ensembldb/DESCRIPTION0000644000175400017540000000336613013177517015256 0ustar00biocbuildbiocbuildPackage: ensembldb Type: Package Title: Utilities to create and use an Ensembl based annotation database Version: 1.6.2 Author: Johannes Rainer , Tim Triche Maintainer: Johannes Rainer URL: https://github.com/jotsetung/ensembldb BugReports: https://github.com/jotsetung/ensembldb/issues Imports: methods, RSQLite, DBI, Biobase, GenomeInfoDb, AnnotationDbi (>= 1.31.19), rtracklayer, S4Vectors, AnnotationHub, Rsamtools, IRanges Depends: BiocGenerics (>= 0.15.10), GenomicRanges (>= 1.23.21), GenomicFeatures (>= 1.23.18) Suggests: BiocStyle, knitr, rmarkdown, EnsDb.Hsapiens.v75 (>= 0.99.7), RUnit, shiny, Gviz, BSgenome.Hsapiens.UCSC.hg19 Enhances: RMySQL VignetteBuilder: knitr Description: The package provides functions to create and use transcript centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, the ensembldb package provides also a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes. Collate: Classes.R Generics.R functions-utils.R dbhelpers.R Methods.R Methods-Filter.R loadEnsDb.R makeEnsemblDbPackage.R EnsDbFromGTF.R runEnsDbApp.R select-methods.R seqname-utils.R zzz.R biocViews: Genetics, AnnotationData, Sequencing, Coverage License: LGPL RoxygenNote: 5.0.1 NeedsCompilation: no Packaged: 2016-11-17 00:52:31 UTC; biocbuild ensembldb/NAMESPACE0000644000175400017540000000526213001231247014750 0ustar00biocbuildbiocbuild## ensembldb NAMESPACE import(methods) importFrom("utils", "read.table", "str") import(BiocGenerics) import(S4Vectors) importFrom(DBI, dbDriver) importFrom(Biobase, createPackage) importFrom(GenomeInfoDb, Seqinfo, isCircular, genome, seqlengths, seqnames, seqlevels, keepSeqlevels, seqlevelsStyle, "seqlevelsStyle<-", genomeStyles) importMethodsFrom(AnnotationDbi, dbconn, columns, keytypes, keys, select, mapIds) importFrom(rtracklayer, import) import(RSQLite) import(GenomicFeatures) ##importMethodsFrom(GenomicFeatures, extractTranscriptSeqs) import(GenomicRanges) importFrom(IRanges, IRanges) importMethodsFrom(IRanges,subsetByOverlaps) ## AnnotationHub importFrom(AnnotationHub, AnnotationHub) importClassesFrom(AnnotationHub, AnnotationHub) importMethodsFrom(AnnotationHub, query, mcols) ## Rsamtools importClassesFrom(Rsamtools, FaFile, RsamtoolsFile) importFrom(Rsamtools, FaFile) importMethodsFrom(Rsamtools, getSeq, indexFa, path) importFrom(Rsamtools, index) ## biovizBase ##importMethodsFrom(biovizBase, crunch) #exportPattern("^[[:alpha:]]+") export(fetchTablesFromEnsembl, makeEnsemblSQLiteFromTables, makeEnsembldbPackage, ensDbFromGtf, ensDbFromGff, ensDbFromGRanges, ensDbFromAH, runEnsDbApp, listEnsDbs) exportClasses(EnsDb, BasicFilter, EntrezidFilter, GeneidFilter, GenebiotypeFilter, GenenameFilter, TxidFilter, TxbiotypeFilter, ExonidFilter, SeqnameFilter, SeqstrandFilter, SeqstartFilter, SeqendFilter, GRangesFilter, ExonrankFilter, SymbolFilter) ## for EnsFilter exportMethods(column, print, show, value, where, "condition<-", "value<-", seqnames, start, end, strand, seqlevels) ## for class EnsDb: exportMethods(dbconn, condition, buildQuery, ensemblVersion, exons, exonsBy, genes, getGenomeFaFile, lengthOf, listColumns, listGenebiotypes, listTxbiotypes, listTables, organism, seqinfo, toSAF, transcripts, transcriptsBy, disjointExons, metadata, promoters, cdsBy, fiveUTRsByTranscript, threeUTRsByTranscript, getGeneRegionTrackForGviz, updateEnsDb, transcriptsByOverlaps, exonsByOverlaps, returnFilterColumns, "returnFilterColumns<-", useMySQL) ## Methods for AnnotationDbi exportMethods(columns, keytypes, keys, select, mapIds) ## Methods for GenomeInfoDb and related stuff exportMethods("seqlevelsStyle", "seqlevelsStyle<-", "supportedSeqlevelsStyles", seqlevels) ## constructors export(EntrezidFilter, GeneidFilter, GenenameFilter, GenebiotypeFilter, TxidFilter, TxbiotypeFilter, ExonidFilter, SeqnameFilter, SeqstrandFilter, SeqstartFilter, SeqendFilter, EnsDb, GRangesFilter, ExonrankFilter, SymbolFilter) ensembldb/R/0000755000175400017540000000000013012705075013734 5ustar00biocbuildbiocbuildensembldb/R/Classes.R0000644000175400017540000002705513001231247015456 0ustar00biocbuildbiocbuild##*********************************************************************** ## ## EnsBb classes ## ## Main class providing access and functionality for the database. ## ##*********************************************************************** setClass("EnsDb", representation(ensdb="DBIConnection", tables="list", .properties="list"), prototype=list(ensdb=NULL, tables=list(), .properties=list()) ) ##*********************************************************************** ## ## BasicFilter classes ## ## Allow to filter the results fetched from the database. ## ## gene: ## - GeneidFilter ## - GenebiotypeFilter ## - GenenameFilter ## - EntrezidFilter ## ## transcript: ## - TxidFilter ## - TxbiotypeFilter ## ## exon: ## - ExonidFilter ## ## chrom position (using info from exon): ## - SeqnameFilter ## - SeqstartFilter ## - SeqendFilter ## - SeqstrandFilter ## alternative: GRangesFilter. See below. ## ##*********************************************************************** setClass("BasicFilter", representation( "VIRTUAL", condition="character", value="character", .valueIsCharacter="logical" ), prototype=list( condition="=", value="", .valueIsCharacter=TRUE ) ) ## Table gene ## filter for gene_id setClass("GeneidFilter", contains="BasicFilter", prototype=list( condition="=", value="", .valueIsCharacter=TRUE ) ) GeneidFilter <- function(value, condition="="){ if(missing(value)){ stop("A filter without a value makes no sense!") } if(length(value) > 1){ if(condition=="=") condition="in" if(condition=="!=") condition="not in" } return(new("GeneidFilter", condition=condition, value=as.character(value))) } ## filter for gene_biotype setClass("GenebiotypeFilter", contains="BasicFilter", prototype=list( condition="=", value="", .valueIsCharacter=TRUE ) ) GenebiotypeFilter <- function(value, condition="="){ if(missing(value)){ stop("A filter without a value makes no sense!") } if(length(value) > 1){ if(condition=="=") condition="in" if(condition=="!=") condition="not in" } return(new("GenebiotypeFilter", condition=condition, value=as.character(value))) } ## filter for gene_name setClass("GenenameFilter", contains="BasicFilter", prototype=list( condition="=", value="", .valueIsCharacter=TRUE ) ) GenenameFilter <- function(value, condition="="){ if(missing(value)){ stop("A filter without a value makes no sense!") } if(length(value) > 1){ if(condition=="=") condition="in" if(condition=="!=") condition="not in" } return(new("GenenameFilter", condition=condition, value=as.character(value))) } ## filter for entrezid setClass("EntrezidFilter", contains="BasicFilter", prototype=list( condition="=", value="", .valueIsCharacter=TRUE ) ) EntrezidFilter <- function(value, condition="="){ if(missing(value)){ stop("A filter without a value makes no sense!") } if(length(value) > 1){ if(condition=="=") condition="in" if(condition=="!=") condition="not in" } return(new("EntrezidFilter", condition=condition, value=as.character(value))) } ## Table transcript ## filter for tx_id setClass("TxidFilter", contains="BasicFilter", prototype=list( condition="=", value="", .valueIsCharacter=TRUE ) ) TxidFilter <- function(value, condition="="){ if(missing(value)){ stop("A filter without a value makes no sense!") } if(length(value) > 1){ if(condition=="=") condition="in" if(condition=="!=") condition="not in" } return(new("TxidFilter", condition=condition, value=as.character(value))) } ## filter for gene_biotype setClass("TxbiotypeFilter", contains="BasicFilter", prototype=list( condition="=", value="", .valueIsCharacter=TRUE ) ) TxbiotypeFilter <- function(value, condition="="){ if(missing(value)){ stop("A filter without a value makes no sense!") } if(length(value) > 1){ if(condition=="=") condition="in" if(condition=="!=") condition="not in" } return(new("TxbiotypeFilter", condition=condition, value=as.character(value))) } ## Table exon ## filter for exon_id setClass("ExonidFilter", contains="BasicFilter", prototype=list( condition="=", value="", .valueIsCharacter=TRUE ) ) ExonidFilter <- function(value, condition="="){ if(missing(value)){ stop("A filter without a value makes no sense!") } if(length(value) > 1){ if(condition=="=") condition="in" if(condition=="!=") condition="not in" } return(new("ExonidFilter", condition=condition, value=as.character(value))) } ## Table tx2exon ## filter for exon_idx setClass("ExonrankFilter", contains="BasicFilter", prototype=list( condition="=", value="", .valueIsCharacter=FALSE ) ) ExonrankFilter <- function(value, condition="="){ if(missing(value)){ stop("A filter without a value makes no sense!") } if(any(is.na(as.numeric(value)))) stop("Argument 'value' has to be numeric!") if(length(value) > 1){ if(condition=="=") condition="in" if(condition=="!=") condition="not in" } return(new("ExonrankFilter", condition=condition, value=as.character(value))) } ## chromosome positions ## basic chromosome/seqname filter. setClass("SeqnameFilter", contains="BasicFilter", prototype=list( condition="=", value="", .valueIsCharacter=TRUE ) ) ## builder... SeqnameFilter <- function(value, condition="="){ if(missing(value)){ stop("A filter without a value makes no sense!") } if(length(value) > 1){ if(condition=="=") condition="in" if(condition=="!=") condition="not in" } return(new("SeqnameFilter", condition=condition, value=as.character(value))) } ## basic chromosome strand filter. setClass("SeqstrandFilter", contains="BasicFilter", prototype=list( condition="=", value="", .valueIsCharacter=FALSE ) ) ## builder... SeqstrandFilter <- function(value, condition="="){ if(missing(value)){ stop("A filter without a value makes no sense!") } ## checking value: should be +, -, will however be translated to -1, 1 if(class(value)=="character"){ value <- match.arg(value, c("1", "-1", "+1", "-", "+")) if(value=="-") value <- "-1" if(value=="+") value <- "+1" ## OK, now transforming to number value <- as.numeric(value) } if(!(value==1 | value==-1)) stop("The strand has to be either 1 or -1 (or \"+\" or \"-\")") return(new("SeqstrandFilter", condition=condition, value=as.character(value))) } ## chromstart filter setClass("SeqstartFilter", contains="BasicFilter", representation( feature="character" ), prototype=list( condition=">", value="", .valueIsCharacter=FALSE, feature="gene" ) ) SeqstartFilter <- function(value, condition="=", feature="gene"){ if(missing(value)){ stop("A filter without a value makes no sense!") } if(length(value) > 1){ value <- value[ 1 ] warning("Multiple values provided, but only the first (", value,") will be considered") } return(new("SeqstartFilter", condition=condition, value=as.character(value), feature=feature)) } ## chromend filter setClass("SeqendFilter", contains="BasicFilter", representation( feature="character" ), prototype=list( condition="<", value="", .valueIsCharacter=FALSE, feature="gene" ) ) SeqendFilter <- function(value, condition="=", feature="gene"){ if(missing(value)){ stop("A filter without a value makes no sense!") } if(length(value) > 1){ value <- value[ 1 ] warning("Multiple values provided, but only the first (", value,") will be considered") } return(new("SeqendFilter", condition=condition, value=as.character(value), feature=feature)) } ###============================================================ ## GRangesFilter ## adding new arguments since we can not overwrite the data type ## of the BasicFilter class... unfortunately. ## + grange <- value ## + location <- condition ###------------------------------------------------------------ setClass("GRangesFilter", contains="BasicFilter", representation(grange="GRanges", feature="character", location="character"), prototype=list( grange=GRanges(), .valueIsCharacter=FALSE, condition="=", location="within", feature="gene", value="" )) ## Constructor GRangesFilter <- function(value, condition="within", feature="gene"){ if(missing(value)) stop("No value provided for the filter!") if(!is(value, "GRanges")) stop("'value' has to be a GRanges object!") if(length(value) == 0) stop("No value provided for the filter!") ## if(length(value) > 1){ ## warning(paste0("GRanges in 'value' has length ", length(value), ## "! Using only the first element!")) ## value <- value[1] ## } grf <- new("GRangesFilter", grange=value, location=condition, feature=feature) ##validObject(grf) return(grf) } ###------------------------------------------------------------ ###============================================================ ## SymbolFilter ###------------------------------------------------------------ setClass("SymbolFilter", contains = "BasicFilter", prototype = list( condition = "=", value = "", .valueIsCharacter = TRUE ) ) SymbolFilter <- function(value, condition = "=") { if(missing(value)){ stop("A filter without a value makes no sense!") } if(length(value) > 1) { if(condition == "=") condition = "in" if(condition == "!=") condition = "not in" } return(new("SymbolFilter", condition = condition, value = as.character(value))) } ############################################################ ## OnlyCodingTx ## ## That's a special case filter that just returns transcripts ## that have tx_cds_seq_start defined (i.e. not NULL). setClass("OnlyCodingTx", contains = "BasicFilter", prototype = list( condition = "=", value = "", .valueIsCharacter = TRUE )) OnlyCodingTx <- function() { return(new("OnlyCodingTx")) } ensembldb/R/EnsDbFromGTF.R0000644000175400017540000013600013001231246016227 0ustar00biocbuildbiocbuild#### ## function to create a EnsDb object (or rather the SQLite database) from ## a Ensembl GTF file. ## Limitation: ## + There is no way to get the Entrezgene ID from this file. ## + Assuming that the element 2 in a row for a transcript represents its biotype, since ## there is no explicit key transcript_biotype in element 9. ## + The CDS features in the GTF are somewhat problematic, while we're used to get just the ## coding start and end for a transcript from the Ensembl perl API, here we get the coding ## start and end for each exon. ensDbFromGtf <- function(gtf, outfile, path, organism, genomeVersion, version){ options(useFancyQuotes=FALSE) message("Importing GTF file...", appendLF=FALSE) ## wanted.features <- c("gene", "transcript", "exon", "CDS") wanted.features <- c("exon") ## GTF <- import(con=gtf, format="gtf", feature.type=wanted.features) GTF <- import(con=gtf, format="gtf") message("OK") ## check what we've got... ## all wanted features? if(any(!(wanted.features %in% levels(GTF$type)))){ stop(paste0("One or more required types are not in the gtf file. Need ", paste(wanted.features, collapse=","), " but got only ", paste(wanted.features[wanted.features %in% levels(GTF$type)], collapse=","), ".")) } ## transcript biotype? if(any(colnames(mcols(GTF))=="transcript_biotype")){ txBiotypeCol <- "transcript_biotype" }else{ ## that's a little weird, but it seems that certain gtf files from Ensembl ## provide the transcript biotype in the element "source" txBiotypeCol <- "source" } ## processing the metadata: ## first read the header... tmp <- readLines(gtf, n=10) tmp <- tmp[grep(tmp, pattern="^#")] haveHeader <- FALSE if(length(tmp) > 0){ ##message("GTF file has a header.") tmp <- gsub(tmp, pattern="^#", replacement="") tmp <- gsub(tmp, pattern="^!", replacement="") Header <- do.call(rbind, strsplit(tmp, split=" ", fixed=TRUE)) colnames(Header) <- c("name", "value") haveHeader <- TRUE } ## Check parameters Parms <- .checkExtractVersions(gtf, organism, genomeVersion, version) ensemblVersion <- Parms["version"] organism <- Parms["organism"] genomeVersion <- Parms["genomeVersion"] if(haveHeader){ if(genomeVersion!=Header[Header[, "name"] == "genome-version", "value"]){ stop(paste0("The GTF file name is not as expected: .", "..gtf!", " I've got genome version ", genomeVersion, " but in the header of the GTF file ", Header[Header[, "name"] == "genome-version", "value"], " is specified!")) } } GTF <- fixCDStypeInEnsemblGTF(GTF) ## here on -> call ensDbFromGRanges. dbname <- ensDbFromGRanges(GTF, outfile=outfile, path=path, organism=organism, genomeVersion=genomeVersion, version=ensemblVersion) gtfFilename <- unlist(strsplit(gtf, split=.Platform$file.sep)) gtfFilename <- gtfFilename[length(gtfFilename)] ## updating the Metadata information... lite <- dbDriver("SQLite") con <- dbConnect(lite, dbname = dbname ) bla <- dbGetQuery(con, paste0("update metadata set value='", gtfFilename, "' where name='source_file';")) dbDisconnect(con) return(dbname) } ####============================================================ ## fixCDStypeInEnsemblGTF ## ## Takes an GRanges object as input and returns a GRanges object in ## which the feature type stop_codon and start_codon is replaced by ## feature type CDS. This is to fix a potential problem (bug?) in ## GTF files from Ensembl, in which the stop_codon or start_codon for ## some transcripts is outside of the CDS. ####------------------------------------------------------------ fixCDStypeInEnsemblGTF <- function(x){ if(any(unique(x$type) %in% c("start_codon", "stop_codon"))){ x$type[x$type %in% c("start_codon", "stop_codon")] <- "CDS" } return(x) } ####============================================================ ## ensDbFromAH ## ## Retrieve a GTF file from AnnotationHub and build a EnsDb object from that. ## ####------------------------------------------------------------ ensDbFromAH <- function(ah, outfile, path, organism, genomeVersion, version){ options(useFancyQuotes=FALSE) ## Input checking... if(!is(ah, "AnnotationHub")) stop("Argument 'ah' has to be a (single) AnnotationHub object.") if(length(ah) != 1) stop("Argument 'ah' has to be a single AnnotationHub resource!") if(tolower(ah$dataprovider) != "ensembl") stop("Can only process GTF files provided by Ensembl!") if(tolower(ah$sourcetype) != "gtf") stop("Resource is not a GTF file!") ## Check parameters Parms <- .checkExtractVersions(ah$title, organism, genomeVersion, version) ensFromAH <- Parms["version"] orgFromAH <- Parms["organism"] genFromAH <- Parms["genomeVersion"] gtfFilename <- ah$title message("Fetching data ...", appendLF=FALSE) suppressMessages( gff <- ah[[1]] ) message("OK") message(" -------------") message("Proceeding to create the database.") gff <- fixCDStypeInEnsemblGTF(gff) ## Proceed. dbname <- ensDbFromGRanges(gff, outfile=outfile, path=path, organism=orgFromAH, genomeVersion=genFromAH, version=ensFromAH) ## updating the Metadata information... lite <- dbDriver("SQLite") con <- dbConnect(lite, dbname = dbname ) bla <- dbGetQuery(con, paste0("update metadata set value='", gtfFilename, "' where name='source_file';")) dbDisconnect(con) return(dbname) } .checkExtractVersions <- function(filename, organism, genomeVersion, version){ if(isEnsemblFileName(filename)){ ensFromFile <- ensemblVersionFromGtfFileName(filename) orgFromFile <- organismFromGtfFileName(filename) genFromFile <- genomeVersionFromGtfFileName(filename) }else{ ensFromFile <- NA orgFromFile <- NA genFromFile <- NA if(missing(organism) | missing(genomeVersion) | missing(version)) stop("The file name does not match the expected naming scheme of Ensembl", " files hence I cannot extract any information from it! Parameters", " 'organism', 'genomeVersion' and 'version' are thus required!") } ## Do some more testing with versions provided from the user. if(!missing(organism)){ if(!is.na(orgFromFile)){ if(organism != orgFromFile){ warning("User specified organism (", organism, ") is different to the one extracted", " from the file name (", orgFromFile, ")! Using the one defined by the user.") } } orgFromFile <- organism } if(!missing(genomeVersion)){ if(!is.na(genFromFile)){ if(genomeVersion != genFromFile){ warning("User specified genome version (", genomeVersion, ") is different to the one extracted", " from the file name (", genFromFile, ")! Using the one defined by the user.") } } genFromFile <- genomeVersion } if(!missing(version)){ if(!is.na(ensFromFile)){ if(version != ensFromFile){ warning("User specified Ensembl version (", version, ") is different to the one extracted", " from the file name (", ensFromFile, ")! Using the one defined by the user.") } } ensFromFile <- version } res <- c(orgFromFile, genFromFile, ensFromFile) names(res) <- c("organism", "genomeVersion", "version") return(res) } ####============================================================ ## ## ensDbFromGff ## ####------------------------------------------------------------ ensDbFromGff <- function(gff, outfile, path, organism, genomeVersion, version){ options(useFancyQuotes=FALSE) ## Check parameters Parms <- .checkExtractVersions(gff, organism, genomeVersion, version) ensFromFile <- Parms["version"] orgFromFile <- Parms["organism"] genFromFile <- Parms["genomeVersion"] ## Reading some info from the header. tmp <- readLines(gff, n=500) if(length(grep(tmp[1], pattern="##gff-version")) == 0) stop("File ", gff, " does not seem to be a correct GFF file! ", "The ##gff-version line is missing!") gffVersion <- unlist(strsplit(tmp[1], split="[ ]+"))[2] if(gffVersion != "3") stop("This function supports only GFF version 3 files!") tmp <- tmp[grep(tmp, pattern="^#!")] if(length(tmp) > 0){ tmp <- gsub(tmp, pattern="^#!", replacement="") Header <- do.call(rbind, strsplit(tmp, split="[ ]+")) colnames(Header) <- c("name", "value") if(any(Header[, "name"] == "genome-version")){ genFromHeader <- Header[Header[, "name"] == "genome-version", "value"] if(genFromHeader != genFromFile){ warning("Genome version extracted from file name (", genFromFile, ") does not match the genome version specified inside the file (", genFromHeader, "). Will consider the one defined inside the file.") genFromFile <- genFromHeader } } } message("Importing GFF...", appendLF=FALSE) suppressWarnings( theGff <- import(gff, format=paste0("gff", gffVersion)) ) message("OK") ## Works with Ensembl 83; eventually not for updated Ensembl gff files! ## what seems a little strange: exons have an ID of NA. ## Ensembl specific fields: gene_id, transcript_id, exon_id, rank, biotype. ## GFF3 fields: type, ID, Name, Parent ## check columns and subset... gffcols <- c("type", "ID", "Name", "Parent") if(!all(gffcols %in% colnames(mcols(theGff)))) stop("Required columns/fields ", paste(gffcols[!(gffcols %in% colnames(mcols(theGff)))], collapse=";"), " not present in the GFF file!") enscols <- c("gene_id", "transcript_id", "exon_id", "rank", "biotype") if(!all(enscols %in% colnames(mcols(theGff)))) stop("Required columns/fields ", paste(enscols[!(enscols %in% colnames(mcols(theGff)))], collapse=";"), " not present in the GFF file!") ## Subsetting to eventually speed up further processing. theGff <- theGff[, c(gffcols, enscols)] ## Renaming and fixing some columns: CN <- colnames(mcols(theGff)) colnames(mcols(theGff))[CN == "Name"] <- "gene_name" colnames(mcols(theGff))[CN == "biotype"] <- "gene_biotype" colnames(mcols(theGff))[CN == "rank"] <- "exon_number" theGff$transcript_biotype <- theGff$gene_biotype ## Processing that stuff... ## Replace the ID format type:ID. ids <- strsplit(theGff$ID, split=":") message("Fixing IDs...", appendLF=FALSE) ## For those that have length > 1 use the second element. theGff$ID <- unlist(lapply(ids, function(z){ if(length(z) > 1) return(z[2]) return(z) })) message("OK") ## Process genes... message("Processing genes...", appendLF=FALSE) ## Bring the GFF into the correct format for EnsDb/ensDbFromGRanges. idx <- which(!is.na(theGff$gene_id)) theGff$type[idx] <- "gene" message("OK") ## ## Can not use the lengths of chromosomes provided in the chromosome features!!! ## ## For whatever reasons chromosome Y length is incorrect!!! ## message("Processing seqinfo...", appendLF=FALSE) ## SI <- seqinfo(theGff) ## tmp <- theGff[theGff$ID %in% seqlevels(SI)] ## ## Check if we've got length for all. ## message("OK") ## Process transcripts... message("Processing transcripts...", appendLF=FALSE) idx <- which(!is.na(theGff$transcript_id)) ## Check if I've got multiple parents... parentGenes <- theGff$Parent[idx] if(any(lengths(parentGenes) > 1)) stop("Transcripts with multiple parents in GFF element 'Parent' not (yet) supported!") theGff$type[idx] <- "transcript" ## Setting the gene_id for these guys... theGff$gene_id[idx] <- unlist(sub(parentGenes, pattern="gene:", replacement="", fixed=TRUE)) ## The CDS: idx <- which(theGff$type == "CDS") parentTx <- theGff$Parent[idx] if(any(lengths(parentTx) > 1)) stop("CDS with multiple parent transcripts in GFF element 'Parent' not (yet) supported!") theGff$transcript_id[idx] <- unlist(sub(parentTx, pattern="transcript:", replacement="", fixed=TRUE)) message("OK") message("Processing exons...", appendLF=FALSE) idx <- which(!is.na(theGff$exon_id)) parentTx <- theGff$Parent[idx] if(any(lengths(parentTx) > 1)) stop("Exons with multiple parent transcripts in GFF element 'Parent' not (yet) supported!") theGff$transcript_id[idx] <- unlist(sub(parentTx, pattern="transcript:", replacement="", fixed=TRUE)) message("OK") theGff <- theGff[theGff$type %in% c("gene", "transcript", "exon", "CDS")] theGff <- keepSeqlevels(theGff, as.character(unique(seqnames(theGff)))) ## Now we can proceed and pass that to the next function! message(" -------------") message("Proceeding to create the database.") ## Proceed. dbname <- ensDbFromGRanges(theGff, outfile=outfile, path=path, organism=orgFromFile, genomeVersion=genFromFile, version=ensFromFile) gtfFilename <- unlist(strsplit(gff, split=.Platform$file.sep)) gtfFilename <- gtfFilename[length(gtfFilename)] ## updating the Metadata information... lite <- dbDriver("SQLite") con <- dbConnect(lite, dbname = dbname ) bla <- dbGetQuery(con, paste0("update metadata set value='", gtfFilename, "' where name='source_file';")) dbDisconnect(con) return(dbname) } #### build a EnsDb SQLite database from the GRanges. ## we can however not get all of the information from the GRanges (yet), for example, ## the seqinfo might not be available in all GRanges objects. Also, there is no way ## we can guess the organism or the Ensembl version from the GRanges, thus, this ## information has to be provided by the user. ## x: the GRanges object or file name. If file name, the function tries to guess ## the organism, genome build and ensembl version from the file name, if not ## provided. ## ensDbFromGRanges <- function(x, outfile, path, organism, genomeVersion, version){ if(!is(x, "GRanges")) stop("This method can only be called on GRanges objects!") ## check for missing parameters if(missing(organism)){ stop("The organism has to be specified (e.g. using organism=\"Homo_sapiens\")") } if(missing(version)){ stop("The Ensembl version has to be specified!") } ## checking the seqinfo in the GRanges object... Seqinfo <- seqinfo(x) fetchSeqinfo <- FALSE ## check if we've got some information... if(any(is.na(seqlengths(Seqinfo)))){ fetchSeqinfo <- TRUE ## means we have to fetch the seqinfo ourselfs... } if(missing(genomeVersion)){ ## is there a seqinfo in x that I could use??? if(!fetchSeqinfo){ genomeVersion <- unique(genome(Seqinfo)) if(is.na(genomeVersion) | length(genomeVersion) > 1){ stop(paste0("The genome version has to be specified as", " it can not be extracted from the seqinfo!")) } }else{ stop("The genome version has to be specified!") } } if(missing(outfile)){ ## use the organism, genome version and ensembl version as the file name. outfile <- paste0(c(organism, genomeVersion, version, "sqlite"), collapse=".") if(missing(path)) path <- "." dbname <- paste0(path, .Platform$file.sep, outfile) }else{ if(!missing(path)) warning("outfile specified, thus I will discard the path argument.") dbname <- outfile } ## that's quite some hack ## transcript biotype? if(any(colnames(mcols(x))=="transcript_biotype")){ txBiotypeCol <- "transcript_biotype" }else{ ## that's a little weird, but it seems that certain gtf files from Ensembl ## provide the transcript biotype in the element "source" txBiotypeCol <- "source" } con <- dbConnect(dbDriver("SQLite"), dbname=dbname) on.exit(dbDisconnect(con)) ## ---------------------------- ## metadata table: message("Processing metadata...", appendLF=FALSE) Metadata <- buildMetadata(organism, version, host="unknown", sourceFile="GRanges object", genomeVersion=genomeVersion) dbWriteTable(con, name="metadata", Metadata, overwrite=TRUE, row.names=FALSE) message("OK") ## Check if we've got column "type" if(!any(colnames(mcols(x)) == "type")) stop("The GRanges object lacks the required column 'type', sorry.") gotTypes <- as.character(unique(x$type)) gotColumns <- colnames(mcols(x)) ## ---------------------------- ## ## process genes ## we're lacking NCBI Entrezids and also the coord system, but these are not ## required columns anyway... message("Processing genes...") ## want to have: gene_id, gene_name, entrezid, gene_biotype, gene_seq_start, ## gene_seq_end, seq_name, seq_strand, seq_coord_system. wouldBeNice <- c("gene_id", "gene_name", "entrezid", "gene_biotype") dontHave <- wouldBeNice[!(wouldBeNice %in% gotColumns)] haveGot <- wouldBeNice[wouldBeNice %in% gotColumns] ## Just really require the gene_id... reqCols <- c("gene_id") if(length(dontHave) > 0){ mess <- paste0(" I'm missing column(s): ", paste0(sQuote(dontHave), collapse=","), ".") warning(mess, " The corresponding database column(s) will be empty!") } message(" Attribute availability:", appendLF=TRUE) for(i in 1:length(wouldBeNice)){ message(" o ", wouldBeNice[i], "...", ifelse(any(gotColumns == wouldBeNice[i]), yes=" OK", no=" Nope")) } if(!any(reqCols %in% haveGot)) stop(paste0("One or more required fields are not defined in the", " submitted GRanges object! Need ", paste(sQuote(reqCols), collapse=","), " but got only ", paste(reqCols[reqCols %in% gotColumns], collapse=","), ".")) ## Now gets tricky; special case Ensembl < 75: we've got NO gene type. if(any(gotTypes == "gene")){ ## All is fine. genes <- as.data.frame(x[x$type == "gene", haveGot]) }else{ ## Well, have to split by gene_id and process... genes <- split(x[ , haveGot], x$gene_id) gnRanges <- unlist(range(genes)) gnMcol <- as.data.frame(unique(mcols(unlist(genes)))) genes <- as.data.frame(gnRanges) ## Adding mcols again. genes <- cbind(genes, gnMcol[match(rownames(genes), gnMcol$gene_id), ]) rm(gnRanges) rm(gnMcol) } colnames(genes) <- c("seq_name", "gene_seq_start", "gene_seq_end", "width", "seq_strand", haveGot) ## Add missing cols... if(length(dontHave) > 0){ cn <- colnames(genes) for(i in 1:length(dontHave)){ genes <- cbind(genes, rep(NA, nrow(genes))) } colnames(genes) <- c(cn, dontHave) } genes <- cbind(genes, seq_coord_system=rep(NA, nrow(genes))) ## transforming seq_strand from +/- to +1, -1. strand <- rep(0L, nrow(genes)) strand[as.character(genes$seq_strand) == "+"] <- 1L strand[as.character(genes$seq_strand) == "-"] <- -1L genes[ , "seq_strand"] <- strand ## rearranging data.frame... genes <- genes[ , c("gene_id", "gene_name", "entrezid", "gene_biotype", "gene_seq_start", "gene_seq_end", "seq_name", "seq_strand", "seq_coord_system")] OK <- .checkIntegerCols(genes) dbWriteTable(con, name="gene", genes, overwrite=TRUE, row.names=FALSE) ## Done. message("OK") ## ---------------------------- ## ## process transcripts message("Processing transcripts...", appendLF=TRUE) ## want to have: tx_id, tx_biotype, tx_seq_start, tx_seq_end, tx_cds_seq_start, ## tx_cds_seq_end, gene_id wouldBeNice <- c("transcript_id", "gene_id", txBiotypeCol) dontHave <- wouldBeNice[!(wouldBeNice %in% gotColumns)] if(length(dontHave) > 0){ mess <- paste0("I'm missing column(s): ", paste0(sQuote(dontHave), collapse=","), ".") warning(mess, " The corresponding database columns will be empty!") } haveGot <- wouldBeNice[wouldBeNice %in% gotColumns] message(" Attribute availability:", appendLF=TRUE) for(i in 1:length(wouldBeNice)){ message(" o ", wouldBeNice[i], "...", ifelse(any(gotColumns == wouldBeNice[i]), yes=" OK", no=" Nope")) } reqCols <- c("transcript_id", "gene_id") if(!any(reqCols %in% gotColumns)) stop(paste0("One or more required fields are not defined in", " the submitted GRanges object! Need ", paste(reqCols, collapse=","), " but got only ", paste(reqCols[reqCols %in% gotColumns], collapse=","), ".")) if(any(gotTypes == "transcript")){ tx <- as.data.frame(x[x$type == "transcript" , haveGot]) }else{ tx <- split(x[, haveGot], x$transcript_id) txRanges <- unlist(range(tx)) txMcol <- as.data.frame(unique(mcols(unlist(tx)))) tx <- as.data.frame(txRanges) tx <- cbind(tx, txMcol[match(rownames(tx), txMcol$transcript_id), ]) rm(txRanges) rm(txMcol) } ## Drop columns seqnames, width and strand tx <- tx[, -c(1, 4, 5)] ## Add empty columns, eventually if(length(dontHave) > 0){ cn <- colnames(tx) for(i in 1:length(dontHave)){ tx <- cbind(tx, rep(NA, nrow(tx))) } colnames(tx) <- c(cn, dontHave) } ## Add columns for UTR tx <- cbind(tx, tx_cds_seq_start=rep(NA, nrow(tx)), tx_cds_seq_end=rep(NA, nrow(tx))) ## Process CDS... if(any(gotTypes == "CDS")){ ## Only do that if we've got type == "CDS"! ## process the CDS features to get the cds start and end of the transcript. CDS <- as.data.frame(x[x$type == "CDS", "transcript_id"]) ## startByTx <- split(CDS$start, f=CDS$transcript_id) cdsStarts <- unlist(lapply(startByTx, function(z){return(min(z, na.rm=TRUE))})) endByTx <- split(CDS$end, f=CDS$transcript_id) cdsEnds <- unlist(lapply(endByTx, function(z){return(max(z, na.rm=TRUE))})) idx <- match(names(cdsStarts), tx$transcript_id) areNas <- is.na(idx) idx <- idx[!areNas] cdsStarts <- cdsStarts[!areNas] cdsEnds <- cdsEnds[!areNas] tx[idx, "tx_cds_seq_start"] <- cdsStarts tx[idx, "tx_cds_seq_end"] <- cdsEnds }else{ mess <- " I can't find type=='CDS'! The resulting database will lack CDS information!" message(mess, appendLF = TRUE) warning(mess) } colnames(tx) <- c("tx_seq_start", "tx_seq_end", "tx_id", "gene_id", "tx_biotype", "tx_cds_seq_start", "tx_cds_seq_end") ## rearranging data.frame: tx <- tx[ , c("tx_id", "tx_biotype", "tx_seq_start", "tx_seq_end", "tx_cds_seq_start", "tx_cds_seq_end", "gene_id")] ## write the table. OK <- .checkIntegerCols(tx) dbWriteTable(con, name="tx", tx, overwrite=TRUE, row.names=FALSE) rm(tx) rm(CDS) rm(cdsStarts) rm(cdsEnds) message("OK") ## ---------------------------- ## ## process exons message("Processing exons...", appendLF=FALSE) reqCols <- c("exon_id", "transcript_id", "exon_number") if(!any(reqCols %in% gotColumns)) stop(paste0("One or more required fields are not defined in", " the submitted GRanges object! Need ", paste(reqCols, collapse=","), " but got only ", paste(reqCols[reqCols %in% gotColumns], collapse=","), ".")) exons <- as.data.frame(x[x$type == "exon", reqCols])[, -c(1, 4, 5)] ## for table tx2exon we want to have: ## tx_id, exon_id, exon_idx t2e <- unique(exons[ , c("transcript_id", "exon_id", "exon_number")]) colnames(t2e) <- c("tx_id", "exon_id", "exon_idx") ## Force exon_idx to be an integer! t2e[, "exon_idx"] <- as.integer(t2e[, "exon_idx"]) ## Cross-check that we've got the corresponding tx_ids in the tx table! ## for table exons we want to have: ## exon_id, exon_seq_start, exon_seq_end exons <- unique(exons[ , c("exon_id", "start", "end")]) colnames(exons) <- c("exon_id", "exon_seq_start", "exon_seq_end") ## writing the tables. .checkIntegerCols(exons) .checkIntegerCols(t2e) dbWriteTable(con, name="exon", exons, overwrite=TRUE, row.names=FALSE) dbWriteTable(con, name="tx2exon", t2e, overwrite=TRUE, row.names=FALSE) message("OK") ## ---------------------------- ## ## process chromosomes message("Processing chromosomes...", appendLF=FALSE) if(fetchSeqinfo){ ## problem is I don't have these available... chroms <- data.frame(seq_name=unique(as.character(genes$seq_name))) chroms <- cbind(chroms, seq_length=rep(NA, nrow(chroms)), is_circular=rep(NA, nrow(chroms))) rownames(chroms) <- chroms$seq_name ## now trying to get the sequence lengths directly from Ensembl using internal ## functions from the GenomicFeatures package. I will use "try" to not break ## the call if no seqlengths are available. seqlengths <- tryGetSeqinfoFromEnsembl(organism, version, seqnames=chroms$seq_name) if(nrow(seqlengths)>0){ seqlengths <- seqlengths[seqlengths[, "name"] %in% rownames(chroms), ] chroms[seqlengths[, "name"], "seq_length"] <- seqlengths[, "length"] } }else{ ## have seqinfo available. chroms <- data.frame(seq_name=seqnames(Seqinfo), seq_length=seqlengths(Seqinfo), is_circular=isCircular(Seqinfo)) } ## write the table. dbWriteTable(con, name="chromosome", chroms, overwrite=TRUE, row.names=FALSE) rm(genes) message("OK") message("Generating index...", appendLF=FALSE) ## generating all indices... .createEnsDbIndices(con) message("OK") message(" -------------") message("Verifying validity of the information in the database:") checkValidEnsDb(EnsDb(dbname)) return(dbname) } ## helper function that checks that the gene, transcript and exon data in the ## EnsDb database is correct (i.e. transcript within gene coordinates, exons within ## transcript coordinates, cds within transcript) checkValidEnsDb <- function(x){ message("Checking transcripts...", appendLF=FALSE) tx <- transcripts(x, columns=c("gene_id", "tx_id", "gene_seq_start", "gene_seq_end", "tx_seq_start", "tx_seq_end", "tx_cds_seq_start", "tx_cds_seq_end"), return.type="DataFrame") ## check if the tx are inside the genes... isInside <- tx$tx_seq_start >= tx$gene_seq_start & tx$tx_seq_end <= tx$gene_seq_end if(any(!isInside)) stop("Start and end coordinates for ", sum(!isInside), "transcripts are not within the gene coordinates!") ## check cds coordinates notInside <- which(!(tx$tx_cds_seq_start >= tx$tx_seq_start & tx$tx_cds_seq_end <= tx$tx_seq_end)) if(length(notInside) > 0){ stop("The CDS start and end coordinates for ", length(notInside), " transcripts are not within the transcript coordinates!") } rm(tx) message("OK\nChecking exons...", appendLF=FALSE) ex <- exons(x, columns=c("exon_id", "tx_id", "exon_seq_start", "exon_seq_end", "tx_seq_start", "tx_seq_end", "seq_strand", "exon_idx"), return.type="data.frame") ## check if exons are within tx isInside <- ex$exon_seq_start >= ex$tx_seq_start & ex$exon_seq_end <= ex$tx_seq_end if(any(!isInside)) stop("Start and end coordinates for ", sum(!isInside), " exons are not within the transcript coordinates!") ## checking the exon index... extmp <- ex[ex$seq_strand==1, c("exon_idx", "tx_id", "exon_seq_start")] extmp <- extmp[order(extmp$exon_seq_start), ] extmp.split <- split(extmp[ , c("exon_idx")], f=factor(extmp$tx_id)) Different <- unlist(lapply(extmp.split, FUN=function(z){ return(any(z != seq(1, length(z)))) })) if(any(Different)){ stop(paste0("Provided exon index in transcript does not match with ordering", " of the exons by chromosomal coordinates for", sum(Different), "of the", length(Different), "transcripts encoded on the + strand!")) } extmp <- ex[ex$seq_strand==-1, c("exon_idx", "tx_id", "exon_seq_end")] extmp <- extmp[order(extmp$exon_seq_end, decreasing=TRUE), ] extmp.split <- split(extmp[ , c("exon_idx")], f=factor(extmp$tx_id)) Different <- unlist(lapply(extmp.split, FUN=function(z){ return(any(z != seq(1, length(z)))) })) if(any(Different)){ stop(paste0("Provided exon index in transcript does not match with ordering", " of the exons by chromosomal coordinates for", sum(Different), "of the", length(Different), "transcripts encoded on the - strand!")) } message("OK") } ## organism is expected to be e.g. Homo_sapiens, so the full organism name, with ## _ as a separator tryGetSeqinfoFromEnsembl <- function(organism, ensemblVersion, seqnames){ ## Quick fix if organism contains whitespace instead of _: organism <- gsub(organism, pattern=" ", replacement="_", fixed=TRUE) Dataset <- paste0(c(tolower(.abbrevOrganismName(organism)), "gene_ensembl"), collapse="_") message("Fetch seqlengths from ensembl, dataset ", Dataset, " version ", ensemblVersion, "...", appendLF=FALSE) ## get it all from the ensemblgenomes.org host??? tmp <- try( GenomicFeatures:::fetchChromLengthsFromEnsembl(dataset=Dataset, release=ensemblVersion, extra_seqnames=seqnames), silent=TRUE) if(class(tmp)=="try-error"){ message(paste0("Unable to get sequence lengths from Ensembl for dataset: ", Dataset, ". Error was: ", message(tmp), "\n")) }else{ message("OK") return(tmp) } ## try plant genomes... tmp <- try( GenomicFeatures:::fetchChromLengthsFromEnsemblPlants(dataset=Dataset, extra_seqnames=seqnames), silent=TRUE) if(class(tmp)=="try-error"){ message(paste0("Unable to get sequence lengths from Ensembl plants for dataset: ", Dataset, ". Error was: ", message(tmp), "\n")) }else{ message("OK") return(tmp) } message("FAIL") return(matrix(ncol=2, nrow=0)) } buildMetadata <- function(organism="", ensemblVersion="", genomeVersion="", host="", sourceFile=""){ MetaData <- data.frame(matrix(ncol=2, nrow=11)) colnames(MetaData) <- c("name", "value") MetaData[1, ] <- c("Db type", "EnsDb") MetaData[2, ] <- c("Type of Gene ID", "Ensembl Gene ID") MetaData[3, ] <- c("Supporting package", "ensembldb") MetaData[4, ] <- c("Db created by", "ensembldb package from Bioconductor") MetaData[5, ] <- c("script_version", "0.0.1") MetaData[6, ] <- c("Creation time", date()) MetaData[7, ] <- c("ensembl_version", ensemblVersion) MetaData[8, ] <- c("ensembl_host", host) MetaData[9, ] <- c("Organism", organism ) MetaData[10, ] <- c("genome_build", genomeVersion) MetaData[11, ] <- c("DBSCHEMAVERSION", "1.0") MetaData[12, ] <- c("source_file", sourceFile) return(MetaData) } ## compare the contents of the EnsDb sqlite database generated from a GTF (file name submitted ## with x ) with the one provided by package "lib". compareEnsDbs <- function(x, y){ ## compare two EnsDbs... if(organism(x)!=organism(y)) stop("Well, at least the organism should be the same for both databases!") Messages <- rep("OK", 5) names(Messages) <- c("metadata", "chromosome", "gene", "transcript", "exon") ## comparing metadata. metadataX <- metadata(x) metadataY <- metadata(y) rownames(metadataX) <- metadataX[, 1] rownames(metadataY) <- metadataY[, 1] metadataY <- metadataY[rownames(metadataX),] cat("\nComparing metadata:\n") idx <- which(metadataX[, "value"]!=metadataY[, "value"]) if(length(idx)>0) Messages["metadata"] <- "NOTE" ## check ensembl version if(metadataX["ensembl_version", "value"] == metadataY["ensembl_version", "value"]){ cat(" Ensembl versions match.\n") }else{ cat(" WARNING: databases base on different Ensembl versions! Expect considerable differences!\n") Messages["metadata"] <- "WARN" } ## genome build if(metadataX["genome_build", "value"] == metadataY["genome_build", "value"]){ cat(" Genome builds match.\n") }else{ cat(" WARNING: databases base on different Genome builds! Expect considerable differences!\n") Messages["metadata"] <- "WARN" } if(length(idx)>0){ cat(" All differences: : != \n") for(i in idx){ cat(paste(" - ", metadataX[i, "name"], ":", metadataX[i, "value"], " != ", metadataY[i, "value"], "\n")) } } cat(paste0("Done. Result: ", Messages["metadata"],"\n")) ## now comparing chromosomes Messages["chromosome"] <- compareChromosomes(x, y) ## comparing genes Messages["gene"] <- compareGenes(x, y) ## comparing transcripts Messages["transcript"] <- compareTx(x, y) ## comparing exons Messages["exon"] <- compareExons(x, y) return(Messages) } compareChromosomes <- function(x, y){ Ret <- "OK" cat("\nComparing chromosome data:\n") chromX <- as.data.frame(seqinfo(x)) chromY <- as.data.frame(seqinfo(y)) ## compare seqnames inboth <- rownames(chromX)[rownames(chromX) %in% rownames(chromY)] onlyX <- rownames(chromX)[!(rownames(chromX) %in% rownames(chromY))] onlyY <- rownames(chromY)[!(rownames(chromY) %in% rownames(chromX))] if(length(onlyX) > 0 | length(onlyY) > 0) Ret <- "WARN" cat(paste0( " Sequence names: (", length(inboth), ") common, (", length(onlyX), ") only in x, (", length(onlyY), ") only in y.\n" )) same <- length(which(chromX[inboth, "seqlengths"]==chromY[inboth, "seqlengths"])) different <- length(inboth) - same cat(paste0( " Sequence lengths: (",same, ") identical, (", different, ") different.\n" )) if(different > 0) Ret <- "WARN" cat(paste0("Done. Result: ", Ret,"\n")) return(Ret) } compareGenes <- function(x, y){ cat("\nComparing gene data:\n") Ret <- "OK" genesX <- genes(x) genesY <- genes(y) inboth <- names(genesX)[names(genesX) %in% names(genesY)] onlyX <- names(genesX)[!(names(genesX) %in% names(genesY))] onlyY <- names(genesY)[!(names(genesY) %in% names(genesX))] if(length(onlyX) > 0 | length(onlyY) > 0) Ret <- "WARN" cat(paste0(" gene IDs: (", length(inboth), ") common, (", length(onlyX), ") only in x, (", length(onlyY), ") only in y.\n")) ## seq names same <- length( which(as.character(seqnames(genesX[inboth]))==as.character(seqnames(genesY[inboth]))) ) different <- length(inboth) - same if(different > 0) Ret <- "ERROR" cat(paste0( " Sequence names: (",same, ") identical, (", different, ") different.\n" )) ## start same <- length( which(start(genesX[inboth]) == start(genesY[inboth])) ) different <- length(inboth) - same if(different > 0) Ret <- "ERROR" cat(paste0( " Gene start coordinates: (",same, ") identical, (", different, ") different.\n" )) ## end same <- length( which(end(genesX[inboth]) == end(genesY[inboth])) ) different <- length(inboth) - same if(different > 0) Ret <- "ERROR" cat(paste0( " Gene end coordinates: (",same, ") identical, (", different, ") different.\n" )) ## strand same <- length( which(as.character(strand(genesX[inboth])) == as.character(strand(genesY[inboth]))) ) different <- length(inboth) - same if(different > 0) Ret <- "ERROR" cat(paste0( " Gene strand: (",same, ") identical, (", different, ") different.\n" )) ## name same <- length( which(genesX[inboth]$gene_name == genesY[inboth]$gene_name) ) different <- length(inboth) - same if(different > 0 & Ret!="ERROR") Ret <- "WARN" cat(paste0( " Gene names: (",same, ") identical, (", different, ") different.\n" )) ## entrezid same <- length( which(genesX[inboth]$entrezid == genesY[inboth]$entrezid) ) different <- length(inboth) - same if(different > 0 & Ret!="ERROR") Ret <- "WARN" cat(paste0( " Entrezgene IDs: (",same, ") identical, (", different, ") different.\n" )) ## gene biotype same <- length( which(genesX[inboth]$gene_biotype == genesY[inboth]$gene_biotype) ) different <- length(inboth) - same if(different > 0 & Ret!="ERROR") Ret <- "WARN" cat(paste0( " Gene biotypes: (",same, ") identical, (", different, ") different.\n" )) cat(paste0("Done. Result: ", Ret,"\n")) return(Ret) } compareTx <- function(x, y){ cat("\nComparing transcript data:\n") Ret <- "OK" txX <- transcripts(x) txY <- transcripts(y) inboth <- names(txX)[names(txX) %in% names(txY)] onlyX <- names(txX)[!(names(txX) %in% names(txY))] onlyY <- names(txY)[!(names(txY) %in% names(txX))] if(length(onlyX) > 0 | length(onlyY) > 0) Ret <- "WARN" cat(paste0(" transcript IDs: (", length(inboth), ") common, (", length(onlyX), ") only in x, (", length(onlyY), ") only in y.\n")) ## start same <- length( which(start(txX[inboth]) == start(txY[inboth])) ) different <- length(inboth) - same if(different > 0) Ret <- "ERROR" cat(paste0( " Transcript start coordinates: (",same, ") identical, (", different, ") different.\n" )) ## end same <- length( which(end(txX[inboth]) == end(txY[inboth])) ) different <- length(inboth) - same if(different > 0) Ret <- "ERROR" cat(paste0( " Transcript end coordinates: (",same, ") identical, (", different, ") different.\n" )) ## tx biotype same <- length( which(txX[inboth]$tx_biotype == txY[inboth]$tx_biotype) ) different <- length(inboth) - same if(different > 0 & Ret!="ERROR") Ret <- "WARN" cat(paste0( " Transcript biotypes: (",same, ") identical, (", different, ") different.\n" )) ## cds start ## Makes sense to just compare for those that have the same tx! txXSub <- txX[inboth] txYSub <- txY[inboth] txCdsX <- names(txXSub)[!is.na(txXSub$tx_cds_seq_start)] txCdsY <- names(txYSub)[!is.na(txYSub$tx_cds_seq_start)] cdsInBoth <- txCdsX[txCdsX %in% txCdsY] cdsOnlyX <- txCdsX[!(txCdsX %in% txCdsY)] cdsOnlyY <- txCdsY[!(txCdsY %in% txCdsX)] if((length(cdsOnlyX) > 0 | length(cdsOnlyY)) & Ret!="ERROR") Ret <- "ERROR" cat(paste0(" Common transcripts with defined CDS: (",length(cdsInBoth), ") common, (", length(cdsOnlyX), ") only in x, (", length(cdsOnlyY), ") only in y.\n")) same <- length( which(txX[cdsInBoth]$tx_cds_seq_start == txY[cdsInBoth]$tx_cds_seq_start) ) different <- length(cdsInBoth) - same if(different > 0 & Ret!="ERROR") Ret <- "ERROR" cat(paste0( " CDS start coordinates: (",same, ") identical, (", different, ") different.\n" )) ## cds end same <- length( which(txX[cdsInBoth]$tx_cds_seq_end == txY[cdsInBoth]$tx_cds_seq_end) ) different <- length(cdsInBoth) - same if(different > 0 & Ret!="ERROR") Ret <- "ERROR" cat(paste0( " CDS end coordinates: (",same, ") identical, (", different, ") different.\n" )) ## gene id same <- length( which(txX[inboth]$gene_id == txY[inboth]$gene_id) ) different <- length(inboth) - same if(different > 0) Ret <- "ERROR" cat(paste0( " Associated gene IDs: (",same, ") identical, (", different, ") different.\n" )) cat(paste0("Done. Result: ", Ret,"\n")) return(Ret) } compareExons <- function(x, y){ cat("\nComparing exon data:\n") Ret <- "OK" exonX <- exons(x) exonY <- exons(y) inboth <- names(exonX)[names(exonX) %in% names(exonY)] onlyX <- names(exonX)[!(names(exonX) %in% names(exonY))] onlyY <- names(exonY)[!(names(exonY) %in% names(exonX))] if(length(onlyX) > 0 | length(onlyY) > 0) Ret <- "WARN" cat(paste0(" exon IDs: (", length(inboth), ") common, (", length(onlyX), ") only in x, (", length(onlyY), ") only in y.\n")) ## start same <- length( which(start(exonX[inboth]) == start(exonY[inboth])) ) different <- length(inboth) - same if(different > 0) Ret <- "ERROR" cat(paste0( " Exon start coordinates: (",same, ") identical, (", different, ") different.\n" )) ## end same <- length( which(end(exonX[inboth]) == end(exonY[inboth])) ) different <- length(inboth) - same if(different > 0) Ret <- "ERROR" cat(paste0( " Exon end coordinates: (",same, ") identical, (", different, ") different.\n" )) ## now getting also the exon index in tx: exonX <- exons(x, columns=c("exon_id", "tx_id", "exon_idx"), return.type="DataFrame") rownames(exonX) <- paste(exonX$tx_id, exonX$exon_id, sep=":") exonY <- exons(y, columns=c("exon_id", "tx_id", "exon_idx"), return.type="DataFrame") rownames(exonY) <- paste(exonY$tx_id, exonY$exon_id, sep=":") inboth <- rownames(exonX)[rownames(exonX) %in% rownames(exonY)] onlyX <- rownames(exonX)[!(rownames(exonX) %in% rownames(exonY))] onlyY <- rownames(exonY)[!(rownames(exonY) %in% rownames(exonX))] ## tx exon idx same <- length( which(exonX[inboth, ]$exon_idx == exonY[inboth, ]$exon_idx) ) different <- length(inboth) - same if(different > 0 ) Ret <- "ERROR" cat(paste0( " Exon index in transcript models: (",same, ") identical, (", different, ") different.\n" )) cat(paste0("Done. Result: ", Ret,"\n")) return(Ret) } ####============================================================ ## isEnsemblFileName ## ## evaluate whether the file name is "most likely" corresponding ## to a file name from Ensembl, i.e. following the convention ## ...[chr].gff/gtf.gz ## The problem is that the genome version can also be . separated. ####------------------------------------------------------------ isEnsemblFileName <- function(x){ x <- file.name(x) ## If we split by ., do we get at least 4 elements? els <- unlist(strsplit(x, split=".", fixed=TRUE)) if(length(els) < 4) return(FALSE) ## Can we get an Ensembl version? ensVer <- ensemblVersionFromGtfFileName(x) if(is.na(ensVer)) return(FALSE) ## If we got one, do we still have enough fields left of the version? idx <- which(els == ensVer) idx <- idx[length(idx)] if(idx < 3){ ## No way, we're missing the organism and the genome build field! return(FALSE) } ## Well, can not think of any other torture... let's assume it's OK. return(TRUE) } organismFromGtfFileName <- function(x){ return(elementFromEnsemblFilename(x, 1)) } ####============================================================ ## ensemblVersionFromGtfFileName ## ## Tries to extract the Ensembl version from the file name. If it ## finds a numeric value it returns it, otherwise it returns NA. ####------------------------------------------------------------ ensemblVersionFromGtfFileName <- function(x){ x <- file.name(x) els <- unlist(strsplit(x, split=".", fixed=TRUE)) ## Ensembl version is the last numeric value in the file name. for(elm in rev(els)){ suppressWarnings( if(!is.na(as.numeric(elm))){ return(elm) } ) } return(NA) } ####============================================================ ## genomeVersionFromGtfFileName ## ## the genome build can also contain .! thus, I return everything which is not ## the first element (i.e. organism), or the ensembl version, that is one left of ## the gtf. genomeVersionFromGtfFileName <- function(x){ x <- file.name(x) els <- unlist(strsplit(x, split=".", fixed=TRUE)) ensVer <- ensemblVersionFromGtfFileName(x) if(is.na(ensVer)){ stop("Can not extract the genome version from the file name!", " The file name does not follow the expected naming convention from Ensembl!") } idx <- which(els == ensVer) idx <- idx[length(idx)] if(idx < 3) stop("Can not extract the genome version from the file name!", " The file name does not follow the expected naming convention from Ensembl!") return(paste(els[2:(idx-1)], collapse=".")) } old_ensemblVersionFromGtfFileName <- function(x){ tmp <- unlist(strsplit(x, split=.Platform$file.sep, fixed=TRUE)) splitty <- unlist(strsplit(tmp[length(tmp)], split=".", fixed=TRUE)) return(splitty[(grep(splitty, pattern="gtf")-1)]) } ## the genome build can also contain .! thus, I return everything which is not ## the first element (i.e. organism), or the ensembl version, that is one left of ## the gtf. old_genomeVersionFromGtfFileName <- function(x){ tmp <- unlist(strsplit(x, split=.Platform$file.sep, fixed=TRUE)) splitty <- unlist(strsplit(tmp[length(tmp)], split=".", fixed=TRUE)) gvparts <- splitty[2:(grep(splitty, pattern="gtf")-2)] return(paste(gvparts, collapse=".")) } ## Returns NULL if there was a problem. elementFromEnsemblFilename <- function(x, which=1){ tmp <- unlist(strsplit(x, split=.Platform$file.sep, fixed=TRUE)) splitty <- unlist(strsplit(tmp[length(tmp)], split=".", fixed=TRUE)) if(length(splitty) < which){ warning("File ", x, " does not conform to the Ensembl file naming convention.") return(NULL) } return(splitty[which]) } file.name <- function(x){ fn <- unlist(strsplit(x, split=.Platform$file.sep, fixed=TRUE)) fn <- fn[length(fn)] return(fn) } ensembldb/R/Generics.R0000644000175400017540000001262713001231247015617 0ustar00biocbuildbiocbuild##*********************************************************************** ## ## Generic methods ## ##*********************************************************************** if(!isGeneric("column")) setGeneric("column", function(object, db, with.tables, ...) standardGeneric("column")) if(!isGeneric("buildQuery")) setGeneric("buildQuery", function(x, ...) standardGeneric("buildQuery")) if(!isGeneric("cleanColumns")) setGeneric("cleanColumns", function(x, columns, ...) starndardGeneric("cleanColumns")) if(!isGeneric("condition")) setGeneric("condition", function(x, ...) standardGeneric("condition")) setGeneric("condition<-", function(x, value) standardGeneric("condition<-")) setGeneric("dbSeqlevelsStyle", function(x, ...) standardGeneric("dbSeqlevelsStyle")) if(!isGeneric("genes")) setGeneric("genes", function(x, ...) standardGeneric("genes")) if(!isGeneric("getWhat")) setGeneric("getWhat", function(x, ...) standardGeneric("getWhat")) if(!isGeneric("ensemblVersion")) setGeneric("ensemblVersion", function(x) standardGeneric("ensemblVersion")) if(!isGeneric("exons")) setGeneric("exons", function(x, ...) standardGeneric("exons")) if(!isGeneric("exonsBy")) setGeneric("exonsBy", function(x, ...) standardGeneric("exonsBy")) setGeneric("getGeneRegionTrackForGviz", function(x, ...) standardGeneric("getGeneRegionTrackForGviz")) if(!isGeneric("getGenomeFaFile")) setGeneric("getGenomeFaFile", function(x, ...) standardGeneric("getGenomeFaFile")) if(!isGeneric("getGenomeTwoBitFile")) setGeneric("getGenomeTwoBitFile", function(x, ...) standardGeneric("getGenomeTwoBitFile")) if(!isGeneric("getMetadataValue")) setGeneric("getMetadataValue", function(x, name) standardGeneric("getMetadataValue")) if(!isGeneric("listColumns")){ setGeneric("listColumns", function(x, ...) standardGeneric("listColumns")) } if(!isGeneric("listGenebiotypes")){ setGeneric("listGenebiotypes", function(x, ...) standardGeneric("listGenebiotypes")) } if(!isGeneric("listTxbiotypes")){ setGeneric("listTxbiotypes", function(x, ...) standardGeneric("listTxbiotypes")) } if(!isGeneric("lengthOf")) setGeneric("lengthOf", function(x, ...) standardGeneric("lengthOf")) if(!isGeneric("print")) setGeneric("print", function(x, ...) standardGeneric("print")) if(!isGeneric("requireTable")) setGeneric("requireTable", function(x, db, ...) standardGeneric("requireTable")) setGeneric("supportedSeqlevelsStyles", function(x) standardGeneric("supportedSeqlevelsStyles")) if(!isGeneric("seqinfo")) setGeneric("seqinfo", function(x) standardGeneric("seqinfo")) if(!isGeneric("show")) setGeneric("show", function(object, ...) standardGeneric("show")) if(!isGeneric("toSAF")) setGeneric("toSAF", function(x, ...) standardGeneric("toSAF")) if(!isGeneric("listTables")){ setGeneric("listTables", function(x, ...) standardGeneric("listTables")) } setGeneric("returnFilterColumns", function(x) standardGeneric("returnFilterColumns")) setGeneric("returnFilterColumns<-", function(x, value) standardGeneric("returnFilterColumns<-")) if(!isGeneric("tablesByDegree")){ setGeneric("tablesByDegree", function(x, ...) standardGeneric("tablesByDegree")) } if(!isGeneric("tablesForColumns")) setGeneric("tablesForColumns", function(x, attributes, ...) standardGeneric("tablesForColumns")) if(!isGeneric("transcriptLengths")) setGeneric("transcriptLengths", function(x, with.cds_len=FALSE, with.utr5_len=FALSE, with.utr3_len=FALSE, ...) standardGeneric("transcriptLengths")) if(!isGeneric("transcripts")) setGeneric("transcripts", function(x, ...) standardGeneric("transcripts")) if(!isGeneric("transcriptsBy")) setGeneric("transcriptsBy", function(x, ...) standardGeneric("transcriptsBy")) setGeneric("updateEnsDb", function(x, ...) standardGeneric("updateEnsDb")) ##if(!isGeneric("value")) setGeneric("value", function(x, db, ...) standardGeneric("value")) setGeneric("value<-", function(x, value) standardGeneric("value<-")) if(!isGeneric("where")) setGeneric("where", function(object, db, with.tables, ...) standardGeneric("where")) ####============================================================ ## Private methods ## ####------------------------------------------------------------ setGeneric("properties", function(x, ...) standardGeneric("properties")) ## setGeneric("properties<-", function(x, name, value, ...) ## standardGeneric("properties<-")) setGeneric("getProperty", function(x, name=NULL, ...) standardGeneric("getProperty")) setGeneric("setProperty", function(x, value=NULL, ...) standardGeneric("setProperty")) setGeneric("formatSeqnamesForQuery", function(x, sn, ...) standardGeneric("formatSeqnamesForQuery")) setGeneric("formatSeqnamesFromQuery", function(x, sn, ...) standardGeneric("formatSeqnamesFromQuery")) setGeneric("orderResultsInR", function(x) standardGeneric("orderResultsInR")) setGeneric("orderResultsInR<-", function(x, value) standardGeneric("orderResultsInR<-")) setGeneric("useMySQL", function(x, host = "localhost", port = 3306, user, pass) standardGeneric("useMySQL")) ensembldb/R/Methods-Filter.R0000644000175400017540000011561113001231247016703 0ustar00biocbuildbiocbuild##*********************************************************************** ## ## Methods for BasicFilter classes. ## ##*********************************************************************** validateConditionFilter <- function(object){ if(object@.valueIsCharacter){ ## condition has to be either = or in if(!any(c("=", "in", "not in", "like", "!=")==object@condition)){ return(paste("only \"=\", \"!=\", \"in\" , \"not in\" and \"like\"", "allowed for condition", ", I've got", object@condition)) } }else{ ## condition has to be = < > >= <= if(!any(c("=", ">", "<", ">=", "<=", "in", "not in")==object@condition)){ return(paste("only \"=\", \">\", \"<\", \">=\", \"<=\" , \"in\" and \"not in\"", " are allowed for condition, I've got", object@condition)) } } if(length(object@value) > 1){ if(any(!object@condition %in% c("in", "not in"))) return(paste("only \"in\" and \"not in\" are allowed if value", "is a vector with more than one value!")) } if(!object@.valueIsCharacter){ vals <- object@value if(length(vals) == 1){ if(vals == ""){ vals <- "0" } } ## value has to be numeric!!! suppressWarnings( if(any(is.na(is.numeric(vals)))) return(paste("value has to be numeric!!!")) ) } return(TRUE) } setValidity("BasicFilter", validateConditionFilter) setMethod("initialize", "BasicFilter", function(.Object, ...){ OK <- validateConditionFilter(.Object) if(class(OK)=="character"){ stop(OK) } callNextMethod(.Object, ...) }) .where <- function(object, db=NULL){ if(is.null(db)){ Vals <- value(object) }else{ Vals <- value(object, db) } ## if not a number we have to single quote! if(object@.valueIsCharacter){ Vals <- sQuote(gsub(unique(Vals),pattern="'",replacement="''")) }else{ Vals <- unique(Vals) } ## check, if there are more than one, concatenate in that case on put () aroung if(length(Vals) > 1){ Vals <- paste0("(", paste(Vals, collapse=",") ,")") } return(paste(condition(object), Vals)) } setMethod("where", signature(object="BasicFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ return(.where(object)) }) setMethod("where", signature(object="BasicFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ return(.where(object, db=db)) }) setMethod("where", signature(object="BasicFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ return(.where(object, db=db)) }) setMethod("condition", "BasicFilter", function(x, ...){ if(length(unique(value(x))) > 1){ if(x@condition=="in" | x@condition=="not in") return(x@condition) if(x@condition=="!="){ return("not in") }else if(x@condition=="="){ return("in") }else{ stop("With more than 1 value only conditions \"=\" and \"!=\" are allowed!") } }else{ ## check first if we do have "in" or "not in" and if ## cast it to a = and != respectively if(x@condition=="in") return("=") if(x@condition=="not in") return("!=") return(x@condition) } }) setReplaceMethod("condition", "BasicFilter", function(x, value){ if(x@.valueIsCharacter){ allowed <- c("=", "!=", "in", "not in", "like") if(!any(allowed == value)){ stop("Only ", paste(allowed, collapse=", "), " are allowed if the value from", " the filter is of type character!") } if(value == "=" & length(x@value) > 1) value <- "in" if(value == "!=" & length(x@value) > 1) value <- "not in" if(value == "in" & length(x@value) == 1) value <- "=" if(value == "not in" & length(x@value) == 1) value <- "!=" }else{ allowed <- c("=", ">", "<", ">=", "<=") if(!any(allowed == value)){ stop("Only ", paste(allowed, collapse=", "), " are allowed if the value from", " the filter is numeric!") } } x@condition <- value validObject(x) return(x) }) setMethod("value", signature(x="BasicFilter", db="missing"), function(x, db, ...){ return(x@value) }) setMethod("value", signature(x="BasicFilter", db="EnsDb"), function(x, db, ...){ return(x@value) }) setReplaceMethod("value", "BasicFilter", function(x, value){ if(is.numeric(value)){ x@.valueIsCharacter <- FALSE }else{ x@.valueIsCharacter <- TRUE } x@value <- as.character(value) ## Checking if condition matches the value. if(length(value) > 1){ if(x@condition == "=") x@condition <- "in" if(x@condition == "!=") x@condition <- "not in" }else{ if(x@condition == "in") x@condition <- "=" if(x@condition == "not in") x@condition <- "!=" } ## Test validity validObject(x) return(x) }) ## setMethod("requireTable", "EnsFilter", function(object, ...){ ## return(object@required.table) ## }) setMethod("print", "BasicFilter", function(x, ...){ show(x) }) setMethod("show", "BasicFilter", function(object){ cat("| Object of class:", class(object), "\n") cat("| condition:", object@condition, "\n") cat("| value:", value(object), "\n") }) ##*********************************************************************** ## ## where for a list. ## ##*********************************************************************** setMethod("where", signature(object="list",db="missing", with.tables="missing"), function(object, db, with.tables, ...){ wherequery <- paste(" where", paste(unlist(lapply(object, where)), collapse=" and ")) return(wherequery) }) setMethod("where", signature(object="list",db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ wherequery <- paste(" where", paste(unlist(lapply(object, where, db)), collapse=" and ")) return(wherequery) }) setMethod("where", signature(object="list",db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ wherequery <- paste(" where", paste(unlist(lapply(object, where, db, with.tables=with.tables)), collapse=" and ")) return(wherequery) }) ##*********************************************************************** ## ## Methods for GeneidFilter classes. ## ##*********************************************************************** setMethod("where", signature(object="GeneidFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ suff <- callNextMethod() return(paste(column(object), suff)) }) setMethod("column", signature(object="GeneidFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ return("gene_id") }) setMethod("where", signature(object="GeneidFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(where(object, db, with.tables=tn)) }) setMethod("column", signature(object="GeneidFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(column(object, db, with.tables=tn)) }) setMethod("where", signature(object="GeneidFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ suff <- callNextMethod() return(paste(column(object, db, with.tables), suff)) }) setMethod("column", signature("GeneidFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ return(unlist(prefixColumns(db, column(object), with.tables=with.tables), use.names=FALSE)) }) ##*********************************************************************** ## ## Methods for EntrezidFilter classes. ## ##*********************************************************************** setMethod("where", signature(object="EntrezidFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ suff <- callNextMethod() return(paste(column(object), suff)) }) setMethod("column", signature(object="EntrezidFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ return("entrezid") }) setMethod("where", signature(object="EntrezidFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(where(object, db, with.tables=tn)) }) setMethod("column", signature(object="EntrezidFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(column(object, db, with.tables=tn)) }) setMethod("where", signature(object="EntrezidFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ suff <- callNextMethod() return(paste(column(object, db, with.tables=with.tables), suff)) }) setMethod("column", signature("EntrezidFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ return(unlist(prefixColumns(db, column(object), with.tables=with.tables), use.names=FALSE)) }) ##*********************************************************************** ## ## Methods for GenebiotypeFilter classes. ## ##*********************************************************************** setMethod("where", signature(object="GenebiotypeFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ suff <- callNextMethod() return(paste(column(object), suff)) }) setMethod("column", signature(object="GenebiotypeFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ return("gene_biotype") }) setMethod("where", signature(object="GenebiotypeFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(where(object, db, with.tables=tn)) }) setMethod("column", signature(object="GenebiotypeFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(column(object, db, with.tables=tn)) }) setMethod("where", signature(object="GenebiotypeFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ suff <- callNextMethod() return(paste(column(object, db, with.tables=with.tables), suff)) }) setMethod("column", signature(object="GenebiotypeFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ return(unlist(prefixColumns(db, column(object), with.tables=with.tables), use.names=FALSE)) }) ##*********************************************************************** ## ## Methods for GenenameFilter classes. ## ##*********************************************************************** setMethod("where", signature(object="GenenameFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ suff <- callNextMethod() return(paste(column(object), suff)) }) setMethod("column", signature(object="GenenameFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ return("gene_name") }) setMethod("where", signature(object="GenenameFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(where(object, db, with.tables=tn)) }) setMethod("column", signature(object="GenenameFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(column(object, db, with.tables=tn)) }) setMethod("where", signature(object="GenenameFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables="character", ...){ suff <- callNextMethod() return(paste(column(object, db, with.tables=with.tables), suff)) }) setMethod("column", signature(object="GenenameFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ return(unlist(prefixColumns(db, column(object), with.tables=with.tables), use.names=FALSE)) }) ##*********************************************************************** ## ## Methods for TxidFilter classes. ## ##*********************************************************************** setMethod("where", signature(object="TxidFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ suff <- callNextMethod() return(paste(column(object), suff)) }) setMethod("column", signature(object="TxidFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ return("tx_id") }) setMethod("where", signature(object="TxidFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(where(object, db, with.tables=tn)) }) setMethod("column", signature(object="TxidFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(column(object, db, with.tables=tn)) }) setMethod("where", signature(object="TxidFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ suff <- callNextMethod() return(paste(column(object, db, with.tables=with.tables), suff)) }) setMethod("column", signature(object="TxidFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ return(unlist(prefixColumns(db, column(object), with.tables=with.tables), use.names=FALSE)) }) ##*********************************************************************** ## ## Methods for TxbiotypeFilter classes. ## ##*********************************************************************** setMethod("where", signature(object="TxbiotypeFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ suff <- callNextMethod() return(paste(column(object), suff)) }) setMethod("column", signature(object="TxbiotypeFilter", db="missing", with.tables="missing"), function(object, db, with.tables...){ return("tx_biotype") }) setMethod("where", signature(object="TxbiotypeFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(where(object, db, with.tables=tn)) }) setMethod("column", signature(object="TxbiotypeFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(column(object, db, with.tables=tn)) }) setMethod("where", signature(object="TxbiotypeFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ suff <- callNextMethod() return(paste(column(object, db, with.tables=with.tables), suff)) }) setMethod("column", signature(object="TxbiotypeFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ return(unlist(prefixColumns(db, column(object), with.tables=with.tables), use.names=FALSE)) }) ##*********************************************************************** ## ## Methods for ExonidFilter classes. ## ##*********************************************************************** setMethod("where", signature(object="ExonidFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ suff <- callNextMethod() return(paste(column(object), suff)) }) setMethod("column", signature(object="ExonidFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ return("exon_id") }) setMethod("where", signature(object="ExonidFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(where(object, db, with.tables=tn)) }) setMethod("column", signature(object="ExonidFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(column(object, db, with.tables=tn)) }) setMethod("where", signature(object="ExonidFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ suff <- callNextMethod() return(paste(column(object, db, with.tables=with.tables), suff)) }) setMethod("column", signature(object="ExonidFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ return(unlist(prefixColumns(db, column(object), with.tables=with.tables), use.names=FALSE)) }) ##*********************************************************************** ## ## Methods for ExonrankFilter classes. ## ##*********************************************************************** setMethod("where", signature(object="ExonrankFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ suff <- callNextMethod() return(paste(column(object), suff)) }) setMethod("column", signature(object="ExonrankFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ return("exon_idx") }) setMethod("where", signature(object="ExonrankFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(where(object, db, with.tables=tn)) }) setMethod("column", signature(object="ExonrankFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(column(object, db, with.tables=tn)) }) setMethod("where", signature(object="ExonrankFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ suff <- callNextMethod() return(paste(column(object, db, with.tables=with.tables), suff)) }) setMethod("column", signature(object="ExonrankFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ return(unlist(prefixColumns(db, column(object), with.tables=with.tables), use.names=FALSE)) }) setReplaceMethod("value", "ExonrankFilter", function(x, value){ if(any(is.na(as.numeric(value)))) stop("Argument 'value' has to be numeric!") x@value <- value validObject(x) return(x) }) ##*********************************************************************** ## ## Methods for SeqnameFilter classes. ## ##*********************************************************************** setMethod("where", signature(object="SeqnameFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ suff <- callNextMethod() return(paste(column(object), suff)) }) setMethod("column", signature(object="SeqnameFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ return("seq_name") }) setMethod("where", signature(object="SeqnameFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(where(object, db, with.tables=tn)) }) setMethod("column", signature(object="SeqnameFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(column(object, db, with.tables=tn)) }) setMethod("where", signature(object="SeqnameFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ suff <- callNextMethod() return(paste(column(object, db, with.tables=with.tables), suff)) }) setMethod("column", signature(object="SeqnameFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ return(unlist(prefixColumns(db, column(object), with.tables=with.tables), use.names=FALSE)) }) ## Overwriting the value method allows us to fix chromosome names (e.g. with prefix chr) ## to be usable for EnsDb and Ensembl based chromosome names (i.e. without chr). setMethod("value", signature(x="SeqnameFilter", db="EnsDb"), function(x, db, ...){ val <- formatSeqnamesForQuery(db, value(x)) if(any(is.na(val))){ stop("A value of is not allowed for a SeqnameFilter!") } return(val) ##return(ucscToEns(value(x))) }) ##*********************************************************************** ## ## Methods for SeqstrandFilter classes. ## ##*********************************************************************** setMethod("where", signature(object="SeqstrandFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ suff <- callNextMethod() return(paste(column(object), suff)) }) setMethod("column", signature(object="SeqstrandFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ return("seq_strand") }) setMethod("where", signature(object="SeqstrandFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(where(object, db, with.tables=tn)) }) setMethod("column", signature(object="SeqstrandFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(column(object, db, with.tables=tn)) }) setMethod("where", signature(object="SeqstrandFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ suff <- callNextMethod() return(paste(column(object, db, with.tables=with.tables), suff)) }) setMethod("column", signature(object="SeqstrandFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ return(unlist(prefixColumns(db, column(object), with.tables=with.tables), use.names=FALSE)) }) ##*********************************************************************** ## ## Methods for SeqstartFilter classes. ## ##*********************************************************************** setMethod("where", signature(object="SeqstartFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ suff <- callNextMethod() return(paste(column(object), suff)) }) setMethod("column", signature(object="SeqstartFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ ## assuming that we follow the naming convention: ## _seq_end for the naming of the database columns. feature <- object@feature feature <- match.arg(feature, c("gene", "transcript", "exon", "tx")) if(object@feature=="transcript") feature <- "tx" return(paste0(feature, "_seq_start")) }) setMethod("where", signature(object="SeqstartFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(where(object, db, with.tables=tn)) }) setMethod("column", signature(object="SeqstartFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(column(object, db, with.tables=tn)) }) setMethod("where", signature(object="SeqstartFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ suff <- callNextMethod() return(paste(column(object, db, with.tables=with.tables), suff)) }) setMethod("column", signature(object="SeqstartFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ return(unlist(prefixColumns(db, column(object), with.tables=with.tables), use.names=FALSE)) }) ##*********************************************************************** ## ## Methods for SeqendFilter classes. ## ##*********************************************************************** setMethod("where", signature(object="SeqendFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ suff <- callNextMethod() return(paste(column(object), suff)) }) setMethod("column", signature(object="SeqendFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ ## assuming that we follow the naming convention: ## _seq_end for the naming of the database columns. feature <- object@feature feature <- match.arg(feature, c("gene", "transcript", "exon", "tx")) if(object@feature=="transcript") feature <- "tx" return(paste0(feature, "_seq_end")) }) setMethod("where", signature(object="SeqendFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(where(object, db, with.tables=tn)) }) setMethod("column", signature(object="SeqendFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(column(object, db, with.tables=tn)) }) setMethod("where", signature(object="SeqendFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ suff <- callNextMethod() return(paste(column(object, db, with.tables=with.tables), suff)) }) setMethod("column", signature(object="SeqendFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ return(unlist(prefixColumns(db, column(object), with.tables=with.tables), use.names=FALSE)) }) ###============================================================ ## Methods for GRangesFilter ## + show ## + condition ## + value ## + where ## + column ## + start ## + end ## + seqnames ## + strand ###------------------------------------------------------------ ## Overwrite the validation method. setValidity("GRangesFilter", function(object){ if(!any(object@location == c("within", "overlapping") )){ return(paste0("Argument condition should be either 'within' or 'overlapping'! Got ", object@location, "!")) } ## GRanges has to have valid values for start, end and seqnames! if(length(start(object)) == 0) return("start coordinate of the range is missing!") if(length(end(object)) == 0) return("end coordinate of the range is missing!") if(length(seqnames(object)) == 0) return("A valid seqname is required from the submitted GRanges!") return(TRUE) }) setMethod("show", "GRangesFilter", function(object){ cat("| Object of class:" , class(object), "\n") cat("| region:\n") cat("| + start:", paste0(start(object), collapse=", "), "\n") cat("| + end: ", paste0(end(object), collapse=", "), "\n") cat("| + seqname:", paste0(seqnames(object), collapse=", "), "\n") cat("| + strand: ", paste0(strand(object), collapse=", "), "\n") cat("| condition:", condition(object), "\n") }) setMethod("condition", "GRangesFilter", function(x, ...){ return(x@location) }) setReplaceMethod("condition", "GRangesFilter", function(x, value){ value <- match.arg(value, c("within", "overlapping")) x@location <- value validObject(x) return(x) }) setMethod("value", signature(x="GRangesFilter", db="missing"), function(x, db, ...){ return(x@grange) }) setMethod("value", signature(x="GRangesFilter", db="EnsDb"), function(x, db, ...){ return(x@grange) }) setMethod("start", signature(x="GRangesFilter"), function(x, ...){ return(start(value(x))) }) setMethod("end", signature(x="GRangesFilter"), function(x, ...){ return(end(value(x))) }) setMethod("strand", signature(x="GRangesFilter"), function(x, ...){ strnd <- as.character(strand(value(x))) return(strnd) }) setMethod("seqnames", signature(x="GRangesFilter"), function(x){ return(as.character(seqnames(value(x)))) }) setMethod("seqlevels", signature(x="GRangesFilter"), function(x){ return(seqlevels(value(x))) }) ## The column method for GRangesFilter returns all columns required for the query, i.e. ## the _seq_start, _seq_end for the feature, seq_name and seq_strand. ## Note: this method has to return a named vector! setMethod("column", signature(object="GRangesFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ ## assuming that we follow the naming convention: ## _seq_end for the naming of the database columns. feature <- object@feature feature <- match.arg(feature, c("gene", "transcript", "exon", "tx")) if(object@feature=="transcript") feature <- "tx" cols <- c(start=paste0(feature, "_seq_start"), end=paste0(feature, "_seq_end"), seqname="seq_name", strand="seq_strand") return(cols) }) setMethod("column", signature(object="GRangesFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(column(object, db, with.tables=tn)) }) ## Providing also the columns. setMethod("column", signature(object="GRangesFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ cols <- unlist(prefixColumns(db, column(object), with.tables=with.tables), use.names=FALSE) ## We have to give the vector the required names! names(cols) <- 1:length(cols) names(cols)[grep(cols, pattern="seq_name")] <- "seqname" names(cols)[grep(cols, pattern="seq_strand")] <- "strand" names(cols)[grep(cols, pattern="seq_start")] <- "start" names(cols)[grep(cols, pattern="seq_end")] <- "end" return(cols[c("start", "end", "seqname", "strand")]) }) ## Where for GRangesFilter only. setMethod("where", signature(object="GRangesFilter", db="missing", with.tables="missing"), function(object, db, with.tables, ...){ ## Get the names of the columns we're going to query. cols <- column(object) query <- buildWhereForGRanges(object, cols) return(query) }) setMethod("where", signature(object="GRangesFilter", db="EnsDb", with.tables="missing"), function(object, db, with.tables, ...){ tn <- names(listTables(db)) return(where(object, db, with.tables=tn)) }) setMethod("where", signature(object="GRangesFilter", db="EnsDb", with.tables="character"), function(object, db, with.tables, ...){ cols <- column(object, db, with.tables) query <- buildWhereForGRanges(object, cols, db=db) return(query) }) ## grf: GRangesFilter buildWhereForGRanges <- function(grf, columns, db=NULL){ condition <- condition(grf) if(!any(condition == c("within", "overlapping"))) stop(paste0("'condition' for GRangesFilter should either be ", "'within' or 'overlapping', got ", condition, ".")) if(is.null(names(columns))){ stop(paste0("The vector with the required column names for the", " GRangesFilter query has to have names!")) } if(!all(c("start", "end", "seqname", "strand") %in% names(columns))) stop(paste0("'columns' has to be a named vector with names being ", "'start', 'end', 'seqname', 'strand'!")) ## Build the query to fetch all features that are located within the range quers <- sapply(value(grf), function(z){ if(!is.null(db)){ seqn <- formatSeqnamesForQuery(db, as.character(seqnames(z))) }else{ seqn <- as.character(seqnames(z)) } if(condition == "within"){ query <- paste0(columns["start"], " >= ", start(z), " and ", columns["end"], " <= ", end(z), " and ", columns["seqname"], " == '", seqn, "'") } ## Build the query to fetch all features (partially) overlapping the range. This ## includes also all features (genes or transcripts) that have an intron at that ## position. if(condition == "overlapping"){ query <- paste0(columns["start"], " <= ", end(z), " and ", columns["end"], " >= ", start(z), " and ", columns["seqname"], " = '", seqn, "'") } ## Include the strand, if it's not "*" if(as.character(strand(z)) != "*"){ query <- paste0(query, " and ", columns["strand"], " = ", strand2num(as.character(strand(z)))) } return(query) }) if(length(quers) > 1) quers <- paste0("(", quers, ")") query <- paste0(quers, collapse=" or ") ## Collapse now the queries. return(query) } ## map chromosome strand... strand2num <- function(x){ if(x == "+" | x == "-"){ return(as.numeric(paste0(x, 1))) }else{ stop("Only '+' and '-' supported!") } } num2strand <- function(x){ if(x < 0){ return("-") }else{ return("+") } } ##*********************************************************************** ## ## Methods for SymbolFilter classes. ## ##*********************************************************************** setMethod("where", signature(object = "SymbolFilter", db = "missing", with.tables = "missing"), function(object, db, with.tables, ...) { suff <- callNextMethod() return(paste(column(object), suff)) }) setMethod("column", signature(object = "SymbolFilter", db = "missing", with.tables = "missing"), function(object, db, with.tables, ...) { return("symbol") }) setMethod("where", signature(object = "SymbolFilter", db = "EnsDb", with.tables = "missing"), function(object, db, with.tables, ...) { tn <- names(listTables(db)) return(where(object, db, with.tables = tn)) }) setMethod("column", signature(object = "SymbolFilter", db = "EnsDb", with.tables = "missing"), function(object, db, with.tables, ...) { tn <- names(listTables(db)) return(column(object, db, with.tables = tn)) }) setMethod("where", signature(object = "SymbolFilter", db = "EnsDb", with.tables="character"), function(object, db, with.tables = "character", ...) { suff <- callNextMethod() return(paste(column(object, db, with.tables = with.tables), suff)) }) setMethod("column", signature(object = "SymbolFilter", db = "EnsDb", with.tables = "character"), function(object, db, with.tables, ...) { return(unlist(prefixColumns(db, "gene_name", with.tables = with.tables), use.names = FALSE)) }) ##*********************************************************************** ## ## Methods for OnlyCodingTx classes. ## ##*********************************************************************** setMethod("where", signature(object = "OnlyCodingTx", db = "EnsDb", with.tables = "missing"), function(object, db, with.tables, ...) { tn <- names(listTables(db)) return(where(object, db, with.tables = tn)) }) setMethod("column", signature(object = "OnlyCodingTx", db = "EnsDb", with.tables = "missing"), function(object, db, with.tables, ...) { tn <- names(listTables(db)) return(column(object, db, with.tables = tn)) }) setMethod("where", signature(object = "OnlyCodingTx", db = "EnsDb", with.tables="character"), function(object, db, with.tables = "character", ...) { ## Hard coded. return("tx.tx_cds_seq_start is not null") }) setMethod("column", signature(object = "OnlyCodingTx", db = "EnsDb", with.tables = "character"), function(object, db, with.tables, ...) { return("tx.tx_cds_seq_start") }) ensembldb/R/Methods.R0000644000175400017540000021566413012705075015500 0ustar00biocbuildbiocbuild##*********************************************************************** ## ## Methods for EnsDb classes ## ##*********************************************************************** setMethod("show", "EnsDb", function(object) { if (is.null(object@ensdb)) { cat("Dash it! Got an empty thing!