 m 164.08 242.70 l S 164.08 236.53 m 164.13 242.49 l S 164.13 236.53 m 164.17 246.19 l S 164.17 236.53 m 164.22 238.38 l S 164.22 236.53 m 164.27 237.15 l S 164.27 236.53 m 164.31 241.47 l S 164.31 236.53 m 164.36 242.29 l S 164.36 236.53 m 164.40 237.56 l S 164.40 236.53 m 164.45 237.36 l S 164.45 236.53 m 164.50 237.36 l S 164.50 236.53 m 164.54 237.36 l S 164.54 236.53 m 164.59 237.36 l S 164.59 236.53 m 164.63 239.00 l S 164.63 236.53 m 164.68 237.56 l S 164.68 236.53 m 164.73 238.38 l S 164.73 236.53 m 164.77 237.15 l S 164.77 236.53 m 164.82 237.15 l S 164.82 236.53 m 164.86 238.59 l S 164.86 236.53 m 164.91 237.15 l S 164.91 236.53 m 164.96 237.56 l S 164.96 236.53 m 165.00 237.36 l S 165.00 236.53 m 165.05 236.94 l S 165.05 236.53 m 165.09 237.36 l S 165.09 236.53 m 165.14 237.15 l S 165.14 236.53 m 165.19 237.77 l S 165.19 236.53 m 165.23 237.36 l S 165.23 236.53 m 165.28 237.15 l S 165.28 236.53 m 165.32 239.41 l S 165.32 236.53 m 165.37 237.97 l S 165.37 236.53 m 165.42 237.56 l S 165.42 236.53 m 165.46 237.36 l S 165.46 236.53 m 165.51 237.15 l S 165.51 236.53 m 165.55 237.77 l S 165.55 236.53 m 165.60 237.97 l S 165.60 236.53 m 165.65 237.15 l S 165.65 236.53 m 165.69 237.56 l S 165.69 236.53 m 165.74 237.56 l S 165.74 236.53 m 165.78 239.41 l S 165.78 236.53 m 165.83 239.41 l S 165.83 236.53 m 165.88 247.84 l S 165.88 236.53 m 165.92 249.07 l S 165.92 236.53 m 165.97 240.64 l S 165.97 236.53 m 166.01 241.05 l S 166.01 236.53 m 166.06 239.41 l S 166.06 236.53 m 166.11 238.59 l S 166.11 236.53 m 166.15 237.77 l S 166.15 236.53 m 166.20 237.56 l S 166.20 236.53 m 166.24 237.36 l S 166.24 236.53 m 166.29 237.36 l S 166.29 236.53 m 166.34 241.88 l S 166.34 236.53 m 166.38 245.37 l S 166.38 236.53 m 166.43 242.08 l S 166.43 236.53 m 166.48 237.36 l S 166.48 236.53 m 166.52 237.15 l S 166.52 236.53 m 166.57 242.90 l S 166.57 236.53 m 166.61 241.26 l S 166.61 236.53 m 166.66 243.32 l S 166.66 236.53 m 166.71 248.25 l S 166.71 236.53 m 166.75 240.64 l S 166.75 236.53 m 166.80 237.36 l S 166.80 236.53 m 166.84 236.74 l S 166.84 236.53 m 166.89 237.15 l S 166.89 236.53 m 166.94 238.59 l S 166.94 236.53 m 166.98 236.94 l S 166.98 236.53 m 167.03 237.97 l S 167.03 236.53 m 167.07 238.18 l S 167.07 236.53 m 167.12 237.77 l S 167.12 236.53 m 167.17 237.15 l S 167.17 236.53 m 167.21 239.62 l S 167.21 236.53 m 167.26 238.18 l S 167.26 236.53 m 167.30 237.77 l S 167.30 236.53 m 167.35 237.36 l S 167.35 236.53 m 167.40 236.94 l S 167.40 236.53 m 167.44 240.03 l S 167.44 236.53 m 167.49 237.36 l S 167.49 236.53 m 167.53 238.38 l S 167.53 236.53 m 167.58 237.15 l S 167.58 236.53 m 167.63 237.15 l S 167.63 236.53 m 167.67 236.94 l S 167.67 236.53 m 167.72 237.77 l S 167.72 236.53 m 167.76 238.79 l S 167.76 236.53 m 167.81 237.56 l S 167.81 236.53 m 167.86 239.41 l S 167.86 236.53 m 167.90 237.15 l S 167.90 236.53 m 167.95 240.44 l S 167.95 236.53 m 167.99 236.74 l S 167.99 236.53 m 168.04 236.94 l S 168.04 236.53 m 168.09 240.64 l S 168.09 236.53 m 168.13 237.36 l S 168.13 236.53 m 168.18 240.64 l S 168.18 236.53 m 168.22 238.59 l S 168.22 236.53 m 168.27 239.00 l S 168.27 236.53 m 168.32 239.00 l S 168.32 236.53 m 168.36 237.36 l S 168.36 236.53 m 168.41 239.62 l S 168.41 236.53 m 168.45 237.36 l S 168.45 236.53 m 168.50 237.97 l S 168.50 236.53 m 168.55 237.36 l S 168.55 236.53 m 168.59 237.97 l S 168.59 236.53 m 168.64 241.88 l S 168.64 236.53 m 168.68 242.29 l S 168.68 236.53 m 168.73 243.73 l S 168.73 236.53 m 168.78 244.34 l S 168.78 236.53 m 168.82 241.05 l S 168.82 236.53 m 168.87 239.21 l S 168.87 236.53 m 168.91 242.29 l S 168.91 236.53 m 168.96 240.23 l S 168.96 236.53 m 169.01 247.84 l S 169.01 236.53 m 169.05 241.26 l S 169.05 236.53 m 169.10 237.56 l S 169.10 236.53 m 169.14 237.77 l S 169.14 236.53 m 169.19 239.00 l S 169.19 236.53 m 169.24 239.00 l S 169.24 236.53 m 169.28 237.15 l S 169.28 236.53 m 169.33 237.15 l S 169.33 236.53 m 169.37 238.38 l S 169.37 236.53 m 169.42 238.18 l S 169.42 236.53 m 169.47 237.15 l S 169.47 236.53 m 169.51 237.56 l S 169.51 236.53 m 169.56 237.36 l S 169.56 236.53 m 169.60 237.15 l S 169.60 236.53 m 169.65 237.15 l S 169.65 236.53 m 169.70 237.15 l S 169.70 236.53 m 169.74 237.56 l S 169.74 236.53 m 169.79 237.97 l S 169.79 236.53 m 169.83 236.94 l S 169.83 236.53 m 169.88 236.94 l S 169.88 236.53 m 169.93 237.36 l S 169.93 236.53 m 169.97 241.26 l S 169.97 236.53 m 170.02 237.15 l S 170.02 236.53 m 170.06 236.94 l S 170.06 236.53 m 170.11 240.85 l S 170.11 236.53 m 170.16 241.88 l S 170.16 236.53 m 170.20 241.88 l S 170.20 236.53 m 170.25 239.41 l S 170.25 236.53 m 170.29 240.03 l S 170.29 236.53 m 170.34 241.05 l S 170.34 236.53 m 170.39 238.38 l S 170.39 236.53 m 170.43 238.18 l S 170.43 236.53 m 170.48 240.03 l S 170.48 236.53 m 170.52 239.62 l S 170.52 236.53 m 170.57 240.03 l S 170.57 236.53 m 170.62 249.89 l S 170.62 236.53 m 170.66 239.00 l S 170.66 236.53 m 170.71 237.15 l S 170.71 236.53 m 170.75 237.56 l S 170.75 236.53 m 170.80 237.36 l S 170.80 236.53 m 170.85 236.94 l S 170.85 236.53 m 170.89 237.77 l S 170.89 236.53 m 170.94 237.36 l S 170.94 236.53 m 170.98 236.94 l S 170.98 236.53 m 171.03 236.94 l S 171.03 236.53 m 171.08 237.15 l S 171.08 236.53 m 171.12 236.94 l S 171.12 236.53 m 171.17 237.56 l S 171.17 236.53 m 171.21 237.36 l S 171.21 236.53 m 171.26 236.94 l S 171.26 236.53 m 171.31 236.94 l S 171.31 236.53 m 171.35 237.36 l S 171.35 236.53 m 171.40 237.56 l S 171.40 236.53 m 171.44 236.94 l S 171.44 236.53 m 171.49 237.36 l S 171.49 236.53 m 171.54 236.94 l S 171.54 236.53 m 171.58 237.36 l S 171.58 236.53 m 171.63 237.15 l S 171.63 236.53 m 171.67 236.94 l S 171.67 236.53 m 171.72 236.94 l S 171.72 236.53 m 171.77 237.15 l S 171.77 236.53 m 171.81 236.94 l S 171.81 236.53 m 171.86 237.15 l S 171.86 236.53 m 171.90 236.94 l S 171.90 236.53 m 171.95 237.77 l S 171.95 236.53 m 172.00 236.74 l S 172.00 236.53 m 172.04 237.15 l S 172.04 236.53 m 172.09 237.15 l S 172.09 236.53 m 172.13 236.94 l S 172.13 236.53 m 172.18 236.74 l S 172.18 236.53 m 172.23 237.15 l S 172.23 236.53 m 172.27 237.15 l S 172.27 236.53 m 172.32 237.36 l S 172.32 236.53 m 172.36 236.94 l S 172.36 236.53 m 172.41 236.94 l S 172.41 236.53 m 172.46 236.94 l S 172.46 236.53 m 172.50 236.94 l S 172.50 236.53 m 172.55 237.56 l S 172.55 236.53 m 172.59 236.94 l S 172.59 236.53 m 172.64 236.94 l S 172.64 236.53 m 172.69 237.15 l S 172.69 236.53 m 172.73 237.15 l S 172.73 236.53 m 172.78 236.94 l S 172.78 236.53 m 172.82 237.15 l S 172.82 236.53 m 172.87 237.36 l S 172.87 236.53 m 172.92 236.94 l S 172.92 236.53 m 172.96 237.15 l S 172.96 236.53 m 173.01 237.15 l S 173.01 236.53 m 173.05 237.15 l S 173.05 236.53 m 173.10 239.00 l S 173.10 236.53 m 173.15 237.15 l S 173.15 236.53 m 173.19 237.15 l S 173.19 236.53 m 173.24 237.15 l S 173.24 236.53 m 173.28 236.94 l S 173.28 236.53 m 173.33 237.15 l S 173.33 236.53 m 173.38 237.15 l S 173.38 236.53 m 173.42 237.56 l S 173.42 236.53 m 173.47 236.94 l S 173.47 236.53 m 173.51 236.94 l S 173.51 236.53 m 173.56 236.94 l S 173.56 236.53 m 173.61 236.94 l S 173.61 236.53 m 173.65 237.15 l S 173.65 236.53 m 173.70 237.15 l S 173.70 236.53 m 173.74 237.56 l S 173.74 236.53 m 173.79 237.15 l S 173.79 236.53 m 173.84 237.97 l S 173.84 236.53 m 173.88 237.15 l S 173.88 236.53 m 173.93 237.36 l S 173.93 236.53 m 173.97 236.94 l S 173.97 236.53 m 174.02 237.56 l S 174.02 236.53 m 174.07 237.15 l S 174.07 236.53 m 174.11 237.15 l S 174.11 236.53 m 174.16 237.97 l S 174.16 236.53 m 174.20 239.21 l S 174.20 236.53 m 174.25 239.41 l S 174.25 236.53 m 174.30 237.97 l S 174.30 236.53 m 174.34 237.15 l S 174.34 236.53 m 174.39 239.00 l S 174.39 236.53 m 174.44 241.67 l S 174.44 236.53 m 174.48 237.97 l S 174.48 236.53 m 174.53 237.36 l S 174.53 236.53 m 174.57 236.94 l S 174.57 236.53 m 174.62 237.36 l S 174.62 236.53 m 174.67 237.15 l S 174.67 236.53 m 174.71 237.56 l S 174.71 236.53 m 174.76 238.18 l S 174.76 236.53 m 174.80 241.67 l S 174.80 236.53 m 174.85 238.59 l S 174.85 236.53 m 174.90 239.62 l S 174.90 236.53 m 174.94 237.77 l S 174.94 236.53 m 174.99 237.36 l S 174.99 236.53 m 175.03 237.36 l S 175.03 236.53 m 175.08 237.15 l S 175.08 236.53 m 175.13 239.00 l S 175.13 236.53 m 175.17 237.56 l S 175.17 236.53 m 175.22 237.36 l S 175.22 236.53 m 175.26 237.36 l S 175.26 236.53 m 175.31 236.94 l S 175.31 236.53 m 175.36 237.15 l S 175.36 236.53 m 175.40 238.59 l S 175.40 236.53 m 175.45 237.56 l S 175.45 236.53 m 175.49 237.56 l S 175.49 236.53 m 175.54 237.36 l S 175.54 236.53 m 175.59 237.15 l S 175.59 236.53 m 175.63 237.77 l S 175.63 236.53 m 175.68 238.38 l S 175.68 236.53 m 175.72 238.59 l S 175.72 236.53 m 175.77 237.56 l S 175.77 236.53 m 175.82 243.11 l S 175.82 236.53 m 175.86 241.88 l S 175.86 236.53 m 175.91 244.55 l S 175.91 236.53 m 175.95 242.29 l S 175.95 236.53 m 176.00 237.97 l S 176.00 236.53 m 176.05 237.97 l S 176.05 236.53 m 176.09 244.55 l S 176.09 236.53 m 176.14 243.93 l S 176.14 236.53 m 176.18 244.14 l S 176.18 236.53 m 176.23 248.04 l S 176.23 236.53 m 176.28 237.36 l S 176.28 236.53 m 176.32 237.36 l S 176.32 236.53 m 176.37 243.52 l S 176.37 236.53 m 176.41 237.15 l S 176.41 236.53 m 176.46 236.74 l S 176.46 236.53 m 176.51 236.53 l S 176.51 236.53 m 176.55 236.53 l S 176.55 236.53 m 176.60 236.53 l S 176.60 236.53 m 176.64 236.53 l S 176.64 236.53 m 176.69 236.74 l S 176.69 236.53 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179.40 236.53 m 179.45 236.74 l S 179.45 236.53 m 179.50 237.15 l S 179.50 236.53 m 179.54 236.74 l S 179.54 236.53 m 179.59 237.15 l S 179.59 236.53 m 179.63 236.94 l S 179.63 236.53 m 179.68 237.15 l S 179.68 236.53 m 179.73 236.74 l S 179.73 236.53 m 179.77 236.94 l S 179.77 236.53 m 179.82 236.94 l S 179.82 236.53 m 179.86 236.94 l S 179.86 236.53 m 179.91 237.15 l S 179.91 236.53 m 179.96 236.94 l S 179.96 236.53 m 180.00 236.94 l S 180.00 236.53 m 180.05 236.94 l S 180.05 236.53 m 180.09 237.15 l S 180.09 236.53 m 180.14 236.94 l S 180.14 236.53 m 180.19 237.56 l S 180.19 236.53 m 180.23 236.94 l S 180.23 236.53 m 180.28 236.94 l S 180.28 236.53 m 180.32 237.36 l S 180.32 236.53 m 180.37 236.94 l S 180.37 236.53 m 180.42 236.94 l S 180.42 236.53 m 180.46 236.94 l S 180.46 236.53 m 180.51 236.94 l S 180.51 236.53 m 180.55 236.94 l S 180.55 236.53 m 180.60 236.94 l S 180.60 236.53 m 180.65 237.56 l S 180.65 236.53 m 180.69 237.15 l S 180.69 236.53 m 180.74 237.15 l S 180.74 236.53 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236.94 l S 182.12 236.53 m 182.16 236.74 l S 182.16 236.53 m 182.21 236.94 l S 182.21 236.53 m 182.26 236.94 l S 182.26 236.53 m 182.30 236.94 l S 182.30 236.53 m 182.35 236.94 l S 182.35 236.53 m 182.39 236.94 l S 182.39 236.53 m 182.44 237.36 l S 182.44 236.53 m 182.49 236.94 l S 182.49 236.53 m 182.53 237.77 l S 182.53 236.53 m 182.58 237.77 l S 182.58 236.53 m 182.63 236.94 l S 182.63 236.53 m 182.67 237.15 l S 182.67 236.53 m 182.72 236.94 l S 182.72 236.53 m 182.76 237.15 l S 182.76 236.53 m 182.81 237.15 l S 182.81 236.53 m 182.86 236.94 l S 182.86 236.53 m 182.90 237.15 l S 182.90 236.53 m 182.95 237.15 l S 182.95 236.53 m 182.99 237.77 l S 182.99 236.53 m 183.04 237.15 l S 183.04 236.53 m 183.09 237.15 l S 183.09 236.53 m 183.13 236.94 l S 183.13 236.53 m 183.18 244.55 l S 183.18 236.53 m 183.22 250.92 l S 183.22 236.53 m 183.27 240.03 l S 183.27 236.53 m 183.32 237.36 l S 183.32 236.53 m 183.36 238.18 l S 183.36 236.53 m 183.41 240.64 l S 183.41 236.53 m 183.45 241.05 l S 183.45 236.53 m 183.50 242.29 l S 183.50 236.53 m 183.55 238.79 l S 183.55 236.53 m 183.59 240.85 l S 183.59 236.53 m 183.64 237.77 l S 183.64 236.53 m 183.68 239.00 l S 183.68 236.53 m 183.73 238.18 l S 183.73 236.53 m 183.78 236.94 l S 183.78 236.53 m 183.82 237.15 l S 183.82 236.53 m 183.87 236.94 l S 183.87 236.53 m 183.91 237.36 l S 183.91 236.53 m 183.96 236.94 l S 183.96 236.53 m 184.01 236.74 l S 184.01 236.53 m 184.05 237.15 l S 184.05 236.53 m 184.10 237.15 l S 184.10 236.53 m 184.14 236.94 l S 184.14 236.53 m 184.19 236.74 l S 184.19 236.53 m 184.24 237.15 l S 184.24 236.53 m 184.28 237.15 l S 184.28 236.53 m 184.33 236.74 l S 184.33 236.53 m 184.37 236.94 l S 184.37 236.53 m 184.42 236.74 l S 184.42 236.53 m 184.47 237.15 l S 184.47 236.53 m 184.51 237.15 l S 184.51 236.53 m 184.56 237.15 l S 184.56 236.53 m 184.60 236.94 l S 184.60 236.53 m 184.65 237.36 l S 184.65 236.53 m 184.70 237.15 l S 184.70 236.53 m 184.74 236.74 l S 184.74 236.53 m 184.79 236.94 l S 184.79 236.53 m 184.83 236.94 l S 184.83 236.53 m 184.88 236.94 l S 184.88 236.53 m 184.93 237.15 l S 184.93 236.53 m 184.97 236.94 l S 184.97 236.53 m 185.02 236.74 l S 185.02 236.53 m 185.06 237.15 l S 185.06 236.53 m 185.11 237.56 l S 185.11 236.53 m 185.16 239.62 l S 185.16 236.53 m 185.20 244.75 l S 185.20 236.53 m 185.25 237.56 l S 185.25 236.53 m 185.29 236.94 l S 185.29 236.53 m 185.34 237.15 l S 185.34 236.53 m 185.39 237.56 l S 185.39 236.53 m 185.43 237.36 l S 185.43 236.53 m 185.48 236.74 l S 185.48 236.53 m 185.52 237.77 l S 185.52 236.53 m 185.57 237.15 l S 185.57 236.53 m 185.62 237.15 l S 