\n") } else { info <- dbGetQuery(object@ensdb, "select * from metadata") cat("EnsDb for Ensembl:\n") if (inherits(object@ensdb, "SQLiteConnection")) cat(paste0("|Backend: SQLite\n")) if (inherits(object@ensdb, "MySQLConnection")) cat(paste0("|Backend: MySQL\n")) for (i in 1:nrow(info)) { cat(paste0("|", info[ i, "name" ], ": ", info[ i, "value" ], "\n")) } ## gene and transcript info. cat(paste0("| No. of genes: ", dbGetQuery(object@ensdb, "select count(distinct gene_id) from gene")[1, 1], ".\n")) cat(paste0("| No. of transcripts: ", dbGetQuery(object@ensdb, "select count(distinct tx_id) from tx")[1, 1], ".\n")) } }) setMethod("organism", "EnsDb", function(object){ Species <- .getMetaDataValue(object@ensdb, "Organism") ## reformat the e.g. homo_sapiens string into Homo sapiens # Species <- gsub(Species, pattern="_", replacement=" ", fixed=TRUE) Species <- .organismName(Species) return(Species) }) setMethod("metadata", "EnsDb", function(x, ...){ Res <- dbGetQuery(dbconn(x), "select * from metadata") return(Res) }) ##### ## Validation ## validateEnsDb <- function(object){ ## check if the database contains all required tables... if(!is.null(object@ensdb)){ OK <- dbHasRequiredTables(object@ensdb) if (is.character(OK)) return(OK) OK <- dbHasValidTables(object@ensdb) if (is.character(OK)) return(OK) } return(TRUE) } setValidity("EnsDb", validateEnsDb) setMethod("initialize", "EnsDb", function(.Object,...){ OK <- validateEnsDb(.Object) if(class(OK)=="character"){ stop(OK) } callNextMethod(.Object, ...) }) ### connection: ## returns the connection object to the SQL database setMethod("dbconn", "EnsDb", function(x){ return(x@ensdb) }) ### ensemblVersion ## returns the ensembl version of the package. setMethod("ensemblVersion", "EnsDb", function(x){ eVersion <- getMetadataValue(x, "ensembl_version") return(eVersion) }) ### getMetadataValue ## returns the metadata value for the specified name/key setMethod("getMetadataValue", "EnsDb", function(x, name){ if(missing(name)) stop("Argument name has to be specified!") return(metadata(x)[metadata(x)$name==name, "value"]) }) ### seqinfo ## returns the sequence/chromosome information from the database. setMethod("seqinfo", "EnsDb", function(x){ Chrs <- dbGetQuery(dbconn(x), "select * from chromosome") Chr.build <- .getMetaDataValue(dbconn(x), "genome_build") Chrs$seq_name <- formatSeqnamesFromQuery(x, Chrs$seq_name) SI <- Seqinfo(seqnames=Chrs$seq_name, seqlengths=Chrs$seq_length, isCircular=Chrs$is_circular==1, genome=Chr.build) return(SI) }) ### seqlevels setMethod("seqlevels", "EnsDb", function(x){ Chrs <- dbGetQuery(dbconn(x), "select distinct seq_name from chromosome") Chrs <- formatSeqnamesFromQuery(x, Chrs$seq_name) return(Chrs) }) ### getGenomeFaFile ## queries the dna.toplevel.fa file from AnnotationHub matching the current ## Ensembl version ## Update: if we can't find a FaFile matching the Ensembl version we suggest ones ## that might match. setMethod("getGenomeFaFile", "EnsDb", function(x, pattern="dna.toplevel.fa"){ ah <- AnnotationHub() ## Reduce the AnnotationHub to species, provider and genome version. ah <- .reduceAH(ah, organism=organism(x), dataprovider="Ensembl", genome=unique(genome(x))) if(length(ah) == 0) stop("Can not find any ressources in AnnotationHub for organism: ", organism(x), ", data provider: Ensembl and genome version: ", unique(genome(x)), "!") ## Reduce to all Fasta files with toplevel or primary_assembly. ah <- ah[ah$rdataclass == "FaFile", ] if(length(ah) == 0) stop("No FaFiles available in AnnotationHub for organism: ", organism(x), ", data provider: Ensembl and genome version: ", unique(genome(x)), "! You might also try to use the", " 'getGenomeTwoBitFile' method instead.") ## Reduce to dna.toplevel or dna.primary_assembly. idx <- c(grep(ah$title, pattern="dna.toplevel"), grep(ah$title, pattern="dna.primary_assembly")) if(length(idx) == 0) stop("No genome assembly fasta file available for organism: ", organism(x), ", data provider: Ensembl and genome version: ", unique(genome(x)), "!") ah <- ah[idx, ] ## Get the Ensembl version from the source url. ensVers <- .ensVersionFromSourceUrl(ah$sourceurl) if(any(ensVers == ensemblVersion(x))){ ## Got it. itIs <- which(ensVers == ensemblVersion(x)) }else{ ## Get the "closest" one. diffs <- abs(ensVers - as.numeric(ensemblVersion(x))) itIs <- which(diffs == min(diffs))[1] message("Returning the Fasta file for Ensembl version ", ensVers[itIs], " since no file for Ensembl version ", ensemblVersion(x), " is available.") } ## Getting the ressource. Dna <- ah[[names(ah)[itIs]]] ## generate an index if none is available if(is.na(index(Dna))){ indexFa(Dna) Dna <- FaFile(path(Dna)) } return(Dna) }) ## Just restricting the Annotation Hub to entries matching the species and the ## genome; not yet the Ensembl version. .reduceAH <- function(ah, organism=NULL, dataprovider="Ensembl", genome=NULL){ if(!is.null(dataprovider)) ah <- ah[ah$dataprovider == dataprovider, ] if(!is.null(organism)) ah <- ah[ah$species == organism, ] if(!is.null(genome)) ah <- ah[ah$genome == genome, ] return(ah) } .ensVersionFromSourceUrl <- function(url){ url <- strsplit(url, split="/", fixed=TRUE) ensVers <- unlist(lapply(url, function(z){ idx <- grep(z, pattern="^release") if(length(idx) == 0) return(-1) return(as.numeric(unlist(strsplit(z[idx], split="-"))[2])) })) return(ensVers) } ####============================================================ ## getGenomeTwoBitFile ## ## Search and retrieve a genomic DNA resource through a TwoBitFile ## from AnnotationHub. ####------------------------------------------------------------ setMethod("getGenomeTwoBitFile", "EnsDb", function(x){ ah <- AnnotationHub() ## Reduce the AnnotationHub to species, provider and genome version. ah <- .reduceAH(ah, organism=organism(x), dataprovider="Ensembl", genome=unique(genome(x))) if(length(ah) == 0) stop("Can not find any ressources in AnnotationHub for organism: ", organism(x), ", data provider: Ensembl and genome version: ", unique(genome(x)), "!") ## Reduce to all Fasta files with toplevel or primary_assembly. ah <- ah[ah$rdataclass == "TwoBitFile", ] if(length(ah) == 0) stop("No TwoBitFile available in AnnotationHub for organism: ", organism(x), ", data provider: Ensembl and genome version: ", unique(genome(x)), "!") ## Reduce to dna.toplevel or dna.primary_assembly. idx <- c(grep(ah$title, pattern="dna.toplevel"), grep(ah$title, pattern="dna.primary_assembly")) if(length(idx) == 0) stop("No genome assembly fasta file available for organism: ", organism(x), ", data provider: Ensembl and genome version: ", unique(genome(x)), "!") ah <- ah[idx, ] ## Get the Ensembl version from the source url. ensVers <- .ensVersionFromSourceUrl(ah$sourceurl) if(any(ensVers == ensemblVersion(x))){ ## Got it. itIs <- which(ensVers == ensemblVersion(x)) }else{ ## Get the "closest" one. diffs <- abs(ensVers - as.numeric(ensemblVersion(x))) itIs <- which(diffs == min(diffs))[1] message("Returning the TwoBit file for Ensembl version ", ensVers[itIs], " since no file for Ensembl version ", ensemblVersion(x), " is available.") } ## Getting the ressource. Dna <- ah[[names(ah)[itIs]]] return(Dna) }) ### listTables ## returns a named list with database table columns setMethod("listTables", "EnsDb", function(x, ...){ if(length(x@tables)==0){ tables <- dbListTables(dbconn(x)) ## Quick fix for EnsDbs containing also protein data (issue #30): tables <- tables[!(tables %in% c("protein", "uniprot", "protein_domain"))] ## read the columns for these tables. Tables <- vector(length=length(tables), "list") for(i in 1:length(Tables)){ Tables[[ i ]] <- colnames(dbGetQuery(dbconn(x), paste0("select * from ", tables[ i ], " limit 1"))) } names(Tables) <- tables x@tables <- Tables } Tab <- x@tables Tab <- Tab[tablesByDegree(x, tab=names(Tab))] ## Manually add tx_name as a "virtual" column; getWhat will insert the tx_id into that. Tab$tx <- unique(c(Tab$tx, "tx_name")) ## Manually add the symbol as a "virtual" column. Tab$gene <- unique(c(Tab$gene, "symbol")) return(Tab) }) ### listColumns ## lists all columns. setMethod("listColumns", "EnsDb", function(x, table, skip.keys=TRUE, ...){ if(length(x@tables)==0){ tables <- dbListTables(dbconn(x)) ## Quick fix for EnsDbs containing also protein data (issue #30): tables <- tables[!(tables %in% c("protein", "uniprot", "protein_domain"))] ## read the columns for these tables. Tables <- vector(length=length(tables), "list") for(i in 1:length(Tables)){ Tables[[ i ]] <- colnames(dbGetQuery(dbconn(x), paste0("select * from ", tables[ i ], " limit 1"))) } names(Tables) <- tables x@tables <- Tables } Tab <- x@tables ## Manually add tx_name as a "virtual" column; getWhat will insert the tx_id into that. Tab$tx <- unique(c(Tab$tx, "tx_name")) ## Manually add the symbol as a "virtual" column. Tab$gene <- unique(c(Tab$gene, "symbol")) if(!missing(table)){ columns <- Tab[[ table ]] }else{ columns <- unlist(Tab, use.names=FALSE) } if(skip.keys){ ## remove everything that has a _pk or _fk... idx <- grep(columns, pattern="_fk$") if(length(idx) > 0) columns <- columns[ -idx ] idx <- grep(columns, pattern="_pk$") if(length(idx) > 0) columns <- columns[ -idx ] } return(columns) }) setMethod("listGenebiotypes", "EnsDb", function(x, ...){ return(dbGetQuery(dbconn(x), "select distinct gene_biotype from gene")[,1]) }) setMethod("listTxbiotypes", "EnsDb", function(x, ...){ return(dbGetQuery(dbconn(x), "select distinct tx_biotype from tx")[,1]) }) ### cleanColumns ## checks columns and removes all that are not present in database tables ## the method checks internally whether the columns are in the full form, ## i.e. gene.gene_id (.) setMethod("cleanColumns", "EnsDb", function(x, columns, ...){ if(missing(columns)) stop("No columns submitted!") ## vote of the majority full.name <- length(grep(columns, pattern=".", fixed=TRUE)) > floor(length(columns) /2) if(full.name){ suppressWarnings( full.columns <- unlist(prefixColumns(x, unlist(listTables(x)), clean=FALSE), use.names=TRUE) ) bm <- columns %in% full.columns removed <- columns[ !bm ] }else{ bm <- columns %in% unlist(listTables(x)[ c("gene", "tx", "exon", "tx2exon", "chromosome") ]) removed <- columns[ !bm ] } if(length(removed) > 0){ warning("Columns ", paste(sQuote(removed), collapse=", "), " are not valid and have been removed") } return(columns[ bm ]) }) ### tablesForColumns ## returns the tables for the specified columns. setMethod("tablesForColumns", "EnsDb", function(x, columns, ...){ if(missing(columns)) stop("No columns submitted!") bm <- unlist(lapply(listTables(x), function(z){ return(any(z %in% columns)) })) if(!any(bm)) return(NULL) Tables <- names(bm)[ bm ] Tables <- Tables[ !(Tables %in% c("metadata")) ] return(Tables) }) ## returns the table names ordered by degree, i.e. edges to other tables setMethod("tablesByDegree", "EnsDb", function(x, tab=names(listTables(x)), ...){ ## ## to do this with a graph: ## DBgraph <- graphNEL(nodes=c("gene", "tx", "tx2exon", "exon", "chromosome", "information"), ## edgeL=list(gene=c("tx", "chromosome"), ## tx=c("gene", "tx2exon"), ## tx2exon=c("tx", "exon"), ## exon="tx2exon", ## chromosome="gene" ## )) ## Tab <- names(sort(degree(DBgraph), decreasing=TRUE)) Table.order <- c(gene=1, tx=2, tx2exon=3, exon=4, chromosome=5, metadata=6) ##Table.order <- c(gene=2, tx=1, tx2exon=3, exon=4, chromosome=5, metadata=6) Tab <- tab[ order(Table.order[ tab ]) ] return(Tab) }) ### genes: ## get genes from the database. setMethod("genes", "EnsDb", function(x, columns=listColumns(x, "gene"), filter, order.by="", order.type="asc", return.type="GRanges"){ return.type <- match.arg(return.type, c("data.frame", "GRanges", "DataFrame")) columns <- unique(c(columns, "gene_id")) ## if return.type is GRanges we require columns: seq_name, gene_seq_start ## and gene_seq_end and seq_strand if(return.type=="GRanges"){ columns <- unique(c(columns, c("gene_seq_start", "gene_seq_end", "seq_name", "seq_strand"))) } if(missing(filter)){ filter=list() }else{ filter <- checkFilter(filter) } filter <- setFeatureInGRangesFilter(filter, "gene") ## Eventually add columns for the filters: columns <- addFilterColumns(columns, filter, x) retColumns <- columns ## If we don't have an order.by define one. if(all(order.by == "")){ order.by <- NULL if (any(columns == "seq_name")) order.by <- c(order.by, "seq_name") if( any(columns == "gene_seq_start")) order.by <- c(order.by, "gene_seq_start") if(is.null(order.by)) order.by <- "" } Res <- getWhat(x, columns=columns, filter=filter, order.by=order.by, order.type=order.type) if(return.type=="data.frame" | return.type=="DataFrame"){ notThere <- !(retColumns %in% colnames(Res)) if(any(notThere)) warning(paste0("Columns ", paste(retColumns[notThere], collapse=", "), " not present in the result data.frame!")) retColumns <- retColumns[!notThere] Res <- Res[, retColumns] if(return.type=="DataFrame") Res <- DataFrame(Res) return(Res) } if(return.type=="GRanges"){ metacols <- columns[ !(columns %in% c("seq_name", "seq_strand", "gene_seq_start", "gene_seq_end")) ] suppressWarnings( SI <- seqinfo(x) ) SI <- SI[as.character(unique(Res$seq_name))] GR <- GRanges(seqnames=Rle(Res$seq_name), ranges=IRanges(start=Res$gene_seq_start, end=Res$gene_seq_end), strand=Rle(Res$seq_strand), seqinfo=SI[as.character(unique(Res$seq_name))], Res[ , metacols, drop=FALSE ] ) names(GR) <- Res$gene_id return(GR) } }) ### transcripts: ## get transcripts from the database. setMethod("transcripts", "EnsDb", function(x, columns=listColumns(x, "tx"), filter, order.by="", order.type="asc", return.type="GRanges"){ return.type <- match.arg(return.type, c("data.frame", "GRanges", "DataFrame")) columns <- unique(c(columns, "tx_id")) ## if return.type is GRanges we require columns: seq_name, gene_seq_start ## and gene_seq_end and seq_strand if(return.type=="GRanges"){ columns <- unique(c(columns, c("tx_seq_start", "tx_seq_end", "seq_name", "seq_strand"))) } if(missing(filter)){ filter=list() }else{ filter <- checkFilter(filter) } filter <- setFeatureInGRangesFilter(filter, "tx") ## Eventually add columns for the filters: columns <- addFilterColumns(columns, filter, x) retColumns <- columns ## If we don't have an order.by define one. if(all(order.by == "")){ order.by <- NULL if(any(columns == "seq_name")) order.by <- c(order.by, "seq_name") if(any(columns == "tx_seq_start")) order.by <- c(order.by, "tx_seq_start") if(is.null(order.by)) order.by <- "" } Res <- getWhat(x, columns=columns, filter=filter, order.by=order.by, order.type=order.type) if(return.type=="data.frame" | return.type=="DataFrame"){ notThere <- !(retColumns %in% colnames(Res)) if(any(notThere)) warning(paste0("Columns ", paste(retColumns[notThere], collapse=", "), " not present in the result data.frame!")) retColumns <- retColumns[!notThere] Res <- Res[, retColumns] if(return.type=="DataFrame") Res <- DataFrame(Res) return(Res) } if(return.type=="GRanges"){ notThere <- !(columns %in% colnames(Res)) if(any(notThere)) warning(paste0("Columns ", paste(columns[notThere], collapse=", "), " not present in the result data.frame!")) columns <- columns[!notThere] metacols <- columns[ !(columns %in% c("seq_name", "seq_strand", "tx_seq_start", "tx_seq_end")) ] suppressWarnings( SI <- seqinfo(x) ) SI <- SI[as.character(unique(Res$seq_name))] GR <- GRanges(seqnames=Rle(Res$seq_name), ranges=IRanges(start=Res$tx_seq_start, end=Res$tx_seq_end), strand=Rle(Res$seq_strand), seqinfo=SI[as.character(unique(Res$seq_name))], Res[ , metacols, drop=FALSE ] ) names(GR) <- Res$tx_id return(GR) } }) ### promoters: ## get promoter regions from the database. setMethod("promoters", "EnsDb", function(x, upstream=2000, downstream=200, ...) { gr <- transcripts(x, ...) trim(suppressWarnings(promoters(gr, upstream=upstream, downstream=downstream))) } ) ### exons: ## get exons from the database. setMethod("exons", "EnsDb", function(x, columns=listColumns(x, "exon"), filter, order.by="", order.type="asc", return.type="GRanges"){ return.type <- match.arg(return.type, c("data.frame", "GRanges", "DataFrame")) if(!any(columns %in% c(listColumns(x, "exon"), "exon_idx"))){ ## have to have at least one column from the gene table... columns <- c(columns, "exon_id") } columns <- unique(c(columns, "exon_id")) ## if return.type is GRanges we require columns: seq_name, gene_seq_start ## and gene_seq_end and seq_strand if(return.type=="GRanges"){ columns <- unique(c(columns, c("exon_seq_start", "exon_seq_end", "seq_name", "seq_strand"))) } if(missing(filter)){ filter=list() }else{ filter <- checkFilter(filter) } filter <- setFeatureInGRangesFilter(filter, "exon") ## Eventually add columns for the filters: columns <- addFilterColumns(columns, filter, x) retColumns <- columns ## If we don't have an order.by define one. if (order.by == "") { order.by <- NULL if (any(columns == "seq_name")) order.by <- c(order.by, "seq_name") if (any(columns == "exon_seq_start")) order.by <- c(order.by, "exon_seq_start") if(is.null(order.by)) order.by <- "" } Res <- getWhat(x, columns=columns, filter=filter, order.by=order.by, order.type=order.type) if(return.type=="data.frame" | return.type=="DataFrame"){ notThere <- !(retColumns %in% colnames(Res)) if(any(notThere)) warning(paste0("Columns ", paste(retColumns[notThere], collapse=", "), " not present in the result data.frame!")) retColumns <- retColumns[!notThere] Res <- Res[, retColumns] if(return.type=="DataFrame") Res <- DataFrame(Res) return(Res) } if(return.type=="GRanges"){ notThere <- !(columns %in% colnames(Res)) if(any(notThere)) warning(paste0("Columns ", paste(columns[notThere], collapse=", "), " not present in the result data.frame!")) columns <- columns[!notThere] metacols <- columns[ !(columns %in% c("seq_name", "seq_strand", "exon_seq_start", "exon_seq_end")) ] suppressWarnings( SI <- seqinfo(x) ) SI <- SI[as.character(unique(Res$seq_name))] GR <- GRanges(seqnames=Rle(Res$seq_name), ranges=IRanges(start=Res$exon_seq_start, end=Res$exon_seq_end), strand=Rle(Res$seq_strand), seqinfo=SI[as.character(unique(Res$seq_name))], Res[ , metacols, drop=FALSE ] ) names(GR) <- Res$exon_id return(GR) } }) ## should return a GRangesList ## still considerably slower than the corresponding call in the GenomicFeatures package. setMethod("exonsBy", "EnsDb", function(x, by = c("tx", "gene"), columns = listColumns(x, "exon"), filter, use.names = FALSE) { by <- match.arg(by, c("tx", "gene")) bySuff <- "_id" if (use.names) { if (by == "tx") { use.names <- FALSE warning("Argument use.names ignored as no transcript names are available.") } else { columns <- unique(c(columns, "gene_name")) bySuff <- "_name" } } if (missing(filter)) { filter <- list() } else { filter <- checkFilter(filter) } ## We're applying eventual GRangesFilter to either gene or tx. filter <- setFeatureInGRangesFilter(filter, by) ## Eventually add columns for the filters: columns <- unique(c(columns, "exon_id")) columns <- addFilterColumns(columns, filter, x) ## Quick fix; rename any exon_rank to exon_idx. columns[columns == "exon_rank"] <- "exon_idx" ## The minimum columns we need, in addition to "columns" min.columns <- c(paste0(by, "_id"), "seq_name","exon_seq_start", "exon_seq_end", "exon_id", "seq_strand") by.id.full <- unlist(prefixColumns(x, columns = paste0(by, "_id"), clean = FALSE), use.names = FALSE) if (by == "gene") { ## tx columns have to be removed, since the same exon can be part of ## more than one tx txcolumns <- c(listColumns(x, "tx"), "exon_idx") txcolumns <- txcolumns[txcolumns != "gene_id"] torem <- columns %in% txcolumns if (any(torem)) warning("Columns ", paste(columns[ torem ], collapse = ","), " have been removed as they are not allowed if exons", " are fetched by gene.") columns <- columns[!torem] } else { min.columns <- unique(c(min.columns, "exon_idx")) columns <- c(columns, "exon_idx") } ## define the minimal columns that we need... ret_cols <- unique(columns) ## before adding the "min.columns" columns <- unique(c(columns, min.columns)) ## get the seqinfo: suppressWarnings( SI <- seqinfo(x) ) ## Resolve ordering problems. orderR <- orderResultsInR(x) if (orderR) { order.by <- "" } else { if (by == "gene") { order.by <- paste0("gene.gene_id, ", "case when seq_strand = 1 then exon_seq_start", " when seq_strand = -1 then (exon_seq_end * -1)", " end") } else { ## Funny thing is the query takes longer if I use tx2exon.tx_id! order.by <- "tx.tx_id, tx2exon.exon_idx" } } Res <- getWhat(x, columns = columns, filter = filter, order.by = order.by, skip.order.check = TRUE) ## Now, order in R, if not already done in SQL. if (orderR) { if (by == "gene") { startend <- (Res$seq_strand == 1) * Res$exon_seq_start + (Res$seq_strand == -1) * (Res$exon_seq_end * -1) Res <- Res[order(Res$gene_id, startend, method = "radix"), ] } else { Res <- Res[order(Res$tx_id, Res$exon_idx, method = "radix"), ] } } SI <- SI[as.character(unique(Res$seq_name))] ## replace exon_idx with exon_rank colnames(Res)[colnames(Res) == "exon_idx"] <- "exon_rank" columns[columns == "exon_idx"] <- "exon_rank" ret_cols[ret_cols == "exon_idx"] <- "exon_rank" notThere <- !(ret_cols %in% colnames(Res)) if (any(notThere)) warning(paste0("Columns ", paste(ret_cols[notThere], collapse = ", "), " not present in the result data.frame!")) ret_cols <- ret_cols[!notThere] columns.metadata <- ret_cols[!(ret_cols %in% c("seq_name", "seq_strand", "exon_seq_start", "exon_seq_end"))] columns.metadata <- match(columns.metadata, colnames(Res)) GR <- GRanges(seqnames = Rle(Res$seq_name), strand = Rle(Res$seq_strand), ranges = IRanges(start = Res$exon_seq_start, end = Res$exon_seq_end), seqinfo = SI, Res[, columns.metadata, drop=FALSE] ) return(split(GR, Res[, paste0(by, bySuff)])) }) ############################################################ ## transcriptsBy setMethod("transcriptsBy", "EnsDb", function(x, by = c("gene", "exon"), columns = listColumns(x, "tx"), filter){ if (any(by == "cds")) stop("fetching transcripts by cds is not (yet) implemented.") by <- match.arg(by, c("gene", "exon")) byId <- paste0(by, "_id") min.columns <- c(paste0(by, "_id"), "seq_name", "tx_seq_start", "tx_seq_end", "tx_id", "seq_strand") ## can not have exon columns! ex_cols <- c(listColumns(x, "exon"), "exon_idx") ex_cols <- ex_cols[ex_cols != "tx_id"] torem <- columns %in% ex_cols if (any(torem)) warning("Columns ", paste(columns[ torem ], collapse=","), " have been removed as they are not allowed if", " transcripts are fetched.") columns <- columns[!torem] ## Process filters if (missing(filter)) { filter <- list() } else { filter <- checkFilter(filter) } ## GRanges filter should be based on either gene or exon coors. filter <- setFeatureInGRangesFilter(filter, by) ## Eventually add columns for the filters: columns <- addFilterColumns(columns, filter, x) ret_cols <- unique(columns) ## define the minimal columns that we need... columns <- unique(c(columns, min.columns)) ## get the seqinfo: suppressWarnings( SI <- seqinfo(x) ) byIdFull <- unlist(prefixColumns(x, columns = byId, clean = FALSE), use.names = FALSE) orderR <- orderResultsInR(x) if (orderR) { order.by <- "" } else { order.by <- paste0(byIdFull , ", case when seq_strand = 1 then tx_seq_start", " when seq_strand = -1 then (tx_seq_end * -1) end") } Res <- getWhat(x, columns=columns, filter=filter, order.by=order.by, skip.order.check=TRUE) if (orderR) { startEnd <- (Res$seq_strand == 1) * Res$tx_seq_start + (Res$seq_strand == -1) * (Res$tx_seq_end * -1) Res <- Res[order(Res[, byId], startEnd, method = "radix"), ] } SI <- SI[as.character(unique(Res$seq_name))] ## Replace exon_idx with exon_rank colnames(Res) <- gsub(colnames(Res), pattern = "exon_idx", replacement = "exon_rank", fixed = TRUE) ret_cols[ret_cols == "exon_idx"] <- "exon_rank" notThere <- !(ret_cols %in% colnames(Res)) if(any(notThere)) warning(paste0("Columns ", paste(ret_cols[notThere], collapse=", "), " not present in the result data.frame!")) ret_cols <- ret_cols[!notThere] columns.metadata <- ret_cols[!(ret_cols %in% c("seq_name", "seq_strand", "tx_seq_start", "tx_seq_end"))] columns.metadata <- match(columns.metadata, colnames(Res)) ## presumably faster... GR <- GRanges(seqnames=Rle(Res$seq_name), strand=Rle(Res$seq_strand), ranges=IRanges(start=Res$tx_seq_start, end=Res$tx_seq_end), seqinfo=SI, Res[ , columns.metadata, drop=FALSE ] ) return(split(GR, Res[ , byId])) }) ## for GRangesList... setMethod("lengthOf", "GRangesList", function(x, ...){ return(sum(width(reduce(x)))) ## return(unlist(lapply(width(reduce(x)), sum))) }) ## return the length of genes or transcripts setMethod("lengthOf", "EnsDb", function(x, of="gene", filter=list()){ of <- match.arg(of, c("gene", "tx")) ## get the exons by gene or transcript from the database... suppressWarnings( GRL <- exonsBy(x, by=of, filter=filter) ) return(lengthOf(GRL)) }) ####============================================================ ## transcriptLengths ## ## For TxDb: calls just the function (not method!) from the GenomicFeatures ## package. ## For EnsDb: calls the .transcriptLengths function. ####------------------------------------------------------------ ## setMethod("transcriptLengths", "TxDb", function(x, with.cds_len=FALSE, with.utr5_len=FALSE, ## with.utr3_len=FALSE){ ## return(GenomicFeatures::transcriptLengths(x, with.cds_len=with.cds_len, ## with.utr5_len=with.utr5_len, ## with.utr3_len=with.utr3_len)) ## }) ## setMethod("transcriptLengths", "EnsDb", function(x, with.cds_len=FALSE, with.utr5_len=FALSE, ## with.utr3_len=FALSE, filter=list()){ ## return(.transcriptLengths(x, with.cds_len=with.cds_len, with.utr5_len=with.utr3_len, ## with.utr3_len=with.utr3_len, filter=filter)) ## }) ## implement the method from the GenomicFeatures package .transcriptLengths <- function(x, with.cds_len=FALSE, with.utr5_len=FALSE, with.utr3_len=FALSE, filter=list()){ ## First we're going to fetch the exonsBy. ## Or use getWhat??? ## Dash, have to make two queries! allTxs <- transcripts(x, filter=filter) exns <- exonsBy(x, filter=filter) ## Match ordering exns <- exns[match(allTxs$tx_id, names(exns))] ## Calculate length of transcripts. txLengths <- sum(width(reduce(exns))) ## Calculate no. of exons. ## build result data frame: Res <- data.frame(tx_id=allTxs$tx_id, gene_id=allTxs$gene_id, nexon=lengths(exns), tx_len=txLengths, stringsAsFactors=FALSE) if(!any(c(with.cds_len, with.utr5_len, with.utr3_len))){ ## Return what we've got thus far. return(Res) } if(with.cds_len) Res <- cbind(Res, cds_len=rep(NA, nrow(Res))) if(with.utr5_len) Res <- cbind(Res, utr5_len=rep(NA, nrow(Res))) if(with.utr3_len) Res <- cbind(Res, utr3_len=rep(NA, nrow(Res))) ## Otherwise do the remaining stuff... txs <- allTxs[!is.na(allTxs$tx_cds_seq_start)] if(length(txs) > 0){ cExns <- exns[txs$tx_id] cReg <- GRanges(seqnames=seqnames(txs), ranges=IRanges(txs$tx_cds_seq_start, txs$tx_cds_seq_end), strand=strand(txs), tx_id=txs$tx_id) cReg <- split(cReg, f=cReg$tx_id) ## Match order. cReg <- cReg[match(txs$tx_id, names(cReg))] cdsExns <- intersect(cReg, cExns) ## cExns: all exons of coding transcripts (includes untranslated ## and translated region) ## cReg: just the start-end position of the coding region of the tx. ## cdsExns: the coding part of all exons of the tx. if(with.cds_len){ ## Calculate CDS length cdsLengths <- sum(width(reduce(cdsExns))) Res[names(cdsLengths), "cds_len"] <- cdsLengths } if(with.utr3_len | with.utr5_len){ ## ! UTR is the difference between the exons and the cds-exons ## Note: order of parameters is important! utrReg <- setdiff(cExns, cdsExns) leftOfCds <- utrReg[end(utrReg) < start(cReg)] rightOfCds <- utrReg[start(utrReg) > end(cReg)] ## Calculate lengths. leftOfLengths <- sum(width(reduce(leftOfCds))) rightOfLengths <- sum(width(reduce(rightOfCds))) minusTx <- which(as.character(strand(txs)) == "-" ) if(with.utr3_len){ ## Ordering of txs and all other stuff matches. tmp <- rightOfLengths tmp[minusTx] <- leftOfLengths[minusTx] Res[names(tmp), "utr3_len"] <- tmp } if(with.utr5_len){ tmp <- leftOfLengths tmp[minusTx] <- rightOfLengths[minusTx] Res[names(tmp), "utr5_len"] <- tmp } } } return(Res) } ## cdsBy... return coding region ranges by tx or by gene. setMethod("cdsBy", "EnsDb", function(x, by = c("tx", "gene"), columns = NULL, filter, use.names = FALSE){ by <- match.arg(by, c("tx", "gene")) if (missing(filter)) { filter = list() } else { filter <- checkFilter(filter) } filter <- setFeatureInGRangesFilter(filter, by) ## Eventually add columns for the filters: columns <- addFilterColumns(columns, filter, x) ## Add a filter ensuring that only coding transcripts are queried. filter <- c(list(OnlyCodingTx()), filter) bySuff <- "_id" if (by == "tx") { ## adding exon_id, exon_idx to the columns. columns <- unique(c(columns, "exon_id", "exon_idx")) if (use.names) warning("Not considering use.names as no transcript names are available.") } else { columns <- unique(c("gene_id", columns)) if( use.names) { bySuff <- "_name" columns <- c(columns, "gene_name") } } byId <- paste0(by, bySuff) ## Query the data fetchCols <- unique(c(byId, columns, "tx_cds_seq_start", "tx_cds_seq_end", "seq_name", "seq_strand", "exon_idx", "exon_id", "exon_seq_start", "exon_seq_end")) ## Ordering of the results: ## Force ordering in R by default here to fix issue #11 ##orderR <- orderResultsInR(x) orderR <- TRUE if (orderR) { order.by <- "" } else { if (by == "tx") { ## Here we want to sort the exons by exon_idx order.by <- "tx.tx_id, tx2exon.exon_idx" } else { ## Here we want to sort the transcripts by tx start. order.by <- "gene.gene_id, case when seq_strand = 1 then tx_cds_seq_start when seq_strand = -1 then (tx_cds_seq_end * -1) end" } } Res <- getWhat(x, columns = fetchCols, filter = filter, order.by = order.by, skip.order.check = TRUE) ## Remove rows with NA in tx_cds_seq_start; that's the case for "old" databases. nas <- is.na(Res$tx_cds_seq_start) if (any(nas)) Res <- Res[!nas, ] ## Remove exons that are not within the cds. Res <- Res[Res$exon_seq_end >= Res$tx_cds_seq_start & Res$exon_seq_start <= Res$tx_cds_seq_end, , drop = FALSE] if (orderR) { ## And finally ordering them. if (by == "tx") { Res <- Res[order(Res$tx_id, Res$exon_idx, method = "radix"), ] } else { startend <- (Res$seq_strand == 1) * Res$tx_cds_seq_start + (Res$seq_strand == -1) * (Res$tx_cds_seq_end * -1) Res <- Res[order(Res$gene_id, startend, method = "radix"), ] } } if(nrow(Res)==0){ warning("No cds found!") return(NULL) } cdsStarts <- pmax.int(Res$exon_seq_start, Res$tx_cds_seq_start) cdsEnds <- pmin.int(Res$exon_seq_end, Res$tx_cds_seq_end) ## get the seqinfo: suppressWarnings( SI <- seqinfo(x) ) SI <- SI[as.character(unique(Res$seq_name))] ## Rename columns exon_idx to exon_rank, if present if(any(colnames(Res) == "exon_idx")){ colnames(Res)[colnames(Res) == "exon_idx"] <- "exon_rank" columns[columns == "exon_idx"] <- "exon_rank" } ## Building the result. if(length(columns) > 0){ notThere <- !(columns %in% colnames(Res)) if(any(notThere)) warning(paste0("Columns ", paste(columns[notThere], collapse=", "), " not present in the result data.frame!")) columns <- columns[!notThere] GR <- GRanges(seqnames=Rle(Res$seq_name), strand=Rle(Res$seq_strand), ranges=IRanges(start=cdsStarts, end=cdsEnds), seqinfo=SI, Res[, columns, drop=FALSE]) }else{ GR <- GRanges(seqnames=Rle(Res$seq_name), strand=Rle(Res$seq_strand), ranges=IRanges(start=cdsStarts, end=cdsEnds), seqinfo=SI) } GR <- split(GR, Res[, paste0(by, bySuff)]) ## For "by gene" we reduce the redundant ranges; ## that way we loose however all additional columns! if(by == "gene") GR <- reduce(GR) return(GR) }) ############################################################ ## getUTRsByTranscript getUTRsByTranscript <- function(x, what, columns = NULL, filter) { if (missing(filter)) { filter <- list() } else { filter <- checkFilter(filter) } filter <- setFeatureInGRangesFilter(filter, "tx") ## Eventually add columns for the filters: columns <- addFilterColumns(columns, filter, x) columns <- unique(c(columns, "exon_id", "exon_idx")) ## Add the filter for coding tx only. filter <- c(list(OnlyCodingTx()), filter) ## what do we need: tx_cds_seq_start, tx_cds_seq_end and exon_idx fetchCols <- unique(c("tx_id", columns, "tx_cds_seq_start", "tx_cds_seq_end", "seq_name", "seq_strand", "exon_seq_start", "exon_seq_end")) order.by <- "tx.tx_id" ## get the seqinfo: suppressWarnings( SI <- seqinfo(x) ) ## Note: doing that with a single query and some coordinate juggling ## is faster than calling exonsBy and GRangesList setdiff etc. Res <- getWhat(x, columns=fetchCols, filter=filter, order.by=order.by, skip.order.check=TRUE) nas <- is.na(Res$tx_cds_seq_start) if (any(nas)) Res <- Res[!nas, ] ## Remove exons that are within the cds. Res <- Res[Res$exon_seq_start < Res$tx_cds_seq_start | Res$exon_seq_end > Res$tx_cds_seq_end, , drop=FALSE] if (nrow(Res) == 0) { warning(paste0("No ", what, "UTR found!")) return(NULL) } ## Rename columns exon_idx to exon_rank, if present if (any(colnames(Res) == "exon_idx")) { colnames(Res) <- sub(colnames(Res), pattern = "exon_idx", replacement = "exon_rank", fixed = TRUE) columns[columns == "exon_idx"] <- "exon_rank" } if (what == "five") { ## All those on the forward strand for which the exon start is smaller ## than the cds start and those on the reverse strand with an exon end ## larger than the cds end. Res <- Res[(Res$seq_strand > 0 & Res$exon_seq_start < Res$tx_cds_seq_start) | (Res$seq_strand < 0 & Res$exon_seq_end > Res$tx_cds_seq_end), , drop=FALSE] } else { ## Other way round. Res <- Res[(Res$seq_strand > 0 & Res$exon_seq_end > Res$tx_cds_seq_end) | (Res$seq_strand < 0 & Res$exon_seq_start < Res$tx_cds_seq_start), , drop=FALSE] } if (nrow(Res) == 0) { warning(paste0("No ", what, "UTR found!")) return(NULL) } ## Increase the cds end by 1 and decrease the start by 1, thus, ## avoiding that the UTR overlaps the cds Res$tx_cds_seq_end <- Res$tx_cds_seq_end + 1L Res$tx_cds_seq_start <- Res$tx_cds_seq_start - 1L utrStarts <- rep(0, nrow(Res)) utrEnds <- utrStarts ## Distinguish between stuff which is left of and right of the CDS: ## Left of the CDS: can be either 5' for + strand or 3' for - strand. bm <- which(Res$exon_seq_start <= Res$tx_cds_seq_start) if (length(bm) > 0) { if (what == "five") { ## 5' and left of CDS means we're having 5' CDSs bm <- bm[Res$seq_strand[bm] > 0] if(length(bm) > 0){ utrStarts[bm] <- Res$exon_seq_start[bm] utrEnds[bm] <- pmin.int(Res$exon_seq_end[bm], Res$tx_cds_seq_start[bm]) } } else { bm <- bm[Res$seq_strand[bm] < 0] if (length(bm) > 0) { utrStarts[bm] <- Res$exon_seq_start[bm] utrEnds[bm] <- pmin.int(Res$exon_seq_end[bm], Res$tx_cds_seq_start[bm]) } } } ## Right of the CDS: can be either 5' for - strand of 3' for + strand. bm <- which(Res$exon_seq_end >= Res$tx_cds_seq_end) if (length(bm) > 0) { if (what == "five") { ## Right of CDS is 5' for - strand. bm <- bm[Res$seq_strand[bm] < 0] if (length(bm) > 0) { utrStarts[bm] <- pmax.int(Res$exon_seq_start[bm], Res$tx_cds_seq_end[bm]) utrEnds[bm] <- Res$exon_seq_end[bm] } } else { ## Right of CDS is 3' for + strand bm <- bm[Res$seq_strand[bm] > 0] if (length(bm) > 0) { utrStarts[bm] <- pmax.int(Res$exon_seq_start[bm], Res$tx_cds_seq_end[bm]) utrEnds[bm] <- Res$exon_seq_end[bm] } } } notThere <- !(columns %in% colnames(Res)) if (any(notThere)) warning(paste0("Columns ", paste(columns[notThere], collapse=", "), " not present in the result data.frame!")) columns <- columns[!notThere] SI <- SI[as.character(unique(Res$seq_name))] GR <- GRanges(seqnames = Rle(Res$seq_name), strand = Rle(Res$seq_strand), ranges = IRanges(start=utrStarts, end=utrEnds), seqinfo = SI, Res[, columns, drop = FALSE]) GR <- split(GR, Res[, "tx_id"]) return(GR) } ## threeUTRsByTranscript setMethod("threeUTRsByTranscript", "EnsDb", function(x, columns=NULL, filter){ if(missing(filter)){ filter=list() }else{ filter <- checkFilter(filter) } return(getUTRsByTranscript(x=x, what="three", columns=columns, filter=filter)) }) ## fiveUTRsByTranscript setMethod("fiveUTRsByTranscript", "EnsDb", function(x, columns=NULL, filter){ if(missing(filter)){ filter=list() }else{ filter <- checkFilter(filter) } return(getUTRsByTranscript(x=x, what="five", columns=columns, filter=filter)) }) ## toSAF... function to transform a GRangesList into a data.frame ## corresponding to the SAF format. ## assuming the names of the GRangesList to be the GeneID and the ## element (GRanges) the start/end coordinates ## of an exon, transcript or the gene itself. .toSaf <- function(x){ DF <- as.data.frame(x) colnames(DF)[ colnames(DF)=="group_name" ] <- "GeneID" colnames(DF)[ colnames(DF)=="seqnames" ] <- "Chr" colnames(DF)[ colnames(DF)=="start" ] <- "Start" colnames(DF)[ colnames(DF)=="end" ] <- "End" colnames(DF)[ colnames(DF)=="strand" ] <- "Strand" return(DF[ , c("GeneID", "Chr", "Start", "End", "Strand")]) } ## for GRangesList... setMethod("toSAF", "GRangesList", function(x, ...){ return(.toSaf(x)) }) .requireTable <- function(db, attr){ return(names(prefixColumns(db, columns=attr))) } ## these function determine which tables we need for the submitted filters. setMethod("requireTable", signature(x="GeneidFilter", db="EnsDb"), function(x, db, ...){ return(.requireTable(db=db, attr="gene_id")) }) setMethod("requireTable", signature(x="EntrezidFilter", db="EnsDb"), function(x, db, ...){ return(.requireTable(db=db, attr="entrezid")) }) setMethod("requireTable", signature(x="GenebiotypeFilter", db="EnsDb"), function(x, db, ...){ return(.requireTable(db=db, attr="gene_biotype")) }) setMethod("requireTable", signature(x="GenenameFilter", db="EnsDb"), function(x, db, ...){ return(.requireTable(db=db, attr="gene_name")) }) setMethod("requireTable", signature(x="TxidFilter", db="EnsDb"), function(x, db, ...){ return(.requireTable(db=db, attr="tx_id")) }) setMethod("requireTable", signature(x="TxbiotypeFilter", db="EnsDb"), function(x, db, ...){ return(.requireTable(db=db, attr="tx_biotype")) }) setMethod("requireTable", signature(x="ExonidFilter", db="EnsDb"), function(x, db, ...){ return(.requireTable(db=db, attr="exon_id")) }) setMethod("requireTable", signature(x="SeqnameFilter", db="EnsDb"), function(x, db, ...){ return(.requireTable(db=db, attr="seq_name")) }) setMethod("requireTable", signature(x="SeqstrandFilter", db="EnsDb"), function(x, db, ...){ return(.requireTable(db=db, attr="seq_name")) }) setMethod("requireTable", signature(x="SeqstartFilter", db="EnsDb"), function(x, db, ...){ if(x@feature=="gene") return(.requireTable(db=db, attr="gene_seq_start")) if(x@feature=="transcript" | x@feature=="tx") return(.requireTable(db=db, attr="tx_seq_start")) if(x@feature=="exon") return(.requireTable(db=db, attr="exon_seq_start")) return(NA) }) setMethod("requireTable", signature(x="SeqendFilter", db="EnsDb"), function(x, db, ...){ if(x@feature=="gene") return(.requireTable(db=db, attr="gene_seq_end")) if(x@feature=="transcript" | x@feature=="tx") return(.requireTable(db=db, attr="tx_seq_end")) if(x@feature=="exon") return(.requireTable(db=db, attr="exon_seq_end")) return(NA) }) setMethod("requireTable", signature(x = "SymbolFilter", db = "EnsDb"), function(x, db, ...) { return(.requireTable(db = db, attr = "gene_name")) }) setMethod("buildQuery", "EnsDb", function(x, columns=c("gene_id", "gene_biotype", "gene_name"), filter=list(), order.by="", order.type="asc", skip.order.check=FALSE){ return(.buildQuery(x=x, columns=columns, filter=filter, order.by=order.by, order.type=order.type, skip.order.check=skip.order.check)) }) #### ## Method that wraps the internal .getWhat function to retrieve data from the ## database. In addition, if present, we're renaming chromosome names depending ## on the ucscChromosomeNames option. setMethod("getWhat", "EnsDb", function(x, columns = c("gene_id", "gene_biotype", "gene_name"), filter = list(), order.by = "", order.type = "asc", group.by = NULL, skip.order.check = FALSE) { Res <- .getWhat(x = x, columns = columns, filter = filter, order.by = order.by, order.type = order.type, group.by = group.by, skip.order.check = skip.order.check) ## Eventually renaming seqnames according to the specified style. if(any(colnames(Res) == "seq_name")) Res$seq_name <- formatSeqnamesFromQuery(x, Res$seq_name) return(Res) }) ## that's basically a copy of the code from the GenomicFeatures package. setMethod("disjointExons", "EnsDb", function(x, aggregateGenes=FALSE, includeTranscripts=TRUE, filter, ...){ if(missing(filter)){ filter <- list() }else{ filter <- checkFilter(filter) } exonsByGene <- exonsBy(x, by="gene", filter=filter) exonicParts <- disjoin(unlist(exonsByGene, use.names=FALSE)) if (aggregateGenes) { foGG <- findOverlaps(exonsByGene, exonsByGene) aggregateNames <- GenomicFeatures:::.listNames(names(exonsByGene), as.list(foGG)) foEG <- findOverlaps(exonicParts, exonsByGene, select="first") gene_id <- aggregateNames[foEG] pasteNames <- GenomicFeatures:::.pasteNames(names(exonsByGene), as.list(foGG))[foEG] orderByGeneName <- order(pasteNames) exonic_rle <- runLength(Rle(pasteNames[orderByGeneName])) } else { ## drop exonic parts that overlap > 1 gene foEG <- findOverlaps(exonicParts, exonsByGene) idxList <- as.list(foEG) if (any(keep <- countQueryHits(foEG) == 1)) { idxList <- idxList[keep] exonicParts <- exonicParts[keep] } gene_id <- GenomicFeatures:::.listNames(names(exonsByGene), idxList) orderByGeneName <- order(unlist(gene_id, use.names=FALSE)) exonic_rle <- runLength(Rle(unlist(gene_id[orderByGeneName], use.names=FALSE))) } values <- DataFrame(gene_id) if (includeTranscripts) { exonsByTx <- exonsBy(x, by="tx", filter=filter) foET <- findOverlaps(exonicParts, exonsByTx) values$tx_name <- GenomicFeatures:::.listNames(names(exonsByTx), as.list(foET)) } mcols(exonicParts) <- values exonicParts <- exonicParts[orderByGeneName] exonic_part <- unlist(lapply(exonic_rle, seq_len), use.names=FALSE) exonicParts$exonic_part <- exonic_part return(exonicParts) } ) ### utility functions ## checkFilter: ## checks the filter argument and ensures that a list of Filter object is returned checkFilter <- function(x){ if(is(x, "list")){ if(length(x)==0) return(x) ## check if all elements are Filter classes. IsAFilter <- unlist(lapply(x, function(z){ return(is(z, "BasicFilter")) })) if(any(!IsAFilter)) stop("One of more elements in filter are not filter objects!") }else{ if(is(x, "BasicFilter")){ x <- list(x) }else{ stop("filter has to be a filter object or a list of filter objects!") } } return(x) } ## Fetch data to add as a GeneTrack. ## filter ... Used to filter the result. ## chromosome, start, end ... Either all or none has to be specified. If specified, the function ## first retrieves all transcripts that have an exon in the specified ## range and adds them as a TranscriptidFilter to the filters. The ## query to fetch the "real" data is performed after. ## featureIs ... Wheter gene_biotype or tx_biotype should be mapped to the column ## feature. setMethod("getGeneRegionTrackForGviz", "EnsDb", function(x, filter=list(), chromosome=NULL, start=NULL, end=NULL, featureIs="gene_biotype"){ featureIs <- match.arg(featureIs, c("gene_biotype", "tx_biotype")) filter <- checkFilter(filter) if(missing(chromosome)) chromosome <- NULL if(missing(start)) start <- NULL if(missing(end)) end <- NULL ## if only chromosome is specified, create a SeqnameFilter and add it to the filter if(is.null(start) & is.null(end) & !is.null(chromosome)){ filter <- c(filter, list(SeqnameFilter(chromosome))) chromosome <- NULL } if(any(c(!is.null(chromosome), !is.null(start), !is.null(end)))){ ## Require however that all are defined!!! if(all(c(!is.null(chromosome), !is.null(start), !is.null(end)))){ ## Fix eventually provided UCSC chromosome names: chromosome <- ucscToEns(chromosome) ## Fetch all transcripts in that region: tids <- dbGetQuery(dbconn(x), paste0("select distinct tx.tx_id from tx join gene on", " (tx.gene_id=gene.gene_id)", " where seq_name='", chromosome, "' and (", "(tx_seq_start >=",start," and tx_seq_start <=",end,") or ", "(tx_seq_end >=",start," and tx_seq_end <=",end,") or ", "(tx_seq_start <=",start," and tx_seq_end >=",end,")", ")"))[, "tx_id"] if(length(tids) == 0) stop(paste0("Did not find any transcript on chromosome ", chromosome, " from ", start, " to ", end, "!")) filter <- c(filter, TxidFilter(tids)) }else{ stop(paste0("Either all or none of arguments 'chromosome', 'start' and 'end' ", " have to be specified!")) } } ## Return a data.frame with columns: chromosome, start, end, width, strand, feature, ## gene, exon, transcript and symbol. ## 1) Query the data as we usually would. ## 2) Perform an additional query to get cds and utr, remove all entries from the ## first result for the same transcripts and rbind the data.frames. needCols <- c("seq_name", "exon_seq_start", "exon_seq_end", "seq_strand", featureIs, "gene_id", "exon_id", "exon_idx", "tx_id", "gene_name") ## That's the names to which we map the original columns from the EnsDb. names(needCols) <- c("chromosome", "start", "end", "strand", "feature", "gene", "exon", "exon_rank", "transcript", "symbol") txs <- transcripts(x, filter=filter, columns=needCols, return.type="data.frame") ## Rename columns idx <- match(needCols, colnames(txs)) notThere <- is.na(idx) idx <- idx[!notThere] colnames(txs)[idx] <- names(needCols)[!notThere] ## now processing the 5utr fUtr <- fiveUTRsByTranscript(x, filter=filter, columns=needCols) if(length(fUtr) > 0){ fUtr <- as(unlist(fUtr, use.names=FALSE), "data.frame") fUtr <- fUtr[, !(colnames(fUtr) %in% c("width", "seq_name", "exon_seq_start", "exon_seq_end", "strand"))] colnames(fUtr)[1] <- "chromosome" idx <- match(needCols, colnames(fUtr)) notThere <- is.na(idx) idx <- idx[!notThere] colnames(fUtr)[idx] <- names(needCols)[!notThere] ## Force being in the correct ordering: fUtr <- fUtr[, names(needCols)] fUtr$feature <- "utr5" ## Remove transcripts from the txs data.frame txs <- txs[!(txs$transcript %in% fUtr$transcript), , drop=FALSE] } tUtr <- threeUTRsByTranscript(x, filter=filter, columns=needCols) if(length(tUtr) > 0){ tUtr <- as(unlist(tUtr, use.names=FALSE), "data.frame") tUtr <- tUtr[, !(colnames(tUtr) %in% c("width", "seq_name", "exon_seq_start", "exon_seq_end", "strand"))] colnames(tUtr)[1] <- "chromosome" idx <- match(needCols, colnames(tUtr)) notThere <- is.na(idx) idx <- idx[!notThere] colnames(tUtr)[idx] <- names(needCols)[!notThere] ## Force being in the correct ordering: tUtr <- tUtr[, names(needCols)] tUtr$feature <- "utr3" ## Remove transcripts from the txs data.frame if(nrow(txs) > 0){ txs <- txs[!(txs$transcript %in% tUtr$transcript), , drop=FALSE] } } cds <- cdsBy(x, filter=filter, columns=needCols) if(length(cds) > 0){ cds <- as(unlist(cds, use.names=FALSE), "data.frame") cds <- cds[, !(colnames(cds) %in% c("width", "seq_name", "exon_seq_start", "exon_seq_end", "strand"))] colnames(cds)[1] <- "chromosome" idx <- match(needCols, colnames(cds)) notThere <- is.na(idx) idx <- idx[!notThere] colnames(cds)[idx] <- names(needCols)[!notThere] ## Force being in the correct ordering: cds <- cds[, names(needCols)] ## Remove transcripts from the txs data.frame if(nrow(txs) > 0){ txs <- txs[!(txs$transcript %in% cds$transcript), , drop=FALSE] } } if(length(fUtr) > 0){ txs <- rbind(txs, fUtr) } if(length(tUtr) > 0){ txs <- rbind(txs, tUtr) } if(length(cds) > 0){ txs <- rbind(txs, cds) } ## Convert into GRanges. suppressWarnings( SI <- seqinfo(x) ) SI <- SI[as.character(unique(txs$chromosome))] GR <- GRanges(seqnames=Rle(txs$chromosome), strand=Rle(txs$strand), ranges=IRanges(start=txs$start, end=txs$end), seqinfo=SI, txs[, c("feature", "gene", "exon", "exon_rank", "transcript", "symbol"), drop=FALSE]) return(GR) }) ## Simple helper function to set the @feature in GRangesFilter depending on the calling method. setFeatureInGRangesFilter <- function(x, feature){ for(i in seq(along.with=x)){ if(is(x[[i]], "GRangesFilter")){ x[[i]]@feature <- feature } } return(x) } ####============================================================ ## properties ## ## Get access to the "hidden" .properties slot and return it. ## This ensures that we're not generating an error for objects that ## do not have yet that slot. ####------------------------------------------------------------ setMethod("properties", "EnsDb", function(x, ...){ if(.hasSlot(x, ".properties")){ return(x@.properties) }else{ warning("The present EnsDb instance has no .properties slot! ", "Please use 'updateEnsDb' to update the object!") return(list()) } }) ####============================================================ ## getProperty ## ## Return the value for the property with the specified name or ## NA if not present. ####------------------------------------------------------------ setMethod("getProperty", "EnsDb", function(x, name, default = NA){ props <- properties(x) if(any(names(props) == name)){ return(props[[name]]) }else{ return(default) } }) ####============================================================ ## setProperty ## ## Sets a property in the object. The value has to be a named vector. ####------------------------------------------------------------ setMethod("setProperty", "EnsDb", function(x, ...){ dotL <- list(...) if(length(dotL) == 0){ stop("No property specified! The property has to be submitted ", "in the format name=value!") return(x) } if(length(dotL) > 1){ warning("'setProperty' does only support setting of a single property!", " Using the first submitted one.") dotL <- dotL[1] } if(is.null(names(dotL)) | names(dotL) == "") stop("A name is required! Use name=value!") if(.hasSlot(x, ".properties")){ x@.properties[names(dotL)] <- dotL[[1]] }else{ warning("The present EnsDb instance has no .properties slot! ", "Please use 'updateEnsDb' to update the object!") } return(x) }) ####============================================================ ## updateEnsDb ## ## Update any "old" EnsDb instance to the most recent implementation. ####------------------------------------------------------------ setMethod("updateEnsDb", "EnsDb", function(x, ...){ newE <- new("EnsDb", ensdb=x@ensdb, tables=x@tables) if(.hasSlot(x, ".properties")) newE@.properties <- x@.properties return(newE) }) ####============================================================ ## transcriptsByOverlaps ## ## Just "re-implementing" the transcriptsByOverlaps methods from the ## GenomicFeature package, finetuning and adapting it for EnsDbs ####------------------------------------------------------------ setMethod("transcriptsByOverlaps", "EnsDb", function(x, ranges, maxgap = 0L, minoverlap = 1L, type = c("any", "start", "end"), columns=listColumns(x, "tx"), filter) { if(missing(ranges)) stop("Parameter 'ranges' is missing!") if(missing(filter)){ filter <- list() }else{ filter <- checkFilter(filter) } SLs <- unique(as.character(seqnames(ranges))) filter <- c(filter, SeqnameFilter(SLs)) return(subsetByOverlaps(transcripts(x, columns=columns, filter=filter), ranges, maxgap=maxgap, minoverlap=minoverlap, type=match.arg(type))) }) ####============================================================ ## exonsByOverlaps ## ####------------------------------------------------------------ setMethod("exonsByOverlaps", "EnsDb", function(x, ranges, maxgap=0L, minoverlap=1L, type=c("any", "start", "end"), columns=listColumns(x, "exon"), filter) { if(missing(ranges)) stop("Parameter 'ranges' is missing!") if(missing(filter)){ filter <- list() }else{ filter <- checkFilter(filter) } SLs <- unique(as.character(seqnames(ranges))) filter <- c(filter, SeqnameFilter(SLs)) return(subsetByOverlaps(exons(x, columns=columns, filter=filter), ranges, maxgap=maxgap, minoverlap=minoverlap, type=match.arg(type))) }) ############################################################ ## returnFilterColumns ## ## Method to set the option whether or not the filter columns should be ## returned too. setMethod("returnFilterColumns", "EnsDb", function(x) { return(getProperty(x, "returnFilterColumns")) }) setReplaceMethod("returnFilterColumns", "EnsDb", function(x, value) { if(!is.logical(value)) stop("'value' has to be a logical!") if(length(value) > 1) stop("'value' has to be a logical of length 1!") x <- setProperty(x, returnFilterColumns=value) return(x) }) ############################################################ ## orderResultsInR ## ## Whether the results should be ordered in R instead of in the ## SQL call setMethod("orderResultsInR", "EnsDb", function(x) { return(getProperty(x, "orderResultsInR", default = FALSE)) }) setReplaceMethod("orderResultsInR", "EnsDb", function(x, value) { if(!is.logical(value)) stop("'value' has to be a logical!") if(length(value) > 1) stop("'value' has to be a logical of length 1!") x <- setProperty(x, orderResultsInR = value) return(x) }) ############################################################ ## useMySQL ## ## Switch from RSQlite backend to a MySQL backend. ##' @title Use a MySQL backend ##' @aliases useMySQL ##' ##' @description Change the SQL backend from \emph{SQLite} to \emph{MySQL}. ##' When first called on an \code{\linkS4class{EnsDb}} object, the function ##' tries to create and save all of the data into a MySQL database. All ##' subsequent calls will connect to the already existing MySQL database. ##' ##' @details This functionality requires that the \code{RMySQL} package is ##' installed and that the user has (write) access to a running MySQL server. ##' If the corresponding database does already exist users without write access ##' can use this functionality. ##' ##' @note At present the function does not evaluate whether the versions ##' between the SQLite and MySQL database differ. ##' ##' @param x The \code{\linkS4class{EnsDb}} object. ##' @param host Character vector specifying the host on which the MySQL ##' server runs. ##' @param port The port on which the MySQL server can be accessed. ##' @param user The user name for the MySQL server. ##' @param pass The password for the MySQL server. ##' @return A \code{\linkS4class{EnsDb}} object providing access to the ##' data stored in the MySQL backend. ##' @author Johannes Rainer ##' @examples ##' ## Load the EnsDb database (SQLite backend). ##' library(EnsDb.Hsapiens.v75) ##' edb <- EnsDb.Hsapiens.v75 ##' ## Now change the backend to MySQL; my_user and my_pass should ##' ## be the user name and password to access the MySQL server. ##' \dontrun{ ##' edb_mysql <- useMySQL(edb, host = "localhost", user = my_user, pass = my_pass) ##' } setMethod("useMySQL", "EnsDb", function(x, host = "localhost", port = 3306, user, pass) { if (missing(user)) stop("'user' has to be specified.") if (missing(pass)) stop("'pass' has to be specified.") ## Check if RMySQL package is available. if(requireNamespace("RMySQL", quietly = TRUE)) { ## Check if we can connect to MySQL. driva <- dbDriver("MySQL") con <- dbConnect(driva, host = host, user = user, pass = pass, port = port) ## Check if database is available. dbs <- dbGetQuery(con, "show databases;") sqliteName <- sub(basename(dbfile(dbconn(x))), pattern = ".sqlite", replacement = "", fixed = TRUE) mysqlName <- SQLiteName2MySQL(sqliteName) if (nrow(dbs) == 0 | !any(dbs$Database == mysqlName)) { message("Database not available, trying to create it...", appendLF = FALSE) dbGetQuery(con, paste0("create database ", mysqlName)) message("OK") } dbDisconnect(con) ## Connect to the database and check if we've got all tables. con <- dbConnect(driva, host = host, user = user, pass = pass, dbname = mysqlName) ## If we've got no tables we try to feed the SQLite database if (length(dbListTables(con)) == 0) feedEnsDb2MySQL(x, mysql = con) ## Check if we've got all required tables. OK <- dbHasRequiredTables(con) if (is.character(OK)) stop(OK) OK <- dbHasValidTables(con) if (is.character(OK)) stop(OK) ## Check if the versions/creation date differ. metadata_pkg <- metadata(x) ## Now store the connection into the @ensdb slot ## dbDisconnect(x@ensdb) ## x@ensdb <- NULL x@ensdb <- con metadata_db <- metadata(x) cre_pkg <- metadata_pkg[metadata_pkg$name == "Creation time", "value"] cre_db <- metadata_db[metadata_db$name == "Creation time", "value"] if (cre_pkg != cre_db) { message("Creation date between the package and the information in", " the database differ:\n o package: ", cre_pkg, "\n o database: ", cre_db, ".\nYou might consider to delete", " the database and re-install it calling this function.") } return(x) } else { stop("Package 'RMySQL' not available.") } }) ensembldb/R/dbhelpers.R0000644000175400017540000006167713012705075016050 0ustar00biocbuildbiocbuild############################################################ ## EnsDb ## Constructor function. ##' @title Connect to an EnsDb object ##' ##' @description The \code{EnsDb} constructor function connects to the database ##' specified with argument \code{x} and returns a corresponding ##' \code{\linkS4class{EnsDb}} object. ##' ##' @details By providing the connection to a MySQL database, it is possible ##' to use MySQL as the database backend and queries will be performed on that ##' database. Note however that this requires the package \code{RMySQL} to be ##' installed. In addition, the user needs to have access to a MySQL server ##' providing already an EnsDb database, or must have write privileges on a ##' MySQL server, in which case the \code{\link{useMySQL}} method can be used ##' to insert the annotations from an EnsDB package into a MySQL database. ##' @param x Either a character specifying the \emph{SQLite} database file, or ##' a \code{DBIConnection} to e.g. a MySQL database. ##' @return A \code{\linkS4class{EnsDb}} object. ##' @author Johannes Rainer ##' @examples ##' ## "Standard" way to create an EnsDb object: ##' library(EnsDb.Hsapiens.v75) ##' EnsDb.Hsapiens.v75 ##' ##' ## Alternatively, provide the full file name of a SQLite database file ##' dbfile <- system.file("extdata/EnsDb.Hsapiens.v75.sqlite", package = "EnsDb.Hsapiens.v75") ##' edb <- EnsDb(dbfile) ##' edb ##' ##' ## Third way: connect to a MySQL database ##' \dontrun{ ##' library(RMySQL) ##' dbcon <- dbConnect(MySQL(), user = my_user, pass = my_pass, host = my_host, dbname = "ensdb_hsapiens_v75") ##' edb <- EnsDb(dbcon) ##' } EnsDb <- function(x){ options(useFancyQuotes=FALSE) if(missing(x)){ stop("No sqlite file provided!") } if (is.character(x)) { lite <- dbDriver("SQLite") con <- dbConnect(lite, dbname = x, flags=SQLITE_RO) } else if (is(x, "DBIConnection")) { con <- x } else { stop("'x' should be either a character specifying the SQLite file to", " be loaded, or a DBIConnection object providing the connection", " to the database.") } ## Check if the database is valid. OK <- dbHasRequiredTables(con) if (is.character(OK)) stop(OK) OK <- dbHasValidTables(con) if (is.character(OK)) stop(OK) tables <- dbListTables(con) ## Quick fix for EnsDbs containing also protein data (issue #30): tables <- tables[!(tables %in% c("protein", "uniprot", "protein_domain"))] ## read the columns for these tables. Tables <- vector(length=length(tables), "list") for(i in 1:length(Tables)){ Tables[[ i ]] <- colnames(dbGetQuery(con, paste0("select * from ", tables[ i ], " limit 1"))) } names(Tables) <- tables EDB <- new("EnsDb", ensdb=con, tables=Tables) EDB <- setProperty(EDB, dbSeqlevelsStyle="Ensembl") ## Setting the default for the returnFilterColumns returnFilterColumns(EDB) <- TRUE ## Defining the default for the ordering orderResultsInR(EDB) <- FALSE return(EDB) } ## x is the connection to the database, name is the name of the entry to fetch .getMetaDataValue <- function(x, name){ return(dbGetQuery(x, paste0("select value from metadata where name='", name, "'"))[ 1, 1]) } #### ## Note: that's the central function that checks which tables are needed for the ## least expensive join!!! The names of the tables should then also be submitted ## to any other method that calls prefixColumns (e.g. where of the Filter classes) ## ## this function checks: ## a) for multi-table columns, selects the table with the highest degree ## b) pre-pend (inverse of append ;)) the table name to the column name. ## returns a list, names being the tables and the values being the columns ## named:
. ## clean: whether a cleanColumns should be called on the submitted columns. ## with.tables: force the prefix to be specifically on the submitted tables. prefixColumns <- function(x, columns, clean = TRUE, with.tables){ if (missing(columns)) stop("columns is empty! No columns provided!") ## first get to the tables that contain these columns Tab <- listTables(x) ## returns the tables by degree! if (!missing(with.tables)) { with.tables <- with.tables[ with.tables %in% names(Tab) ] if (length(with.tables) > 0) { Tab <- Tab[ with.tables ] } else { warning("The submitted table names are not valid in the database and were thus dropped.") } if (length(Tab) == 0) stop("None of the tables submitted with with.tables is present in the database!") } if (clean) columns <- cleanColumns(x, columns) if (length(columns) == 0) { return(NULL) } ## group the columns by table. columns.bytable <- sapply(Tab, function(z){ return(z[ z %in% columns ]) }, simplify=FALSE, USE.NAMES=TRUE) ## kick out empty tables... columns.bytable <- columns.bytable[ unlist(lapply(columns.bytable, function(z){ return(length(z) > 0) })) ] if(length(columns.bytable)==0) stop("No columns available!") have.columns <- NULL ## new approach! order the tables by number of elements, and after that, re-order them. columns.bytable <- columns.bytable[ order(unlist(lapply(columns.bytable, length)), decreasing=TRUE) ] ## has to be a for loop!!! ## loop throught the columns by table and sequentially kick out columns for the current table if they where already ## in a previous (more relevant) table ## however, prefer also cases were fewer tables are returned. for(i in 1:length(columns.bytable)){ bm <- columns.bytable[[ i ]] %in% have.columns keepvals <- columns.bytable[[ i ]][ !bm ] ## keep those if(length(keepvals) > 0){ have.columns <- c(have.columns, keepvals) } if(length(keepvals) > 0){ columns.bytable[[ i ]] <- paste(names(columns.bytable)[ i ], keepvals, sep=".") }else{ columns.bytable[[ i ]] <- keepvals } } ## kick out those tables with no elements left... columns.bytable <- columns.bytable[ unlist(lapply(columns.bytable, function(z){ return(length(z) > 0) })) ] ## re-order by degree. columns.bytable <- columns.bytable[ tablesByDegree(x, names(columns.bytable)) ] return(columns.bytable) } ############################################################ ## call the prefixColumns function and return just the column ## names, but in the same order than the provided columns. prefixColumnsKeepOrder <- function(x, columns, clean = TRUE, with.tables) { res <- unlist(prefixColumns(x, columns, clean, with.tables), use.names = FALSE) res_order <- sapply(columns, function(z) { idx <- grep(res, pattern = paste0("\\.", z, "$")) if (length(idx) == 0) return(NULL) return(res[idx[1]]) }) return(res_order[!is.null(res_order)]) } ## define a function to create a join query based on columns ## this function has to first get all tables that contain the columns, ## and then select, for columns present in more than one ## x... EnsDb ## columns... the columns joinQueryOnColumns <- function(x, columns){ columns.bytable <- prefixColumns(x, columns) ## based on that we can build the query based on the tables we've got. Note that the ## function internally ## adds tables that might be needed for the join. Query <- joinQueryOnTables(x, names(columns.bytable)) return(Query) } ## only list direct joins!!! .JOINS <- rbind( c("gene", "tx", "join tx on (gene.gene_id=tx.gene_id)"), c("gene", "chromosome", "join chromosome on (gene.seq_name=chromosome.seq_name)"), c("tx", "tx2exon", "join tx2exon on (tx.tx_id=tx2exon.tx_id)"), c("tx2exon", "exon", "join exon on (tx2exon.exon_id=exon.exon_id)") ) ## tx is now no 1: ## .JOINS <- rbind( ## c("tx", "gene", "join gene on (tx.gene_id=gene.gene_id)"), ## c("gene", "chromosome", "join chromosome on (gene.seq_name=chromosome.seq_name)"), ## c("tx", "tx2exon", "join tx2exon on (tx.tx_id=tx2exon.tx_id)"), ## c("tx2exon", "exon", "join exon on (tx2exon.exon_id=exon.exon_id)") ## ) joinQueryOnTables <- function(x, tab){ ## just to be on the save side: evaluate whether we have all required tables to join; ## this will also ensure that the order is by degree. tab <- addRequiredTables(x, tab) Query <- tab[ 1 ] previous.table <- tab[ 1 ] for(i in 1:length(tab)){ if(i > 1){ Query <- paste(Query, .JOINS[ .JOINS[ , 2 ]==tab[ i ], 3 ]) } } return(Query) } ### ## Add additional tables in case the submitted tables are not directly connected ## and can thus not be joined. That's however not so complicated, since the database ## layout is pretty simple. ## The tables are: ## ## exon -(exon_id=t2e_exon_id)- tx2exon -(t2e_tx_id=tx_id)- tx -(gene_id=gene_id)- gene ## | ## chromosome -(seq_name=seq_name)-´ addRequiredTables <- function(x, tab){ ## dash it, as long as I can't find a way to get connected objects in a ## graph I'll do it manually... ## if we have exon and any other table, we need definitely tx2exon if(any(tab=="exon") & length(tab) > 1){ tab <- unique(c(tab, "tx2exon")) } ## if we have chromosome and any other table, we'll need gene if(any(tab=="chromosome") & length(tab) > 1){ tab <- unique(c(tab, "gene")) } ## if we have exon and we have gene, we'll need also tx if((any(tab=="exon") | (any(tab=="tx2exon"))) & any(tab=="gene")){ tab <- unique(c(tab, "tx")) } return(tablesByDegree(x, tab)) } ############################################################ ## .buildQuery ## ## The "backbone" function that builds the SQL query based on the specified ## columns, the provided filters etc. ## x an EnsDb object .buildQuery <- function(x, columns, filter = list(), order.by = "", order.type = "asc", group.by, skip.order.check=FALSE, return.all.columns = TRUE) { resultcolumns <- columns ## just to remember what we really want to give back ## 1) get all column names from the filters also removing the prefix. if (class(filter)!="list") stop("parameter filter has to be a list of BasicFilter classes!") if (length(filter) > 0) { ## check filter! ## add the columns needed for the filter filtercolumns <- unlist(lapply(filter, column, x)) ## remove the prefix (column name for these) filtercolumns <- sapply(filtercolumns, removePrefix, USE.NAMES = FALSE) columns <- unique(c(columns, filtercolumns)) } ## 2) get all column names for the order.by: if (any(order.by != "")) { ## if we have skip.order.check set we use the order.by as is. if (!skip.order.check) { order.by <- checkOrderBy(orderBy = order.by, supported = columns) } }else{ order.by <- "" } ## Note: order by is now a vector!!! ## columns are now all columns that we want to fetch or that we need to ## filter or to sort. ## 3) check which tables we need for all of these columns: need.tables <- names(prefixColumns(x, columns)) ## ## Now we can begin to build the query parts! ## a) the query part that joins all required tables. joinquery <- joinQueryOnColumns(x, columns=columns) ## b) the filter part of the query if (length(filter) > 0) { filterquery <- paste(" where", paste(unlist(lapply(filter, where, x, with.tables = need.tables)), collapse=" and ")) } else { filterquery <- "" } ## c) the order part of the query if (any(order.by != "")) { if (!skip.order.check) { ## order.by <- paste(unlist(prefixColumns(x=x, columns=order.by, ## with.tables=need.tables), ## use.names=FALSE), collapse=",") order.by <- paste(prefixColumnsKeepOrder(x = x, columns = order.by, with.tables = need.tables), collapse=",") } orderquery <- paste(" order by", order.by, order.type) }else{ orderquery <- "" } ## And finally build the final query if(return.all.columns){ resultcolumns <- columns } finalquery <- paste0("select distinct ", ## paste(unlist(prefixColumns(x, ## resultcolumns, ## with.tables=need.tables), ## use.names=FALSE), collapse=","), paste(prefixColumnsKeepOrder(x, resultcolumns, with.tables = need.tables), collapse=","), " from ", joinquery, filterquery, orderquery ) return(finalquery) } ## remove the prefix again... removePrefix <- function(x, split=".", fixed=TRUE){ return(sapply(x, function(z){ tmp <- unlist(strsplit(z, split=split, fixed=fixed)) return(tmp[ length(tmp) ]) })) } ## just to add another layer; basically just calls buildQuery and executes the query .getWhat <- function(x, columns, filter = list(), order.by = "", order.type = "asc", group.by = NULL, skip.order.check = FALSE) { ## That's nasty stuff; for now we support the column tx_name, which we however ## don't have in the database. Thus, we are querying everything except that ## column and filling it later with the values from tx_id. fetchColumns <- columns if(any(columns == "tx_name")) fetchColumns <- unique(c("tx_id", fetchColumns[fetchColumns != "tx_name"])) if (class(filter) != "list") stop("parameter filter has to be a list of BasicFilter classes!") ## If any of the filter is a SymbolFilter, add "symbol" to the return columns. if (length(filter) > 0) { if (any(unlist(lapply(filter, function(z) { return(is(z, "SymbolFilter")) })))) columns <- unique(c(columns, "symbol")) ## append a filter column. } ## Catch also a "symbol" in columns if(any(columns == "symbol")) fetchColumns <- unique(c(fetchColumns[fetchColumns != "symbol"], "gene_name")) ## Shall we do the ordering in R or in SQL? if (orderResultsInR(x) & !skip.order.check) { ## Build the query Q <- .buildQuery(x = x, columns = fetchColumns, filter = filter, order.by = "", order.type = order.type, group.by = group.by, skip.order.check = skip.order.check) ## Get the data Res <- dbGetQuery(dbconn(x), Q) ## Note: we can only order by the columns that we did get back from the ## database; that might be different for the SQL sorting! Res <- orderDataFrameBy(Res, by = checkOrderBy(order.by, fetchColumns), decreasing = order.type != "asc") } else { ## Build the query Q <- .buildQuery(x = x, columns = fetchColumns, filter = filter, order.by = order.by, order.type = order.type, group.by = group.by, skip.order.check = skip.order.check) ## Get the data Res <- dbGetQuery(dbconn(x), Q) } ## cat("Query:\n", Q, "\n") if(any(columns == "tx_cds_seq_start")) { if (!is.integer(Res[, "tx_cds_seq_start"])) { suppressWarnings( ## column contains "NULL" if not defined and coordinates are ## characters as.numeric transforms "NULL" into NA, and ensures ## coords are numeric. Res[ , "tx_cds_seq_start"] <- as.integer(Res[ , "tx_cds_seq_start"]) ) } } if(any(columns=="tx_cds_seq_end")){ if (!is.integer(Res[, "tx_cds_seq_end"])) { suppressWarnings( ## column contains "NULL" if not defined and coordinates are ## characters as.numeric transforms "NULL" into NA, and ensures ## coords are numeric. Res[ , "tx_cds_seq_end" ] <- as.integer(Res[ , "tx_cds_seq_end" ]) ) } } ## Fix for MySQL returning 'numeric' instead of 'integer'. ## THIS SHOULD BE REMOVED ONCE THE PROBLEM IS FIXED IN RMySQL!!! int_cols <- c("exon_seq_start", "exon_seq_end", "exon_idx", "tx_seq_start", "tx_seq_end", "tx_cds_seq_start", "tx_cds_seq_end", "gene_seq_start", "gene_seq_end", "seq_strand") for (the_col in int_cols) { if (any(colnames(Res) == the_col)) if (!is.integer(Res[, the_col])) Res[, the_col] <- as.integer(Res[, the_col]) } ## Resolving the "symlinks" again. if(any(columns == "tx_name")) { Res <- data.frame(Res, tx_name = Res$tx_id, stringsAsFactors = FALSE) } if(any(columns == "symbol")) { Res <- data.frame(Res, symbol = Res$gene_name, stringsAsFactors = FALSE) } ## Ensure that the ordering is as requested. Res <- Res[, columns, drop=FALSE] return(Res) } ############################################################ ## Check database validity. .ENSDB_TABLES <- list(gene = c("gene_id", "gene_name", "entrezid", "gene_biotype", "gene_seq_start", "gene_seq_end", "seq_name", "seq_strand", "seq_coord_system"), tx = c("tx_id", "tx_biotype", "tx_seq_start", "tx_seq_end", "tx_cds_seq_start", "tx_cds_seq_end", "gene_id"), tx2exon = c("tx_id", "exon_id", "exon_idx"), exon = c("exon_id", "exon_seq_start", "exon_seq_end"), chromosome = c("seq_name", "seq_length", "is_circular"), metadata = c("name", "value")) dbHasRequiredTables <- function(con, returnError = TRUE) { tabs <- dbListTables(con) if (length(tabs) == 0) { if (returnError) return("Database does not have any tables!") return(FALSE) } not_there <- names(.ENSDB_TABLES)[!(names(.ENSDB_TABLES) %in% tabs)] if (length(not_there) > 0) { if (returnError) return(paste0("Required tables ", paste(not_there, collapse = ", "), " are not present in the database!")) return(FALSE) } return(TRUE) } dbHasValidTables <- function(con, returnError = TRUE) { for (tab in names(.ENSDB_TABLES)) { cols <- .ENSDB_TABLES[[tab]] from_db <- colnames(dbGetQuery(con, paste0("select * from ", tab, " limit 1"))) not_there <- cols[!(cols %in% from_db)] if (length(not_there) > 0) { if (returnError) return(paste0("Table ", tab, " is missing required columns ", paste(not_there, collapse = ", "), "!")) return(FALSE) } } return(TRUE) } ############################################################ ## feedEnsDb2MySQL ## ## feedEnsDb2MySQL <- function(x, mysql, verbose = TRUE) { if (!inherits(mysql, "MySQLConnection")) stop("'mysql' is supposed to be a connection to a MySQL database.") ## Fetch the tables and feed them to MySQL. sqlite_con <- dbconn(x) tabs <- dbListTables(sqlite_con) for (the_table in tabs) { if (verbose) message("Fetch table ", the_table, "...", appendLF = FALSE) tmp <- dbGetQuery(sqlite_con, paste0("select * from ", the_table, ";")) if (verbose) message("OK\nStoring the table in MySQL...", appendLF = FALSE) ## Fix tx_cds_seq_start being a character in old databases if (any(colnames(tmp) == "tx_cds_seq_start")) { suppressWarnings( tmp[, "tx_cds_seq_start"] <- as.integer(tmp[, "tx_cds_seq_start"]) ) suppressWarnings( tmp[, "tx_cds_seq_end"] <- as.integer(tmp[, "tx_cds_seq_end"]) ) } dbWriteTable(mysql, tmp, name = the_table, row.names = FALSE) if (verbose) message("OK") } ## Create the indices. if (verbose) message("Creating indices...", appendLF = FALSE) .createEnsDbIndices(mysql, indexLength = "(20)") if (verbose) message("OK") return(TRUE) } ## Small helper function to cfeate all the indices. .createEnsDbIndices <- function(con, indexLength = "") { dbGetQuery(con, paste0("create index seq_name_idx on chromosome (seq_name", indexLength, ");")) dbGetQuery(con, paste0("create index gene_gene_id_idx on gene (gene_id", indexLength, ");")) dbGetQuery(con, paste0("create index gene_gene_name_idx on gene (gene_name", indexLength, ");")) dbGetQuery(con, paste0("create index gene_seq_name_idx on gene (seq_name", indexLength, ");")) dbGetQuery(con, paste0("create index tx_tx_id_idx on tx (tx_id", indexLength, ");")) dbGetQuery(con, paste0("create index tx_gene_id_idx on tx (gene_id", indexLength, ");")) dbGetQuery(con, paste0("create index exon_exon_id_idx on exon (exon_id", indexLength, ");")) dbGetQuery(con, paste0("create index t2e_tx_id_idx on tx2exon (tx_id", indexLength, ");")) dbGetQuery(con, paste0("create index t2e_exon_id_idx on tx2exon (exon_id", indexLength, ");")) dbGetQuery(con, "create index t2e_exon_idx_idx on tx2exon (exon_idx);") } ############################################################ ## listEnsDbs ## list databases ##' @title List EnsDb databases in a MySQL server ##' @description The \code{listEnsDbs} function lists EnsDb databases in a ##' MySQL server. ##' ##' @details The use of this function requires that the \code{RMySQL} package ##' is installed and that the user has either access to a MySQL server with ##' already installed EnsDb databases, or write access to a MySQL server in ##' which case EnsDb databases could be added with the \code{\link{useMySQL}} ##' method. EnsDb databases follow the same naming conventions than the EnsDb ##' packages, with the exception that the name is all lower case and that ##' \code{"."} is replaced by \code{"_"}. ##' @param dbcon A \code{DBIConnection} object providing access to a MySQL ##' database. Either \code{dbcon} or all of the other arguments have to be ##' specified. ##' @param host Character specifying the host on which the MySQL server is ##' running. ##' @param port The port of the MySQL server (usually \code{3306}). ##' @param user The username for the MySQL server. ##' @param pass The password for the MySQL server. ##' @return A \code{data.frame} listing the database names, organism name ##' and Ensembl version of the EnsDb databases found on the server. ##' @author Johannes Rainer ##' @seealso \code{\link{useMySQL}} ##' @examples ##' \dontrun{ ##' library(RMySQL) ##' dbcon <- dbConnect(MySQL(), host = "localhost", user = my_user, pass = my_pass) ##' listEnsDbs(dbcon) ##' } listEnsDbs <- function(dbcon, host, port, user, pass) { if(requireNamespace("RMySQL", quietly = TRUE)) { if (missing(dbcon)) { if (missing(host) | missing(user) | missing(port) | missing(host)) stop("Arguments 'host', 'port', 'user' and 'pass' are required", " if 'dbcon' is not specified.") dbcon <- dbConnect(RMySQL::MySQL(), host = host, port = port, user = user, pass = pass) } dbs <- dbGetQuery(dbcon, "show databases;") edbs <- dbs[grep(dbs$Database, pattern = "^ensdb_"), "Database"] edbTable <- data.frame(matrix(ncol = 3, nrow = length(edbs))) colnames (edbTable) <- c("dbname", "organism", "ensembl_version") for (i in seq_along(edbs)) { edbTable[i, "dbname"] <- edbs[i] tmp <- unlist(strsplit(edbs[i], split = "_")) edbTable[i, "organism"] <- tmp[2] edbTable[i, "ensembl_version"] <- as.numeric(gsub(pattern = "v", replacement = "", tmp[3])) } return(edbTable) } else { stop("Required package 'RMySQL' is not installed.") } } ensembldb/R/functions-utils.R0000644000175400017540000000565313001231246017226 0ustar00biocbuildbiocbuild############################################################ ## Utility functions ############################################################ ## orderDataFrameBy ## ## Simply orders the data.frame x based on the columns specified ## with by. orderDataFrameBy <- function(x, by = "", decreasing = FALSE) { if (all(by == "") | all(is.null(by))) return(x) return(x[do.call(order, args = c(list(method = "radix", decreasing = decreasing), as.list(x[, by, drop = FALSE]))), ]) } ############################################################ ## checkOrderBy ## ## Check the orderBy argument. ## o orderBy can be a character vector or a , separated list. ## o Ensure that the columns are valid by comparing with 'supported'. ## Returns a character vector, each element representing a column ## on which sorting should be performed. checkOrderBy <- function(orderBy, supported = character()) { if (is.null(orderBy) | all(orderBy == "")) { return(orderBy) } if (length(orderBy) == 1 & length(grep(orderBy, pattern = ",")) > 0) { orderBy <- unlist(strsplit(orderBy, split = ","), use.names = FALSE) orderBy <- gsub(orderBy, pattern = " ", replacement = "", fixed = TRUE) } not_supported <- !(orderBy %in% supported) if (any(not_supported)) { warning("Columns in 'order.by' (", paste(orderBy[not_supported], collapse = ", "), ") are not in 'columns' and were thus removed.") orderBy <- orderBy[!not_supported] if (length(orderBy) == 0) orderBy <- "" } return(orderBy) } ############################################################ ## addFilterColumns ## ## This function checks the filter objects and adds, depending on the ## returnFilterColumns setting of the EnsDb, also columns for each of the ## filters, ensuring that: ## a) "Symlink" filters are added correctly (the column returned by the ## column call without db are added). ## b) GRangesFilter: the feature is set based on the specified feature parameter ## Args: addFilterColumns <- function(cols, filter = list(), edb) { gimmeAll <- returnFilterColumns(edb) if (!missing(filter)) { if(!is.list(filter)) filter <- list(filter) } else { return(cols) } if (!gimmeAll) return(cols) ## Or alternatively process the filters and add columns. symFilts <- c("SymbolFilter") addC <- unlist(lapply(filter, function(z) { if(class(z) %in% symFilts) return(column(z)) return(column(z)) })) return(unique(c(cols, addC))) } ############################################################ ## SQLiteName2MySQL ## ## Convert the SQLite database name (file name) to the corresponding ## MySQL database name. SQLiteName2MySQL <- function(x) { return(tolower(gsub(x, pattern = ".", replacement = "_", fixed = TRUE))) } ensembldb/R/loadEnsDb.R0000644000175400017540000000017013001231247015701 0ustar00biocbuildbiocbuildloadEnsDb <- function( x ){ ## con <- ensDb( x ) ## EDB <- new( "EnsDb", ensdb=con ) return( EnsDb( x ) ) } ensembldb/R/makeEnsemblDbPackage.R0000644000175400017540000002075713001231247020030 0ustar00biocbuildbiocbuild## part of this code is from GenomicFeatures makeTxDbPackage.R ## So to make a package we need a couple things: ## 1) we need a method called makeTxDbPackage (that will take a txdb object) ## 2) we will need a package template to use ## Separate helper function for abbreviating the genus and species name strings ## this simply makes the first character uppercase .organismName <- function(x){ substring(x, 1, 1) <- toupper(substring(x, 1, 1)) return(x) } .abbrevOrganismName <- function(organism){ spc <- unlist(strsplit(organism, "_")) ## this assumes a binomial nomenclature has been maintained. return(paste0(substr(spc[[1]], 1, 1), spc[[2]])) } ## x has to be the connection to the database. .makePackageName <- function(x){ species <- .getMetaDataValue(x, "Organism") ensembl_version <- .getMetaDataValue(x, "ensembl_version") pkgName <- paste0("EnsDb.",.abbrevOrganismName(.organismName(species)), ".v", ensembl_version) return(pkgName) } .makeObjectName <- function(pkgName){ strs <- unlist(strsplit(pkgName, "\\.")) paste(c(strs[2:length(strs)],strs[1]), collapse="_") } ## retrieve Ensembl data ## save all files to local folder. ## returns the path where files have been saved to. fetchTablesFromEnsembl <- function(version, ensemblapi, user="anonymous", host="ensembldb.ensembl.org", pass="", port=5306, species="human"){ if(missing(version)) stop("The version of the Ensembl database has to be provided!") ## setting the stage for perl: fn <- system.file("perl", "get_gene_transcript_exon_tables.pl", package="ensembldb") ## parameters: s, U, H, P, e ## replacing white spaces with _ species <- gsub(species, pattern=" ", replacement="_") cmd <- paste0("perl ", fn, " -s ", species," -e ", version, " -U ", user, " -H ", host, " -p ", port, " -P ", pass) if(!missing(ensemblapi)){ Sys.setenv(ENS=ensemblapi) } system(cmd) if(!missing(ensemblapi)){ Sys.unsetenv("ENS") } ## we should now have the files: in_files <- c("ens_gene.txt", "ens_tx.txt", "ens_exon.txt", "ens_tx2exon.txt", "ens_chromosome.txt", "ens_metadata.txt") ## check if we have all files... all_files <- dir(pattern="txt") if(sum(in_files %in% all_files)!=length(in_files)) stop("Something went wrong! I'm missing some of the txt files the perl script should have generated.") } #### ## ## create a SQLite database containing the information defined in the txt files. makeEnsemblSQLiteFromTables <- function(path=".", dbname){ ## check if we have all files... in_files <- c("ens_gene.txt", "ens_tx.txt", "ens_exon.txt", "ens_tx2exon.txt", "ens_chromosome.txt", "ens_metadata.txt") ## check if we have all files... all_files <- dir(path, pattern="txt") if(sum(in_files %in% all_files)!=length(in_files)) stop("Something went wrong! I'm missing some of the txt files the perl script should have generated.") ## read information info <- read.table(paste0(path, .Platform$file.sep ,"ens_metadata.txt"), sep="\t", as.is=TRUE, header=TRUE) species <- .organismName(info[ info$name=="Organism", "value" ]) ##substring(species, 1, 1) <- toupper(substring(species, 1, 1)) if(missing(dbname)){ dbname <- paste0("EnsDb.",substring(species, 1, 1), unlist(strsplit(species, split="_"))[ 2 ], ".v", info[ info$name=="ensembl_version", "value" ], ".sqlite") } con <- dbConnect(dbDriver("SQLite"), dbname=dbname) ## write information table dbWriteTable(con, name="metadata", info, row.names=FALSE) ## process chromosome tmp <- read.table(paste0(path, .Platform$file.sep ,"ens_chromosome.txt"), sep="\t", as.is=TRUE, header=TRUE) tmp[, "seq_name"] <- as.character(tmp[, "seq_name"]) dbWriteTable(con, name="chromosome", tmp, row.names=FALSE) rm(tmp) ## process genes: some gene names might have fancy names... tmp <- read.table(paste0(path, .Platform$file.sep, "ens_gene.txt"), sep="\t", as.is=TRUE, header=TRUE, quote="", comment.char="" ) OK <- .checkIntegerCols(tmp) dbWriteTable(con, name="gene", tmp, row.names=FALSE) rm(tmp) ## process transcripts: tmp <- read.table(paste0(path, .Platform$file.sep, "ens_tx.txt"), sep="\t", as.is=TRUE, header=TRUE) ## Fix the tx_cds_seq_start and tx_cds_seq_end columns: these should be integer! suppressWarnings( tmp[, "tx_cds_seq_start"] <- as.integer(tmp[, "tx_cds_seq_start"]) ) suppressWarnings( tmp[, "tx_cds_seq_end"] <- as.integer(tmp[, "tx_cds_seq_end"]) ) OK <- .checkIntegerCols(tmp) dbWriteTable(con, name="tx", tmp, row.names=FALSE) rm(tmp) ## process exons: tmp <- read.table(paste0(path, .Platform$file.sep, "ens_exon.txt"), sep="\t", as.is=TRUE, header=TRUE) OK <- .checkIntegerCols(tmp) dbWriteTable(con, name="exon", tmp, row.names=FALSE) rm(tmp) tmp <- read.table(paste0(path, .Platform$file.sep, "ens_tx2exon.txt"), sep="\t", as.is=TRUE, header=TRUE) OK <- .checkIntegerCols(tmp) dbWriteTable(con, name="tx2exon", tmp, row.names=FALSE) rm(tmp) ## Create indices .createEnsDbIndices(con) dbDisconnect(con) ## done. return(dbname) } ############################################################ ## Simply checking that some columns are integer .checkIntegerCols <- function(x, columns = c("gene_seq_start", "gene_seq_end", "tx_seq_start", "tx_seq_start", "exon_seq_start", "exon_seq_end", "exon_idx", "tx_cds_seq_start", "tx_cds_seq_end")) { cols <- columns[columns %in% colnames(x)] if(length(cols) > 0) { sapply(cols, function(z) { if(!is.integer(x[, z])) stop("Column '", z,"' is not of type integer!") }) } return(TRUE) } #### ## the function that creates the annotation package. ## ensdb should be a connection to an SQLite database, or a character string... makeEnsembldbPackage <- function(ensdb, version, maintainer, author, destDir=".", license="Artistic-2.0"){ if(class(ensdb)!="character") stop("ensdb has to be the name of the SQLite database!") ensdbfile <- ensdb ensdb <- EnsDb(x=ensdbfile) con <- dbconn(ensdb) pkgName <- .makePackageName(con) ensembl_version <- .getMetaDataValue(con, "ensembl_version") ## there should only be one template template_path <- system.file("pkg-template",package="ensembldb") ## We need to define some symbols in order to have the ## template filled out correctly. symvals <- list( PKGTITLE=paste("Ensembl based annotation package"), PKGDESCRIPTION=paste("Exposes an annotation databases generated from Ensembl."), PKGVERSION=version, AUTHOR=author, MAINTAINER=maintainer, LIC=license, ORGANISM=.organismName(.getMetaDataValue(con ,'Organism')), SPECIES=.organismName(.getMetaDataValue(con,'Organism')), PROVIDER="Ensembl", PROVIDERVERSION=as.character(ensembl_version), RELEASEDATE= .getMetaDataValue(con ,'Creation time'), SOURCEURL= .getMetaDataValue(con ,'ensembl_host'), ORGANISMBIOCVIEW=gsub(" ","_",.organismName(.getMetaDataValue(con ,'Organism'))), TXDBOBJNAME=pkgName ## .makeObjectName(pkgName) ) ## Should never happen if (any(duplicated(names(symvals)))) { str(symvals) stop("'symvals' contains duplicated symbols") } createPackage(pkgname=pkgName, destinationDir=destDir, originDir=template_path, symbolValues=symvals) ## then copy the contents of the database into the extdata dir sqlfilename <- unlist(strsplit(ensdbfile, split=.Platform$file.sep)) sqlfilename <- sqlfilename[ length(sqlfilename) ] dir.create(paste(c(destDir, pkgName, "inst", "extdata"), collapse=.Platform$file.sep), showWarnings=FALSE, recursive=TRUE) db_path <- file.path(destDir, pkgName, "inst", "extdata", paste(pkgName,"sqlite",sep=".")) file.copy(ensdbfile, to=db_path) } ensembldb/R/runEnsDbApp.R0000644000175400017540000000051713001231246016233 0ustar00biocbuildbiocbuild## running the shiny web app. runEnsDbApp <- function(...){ if(requireNamespace("shiny", quietly=TRUE)){ message("Starting the EnsDb shiny web app. Use Ctrl-C to stop.") shiny::runApp(appDir=system.file("shinyHappyPeople", package="ensembldb"), ...) }else{ stop("Package shiny not installed!") } } ensembldb/R/select-methods.R0000644000175400017540000003001513001231247016767 0ustar00biocbuildbiocbuild## That's to support and interface the AnnotionDbi package. ####============================================================ ## .getColMappings ## ## That returns a character vector of abbreviated column names ## which can be/are used by AnnotationDbi with the names correponding ## to the column names from ensembldb. ## x: is supposed to be an EnsDb object. ## all: if TRUE we return all of them, otherwise we just return those ## that should be visible for the user. ####------------------------------------------------------------ .getColMappings <- function(x, all=FALSE){ cols <- listColumns(x) if(!all){ cols <- cols[!(cols %in% c("name", "value"))] } ret <- toupper(gsub("_", replacement="", cols)) names(ret) <- cols return(ret) } ####============================================================ ## columnForKeytype ## ## Returns the appropriate column name in the database for the ## given keytypes. ####------------------------------------------------------------ ensDbColumnForColumn <- function(x, column){ maps <- .getColMappings(x) revmaps <- names(maps) names(revmaps) <- maps cols <- revmaps[column] if(any(is.na(cols))){ warning("The following columns can not be mapped to column names in the", " db: ", paste(column[is.na(cols)], collapse=", ")) cols <- cols[!is.na(cols)] } ## Fixing tx_name; tx_name should be mapped to tx_id in the database! ##cols[cols == "tx_name"] <- "tx_id" return(cols) } ####============================================================ ## columns method ## ## Just return the attributes, but as expected by the AnnotationDbi ## interface (i.e. upper case, no _). ####------------------------------------------------------------ .getColumns <- function(x){ cols <- .getColMappings(x, all=FALSE) names(cols) <- NULL return(unique(cols)) } setMethod("columns", "EnsDb", function(x) .getColumns(x) ) ####============================================================ ## keytypes method ## ## I will essentially use all of the filters here. ####------------------------------------------------------------ setMethod("keytypes", "EnsDb", function(x){ return(.filterKeytypes()) } ) ## This just returns some (eventually) usefull names for keys .simpleKeytypes <- function(x){ return(c("GENEID","TXID","TXNAME","EXONID","EXONNAME","CDSID","CDSNAME")) } .filterKeytypes <- function(x){ return(names(.keytype2FilterMapping())) } ## returns a vector mapping keytypes (names of vector) to filter names (elements). .keytype2FilterMapping <- function(){ filters <- c("EntrezidFilter", "GeneidFilter", "GenebiotypeFilter", "GenenameFilter", "TxidFilter", "TxbiotypeFilter", "ExonidFilter", "SeqnameFilter", "SeqstrandFilter", "TxidFilter", "SymbolFilter") names(filters) <- c("ENTREZID", "GENEID", "GENEBIOTYPE", "GENENAME", "TXID", "TXBIOTYPE", "EXONID", "SEQNAME", "SEQSTRAND", "TXNAME", "SYMBOL") return(filters) } filterForKeytype <- function(keytype){ filters <- .keytype2FilterMapping() if(any(names(filters) == keytype)){ filt <- new(filters[keytype]) return(filt) }else{ stop("No filter for that keytype!") } } ####============================================================ ## keys method ## ## This keys method returns all of the keys for a specified keytype. ## There should also be an implementation without keytypes, which ## returns in our case the gene_ids ## ####------------------------------------------------------------ setMethod("keys", "EnsDb", function(x, keytype, filter,...){ if(missing(keytype)) keytype <- "GENEID" if(missing(filter)) filter <- list() if(is(filter, "BasicFilter")) filter <- list(filter) keyt <- keytypes(x) keytype <- match.arg(keytype, keyt) ## Map the keytype to the appropriate column name. dbColumn <- ensDbColumnForColumn(x, keytype) ## Perform the query. res <- getWhat(x, columns=dbColumn, filter=filter)[, dbColumn] return(res) }) ####============================================================ ## select method ## ## ####------------------------------------------------------------ setMethod("select", "EnsDb", function(x, keys, columns, keytype, ...) { if (missing(keys)) keys <- NULL if (missing(columns)) columns <- NULL if (missing(keytype)) keytype <- NULL return(.select(x = x, keys = keys, columns = columns, keytype = keytype, ...)) }) .select <- function(x, keys = NULL, columns = NULL, keytype = NULL, ...) { extraArgs <- list(...) ## Perform argument checking: ## columns: if (missing(columns) | is.null(columns)) columns <- columns(x) notAvailable <- !(columns %in% columns(x)) if (all(notAvailable)) stop("None of the specified columns are avaliable in the database!") if (any(notAvailable)){ warning("The following columns are not available in the database and have", " thus been removed: ", paste(columns[notAvailable], collapse = ", ")) columns <- columns[!notAvailable] } ## keys: if (is.null(keys) | missing(keys)) { ## Get everything from the database... keys <- list() } else { if (!(is(keys, "character") | is(keys, "list") | is(keys, "BasicFilter"))) stop("Argument keys should be a character vector, an object extending BasicFilter ", "or a list of objects extending BasicFilter.") if (is(keys, "list")) { if (!all(vapply(keys, is, logical(1L), "BasicFilter"))) stop("If keys is a list it should be a list of objects extending BasicFilter!") } if (is(keys, "BasicFilter")) { keys <- list(keys) } if (is(keys, "character")) { if (is.null(keytype)) { stop("Argument keytype is mandatory if keys is a character vector!") } ## Check also keytype: if (!(keytype %in% keytypes(x))) stop("keytype ", keytype, " not available in the database.", " Use keytypes method to list all available keytypes.") ## Generate a filter object for the filters. keyFilter <- filterForKeytype(keytype) value(keyFilter) <- keys keys <- list(keyFilter) ## Add also the keytype itself to the columns. if (!any(columns == keytype)) columns <- c(keytype, columns) } } ## Map the columns to column names we have in the database and add filter columns too. ensCols <- unique(c(ensDbColumnForColumn(x, columns), addFilterColumns(character(), filter = keys, x))) ## OK, now perform the query given the filters we've got. res <- getWhat(x, columns = ensCols, filter = keys) ## Order results if length of filters is 1. if (length(keys) == 1) { ## Define the filters on which we could sort. sortFilts <- c("GenenameFilter", "GeneidFilter", "EntrezidFilter", "GenebiotypeFilter", "SymbolFilter", "TxidFilter", "TxbiotypeFilter", "ExonidFilter", "ExonrankFilter", "SeqnameFilter") if (class(keys[[1]]) %in% sortFilts) { keyvals <- value(keys[[1]]) ## Handle symlink Filter differently: if (is(keys[[1]], "SymbolFilter")) { sortCol <- column(keys[[1]]) } else { sortCol <- removePrefix(column(keys[[1]], x)) } res <- res[order(match(res[, sortCol], keyvals)), ] } } else { ## Show a mild warning message message("Note: ordering of the results might not match ordering of keys!") } colMap <- .getColMappings(x) colnames(res) <- colMap[colnames(res)] rownames(res) <- NULL if (returnFilterColumns(x)) return(res) ## ## Now, if we've got a "TXNAME" in columns, we have to replace at least one of the "TXID"s ## ## in the colnames... ## if(any(columns == "TXNAME")) ## colnames(res)[match("TXID", colnames(res))] <- "TXNAME" return(res[, columns]) } ####============================================================ ## mapIds method ## ## maps the submitted keys (names of the returned vector) to values ## of the column specified by column. ## x, key, column, keytype, ..., multiVals ####------------------------------------------------------------ setMethod("mapIds", "EnsDb", function(x, keys, column, keytype, ..., multiVals){ if(missing(keys)) keys <- NULL if(missing(column)) column <- NULL if(missing(keytype)) keytype <- NULL if(missing(multiVals)) multiVals <- NULL return(.mapIds(x=x, keys=keys, column=column, keytype=keytype, multiVals=multiVals, ...)) }) ## Other methods: saveDb, species, dbfile, dbconn, taxonomyId .mapIds <- function(x, keys=NULL, column=NULL, keytype=NULL, ..., multiVals=NULL){ if(is.null(keys)) stop("Argument keys has to be provided!") if(!(is(keys, "character") | is(keys, "list") | is(keys, "BasicFilter"))) stop("Argument keys should be a character vector, an object extending BasicFilter ", "or a list of objects extending BasicFilter.") if(is.null(column)) column <- "GENEID" ## Have to specify the columns argument. Has to be keytype and column. if(is(keys, "character")){ if(is.null(keytype)) stop("Argument keytype is mandatory if keys is a character vector!") columns <- c(keytype, column) } if(is(keys, "list") | is(keys, "BasicFilter")){ if(is(keys, "list")){ if(length(keys) > 1) warning("Got ", length(keys), " filter objects.", " Will use the keys of the first for the mapping!") cn <- class(keys[[1]])[1] }else{ cn <- class(keys)[1] } ## Use the first element to determine the keytype... mapping <- .keytype2FilterMapping() columns <- c(names(mapping)[mapping == cn], column) keytype <- NULL } res <- select(x, keys=keys, columns=columns, keytype=keytype) if(nrow(res) == 0) return(character()) ## Handling multiVals. if(is.null(multiVals)) multiVals <- "first" if(is(multiVals, "function")) stop("Not yet implemented!") ## Eventually re-order the data.frame in the same order than the keys... ## That's amazingly slow!!! ## if(is.character(keys)){ ## res <- split(res, f=factor(res[, 1], levels=keys)) ## res <- do.call(rbind, res) ## rownames(res) <- NULL ## } if(is.character(keys)){ theNames <- keys }else{ theNames <- unique(res[, 1]) } switch(multiVals, first={ vals <- res[match(theNames, res[, 1]), 2] names(vals) <- theNames return(vals) }, list={ ## vals <- split(res[, 2], f=factor(res[, 1], levels=unique(res[, 1]))) vals <- split(res[, 2], f=factor(res[, 1], levels=unique(theNames))) return(vals) }, filter={ vals <- split(res[, 2], f=factor(res[, 1], levels=unique(theNames))) vals <- vals[unlist(lapply(vals, length)) == 1] return(unlist(vals)) }, asNA={ ## Split the vector, set all those with multi mappings NA. vals <- split(res[, 2], f=factor(res[, 1], levels=unique(theNames))) vals[unlist(lapply(vals, length)) > 1] <- NA return(unlist(vals)) }, CharacterList={ stop("Not yet implemented!") }) } ensembldb/R/seqname-utils.R0000644000175400017540000002416113001231247016643 0ustar00biocbuildbiocbuild####============================================================ ## Methods and functions to allow usage of EnsDb objects also ## with genomic resources that do not use Ensembl based ## seqnames ## We're storing the seqname style into the .properties slot ## of the EnsDb object. ####------------------------------------------------------------ .ENSOPT.SEQNOTFOUND="ensembldb.seqnameNotFound" ####============================================================ ## formatSeqnamesForQuery ## ## Formating/renamaing the seqname(s) according to the specified ## style. ## x is an EnsDb, ## sn the seqnames to convert... ## If a seqname can not be mapped NA will be returned. ####------------------------------------------------------------ setMethod("formatSeqnamesForQuery", "EnsDb", function(x, sn, ifNotFound){ return(.formatSeqnameByStyleForQuery(x, sn, ifNotFound)) }) ## Little helper function that returns eventually the argument. ## Returns MISSING if the argument was not set. .getSeqnameNotFoundOption <- function(){ notFound <- "MISSING" if(any(names(options()) == .ENSOPT.SEQNOTFOUND)){ notFound <- getOption(.ENSOPT.SEQNOTFOUND) ## Do some sanity checks? } return(notFound) } .formatSeqnameByStyleForQuery <- function(x, sn, ifNotFound){ ## Fixing ifNotFound, allowing that this can be set using options. if(missing(ifNotFound)){ ifNotFound <- .getSeqnameNotFoundOption() } ## Map whatever to Ensembl seqnames, such that we can perform queries. ## Use mapSeqlevels, or rather genomeStyles and do it hand-crafted! sst <- seqlevelsStyle(x) dbSst <- dbSeqlevelsStyle(x) if(sst == dbSst) return(sn) ## Don't like that the genomeStyles is reading the stuff form file. map <- getProperty(x, "genomeStyle") if(!is(map, "data.frame")) map <- genomeStyles(organism(x)) ## sn are supposed to be in sst style, map them to dbSst idx <- match(sn, map[, sst]) mapped <- map[idx, dbSst] if(any(is.na(mapped))){ noMap <- which(is.na(mapped)) seqNoMap <- unique(sn[noMap]) if(length(seqNoMap) > 5){ theMess <- paste0("More than 5 seqnames could not be mapped to ", "the seqlevels style of the database (", dbSst, ")!") }else{ theMess <- paste0("Seqnames: ", paste(seqNoMap, collapse=", "), " could not be mapped to ", " the seqlevels style of the database (", dbSst, ")!") } if(is.na(ifNotFound) | is.null(ifNotFound)){ ## Replacing the missing seqname mappings with ifNotFound. mapped[noMap] <- ifNotFound warnMess <- paste0(" Returning ", ifNotFound, " for these.") }else{ ## If MISSING -> STOP if(ifNotFound == "MISSING"){ stop(theMess) }else{ ## Next special case: use the original names, i.e. don't map at all. if(ifNotFound == "ORIGINAL"){ mapped[noMap] <- sn[noMap] warnMess <- "Returning the orginal seqnames for these." }else{ mapped[noMap] <- ifNotFound warnMess <- paste0(" Returning ", ifNotFound, " for these.") } } } warning(theMess, warnMess) } return(mapped) } setMethod("formatSeqnamesFromQuery", "EnsDb", function(x, sn, ifNotFound){ return(.formatSeqnameByStyleFromQuery(x, sn, ifNotFound)) }) .formatSeqnameByStyleFromQuery <- function(x, sn, ifNotFound){ ## Fixing ifNotFound, allowing that this can be set using options. if(missing(ifNotFound)){ ifNotFound <- .getSeqnameNotFoundOption() } ## Map Ensembl seqnames resulting form queries to the seqlevel style by ## seqlevelsStyle. sst <- seqlevelsStyle(x) dbSst <- dbSeqlevelsStyle(x) if(sst == dbSst) return(sn) ## Otherwise... map <- getProperty(x, "genomeStyle") if(!is(map, "data.frame")) map <- genomeStyles(organism(x)) ## sn are supposed to be in sst style, map them to dbSst idx <- match(sn, map[, dbSst]) mapped <- map[idx, sst] if(any(is.na(mapped))){ noMap <- which(is.na(mapped)) seqNoMap <- unique(sn[noMap]) if(length(seqNoMap) > 5){ theMess <- paste0("More than 5 seqnames with seqlevels style of the database (", dbSst, ") could not be mapped to the seqlevels style: ", sst, "!)") }else{ theMess <- paste0("Seqnames: ", paste(seqNoMap, collapse=", "), " with seqlevels style of the database (", dbSst, ") could not be mapped to seqlevels style: ", sst, "!") } if(is.na(ifNotFound) | is.null(ifNotFound)){ ## Replacing the missing seqname mappings with ifNotFound. mapped[noMap] <- ifNotFound warnMess <- paste0(" Returning ", ifNotFound, " for these.") }else{ ## If MISSING -> STOP if(ifNotFound == "MISSING"){ stop(theMess) }else{ ## Next special case: use the original names, i.e. don't map at all. if(ifNotFound == "ORIGINAL"){ mapped[noMap] <- sn[noMap] warnMess <- " Returning the orginal seqnames for these." }else{ mapped[noMap] <- ifNotFound warnMess <- paste0(" Returning ", ifNotFound, " for these.") } } } warning(theMess, warnMess) } return(mapped) } ####============================================================ ## dbSeqlevelsStyle ## ## Returns the seqname style used by the database. Defaults to ## Ensembl and reads the property: dbSeqlevelsStyle. ####------------------------------------------------------------ setMethod("dbSeqlevelsStyle", "EnsDb", function(x){ stl <- getProperty(x, "dbSeqlevelsStyle") if(is.na(stl)) stl <- "Ensembl" return(stl) }) ####============================================================ ## seqlevelStyle ## ## Get or set the seqlevel style. If we can't find the stype in ## GenomeInfoDb throw and error. ####------------------------------------------------------------ setMethod("seqlevelsStyle", "EnsDb", function(x){ st <- getProperty(x, "seqlevelsStyle") if(is.na(st)) st <- "Ensembl" return(st) }) setReplaceMethod("seqlevelsStyle", "EnsDb", function(x, value){ if(value == dbSeqlevelsStyle(x)){ ## Not much to do; that's absolutely fine. x <- setProperty(x, seqlevelsStyle=value) }else{ ## Have to check whether I have the mapping available in GenomeInfoDb, if not ## -> throw an error. dbStyle <- dbSeqlevelsStyle(x) ## Note that both, the db seqlevel style and the style have to be available! ## Check if we could use the mapping provided by GenomeInfoDb. genSt <- try(genomeStyles(organism(x)), silent=TRUE) if(is(genSt, "try-error")){ stop("No mapping of seqlevel styles available in GenomeInfoDb for", " species ", organism(x), "! Please refer to the Vignette of the", " GenomeInfoDb package if you would like to provide this mapping.") } if(!any(colnames(genSt) == value)){ stop("The provided seqlevels style is not known to GenomeInfoDb!") } if(!any(colnames(genSt) == dbStyle)){ stop("The seqlevels style of the database (", dbStyle, ") is not known to GenomeInfoDb!") } ## If we got that far it should be OK x <- setProperty(x, seqlevelsStyle=value) x <- setProperty(x, genomeStyle=list(genSt)) } return(x) }) ####============================================================ ## supportedSeqlevelsStyles ## ## Get all supported seqlevels styles for the species of the EnsDb ####------------------------------------------------------------ setMethod("supportedSeqlevelsStyles", "EnsDb", function(x){ map <- genomeStyles(organism(x)) cn <- colnames(map) cn <- cn[!(cn %in% c("circular", "auto", "sex"))] return(colnames(cn)) }) ####==================== OLD STUFF BELOW ==================== ###============================================================== ## Prefix chromosome names with "chr" if ucscChromosomeNames option ## is set, otherwise, use chromosome names "as is". ## This function should be used in functions that return results from ## EnsDbs. ###-------------------------------------------------------------- prefixChromName <- function(x, prefix="chr"){ ucsc <- getOption("ucscChromosomeNames", default=FALSE) if(ucsc){ ## TODO fix also the mitochondrial chromosome name. mapping <- ucscToEnsMapping() for(i in 1:length(mapping)){ x <- sub(x, pattern=names(mapping)[i], replacement=mapping[i], fixed=TRUE) } ## Replace chr if it's already there x <- gsub(x, pattern="^chr", replacement="", ignore.case=TRUE) x <- paste0(prefix, x) } return(x) } ###============================================================== ## Remove leading "chr" to fit Ensembl based chromosome names. ## This function should be called in functions that fetch data from ## EnsDbs. ###-------------------------------------------------------------- ucscToEns <- function(x){ ## TODO rename all additional chromosome names. mapping <- ucscToEnsMapping() for(i in 1:length(mapping)){ x <- sub(x, pattern=mapping[i], replacement=names(mapping)[i], fixed=TRUE) } x <- gsub(x, pattern="^chr", replacement="", ignore.case=TRUE) return(x) } ###============================================================ ## Returns a character vector, elements representing UCSC chromosome ## names with their names corresponding to the respective Ensembl ## chromosome names. ###------------------------------------------------------------ ucscToEnsMapping <- function(){ theMap <- c(MT="chrM") return(theMap) } ensembldb/R/zzz.R0000644000175400017540000000076713001231246014716 0ustar00biocbuildbiocbuild .onLoad <- function(libname, pkgname){ op <- options() ## What should be returned by default if the seqnames can not be mapped based ## on the style set by seqlevelsStyle<- ## Options: ## + NA or any other value: return this value for such cases. ## + MISSING: stop and throw an error. ## + ORIGINAL: return the original seqnames. opts.ens <- list(useFancyQuotes=FALSE, ensembldb.seqnameNotFound="ORIGINAL") options(opts.ens) invisible() } ensembldb/build/0000755000175400017540000000000013013177517014637 5ustar00biocbuildbiocbuildensembldb/build/vignette.rds0000644000175400017540000000050513013177517017176 0ustar00biocbuildbiocbuildPMS0L V_z[ǻ3Z ֌ԓHZ/=6w! `1C4"珫UD/&%KÈ Ų(M" vUoDbbÙ̘`(ȷVnLXK` z.>,5/'|N{cxg((ZQbCZ(STKyB+/?Ssإ%+螭ȓ2FM_8^)U6&~!Aensembldb/inst/0000755000175400017540000000000013013177517014515 5ustar00biocbuildbiocbuildensembldb/inst/NEWS0000644000175400017540000003020113012705075015203 0ustar00biocbuildbiocbuildCHANGES IN VERSION 1.6.2 ------------------------ BUG FIXES: o Avoid errors when using EnsDbs with protein annotations. CHANGES IN VERSION 1.6.1 ------------------------ BUG FIXES: o Fix plain return statements in shiny server.R. CHANGES IN VERSION 1.5.14 ------------------------- NEW FEATURES: o listEnsDbs function to list EnsDb databases in a MySQL server. o EnsDb constructor function allows to directly connect to a EnsDb database in a MySQL server. o useMySQL compares the creation date between database and SQLite version and proposes to update database if different. CHANGES IN VERSION 1.5.13 ------------------------- NEW FEATURES: o useMySQL method to insert the data into a MySQL database and switch backend from SQLite to MySQL. CHANGES IN VERSION 1.5.12 ------------------------- USER VISIBLE CHANGES: o Add additional indices on newly created database which improves performance considerably. BUG FIXES o Fix issue #11: performance problems with RSQLite 1.0.9011. Ordering for cdsBy, transcriptsBy, UTRs by is performed in R and not in SQL. o Fix ordering bug: results were sorted by columns in alphabetical order (e.g. if order.by = "seq_name, gene_seq_start" was provided they were sorted by gene_seq_start and then by seq_name CHANGES IN VERSION 1.5.11 ------------------------- BUG FIXES o makeEnsemblSQLiteFromTables and ensDbFromGRanges perform sanity checks on the input tables. CHANGES IN VERSION 1.5.10 ------------------------- USER VISIBLE CHANGES: o Using html_document2 style for the vignette. CHANGES IN VERSION 1.5.9 ------------------------- NEW FEATURES: o New SymbolFilter. o returnFilterColumns method to enable/disable that filter columns are also returned by the methods (which is the default). o select method support for SYMBOL keys, columns and filter. o Select method does ensure result ordering matches the input keys if a single filter or only keys are provided. CHANGES IN VERSION 1.5.8 ------------------------- BUG FIXES o Fix problem with white space separated species name in ensDbFromGRanges. CHANGES IN VERSION 1.5.7 ------------------------- OTHER CHANGES o Fixed typos in documentation CHANGES IN VERSION 1.5.6 ------------------------- BUG FIXES o Fix warning fo validation of numeric BasicFilter. CHANGES IN VERSION 1.5.5 ------------------------- BUG FIXES o exonsBy: did always return tx_id, even if not present in columns argument. CHANGES IN VERSION 1.5.4 ------------------------- Bug fixes o Column tx_id was always removed from exonsBy result even if in the columns argument. o exon_idx was of type character if database generated from a GTF file. CHANGES IN VERSION 1.5.2 ------------------------- NEW FEATURES: o Added support for column tx_name in all methods and in the keys and select methods. Values in the returned tx_name columns correspond to the tx_id. o Update documentation. CHANGES IN VERSION 1.5.1 ------------------------- BUG FIXES o tx_id was removed from metadata columns in txBy. o Fixed a bug that caused exon_idx column to be character if database created from a GTF. CHANGES IN VERSION 1.3.20 ------------------------- BUG FIXES o methods transcripts, genes etc don't result in an error when columns are specified which are not present in the database and the return.type is GRanges. o Removed the transcriptLengths method implemented in ensembldb in favor of using the one from GenomicFeatures. CHANGES IN VERSION 1.3.19 ------------------------- BUG FIXES o ensDbFromGRanges (and thus ensDbFromGtf, ensDbFromGff and ensDbFromAH) support now Ensembl GTF file formats from version 74 and before. CHANGES IN VERSION 1.3.18 ------------------------- NEW FEATURES o New ExonrankFilter to filter based on exon index/rank. CHANGES IN VERSION 1.3.17 ------------------------- BUG FIXES o Use setdiff/intersect instead of psetdiff/pintersect. CHANGES IN VERSION 1.3.16 ------------------------- BUG FIXES o Fixed failing test. CHANGES IN VERSION 1.3.15 ------------------------- NEW FEATURES o GRangesFilter now supports GRanges of length > 1. o seqlevels method for GRangesFilter. o New methods exonsByOverlaps and transcriptsByOverlaps. CHANGES IN VERSION 1.3.14 ------------------------- NEW FEATURES o seqlevelsStyle getter and setter method to change the enable easier integration of EnsDb objects with UCSC based packages. supportedSeqlevelsStyle method to list possible values. Global option "ensembldb.seqnameNotFound" allows to adapt the behaviour of the mapping functions when a seqname can not be mapped properly. o Added a seqlevels method for EnsDb objects. SIGNIFICANT USER-VISIBLE CHANGES o Add an example to extract transcript sequences directly from an EnsDb object to the vignette. o Add examples to use EnsDb objects with UCSC chromosome names to the vignette. BUG FIXES o Seqinfo for genes, transcripts and exons contain now only the seqnames returned in the GRanges, not all that are in the database. CHANGES IN VERSION 1.3.13 ------------------------- NEW FEATURES o EnsDb: new "hidden" slot to store additional properties and a method updateEnsDb to update objects to the new implementation. o New method "transcriptLengths" for EnsDb that creates a similar data.frame than the same named function in the GenomicFeatures package. BUG FIXES o fiveUTRsByTranscript and threeUTRsByTranscript returned wrong UTRs for some special cases in which the CDS start and end were in the same exon. This has been fixed. CHANGES IN VERSION 1.3.12 ------------------------- NEW FEATURES o ensDbFromGff and ensDbFromAH functions to build EnsDb objects from GFF3 files or directly from AnnotationHub ressources. o getGenomeFaFile does now also retrieve Fasta files for the "closest" Ensembl release if none is available for the matching version. SIGNIFICANT USER-VISIBLE CHANGES o Removed argument 'verbose' in ensDbFromGRanges and ensDbFromGtf. o Updated parts of the vignette. o Removed method extractTranscriptSeqs again due to some compatibility problems with GenomicRanges. BUG FIXES o Avoid wrong CDS start/end position definition for Ensembl gtf files in which the start or end codon is outside the CDS. CHANGES IN VERSION 1.3.11 ------------------------- BUG FIXES o "select" method returns now also the keytype as a column from the database. CHANGES IN VERSION 1.3.10 ------------------------- NEW FEATURES o Implemented methods columns, keys, keytypes, mapIds and select from AnnotationDbi. o Methods condition<- and value<- for BasicFilter. CHANGES IN VERSION 1.3.9 ------------------------ SIGNIFICANT USER-VISIBLE CHANGES o The shiny app now allows to return the search results. CHANGES IN VERSION 1.3.7 ------------------------ SIGNIFICANT USER-VISIBLE CHANGES o Some small changes to the vignette. BUG FIXES o Fixed a problem in an unit test. CHANGES IN VERSION 1.3.6 ------------------------ BUG FIXES o Fixed a bug in ensDbFromGRanges. CHANGES IN VERSION 1.3.5 ------------------------ NEW FEATURES o Added GRangesFilter enabling filtering using a (single!) GRanges object. o Better usability and compatibility with chromosome names: SeqnameFilter and GRangesFilter support both Ensembl and UCSC chromosome names, if option ucscChromosomeNames is set to TRUE returned chromosome/seqnames are in UCSC format. SIGNIFICANT USER-VISIBLE CHANGES o Added method "value" for BasicFilter objects. BUG FIXES o transcripts, genes, exons return now results sorted by seq name and start coordinate. CHANGES IN VERSION 1.3.4 ------------------------ NEW FEATURES o Added extractTranscriptSeqs method for EnsDb objects. SIGNIFICANT USER-VISIBLE CHANGES o Added a section to the vignette describing the use of ensembldb in Gviz. o Fixed the vignette to conform the "Bioconductor style". o Added argument use.names to exonsBy. BUG FIXES o Fixed bug with getGeneRegionTrackForGviz with only chromosome specified. o Fixed an internal problem subsetting a seqinfo. CHANGES IN VERSION 1.3.3 ------------------------ NEW FEATURES o Add method getGeneRegionTrackForGviz to enable using EnsDb databases for Gviz. BUG FIXES o cdsBy, fiveUTRsForTranscript and threeUTRsForTranscript do no longer throw an error if nothing was found but return NULL and produce a warning. CHANGES IN VERSION 1.3.2 ------------------------ NEW FEATURES o Implemented methods cdsBy, fiveUTRsForTranscript and threeUTRsForTranscript for EnsDb. CHANGES IN VERSION 1.3.1 ------------------------ BUG FIXES o Ensuring that methods exons, genes and transcripts return columns in the same order than provided with argument 'columns' for return.type 'data.frame' or 'DataFrame'. CHANGES IN VERSION 1.1.9 ------------------------ BUG FIXES o Fixed a figure placement problem that can result in an error on certain systems using a recent TexLive distribution. CHANGES IN VERSION 1.1.6 ------------------------ BUG FIXES o Fix a bug in lengthOf that caused an error if no filter was supplied. CHANGES IN VERSION 1.1.5 ------------------------ NEW FEATURES o Implemented a shiny web app to search for genes/transcripts/exons using annotation of an EnsDb annotation package (function runEnsDbApp). CHANGES IN VERSION 1.1.4 ------------------------ NEW FEATURES o Added promoters method. CHANGES IN VERSION 1.1.3 ------------------------ SIGNIFICANT USER-VISIBLE CHANGES o Added method ensemblVersion that returns the Ensembl version the package bases on. o Added method getGenomeFaFile that queries AnnotationHub to retrieve the Genome FaFile matching the Ensembl version of the EnsDb object. CHANGES IN VERSION 1.1.2 ------------------------ SIGNIFICANT USER-VISIBLE CHANGES o Added examples to the vignette for building an EnsDb using AnnotationHub along with the matching genomic sequence. o Added an example for fetching the sequences of genes, transcripts and exons to the vignette. BUG FIXES o Fixed a bug in ensDbFromGRanges and ensDbFromGtf in which the genome build version was not set even if provided. CHANGES IN VERSION 1.1.1 ------------------------ SIGNIFICANT USER-VISIBLE CHANGES o The filter argument in all functions supports now also submission of a filter object, not only of a list of filter objects. CHANGES IN VERSION 0.99.18 -------------------------- BUG FIXES o Fixed a problem in processing GTF files without header information. o Fixed a bug failing to throw an error if not all required feature types are available in the GTF. CHANGES IN VERSION 0.99.17 -------------------------- NEW FEATURES o Added new function ensDbFromGRanges that builds an EnsDB database from information provided in a GRanges object (e.g. retrieved from the AnnotationHub). CHANGES IN VERSION 0.99.16 -------------------------- SIGNIFICANT USER-VISIBLE CHANGES o Added argument outfile to ensDbFromGtf that allows to manually specify the file name of the database file. o ensDbFromGtf tries now to automatically fetch the sequence lengths from Ensembl. BUG FIXES o Fixed the function that extracts the genome build version from the gtf file name. CHANGES IN VERSION 0.99.15 -------------------------- NEW FEATURES o metadata method to extract the information from the metadata database table. o ensDbFromGtf function to generate a EnsDb SQLite file from an (Ensembl) GTF file. CHANGES IN VERSION 0.99.14 -------------------------- BUG FIXES o Fixed a problem when reading tables fetched from Ensembl that contained ' or #. CHANGES IN VERSION 0.99.13 -------------------------- SIGNIFICANT USER-VISIBLE CHANGES o Added argument "port" to the fetchTablesFromEnsembl to allow specifying the MySQL port of the database. 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gene_seq_start gene_seq_end seq_name seq_strand seq_coord_system ENSG00000012817 KDM5D 8284 protein_coding 21865751 21906825 Y -1 chromosome ENSG00000067048 DDX3Y 8653 protein_coding 15016019 15032390 Y 1 chromosome ENSG00000067646 ZFY 7544 protein_coding 2803112 2850547 Y 1 chromosome ENSG00000092377 TBL1Y 90665 protein_coding 6778727 6959724 Y 1 chromosome ENSG00000099715 PCDH11Y 83259;27328 protein_coding 4868267 5610265 Y 1 chromosome ENSG00000099721 AMELY 266 protein_coding 6733959 6742068 Y -1 chromosome ENSG00000099725 PRKY 5616 pseudogene 7142013 7249589 Y 1 chromosome ENSG00000114374 USP9Y 8287 protein_coding 14813160 14972764 Y 1 chromosome ENSG00000129816 TTTY1B 50858;100101116 lincRNA 6258472 6279605 Y 1 chromosome ENSG00000129824 RPS4Y1 6192 protein_coding 2709527 2800041 Y 1 chromosome ENSG00000129845 TTTY1 50858;100101116 lincRNA 9590765 9611898 Y -1 chromosome ENSG00000129862 VCY1B 9084;353513 protein_coding 16168097 16168838 Y 1 chromosome ENSG00000129864 VCY 9084;353513 protein_coding 16097652 16098393 Y -1 chromosome ENSG00000129873 CDY2B 9426;203611 protein_coding 19989290 19992100 Y -1 chromosome ENSG00000131002 TXLNG2P 246126 pseudogene 21729199 21768160 Y 1 chromosome ENSG00000131007 TTTY9B 83864;425057 antisense 20743092 20752407 Y -1 chromosome ENSG00000131538 TTTY6 84672;441543 lincRNA 24585740 24587605 Y -1 chromosome ENSG00000131548 TTTY6B 84672;441543 antisense 24291113 24292978 Y 1 chromosome ENSG00000147753 TTTY7 246122 lincRNA 6317509 6325947 Y 1 chromosome ENSG00000147761 TTTY7B 100101120 lincRNA 9544433 9552871 Y -1 chromosome ENSG00000154620 TMSB4Y 9087 protein_coding 15815447 15817904 Y 1 chromosome ENSG00000157828 RPS4Y2 140032 protein_coding 22918050 22942918 Y 1 chromosome ENSG00000165246 NLGN4Y 22829 protein_coding 16634518 16957530 Y 1 chromosome ENSG00000168757 TSPY2 64591 protein_coding 6114264 6117060 Y 1 chromosome ENSG00000169763 PRYP3 pseudogene 25827587 25840726 Y -1 chromosome ENSG00000169789 PRY 442862;100509646;9081 protein_coding 24636544 24660784 Y 1 chromosome ENSG00000169800 RBMY1F 159163;378951 protein_coding 24314689 24329129 Y -1 chromosome ENSG00000169807 PRY2 442862;100509646;9081 protein_coding 24217903 24242154 Y -1 chromosome ENSG00000169811 RBMY1HP pseudogene 23655405 23663854 Y 1 chromosome ENSG00000169849 TSPY14P pseudogene 23630044 23632569 Y 1 chromosome ENSG00000169953 HSFY2 159119;86614 protein_coding 20893326 20990548 Y -1 chromosome ENSG00000172283 PRYP4 pseudogene 28121660 28134216 Y 1 chromosome ENSG00000172288 CDY1 9085;253175 protein_coding 27768264 27771049 Y 1 chromosome ENSG00000172294 CSPG4P4Y 114758 pseudogene 27624416 27632852 Y 1 chromosome ENSG00000172297 GOLGA2P3Y 401634;84559 pseudogene 27600708 27606719 Y 1 chromosome ENSG00000172332 AC012005.2 401634;84559 pseudogene 26355714 26361728 Y -1 chromosome ENSG00000172342 CSPG4P3Y pseudogene 26332656 26338017 Y -1 chromosome ENSG00000172352 CDY1B 9085;253175 protein_coding 26191376 26194166 Y -1 chromosome ENSG00000172468 HSFY1 159119;86614 protein_coding 20708557 20750849 Y 1 chromosome ENSG00000173357 AC007967.3 pseudogene 8774265 8784114 Y 1 chromosome ENSG00000176679 TGIF2LY 90655 protein_coding 3447082 3448082 Y 1 chromosome ENSG00000176728 TTTY14 55410;83869 lincRNA 21034387 21239302 Y -1 chromosome ENSG00000180910 TTTY11 83866 lincRNA 8651351 8685423 Y -1 chromosome ENSG00000182415 CDY2A 9085;9426;203611;253175 protein_coding 20137667 20140477 Y 1 chromosome ENSG00000183146 CYorf17 100533178 protein_coding 23544840 23548246 Y -1 chromosome ENSG00000183385 TTTY8 84673;100101118 lincRNA 9528709 9531566 Y 1 chromosome ENSG00000183704 SLC9B1P1 protein_coding 13496241 13524717 Y -1 chromosome ENSG00000183753 BPY2 442867;9083;442868 protein_coding 25119966 25151612 Y 1 chromosome ENSG00000183795 BPY2B 442867;9083;442868 protein_coding 26753707 26785354 Y 1 chromosome ENSG00000183878 UTY 7404 protein_coding 15360259 15592553 Y -1 chromosome ENSG00000184895 SRY 6736 protein_coding 2654896 2655740 Y -1 chromosome ENSG00000184991 TTTY13 83868 lincRNA 23745486 23756552 Y -1 chromosome ENSG00000185275 CD24P4 pseudogene 21154353 21154595 Y -1 chromosome ENSG00000185700 TTTY8B 84673;100101118 lincRNA 6338814 6341671 Y -1 chromosome ENSG00000185894 BPY2C 442867;9083;442868 protein_coding 27177048 27208695 Y -1 chromosome ENSG00000187191 DAZ3 57054;57055 protein_coding 26909216 26959626 Y -1 chromosome ENSG00000187657 TSPY13P pseudogene 9743204 9745748 Y -1 chromosome ENSG00000188120 DAZ1 57135;1617 protein_coding 25275502 25345241 Y -1 chromosome ENSG00000188399 ANKRD36P1 pseudogene 28555962 28566682 Y 1 chromosome ENSG00000188656 TSPY7P pseudogene 9215731 9218480 Y 1 chromosome ENSG00000197038 RBMY1A3P 286557 pseudogene 9148739 9160483 Y -1 chromosome ENSG00000197092 GOLGA6L16P pseudogene 27641798 27648105 Y -1 chromosome ENSG00000198692 EIF1AY 9086;101060318 protein_coding 22737611 22755040 Y 1 chromosome ENSG00000205916 DAZ4 57055;57135;1617 protein_coding 26980008 27053183 Y 1 chromosome ENSG00000205936 PPP1R12BP2 pseudogene 25525631 25538844 Y 1 chromosome ENSG00000205944 DAZ2 57055;57135;1617 protein_coding 25365594 25437503 Y 1 chromosome ENSG00000206159 GYG2P1 352887 pseudogene 14475147 14532255 Y -1 chromosome ENSG00000212855 TTTY2 lincRNA 9578193 9596085 Y 1 chromosome ENSG00000212856 TTTY2B lincRNA 6274285 6292186 Y -1 chromosome ENSG00000214207 KRT18P10 pseudogene 5441186 5442472 Y 1 chromosome ENSG00000215414 PSMA6P1 5687 pseudogene 15398518 15399258 Y 1 chromosome ENSG00000215506 TPTE2P4 pseudogene 28654360 28725837 Y 1 chromosome ENSG00000215507 RBMY2DP pseudogene 28269821 28279455 Y 1 chromosome ENSG00000215537 ZNF736P11Y pseudogene 25012691 25013903 Y -1 chromosome ENSG00000215540 AC009947.3 pseudogene 25683455 25693089 Y -1 chromosome ENSG00000215560 TTTY5 83863 lincRNA 24442945 24445023 Y -1 chromosome ENSG00000215580 BCORP1 286554 pseudogene 21617317 21665039 Y -1 chromosome ENSG00000215583 ASS1P6 pseudogene 14043242 14044475 Y -1 chromosome ENSG00000215601 TSPY24P pseudogene 8148239 8150250 Y 1 chromosome ENSG00000215603 ZNF92P1Y pseudogene 7721727 7722917 Y -1 chromosome ENSG00000216777 PRRC2CP1 pseudogene 3417693 3417851 Y -1 chromosome ENSG00000216824 ZNF736P10Y pseudogene 8286832 8287941 Y -1 chromosome ENSG00000216844 AC009494.3 pseudogene 23005142 23005596 Y -1 chromosome ENSG00000217179 MTCYBP2 pseudogene 21033988 21034158 Y 1 chromosome ENSG00000217896 ZNF839P1 pseudogene 21147061 21148284 Y 1 chromosome ENSG00000218410 AC012078.2 pseudogene 3719265 3720910 Y -1 chromosome ENSG00000223362 CDY15P pseudogene 26001669 26003238 Y -1 chromosome ENSG00000223406 XKRYP5 pseudogene 27898535 27899017 Y 1 chromosome ENSG00000223407 USP9YP18 pseudogene 25805613 25813969 Y -1 chromosome ENSG00000223422 AC007274.2 pseudogene 7546940 7549069 Y 1 chromosome ENSG00000223517 AC010723.1 lincRNA 16388092 16389369 Y -1 chromosome ENSG00000223555 USP9YP23 pseudogene 19894090 19896695 Y 1 chromosome ENSG00000223600 EEF1A1P41 pseudogene 2863108 2863314 Y -1 chromosome ENSG00000223636 UBE2Q2P5Y pseudogene 27577303 27583462 Y 1 chromosome ENSG00000223637 RBMY2EP 159125 pseudogene 23556877 23563471 Y -1 chromosome ENSG00000223641 TTTY17C 474152;474151;252949 lincRNA 27329790 27330920 Y -1 chromosome ENSG00000223655 RAB9AP3 pseudogene 28064470 28065042 Y -1 chromosome ENSG00000223698 GOLGA6L11P pseudogene 26314334 26320641 Y 1 chromosome ENSG00000223744 RBMY2GP pseudogene 6196093 6211364 Y 1 chromosome ENSG00000223856 RAB9AP2 pseudogene 27856055 27859704 Y -1 chromosome ENSG00000223915 DPPA2P1 pseudogene 15342765 15343320 Y 1 chromosome ENSG00000223955 MTND6P1 pseudogene 8231577 8232000 Y -1 chromosome ENSG00000223978 ZNF736P1Y pseudogene 27131348 27132623 Y -1 chromosome ENSG00000224033 CDY8P pseudogene 20134179 20135788 Y 1 chromosome ENSG00000224035 SFPQP1 pseudogene 15195704 15207719 Y 1 chromosome ENSG00000224060 ARSEP1 pseudogene 14460540 14468226 Y 1 chromosome ENSG00000224075 TTTY22 252954 lincRNA 9638762 9650854 Y 1 chromosome ENSG00000224151 USP9YP28 pseudogene 20994888 21001199 Y -1 chromosome ENSG00000224166 PRYP1 pseudogene 19841408 19856476 Y -1 chromosome ENSG00000224169 HSFY6P pseudogene 25803986 25805205 Y -1 chromosome ENSG00000224210 TRIM60P5Y pseudogene 26640856 26641730 Y -1 chromosome ENSG00000224240 CYCSP49 pseudogene 28695572 28695890 Y 1 chromosome ENSG00000224336 FAM197Y1 protein_coding 9374241 9384693 Y -1 chromosome ENSG00000224408 USP9YP22 pseudogene 9021303 9023412 Y 1 chromosome ENSG00000224482 HSFY4P pseudogene 22970662 22973695 Y -1 chromosome ENSG00000224485 USP9YP7 pseudogene 20020635 20022686 Y 1 chromosome ENSG00000224518 AC006989.2 pseudogene 17053626 17054595 Y 1 chromosome ENSG00000224571 USP9YP13 pseudogene 26066052 26067657 Y 1 chromosome ENSG00000224634 ZNF736P6Y pseudogene 8218972 8220184 Y 1 chromosome ENSG00000224657 RBMY2BP pseudogene 24795392 24805025 Y 1 chromosome ENSG00000224827 LINC00265-2P pseudogene 26422392 26423173 Y 1 chromosome ENSG00000224866 USP9YP25 pseudogene 25880340 25882396 Y 1 chromosome ENSG00000224873 CDY13P pseudogene 24666828 24667842 Y -1 chromosome ENSG00000224917 AC016694.2 pseudogene 24816763 24819006 Y -1 chromosome ENSG00000224953 SRIP3 pseudogene 6587003 6587221 Y -1 chromosome ENSG00000224964 TRAPPC2P3 pseudogene 19909863 19912576 Y -1 chromosome ENSG00000224989 FAM41AY1 340618;100302526 lincRNA 19612838 19626898 Y 1 chromosome ENSG00000225117 ARSDP1 pseudogene 14474827 14499123 Y 1 chromosome ENSG00000225189 REREP1Y pseudogene 25608612 25618427 Y 1 chromosome ENSG00000225256 TRAPPC2P5 pseudogene 27845598 27848709 Y 1 chromosome ENSG00000225287 OFD1P13Y pseudogene 27821207 27843493 Y -1 chromosome ENSG00000225326 USP9YP19 pseudogene 27894743 27896353 Y -1 chromosome ENSG00000225466 OFD1P10Y pseudogene 25727742 25760787 Y 1 chromosome ENSG00000225491 UBE2Q2P4Y pseudogene 26378973 26385131 Y -1 chromosome ENSG00000225516 AC006156.1 protein_coding 9293012 9344176 Y -1 chromosome ENSG00000225520 TTTY16 252948 lincRNA 7567398 7569288 Y -1 chromosome ENSG00000225560 FAM197Y8 252946;100287826;100289150 antisense 9185120 9193010 Y -1 chromosome ENSG00000225609 CDY20P pseudogene 27764791 27766400 Y 1 chromosome ENSG00000225615 RBMY2UP pseudogene 24344576 24349859 Y 1 chromosome ENSG00000225624 TBL1YP1 pseudogene 22889140 22904786 Y 1 chromosome ENSG00000225653 RNF19BPY pseudogene 3646038 3647587 Y -1 chromosome ENSG00000225685 TSPY5P pseudogene 9904163 9906760 Y -1 chromosome ENSG00000225716 TCEB1P13 pseudogene 20602683 20603011 Y 1 chromosome ENSG00000225740 TCEB1P6 pseudogene 19949081 19949408 Y -1 chromosome ENSG00000225809 RBMY2KP pseudogene 8132490 8145229 Y -1 chromosome ENSG00000225840 AC010970.2 pseudogene 10036113 10036711 Y -1 chromosome ENSG00000225876 AC024067.1 pseudogene 28390498 28390720 Y -1 chromosome ENSG00000225878 SERBP1P2 pseudogene 4669726 4670889 Y -1 chromosome ENSG00000225895 TRAPPC2P8 pseudogene 20267200 20269988 Y 1 chromosome ENSG00000225896 AC007742.3 pseudogene 19838632 19838960 Y 1 chromosome ENSG00000226011 OFD1P1Y pseudogene 19921687 19937209 Y 1 chromosome ENSG00000226042 CDY10P pseudogene 23799361 23800927 Y -1 chromosome ENSG00000226061 PCMTD1P1 pseudogene 10011462 10011816 Y 1 chromosome ENSG00000226092 RBMY2AP pseudogene 24073668 24083297 Y -1 chromosome ENSG00000226116 USP9YP6 pseudogene 20024579 20033510 Y -1 chromosome ENSG00000226223 TSPY16P pseudogene 9477858 9478325 Y -1 chromosome ENSG00000226270 ZNF736P2Y pseudogene 26829804 26831082 Y 1 chromosome ENSG00000226353 TAF9P1 pseudogene 19740393 19741282 Y 1 chromosome ENSG00000226362 FAM41AY2 340618;100302526 lincRNA 20552880 20566932 Y -1 chromosome ENSG00000226369 USP9YP11 pseudogene 26081100 26086248 Y 1 chromosome ENSG00000226449 CDY5P pseudogene 19833478 19835057 Y 1 chromosome ENSG00000226504 TMEM167AP1 pseudogene 23069230 23069448 Y -1 chromosome ENSG00000226529 MTND1P1 pseudogene 8239704 8240071 Y 1 chromosome ENSG00000226555 AGKP1 pseudogene 16750976 16752238 Y 1 chromosome ENSG00000226611 OFD1P2Y pseudogene 20242567 20258088 Y -1 chromosome ENSG00000226863 SHROOM2P1 pseudogene 14649708 14656818 Y 1 chromosome ENSG00000226873 CDY14P pseudogene 25820526 25821539 Y 1 chromosome ENSG00000226906 TTTY4 474149;474150;114761 lincRNA 25082602 25119431 Y 1 chromosome ENSG00000226918 AC010086.1 pseudogene 23473154 23480781 Y 1 chromosome ENSG00000226941 RBMY1J 378949;5940;378950;159163;378948;378951 protein_coding 24454970 24564028 Y 1 chromosome ENSG00000226975 AC006987.6 pseudogene 10007397 10007923 Y -1 chromosome ENSG00000227166 STSP1 pseudogene 17659096 17705211 Y 1 chromosome ENSG00000227204 RBMY2JP pseudogene 7995171 8012244 Y 1 chromosome ENSG00000227251 TRIM60P9Y pseudogene 25204177 25205333 Y 1 chromosome ENSG00000227289 HSFY3P pseudogene 2749724 2751693 Y -1 chromosome ENSG00000227439 TTTY17B 474152;474151;252949 lincRNA 26631479 26632610 Y 1 chromosome ENSG00000227444 AC007322.5 pseudogene 24065082 24067253 Y 1 chromosome ENSG00000227447 XGPY pseudogene 14551845 14619171 Y -1 chromosome ENSG00000227494 USP9YP14 pseudogene 20642973 20649286 Y 1 chromosome ENSG00000227629 SLC25A15P1 pseudogene 28732789 28737748 Y -1 chromosome ENSG00000227633 RBMY2YP pseudogene 27412731 27419678 Y 1 chromosome ENSG00000227635 USP9YP21 pseudogene 28079980 28082028 Y -1 chromosome ENSG00000227830 TRIM60P3Y pseudogene 8281078 8282095 Y -1 chromosome ENSG00000227837 TRIM60P11Y pseudogene 26837938 26839079 Y 1 chromosome ENSG00000227867 TCEB1P11 pseudogene 28137388 28137719 Y -1 chromosome ENSG00000227871 USP9YP12 pseudogene 26092074 26098440 Y -1 chromosome ENSG00000227915 TCEB1P16 pseudogene 25824651 25824982 Y 1 chromosome ENSG00000227949 CYCSP46 pseudogene 17053427 17053630 Y -1 chromosome ENSG00000227989 AC010141.8 pseudogene 23717738 23719956 Y -1 chromosome ENSG00000228193 TCEB1P14 pseudogene 20949636 20949966 Y 1 chromosome ENSG00000228207 AC007967.2 pseudogene 8769244 8771317 Y -1 chromosome ENSG00000228240 TTTY17A 474152;474151;252949 lincRNA 24997731 24998862 Y 1 chromosome ENSG00000228257 AC010141.6 pseudogene 23693725 23695897 Y -1 chromosome ENSG00000228296 TTTY4C 474149;474150;114761 lincRNA 27209230 27246039 Y -1 chromosome ENSG00000228379 AC010891.2 lincRNA 9650924 9655122 Y 1 chromosome ENSG00000228383 FAM197Y7 252946;100287826;100289150 antisense 9205412 9235047 Y -1 chromosome ENSG00000228411 CDY4P pseudogene 14913906 14915763 Y -1 chromosome ENSG00000228465 TRAPPC2P10 pseudogene 26113711 26116841 Y -1 chromosome ENSG00000228518 SURF6P1 pseudogene 19304706 19305532 Y 1 chromosome ENSG00000228571 HSFY7P pseudogene 24683194 24683996 Y 1 chromosome ENSG00000228578 TUBB1P2 pseudogene 19628716 19630918 Y -1 chromosome ENSG00000228764 ZNF885P pseudogene 22551937 22558682 Y 1 chromosome ENSG00000228786 LINC00266-4P lincRNA 27524447 27540866 Y -1 chromosome ENSG00000228787 NLGN4Y-AS1 100874056 antisense 16905522 16915913 Y -1 chromosome ENSG00000228850 CDY12P pseudogene 24210845 24211859 Y 1 chromosome ENSG00000228890 TTTY21 252953;100101115 lincRNA 9555262 9558905 Y -1 chromosome ENSG00000228927 TSPY3 728137;100289087;7258 protein_coding 9236030 9307357 Y 1 chromosome ENSG00000228945 CLUHP2 pseudogene 20442064 20446647 Y 1 chromosome ENSG00000229129 ACTG1P2 pseudogene 19868881 19870005 Y -1 chromosome ENSG00000229138 CDY6P pseudogene 19993979 19995588 Y -1 chromosome ENSG00000229159 TSPY23P pseudogene 24329997 24332168 Y 1 chromosome ENSG00000229163 NAP1L1P2 pseudogene 2797042 2799161 Y -1 chromosome ENSG00000229208 RBMY2NP pseudogene 9669027 9684305 Y -1 chromosome ENSG00000229234 RBMY1KP pseudogene 24355478 24362727 Y 1 chromosome ENSG00000229236 TTTY10 246119 lincRNA 22627554 22681114 Y -1 chromosome ENSG00000229238 PPP1R12BP1 pseudogene 28424070 28500565 Y -1 chromosome ENSG00000229250 USP9YP31 pseudogene 26227851 26236493 Y -1 chromosome ENSG00000229302 TAF9P2 pseudogene 20438493 20439381 Y -1 chromosome ENSG00000229308 AC010084.1 lincRNA 3904538 3968361 Y 1 chromosome ENSG00000229343 CDY22P pseudogene 27959160 27960713 Y 1 chromosome ENSG00000229406 OFD1P4Y pseudogene 20615036 20634023 Y -1 chromosome ENSG00000229416 USP9YP8 pseudogene 23839132 23843004 Y -1 chromosome ENSG00000229465 ACTG1P11 pseudogene 20309770 20310894 Y 1 chromosome ENSG00000229518 UBE2V1P3 pseudogene 3734347 3734763 Y -1 chromosome ENSG00000229549 TSPY8 728403 protein_coding 9195406 9218479 Y 1 chromosome ENSG00000229551 GAPDHP17 pseudogene 23023223 23024223 Y 1 chromosome ENSG00000229553 USP9YP17 pseudogene 24195902 24204260 Y -1 chromosome ENSG00000229643 LINC00280 lincRNA 6225260 6229454 Y -1 chromosome ENSG00000229709 USP9YP36 pseudogene 27725937 27734591 Y 1 chromosome ENSG00000229725 AC007322.1 pseudogene 24017478 24019697 Y 1 chromosome ENSG00000229745 BPY2DP pseudogene 7801038 7805681 Y -1 chromosome ENSG00000229940 TSPY22P pseudogene 24452072 24454098 Y -1 chromosome ENSG00000230025 AC007967.4 pseudogene 8839792 8842136 Y -1 chromosome ENSG00000230029 CDY11P pseudogene 23858456 23860106 Y 1 chromosome ENSG00000230066 FAM197Y3 antisense 9334896 9342768 Y -1 chromosome ENSG00000230073 AC009947.2 pseudogene 25669468 25671516 Y 1 chromosome ENSG00000230377 TCEB1P7 pseudogene 20694212 20694542 Y -1 chromosome ENSG00000230412 TCEB1P12 pseudogene 20230369 20230696 Y 1 chromosome ENSG00000230458 GPM6BP1 pseudogene 20740303 20740446 Y 1 chromosome ENSG00000230476 OFD1P9Y pseudogene 24727136 24760228 Y -1 chromosome ENSG00000230663 FAM224B lincRNA 19664680 19691634 Y -1 chromosome ENSG00000230727 RBMY2WP pseudogene 24908998 24915934 Y -1 chromosome ENSG00000230814 USP9YP24 pseudogene 24674428 24682786 Y 1 chromosome ENSG00000230819 ZNF736P5Y pseudogene 27164646 27165450 Y -1 chromosome ENSG00000230854 USP9YP20 pseudogene 28069535 28075973 Y 1 chromosome ENSG00000230904 XKRYP2 pseudogene 20973224 20973715 Y -1 chromosome ENSG00000230977 AC023274.6 pseudogene 26328624 26329218 Y 1 chromosome ENSG00000231026 XKRYP4 pseudogene 26063388 26063870 Y -1 chromosome ENSG00000231141 TTTY3 114760;474148 lincRNA 27874637 27879535 Y 1 chromosome ENSG00000231159 OFD1P8Y pseudogene 24118460 24151496 Y 1 chromosome ENSG00000231311 PRYP2 pseudogene 20323252 20338370 Y 1 chromosome ENSG00000231341 VDAC1P6 pseudogene 5075256 5076110 Y -1 chromosome ENSG00000231375 CDY17P pseudogene 26196025 26197634 Y -1 chromosome ENSG00000231411 AC006987.7 pseudogene 10009199 10010334 Y -1 chromosome ENSG00000231423 RAB9AP5 pseudogene 26102716 26106365 Y 1 chromosome ENSG00000231436 RBMY3AP pseudogene 9448180 9458885 Y -1 chromosome ENSG00000231514 FAM58CP pseudogene 28772667 28773306 Y -1 chromosome ENSG00000231535 LINC00278 100873962 lincRNA 2870953 2970313 Y 1 chromosome ENSG00000231540 TCEB1P9 pseudogene 25954968 25955206 Y -1 chromosome ENSG00000231716 CDY23P pseudogene 28140831 28141844 Y -1 chromosome ENSG00000231874 TSPY18P pseudogene 9707273 9708390 Y 1 chromosome ENSG00000231988 OFD1P3Y pseudogene 8898635 8908029 Y 1 chromosome ENSG00000232003 ZNF736P12Y pseudogene 26646410 26647652 Y -1 chromosome ENSG00000232029 TCEB1P15 pseudogene 24214967 24215298 Y 1 chromosome ENSG00000232064 USP9YP33 pseudogene 27736470 27738492 Y -1 chromosome ENSG00000232195 TOMM22P2 pseudogene 2696023 2696259 Y 1 chromosome ENSG00000232205 CDY18P pseudogene 26250254 26252165 Y 1 chromosome ENSG00000232226 ARSFP1 pseudogene 14373025 14378737 Y -1 chromosome ENSG00000232235 CDY3P pseudogene 9003694 9005311 Y -1 chromosome ENSG00000232348 LINC00279 lincRNA 8506335 8512883 Y 1 chromosome ENSG00000232419 TTTY19 252952 lincRNA 8572513 8573324 Y 1 chromosome ENSG00000232424 USP9YP29 pseudogene 25886405 25892840 Y -1 chromosome ENSG00000232475 HSFY5P pseudogene 24194701 24195494 Y -1 chromosome ENSG00000232522 ZNF886P pseudogene 22163071 22174090 Y -1 chromosome ENSG00000232583 GPR143P pseudogene 6968774 6974543 Y -1 chromosome ENSG00000232585 OFD1P12Y pseudogene 26118927 26141228 Y 1 chromosome ENSG00000232614 USP9YP9 pseudogene 27876158 27881307 Y -1 chromosome ENSG00000232617 AC017019.1 pseudogene 9502838 9506619 Y 1 chromosome ENSG00000232620 TSPY17P pseudogene 6388504 6388970 Y 1 chromosome ENSG00000232634 NEFLP1 pseudogene 23382992 23383975 Y -1 chromosome ENSG00000232695 TCEB1P17 pseudogene 28007090 28007415 Y 1 chromosome ENSG00000232730 FAM8A4P pseudogene 14442350 14443562 Y 1 chromosome ENSG00000232744 USP9YP16 pseudogene 20283081 20285685 Y -1 chromosome ENSG00000232764 TRIM60P8Y pseudogene 25171741 25172810 Y 1 chromosome ENSG00000232808 TTTY20 252951 antisense 9167489 9172441 Y -1 chromosome ENSG00000232845 TRAPPC2P9 pseudogene 25908985 25911730 Y -1 chromosome ENSG00000232899 CDY9P pseudogene 20344721 20346300 Y -1 chromosome ENSG00000232910 RAB9AP1 pseudogene 25897335 25897907 Y 1 chromosome ENSG00000232914 TRAPPC2P4 pseudogene 28050665 28053397 Y 1 chromosome ENSG00000232924 TCEB1P4 pseudogene 9068524 9068856 Y 1 chromosome ENSG00000232927 USP12PY pseudogene 3550846 3551909 Y 1 chromosome ENSG00000232976 TRIM60P10Y pseudogene 26805484 26806553 Y 1 chromosome ENSG00000233070 ZFY-AS1 antisense 2834885 2870667 Y -1 chromosome ENSG00000233120 USP9YP15 pseudogene 20278413 20279581 Y 1 chromosome ENSG00000233126 ZNF736P3Y pseudogene 25195394 25197342 Y 1 chromosome ENSG00000233156 HSFY8P pseudogene 28157582 28158384 Y 1 chromosome ENSG00000233378 USP9YP34 pseudogene 20096229 20105140 Y 1 chromosome ENSG00000233522 FAM224A lincRNA 20488139 20515096 Y 1 chromosome ENSG00000233546 PRYP5 pseudogene 20691244 20691509 Y 1 chromosome ENSG00000233619 AC006328.9 pseudogene 27633431 27633809 Y -1 chromosome ENSG00000233634 GOT2P5 pseudogene 6705748 6706293 Y 1 chromosome ENSG00000233652 CICP1 pseudogene 27535139 27537958 Y 1 chromosome ENSG00000233699 TTTY18 252950 lincRNA 8551411 8551919 Y -1 chromosome ENSG00000233740 CICP2 pseudogene 26424484 26427287 Y -1 chromosome ENSG00000233774 MED14P1 pseudogene 14730915 14746333 Y -1 chromosome ENSG00000233803 TSPY4 728395 protein_coding 9175073 9177893 Y 1 chromosome ENSG00000233843 CYCSP48 pseudogene 28546758 28547377 Y 1 chromosome ENSG00000233864 TTTY15 64595 lincRNA 14774265 14804162 Y 1 chromosome ENSG00000233944 LINC00265-3P pseudogene 27539301 27540203 Y -1 chromosome ENSG00000234059 CASKP1 pseudogene 15042075 15060090 Y -1 chromosome ENSG00000234081 TCEB1P10 pseudogene 26153058 26153384 Y -1 chromosome ENSG00000234110 TSPY25P pseudogene 9461792 9463961 Y 1 chromosome ENSG00000234131 TCEB1P8 pseudogene 24663389 24663720 Y -1 chromosome ENSG00000234179 MTCYBP1 pseudogene 8232073 8233191 Y 1 chromosome ENSG00000234385 RCC2P1 pseudogene 13901758 13903233 Y -1 chromosome ENSG00000234399 RBMY2XP pseudogene 26542751 26549673 Y -1 chromosome ENSG00000234414 RBMY1A1 5940 protein_coding 23673258 23711212 Y 1 chromosome ENSG00000234529 GAPDHP19 pseudogene 21489455 21490459 Y 1 chromosome ENSG00000234583 TSPY19P pseudogene 6171998 6173115 Y -1 chromosome ENSG00000234620 HDHD1P1 pseudogene 17460542 17567954 Y -1 chromosome ENSG00000234652 AGPAT5P1 pseudogene 3161849 3162867 Y 1 chromosome ENSG00000234744 USP9YP26 pseudogene 28148401 28155003 Y 1 chromosome ENSG00000234795 RFTN1P1 pseudogene 7581026 7677302 Y -1 chromosome ENSG00000234803 FAM197Y2 252946;100287826;100289150 antisense 9355208 9364506 Y -1 chromosome ENSG00000234830 FAM197Y9 pseudogene 6124308 6131994 Y -1 chromosome ENSG00000234850 MTND2P3 pseudogene 8240282 8240751 Y 1 chromosome ENSG00000234888 OFD1P15Y pseudogene 28201926 28234640 Y -1 chromosome ENSG00000234950 RBMY2OP pseudogene 9789162 9797032 Y 1 chromosome ENSG00000235001 EIF4A1P2 pseudogene 5205786 5207005 Y -1 chromosome ENSG00000235004 USP9YP30 pseudogene 27869163 27870333 Y 1 chromosome ENSG00000235014 REREP2Y pseudogene 28344478 28354295 Y -1 chromosome ENSG00000235059 AC008175.1 101929148 lincRNA 24585087 24631739 Y 1 chromosome ENSG00000235094 AC006335.10 pseudogene 6174083 6175961 Y 1 chromosome ENSG00000235175 RPL26P37 pseudogene 5661341 5661778 Y -1 chromosome ENSG00000235193 AC006987.4 pseudogene 9925635 9927195 Y 1 chromosome ENSG00000235412 TTTY4B 474149;474150;114761 lincRNA 26716349 26753172 Y 1 chromosome ENSG00000235451 PNPLA4P1 pseudogene 16192955 16198480 Y 1 chromosome ENSG00000235462 TAB3P1 pseudogene 15265842 15274125 Y -1 chromosome ENSG00000235479 RAB9AP4 pseudogene 20811557 20812165 Y 1 chromosome ENSG00000235511 OFD1P18Y pseudogene 28018078 28044800 Y -1 chromosome ENSG00000235521 USP9YP27 pseudogene 19900195 19901364 Y -1 chromosome ENSG00000235583 AC007562.1 pseudogene 27658448 27673933 Y -1 chromosome ENSG00000235649 MXRA5P1 pseudogene 14077914 14108092 Y 1 chromosome ENSG00000235691 AC006987.5 pseudogene 9928411 9928816 Y 1 chromosome ENSG00000235719 AC010141.1 pseudogene 23627270 23629049 Y -1 chromosome ENSG00000235857 CTBP2P1 pseudogene 59001391 59001635 Y 1 chromosome ENSG00000235895 AC010154.2 pseudogene 6361017 6364798 Y -1 chromosome ENSG00000235981 AC023274.4 pseudogene 26288505 26303990 Y 1 chromosome ENSG00000236131 MED13P1 pseudogene 17019778 17019948 Y 1 chromosome ENSG00000236379 ZNF736P4Y pseudogene 27314746 27315988 Y 1 chromosome ENSG00000236424 TSPY10 728137;100289087;7258 protein_coding 9365489 9368291 Y 1 chromosome ENSG00000236429 GPM6BP2 pseudogene 20903729 20903872 Y -1 chromosome ENSG00000236435 TSPY12P pseudogene 7555780 7557589 Y -1 chromosome ENSG00000236477 RPS24P1 pseudogene 14365457 14366162 Y 1 chromosome ENSG00000236599 TCEB1P26 pseudogene 