185.62 236.53 m 185.66 237.15 l S 185.66 236.53 m 185.71 237.36 l S 185.71 236.53 m 185.75 237.97 l S 185.75 236.53 m 185.80 238.59 l S 185.80 236.53 m 185.85 237.56 l S 185.85 236.53 m 185.89 237.15 l S 185.89 236.53 m 185.94 237.15 l S 185.94 236.53 m 185.98 237.15 l S 185.98 236.53 m 186.03 237.15 l S 186.03 236.53 m 186.08 237.15 l S 186.08 236.53 m 186.12 238.18 l S 186.12 236.53 m 186.17 237.15 l S 186.17 236.53 m 186.21 239.82 l S 186.21 236.53 m 186.26 240.85 l S 186.26 236.53 m 186.31 242.49 l S 186.31 236.53 m 186.35 237.77 l S 186.35 236.53 m 186.40 239.41 l S 186.40 236.53 m 186.44 239.62 l S 186.44 236.53 m 186.49 236.94 l S 186.49 236.53 m 186.54 239.21 l S 186.54 236.53 m 186.58 236.94 l S 186.58 236.53 m 186.63 237.36 l S 186.63 236.53 m 186.67 239.82 l S 186.67 236.53 m 186.72 239.21 l S 186.72 236.53 m 186.77 237.36 l S 186.77 236.53 m 186.81 238.18 l S 186.81 236.53 m 186.86 237.36 l S 186.86 236.53 m 186.90 237.36 l S 186.90 236.53 m 186.95 237.15 l S 186.95 236.53 m 187.00 238.18 l S 187.00 236.53 m 187.04 239.00 l S 187.04 236.53 m 187.09 237.15 l S 187.09 236.53 m 187.13 237.15 l S 187.13 236.53 m 187.18 239.41 l S 187.18 236.53 m 187.23 239.62 l S 187.23 236.53 m 187.27 236.94 l S 187.27 236.53 m 187.32 236.94 l S 187.32 236.53 m 187.36 237.77 l S 187.36 236.53 m 187.41 236.94 l S 187.41 236.53 m 187.46 236.74 l S 187.46 236.53 m 187.50 236.94 l S 187.50 236.53 m 187.55 236.94 l S 187.55 236.53 m 187.59 236.74 l S 187.59 236.53 m 187.64 237.15 l S 187.64 236.53 m 187.69 236.74 l S 187.69 236.53 m 187.73 236.74 l S 187.73 236.53 m 187.78 238.18 l S 187.78 236.53 m 187.82 240.64 l S 187.82 236.53 m 187.87 237.36 l S 187.87 236.53 m 187.92 236.94 l S 187.92 236.53 m 187.96 237.36 l S 187.96 236.53 m 188.01 237.36 l S 188.01 236.53 m 188.05 237.36 l S 188.05 236.53 m 188.10 237.77 l S 188.10 236.53 m 188.15 238.38 l S 188.15 236.53 m 188.19 239.62 l S 188.19 236.53 m 188.24 240.03 l S 188.24 236.53 m 188.28 239.00 l S 188.28 236.53 m 188.33 244.75 l S 188.33 236.53 m 188.38 237.97 l S 188.38 236.53 m 188.42 237.15 l S 188.42 236.53 m 188.47 239.21 l S 188.47 236.53 m 188.51 241.67 l S 188.51 236.53 m 188.56 240.23 l S 188.56 236.53 m 188.61 239.62 l S 188.61 236.53 m 188.65 242.70 l S 188.65 236.53 m 188.70 242.29 l S 188.70 236.53 m 188.74 237.36 l S 188.74 236.53 m 188.79 240.85 l S 188.79 236.53 m 188.84 238.59 l S 188.84 236.53 m 188.88 236.94 l S 188.88 236.53 m 188.93 237.15 l S 188.93 236.53 m 188.97 243.32 l S 188.97 236.53 m 189.02 237.77 l S 189.02 236.53 m 189.07 237.15 l S 189.07 236.53 m 189.11 236.94 l S 189.11 236.53 m 189.16 236.94 l S 189.16 236.53 m 189.20 237.56 l S 189.20 236.53 m 189.25 237.97 l S 189.25 236.53 m 189.30 236.94 l S 189.30 236.53 m 189.34 237.15 l S 189.34 236.53 m 189.39 236.94 l S 189.39 236.53 m 189.43 236.94 l S 189.43 236.53 m 189.48 238.38 l S 189.48 236.53 m 189.53 237.36 l S 189.53 236.53 m 189.57 241.88 l S 189.57 236.53 m 189.62 238.79 l S 189.62 236.53 m 189.66 242.29 l S 189.66 236.53 m 189.71 244.96 l S 189.71 236.53 m 189.76 244.14 l S 189.76 236.53 m 189.80 239.62 l S 189.80 236.53 m 189.85 237.97 l S 189.85 236.53 m 189.89 238.18 l S 189.89 236.53 m 189.94 237.77 l S 189.94 236.53 m 189.99 237.56 l S 189.99 236.53 m 190.03 238.59 l S 190.03 236.53 m 190.08 239.82 l S 190.08 236.53 m 190.12 239.41 l S 190.12 236.53 m 190.17 237.36 l S 190.17 236.53 m 190.22 239.82 l S 190.22 236.53 m 190.26 242.70 l S 190.26 236.53 m 190.31 238.38 l S 190.31 236.53 m 190.35 241.05 l S 190.35 236.53 m 190.40 237.15 l S 190.40 236.53 m 190.45 236.94 l S 190.45 236.53 m 190.49 237.15 l S 190.49 236.53 m 190.54 237.15 l S 190.54 236.53 m 190.59 237.15 l S 190.59 236.53 m 190.63 238.18 l S 190.63 236.53 m 190.68 237.97 l S 190.68 236.53 m 190.72 236.94 l S 190.72 236.53 m 190.77 239.62 l S 190.77 236.53 m 190.82 237.56 l S 190.82 236.53 m 190.86 237.15 l S 190.86 236.53 m 190.91 236.94 l S 190.91 236.53 m 190.95 238.18 l S 190.95 236.53 m 191.00 236.94 l S 191.00 236.53 m 191.05 237.15 l S 191.05 236.53 m 191.09 237.15 l S 191.09 236.53 m 191.14 236.94 l S 191.14 236.53 m 191.18 237.15 l S 191.18 236.53 m 191.23 236.94 l S 191.23 236.53 m 191.28 236.74 l S 191.28 236.53 m 191.32 236.94 l S 191.32 236.53 m 191.37 236.94 l S 191.37 236.53 m 191.41 237.15 l S 191.41 236.53 m 191.46 237.15 l S 191.46 236.53 m 191.51 236.94 l S 191.51 236.53 m 191.55 236.94 l S 191.55 236.53 m 191.60 236.94 l S 191.60 236.53 m 191.64 236.74 l S 191.64 236.53 m 191.69 236.94 l S 191.69 236.53 m 191.74 236.74 l S 191.74 236.53 m 191.78 237.15 l S 191.78 236.53 m 191.83 236.74 l S 191.83 236.53 m 191.87 236.74 l S 191.87 236.53 m 191.92 236.94 l S 191.92 236.53 m 191.97 237.15 l S 191.97 236.53 m 192.01 237.15 l S 192.01 236.53 m 192.06 236.94 l S 192.06 236.53 m 192.10 236.74 l S 192.10 236.53 m 192.15 238.79 l S 192.15 236.53 m 192.20 237.36 l S 192.20 236.53 m 192.24 236.94 l S 192.24 236.53 m 192.29 236.94 l S 192.29 236.53 m 192.33 241.67 l S 192.33 236.53 m 192.38 237.36 l S 192.38 236.53 m 192.43 238.59 l S 192.43 236.53 m 192.47 237.15 l S 192.47 236.53 m 192.52 238.59 l S 192.52 236.53 m 192.56 239.41 l S 192.56 236.53 m 192.61 236.94 l S 192.61 236.53 m 192.66 237.15 l S 192.66 236.53 m 192.70 236.94 l S 192.70 236.53 m 192.75 239.41 l S 192.75 236.53 m 192.79 248.45 l S 192.79 236.53 m 192.84 241.05 l S 192.84 236.53 m 192.89 237.15 l S 192.89 236.53 m 192.93 237.77 l S 192.93 236.53 m 192.98 237.15 l S 192.98 236.53 m 193.02 238.18 l S 193.02 236.53 m 193.07 237.77 l S 193.07 236.53 m 193.12 236.94 l S 193.12 236.53 m 193.16 236.94 l S 193.16 236.53 m 193.21 237.15 l S 193.21 236.53 m 193.25 237.15 l S 193.25 236.53 m 193.30 237.15 l S 193.30 236.53 m 193.35 237.15 l S 193.35 236.53 m 193.39 237.56 l S 193.39 236.53 m 193.44 237.36 l S 193.44 236.53 m 193.48 237.36 l S 193.48 236.53 m 193.53 236.94 l S 193.53 236.53 m 193.58 237.36 l S 193.58 236.53 m 193.62 242.70 l S 193.62 236.53 m 193.67 236.94 l S 193.67 236.53 m 193.71 238.18 l S 193.71 236.53 m 193.76 239.00 l S 193.76 236.53 m 193.81 238.79 l S 193.81 236.53 m 193.85 238.18 l S 193.85 236.53 m 193.90 237.77 l S 193.90 236.53 m 193.94 246.81 l S 193.94 236.53 m 193.99 236.94 l S 193.99 236.53 m 194.04 237.36 l S 194.04 236.53 m 194.08 237.15 l S 194.08 236.53 m 194.13 236.94 l S 194.13 236.53 m 194.17 237.36 l S 194.17 236.53 m 194.22 237.56 l S 194.22 236.53 m 194.27 239.62 l S 194.27 236.53 m 194.31 238.38 l S 194.31 236.53 m 194.36 237.15 l S 194.36 236.53 m 194.40 236.94 l S 194.40 236.53 m 194.45 237.36 l S 194.45 236.53 m 194.50 237.15 l S 194.50 236.53 m 194.54 237.15 l S 194.54 236.53 m 194.59 237.77 l S 194.59 236.53 m 194.63 240.44 l S 194.63 236.53 m 194.68 240.23 l S 194.68 236.53 m 194.73 236.94 l S 194.73 236.53 m 194.77 237.15 l S 194.77 236.53 m 194.82 238.38 l S 194.82 236.53 m 194.86 236.94 l S 194.86 236.53 m 194.91 237.15 l S 194.91 236.53 m 194.96 237.15 l S 194.96 236.53 m 195.00 238.38 l S 195.00 236.53 m 195.05 238.18 l S 195.05 236.53 m 195.09 238.18 l S 195.09 236.53 m 195.14 237.36 l S 195.14 236.53 m 195.19 237.15 l S 195.19 236.53 m 195.23 237.15 l S 195.23 236.53 m 195.28 236.94 l S 195.28 236.53 m 195.32 237.97 l S 195.32 236.53 m 195.37 237.15 l S 195.37 236.53 m 195.42 242.08 l S 195.42 236.53 m 195.46 239.00 l S 195.46 236.53 m 195.51 237.56 l S 195.51 236.53 m 195.55 237.15 l S 195.55 236.53 m 195.60 237.15 l S 195.60 236.53 m 195.65 236.74 l S 195.65 236.53 m 195.69 237.36 l S 195.69 236.53 m 195.74 237.56 l S 195.74 236.53 m 195.78 241.67 l S 195.78 236.53 m 195.83 237.36 l S 195.83 236.53 m 195.88 237.97 l S 195.88 236.53 m 195.92 237.97 l S 195.92 236.53 m 195.97 237.15 l S 195.97 236.53 m 196.01 237.77 l S 196.01 236.53 m 196.06 237.77 l S 196.06 236.53 m 196.11 237.15 l S 196.11 236.53 m 196.15 236.94 l S 196.15 236.53 m 196.20 239.41 l S 196.20 236.53 m 196.24 239.21 l S 196.24 236.53 m 196.29 236.94 l S 196.29 236.53 m 196.34 236.94 l S 196.34 236.53 m 196.38 237.97 l S 196.38 236.53 m 196.43 236.94 l S 196.43 236.53 m 196.47 240.23 l S 196.47 236.53 m 196.52 236.94 l S 196.52 236.53 m 196.57 236.94 l S 196.57 236.53 m 196.61 236.94 l S 196.61 236.53 m 196.66 236.74 l S 196.66 236.53 m 196.70 236.94 l S 196.70 236.53 m 196.75 237.36 l S 196.75 236.53 m 196.80 237.15 l S 196.80 236.53 m 196.84 237.36 l S 196.84 236.53 m 196.89 237.36 l S 196.89 236.53 m 196.93 236.74 l S 196.93 236.53 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203.70 236.53 m 203.74 236.94 l S 203.74 236.53 m 203.79 236.94 l S 203.79 236.53 m 203.84 237.77 l S 203.84 236.53 m 203.88 237.36 l S 203.88 236.53 m 203.93 237.56 l S 203.93 236.53 m 203.97 240.85 l S 203.97 236.53 m 204.02 237.15 l S 204.02 236.53 m 204.07 237.15 l S 204.07 236.53 m 204.11 237.15 l S 204.11 236.53 m 204.16 237.15 l S 204.16 236.53 m 204.20 237.36 l S 204.20 236.53 m 204.25 239.41 l S 204.25 236.53 m 204.30 240.03 l S 204.30 236.53 m 204.34 237.15 l S 204.34 236.53 m 204.39 240.23 l S 204.39 236.53 m 204.43 238.38 l S 204.43 236.53 m 204.48 237.15 l S 204.48 236.53 m 204.53 237.15 l S 204.53 236.53 m 204.57 238.59 l S 204.57 236.53 m 204.62 237.15 l S 204.62 236.53 m 204.66 237.15 l S 204.66 236.53 m 204.71 237.15 l S 204.71 236.53 m 204.76 241.26 l S 204.76 236.53 m 204.80 237.15 l S 204.80 236.53 m 204.85 236.94 l S 204.85 236.53 m 204.89 237.56 l S 204.89 236.53 m 204.94 237.77 l S 204.94 236.53 m 204.99 244.75 l S 204.99 236.53 m 205.03 237.36 l S 205.03 236.53 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237.15 l S 214.51 236.53 m 214.56 237.15 l S 214.56 236.53 m 214.60 237.15 l S 214.60 236.53 m 214.65 237.15 l S 214.65 236.53 m 214.70 237.15 l S 214.70 236.53 m 214.74 236.94 l S 214.74 236.53 m 214.79 237.15 l S 214.79 236.53 m 214.83 236.94 l S 214.83 236.53 m 214.88 236.94 l S 214.88 236.53 m 214.93 236.74 l S 214.93 236.53 m 214.97 236.94 l S 214.97 236.53 m 215.02 236.74 l S 215.02 236.53 m 215.06 236.94 l S 215.06 236.53 m 215.11 237.36 l S 215.11 236.53 m 215.16 237.36 l S 215.16 236.53 m 215.20 236.74 l S 215.20 236.53 m 215.25 236.94 l S 215.25 236.53 m 215.29 236.74 l S 215.29 236.53 m 215.34 236.94 l S 215.34 236.53 m 215.39 236.94 l S 215.39 236.53 m 215.43 237.15 l S 215.43 236.53 m 215.48 236.94 l S 215.48 236.53 m 215.52 236.74 l S 215.52 236.53 m 215.57 237.15 l S 215.57 236.53 m 215.62 237.15 l S 215.62 236.53 m 215.66 236.74 l S 215.66 236.53 m 215.71 237.15 l S 215.71 236.53 m 215.75 236.94 l S 215.75 236.53 m 215.80 236.94 l S 215.80 236.53 m 215.85 236.94 l S 215.85 236.53 m 215.89 237.15 l S 215.89 236.53 m 215.94 236.74 l S 215.94 236.53 m 215.98 236.94 l S 215.98 236.53 m 216.03 236.94 l S 216.03 236.53 m 216.08 236.74 l S 216.08 236.53 m 216.12 236.94 l S 216.12 236.53 m 216.17 236.94 l S 216.17 236.53 m 216.21 236.94 l S 216.21 236.53 m 216.26 236.94 l S 216.26 236.53 m 216.31 237.15 l S 216.31 236.53 m 216.35 236.94 l S 216.35 236.53 m 216.40 236.74 l S 216.40 236.53 m 216.44 236.94 l S 216.44 236.53 m 216.49 236.94 l S 216.49 236.53 m 216.54 236.94 l S 216.54 236.53 m 216.58 236.94 l S 216.58 236.53 m 216.63 236.74 l S 216.63 236.53 m 216.67 236.94 l S 216.67 236.53 m 216.72 236.94 l S 216.72 236.53 m 216.77 236.94 l S 216.77 236.53 m 216.81 237.15 l S 216.81 236.53 m 216.86 237.36 l S 216.86 236.53 m 216.90 236.94 l S 216.90 236.53 m 216.95 236.94 l S 216.95 236.53 m 217.00 236.94 l S 217.00 236.53 m 217.04 236.94 l S 217.04 236.53 m 217.09 237.15 l S 217.09 236.53 m 217.13 236.94 l S 217.13 236.53 m 217.18 236.94 l S 217.18 236.53 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223.94 236.53 m 223.99 238.38 l S 223.99 236.53 m 224.04 238.79 l S 224.04 236.53 m 224.08 236.94 l S 224.08 236.53 m 224.13 237.15 l S 224.13 236.53 m 224.17 237.36 l S 224.17 236.53 m 224.22 238.18 l S 224.22 236.53 m 224.27 237.15 l S 224.27 236.53 m 224.31 237.15 l S 224.31 236.53 m 224.36 237.77 l S 224.36 236.53 m 224.40 237.56 l S 224.40 236.53 m 224.45 246.19 l S 224.45 236.53 m 224.50 246.60 l S 224.50 236.53 m 224.54 242.90 l S 224.54 236.53 m 224.59 237.97 l S 224.59 236.53 m 224.63 237.56 l S 224.63 236.53 m 224.68 237.36 l S 224.68 236.53 m 224.73 238.38 l S 224.73 236.53 m 224.77 237.36 l S 224.77 236.53 m 224.82 238.18 l S 224.82 236.53 m 224.86 238.38 l S 224.86 236.53 m 224.91 239.21 l S 224.91 236.53 m 224.96 237.15 l S 224.96 236.53 m 225.00 237.77 l S 225.00 236.53 m 225.05 238.79 l S 225.05 236.53 m 225.09 238.38 l S 225.09 236.53 m 225.14 236.94 l S 225.14 236.53 m 225.19 236.94 l S 225.19 236.53 m 225.23 237.15 l S 225.23 236.53 m 225.28 237.36 l S 225.28 236.53 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227.99 236.53 m 228.04 237.36 l S 228.04 236.53 m 228.08 238.59 l S 228.08 236.53 m 228.13 237.36 l S 228.13 236.53 m 228.18 237.97 l S 228.18 236.53 m 228.22 237.56 l S 228.22 236.53 m 228.27 237.36 l S 228.27 236.53 m 228.31 238.79 l S 228.31 236.53 m 228.36 238.59 l S 228.36 236.53 m 228.41 237.15 l S 228.41 236.53 m 228.45 237.36 l S 228.45 236.53 m 228.50 237.15 l S 228.50 236.53 m 228.54 237.36 l S 228.54 236.53 m 228.59 237.56 l S 228.59 236.53 m 228.64 