20340818 20341146 Y -1 chromosome ENSG00000236615 AC010086.5 pseudogene 23567656 23567881 Y 1 chromosome ENSG00000236620 XKRYP3 pseudogene 25862069 25872955 Y -1 chromosome ENSG00000236647 AC009947.5 pseudogene 25695956 25697532 Y 1 chromosome ENSG00000236690 AC007274.5 pseudogene 7558552 7560719 Y 1 chromosome ENSG00000236718 RBMY2QP pseudogene 9860098 9871796 Y -1 chromosome ENSG00000236786 TSPY15P pseudogene 9385717 9388290 Y 1 chromosome ENSG00000236951 AC007359.6 101929148 lincRNA 24246961 24293631 Y -1 chromosome ENSG00000237023 USP9YP3 pseudogene 23823812 23835894 Y 1 chromosome ENSG00000237048 TTTY12 83867 lincRNA 7672965 7678724 Y 1 chromosome ENSG00000237069 TTTY23B 252955;100101121 lincRNA 6110487 6111651 Y -1 chromosome ENSG00000237195 DLGAP5P1 pseudogene 6026843 6027306 Y 1 chromosome ENSG00000237269 RBMY2TP pseudogene 23592583 23599131 Y -1 chromosome ENSG00000237302 OFD1P11Y pseudogene 25917379 25944297 Y 1 chromosome ENSG00000237427 TOMM22P1 pseudogene 23292756 23293067 Y 1 chromosome ENSG00000237447 CDC27P2 pseudogene 10027986 10029907 Y -1 chromosome ENSG00000237467 USP9YP35 pseudogene 26223946 26225959 Y 1 chromosome ENSG00000237546 XKRYP6 pseudogene 28089415 28100299 Y 1 chromosome ENSG00000237558 CDY7P pseudogene 20063469 20065380 Y 1 chromosome ENSG00000237563 TTTY21B 252953;100101115 lincRNA 6311475 6315118 Y 1 chromosome ENSG00000237616 USP9YP32 pseudogene 20107081 20109132 Y -1 chromosome ENSG00000237659 RNASEH2CP1 pseudogene 2657868 2658369 Y 1 chromosome ENSG00000237701 ATP5JP1 pseudogene 6768794 6769413 Y 1 chromosome ENSG00000237802 FAM197Y6 100289188 antisense 9225731 9233636 Y -1 chromosome ENSG00000237823 CDY19P pseudogene 27710269 27712180 Y -1 chromosome ENSG00000237902 TSPY21P pseudogene 24546550 24548715 Y -1 chromosome ENSG00000237917 PARP4P1 pseudogene 28740998 28780799 Y -1 chromosome ENSG00000237968 AC007322.7 pseudogene 24086159 24087740 Y 1 chromosome ENSG00000237997 RCC2P2 pseudogene 22148461 22149738 Y -1 chromosome ENSG00000238067 XKRYP1 pseudogene 20670463 20670954 Y 1 chromosome ENSG00000238073 RBMY2HP pseudogene 7539784 7544065 Y -1 chromosome ENSG00000238074 TSPY6P 7258 protein_coding 9324922 9327689 Y 1 chromosome ENSG00000238088 OFD1P7Y pseudogene 21010150 21029166 Y 1 chromosome ENSG00000238135 USP9YP10 pseudogene 20987993 20991373 Y 1 chromosome ENSG00000238154 USP9YP4 pseudogene 9027238 9042220 Y -1 chromosome ENSG00000238191 CLUHP1 pseudogene 19733139 19737712 Y -1 chromosome ENSG00000238235 TSPY11P pseudogene 6134634 6137316 Y 1 chromosome ENSG00000239225 TTTY23 252955;100101121 lincRNA 9748407 9749571 Y 1 chromosome ENSG00000239304 DNM1P48 pseudogene 27632787 27633469 Y 1 chromosome ENSG00000239533 GOLGA2P2Y processed_transcript 26356114 26360978 Y -1 chromosome ENSG00000239893 ZNF736P9Y pseudogene 7936937 7938764 Y -1 chromosome ENSG00000240438 OFD1P5Y pseudogene 20743949 20790963 Y -1 chromosome ENSG00000240450 CSPG4P1Y 114758 lincRNA 27629055 27632852 Y 1 chromosome ENSG00000240566 AC010153.3 pseudogene 26796955 26797681 Y 1 chromosome ENSG00000241200 ZNF736P7Y pseudogene 7859028 7859847 Y -1 chromosome ENSG00000241859 KALP pseudogene 15863536 16027704 Y 1 chromosome ENSG00000242153 OFD1P6Y 83864;425057 pseudogene 20835919 20901083 Y 1 chromosome ENSG00000242389 RBMY1E 378949;378950 protein_coding 24026223 24064214 Y -1 chromosome ENSG00000242393 AC010141.4 pseudogene 23670185 23672356 Y -1 chromosome ENSG00000242425 RN7SL818P misc_RNA 26357107 26357382 Y -1 chromosome ENSG00000242854 DNM1P24 pseudogene 26325463 26329652 Y -1 chromosome ENSG00000242875 RBMY1B 378949;378948 protein_coding 23673224 23687672 Y 1 chromosome ENSG00000242879 AC006335.11 pseudogene 6175751 6177628 Y 1 chromosome ENSG00000243040 RBMY2FP 159162;100652931 pseudogene 24455006 24467972 Y 1 chromosome ENSG00000243643 TSPY20P pseudogene 9873804 9875984 Y 1 chromosome ENSG00000243980 RN7SL702P misc_RNA 14394177 14394465 Y 1 chromosome ENSG00000244000 AC006366.3 pseudogene 25163210 25163968 Y 1 chromosome ENSG00000244231 CSPG4P2Y 114758 processed_transcript 26329581 26333378 Y -1 chromosome ENSG00000244246 ZNF736P8Y pseudogene 7781463 7782131 Y -1 chromosome ENSG00000244395 RBMY1D 378949;5940;378948 protein_coding 24026223 24040673 Y -1 chromosome ENSG00000244646 AC024183.3 pseudogene 20290496 20298913 Y 1 chromosome ENSG00000248573 PRYP6 pseudogene 20952595 20952937 Y -1 chromosome ENSG00000248792 LINC00266-2P pseudogene 26424828 26437493 Y 1 chromosome ENSG00000249501 USP9YP2 pseudogene 20938165 20941313 Y -1 chromosome ENSG00000249606 TRAPPC2P7 pseudogene 20817406 20818076 Y -1 chromosome ENSG00000249634 USP9YP5 pseudogene 20652801 20656181 Y -1 chromosome ENSG00000249726 TUBB1P1 pseudogene 20548860 20551037 Y 1 chromosome ENSG00000250204 AC006335.6 pseudogene 6111336 6113324 Y -1 chromosome ENSG00000250868 AC007742.7 pseudogene 19880862 19889280 Y -1 chromosome ENSG00000250951 USP9YP1 pseudogene 20702866 20706014 Y 1 chromosome ENSG00000251275 AC006335.2 pseudogene 6109809 6111670 Y -1 chromosome ENSG00000251510 AC022486.1 lincRNA 20653626 20709584 Y 1 chromosome ENSG00000251618 AC007322.3 pseudogene 24041541 24043706 Y 1 chromosome ENSG00000251705 RNA5-8SP6 rRNA 10037764 10037915 Y 1 chromosome ENSG00000251766 RNA5SP518 rRNA 9928019 9928137 Y -1 chromosome ENSG00000251796 SNORA70 snoRNA 28393531 28393668 Y 1 chromosome ENSG00000251841 RNU6-1334P snRNA 2652790 2652894 Y 1 chromosome ENSG00000251879 AC010874.1 miRNA 5742287 5742379 Y 1 chromosome ENSG00000251917 RNU1-86P snRNA 26092765 26092918 Y -1 chromosome ENSG00000251925 SNORA70 snoRNA 25569246 25569383 Y -1 chromosome ENSG00000251953 RNA5SP522 rRNA 20508009 20508124 Y -1 chromosome ENSG00000251966 AC010970.1 miRNA 10033981 10034093 Y 1 chromosome ENSG00000251970 RNU1-41P snRNA 20995615 20995776 Y -1 chromosome ENSG00000251996 Y_RNA misc_RNA 7209572 7209683 Y 1 chromosome ENSG00000252012 RNA5SP521 rRNA 19671654 19671769 Y 1 chromosome ENSG00000252038 AC068704.1 miRNA 19734941 19735035 Y -1 chromosome ENSG00000252059 AC012667.1 miRNA 5441969 5442060 Y 1 chromosome ENSG00000252155 RNU6-941P snRNA 7246713 7246820 Y 1 chromosome ENSG00000252166 RNU1-95P snRNA 20278734 20278893 Y 1 chromosome ENSG00000252173 RNU6-109P snRNA 18360815 18360921 Y 1 chromosome ENSG00000252209 RNU1-48P snRNA 20648397 20648558 Y 1 chromosome ENSG00000252289 RNA5SP519 rRNA 9930484 9930602 Y -1 chromosome ENSG00000252315 RNA5SP520 rRNA 19669744 19669856 Y 1 chromosome ENSG00000252323 RNU6-184P snRNA 18448163 18448269 Y -1 chromosome ENSG00000252426 RNU1-107P snRNA 27869489 27869642 Y 1 chromosome ENSG00000252439 AC007241.1 miRNA 20444739 20444833 Y 1 chromosome ENSG00000252468 RNU2-57P snRNA 4887117 4887307 Y 1 chromosome ENSG00000252471 AC006328.1 miRNA 27606157 27606239 Y 1 chromosome ENSG00000252472 RNU6-521P snRNA 7291095 7291199 Y -1 chromosome ENSG00000252513 RNU1-128P snRNA 19900883 19901042 Y -1 chromosome ENSG00000252586 AC002992.1 miRNA 14482123 14482230 Y 1 chromosome ENSG00000252625 RNU1-40P snRNA 28075126 28075289 Y 1 chromosome ENSG00000252633 RN7SKP282 misc_RNA 7192338 7192636 Y -1 chromosome ENSG00000252664 AC017020.1 miRNA 18174646 18174709 Y 1 chromosome ENSG00000252667 RNA5SP523 rRNA 20509921 20510033 Y -1 chromosome ENSG00000252681 RNU1-97P snRNA 25887089 25887252 Y -1 chromosome ENSG00000252689 SNORA20 snoRNA 18250128 18250259 Y -1 chromosome ENSG00000252694 AC006371.1 miRNA 15779840 15779936 Y -1 chromosome ENSG00000252766 RNU6-255P snRNA 21180869 21180973 Y -1 chromosome ENSG00000252855 AC134878.1 miRNA 13340551 13340633 Y -1 chromosome ENSG00000252900 RNU6-303P snRNA 4043026 4043131 Y -1 chromosome ENSG00000252948 RNU6-1314P snRNA 28507136 28507239 Y 1 chromosome ENSG00000254488 RP11-65G9.1 lincRNA 23200175 23206610 Y -1 chromosome ENSG00000258567 DUX4L16 pseudogene 13462594 13463857 Y 1 chromosome ENSG00000258991 DUX4L19 pseudogene 13488005 13489271 Y 1 chromosome ENSG00000258992 TSPY1 7258 protein_coding 9236076 9307357 Y 1 chromosome ENSG00000259029 DUX4L18 pseudogene 13477233 13478499 Y 1 chromosome ENSG00000259154 DUX4L17 pseudogene 13470597 13471863 Y 1 chromosome ENSG00000259247 TTTY25P pseudogene 24476599 24478647 Y 1 chromosome ENSG00000260197 RP11-424G14.1 lincRNA 21853827 21856492 Y -1 chromosome ENSG00000263502 AC134878.2 miRNA 13340359 13340440 Y -1 chromosome ENSG00000265161 AC011293.1 miRNA 13947443 13947512 Y -1 chromosome ENSG00000265197 AC053516.1 miRNA 18398127 18398238 Y -1 chromosome ENSG00000266220 AC010723.2 miRNA 16364065 16364171 Y -1 chromosome ENSG00000267793 RP11-576C2.1 pseudogene 21760074 21760643 Y 1 chromosome ENSG00000267935 AC016752.1 protein_coding 25847479 25850592 Y 1 chromosome ENSG00000269084 AC009977.1 protein_coding 21737895 21738068 Y -1 chromosome ENSG00000269291 AC010877.1 protein_coding 15418467 15429181 Y 1 chromosome ENSG00000269393 AC007965.1 protein_coding 28111776 28114889 Y -1 chromosome ENSG00000269464 AC012067.1 protein_coding 5306691 5312605 Y -1 chromosome ENSG00000270073 AC006156.2 pseudogene 9345205 9347784 Y 1 chromosome ENSG00000270242 RP11-295P22.1 pseudogene 13551375 13552752 Y 1 chromosome ENSG00000270455 PABPC1P5 pseudogene 13491303 13493369 Y 1 chromosome ENSG00000270535 TCEB1P34 pseudogene 27809047 27809373 Y 1 chromosome ENSG00000270570 RP1-85D24.1 pseudogene 13263395 13263563 Y -1 chromosome ENSG00000271123 TCEB1P5 pseudogene 23793569 23793901 Y -1 chromosome ENSG00000271309 RP1-85D24.2 pseudogene 13263065 13263272 Y -1 chromosome ENSG00000271365 RP1-85D24.3 pseudogene 13262741 13262939 Y -1 chromosome ENSG00000271375 RP11-295P22.2 pseudogene 13629403 13629913 Y -1 chromosome ENSG00000271595 TCEB1P35 pseudogene 19576759 19577094 Y -1 chromosome ENSG00000272042 Metazoa_SRP misc_RNA 27605054 27605329 Y 1 chromosome ensembldb/inst/chrY/ens_metadata.txt0000644000175400017540000000046413001231245020576 0ustar00biocbuildbiocbuildname value Db type EnsDb Type of Gene ID Ensembl Gene ID Supporting package ensembldb Db created by ensembldb package from Bioconductor script_version 0.1.2 Creation time Wed Mar 18 09:30:54 2015 ensembl_version 75 ensembl_host manny.i-med.ac.at Organism homo_sapiens genome_build GRCh37 DBSCHEMAVERSION 1.0 ensembldb/inst/chrY/ens_tx.txt0000644000175400017540000016134513001231245017457 0ustar00biocbuildbiocbuildtx_id tx_biotype tx_seq_start tx_seq_end tx_cds_seq_start tx_cds_seq_end gene_id ENST00000469599 retained_intron 21865751 21878581 NULL NULL ENSG00000012817 ENST00000317961 protein_coding 21867301 21906809 21867881 21906420 ENSG00000012817 ENST00000382806 protein_coding 21867306 21906647 21867881 21906420 ENSG00000012817 ENST00000492117 retained_intron 21867311 21884353 NULL NULL ENSG00000012817 ENST00000440077 protein_coding 21867949 21906809 21867949 21906420 ENSG00000012817 ENST00000415360 protein_coding 21869068 21870835 21869068 21870835 ENSG00000012817 ENST00000485154 retained_intron 21871586 21872492 NULL NULL ENSG00000012817 ENST00000478891 retained_intron 21877022 21878234 NULL NULL ENSG00000012817 ENST00000447300 protein_coding 21883158 21906597 21883158 21906420 ENSG00000012817 ENST00000541639 protein_coding 21867303 21906825 21867881 21906420 ENSG00000012817 ENST00000360160 protein_coding 15016019 15030451 15016848 15030034 ENSG00000067048 ENST00000454054 protein_coding 15016029 15025765 15016848 15025765 ENSG00000067048 ENST00000336079 protein_coding 15016742 15032390 15016848 15030034 ENSG00000067048 ENST00000493363 processed_transcript 15016760 15021607 NULL NULL ENSG00000067048 ENST00000440554 protein_coding 15017649 15026561 15017691 15026561 ENSG00000067048 ENST00000469101 processed_transcript 15024552 15025713 NULL NULL ENSG00000067048 ENST00000472510 processed_transcript 15024875 15026811 NULL NULL ENSG00000067048 ENST00000463199 processed_transcript 15024920 15027139 NULL NULL ENSG00000067048 ENST00000495478 processed_transcript 15027408 15028265 NULL NULL ENSG00000067048 ENST00000383052 protein_coding 2803322 2850547 2821978 2848034 ENSG00000067646 ENST00000469869 processed_transcript 2803541 2846094 NULL NULL ENSG00000067646 ENST00000443793 protein_coding 2803546 2829327 2821978 2829327 ENSG00000067646 ENST00000478783 processed_transcript 2845860 2847391 NULL NULL ENSG00000067646 ENST00000431102 protein_coding 2803112 2850546 2821978 2848034 ENSG00000067646 ENST00000155093 protein_coding 2803518 2850546 2821978 2848034 ENSG00000067646 ENST00000449237 protein_coding 2803518 2850546 2829132 2848034 ENSG00000067646 ENST00000383032 protein_coding 6778727 6959724 6893126 6959533 ENSG00000092377 ENST00000355162 protein_coding 6778727 6959724 6893126 6959533 ENSG00000092377 ENST00000346432 protein_coding 6778727 6959724 6893126 6959533 ENSG00000092377 ENST00000333703 protein_coding 4868267 4973485 4900738 4972402 ENSG00000099715 ENST00000362095 protein_coding 4924131 4972741 4924865 4972402 ENSG00000099715 ENST00000400457 protein_coding 4924930 5610265 4924930 5605983 ENSG00000099715 ENST00000215473 protein_coding 4924865 5605983 4924865 5605983 ENSG00000099715 ENST00000215479 protein_coding 6733959 6742068 6734114 6740649 ENSG00000099721 ENST00000383036 protein_coding 6734114 6740649 6734114 6740649 ENSG00000099721 ENST00000383037 protein_coding 6736078 6740649 6736078 6740649 ENSG00000099721 ENST00000528056 processed_transcript 7142013 7249589 NULL NULL ENSG00000099725 ENST00000533551 transcribed_unprocessed_pseudogene 7142354 7239909 NULL NULL ENSG00000099725 ENST00000495163 processed_transcript 7194108 7196444 NULL NULL ENSG00000099725 ENST00000472666 processed_transcript 7201071 7224264 NULL NULL ENSG00000099725 ENST00000362758 transcribed_unprocessed_pseudogene 7142336 7235456 NULL NULL ENSG00000099725 ENST00000338981 protein_coding 14813160 14972764 14821381 14971341 ENSG00000114374 ENST00000493168 processed_transcript 14813969 14833136 NULL NULL ENSG00000114374 ENST00000426564 processed_transcript 14821369 14972764 NULL NULL ENSG00000114374 ENST00000453031 protein_coding 14958970 14971537 14958970 14971341 ENSG00000114374 ENST00000471409 processed_transcript 14968738 14972764 NULL NULL ENSG00000114374 ENST00000250776 lincRNA 6258472 6279605 NULL NULL ENSG00000129816 ENST00000250784 protein_coding 2709527 2735309 2709666 2734935 ENSG00000129824 ENST00000430575 protein_coding 2709961 2734903 2709985 2734903 ENSG00000129824 ENST00000477725 processed_transcript 2722137 2734997 NULL NULL ENSG00000129824 ENST00000515575 processed_transcript 2722771 2800041 NULL NULL ENSG00000129824 ENST00000250805 lincRNA 9590765 9611898 NULL NULL ENSG00000129845 ENST00000250823 protein_coding 16168097 16168838 16168170 16168739 ENSG00000129862 ENST00000250825 protein_coding 16097652 16098393 16097751 16098320 ENSG00000129864 ENST00000382867 protein_coding 19990147 19992100 19990147 19991772 ENSG00000129873 ENST00000544303 protein_coding 19989290 19992098 19989665 19991772 ENSG00000129873 ENST00000407724 processed_transcript 21729199 21752133 NULL NULL ENSG00000131002 ENST00000459719 retained_intron 21729236 21752304 NULL NULL ENSG00000131002 ENST00000447520 processed_transcript 21729268 21752309 NULL NULL ENSG00000131002 ENST00000445715 transcribed_unprocessed_pseudogene 21729268 21766006 NULL NULL ENSG00000131002 ENST00000538014 retained_intron 21729673 21751735 NULL NULL ENSG00000131002 ENST00000447202 retained_intron 21729715 21751733 NULL NULL ENSG00000131002 ENST00000588613 processed_transcript 21750429 21755603 NULL NULL ENSG00000131002 ENST00000589075 processed_transcript 21750456 21752305 NULL NULL ENSG00000131002 ENST00000585549 processed_transcript 21750495 21755488 NULL NULL ENSG00000131002 ENST00000587095 processed_transcript 21750497 21755488 NULL NULL ENSG00000131002 ENST00000488280 processed_transcript 21752639 21755537 NULL NULL ENSG00000131002 ENST00000253320 processed_transcript 21754336 21768160 NULL NULL ENSG00000131002 ENST00000593000 retained_intron 21754974 21756047 NULL NULL ENSG00000131002 ENST00000592697 processed_transcript 21759243 21765937 NULL NULL ENSG00000131002 ENST00000251749 transcribed_unprocessed_pseudogene 21729235 21752308 NULL NULL ENSG00000131002 ENST00000382832 transcribed_unprocessed_pseudogene 21758442 21767698 NULL NULL ENSG00000131002 ENST00000433794 antisense 20743092 20752407 NULL NULL ENSG00000131007 ENST00000545582 antisense 20743092 20750598 NULL NULL ENSG00000131007 ENST00000253838 lincRNA 24585740 24587605 NULL NULL ENSG00000131538 ENST00000538537 lincRNA 24585887 24587549 NULL NULL ENSG00000131538 ENST00000253848 antisense 24291113 24292978 NULL NULL ENSG00000131548 ENST00000545808 antisense 24291169 24292831 NULL NULL ENSG00000131548 ENST00000457100 lincRNA 6317509 6325947 NULL NULL ENSG00000147753 ENST00000276770 lincRNA 6317509 6325947 NULL NULL ENSG00000147753 ENST00000449828 lincRNA 6321946 6325947 NULL NULL ENSG00000147753 ENST00000447655 lincRNA 9544433 9548434 NULL NULL ENSG00000147761 ENST00000276779 lincRNA 9544433 9552871 NULL NULL ENSG00000147761 ENST00000415405 lincRNA 9544433 9552871 NULL NULL ENSG00000147761 ENST00000284856 protein_coding 15815447 15817904 15816216 15817139 ENSG00000154620 ENST00000288666 protein_coding 22918050 22942918 22918050 22942918 ENSG00000157828 ENST00000471252 processed_transcript 16634518 16734377 NULL NULL ENSG00000165246 ENST00000382872 protein_coding 16634632 16957530 16835029 16953142 ENSG00000165246 ENST00000355905 protein_coding 16635626 16955606 16734000 16953142 ENSG00000165246 ENST00000382868 protein_coding 16635626 16955606 16734000 16953142 ENSG00000165246 ENST00000476359 processed_transcript 16635626 16955606 NULL NULL ENSG00000165246 ENST00000481089 processed_transcript 16636409 16734248 NULL NULL ENSG00000165246 ENST00000339174 protein_coding 16636454 16955527 16734000 16953142 ENSG00000165246 ENST00000413217 protein_coding 16734061 16845429 16734061 16845417 ENSG00000165246 ENST00000297967 protein_coding 16733901 16845429 16734000 16845417 ENSG00000165246 ENST00000320701 protein_coding 6114264 6117054 6114310 6116866 ENSG00000168757 ENST00000383042 protein_coding 6114310 6117053 6114310 6116119 ENSG00000168757 ENST00000470569 retained_intron 6114310 6117060 NULL NULL ENSG00000168757 ENST00000464674 retained_intron 6115664 6117045 NULL NULL ENSG00000168757 ENST00000343584 processed_transcript 25827587 25840726 NULL NULL ENSG00000169763 ENST00000607210 transcribed_unprocessed_pseudogene 25828154 25840674 NULL NULL ENSG00000169763 ENST00000303593 transcribed_unprocessed_pseudogene 25829559 25840710 NULL NULL ENSG00000169763 ENST00000303728 protein_coding 24636544 24660784 24647712 24660217 ENSG00000169789 ENST00000477123 nonsense_mediated_decay 24636544 24660784 24647712 24658815 ENSG00000169789 ENST00000338793 protein_coding 24636544 24658815 24647712 24658815 ENSG00000169789 ENST00000303766 protein_coding 24314689 24329095 24314967 24327116 ENSG00000169800 ENST00000481858 processed_transcript 24314689 24329104 NULL NULL ENSG00000169800 ENST00000454978 protein_coding 24314689 24329129 24314967 24327116 ENSG00000169800 ENST00000303804 protein_coding 24217903 24242154 24218470 24230986 ENSG00000169807 ENST00000472391 nonsense_mediated_decay 24217903 24242154 24219875 24230986 ENSG00000169807 ENST00000341740 protein_coding 24219875 24242154 24219875 24230986 ENSG00000169807 ENST00000382759 unprocessed_pseudogene 23655405 23663854 NULL NULL ENSG00000169811 ENST00000326985 unprocessed_pseudogene 23655405 23662391 NULL NULL ENSG00000169811 ENST00000426043 unprocessed_pseudogene 23661276 23663854 NULL NULL ENSG00000169811 ENST00000303979 unprocessed_pseudogene 23630044 23632569 NULL NULL ENSG00000169849 ENST00000344884 protein_coding 20893326 20935601 20893655 20935504 ENSG00000169953 ENST00000491902 processed_transcript 20893326 20935601 NULL NULL ENSG00000169953 ENST00000382852 protein_coding 20930807 20935572 20931484 20935504 ENSG00000169953 ENST00000304790 protein_coding 20933700 20935621 20933821 20935504 ENSG00000169953 ENST00000505047 processed_transcript 20934594 20990548 NULL NULL ENSG00000169953 ENST00000306589 unprocessed_pseudogene 28121696 28134216 NULL NULL ENSG00000172283 ENST00000338673 unprocessed_pseudogene 28121660 28132811 NULL NULL ENSG00000172283 ENST00000306609 protein_coding 27768309 27771049 27768590 27770674 ENSG00000172288 ENST00000361963 protein_coding 27768264 27770483 27768590 27770212 ENSG00000172288 ENST00000333235 unprocessed_pseudogene 27624416 27629777 NULL NULL ENSG00000172294 ENST00000539489 unprocessed_pseudogene 27629055 27632852 NULL NULL ENSG00000172294 ENST00000416946 unprocessed_pseudogene 27600708 27606719 NULL NULL ENSG00000172297 ENST00000306667 unprocessed_pseudogene 27601458 27606322 NULL NULL ENSG00000172297 ENST00000423852 unprocessed_pseudogene 26355714 26361728 NULL NULL ENSG00000172332 ENST00000338706 unprocessed_pseudogene 26356114 26360978 NULL NULL ENSG00000172332 ENST00000418188 unprocessed_pseudogene 26332656 26338017 NULL NULL ENSG00000172342 ENST00000306882 protein_coding 26191376 26194116 26191751 26193835 ENSG00000172352 ENST00000382407 protein_coding 26191940 26194166 26192213 26193835 ENSG00000172352 ENST00000307393 protein_coding 20708557 20710478 20708674 20710357 ENSG00000172468 ENST00000309834 protein_coding 20708577 20750849 20708674 20750520 ENSG00000172468 ENST00000338876 nonsense_mediated_decay 20708577 20750849 20708674 20712690 ENSG00000172468 ENST00000382856 protein_coding 20708606 20713351 20708674 20712690 ENSG00000172468 ENST00000455422 transcribed_unprocessed_pseudogene 8774265 8784114 NULL NULL ENSG00000173357 ENST00000311828 processed_transcript 8777989 8782196 NULL NULL ENSG00000173357 ENST00000416687 transcribed_unprocessed_pseudogene 8774265 8782184 NULL NULL ENSG00000173357 ENST00000321217 protein_coding 3447082 3448082 3447286 3447843 ENSG00000176679 ENST00000559055 protein_coding 3447156 3448082 3447286 3447843 ENSG00000176679 ENST00000324446 lincRNA 21034387 21040114 NULL NULL ENSG00000176728 ENST00000454875 lincRNA 21034389 21239004 NULL NULL ENSG00000176728 ENST00000452584 lincRNA 21094203 21237882 NULL NULL ENSG00000176728 ENST00000331787 lincRNA 21094585 21239302 NULL NULL ENSG00000176728 ENST00000447937 lincRNA 21203384 21239281 NULL NULL ENSG00000176728 ENST00000253470 lincRNA 8651351 8685423 NULL NULL ENSG00000180910 ENST00000426790 protein_coding 20137669 20140477 20137995 20140102 ENSG00000182415 ENST00000250838 protein_coding 20137667 20139626 20137995 20139620 ENSG00000182415 ENST00000382764 protein_coding 23544840 23548246 23545072 23548149 ENSG00000183146 ENST00000329684 lincRNA 9528709 9531308 NULL NULL ENSG00000183385 ENST00000426035 lincRNA 9528709 9531566 NULL NULL ENSG00000183385 ENST00000331172 protein_coding 13496241 13524717 13496255 13524717 ENSG00000183704 ENST00000602732 protein_coding 25119966 25138523 25138491 25138523 ENSG00000183753 ENST00000331070 protein_coding 25130410 25151606 25138491 25144415 ENSG00000183753 ENST00000602818 processed_transcript 25130434 25151553 NULL NULL ENSG00000183753 ENST00000382585 protein_coding 25130410 25151612 25138491 25144415 ENSG00000183753 ENST00000602770 protein_coding 26753707 26772264 26772232 26772264 ENSG00000183795 ENST00000382392 protein_coding 26764151 26785354 26772232 26778157 ENSG00000183795 ENST00000602549 processed_transcript 26764175 26785295 NULL NULL ENSG00000183795 ENST00000331397 protein_coding 15360259 15592553 15361736 15591545 ENSG00000183878 ENST00000362096 protein_coding 15409321 15592550 15409583 15591545 ENSG00000183878 ENST00000329134 protein_coding 15434914 15592550 15434994 15591545 ENSG00000183878 ENST00000478900 processed_transcript 15472368 15591420 NULL NULL ENSG00000183878 ENST00000474365 processed_transcript 15505032 15591384 NULL NULL ENSG00000183878 ENST00000382893 protein_coding 15508182 15591858 15508784 15591545 ENSG00000183878 ENST00000479713 processed_transcript 15590922 15591803 NULL NULL ENSG00000183878 ENST00000382896 protein_coding 15361736 15591545 15361736 15591545 ENSG00000183878 ENST00000537580 protein_coding 15361736 15591545 15361736 15591545 ENSG00000183878 ENST00000538878 protein_coding 15434927 15591551 15434994 15591545 ENSG00000183878 ENST00000540140 protein_coding 15434948 15591545 15435229 15591545 ENSG00000183878 ENST00000545955 protein_coding 15435435 15591545 15435435 15591545 ENSG00000183878 ENST00000383070 protein_coding 2654896 2655740 2655030 2655644 ENSG00000184895 ENST00000525526 protein_coding 2655049 2655644 2655049 2655644 ENSG00000184895 ENST00000534739 protein_coding 2655145 2655644 2655145 2655644 ENSG00000184895 ENST00000330337 lincRNA 23745486 23756552 NULL NULL ENSG00000184991 ENST00000382840 processed_pseudogene 21154353 21154595 NULL NULL ENSG00000185275 ENST00000455570 lincRNA 6338814 6341671 NULL NULL ENSG00000185700 ENST00000328819 lincRNA 6339072 6341671 NULL NULL ENSG00000185700 ENST00000382287 protein_coding 27177048 27198251 27184245 27190170 ENSG00000185894 ENST00000602559 processed_transcript 27177107 27198227 NULL NULL ENSG00000185894 ENST00000602680 protein_coding 27190138 27208695 27190138 27190170 ENSG00000185894 ENST00000382365 protein_coding 26909216 26959626 26915081 26959332 ENSG00000187191 ENST00000446723 protein_coding 26909220 26959542 26915081 26959332 ENSG00000187191 ENST00000315357 protein_coding 26909222 26959540 26915081 26959332 ENSG00000187191 ENST00000400212 protein_coding 26929528 26959519 26929528 26959332 ENSG00000187191 ENST00000306737 protein_coding 26934285 26959519 26934285 26959332 ENSG00000187191 ENST00000338964 unprocessed_pseudogene 9743204 9745748 NULL NULL ENSG00000187657 ENST00000405239 protein_coding 25275502 25345241 25281357 25344947 ENSG00000188120 ENST00000466332 processed_transcript 25275510 25286959 NULL NULL ENSG00000188120 ENST00000382510 protein_coding 25276010 25345070 25276427 25344947 ENSG00000188120 ENST00000426000 protein_coding 25275506 25345140 25281357 25344947 ENSG00000188120 ENST00000540248 protein_coding 25275509 25345157 25281357 25344947 ENSG00000188120 ENST00000344424 unprocessed_pseudogene 28555962 28566682 NULL NULL ENSG00000188399 ENST00000431358 unprocessed_pseudogene 9215731 9218480 NULL NULL ENSG00000188656 ENST00000382670 unprocessed_pseudogene 9148739 9160478 NULL NULL ENSG00000197038 ENST00000538925 unprocessed_pseudogene 9154670 9160483 NULL NULL ENSG00000197038 ENST00000441780 unprocessed_pseudogene 27641798 27648105 NULL NULL ENSG00000197092 ENST00000361365 protein_coding 22737611 22755040 22737758 22754232 ENSG00000198692 ENST00000465253 processed_transcript 22737664 22750415 NULL NULL ENSG00000198692 ENST00000382772 protein_coding 22737680 22754516 22737758 22754232 ENSG00000198692 ENST00000464196 processed_transcript 22748604 22754516 NULL NULL ENSG00000198692 ENST00000485584 processed_transcript 22749733 22754516 NULL NULL ENSG00000198692 ENST00000382314 protein_coding 26980064 27053183 26980274 27047322 ENSG00000205916 ENST00000382296 protein_coding 26997726 27047331 26997726 27047322 ENSG00000205916 ENST00000449750 protein_coding 26980064 27053183 26980274 27047322 ENSG00000205916 ENST00000415508 protein_coding 26980066 27053181 26980274 27047322 ENSG00000205916 ENST00000440066 protein_coding 26980081 27053183 26980274 27047322 ENSG00000205916 ENST00000382432 protein_coding 26980008 27053183 26980274 27020194 ENSG00000205916 ENST00000400494 protein_coding 26980087 27053181 26980274 27020194 ENSG00000205916 ENST00000382290 protein_coding 26980008 27053183 26980274 27015437 ENSG00000205916 ENST00000416956 unprocessed_pseudogene 25525631 25538844 NULL NULL ENSG00000205936 ENST00000382449 protein_coding 25365594 25437503 25365888 25431648 ENSG00000205944 ENST00000382440 protein_coding 25365622 25437499 25365888 25431648 ENSG00000205944 ENST00000382433 protein_coding 25365695 25436146 25365888 25431648 ENSG00000205944 ENST00000382306 protein_coding 25365622 25437496 25365888 25431648 ENSG00000205944 ENST00000449947 protein_coding 25365678 25437499 25365888 25431648 ENSG00000205944 ENST00000382294 protein_coding 25365680 25437497 25365888 25431648 ENSG00000205944 ENST00000382424 protein_coding 25365695 25437497 25365888 25431648 ENSG00000205944 ENST00000400493 protein_coding 25365701 25437497 25365888 25431648 ENSG00000205944 ENST00000382431 protein_coding 25365701 25435854 25365888 25431648 ENSG00000205944 ENST00000382434 protein_coding 25365622 25395738 25365888 25395738 ENSG00000205944 ENST00000382966 processed_transcript 14475147 14532255 NULL NULL ENSG00000206159 ENST00000493160 processed_transcript 14494992 14530093 NULL NULL ENSG00000206159 ENST00000357871 processed_transcript 14514046 14532255 NULL NULL ENSG00000206159 ENST00000382963 processed_transcript 14517922 14532171 NULL NULL ENSG00000206159 ENST00000382965 transcribed_unprocessed_pseudogene 14518595 14532121 NULL NULL ENSG00000206159 ENST00000417072 lincRNA 9578193 9596085 NULL NULL ENSG00000212855 ENST00000450591 lincRNA 6274285 6292186 NULL NULL ENSG00000212856 ENST00000388836 processed_pseudogene 5441186 5442472 NULL NULL ENSG00000214207 ENST00000400275 processed_pseudogene 15398518 15399258 NULL NULL ENSG00000215414 ENST00000536206 processed_pseudogene 15398518 15399258 NULL NULL ENSG00000215414 ENST00000258589 unprocessed_pseudogene 28654360 28725837 NULL NULL ENSG00000215506 ENST00000448881 transcribed_processed_pseudogene 28269821 28279455 NULL NULL ENSG00000215507 ENST00000400476 processed_transcript 28269867 28275354 NULL NULL ENSG00000215507 ENST00000442535 processed_pseudogene 25012691 25013903 NULL NULL ENSG00000215537 ENST00000448575 transcribed_processed_pseudogene 25683455 25693089 NULL NULL ENSG00000215540 ENST00000458444 processed_transcript 25687556 25693043 NULL NULL ENSG00000215540 ENST00000400581 lincRNA 24442945 24445023 NULL NULL ENSG00000215560 ENST00000441139 processed_transcript 21617317 21665039 NULL NULL ENSG00000215580 ENST00000400605 processed_transcript 21618198 21665022 NULL NULL ENSG00000215580 ENST00000513194 transcribed_unprocessed_pseudogene 21618471 21646770 NULL NULL ENSG00000215580 ENST00000421118 processed_pseudogene 14043242 14044475 NULL NULL ENSG00000215583 ENST00000431340 unprocessed_pseudogene 8148239 8150250 NULL NULL ENSG00000215601 ENST00000415010 processed_pseudogene 7721727 7722917 NULL NULL ENSG00000215603 ENST00000404428 processed_pseudogene 3417693 3417851 NULL NULL ENSG00000216777 ENST00000403990 processed_pseudogene 8286832 8287941 NULL NULL ENSG00000216824 ENST00000406090 processed_pseudogene 23005142 23005596 NULL NULL ENSG00000216844 ENST00000407745 processed_pseudogene 21033988 21034158 NULL NULL ENSG00000217179 ENST00000403487 processed_pseudogene 21147061 21148284 NULL NULL ENSG00000217896 ENST00000405035 processed_pseudogene 3719265 3720910 NULL NULL ENSG00000218410 ENST00000421995 unprocessed_pseudogene 26001669 26003238 NULL NULL ENSG00000223362 ENST00000449659 unprocessed_pseudogene 27898535 27899017 NULL NULL ENSG00000223406 ENST00000425912 unprocessed_pseudogene 25805613 25813969 NULL NULL ENSG00000223407 ENST00000439805 unprocessed_pseudogene 7546940 7549069 NULL NULL ENSG00000223422 ENST00000435111 lincRNA 16388092 16389369 NULL NULL ENSG00000223517 ENST00000455085 unprocessed_pseudogene 19894090 19896695 NULL NULL ENSG00000223555 ENST00000414667 processed_pseudogene 2863108 2863314 NULL NULL ENSG00000223600 ENST00000413867 unprocessed_pseudogene 27577303 27583462 NULL NULL ENSG00000223636 ENST00000456360 transcribed_unprocessed_pseudogene 23556877 23563471 NULL NULL ENSG00000223637 ENST00000444169 processed_transcript 23557034 23563448 NULL NULL ENSG00000223637 ENST00000421387 lincRNA 27329790 27330920 NULL NULL ENSG00000223641 ENST00000412737 processed_pseudogene 28064470 28065042 NULL NULL ENSG00000223655 ENST00000417252 unprocessed_pseudogene 26314334 26320641 NULL NULL ENSG00000223698 ENST00000418671 unprocessed_pseudogene 6196093 6211364 NULL NULL ENSG00000223744 ENST00000443911 unprocessed_pseudogene 27856055 27859704 NULL NULL ENSG00000223856 ENST00000428060 processed_pseudogene 15342765 15343320 NULL NULL ENSG00000223915 ENST00000417434 processed_pseudogene 8231577 8232000 NULL NULL ENSG00000223955 ENST00000440402 processed_pseudogene 27131348 27132623 NULL NULL ENSG00000223978 ENST00000428380 unprocessed_pseudogene 20134179 20135788 NULL NULL ENSG00000224033 ENST00000446621 processed_pseudogene 15195704 15207719 NULL NULL ENSG00000224035 ENST00000420443 transcribed_unprocessed_pseudogene 14460540 14468218 NULL NULL ENSG00000224060 ENST00000430152 processed_transcript 14465804 14468226 NULL NULL ENSG00000224060 ENST00000445253 lincRNA 9638762 9650854 NULL NULL ENSG00000224075 ENST00000435142 unprocessed_pseudogene 20994888 21001199 NULL NULL ENSG00000224151 ENST00000412474 unprocessed_pseudogene 19841408 19856476 NULL NULL ENSG00000224166 ENST00000445573 unprocessed_pseudogene 25803986 25805205 NULL NULL ENSG00000224169 ENST00000413946 processed_pseudogene 26640856 26641730 NULL NULL ENSG00000224210 ENST00000420810 processed_pseudogene 28695572 28695890 NULL NULL ENSG00000224240 ENST00000421178 protein_coding 9374241 9384693 9374241 9384693 ENSG00000224336 ENST00000438971 unprocessed_pseudogene 9021303 9023412 NULL NULL ENSG00000224408 ENST00000415994 unprocessed_pseudogene 22970662 22973695 NULL NULL ENSG00000224482 ENST00000436723 unprocessed_pseudogene 20020635 20022686 NULL NULL ENSG00000224485 ENST00000427212 processed_pseudogene 17053626 17054595 NULL NULL ENSG00000224518 ENST00000417797 unprocessed_pseudogene 26066052 26067657 NULL NULL ENSG00000224571 ENST00000447526 processed_pseudogene 8218972 8220184 NULL NULL ENSG00000224634 ENST00000451071 transcribed_unprocessed_pseudogene 24795392 24805025 NULL NULL ENSG00000224657 ENST00000400578 processed_transcript 24795438 24800925 NULL NULL ENSG00000224657 ENST00000413372 processed_pseudogene 26422392 26423173 NULL NULL ENSG00000224827 ENST00000457708 unprocessed_pseudogene 25880340 25882396 NULL NULL ENSG00000224866 ENST00000445813 unprocessed_pseudogene 24666828 24667842 NULL NULL ENSG00000224873 ENST00000434179 unprocessed_pseudogene 24816763 24819006 NULL NULL ENSG00000224917 ENST00000413200 processed_pseudogene 6587003 6587221 NULL NULL ENSG00000224953 ENST00000443200 unprocessed_pseudogene 19909863 19912576 NULL NULL ENSG00000224964 ENST00000421205 lincRNA 19612838 19626898 NULL NULL ENSG00000224989 ENST00000443820 unprocessed_pseudogene 14474827 14499123 NULL NULL ENSG00000225117 ENST00000422655 unprocessed_pseudogene 25608612 25618427 NULL NULL ENSG00000225189 ENST00000426293 unprocessed_pseudogene 27845598 27848709 NULL NULL ENSG00000225256 ENST00000431179 unprocessed_pseudogene 27821207 27843493 NULL NULL ENSG00000225287 ENST00000412165 unprocessed_pseudogene 27894743 27896353 NULL NULL ENSG00000225326 ENST00000434110 unprocessed_pseudogene 25727742 25760787 NULL NULL ENSG00000225466 ENST00000433767 unprocessed_pseudogene 26378973 26385131 NULL NULL ENSG00000225491 ENST00000450145 protein_coding 9293012 9323893 9293012 9323893 ENSG00000225516 ENST00000423213 protein_coding 9301461 9323893 9301461 9323893 ENSG00000225516 ENST00000430032 protein_coding 9323618 9344176 9323618 9344176 ENSG00000225516 ENST00000437686 lincRNA 7567398 7569288 NULL NULL ENSG00000225520 ENST00000426661 antisense 9185120 9193010 NULL NULL ENSG00000225560 ENST00000432394 antisense 9187188 9192791 NULL NULL ENSG00000225560 ENST00000447528 unprocessed_pseudogene 27764791 27766400 NULL NULL ENSG00000225609 ENST00000453474 unprocessed_pseudogene 24344576 24349859 NULL NULL ENSG00000225615 ENST00000440297 unprocessed_pseudogene 22889140 22904786 NULL NULL ENSG00000225624 ENST00000458209 processed_pseudogene 3646038 3647587 NULL NULL ENSG00000225653 ENST00000456541 processed_transcript 9904163 9904995 NULL NULL ENSG00000225685 ENST00000426936 transcribed_unprocessed_pseudogene 9904196 9906760 NULL NULL ENSG00000225685 ENST00000443934 processed_pseudogene 20602683 20603011 NULL NULL ENSG00000225716 ENST00000429066 processed_pseudogene 19949081 19949408 NULL NULL ENSG00000225740 ENST00000418221 unprocessed_pseudogene 8132490 8145229 NULL NULL ENSG00000225809 ENST00000445125 processed_pseudogene 10036113 10036711 NULL NULL ENSG00000225840 ENST00000426199 unprocessed_pseudogene 28390498 28390720 NULL NULL ENSG00000225876 ENST00000436888 processed_pseudogene 4669726 4670889 NULL NULL ENSG00000225878 ENST00000440676 unprocessed_pseudogene 20267200 20269988 NULL NULL ENSG00000225895 ENST00000425789 processed_pseudogene 19838632 19838960 NULL NULL ENSG00000225896 ENST00000411756 unprocessed_pseudogene 19921687 19937209 NULL NULL ENSG00000226011 ENST00000452415 unprocessed_pseudogene 23799361 23800927 NULL NULL ENSG00000226042 ENST00000455560 unprocessed_pseudogene 10011462 10011816 NULL NULL ENSG00000226061 ENST00000445405 unprocessed_pseudogene 24073668 24083297 NULL NULL ENSG00000226092 ENST00000413550 unprocessed_pseudogene 20024579 20033510 NULL NULL ENSG00000226116 ENST00000449843 unprocessed_pseudogene 9477858 9478325 NULL NULL ENSG00000226223 ENST00000416040 processed_pseudogene 26829804 26831082 NULL NULL ENSG00000226270 ENST00000445421 processed_pseudogene 19740393 19741282 NULL NULL ENSG00000226353 ENST00000458627 lincRNA 20552880 20566932 NULL NULL ENSG00000226362 ENST00000427537 unprocessed_pseudogene 26081100 26086248 NULL NULL ENSG00000226369 ENST00000411487 unprocessed_pseudogene 19833478 19835057 NULL NULL ENSG00000226449 ENST00000423409 processed_pseudogene 23069230 23069448 NULL NULL ENSG00000226504 ENST00000445453 processed_pseudogene 8239704 8240071 NULL NULL ENSG00000226529 ENST00000455254 processed_pseudogene 16750976 16752238 NULL NULL ENSG00000226555 ENST00000421895 unprocessed_pseudogene 20242567 20258088 NULL NULL ENSG00000226611 ENST00000434773 unprocessed_pseudogene 14649708 14656818 NULL NULL ENSG00000226863 ENST00000431260 unprocessed_pseudogene 25820526 25821539 NULL NULL ENSG00000226873 ENST00000436568 lincRNA 25082602 25119431 NULL NULL ENSG00000226906 ENST00000437359 unprocessed_pseudogene 23473154 23480781 NULL NULL ENSG00000226918 ENST00000470460 processed_transcript 24549608 24564028 NULL NULL ENSG00000226941 ENST00000250831 protein_coding 24549617 24564028 24551595 24563750 ENSG00000226941 ENST00000414629 protein_coding 24454970 24564028 24551595 24563750 ENSG00000226941 ENST00000445779 protein_coding 24454970 24564028 24551595 24563750 ENSG00000226941 ENST00000413610 unprocessed_pseudogene 10007397 10007923 NULL NULL ENSG00000226975 ENST00000412493 unprocessed_pseudogene 17659096 17705211 NULL NULL ENSG00000227166 ENST00000425544 unprocessed_pseudogene 7995171 8012244 NULL NULL ENSG00000227204 ENST00000438459 processed_pseudogene 25204177 25205333 NULL NULL ENSG00000227251 ENST00000444242 unprocessed_pseudogene 2749724 2751693 NULL NULL ENSG00000227289 ENST00000441906 lincRNA 26631479 26632610 NULL NULL ENSG00000227439 ENST00000426983 unprocessed_pseudogene 24065082 24067253 NULL NULL ENSG00000227444 ENST00000381172 unprocessed_pseudogene 14551845 14619171 NULL NULL ENSG00000227447 ENST00000430663 unprocessed_pseudogene 20642973 20649286 NULL NULL ENSG00000227494 ENST00000456738 unprocessed_pseudogene 28732789 28737748 NULL NULL ENSG00000227629 ENST00000423333 unprocessed_pseudogene 27412731 27419678 NULL NULL ENSG00000227633 ENST00000420603 unprocessed_pseudogene 28079980 28082028 NULL NULL ENSG00000227635 ENST00000432201 processed_pseudogene 8281078 8282095 NULL NULL ENSG00000227830 ENST00000445313 processed_pseudogene 26837938 26839079 NULL NULL ENSG00000227837 ENST00000436690 processed_pseudogene 28137388 28137719 NULL NULL ENSG00000227867 ENST00000450481 unprocessed_pseudogene 26092074 26098440 NULL NULL ENSG00000227871 ENST00000444494 unprocessed_pseudogene 25824651 25824982 NULL NULL ENSG00000227915 ENST00000423569 processed_pseudogene 17053427 17053630 NULL NULL ENSG00000227949 ENST00000411585 unprocessed_pseudogene 23717738 23719956 NULL NULL ENSG00000227989 ENST00000428070 processed_pseudogene 20949636 20949966 NULL NULL ENSG00000228193 ENST00000432613 unprocessed_pseudogene 8769244 8771317 NULL NULL ENSG00000228207 ENST00000416110 lincRNA 24997731 24998862 NULL NULL ENSG00000228240 ENST00000452426 unprocessed_pseudogene 23693725 23695897 NULL NULL ENSG00000228257 ENST00000456123 lincRNA 27209230 27246039 NULL NULL ENSG00000228296 ENST00000449963 lincRNA 9650924 9655122 NULL NULL ENSG00000228379 ENST00000433321 antisense 9205412 9213290 NULL NULL ENSG00000228383 ENST00000436801 antisense 9207466 9213071 NULL NULL ENSG00000228383 ENST00000452256 antisense 9212578 9235047 NULL NULL ENSG00000228383 ENST00000432892 antisense 9212578 9214701 NULL NULL ENSG00000228383 ENST00000420524 antisense 9232924 9235047 NULL NULL ENSG00000228383 ENST00000418290 processed_pseudogene 14913906 14915763 NULL NULL ENSG00000228411 ENST00000415662 unprocessed_pseudogene 26113711 26116841 NULL NULL ENSG00000228465 ENST00000451854 processed_pseudogene 19304706 19305532 NULL NULL ENSG00000228518 ENST00000432672 unprocessed_pseudogene 24683194 24683996 NULL NULL ENSG00000228571 ENST00000422712 unprocessed_pseudogene 19628716 19630918 NULL NULL ENSG00000228578 ENST00000420174 processed_pseudogene 22551937 22558682 NULL NULL ENSG00000228764 ENST00000427373 lincRNA 27524447 27540866 NULL NULL ENSG00000228786 ENST00000434164 antisense 16905522 16915913 NULL NULL ENSG00000228787 ENST00000440679 unprocessed_pseudogene 24210845 24211859 NULL NULL ENSG00000228850 ENST00000430228 lincRNA 9555262 9558905 NULL NULL ENSG00000228890 ENST00000457222 protein_coding 9236030 9238826 9236076 9238638 ENSG00000228927 ENST00000424594 protein_coding 9236076 9238825 9236076 9237891 ENSG00000228927 ENST00000491844 processed_transcript 9236076 9238832 NULL NULL ENSG00000228927 ENST00000469322 processed_transcript 9237436 9238817 NULL NULL ENSG00000228927 ENST00000440483 protein_coding 9236030 9307357 9236076 9307170 ENSG00000228927 ENST00000427871 protein_coding 9236076 9238832 9236076 9237587 ENSG00000228927 ENST00000426660 unprocessed_pseudogene 20442064 20446647 NULL NULL ENSG00000228945 ENST00000415776 processed_pseudogene 19868881 19870005 NULL NULL ENSG00000229129 ENST00000413320 unprocessed_pseudogene 19993979 19995588 NULL NULL ENSG00000229138 ENST00000440136 unprocessed_pseudogene 24329997 24332168 NULL NULL ENSG00000229159 ENST00000414182 processed_pseudogene 2797042 2799161 NULL NULL ENSG00000229163 ENST00000434454 unprocessed_pseudogene 9669027 9684305 NULL NULL ENSG00000229208 ENST00000423480 unprocessed_pseudogene 24355478 24362727 NULL NULL ENSG00000229234 ENST00000455084 lincRNA 22627554 22681114 NULL NULL ENSG00000229236 ENST00000439472 lincRNA 22669140 22680293 NULL NULL ENSG00000229236 ENST00000367272 unprocessed_pseudogene 28424070 28500565 NULL NULL ENSG00000229238 ENST00000431405 unprocessed_pseudogene 26227851 26236493 NULL NULL ENSG00000229250 ENST00000454958 processed_pseudogene 20438493 20439381 NULL NULL ENSG00000229302 ENST00000426699 lincRNA 3904538 3968361 NULL NULL ENSG00000229308 ENST00000451423 unprocessed_pseudogene 27959160 27960713 NULL NULL ENSG00000229343 ENST00000455273 unprocessed_pseudogene 20615036 20634023 NULL NULL ENSG00000229406 ENST00000441913 unprocessed_pseudogene 23839132 23843004 NULL NULL ENSG00000229416 ENST00000425411 processed_pseudogene 20309770 20310894 NULL NULL ENSG00000229465 ENST00000439651 processed_pseudogene 3734347 3734763 NULL NULL ENSG00000229518 ENST00000287721 protein_coding 9195406 9198202 9195452 9198014 ENSG00000229549 ENST00000383000 protein_coding 9195452 9198201 9195452 9197267 ENSG00000229549 ENST00000477879 processed_transcript 9195452 9198208 NULL NULL ENSG00000229549 ENST00000436159 processed_transcript 9196812 9198193 NULL NULL ENSG00000229549 ENST00000383005 protein_coding 9195406 9218479 9195452 9218292 ENSG00000229549 ENST00000330628 protein_coding 9195452 9218479 9195452 9218292 ENSG00000229549 ENST00000537415 protein_coding 9195501 9197317 9195758 9197317 ENSG00000229549 ENST00000449858 processed_pseudogene 23023223 23024223 NULL NULL ENSG00000229551 ENST00000444617 unprocessed_pseudogene 24195902 24204260 NULL NULL ENSG00000229553 ENST00000439525 lincRNA 6225260 6229454 NULL NULL ENSG00000229643 ENST00000424581 unprocessed_pseudogene 27725937 27734591 NULL NULL ENSG00000229709 ENST00000414540 unprocessed_pseudogene 24017478 24019697 NULL NULL ENSG00000229725 ENST00000422208 unprocessed_pseudogene 7801038 7805681 NULL NULL ENSG00000229745 ENST00000427496 unprocessed_pseudogene 24452072 24454098 NULL NULL ENSG00000229940 ENST00000434308 unprocessed_pseudogene 8839792 8842136 NULL NULL ENSG00000230025 ENST00000447168 unprocessed_pseudogene 23858456 23860106 NULL NULL ENSG00000230029 ENST00000427622 antisense 9334896 9342768 NULL NULL ENSG00000230066 ENST00000414596 antisense 9336950 9342550 NULL NULL ENSG00000230066 ENST00000444350 unprocessed_pseudogene 25669468 25671516 NULL NULL ENSG00000230073 ENST00000439968 processed_pseudogene 20694212 20694542 NULL NULL ENSG00000230377 ENST00000458706 processed_pseudogene 20230369 20230696 NULL NULL ENSG00000230412 ENST00000446387 unprocessed_pseudogene 20740303 20740446 NULL NULL ENSG00000230458 ENST00000417699 unprocessed_pseudogene 24727136 24760228 NULL NULL ENSG00000230476 ENST00000458667 lincRNA 19664680 19691634 NULL NULL ENSG00000230663 ENST00000424230 unprocessed_pseudogene 24908998 24915934 NULL NULL ENSG00000230727 ENST00000434374 unprocessed_pseudogene 24674428 24682786 NULL NULL ENSG00000230814 ENST00000451173 processed_pseudogene 27164646 27165450 NULL NULL ENSG00000230819 ENST00000419224 unprocessed_pseudogene 28069535 28075973 NULL NULL ENSG00000230854 ENST00000432915 unprocessed_pseudogene 20973224 20973715 NULL NULL ENSG00000230904 ENST00000418278 unprocessed_pseudogene 26328624 26329218 NULL NULL ENSG00000230977 ENST00000414395 unprocessed_pseudogene 26063388 26063870 NULL NULL ENSG00000231026 ENST00000417334 lincRNA 27874637 27879535 NULL NULL ENSG00000231141 ENST00000449148 unprocessed_pseudogene 24118460 24151496 NULL NULL ENSG00000231159 ENST00000416652 unprocessed_pseudogene 20323252 20338370 NULL NULL ENSG00000231311 ENST00000430062 processed_pseudogene 5075256 5076110 NULL NULL ENSG00000231341 ENST00000414121 unprocessed_pseudogene 26196025 26197634 NULL NULL ENSG00000231375 ENST00000446466 unprocessed_pseudogene 10009199 10010334 NULL NULL ENSG00000231411 ENST00000437794 unprocessed_pseudogene 26102716 26106365 NULL NULL ENSG00000231423 ENST00000449381 unprocessed_pseudogene 9448180 9458885 NULL NULL ENSG00000231436 ENST00000435741 processed_pseudogene 28772667 28773306 NULL NULL ENSG00000231514 ENST00000444263 lincRNA 2870953 2965258 NULL NULL ENSG00000231535 ENST00000425031 lincRNA 2871039 2970313 NULL NULL ENSG00000231535 ENST00000418624 processed_pseudogene 25954968 25955206 NULL NULL ENSG00000231540 ENST00000451661 unprocessed_pseudogene 28140831 28141844 NULL NULL ENSG00000231716 ENST00000441642 unprocessed_pseudogene 9707273 9708390 NULL NULL ENSG00000231874 ENST00000453312 unprocessed_pseudogene 8898635 8908029 NULL NULL ENSG00000231988 ENST00000443152 processed_pseudogene 26646410 26647652 NULL NULL ENSG00000232003 ENST00000442090 processed_pseudogene 24214967 24215298 NULL NULL ENSG00000232029 ENST00000412220 unprocessed_pseudogene 27736470 27738492 NULL NULL ENSG00000232064 ENST00000430735 processed_pseudogene 2696023 2696259 NULL NULL ENSG00000232195 ENST00000438294 unprocessed_pseudogene 26250254 26252165 NULL NULL ENSG00000232205 ENST00000398758 unprocessed_pseudogene 14373025 14378737 NULL NULL ENSG00000232226 ENST00000454543 processed_pseudogene 9003694 9005311 NULL NULL ENSG00000232235 ENST00000413486 lincRNA 8506335 8512883 NULL NULL ENSG00000232348 ENST00000453955 lincRNA 8572513 8573324 NULL NULL ENSG00000232419 ENST00000445112 unprocessed_pseudogene 25886405 25892840 NULL NULL ENSG00000232424 ENST00000453726 unprocessed_pseudogene 24194701 24195494 NULL NULL ENSG00000232475 ENST00000450781 unprocessed_pseudogene 22163071 22174090 NULL NULL ENSG00000232522 ENST00000434155 unprocessed_pseudogene 6968774 6974543 NULL NULL ENSG00000232583 ENST00000427963 unprocessed_pseudogene 26118927 26141228 NULL NULL ENSG00000232585 ENST00000429406 unprocessed_pseudogene 27876158 27881307 NULL NULL ENSG00000232614 ENST00000426129 unprocessed_pseudogene 9502838 9506619 NULL NULL ENSG00000232617 ENST00000450329 unprocessed_pseudogene 6388504 6388970 NULL NULL ENSG00000232620 ENST00000436364 processed_pseudogene 23382992 23383975 NULL NULL ENSG00000232634 ENST00000414751 processed_pseudogene 28007090 28007415 NULL NULL ENSG00000232695 ENST00000435433 processed_pseudogene 14442350 14443562 NULL NULL ENSG00000232730 ENST00000430287 unprocessed_pseudogene 20283081 20285685 NULL NULL ENSG00000232744 ENST00000436338 processed_pseudogene 25171741 25172810 NULL NULL ENSG00000232764 ENST00000434487 antisense 9167489 9172441 NULL NULL ENSG00000232808 ENST00000434556 unprocessed_pseudogene 25908985 25911730 NULL NULL ENSG00000232845 ENST00000451397 unprocessed_pseudogene 20344721 20346300 NULL NULL ENSG00000232899 ENST00000449393 unprocessed_pseudogene 25897335 25897907 NULL NULL ENSG00000232910 ENST00000421750 unprocessed_pseudogene 28050665 28053397 NULL NULL ENSG00000232914 ENST00000420090 processed_pseudogene 9068524 9068856 NULL NULL ENSG00000232924 ENST00000427677 processed_pseudogene 3550846 3551909 NULL NULL ENSG00000232927 ENST00000425589 processed_pseudogene 26805484 26806553 NULL NULL ENSG00000232976 ENST00000417305 antisense 2834885 2870667 NULL NULL ENSG00000233070 ENST00000431145 antisense 2869669 2870612 NULL NULL ENSG00000233070 ENST00000442391 unprocessed_pseudogene 20278413 20279581 NULL NULL ENSG00000233120 ENST00000428264 processed_pseudogene 25195394 25197342 NULL NULL ENSG00000233126 ENST00000437934 unprocessed_pseudogene 28157582 28158384 NULL NULL ENSG00000233156 ENST00000453983 unprocessed_pseudogene 20096229 20105140 NULL NULL ENSG00000233378 ENST00000419557 lincRNA 20488139 20515096 NULL NULL ENSG00000233522 ENST00000451909 unprocessed_pseudogene 20691244 20691509 NULL NULL ENSG00000233546 ENST00000437571 processed_pseudogene 27633431 27633809 NULL NULL ENSG00000233619 ENST00000433995 processed_pseudogene 6705748 6706293 NULL NULL ENSG00000233634 ENST00000416803 processed_pseudogene 27535139 27537958 NULL NULL ENSG00000233652 ENST00000438677 lincRNA 8551411 8551919 NULL NULL ENSG00000233699 ENST00000420675 processed_pseudogene 26424484 26427287 NULL NULL ENSG00000233740 ENST00000430517 unprocessed_pseudogene 14730915 14746333 NULL NULL ENSG00000233774 ENST00000426950 protein_coding 9175073 9177887 9175119 9177699 ENSG00000233803 ENST00000383008 protein_coding 9175119 9177886 9175119 9176952 ENSG00000233803 ENST00000466036 processed_transcript 9175119 9177893 NULL NULL ENSG00000233803 ENST00000482082 processed_transcript 9176497 9177878 NULL NULL ENSG00000233803 ENST00000417124 processed_pseudogene 28546758 28547377 NULL NULL ENSG00000233843 ENST00000457658 lincRNA 14774265 14802370 NULL NULL ENSG00000233864 ENST00000440408 lincRNA 14774265 14804162 NULL NULL ENSG00000233864 ENST00000417071 lincRNA 14774468 14800184 NULL NULL ENSG00000233864 ENST00000543097 lincRNA 14774284 14776614 NULL NULL ENSG00000233864 ENST00000447588 processed_pseudogene 27539301 27540203 NULL NULL ENSG00000233944 ENST00000412870 unprocessed_pseudogene 15042075 15060090 NULL NULL ENSG00000234059 ENST00000440624 processed_pseudogene 26153058 26153384 NULL NULL ENSG00000234081 ENST00000429463 unprocessed_pseudogene 9461792 9463961 NULL NULL ENSG00000234110 ENST00000439309 processed_pseudogene 24663389 24663720 NULL NULL ENSG00000234131 ENST00000454315 processed_pseudogene 8232073 8233191 NULL NULL ENSG00000234179 ENST00000452257 processed_pseudogene 13901758 13903233 NULL NULL ENSG00000234385 ENST00000447471 unprocessed_pseudogene 26542751 26549673 NULL NULL ENSG00000234399 ENST00000382707 protein_coding 23696765 23711212 23698778 23710934 ENSG00000234414 ENST00000361046 nonsense_mediated_decay 23696790 23711212 23698778 23704609 ENSG00000234414 ENST00000303902 protein_coding 23698778 23711210 23698778 23710934 ENSG00000234414 ENST00000439108 protein_coding 23673258 23711210 23706817 23710934 ENSG00000234414 ENST00000431768 processed_pseudogene 21489455 21490459 NULL NULL ENSG00000234529 ENST00000418461 unprocessed_pseudogene 6171998 6173115 NULL NULL ENSG00000234583 ENST00000421058 unprocessed_pseudogene 17460542 17567954 NULL NULL ENSG00000234620 ENST00000455855 processed_pseudogene 3161849 3162867 NULL NULL ENSG00000234652 ENST00000421008 unprocessed_pseudogene 28148401 28155003 NULL NULL ENSG00000234744 ENST00000455527 transcribed_unprocessed_pseudogene 7581026 7644748 NULL NULL ENSG00000234795 ENST00000442584 processed_transcript 7610636 7677302 NULL NULL ENSG00000234795 ENST00000448006 antisense 9355208 9363096 NULL NULL ENSG00000234803 ENST00000418016 antisense 9357275 9362877 NULL NULL ENSG00000234803 ENST00000598351 antisense 9362384 9364506 NULL NULL ENSG00000234803 ENST00000451062 processed_transcript 6124308 6131994 NULL NULL ENSG00000234830 ENST00000452103 processed_transcript 6126374 6131976 NULL NULL ENSG00000234830 ENST00000505707 transcribed_unprocessed_pseudogene 6130047 6131976 NULL NULL ENSG00000234830 ENST00000452458 processed_pseudogene 8240282 8240751 NULL NULL ENSG00000234850 ENST00000411536 unprocessed_pseudogene 28201926 28234640 NULL NULL ENSG00000234888 ENST00000447105 unprocessed_pseudogene 9789162 9797032 NULL NULL ENSG00000234950 ENST00000422633 processed_pseudogene 5205786 5207005 NULL NULL ENSG00000235001 ENST00000545933 processed_pseudogene 5205788 5206981 NULL NULL ENSG00000235001 ENST00000430729 unprocessed_pseudogene 27869163 27870333 NULL NULL ENSG00000235004 ENST00000439103 unprocessed_pseudogene 28344478 28354295 NULL NULL ENSG00000235014 ENST00000446299 lincRNA 24585087 24630861 NULL NULL ENSG00000235059 ENST00000439653 lincRNA 24626684 24631739 NULL NULL ENSG00000235059 ENST00000445200 unprocessed_pseudogene 6174083 6175961 NULL NULL ENSG00000235094 ENST00000411983 processed_pseudogene 5661341 5661778 NULL NULL ENSG00000235175 ENST00000434709 unprocessed_pseudogene 9925635 9927195 NULL NULL ENSG00000235193 ENST00000420149 lincRNA 26716349 26753172 NULL NULL ENSG00000235412 ENST00000458328 unprocessed_pseudogene 16192955 16198480 NULL NULL ENSG00000235451 ENST00000452645 transcribed_unprocessed_pseudogene 15265842 15274125 NULL NULL ENSG00000235462 ENST00000439217 processed_transcript 15271184 15273460 NULL NULL ENSG00000235462 ENST00000415230 unprocessed_pseudogene 20811557 20812165 NULL NULL ENSG00000235479 ENST00000420889 unprocessed_pseudogene 28018078 28044800 NULL NULL ENSG00000235511 ENST00000418455 unprocessed_pseudogene 19900195 19901364 NULL NULL ENSG00000235521 ENST00000421819 unprocessed_pseudogene 27658448 27673933 NULL NULL ENSG00000235583 ENST00000420610 unprocessed_pseudogene 14077914 14108092 NULL NULL ENSG00000235649 ENST00000416843 unprocessed_pseudogene 9928411 9928816 NULL NULL ENSG00000235691 ENST00000425318 unprocessed_pseudogene 23627270 23629049 NULL NULL ENSG00000235719 ENST00000431853 processed_pseudogene 59001391 59001635 NULL NULL ENSG00000235857 ENST00000425857 unprocessed_pseudogene 6361017 6364798 NULL NULL ENSG00000235895 ENST00000445264 unprocessed_pseudogene 26288505 26303990 NULL NULL ENSG00000235981 ENST00000428342 processed_pseudogene 17019778 17019948 NULL NULL ENSG00000236131 ENST00000454995 processed_pseudogene 27314746 27315988 NULL NULL ENSG00000236379 ENST00000428845 protein_coding 9365489 9368285 9365535 9368097 ENSG00000236424 ENST00000444056 protein_coding 9365535 9368284 9365535 9367350 ENSG00000236424 ENST00000489397 processed_transcript 9365535 9368291 NULL NULL ENSG00000236424 ENST00000495839 processed_transcript 9366895 9368276 NULL NULL ENSG00000236424 ENST00000429039 protein_coding 9365535 9368284 9365535 9367069 ENSG00000236424 ENST00000432046 unprocessed_pseudogene 20903729 20903872 NULL NULL ENSG00000236429 ENST00000421279 unprocessed_pseudogene 7555780 7557589 NULL NULL ENSG00000236435 ENST00000438550 processed_pseudogene 14365457 14366162 NULL NULL ENSG00000236477 ENST00000436067 processed_pseudogene 20340818 20341146 NULL NULL ENSG00000236599 ENST00000428616 unprocessed_pseudogene 23567656 23567881 NULL NULL ENSG00000236615 ENST00000442113 unprocessed_pseudogene 25862069 25872955 NULL NULL ENSG00000236620 ENST00000444014 unprocessed_pseudogene 25695956 25697532 NULL NULL ENSG00000236647 ENST00000421675 unprocessed_pseudogene 7558552 7560719 NULL NULL ENSG00000236690 ENST00000429799 unprocessed_pseudogene 9860098 9871796 NULL NULL ENSG00000236718 ENST00000420376 unprocessed_pseudogene 9868005 9868142 NULL NULL ENSG00000236718 ENST00000457163 unprocessed_pseudogene 9385717 9388290 NULL NULL ENSG00000236786 ENST00000417910 lincRNA 24246961 24252016 NULL NULL ENSG00000236951 ENST00000419158 lincRNA 24247839 24293631 NULL NULL ENSG00000236951 ENST00000434481 unprocessed_pseudogene 23823812 23835894 NULL NULL ENSG00000237023 ENST00000413466 lincRNA 7672965 7678724 NULL NULL ENSG00000237048 ENST00000451467 lincRNA 6110487 6111651 NULL NULL ENSG00000237069 ENST00000430307 processed_pseudogene 6026843 6027306 NULL NULL ENSG00000237195 ENST00000451162 unprocessed_pseudogene 23592583 23599131 NULL NULL ENSG00000237269 ENST00000418213 unprocessed_pseudogene 25917379 25944297 NULL NULL ENSG00000237302 ENST00000418578 processed_pseudogene 23292756 23293067 NULL NULL ENSG00000237427 ENST00000425026 processed_pseudogene 10027986 10029907 NULL NULL ENSG00000237447 ENST00000435696 unprocessed_pseudogene 26223946 26225959 NULL NULL ENSG00000237467 ENST00000440468 unprocessed_pseudogene 28089415 28100299 NULL NULL ENSG00000237546 ENST00000429883 unprocessed_pseudogene 20063469 20065380 NULL NULL ENSG00000237558 ENST00000421353 lincRNA 6311475 6315118 NULL NULL ENSG00000237563 ENST00000452432 unprocessed_pseudogene 20107081 20109132 NULL NULL ENSG00000237616 ENST00000454281 processed_pseudogene 2657868 2658369 NULL NULL ENSG00000237659 ENST00000431631 processed_pseudogene 6768794 6769413 NULL NULL ENSG00000237701 ENST00000442145 antisense 9225731 9233636 NULL NULL ENSG00000237802 ENST00000450658 antisense 9227800 9233417 NULL NULL ENSG00000237802 ENST00000411668 unprocessed_pseudogene 27710269 27712180 NULL NULL ENSG00000237823 ENST00000433481 unprocessed_pseudogene 24546550 24548715 NULL NULL ENSG00000237902 ENST00000435945 unprocessed_pseudogene 28740998 28780799 NULL NULL ENSG00000237917 ENST00000426526 unprocessed_pseudogene 24086159 24087740 NULL NULL ENSG00000237968 ENST00000422174 processed_pseudogene 22148461 22149738 NULL NULL ENSG00000237997 ENST00000423438 unprocessed_pseudogene 20670463 20670954 NULL NULL ENSG00000238067 ENST00000457961 unprocessed_pseudogene 7539784 7544065 NULL NULL ENSG00000238073 ENST00000440215 protein_coding 9324922 9327689 9324922 9327502 ENSG00000238074 ENST00000446779 protein_coding 9324922 9327689 9324922 9326349 ENSG00000238074 ENST00000454643 unprocessed_pseudogene 21010150 21029166 NULL NULL ENSG00000238088 ENST00000454868 unprocessed_pseudogene 20987993 20991373 NULL NULL ENSG00000238135 ENST00000424401 unprocessed_pseudogene 9027238 9042220 NULL NULL ENSG00000238154 ENST00000435012 unprocessed_pseudogene 19733139 19737712 NULL NULL ENSG00000238191 ENST00000442607 unprocessed_pseudogene 6134634 6137316 NULL NULL ENSG00000238235 ENST00000452889 lincRNA 9748407 9749571 NULL NULL ENSG00000239225 ENST00000419538 processed_pseudogene 27632787 27633469 NULL NULL ENSG00000239304 ENST00000398377 processed_transcript 26356114 26360978 NULL NULL ENSG00000239533 ENST00000516761 miRNA 26356197 26356279 NULL NULL ENSG00000239533 ENST00000439586 processed_pseudogene 7936937 7938764 NULL NULL ENSG00000239893 ENST00000447585 unprocessed_pseudogene 20743949 20790963 NULL NULL ENSG00000240438 ENST00000306641 lincRNA 27629055 27632852 NULL NULL ENSG00000240450 ENST00000432862 processed_pseudogene 26796955 26797681 NULL NULL ENSG00000240566 ENST00000425158 processed_pseudogene 7859028 7859847 NULL NULL ENSG00000241200 ENST00000472227 transcribed_unprocessed_pseudogene 15863536 16027704 NULL NULL ENSG00000241859 ENST00000430079 processed_transcript 15863673 15970474 NULL NULL ENSG00000241859 ENST00000460561 processed_transcript 15864978 15983586 NULL NULL ENSG00000241859 ENST00000451061 transcribed_unprocessed_pseudogene 20835919 20899975 NULL NULL ENSG00000242153 ENST00000432335 processed_transcript 20891768 20901083 NULL NULL ENSG00000242153 ENST00000538268 transcribed_unprocessed_pseudogene 20893577 20901083 NULL NULL ENSG00000242153 ENST00000358944 nonsense_mediated_decay 24049765 24064189 24056368 24062201 ENSG00000242389 ENST00000382659 protein_coding 24049765 24064214 24050043 24062201 ENSG00000242389 ENST00000382673 protein_coding 24026223 24064174 24026501 24062201 ENSG00000242389 ENST00000382658 protein_coding 24049997 24064127 24050043 24062201 ENSG00000242389 ENST00000456659 unprocessed_pseudogene 23670185 23672356 NULL NULL ENSG00000242393 ENST00000485099 misc_RNA 26357107 26357382 NULL NULL ENSG00000242425 ENST00000441091 unprocessed_pseudogene 26325463 26329652 NULL NULL ENSG00000242854 ENST00000383020 protein_coding 23673224 23687672 23675237 23687394 ENSG00000242875 ENST00000382639 nonsense_mediated_decay 23673249 23687672 23675237 23681068 ENSG00000242875 ENST00000437993 unprocessed_pseudogene 6175751 6177628 NULL NULL ENSG00000242879 ENST00000303922 processed_transcript 24455006 24462352 NULL NULL ENSG00000243040 ENST00000420346 transcribed_unprocessed_pseudogene 24457011 24467972 NULL NULL ENSG00000243040 ENST00000450910 unprocessed_pseudogene 9873804 9875984 NULL NULL ENSG00000243643 ENST00000488394 misc_RNA 14394177 14394465 NULL NULL ENSG00000243980 ENST00000422002 processed_pseudogene 25163210 25163968 NULL NULL ENSG00000244000 ENST00000306853 processed_transcript 26329581 26333378 NULL NULL ENSG00000244231 ENST00000451913 processed_pseudogene 7781463 7782131 NULL NULL ENSG00000244246 ENST00000382653 nonsense_mediated_decay 24026223 24040648 24032827 24038660 ENSG00000244395 ENST00000382680 protein_coding 24026223 24040673 24026501 24038660 ENSG00000244395 ENST00000382677 protein_coding 24026223 24038660 24026501 24038660 ENSG00000244395 ENST00000418956 protein_coding 24026455 24040586 24026501 24038660 ENSG00000244395 ENST00000442362 unprocessed_pseudogene 20290496 20298506 NULL NULL ENSG00000244646 ENST00000535771 pseudogene 20297335 20298913 NULL NULL ENSG00000244646 ENST00000514804 transcribed_unprocessed_pseudogene 20952595 20952937 NULL NULL ENSG00000248573 ENST00000509776 unprocessed_pseudogene 26424828 26437493 NULL NULL ENSG00000248792 ENST00000504503 unprocessed_pseudogene 20938165 20941313 NULL NULL ENSG00000249501 ENST00000509650 unprocessed_pseudogene 20817406 20818076 NULL NULL ENSG00000249606 ENST00000511770 unprocessed_pseudogene 20652801 20656181 NULL NULL ENSG00000249634 ENST00000503144 unprocessed_pseudogene 20548860 20551037 NULL NULL ENSG00000249726 ENST00000442790 unprocessed_pseudogene 6111336 6113324 NULL NULL ENSG00000250204 ENST00000510392 unprocessed_pseudogene 19880996 19889280 NULL NULL ENSG00000250868 ENST00000354494 pseudogene 19880862 19882440 NULL NULL ENSG00000250868 ENST00000513521 unprocessed_pseudogene 20702866 20706014 NULL NULL ENSG00000250951 ENST00000506069 unprocessed_pseudogene 6109809 6111670 NULL NULL ENSG00000251275 ENST00000510613 lincRNA 20653626 20709584 NULL NULL ENSG00000251510 ENST00000509611 unprocessed_pseudogene 24041541 24043706 NULL NULL ENSG00000251618 ENST00000515896 rRNA 10037764 10037915 NULL NULL ENSG00000251705 ENST00000515957 rRNA 9928019 9928137 NULL NULL ENSG00000251766 ENST00000515987 snoRNA 28393531 28393668 NULL NULL ENSG00000251796 ENST00000516032 snRNA 2652790 2652894 NULL NULL ENSG00000251841 ENST00000516070 miRNA 5742287 5742379 NULL NULL ENSG00000251879 ENST00000516108 snRNA 26092765 26092918 NULL NULL ENSG00000251917 ENST00000516116 snoRNA 25569246 25569383 NULL NULL ENSG00000251925 ENST00000516144 rRNA 20508009 20508124 NULL NULL ENSG00000251953 ENST00000516157 miRNA 10033981 10034093 NULL NULL ENSG00000251966 ENST00000516161 snRNA 20995615 20995776 NULL NULL ENSG00000251970 ENST00000516187 misc_RNA 7209572 7209683 NULL NULL ENSG00000251996 ENST00000516203 rRNA 19671654 19671769 NULL NULL ENSG00000252012 ENST00000516229 miRNA 19734941 19735035 NULL NULL ENSG00000252038 ENST00000516250 miRNA 5441969 5442060 NULL NULL ENSG00000252059 ENST00000516346 snRNA 7246713 7246820 NULL NULL ENSG00000252155 ENST00000516357 snRNA 20278734 20278893 NULL NULL ENSG00000252166 ENST00000516364 snRNA 18360815 18360921 NULL NULL ENSG00000252173 ENST00000516400 snRNA 20648397 20648558 NULL NULL ENSG00000252209 ENST00000516480 rRNA 9930484 9930602 NULL NULL ENSG00000252289 ENST00000516506 rRNA 19669744 19669856 NULL NULL ENSG00000252315 ENST00000516514 snRNA 18448163 18448269 NULL NULL ENSG00000252323 ENST00000516617 snRNA 27869489 27869642 NULL NULL ENSG00000252426 ENST00000516630 miRNA 20444739 20444833 NULL NULL ENSG00000252439 ENST00000516659 snRNA 4887117 4887307 NULL NULL ENSG00000252468 ENST00000516662 miRNA 27606157 27606239 NULL NULL ENSG00000252471 ENST00000516663 snRNA 7291095 7291199 NULL NULL ENSG00000252472 ENST00000516704 snRNA 19900883 19901042 NULL NULL ENSG00000252513 ENST00000516777 miRNA 14482123 14482230 NULL NULL ENSG00000252586 ENST00000516816 snRNA 28075126 28075289 NULL NULL ENSG00000252625 ENST00000516824 misc_RNA 7192338 7192636 NULL NULL ENSG00000252633 ENST00000516855 miRNA 18174646 18174709 NULL NULL ENSG00000252664 ENST00000516858 rRNA 20509921 20510033 NULL NULL ENSG00000252667 ENST00000516872 snRNA 25887089 25887252 NULL NULL ENSG00000252681 ENST00000516880 snoRNA 18250128 18250259 NULL NULL ENSG00000252689 ENST00000516885 miRNA 15779840 15779936 NULL NULL ENSG00000252694 ENST00000516957 snRNA 21180869 21180973 NULL NULL ENSG00000252766 ENST00000517046 miRNA 13340551 13340633 NULL NULL ENSG00000252855 ENST00000517091 snRNA 4043026 4043131 NULL NULL ENSG00000252900 ENST00000517139 snRNA 28507136 28507239 NULL NULL ENSG00000252948 ENST00000527562 lincRNA 23200175 23206610 NULL NULL ENSG00000254488 ENST00000555130 unprocessed_pseudogene 13462594 13463857 NULL NULL ENSG00000258567 ENST00000557448 unprocessed_pseudogene 13488005 13489271 NULL NULL ENSG00000258991 ENST00000451548 protein_coding 9304564 9307357 9304610 9307170 ENSG00000258992 ENST00000423647 protein_coding 9236076 9307357 9236076 9307170 ENSG00000258992 ENST00000553347 unprocessed_pseudogene 13477233 13478499 NULL NULL ENSG00000259029 ENST00000557360 unprocessed_pseudogene 13470597 13471863 NULL NULL ENSG00000259154 ENST00000558356 unprocessed_pseudogene 24476599 24478647 NULL NULL ENSG00000259247 ENST00000566193 lincRNA 21853827 21856492 NULL NULL ENSG00000260197 ENST00000584011 miRNA 13340359 13340440 NULL NULL ENSG00000263502 ENST00000580394 miRNA 13947443 13947512 NULL NULL ENSG00000265161 ENST00000584045 miRNA 18398127 18398238 NULL NULL ENSG00000265197 ENST00000578366 miRNA 16364065 16364171 NULL NULL ENSG00000266220 ENST00000586015 transcribed_processed_pseudogene 21760074 21760643 NULL NULL ENSG00000267793 ENST00000601700 protein_coding 25847479 25850592 25847479 25850592 ENSG00000267935 ENST00000599485 protein_coding 21737895 21738068 21737895 21738068 ENSG00000269084 ENST00000595988 protein_coding 15418467 15429181 15418467 15429181 ENSG00000269291 ENST00000598545 protein_coding 28111776 28114889 28111776 28114889 ENSG00000269393 ENST00000601705 protein_coding 5306691 5312605 5306691 5312605 ENSG00000269464 ENST00000448518 unprocessed_pseudogene 9345205 9347784 NULL NULL ENSG00000270073 ENST00000605584 processed_pseudogene 13551375 13552752 NULL NULL ENSG00000270242 ENST00000603738 processed_pseudogene 13491303 13493369 NULL NULL ENSG00000270455 ENST00000604436 processed_pseudogene 27809047 27809373 NULL NULL ENSG00000270535 ENST00000603467 processed_pseudogene 13263395 13263563 NULL NULL ENSG00000270570 ENST00000604924 processed_pseudogene 23793569 23793901 NULL NULL ENSG00000271123 ENST00000604370 processed_pseudogene 13263065 13263272 NULL NULL ENSG00000271309 ENST00000605663 processed_pseudogene 13262741 13262939 NULL NULL ENSG00000271365 ENST00000604178 processed_pseudogene 13629403 13629913 NULL NULL ENSG00000271375 ENST00000604289 processed_pseudogene 19576759 19577094 NULL NULL ENSG00000271595 ENST00000606439 misc_RNA 27605054 27605329 NULL NULL ENSG00000272042 ensembldb/inst/chrY/ens_tx2exon.txt0000644000175400017540000037207413001231247020440 0ustar00biocbuildbiocbuildtx_id exon_id exon_idx ENST00000469599 ENSE00001902471 1 ENST00000469599 ENSE00003665408 2 ENST00000469599 ENSE00003572891 3 ENST00000469599 ENSE00003452486 4 ENST00000469599 ENSE00003535737 5 ENST00000469599 ENSE00003306433 6 ENST00000469599 ENSE00003587057 7 ENST00000469599 ENSE00003522786 8 ENST00000469599 ENSE00003674105 9 ENST00000469599 ENSE00003605358 10 ENST00000469599 ENSE00001928834 11 ENST00000469599 ENSE00001954294 12 ENST00000317961 ENSE00001733393 1 ENST00000317961 ENSE00000891759 2 ENST00000317961 ENSE00000652508 3 ENST00000317961 ENSE00000652506 4 ENST00000317961 ENSE00001788914 5 ENST00000317961 ENSE00001805865 6 ENST00000317961 ENSE00000652503 7 ENST00000317961 ENSE00000652502 8 ENST00000317961 ENSE00000652501 9 ENST00000317961 ENSE00001799734 10 ENST00000317961 ENSE00000652498 11 ENST00000317961 ENSE00001786866 12 ENST00000317961 ENSE00003497148 13 ENST00000317961 ENSE00003688313 14 ENST00000317961 ENSE00003664560 15 ENST00000317961 ENSE00003602862 16 ENST00000317961 ENSE00003491684 17 ENST00000317961 ENSE00003488142 18 ENST00000317961 ENSE00003534652 19 ENST00000317961 ENSE00003607600 20 ENST00000317961 ENSE00003544083 21 ENST00000317961 ENSE00003591494 22 ENST00000317961 ENSE00003484526 23 ENST00000317961 ENSE00003539670 24 ENST00000317961 ENSE00001670444 25 ENST00000317961 ENSE00001594027 26 ENST00000317961 ENSE00001945799 27 ENST00000382806 ENSE00001277406 1 ENST00000382806 ENSE00000891759 2 ENST00000382806 ENSE00000652508 3 ENST00000382806 ENSE00000652506 4 ENST00000382806 ENSE00001805865 5 ENST00000382806 ENSE00000652503 6 ENST00000382806 ENSE00000652502 7 ENST00000382806 ENSE00000652501 8 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ENSE00003522786 11 ENST00000492117 ENSE00003674105 12 ENST00000492117 ENSE00003605358 13 ENST00000492117 ENSE00001928834 14 ENST00000492117 ENSE00001941473 15 ENST00000440077 ENSE00001733393 1 ENST00000440077 ENSE00000891759 2 ENST00000440077 ENSE00000652508 3 ENST00000440077 ENSE00001788914 4 ENST00000440077 ENSE00001805865 5 ENST00000440077 ENSE00000652503 6 ENST00000440077 ENSE00000652502 7 ENST00000440077 ENSE00000652501 8 ENST00000440077 ENSE00001799734 9 ENST00000440077 ENSE00000652498 10 ENST00000440077 ENSE00001786866 11 ENST00000440077 ENSE00003497148 12 ENST00000440077 ENSE00003688313 13 ENST00000440077 ENSE00003664560 14 ENST00000440077 ENSE00003602862 15 ENST00000440077 ENSE00003491684 16 ENST00000440077 ENSE00003488142 17 ENST00000440077 ENSE00003534652 18 ENST00000440077 ENSE00003607600 19 ENST00000440077 ENSE00003544083 20 ENST00000440077 ENSE00003591494 21 ENST00000440077 ENSE00003484526 22 ENST00000440077 ENSE00003539670 23 ENST00000440077 ENSE00001670444 24 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ENSE00003611496 5 ENST00000155093 ENSE00001649504 6 ENST00000155093 ENSE00001777381 7 ENST00000155093 ENSE00001368923 8 ENST00000449237 ENSE00001648585 1 ENST00000449237 ENSE00003604519 2 ENST00000449237 ENSE00003548678 3 ENST00000449237 ENSE00003611496 4 ENST00000449237 ENSE00001777381 5 ENST00000449237 ENSE00001368923 6 ENST00000383032 ENSE00001408731 1 ENST00000383032 ENSE00001494437 2 ENST00000383032 ENSE00001494434 3 ENST00000383032 ENSE00001494433 4 ENST00000383032 ENSE00001494431 5 ENST00000383032 ENSE00001793373 6 ENST00000383032 ENSE00003432678 7 ENST00000383032 ENSE00003437631 8 ENST00000383032 ENSE00003379331 9 ENST00000383032 ENSE00001213593 10 ENST00000383032 ENSE00001607530 11 ENST00000383032 ENSE00001716749 12 ENST00000383032 ENSE00001744765 13 ENST00000383032 ENSE00001731081 14 ENST00000383032 ENSE00001654905 15 ENST00000383032 ENSE00001729171 16 ENST00000383032 ENSE00001763966 17 ENST00000383032 ENSE00001593786 18 ENST00000383032 ENSE00001370395 19 ENST00000355162 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ENSE00001744765 12 ENST00000346432 ENSE00001731081 13 ENST00000346432 ENSE00001654905 14 ENST00000346432 ENSE00001729171 15 ENST00000346432 ENSE00001763966 16 ENST00000346432 ENSE00001593786 17 ENST00000346432 ENSE00001370395 18 ENST00000333703 ENSE00001631277 1 ENST00000333703 ENSE00001619635 2 ENST00000333703 ENSE00001755808 3 ENST00000333703 ENSE00000981568 4 ENST00000333703 ENSE00001640924 5 ENST00000333703 ENSE00001602875 6 ENST00000362095 ENSE00001350198 1 ENST00000362095 ENSE00001640924 2 ENST00000362095 ENSE00001944263 3 ENST00000400457 ENSE00000981568 1 ENST00000400457 ENSE00001640924 2 ENST00000400457 ENSE00001803775 3 ENST00000400457 ENSE00001677522 4 ENST00000400457 ENSE00001322750 5 ENST00000215473 ENSE00001436852 1 ENST00000215473 ENSE00001640924 2 ENST00000215473 ENSE00001803775 3 ENST00000215473 ENSE00001731866 4 ENST00000215473 ENSE00001711324 5 ENST00000215473 ENSE00001779807 6 ENST00000215479 ENSE00001348274 1 ENST00000215479 ENSE00001671586 2 ENST00000215479 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ENSE00002194098 7 ENST00000495163 ENSE00001877035 1 ENST00000495163 ENSE00001918349 2 ENST00000472666 ENSE00001948825 1 ENST00000472666 ENSE00003507474 2 ENST00000472666 ENSE00001928357 3 ENST00000362758 ENSE00002210406 1 ENST00000362758 ENSE00003574133 2 ENST00000362758 ENSE00003534383 3 ENST00000362758 ENSE00003600341 4 ENST00000362758 ENSE00003520510 5 ENST00000362758 ENSE00001661914 6 ENST00000338981 ENSE00001020131 1 ENST00000338981 ENSE00001020125 2 ENST00000338981 ENSE00003651478 3 ENST00000338981 ENSE00003476271 4 ENST00000338981 ENSE00003602419 5 ENST00000338981 ENSE00003467347 6 ENST00000338981 ENSE00003693194 7 ENST00000338981 ENSE00003654508 8 ENST00000338981 ENSE00003550081 9 ENST00000338981 ENSE00003624888 10 ENST00000338981 ENSE00003487242 11 ENST00000338981 ENSE00003601630 12 ENST00000338981 ENSE00003666147 13 ENST00000338981 ENSE00003566318 14 ENST00000338981 ENSE00003557039 15 ENST00000338981 ENSE00003515132 16 ENST00000338981 ENSE00003621719 17 ENST00000338981 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----eval=FALSE---------------------------------------------------------- # library(ensembldb) # ## Load the EnsDb package that should be installed on the MySQL server # library(EnsDb.Hsapiens.v75) # # ## Call the useMySQL method providing the required credentials to create # ## databases and inserting data on the MySQL server # edb_mysql <- useMySQL(EnsDb.Hsapiens.v75, host = "localhost", user = "userwrite", # pass = "userpass") # # ## Use this EnsDb object # genes(edb_mysql) ## ----eval=FALSE---------------------------------------------------------- # library(ensembldb) # library(RMySQL) # # ## Connect to the MySQL database to list the databases. # dbcon <- dbConnect(MySQL(), host = "localhost", user = "readonly", # pass = "readonly") # # ## List the available databases # listEnsDbs(dbcon) # # ## Connect to one of the databases and use that one. # dbcon <- dbConnect(MySQL(), host = "localhost", user = "readonly", # pass = "readonly", dbname = "ensdb_hsapiens_v75") # edb <- EnsDb(dbcon) # edb ensembldb/inst/doc/MySQL-backend.Rmd0000644000175400017540000000501413013177517020260 0ustar00biocbuildbiocbuild--- title: "Using a MySQL server backend" graphics: yes output: BiocStyle::html_document2 vignette: > %\VignetteIndexEntry{Using a MySQL server backend} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} %\VignetteDepends{ensembldb,EnsDb.Hsapiens.v75,BiocStyle} %\VignettePackage{ensembldb} %\VignetteKeywords{annotation,database} --- **Package**: `r BiocStyle::Biocpkg("ensembldb")`
**Authors**: `r packageDescription("ensembldb")$Author`
**Modified**: 20 September, 2016
**Compiled**: `r date()` # Introduction `ensembldb` uses by default, similar to other annotation packages in Bioconductor, a SQLite database backend, i.e. annotations are retrieved from file-based SQLite databases that are provided *via* packages, such as the `EnsDb.Hsapiens.v75` package. In addition, `ensembldb` allows to switch the backend from SQLite to MySQL and thus to retrieve annotations from a MySQL server instead. Such a setup might be useful for a lab running a well-configured MySQL server that would require installation of EnsDb databases only on the database server and not on the individual clients. **Note** the code in this document is not executed during vignette generation as this would require access to a MySQL server. # Using `ensembldb` with a MySQL server Installation of `EnsDb` databases in a MySQL server is straight forward - given that the user has write access to the server: ```{r eval=FALSE} library(ensembldb) ## Load the EnsDb package that should be installed on the MySQL server library(EnsDb.Hsapiens.v75) ## Call the useMySQL method providing the required credentials to create ## databases and inserting data on the MySQL server edb_mysql <- useMySQL(EnsDb.Hsapiens.v75, host = "localhost", user = "userwrite", pass = "userpass") ## Use this EnsDb object genes(edb_mysql) ``` To use an `EnsDb` in a MySQL server without the need to install the corresponding R-package, the connection to the database can be passed to the `EnsDb` constructor function. With the resulting `EnsDb` object annotations can be retrieved from the MySQL database. ```{r eval=FALSE} library(ensembldb) library(RMySQL) ## Connect to the MySQL database to list the databases. dbcon <- dbConnect(MySQL(), host = "localhost", user = "readonly", pass = "readonly") ## List the available databases listEnsDbs(dbcon) ## Connect to one of the databases and use that one. dbcon <- dbConnect(MySQL(), host = "localhost", user = "readonly", pass = "readonly", dbname = "ensdb_hsapiens_v75") edb <- EnsDb(dbcon) edb ``` ensembldb/inst/doc/MySQL-backend.html0000644000175400017540000257176113013177517020525 0ustar00biocbuildbiocbuild Using a MySQL server backend

Contents

Package: ensembldb
Authors: Johannes Rainer johannes.rainer@eurac.edu, Tim Triche tim.triche@usc.edu
Modified: 20 September, 2016
Compiled: Wed Nov 16 19:52:05 2016

1 Introduction

ensembldb uses by default, similar to other annotation packages in Bioconductor, a SQLite database backend, i.e. annotations are retrieved from file-based SQLite databases that are provided via packages, such as the EnsDb.Hsapiens.v75 package. In addition, ensembldb allows to switch the backend from SQLite to MySQL and thus to retrieve annotations from a MySQL server instead. Such a setup might be useful for a lab running a well-configured MySQL server that would require installation of EnsDb databases only on the database server and not on the individual clients.

Note the code in this document is not executed during vignette generation as this would require access to a MySQL server.

2 Using ensembldb with a MySQL server

Installation of EnsDb databases in a MySQL server is straight forward - given that the user has write access to the server:

library(ensembldb)
## Load the EnsDb package that should be installed on the MySQL server
library(EnsDb.Hsapiens.v75)

## Call the useMySQL method providing the required credentials to create
## databases and inserting data on the MySQL server
edb_mysql <- useMySQL(EnsDb.Hsapiens.v75, host = "localhost", user = "userwrite",
              pass = "userpass")

## Use this EnsDb object
genes(edb_mysql)

To use an EnsDb in a MySQL server without the need to install the corresponding R-package, the connection to the database can be passed to the EnsDb constructor function. With the resulting EnsDb object annotations can be retrieved from the MySQL database.

library(ensembldb)
library(RMySQL)

## Connect to the MySQL database to list the databases.
dbcon <- dbConnect(MySQL(), host = "localhost", user = "readonly",
           pass = "readonly")

## List the available databases
listEnsDbs(dbcon)

## Connect to one of the databases and use that one.
dbcon <- dbConnect(MySQL(), host = "localhost", user = "readonly",
           pass = "readonly", dbname = "ensdb_hsapiens_v75")
edb <- EnsDb(dbcon)
edb
ensembldb/inst/doc/ensembldb.R0000644000175400017540000003361413013177517017347 0ustar00biocbuildbiocbuild## ----warning=FALSE, message=FALSE---------------------------------------- library(EnsDb.Hsapiens.v75) ## Making a "short cut" edb <- EnsDb.Hsapiens.v75 ## print some informations for this package edb ## for what organism was the database generated? organism(edb) ## ------------------------------------------------------------------------ Tx <- transcripts(edb, filter = list(GenenameFilter("BCL2L11"))) Tx ## as this is a GRanges object we can access e.g. the start coordinates with head(start(Tx)) ## or extract the biotype with head(Tx$tx_biotype) ## ------------------------------------------------------------------------ ## list all database tables along with their columns listTables(edb) ## list columns from a specific table listColumns(edb, "tx") ## ------------------------------------------------------------------------ Tx <- transcripts(edb, columns = c(listColumns(edb , "tx"), "gene_name"), filter = TxbiotypeFilter("nonsense_mediated_decay"), return.type = "DataFrame") nrow(Tx) Tx ## ------------------------------------------------------------------------ yCds <- cdsBy(edb, filter = SeqnameFilter("Y")) yCds ## ------------------------------------------------------------------------ ## Define the filter grf <- GRangesFilter(GRanges("11", ranges = IRanges(114000000, 114000050), strand = "+"), condition = "overlapping") ## Query genes: gn <- genes(edb, filter = grf) gn ## Next we retrieve all transcripts for that gene so that we can plot them. txs <- transcripts(edb, filter = GenenameFilter(gn$gene_name)) ## ----tx-for-zbtb16, message=FALSE, fig.align='center', fig.width=7.5, fig.height=5---- plot(3, 3, pch = NA, xlim = c(start(gn), end(gn)), ylim = c(0, length(txs)), yaxt = "n", ylab = "") ## Highlight the GRangesFilter region rect(xleft = start(grf), xright = end(grf), ybottom = 0, ytop = length(txs), col = "red", border = "red") for(i in 1:length(txs)) { current <- txs[i] rect(xleft = start(current), xright = end(current), ybottom = i-0.975, ytop = i-0.125, border = "grey") text(start(current), y = i-0.5, pos = 4, cex = 0.75, labels = current$tx_id) } ## ------------------------------------------------------------------------ transcripts(edb, filter = grf) ## ------------------------------------------------------------------------ ## Get all gene biotypes from the database. The GenebiotypeFilter ## allows to filter on these values. listGenebiotypes(edb) ## Get all transcript biotypes from the database. listTxbiotypes(edb) ## ------------------------------------------------------------------------ ## We're going to fetch all genes which names start with BCL. To this end ## we define a GenenameFilter with partial matching, i.e. condition "like" ## and a % for any character/string. BCLs <- genes(edb, columns = c("gene_name", "entrezid", "gene_biotype"), filter = list(GenenameFilter("BCL%", condition = "like")), return.type = "DataFrame") nrow(BCLs) BCLs ## ------------------------------------------------------------------------ ## determine the average length of snRNA, snoRNA and rRNA genes encoded on ## chromosomes X and Y. mean(lengthOf(edb, of = "tx", filter = list(GenebiotypeFilter(c("snRNA", "snoRNA", "rRNA")), SeqnameFilter(c("X", "Y"))))) ## determine the average length of protein coding genes encoded on the same ## chromosomes. mean(lengthOf(edb, of = "tx", filter = list(GenebiotypeFilter("protein_coding"), SeqnameFilter(c("X", "Y"))))) ## ------------------------------------------------------------------------ ## Extract all exons 1 and (if present) 2 for all genes encoded on the ## Y chromosome exons(edb, columns = c("tx_id", "exon_idx"), filter = list(SeqnameFilter("Y"), ExonrankFilter(3, condition = "<"))) ## ------------------------------------------------------------------------ TxByGns <- transcriptsBy(edb, by = "gene", filter = list(SeqnameFilter(c("X", "Y"))) ) TxByGns ## ----eval=FALSE---------------------------------------------------------- # ## will just get exons for all genes on chromosomes 1 to 22, X and Y. # ## Note: want to get rid of the "LRG" genes!!! # EnsGenes <- exonsBy(edb, by = "gene", # filter = list(SeqnameFilter(c(1:22, "X", "Y")), # GeneidFilter("ENSG%", "like"))) ## ----eval=FALSE---------------------------------------------------------- # ## Transforming the GRangesList into a data.frame in SAF format # EnsGenes.SAF <- toSAF(EnsGenes) ## ----eval=FALSE---------------------------------------------------------- # ## Create a GRanges of non-overlapping exon parts. # DJE <- disjointExons(edb, # filter = list(SeqnameFilter(c(1:22, "X", "Y")), # GeneidFilter("ENSG%", "like"))) ## ----eval=FALSE---------------------------------------------------------- # library(EnsDb.Hsapiens.v75) # library(Rsamtools) # edb <- EnsDb.Hsapiens.v75 # # ## Get the FaFile with the genomic sequence matching the Ensembl version # ## using the AnnotationHub package. # Dna <- getGenomeFaFile(edb) # # ## Get start/end coordinates of all genes. # genes <- genes(edb) # ## Subset to all genes that are encoded on chromosomes for which # ## we do have DNA sequence available. # genes <- genes[seqnames(genes) %in% seqnames(seqinfo(Dna))] # # ## Get the gene sequences, i.e. the sequence including the sequence of # ## all of the gene's exons and introns. # geneSeqs <- getSeq(Dna, genes) ## ----eval=FALSE---------------------------------------------------------- # ## get all exons of all transcripts encoded on chromosome Y # yTx <- exonsBy(edb, filter = SeqnameFilter("Y")) # # ## Retrieve the sequences for these transcripts from the FaFile. # library(GenomicFeatures) # yTxSeqs <- extractTranscriptSeqs(Dna, yTx) # yTxSeqs # # ## Extract the sequences of all transcripts encoded on chromosome Y. # yTx <- extractTranscriptSeqs(Dna, edb, filter = SeqnameFilter("Y")) # # ## Along these lines, we could use the method also to retrieve the coding sequence # ## of all transcripts on the Y chromosome. # cdsY <- cdsBy(edb, filter = SeqnameFilter("Y")) # extractTranscriptSeqs(Dna, cdsY) ## ----message=FALSE------------------------------------------------------- ## Change the seqlevels style form Ensembl (default) to UCSC: seqlevelsStyle(edb) <- "UCSC" ## Now we can use UCSC style seqnames in SeqnameFilters or GRangesFilter: genesY <- genes(edb, filter = SeqnameFilter("chrY")) ## The seqlevels of the returned GRanges are also in UCSC style seqlevels(genesY) ## ------------------------------------------------------------------------ seqlevelsStyle(edb) <- "UCSC" ## Getting the default option: getOption("ensembldb.seqnameNotFound") ## Listing all seqlevels in the database. seqlevels(edb)[1:30] ## Setting the option to NA, thus, for each seqname for which no mapping is available, ## NA is returned. options(ensembldb.seqnameNotFound=NA) seqlevels(edb)[1:30] ## Resetting the option. options(ensembldb.seqnameNotFound = "ORIGINAL") ## ----warning=FALSE, message=FALSE---------------------------------------- library(BSgenome.Hsapiens.UCSC.hg19) bsg <- BSgenome.Hsapiens.UCSC.hg19 ## Get the genome version unique(genome(bsg)) unique(genome(edb)) ## Although differently named, both represent genome build GRCh37. ## Extract the full transcript sequences. yTxSeqs <- extractTranscriptSeqs(bsg, exonsBy(edb, "tx", filter = SeqnameFilter("chrY"))) yTxSeqs ## Extract just the CDS Test <- cdsBy(edb, "tx", filter = SeqnameFilter("chrY")) yTxCds <- extractTranscriptSeqs(bsg, cdsBy(edb, "tx", filter = SeqnameFilter("chrY"))) yTxCds ## ------------------------------------------------------------------------ seqlevelsStyle(edb) <- "Ensembl" ## ----gviz-plot, message=FALSE, fig.align='center', fig.width=7.5, fig.height=2.25---- ## Loading the Gviz library library(Gviz) library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 ## Retrieving a Gviz compatible GRanges object with all genes ## encoded on chromosome Y. gr <- getGeneRegionTrackForGviz(edb, chromosome = "Y", start = 20400000, end = 21400000) ## Define a genome axis track gat <- GenomeAxisTrack() ## We have to change the ucscChromosomeNames option to FALSE to enable Gviz usage ## with non-UCSC chromosome names. options(ucscChromosomeNames = FALSE) plotTracks(list(gat, GeneRegionTrack(gr))) options(ucscChromosomeNames = TRUE) ## ----message=FALSE------------------------------------------------------- seqlevelsStyle(edb) <- "UCSC" ## Retrieving the GRanges objects with seqnames corresponding to UCSC chromosome names. gr <- getGeneRegionTrackForGviz(edb, chromosome = "chrY", start = 20400000, end = 21400000) seqnames(gr) ## Define a genome axis track gat <- GenomeAxisTrack() plotTracks(list(gat, GeneRegionTrack(gr))) ## ----gviz-separate-tracks, message=FALSE, warning=FALSE, fig.align='center', fig.width=7.5, fig.height=2.25---- protCod <- getGeneRegionTrackForGviz(edb, chromosome = "chrY", start = 20400000, end = 21400000, filter = GenebiotypeFilter("protein_coding")) lincs <- getGeneRegionTrackForGviz(edb, chromosome = "chrY", start = 20400000, end = 21400000, filter = GenebiotypeFilter("lincRNA")) plotTracks(list(gat, GeneRegionTrack(protCod, name = "protein coding"), GeneRegionTrack(lincs, name = "lincRNAs")), transcriptAnnotation = "symbol") ## At last we change the seqlevels style again to Ensembl seqlevelsStyle <- "Ensembl" ## ------------------------------------------------------------------------ library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 ## List all available columns in the database. columns(edb) ## Note that these do *not* correspond to the actual column names ## of the database that can be passed to methods like exons, genes, ## transcripts etc. These column names can be listed with the listColumns ## method. listColumns(edb) ## List all of the supported key types. keytypes(edb) ## Get all gene ids from the database. gids <- keys(edb, keytype = "GENEID") length(gids) ## Get all gene names for genes encoded on chromosome Y. gnames <- keys(edb, keytype = "GENENAME", filter = SeqnameFilter("Y")) head(gnames) ## ----warning=FALSE------------------------------------------------------- ## Use the /standard/ way to fetch data. select(edb, keys = c("BCL2", "BCL2L11"), keytype = "GENENAME", columns = c("GENEID", "GENENAME", "TXID", "TXBIOTYPE")) ## Use the filtering system of ensembldb select(edb, keys = list(GenenameFilter(c("BCL2", "BCL2L11")), TxbiotypeFilter("protein_coding")), columns = c("GENEID", "GENENAME", "TXID", "TXBIOTYPE")) ## ------------------------------------------------------------------------ ## Use the default method, which just returns the first value for multi mappings. mapIds(edb, keys = c("BCL2", "BCL2L11"), column = "TXID", keytype = "GENENAME") ## Alternatively, specify multiVals="list" to return all mappings. mapIds(edb, keys = c("BCL2", "BCL2L11"), column = "TXID", keytype = "GENENAME", multiVals = "list") ## And, just like before, we can use filters to map only to protein coding transcripts. mapIds(edb, keys = list(GenenameFilter(c("BCL2", "BCL2L11")), TxbiotypeFilter("protein_coding")), column = "TXID", multiVals = "list") ## ----eval=FALSE---------------------------------------------------------- # library(ensembldb) # # ## get all human gene/transcript/exon annotations from Ensembl (75) # ## the resulting tables will be stored by default to the current working # ## directory # fetchTablesFromEnsembl(75, species = "human") # # ## These tables can then be processed to generate a SQLite database # ## containing the annotations (again, the function assumes the required # ## txt files to be present in the current working directory) # DBFile <- makeEnsemblSQLiteFromTables() # # ## and finally we can generate the package # makeEnsembldbPackage(ensdb = DBFile, version = "0.99.12", # maintainer = "Johannes Rainer ", # author = "J Rainer") ## ----eval=FALSE---------------------------------------------------------- # ## Load the AnnotationHub data. # library(AnnotationHub) # ah <- AnnotationHub() # # ## Query all available files for Ensembl release 77 for # ## Mus musculus. # query(ah, c("Mus musculus", "release-77")) # # ## Get the resource for the gtf file with the gene/transcript definitions. # Gtf <- ah["AH28822"] # ## Create a EnsDb database file from this. # DbFile <- ensDbFromAH(Gtf) # ## We can either generate a database package, or directly load the data # edb <- EnsDb(DbFile) # # # ## Identify and get the FaFile object with the genomic DNA sequence matching # ## the EnsDb annotation. # Dna <- getGenomeFaFile(edb) # library(Rsamtools) # ## We next retrieve the sequence of all exons on chromosome Y. # exons <- exons(edb, filter = SeqnameFilter("Y")) # exonSeq <- getSeq(Dna, exons) # # ## Alternatively, look up and retrieve the toplevel DNA sequence manually. # Dna <- ah[["AH22042"]] ## ----message=FALSE------------------------------------------------------- ## Generate a sqlite database from a GRanges object specifying ## genes encoded on chromosome Y load(system.file("YGRanges.RData", package = "ensembldb")) Y DB <- ensDbFromGRanges(Y, path = tempdir(), version = 75, organism = "Homo_sapiens") edb <- EnsDb(DB) edb ## As shown in the example below, we could make an EnsDb package on ## this DB object using the makeEnsembldbPackage function. ## ----eval=FALSE---------------------------------------------------------- # library(ensembldb) # # ## the GTF file can be downloaded from # ## ftp://ftp.ensembl.org/pub/release-75/gtf/homo_sapiens/ # gtffile <- "Homo_sapiens.GRCh37.75.gtf.gz" # ## generate the SQLite database file # DB <- ensDbFromGtf(gtf = gtffile) # # ## load the DB file directly # EDB <- EnsDb(DB) # # ## alternatively, build the annotation package # ## and finally we can generate the package # makeEnsembldbPackage(ensdb = DB, version = "0.99.12", # maintainer = "Johannes Rainer ", # author = "J Rainer") ensembldb/inst/doc/ensembldb.Rmd0000644000175400017540000011541513013177517017670 0ustar00biocbuildbiocbuild--- title: "Generating an using Ensembl based annotation packages" graphics: yes output: BiocStyle::html_document2 vignette: > %\VignetteIndexEntry{Generating an using Ensembl based annotation packages} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} %\VignetteDepends{ensembldb,EnsDb.Hsapiens.v75,Gviz,BiocStyle} %\VignettePackage{ensembldb} %\VignetteKeywords{annotation,database} --- **Package**: `r BiocStyle::Biocpkg("ensembldb")`
**Authors**: `r packageDescription("ensembldb")$Author`
**Modified**: 12 September, 2016
**Compiled**: `r date()` # Introduction The `ensembldb` package provides functions to create and use transcript centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl 1 using their Perl API. The functionality and data is similar to that of the `TxDb` packages from the `GenomicFeatures` package, but, in addition to retrieve all gene/transcript models and annotations from the database, the `ensembldb` package provides also a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes. In the databases, along with the gene and transcript models and their chromosomal coordinates, additional annotations including the gene name (symbol) and NCBI Entrezgene identifiers as well as the gene and transcript biotypes are stored too (see Section [11](#orgtarget1) for the database layout and an overview of available attributes/columns). Another main goal of this package is to generate *versioned* annotation packages, i.e. annotation packages that are build for a specific Ensembl release, and are also named according to that (e.g. `EnsDb.Hsapiens.v75` for human gene definitions of the Ensembl code database version 75). This ensures reproducibility, as it allows to load annotations from a specific Ensembl release also if newer versions of annotation packages/releases are available. It also allows to load multiple annotation packages at the same time in order to e.g. compare gene models between Ensembl releases. In the example below we load an Ensembl based annotation package for Homo sapiens, Ensembl version 75. The connection to the database is bound to the variable `EnsDb.Hsapiens.v75`. ```{r warning=FALSE, message=FALSE} library(EnsDb.Hsapiens.v75) ## Making a "short cut" edb <- EnsDb.Hsapiens.v75 ## print some informations for this package edb ## for what organism was the database generated? organism(edb) ``` # Using `ensembldb` annotation packages to retrieve specific annotations The `ensembldb` package provides a set of filter objects allowing to specify which entries should be fetched from the database. The complete list of filters, which can be used individually or can be combined, is shown below (in alphabetical order): - `ExonidFilter`: allows to filter the result based on the (Ensembl) exon identifiers. - `ExonrankFilter`: filter results on the rank (index) of an exon within the transcript model. Exons are always numbered from 5' to 3' end of the transcript, thus, also on the reverse strand, the exon 1 is the most 5' exon of the transcript. - `EntrezidFilter`: allows to filter results based on NCBI Entrezgene identifiers of the genes. - `GenebiotypeFilter`: allows to filter for the gene biotypes defined in the Ensembl database; use the `listGenebiotypes` method to list all available biotypes. - `GeneidFilter`: allows to filter based on the Ensembl gene IDs. - `GenenameFilter`: allows to filter based on the names (symbols) of the genes. - `SymbolFilter`: allows to filter on gene symbols; note that no database columns *symbol* is available in an `EnsDb` database and hence the gene name is used for filtering. - `GRangesFilter`: allows to retrieve all features (genes, transcripts or exons) that are either within (setting `condition` to "within") or partially overlapping (setting `condition` to "overlapping") the defined genomic region/range. Note that, depending on the called method (`genes`, `transcripts` or `exons`) the start and end coordinates of either the genes, transcripts or exons are used for the filter. For methods `exonsBy`, `cdsBy` and `txBy` the coordinates of `by` are used. - `SeqendFilter`: filter based on the chromosomal end coordinate of the exons, transcripts or genes (correspondingly set =feature = "exon"=, =feature = "tx"= or =feature = "gene"=). - `SeqnameFilter`: filter by the name of the chromosomes the genes are encoded on. - `SeqstartFilter`: filter based on the chromosomal start coordinates of the exons, transcripts or genes (correspondingly set =feature = "exon"=, =feature = "tx"= or =feature = "gene"=). - `SeqstrandFilter`: filter for the chromosome strand on which the genes are encoded. - `TxbiotypeFilter`: filter on the transcript biotype defined in Ensembl; use the `listTxbiotypes` method to list all available biotypes. - `TxidFilter`: filter on the Ensembl transcript identifiers. Each of the filter classes can take a single value or a vector of values (with the exception of the `SeqendFilter` and `SeqstartFilter`) for comparison. In addition, it is possible to specify the *condition* for the filter, e.g. setting `condition` to = to retrieve all entries matching the filter value, to != to negate the filter or setting `condition = "like"= to allow partial matching. The =condition` parameter for `SeqendFilter` and `SeqendFilter` can take the values = , >, >=, < and <= (since these filters base on numeric values). A simple example would be to get all transcripts for the gene *BCL2L11*. To this end we specify a `GenenameFilter` with the value *BCL2L11*. As a result we get a `GRanges` object with `start`, `end`, `strand` and `seqname` of the `GRanges` object being the start coordinate, end coordinate, chromosome name and strand for the respective transcripts. All additional annotations are available as metadata columns. Alternatively, by setting `return.type` to "DataFrame", or "data.frame" the method would return a `DataFrame` or `data.frame` object. ```{r } Tx <- transcripts(edb, filter = list(GenenameFilter("BCL2L11"))) Tx ## as this is a GRanges object we can access e.g. the start coordinates with head(start(Tx)) ## or extract the biotype with head(Tx$tx_biotype) ``` The parameter `columns` of the `exons`, `genes` and `transcripts` method allows to specify which database attributes (columns) should be retrieved. The `exons` method returns by default all exon-related columns, the `transcripts` all columns from the transcript database table and the `genes` all from the gene table. Note however that in the example above we got also a column `gene_name` although this column is not present in the transcript database table. By default the methods return also all columns that are used by any of the filters submitted with the `filter` argument (thus, because a `GenenameFilter` was used, the column `gene_name` is also returned). Setting `returnFilterColumns(edb) <- FALSE` disables this option and only the columns specified by the `columns` parameter are retrieved. To get an overview of database tables and available columns the function `listTables` can be used. The method `listColumns` on the other hand lists columns for the specified database table. ```{r } ## list all database tables along with their columns listTables(edb) ## list columns from a specific table listColumns(edb, "tx") ``` Thus, we could retrieve all transcripts of the biotype *nonsense\_mediated\_decay* (which, according to the definitions by Ensembl are transcribed, but most likely not translated in a protein, but rather degraded after transcription) along with the name of the gene for each transcript. Note that we are changing here the `return.type` to `DataFrame`, so the method will return a `DataFrame` with the results instead of the default `GRanges`. ```{r } Tx <- transcripts(edb, columns = c(listColumns(edb , "tx"), "gene_name"), filter = TxbiotypeFilter("nonsense_mediated_decay"), return.type = "DataFrame") nrow(Tx) Tx ``` For protein coding transcripts, we can also specifically extract their coding region. In the example below we extract the CDS for all transcripts encoded on chromosome Y. ```{r } yCds <- cdsBy(edb, filter = SeqnameFilter("Y")) yCds ``` Using a `GRangesFilter` we can retrieve all features from the database that are either within or overlapping the specified genomic region. In the example below we query all genes that are partially overlapping with a small region on chromosome 11. The filter restricts to all genes for which either an exon or an intron is partially overlapping with the region. ```{r } ## Define the filter grf <- GRangesFilter(GRanges("11", ranges = IRanges(114000000, 114000050), strand = "+"), condition = "overlapping") ## Query genes: gn <- genes(edb, filter = grf) gn ## Next we retrieve all transcripts for that gene so that we can plot them. txs <- transcripts(edb, filter = GenenameFilter(gn$gene_name)) ``` ```{r tx-for-zbtb16, message=FALSE, fig.align='center', fig.width=7.5, fig.height=5} plot(3, 3, pch = NA, xlim = c(start(gn), end(gn)), ylim = c(0, length(txs)), yaxt = "n", ylab = "") ## Highlight the GRangesFilter region rect(xleft = start(grf), xright = end(grf), ybottom = 0, ytop = length(txs), col = "red", border = "red") for(i in 1:length(txs)) { current <- txs[i] rect(xleft = start(current), xright = end(current), ybottom = i-0.975, ytop = i-0.125, border = "grey") text(start(current), y = i-0.5, pos = 4, cex = 0.75, labels = current$tx_id) } ``` As we can see, 4 transcripts of the gene ZBTB16 are also overlapping the region. Below we fetch these 4 transcripts. Note, that a call to `exons` will not return any features from the database, as no exon is overlapping with the region. ```{r } transcripts(edb, filter = grf) ``` The `GRangesFilter` supports also `GRanges` defining multiple regions and a query will return all features overlapping any of these regions. Besides using the `GRangesFilter` it is also possible to search for transcripts or exons overlapping genomic regions using the `exonsByOverlaps` or `transcriptsByOverlaps` known from the `GenomicFeatures` package. Note that the implementation of these methods for `EnsDb` objects supports also to use filters to further fine-tune the query. To get an overview of allowed/available gene and transcript biotype the functions `listGenebiotypes` and `listTxbiotypes` can be used. ```{r } ## Get all gene biotypes from the database. The GenebiotypeFilter ## allows to filter on these values. listGenebiotypes(edb) ## Get all transcript biotypes from the database. listTxbiotypes(edb) ``` Data can be fetched in an analogous way using the `exons` and `genes` methods. In the example below we retrieve `gene_name`, `entrezid` and the `gene_biotype` of all genes in the database which names start with "BCL2". ```{r } ## We're going to fetch all genes which names start with BCL. To this end ## we define a GenenameFilter with partial matching, i.e. condition "like" ## and a % for any character/string. BCLs <- genes(edb, columns = c("gene_name", "entrezid", "gene_biotype"), filter = list(GenenameFilter("BCL%", condition = "like")), return.type = "DataFrame") nrow(BCLs) BCLs ``` Sometimes it might be useful to know the length of genes or transcripts (i.e. the total sum of nucleotides covered by their exons). Below we calculate the mean length of transcripts from protein coding genes on chromosomes X and Y as well as the average length of snoRNA, snRNA and rRNA transcripts encoded on these chromosomes. ```{r } ## determine the average length of snRNA, snoRNA and rRNA genes encoded on ## chromosomes X and Y. mean(lengthOf(edb, of = "tx", filter = list(GenebiotypeFilter(c("snRNA", "snoRNA", "rRNA")), SeqnameFilter(c("X", "Y"))))) ## determine the average length of protein coding genes encoded on the same ## chromosomes. mean(lengthOf(edb, of = "tx", filter = list(GenebiotypeFilter("protein_coding"), SeqnameFilter(c("X", "Y"))))) ``` Not unexpectedly, transcripts of protein coding genes are longer than those of snRNA, snoRNA or rRNA genes. At last we extract the first two exons of each transcript model from the database. ```{r } ## Extract all exons 1 and (if present) 2 for all genes encoded on the ## Y chromosome exons(edb, columns = c("tx_id", "exon_idx"), filter = list(SeqnameFilter("Y"), ExonrankFilter(3, condition = "<"))) ``` # Extracting gene/transcript/exon models for RNASeq feature counting For the feature counting step of an RNAseq experiment, the gene or transcript models (defined by the chromosomal start and end positions of their exons) have to be known. To extract these from an Ensembl based annotation package, the `exonsBy`, `genesBy` and `transcriptsBy` methods can be used in an analogous way as in `TxDb` packages generated by the `GenomicFeatures` package. However, the `transcriptsBy` method does not, in contrast to the method in the `GenomicFeatures` package, allow to return transcripts by "cds". While the annotation packages built by the `ensembldb` contain the chromosomal start and end coordinates of the coding region (for protein coding genes) they do not assign an ID to each CDS. A simple use case is to retrieve all genes encoded on chromosomes X and Y from the database. ```{r } TxByGns <- transcriptsBy(edb, by = "gene", filter = list(SeqnameFilter(c("X", "Y"))) ) TxByGns ``` Since Ensembl contains also definitions of genes that are on chromosome variants (supercontigs), it is advisable to specify the chromosome names for which the gene models should be returned. In a real use case, we might thus want to retrieve all genes encoded on the *standard* chromosomes. In addition it is advisable to use a `GeneidFilter` to restrict to Ensembl genes only, as also *LRG* (Locus Reference Genomic) genes2 are defined in the database, which are partially redundant with Ensembl genes. ```{r eval=FALSE} ## will just get exons for all genes on chromosomes 1 to 22, X and Y. ## Note: want to get rid of the "LRG" genes!!! EnsGenes <- exonsBy(edb, by = "gene", filter = list(SeqnameFilter(c(1:22, "X", "Y")), GeneidFilter("ENSG%", "like"))) ``` The code above returns a `GRangesList` that can be used directly as an input for the `summarizeOverlaps` function from the `GenomicAlignments` package 3. Alternatively, the above `GRangesList` can be transformed to a `data.frame` in *SAF* format that can be used as an input to the `featureCounts` function of the `Rsubread` package 4. ```{r eval=FALSE} ## Transforming the GRangesList into a data.frame in SAF format EnsGenes.SAF <- toSAF(EnsGenes) ``` Note that the ID by which the `GRangesList` is split is used in the SAF formatted `data.frame` as the `GeneID`. In the example below this would be the Ensembl gene IDs, while the start, end coordinates (along with the strand and chromosomes) are those of the the exons. In addition, the `disjointExons` function (similar to the one defined in `GenomicFeatures`) can be used to generate a `GRanges` of non-overlapping exon parts which can be used in the `DEXSeq` package. ```{r eval=FALSE} ## Create a GRanges of non-overlapping exon parts. DJE <- disjointExons(edb, filter = list(SeqnameFilter(c(1:22, "X", "Y")), GeneidFilter("ENSG%", "like"))) ``` # Retrieving sequences for gene/transcript/exon models The methods to retrieve exons, transcripts and genes (i.e. `exons`, `transcripts` and `genes`) return by default `GRanges` objects that can be used to retrieve sequences using the `getSeq` method e.g. from BSgenome packages. The basic workflow is thus identical to the one for `TxDb` packages, however, it is not straight forward to identify the BSgenome package with the matching genomic sequence. Most BSgenome packages are named according to the genome build identifier used in UCSC which does not (always) match the genome build name used by Ensembl. Using the Ensembl version provided by the `EnsDb`, the correct genomic sequence can however be retrieved easily from the `AnnotationHub` using the `getGenomeFaFile`. If no Fasta file matching the Ensembl version is available, the function tries to identify a Fasta file with the correct genome build from the *closest* Ensembl release and returns that instead. In the code block below we retrieve first the `FaFile` with the genomic DNA sequence, extract the genomic start and end coordinates for all genes defined in the package, subset to genes encoded on sequences available in the `FaFile` and extract all of their sequences. Note: these sequences represent the sequence between the chromosomal start and end coordinates of the gene. ```{r eval=FALSE} library(EnsDb.Hsapiens.v75) library(Rsamtools) edb <- EnsDb.Hsapiens.v75 ## Get the FaFile with the genomic sequence matching the Ensembl version ## using the AnnotationHub package. Dna <- getGenomeFaFile(edb) ## Get start/end coordinates of all genes. genes <- genes(edb) ## Subset to all genes that are encoded on chromosomes for which ## we do have DNA sequence available. genes <- genes[seqnames(genes) %in% seqnames(seqinfo(Dna))] ## Get the gene sequences, i.e. the sequence including the sequence of ## all of the gene's exons and introns. geneSeqs <- getSeq(Dna, genes) ``` To retrieve the (exonic) sequence of transcripts (i.e. without introns) we can use directly the `extractTranscriptSeqs` method defined in the `GenomicFeatures` on the `EnsDb` object, eventually using a filter to restrict the query. ```{r eval=FALSE} ## get all exons of all transcripts encoded on chromosome Y yTx <- exonsBy(edb, filter = SeqnameFilter("Y")) ## Retrieve the sequences for these transcripts from the FaFile. library(GenomicFeatures) yTxSeqs <- extractTranscriptSeqs(Dna, yTx) yTxSeqs ## Extract the sequences of all transcripts encoded on chromosome Y. yTx <- extractTranscriptSeqs(Dna, edb, filter = SeqnameFilter("Y")) ## Along these lines, we could use the method also to retrieve the coding sequence ## of all transcripts on the Y chromosome. cdsY <- cdsBy(edb, filter = SeqnameFilter("Y")) extractTranscriptSeqs(Dna, cdsY) ``` Note: in the next section we describe how transcript sequences can be retrieved from a `BSgenome` package that is based on UCSC, not Ensembl. # Integrating annotations from Ensembl based `EnsDb` packages with UCSC based annotations Sometimes it might be useful to combine (Ensembl based) annotations from `EnsDb` packages/objects with annotations from other Bioconductor packages, that might base on UCSC annotations. To support such an integration of annotations, the `ensembldb` packages implements the `seqlevelsStyle` and `seqlevelsStyle<-` from the `GenomeInfoDb` package that allow to change the style of chromosome naming. Thus, sequence/chromosome names other than those used by Ensembl can be used in, and are returned by, the queries to `EnsDb` objects as long as a mapping for them is provided by the `GenomeInfoDb` package (which provides a mapping mostly between UCSC, NCBI and Ensembl chromosome names for the *main* chromosomes). In the example below we change the seqnames style to UCSC. ```{r message=FALSE} ## Change the seqlevels style form Ensembl (default) to UCSC: seqlevelsStyle(edb) <- "UCSC" ## Now we can use UCSC style seqnames in SeqnameFilters or GRangesFilter: genesY <- genes(edb, filter = SeqnameFilter("chrY")) ## The seqlevels of the returned GRanges are also in UCSC style seqlevels(genesY) ``` Note that in most instances no mapping is available for sequences not corresponding to the main chromosomes (i.e. contigs, patched chromosomes etc). What is returned in cases in which no mapping is available can be specified with the global `ensembldb.seqnameNotFound` option. By default (with `ensembldb.seqnameNotFound` set to "ORIGINAL"), the original seqnames (i.e. the ones from Ensembl) are returned. With `ensembldb.seqnameNotFound` "MISSING" each time a seqname can not be found an error is thrown. For all other cases (e.g. `ensembldb.seqnameNotFound = NA`) the value of the option is returned. ```{r } seqlevelsStyle(edb) <- "UCSC" ## Getting the default option: getOption("ensembldb.seqnameNotFound") ## Listing all seqlevels in the database. seqlevels(edb)[1:30] ## Setting the option to NA, thus, for each seqname for which no mapping is available, ## NA is returned. options(ensembldb.seqnameNotFound=NA) seqlevels(edb)[1:30] ## Resetting the option. options(ensembldb.seqnameNotFound = "ORIGINAL") ``` Next we retrieve transcript sequences from genes encoded on chromosome Y using the `BSGenome` package for the human genome from UCSC. The specified version `hg19` matches the genome build of Ensembl version 75, i.e. `GRCh37`. Note that while we changed the style of the seqnames to UCSC we did not change the naming of the genome release. ```{r warning=FALSE, message=FALSE} library(BSgenome.Hsapiens.UCSC.hg19) bsg <- BSgenome.Hsapiens.UCSC.hg19 ## Get the genome version unique(genome(bsg)) unique(genome(edb)) ## Although differently named, both represent genome build GRCh37. ## Extract the full transcript sequences. yTxSeqs <- extractTranscriptSeqs(bsg, exonsBy(edb, "tx", filter = SeqnameFilter("chrY"))) yTxSeqs ## Extract just the CDS Test <- cdsBy(edb, "tx", filter = SeqnameFilter("chrY")) yTxCds <- extractTranscriptSeqs(bsg, cdsBy(edb, "tx", filter = SeqnameFilter("chrY"))) yTxCds ``` At last changing the seqname style to the default value ="Ensembl"=. ```{r } seqlevelsStyle(edb) <- "Ensembl" ``` # Interactive annotation lookup using the `shiny` web app In addition to the `genes`, `transcripts` and `exons` methods it is possibly to search interactively for gene/transcript/exon annotations using the internal, `shiny` based, web application. The application can be started with the `runEnsDbApp()` function. The search results from this app can also be returned to the R workspace either as a `data.frame` or `GRanges` object. # Plotting gene/transcript features using `ensembldb` and `Gviz` The `Gviz` package provides functions to plot genes and transcripts along with other data on a genomic scale. Gene models can be provided either as a `data.frame`, `GRanges`, `TxDB` database, can be fetched from biomart and can also be retrieved from `ensembldb`. Below we generate a `GeneRegionTrack` fetching all transcripts from a certain region on chromosome Y. Note that if we want in addition to work also with BAM files that were aligned against DNA sequences retrieved from Ensembl or FASTA files representing genomic DNA sequences from Ensembl we should change the `ucscChromosomeNames` option from `Gviz` to `FALSE` (i.e. by calling `options(ucscChromosomeNames = FALSE)`). This is not necessary if we just want to retrieve gene models from an `EnsDb` object, as the `ensembldb` package internally checks the `ucscChromosomeNames` option and, depending on that, maps Ensembl chromosome names to UCSC chromosome names. ```{r gviz-plot, message=FALSE, fig.align='center', fig.width=7.5, fig.height=2.25} ## Loading the Gviz library library(Gviz) library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 ## Retrieving a Gviz compatible GRanges object with all genes ## encoded on chromosome Y. gr <- getGeneRegionTrackForGviz(edb, chromosome = "Y", start = 20400000, end = 21400000) ## Define a genome axis track gat <- GenomeAxisTrack() ## We have to change the ucscChromosomeNames option to FALSE to enable Gviz usage ## with non-UCSC chromosome names. options(ucscChromosomeNames = FALSE) plotTracks(list(gat, GeneRegionTrack(gr))) options(ucscChromosomeNames = TRUE) ``` Above we had to change the option `ucscChromosomeNames` to `FALSE` in order to use it with non-UCSC chromosome names. Alternatively, we could however also change the `seqnamesStyle` of the `EnsDb` object to `UCSC`. Note that we have to use now also chromosome names in the *UCSC style* in the `SeqnameFilter` (i.e. "chrY" instead of `Y`). ```{r message=FALSE} seqlevelsStyle(edb) <- "UCSC" ## Retrieving the GRanges objects with seqnames corresponding to UCSC chromosome names. gr <- getGeneRegionTrackForGviz(edb, chromosome = "chrY", start = 20400000, end = 21400000) seqnames(gr) ## Define a genome axis track gat <- GenomeAxisTrack() plotTracks(list(gat, GeneRegionTrack(gr))) ``` We can also use the filters from the `ensembldb` package to further refine what transcripts are fetched, like in the example below, in which we create two different gene region tracks, one for protein coding genes and one for lincRNAs. ```{r gviz-separate-tracks, message=FALSE, warning=FALSE, fig.align='center', fig.width=7.5, fig.height=2.25} protCod <- getGeneRegionTrackForGviz(edb, chromosome = "chrY", start = 20400000, end = 21400000, filter = GenebiotypeFilter("protein_coding")) lincs <- getGeneRegionTrackForGviz(edb, chromosome = "chrY", start = 20400000, end = 21400000, filter = GenebiotypeFilter("lincRNA")) plotTracks(list(gat, GeneRegionTrack(protCod, name = "protein coding"), GeneRegionTrack(lincs, name = "lincRNAs")), transcriptAnnotation = "symbol") ## At last we change the seqlevels style again to Ensembl seqlevelsStyle <- "Ensembl" ``` # Using `EnsDb` objects in the `AnnotationDbi` framework Most of the methods defined for objects extending the basic annotation package class `AnnotationDbi` are also defined for `EnsDb` objects (i.e. methods `columns`, `keytypes`, `keys`, `mapIds` and `select`). While these methods can be used analogously to basic annotation packages, the implementation for `EnsDb` objects also support the filtering framework of the `ensembldb` package. In the example below we first evaluate all the available columns and keytypes in the database and extract then the gene names for all genes encoded on chromosome X. ```{r } library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 ## List all available columns in the database. columns(edb) ## Note that these do *not* correspond to the actual column names ## of the database that can be passed to methods like exons, genes, ## transcripts etc. These column names can be listed with the listColumns ## method. listColumns(edb) ## List all of the supported key types. keytypes(edb) ## Get all gene ids from the database. gids <- keys(edb, keytype = "GENEID") length(gids) ## Get all gene names for genes encoded on chromosome Y. gnames <- keys(edb, keytype = "GENENAME", filter = SeqnameFilter("Y")) head(gnames) ``` In the next example we retrieve specific information from the database using the `select` method. First we fetch all transcripts for the genes *BCL2* and *BCL2L11*. In the first call we provide the gene names, while in the second call we employ the filtering system to perform a more fine-grained query to fetch only the protein coding transcripts for these genes. ```{r warning=FALSE} ## Use the /standard/ way to fetch data. select(edb, keys = c("BCL2", "BCL2L11"), keytype = "GENENAME", columns = c("GENEID", "GENENAME", "TXID", "TXBIOTYPE")) ## Use the filtering system of ensembldb select(edb, keys = list(GenenameFilter(c("BCL2", "BCL2L11")), TxbiotypeFilter("protein_coding")), columns = c("GENEID", "GENENAME", "TXID", "TXBIOTYPE")) ``` Finally, we use the `mapIds` method to establish a mapping between ids and values. In the example below we fetch transcript ids for the two genes from the example above. ```{r } ## Use the default method, which just returns the first value for multi mappings. mapIds(edb, keys = c("BCL2", "BCL2L11"), column = "TXID", keytype = "GENENAME") ## Alternatively, specify multiVals="list" to return all mappings. mapIds(edb, keys = c("BCL2", "BCL2L11"), column = "TXID", keytype = "GENENAME", multiVals = "list") ## And, just like before, we can use filters to map only to protein coding transcripts. mapIds(edb, keys = list(GenenameFilter(c("BCL2", "BCL2L11")), TxbiotypeFilter("protein_coding")), column = "TXID", multiVals = "list") ``` Note that, if the filters are used, the ordering of the result does no longer match the ordering of the genes. # Important notes These notes might explain eventually unexpected results (and, more importantly, help avoiding them): - The ordering of the results returned by the `genes`, `exons`, `transcripts` methods can be specified with the `order.by` parameter. The ordering of the results does however **not** correspond to the ordering of values in submitted filter objects. The exception is the `select` method. If a character vector of values or a single filter is passed with argument `keys` the ordering of results of this method matches the ordering of the key values or the values of the filter. - Results of `exonsBy`, `transcriptsBy` are always ordered by the `by` argument. - The CDS provided by `EnsDb` objects **always** includes both, the start and the stop codon. - Transcripts with multiple CDS are at present not supported by `EnsDb`. - At present, `EnsDb` support only genes/transcripts for which all of their exons are encoded on the same chromosome and the same strand. # Building an transcript-centric database package based on Ensembl annotation The code in this section is not supposed to be automatically executed when the vignette is built, as this would require a working installation of the Ensembl Perl API, which is not expected to be available on each system. Also, building `EnsDb` from alternative sources, like GFF or GTF files takes some time and thus also these examples are not directly executed when the vignette is build. ## Requirements The `fetchTablesFromEnsembl` function of the package uses the Ensembl Perl API to retrieve the required annotations from an Ensembl database (e.g. from the main site *ensembldb.ensembl.org*). Thus, to use the functionality to built databases, the Ensembl Perl API needs to be installed (see 5 for details). Alternatively, the `ensDbFromAH`, `ensDbFromGff`, `ensDbFromGRanges` and `ensDbFromGtf` functions allow to build EnsDb SQLite files from a `GRanges` object or GFF/GTF files from Ensembl (either provided as files or *via* `AnnotationHub`). These functions do not depend on the Ensembl Perl API, but require a working internet connection to fetch the chromosome lengths from Ensembl as these are not provided within GTF or GFF files. ## Building annotation packages The functions below use the Ensembl Perl API to fetch the required data directly from the Ensembl core databases. Thus, the path to the Perl API specific for the desired Ensembl version needs to be added to the `PERL5LIB` environment variable. An annotation package containing all human genes for Ensembl version 75 can be created using the code in the block below. ```{r eval=FALSE} library(ensembldb) ## get all human gene/transcript/exon annotations from Ensembl (75) ## the resulting tables will be stored by default to the current working ## directory fetchTablesFromEnsembl(75, species = "human") ## These tables can then be processed to generate a SQLite database ## containing the annotations (again, the function assumes the required ## txt files to be present in the current working directory) DBFile <- makeEnsemblSQLiteFromTables() ## and finally we can generate the package makeEnsembldbPackage(ensdb = DBFile, version = "0.99.12", maintainer = "Johannes Rainer ", author = "J Rainer") ``` The generated package can then be build using `R CMD build EnsDb.Hsapiens.v75` and installed with `R CMD INSTALL EnsDb.Hsapiens.v75*`. Note that we could directly generate an `EnsDb` instance by loading the database file, i.e. by calling `edb <- EnsDb(DBFile)` and work with that annotation object. To fetch and build annotation packages for plant genomes (e.g. arabidopsis thaliana), the *Ensembl genomes* should be specified as a host, i.e. setting `host` to "mysql-eg-publicsql.ebi.ac.uk", `port` to `4157` and `species` to e.g. "arabidopsis thaliana". In the next example we create an `EnsDb` database using the `AnnotationHub` package and load also the corresponding genomic DNA sequence matching the Ensembl version. We thus first query the `AnnotationHub` package for all resources available for `Mus musculus` and the Ensembl release 77. Next we create the `EnsDb` object from the appropriate `AnnotationHub` resource. We then use the `getGenomeFaFile` method on the `EnsDb` to directly look up and retrieve the correct or best matching `FaFile` with the genomic DNA sequence. At last we retrieve the sequences of all exons using the `getSeq` method. ```{r eval=FALSE} ## Load the AnnotationHub data. library(AnnotationHub) ah <- AnnotationHub() ## Query all available files for Ensembl release 77 for ## Mus musculus. query(ah, c("Mus musculus", "release-77")) ## Get the resource for the gtf file with the gene/transcript definitions. Gtf <- ah["AH28822"] ## Create a EnsDb database file from this. DbFile <- ensDbFromAH(Gtf) ## We can either generate a database package, or directly load the data edb <- EnsDb(DbFile) ## Identify and get the FaFile object with the genomic DNA sequence matching ## the EnsDb annotation. Dna <- getGenomeFaFile(edb) library(Rsamtools) ## We next retrieve the sequence of all exons on chromosome Y. exons <- exons(edb, filter = SeqnameFilter("Y")) exonSeq <- getSeq(Dna, exons) ## Alternatively, look up and retrieve the toplevel DNA sequence manually. Dna <- ah[["AH22042"]] ``` In the example below we load a `GRanges` containing gene definitions for genes encoded on chromosome Y and generate a EnsDb SQLite database from that information. ```{r message=FALSE} ## Generate a sqlite database from a GRanges object specifying ## genes encoded on chromosome Y load(system.file("YGRanges.RData", package = "ensembldb")) Y DB <- ensDbFromGRanges(Y, path = tempdir(), version = 75, organism = "Homo_sapiens") edb <- EnsDb(DB) edb ## As shown in the example below, we could make an EnsDb package on ## this DB object using the makeEnsembldbPackage function. ``` Alternatively we can build the annotation database using the `ensDbFromGtf` `ensDbFromGff` functions, that extracts most of the required data from a GTF respectively GFF (version 3) file which can be downloaded from Ensembl (e.g. from for human gene definitions from Ensembl version 75; for plant genomes etc files can be retrieved from ). All information except the chromosome lengths and the NCBI Entrezgene IDs can be extracted from these GTF files. The function also tries to retrieve chromosome length information automatically from Ensembl. Below we create the annotation from a gtf file that we fetch directly from Ensembl. ```{r eval=FALSE} library(ensembldb) ## the GTF file can be downloaded from ## ftp://ftp.ensembl.org/pub/release-75/gtf/homo_sapiens/ gtffile <- "Homo_sapiens.GRCh37.75.gtf.gz" ## generate the SQLite database file DB <- ensDbFromGtf(gtf = gtffile) ## load the DB file directly EDB <- EnsDb(DB) ## alternatively, build the annotation package ## and finally we can generate the package makeEnsembldbPackage(ensdb = DB, version = "0.99.12", maintainer = "Johannes Rainer ", author = "J Rainer") ``` # Database layout The database consists of the following tables and attributes (the layout is also shown in Figure [115](#orgparagraph1)): - **gene**: all gene specific annotations. - `gene_id`: the Ensembl ID of the gene. - `gene_name`: the name (symbol) of the gene. - `entrezid`: the NCBI Entrezgene ID(s) of the gene. Note that this can be a `;` separated list of IDs for genes that are mapped to more than one Entrezgene. - `gene_biotype`: the biotype of the gene. - `gene_seq_start`: the start coordinate of the gene on the sequence (usually a chromosome). - `gene_seq_end`: the end coordinate of the gene on the sequence. - `seq_name`: the name of the sequence (usually the chromosome name). - `seq_strand`: the strand on which the gene is encoded. - `seq_coord_system`: the coordinate system of the sequence. - **tx**: all transcript related annotations. Note that while no `tx_name` column is available in this database column, all methods to retrieve data from the database support also this column. The returned values are however the ID of the transcripts. - `tx_id`: the Ensembl transcript ID. - `tx_biotype`: the biotype of the transcript. - `tx_seq_start`: the start coordinate of the transcript. - `tx_seq_end`: the end coordinate of the transcript. - `tx_cds_seq_start`: the start coordinate of the coding region of the transcript (NULL for non-coding transcripts). - `tx_cds_seq_end`: the end coordinate of the coding region of the transcript. - `gene_id`: the gene to which the transcript belongs. - **exon**: all exon related annotation. - `exon_id`: the Ensembl exon ID. - `exon_seq_start`: the start coordinate of the exon. - `exon_seq_end`: the end coordinate of the exon. - **tx2exon**: provides the n:m mapping between transcripts and exons. - `tx_id`: the Ensembl transcript ID. - `exon_id`: the Ensembl exon ID. - `exon_idx`: the index of the exon in the corresponding transcript, always from 5' to 3' of the transcript. - **chromosome**: provides some information about the chromosomes. - `seq_name`: the name of the sequence/chromosome. - `seq_length`: the length of the sequence. - `is_circular`: whether the sequence in circular. - **information**: some additional, internal, informations (Genome build, Ensembl version etc). - `key` - `value` - *virtual* columns: - `symbol`: the database does not have such a database column, but it is still possible to use it in the `columns` parameter. This column is *symlinked* to the `gene_name` column. - `tx_name`: similar to the `symbol` column, this column is *symlinked* to the `tx_id` column. ![img](images/dblayout.png "Database layout.")

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ensembldb/inst/doc/ensembldb.html0000644000175400017540000560074613013177517020125 0ustar00biocbuildbiocbuild Generating an using Ensembl based annotation packages

Contents

Package: ensembldb
Authors: Johannes Rainer johannes.rainer@eurac.edu, Tim Triche tim.triche@usc.edu
Modified: 12 September, 2016
Compiled: Wed Nov 16 19:52:05 2016

1 Introduction

The ensembldb package provides functions to create and use transcript centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl 1 using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, the ensembldb package provides also a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes. In the databases, along with the gene and transcript models and their chromosomal coordinates, additional annotations including the gene name (symbol) and NCBI Entrezgene identifiers as well as the gene and transcript biotypes are stored too (see Section 11 for the database layout and an overview of available attributes/columns).

Another main goal of this package is to generate versioned annotation packages, i.e. annotation packages that are build for a specific Ensembl release, and are also named according to that (e.g. EnsDb.Hsapiens.v75 for human gene definitions of the Ensembl code database version 75). This ensures reproducibility, as it allows to load annotations from a specific Ensembl release also if newer versions of annotation packages/releases are available. It also allows to load multiple annotation packages at the same time in order to e.g. compare gene models between Ensembl releases.