237.15 l S 228.64 236.53 m 228.68 237.15 l S 228.68 236.53 m 228.73 237.15 l S 228.73 236.53 m 228.77 237.15 l S 228.77 236.53 m 228.82 236.94 l S 228.82 236.53 m 228.87 237.36 l S 228.87 236.53 m 228.91 237.36 l S 228.91 236.53 m 228.96 236.94 l S 228.96 236.53 m 229.00 238.59 l S 229.00 236.53 m 229.05 237.36 l S 229.05 236.53 m 229.10 237.15 l S 229.10 236.53 m 229.14 237.15 l S 229.14 236.53 m 229.19 237.36 l S 229.19 236.53 m 229.23 238.18 l S 229.23 236.53 m 229.28 239.62 l S 229.28 236.53 m 229.33 237.15 l S 229.33 236.53 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239.41 l S 230.71 236.53 m 230.75 239.41 l S 230.75 236.53 m 230.80 238.38 l S 230.80 236.53 m 230.85 239.62 l S 230.85 236.53 m 230.89 240.03 l S 230.89 236.53 m 230.94 248.25 l S 230.94 236.53 m 230.98 247.22 l S 230.98 236.53 m 231.03 252.77 l S 231.03 236.53 m 231.08 238.59 l S 231.08 236.53 m 231.12 238.38 l S 231.12 236.53 m 231.17 238.79 l S 231.17 236.53 m 231.21 244.55 l S 231.21 236.53 m 231.26 241.26 l S 231.26 236.53 m 231.31 243.93 l S 231.31 236.53 m 231.35 237.15 l S 231.35 236.53 m 231.40 241.47 l S 231.40 236.53 m 231.44 239.00 l S 231.44 236.53 m 231.49 240.44 l S 231.49 236.53 m 231.54 239.21 l S 231.54 236.53 m 231.58 240.03 l S 231.58 236.53 m 231.63 237.77 l S 231.63 236.53 m 231.67 238.38 l S 231.67 236.53 m 231.72 237.36 l S 231.72 236.53 m 231.77 241.05 l S 231.77 236.53 m 231.81 240.85 l S 231.81 236.53 m 231.86 237.56 l S 231.86 236.53 m 231.90 237.15 l S 231.90 236.53 m 231.95 236.94 l S 231.95 236.53 m 232.00 236.94 l S 232.00 236.53 m 232.04 236.94 l S 232.04 236.53 m 232.09 237.15 l S 232.09 236.53 m 232.13 236.94 l S 232.13 236.53 m 232.18 237.15 l S 232.18 236.53 m 232.23 236.74 l S 232.23 236.53 m 232.27 236.94 l S 232.27 236.53 m 232.32 236.94 l S 232.32 236.53 m 232.36 237.15 l S 232.36 236.53 m 232.41 237.15 l S 232.41 236.53 m 232.46 236.94 l S 232.46 236.53 m 232.50 236.94 l S 232.50 236.53 m 232.55 237.15 l S 232.55 236.53 m 232.59 237.15 l S 232.59 236.53 m 232.64 237.15 l S 232.64 236.53 m 232.69 237.15 l S 232.69 236.53 m 232.73 237.15 l S 232.73 236.53 m 232.78 236.94 l S 232.78 236.53 m 232.82 237.15 l S 232.82 236.53 m 232.87 238.38 l S 232.87 236.53 m 232.92 237.56 l S 232.92 236.53 m 232.96 236.94 l S 232.96 236.53 m 233.01 237.36 l S 233.01 236.53 m 233.05 237.56 l S 233.05 236.53 m 233.10 241.26 l S 233.10 236.53 m 233.15 237.36 l S 233.15 236.53 m 233.19 236.94 l S 233.19 236.53 m 233.24 237.97 l S 233.24 236.53 m 233.28 237.56 l S 233.28 236.53 m 233.33 236.94 l S 233.33 236.53 m 233.38 236.94 l S 233.38 236.53 m 233.42 237.56 l S 233.42 236.53 m 233.47 236.94 l S 233.47 236.53 m 233.51 237.15 l S 233.51 236.53 m 233.56 236.94 l S 233.56 236.53 m 233.61 236.94 l S 233.61 236.53 m 233.65 237.36 l S 233.65 236.53 m 233.70 236.94 l S 233.70 236.53 m 233.74 237.15 l S 233.74 236.53 m 233.79 237.15 l S 233.79 236.53 m 233.84 236.94 l S 233.84 236.53 m 233.88 237.15 l S 233.88 236.53 m 233.93 236.94 l S 233.93 236.53 m 233.97 236.94 l S 233.97 236.53 m 234.02 237.15 l S 234.02 236.53 m 234.07 237.15 l S 234.07 236.53 m 234.11 237.36 l S 234.11 236.53 m 234.16 236.94 l S 234.16 236.53 m 234.20 237.15 l S 234.20 236.53 m 234.25 237.36 l S 234.25 236.53 m 234.30 237.15 l S 234.30 236.53 m 234.34 236.94 l S 234.34 236.53 m 234.39 237.36 l S 234.39 236.53 m 234.43 237.36 l S 234.43 236.53 m 234.48 237.77 l S 234.48 236.53 m 234.53 237.15 l S 234.53 236.53 m 234.57 238.38 l S 234.57 236.53 m 234.62 241.26 l S 234.62 236.53 m 234.66 237.15 l S 234.66 236.53 m 234.71 237.15 l S 234.71 236.53 m 234.76 236.94 l S 234.76 236.53 m 234.80 237.15 l S 234.80 236.53 m 234.85 236.94 l S 234.85 236.53 m 234.89 237.56 l S 234.89 236.53 m 234.94 237.15 l S 234.94 236.53 m 234.99 236.94 l S 234.99 236.53 m 235.03 237.36 l S 235.03 236.53 m 235.08 239.21 l S 235.08 236.53 m 235.12 237.36 l S 235.12 236.53 m 235.17 239.82 l S 235.17 236.53 m 235.22 241.26 l S 235.22 236.53 m 235.26 237.36 l S 235.26 236.53 m 235.31 237.77 l S 235.31 236.53 m 235.35 242.70 l S 235.35 236.53 m 235.40 244.14 l S 235.40 236.53 m 235.45 239.00 l S 235.45 236.53 m 235.49 236.94 l S 235.49 236.53 m 235.54 237.36 l S 235.54 236.53 m 235.58 237.15 l S 235.58 236.53 m 235.63 237.97 l S 235.63 236.53 m 235.68 238.18 l S 235.68 236.53 m 235.72 237.15 l S 235.72 236.53 m 235.77 236.94 l S 235.77 236.53 m 235.81 238.18 l S 235.81 236.53 m 235.86 239.00 l S 235.86 236.53 m 235.91 237.36 l S 235.91 236.53 m 235.95 238.38 l S 235.95 236.53 m 236.00 238.38 l S 236.00 236.53 m 236.04 240.64 l S 236.04 236.53 m 236.09 237.97 l S 236.09 236.53 m 236.14 236.94 l S 236.14 236.53 m 236.18 237.56 l S 236.18 236.53 m 236.23 243.11 l S 236.23 236.53 m 236.27 237.77 l S 236.27 236.53 m 236.32 238.18 l S 236.32 236.53 m 236.37 241.88 l S 236.37 236.53 m 236.41 237.15 l S 236.41 236.53 m 236.46 237.15 l S 236.46 236.53 m 236.50 237.15 l S 236.50 236.53 m 236.55 237.15 l S 236.55 236.53 m 236.60 237.36 l S 236.60 236.53 m 236.64 237.56 l S 236.64 236.53 m 236.69 237.15 l S 236.69 236.53 m 236.73 237.15 l S 236.73 236.53 m 236.78 237.15 l S 236.78 236.53 m 236.83 236.94 l S 236.83 236.53 m 236.87 237.15 l S 236.87 236.53 m 236.92 237.15 l S 236.92 236.53 m 236.96 237.36 l S 236.96 236.53 m 237.01 237.15 l S 237.01 236.53 m 237.06 237.36 l S 237.06 236.53 m 237.10 236.94 l S 237.10 236.53 m 237.15 237.36 l S 237.15 236.53 m 237.19 237.15 l S 237.19 236.53 m 237.24 237.15 l S 237.24 236.53 m 237.29 237.56 l S 237.29 236.53 m 237.33 237.36 l S 237.33 236.53 m 237.38 237.36 l S 237.38 236.53 m 237.42 238.18 l S 237.42 236.53 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236.53 l S 161.51 236.53 m 161.55 236.53 l S 161.55 236.53 m 161.60 236.53 l S 161.60 236.53 m 161.64 236.53 l S 161.64 236.53 m 161.69 236.53 l S 161.69 236.53 m 161.74 236.53 l S 161.74 236.53 m 161.78 236.53 l S 161.78 236.53 m 161.83 236.53 l S 161.83 236.53 m 161.87 239.46 l S 161.87 236.53 m 161.92 238.49 l S 161.92 236.53 m 161.97 236.53 l S 161.97 236.53 m 162.01 236.53 l S 162.01 236.53 m 162.06 236.53 l S 162.06 236.53 m 162.10 236.53 l S 162.10 236.53 m 162.15 237.51 l S 162.15 236.53 m 162.20 238.49 l S 162.20 236.53 m 162.24 236.53 l S 162.24 236.53 m 162.29 236.53 l S 162.29 236.53 m 162.33 236.53 l S 162.33 236.53 m 162.38 236.53 l S 162.38 236.53 m 162.43 237.51 l S 162.43 236.53 m 162.47 237.51 l S 162.47 236.53 m 162.52 236.53 l S 162.52 236.53 m 162.56 236.53 l S 162.56 236.53 m 162.61 236.53 l S 162.61 236.53 m 162.66 236.53 l S 162.66 236.53 m 162.70 236.53 l S 162.70 236.53 m 162.75 236.53 l S 162.75 236.53 m 162.79 236.53 l S 162.79 236.53 m 162.84 236.53 l S 162.84 236.53 m 162.89 236.53 l S 162.89 236.53 m 162.93 236.53 l S 162.93 236.53 m 162.98 236.53 l S 162.98 236.53 m 163.02 236.53 l S 163.02 236.53 m 163.07 236.53 l S 163.07 236.53 m 163.12 236.53 l S 163.12 236.53 m 163.16 236.53 l S 163.16 236.53 m 163.21 236.53 l S 163.21 236.53 m 163.25 236.53 l S 163.25 236.53 m 163.30 236.53 l S 163.30 236.53 m 163.35 236.53 l S 163.35 236.53 m 163.39 236.53 l S 163.39 236.53 m 163.44 236.53 l S 163.44 236.53 m 163.48 236.53 l S 163.48 236.53 m 163.53 237.51 l S 163.53 236.53 m 163.58 236.53 l S 163.58 236.53 m 163.62 236.53 l S 163.62 236.53 m 163.67 236.53 l S 163.67 236.53 m 163.71 236.53 l S 163.71 236.53 m 163.76 236.53 l S 163.76 236.53 m 163.81 236.53 l S 163.81 236.53 m 163.85 236.53 l S 163.85 236.53 m 163.90 236.53 l S 163.90 236.53 m 163.94 236.53 l S 163.94 236.53 m 163.99 236.53 l S 163.99 236.53 m 164.04 236.53 l S 164.04 236.53 m 164.08 236.53 l S 164.08 236.53 m 164.13 236.53 l S 164.13 236.53 m 164.17 236.53 l S 164.17 236.53 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237.51 l S 165.55 236.53 m 165.60 236.53 l S 165.60 236.53 m 165.65 236.53 l S 165.65 236.53 m 165.69 236.53 l S 165.69 236.53 m 165.74 236.53 l S 165.74 236.53 m 165.78 237.51 l S 165.78 236.53 m 165.83 236.53 l S 165.83 236.53 m 165.88 236.53 l S 165.88 236.53 m 165.92 236.53 l S 165.92 236.53 m 165.97 236.53 l S 165.97 236.53 m 166.01 236.53 l S 166.01 236.53 m 166.06 236.53 l S 166.06 236.53 m 166.11 236.53 l S 166.11 236.53 m 166.15 236.53 l S 166.15 236.53 m 166.20 236.53 l S 166.20 236.53 m 166.24 236.53 l S 166.24 236.53 m 166.29 236.53 l S 166.29 236.53 m 166.34 236.53 l S 166.34 236.53 m 166.38 236.53 l S 166.38 236.53 m 166.43 236.53 l S 166.43 236.53 m 166.48 236.53 l S 166.48 236.53 m 166.52 236.53 l S 166.52 236.53 m 166.57 236.53 l S 166.57 236.53 m 166.61 236.53 l S 166.61 236.53 m 166.66 236.53 l S 166.66 236.53 m 166.71 236.53 l S 166.71 236.53 m 166.75 237.51 l S 166.75 236.53 m 166.80 236.53 l S 166.80 236.53 m 166.84 236.53 l S 166.84 236.53 m 166.89 236.53 l S 166.89 236.53 m 166.94 236.53 l S 166.94 236.53 m 166.98 237.51 l S 166.98 236.53 m 167.03 236.53 l S 167.03 236.53 m 167.07 236.53 l S 167.07 236.53 m 167.12 236.53 l S 167.12 236.53 m 167.17 236.53 l S 167.17 236.53 m 167.21 236.53 l S 167.21 236.53 m 167.26 236.53 l S 167.26 236.53 m 167.30 236.53 l S 167.30 236.53 m 167.35 236.53 l S 167.35 236.53 m 167.40 236.53 l S 167.40 236.53 m 167.44 236.53 l S 167.44 236.53 m 167.49 236.53 l S 167.49 236.53 m 167.53 236.53 l S 167.53 236.53 m 167.58 236.53 l S 167.58 236.53 m 167.63 236.53 l S 167.63 236.53 m 167.67 236.53 l S 167.67 236.53 m 167.72 236.53 l S 167.72 236.53 m 167.76 236.53 l S 167.76 236.53 m 167.81 236.53 l S 167.81 236.53 m 167.86 236.53 l S 167.86 236.53 m 167.90 236.53 l S 167.90 236.53 m 167.95 236.53 l S 167.95 236.53 m 167.99 236.53 l S 167.99 236.53 m 168.04 236.53 l S 168.04 236.53 m 168.09 236.53 l S 168.09 236.53 m 168.13 236.53 l S 168.13 236.53 m 168.18 236.53 l S 168.18 236.53 m 168.22 236.53 l S 168.22 236.53 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170.94 236.53 m 170.98 238.49 l S 170.98 236.53 m 171.03 238.49 l S 171.03 236.53 m 171.08 236.53 l S 171.08 236.53 m 171.12 237.51 l S 171.12 236.53 m 171.17 237.51 l S 171.17 236.53 m 171.21 236.53 l S 171.21 236.53 m 171.26 236.53 l S 171.26 236.53 m 171.31 236.53 l S 171.31 236.53 m 171.35 236.53 l S 171.35 236.53 m 171.40 236.53 l S 171.40 236.53 m 171.44 236.53 l S 171.44 236.53 m 171.49 236.53 l S 171.49 236.53 m 171.54 236.53 l S 171.54 236.53 m 171.58 236.53 l S 171.58 236.53 m 171.63 236.53 l S 171.63 236.53 m 171.67 236.53 l S 171.67 236.53 m 171.72 236.53 l S 171.72 236.53 m 171.77 236.53 l S 171.77 236.53 m 171.81 236.53 l S 171.81 236.53 m 171.86 236.53 l S 171.86 236.53 m 171.90 236.53 l S 171.90 236.53 m 171.95 236.53 l S 171.95 236.53 m 172.00 236.53 l S 172.00 236.53 m 172.04 236.53 l S 172.04 236.53 m 172.09 236.53 l S 172.09 236.53 m 172.13 236.53 l S 172.13 236.53 m 172.18 236.53 l S 172.18 236.53 m 172.23 236.53 l S 172.23 236.53 m 172.27 236.53 l S 172.27 236.53 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179.04 236.53 m 179.08 236.53 l S 179.08 236.53 m 179.13 236.53 l S 179.13 236.53 m 179.17 236.53 l S 179.17 236.53 m 179.22 236.53 l S 179.22 236.53 m 179.27 236.53 l S 179.27 236.53 m 179.31 236.53 l S 179.31 236.53 m 179.36 236.53 l S 179.36 236.53 m 179.40 236.53 l S 179.40 236.53 m 179.45 236.53 l S 179.45 236.53 m 179.50 236.53 l S 179.50 236.53 m 179.54 236.53 l S 179.54 236.53 m 179.59 236.53 l S 179.59 236.53 m 179.63 236.53 l S 179.63 236.53 m 179.68 236.53 l S 179.68 236.53 m 179.73 236.53 l S 179.73 236.53 m 179.77 237.51 l S 179.77 236.53 m 179.82 237.51 l S 179.82 236.53 m 179.86 236.53 l S 179.86 236.53 m 179.91 236.53 l S 179.91 236.53 m 179.96 236.53 l S 179.96 236.53 m 180.00 236.53 l S 180.00 236.53 m 180.05 236.53 l S 180.05 236.53 m 180.09 236.53 l S 180.09 236.53 m 180.14 236.53 l S 180.14 236.53 m 180.19 236.53 l S 180.19 236.53 m 180.23 236.53 l S 180.23 236.53 m 180.28 236.53 l S 180.28 236.53 m 180.32 236.53 l S 180.32 236.53 m 180.37 236.53 l S 180.37 236.53 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236.53 l S 181.75 236.53 m 181.80 236.53 l S 181.80 236.53 m 181.84 236.53 l S 181.84 236.53 m 181.89 236.53 l S 181.89 236.53 m 181.93 236.53 l S 181.93 236.53 m 181.98 237.51 l S 181.98 236.53 m 182.03 236.53 l S 182.03 236.53 m 182.07 237.51 l S 182.07 236.53 m 182.12 237.51 l S 182.12 236.53 m 182.16 236.53 l S 182.16 236.53 m 182.21 236.53 l S 182.21 236.53 m 182.26 236.53 l S 182.26 236.53 m 182.30 236.53 l S 182.30 236.53 m 182.35 236.53 l S 182.35 236.53 m 182.39 236.53 l S 182.39 236.53 m 182.44 236.53 l S 182.44 236.53 m 182.49 236.53 l S 182.49 236.53 m 182.53 236.53 l S 182.53 236.53 m 182.58 236.53 l S 182.58 236.53 m 182.63 236.53 l S 182.63 236.53 m 182.67 236.53 l S 182.67 236.53 m 182.72 236.53 l S 182.72 236.53 m 182.76 236.53 l S 182.76 236.53 m 182.81 236.53 l S 182.81 236.53 m 182.86 236.53 l S 182.86 236.53 m 182.90 236.53 l S 182.90 236.53 m 182.95 236.53 l S 182.95 236.53 m 182.99 236.53 l S 182.99 236.53 m 183.04 236.53 l S 183.04 236.53 m 183.09 236.53 l S 183.09 236.53 m 183.13 236.53 l S 183.13 236.53 m 183.18 236.53 l S 183.18 236.53 m 183.22 236.53 l S 183.22 236.53 m 183.27 236.53 l S 183.27 236.53 m 183.32 236.53 l S 183.32 236.53 m 183.36 236.53 l S 183.36 236.53 