In the example below we load an Ensembl based annotation package for Homo sapiens, Ensembl version 75. The connection to the database is bound to the variable EnsDb.Hsapiens.v75.

library(EnsDb.Hsapiens.v75)

## Making a "short cut"
edb <- EnsDb.Hsapiens.v75
## print some informations for this package
edb
## EnsDb for Ensembl:
## |Backend: SQLite
## |Db type: EnsDb
## |Type of Gene ID: Ensembl Gene ID
## |Supporting package: ensembldb
## |Db created by: ensembldb package from Bioconductor
## |script_version: 0.1.3
## |Creation time: Thu Sep 15 13:16:58 2016
## |ensembl_version: 75
## |ensembl_host: localhost
## |Organism: homo_sapiens
## |genome_build: GRCh37
## |DBSCHEMAVERSION: 1.0
## | No. of genes: 64102.
## | No. of transcripts: 215647.
## for what organism was the database generated?
organism(edb)
## [1] "Homo sapiens"

2 Using ensembldb annotation packages to retrieve specific annotations

The ensembldb package provides a set of filter objects allowing to specify which entries should be fetched from the database. The complete list of filters, which can be used individually or can be combined, is shown below (in alphabetical order):

  • ExonidFilter: allows to filter the result based on the (Ensembl) exon identifiers.
  • ExonrankFilter: filter results on the rank (index) of an exon within the transcript model. Exons are always numbered from 5’ to 3’ end of the transcript, thus, also on the reverse strand, the exon 1 is the most 5’ exon of the transcript.
  • EntrezidFilter: allows to filter results based on NCBI Entrezgene identifiers of the genes.
  • GenebiotypeFilter: allows to filter for the gene biotypes defined in the Ensembl database; use the listGenebiotypes method to list all available biotypes.
  • GeneidFilter: allows to filter based on the Ensembl gene IDs.
  • GenenameFilter: allows to filter based on the names (symbols) of the genes.
  • SymbolFilter: allows to filter on gene symbols; note that no database columns symbol is available in an EnsDb database and hence the gene name is used for filtering.
  • GRangesFilter: allows to retrieve all features (genes, transcripts or exons) that are either within (setting condition to “within”) or partially overlapping (setting condition to “overlapping”) the defined genomic region/range. Note that, depending on the called method (genes, transcripts or exons) the start and end coordinates of either the genes, transcripts or exons are used for the filter. For methods exonsBy, cdsBy and txBy the coordinates of by are used.
  • SeqendFilter: filter based on the chromosomal end coordinate of the exons, transcripts or genes (correspondingly set =feature = “exon”=, =feature = “tx”= or =feature = “gene”=).
  • SeqnameFilter: filter by the name of the chromosomes the genes are encoded on.
  • SeqstartFilter: filter based on the chromosomal start coordinates of the exons, transcripts or genes (correspondingly set =feature = “exon”=, =feature = “tx”= or =feature = “gene”=).
  • SeqstrandFilter: filter for the chromosome strand on which the genes are encoded.
  • TxbiotypeFilter: filter on the transcript biotype defined in Ensembl; use the listTxbiotypes method to list all available biotypes.
  • TxidFilter: filter on the Ensembl transcript identifiers.

Each of the filter classes can take a single value or a vector of values (with the exception of the SeqendFilter and SeqstartFilter) for comparison. In addition, it is possible to specify the condition for the filter, e.g. setting condition to = to retrieve all entries matching the filter value, to != to negate the filter or setting condition = "like"= to allow partial matching. The =condition parameter for SeqendFilter and SeqendFilter can take the values = , >, >=, < and <= (since these filters base on numeric values).

A simple example would be to get all transcripts for the gene BCL2L11. To this end we specify a GenenameFilter with the value BCL2L11. As a result we get a GRanges object with start, end, strand and seqname of the GRanges object being the start coordinate, end coordinate, chromosome name and strand for the respective transcripts. All additional annotations are available as metadata columns. Alternatively, by setting return.type to “DataFrame”, or “data.frame” the method would return a DataFrame or data.frame object.

Tx <- transcripts(edb, filter = list(GenenameFilter("BCL2L11")))

Tx
## GRanges object with 17 ranges and 7 metadata columns:
##                   seqnames                 ranges strand |           tx_id
##                      <Rle>              <IRanges>  <Rle> |     <character>
##   ENST00000432179        2 [111876955, 111881689]      + | ENST00000432179
##   ENST00000308659        2 [111878491, 111922625]      + | ENST00000308659
##   ENST00000357757        2 [111878491, 111919016]      + | ENST00000357757
##   ENST00000393253        2 [111878491, 111909428]      + | ENST00000393253
##   ENST00000337565        2 [111878491, 111886423]      + | ENST00000337565
##               ...      ...                    ...    ... .             ...
##   ENST00000452231        2 [111881323, 111921808]      + | ENST00000452231
##   ENST00000361493        2 [111881323, 111921808]      + | ENST00000361493
##   ENST00000431217        2 [111881323, 111921929]      + | ENST00000431217
##   ENST00000439718        2 [111881323, 111922220]      + | ENST00000439718
##   ENST00000438054        2 [111881329, 111903861]      + | ENST00000438054
##                                tx_biotype tx_cds_seq_start tx_cds_seq_end
##                               <character>        <integer>      <integer>
##   ENST00000432179          protein_coding        111881323      111881689
##   ENST00000308659          protein_coding        111881323      111921808
##   ENST00000357757          protein_coding        111881323      111919016
##   ENST00000393253          protein_coding        111881323      111909428
##   ENST00000337565          protein_coding        111881323      111886328
##               ...                     ...              ...            ...
##   ENST00000452231 nonsense_mediated_decay        111881323      111919016
##   ENST00000361493 nonsense_mediated_decay        111881323      111887812
##   ENST00000431217 nonsense_mediated_decay        111881323      111902078
##   ENST00000439718 nonsense_mediated_decay        111881323      111909428
##   ENST00000438054          protein_coding        111881329      111902068
##                           gene_id         tx_name   gene_name
##                       <character>     <character> <character>
##   ENST00000432179 ENSG00000153094 ENST00000432179     BCL2L11
##   ENST00000308659 ENSG00000153094 ENST00000308659     BCL2L11
##   ENST00000357757 ENSG00000153094 ENST00000357757     BCL2L11
##   ENST00000393253 ENSG00000153094 ENST00000393253     BCL2L11
##   ENST00000337565 ENSG00000153094 ENST00000337565     BCL2L11
##               ...             ...             ...         ...
##   ENST00000452231 ENSG00000153094 ENST00000452231     BCL2L11
##   ENST00000361493 ENSG00000153094 ENST00000361493     BCL2L11
##   ENST00000431217 ENSG00000153094 ENST00000431217     BCL2L11
##   ENST00000439718 ENSG00000153094 ENST00000439718     BCL2L11
##   ENST00000438054 ENSG00000153094 ENST00000438054     BCL2L11
##   -------
##   seqinfo: 1 sequence from GRCh37 genome
## as this is a GRanges object we can access e.g. the start coordinates with
head(start(Tx))
## [1] 111876955 111878491 111878491 111878491 111878491 111878506
## or extract the biotype with
head(Tx$tx_biotype)
## [1] "protein_coding" "protein_coding" "protein_coding" "protein_coding"
## [5] "protein_coding" "protein_coding"

The parameter columns of the exons, genes and transcripts method allows to specify which database attributes (columns) should be retrieved. The exons method returns by default all exon-related columns, the transcripts all columns from the transcript database table and the genes all from the gene table. Note however that in the example above we got also a column gene_name although this column is not present in the transcript database table. By default the methods return also all columns that are used by any of the filters submitted with the filter argument (thus, because a GenenameFilter was used, the column gene_name is also returned). Setting returnFilterColumns(edb) <- FALSE disables this option and only the columns specified by the columns parameter are retrieved.

To get an overview of database tables and available columns the function listTables can be used. The method listColumns on the other hand lists columns for the specified database table.

## list all database tables along with their columns
listTables(edb)
## $gene
##  [1] "gene_id"          "gene_name"        "entrezid"        
##  [4] "gene_biotype"     "gene_seq_start"   "gene_seq_end"    
##  [7] "seq_name"         "seq_strand"       "seq_coord_system"
## [10] "symbol"          
## 
## $tx
## [1] "tx_id"            "tx_biotype"       "tx_seq_start"    
## [4] "tx_seq_end"       "tx_cds_seq_start" "tx_cds_seq_end"  
## [7] "gene_id"          "tx_name"         
## 
## $tx2exon
## [1] "tx_id"    "exon_id"  "exon_idx"
## 
## $exon
## [1] "exon_id"        "exon_seq_start" "exon_seq_end"  
## 
## $chromosome
## [1] "seq_name"    "seq_length"  "is_circular"
## 
## $metadata
## [1] "name"  "value"
## list columns from a specific table
listColumns(edb, "tx")
## [1] "tx_id"            "tx_biotype"       "tx_seq_start"    
## [4] "tx_seq_end"       "tx_cds_seq_start" "tx_cds_seq_end"  
## [7] "gene_id"          "tx_name"

Thus, we could retrieve all transcripts of the biotype nonsense_mediated_decay (which, according to the definitions by Ensembl are transcribed, but most likely not translated in a protein, but rather degraded after transcription) along with the name of the gene for each transcript. Note that we are changing here the return.type to DataFrame, so the method will return a DataFrame with the results instead of the default GRanges.

Tx <- transcripts(edb,
          columns = c(listColumns(edb , "tx"), "gene_name"),
          filter = TxbiotypeFilter("nonsense_mediated_decay"),
          return.type = "DataFrame")
nrow(Tx)
## [1] 13812
Tx
## DataFrame with 13812 rows and 9 columns
##                 tx_id              tx_biotype tx_seq_start tx_seq_end
##           <character>             <character>    <integer>  <integer>
## 1     ENST00000495251 nonsense_mediated_decay        64085      69409
## 2     ENST00000462860 nonsense_mediated_decay        64085      69452
## 3     ENST00000483390 nonsense_mediated_decay        65739      68764
## 4     ENST00000538848 nonsense_mediated_decay        66411      68843
## 5     ENST00000567466 nonsense_mediated_decay        97578      99521
## ...               ...                     ...          ...        ...
## 13808 ENST00000496411 nonsense_mediated_decay    249149927  249153217
## 13809 ENST00000483223 nonsense_mediated_decay    249150714  249152728
## 13810 ENST00000533647 nonsense_mediated_decay    249151472  249152523
## 13811 ENST00000528141 nonsense_mediated_decay    249151590  249153284
## 13812 ENST00000530986 nonsense_mediated_decay    249151668  249153284
##       tx_cds_seq_start tx_cds_seq_end         gene_id         tx_name
##              <integer>      <integer>     <character>     <character>
## 1                68052          68789 ENSG00000234769 ENST00000495251
## 2                68052          68789 ENSG00000234769 ENST00000462860
## 3                66428          68764 ENSG00000234769 ENST00000483390
## 4                67418          68789 ENSG00000234769 ENST00000538848
## 5                98546          98893 ENSG00000261456 ENST00000567466
## ...                ...            ...             ...             ...
## 13808        249152153      249152508 ENSG00000171163 ENST00000496411
## 13809        249152153      249152508 ENSG00000171163 ENST00000483223
## 13810        249152153      249152508 ENSG00000171163 ENST00000533647
## 13811        249152203      249152508 ENSG00000171163 ENST00000528141
## 13812        249152203      249152508 ENSG00000171163 ENST00000530986
##         gene_name
##       <character>
## 1          WASH4P
## 2          WASH4P
## 3          WASH4P
## 4          WASH4P
## 5           TUBB8
## ...           ...
## 13808      ZNF692
## 13809      ZNF692
## 13810      ZNF692
## 13811      ZNF692
## 13812      ZNF692

For protein coding transcripts, we can also specifically extract their coding region. In the example below we extract the CDS for all transcripts encoded on chromosome Y.

yCds <- cdsBy(edb, filter = SeqnameFilter("Y"))
yCds
## GRangesList object of length 160:
## $ENST00000155093 
## GRanges object with 7 ranges and 3 metadata columns:
##       seqnames             ranges strand |    seq_name         exon_id
##          <Rle>          <IRanges>  <Rle> | <character>     <character>
##   [1]        Y [2821978, 2822038]      + |           Y ENSE00002223884
##   [2]        Y [2829115, 2829687]      + |           Y ENSE00003645989
##   [3]        Y [2843136, 2843285]      + |           Y ENSE00003548678
##   [4]        Y [2843552, 2843695]      + |           Y ENSE00003611496
##   [5]        Y [2844711, 2844863]      + |           Y ENSE00001649504
##   [6]        Y [2845981, 2846121]      + |           Y ENSE00001777381
##   [7]        Y [2846851, 2848034]      + |           Y ENSE00001368923
##       exon_rank
##       <integer>
##   [1]         2
##   [2]         3
##   [3]         4
##   [4]         5
##   [5]         6
##   [6]         7
##   [7]         8
## 
## $ENST00000215473 
## GRanges object with 6 ranges and 3 metadata columns:
##       seqnames             ranges strand | seq_name         exon_id
##   [1]        Y [4924865, 4925500]      + |        Y ENSE00001436852
##   [2]        Y [4966256, 4968748]      + |        Y ENSE00001640924
##   [3]        Y [5369098, 5369296]      + |        Y ENSE00001803775
##   [4]        Y [5483308, 5483316]      + |        Y ENSE00001731866
##   [5]        Y [5491131, 5491145]      + |        Y ENSE00001711324
##   [6]        Y [5605313, 5605983]      + |        Y ENSE00001779807
##       exon_rank
##   [1]         1
##   [2]         2
##   [3]         3
##   [4]         4
##   [5]         5
##   [6]         6
## 
## $ENST00000215479 
## GRanges object with 5 ranges and 3 metadata columns:
##       seqnames             ranges strand | seq_name         exon_id
##   [1]        Y [6740596, 6740649]      - |        Y ENSE00001671586
##   [2]        Y [6738047, 6738094]      - |        Y ENSE00001645681
##   [3]        Y [6736773, 6736817]      - |        Y ENSE00000652250
##   [4]        Y [6736078, 6736503]      - |        Y ENSE00001667251
##   [5]        Y [6734114, 6734119]      - |        Y ENSE00001494454
##       exon_rank
##   [1]         2
##   [2]         3
##   [3]         4
##   [4]         5
##   [5]         6
## 
## ...
## <157 more elements>
## -------
## seqinfo: 1 sequence from GRCh37 genome

Using a GRangesFilter we can retrieve all features from the database that are either within or overlapping the specified genomic region. In the example below we query all genes that are partially overlapping with a small region on chromosome 11. The filter restricts to all genes for which either an exon or an intron is partially overlapping with the region.

## Define the filter
grf <- GRangesFilter(GRanges("11", ranges = IRanges(114000000, 114000050),
                 strand = "+"), condition = "overlapping")

## Query genes:
gn <- genes(edb, filter = grf)
gn
## GRanges object with 1 range and 6 metadata columns:
##                   seqnames                 ranges strand |         gene_id
##                      <Rle>              <IRanges>  <Rle> |     <character>
##   ENSG00000109906       11 [113930315, 114121398]      + | ENSG00000109906
##                     gene_name    entrezid   gene_biotype seq_coord_system
##                   <character> <character>    <character>      <character>
##   ENSG00000109906      ZBTB16        7704 protein_coding       chromosome
##                        symbol
##                   <character>
##   ENSG00000109906      ZBTB16
##   -------
##   seqinfo: 1 sequence from GRCh37 genome
## Next we retrieve all transcripts for that gene so that we can plot them.
txs <- transcripts(edb, filter = GenenameFilter(gn$gene_name))
plot(3, 3, pch = NA, xlim = c(start(gn), end(gn)), ylim = c(0, length(txs)),
     yaxt = "n", ylab = "")
## Highlight the GRangesFilter region
rect(xleft = start(grf), xright = end(grf), ybottom = 0, ytop = length(txs),
     col = "red", border = "red")
for(i in 1:length(txs)) {
    current <- txs[i]
    rect(xleft = start(current), xright = end(current), ybottom = i-0.975,
     ytop = i-0.125, border = "grey")
    text(start(current), y = i-0.5, pos = 4, cex = 0.75, labels = current$tx_id)
}

As we can see, 4 transcripts of the gene ZBTB16 are also overlapping the region. Below we fetch these 4 transcripts. Note, that a call to exons will not return any features from the database, as no exon is overlapping with the region.

transcripts(edb, filter = grf)
## GRanges object with 4 ranges and 6 metadata columns:
##                   seqnames                 ranges strand |           tx_id
##                      <Rle>              <IRanges>  <Rle> |     <character>
##   ENST00000335953       11 [113930315, 114121398]      + | ENST00000335953
##   ENST00000541602       11 [113930447, 114060486]      + | ENST00000541602
##   ENST00000392996       11 [113931229, 114121374]      + | ENST00000392996
##   ENST00000539918       11 [113935134, 114118066]      + | ENST00000539918
##                                tx_biotype tx_cds_seq_start tx_cds_seq_end
##                               <character>        <integer>      <integer>
##   ENST00000335953          protein_coding        113934023      114121277
##   ENST00000541602         retained_intron             <NA>           <NA>
##   ENST00000392996          protein_coding        113934023      114121277
##   ENST00000539918 nonsense_mediated_decay        113935134      113992549
##                           gene_id         tx_name
##                       <character>     <character>
##   ENST00000335953 ENSG00000109906 ENST00000335953
##   ENST00000541602 ENSG00000109906 ENST00000541602
##   ENST00000392996 ENSG00000109906 ENST00000392996
##   ENST00000539918 ENSG00000109906 ENST00000539918
##   -------
##   seqinfo: 1 sequence from GRCh37 genome

The GRangesFilter supports also GRanges defining multiple regions and a query will return all features overlapping any of these regions. Besides using the GRangesFilter it is also possible to search for transcripts or exons overlapping genomic regions using the exonsByOverlaps or transcriptsByOverlaps known from the GenomicFeatures package. Note that the implementation of these methods for EnsDb objects supports also to use filters to further fine-tune the query.

To get an overview of allowed/available gene and transcript biotype the functions listGenebiotypes and listTxbiotypes can be used.

## Get all gene biotypes from the database. The GenebiotypeFilter
## allows to filter on these values.
listGenebiotypes(edb)
##  [1] "protein_coding"           "pseudogene"              
##  [3] "processed_transcript"     "antisense"               
##  [5] "lincRNA"                  "polymorphic_pseudogene"  
##  [7] "IG_V_pseudogene"          "IG_V_gene"               
##  [9] "sense_overlapping"        "sense_intronic"          
## [11] "TR_V_gene"                "misc_RNA"                
## [13] "snRNA"                    "miRNA"                   
## [15] "snoRNA"                   "rRNA"                    
## [17] "Mt_tRNA"                  "Mt_rRNA"                 
## [19] "IG_C_gene"                "IG_J_gene"               
## [21] "TR_J_gene"                "TR_C_gene"               
## [23] "TR_V_pseudogene"          "TR_J_pseudogene"         
## [25] "IG_D_gene"                "IG_C_pseudogene"         
## [27] "TR_D_gene"                "IG_J_pseudogene"         
## [29] "3prime_overlapping_ncrna" "processed_pseudogene"    
## [31] "LRG_gene"
## Get all transcript biotypes from the database.
listTxbiotypes(edb)
##  [1] "protein_coding"                    
##  [2] "processed_transcript"              
##  [3] "retained_intron"                   
##  [4] "nonsense_mediated_decay"           
##  [5] "unitary_pseudogene"                
##  [6] "non_stop_decay"                    
##  [7] "unprocessed_pseudogene"            
##  [8] "processed_pseudogene"              
##  [9] "transcribed_unprocessed_pseudogene"
## [10] "antisense"                         
## [11] "lincRNA"                           
## [12] "polymorphic_pseudogene"            
## [13] "transcribed_processed_pseudogene"  
## [14] "miRNA"                             
## [15] "pseudogene"                        
## [16] "IG_V_pseudogene"                   
## [17] "snoRNA"                            
## [18] "IG_V_gene"                         
## [19] "sense_overlapping"                 
## [20] "sense_intronic"                    
## [21] "TR_V_gene"                         
## [22] "snRNA"                             
## [23] "misc_RNA"                          
## [24] "rRNA"                              
## [25] "Mt_tRNA"                           
## [26] "Mt_rRNA"                           
## [27] "IG_C_gene"                         
## [28] "IG_J_gene"                         
## [29] "TR_J_gene"                         
## [30] "TR_C_gene"                         
## [31] "TR_V_pseudogene"                   
## [32] "TR_J_pseudogene"                   
## [33] "IG_D_gene"                         
## [34] "IG_C_pseudogene"                   
## [35] "TR_D_gene"                         
## [36] "IG_J_pseudogene"                   
## [37] "3prime_overlapping_ncrna"          
## [38] "translated_processed_pseudogene"   
## [39] "LRG_gene"

Data can be fetched in an analogous way using the exons and genes methods. In the example below we retrieve gene_name, entrezid and the gene_biotype of all genes in the database which names start with “BCL2”.

## We're going to fetch all genes which names start with BCL. To this end
## we define a GenenameFilter with partial matching, i.e. condition "like"
## and a % for any character/string.
BCLs <- genes(edb,
          columns = c("gene_name", "entrezid", "gene_biotype"),
          filter = list(GenenameFilter("BCL%", condition = "like")),
          return.type = "DataFrame")
nrow(BCLs)
## [1] 25
BCLs
## DataFrame with 25 rows and 4 columns
##       gene_name    entrezid   gene_biotype         gene_id
##     <character> <character>    <character>     <character>
## 1         BCL10        8915 protein_coding ENSG00000142867
## 2        BCL11A       53335 protein_coding ENSG00000119866
## 3        BCL11B       64919 protein_coding ENSG00000127152
## 4          BCL2         596 protein_coding ENSG00000171791
## 5        BCL2A1         597 protein_coding ENSG00000140379
## ...         ...         ...            ...             ...
## 21        BCL7C        9274 protein_coding ENSG00000099385
## 22         BCL9         607 protein_coding ENSG00000116128
## 23         BCL9         607 protein_coding ENSG00000266095
## 24        BCL9L      283149 protein_coding ENSG00000186174
## 25       BCLAF1        9774 protein_coding ENSG00000029363

Sometimes it might be useful to know the length of genes or transcripts (i.e. the total sum of nucleotides covered by their exons). Below we calculate the mean length of transcripts from protein coding genes on chromosomes X and Y as well as the average length of snoRNA, snRNA and rRNA transcripts encoded on these chromosomes.

## determine the average length of snRNA, snoRNA and rRNA genes encoded on
## chromosomes X and Y.
mean(lengthOf(edb, of = "tx",
          filter = list(GenebiotypeFilter(c("snRNA", "snoRNA", "rRNA")),
                SeqnameFilter(c("X", "Y")))))
## [1] 116.3046
## determine the average length of protein coding genes encoded on the same
## chromosomes.
mean(lengthOf(edb, of = "tx",
          filter = list(GenebiotypeFilter("protein_coding"),
                SeqnameFilter(c("X", "Y")))))
## [1] 1920

Not unexpectedly, transcripts of protein coding genes are longer than those of snRNA, snoRNA or rRNA genes.

At last we extract the first two exons of each transcript model from the database.

## Extract all exons 1 and (if present) 2 for all genes encoded on the
## Y chromosome
exons(edb, columns = c("tx_id", "exon_idx"),
      filter = list(SeqnameFilter("Y"),
            ExonrankFilter(3, condition = "<")))
## GRanges object with 1287 ranges and 3 metadata columns:
##                   seqnames               ranges strand |           tx_id
##                      <Rle>            <IRanges>  <Rle> |     <character>
##   ENSE00002088309        Y   [2652790, 2652894]      + | ENST00000516032
##   ENSE00001494622        Y   [2654896, 2655740]      - | ENST00000383070
##   ENSE00002323146        Y   [2655049, 2655069]      - | ENST00000525526
##   ENSE00002201849        Y   [2655075, 2655644]      - | ENST00000525526
##   ENSE00002214525        Y   [2655145, 2655168]      - | ENST00000534739
##               ...      ...                  ...    ... .             ...
##   ENSE00001632993        Y [28737695, 28737748]      - | ENST00000456738
##   ENSE00001616687        Y [28772667, 28773306]      - | ENST00000435741
##   ENSE00001638296        Y [28779492, 28779578]      - | ENST00000435945
##   ENSE00001797328        Y [28780670, 28780799]      - | ENST00000435945
##   ENSE00001794473        Y [59001391, 59001635]      + | ENST00000431853
##                    exon_idx         exon_id
##                   <integer>     <character>
##   ENSE00002088309         1 ENSE00002088309
##   ENSE00001494622         1 ENSE00001494622
##   ENSE00002323146         2 ENSE00002323146
##   ENSE00002201849         1 ENSE00002201849
##   ENSE00002214525         2 ENSE00002214525
##               ...       ...             ...
##   ENSE00001632993         1 ENSE00001632993
##   ENSE00001616687         1 ENSE00001616687
##   ENSE00001638296         2 ENSE00001638296
##   ENSE00001797328         1 ENSE00001797328
##   ENSE00001794473         1 ENSE00001794473
##   -------
##   seqinfo: 1 sequence from GRCh37 genome

3 Extracting gene/transcript/exon models for RNASeq feature counting

For the feature counting step of an RNAseq experiment, the gene or transcript models (defined by the chromosomal start and end positions of their exons) have to be known. To extract these from an Ensembl based annotation package, the exonsBy, genesBy and transcriptsBy methods can be used in an analogous way as in TxDb packages generated by the GenomicFeatures package. However, the transcriptsBy method does not, in contrast to the method in the GenomicFeatures package, allow to return transcripts by “cds”. While the annotation packages built by the ensembldb contain the chromosomal start and end coordinates of the coding region (for protein coding genes) they do not assign an ID to each CDS.

A simple use case is to retrieve all genes encoded on chromosomes X and Y from the database.

TxByGns <- transcriptsBy(edb, by = "gene",
             filter = list(SeqnameFilter(c("X", "Y")))
             )
TxByGns
## GRangesList object of length 2908:
## $ENSG00000000003 
## GRanges object with 3 ranges and 6 metadata columns:
##       seqnames               ranges strand |           tx_id
##          <Rle>            <IRanges>  <Rle> |     <character>
##   [1]        X [99888439, 99894988]      - | ENST00000494424
##   [2]        X [99883667, 99891803]      - | ENST00000373020
##   [3]        X [99887538, 99891686]      - | ENST00000496771
##                 tx_biotype tx_cds_seq_start tx_cds_seq_end         gene_id
##                <character>        <integer>      <integer>     <character>
##   [1] processed_transcript             <NA>           <NA> ENSG00000000003
##   [2]       protein_coding         99885795       99891691 ENSG00000000003
##   [3] processed_transcript             <NA>           <NA> ENSG00000000003
##               tx_name
##           <character>
##   [1] ENST00000494424
##   [2] ENST00000373020
##   [3] ENST00000496771
## 
## $ENSG00000000005 
## GRanges object with 2 ranges and 6 metadata columns:
##       seqnames               ranges strand |           tx_id
##   [1]        X [99839799, 99854882]      + | ENST00000373031
##   [2]        X [99848621, 99852528]      + | ENST00000485971
##                 tx_biotype tx_cds_seq_start tx_cds_seq_end         gene_id
##   [1]       protein_coding         99840016       99854714 ENSG00000000005
##   [2] processed_transcript             <NA>           <NA> ENSG00000000005
##               tx_name
##   [1] ENST00000373031
##   [2] ENST00000485971
## 
## $ENSG00000001497 
## GRanges object with 6 ranges and 6 metadata columns:
##       seqnames               ranges strand |           tx_id
##   [1]        X [64732463, 64754655]      - | ENST00000484069
##   [2]        X [64732462, 64754636]      - | ENST00000374811
##   [3]        X [64732463, 64754636]      - | ENST00000374804
##   [4]        X [64732463, 64754636]      - | ENST00000312391
##   [5]        X [64732462, 64754634]      - | ENST00000374807
##   [6]        X [64740309, 64743497]      - | ENST00000469091
##                    tx_biotype tx_cds_seq_start tx_cds_seq_end
##   [1] nonsense_mediated_decay         64744901       64754595
##   [2]          protein_coding         64732655       64754595
##   [3]          protein_coding         64732655       64754595
##   [4]          protein_coding         64744901       64754595
##   [5]          protein_coding         64732655       64754595
##   [6]          protein_coding         64740535       64743497
##               gene_id         tx_name
##   [1] ENSG00000001497 ENST00000484069
##   [2] ENSG00000001497 ENST00000374811
##   [3] ENSG00000001497 ENST00000374804
##   [4] ENSG00000001497 ENST00000312391
##   [5] ENSG00000001497 ENST00000374807
##   [6] ENSG00000001497 ENST00000469091
## 
## ...
## <2905 more elements>
## -------
## seqinfo: 2 sequences from GRCh37 genome

Since Ensembl contains also definitions of genes that are on chromosome variants (supercontigs), it is advisable to specify the chromosome names for which the gene models should be returned.

In a real use case, we might thus want to retrieve all genes encoded on the standard chromosomes. In addition it is advisable to use a GeneidFilter to restrict to Ensembl genes only, as also LRG (Locus Reference Genomic) genes2 are defined in the database, which are partially redundant with Ensembl genes.

## will just get exons for all genes on chromosomes 1 to 22, X and Y.
## Note: want to get rid of the "LRG" genes!!!
EnsGenes <- exonsBy(edb, by = "gene",
            filter = list(SeqnameFilter(c(1:22, "X", "Y")),
                  GeneidFilter("ENSG%", "like")))

The code above returns a GRangesList that can be used directly as an input for the summarizeOverlaps function from the GenomicAlignments package 3.

Alternatively, the above GRangesList can be transformed to a data.frame in SAF format that can be used as an input to the featureCounts function of the Rsubread package 4.

## Transforming the GRangesList into a data.frame in SAF format
EnsGenes.SAF <- toSAF(EnsGenes)

Note that the ID by which the GRangesList is split is used in the SAF formatted data.frame as the GeneID. In the example below this would be the Ensembl gene IDs, while the start, end coordinates (along with the strand and chromosomes) are those of the the exons.

In addition, the disjointExons function (similar to the one defined in GenomicFeatures) can be used to generate a GRanges of non-overlapping exon parts which can be used in the DEXSeq package.

## Create a GRanges of non-overlapping exon parts.
DJE <- disjointExons(edb,
             filter = list(SeqnameFilter(c(1:22, "X", "Y")),
                   GeneidFilter("ENSG%", "like")))

4 Retrieving sequences for gene/transcript/exon models

The methods to retrieve exons, transcripts and genes (i.e. exons, transcripts and genes) return by default GRanges objects that can be used to retrieve sequences using the getSeq method e.g. from BSgenome packages. The basic workflow is thus identical to the one for TxDb packages, however, it is not straight forward to identify the BSgenome package with the matching genomic sequence. Most BSgenome packages are named according to the genome build identifier used in UCSC which does not (always) match the genome build name used by Ensembl. Using the Ensembl version provided by the EnsDb, the correct genomic sequence can however be retrieved easily from the AnnotationHub using the getGenomeFaFile. If no Fasta file matching the Ensembl version is available, the function tries to identify a Fasta file with the correct genome build from the closest Ensembl release and returns that instead.

In the code block below we retrieve first the FaFile with the genomic DNA sequence, extract the genomic start and end coordinates for all genes defined in the package, subset to genes encoded on sequences available in the FaFile and extract all of their sequences. Note: these sequences represent the sequence between the chromosomal start and end coordinates of the gene.

library(EnsDb.Hsapiens.v75)
library(Rsamtools)
edb <- EnsDb.Hsapiens.v75

## Get the FaFile with the genomic sequence matching the Ensembl version
## using the AnnotationHub package.
Dna <- getGenomeFaFile(edb)

## Get start/end coordinates of all genes.
genes <- genes(edb)
## Subset to all genes that are encoded on chromosomes for which
## we do have DNA sequence available.
genes <- genes[seqnames(genes) %in% seqnames(seqinfo(Dna))]

## Get the gene sequences, i.e. the sequence including the sequence of
## all of the gene's exons and introns.
geneSeqs <- getSeq(Dna, genes)

To retrieve the (exonic) sequence of transcripts (i.e. without introns) we can use directly the extractTranscriptSeqs method defined in the GenomicFeatures on the EnsDb object, eventually using a filter to restrict the query.

## get all exons of all transcripts encoded on chromosome Y
yTx <- exonsBy(edb, filter = SeqnameFilter("Y"))

## Retrieve the sequences for these transcripts from the FaFile.
library(GenomicFeatures)
yTxSeqs <- extractTranscriptSeqs(Dna, yTx)
yTxSeqs

## Extract the sequences of all transcripts encoded on chromosome Y.
yTx <- extractTranscriptSeqs(Dna, edb, filter = SeqnameFilter("Y"))

## Along these lines, we could use the method also to retrieve the coding sequence
## of all transcripts on the Y chromosome.
cdsY <- cdsBy(edb, filter = SeqnameFilter("Y"))
extractTranscriptSeqs(Dna, cdsY)

Note: in the next section we describe how transcript sequences can be retrieved from a BSgenome package that is based on UCSC, not Ensembl.

5 Integrating annotations from Ensembl based EnsDb packages with UCSC based annotations

Sometimes it might be useful to combine (Ensembl based) annotations from EnsDb packages/objects with annotations from other Bioconductor packages, that might base on UCSC annotations. To support such an integration of annotations, the ensembldb packages implements the seqlevelsStyle and seqlevelsStyle<- from the GenomeInfoDb package that allow to change the style of chromosome naming. Thus, sequence/chromosome names other than those used by Ensembl can be used in, and are returned by, the queries to EnsDb objects as long as a mapping for them is provided by the GenomeInfoDb package (which provides a mapping mostly between UCSC, NCBI and Ensembl chromosome names for the main chromosomes).

In the example below we change the seqnames style to UCSC.

## Change the seqlevels style form Ensembl (default) to UCSC:
seqlevelsStyle(edb) <- "UCSC"

## Now we can use UCSC style seqnames in SeqnameFilters or GRangesFilter:
genesY <- genes(edb, filter = SeqnameFilter("chrY"))
## The seqlevels of the returned GRanges are also in UCSC style
seqlevels(genesY)
## [1] "chrY"

Note that in most instances no mapping is available for sequences not corresponding to the main chromosomes (i.e. contigs, patched chromosomes etc). What is returned in cases in which no mapping is available can be specified with the global ensembldb.seqnameNotFound option. By default (with ensembldb.seqnameNotFound set to “ORIGINAL”), the original seqnames (i.e. the ones from Ensembl) are returned. With ensembldb.seqnameNotFound “MISSING” each time a seqname can not be found an error is thrown. For all other cases (e.g. ensembldb.seqnameNotFound = NA) the value of the option is returned.

seqlevelsStyle(edb) <- "UCSC"

## Getting the default option:
getOption("ensembldb.seqnameNotFound")
## [1] "ORIGINAL"
## Listing all seqlevels in the database.
seqlevels(edb)[1:30]
## Warning in .formatSeqnameByStyleFromQuery(x, sn, ifNotFound): More than 5
## seqnames with seqlevels style of the database (Ensembl) could not be mapped
## to the seqlevels style: UCSC!) Returning the orginal seqnames for these.
##  [1] "chr1"       "chr10"      "chr11"      "chr12"      "chr13"     
##  [6] "chr14"      "chr15"      "chr16"      "chr17"      "chr18"     
## [11] "chr19"      "chr2"       "chr20"      "chr21"      "chr22"     
## [16] "chr3"       "chr4"       "chr5"       "chr6"       "chr7"      
## [21] "chr8"       "chr9"       "GL000191.1" "GL000192.1" "GL000193.1"
## [26] "GL000194.1" "GL000195.1" "GL000196.1" "GL000199.1" "GL000201.1"
## Setting the option to NA, thus, for each seqname for which no mapping is available,
## NA is returned.
options(ensembldb.seqnameNotFound=NA)
seqlevels(edb)[1:30]
## Warning in .formatSeqnameByStyleFromQuery(x, sn, ifNotFound): More than 5
## seqnames with seqlevels style of the database (Ensembl) could not be mapped
## to the seqlevels style: UCSC!) Returning NA for these.
##  [1] "chr1"  "chr10" "chr11" "chr12" "chr13" "chr14" "chr15" "chr16"
##  [9] "chr17" "chr18" "chr19" "chr2"  "chr20" "chr21" "chr22" "chr3" 
## [17] "chr4"  "chr5"  "chr6"  "chr7"  "chr8"  "chr9"  NA      NA     
## [25] NA      NA      NA      NA      NA      NA
## Resetting the option.
options(ensembldb.seqnameNotFound = "ORIGINAL")

Next we retrieve transcript sequences from genes encoded on chromosome Y using the BSGenome package for the human genome from UCSC. The specified version hg19 matches the genome build of Ensembl version 75, i.e. GRCh37. Note that while we changed the style of the seqnames to UCSC we did not change the naming of the genome release.

library(BSgenome.Hsapiens.UCSC.hg19)
bsg <- BSgenome.Hsapiens.UCSC.hg19

## Get the genome version
unique(genome(bsg))
## [1] "hg19"
unique(genome(edb))
## [1] "GRCh37"
## Although differently named, both represent genome build GRCh37.

## Extract the full transcript sequences.
yTxSeqs <- extractTranscriptSeqs(bsg, exonsBy(edb, "tx", filter = SeqnameFilter("chrY")))

yTxSeqs
##   A DNAStringSet instance of length 731
##       width seq                                        names               
##   [1]  5239 GCCTAGTGCGCGCGCAGTAA...AAATGTTTACTTGTATATG ENST00000155093
##   [2]  4023 ATGTTTAGGGTTGGCTTCTT...GGAAACACATCCCTTGTAA ENST00000215473
##   [3]   802 AGAGGACCAAGCCTCCCTGT...TAAAATGTTTTAAAAATCA ENST00000215479
##   [4]   910 TGTCTGTCAGAGCTGTCAGC...ACACTGGTATATTTCTGTT ENST00000250776
##   [5]  1305 TTCCAGGATATGAACTCTAC...ATCCTGTGGCTGTAGGAAA ENST00000250784
##   ...   ... ...
## [727]   333 ATGGATGAAGAAGAGAAAAC...TGAACTTTCTAGATTGCAT ENST00000604924
## [728]  1247 CATGGCGGGGTTCCTGCCTT...TTTGGAGTAATGTCTTAGT ENST00000605584
## [729]   199 CAGTTCTCGCTCCTGTGCAG...GGTCTGGGTGGCTTCTGGA ENST00000605663
## [730]   276 GCCCCAGGAGGAAAGGGGGA...AATAAAGAACAGCGCATTC ENST00000606439
## [731]   444 ATGGGAGCCACTGGGCTTGG...CGTTCATGAAGAAGACTAA ENST00000607210
## Extract just the CDS
Test <- cdsBy(edb, "tx", filter = SeqnameFilter("chrY"))
yTxCds <- extractTranscriptSeqs(bsg, cdsBy(edb, "tx", filter = SeqnameFilter("chrY")))
yTxCds
##   A DNAStringSet instance of length 160
##       width seq                                        names               
##   [1]  2406 ATGGATGAAGATGAATTTGA...AGAAGTTGGTCTGCCCTAA ENST00000155093
##   [2]  4023 ATGTTTAGGGTTGGCTTCTT...GGAAACACATCCCTTGTAA ENST00000215473
##   [3]   579 ATGGGGACCTGGATTTTGTT...GCAGGAGGAAGTGGATTAA ENST00000215479
##   [4]   792 ATGGCCCGGGGCCCCAAGAA...CAAACAGAGCAGTGGCTAA ENST00000250784
##   [5]   378 ATGAGTCCAAAGCCGAGAGC...TACTCCCCTATCTCCCTGA ENST00000250823
##   ...   ... ...
## [156]    63 CGCAAGGATTTAAAAGAGAT...ACCCTGTTGGCCAGGCTAG ENST00000601700
## [157]    42 CTTGATACAAAGAATCAATTTAATTTTAAGATTGTCTATCTT ENST00000601705
## [158]    33 ATGATGACGCTTGTCCCCAGAGCCAGGACACGT          ENST00000602680
## [159]    33 ATGATGACGCTTGTCCCCAGAGCCAGGACACGT          ENST00000602732
## [160]    33 ATGATGACGCTTGTCCCCAGAGCCAGGACACGT          ENST00000602770

At last changing the seqname style to the default value =“Ensembl”=.

seqlevelsStyle(edb) <- "Ensembl"

6 Interactive annotation lookup using the shiny web app

In addition to the genes, transcripts and exons methods it is possibly to search interactively for gene/transcript/exon annotations using the internal, shiny based, web application. The application can be started with the runEnsDbApp() function. The search results from this app can also be returned to the R workspace either as a data.frame or GRanges object.

7 Plotting gene/transcript features using ensembldb and Gviz

The Gviz package provides functions to plot genes and transcripts along with other data on a genomic scale. Gene models can be provided either as a data.frame, GRanges, TxDB database, can be fetched from biomart and can also be retrieved from ensembldb.

Below we generate a GeneRegionTrack fetching all transcripts from a certain region on chromosome Y.

Note that if we want in addition to work also with BAM files that were aligned against DNA sequences retrieved from Ensembl or FASTA files representing genomic DNA sequences from Ensembl we should change the ucscChromosomeNames option from Gviz to FALSE (i.e. by calling options(ucscChromosomeNames = FALSE)). This is not necessary if we just want to retrieve gene models from an EnsDb object, as the ensembldb package internally checks the ucscChromosomeNames option and, depending on that, maps Ensembl chromosome names to UCSC chromosome names.

## Loading the Gviz library
library(Gviz)
library(EnsDb.Hsapiens.v75)
edb <- EnsDb.Hsapiens.v75

## Retrieving a Gviz compatible GRanges object with all genes
## encoded on chromosome Y.
gr <- getGeneRegionTrackForGviz(edb, chromosome = "Y",
                start = 20400000, end = 21400000)
## Define a genome axis track
gat <- GenomeAxisTrack()

## We have to change the ucscChromosomeNames option to FALSE to enable Gviz usage
## with non-UCSC chromosome names.
options(ucscChromosomeNames = FALSE)

plotTracks(list(gat, GeneRegionTrack(gr)))

options(ucscChromosomeNames = TRUE)

Above we had to change the option ucscChromosomeNames to FALSE in order to use it with non-UCSC chromosome names. Alternatively, we could however also change the seqnamesStyle of the EnsDb object to UCSC. Note that we have to use now also chromosome names in the UCSC style in the SeqnameFilter (i.e. “chrY” instead of Y).

seqlevelsStyle(edb) <- "UCSC"
## Retrieving the GRanges objects with seqnames corresponding to UCSC chromosome names.
gr <- getGeneRegionTrackForGviz(edb, chromosome = "chrY",
                start = 20400000, end = 21400000)
seqnames(gr)
## factor-Rle of length 218 with 1 run
##   Lengths:  218
##   Values : chrY
## Levels(1): chrY
## Define a genome axis track
gat <- GenomeAxisTrack()
plotTracks(list(gat, GeneRegionTrack(gr)))

We can also use the filters from the ensembldb package to further refine what transcripts are fetched, like in the example below, in which we create two different gene region tracks, one for protein coding genes and one for lincRNAs.

protCod <- getGeneRegionTrackForGviz(edb, chromosome = "chrY",
                     start = 20400000, end = 21400000,
                     filter = GenebiotypeFilter("protein_coding"))
lincs <- getGeneRegionTrackForGviz(edb, chromosome = "chrY",
                   start = 20400000, end = 21400000,
                   filter = GenebiotypeFilter("lincRNA"))

plotTracks(list(gat, GeneRegionTrack(protCod, name = "protein coding"),
        GeneRegionTrack(lincs, name = "lincRNAs")), transcriptAnnotation = "symbol")

## At last we change the seqlevels style again to Ensembl
seqlevelsStyle <- "Ensembl"

8 Using EnsDb objects in the AnnotationDbi framework

Most of the methods defined for objects extending the basic annotation package class AnnotationDbi are also defined for EnsDb objects (i.e. methods columns, keytypes, keys, mapIds and select). While these methods can be used analogously to basic annotation packages, the implementation for EnsDb objects also support the filtering framework of the ensembldb package.

In the example below we first evaluate all the available columns and keytypes in the database and extract then the gene names for all genes encoded on chromosome X.

library(EnsDb.Hsapiens.v75)
edb <- EnsDb.Hsapiens.v75

## List all available columns in the database.
columns(edb)
##  [1] "ENTREZID"       "EXONID"         "EXONIDX"        "EXONSEQEND"    
##  [5] "EXONSEQSTART"   "GENEBIOTYPE"    "GENEID"         "GENENAME"      
##  [9] "GENESEQEND"     "GENESEQSTART"   "ISCIRCULAR"     "SEQCOORDSYSTEM"
## [13] "SEQLENGTH"      "SEQNAME"        "SEQSTRAND"      "SYMBOL"        
## [17] "TXBIOTYPE"      "TXCDSSEQEND"    "TXCDSSEQSTART"  "TXID"          
## [21] "TXNAME"         "TXSEQEND"       "TXSEQSTART"
## Note that these do *not* correspond to the actual column names
## of the database that can be passed to methods like exons, genes,
## transcripts etc. These column names can be listed with the listColumns
## method.
listColumns(edb)
##  [1] "seq_name"         "seq_length"       "is_circular"     
##  [4] "exon_id"          "exon_seq_start"   "exon_seq_end"    
##  [7] "gene_id"          "gene_name"        "entrezid"        
## [10] "gene_biotype"     "gene_seq_start"   "gene_seq_end"    
## [13] "seq_name"         "seq_strand"       "seq_coord_system"
## [16] "symbol"           "name"             "value"           
## [19] "tx_id"            "tx_biotype"       "tx_seq_start"    
## [22] "tx_seq_end"       "tx_cds_seq_start" "tx_cds_seq_end"  
## [25] "gene_id"          "tx_name"          "tx_id"           
## [28] "exon_id"          "exon_idx"
## List all of the supported key types.
keytypes(edb)
##  [1] "ENTREZID"    "EXONID"      "GENEBIOTYPE" "GENEID"      "GENENAME"   
##  [6] "SEQNAME"     "SEQSTRAND"   "SYMBOL"      "TXBIOTYPE"   "TXID"       
## [11] "TXNAME"
## Get all gene ids from the database.
gids <- keys(edb, keytype = "GENEID")
length(gids)
## [1] 64102
## Get all gene names for genes encoded on chromosome Y.
gnames <- keys(edb, keytype = "GENENAME", filter = SeqnameFilter("Y"))
head(gnames)
## [1] "KDM5D"   "DDX3Y"   "ZFY"     "TBL1Y"   "PCDH11Y" "AMELY"

In the next example we retrieve specific information from the database using the select method. First we fetch all transcripts for the genes BCL2 and BCL2L11. In the first call we provide the gene names, while in the second call we employ the filtering system to perform a more fine-grained query to fetch only the protein coding transcripts for these genes.

## Use the /standard/ way to fetch data.
select(edb, keys = c("BCL2", "BCL2L11"), keytype = "GENENAME",
       columns = c("GENEID", "GENENAME", "TXID", "TXBIOTYPE"))
##             GENEID GENENAME            TXID               TXBIOTYPE
## 1  ENSG00000171791     BCL2 ENST00000398117          protein_coding
## 2  ENSG00000171791     BCL2 ENST00000333681          protein_coding
## 3  ENSG00000171791     BCL2 ENST00000590515    processed_transcript
## 4  ENSG00000171791     BCL2 ENST00000589955          protein_coding
## 5  ENSG00000171791     BCL2 ENST00000444484          protein_coding
## 6  ENSG00000153094  BCL2L11 ENST00000432179          protein_coding
## 7  ENSG00000153094  BCL2L11 ENST00000308659          protein_coding
## 8  ENSG00000153094  BCL2L11 ENST00000393256          protein_coding
## 9  ENSG00000153094  BCL2L11 ENST00000393252          protein_coding
## 10 ENSG00000153094  BCL2L11 ENST00000433098 nonsense_mediated_decay
## 11 ENSG00000153094  BCL2L11 ENST00000405953          protein_coding
## 12 ENSG00000153094  BCL2L11 ENST00000415458 nonsense_mediated_decay
## 13 ENSG00000153094  BCL2L11 ENST00000436733 nonsense_mediated_decay
## 14 ENSG00000153094  BCL2L11 ENST00000437029 nonsense_mediated_decay
## 15 ENSG00000153094  BCL2L11 ENST00000452231 nonsense_mediated_decay
## 16 ENSG00000153094  BCL2L11 ENST00000361493 nonsense_mediated_decay
## 17 ENSG00000153094  BCL2L11 ENST00000431217 nonsense_mediated_decay
## 18 ENSG00000153094  BCL2L11 ENST00000439718 nonsense_mediated_decay
## 19 ENSG00000153094  BCL2L11 ENST00000438054          protein_coding
## 20 ENSG00000153094  BCL2L11 ENST00000357757          protein_coding
## 21 ENSG00000153094  BCL2L11 ENST00000393253          protein_coding
## 22 ENSG00000153094  BCL2L11 ENST00000337565          protein_coding
## Use the filtering system of ensembldb
select(edb, keys = list(GenenameFilter(c("BCL2", "BCL2L11")),
            TxbiotypeFilter("protein_coding")),
       columns = c("GENEID", "GENENAME", "TXID", "TXBIOTYPE"))
## Note: ordering of the results might not match ordering of keys!
##             GENEID GENENAME            TXID      TXBIOTYPE
## 1  ENSG00000171791     BCL2 ENST00000398117 protein_coding
## 2  ENSG00000171791     BCL2 ENST00000333681 protein_coding
## 3  ENSG00000171791     BCL2 ENST00000589955 protein_coding
## 4  ENSG00000171791     BCL2 ENST00000444484 protein_coding
## 5  ENSG00000153094  BCL2L11 ENST00000432179 protein_coding
## 6  ENSG00000153094  BCL2L11 ENST00000308659 protein_coding
## 7  ENSG00000153094  BCL2L11 ENST00000393256 protein_coding
## 8  ENSG00000153094  BCL2L11 ENST00000393252 protein_coding
## 9  ENSG00000153094  BCL2L11 ENST00000405953 protein_coding
## 10 ENSG00000153094  BCL2L11 ENST00000438054 protein_coding
## 11 ENSG00000153094  BCL2L11 ENST00000357757 protein_coding
## 12 ENSG00000153094  BCL2L11 ENST00000393253 protein_coding
## 13 ENSG00000153094  BCL2L11 ENST00000337565 protein_coding

Finally, we use the mapIds method to establish a mapping between ids and values. In the example below we fetch transcript ids for the two genes from the example above.

## Use the default method, which just returns the first value for multi mappings.
mapIds(edb, keys = c("BCL2", "BCL2L11"), column = "TXID", keytype = "GENENAME")
##              BCL2           BCL2L11 
## "ENST00000398117" "ENST00000432179"
## Alternatively, specify multiVals="list" to return all mappings.
mapIds(edb, keys = c("BCL2", "BCL2L11"), column = "TXID", keytype = "GENENAME",
       multiVals = "list")
## $BCL2
## [1] "ENST00000398117" "ENST00000333681" "ENST00000590515" "ENST00000589955"
## [5] "ENST00000444484"
## 
## $BCL2L11
##  [1] "ENST00000432179" "ENST00000308659" "ENST00000393256"
##  [4] "ENST00000393252" "ENST00000433098" "ENST00000405953"
##  [7] "ENST00000415458" "ENST00000436733" "ENST00000437029"
## [10] "ENST00000452231" "ENST00000361493" "ENST00000431217"
## [13] "ENST00000439718" "ENST00000438054" "ENST00000357757"
## [16] "ENST00000393253" "ENST00000337565"
## And, just like before, we can use filters to map only to protein coding transcripts.
mapIds(edb, keys = list(GenenameFilter(c("BCL2", "BCL2L11")),
            TxbiotypeFilter("protein_coding")), column = "TXID",
       multiVals = "list")
## Warning in .mapIds(x = x, keys = keys, column = column, keytype =
## keytype, : Got 2 filter objects. Will use the keys of the first for the
## mapping!
## Note: ordering of the results might not match ordering of keys!
## $BCL2
## [1] "ENST00000398117" "ENST00000333681" "ENST00000589955" "ENST00000444484"
## 
## $BCL2L11
## [1] "ENST00000432179" "ENST00000308659" "ENST00000393256" "ENST00000393252"
## [5] "ENST00000405953" "ENST00000438054" "ENST00000357757" "ENST00000393253"
## [9] "ENST00000337565"

Note that, if the filters are used, the ordering of the result does no longer match the ordering of the genes.

9 Important notes

These notes might explain eventually unexpected results (and, more importantly, help avoiding them):

  • The ordering of the results returned by the genes, exons, transcripts methods can be specified with the order.by parameter. The ordering of the results does however not correspond to the ordering of values in submitted filter objects. The exception is the select method. If a character vector of values or a single filter is passed with argument keys the ordering of results of this method matches the ordering of the key values or the values of the filter.

  • Results of exonsBy, transcriptsBy are always ordered by the by argument.

  • The CDS provided by EnsDb objects always includes both, the start and the stop codon.

  • Transcripts with multiple CDS are at present not supported by EnsDb.

  • At present, EnsDb support only genes/transcripts for which all of their exons are encoded on the same chromosome and the same strand.

10 Building an transcript-centric database package based on Ensembl annotation

The code in this section is not supposed to be automatically executed when the vignette is built, as this would require a working installation of the Ensembl Perl API, which is not expected to be available on each system. Also, building EnsDb from alternative sources, like GFF or GTF files takes some time and thus also these examples are not directly executed when the vignette is build.

10.1 Requirements

The fetchTablesFromEnsembl function of the package uses the Ensembl Perl API to retrieve the required annotations from an Ensembl database (e.g. from the main site ensembldb.ensembl.org). Thus, to use the functionality to built databases, the Ensembl Perl API needs to be installed (see 5 for details).

Alternatively, the ensDbFromAH, ensDbFromGff, ensDbFromGRanges and ensDbFromGtf functions allow to build EnsDb SQLite files from a GRanges object or GFF/GTF files from Ensembl (either provided as files or via AnnotationHub). These functions do not depend on the Ensembl Perl API, but require a working internet connection to fetch the chromosome lengths from Ensembl as these are not provided within GTF or GFF files.

10.2 Building annotation packages

The functions below use the Ensembl Perl API to fetch the required data directly from the Ensembl core databases. Thus, the path to the Perl API specific for the desired Ensembl version needs to be added to the PERL5LIB environment variable.

An annotation package containing all human genes for Ensembl version 75 can be created using the code in the block below.

library(ensembldb)

## get all human gene/transcript/exon annotations from Ensembl (75)
## the resulting tables will be stored by default to the current working
## directory
fetchTablesFromEnsembl(75, species = "human")

## These tables can then be processed to generate a SQLite database
## containing the annotations (again, the function assumes the required
## txt files to be present in the current working directory)
DBFile <- makeEnsemblSQLiteFromTables()

## and finally we can generate the package
makeEnsembldbPackage(ensdb = DBFile, version = "0.99.12",
             maintainer = "Johannes Rainer <johannes.rainer@eurac.edu>",
             author = "J Rainer")

The generated package can then be build using R CMD build EnsDb.Hsapiens.v75 and installed with R CMD INSTALL EnsDb.Hsapiens.v75*. Note that we could directly generate an EnsDb instance by loading the database file, i.e. by calling edb <- EnsDb(DBFile) and work with that annotation object.

To fetch and build annotation packages for plant genomes (e.g. arabidopsis thaliana), the Ensembl genomes should be specified as a host, i.e. setting host to “mysql-eg-publicsql.ebi.ac.uk”, port to 4157 and species to e.g. “arabidopsis thaliana”.

In the next example we create an EnsDb database using the AnnotationHub package and load also the corresponding genomic DNA sequence matching the Ensembl version. We thus first query the AnnotationHub package for all resources available for Mus musculus and the Ensembl release 77. Next we create the EnsDb object from the appropriate AnnotationHub resource. We then use the getGenomeFaFile method on the EnsDb to directly look up and retrieve the correct or best matching FaFile with the genomic DNA sequence. At last we retrieve the sequences of all exons using the getSeq method.

## Load the AnnotationHub data.
library(AnnotationHub)
ah <- AnnotationHub()

## Query all available files for Ensembl release 77 for
## Mus musculus.
query(ah, c("Mus musculus", "release-77"))

## Get the resource for the gtf file with the gene/transcript definitions.
Gtf <- ah["AH28822"]
## Create a EnsDb database file from this.
DbFile <- ensDbFromAH(Gtf)
## We can either generate a database package, or directly load the data
edb <- EnsDb(DbFile)


## Identify and get the FaFile object with the genomic DNA sequence matching
## the EnsDb annotation.
Dna <- getGenomeFaFile(edb)
library(Rsamtools)
## We next retrieve the sequence of all exons on chromosome Y.
exons <- exons(edb, filter = SeqnameFilter("Y"))
exonSeq <- getSeq(Dna, exons)

## Alternatively, look up and retrieve the toplevel DNA sequence manually.
Dna <- ah[["AH22042"]]

In the example below we load a GRanges containing gene definitions for genes encoded on chromosome Y and generate a EnsDb SQLite database from that information.

## Generate a sqlite database from a GRanges object specifying
## genes encoded on chromosome Y
load(system.file("YGRanges.RData", package = "ensembldb"))
Y
## GRanges object with 7155 ranges and 16 metadata columns:
##          seqnames               ranges strand |               source
##             <Rle>            <IRanges>  <Rle> |             <factor>
##      [1]        Y   [2652790, 2652894]      + |                snRNA
##      [2]        Y   [2652790, 2652894]      + |                snRNA
##      [3]        Y   [2652790, 2652894]      + |                snRNA
##      [4]        Y   [2654896, 2655740]      - |       protein_coding
##      [5]        Y   [2654896, 2655740]      - |       protein_coding
##      ...      ...                  ...    ... .                  ...
##   [7151]        Y [28772667, 28773306]      - | processed_pseudogene
##   [7152]        Y [28772667, 28773306]      - | processed_pseudogene
##   [7153]        Y [59001391, 59001635]      + |           pseudogene
##   [7154]        Y [59001391, 59001635]      + | processed_pseudogene
##   [7155]        Y [59001391, 59001635]      + | processed_pseudogene
##                type     score     phase         gene_id   gene_name
##            <factor> <numeric> <integer>     <character> <character>
##      [1]       gene      <NA>      <NA> ENSG00000251841  RNU6-1334P
##      [2] transcript      <NA>      <NA> ENSG00000251841  RNU6-1334P
##      [3]       exon      <NA>      <NA> ENSG00000251841  RNU6-1334P
##      [4]       gene      <NA>      <NA> ENSG00000184895         SRY
##      [5] transcript      <NA>      <NA> ENSG00000184895         SRY
##      ...        ...       ...       ...             ...         ...
##   [7151] transcript      <NA>      <NA> ENSG00000231514     FAM58CP
##   [7152]       exon      <NA>      <NA> ENSG00000231514     FAM58CP
##   [7153]       gene      <NA>      <NA> ENSG00000235857     CTBP2P1
##   [7154] transcript      <NA>      <NA> ENSG00000235857     CTBP2P1
##   [7155]       exon      <NA>      <NA> ENSG00000235857     CTBP2P1
##             gene_source   gene_biotype   transcript_id transcript_name
##             <character>    <character>     <character>     <character>
##      [1]        ensembl          snRNA            <NA>            <NA>
##      [2]        ensembl          snRNA ENST00000516032  RNU6-1334P-201
##      [3]        ensembl          snRNA ENST00000516032  RNU6-1334P-201
##      [4] ensembl_havana protein_coding            <NA>            <NA>
##      [5] ensembl_havana protein_coding ENST00000383070         SRY-001
##      ...            ...            ...             ...             ...
##   [7151]         havana     pseudogene ENST00000435741     FAM58CP-001
##   [7152]         havana     pseudogene ENST00000435741     FAM58CP-001
##   [7153]         havana     pseudogene            <NA>            <NA>
##   [7154]         havana     pseudogene ENST00000431853     CTBP2P1-001
##   [7155]         havana     pseudogene ENST00000431853     CTBP2P1-001
##          transcript_source exon_number         exon_id         tag
##                <character>   <numeric>     <character> <character>
##      [1]              <NA>        <NA>            <NA>        <NA>
##      [2]           ensembl        <NA>            <NA>        <NA>
##      [3]           ensembl           1 ENSE00002088309        <NA>
##      [4]              <NA>        <NA>            <NA>        <NA>
##      [5]    ensembl_havana        <NA>            <NA>        CCDS
##      ...               ...         ...             ...         ...
##   [7151]            havana        <NA>            <NA>        <NA>
##   [7152]            havana           1 ENSE00001616687        <NA>
##   [7153]              <NA>        <NA>            <NA>        <NA>
##   [7154]            havana        <NA>            <NA>        <NA>
##   [7155]            havana           1 ENSE00001794473        <NA>
##              ccds_id  protein_id
##          <character> <character>
##      [1]        <NA>        <NA>
##      [2]        <NA>        <NA>
##      [3]        <NA>        <NA>
##      [4]        <NA>        <NA>
##      [5]   CCDS14772        <NA>
##      ...         ...         ...
##   [7151]        <NA>        <NA>
##   [7152]        <NA>        <NA>
##   [7153]        <NA>        <NA>
##   [7154]        <NA>        <NA>
##   [7155]        <NA>        <NA>
##   -------
##   seqinfo: 1 sequence from GRCh37 genome
DB <- ensDbFromGRanges(Y, path = tempdir(), version = 75,
               organism = "Homo_sapiens")
## Warning in ensDbFromGRanges(Y, path = tempdir(), version = 75, organism
## = "Homo_sapiens"): I'm missing column(s): 'entrezid'. The corresponding
## database column(s) will be empty!
edb <- EnsDb(DB)
edb
## EnsDb for Ensembl:
## |Backend: SQLite
## |Db type: EnsDb
## |Type of Gene ID: Ensembl Gene ID
## |Supporting package: ensembldb
## |Db created by: ensembldb package from Bioconductor
## |script_version: 0.0.1
## |Creation time: Wed Nov 16 19:52:30 2016
## |ensembl_version: 75
## |ensembl_host: unknown
## |Organism: Homo_sapiens
## |genome_build: GRCh37
## |DBSCHEMAVERSION: 1.0
## |source_file: GRanges object
## | No. of genes: 495.
## | No. of transcripts: 731.
## As shown in the example below, we could make an EnsDb package on
## this DB object using the makeEnsembldbPackage function.

Alternatively we can build the annotation database using the ensDbFromGtf ensDbFromGff functions, that extracts most of the required data from a GTF respectively GFF (version 3) file which can be downloaded from Ensembl (e.g. from ftp://ftp.ensembl.org/pub/release-75/gtf/homo_sapiens for human gene definitions from Ensembl version 75; for plant genomes etc files can be retrieved from ftp://ftp.ensemblgenomes.org). All information except the chromosome lengths and the NCBI Entrezgene IDs can be extracted from these GTF files. The function also tries to retrieve chromosome length information automatically from Ensembl.

Below we create the annotation from a gtf file that we fetch directly from Ensembl.

library(ensembldb)

## the GTF file can be downloaded from
## ftp://ftp.ensembl.org/pub/release-75/gtf/homo_sapiens/
gtffile <- "Homo_sapiens.GRCh37.75.gtf.gz"
## generate the SQLite database file
DB <- ensDbFromGtf(gtf = gtffile)

## load the DB file directly
EDB <- EnsDb(DB)

## alternatively, build the annotation package
## and finally we can generate the package
makeEnsembldbPackage(ensdb = DB, version = "0.99.12",
             maintainer = "Johannes Rainer <johannes.rainer@eurac.edu>",
             author = "J Rainer")

11 Database layout

The database consists of the following tables and attributes (the layout is also shown in Figure 115):

  • gene: all gene specific annotations.
    • gene_id: the Ensembl ID of the gene.
    • gene_name: the name (symbol) of the gene.
    • entrezid: the NCBI Entrezgene ID(s) of the gene. Note that this can be a ; separated list of IDs for genes that are mapped to more than one Entrezgene.
    • gene_biotype: the biotype of the gene.
    • gene_seq_start: the start coordinate of the gene on the sequence (usually a chromosome).
    • gene_seq_end: the end coordinate of the gene on the sequence.
    • seq_name: the name of the sequence (usually the chromosome name).
    • seq_strand: the strand on which the gene is encoded.
    • seq_coord_system: the coordinate system of the sequence.
  • tx: all transcript related annotations. Note that while no tx_name column is available in this database column, all methods to retrieve data from the database support also this column. The returned values are however the ID of the transcripts.
    • tx_id: the Ensembl transcript ID.
    • tx_biotype: the biotype of the transcript.
    • tx_seq_start: the start coordinate of the transcript.
    • tx_seq_end: the end coordinate of the transcript.
    • tx_cds_seq_start: the start coordinate of the coding region of the transcript (NULL for non-coding transcripts).
    • tx_cds_seq_end: the end coordinate of the coding region of the transcript.
    • gene_id: the gene to which the transcript belongs.
  • exon: all exon related annotation.
    • exon_id: the Ensembl exon ID.
    • exon_seq_start: the start coordinate of the exon.
    • exon_seq_end: the end coordinate of the exon.
  • tx2exon: provides the n:m mapping between transcripts and exons.
    • tx_id: the Ensembl transcript ID.
    • exon_id: the Ensembl exon ID.
    • exon_idx: the index of the exon in the corresponding transcript, always from 5’ to 3’ of the transcript.
  • chromosome: provides some information about the chromosomes.
    • seq_name: the name of the sequence/chromosome.
    • seq_length: the length of the sequence.
    • is_circular: whether the sequence in circular.
  • information: some additional, internal, informations (Genome build, Ensembl version etc).
    • key
    • value
  • virtual columns:
    • symbol: the database does not have such a database column, but it is still possible to use it in the columns parameter. This column is symlinked to the gene_name column.
    • tx_name: similar to the symbol column, this column is symlinked to the tx_id column.
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ensembldb/inst/perl/0000755000175400017540000000000013001231247015443 5ustar00biocbuildbiocbuildensembldb/inst/perl/get_gene_transcript_exon_tables.pl0000644000175400017540000002366613001231247024426 0ustar00biocbuildbiocbuild#!/usr/bin/perl ##################################### ## version 0.0.2: * get also gene_seq_start, gene_seq_end, tx_seq_start and tx_seq_end from the database! ## * did rename chrom_start to seq_start. ## uses environment variable ENS pointing to the ## ENSEMBL API on the computer use lib $ENV{ENS} || $ENV{PERL5LIB}; use IO::File; use Getopt::Std; use strict; use warnings; use Bio::EnsEMBL::ApiVersion; use Bio::EnsEMBL::Registry; ## unification function for arrays use List::MoreUtils qw/ uniq /; my $script_version = "0.1.3"; ## connecting to the ENSEMBL data base use Bio::EnsEMBL::Registry; use Bio::EnsEMBL::ApiVersion; my $user = "anonymous"; my $host = "ensembldb.ensembl.org"; my $port = 5306; my $pass = ""; my $registry = 'Bio::EnsEMBL::Registry'; my $ensembl_version="none"; my $ensembl_database="core"; my $species = "human"; my $slice; my $coord_system_version="unknown"; ## get all gene ids defined in the database... my @gene_ids = (); my %option=(); getopts("e:hH:P:p:U:s:",\%option); if($option{ h }){ ## print help and exit. print("\nget_gene_transcript_exon_tables version ".$script_version.".\n"); print("Retrieves gene/transcript/exon annotations from Ensembl and stores it as tabulator delimited text files.\n\n"); print("usage: perl get_gene_transcript_exon_tables -e:hH:P:U:s:\n"); print("-e (required): the Ensembl version (e.g. -e 75). The function will internally check if the submitted version matches the Ensembl API version and database queried.\n"); print("-H (optional): the hostname of the Ensembl database; defaults to ensembldb.ensembl.org.\n"); print("-h (optional): print this help message.\n"); print("-p (optional): the port to access the Ensembl database.\n"); print("-P (optional): the password to access the Ensembl database.\n"); print("-U (optional): the username to access the Ensembl database.\n"); print("-s (optional): the species; defaults to human.\n"); print("\n\nThe script will generate the following tables:\n"); print("- ens_gene.txt: contains all genes defined in Ensembl.\n"); print("- ens_transcript.txt: contains all transcripts of all genes.\n"); print("- ens_exon.txt: contains all (unique) exons, along with their genomic alignment.\n"); print("- ens_tx2exon.txt: relates transcript ids to exon ids (m:n), along with the index of the exon in the respective transcript (since the same exon can be part of different transcripts and have a different index in each transcript).\n"); print("- ens_chromosome.txt: the information of all chromosomes (chromosome/sequence/contig names). \n"); print("- ens_metadata.txt\n"); exit 0; } if(defined($option{ s })){ $species=$option{ s }; } if(defined($option{ U })){ $user=$option{ U }; } if(defined($option{ H })){ $host=$option{ H }; } if(defined($option{ P })){ $pass=$option{ P }; } if(defined($option{ p })){ $port=$option{ p }; } if(defined($option{ e })){ $ensembl_version=$option{ e }; }else{ die("The ensembl version has to be specified with the -e parameter (e.g. -e 75)"); } my $api_version="".software_version().""; if($ensembl_version ne $api_version){ die "The submitted Ensembl version (".$ensembl_version.") does not match the version of the Ensembl API (".$api_version."). Please configure the environment variable ENS to point to the correct API."; } print "Connecting to ".$host." at port ".$port."\n"; # $registry->load_registry_from_db(-host => $host, -user => $user, # -pass => $pass, -port => $port, # -verbose => "1"); $registry->load_registry_from_db(-host => $host, -user => $user, -pass => $pass, -port => $port); my $gene_adaptor = $registry->get_adaptor($species, $ensembl_database, "gene"); my $slice_adaptor = $registry->get_adaptor($species, $ensembl_database, "slice"); ## determine the species: my $species_id = $gene_adaptor->db->species_id; my $species_ens = $gene_adaptor->db->species; my $infostring = "# get_gene_transcript_exon_tables.pl version $script_version:\nRetrieve gene models for Ensembl version $ensembl_version, species $species from Ensembl database at host: $host\n"; print $infostring; ## preparing output files: open(GENE , ">ens_gene.txt"); print GENE "gene_id\tgene_name\tentrezid\tgene_biotype\tgene_seq_start\tgene_seq_end\tseq_name\tseq_strand\tseq_coord_system\n"; open(TRANSCRIPT , ">ens_tx.txt"); print TRANSCRIPT "tx_id\ttx_biotype\ttx_seq_start\ttx_seq_end\ttx_cds_seq_start\ttx_cds_seq_end\tgene_id\n"; open(EXON , ">ens_exon.txt"); print EXON "exon_id\texon_seq_start\texon_seq_end\n"; # open(G2T , ">ens_gene2transcript.txt"); # print G2T "g2t_gene_id\tg2t_tx_id\n"; open(T2E , ">ens_tx2exon.txt"); print T2E "tx_id\texon_id\texon_idx\n"; open(CHR , ">ens_chromosome.txt"); print CHR "seq_name\tseq_length\tis_circular\n"; ##OK now running the stuff: print "Start fetching data\n"; my %done_chromosomes=(); my %done_exons=(); ## to keep track of which exons have already been saved. my $counta = 0; @gene_ids = @{$gene_adaptor->list_stable_ids()}; foreach my $gene_id (@gene_ids){ $counta++; if(($counta % 2000) == 0){ print "processed $counta genes\n"; } my $orig_gene; $orig_gene = $gene_adaptor->fetch_by_stable_id($gene_id); if(defined $orig_gene){ my $do_transform=1; my $gene = $orig_gene->transform("chromosome"); if(!defined $gene){ ## gene is not on known defined chromosomes! $gene = $orig_gene; $do_transform=0; } my $coord_system = $gene->coord_system_name; my $chrom = $gene->slice->seq_region_name; my $strand = $gene->strand; ## check if we did already fetch some info for that chromosome if(exists($done_chromosomes{ $chrom })){ ## don't do anything... }else{ $done_chromosomes{ $chrom } = "done"; my $chr_slice = $gene->slice->seq_region_Slice(); my $name = $chr_slice->seq_region_name; my $length = $chr_slice->length; my $is_circular = $chr_slice->is_circular; print CHR "$name\t$length\t$is_circular\n"; my $chr_slice_again = $slice_adaptor->fetch_by_region('chromosome', $chrom); if(defined($chr_slice_again)){ $coord_system_version = $chr_slice_again->coord_system()->version(); } # if(defined $chr_slice){ # my $name = $chr_slice->seq_region_name; # my $length = $chr_slice->length; # my $is_circular = $chr_slice->is_circular; # $coord_system_version = $chr_slice->coord_system()->version(); # print CHR "$name\t$length\t$is_circular\n"; # }else{ # my $length = $gene->slice->seq_region_length(); # print CHR "$chrom\t0\t0\n"; # } } ## get information for the gene. my $gene_external_name= $gene->external_name; if(!defined($gene_external_name)){ $gene_external_name=""; } my $gene_biotype = $gene->biotype; my $gene_seq_start = $gene->start; my $gene_seq_end = $gene->end; ## get entrezgene ID, if any... my $all_entries = $gene->get_all_DBLinks("EntrezGene"); my %entrezgene_hash=(); foreach my $dbe (@{$all_entries}){ $entrezgene_hash{ $dbe->primary_id } = 1; } my $hash_size = keys %entrezgene_hash; my $entrezid = ""; if($hash_size > 0){ $entrezid = join(";", keys %entrezgene_hash); } print GENE "$gene_id\t$gene_external_name\t$entrezid\t$gene_biotype\t$gene_seq_start\t$gene_seq_end\t$chrom\t$strand\t$coord_system\n"; ## process transcript(s) my @transcripts = @{ $gene->get_all_Transcripts }; ## ok looping through the transcripts foreach my $transcript (@transcripts){ if($do_transform==1){ ## just to be shure that we have the transcript in chromosomal coordinations. $transcript = $transcript->transform("chromosome"); } ##my $tx_start = $transcript->start; ##my $tx_end = $transcript->end; ## caution!!! will get undef if transcript is non-coding! my $tx_cds_start = $transcript->coding_region_start; if(!defined($tx_cds_start)){ $tx_cds_start = "NULL"; } my $tx_cds_end = $transcript->coding_region_end; if(!defined($tx_cds_end)){ $tx_cds_end = "NULL"; } my $tx_id = $transcript->stable_id; my $tx_biotype = $transcript->biotype; my $tx_seq_start = $transcript->start; my $tx_seq_end = $transcript->end; ## write info. print TRANSCRIPT "$tx_id\t$tx_biotype\t$tx_seq_start\t$tx_seq_end\t$tx_cds_start\t$tx_cds_end\t$gene_id\n"; ## print G2T "$gene_id\t$tx_id\n"; ## process exon(s) ##my @exons = @{ $transcript->get_all_Exons(-constitutive => 1) }; my @exons = @{ $transcript->get_all_Exons() }; ## exons always returned 5' 3' of transcript! my $current_exon_idx = 1; foreach my $exon (@exons){ if($do_transform==1){ $exon->transform("chromosome"); } my $exon_start = $exon->start; my $exon_end = $exon->end; my $exon_id = $exon->stable_id; ## write info, but only if we didn't already saved this exon (exon can be ## part of more than one transcript). if(exists($done_exons{ $exon_id })){ ## don't do anything. }else{ $done_exons{ $exon_id } = 1; print EXON "$exon_id\t$exon_start\t$exon_end\n"; } ## saving the exon id to this file that provides the n:m mappint; also saving ## the index of the exon in the present transcript to that. print T2E "$tx_id\t$exon_id\t$current_exon_idx\n"; $current_exon_idx++; } } } } ## want to save: ## data, ensembl host, species, ensembl version, genome build? open(INFO , ">ens_metadata.txt"); print INFO "name\tvalue\n"; print INFO "Db type\tEnsDb\n"; print INFO "Type of Gene ID\tEnsembl Gene ID\n"; print INFO "Supporting package\tensembldb\n"; print INFO "Db created by\tensembldb package from Bioconductor\n"; print INFO "script_version\t$script_version\n"; print INFO "Creation time\t".localtime()."\n"; print INFO "ensembl_version\t$ensembl_version\n"; print INFO "ensembl_host\t$host\n"; print INFO "Organism\t$species_ens\n"; print INFO "genome_build\t$coord_system_version\n"; print INFO "DBSCHEMAVERSION\t1.0\n"; close(INFO); close(GENE); close(TRANSCRIPT); close(EXON); ##close(G2T); close(T2E); close(CHR); ensembldb/inst/pkg-template/0000755000175400017540000000000013001231247017073 5ustar00biocbuildbiocbuildensembldb/inst/pkg-template/DESCRIPTION0000644000175400017540000000056113001231247020603 0ustar00biocbuildbiocbuildPackage: @PKGNAME@ Title: @PKGTITLE@ Description: @PKGDESCRIPTION@ Version: @PKGVERSION@ Author: @AUTHOR@ Maintainer: @MAINTAINER@ Depends: ensembldb License: @LIC@ organism: @ORGANISM@ species: @SPECIES@ provider: @PROVIDER@ provider_version: @PROVIDERVERSION@ release_date: @RELEASEDATE@ resource_url: @SOURCEURL@ biocViews: AnnotationData, EnsDb, @ORGANISMBIOCVIEW@ ensembldb/inst/pkg-template/NAMESPACE0000644000175400017540000000035313001231247020313 0ustar00biocbuildbiocbuild##import(AnnotationDbi) #import(GenomicFeatures) import(ensembldb) ### Don't export @TXDBOBJNAME@ (the object defined in this ### package): it is created and dynamically exported at load time (refer ### to R/zzz.R for the details). ensembldb/inst/pkg-template/R/0000755000175400017540000000000013001231245017272 5ustar00biocbuildbiocbuildensembldb/inst/pkg-template/R/zzz.R0000644000175400017540000000074113001231245020254 0ustar00biocbuildbiocbuild### ### Load any db objects whenever the package is loaded. ### .onLoad <- function(libname, pkgname) { ns <- asNamespace(pkgname) path <- system.file("extdata", package=pkgname, lib.loc=libname) files <- dir(path) for(i in seq_len(length(files))){ db <- EnsDb(system.file("extdata", files[[i]], package=pkgname, lib.loc=libname)) objname <- sub(".sqlite$","",files[[i]]) assign(objname, db, envir=ns) namespaceExport(ns, objname) } } ensembldb/inst/pkg-template/man/0000755000175400017540000000000013001231245017644 5ustar00biocbuildbiocbuildensembldb/inst/pkg-template/man/package.Rd0000644000175400017540000000135713001231245021534 0ustar00biocbuildbiocbuild\name{@TXDBOBJNAME@} \docType{package} \alias{@PKGNAME@-package} \alias{@PKGNAME@} \alias{@TXDBOBJNAME@} \title{@PKGTITLE@} \description{ This package loads an SQL connection to a database containing annotations from Ensembl. For examples and help on functions see the help pages from the \code{ensembldb} package! } \note{ This data package was made from resources at @PROVIDER@ on @RELEASEDATE@ and based on the @PROVIDERVERSION@ } \author{@AUTHOR@} \examples{ ## load the library ##library(@PKGNAME@) ## list the contents that are loaded into memory ls('package:@PKGNAME@') ## show the db object that is loaded by calling it's name @PKGNAME@ ## for more examples see the ensembldb package. } \keyword{package} \keyword{data} ensembldb/inst/shinyHappyPeople/0000755000175400017540000000000013012434305020004 5ustar00biocbuildbiocbuildensembldb/inst/shinyHappyPeople/server.R0000644000175400017540000002004313012434305021434 0ustar00biocbuildbiocbuild## list all packages... packs <- installed.packages() epacks <- packs[grep(packs, pattern="^Ens")] ## library(EnsDb.Hsapiens.v75) ## edb <- EnsDb.Hsapiens.v75 TheFilter <- function(input){ Cond <- input$condition ## check if we've got something to split... Vals <- input$geneName ## check if we've got , if(length(grep(Vals, pattern=",")) > 0){ ## don't want whitespaces here... Vals <- gsub(Vals, pattern=" ", replacement="", fixed=TRUE) Vals <- unlist(strsplit(Vals, split=",")) } if(length(grep(Vals, pattern=" ", fixed=TRUE)) > 0){ Vals <- unlist(strsplit(Vals, split=" ", fixed=TRUE)) } if(input$type=="Gene name"){ return(GenenameFilter(Vals, condition=Cond)) } if(input$type=="Chrom name"){ return(SeqnameFilter(Vals, condition=Cond)) } if(input$type=="Gene biotype"){ return(GenebiotypeFilter(Vals, condition=Cond)) } if(input$type=="Tx biotype"){ return(TxbiotypeFilter(Vals, condition=Cond)) } } ## checkSelectedPackage <- function(input){ ## if(is.null(input$package)){ ## return(FALSE) ## }else{ ## require(input$package, character.only=TRUE) ## message("Assigning ", input$package, " to variable edb.") ## assign("edb", get(input$package), envir=globalenv()) ## return(TRUE) ## } ## } ## Based on the given EnsDb package name it loads the library and returns ## the object. getEdb <- function(x){ require(x, character.only=TRUE) return(get(x)) } ## Define server logic required to draw a histogram shinyServer(function(input, output) { ## Generate the select field for the package... output$packages <- renderUI( selectInput("package", "Select installed EnsDb package", as.list(epacks)) ) selectedPackage <- reactive({ names(epacks) <- epacks ## epacks <- sapply(epacks, as.symbol) ## load the package. if(length(input$package) > 0){ require(input$package, character.only=TRUE) ## Actually, should be enough to just return input$package... ##return(switch(input$package, epacks)) return(getEdb(input$package)) }else{ return(NULL) } }) ## Metadata infos output$metadata_organism <- renderText({ edb <- selectedPackage() if(!is.null(edb)){ ## db <- getEdb(edb) paste0("Organism: ", organism(edb)) } }) output$metadata_ensembl <- renderText({ edb <- selectedPackage() if(!is.null(edb)){ ## db <- getEdb(edb) md <- metadata(edb) rownames(md) <- md$name paste0("Ensembl version: ", md["ensembl_version", "value"]) } }) output$metadata_genome <- renderText({ edb <- selectedPackage() if(!is.null(edb)){ ## db <- getEdb(edb) md <- metadata(edb) rownames(md) <- md$name paste0("Genome build: ", md["genome_build", "value"]) } }) output$genename <- renderText({ if(length(input$geneName) > 0){ input$geneName }else{ return() } }) ## That's the actual queries for genes, transcripts and exons... output$Genes <- renderDataTable({ ## if(!checkSelectedPackage(input)) ## return() if(length(input$package) == 0) return(NULL) if(!is.na(input$geneName) & length(input$geneName) > 0 & input$geneName!=""){ edb <- selectedPackage() res <- genes(edb, filter=TheFilter(input), return.type="data.frame") assign(".ENS_TMP_RES", res, envir=globalenv()) return(res) } }) output$Transcripts <- renderDataTable({ if(length(input$package) == 0) return(NULL) if(!is.na(input$geneName) & length(input$geneName) > 0 & input$geneName!=""){ edb <- selectedPackage() res <- transcripts(edb, filter=TheFilter(input), return.type="data.frame") assign(".ENS_TMP_RES", res, envir=globalenv()) return(res) } }) output$Exons <- renderDataTable({ if(length(input$package) == 0) return(NULL) if(!is.na(input$geneName) & length(input$geneName) > 0 & input$geneName!=""){ edb <- selectedPackage() res <- exons(edb, filter=TheFilter(input), return.type="data.frame") assign(".ENS_TMP_RES", res, envir=globalenv()) return(res) } }) observe({ if(input$closeButton > 0){ ## OK, now, gather all the data and return it in the selected format. edb <- selectedPackage() resType <- input$returnType resTab <- input$resultTab res <- NULL ## If result type is data.frame we just return what we've got. if(resType == "data.frame"){ res <- get(".ENS_TMP_RES") }else{ ## Otherwise we have to fetch a little bit more data, thus, we perform the ## query again and return it as GRanges. if(resTab == "Genes") res <- genes(edb, filter=TheFilter(input), return.type="GRanges") if(resTab == "Transcripts") res <- transcripts(edb,filter=TheFilter(input), return.type="GRanges") if(resTab == "Exons") res <- exons(edb,filter=TheFilter(input), return.type="GRanges") } rm(".ENS_TMP_RES", envir=globalenv()) stopApp(res) } }) }) ## ## Define server logic required to draw a histogram ## shinyServer(function(input, output) { ## ## generate the select field for the package... ## output$packages <- renderUI( ## selectInput("package", "Select EnsDb package", as.list(epacks)) ## ) ## ## generating metadata info. ## output$metadata_organism <- renderText({ ## if(!checkSelectedPackage(input)) ## return() ## paste0("Organism: ", organism(edb)) ## }) ## output$metadata_ensembl <- renderText({ ## if(!checkSelectedPackage(input)) ## return() ## md <- metadata(edb) ## rownames(md) <- md$name ## paste0("Ensembl version: ", md["ensembl_version", "value"]) ## }) ## output$metadata_genome <- renderText({ ## if(!checkSelectedPackage(input)) ## return() ## md <- metadata(edb) ## rownames(md) <- md$name ## paste0("Genome build: ", md["genome_build", "value"]) ## }) ## ## output$genename <- renderText({ ## ## if(!checkSelectedPackage(input)) ## ## return() ## ## input$geneName ## ## }) ## ## That's the actual queries for genes, transcripts and exons... ## output$Genes <- renderDataTable({ ## if(!checkSelectedPackage(input)) ## return() ## if(!is.na(input$geneName) & length(input$geneName) > 0 & input$geneName!=""){ ## res <- genes(edb, filter=TheFilter(input), ## return.type="data.frame") ## return(res) ## } ## }) ## output$Transcripts <- renderDataTable({ ## if(!checkSelectedPackage(input)) ## return() ## if(!is.na(input$geneName) & length(input$geneName) > 0 & input$geneName!=""){ ## res <- transcripts(edb, filter=TheFilter(input), ## return.type="data.frame") ## return(res) ## } ## }) ## output$Exons <- renderDataTable({ ## if(!checkSelectedPackage(input)) ## return() ## if(!is.na(input$geneName) & length(input$geneName) > 0 & input$geneName!=""){ ## res <- exons(edb, filter=TheFilter(input), ## return.type="data.frame") ## return(res) ## } ## }) ## ## observe({ ## ## if(input$Close > 0){ ## ## stopApp("AAARGHHH") ## ## } ## ## }) ## }) ensembldb/inst/shinyHappyPeople/ui.R0000644000175400017540000001330613001231247020545 0ustar00biocbuildbiocbuildlibrary(shiny) ## start with runApp("jo_test") shinyUI(fluidPage( ## Application title titlePanel("Get gene/transcript/exon annotations"), fluidRow( shiny::column(3, uiOutput("packages") ), shiny::column(3, ## div( h4("EnsDb annotation:"), textOutput("metadata_organism"), textOutput("metadata_ensembl"), textOutput("metadata_genome"), " " ## ) ), shiny::column(4, h4("Hints:"), tags$li("Enter comma or whitespace separated values to search for multiple e.g. genes."), tags$li("Use % and condition like for partial matching.")) ), fluidRow( shiny::column(4, " " ) ), fluidRow( shiny::column(2, selectInput("type", NA, choices=c("Gene name", "Chrom name", "Gene biotype", "Tx biotype"), selected="Gene name") ), shiny::column(1, selectInput("condition", NA, choices=c("=", "!=", "like", "in"), selected="=") ), shiny::column(2, textInput("geneName", NA, value="") ) ), fluidRow( mainPanel( tabsetPanel( tabPanel('Genes', dataTableOutput("Genes") ), tabPanel('Transcripts', dataTableOutput("Transcripts") ), tabPanel('Exons', dataTableOutput("Exons") ) , id="resultTab" ) ) ), wellPanel( fluidRow( shiny::column(2, "Return results as " ), shiny::column(2, selectInput("returnType", NA, choices=c("data.frame", "GRanges"), selected="data.frame") ), shiny::column(2, actionButton("closeButton", "Return & close") ) ) ) )) ## shinyUI(fluidPage( ## ## Application title ## titlePanel("Get gene/transcript/exon annotations"), ## fluidRow( ## shiny::column(3, ## uiOutput("packages") ## ), ## shiny::column(3, ## ## div( ## h4("EnsDb annotation:"), ## textOutput("metadata_organism"), ## textOutput("metadata_ensembl"), ## textOutput("metadata_genome"), ## " " ## ## ) ## ), ## shiny::column(4, ## h4("Hints:"), ## tags$li("Enter comma or whitespace separated values to search for multiple e.g. genes."), ## tags$li("Use % and condition like for partial matching."), ## tags$li("The selected database is assigned to the environment variable ENS_DB.")) ## ), ## fluidRow( ## shiny::column(4, ## " " ## ) ## ## ) ## ), ## fluidRow( ## shiny::column(2, ## selectInput("type", NA, ## choices=c("Gene name", "Chrom name", ## "Gene biotype", "Tx biotype"), ## selected="Gene name") ## ), ## shiny::column(1, ## selectInput("condition", NA, ## choices=c("=", "!=", "like", "in"), ## selected="=") ## ), ## shiny::column(2, ## textInput("geneName", NA, value="") ## ), ## ## shiny::column(2, ## ## submitButton("Go!") ## ## ), ## shiny::column(2, ## actionButton("closeButton", "Return result") ## ) ## ), ## ## fluidRow( ## ## mainPanel( ## ## tabsetPanel( ## ## tabPanel('Genes', ## ## dataTableOutput("Genes") ## ## ), ## ## tabPanel('Transcripts', ## ## dataTableOutput("Transcripts") ## ## ), ## ## tabPanel('Exons', ## ## dataTableOutput("Exons") ## ## ) ## ## ) ## ## ##h4(textOutput("genename")) ## ## ) ## ## ) ## )) ensembldb/inst/test/0000755000175400017540000000000013001231247015460 5ustar00biocbuildbiocbuildensembldb/inst/test/testFunctionality.R0000644000175400017540000002320113001231247021331 0ustar00biocbuildbiocbuild## check namespace. detachem <- function( x ){ NS <- loadedNamespaces() if( any( NS==x ) ){ pkgn <- paste0( "package:", x ) detach( pkgn, unload=TRUE, character.only=TRUE ) } } Pkgs <- c( "EnsDb.Hsapiens.v75", "ensembldb" ) tmp <- sapply( Pkgs, detachem ) tmp <- sapply( Pkgs, library, character.only=TRUE ) ### ## just get all genes. cat( "getting all genes..." ) Gns <- genes( EnsDb.Hsapiens.v75 ) Gns cat("done\n") cat( "getting all transcripts..." ) Gns <- transcripts( EnsDb.Hsapiens.v75 ) Gns cat("done\n") cat( "getting all exons..." ) Gns <- exons( EnsDb.Hsapiens.v75 ) Gns cat("done\n") ## get exons, sort by exon_seq_start Gns <- exons( EnsDb.Hsapiens.v75, columns=c( "exon_id", "tx_id" ), filter=list( TxidFilter( "a" ) ) ) ensembldb:::.buildQuery( EnsDb.Hsapiens.v75, columns=c( "exon_id", "tx_id" ), filter=list( TxidFilter( "a" ) )) cat( "all transcripts by..." ) tmp <- transcriptsBy( EnsDb.Hsapiens.v75 ) tmp cat("done\n") cat( "all exons by..." ) tmp <- exonsBy( EnsDb.Hsapiens.v75 ) tmp cat("done\n") ########### ## getWhat... generic query interface to the database. Test <- ensembldb:::getWhat( EnsDb.Hsapiens.v75, columns=c( "gene_id", "gene_biotype", "gene_name", "seq_name" ), filter=list( SeqnameFilter( "Y" ) ) ) head(Test) dim(Test) ## now let's joind exon... Test <- ensembldb:::getWhat( EnsDb.Hsapiens.v75, columns=c( "gene_id", "gene_biotype", "gene_name", "seq_name", "exon_id", "exon_seq_start", "exon_seq_end" ), order.by="exon_seq_end", order.type="desc", filter=list( SeqnameFilter( "Y" ) ) ) head(Test) dim(Test) ## throws a warning since exon_chrom_end is not valid. Test <- ensembldb:::getWhat( EnsDb.Hsapiens.v75, columns=c( "gene_id", "gene_biotype", "gene_name", "seq_name", "exon_id", "exon_seq_start", "exon_seq_end" ), order.by="exon_chrom_end", order.type="desc", filter=list( SeqnameFilter( "Y" ) ) ) head(Test) dim(Test) ## add a Txid Filter. Test <- ensembldb:::getWhat( EnsDb.Hsapiens.v75, columns=c( "gene_id", "gene_biotype", "gene_name", "seq_name", "exon_id", "exon_seq_start", "exon_seq_end", "tx_id" ), order.by="exon_seq_end", order.type="desc", filter=list( TxidFilter( "ENST00000028008" ) ) ) Test Test <- ensembldb:::getWhat( EnsDb.Hsapiens.v75, columns=c( "gene_id", "gene_biotype", "gene_name", "seq_name" ), filter=list( TxidFilter( "ENST00000028008" ) ) ) Test ###### ## exonsBy ## get all Exons by gene for genes encoded on chromosomes 1, 2, 4 Test <- exonsBy( EnsDb.Hsapiens.v75, by="gene", columns=c( "gene_id", "gene_name", "gene_biotype" ), filter=list( SeqnameFilter( c( 1, 2,4 ) ), SeqstrandFilter( "-" ) ) ) Test ## tx_biotype and tx_id have been removed. Test <- exonsBy( EnsDb.Hsapiens.v75, by="gene", columns=c( "gene_id", "gene_name", "gene_biotype", "tx_biotype", "tx_id" ), filter=list( SeqnameFilter( c( 1, 2,4 ) ), SeqstrandFilter( "-" ) ) ) Test Test <- exonsBy( EnsDb.Hsapiens.v75, by="tx", columns=c( "gene_id", "tx_id", "tx_biotype" ), filter=list( SeqnameFilter( c( 1, 2,4 ) ) ) ) Test ## exons for a specific transcript Test <- exonsBy( EnsDb.Hsapiens.v75, by="tx", columns=c( "gene_id", "tx_id", "tx_biotype" ), filter=list( TxidFilter( "ENST00000028008" ) ) ) Test ## that also works, albeit throwing an warning. Test <- exonsBy( EnsDb.Hsapiens.v75, by="gene", columns=c( "gene_id", "tx_id", "tx_biotype" ), filter=list( TxidFilter( "ENST00000028008" ) ) ) Test ######## ## transcriptsBy Test <- transcriptsBy( EnsDb.Hsapiens.v75, by="gene", filter=list( SeqstrandFilter( "+" ), SeqnameFilter( "X" ) ) ) Test ## that should throw a warning Test <- transcriptsBy( EnsDb.Hsapiens.v75, by="gene", filter=list( SeqstrandFilter( "+" ), SeqnameFilter( "X" ) ), columns=c( "exon_id", "exon_seq_start" ) ) Test Test <- transcriptsBy( EnsDb.Hsapiens.v75, by="exon", filter=list( SeqstrandFilter( "+" ), SeqnameFilter( "X" ) ), columns="tx_biotype" ) Test ## that should throw a warning Test <- transcriptsBy( EnsDb.Hsapiens.v75, by="exon", filter=list( SeqstrandFilter( "+" ), SeqnameFilter( "X" ) ), columns=c( "exon_id", "exon_seq_start", "tx_biotype" ) ) Test ###### ## genes Test <- genes( EnsDb.Hsapiens.v75, filter=list( GenebiotypeFilter( "lincRNA" ) ) ) head( Test ) length( Test ) ## adding tx properties along with gene columns; this will return a data.frame with the ## additional information; gene columns can however no longer be unique in the data.frame Test <- genes( EnsDb.Hsapiens.v75, filter=list( GenebiotypeFilter( "lincRNA" ) ), columns=c( listColumns( EnsDb.Hsapiens.v75, "gene"), "tx_id", "tx_biotype" ) ) head( Test ) length( Test ) ###### ## transcripts ## get all transcripts that are target to nonsense mediated decay Test <- transcripts( EnsDb.Hsapiens.v75, filter=list( TxbiotypeFilter( "nonsense_mediated_decay" ) ) ) head( Test ) length( Test ) ## order the transcripts by seq_name; this does not work. Test <- transcripts( EnsDb.Hsapiens.v75, filter=list( TxbiotypeFilter( "nonsense_mediated_decay" ) ), order.by="seq_name" ) head( Test ) nrow( Test ) ## order the transcripts by seq_name; have to explicitely add seq_name to the columns. Test <- transcripts( EnsDb.Hsapiens.v75, filter=list( TxbiotypeFilter( "nonsense_mediated_decay" ) ), order.by="seq_name", columns=c( listColumns( EnsDb.Hsapiens.v75, "tx" ), "seq_name" ) ) head( Test ) nrow( Test ) ## get in addition the gene_name and gene_id Test <- transcripts( EnsDb.Hsapiens.v75, filter=list( TxbiotypeFilter( "nonsense_mediated_decay" ) ), columns=c( listColumns( EnsDb.Hsapiens.v75, "tx" ), "gene_id", "gene_name" ) ) head( Test ) nrow( Test ) ## get in addition the gene_name and gene_id and also exon_id and exon_idx Test <- transcripts( EnsDb.Hsapiens.v75, filter=list( TxbiotypeFilter( "nonsense_mediated_decay" ) ), columns=c( listColumns( EnsDb.Hsapiens.v75, "tx" ), "gene_id", "gene_name", "exon_id", "exon_idx" ) ) head( Test ) nrow( Test ) ##### ## exons ## Test <- exons( EnsDb.Hsapiens.v75, filter=list( TxidFilter( "ENST00000028008" ) ), columns=c( "gene_id","gene_name", "gene_biotype" ) ) Test ################## ## examples from EnsDb-class: ## display some information: EnsDb.Hsapiens.v75 organism( EnsDb.Hsapiens.v75 ) seqinfo( EnsDb.Hsapiens.v75 ) ## show the tables listTables( EnsDb.Hsapiens.v75 ) ###### buildQuery ## ## join tables gene and transcript and return gene_id and tx_id buildQuery( EnsDb.Hsapiens.v75, columns=c( "gene_id", "tx_id" ) ) ## get all exon_ids and transcript ids of genes encoded on chromosome Y. buildQuery( EnsDb.Hsapiens.v75, columns=c( "exon_id", "tx_id" ), filter=list( SeqnameFilter( "Y") ) ) ###### genes ## ## get all genes coded on chromosome Y AllY <- genes( EnsDb.Hsapiens.v75, filter=list( SeqnameFilter( "Y" ) ) ) head( AllY ) ## return result as GRanges. AllY.granges <- genes( EnsDb.Hsapiens.v75, filter=list( SeqnameFilter( "Y" ) ), return.type="GRanges" ) AllY.granges ## include all transcripts of the gene and their chromosomal ## coordinates, sort by chrom start of transcripts and return as ## GRanges. AllY.granges.tx <- genes( EnsDb.Hsapiens.v75, filter=list( SeqnameFilter( "Y" ) ), return.type="GRanges", columns=c( "gene_id", "seq_name", "seq_strand", "tx_id", "tx_biotype", "tx_seq_start", "tx_seq_end" ), order.by="tx_seq_start" ) AllY.granges.tx ###### transcripts ## ## get all transcripts of a gene Tx <- transcripts( EnsDb.Hsapiens.v75, filter=list( GeneidFilter( "ENSG00000184895" ) ), order.by="tx_seq_start" ) Tx ## get all transcripts of two genes along with some information on the ## gene and transcript Tx.granges <- transcripts( EnsDb.Hsapiens.v75, filter=list( GeneidFilter( c( "ENSG00000184895", "ENSG00000092377" ), condition="in" )), return.type="GRanges", order.by="tx_seq_start", columns=c( "gene_id", "gene_seq_start", "gene_seq_end", "gene_biotype", "tx_biotype" ) ) Tx.granges ###### exons ## ## get all exons of the provided genes Exon.granges <- exons( EnsDb.Hsapiens.v75, filter=list( GeneidFilter( c( "ENSG00000184895", "ENSG00000092377" ) )), return.type="GRanges", order.by="exon_seq_start", columns=c( "gene_id", "gene_seq_start", "gene_seq_end", "gene_biotype" ) ) Exon.granges ##### exonsBy ## ## get all exons for transcripts encoded on chromosomes 1 to 22, X and Y. ETx <- exonsBy( EnsDb.Hsapiens.v75, by="tx", filter=list( SeqnameFilter( c( 1:22, "X", "Y" ) ) ) ) ETx ## get all exons for genes encoded on chromosome 1 to 22, X and Y and ## include additional annotation columns in the result EGenes <- exonsBy( EnsDb.Hsapiens.v75, by="gene", filter=list( SeqnameFilter( c( 1:22, "X", "Y" ) ) ), columns=c( "gene_biotype", "gene_name" ) ) EGenes ## Note that this might also contain "LRG" genes. sum( grep( names( EGenes ), pattern="LRG" ) ) ## fetch just Ensembl genes: EGenes <- exonsBy( EnsDb.Hsapiens.v75, by="gene", filter=list( SeqnameFilter( c( 1:22, "X", "Y" ) ), GeneidFilter( "ENS%", "like" ) ), columns=c( "gene_biotype", "gene_name" ) ) sum( grep( names( EGenes ), pattern="LRG" ) ) ##### transcriptsBy ## TGenes <- transcriptsBy( EnsDb.Hsapiens.v75, by="gene", filter=list( SeqnameFilter( c( 1:22, "X", "Y" ) ) ) ) TGenes ##### lengthOf ## ## length of a specific gene. lengthOf( EnsDb.Hsapiens.v75, filter=list( GeneidFilter( "ENSG00000000003" ) ) ) ## length of a transcript lengthOf( EnsDb.Hsapiens.v75, of="tx", filter=list( TxidFilter( "ENST00000494424" ) ) ) ## average length of all protein coding genes mean( lengthOf( EnsDb.Hsapiens.v75, of="gene", filter=list( GenebiotypeFilter( "protein_coding" ), SeqnameFilter( c( 1:22, "X", "Y" ) ) ) ) ) ## average length of all snoRNAs mean( lengthOf( EnsDb.Hsapiens.v75, of="gene", filter=list( GenebiotypeFilter( "snoRNA" ), SeqnameFilter( c( 1:22, "X", "Y" ) ) ) ) ) listGenebiotypes(EnsDb.Hsapiens.v75) listTxbiotypes(EnsDb.Hsapiens.v75) ensembldb/inst/test/testInternals.R0000644000175400017540000001040313001231245020436 0ustar00biocbuildbiocbuilddetachem <- function(x){ NS <- loadedNamespaces() if(any(NS==x)){ pkgn <- paste0("package:", x) detach(pkgn, unload=TRUE, character.only=TRUE) } } Pkgs <- c("EnsDb.Hsapiens.v75", "ensembldb") tmp <- sapply(Pkgs, detachem) tmp <- sapply(Pkgs, library, character.only=TRUE) DB <- EnsDb.Hsapiens.v75 ####################################################### ## ## add required tables if needed. ## ## check if we get what we want... Expect <- c("exon", "tx2exon", "tx") Get <- ensembldb:::addRequiredTables(EnsDb.Hsapiens.v75, c("exon", "tx")) Get if(sum(Get %in% Expect)!=length(Expect)) stop("Didn't get what I expected!") Expect <- c("exon", "tx2exon", "tx", "gene") Get <- ensembldb:::addRequiredTables(EnsDb.Hsapiens.v75, c("exon", "gene")) Get if(sum(Get %in% Expect)!=length(Expect)) stop("Didn't get what I expected!") Expect <- c("exon", "tx2exon", "tx", "gene") Get <- ensembldb:::addRequiredTables(EnsDb.Hsapiens.v75, c("exon", "gene", "tx")) Get if(sum(Get %in% Expect)!=length(Expect)) stop("Didn't get what I expected!") ####################################################### ## ## join queries ## ensembldb:::joinQueryOnTables(EnsDb.Hsapiens.v75, c("exon", "t2exon", "tx")) ensembldb:::joinQueryOnTables(EnsDb.Hsapiens.v75, c("exon")) ensembldb:::joinQueryOnTables(EnsDb.Hsapiens.v75, c("exon", "t2exon", "tx", "gene")) ensembldb:::joinQueryOnTables(EnsDb.Hsapiens.v75, c("tx", "gene")) ensembldb:::joinQueryOnTables(EnsDb.Hsapiens.v75, c("chromosome", "gene")) ####################################################### ## ## join queries on column names ## ## for that query we don't need the exon table ensembldb:::cleanColumns(EnsDb.Hsapiens.v75, c("gene_id","tx_id", "bla", "value")) ## don't require the exon table here, exon_id is also in tx2exon. ensembldb:::joinQueryOnColumns(EnsDb.Hsapiens.v75, c("gene_id", "tx_id", "gene_name", "exon_id")) ## ensembldb:::joinQueryOnColumns(EnsDb.Hsapiens.v75, c("gene_id", "tx_id", "gene_name", "exon_idx")) ensembldb:::joinQueryOnColumns(EnsDb.Hsapiens.v75, c("gene_id", "tx_id", "gene_name", "exon_id", "exon_seq_start")) ####################################################### ## ## clean columns ## ensembldb:::cleanColumns(EnsDb.Hsapiens.v75, c("gene_id" ,"bma", "gene.gene_biotype")) ensembldb:::cleanColumns(EnsDb.Hsapiens.v75, c("gene_id" ,"gene.gene_name", "gene.gene_biotype")) ####################################################### ## ## check built queries ## ensembldb:::.buildQuery(EnsDb.Hsapiens.v75, columns=c("gene_id", "gene_name", "tx_id", "exon_id"), filter=list(SeqnameFilter("Y"), SeqstrandFilter("-"))) ## throws a warning ensembldb:::.buildQuery(EnsDb.Hsapiens.v75, columns=c("gene_id", "gene_name", "tx_id", "exon_id"), filter=list(SeqnameFilter("Y"), SeqstrandFilter("-")), order.by="exon_seq_end", order.type="desc") ## works ensembldb:::.buildQuery(EnsDb.Hsapiens.v75, columns=c("gene_id", "gene_name", "tx_id", "exon_id", "exon_seq_end"), filter=list(SeqnameFilter("Y"), SeqstrandFilter("-")), order.by="exon_seq_end", order.type="desc") ensembldb:::.buildQuery(EnsDb.Hsapiens.v75, columns=c("tx_id", "exon_id", "exon_seq_end"), filter=list(SeqnameFilter("Y"), SeqstrandFilter("-")), order.by="exon_seq_end", order.type="desc") ensembldb:::.buildQuery(EnsDb.Hsapiens.v75, columns=c("tx_id", "gene_id")) ## check the new filter thingy. 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Filt <- GenenameFilter("I'm a gene") checkEquals(where(Filt, EnsDb.Hsapiens.v75), "gene.gene_name = 'I''m a gene'") } test_TxidFilter <- function(){ Filt <- TxidFilter("a") checkEquals(column(Filt, EnsDb.Hsapiens.v75), "tx.tx_id") checkException(TxidFilter("a", ">")) ## expect the filter to change the condition if lenght of values ## is > 1 checkMultiValsIn(TxidFilter(c("a", "b"), "=")) checkMultiValsNotIn(TxidFilter(c("a", "b"), "!=")) } test_TxbiotypeFilter <- function(){ Filt <- TxbiotypeFilter("a") checkEquals(column(Filt, EnsDb.Hsapiens.v75), "tx.tx_biotype") checkException(TxbiotypeFilter("a", ">")) ## expect the filter to change the condition if lenght of values ## is > 1 checkMultiValsIn(TxbiotypeFilter(c("a", "b"), "=")) checkMultiValsNotIn(TxbiotypeFilter(c("a", "b"), "!=")) } test_ExonidFilter <- function(){ Filt <- ExonidFilter("a") checkEquals(column(Filt, EnsDb.Hsapiens.v75), "tx2exon.exon_id") checkException(ExonidFilter("a", ">")) ## expect the filter to change the condition if lenght of values ## is > 1 checkMultiValsIn(ExonidFilter(c("a", "b"), "=")) checkMultiValsNotIn(ExonidFilter(c("a", "b"), "!=")) } ## SeqnameFilter test_SeqnameFilter <- function(){ Filt <- SeqnameFilter("a") checkEquals(column(Filt, EnsDb.Hsapiens.v75), "gene.seq_name") checkException(SeqnameFilter("a", ">")) } ## SeqstrandFilter test_SeqstrandFilter <- function(){ checkException(SeqstrandFilter("a")) Filt <- SeqstrandFilter("-") checkEquals(column(Filt, EnsDb.Hsapiens.v75), "gene.seq_strand") } ## SeqstartFilter, feature test_SeqstartFilter <- function(){ Filt <- SeqstartFilter(123, feature="gene") checkEquals(column(Filt, EnsDb.Hsapiens.v75), "gene.gene_seq_start") Filt <- SeqstartFilter(123, feature="transcript") checkEquals(column(Filt, EnsDb.Hsapiens.v75), "tx.tx_seq_start") } ## SeqendFilter test_SeqendFilter <- function(){ Filt <- SeqendFilter(123, feature="gene") checkEquals(column(Filt, EnsDb.Hsapiens.v75), "gene.gene_seq_end") Filt <- SeqendFilter(123, feature="transcript") checkEquals(column(Filt, EnsDb.Hsapiens.v75), "tx.tx_seq_end") } ## checks if "condition" of the filter is "in" checkMultiValsIn <- function(filt){ checkEquals(condition(filt), "in") } ## checks if "condition" of the filter is "in" checkMultiValsNotIn <- function(filt){ checkEquals(condition(filt), "not in") } test_ExonrankFilter <- function(){ Filt <- ExonrankFilter(123) checkException(ExonrankFilter("a")) edb <- EnsDb.Hsapiens.v75 checkException(value(Filt) <- "b") checkEquals(column(Filt), "exon_idx") checkEquals(column(Filt, edb), "tx2exon.exon_idx") where(Filt, edb) } ## SymbolFilter test_SymbolFilter <- function() { edb <- EnsDb.Hsapiens.v75 sf <- SymbolFilter("SKA2") ## Check the column method. checkEquals(column(sf), "symbol") ## For EnsDb we want it to link to gene_name checkEquals(column(sf, edb), "gene.gene_name") checkException(column(sf, edb, with.tables = c("tx", "exon"))) ## Check the where method. checkEquals(where(sf), "symbol = 'SKA2'") condition(sf) <- "!=" checkEquals(where(sf, edb), "gene.gene_name != 'SKA2'") ## Test if we can use it: condition(sf) <- "=" Res <- genes(edb, filter = sf, return.type = "data.frame") checkEquals(Res$gene_id, "ENSG00000182628") ## We need now also a column "symbol"! checkEquals(Res$symbol, Res$gene_name) ## Asking explicitely for symbol Res <- genes(edb, filter = sf, return.type = "data.frame", columns = c("symbol", "gene_id")) checkEquals(colnames(Res), c("symbol", "gene_id")) ## Some more stuff, also shuffling the order. Res <- genes(edb, filter = sf, return.type = "data.frame", columns = c("gene_name", "symbol", "gene_id")) checkEquals(colnames(Res), c("gene_name", "symbol", "gene_id")) Res <- genes(edb, filter = sf, return.type = "data.frame", columns = c("gene_id", "gene_name", "symbol")) checkEquals(colnames(Res), c("gene_id", "gene_name", "symbol")) ## And with GRanges as return type. Res <- genes(edb, filter = sf, return.type = "GRanges", columns = c("gene_id", "gene_name", "symbol")) checkEquals(colnames(mcols(Res)), c("gene_id", "gene_name", "symbol")) ## Combine tx_name and symbol Res <- genes(edb, filter = sf, columns = c("tx_name", "symbol"), return.type = "data.frame") checkEquals(colnames(Res), c("tx_name", "symbol", "gene_id")) checkTrue(all(Res$symbol == "SKA2")) ## Test for transcripts Res <- transcripts(edb, filter=sf, return.type="data.frame") checkTrue(all(Res$symbol == "SKA2")) Res <- transcripts(edb, filter = sf, return.type = "data.frame", columns = c("symbol", "tx_id", "gene_name")) checkTrue(all(Res$symbol == "SKA2")) checkEquals(Res$symbol, Res$gene_name) checkEquals(colnames(Res), c("symbol", "tx_id", "gene_name")) ## Test for exons Res <- exons(edb, filter=sf, return.type="data.frame") checkTrue(all(Res$symbol == "SKA2")) Res <- exons(edb, filter = c(sf, TxbiotypeFilter("nonsense_mediated_decay")), return.type = "data.frame", columns = c("symbol", "tx_id", "gene_name")) checkTrue(all(Res$symbol == "SKA2")) checkEquals(Res$symbol, Res$gene_name) checkEquals(colnames(Res), c("symbol", "tx_id", "gene_name", "exon_id", "tx_biotype")) ## Test for exonsBy Res <- exonsBy(edb, filter=sf) checkTrue(all(unlist(Res)$symbol == "SKA2")) Res <- exonsBy(edb, filter = c(sf, TxbiotypeFilter("nonsense_mediated_decay")), columns = c("symbol", "tx_id", "gene_name")) checkTrue(all(unlist(Res)$symbol == "SKA2")) checkEquals(unlist(Res)$symbol, unlist(Res)$gene_name) ## Test for transcriptsBy too } ## Here we want to test if we get always also the filter columns back. test_multiFilterReturnCols <- function() { cols <- ensembldb:::addFilterColumns(edb, cols = c("exon_id"), filter = SymbolFilter("SKA2")) checkEquals(cols, c("exon_id", "symbol")) ## Two filter cols <- ensembldb:::addFilterColumns(edb, cols = c("exon_id"), filter = list(SymbolFilter("SKA2"), GenenameFilter("SKA2"))) checkEquals(cols, c("exon_id", "symbol", "gene_name")) cols <- ensembldb:::addFilterColumns(edb, cols = c("exon_id"), filter = list(SymbolFilter("SKA2"), GenenameFilter("SKA2"), GRangesFilter(GRanges("3", IRanges(3, 5) )))) checkEquals(cols, c("exon_id", "symbol", "gene_name", "gene_seq_start", "gene_seq_end", "seq_name", "seq_strand")) cols <- ensembldb:::addFilterColumns(edb, cols = c("exon_id"), filter = list(SymbolFilter("SKA2"), GenenameFilter("SKA2"), GRangesFilter(GRanges("3", IRanges(3, 5) ), feature = "exon"))) checkEquals(cols, c("exon_id", "symbol", "gene_name", "exon_seq_start", "exon_seq_end", "seq_name", "seq_strand")) ## SeqstartFilter and GRangesFilter ssf <- SeqstartFilter(123, feature="tx") cols <- ensembldb:::addFilterColumns(edb, cols = c("exon_id"), filter = list(SymbolFilter("SKA2"), GenenameFilter("SKA2"), GRangesFilter(GRanges("3", IRanges(3, 5) ), feature = "exon"), ssf)) checkEquals(cols, c("exon_id", "symbol", "gene_name", "exon_seq_start", "exon_seq_end", "seq_name", "seq_strand", "tx_seq_start")) } ensembldb/inst/unitTests/test_Functionality.R0000644000175400017540000005266113001231247022527 0ustar00biocbuildbiocbuild## that's just a plain simple R-script calling the standard methods. library( "EnsDb.Hsapiens.v75" ) DB <- EnsDb.Hsapiens.v75 ## testing genes method. test_genes <- function(){ Gns <- genes(DB, filter=SeqnameFilter("Y")) Gns <- genes(DB, filter=SeqnameFilter("Y"), return.type="DataFrame") checkEquals(sort(colnames(Gns)), sort(listColumns(DB, "gene"))) Gns <- genes(DB, filter=SeqnameFilter("Y"), return.type="DataFrame", columns=c("gene_id", "tx_name")) checkEquals(colnames(Gns), c("gene_id", "tx_name", "seq_name")) Gns <- genes(DB, filter=SeqnameFilter("Y"), columns=c("gene_id", "gene_name")) ## Here we don't need the seqnames in mcols! checkEquals(colnames(mcols(Gns)), c("gene_id", "gene_name")) ## checkEquals(class(genes(DB, return.type="DataFrame", ## filter=list(SeqnameFilter("Y")))), "DataFrame" ) } test_transcripts <- function(){ Tns <- transcripts(DB, filter=SeqnameFilter("Y"), return.type="DataFrame") checkEquals(sort(colnames(Tns)), sort(c(listColumns(DB, "tx"), "seq_name"))) Tns <- transcripts(DB, columns=c("tx_id", "tx_name"), filter=SeqnameFilter("Y")) checkEquals(sort(colnames(mcols(Tns))), sort(c("tx_id", "tx_name"))) ## Check the default ordering. Tns <- transcripts(DB, filter = TxbiotypeFilter("protein_coding"), return.type = "data.frame", columns = c("seq_name", listColumns(DB, "tx"))) checkEquals(order(Tns$seq_name, method = "radix"), 1:nrow(Tns)) } test_transcriptsBy <- function(){ ## Expect results on the forward strand to be ordered by tx_seq_start res <- transcriptsBy(DB, filter = list(SeqnameFilter("Y"), SeqstrandFilter("+")), by = "gene") fw <- res[[3]] checkEquals(order(start(fw)), 1:length(fw)) ## Expect results on the reverse strand to be ordered by -tx_seq_end res <- transcriptsBy(DB, filter = list(SeqnameFilter("Y"), SeqstrandFilter("-")), by = "gene") rv <- res[[3]] checkEquals(order(start(rv), decreasing = TRUE), 1:length(rv)) } test_exons <- function(){ Exns <- exons(DB, filter=SeqnameFilter("Y"), return.type="DataFrame") checkEquals(sort(colnames(Exns)), sort(c(listColumns(DB, "exon"), "seq_name"))) ## Check correct ordering. Exns <- exons(DB, return.type = "data.frame", filter = SeqnameFilter(20:23)) checkEquals(order(Exns$seq_name, method = "radix"), 1:nrow(Exns)) } test_exonsBy <- function() { ##ExnsBy <- exonsBy(DB, filter=list(SeqnameFilter("X")), by="tx") ExnsBy <- exonsBy(DB, filter = list(SeqnameFilter("Y")), by = "tx", columns = c("tx_name")) checkEquals(sort(colnames(mcols(ExnsBy[[1]]))), sort(c("exon_id", "exon_rank", "tx_name"))) ## Check what happens if we specify tx_id. ExnsBy <- exonsBy(DB, filter=list(SeqnameFilter("Y")), by="tx", columns=c("tx_id")) checkEquals(sort(colnames(mcols(ExnsBy[[1]]))), sort(c("exon_id", "exon_rank", "tx_id"))) ## ExnsBy <- exonsBy(DB, filter=list(SeqnameFilter("Y")), by="tx", ## columns=c("exon_rank")) ## checkEquals(sort(colnames(mcols(ExnsBy[[1]]))), ## sort(c("exon_id", "exon_rank"))) ExnsBy <- exonsBy(DB, filter=list(SeqnameFilter("Y"), SeqstrandFilter("+")), by="gene") ## Check that ordering is on start on the forward strand. fw <- ExnsBy[[3]] checkEquals(order(start(fw)), 1:length(fw)) ## ExnsBy <- exonsBy(DB, filter=list(SeqnameFilter("Y"), SeqstrandFilter("-")), by="gene") ## Check that ordering is on start on the forward strand. rv <- ExnsBy[[3]] checkEquals(order(end(rv), decreasing = TRUE), 1:length(rv)) } test_dbfunctionality <- function(){ GBT <- listGenebiotypes(DB) TBT <- listTxbiotypes(DB) } ## test if we get the expected exceptions if we're not submitting ## correct filter objects test_filterExceptions <- function(){ checkException(genes(DB, filter="d")) checkException(genes(DB, filter=list(SeqnameFilter("X"), "z"))) checkException(transcripts(DB, filter="d")) checkException(transcripts(DB, filter=list(SeqnameFilter("X"), "z"))) checkException(exons(DB, filter="d")) checkException(exons(DB, filter=list(SeqnameFilter("X"), "z"))) checkException(exonsBy(DB, filter="d")) checkException(exonsBy(DB, filter=list(SeqnameFilter("X"), "z"))) checkException(transcriptsBy(DB, filter="d")) checkException(transcriptsBy(DB, filter=list(SeqnameFilter("X"), "z"))) } test_promoters <- function(){ promoters(EnsDb.Hsapiens.v75, filter=GeneidFilter(c("ENSG00000184895", "ENSG00000092377"))) } test_return_columns_gene <- function(){ cols <- c("gene_name", "tx_id") Resu <- genes(DB, filter=SeqnameFilter("Y"), columns=cols, return.type="data.frame") checkEquals(sort(c(cols, "seq_name", "gene_id")), sort(colnames(Resu))) Resu <- genes(DB, filter=SeqnameFilter("Y"), columns=cols, return.type="DataFrame") checkEquals(sort(c(cols, "seq_name", "gene_id")), sort(colnames(Resu))) Resu <- genes(DB, filter=SeqnameFilter("Y"), columns=cols) checkEquals(sort(c(cols, "gene_id")), sort(colnames(mcols(Resu)))) } test_return_columns_tx <- function(){ cols <- c("tx_id", "exon_id", "tx_biotype") Resu <- transcripts(DB, filter=SeqnameFilter("Y"), columns=cols, return.type="data.frame") checkEquals(sort(c(cols, "seq_name")), sort(colnames(Resu))) Resu <- transcripts(DB, filter=SeqnameFilter("Y"), columns=cols, return.type="DataFrame") checkEquals(sort(c(cols, "seq_name")), sort(colnames(Resu))) Resu <- transcripts(DB, filter=SeqnameFilter("Y"), columns=cols) checkEquals(sort(cols), sort(colnames(mcols(Resu)))) } test_return_columns_exon <- function(){ cols <- c("tx_id", "exon_id", "tx_biotype") Resu <- exons(DB, filter=SeqnameFilter("Y"), columns=cols, return.type="data.frame") checkEquals(sort(c(cols, "seq_name")), sort(colnames(Resu))) Resu <- exons(DB, filter=SeqnameFilter("Y"), columns=cols, return.type="DataFrame") checkEquals(sort(c(cols, "seq_name")), sort(colnames(Resu))) Resu <- exons(DB, filter=SeqnameFilter("Y"), columns=cols) checkEquals(sort(cols), sort(colnames(mcols(Resu)))) } test_cdsBy <- function(){ ## Just checking if we get also tx_name cs <- cdsBy(DB, filter=SeqnameFilter("Y"), column="tx_name") checkTrue(any(colnames(mcols(cs[[1]])) == "tx_name")) do.plot <- FALSE ## By tx cs <- cdsBy(DB, filter=list(SeqnameFilter("Y"), SeqstrandFilter("+"))) tx <- exonsBy(DB, filter=list(SeqnameFilter("Y"), SeqstrandFilter("+"))) ## Check for the first if it makes sense: whichTx <- names(cs)[1] whichCs <- cs[[1]] tx <- transcripts(DB, filter=TxidFilter(whichTx), columns=c("tx_seq_start", "tx_seq_end", "tx_cds_seq_start", "tx_cds_seq_end", "exon_seq_start", "exon_seq_end", "exon_idx", "exon_id", "seq_strand"), return.type="data.frame") checkSingleTx(tx=tx, cds=whichCs, do.plot=do.plot) ## Next one: whichTx <- names(cs)[2] tx <- transcripts(DB, filter=TxidFilter(whichTx), columns=c("tx_seq_start", "tx_seq_end", "tx_cds_seq_start", "tx_cds_seq_end", "exon_seq_start", "exon_seq_end", "exon_idx", "exon_id"), return.type="data.frame") checkSingleTx(tx=tx, cds=cs[[2]], do.plot=do.plot) ## Now for reverse strand: cs <- cdsBy(DB, filter=list(SeqnameFilter("Y"), SeqstrandFilter("-"))) whichTx <- names(cs)[1] whichCs <- cs[[1]] tx <- transcripts(DB, filter=TxidFilter(whichTx), columns=c("tx_seq_start", "tx_seq_end", "tx_cds_seq_start", "tx_cds_seq_end", "exon_seq_start", "exon_seq_end", "exon_idx", "exon_id"), return.type="data.frame") ## order the guys by seq_start whichCs <- whichCs[order(start(whichCs))] checkSingleTx(tx=tx, cds=whichCs, do.plot=do.plot) ## Next one: whichTx <- names(cs)[2] whichCs <- cs[[2]] tx <- transcripts(DB, filter=TxidFilter(whichTx), columns=c("tx_seq_start", "tx_seq_end", "tx_cds_seq_start", "tx_cds_seq_end", "exon_seq_start", "exon_seq_end", "exon_idx", "exon_id"), return.type="data.frame") ## order the guys by seq_start whichCs <- whichCs[order(start(whichCs))] checkSingleTx(tx=tx, cds=whichCs, do.plot=do.plot) ## Check adding columns Test <- cdsBy(DB, filter=list(SeqnameFilter("Y")), columns=c("gene_biotype", "gene_name")) } test_cdsByGene <- function(){ do.plot <- FALSE ## By gene. cs <- cdsBy(DB, filter=list(SeqnameFilter("Y"), SeqstrandFilter("+")), by="gene", columns=NULL) checkSingleGene(cs[[1]], gene=names(cs)[[1]], do.plot=do.plot) checkSingleGene(cs[[2]], gene=names(cs)[[2]], do.plot=do.plot) ## - strand cs <- cdsBy(DB, filter=list(SeqnameFilter("Y"), SeqstrandFilter("-")), by="gene", columns=NULL) checkSingleGene(cs[[1]], gene=names(cs)[[1]], do.plot=do.plot) checkSingleGene(cs[[2]], gene=names(cs)[[2]], do.plot=do.plot) ## looks good! cs2 <- cdsBy(DB, filter=list(SeqnameFilter("Y"), SeqstrandFilter("+")), by="gene", use.names=TRUE) } test_UTRs <- function() { ## check presence of tx_name fUTRs <- fiveUTRsByTranscript(DB, filter = TxidFilter("ENST00000155093"), column = "tx_name") checkTrue(any(colnames(mcols(fUTRs[[1]])) == "tx_name")) do.plot <- FALSE fUTRs <- fiveUTRsByTranscript(DB, filter = list(SeqnameFilter("Y"), SeqstrandFilter("+"))) tUTRs <- threeUTRsByTranscript(DB, filter = list(SeqnameFilter("Y"), SeqstrandFilter("+"))) cds <- cdsBy(DB, "tx", filter = list(SeqnameFilter("Y"), SeqstrandFilter("+"))) ## Check a TX: tx <- names(fUTRs)[1] checkGeneUTRs(fUTRs[[tx]], tUTRs[[tx]], cds[[tx]], tx = tx, do.plot = do.plot) tx <- names(fUTRs)[2] checkGeneUTRs(fUTRs[[tx]], tUTRs[[tx]], cds[[tx]], tx = tx, do.plot = do.plot) tx <- names(fUTRs)[3] checkGeneUTRs(fUTRs[[tx]], tUTRs[[tx]], cds[[tx]], tx = tx, do.plot = do.plot) ## Reverse strand fUTRs <- fiveUTRsByTranscript(DB, filter = list(SeqnameFilter("Y"), SeqstrandFilter("-"))) tUTRs <- threeUTRsByTranscript(DB, filter = list(SeqnameFilter("Y"), SeqstrandFilter("-"))) cds <- cdsBy(DB, "tx", filter = list(SeqnameFilter("Y"), SeqstrandFilter("-"))) ## Check a TX: tx <- names(fUTRs)[1] checkGeneUTRs(fUTRs[[tx]], tUTRs[[tx]], cds[[tx]], tx = tx, do.plot = do.plot) tx <- names(fUTRs)[2] checkGeneUTRs(fUTRs[[tx]], tUTRs[[tx]], cds[[tx]], tx = tx, do.plot = do.plot) tx <- names(fUTRs)[3] checkGeneUTRs(fUTRs[[tx]], tUTRs[[tx]], cds[[tx]], tx = tx, do.plot = do.plot) } ## The "test_UTRs" has a very poor performance with the RSQLite 1.0.9011 ## release candidate. Here we want to evaluate the performance. dontrun_test_UTRs_performance <- function() { system.time(fUTRs <- fiveUTRsByTranscript(DB, filter = list(SeqnameFilter("Y"), SeqstrandFilter("+")), column = "tx_name") ) ## 6.4 secs. system.time(fUTRs <- fiveUTRsByTranscript(DB, filter = list(SeqnameFilter("Y"), SeqstrandFilter("+")))) ## 6.4 secs. system.time(tUTRs <- threeUTRsByTranscript(DB, filter = list(SeqnameFilter("Y"), SeqstrandFilter("+")))) ## 6.3 secs. system.time(cds <- cdsBy(DB, "tx", filter = list(SeqnameFilter("Y"), SeqstrandFilter("+")))) ## 6.3 secs. system.time(fUTRs <- fiveUTRsByTranscript(DB, filter = list(SeqnameFilter("Y"), SeqstrandFilter("-")))) ## 6.4 secs. system.time(tUTRs <- threeUTRsByTranscript(DB, filter = list(SeqnameFilter("Y"), SeqstrandFilter("-")))) ## 6.6 secs. system.time(cds <- cdsBy(DB, "tx", filter = list(SeqnameFilter("Y"), SeqstrandFilter("-")))) ## 6.3 secs. } checkGeneUTRs <- function(f, t, c, tx, do.plot=FALSE){ if(any(strand(c) == "+")){ ## End of five UTR has to be smaller than any start of cds checkTrue(max(end(f)) < min(start(c))) ## 3' checkTrue(min(start(t)) > max(end(c))) }else{ ## 5' checkTrue(min(start(f)) > max(end(c))) ## 3' checkTrue(max(end(t)) < min(start(c))) } ## just plot... if(do.plot){ tx <- transcripts(DB, filter=TxidFilter(tx), columns=c("exon_seq_start", "exon_seq_end"), return.type="data.frame") XL <- range(c(start(f), start(c), start(t), end(f), end(c), end(t))) YL <- c(0, 4) plot(4, 4, pch=NA, xlim=XL, ylim=YL, yaxt="n", ylab="", xlab="") ## five UTR rect(xleft=start(f), xright=end(f), ybottom=0.1, ytop=0.9, col="blue") ## cds rect(xleft=start(c), xright=end(c), ybottom=1.1, ytop=1.9) ## three UTR rect(xleft=start(t), xright=end(t), ybottom=2.1, ytop=2.9, col="red") ## all exons rect(xleft=tx$exon_seq_start, xright=tx$exon_seq_end, ybottom=3.1, ytop=3.9) } } checkSingleGene <- function(whichCs, gene, do.plot=FALSE){ tx <- transcripts(DB, filter=GeneidFilter(gene), columns=c("tx_seq_start", "tx_seq_end", "tx_cds_seq_start", "tx_cds_seq_end", "tx_id", "exon_id", "exon_seq_start", "exon_seq_end"), return.type="data.frame") XL <- range(tx[, c("tx_seq_start", "tx_seq_end")]) tx <- split(tx, f=tx$tx_id) if(do.plot){ ##XL <- range(c(start(whichCs), end(whichCs))) YL <- c(0, length(tx) + 1) plot(4, 4, pch=NA, xlim=XL, ylim=YL, yaxt="n", ylab="", xlab="") ## plot the txses for(i in 1:length(tx)){ current <- tx[[i]] rect(xleft=current$exon_seq_start, xright=current$exon_seq_end, ybottom=rep((i-1+0.1), nrow(current)), ytop=rep((i-0.1), nrow(current))) ## coding: rect(xleft=current$tx_cds_seq_start, xright=current$tx_cds_seq_end, ybottom=rep((i-1+0.1), nrow(current)), ytop=rep((i-0.1), nrow(current)), border="blue") } rect(xleft=start(whichCs), xright=end(whichCs), ybottom=rep(length(tx)+0.1, length(whichCs)), ytop=rep(length(tx)+0.9, length(whichCs)), border="red") } } checkSingleTx <- function(tx, cds, do.plot=FALSE){ rownames(tx) <- tx$exon_id tx <- tx[cds$exon_id, ] ## cds start and end have to be within the correct range. checkTrue(all(start(cds) >= min(tx$tx_cds_seq_start))) checkTrue(all(end(cds) <= max(tx$tx_cds_seq_end))) ## For all except the first and the last we have to assume that exon_seq_start ## is equal to start of cds. checkTrue(all(start(cds)[-1] == tx$exon_seq_start[-1])) checkTrue(all(end(cds)[-nrow(tx)] == tx$exon_seq_end[-nrow(tx)])) ## just plotting the stuff... if(do.plot){ XL <- range(tx[, c("exon_seq_start", "exon_seq_end")]) YL <- c(0, 4) plot(3, 3, pch=NA, xlim=XL, ylim=YL, xlab="", yaxt="n", ylab="") ## plotting the "real" exons: rect(xleft=tx$exon_seq_start, xright=tx$exon_seq_end, ybottom=rep(0, nrow(tx)), ytop=rep(1, nrow(tx))) ## plotting the cds: rect(xleft=start(cds), xright=end(cds), ybottom=rep(1.2, nrow(tx)), ytop=rep(2.2, nrow(tx)), col="blue") } } ##***************************************************************** ## Gviz stuff notrun_test_genetrack_df <- function(){ do.plot <- FALSE if(do.plot){ library(Gviz) options(ucscChromosomeNames=FALSE) data(geneModels) geneModels$chromosome <- 7 chr <- 7 start <- min(geneModels$start) end <- max(geneModels$end) myGeneModels <- getGeneRegionTrackForGviz(DB, chromosome=chr, start=start, end=end) ## chromosome has to be the same.... gtrack <- GenomeAxisTrack() gvizTrack <- GeneRegionTrack(geneModels, name="Gviz") ensdbTrack <- GeneRegionTrack(myGeneModels, name="ensdb") plotTracks(list(gtrack, gvizTrack, ensdbTrack)) plotTracks(list(gtrack, gvizTrack, ensdbTrack), from=26700000, to=26780000) ## Looks very nice... } ## Put the stuff below into the vignette: ## Next we get all lincRNAs on chromosome Y Lncs <- getGeneRegionTrackForGviz(DB, filter=list(SeqnameFilter("Y"), GenebiotypeFilter("lincRNA"))) Prots <- getGeneRegionTrackForGviz(DB, filter=list(SeqnameFilter("Y"), GenebiotypeFilter("protein_coding"))) if(do.plot){ plotTracks(list(gtrack, GeneRegionTrack(Lncs, name="lincRNAs"), GeneRegionTrack(Prots, name="proteins"))) plotTracks(list(gtrack, GeneRegionTrack(Lncs, name="lincRNAs"), GeneRegionTrack(Prots, name="proteins")), from=5000000, to=7000000, transcriptAnnotation="symbol") } ## is that the same than: TestL <- getGeneRegionTrackForGviz(DB, filter=list(GenebiotypeFilter("lincRNA")), chromosome="Y", start=5000000, end=7000000) TestP <- getGeneRegionTrackForGviz(DB, filter=list(GenebiotypeFilter("protein_coding")), chromosome="Y", start=5000000, end=7000000) if(do.plot){ plotTracks(list(gtrack, GeneRegionTrack(Lncs, name="lincRNAs"), GeneRegionTrack(Prots, name="proteins"), GeneRegionTrack(TestL, name="compareL"), GeneRegionTrack(TestP, name="compareP")), from=5000000, to=7000000, transcriptAnnotation="symbol") } checkTrue(all(TestL$exon %in% Lncs$exon)) checkTrue(all(TestP$exon %in% Prots$exon)) ## Crazy amazing stuff ## system.time( ## All <- getGeneRegionTrackForGviz(DB) ## ) } ####============================================================ ## length stuff ## ####------------------------------------------------------------ test_lengthOf <- function(){ system.time( lenY <- lengthOf(DB, "tx", filter=SeqnameFilter("Y")) ) ## Check what would happen if we do it ourselfs... system.time( lenY2 <- sum(width(reduce(exonsBy(DB, "tx", filter=SeqnameFilter("Y"))))) ) checkEquals(lenY, lenY2) ## Same for genes. system.time( lenY <- lengthOf(DB, "gene", filter=SeqnameFilter("Y")) ) ## Check what would happen if we do it ourselfs... system.time( lenY2 <- sum(width(reduce(exonsBy(DB, "gene", filter=SeqnameFilter("Y"))))) ) checkEquals(lenY, lenY2) ## Just using the transcriptLengths } ####============================================================ ## ExonrankFilter ## ####------------------------------------------------------------ test_ExonrankFilter <- function(){ txs <- transcripts(DB, columns=c("exon_id", "exon_idx"), filter=SeqnameFilter(c("Y"))) txs <- txs[order(names(txs))] txs2 <- transcripts(DB, columns=c("exon_id"), filter=list(SeqnameFilter(c("Y")), ExonrankFilter(3))) txs2 <- txs[order(names(txs2))] ## hm, that's weird somehow. exns <- exons(DB, columns=c("tx_id", "exon_idx"), filter=list(SeqnameFilter("Y"), ExonrankFilter(3))) checkTrue(all(exns$exon_idx == 3)) exns <- exons(DB, columns=c("tx_id", "exon_idx"), filter=list(SeqnameFilter("Y"), ExonrankFilter(3, condition="<"))) checkTrue(all(exns$exon_idx < 3)) } notrun_lengthOf <- function(){ ## How does TxDb do that?s library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene Test <- transcriptLengths(txdb) head(Test) } ensembldb/inst/unitTests/test_GFF.R0000644000175400017540000001364513001231247020300 0ustar00biocbuildbiocbuildnotrun_test_builds <- function(){ input <- "/Users/jo/Projects/EnsDbs/83/Homo_sapiens.GRCh38.83.gtf.gz" fromGtf <- ensDbFromGtf(input, outfile=tempfile()) ## provide wrong ensembl version fromGtf <- ensDbFromGtf(input, outfile=tempfile(), version="75") ## provide wrong genome version fromGtf <- ensDbFromGtf(input, outfile=tempfile(), genomeVersion="75") EnsDb(fromGtf) ## provide wrong organism fromGtf <- ensDbFromGtf(input, outfile=tempfile(), organism="blalba") EnsDb(fromGtf) ## GFF input <- "/Users/jo/Projects/EnsDbs/83/Homo_sapiens.GRCh38.83.chr.gff3.gz" fromGff <- ensDbFromGff(input, outfile=tempfile()) EnsDb(fromGff) fromGff <- ensDbFromGff(input, outfile=tempfile(), version="75") EnsDb(fromGff) fromGff <- ensDbFromGff(input, outfile=tempfile(), genomeVersion="bla") EnsDb(fromGff) fromGff <- ensDbFromGff(input, outfile=tempfile(), organism="blabla") EnsDb(fromGff) ## AH library(AnnotationHub) ah <- AnnotationHub() fromAh <- ensDbFromAH(ah["AH47963"], outfile=tempfile()) EnsDb(fromAH) fromAh <- ensDbFromAH(ah["AH47963"], outfile=tempfile(), version="75") EnsDb(fromAH) fromAh <- ensDbFromAH(ah["AH47963"], outfile=tempfile(), genomeVersion="bla") EnsDb(fromAH) fromAh <- ensDbFromAH(ah["AH47963"], outfile=tempfile(), organism="blabla") EnsDb(fromAH) } notrun_test_ensdbFromGFF <- function(){ library(ensembldb) ##library(rtracklayer) ## VERSION 83 gtf <- "/Users/jo/Projects/EnsDbs/83/Homo_sapiens.GRCh38.83.gtf.gz" fromGtf <- ensDbFromGtf(gtf, outfile=tempfile()) egtf <- EnsDb(fromGtf) gff <- "/Users/jo/Projects/EnsDbs/83/Homo_sapiens.GRCh38.83.gff3.gz" fromGff <- ensDbFromGff(gff, outfile=tempfile()) egff <- EnsDb(fromGff) ## Compare EnsDbs ensembldb:::compareEnsDbs(egtf, egff) ## OK, only Entrezgene ID "problems" ## Compare with the one built with the Perl API library(EnsDb.Hsapiens.v83) edb <- EnsDb.Hsapiens.v83 ensembldb:::compareEnsDbs(egtf, edb) ensembldb:::compareEnsDbs(egff, edb) ## OK, I get different genes... genes1 <- genes(egtf) genes2 <- genes(edb) only2 <- genes2[!(genes2$gene_id %in% genes1$gene_id)] ## That below was before the fix to include feature type start_codon and stop_codon ## to the CDS type. ## Identify which are the different transcripts: txGtf <- transcripts(egtf) txGff <- transcripts(egff) commonIds <- intersect(names(txGtf), names(txGff)) haveCds <- commonIds[!is.na(txGtf[commonIds]$tx_cds_seq_start) & !is.na(txGff[commonIds]$tx_cds_seq_start)] diffs <- haveCds[txGtf[haveCds]$tx_cds_seq_start != txGff[haveCds]$tx_cds_seq_start] head(diffs) ## What could be reasons? ## 1) alternative CDS? ## Checking the GTF: ## tx ENST00000623834: start_codon: 195409 195411. ## first CDS: 195259 195411. ## last CDS: 185220 185350. ## stop_codon: 185217 185219. ## So, why the heck is the stop codon OUTSIDE the CDS??? ## library(rtracklayer) ## theGtf <- import(gtf, format="gtf") ## ## Apparently, the GTF contains the additional elements start_codon/stop_codon. ## theGff <- import(gff, format="gff3") ## transcripts(egtf, filter=TxidFilter(diffs[1])) ## transcripts(egff, filter=TxidFilter(diffs[1])) ## VERSION 81 ## Try to get the same via AnnotationHub gff <- "/Users/jo/Projects/EnsDbs/81/homo_sapiens/Homo_sapiens.GRCh38.81.gff3.gz" fromGff <- ensDbFromGff(gff, outfile=tempfile()) egff <- EnsDb(fromGff) gtf <- "/Users/jo/Projects/EnsDbs/81/homo_sapiens/Homo_sapiens.GRCh38.81.gtf.gz" fromGtf <- ensDbFromGtf(gtf, outfile=tempfile()) egtf <- EnsDb(fromGtf) ## Compare those two: ensembldb:::compareEnsDbs(egff, egtf) ## Why are there some differences in the transcripts??? trans1 <- transcripts(egff) trans2 <- transcripts(egtf) onlyInGtf <- trans2[!