m 183.41 236.53 l S 183.41 236.53 m 183.45 237.51 l S 183.45 236.53 m 183.50 237.51 l S 183.50 236.53 m 183.55 236.53 l S 183.55 236.53 m 183.59 236.53 l S 183.59 236.53 m 183.64 236.53 l S 183.64 236.53 m 183.68 236.53 l S 183.68 236.53 m 183.73 236.53 l S 183.73 236.53 m 183.78 236.53 l S 183.78 236.53 m 183.82 236.53 l S 183.82 236.53 m 183.87 236.53 l S 183.87 236.53 m 183.91 236.53 l S 183.91 236.53 m 183.96 236.53 l S 183.96 236.53 m 184.01 236.53 l S 184.01 236.53 m 184.05 236.53 l S 184.05 236.53 m 184.10 236.53 l S 184.10 236.53 m 184.14 236.53 l S 184.14 236.53 m 184.19 236.53 l S 184.19 236.53 m 184.24 236.53 l S 184.24 236.53 m 184.28 236.53 l S 184.28 236.53 m 184.33 236.53 l S 184.33 236.53 m 184.37 236.53 l S 184.37 236.53 m 184.42 236.53 l S 184.42 236.53 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187.13 236.53 m 187.18 236.53 l S 187.18 236.53 m 187.23 236.53 l S 187.23 236.53 m 187.27 236.53 l S 187.27 236.53 m 187.32 236.53 l S 187.32 236.53 m 187.36 237.51 l S 187.36 236.53 m 187.41 237.51 l S 187.41 236.53 m 187.46 236.53 l S 187.46 236.53 m 187.50 236.53 l S 187.50 236.53 m 187.55 236.53 l S 187.55 236.53 m 187.59 236.53 l S 187.59 236.53 m 187.64 236.53 l S 187.64 236.53 m 187.69 236.53 l S 187.69 236.53 m 187.73 236.53 l S 187.73 236.53 m 187.78 236.53 l S 187.78 236.53 m 187.82 236.53 l S 187.82 236.53 m 187.87 236.53 l S 187.87 236.53 m 187.92 237.51 l S 187.92 236.53 m 187.96 237.51 l S 187.96 236.53 m 188.01 236.53 l S 188.01 236.53 m 188.05 236.53 l S 188.05 236.53 m 188.10 236.53 l S 188.10 236.53 m 188.15 236.53 l S 188.15 236.53 m 188.19 236.53 l S 188.19 236.53 m 188.24 236.53 l S 188.24 236.53 m 188.28 236.53 l S 188.28 236.53 m 188.33 236.53 l S 188.33 236.53 m 188.38 236.53 l S 188.38 236.53 m 188.42 236.53 l S 188.42 236.53 m 188.47 236.53 l S 188.47 236.53 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(14) Tj ET 187.20 43.20 72.00 72.00 re S 1.000 0.000 0.000 RG 189.45 45.45 m 193.95 45.45 l 193.95 49.95 l 189.45 49.95 l 189.45 54.45 l 189.45 58.95 l 193.95 58.95 l 193.95 54.45 l 198.45 54.45 l 198.45 58.95 l 202.95 58.95 l 202.95 54.45 l 202.95 49.95 l 198.45 49.95 l 198.45 45.45 l 202.95 45.45 l 207.45 45.45 l 207.45 49.95 l 211.95 49.95 l 211.95 45.45 l 216.45 45.45 l 220.95 45.45 l 220.95 49.95 l 216.45 49.95 l 216.45 54.45 l 220.95 54.45 l 220.95 58.95 l 216.45 58.95 l 211.95 58.95 l 211.95 54.45 l 207.45 54.45 l 207.45 58.95 l 207.45 63.45 l 207.45 67.95 l 211.95 67.95 l 211.95 63.45 l 216.45 63.45 l 220.95 63.45 l 220.95 67.95 l 216.45 67.95 l 216.45 72.45 l 220.95 72.45 l 220.95 76.95 l 216.45 76.95 l 211.95 76.95 l 211.95 72.45 l 207.45 72.45 l 207.45 76.95 l 202.95 76.95 l 198.45 76.95 l 198.45 72.45 l 202.95 72.45 l 202.95 67.95 l 202.95 63.45 l 198.45 63.45 l 198.45 67.95 l 193.95 67.95 l 193.95 63.45 l 189.45 63.45 l 189.45 67.95 l 189.45 72.45 l 193.95 72.45 l 193.95 76.95 l 189.45 76.95 l 189.45 81.45 l 189.45 85.95 l 193.95 85.95 l 193.95 81.45 l 198.45 81.45 l 202.95 81.45 l 202.95 85.95 l 198.45 85.95 l 198.45 90.45 l 202.95 90.45 l 202.95 94.95 l 198.45 94.95 l 193.95 94.95 l 193.95 90.45 l 189.45 90.45 l 189.45 94.95 l 189.45 99.45 l 193.95 99.45 l 193.95 103.95 l 189.45 103.95 l 189.45 108.45 l 189.45 112.95 l 193.95 112.95 l 193.95 108.45 l 198.45 108.45 l 198.45 112.95 l 202.95 112.95 l 202.95 108.45 l 202.95 103.95 l 198.45 103.95 l 198.45 99.45 l 202.95 99.45 l 207.45 99.45 l 211.95 99.45 l 211.95 103.95 l 207.45 103.95 l 207.45 108.45 l 207.45 112.95 l 211.95 112.95 l 211.95 108.45 l 216.45 108.45 l 216.45 112.95 l 220.95 112.95 l 220.95 108.45 l 220.95 103.95 l 216.45 103.95 l 216.45 99.45 l 220.95 99.45 l 220.95 94.95 l 220.95 90.45 l 216.45 90.45 l 216.45 94.95 l 211.95 94.95 l 207.45 94.95 l 207.45 90.45 l 211.95 90.45 l 211.95 85.95 l 207.45 85.95 l 207.45 81.45 l 211.95 81.45 l 216.45 81.45 l 216.45 85.95 l 220.95 85.95 l 220.95 81.45 l 225.45 81.45 l 225.45 85.95 l 229.95 85.95 l 229.95 81.45 l 234.45 81.45 l 238.95 81.45 l 238.95 85.95 l 234.45 85.95 l 234.45 90.45 l 238.95 90.45 l 238.95 94.95 l 234.45 94.95 l 229.95 94.95 l 229.95 90.45 l 225.45 90.45 l 225.45 94.95 l 225.45 99.45 l 229.95 99.45 l 229.95 103.95 l 225.45 103.95 l 225.45 108.45 l 225.45 112.95 l 229.95 112.95 l 229.95 108.45 l 234.45 108.45 l 234.45 112.95 l 238.95 112.95 l 238.95 108.45 l 238.95 103.95 l 234.45 103.95 l 234.45 99.45 l 238.95 99.45 l 243.45 99.45 l 247.95 99.45 l 247.95 103.95 l 243.45 103.95 l 243.45 108.45 l 243.45 112.95 l 247.95 112.95 l 247.95 108.45 l 252.45 108.45 l 252.45 112.95 l 256.95 112.95 l 256.95 108.45 l 256.95 103.95 l 252.45 103.95 l 252.45 99.45 l 256.95 99.45 l 256.95 94.95 l 256.95 90.45 l 252.45 90.45 l 252.45 94.95 l 247.95 94.95 l 243.45 94.95 l 243.45 90.45 l 247.95 90.45 l 247.95 85.95 l 243.45 85.95 l 243.45 81.45 l 247.95 81.45 l 252.45 81.45 l 252.45 85.95 l 256.95 85.95 l 256.95 81.45 l 256.95 76.95 l 252.45 76.95 l 252.45 72.45 l 256.95 72.45 l 256.95 67.95 l 256.95 63.45 l 252.45 63.45 l 252.45 67.95 l 247.95 67.95 l 247.95 63.45 l 243.45 63.45 l 243.45 67.95 l 243.45 72.45 l 247.95 72.45 l 247.95 76.95 l 243.45 76.95 l 238.95 76.95 l 238.95 72.45 l 234.45 72.45 l 234.45 76.95 l 229.95 76.95 l 225.45 76.95 l 225.45 72.45 l 229.95 72.45 l 229.95 67.95 l 225.45 67.95 l 225.45 63.45 l 229.95 63.45 l 234.45 63.45 l 234.45 67.95 l 238.95 67.95 l 238.95 63.45 l 238.95 58.95 l 238.95 54.45 l 234.45 54.45 l 234.45 58.95 l 229.95 58.95 l 225.45 58.95 l 225.45 54.45 l 229.95 54.45 l 229.95 49.95 l 225.45 49.95 l 225.45 45.45 l 229.95 45.45 l 234.45 45.45 l 234.45 49.95 l 238.95 49.95 l 238.95 45.45 l 243.45 45.45 l 247.95 45.45 l 247.95 49.95 l 243.45 49.95 l 243.45 54.45 l 243.45 58.95 l 247.95 58.95 l 247.95 54.45 l 252.45 54.45 l 252.45 58.95 l 256.95 58.95 l 256.95 54.45 l 256.95 49.95 l 252.45 49.95 l 252.45 45.45 l 256.95 45.45 l S 1.000 0.000 1.000 RG BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 188.62 44.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 193.12 44.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 193.12 49.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 188.62 49.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 188.62 53.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 188.62 58.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 193.12 58.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 193.12 53.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 197.62 53.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 197.62 58.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 202.12 58.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 202.12 53.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 202.12 49.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 197.62 49.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 197.62 44.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 202.12 44.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 206.62 44.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 206.62 49.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 211.12 49.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 211.12 44.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 215.62 44.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 220.12 44.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 220.12 49.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 215.62 49.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 215.62 53.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 220.12 53.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 220.12 58.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 215.62 58.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 211.12 58.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 211.12 53.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 206.62 53.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 206.62 58.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 206.62 62.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 206.62 67.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 211.12 67.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 211.12 62.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 215.62 62.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 220.12 62.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 220.12 67.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 215.62 67.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 215.62 71.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 220.12 71.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 220.12 76.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 215.62 76.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 211.12 76.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 211.12 71.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 206.62 71.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 206.62 76.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 202.12 76.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 197.62 76.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 197.62 71.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 202.12 71.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 202.12 67.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 202.12 62.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 197.62 62.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 197.62 67.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 193.12 67.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 193.12 62.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 188.62 62.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 188.62 67.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 188.62 71.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 193.12 71.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 193.12 76.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 188.62 76.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 188.62 80.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 188.62 85.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 193.12 85.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 193.12 80.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 197.62 80.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 202.12 80.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 202.12 85.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 197.62 85.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 197.62 89.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 202.12 89.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 202.12 94.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 197.62 94.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 193.12 94.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 193.12 89.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 188.62 89.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 188.62 94.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 188.62 98.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 193.12 98.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 193.12 103.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 188.62 103.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 188.62 107.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 188.62 112.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 193.12 112.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 193.12 107.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 197.62 107.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 197.62 112.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 202.12 112.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 202.12 107.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 202.12 103.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 197.62 103.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 197.62 98.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 202.12 98.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 206.62 98.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 211.12 98.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 211.12 103.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 206.62 103.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 206.62 107.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 206.62 112.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 211.12 112.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 211.12 107.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 215.62 107.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 215.62 112.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 220.12 112.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 220.12 107.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 220.12 103.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 215.62 103.22 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 215.62 98.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 220.12 98.72 Tm (l) Tj 0 Tr ET BT /F1 1 Tf 1 Tr 2.10 0 0 2.10 220.12 94.22 Tm (l) Tj 0 Tr 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240.42 85.79 l 240.46 85.78 l 240.51 85.81 l 240.56 85.77 l 240.60 85.75 l 240.65 85.75 l 240.69 85.77 l 240.74 85.76 l 240.79 85.75 l 240.83 85.73 l 240.88 85.69 l 240.92 85.64 l 240.97 85.63 l 241.02 85.63 l 241.06 85.65 l 241.11 85.64 l 241.15 85.59 l 241.20 85.54 l 241.25 85.47 l 241.29 85.46 l 241.34 85.48 l 241.38 85.50 l 241.43 85.53 l 241.48 85.48 l 241.52 85.48 l 241.57 85.46 l 241.61 85.47 l 241.66 85.44 l 241.71 85.40 l 241.75 85.41 l 241.80 85.40 l 241.84 85.40 l 241.89 85.36 l 241.94 85.35 l 241.98 85.39 l 242.03 85.37 l 242.07 85.36 l 242.12 85.37 l 242.17 85.37 l 242.21 85.36 l 242.26 85.31 l 242.30 85.28 l 242.35 85.26 l 242.40 85.26 l 242.44 85.25 l 242.49 85.21 l 242.53 85.21 l 242.58 85.23 l 242.63 85.21 l 242.67 85.20 l 242.72 85.14 l 242.76 85.14 l 242.81 85.11 l 242.86 85.11 l 242.90 85.09 l 242.95 85.09 l 242.99 85.08 l 243.04 85.05 l 243.09 85.06 l 243.13 85.05 l 243.18 85.03 l 243.22 85.00 l 243.27 85.00 l 243.32 85.03 l 243.36 85.06 l 243.41 85.00 l 243.45 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246.54 84.52 l 246.58 84.50 l 246.63 84.49 l 246.67 84.47 l 246.72 84.46 l 246.77 84.41 l 246.81 84.40 l 246.86 84.40 l 246.90 84.40 l 246.95 84.44 l 247.00 84.42 l 247.04 84.45 l 247.09 84.42 l 247.13 84.39 l 247.18 84.42 l 247.23 84.44 l 247.27 84.43 l 247.32 84.39 l 247.36 84.34 l 247.41 84.35 l 247.46 84.38 l 247.50 84.36 l 247.55 84.37 l 247.59 84.32 l 247.64 84.30 l 247.69 84.31 l 247.73 84.35 l 247.78 84.39 l 247.82 84.39 l 247.87 84.40 l 247.92 84.37 l 247.96 84.33 l 248.01 84.32 l 248.05 84.33 l 248.10 84.35 l 248.15 84.36 l 248.19 84.34 l 248.24 84.30 l 248.28 84.27 l 248.33 84.26 l 248.38 84.22 l 248.42 84.24 l 248.47 84.26 l 248.51 84.27 l 248.56 84.29 l 248.61 84.30 l 248.65 84.28 l 248.70 84.29 l 248.74 84.27 l 248.79 84.34 l 248.84 84.36 l 248.88 84.35 l 248.93 84.38 l 248.97 84.38 l 249.02 84.37 l 249.07 84.39 l 249.11 84.40 l 249.16 84.37 l 249.20 84.34 l 249.25 84.37 l 249.30 84.38 l 249.34 84.41 l 249.39 84.43 l 249.43 84.40 l 249.48 84.40 l 249.53 84.39 l 249.57 84.36 l 249.62 84.35 l 249.66 84.34 l 249.71 84.37 l 249.76 84.38 l 249.80 84.35 l 249.85 84.33 l 249.89 84.30 l 249.94 84.29 l 249.99 84.29 l 250.03 84.32 l 250.08 84.36 l 250.12 84.39 l 250.17 84.38 l 250.22 84.40 l 250.26 84.41 l 250.31 84.42 l 250.35 84.41 l 250.40 84.43 l S Q q 0.000 0.000 0.000 RG 0.75 w [] 0 d 1 J 1 j 10.00 M 66.40 73.44 m 250.40 73.44 l S 66.40 73.44 m 66.40 66.24 l S 112.40 73.44 m 112.40 66.24 l S 158.40 73.44 m 158.40 66.24 l S 204.40 73.44 m 204.40 66.24 l S 250.40 73.44 m 250.40 66.24 l S BT 0.000 0.000 0.000 rg /F2 1 Tf 12.00 0.00 -0.00 12.00 49.55 47.52 Tm (-2000) Tj ET BT /F2 1 Tf 12.00 0.00 -0.00 12.00 95.55 47.52 Tm (-1000) Tj ET BT /F2 1 Tf 12.00 0.00 -0.00 12.00 155.06 47.52 Tm (0) Tj ET BT /F2 1 Tf 12.00 0.00 -0.00 12.00 191.06 47.52 Tm (1000) Tj ET BT /F2 1 Tf 12.00 0.00 -0.00 12.00 237.06 47.52 Tm (2000) Tj ET 59.04 75.11 m 59.04 239.58 l S 59.04 75.11 m 51.84 75.11 l S 59.04 116.23 m 51.84 116.23 l S 59.04 157.35 m 51.84 157.35 l S 59.04 198.46 m 51.84 198.46 l S 59.04 239.58 m 51.84 239.58 l S BT /F2 1 Tf 0.00 12.00 -12.00 0.00 41.76 60.10 Tm (0.000) Tj ET BT /F2 1 Tf 0.00 12.00 -12.00 0.00 41.76 101.22 Tm (0.001) Tj ET BT /F2 1 Tf 0.00 12.00 -12.00 0.00 41.76 142.34 Tm (0.002) Tj ET BT /F2 1 Tf 0.00 12.00 -12.00 0.00 41.76 183.45 Tm (0.003) Tj ET BT /F2 1 Tf 0.00 12.00 -12.00 0.00 41.76 224.57 Tm (0.004) Tj ET 59.04 73.44 m 257.76 73.44 l 257.76 264.96 l 59.04 264.96 l 59.04 73.44 l S Q q BT 0.000 0.000 0.000 rg /F2 1 Tf 12.00 0.00 -0.00 12.00 116.05 18.72 Tm (distance to TSS) Tj ET Q q 59.04 73.44 198.72 191.52 re W n 0.000 1.000 0.000 RG 0.75 w [] 0 d 1 J 1 j 10.00 M 66.40 85.10 m 66.45 85.10 l 66.49 85.13 l 66.54 85.12 l 66.58 85.11 l 66.63 85.10 l 66.68 85.10 l 66.72 85.10 l 66.77 85.10 l 66.81 85.12 l 66.86 85.12 l 66.91 85.13 l 66.95 85.13 l 67.00 85.14 l 67.04 85.13 l 67.09 85.13 l 67.14 85.11 l 67.18 85.13 l 67.23 85.15 l 67.27 85.17 l 67.32 85.17 l 67.37 85.16 l 67.41 85.16 l 67.46 85.09 l 67.50 85.04 l 67.55 84.99 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70.86 85.00 l 70.91 85.06 l 70.95 85.07 l 71.00 85.08 l 71.05 85.08 l 71.09 85.11 l 71.14 85.09 l 71.18 85.13 l 71.23 85.12 l 71.28 85.12 l 71.32 85.11 l 71.37 85.12 l 71.41 85.13 l 71.46 85.17 l 71.51 85.22 l 71.55 85.24 l 71.60 85.31 l 71.64 85.33 l 71.69 85.30 l 71.74 85.33 l 71.78 85.34 l 71.83 85.37 l 71.87 85.38 l 71.92 85.41 l 71.97 85.45 l 72.01 85.46 l 72.06 85.47 l 72.10 85.48 l 72.15 85.47 l 72.20 85.49 l 72.24 85.46 l 72.29 85.48 l 72.33 85.47 l 72.38 85.46 l 72.43 85.47 l 72.47 85.51 l 72.52 85.52 l 72.56 85.54 l 72.61 85.55 l 72.66 85.56 l 72.70 85.56 l 72.75 85.57 l 72.79 85.60 l 72.84 85.57 l 72.89 85.62 l 72.93 85.61 l 72.98 85.62 l 73.02 85.62 l 73.07 85.62 l 73.12 85.65 l 73.16 85.69 l 73.21 85.73 l 73.25 85.73 l 73.30 85.77 l 73.35 85.76 l 73.39 85.79 l 73.44 85.80 l 73.48 85.80 l 73.53 85.84 l 73.58 85.86 l 73.62 85.86 l 73.67 85.85 l 73.71 85.86 l 73.76 85.85 l 73.81 85.88 l 73.85 85.90 l 73.90 85.88 l 73.94 85.91 l 73.99 85.90 l 74.04 85.89 l 74.08 85.92 l 74.13 85.90 l 74.17 85.90 l 74.22 85.93 l 74.27 85.94 l 74.31 85.94 l 74.36 85.94 l 74.40 85.98 l 74.45 85.97 l 74.50 85.99 l 74.54 86.01 l 74.59 86.05 l 74.63 86.06 l 74.68 86.10 l 74.73 86.10 l 74.77 86.10 l 74.82 86.12 l 74.86 86.12 l 74.91 86.12 l 74.96 86.12 l 75.00 86.12 l 75.05 86.17 l 75.09 86.21 l 75.14 86.25 l 75.19 86.25 l 75.23 86.27 l 75.28 86.26 l 75.32 86.25 l 75.37 86.25 l 75.42 86.24 l 75.46 86.24 l 75.51 86.23 l 75.55 86.23 l 75.60 86.25 l 75.65 86.26 l 75.69 86.25 l 75.74 86.25 l 75.78 86.25 l 75.83 86.23 l 75.88 86.22 l 75.92 86.20 l 75.97 86.20 l 76.01 86.20 l 76.06 86.19 l 76.11 86.20 l 76.15 86.20 l 76.20 86.19 l 76.24 86.22 l 76.29 86.25 l 76.34 86.31 l 76.38 86.35 l 76.43 86.39 l 76.47 86.42 l 76.52 86.44 l 76.57 86.45 l 76.61 86.41 l 76.66 86.43 l 76.70 86.46 l 76.75 86.50 l 76.80 86.56 l 76.84 86.57 l 76.89 86.60 l 76.93 86.60 l 76.98 86.65 l 77.03 86.65 l 77.07 86.66 l 77.12 86.66 l 77.16 86.66 l 77.21 86.63 l 77.26 86.63 l 77.30 86.58 l 77.35 86.59 l 77.39 86.54 l 77.44 86.53 l 77.49 86.56 l 77.53 86.63 l 77.58 86.64 l 77.62 86.72 l 77.67 86.76 l 77.72 86.81 l 77.76 86.83 l 77.81 86.85 l 77.85 86.85 l 77.90 86.88 l 77.95 86.89 l 77.99 86.92 l 78.04 86.93 l 78.08 86.96 l 78.13 87.00 l 78.18 86.99 l 78.22 87.01 l 78.27 87.00 l 78.31 86.99 l 78.36 86.98 l 78.41 86.99 l 78.45 87.07 l 78.50 87.17 l 78.54 87.25 l 78.59 87.28 l 78.64 87.28 l 78.68 87.24 l 78.73 87.24 l 78.77 87.28 l 78.82 87.32 l 78.87 87.33 l 78.91 87.35 l 78.96 87.35 l 79.00 87.36 l 79.05 87.32 l 79.10 87.35 l 79.14 87.38 l 79.19 87.41 l 79.23 87.37 l 79.28 87.41 l 79.33 87.35 l 79.37 87.39 l 79.42 87.38 l 79.46 87.42 l 79.51 87.42 l 79.56 87.47 l 79.60 87.52 l 79.65 87.61 l 79.69 87.61 l 79.74 87.70 l 79.79 87.73 l 79.83 87.72 l 79.88 87.72 l 79.92 87.73 l 79.97 87.73 l 80.02 87.76 l 80.06 87.80 l 80.11 87.81 l 80.15 87.82 l 80.20 87.86 l 80.25 87.87 l 80.29 87.87 l 80.34 87.81 l 80.38 87.79 l 80.43 87.76 l 80.48 87.76 l 80.52 87.76 l 80.57 87.76 l 80.61 87.76 l 80.66 87.81 l 80.71 87.83 l 80.75 87.88 l 80.80 87.88 l 80.84 87.95 l 80.89 87.96 l 80.94 88.01 l 80.98 88.05 l 81.03 88.09 l 81.07 88.09 l 81.12 88.13 l 81.17 88.15 l 81.21 88.14 l 81.26 88.16 l 81.30 88.13 l 81.35 88.16 l 81.40 88.17 l 81.44 88.23 l 81.49 88.22 l 81.53 88.22 l 81.58 88.26 l 81.63 88.26 l 81.67 88.24 l 81.72 88.29 l 81.76 88.38 l 81.81 88.41 l 81.86 88.49 l 81.90 88.51 l 81.95 88.50 l 81.99 88.50 l 82.04 88.47 l 82.09 88.47 l 82.13 88.50 l 82.18 88.50 l 82.22 88.51 l 82.27 88.53 l 82.32 88.55 l 82.36 88.55 l 82.41 88.60 l 82.45 88.61 l 82.50 88.66 l 82.55 88.71 l 82.59 88.71 l 82.64 88.77 l 82.68 88.78 l 82.73 88.79 l 82.78 88.78 l 82.82 88.80 l 82.87 88.80 l 82.91 88.80 l 82.96 88.82 l 83.01 88.81 l 83.05 88.85 l 83.10 88.83 l 83.14 88.84 l 83.19 88.83 l 83.24 88.87 l 83.28 88.88 l 83.33 88.92 l 83.37 88.94 l 83.42 88.95 l 83.47 88.93 l 83.51 88.92 l 83.56 88.92 l 83.60 88.93 l 83.65 88.92 l 83.70 88.93 l 83.74 88.92 l 83.79 88.95 l 83.83 88.97 l 83.88 89.00 l 83.93 89.00 l 83.97 89.04 l 84.02 89.06 l 84.06 89.08 l 84.11 89.07 l 84.16 89.11 l 84.20 89.12 l 84.25 89.14 l 84.29 89.21 l 84.34 89.22 l 84.39 89.25 l 84.43 89.25 l 84.48 89.23 l 84.52 89.26 l 84.57 89.29 l 84.62 89.29 l 84.66 89.30 l 84.71 89.34 l 84.75 89.36 l 84.80 89.35 l 84.85 89.30 l 84.89 89.30 l 84.94 89.27 l 84.98 89.28 l 85.03 89.31 l 85.08 89.34 l 85.12 89.35 l 85.17 89.35 l 85.21 89.32 l 85.26 89.32 l 85.31 89.26 l 85.35 89.28 l 85.40 89.26 l 85.44 89.27 l 85.49 89.22 l 85.54 89.23 l 85.58 89.24 l 85.63 89.25 l 85.67 89.24 l 85.72 89.28 l 85.77 89.31 l 85.81 89.34 l 85.86 89.36 l 85.90 89.38 l 85.95 89.41 l 86.00 89.40 l 86.04 89.40 l 86.09 89.42 l 86.13 89.38 l 86.18 89.37 l 86.23 89.37 l 86.27 89.42 l 86.32 89.43 l 86.36 89.46 l 86.41 89.46 l 86.46 89.50 l 86.50 89.56 l 86.55 89.67 l 86.59 89.66 l 86.64 89.68 l 86.69 89.71 l 86.73 89.75 l 86.78 89.80 l 86.82 89.87 l 86.87 89.93 l 86.92 89.94 l 86.96 89.90 l 87.01 89.80 l 87.05 89.75 l 87.10 89.76 l 87.15 89.80 l 87.19 89.82 l 87.24 89.84 l 87.28 89.84 l 87.33 89.84 l 87.38 89.88 l 87.42 89.89 l 87.47 89.90 l 87.51 89.90 l 87.56 89.97 l 87.61 89.98 l 87.65 89.98 l 87.70 90.01 l 87.74 90.02 l 87.79 90.00 l 87.84 90.02 l 87.88 89.99 l 87.93 89.97 l 87.97 89.94 l 88.02 89.94 l 88.07 89.95 l 88.11 89.97 l 88.16 89.92 l 88.20 89.93 l 88.25 89.86 l 88.30 89.88 l 88.34 89.94 l 88.39 89.97 l 88.43 90.01 l 88.48 90.01 l 88.53 90.01 l 88.57 90.05 l 88.62 90.02 l 88.66 90.03 l 88.71 90.04 l 88.76 90.03 l 88.80 90.04 l 88.85 90.10 l 88.89 90.12 l 88.94 90.14 l 88.99 90.17 l 89.03 90.19 l 89.08 90.29 l 89.12 90.28 l 89.17 90.25 l 89.22 90.25 l 89.26 90.23 l 89.31 90.25 l 89.35 90.22 l 89.40 90.25 l 89.45 90.26 l 89.49 90.31 l 89.54 90.32 l 89.58 90.41 l 89.63 90.42 l 89.68 90.40 l 89.72 90.44 l 89.77 90.49 l 89.81 90.53 l 89.86 90.60 l 89.91 90.64 l 89.95 90.63 l 90.00 90.68 l 90.04 90.73 l 90.09 90.73 l 90.14 90.75 l 90.18 90.78 l 90.23 90.76 l 90.27 90.68 l 90.32 90.63 l 90.37 90.60 l 90.41 90.60 l 90.46 90.59 l 90.50 90.60 l 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167.28 237.02 l 167.32 236.17 l 167.37 235.40 l 167.42 234.66 l 167.46 234.13 l 167.51 233.30 l 167.55 232.71 l 167.60 232.30 l 167.65 231.61 l 167.69 230.71 l 167.74 229.67 l 167.78 229.24 l 167.83 228.67 l 167.88 228.02 l 167.92 227.28 l 167.97 226.47 l 168.01 225.66 l 168.06 224.86 l 168.11 223.98 l 168.15 222.89 l 168.20 222.03 l 168.24 221.23 l 168.29 220.61 l 168.34 220.24 l 168.38 219.63 l 168.43 219.31 l 168.47 218.75 l 168.52 218.10 l 168.57 217.49 l 168.61 216.93 l 168.66 216.43 l 168.70 215.86 l 168.75 215.19 l 168.80 214.74 l 168.84 214.34 l 168.89 213.85 l 168.93 212.98 l 168.98 211.96 l 169.03 211.01 l 169.07 210.44 l 169.12 209.83 l 169.16 209.42 l 169.21 208.95 l 169.26 208.52 l 169.30 207.78 l 169.35 207.19 l 169.39 206.48 l 169.44 206.07 l 169.49 205.70 l 169.53 205.36 l 169.58 205.15 l 169.62 204.79 l 169.67 204.41 l 169.72 203.98 l 169.76 203.71 l 169.81 203.38 l 169.85 203.20 l 169.90 202.94 l 169.95 202.52 l 169.99 202.16 l 170.04 201.78 l 170.08 201.68 l 170.13 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178.78 180.81 l 178.82 180.82 l 178.87 180.78 l 178.92 180.80 l 178.96 180.76 l 179.01 180.59 l 179.05 180.47 l 179.10 180.25 l 179.15 180.15 l 179.19 180.03 l 179.24 179.90 l 179.28 179.84 l 179.33 179.86 l 179.38 179.88 l 179.42 179.89 l 179.47 179.83 l 179.51 179.83 l 179.56 179.84 l 179.61 179.79 l 179.65 179.86 l 179.70 179.90 l 179.74 179.84 l 179.79 179.92 l 179.84 179.86 l 179.88 179.81 l 179.93 179.73 l 179.97 179.59 l 180.02 179.42 l 180.07 179.47 l 180.11 179.53 l 180.16 179.48 l 180.20 179.40 l 180.25 179.35 l 180.30 179.20 l 180.34 179.08 l 180.39 179.14 l 180.43 178.90 l 180.48 178.75 l 180.53 178.51 l 180.57 178.46 l 180.62 178.35 l 180.66 178.25 l 180.71 178.23 l 180.76 178.11 l 180.80 178.06 l 180.85 177.99 l 180.89 177.85 l 180.94 177.77 l 180.99 177.66 l 181.03 177.68 l 181.08 177.63 l 181.12 177.63 l 181.17 177.59 l 181.22 177.37 l 181.26 177.12 l 181.31 177.02 l 181.35 176.84 l 181.40 176.80 l 181.45 176.63 l 181.49 176.49 l 181.54 176.42 l 181.58 176.19 l 181.63 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Annalen}, volume={38}, year={1891}, pages={459}, } @article{PeanoCurve_first, author={Giuseppe Peano}, title={Sur une courbe qui rempli toute une aire plaine}, journal={Mathematische Annalen}, volume={36}, year={1890}, pages={157}, } @article{HilbertVisualization_first, title={Pixel-Oriented Visualization Techniques for Exploring Very Large Data Bases}, Author={Daniel A. Keim}, journal={J. Comp. Graph. Stat.}, Volume={5}, year={1996}, pages={58-77}, url={http://www.jstor.org/stable/1390753}, } @article{HistMeth_ChipSeq, author={Artem Barski and Suresh Cuddapah and Kairong Cui and Tae-Young Roh and Dustin E. Schones and Zhibin Wang and Gang Wei and Iouri Chepelev and Keji Zhao}, title={High-Resolution Profiling of Histone Methylations in the Human Genome}, journal={Cell}, Volume={129}, year={2007}, Pages={823-837}, doi={10.1016/j.cell.2007.05.009} } @article{Maq, author={Heng Li and Jue Ruan and Richard Durbin}, title={Mapping short {DNA} sequencing reads and calling variants using mapping quality scores}, journal = {Genome Res.}, volume={18}, year={2008}, pages={1851}, doi={10.1101/gr.078212.108}, } @manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = 2008, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org} } @Article{BioC, author = {Robert C Gentleman and Vincent J. Carey and Douglas M. Bates and Ben Bolstad and Marcel Dettling and Sandrine Dudoit and Byron Ellis and Laurent Gautier and Yongchao Ge and Jeff Gentry and Kurt Hornik and Torsten Hothorn and Wolfgang Huber and Stefano Iacus and Rafael Irizarry and Friedrich Leisch and Cheng Li and Martin Maechler and Anthony J. Rossini and Gunther Sawitzki and Colin Smith and Gordon Smyth and Luke Tierney and Jean Y. H. Yang and Jianhua Zhang}, title = {Bioconductor: Open software development for computational biology and bioinformatics}, journal = {Genome Biology}, volume = {5}, year = {2004}, pages = {R80}, url = {http://genomebiology.com/2004/5/10/R80} } @misc{gtkmm, title={gtkmm: {C++} Interfaces for {GTK+} and {Gnome}}, author={Murray Cumming and Daniel Elstner and others}, url={http://www.gtkmm.org/} } @Manual{GenomeGraphs, title = {GenomeGraphs: Plotting genomic information from Ensembl}, author = {Steffen Durinck and James Bullard}, year = {}, note = {R package version 0.0.9}, url={http://www.bioconductor.org/packages/bioc/html/GenomeGraphs.html} } @Manual{ShortRead, title = {ShortRead: Base classes and methods for high-throughput short-read sequencing data.}, author = {Martin Morgan}, year = {}, note = {R package version 0.1.23}, } ShortRead/vignettes/images/0000755000175400017540000000000013001230552016737 5ustar00biocbuildbiocbuildShortRead/vignettes/images/HilbertDisplay_GUI.pdf0000644000175400017540000024450013001230552023062 0ustar00biocbuildbiocbuild%PDF-1.4 1 0 obj << /Pages 2 0 R /Type /Catalog >> endobj 2 0 obj << /Type /Pages /Kids [ 3 0 R ] /Count 1 >> endobj 3 0 obj << /Type /Page /Parent 2 0 R /Resources << /XObject << /Im0 8 0 R >> /ProcSet 6 0 R >> /MediaBox [0 0 522 876] /CropBox [0 0 522 876] /Contents 4 0 R /Thumb 11 0 R >> endobj 4 0 obj << /Length 5 0 R >> stream q 522 0 0 876 0 0 cm /Im0 Do Q endstream endobj 5 0 obj 31 endobj 6 0 obj [ /PDF /Text /ImageC ] endobj 7 0 obj << >> endobj 8 0 obj << /Type /XObject /Subtype /Image /Name /Im0 /Filter [ /FlateDecode ] /Width 522 /Height 876 /ColorSpace 10 0 R /BitsPerComponent 8 /SMask 15 0 R /Length 9 0 R >> stream xk_E-wqmEADDP10yEP#((ڶ( *" >iV| W@ޙa3g$5ا~V׬v 6X%jM:wܹz70i?c sɉ;wy=Ƀl 7|1wkΝ;w^O7x_!b֏ҹsΝ5S0?[~xS=9Ou_ҹsΝ5S-fۇFPṛy^w;wy|ݏ=gۇF]?0۾p5gri/|BW| ֮YCuۆ7~tYkC{aӍ/L%.fPPaD LX\RXr/>]CNoeӿr^'/ÒX1]哔-*?ܝz֛?:CCV"oG7ro}mEiASm%uy}S~Gߋpnx,BƩïW9/}~pקqo7?r/YO G?/?9LUS}pզa﫮SobAR*7{F =+~盃.zۖL~zK_79g_mvާ%o&|d9;n;-s|Wǡ0Cv4o%_8|+WX.>wl=<>4Z;o ~;-p֦i [o t-eõ7\ Donn7zA},>h_v/z^=wg .W{>fK}H/y^Sa"O#ޕ>g.G|!r{3|7NɛGݾky/;v?fd>wHNnǐv/=p=6ۼqyO|іn3VS~?}wbលg.|VTeVrvFov}s7>j9_{|3znֱ|=w%/~_~uamzmǶO=5[q̍.:Y3j)LZC[͇9ts6\eчN}QKNxmntjM.iyFq6&'-%mlƗl:3<{./wgn7l];>/cun ;?]ma5}*i;ڐ/=f ÿӁ_}ۜipM+n#4\r v#A},zp<6~sp+Vya̓>gnm=6?m9_^J3ֆmv΅ |9~GXԩ^nz$y/mmu3ÏK3-ny|XixXXwx^ .%7"zL:: >ۧ{K0SFttf$ 󢡯/1"UO,J' Gv[=eϿN'됧zY2qߴóLt+g`gLqI8|tp/}3\|;n9rCgԀ_{Xr=^ޭ|ŜKN|ap4#=BA̕ïm;+O˽ϨCkD:zǒtx<,+=gyp!O<>4[_ma 4Lֆ/ A|K3-n庛nn[p7vK>c|Kvk։==?G9ڋG׋#_Ap?aZDѩ 5LJw3wj 42zg?`ޖ}a܍gl} N9hzn nu&Pȴ6Lkb23qG;ᕫ:EO:й߰ iiZն6פ)bZŇ?Wueo3-n O 7t{1ca~qG~M>/wwimW|n߿ew]/׾p{,_^ ^Ӧ_Z8|g}7~z.zO{vpIAӏYrc[^|DƗl>#]77oyf?\-uLk3 hFBd>t}3q޺ʵI?:6=: ?M<>4_ԽZ2ckC>ˏ;-OCmMKneu >Fyjs+ׯq65/d26 ߿` ~ _3c&ã:g$rxF;śh˱ph#o߶3.~s6Z&To;OǫǒLCvM^̱˞oʽnN[ntĪC:c=c׭7:[m|KNI8+_{mO?~9E#1s^5g*O)G}~9= #U3J[T 7|Sl{l7̟~C\E{m+s5(t9X2Qd''0##;8XT᜼ƕ՚j%2ͬ63ȴ&3t^슇W?w#^I_xYkCm՟|w;-nΝgfȸ?}Ov|yk&NO{;ߋ6d>@Ǯ͖~Мuny6Kz9E<1XFvyF[o/ySxx`jC|ݩZg8W>Ľv[>t}{ϟ4<>4om|u>ޱE<1XFY5ss_Pי󟴆ڷyA?z;wܹ8vNxWsΝ;:wܹhxV^wN.U?;)y1/[6f$\w-]:fX1=k}dhpPڗfmU5uט6GQ.1KW-_%m+s?\gɒ1c1c1_{Y_<.MV>cY*[nRhј_(#g g~2ϙ\T?u\GZy#z% s)tv6t2Ovi}Xe+\hFFϰP*5[`f/u?sS9b]_<2ێ;d{QߕʽgSZY~F{ǘk_ʘO;m̋NgLޛ9^1kjk B!G<0B k;c4Mį@0о<0qM>1;=3=H^meuF:XlE3n:O;:ͨf<QI=۫.鯪tO%V* .aTf)c'GA9-3]qX_ڻ2aigMlǎc=+7抟#^\NF;~EKI<"Se9qcF_@1m][9㚳Vx']a]:X֩N-d+ӖUo52,T)dIA֎Z3FRC֥FFFuT3gϳ`$4Ǎ%[ji41%{&U q(moF:\W?>"f`_F Vwޘw_.YO\_JFde)93??t5%ݩrkAmQsl#tXOe}9\ҁ;MOxa3.Ԃ4ӷE1E'ڝFw/4cU[eӖ?*.wfW^99\BX.B}Et(L38|tIKy~,u5e p]g\k*-ꍱǘ yׁzl&FSgyrSWֱh 2>2/ fCr*tV_ut "K(?̈,ihARt<$a3gYK&:t`% t^0\6-?+Ot2*y^Qh7FFFFFFYJ -òB\wA780{Wѯ>O_Ae-ͺ, uEuѨFFմP<=iG_Cpx>u'W#wVWMPùX#X{yWs o}t1|bo-zݵ Qq-K]NT_Q+h%Xς3Ej2wstf1߫>DEp}z ƌoűdגɍ%t{yO NҕDmW 92p8#*"k;c ꬞1%JOv3ǨgN^N_UϬ3 BG=fn߰㽦o H|Vw_TD]Qr\'P]OrC-IS:.gd髷r…cr{=^*'GN=<s̓SJW=gkL"WNnO`yP9<}̀ 9!6!!< -ٟۃf|}W>]<ތs]_9l)}쫑Zw  gOIw 4G;=[Fn! 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either % version 1 of the License, or any later version. % =============================================================== % Name and version information of the main mbs file: % \ProvidesFile{merlin.mbs}[2004/02/09 4.13 (PWD, AO, DPC)] % For use with BibTeX version 0.99a or later %------------------------------------------------------------------- % This bibliography style file is intended for texts in ENGLISH % This is a labelled citation style similar to the standard alpha.bst, % where labels are of the form Dal90 or DBK89. % It requires no extra package to interface to the main text. % The form of the \bibitem entries is % \bibitem[label]{key}... % Usage of \cite is as follows: % \cite{key} ==>> [label] % \cite[chap. 2]{key} ==>> [label, chap. 2] % The order in the reference list is by label. %--------------------------------------------------------------------- ENTRY { address archive author booktitle chapter collaboration doi edition editor eid eprint howpublished institution journal key month note number numpages organization pages publisher school series title type url volume year } {} { label extra.label sort.label } INTEGERS { output.state before.all mid.sentence after.sentence after.block } FUNCTION {init.state.consts} { #0 'before.all := #1 'mid.sentence := #2 'after.sentence := #3 'after.block := } STRINGS { s t} FUNCTION {output.nonnull} { 's := output.state mid.sentence = { ", " * write$ } { output.state after.block = { add.period$ write$ newline$ "\newblock " write$ } { output.state before.all = 'write$ { add.period$ " " * write$ } if$ } if$ mid.sentence 'output.state := } if$ s } FUNCTION {output} { duplicate$ empty$ 'pop$ 'output.nonnull if$ } FUNCTION {output.check} { 't := duplicate$ empty$ { pop$ "empty " t * " in " * cite$ * warning$ } 'output.nonnull if$ } FUNCTION {fin.entry} { add.period$ write$ newline$ } FUNCTION {new.block} { output.state before.all = 'skip$ { after.block 'output.state := } if$ } FUNCTION {new.sentence} { output.state after.block = 'skip$ { output.state before.all = 'skip$ { after.sentence 'output.state := } if$ } if$ } FUNCTION {add.blank} { " " * before.all 'output.state := } FUNCTION {date.block} { new.block } FUNCTION {not} { { #0 } { #1 } if$ } FUNCTION {and} { 'skip$ { pop$ #0 } if$ } FUNCTION {or} { { pop$ #1 } 'skip$ if$ } FUNCTION {new.block.checka} { empty$ 'skip$ 'new.block if$ } FUNCTION {new.block.checkb} { empty$ swap$ empty$ and 'skip$ 'new.block if$ } FUNCTION {new.sentence.checka} { empty$ 'skip$ 'new.sentence if$ } FUNCTION {new.sentence.checkb} { empty$ swap$ empty$ and 'skip$ 'new.sentence if$ } FUNCTION {field.or.null} { duplicate$ empty$ { pop$ "" } 'skip$ if$ } FUNCTION {emphasize} { duplicate$ empty$ { pop$ "" } { "\emph{" swap$ * "}" * } if$ } FUNCTION {bolden} { duplicate$ empty$ { pop$ "" } { "\textbf{" swap$ * "}" * } if$ } FUNCTION {tie.or.space.prefix} { duplicate$ text.length$ #3 < { "~" } { " " } if$ swap$ } FUNCTION {capitalize} { "u" change.case$ "t" change.case$ } FUNCTION {space.word} { " " swap$ * " " * } % Here are the language-specific definitions for explicit words. % Each function has a name bbl.xxx where xxx is the English word. % The language selected here is ENGLISH FUNCTION {bbl.and} { "and"} FUNCTION {bbl.etal} { "et~al." } FUNCTION {bbl.editors} { "eds." } FUNCTION {bbl.editor} { "ed." } FUNCTION {bbl.edby} { "edited by" } FUNCTION {bbl.edition} { "edn." } FUNCTION {bbl.volume} { "vol." } FUNCTION {bbl.of} { "of" } FUNCTION {bbl.number} { "no." } FUNCTION {bbl.nr} { "no." } FUNCTION {bbl.in} { "in" } FUNCTION {bbl.pages} { "pp." } FUNCTION {bbl.page} { "p." } FUNCTION {bbl.eidpp} { "pages" } FUNCTION {bbl.chapter} { "chap." } FUNCTION {bbl.techrep} { "Tech. Rep." } FUNCTION {bbl.mthesis} { "Master's thesis" } FUNCTION {bbl.phdthesis} { "Ph.D. thesis" } MACRO {jan} {"Jan."} MACRO {feb} {"Feb."} MACRO {mar} {"Mar."} MACRO {apr} {"Apr."} MACRO {may} {"May"} MACRO {jun} {"Jun."} MACRO {jul} {"Jul."} MACRO {aug} {"Aug."} MACRO {sep} {"Sep."} MACRO {oct} {"Oct."} MACRO {nov} {"Nov."} MACRO {dec} {"Dec."} %------------------------------------------------------------------- % Begin module: % \ProvidesFile{physjour.mbs}[2002/01/14 2.2 (PWD)] MACRO {aa}{"Astron. \& Astrophys."} MACRO {aasup}{"Astron. \& Astrophys. Suppl. Ser."} MACRO {aj} {"Astron. J."} MACRO {aph} {"Acta Phys."} MACRO {advp} {"Adv. Phys."} MACRO {ajp} {"Amer. J. Phys."} MACRO {ajm} {"Amer. J. Math."} MACRO {amsci} {"Amer. Sci."} MACRO {anofd} {"Ann. Fluid Dyn."} MACRO {am} {"Ann. Math."} MACRO {ap} {"Ann. Phys. (NY)"} MACRO {adp} {"Ann. Phys. (Leipzig)"} MACRO {ao} {"Appl. Opt."} MACRO {apl} {"Appl. Phys. Lett."} MACRO {app} {"Astroparticle Phys."} MACRO {apj} {"Astrophys. J."} MACRO {apjsup} {"Astrophys. J. Suppl."} MACRO {apss} {"Astrophys. Space Sci."} MACRO {araa} {"Ann. Rev. Astron. Astrophys."} MACRO {baas} {"Bull. Amer. Astron. Soc."} MACRO {baps} {"Bull. Amer. Phys. Soc."} MACRO {cmp} {"Comm. Math. Phys."} MACRO {cpam} {"Commun. Pure Appl. Math."} MACRO {cppcf} {"Comm. Plasma Phys. \& Controlled Fusion"} MACRO {cpc} {"Comp. Phys. Comm."} MACRO {cqg} {"Class. Quant. Grav."} MACRO {cra} {"C. R. Acad. Sci. A"} MACRO {fed} {"Fusion Eng. \& Design"} MACRO {ft} {"Fusion Tech."} MACRO {grg} {"Gen. Relativ. Gravit."} MACRO {ieeens} {"IEEE Trans. Nucl. Sci."} MACRO {ieeeps} {"IEEE Trans. Plasma Sci."} MACRO {ijimw} {"Interntl. J. Infrared \& Millimeter Waves"} MACRO {ip} {"Infrared Phys."} MACRO {irp} {"Infrared Phys."} MACRO {jap} {"J. Appl. Phys."} MACRO {jasa} {"J. Acoust. Soc. America"} MACRO {jcp} {"J. Comp. Phys."} MACRO {jetp} {"Sov. Phys.--JETP"} MACRO {jfe} {"J. Fusion Energy"} MACRO {jfm} {"J. Fluid Mech."} MACRO {jmp} {"J. Math. Phys."} MACRO {jne} {"J. Nucl. Energy"} MACRO {jnec} {"J. Nucl. Energy, C: Plasma Phys., Accelerators, Thermonucl. Res."} MACRO {jnm} {"J. Nucl. Mat."} MACRO {jpc} {"J. Phys. Chem."} MACRO {jpp} {"J. Plasma Phys."} MACRO {jpsj} {"J. Phys. Soc. Japan"} MACRO {jsi} {"J. Sci. Instrum."} MACRO {jvst} {"J. Vac. Sci. \& Tech."} MACRO {nat} {"Nature"} MACRO {nature} {"Nature"} MACRO {nedf} {"Nucl. Eng. \& Design/Fusion"} MACRO {nf} {"Nucl. Fusion"} MACRO {nim} {"Nucl. Inst. \& Meth."} MACRO {nimpr} {"Nucl. Inst. \& Meth. in Phys. Res."} MACRO {np} {"Nucl. Phys."} MACRO {npb} {"Nucl. Phys. B"} MACRO {nt/f} {"Nucl. Tech./Fusion"} MACRO {npbpc} {"Nucl. Phys. B (Proc. Suppl.)"} MACRO {inc} {"Nuovo Cimento"} MACRO {nc} {"Nuovo Cimento"} MACRO {pf} {"Phys. Fluids"} MACRO {pfa} {"Phys. Fluids A: Fluid Dyn."} MACRO {pfb} {"Phys. Fluids B: Plasma Phys."} MACRO {pl} {"Phys. Lett."} MACRO {pla} {"Phys. Lett. A"} MACRO {plb} {"Phys. Lett. B"} MACRO {prep} {"Phys. Rep."} MACRO {pnas} {"Proc. Nat. Acad. Sci. USA"} MACRO {pp} {"Phys. Plasmas"} MACRO {ppcf} {"Plasma Phys. \& Controlled Fusion"} MACRO {phitrsl} {"Philos. Trans. Roy. Soc. London"} MACRO {prl} {"Phys. Rev. Lett."} MACRO {pr} {"Phys. Rev."} MACRO {physrev} {"Phys. Rev."} MACRO {pra} {"Phys. Rev. A"} MACRO {prb} {"Phys. Rev. B"} MACRO {prc} {"Phys. Rev. C"} MACRO {prd} {"Phys. Rev. D"} MACRO {pre} {"Phys. Rev. E"} MACRO {ps} {"Phys. Scripta"} MACRO {procrsl} {"Proc. Roy. Soc. London"} MACRO {rmp} {"Rev. Mod. Phys."} MACRO {rsi} {"Rev. Sci. Inst."} MACRO {science} {"Science"} MACRO {sciam} {"Sci. Am."} MACRO {sam} {"Stud. Appl. Math."} MACRO {sjpp} {"Sov. J. Plasma Phys."} MACRO {spd} {"Sov. Phys.--Doklady"} MACRO {sptp} {"Sov. Phys.--Tech. Phys."} MACRO {spu} {"Sov. Phys.--Uspeki"} MACRO {st} {"Sky and Telesc."} % End module: physjour.mbs %------------------------------------------------------------------- % Begin module: % \ProvidesFile{geojour.mbs}[2002/07/10 2.0h (PWD)] MACRO {aisr} {"Adv. Space Res."} MACRO {ag} {"Ann. Geophys."} MACRO {anigeo} {"Ann. Geofis."} MACRO {angl} {"Ann. Glaciol."} MACRO {andmet} {"Ann. d. Meteor."} MACRO {andgeo} {"Ann. d. Geophys."} MACRO {andphy} {"Ann. Phys.-Paris"} MACRO {afmgb} {"Arch. Meteor. Geophys. Bioklimatol."} MACRO {atph} {"Atm\'osphera"} MACRO {aao} {"Atmos. Ocean"} MACRO {ass}{"Astrophys. Space Sci."} MACRO {atenv} {"Atmos. Environ."} MACRO {aujag} {"Aust. J. Agr. Res."} MACRO {aumet} {"Aust. Meteorol. Mag."} MACRO {blmet} {"Bound.-Lay. Meteorol."} MACRO {bams} {"Bull. Amer. Meteorol. Soc."} MACRO {cch} {"Clim. Change"} MACRO {cdyn} {"Clim. Dynam."} MACRO {cbul} {"Climatol. Bull."} MACRO {cap} {"Contrib. Atmos. Phys."} MACRO {dsr} {"Deep-Sea Res."} MACRO {dhz} {"Dtsch. Hydrogr. Z."} MACRO {dao} {"Dynam. Atmos. Oceans"} MACRO {eco} {"Ecology"} MACRO {empl}{"Earth, Moon and Planets"} MACRO {envres} {"Environ. Res."} MACRO {envst} {"Environ. Sci. Technol."} MACRO {ecms} {"Estuarine Coastal Mar. Sci."} MACRO {expa}{"Exper. Astron."} MACRO {geoint} {"Geofis. Int."} MACRO {geopub} {"Geofys. Publ."} MACRO {geogeo} {"Geol. Geofiz."} MACRO {gafd} {"Geophys. Astrophys. Fluid Dyn."} MACRO {gfd} {"Geophys. Fluid Dyn."} MACRO {geomag} {"Geophys. Mag."} MACRO {georl} {"Geophys. Res. Lett."} MACRO {grl} {"Geophys. Res. Lett."} MACRO {ga} {"Geophysica"} MACRO {gs} {"Geophysics"} MACRO {ieeetap} {"IEEE Trans. Antenn. Propag."} MACRO {ijawp} {"Int. J. Air Water Pollut."} MACRO {ijc} {"Int. J. Climatol."} MACRO {ijrs} {"Int. J. Remote Sens."} MACRO {jam} {"J. Appl. Meteorol."} MACRO {jaot} {"J. Atmos. Ocean. Technol."} MACRO {jatp} {"J. Atmos. Terr. Phys."} MACRO {jastp} {"J. Atmos. Solar-Terr. Phys."} MACRO {jce} {"J. Climate"} MACRO {jcam} {"J. Climate Appl. Meteor."} MACRO {jcm} {"J. Climate Meteor."} MACRO {jcy} {"J. Climatol."} MACRO {jgr} {"J. Geophys. Res."} MACRO {jga} {"J. Glaciol."} MACRO {jh} {"J. Hydrol."} MACRO {jmr} {"J. Mar. Res."} MACRO {jmrj} {"J. Meteor. Res. Japan"} MACRO {jm} {"J. Meteor."} MACRO {jpo} {"J. Phys. Oceanogr."} MACRO {jra} {"J. Rech. Atmos."} MACRO {jaes} {"J. Aeronaut. Sci."} MACRO {japca} {"J. Air Pollut. Control Assoc."} MACRO {jas} {"J. Atmos. Sci."} MACRO {jmts} {"J. Mar. Technol. Soc."} MACRO {jmsj} {"J. Meteorol. Soc. Japan"} MACRO {josj} {"J. Oceanogr. Soc. Japan"} MACRO {jwm} {"J. Wea. Mod."} MACRO {lao} {"Limnol. Oceanogr."} MACRO {mwl} {"Mar. Wea. Log"} MACRO {mau} {"Mausam"} MACRO {meteor} {"``Meteor'' Forschungsergeb."} MACRO {map} {"Meteorol. Atmos. Phys."} MACRO {metmag} {"Meteor. Mag."} MACRO {metmon} {"Meteor. Monogr."} MACRO {metrun} {"Meteor. Rundsch."} MACRO {metzeit} {"Meteor. Z."} MACRO {metgid} {"Meteor. Gidrol."} MACRO {mwr} {"Mon. Weather Rev."} MACRO {nwd} {"Natl. Weather Dig."} MACRO {nzjmfr} {"New Zeal. J. Mar. Freshwater Res."} MACRO {npg} {"Nonlin. Proc. Geophys."} MACRO {om} {"Oceanogr. Meteorol."} MACRO {ocac} {"Oceanol. Acta"} MACRO {oceanus} {"Oceanus"} MACRO {paleoc} {"Paleoceanography"} MACRO {pce} {"Phys. Chem. Earth"} MACRO {pmg} {"Pap. Meteor. Geophys."} MACRO {ppom} {"Pap. Phys. Oceanogr. Meteor."} MACRO {physzeit} {"Phys. Z."} MACRO {pps} {"Planet. Space Sci."} MACRO {pss} {"Planet. Space Sci."} MACRO {pag} {"Pure Appl. Geophys."} MACRO {qjrms} {"Quart. J. Roy. Meteorol. Soc."} MACRO {quatres} {"Quat. Res."} MACRO {rsci} {"Radio Sci."} MACRO {rse} {"Remote Sens. Environ."} MACRO {rgeo} {"Rev. Geophys."} MACRO {rgsp} {"Rev. Geophys. Space Phys."} MACRO {rdgeo} {"Rev. Geofis."} MACRO {revmeta} {"Rev. Meteorol."} MACRO {sgp}{"Surveys in Geophys."} MACRO {sp} {"Solar Phys."} MACRO {ssr} {"Space Sci. Rev."} MACRO {tellus} {"Tellus"} MACRO {tac} {"Theor. Appl. Climatol."} MACRO {tagu} {"Trans. Am. Geophys. Union (EOS)"} MACRO {wrr} {"Water Resour. Res."} MACRO {weather} {"Weather"} MACRO {wafc} {"Weather Forecast."} MACRO {ww} {"Weatherwise"} MACRO {wmob} {"WMO Bull."} MACRO {zeitmet} {"Z. Meteorol."} % End module: geojour.mbs %------------------------------------------------------------------- % Begin module: % \ProvidesFile{photjour.mbs}[1999/02/24 2.0b (PWD)] MACRO {appopt} {"Appl. Opt."} MACRO {bell} {"Bell Syst. Tech. J."} MACRO {ell} {"Electron. Lett."} MACRO {jasp} {"J. Appl. Spectr."} MACRO {jqe} {"IEEE J. Quantum Electron."} MACRO {jlwt} {"J. Lightwave Technol."} MACRO {jmo} {"J. Mod. Opt."} MACRO {josa} {"J. Opt. Soc. America"} MACRO {josaa} {"J. Opt. Soc. Amer.~A"} MACRO {josab} {"J. Opt. Soc. Amer.~B"} MACRO {jdp} {"J. Phys. (Paris)"} MACRO {oc} {"Opt. Commun."} MACRO {ol} {"Opt. Lett."} MACRO {phtl} {"IEEE Photon. Technol. Lett."} MACRO {pspie} {"Proc. Soc. Photo-Opt. Instrum. Eng."} MACRO {sse} {"Solid-State Electron."} MACRO {sjot} {"Sov. J. Opt. Technol."} MACRO {sjqe} {"Sov. J. Quantum Electron."} MACRO {sleb} {"Sov. Phys.--Leb. Inst. Rep."} MACRO {stph} {"Sov. Phys.--Techn. Phys."} MACRO {stphl} {"Sov. Techn. Phys. Lett."} MACRO {vr} {"Vision Res."} MACRO {zph} {"Z. f. Physik"} MACRO {zphb} {"Z. f. Physik~B"} MACRO {zphd} {"Z. f. Physik~D"} MACRO {CLEO} {"CLEO"} MACRO {ASSL} {"Adv. Sol.-State Lasers"} MACRO {OSA} {"OSA"} % End module: photjour.mbs %% Copyright 1994-2004 Patrick W Daly MACRO {acmcs} {"ACM Comput. Surv."} MACRO {acta} {"Acta Inf."} MACRO {cacm} {"Commun. ACM"} MACRO {ibmjrd} {"IBM J. Res. Dev."} MACRO {ibmsj} {"IBM Syst.~J."} MACRO {ieeese} {"IEEE Trans. Software Eng."} MACRO {ieeetc} {"IEEE Trans. Comput."} MACRO {ieeetcad} {"IEEE Trans. Comput. Aid. Des."} MACRO {ipl} {"Inf. Process. Lett."} MACRO {jacm} {"J.~ACM"} MACRO {jcss} {"J.~Comput. Syst. Sci."} MACRO {scp} {"Sci. Comput. Program."} MACRO {sicomp} {"SIAM J. Comput."} MACRO {tocs} {"ACM Trans. Comput. Syst."} MACRO {tods} {"ACM Trans. Database Syst."} MACRO {tog} {"ACM Trans. Graphic."} MACRO {toms} {"ACM Trans. Math. Software"} MACRO {toois} {"ACM Trans. Office Inf. Syst."} MACRO {toplas} {"ACM Trans. Progr. Lang. Syst."} MACRO {tcs} {"Theor. Comput. Sci."} FUNCTION {bibinfo.check} { swap$ duplicate$ missing$ { pop$ pop$ "" } { duplicate$ empty$ { swap$ pop$ } { swap$ pop$ } if$ } if$ } FUNCTION {bibinfo.warn} { swap$ duplicate$ missing$ { swap$ "missing " swap$ * " in " * cite$ * warning$ pop$ "" } { duplicate$ empty$ { swap$ "empty " swap$ * " in " * cite$ * warning$ } { swap$ pop$ } if$ } if$ } FUNCTION {format.eprint} { eprint duplicate$ empty$ 'skip$ { "\eprint" archive empty$ 'skip$ { "[" * archive * "]" * } if$ "{" * swap$ * "}" * } if$ } FUNCTION {format.url} { url empty$ { "" } { "\href{" url * "}{[web link]}" *} if$ } STRINGS { bibinfo} INTEGERS { nameptr namesleft numnames } FUNCTION {format.names} { 'bibinfo := duplicate$ empty$ 'skip$ { 's := "" 't := #1 'nameptr := s num.names$ 'numnames := numnames 'namesleft := { namesleft #0 > } { s nameptr "{f.~}{vv~}{ll}" format.name$ bibinfo bibinfo.check 't := nameptr #1 > { namesleft #1 > { ", " * t * } { "," * s nameptr "{ll}" format.name$ duplicate$ "others" = { 't := } { pop$ } if$ t "others" = { " " * bbl.etal * } { " " * t * } if$ } if$ } 't if$ nameptr #1 + 'nameptr := namesleft #1 - 'namesleft := } while$ } if$ } FUNCTION {format.names.ed} { format.names } FUNCTION {format.authors} { author "author" format.names duplicate$ empty$ 'skip$ { collaboration "collaboration" bibinfo.check duplicate$ empty$ 'skip$ { " (" swap$ * ")" * } if$ * } if$ } FUNCTION {get.bbl.editor} { editor num.names$ #1 > 'bbl.editors 'bbl.editor if$ } FUNCTION {format.editors} { editor "editor" format.names duplicate$ empty$ 'skip$ { " " * get.bbl.editor capitalize "(" swap$ * ")" * * } if$ } FUNCTION {format.doi} { doi "doi" bibinfo.check duplicate$ empty$ 'skip$ { new.block "\doi{" swap$ * "}" * } if$ } FUNCTION {format.note} { note empty$ { "" } { note #1 #1 substring$ duplicate$ "{" = 'skip$ { output.state mid.sentence = { "l" } { "u" } if$ change.case$ } if$ note #2 global.max$ substring$ * "note" bibinfo.check } if$ } FUNCTION {format.title} { title "title" bibinfo.check duplicate$ empty$ 'skip$ { "t" change.case$ emphasize } if$ } FUNCTION {output.bibitem} { newline$ "\bibitem[" label * "]{" * write$ cite$ write$ "}" write$ newline$ "" before.all 'output.state := } FUNCTION {n.dashify} { 't := "" { t empty$ not } { t #1 #1 substring$ "-" = { t #1 #2 substring$ "--" = not { "--" * t #2 global.max$ substring$ 't := } { { t #1 #1 substring$ "-" = } { "-" * t #2 global.max$ substring$ 't := } while$ } if$ } { t #1 #1 substring$ * t #2 global.max$ substring$ 't := } if$ } while$ } FUNCTION {word.in} { bbl.in capitalize ":" * " " * } FUNCTION {format.date} { "" duplicate$ empty$ year "year" bibinfo.check duplicate$ empty$ { swap$ 'skip$ { "there's a month but no year in " cite$ * warning$ } if$ * } { swap$ 'skip$ { swap$ " " * swap$ } if$ * } if$ duplicate$ empty$ 'skip$ { before.all 'output.state := " (" swap$ * ")" * } if$ } FUNCTION{format.year} { year "year" bibinfo.check duplicate$ empty$ { "empty year in " cite$ * warning$ } { " (" swap$ * ")" * } if$ } FUNCTION {format.btitle} { title "title" bibinfo.check duplicate$ empty$ 'skip$ { emphasize } if$ } FUNCTION {either.or.check} { empty$ 'pop$ { "can't use both " swap$ * " fields in " * cite$ * warning$ } if$ } FUNCTION {format.bvolume} { volume empty$ { "" } { bbl.volume volume tie.or.space.prefix "volume" bibinfo.check * * series "series" bibinfo.check duplicate$ empty$ 'pop$ { emphasize ", " * swap$ * } if$ "volume and number" number either.or.check } if$ } FUNCTION {format.number.series} { volume empty$ { number empty$ { series field.or.null } { series empty$ { number "number" bibinfo.check } { output.state mid.sentence = { bbl.number } { bbl.number capitalize } if$ number tie.or.space.prefix "number" bibinfo.check * * bbl.in space.word * series "series" bibinfo.check * } if$ } if$ } { "" } if$ } FUNCTION {format.edition} { edition duplicate$ empty$ 'skip$ { output.state mid.sentence = { "l" } { "t" } if$ change.case$ "edition" bibinfo.check " " * bbl.edition * } if$ } INTEGERS { multiresult } FUNCTION {multi.page.check} { 't := #0 'multiresult := { multiresult not t empty$ not and } { t #1 #1 substring$ duplicate$ "-" = swap$ duplicate$ "," = swap$ "+" = or or { #1 'multiresult := } { t #2 global.max$ substring$ 't := } if$ } while$ multiresult } FUNCTION {format.pages} { pages duplicate$ empty$ 'skip$ { duplicate$ multi.page.check { bbl.pages swap$ n.dashify } { bbl.page swap$ } if$ tie.or.space.prefix "pages" bibinfo.check * * } if$ } FUNCTION {first.page} { 't := "" { t empty$ not t #1 #1 substring$ "-" = not and } { t #1 #1 substring$ * t #2 global.max$ substring$ 't := } while$ } FUNCTION {format.journal.pages} { pages duplicate$ empty$ 'pop$ { swap$ duplicate$ empty$ { pop$ pop$ format.pages } { ", " * swap$ first.page "pages" bibinfo.check * } if$ } if$ } FUNCTION {format.journal.eid} { eid "eid" bibinfo.check duplicate$ empty$ 'pop$ { swap$ duplicate$ empty$ 'skip$ { ", " * } if$ swap$ * numpages empty$ 'skip$ { bbl.eidpp numpages tie.or.space.prefix "numpages" bibinfo.check * * " (" swap$ * ")" * * } if$ } if$ } FUNCTION {format.vol.num.pages} { volume field.or.null duplicate$ empty$ 'skip$ { "volume" bibinfo.check } if$ bolden format.year * eid empty$ { format.journal.pages } { format.journal.eid } if$ } FUNCTION {format.chapter.pages} { chapter empty$ 'format.pages { type empty$ { bbl.chapter } { type "l" change.case$ "type" bibinfo.check } if$ chapter tie.or.space.prefix "chapter" bibinfo.check * * pages empty$ 'skip$ { ", " * format.pages * } if$ } if$ } FUNCTION {format.booktitle} { booktitle "booktitle" bibinfo.check emphasize } FUNCTION {format.in.ed.booktitle} { format.booktitle duplicate$ empty$ 'skip$ { format.bvolume duplicate$ empty$ 'pop$ { ", " swap$ * * } if$ editor "editor" format.names.ed duplicate$ empty$ 'pop$ { " " * get.bbl.editor capitalize "(" swap$ * "), " * * swap$ * } if$ word.in swap$ * } if$ } FUNCTION {empty.misc.check} { author empty$ title empty$ howpublished empty$ month empty$ year empty$ note empty$ and and and and and key empty$ not and { "all relevant fields are empty in " cite$ * warning$ } 'skip$ if$ } FUNCTION {format.thesis.type} { type duplicate$ empty$ 'pop$ { swap$ pop$ "t" change.case$ "type" bibinfo.check } if$ } FUNCTION {format.tr.number} { number "number" bibinfo.check type duplicate$ empty$ { pop$ bbl.techrep } 'skip$ if$ "type" bibinfo.check swap$ duplicate$ empty$ { pop$ "t" change.case$ } { tie.or.space.prefix * * } if$ } FUNCTION {format.article.crossref} { key duplicate$ empty$ { pop$ journal duplicate$ empty$ { "need key or journal for " cite$ * " to crossref " * crossref * warning$ } { "journal" bibinfo.check emphasize word.in swap$ * } if$ } { word.in swap$ * " " *} if$ " \cite{" * crossref * "}" * } FUNCTION {format.crossref.editor} { editor #1 "{vv~}{ll}" format.name$ "editor" bibinfo.check editor num.names$ duplicate$ #2 > { pop$ "editor" bibinfo.check " " * bbl.etal * } { #2 < 'skip$ { editor #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" = { "editor" bibinfo.check " " * bbl.etal * } { bbl.and space.word * editor #2 "{vv~}{ll}" format.name$ "editor" bibinfo.check * } if$ } if$ } if$ } FUNCTION {format.book.crossref} { volume duplicate$ empty$ { "empty volume in " cite$ * "'s crossref of " * crossref * warning$ pop$ word.in } { bbl.volume capitalize swap$ tie.or.space.prefix "volume" bibinfo.check * * bbl.of space.word * } if$ editor empty$ editor field.or.null author field.or.null = or { key empty$ { series empty$ { "need editor, key, or series for " cite$ * " to crossref " * crossref * warning$ "" * } { series emphasize * } if$ } { key * } if$ } { format.crossref.editor * } if$ " \cite{" * crossref * "}" * } FUNCTION {format.incoll.inproc.crossref} { editor empty$ editor field.or.null author field.or.null = or { key empty$ { format.booktitle duplicate$ empty$ { "need editor, key, or booktitle for " cite$ * " to crossref " * crossref * warning$ } { word.in swap$ * } if$ } { word.in key * " " *} if$ } { word.in format.crossref.editor * " " *} if$ " \cite{" * crossref * "}" * } FUNCTION {format.org.or.pub} { 't := "" address empty$ t empty$ and 'skip$ { t empty$ { address "address" bibinfo.check * } { t * address empty$ 'skip$ { ", " * address "address" bibinfo.check * } if$ } if$ } if$ } FUNCTION {format.publisher.address} { publisher "publisher" bibinfo.warn format.org.or.pub } FUNCTION {format.organization.address} { organization "organization" bibinfo.check format.org.or.pub } FUNCTION {article} { output.bibitem format.authors "author" output.check new.block format.title "title" output.check new.block crossref missing$ { doi empty$ url empty$ and { "" } { "{}" output before.all 'output.state := "\href{" doi empty$ {url} {"http://dx.doi.org/" doi *} if$ * "}{" * } if$ * journal "journal" bibinfo.check "journal" output.check add.blank format.vol.num.pages output doi empty$ url empty$ and { "" } { "}" } if$ * } { format.article.crossref output.nonnull format.pages output } if$ new.block format.note output format.eprint output fin.entry } FUNCTION {book} { output.bibitem author empty$ { format.editors "author and editor" output.check } { format.authors output.nonnull crossref missing$ { "author and editor" editor either.or.check } 'skip$ if$ } if$ new.block format.btitle "title" output.check crossref missing$ { format.bvolume output new.block format.number.series output format.edition output new.sentence format.publisher.address output } { new.block format.book.crossref output.nonnull } if$ format.date "year" output.check format.doi output new.block format.note output format.eprint output format.url output fin.entry } FUNCTION {booklet} { output.bibitem format.authors output new.block format.title "title" output.check new.block howpublished "howpublished" bibinfo.check output address "address" bibinfo.check output format.date output format.doi output new.block format.note output format.eprint output format.url output fin.entry } FUNCTION {inbook} { output.bibitem author empty$ { format.editors "author and editor" output.check } { format.authors output.nonnull crossref missing$ { "author and editor" editor either.or.check } 'skip$ if$ } if$ new.block format.btitle "title" output.check crossref missing$ { format.bvolume output format.chapter.pages "chapter and pages" output.check new.block format.number.series output format.edition output new.sentence format.publisher.address output } { format.chapter.pages "chapter and pages" output.check new.block format.book.crossref output.nonnull } if$ format.date "year" output.check format.doi output new.block format.note output format.eprint output format.url output fin.entry } FUNCTION {incollection} { output.bibitem format.authors "author" output.check new.block format.title "title" output.check new.block crossref missing$ { format.in.ed.booktitle "booktitle" output.check format.number.series output format.edition output format.chapter.pages output new.sentence format.publisher.address output format.date "year" output.check } { format.incoll.inproc.crossref output.nonnull format.chapter.pages output } if$ format.doi output new.block format.note output format.eprint output format.url output fin.entry } FUNCTION {inproceedings} { output.bibitem format.authors "author" output.check new.block format.title "title" output.check new.block crossref missing$ { format.in.ed.booktitle "booktitle" output.check format.number.series output format.pages output new.sentence publisher empty$ { format.organization.address output } { organization "organization" bibinfo.check output format.publisher.address output } if$ format.date "year" output.check } { format.incoll.inproc.crossref output.nonnull format.pages output } if$ format.doi output new.block format.note output format.eprint output format.url output fin.entry } FUNCTION {conference} { inproceedings } FUNCTION {manual} { output.bibitem author empty$ { organization "organization" bibinfo.check duplicate$ empty$ 'pop$ { output address "address" bibinfo.check output } if$ } { format.authors output.nonnull } if$ new.block format.btitle "title" output.check author empty$ { organization empty$ { address new.block.checka address "address" bibinfo.check output } 'skip$ if$ } { organization address new.block.checkb organization "organization" bibinfo.check output address "address" bibinfo.check output } if$ format.edition output format.date output format.doi output new.block format.note output format.eprint output format.url output fin.entry } FUNCTION {mastersthesis} { output.bibitem format.authors "author" output.check new.block format.btitle "title" output.check new.block bbl.mthesis format.thesis.type output.nonnull school "school" bibinfo.warn output address "address" bibinfo.check output format.date "year" output.check format.doi output new.block format.note output format.eprint output format.url output fin.entry } FUNCTION {misc} { output.bibitem format.authors output title howpublished new.block.checkb format.title output howpublished new.block.checka howpublished "howpublished" bibinfo.check output format.date output format.doi output new.block format.note output format.eprint output format.url output fin.entry empty.misc.check } FUNCTION {phdthesis} { output.bibitem format.authors "author" output.check new.block format.btitle "title" output.check new.block bbl.phdthesis format.thesis.type output.nonnull school "school" bibinfo.warn output address "address" bibinfo.check output format.date "year" output.check format.doi output new.block format.note output format.eprint output format.url output fin.entry } FUNCTION {proceedings} { output.bibitem editor empty$ { organization "organization" bibinfo.check output } { format.editors output.nonnull } if$ new.block format.btitle "title" output.check format.bvolume output format.number.series output editor empty$ { publisher empty$ 'skip$ { new.sentence format.publisher.address output } if$ } { publisher empty$ { new.sentence format.organization.address output } { new.sentence organization "organization" bibinfo.check output format.publisher.address output } if$ } if$ format.date "year" output.check format.doi output new.block format.note output format.eprint output format.url output fin.entry } FUNCTION {techreport} { output.bibitem format.authors "author" output.check new.block format.title "title" output.check new.block format.tr.number output.nonnull institution "institution" bibinfo.warn output address "address" bibinfo.check output format.date "year" output.check format.doi output new.block format.note output format.eprint output format.url output fin.entry } FUNCTION {unpublished} { output.bibitem format.authors "author" output.check new.block format.title "title" output.check format.date output format.doi output new.block format.note "note" output.check format.eprint output format.url output fin.entry } FUNCTION {default.type} { misc } READ FUNCTION {sortify} { purify$ "l" change.case$ } INTEGERS { len } FUNCTION {chop.word} { 's := 'len := s #1 len substring$ = { s len #1 + global.max$ substring$ } 's if$ } INTEGERS { et.al.char.used } FUNCTION {initialize.et.al.char.used} { #0 'et.al.char.used := } EXECUTE {initialize.et.al.char.used} FUNCTION {format.lab.names} { 's := s num.names$ 'numnames := numnames #1 > { numnames #4 > { #3 'namesleft := } { numnames 'namesleft := } if$ #1 'nameptr := "" { namesleft #0 > } { nameptr numnames = { s nameptr "{ff }{vv }{ll}{ jj}" format.name$ "others" = { "{\etalchar{+}}" * #1 'et.al.char.used := } { s nameptr "{l{}}" format.name$ * } if$ } { s nameptr "{l{}}" format.name$ * } if$ nameptr #1 + 'nameptr := namesleft #1 - 'namesleft := } while$ numnames #4 > { "{\etalchar{+}}" * #1 'et.al.char.used := } 'skip$ if$ } { s #1 "{l{}}" format.name$ duplicate$ text.length$ #2 < { pop$ s #1 "{ll}" format.name$ #3 text.prefix$ } 'skip$ if$ } if$ } FUNCTION {author.key.label} { author empty$ { key empty$ { cite$ #1 #3 substring$ } { key #3 text.prefix$ } if$ } { author format.lab.names } if$ } FUNCTION {author.editor.key.label} { author empty$ { editor empty$ { key empty$ { cite$ #1 #3 substring$ } { key #3 text.prefix$ } if$ } { editor format.lab.names } if$ } { author format.lab.names } if$ } FUNCTION {author.key.organization.label} { author empty$ { key empty$ { organization empty$ { cite$ #1 #3 substring$ } { "The " #4 organization chop.word #3 text.prefix$ } if$ } { key #3 text.prefix$ } if$ } { author format.lab.names } if$ } FUNCTION {editor.key.organization.label} { editor empty$ { key empty$ { organization empty$ { cite$ #1 #3 substring$ } { "The " #4 organization chop.word #3 text.prefix$ } if$ } { key #3 text.prefix$ } if$ } { editor format.lab.names } if$ } FUNCTION {calc.label} { key empty$ not { key duplicate$ 'label := sortify 'sort.label := } { type$ "book" = type$ "inbook" = or 'author.editor.key.label { type$ "proceedings" = 'editor.key.organization.label { type$ "manual" = 'author.key.organization.label 'author.key.label if$ } if$ } if$ duplicate$ year field.or.null purify$ #-1 #2 substring$ * 'label := year field.or.null purify$ #-1 #4 substring$ * sortify 'sort.label := } if$ } FUNCTION {sort.format.names} { 's := #1 'nameptr := "" s num.names$ 'numnames := numnames 'namesleft := { namesleft #0 > } { s nameptr "{ll{ }}{ f{ }}{ jj{ }}" format.name$ 't := nameptr #1 > { " " * namesleft #1 = t "others" = and { "zzzzz" * } { t sortify * } if$ } { t sortify * } if$ nameptr #1 + 'nameptr := namesleft #1 - 'namesleft := } while$ } FUNCTION {sort.format.title} { 't := "A " #2 "An " #3 "The " #4 t chop.word chop.word chop.word sortify #1 global.max$ substring$ } FUNCTION {author.sort} { author empty$ { key empty$ { "to sort, need author or key in " cite$ * warning$ "" } { key sortify } if$ } { author sort.format.names } if$ } FUNCTION {author.editor.sort} { author empty$ { editor empty$ { key empty$ { "to sort, need author, editor, or key in " cite$ * warning$ "" } { key sortify } if$ } { editor sort.format.names } if$ } { author sort.format.names } if$ } FUNCTION {author.organization.sort} { author empty$ { organization empty$ { key empty$ { "to sort, need author, organization, or key in " cite$ * warning$ "" } { key sortify } if$ } { "The " #4 organization chop.word sortify } if$ } { author sort.format.names } if$ } FUNCTION {editor.organization.sort} { editor empty$ { organization empty$ { key empty$ { "to sort, need editor, organization, or key in " cite$ * warning$ "" } { key sortify } if$ } { "The " #4 organization chop.word sortify } if$ } { editor sort.format.names } if$ } FUNCTION {presort} { calc.label sort.label " " * type$ "book" = type$ "inbook" = or 'author.editor.sort { type$ "proceedings" = 'editor.organization.sort { type$ "manual" = 'author.organization.sort 'author.sort if$ } if$ } if$ * " " * year field.or.null sortify * " " * title field.or.null sort.format.title * #1 entry.max$ substring$ 'sort.key$ := } ITERATE {presort} SORT STRINGS { longest.label last.sort.label next.extra } INTEGERS { longest.label.width last.extra.num } FUNCTION {initialize.longest.label} { "" 'longest.label := #0 int.to.chr$ 'last.sort.label := "" 'next.extra := #0 'longest.label.width := #0 'last.extra.num := } FUNCTION {forward.pass} { last.sort.label sort.label = { last.extra.num #1 + 'last.extra.num := last.extra.num int.to.chr$ 'extra.label := } { "a" chr.to.int$ 'last.extra.num := "" 'extra.label := sort.label 'last.sort.label := } if$ } FUNCTION {reverse.pass} { next.extra "b" = { "a" 'extra.label := } 'skip$ if$ label extra.label * 'label := label width$ longest.label.width > { label 'longest.label := label width$ 'longest.label.width := } 'skip$ if$ extra.label 'next.extra := } EXECUTE {initialize.longest.label} ITERATE {forward.pass} REVERSE {reverse.pass} FUNCTION {begin.bib} { et.al.char.used { "\newcommand{\etalchar}[1]{$^{#1}$}" write$ newline$ } 'skip$ if$ preamble$ empty$ 'skip$ { preamble$ write$ newline$ } if$ "\begin{thebibliography}{" longest.label * "}" * write$ newline$ "\providecommand{\url}[1]{#1}" write$ newline$ "\providecommand{\urlprefix}{URL }" write$ newline$ "\expandafter\ifx\csname urlstyle\endcsname\relax" write$ newline$ " \providecommand{\doi}[1]{doi:\discretionary{}{}{}#1}\else" write$ newline$ " \providecommand{\doi}{doi:\discretionary{}{}{}\begingroup \urlstyle{rm}\Url}\fi" write$ newline$ "\providecommand{\eprint}[2][]{#2}" write$ newline$ "\providecommand{\arxiv}[1]{\href{http://www.arxiv.org/abs/#1}{ArXiv: #1}}" write$ newline$ "\providecommand{\arxivs}[2]{\href{http://www.arxiv.org/abs/#1}{ArXiv: #1 [#2]}}" write$ newline$ } EXECUTE {begin.bib} EXECUTE {init.state.consts} ITERATE {call.type$} FUNCTION {end.bib} { newline$ "\end{thebibliography}" write$ newline$ } EXECUTE {end.bib} %% End of customized bst file %% %% End of file `simon.bst'.