(trans2$tx_id %in% trans1$tx_id)] ##gtfGRanges <- ah["AH47963"] library(AnnotationHub) ah <- AnnotationHub() fromAh <- ensDbFromAH(ah["AH47963"], outfile=tempfile()) ## That's human... eah <- EnsDb(fromAh) ## Compare it to gtf: ensembldb:::compareEnsDbs(eah, egtf) ## OK. Same cds starts and cds ends. ## Compare it to gff: ensembldb:::compareEnsDbs(eah, egff) ## hm. ## Compare to EnsDb library(EnsDb.Hsapiens.v81) edb <- EnsDb.Hsapiens.v81 ensembldb:::compareEnsDbs(edb, egtf) ## Problem with CDS ensembldb:::compareEnsDbs(edb, egff) ## That's fine. ## Summary: ## GTF and AH are the same. ## GFF and Perl API are the same. ## OLD STUFF BELOW. ##fromAh <- EnsDbFromAH(ah["AH47963"], outfile=tempfile(), organism="Homo sapiens", version=81) ## Try with a fancy species: gff <- "/Users/jo/Projects/EnsDbs/83/gadus_morhua/Gadus_morhua.gadMor1.83.gff3.gz" fromGtf <- ensDbFromGff(gff, outfile=tempfile()) gff <- "/Users/jo/Projects/EnsDbs/83/rattus_norvegicus/Rattus_norvegicus.Rnor_6.0.83.gff3.gz" fromGff <- ensDbFromGff(gff, outfile=tempfile()) ## That works. ## Try with a file from AnnotationHub: Gorilla gorilla. library(AnnotationHub) ah <- AnnotationHub() ah <- ah["AH47962"] res <- ensDbFromAH(ah, outfile=tempfile()) edb <- EnsDb(res) genes(edb) ## ensRel <- query(ah, c("GTF", "ensembl")) ## gtf <- "/Users/jo/Projects/EnsDbs/83/Homo_sapiens.GRCh38.83.gtf.gz" ## ## GTF ## dir.create("/tmp/fromGtf") ## fromGtf <- ensDbFromGtf(gtf, path="/tmp/fromGtf", verbose=TRUE) ## ## GFF ## dir.create("/tmp/fromGff") ## fromGff <- ensembldb:::ensDbFromGff(gff, path="/tmp/fromGff", verbose=TRUE) ## ## ZBTB16: ## ## exon: ENSE00003606532 is 3rd exon of tx: ENST00000335953 ## ## exon: ENSE00003606532 is 3rd exon of tx: ENST00000392996 ## ## the Ensembl GFF has 2 entries for this exon. } ensembldb/inst/unitTests/test_GRangeFilter.R0000644000175400017540000001041413001231247022176 0ustar00biocbuildbiocbuild###============================================================ ## Testing the GRangesFilter ###------------------------------------------------------------ library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 test_GRangesFilterValidity <- function(){ checkException(GRangesFilter(value="bla")) checkException(GRangesFilter(GRanges(seqnames="X", ranges=IRanges(4, 6)), condition=">")) ## Testing slots gr <- GRanges("X", ranges=IRanges(123, 234), strand="-") grf <- GRangesFilter(gr, condition="within") ## Now check some stuff checkEquals(start(grf), start(gr)) checkEquals(end(grf), end(gr)) checkEquals(as.character(strand(gr)), strand(grf)) checkEquals(as.character(seqnames(gr)), seqnames(grf)) ## Test column: ## filter alone. tocomp <- c(start="gene_seq_start", end="gene_seq_end", seqname="seq_name", strand="seq_strand") checkEquals(column(grf), tocomp) grf@feature <- "tx" tocomp <- c(start="tx_seq_start", end="tx_seq_end", seqname="seq_name", strand="seq_strand") checkEquals(column(grf), tocomp) grf@feature <- "exon" tocomp <- c(start="exon_seq_start", end="exon_seq_end", seqname="seq_name", strand="seq_strand") checkEquals(column(grf), tocomp) ## filter and ensdb. tocomp <- c(start="exon.exon_seq_start", end="exon.exon_seq_end", seqname="gene.seq_name", strand="gene.seq_strand") checkEquals(column(grf, edb), tocomp) grf@feature <- "tx" tocomp <- c(start="tx.tx_seq_start", end="tx.tx_seq_end", seqname="gene.seq_name", strand="gene.seq_strand") checkEquals(column(grf, edb), tocomp) grf@feature <- "gene" tocomp <- c(start="gene.gene_seq_start", end="gene.gene_seq_end", seqname="gene.seq_name", strand="gene.seq_strand") checkEquals(column(grf, edb), tocomp) ## Test where: ## filter alone. tocomp <- "gene_seq_start >= 123 and gene_seq_end <= 234 and seq_name == 'X' and seq_strand = -1" checkEquals(where(grf), tocomp) ## what if we set strand to * grf2 <- GRangesFilter(GRanges("1", IRanges(123, 234))) tocomp <- "gene.gene_seq_start >= 123 and gene.gene_seq_end <= 234 and gene.seq_name == '1'" checkEquals(where(grf2, edb), tocomp) ## Now, using overlapping. grf@location <- "overlapping" grf@feature <- "transcript" tocomp <- "tx.tx_seq_start <= 234 and tx.tx_seq_end >= 123 and gene.seq_name = 'X' and gene.seq_strand = -1" checkEquals(where(grf, edb), tocomp) } ## Here we check if we fetch what we expect from the database. test_GRangesFilterQuery <- function(){ do.plot <- FALSE zbtb <- genes(edb, filter=GenenameFilter("ZBTB16")) txs <- transcripts(edb, filter=GenenameFilter("ZBTB16")) ## Now use the GRangesFilter to fetch all tx txs2 <- transcripts(edb, filter=GRangesFilter(zbtb)) checkEquals(txs$tx_id, txs2$tx_id) ## Exons: exs <- exons(edb, filter=GenenameFilter("ZBTB16")) exs2 <- exons(edb, filter=GRangesFilter(zbtb)) checkEquals(exs$exon_id, exs2$exon_id) ## Now check the filter with "overlapping". intr <- GRanges("11", ranges=IRanges(114000000, 114000050), strand="+") gns <- genes(edb, filter=GRangesFilter(intr, condition="overlapping")) checkEquals(gns$gene_name, "ZBTB16") txs <- transcripts(edb, filter=GRangesFilter(intr, condition="overlapping")) if(do.plot){ plot(3, 3, pch=NA, xlim=c(start(zbtb), end(zbtb)), ylim=c(0, length(txs2))) rect(xleft=start(intr), xright=end(intr), ybottom=0, ytop=length(txs2), col="red", border="red") for(i in 1:length(txs2)){ current <- txs2[i] rect(xleft=start(current), xright=end(current), ybottom=i-0.975, ytop=i-0.125, border="grey") text(start(current), y=i-0.5,pos=4, cex=0.75, labels=current$tx_id) } ## OK, that' OK. } ## OK, now for a GRangesFilter with more than one GRanges. ir2 <- IRanges(start=c(2654890, 2709520, 28111770), end=c(2654900, 2709550, 28111790)) grf2 <- GRangesFilter(GRanges(rep("Y", length(ir2)), ir2), condition="overlapping") Test <- transcripts(edb, filter=grf2) checkEquals(names(Test), c("ENST00000383070", "ENST00000250784", "ENST00000598545")) } ensembldb/inst/unitTests/test_SymbolFilter.R0000644000175400017540000000272013001231245022277 0ustar00biocbuildbiocbuild############################################################ ## Testing the SymbolFilter. library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 test_sf_on_genes <- function(){ sf <- SymbolFilter("SKA2") gnf <- GenenameFilter("SKA2") returnFilterColumns(edb) <- FALSE gns_sf <- genes(edb, filter=sf) gns_gnf <- genes(edb, filter=gnf) checkEquals(gns_sf, gns_gnf) returnFilterColumns(edb) <- TRUE gns_sf <- genes(edb, filter=sf) checkEquals(gns_sf$gene_name, gns_sf$symbol) ## Hm, what happens if we use both? gns <- genes(edb, filter=list(sf, gnf)) ## All fine. } test_sf_on_tx <- function(){ sf <- SymbolFilter("SKA2") gnf <- GenenameFilter("SKA2") returnFilterColumns(edb) <- FALSE tx_sf <- transcripts(edb, filter=sf) tx_gnf <- transcripts(edb, filter=gnf) checkEquals(tx_sf, tx_gnf) returnFilterColumns(edb) <- TRUE tx_sf <- transcripts(edb, filter=sf, columns=c("gene_name")) checkEquals(tx_sf$gene_name, tx_sf$symbol) } test_sf_on_exons <- function(){ sf <- SymbolFilter("SKA2") gnf <- GenenameFilter("SKA2") returnFilterColumns(edb) <- FALSE ex_sf <- exons(edb, filter=sf) ex_gnf <- exons(edb, filter=gnf) checkEquals(ex_sf, ex_gnf) returnFilterColumns(edb) <- TRUE ex_sf <- exons(edb, filter=sf, columns=c("gene_name")) checkEquals(ex_sf$gene_name, ex_sf$symbol) } ############################################################ ## select method ensembldb/inst/unitTests/test_buildEdb.R0000644000175400017540000000365413001231247021407 0ustar00biocbuildbiocbuildtest_ensDbFromGRanges <- function(){ load(system.file("YGRanges.RData", package="ensembldb")) DB <- ensDbFromGRanges(Y, path=tempdir(), version=75, organism="Homo_sapiens") edb <- EnsDb(DB) checkEquals(unname(genome(edb)), "GRCh37") } ## Test some internal functions... test_processEnsemblFileNames <- function(){ Test <- "Homo_sapiens.GRCh38.83.gtf.gz" checkTrue(ensembldb:::isEnsemblFileName(Test)) checkEquals(ensembldb:::organismFromGtfFileName(Test), "Homo_sapiens") checkEquals(ensembldb:::genomeVersionFromGtfFileName(Test), "GRCh38") checkEquals(ensembldb:::ensemblVersionFromGtfFileName(Test), "83") Test <- "Homo_sapiens.GRCh38.83.chr.gff3.gz" checkTrue(ensembldb:::isEnsemblFileName(Test)) checkEquals(ensembldb:::organismFromGtfFileName(Test), "Homo_sapiens") checkEquals(ensembldb:::genomeVersionFromGtfFileName(Test), "GRCh38") checkEquals(ensembldb:::ensemblVersionFromGtfFileName(Test), "83") Test <- "Gadus_morhua.gadMor1.83.gff3.gz" checkTrue(ensembldb:::isEnsemblFileName(Test)) checkEquals(ensembldb:::organismFromGtfFileName(Test), "Gadus_morhua") checkEquals(ensembldb:::genomeVersionFromGtfFileName(Test), "gadMor1") checkEquals(ensembldb:::ensemblVersionFromGtfFileName(Test), "83") Test <- "Solanum_lycopersicum.GCA_000188115.2.30.chr.gtf.gz" checkTrue(ensembldb:::isEnsemblFileName(Test)) checkEquals(ensembldb:::organismFromGtfFileName(Test), "Solanum_lycopersicum") checkEquals(ensembldb:::genomeVersionFromGtfFileName(Test), "GCA_000188115.2") checkEquals(ensembldb:::ensemblVersionFromGtfFileName(Test), "30") Test <- "ref_GRCh38.p2_top_level.gff3.gz" checkEquals(ensembldb:::isEnsemblFileName(Test), FALSE) ensembldb:::organismFromGtfFileName(Test) checkException(ensembldb:::genomeVersionFromGtfFileName(Test)) ##checkException(ensembldb:::ensemblVersionFromGtfFileName(Test)) } ensembldb/inst/unitTests/test_getGenomeFaFile.R0000644000175400017540000000275313001231247022655 0ustar00biocbuildbiocbuildlibrary(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 notrun_test_getGenomeFaFile <- function(){ library(EnsDb.Hsapiens.v82) edb <- EnsDb.Hsapiens.v82 ## We know that there is no Fasta file for that Ensembl release available. Fa <- getGenomeFaFile(edb) ## Got the one from Ensembl 81. genes <- genes(edb, filter=SeqnameFilter("Y")) geneSeqsFa <- getSeq(Fa, genes) ## Get the transcript sequences... txSeqsFa <- extractTranscriptSeqs(Fa, edb, filter=SeqnameFilter("Y")) ## Get the TwoBitFile. twob <- ensembldb:::getGenomeTwoBitFile(edb) ## Get thegene sequences. ## ERROR FIX BELOW WITH UPDATED VERSIONS!!! geneSeqs2b <- getSeq(twob, genes) ## Have to fix the seqnames. si <- seqinfo(twob) sn <- unlist(lapply(strsplit(seqnames(si), split=" ", fixed=TRUE), function(z){ return(z[1]) })) seqnames(si) <- sn seqinfo(twob) <- si ## Do the same with the TwoBitFile geneSeqsTB <- getSeq(twob, genes) ## Subset to all genes that are encoded on chromosomes for which ## we do have DNA sequence available. genes <- genes[seqnames(genes) %in% seqnames(seqinfo(Dna))] ## Get the gene sequences, i.e. the sequence including the sequence of ## all of the gene's exons and introns. geneSeqs <- getSeq(Dna, genes) library(AnnotationHub) ah <- AnnotationHub() quer <- query(ah, c("release-", "Homo sapiens")) ## So, I get 2bit files and toplevel stuff. Test <- ah[["AH50068"]] } ensembldb/inst/unitTests/test_get_sequence.R0000644000175400017540000001771513001231247022347 0ustar00biocbuildbiocbuildlibrary(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 ## That's now using the BSGenome package... test_extractTranscriptSeqs_with_BSGenome <- function(){ library(BSgenome.Hsapiens.UCSC.hg19) bsg <- BSgenome.Hsapiens.UCSC.hg19 ## Changing the seqlevels tyle to UCSC seqlevelsStyle(edb) <- "UCSC" ZBTB <- extractTranscriptSeqs(bsg, edb, filter=GenenameFilter("ZBTB16")) ## Load the sequences for one ZBTB16 transcript from FA. faf <- system.file("txt/ENST00000335953.fa.gz", package="ensembldb") Seqs <- readDNAStringSet(faf) tx <- "ENST00000335953" ## cDNA checkEquals(unname(as.character(ZBTB[tx])), unname(as.character(Seqs[grep(names(Seqs), pattern="cdna")]))) ## CDS cBy <- cdsBy(edb, "tx", filter=TxidFilter(tx)) CDS <- extractTranscriptSeqs(bsg, cBy) checkEquals(unname(as.character(CDS)), unname(as.character(Seqs[grep(names(Seqs), pattern="cds")]))) ## 5' UTR fBy <- fiveUTRsByTranscript(edb, filter=TxidFilter(tx)) UTR <- extractTranscriptSeqs(bsg, fBy) checkEquals(unname(as.character(UTR)), unname(as.character(Seqs[grep(names(Seqs), pattern="utr5")]))) ## 3' UTR tBy <- threeUTRsByTranscript(edb, filter=TxidFilter(tx)) UTR <- extractTranscriptSeqs(bsg, tBy) checkEquals(unname(as.character(UTR)), unname(as.character(Seqs[grep(names(Seqs), pattern="utr3")]))) ## Another gene on the reverse strand: faf <- system.file("txt/ENST00000200135.fa.gz", package="ensembldb") Seqs <- readDNAStringSet(faf) tx <- "ENST00000200135" ## cDNA cDNA <- extractTranscriptSeqs(bsg, edb, filter=TxidFilter(tx)) checkEquals(unname(as.character(cDNA)), unname(as.character(Seqs[grep(names(Seqs), pattern="cdna")]))) ## do the same, but from other strand exns <- exonsBy(edb, "tx", filter=TxidFilter(tx)) cDNA <- extractTranscriptSeqs(bsg, exns) checkEquals(unname(as.character(cDNA)), unname(as.character(Seqs[grep(names(Seqs), pattern="cdna")]))) strand(exns) <- "+" cDNA <- extractTranscriptSeqs(bsg, exns) checkTrue(unname(as.character(cDNA)) != unname(as.character(Seqs[grep(names(Seqs), pattern="cdna")]))) ## CDS cBy <- cdsBy(edb, "tx", filter=TxidFilter(tx)) CDS <- extractTranscriptSeqs(bsg, cBy) checkEquals(unname(as.character(CDS)), unname(as.character(Seqs[grep(names(Seqs), pattern="cds")]))) ## 5' UTR fBy <- fiveUTRsByTranscript(edb, filter=TxidFilter(tx)) UTR <- extractTranscriptSeqs(bsg, fBy) checkEquals(unname(as.character(UTR)), unname(as.character(Seqs[grep(names(Seqs), pattern="utr5")]))) ## 3' UTR tBy <- threeUTRsByTranscript(edb, filter=TxidFilter(tx)) UTR <- extractTranscriptSeqs(bsg, tBy) checkEquals(unname(as.character(UTR)), unname(as.character(Seqs[grep(names(Seqs), pattern="utr3")]))) } notrun_test_extractTranscriptSeqs <- function(){ ## Note: we can't run that by default as we can not assume everybody has ## AnnotationHub and the required ressource installed. ## That's how we want to test the transcript seqs. genome <- getGenomeFaFile(edb) ZBTB <- extractTranscriptSeqs(genome, edb, filter=GenenameFilter("ZBTB16")) ## Load the sequences for one ZBTB16 transcript from FA. faf <- system.file("txt/ENST00000335953.fa.gz", package="ensembldb") Seqs <- readDNAStringSet(faf) tx <- "ENST00000335953" ## cDNA checkEquals(unname(as.character(ZBTB[tx])), unname(as.character(Seqs[grep(names(Seqs), pattern="cdna")]))) ## CDS cBy <- cdsBy(edb, "tx", filter=TxidFilter(tx)) CDS <- extractTranscriptSeqs(genome, cBy) checkEquals(unname(as.character(CDS)), unname(as.character(Seqs[grep(names(Seqs), pattern="cds")]))) ## 5' UTR fBy <- fiveUTRsByTranscript(edb, filter=TxidFilter(tx)) UTR <- extractTranscriptSeqs(genome, fBy) checkEquals(unname(as.character(UTR)), unname(as.character(Seqs[grep(names(Seqs), pattern="utr5")]))) ## 3' UTR tBy <- threeUTRsByTranscript(edb, filter=TxidFilter(tx)) UTR <- extractTranscriptSeqs(genome, tBy) checkEquals(unname(as.character(UTR)), unname(as.character(Seqs[grep(names(Seqs), pattern="utr3")]))) ## Another gene on the reverse strand: faf <- system.file("txt/ENST00000200135.fa.gz", package="ensembldb") Seqs <- readDNAStringSet(faf) tx <- "ENST00000200135" ## cDNA cDNA <- extractTranscriptSeqs(genome, edb, filter=TxidFilter(tx)) checkEquals(unname(as.character(cDNA)), unname(as.character(Seqs[grep(names(Seqs), pattern="cdna")]))) ## do the same, but from other strand exns <- exonsBy(edb, "tx", filter=TxidFilter(tx)) cDNA <- extractTranscriptSeqs(genome, exns) checkEquals(unname(as.character(cDNA)), unname(as.character(Seqs[grep(names(Seqs), pattern="cdna")]))) strand(exns) <- "+" cDNA <- extractTranscriptSeqs(genome, exns) checkTrue(unname(as.character(cDNA)) != unname(as.character(Seqs[grep(names(Seqs), pattern="cdna")]))) ## CDS cBy <- cdsBy(edb, "tx", filter=TxidFilter(tx)) CDS <- extractTranscriptSeqs(genome, cBy) checkEquals(unname(as.character(CDS)), unname(as.character(Seqs[grep(names(Seqs), pattern="cds")]))) ## 5' UTR fBy <- fiveUTRsByTranscript(edb, filter=TxidFilter(tx)) UTR <- extractTranscriptSeqs(genome, fBy) checkEquals(unname(as.character(UTR)), unname(as.character(Seqs[grep(names(Seqs), pattern="utr5")]))) ## 3' UTR tBy <- threeUTRsByTranscript(edb, filter=TxidFilter(tx)) UTR <- extractTranscriptSeqs(genome, tBy) checkEquals(unname(as.character(UTR)), unname(as.character(Seqs[grep(names(Seqs), pattern="utr3")]))) } notrun_test_getCdsSequence <- function(){ ## That's when we like to get the sequence from the coding region. genome <- getGenomeFaFile(edb) tx <- extractTranscriptSeqs(genome, edb, filter=SeqnameFilter("Y")) cdsSeq <- extractTranscriptSeqs(genome, cdsBy(edb, filter=SeqnameFilter("Y"))) ## that's basically to get the CDS sequence. ## UTR sequence: tutr <- extractTranscriptSeqs(genome, threeUTRsByTranscript(edb, filter=SeqnameFilter("Y"))) futr <- extractTranscriptSeqs(genome, fiveUTRsByTranscript(edb, filter=SeqnameFilter("Y"))) theTx <- "ENST00000602770" fullSeq <- as.character(tx[theTx]) ## build the one from 5', cds and 3' compSeq <- "" if(any(names(futr) == theTx)) compSeq <- paste0(compSeq, as.character(futr[theTx])) if(any(names(cdsSeq) == theTx)) compSeq <- paste0(compSeq, as.character(cdsSeq[theTx])) if(any(names(tutr) == theTx)) compSeq <- paste(compSeq, as.character(tutr[theTx])) checkEquals(unname(fullSeq), compSeq) } notrun_test_cds <- function(){ library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene cds <- cds(txdb) cby <- cdsBy(txdb, by="tx") gr <- cby[[7]][1] seqlevels(gr) <- sub(seqlevels(gr), pattern="chr", replacement="") tx <- transcripts(edb, filter=GRangesFilter(gr, condition="overlapping")) cby[[7]] ## Note: so that fits! And we've to include the stop_codon feature for GTF import! ## Make an TxDb from GTF: gtf <- "/Users/jo/Projects/EnsDbs/75/homo_sapiens/Homo_sapiens.GRCh37.75.gtf.gz" library(GenomicFeatures) Test <- makeTxDbFromGFF(gtf, format="gtf", organism="Homo sapiens") scds <- cdsBy(Test, by="tx") gr <- scds[[7]][1] tx <- transcripts(edb, filter=GRangesFilter(gr, condition="overlapping")) scds[[7]] ## Compare: ## TxDb form GTF has: 865692 879533 ## EnsDb: 865692 879533 ## Next test: gr <- scds[[2]][1] tx <- transcripts(edb, filter=GRangesFilter(gr, condition="overlapping")) tx scds[[2]] ## start_codon: 367659 367661, stop_codon: 368595 368597 CDS: 367659 368594. ## TxDb from GTF includes the stop_codon! } ensembldb/inst/unitTests/test_mysql.R0000644000175400017540000000154513001231245021035 0ustar00biocbuildbiocbuild############################################################ ## Can not perform these tests right away, as they require a ## working MySQL connection. library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 dontrun_test_useMySQL <- function() { edb_mysql <- useMySQL(edb, user = "anonuser", host = "localhost", pass = "") } dontrun_test_connect_EnsDb <- function() { library(RMySQL) con <- dbConnect(MySQL(), user = "anonuser", host = "localhost", pass = "") ensembldb:::listEnsDbs(dbcon = con) ## just with user. ensembldb:::listEnsDbs(user = "anonuser", host = "localhost", pass = "", port = 3306) ## Connecting directly to a EnsDb MySQL database. con <- dbConnect(MySQL(), user = "anonuser", host = "localhost", pass = "", dbname = "ensdb_hsapiens_v75") edb_mysql <- EnsDb(con) } ensembldb/inst/unitTests/test_ordering.R0000644000175400017540000003035413001231245021501 0ustar00biocbuildbiocbuild############################################################ ## Some tests on the ordering/sorting of the results. library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 ## Compare the results for genes call with and without ordering in R test_ordering_genes <- function() { orig <- ensembldb:::orderResultsInR(edb) ensembldb:::orderResultsInR(edb) <- FALSE res_sql <- genes(edb, return.type = "data.frame") ensembldb:::orderResultsInR(edb) <- TRUE res_r <- genes(edb, return.type = "data.frame") rownames(res_sql) <- NULL rownames(res_r) <- NULL checkIdentical(res_sql, res_r) ## Join tx table ensembldb:::orderResultsInR(edb) <- FALSE res_sql <- genes(edb, columns = c("gene_id", "tx_id"), return.type = "data.frame") ensembldb:::orderResultsInR(edb) <- TRUE res_r <- genes(edb, columns = c("gene_id", "tx_id"), return.type = "data.frame") rownames(res_sql) <- NULL rownames(res_r) <- NULL checkIdentical(res_sql, res_r) ## Join tx table and use an SeqnameFilter ensembldb:::orderResultsInR(edb) <- FALSE res_sql <- genes(edb, columns = c("gene_id", "tx_id"), filter = SeqnameFilter("Y")) ensembldb:::orderResultsInR(edb) <- TRUE res_r <- genes(edb, columns = c("gene_id", "tx_id"), filter = SeqnameFilter("Y")) checkIdentical(res_sql, res_r) ensembldb:::orderResultsInR(edb) <- orig } dontrun_benchmark_ordering_genes <- function() { .withR <- function(x, ...) { ensembldb:::orderResultsInR(x) <- TRUE genes(x, ...) } .withSQL <- function(x, ...) { ensembldb:::orderResultsInR(x) <- FALSE genes(x, ...) } library(microbenchmark) microbenchmark(.withR(edb), .withSQL(edb), times = 10) ## same microbenchmark(.withR(edb, columns = c("gene_id", "tx_id")), .withSQL(edb, columns = c("gene_id", "tx_id")), times = 10) ## R slightly faster. microbenchmark(.withR(edb, columns = c("gene_id", "tx_id"), SeqnameFilter("Y")), .withSQL(edb, columns = c("gene_id", "tx_id"), SeqnameFilter("Y")), times = 10) ## same. } ## We aim to fix issue #11 by performing the ordering in R instead ## of SQL. Thus, we don't want to run this as a "regular" test ## case. dontrun_test_ordering_cdsBy <- function() { doBench <- FALSE if (doBench) library(microbenchmark) .withR <- function(x, ...) { ensembldb:::orderResultsInR(x) <- TRUE cdsBy(x, ...) } .withSQL <- function(x, ...) { ensembldb:::orderResultsInR(x) <- FALSE cdsBy(x, ...) } res_sql <- .withSQL(edb) res_r <- .withR(edb) checkEquals(res_sql, res_r) if (dobench) microbenchmark(.withSQL(edb), .withR(edb), times = 3) ## R slightly faster. res_sql <- .withSQL(edb, filter = SeqnameFilter("Y")) res_r <- .withR(edb, filter = SeqnameFilter("Y")) checkEquals(res_sql, res_r) if (dobench) microbenchmark(.withSQL(edb, filter = SeqnameFilter("Y")), .withR(edb, filter = SeqnameFilter("Y")), times = 10) ## R 6x faster. } dontrun_test_ordering_exonsBy <- function() { doBench <- FALSE if (doBench) library(microbenchmark) .withR <- function(x, ...) { ensembldb:::orderResultsInR(x) <- TRUE exonsBy(x, ...) } .withSQL <- function(x, ...) { ensembldb:::orderResultsInR(x) <- FALSE exonsBy(x, ...) } res_sql <- .withSQL(edb) res_r <- .withR(edb) checkEquals(res_sql, res_r) if (doBench) microbenchmark(.withSQL(edb), .withR(edb), times = 3) ## about the same; R slightly faster. ## with using a SeqnameFilter in addition. res_sql <- .withSQL(edb, filter = SeqnameFilter("Y")) res_r <- .withR(edb, filter = SeqnameFilter("Y")) ## query takes longer. checkEquals(res_sql, res_r) if (doBench) microbenchmark(.withSQL(edb, filter = SeqnameFilter("Y")), .withR(edb, filter = SeqnameFilter("Y")), times = 3) ## SQL twice as fast. ## Now getting stuff by gene res_sql <- .withSQL(edb, by = "gene") res_r <- .withR(edb, by = "gene") ## checkEquals(res_sql, res_r) ## Differences due to ties if (doBench) microbenchmark(.withSQL(edb, by = "gene"), .withR(edb, by = "gene"), times = 3) ## SQL faster; ??? ## Along with a SeqnameFilter res_sql <- .withSQL(edb, by = "gene", filter = SeqnameFilter("Y")) res_r <- .withR(edb, by = "gene", filter = SeqnameFilter("Y")) ## Why does the query take longer for R??? ## checkEquals(res_sql, res_r) ## Differences due to ties if (doBench) microbenchmark(.withSQL(edb, by = "gene", filter = SeqnameFilter("Y")), .withR(edb, by = "gene", filter = SeqnameFilter("Y")), times = 3) ## SQL faster. ## Along with a GenebiotypeFilter if (doBench) microbenchmark(.withSQL(edb, by = "gene", filter = GenebiotypeFilter("protein_coding")) , .withR(edb, by = "gene", filter = GenebiotypeFilter("protein_coding")) , times = 3) } dontrun_test_ordering_transcriptsBy <- function() { .withR <- function(x, ...) { ensembldb:::orderResultsInR(x) <- TRUE transcriptsBy(x, ...) } .withSQL <- function(x, ...) { ensembldb:::orderResultsInR(x) <- FALSE transcriptsBy(x, ...) } res_sql <- .withSQL(edb) res_r <- .withR(edb) checkEquals(res_sql, res_r) microbenchmark(.withSQL(edb), .withR(edb), times = 3) ## same speed res_sql <- .withSQL(edb, filter = SeqnameFilter("Y")) res_r <- .withR(edb, filter = SeqnameFilter("Y")) checkEquals(res_sql, res_r) microbenchmark(.withSQL(edb, filter = SeqnameFilter("Y")), .withR(edb, filter = SeqnameFilter("Y")), times = 3) ## SQL slighly faster. } dontrun_query_tune <- function() { ## Query tuning: library(RSQLite) con <- dbconn(edb) Q <- "select distinct tx2exon.exon_id,exon.exon_seq_start,exon.exon_seq_end,gene.seq_name,tx2exon.tx_id,gene.seq_strand,tx2exon.exon_idx from gene join tx on (gene.gene_id=tx.gene_id) join tx2exon on (tx.tx_id=tx2exon.tx_id) join exon on (tx2exon.exon_id=exon.exon_id) where gene.seq_name = 'Y'" system.time(dbGetQuery(con, Q)) Q2 <- "select distinct tx2exon.exon_id,exon.exon_seq_start,exon.exon_seq_end,gene.seq_name,tx2exon.tx_id,gene.seq_strand,tx2exon.exon_idx from exon join tx2exon on (tx2exon.exon_id = exon.exon_id) join tx on (tx2exon.tx_id = tx.tx_id) join gene on (gene.gene_id=tx.gene_id) where gene.seq_name = 'Y'" system.time(dbGetQuery(con, Q2)) Q3 <- "select distinct tx2exon.exon_id,exon.exon_seq_start,exon.exon_seq_end,gene.seq_name,tx2exon.tx_id,gene.seq_strand,tx2exon.exon_idx from tx2exon join exon on (tx2exon.exon_id = exon.exon_id) join tx on (tx2exon.tx_id = tx.tx_id) join gene on (gene.gene_id=tx.gene_id) where gene.seq_name = 'Y'" system.time(dbGetQuery(con, Q3)) Q4 <- "select distinct tx2exon.exon_id,exon.exon_seq_start,exon.exon_seq_end,gene.seq_name,tx2exon.tx_id,gene.seq_strand,tx2exon.exon_idx from tx2exon join exon on (tx2exon.exon_id = exon.exon_id) join tx on (tx2exon.tx_id = tx.tx_id) join gene on (gene.gene_id=tx.gene_id) where gene.seq_name = 'Y' order by tx.tx_id" system.time(dbGetQuery(con, Q4)) Q5 <- "select distinct tx2exon.exon_id,exon.exon_seq_start,exon.exon_seq_end,gene.seq_name,tx2exon.tx_id,gene.seq_strand,tx2exon.exon_idx from tx2exon inner join exon on (tx2exon.exon_id = exon.exon_id) inner join tx on (tx2exon.tx_id = tx.tx_id) inner join gene on (gene.gene_id=tx.gene_id) where gene.seq_name = 'Y' order by tx.tx_id" system.time(dbGetQuery(con, Q5)) Q6 <- "select distinct tx2exon.exon_id,exon.exon_seq_start,exon.exon_seq_end,gene.seq_name,tx2exon.tx_id,gene.seq_strand,tx2exon.exon_idx from gene inner join tx on (gene.gene_id=tx.gene_id) inner join tx2exon on (tx.tx_id=tx2exon.tx_id) inner join exon on (tx2exon.exon_id=exon.exon_id) where gene.seq_name = 'Y' order by tx.tx_id asc" system.time(dbGetQuery(con, Q6)) } ## Compare the performance of doing the sorting within R or ## directly in the SQL query. dontrun_test_ordering_performance <- function() { library(RUnit) library(RSQLite) ## gene table: order by in SQL query vs R: db_con <- dbconn(edb) .callWithOrder <- function(con, query, orderBy = "", orderSQL = TRUE) { if (all(orderBy == "")) orderBy <- NULL if (orderSQL & !is.null(orderBy)) { orderBy <- paste(orderBy, collapse = ", ") query <- paste0(query, " order by ", orderBy) } res <- dbGetQuery(con, query) if (!orderSQL & !all(is.null(orderBy))) { if (!all(orderBy %in% colnames(res))) stop("orderBy not in columns!") ## Do the ordering in R res <- res[do.call(order, c(list(method = "radix"), as.list(res[, orderBy, drop = FALSE]))), ] } rownames(res) <- NULL return(res) } ####################### ## gene table ## Simple condition the_q <- "select * from gene" system.time(res1 <- .callWithOrder(db_con, query = the_q)) system.time(res2 <- .callWithOrder(db_con, query = the_q, orderSQL = FALSE)) checkIdentical(res1, res2) ## order by gene_id orderBy <- "gene_id" system.time(res1 <- .callWithOrder(db_con, query = the_q, orderBy = orderBy)) system.time(res2 <- .callWithOrder(db_con, query = the_q, orderBy = orderBy, orderSQL = FALSE)) ## SQL: 0.16, R: 0.164. checkIdentical(res1, res2) ## order by gene_name orderBy <- "gene_name" system.time(res1 <- .callWithOrder(db_con, query = the_q, orderBy = orderBy)) system.time(res2 <- .callWithOrder(db_con, query = the_q, orderBy = orderBy, orderSQL = FALSE)) checkIdentical(res1, res2) ## SQL: 0.245, R: 0.185 ## sort by gene_name and gene_seq_start orderBy <- c("gene_name", "gene_seq_start") system.time(res1 <- .callWithOrder(db_con, query = the_q, orderBy = orderBy)) system.time(res2 <- .callWithOrder(db_con, query = the_q, orderBy = orderBy, orderSQL = FALSE)) ## SQL: 0.26, R: 0.188 checkEquals(res1, res2) ## with subsetting: the_q <- "select * from gene where seq_name in ('5', 'Y')" orderBy <- c("gene_name", "gene_seq_start") system.time(res1 <- .callWithOrder(db_con, query = the_q, orderBy = orderBy)) system.time(res2 <- .callWithOrder(db_con, query = the_q, orderBy = orderBy, orderSQL = FALSE)) ## SQL: 0.031, R: 0.024 checkEquals(res1, res2) ######################## ## joining tables. the_q <- paste0("select * from gene join tx on (gene.gene_id = tx.gene_id)", " join tx2exon on (tx.tx_id = tx2exon.tx_id)") orderBy <- c("tx_id", "exon_id") system.time(res1 <- .callWithOrder(db_con, query = the_q, orderBy = orderBy)) system.time(res2 <- .callWithOrder(db_con, query = the_q, orderBy = orderBy, orderSQL = FALSE)) ## SQL: 9.6, R: 9.032 checkEquals(res1, res2) ## subsetting. the_q <- paste0("select * from gene join tx on (gene.gene_id = tx.gene_id)", " join tx2exon on (tx.tx_id = tx2exon.tx_id) where", " seq_name = 'Y'") orderBy <- c("tx_id", "exon_id") system.time(res1 <- .callWithOrder(db_con, query = the_q, orderBy = orderBy)) system.time(res2 <- .callWithOrder(db_con, query = the_q, orderBy = orderBy, orderSQL = FALSE)) ## SQL: 0.9, R: 1.6 checkEquals(res1, res2) } ## implement: ## .checkOrderBy: checks order.by argument removing columns that are ## not present in the database ## orderBy columns are added to the columns. ## .orderDataFrameBy: orders the dataframe by the specified columns. ensembldb/inst/unitTests/test_performance.R0000644000175400017540000000475713001231245022201 0ustar00biocbuildbiocbuild############################################################ ## These are not test cases to be executed, but performance ## comparisons. library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 ############################################################ ## Compare MySQL vs SQLite backends: ## Amazing how inefficient the MySQL backend seems to be! Most ## likely it's due to RMySQL, not MySQL. dontrun_test_MySQL_vs_SQLite <- function() { ## Compare the performance of the MySQL backend against ## the SQLite backend. edb_mysql <- useMySQL(edb, user = "anonuser", pass = "") library(microbenchmark) ## genes microbenchmark(genes(edb), genes(edb_mysql), times = 5) microbenchmark(genes(edb, filter = GenebiotypeFilter("lincRNA")), genes(edb_mysql, filter = GenebiotypeFilter("lincRNA")), times = 5) microbenchmark(genes(edb, filter = SeqnameFilter(20:23)), genes(edb_mysql, filter = SeqnameFilter(20:23)), times = 5) microbenchmark(genes(edb, columns = "tx_id"), genes(edb_mysql, columns = "tx_id"), times = 5) microbenchmark(genes(edb, filter = GenenameFilter("BCL2L11")), genes(edb_mysql, filter = GenenameFilter("BCL2L11")), times = 5) ## transcripts microbenchmark(transcripts(edb), transcripts(edb_mysql), times = 5) microbenchmark(transcripts(edb, filter = GenenameFilter("BCL2L11")), transcripts(edb_mysql, filter = GenenameFilter("BCL2L11")), times = 5) ## exons microbenchmark(exons(edb), exons(edb_mysql), times = 5) microbenchmark(exons(edb, filter = GenenameFilter("BCL2L11")), exons(edb_mysql, filter = GenenameFilter("BCL2L11")), times = 5) ## exonsBy microbenchmark(exonsBy(edb), exonsBy(edb_mysql), times = 5) microbenchmark(exonsBy(edb, filter = SeqnameFilter("Y")), exonsBy(edb_mysql, filter = SeqnameFilter("Y")), times = 5) ## cdsBy microbenchmark(cdsBy(edb), cdsBy(edb_mysql), times = 5) microbenchmark(cdsBy(edb, by = "gene"), cdsBy(edb_mysql, by = "gene"), times = 5) microbenchmark(cdsBy(edb, filter = SeqstrandFilter("-")), cdsBy(edb_mysql, filter = SeqstrandFilter("-")), times = 5) } ensembldb/inst/unitTests/test_returnCols.R0000644000175400017540000003146713001231247022040 0ustar00biocbuildbiocbuild############################################################ ## Here we're checking the returnFilterColumns setting, i.e. ## whether also filter columns should be returned or not. library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 ## Testing the internal function. test_set_returnFilterColumns <- function(x) { orig <- returnFilterColumns(edb) returnFilterColumns(edb) <- TRUE checkEquals(TRUE, returnFilterColumns(edb)) returnFilterColumns(edb) <- FALSE checkEquals(FALSE, returnFilterColumns(edb)) checkException(returnFilterColumns(edb) <- "d") checkException(returnFilterColumns(edb) <- c(TRUE, FALSE)) ## Restore the "original" setting returnFilterColumns(edb) <- orig } test_with_genes <- function(x) { orig <- returnFilterColumns(edb) returnFilterColumns(edb) <- FALSE ## What happens if we use a GRangesFilter with return filter cols FALSE? grf <- GRangesFilter(GRanges(17, IRanges(57180000, 57233000))) res <- genes(edb, filter = grf) checkEquals(res$gene_id, c("ENSG00000224738", "ENSG00000182628", "ENSG00000252212", "ENSG00000211514", "ENSG00000207996")) cols <- c("gene_id", "gene_name") res <- genes(edb, filter = grf, return.type = "data.frame", columns = cols) ## Expect only the columns checkEquals(colnames(res), cols) returnFilterColumns(edb) <- TRUE res <- genes(edb, filter = grf, return.type = "data.frame", columns = cols) ## Now I expect also the gene coords. checkEquals(colnames(res), c(cols, "gene_seq_start", "gene_seq_end", "seq_name", "seq_strand")) ## Use a gene biotype filter gbt <- GenebiotypeFilter("protein_coding") returnFilterColumns(edb) <- TRUE res <- genes(edb, filter = list(gbt, grf), return.type = "data.frame", columns = cols) checkEquals(res$gene_name, "SKA2") checkEquals(colnames(res), c(cols, "gene_biotype", "gene_seq_start", "gene_seq_end", "seq_name", "seq_strand")) returnFilterColumns(edb) <- FALSE res <- genes(edb, filter = list(gbt, grf), return.type = "data.frame", columns = cols) checkEquals(colnames(res), cols) returnFilterColumns(edb) <- orig } test_with_tx <- function(x) { orig <- returnFilterColumns(edb) returnFilterColumns(edb) <- FALSE ## What happens if we use a GRangesFilter with return filter cols FALSE? grf <- GRangesFilter(GRanges(17, IRanges(57180000, 57233000))) res <- transcripts(edb, filter = grf) cols <- c("tx_id", "gene_name") res <- transcripts(edb, filter = grf, return.type = "data.frame", columns = cols) ## Expect only the columns checkEquals(colnames(res), cols) returnFilterColumns(edb) <- TRUE res <- transcripts(edb, filter = grf, return.type = "data.frame", columns = cols) ## Now I expect also the gene coords. checkEquals(colnames(res), c(cols, "tx_seq_start", "tx_seq_end", "seq_name", "seq_strand")) ## Use a gene biotype filter gbt <- GenebiotypeFilter("protein_coding") returnFilterColumns(edb) <- TRUE res <- transcripts(edb, filter = list(gbt, grf), return.type = "data.frame", columns = cols) checkEquals(unique(res$gene_name), "SKA2") checkEquals(colnames(res), c(cols, "gene_biotype", "tx_seq_start", "tx_seq_end", "seq_name", "seq_strand")) returnFilterColumns(edb) <- FALSE res <- transcripts(edb, filter = list(gbt, grf), return.type = "data.frame", columns = cols) checkEquals(colnames(res), cols) returnFilterColumns(edb) <- orig } test_with_exons <- function(x) { orig <- returnFilterColumns(edb) returnFilterColumns(edb) <- FALSE ## What happens if we use a GRangesFilter with return filter cols FALSE? grf <- GRangesFilter(GRanges(17, IRanges(57180000, 57233000))) res <- exons(edb, filter = grf) cols <- c("exon_id", "gene_name") res <- exons(edb, filter = grf, return.type = "data.frame", columns = cols) ## Expect only the columns checkEquals(colnames(res), cols) returnFilterColumns(edb) <- TRUE res <- exons(edb, filter = grf, return.type = "data.frame", columns = cols) ## Now I expect also the gene coords. checkEquals(colnames(res), c(cols, "exon_seq_start", "exon_seq_end", "seq_name", "seq_strand")) ## Use a gene biotype filter gbt <- GenebiotypeFilter("protein_coding") returnFilterColumns(edb) <- TRUE res <- exons(edb, filter = list(gbt, grf), return.type = "data.frame", columns = cols) checkEquals(unique(res$gene_name), c("TRIM37", "SKA2")) checkEquals(colnames(res), c(cols, "gene_biotype", "exon_seq_start", "exon_seq_end", "seq_name", "seq_strand")) returnFilterColumns(edb) <- FALSE res <- exons(edb, filter = list(gbt, grf), return.type = "data.frame", columns = cols) checkEquals(colnames(res), cols) returnFilterColumns(edb) <- orig } test_with_exonsBy <- function(x) { orig <- returnFilterColumns(edb) returnFilterColumns(edb) <- FALSE ## What happens if we use a GRangesFilter with return filter cols FALSE? grf <- GRangesFilter(GRanges(17, IRanges(57180000, 57233000))) ## By genes cols <- c("exon_id", "gene_name") res <- exonsBy(edb, by = "gene", filter = grf, columns = cols) res <- unlist(res) ## Expect only the columns checkEquals(colnames(mcols(res)), cols) returnFilterColumns(edb) <- TRUE res <- exonsBy(edb, by = "gene", filter = grf, columns = cols) res <- unlist(res) ## Now I expect also the gene coords, but not the seq_name and seq_strand, as these ## are redundant with data which is in the GRanges! checkEquals(colnames(mcols(res)), c(cols, "gene_seq_start", "gene_seq_end")) ## Use a gene biotype filter gbt <- GenebiotypeFilter("protein_coding") returnFilterColumns(edb) <- TRUE res <- unlist(exonsBy(edb, by = "gene", filter = list(gbt, grf), columns = cols)) checkEquals(unique(res$gene_name), c("SKA2")) checkEquals(colnames(mcols(res)), c(cols, "gene_biotype", "gene_seq_start", "gene_seq_end")) returnFilterColumns(edb) <- FALSE res <- unlist(exonsBy(edb, by = "gene", filter = list(gbt, grf), columns = cols)) checkEquals(colnames(mcols(res)), cols) ## By tx returnFilterColumns(edb) <- FALSE cols <- c("exon_id", "gene_name") res <- exonsBy(edb, by = "tx", filter = grf, columns = cols) res <- unlist(res) ## Expect only the columns checkEquals(colnames(mcols(res)), c(cols, "exon_rank")) returnFilterColumns(edb) <- TRUE res <- exonsBy(edb, by = "tx", filter = grf, columns = cols) res <- unlist(res) ## Now I expect also the gene coords. checkEquals(colnames(mcols(res)), c(cols, "tx_seq_start", "tx_seq_end", "exon_rank")) ## Use a gene biotype filter gbt <- GenebiotypeFilter("protein_coding") returnFilterColumns(edb) <- TRUE res <- unlist(exonsBy(edb, by = "tx", filter = list(gbt, grf), columns = cols)) checkEquals(unique(res$gene_name), c("SKA2")) checkEquals(colnames(mcols(res)), c(cols, "gene_biotype", "tx_seq_start", "tx_seq_end", "exon_rank")) returnFilterColumns(edb) <- FALSE res <- unlist(exonsBy(edb, by = "tx", filter = list(gbt, grf), columns = cols)) checkEquals(colnames(mcols(res)), c(cols, "exon_rank")) returnFilterColumns(edb) <- orig } test_with_transcriptsBy <- function(x) { orig <- returnFilterColumns(edb) returnFilterColumns(edb) <- FALSE ## What happens if we use a GRangesFilter with return filter cols FALSE? grf <- GRangesFilter(GRanges(17, IRanges(57180000, 57233000))) ## By genes cols <- c("tx_id", "gene_name") res <- transcriptsBy(edb, by = "gene", filter = grf, columns = cols) res <- unlist(res) ## Expect only the columns checkEquals(colnames(mcols(res)), cols) returnFilterColumns(edb) <- TRUE res <- transcriptsBy(edb, by = "gene", filter = grf, columns = cols) res <- unlist(res) ## Now I expect also the gene coords. checkEquals(colnames(mcols(res)), c(cols, "gene_seq_start", "gene_seq_end")) ## Use a gene biotype filter gbt <- GenebiotypeFilter("protein_coding") returnFilterColumns(edb) <- TRUE res <- unlist(transcriptsBy(edb, by = "gene", filter = list(gbt, grf), columns = cols)) checkEquals(unique(res$gene_name), c("SKA2")) checkEquals(colnames(mcols(res)), c(cols, "gene_biotype", "gene_seq_start", "gene_seq_end")) returnFilterColumns(edb) <- FALSE res <- unlist(transcriptsBy(edb, by = "gene", filter = list(gbt, grf), columns = cols)) checkEquals(colnames(mcols(res)), cols) ## ## By exon ## returnFilterColumns(edb) <- FALSE ## cols <- c("tx_id", "gene_name") ## res <- transcriptsBy(edb, by = "exon", filter = grf, columns = cols) ## res <- unlist(res) ## ## Expect only the columns ## checkEquals(colnames(mcols(res)), c(cols)) ## returnFilterColumns(edb) <- TRUE ## res <- transcriptsBy(edb, by = "exon", filter = grf, columns = cols) ## res <- unlist(res) ## ## Now I expect also the gene coords. ## checkEquals(colnames(mcols(res)), c(cols, "exon_seq_start", "exon_seq_end")) ## ## Use a gene biotype filter ## gbt <- GenebiotypeFilter("protein_coding") ## returnFilterColumns(edb) <- TRUE ## res <- unlist(transcriptsBy(edb, by = "exon", filter = list(gbt, grf), columns = cols)) ## checkEquals(unique(res$gene_name), c("SKA2", "TRIM37")) ## checkEquals(colnames(mcols(res)), c(cols, "gene_biotype", "exon_seq_start", "exon_seq_end")) ## returnFilterColumns(edb) <- FALSE ## res <- unlist(transcriptsBy(edb, by = "exon", filter = list(gbt, grf), columns = cols)) ## checkEquals(colnames(mcols(res)), c(cols)) returnFilterColumns(edb) <- orig } test_with_cdsBy <- function(x) { orig <- returnFilterColumns(edb) grf <- GRangesFilter(GRanges(17, IRanges(57180000, 57233000))) ## By tx returnFilterColumns(edb) <- FALSE cols <- c("gene_id", "gene_name") res <- cdsBy(edb, by = "tx", filter = grf, columns = cols) res <- unlist(res) ## Expect only the columns checkEquals(colnames(mcols(res)), c(cols, "exon_id", "exon_rank")) returnFilterColumns(edb) <- TRUE res <- cdsBy(edb, by = "tx", filter = grf, columns = cols) res <- unlist(res) ## Now I expect also the gene coords. checkEquals(colnames(mcols(res)), c(cols, "tx_seq_start", "tx_seq_end", "seq_name", "seq_strand", "exon_id", "exon_rank")) ## Use a gene biotype filter gbt <- GenebiotypeFilter("protein_coding") returnFilterColumns(edb) <- TRUE res <- unlist(cdsBy(edb, by = "tx", filter = list(gbt, grf), columns = cols)) checkEquals(unique(res$gene_name), c("SKA2")) checkEquals(colnames(mcols(res)), c(cols, "gene_biotype", "tx_seq_start", "tx_seq_end", "seq_name", "seq_strand", "exon_id", "exon_rank")) returnFilterColumns(edb) <- FALSE res <- unlist(cdsBy(edb, by = "tx", filter = list(gbt, grf), columns = cols)) checkEquals(colnames(mcols(res)), c(cols, "exon_id", "exon_rank")) returnFilterColumns(edb) <- orig } test_with_threeUTRsByTranscript <- function(x) { orig <- returnFilterColumns(edb) grf <- GRangesFilter(GRanges(17, IRanges(57180000, 57233000))) ## By tx returnFilterColumns(edb) <- FALSE cols <- c("gene_id", "gene_name") res <- threeUTRsByTranscript(edb, filter = grf, columns = cols) res <- unlist(res) ## Expect only the columns checkEquals(colnames(mcols(res)), c(cols, "exon_id", "exon_rank")) returnFilterColumns(edb) <- TRUE res <- threeUTRsByTranscript(edb, filter = grf, columns = cols) res <- unlist(res) ## Now I expect also the gene coords. checkEquals(colnames(mcols(res)), c(cols, "tx_seq_start", "tx_seq_end", "seq_name", "seq_strand", "exon_id", "exon_rank")) ## Use a gene biotype filter gbt <- GenebiotypeFilter("protein_coding") returnFilterColumns(edb) <- TRUE res <- unlist(threeUTRsByTranscript(edb, filter = list(gbt, grf), columns = cols)) checkEquals(unique(res$gene_name), c("SKA2")) checkEquals(colnames(mcols(res)), c(cols, "gene_biotype", "tx_seq_start", "tx_seq_end", "seq_name", "seq_strand", "exon_id", "exon_rank")) returnFilterColumns(edb) <- FALSE res <- unlist(threeUTRsByTranscript(edb, filter = list(gbt, grf), columns = cols)) checkEquals(colnames(mcols(res)), c(cols, "exon_id", "exon_rank")) returnFilterColumns(edb) <- orig } ensembldb/inst/unitTests/test_select.R0000644000175400017540000002140013001231245021137 0ustar00biocbuildbiocbuild####============================================================ ## test cases for AnnotationDbi methods. ## ####------------------------------------------------------------ library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 test_columns <- function(){ cols <- columns(edb) ## Don't expect to see any _ there... checkEquals(length(grep(cols, pattern="_")), 0) } test_keytypes <- function(){ keyt <- keytypes(edb) checkEquals(all(c("GENEID", "EXONID", "TXID") %in% keyt), TRUE) } test_mapper <- function(){ Test <- ensembldb:::ensDbColumnForColumn(edb, "GENEID") checkEquals(unname(Test), "gene_id") Test <- ensembldb:::ensDbColumnForColumn(edb, c("GENEID", "TXID")) checkEquals(unname(Test), c("gene_id", "tx_id")) Test <- ensembldb:::ensDbColumnForColumn(edb, c("GENEID", "TXID", "bla")) checkEquals(unname(Test), c("gene_id", "tx_id")) } test_keys <- function(){ ## get all gene ids system.time( ids <- keys(edb, "GENEID") ) checkEquals(length(ids), length(unique(ids))) ## get all tx ids system.time( ids <- keys(edb, "TXID") ) ## Get the TXNAME... nms <- keys(edb, "TXNAME") checkEquals(nms, ids) checkEquals(length(ids), length(unique(ids))) ## get all gene names system.time( ids <- keys(edb, "GENENAME") ) checkEquals(length(ids), length(unique(ids))) ## get all seq names system.time( ids <- keys(edb, "SEQNAME") ) checkEquals(length(ids), length(unique(ids))) ## get all seq strands system.time( ids <- keys(edb, "SEQSTRAND") ) checkEquals(length(ids), length(unique(ids))) ## get all gene biotypes system.time( ids <- keys(edb, "GENEBIOTYPE") ) checkEquals(ids, listGenebiotypes(edb)) } test_select <- function(){ ## Test: ## Provide GenenameFilter. gf <- GenenameFilter("BCL2") system.time( Test <- select(edb, keys=gf) ) ## Provide list of GenenameFilter and TxbiotypeFilter. Test2 <- select(edb, keys=list(gf, TxbiotypeFilter("protein_coding"))) checkEquals(Test$EXONID[Test$TXBIOTYPE == "protein_coding"], Test2$EXONID) ## Choose selected columns. Test3 <- select(edb, keys=gf, columns=c("GENEID", "GENENAME", "SEQNAME")) checkEquals(unique(Test[, c("GENEID", "GENENAME", "SEQNAME")]), Test3) ## Provide keys. Test4 <- select(edb, keys="BCL2", keytype="GENENAME") checkEquals(Test[, colnames(Test4)], Test4) txs <- keys(edb, "TXID") ## Just get stuff from the tx table; should be faster. system.time( Test <- select(edb, keys=txs, columns=c("TXID", "TXBIOTYPE", "GENEID"), keytype="TXID") ) checkEquals(all(Test$TXID==txs), TRUE) ## Get all lincRNA genes Test <- select(edb, keys="lincRNA", columns=c("GENEID", "GENEBIOTYPE", "GENENAME"), keytype="GENEBIOTYPE") Test2 <- select(edb, keys=GenebiotypeFilter("lincRNA"), columns=c("GENEID", "GENEBIOTYPE", "GENENAME")) checkEquals(Test[, colnames(Test2)], Test2) ## All on chromosome 21 Test <- select(edb, keys="21", columns=c("GENEID", "GENEBIOTYPE", "GENENAME"), keytype="SEQNAME") Test2 <- select(edb, keys=SeqnameFilter("21"), columns=c("GENEID", "GENEBIOTYPE", "GENENAME")) checkEquals(Test[, colnames(Test2)], Test2) ## What if we can't find it? Test <- select(edb, keys="bla", columns=c("GENEID", "GENENAME"), keytype="GENENAME") ## Run the full thing. ## system.time( ## All <- select(edb) ## ) ## Test <- select(edb, keys=txs, keytype="TXID") ## checkEquals(Test, All) Test <- select(edb, keys="ENST00000000233", columns=c("GENEID", "GENENAME"), keytype="TXNAME") checkEquals(Test$TXNAME, "ENST00000000233") ## Check what happens if we just add TXNAME and also TXID. Test2 <- select(edb, keys=list(gf, TxbiotypeFilter("protein_coding")), columns=c("TXID", "TXNAME", "GENENAME", "GENEID")) } test_mapIds <- function(){ ## Simple... map gene ids to gene names allgenes <- keys(edb, keytype="GENEID") randordergenes <- allgenes[sample(1:length(allgenes), 100)] system.time( mi <- mapIds(edb, keys=allgenes, keytype="GENEID", column = "GENENAME") ) checkEquals(allgenes, names(mi)) ## What happens if the ordering is different: mi <- mapIds(edb, keys=randordergenes, keytype="GENEID", column = "GENENAME") checkEquals(randordergenes, names(mi)) ## Now check the different options: ## Handle multi mappings. ## first first <- mapIds(edb, keys=randordergenes, keytype="GENEID", column="TXID") checkEquals(names(first), randordergenes) ## list lis <- mapIds(edb, keys=randordergenes, keytype="GENEID", column="TXID", multiVals="list") checkEquals(names(lis), randordergenes) Test <- lapply(lis, function(z){return(z[1])}) checkEquals(first, unlist(Test)) ## filter filt <- mapIds(edb, keys=randordergenes, keytype="GENEID", column="TXID", multiVals="filter") checkEquals(filt, unlist(lis[unlist(lapply(lis, length)) == 1])) ## asNA asNA <- mapIds(edb, keys=randordergenes, keytype="GENEID", column="TXID", multiVals="asNA") ## Check what happens if we provide 2 identical keys. Test <- mapIds(edb, keys=c("BCL2", "BCL2L11", "BCL2"), keytype="GENENAME", column="TXID") ## Submit Filter: Test <- mapIds(edb, keys=SeqnameFilter("Y"), column="GENEID", multiVals="list") TestS <- select(edb, keys=Test[[1]], columns="SEQNAME", keytype="GENEID") checkEquals(unique(TestS$SEQNAME), "Y") ## Submit 2 filter. Test <- mapIds(edb, keys=list(SeqnameFilter("Y"), SeqstrandFilter("-")), multiVals="list", column="GENEID") TestS <- select(edb, keys=Test[[1]], keytype="GENEID", columns=c("SEQNAME", "SEQSTRAND")) checkTrue(all(TestS$SEQNAME == "Y")) checkTrue(all(TestS$SEQSTRAND == -1)) } ## Test if the results are properly sorted if we submit a single filter or just keys. test_select_sorted <- function() { ks <- c("ZBTB16", "BCL2", "SKA2", "BCL2L11") ## gene_name res <- select(edb, keys = ks, keytype = "GENENAME") checkEquals(unique(res$GENENAME), ks) res <- select(edb, keys = GenenameFilter(ks)) checkEquals(unique(res$GENENAME), ks) ## Using two filters; res <- select(edb, keys = list(GenenameFilter(ks), TxbiotypeFilter("nonsense_mediated_decay"))) ## We don't expect same sorting here! checkTrue(!all(unique(res$GENENAME) == ks[ks %in% unique(res$GENENAME)])) ## symbol res <- select(edb, keys = ks, keytype = "SYMBOL", columns = c("GENENAME", "SYMBOL", "SEQNAME")) ## tx_biotype ks <- c("retained_intron", "nonsense_mediated_decay") res <- select(edb, keys = ks, keytype = "TXBIOTYPE", columns = c("GENENAME", "TXBIOTYPE")) checkEquals(unique(res$TXBIOTYPE), ks) res <- select(edb, keys = TxbiotypeFilter(ks), keytype = "TXBIOTYPE", columns = c("GENENAME", "TXBIOTYPE")) checkEquals(unique(res$TXBIOTYPE), ks) } test_select_symbol <- function() { ## Can I use SYMBOL as keytype? ks <- c("ZBTB16", "BCL2", "SKA2", "BCL2L11") res <- select(edb, keys = ks, keytype = "GENENAME") res2 <- select(edb, keys = ks, keytype = "SYMBOL") checkEquals(res, res2) ## Can I use the SymbolFilter? res <- select(edb, keys = GenenameFilter(ks), columns = c("TXNAME", "SYMBOL", "GENEID")) checkEquals(colnames(res), c("TXNAME", "SYMBOL", "GENEID", "GENENAME")) res <- select(edb, keys = SymbolFilter(ks), columns=c("GENEID")) checkEquals(colnames(res), c("GENEID", "SYMBOL")) checkEquals(res$SYMBOL, ks) ## Can I ask for SYMBOL? res <- select(edb, keys = list(SeqnameFilter("Y"), GenebiotypeFilter("lincRNA")), columns = c("GENEID", "SYMBOL")) checkEquals(colnames(res), c("GENEID", "SYMBOL", "SEQNAME", "GENEBIOTYPE")) } test_select_symbol_n_txname <- function() { ks <- c("ZBTB16", "BCL2", "SKA2") ## Symbol allowed in keytype res <- select(edb, keys = ks, keytype = "SYMBOL", columns = "GENENAME") checkEquals(colnames(res), c("SYMBOL", "GENENAME")) checkEquals(res$SYMBOL, ks) ## Symbol using SymbolFilter res <- select(edb, keys = SymbolFilter(ks), columns = "GENENAME") checkEquals(colnames(res), c("GENENAME", "SYMBOL")) checkEquals(res$SYMBOL, ks) ## Symbol as a column. res <- select(edb, keys = ks, keytype = "GENENAME", columns = "SYMBOL") checkEquals(colnames(res), c("GENENAME", "SYMBOL")) ## TXNAME as a column res <- select(edb, keys = ks, keytype = "GENENAME", columns = c("TXNAME")) checkEquals(colnames(res), c("GENENAME", "TXNAME")) } ensembldb/inst/unitTests/test_transcript_lengths.R0000644000175400017540000001100713001231247023601 0ustar00biocbuildbiocbuild####============================================================ ## Tests related to transcript/feature length calculations. ## ## ####------------------------------------------------------------ ## Loading data and stuff library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 ## Just run that after Herve has added the mods to the transcriptLengths function. notyetrun_transcriptLengths <- function(){ ## With filter. daFilt <- SeqnameFilter("Y") allTxY <- transcripts(edb, filter=daFilt) txLenY <- transcriptLengths(edb, filter=daFilt) checkEquals(names(allTxY), rownames(txLenY)) ## Check if lengths are OK: txLenY2 <- lengthOf(edb, "tx", filter=daFilt) checkEquals(unname(txLenY2[rownames(txLenY)]), txLenY$tx_len) ## Include the cds, 3' and 5' UTR txLenY <- transcriptLengths(edb, with.cds_len = TRUE, with.utr5_len = TRUE, with.utr3_len = TRUE, filter=daFilt) ## sum of 5' CDS and 3' has to match tx_len: txLen <- rowSums(txLenY[, c("cds_len", "utr5_len", "utr3_len")]) checkEquals(txLenY[!is.na(txLen), "tx_len"], unname(txLen[!is.na(txLen)])) ## just to be sure... checkEquals(txLenY[!is.na(txLenY$utr3_len), "tx_len"], unname(txLen[!is.na(txLenY$utr3_len)])) ## Seems to be OK. ## Next check the 5' UTR lengths: that also verifies the fiveUTR call. futr <- fiveUTRsByTranscript(edb, filter=daFilt) futrLen <- sum(width(futr)) checkEquals(unname(futrLen), txLenY[names(futrLen), "utr5_len"]) ## 3' tutr <- threeUTRsByTranscript(edb, filter=daFilt) tutrLen <- sum(width(tutr)) checkEquals(unname(tutrLen), txLenY[names(tutrLen), "utr3_len"]) } notrun_compare_full <- function(){ ## That's on the full thing. ## Test if the result has the same ordering than the transcripts call. allTx <- transcripts(edb) txLen <- transcriptLengths(edb, with.cds_len=TRUE, with.utr5_len=TRUE, with.utr3_len=TRUE) checkEquals(names(allTx), rownames(txLen)) system.time( futr <- fiveUTRsByTranscript(edb) ) ## 23 secs. futrLen <- sum(width(futr)) ## do I need reduce??? checkEquals(unname(futrLen), txLen[names(futrLen), "utr5_len"]) ## 3' system.time( tutr <- threeUTRsByTranscript(edb) ) system.time( tutrLen <- sum(width(tutr)) ) checkEquals(unname(tutrLen), txLen[names(tutrLen), "utr3_len"]) } notrun_compare_to_genfeat <- function(){ library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene system.time( Len <- transcriptLengths(edb) ) ## Woa, 52 sec system.time( txLen <- lengthOf(edb, "tx") ) ## Faster, 31 sec checkEquals(Len$tx_len, unname(txLen[rownames(Len)])) system.time( Len2 <- transcriptLengths(txdb) ) ## :) 2.5 sec. ## Next. system.time( Len <- transcriptLengths(edb, with.cds_len = TRUE) ) ## 56 sec system.time( Len2 <- transcriptLengths(txdb, with.cds_len=TRUE) ) ## 4 sec. ## Calling the transcriptLengths of GenomicFeatures on the EnsDb. system.time( Def <- GenomicFeatures::transcriptLengths(edb) ) ## 26.5 sec system.time( WithCds <- GenomicFeatures::transcriptLengths(edb, with.cds_len=TRUE) ) ## 55 sec system.time( WithAll <- GenomicFeatures::transcriptLengths(edb, with.cds_len=TRUE, with.utr5_len=TRUE, with.utr3_len=TRUE) ) ## 99 secs ## Get my versions... system.time( MyDef <- ensembldb:::.transcriptLengths(edb) ) ## 31 sec system.time( MyWithCds <- ensembldb:::.transcriptLengths(edb, with.cds_len=TRUE) ) ## 44 sec system.time( MyWithAll <- ensembldb:::.transcriptLengths(edb, with.cds_len=TRUE, with.utr5_len=TRUE, with.utr3_len=TRUE) ) ## 63 sec ## Should be all the same!!! rownames(MyDef) <- NULL checkEquals(Def, MyDef) ## rownames(MyWithCds) <- NULL MyWithCds[is.na(MyWithCds$cds_len), "cds_len"] <- 0 checkEquals(WithCds, MyWithCds) ## rownames(MyWithAll) <- NULL MyWithAll[is.na(MyWithAll$cds_len), "cds_len"] <- 0 MyWithAll[is.na(MyWithAll$utr3_len), "utr3_len"] <- 0 MyWithAll[is.na(MyWithAll$utr5_len), "utr5_len"] <- 0 checkEquals(WithAll, MyWithAll) } ensembldb/inst/unitTests/test_ucscChromosomeNames.R0000644000175400017540000004761013001231245023650 0ustar00biocbuildbiocbuild###================================================ ## Here we check functionality to use EnsDbs with ## UCSC chromosome names ###------------------------------------------------ library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 ## library(EnsDb.Hsapiens.v83) ## edb <- EnsDb.Hsapiens.v83 ## library(EnsDb.Hsapiens.v81) test_seqlevels <- function(){ orig <- getOption("ensembldb.seqnameNotFound") options(ensembldb.seqnameNotFound=NA) edb <- EnsDb.Hsapiens.v75 SL <- seqlevels(edb) ucscs <- paste0("chr", c(1:22, "X", "Y", "M")) seqlevelsStyle(edb) <- "UCSC" SL2 <- seqlevels(edb) checkEquals(sort(ucscs), sort(SL2[!is.na(SL2)])) ## Check if we throw an error message options(ensembldb.seqnameNotFound="MISSING") checkException(seqlevels(edb)) ## Check if returning original names works. options(ensembldb.seqnameNotFound="ORIGINAL") SL3 <- seqlevels(edb) idx <- which(SL3 %in% ucscs) checkEquals(sort(SL[-idx]), sort(SL3[-idx])) options(ensembldb.seqnameNotFound=orig) } test_seqinfo <- function(){ edb <- EnsDb.Hsapiens.v75 orig <- getOption("ensembldb.seqnameNotFound") options(ensembldb.seqnameNotFound="MISSING") seqlevelsStyle(edb) <- "UCSC" checkException(seqinfo(edb)) options(ensembldb.seqnameNotFound="ORIGINAL") si <- seqinfo(edb) options(ensembldb.seqnameNotFound=orig) } ## Testing if getWhat returns what we expect. test_getWhat_seqnames <- function(){ orig <- getOption("ensembldb.seqnameNotFound") edb <- EnsDb.Hsapiens.v75 seqlevelsStyle(edb) <- "Ensembl" ensRes <- ensembldb:::getWhat(edb, columns=c("seq_name", "seq_strand")) seqlevelsStyle(edb) <- "UCSC" ucscRes <- ensembldb:::getWhat(edb, columns=c("seq_name", "seq_strand")) seqlevelsStyle(edb) <- "NCBI" ncbiRes <- ensembldb:::getWhat(edb, columns=c("seq_name", "seq_strand")) options(ensembldb.seqnameNotFound=orig) } test_SeqnameFilter_seqnames <- function(){ orig <- getOption("ensembldb.seqnameNotFound") options(ensembldb.seqnameNotFound="MISSING") edb <- EnsDb.Hsapiens.v75 seqlevelsStyle(edb) <- "Ensembl" snf <- SeqnameFilter("chrX") snfEns <- SeqnameFilter(c("X", "Y")) snfNo <- SeqnameFilter(c("bla", "blu")) snfSomeNo <- SeqnameFilter(c("bla", "X")) seqlevelsStyle(edb) <- "Ensembl" checkEquals(value(snf), "chrX") ## That makes no sense for a query though. checkEquals(value(snf, edb), "chrX") checkEquals(value(snfEns, edb), c("X", "Y")) seqlevelsStyle(edb) <- "UCSC" checkEquals(value(snf, edb), "X") checkException(value(snfEns, edb)) checkException(value(snfNo, edb)) checkException(value(snfSomeNo, edb)) ## Setting the options to "ORIGINAL" options(ensembldb.seqnameNotFound="ORIGINAL") checkEquals(value(snf, edb), "X") checkEquals(value(snfEns, edb), c("X", "Y")) checkEquals(value(snfNo, edb), c("bla", "blu")) checkEquals(value(snfSomeNo, edb), c("bla", "X")) ## snf <- SeqnameFilter(c("chrX", "Y")) checkEquals(value(snf, edb), c("X", "Y")) options(ensembldb.seqnameNotFound=orig) } test_genes_seqnames <- function(){ orig <- getOption("ensembldb.seqnameNotFound") edb <- EnsDb.Hsapiens.v75 ## Here we want to test whether the result returned by the function does really ## work when changing the seqnames. seqlevelsStyle(edb) <- "Ensembl" ensAll <- genes(edb) ens21Y <- genes(edb, filter=SeqnameFilter(c("Y", "21"))) checkEquals(sort(as.character(unique(seqnames(ens21Y)))), c("21", "Y")) gr <- GRanges(seqnames="Y", ranges=IRanges(start=1, end=59373566), strand="+") ensY <- genes(edb, filter=GRangesFilter(gr)) checkEquals(seqlevels(ensY), "Y") checkEquals(unique(as.character(strand(ensY))), "+") ## Check UCSC stuff seqlevelsStyle(edb) <- "UCSC" options(ensembldb.seqnameNotFound="ORIGINAL") ## Just visually inspect the seqinfo and seqnames for the "all" query. ucscAll <- genes(edb) as.character(unique(seqnames(ucscAll))) ucsc21Y <- genes(edb, filter=SeqnameFilter(c("chrY", "chr21"))) checkEquals(sort(seqlevels(ucsc21Y)), c("chr21", "chrY")) checkEquals(sort(names(ens21Y)), sort(names(ucsc21Y))) ## GRangesFilter. gr <- GRanges(seqnames="chrY", ranges=IRanges(start=1, end=59373566), strand="+") ucscY <- genes(edb, filter=GRangesFilter(gr)) checkEquals(seqlevels(ucscY), "chrY") checkEquals(unique(as.character(strand(ucscY))), "+") checkEquals(sort(names(ensY)), sort(names(ucscY))) options(ensembldb.seqnameNotFound=orig) } test_transcripts_seqnames <- function(){ orig <- getOption("ensembldb.seqnameNotFound") edb <- EnsDb.Hsapiens.v75 seqlevelsStyle(edb) <- "Ensembl" ens21Y <- transcripts(edb, filter=SeqnameFilter(c("Y", "21"))) checkEquals(sort(seqlevels(ens21Y)), c("21", "Y")) gr <- GRanges(seqnames="Y", ranges=IRanges(start=1, end=59373566), strand="+") ensY <- transcripts(edb, filter=GRangesFilter(gr)) checkEquals(seqlevels(ensY), "Y") checkEquals(unique(as.character(strand(ensY))), "+") ## Check UCSC stuff seqlevelsStyle(edb) <- "UCSC" options(ensembldb.seqnameNotFound="ORIGINAL") ucsc21Y <- transcripts(edb, filter=SeqnameFilter(c("chrY", "chr21"))) checkEquals(sort(seqlevels(ucsc21Y)), c("chr21", "chrY")) checkEquals(sort(names(ens21Y)), sort(names(ucsc21Y))) ## GRangesFilter. gr <- GRanges(seqnames="chrY", ranges=IRanges(start=1, end=59373566), strand="+") ucscY <- transcripts(edb, filter=GRangesFilter(gr)) checkEquals(seqlevels(ucscY), "chrY") checkEquals(unique(as.character(strand(ucscY))), "+") checkEquals(sort(names(ensY)), sort(names(ucscY))) options(ensembldb.seqnameNotFound=orig) } test_transcriptsBy_seqnames <- function(){ orig <- getOption("ensembldb.seqnameNotFound") edb <- EnsDb.Hsapiens.v75 seqlevelsStyle(edb) <- "Ensembl" ens21Y <- transcriptsBy(edb, filter=SeqnameFilter(c("Y", "21"))) checkEquals(sort(seqlevels(ens21Y)), c("21", "Y")) gr <- GRanges(seqnames="Y", ranges=IRanges(start=1, end=59373566), strand="+") ensY <- transcriptsBy(edb, filter=GRangesFilter(gr)) checkEquals(seqlevels(ensY), "Y") checkEquals(unique(as.character(unlist(strand(ensY)))), "+") ## Check UCSC stuff seqlevelsStyle(edb) <- "UCSC" options(ensembldb.seqnameNotFound="ORIGINAL") ucsc21Y <- transcriptsBy(edb, filter=SeqnameFilter(c("chrY", "chr21"))) checkEquals(sort(seqlevels(ucsc21Y)), c("chr21", "chrY")) checkEquals(sort(names(ens21Y)), sort(names(ucsc21Y))) ## GRangesFilter. gr <- GRanges(seqnames="chrY", ranges=IRanges(start=1, end=59373566), strand="+") ucscY <- transcriptsBy(edb, filter=GRangesFilter(gr)) checkEquals(seqlevels(ucscY), "chrY") checkEquals(unique(as.character(unlist(strand(ucscY)))), "+") checkEquals(sort(names(ensY)), sort(names(ucscY))) options(ensembldb.seqnameNotFound=orig) } test_exons_seqnames <- function(){ orig <- getOption("ensembldb.seqnameNotFound") edb <- EnsDb.Hsapiens.v75 seqlevelsStyle(edb) <- "Ensembl" ens21Y <- exons(edb, filter=SeqnameFilter(c("Y", "21"))) checkEquals(sort(seqlevels(ens21Y)), c("21", "Y")) gr <- GRanges(seqnames="Y", ranges=IRanges(start=1, end=59373566), strand="+") ensY <- exons(edb, filter=GRangesFilter(gr)) checkEquals(seqlevels(ensY), "Y") checkEquals(unique(as.character(strand(ensY))), "+") ## Check UCSC stuff seqlevelsStyle(edb) <- "UCSC" options(ensembldb.seqnameNotFound="ORIGINAL") ucsc21Y <- exons(edb, filter=SeqnameFilter(c("chrY", "chr21"))) checkEquals(sort(seqlevels(ucsc21Y)), c("chr21", "chrY")) checkEquals(sort(names(ens21Y)), sort(names(ucsc21Y))) ## GRangesFilter. gr <- GRanges(seqnames="chrY", ranges=IRanges(start=1, end=59373566), strand="+") ucscY <- exons(edb, filter=GRangesFilter(gr)) checkEquals(seqlevels(ucscY), "chrY") checkEquals(unique(as.character(strand(ucscY))), "+") checkEquals(sort(names(ensY)), sort(names(ucscY))) options(ensembldb.seqnameNotFound=orig) } test_exonsBy_seqnames <- function(){ orig <- getOption("ensembldb.seqnameNotFound") edb <- EnsDb.Hsapiens.v75 seqlevelsStyle(edb) <- "Ensembl" ens21Y <- exonsBy(edb, filter=SeqnameFilter(c("Y", "21"))) checkEquals(sort(seqlevels(ens21Y)), c("21", "Y")) gr <- GRanges(seqnames="Y", ranges=IRanges(start=1, end=59373566), strand="+") ensY <- exonsBy(edb, filter=GRangesFilter(gr)) checkEquals(seqlevels(ensY), "Y") checkEquals(unique(as.character(unlist(strand(ensY)))), "+") ## Check UCSC stuff seqlevelsStyle(edb) <- "UCSC" options(ensembldb.seqnameNotFound="ORIGINAL") ucsc21Y <- exonsBy(edb, filter=SeqnameFilter(c("chrY", "chr21"))) checkEquals(sort(seqlevels(ucsc21Y)), c("chr21", "chrY")) checkEquals(sort(names(ens21Y)), sort(names(ucsc21Y))) ## GRangesFilter. gr <- GRanges(seqnames="chrY", ranges=IRanges(start=1, end=59373566), strand="+") ucscY <- exonsBy(edb, filter=GRangesFilter(gr)) checkEquals(seqlevels(ucscY), "chrY") checkEquals(unique(as.character(unlist(strand(ucscY)))), "+") checkEquals(sort(names(ensY)), sort(names(ucscY))) options(ensembldb.seqnameNotFound=orig) } test_cdsBy_seqnames <- function(){ orig <- getOption("ensembldb.seqnameNotFound") edb <- EnsDb.Hsapiens.v75 seqlevelsStyle(edb) <- "Ensembl" ens21Y <- cdsBy(edb, filter=SeqnameFilter(c("Y", "21"))) checkEquals(sort(seqlevels(ens21Y)), c("21", "Y")) gr <- GRanges(seqnames="Y", ranges=IRanges(start=1, end=59373566), strand="+") ensY <- cdsBy(edb, filter=GRangesFilter(gr)) checkEquals(seqlevels(ensY), "Y") checkEquals(unique(as.character(unlist(strand(ensY)))), "+") ## Check UCSC stuff seqlevelsStyle(edb) <- "UCSC" options(ensembldb.seqnameNotFound="ORIGINAL") ucsc21Y <- cdsBy(edb, filter=SeqnameFilter(c("chrY", "chr21"))) checkEquals(sort(seqlevels(ucsc21Y)), c("chr21", "chrY")) checkEquals(sort(names(ens21Y)), sort(names(ucsc21Y))) ## GRangesFilter. gr <- GRanges(seqnames="chrY", ranges=IRanges(start=1, end=59373566), strand="+") ucscY <- cdsBy(edb, filter=GRangesFilter(gr)) checkEquals(seqlevels(ucscY), "chrY") checkEquals(unique(as.character(unlist(strand(ucscY)))), "+") checkEquals(sort(names(ensY)), sort(names(ucscY))) options(ensembldb.seqnameNotFound=orig) } test_threeUTRsByTranscript_seqnames <- function(){ orig <- getOption("ensembldb.seqnameNotFound") edb <- EnsDb.Hsapiens.v75 seqlevelsStyle(edb) <- "Ensembl" ens21Y <- threeUTRsByTranscript(edb, filter=SeqnameFilter(c("Y", "21"))) checkEquals(sort(seqlevels(ens21Y)), c("21", "Y")) gr <- GRanges(seqnames="Y", ranges=IRanges(start=1, end=59373566), strand="+") ensY <- threeUTRsByTranscript(edb, filter=GRangesFilter(gr)) checkEquals(seqlevels(ensY), "Y") checkEquals(unique(as.character(unlist(strand(ensY)))), "+") ## Check UCSC stuff seqlevelsStyle(edb) <- "UCSC" options(ensembldb.seqnameNotFound="ORIGINAL") ucsc21Y <- threeUTRsByTranscript(edb, filter=SeqnameFilter(c("chrY", "chr21"))) checkEquals(sort(seqlevels(ucsc21Y)), c("chr21", "chrY")) checkEquals(sort(names(ens21Y)), sort(names(ucsc21Y))) ## GRangesFilter. gr <- GRanges(seqnames="chrY", ranges=IRanges(start=1, end=59373566), strand="+") ucscY <- threeUTRsByTranscript(edb, filter=GRangesFilter(gr)) checkEquals(seqlevels(ucscY), "chrY") checkEquals(unique(as.character(unlist(strand(ucscY)))), "+") checkEquals(sort(names(ensY)), sort(names(ucscY))) options(ensembldb.seqnameNotFound=orig) } test_fiveUTRsByTranscript_seqnames <- function(){ orig <- getOption("ensembldb.seqnameNotFound") edb <- EnsDb.Hsapiens.v75 seqlevelsStyle(edb) <- "Ensembl" ens21Y <- fiveUTRsByTranscript(edb, filter=SeqnameFilter(c("Y", "21"))) checkEquals(sort(seqlevels(ens21Y)), c("21", "Y")) gr <- GRanges(seqnames="Y", ranges=IRanges(start=1, end=59373566), strand="+") ensY <- fiveUTRsByTranscript(edb, filter=GRangesFilter(gr)) checkEquals(seqlevels(ensY), "Y") checkEquals(unique(as.character(unlist(strand(ensY)))), "+") ## Check UCSC stuff seqlevelsStyle(edb) <- "UCSC" options(ensembldb.seqnameNotFound="ORIGINAL") ucsc21Y <- fiveUTRsByTranscript(edb, filter=SeqnameFilter(c("chrY", "chr21"))) checkEquals(sort(seqlevels(ucsc21Y)), c("chr21", "chrY")) checkEquals(sort(names(ens21Y)), sort(names(ucsc21Y))) ## GRangesFilter. gr <- GRanges(seqnames="chrY", ranges=IRanges(start=1, end=59373566), strand="+") ucscY <- fiveUTRsByTranscript(edb, filter=GRangesFilter(gr)) checkEquals(seqlevels(ucscY), "chrY") checkEquals(unique(as.character(unlist(strand(ucscY)))), "+") checkEquals(sort(names(ensY)), sort(names(ucscY))) options(ensembldb.seqnameNotFound=orig) } test_updateEnsDb <- function(){ edb2 <- updateEnsDb(edb) checkEquals(edb2@tables, edb@tables) checkTrue(.hasSlot(edb2, ".properties")) } test_properties <- function(){ checkEquals(ensembldb:::getProperty(edb, "foo"), NA) checkException(ensembldb:::setProperty(edb, "foo")) edb <- ensembldb:::setProperty(edb, foo="bar") checkEquals(ensembldb:::getProperty(edb, "foo"), "bar") checkEquals(length(ensembldb:::properties(edb)), 4) } test_set_get_seqlevelsStyle <- function(){ edb <- EnsDb.Hsapiens.v75 ## Testing the getter/setter for the seqlevelsStyle. checkEquals(seqlevelsStyle(edb), "Ensembl") checkEquals(NA, ensembldb:::getProperty(edb, "seqlevelsStyle")) seqlevelsStyle(edb) <- "Ensembl" checkEquals(seqlevelsStyle(edb), "Ensembl") checkEquals("Ensembl", ensembldb:::getProperty(edb, "seqlevelsStyle")) ## Try NCBI. seqlevelsStyle(edb) <- "NCBI" checkEquals(seqlevelsStyle(edb), "NCBI") ## Try UCSC. seqlevelsStyle(edb) <- "UCSC" checkEquals(seqlevelsStyle(edb), "UCSC") ## Error checking: checkException(seqlevelsStyle(edb) <- "bla") } ## Just dry run this without any actual query. test_formatSeqnamesForQuery <- function(){ ## Testing if the formating/mapping between seqnames works as expected ## We want to map anything TO Ensembl. ## Check also the warning messages! ucscs <- c("chr1", "chr3", "chr1", "chr9", "chrM", "chr1", "chrX") enses <- c("1", "3", "1", "9", "MT", "1", "X") ## reset edb <- EnsDb.Hsapiens.v75 ## Shouldn't do anything here. seqlevelsStyle(edb) ensembldb:::dbSeqlevelsStyle(edb) got <- ensembldb:::formatSeqnamesForQuery(edb, enses) checkEquals(got, enses) ## Change the seqlevels to UCSC seqlevelsStyle(edb) <- "UCSC" ## If ifNotFound is not specified we suppose to get an error. options(ensembldb.seqnameNotFound="MISSING") checkException(ensembldb:::formatSeqnamesForQuery(edb, enses)) ## With specifying ifNotFound got <- ensembldb:::formatSeqnamesForQuery(edb, enses, ifNotFound=NA) checkEquals(all(is.na(got)), TRUE) ## Same by setting the option options(ensembldb.seqnameNotFound=NA) got <- ensembldb:::formatSeqnamesForQuery(edb, enses) checkEquals(all(is.na(got)), TRUE) ## Now the working example: got <- ensembldb:::formatSeqnamesForQuery(edb, ucscs) checkEquals(got, enses) ## What if one is not mappable: got <- ensembldb:::formatSeqnamesForQuery(edb, c(ucscs, "asdfd"), ifNotFound=NA) checkEquals(got, c(enses, NA)) } ## Just dry run this without any actual query test_formatSeqnamesFromQuery <- function(){ ucscs <- c("chr1", "chr3", "chr1", "chr9", "chrM", "chr1", "chrX") enses <- c("1", "3", "1", "9", "MT", "1", "X") edb <- EnsDb.Hsapiens.v75 ## Shouldn't do anything here. seqlevelsStyle(edb) ensembldb:::dbSeqlevelsStyle(edb) got <- ensembldb:::formatSeqnamesFromQuery(edb, enses) checkEquals(got, enses) ## Change the seqlevels to UCSC seqlevelsStyle(edb) <- "UCSC" ## If ifNotFound is not specified we suppose to get an error. options(ensembldb.seqnameNotFound="MISSING") checkException(ensembldb:::formatSeqnamesFromQuery(edb, ucsc)) ## With specifying ifNotFound got <- ensembldb:::formatSeqnamesFromQuery(edb, ucscs, ifNotFound=NA) checkEquals(all(is.na(got)), TRUE) ## Same using options options(ensembldb.seqnameNotFound=NA) got <- ensembldb:::formatSeqnamesFromQuery(edb, ucscs, ifNotFound=NA) checkEquals(all(is.na(got)), TRUE) ## Now the working example: got <- ensembldb:::formatSeqnamesFromQuery(edb, enses) checkEquals(got, ucscs) ## What if one is not mappable: got <- ensembldb:::formatSeqnamesFromQuery(edb, c(enses, "asdfd"), ifNotFound=NA) checkEquals(got, c(ucscs, NA)) got <- ensembldb:::formatSeqnamesFromQuery(edb, c(enses, "asdfd")) checkEquals(got, c(ucscs, NA)) } notrun_test_set_seqlevels <- function(){ ## To test what happens if no mapping is available ##gff <- "/Users/jo/Projects/EnsDbs/83/gadus_morhua/Gadus_morhua.gadMor1.83.gff3.gz" library(AnnotationHub) ah <- AnnotationHub() ah <- ah["AH47962"] fromG <- ensDbFromAH(ah, outfile=tempfile()) edb <- EnsDb(fromG) seqlevelsStyle(edb) checkException(seqlevelsStyle(edb) <- "UCSC") } deprecated_test_check_SeqnameFilter <- function(){ Orig <- getOption("ucscChromosomeNames", FALSE) options(ucscChromosomeNames=TRUE) snf <- SeqnameFilter(c("chrX", "chr3")) checkEquals(value(snf), c("chrX", "chr3")) checkEquals(value(snf, edb), c("X", "3")) options(ucscChromosomeNames=FALSE) checkEquals(value(snf, edb), c("X", "3")) ## No matter what, where has to return names without chr! checkEquals(where(snf, edb), "gene.seq_name in ('X','3')") ## GRangesFilter: grf <- GRangesFilter(GRanges("chrX", IRanges(123, 345))) checkEqualsNumeric(length(grep(where(grf), pattern="seq_name == 'chrX'")), 1) checkEqualsNumeric(length(grep(where(grf, edb), pattern="seq_name == 'X'")), 1) ## Check chromosome MT/chrM options(ucscChromosomeNames=FALSE) snf <- SeqnameFilter("MT") checkEquals(where(snf, edb), "gene.seq_name = 'MT'") snf <- SeqnameFilter("chrM") checkEquals(where(snf, edb), "gene.seq_name = 'MT'") options(ucscChromosomeNames=TRUE) snf <- SeqnameFilter("MT") checkEquals(where(snf, edb), "gene.seq_name = 'MT'") snf <- SeqnameFilter("chrM") checkEquals(where(snf, edb), "gene.seq_name = 'MT'") options(ucscChromosomeNames=Orig) } deprecated_test_check_retrieve_data <- function(){ Orig <- getOption("ucscChromosomeNames", FALSE) options(ucscChromosomeNames=FALSE) genes <- genes(edb, filter=SeqnameFilter(c("21", "Y", "X"))) checkEquals(all(seqlevels(genes) %in% c("21", "X", "Y")), TRUE) options(ucscChromosomeNames=TRUE) genes <- genes(edb, filter=SeqnameFilter(c("21", "Y", "X"))) checkEquals(all(seqlevels(genes) %in% c("chr21", "chrX", "chrY")), TRUE) ## Check chromosome MT options(ucscChromosomeNames=FALSE) exons <- exons(edb, filter=SeqnameFilter("MT")) checkEquals(seqlevels(exons), "MT") options(ucscChromosomeNames=TRUE) exons <- exons(edb, filter=SeqnameFilter("MT")) checkEquals(seqlevels(exons), "chrM") options(ucscChromosomeNames=Orig) } notrun_check_get_sequence_bsgenome <- function(){ edb <- EnsDb.Hsapiens.v75 ## Using first the Ensembl fasta stuff. ensSeqs <- extractTranscriptSeqs(getGenomeFaFile(edb), exonsBy(edb, "tx", filter=SeqnameFilter("Y"))) ## Now the same using the BSgenome stuff. seqlevelsStyle(edb) <- "UCSC" options(ensembldb.seqnameNotFound="ORIGINAL") exs <- exonsBy(edb, "tx", filter=SeqnameFilter("chrY")) library(BSgenome.Hsapiens.UCSC.hg19) bsg <- BSgenome.Hsapiens.UCSC.hg19 ucscSeqs <- extractTranscriptSeqs(bsg, exs) checkEquals(as.character(ensSeqs), as.character(ucscSeqs)) } ## Use the stuff from GenomeInfoDb! notrun_test_newstuff <- function(){ library(GenomeInfoDb) Map <- mapSeqlevels(seqlevels(edb), style="Ensembl") Map <- mapSeqlevels(seqlevels(edb), style="UCSC") ## just check what's out there genomeStyles() } ensembldb/inst/unitTests/test_validity.R0000644000175400017540000000041113001231245021504 0ustar00biocbuildbiocbuildlibrary(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 test_validity_functions <- function() { OK <- ensembldb:::dbHasRequiredTables(dbconn(edb)) checkTrue(OK) ## Check the tables OK <- ensembldb:::dbHasValidTables(dbconn(edb)) checkTrue(OK) } ensembldb/inst/unitTests/test_xByOverlap.R0000644000175400017540000001005013001231245021752 0ustar00biocbuildbiocbuild####============================================================ ## tests for exonsByOverlaps, transcriptsByOverlaps ## ####------------------------------------------------------------ library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 test_transcriptsByOverlaps <- function(){ ir2 <- IRanges(start=c(2654890, 2709520, 28111770), end=c(2654900, 2709550, 28111790)) gr2 <- GRanges(rep("Y", length(ir2)), ir2) grf2 <- GRangesFilter(gr2, condition="overlapping") Test <- transcripts(edb, filter=grf2) Test2 <- transcriptsByOverlaps(edb, gr2) checkEquals(names(Test), names(Test2)) ## on one strand. gr2 <- GRanges(rep("Y", length(ir2)), ir2, strand=rep("-", length(ir2))) grf2 <- GRangesFilter(gr2, condition="overlapping") Test <- transcripts(edb, filter=grf2) Test2 <- transcriptsByOverlaps(edb, gr2) checkEquals(names(Test), names(Test2)) ## Combine with filter... gr2 <- GRanges(rep("Y", length(ir2)), ir2) Test3 <- transcriptsByOverlaps(edb, gr2, filter=SeqstrandFilter("-")) checkEquals(names(Test), names(Test3)) } test_exonsByOverlaps <- function(){ ir2 <- IRanges(start=c(2654890, 2709520, 28111770), end=c(2654900, 2709550, 28111790)) gr2 <- GRanges(rep("Y", length(ir2)), ir2) grf2 <- GRangesFilter(gr2, condition="overlapping") Test <- exons(edb, filter=grf2) Test2 <- exonsByOverlaps(edb, gr2) checkEquals(names(Test), names(Test2)) ## on one strand. gr2 <- GRanges(rep("Y", length(ir2)), ir2, strand=rep("-", length(ir2))) grf2 <- GRangesFilter(gr2, condition="overlapping") Test <- exons(edb, filter=grf2) Test2 <- exonsByOverlaps(edb, gr2) checkEquals(names(Test), names(Test2)) ## Combine with filter... gr2 <- GRanges(rep("Y", length(ir2)), ir2) Test3 <- exonsByOverlaps(edb, gr2, filter=SeqstrandFilter("-")) checkEquals(names(Test), names(Test3)) } testing_txByOverlap <- function(){ ## Apparently, a combination between transcripts and findoverlaps. grf <- GRangesFilter(GRanges(seqname="Y", IRanges(start=2655145, end=2655500)), condition="overlapping") grf2 <- GRangesFilter(GRanges(seqname="Y", IRanges(start=28740998, end=28741998)), condition="overlapping") transcripts(edb, filter=list(SeqnameFilter("Y"), GenebiotypeFilter("protein_coding"))) where(grf) con <- dbconn(edb) library(RSQLite) q <- paste0("select * from gene where (", where(grf, edb), ") or (", where(grf2), ")") Test <- dbGetQuery(con, q) ## Here we go... ir <- IRanges(start=c(142999, 231380, 27635900), end=c(143300, 231800, 27636200)) gr <- GRanges(seqname=rep("Y", length(ir)), ir) grf <- GRangesFilter(gr, condition="overlapping") where(grf) where(grf, edb) Test <- transcripts(edb, filter=grf) ## ?? Nothing ?? ir2 <- IRanges(start=c(2654890, 2709520, 28111770), end=c(2654900, 2709550, 28111790)) grf2 <- GRangesFilter(GRanges(rep("Y", length(ir2)), ir2), condition="overlapping") Test <- transcripts(edb, filter=grf2) checkEquals(names(Test), c("ENST00000383070", "ENST00000250784", "ENST00000598545")) ## ## TxDb... ## library(TxDb.Hsapiens.UCSC.hg19.knownGene) ## txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene ## gr <- GRanges(seqname=c("chrY", "chrY", "chrY", "chrY"), ## IRanges(start=c(2655145, 28740998, 2709990, 28111770), ## end=c(2655200, 28741998, 2709999, 28112800))) ## transcriptsByOverlaps(txdb, GRanges(seqname=rep("chrY", length(ir)), ir)) ## transcriptsByOverlaps(txdb, GRanges(seqname=rep("chrY", length(ir2)), ir2)) } notrun_txdb <- function(){ txdb <- loadDb(system.file("extdata", "hg19_knownGene_sample.sqlite", package="GenomicFeatures")) gr <- GRanges(seqnames = rep("chr1",2), ranges = IRanges(start=c(500,10500), end=c(10000,30000)), strand = strand(rep("-",2))) transcriptsByOverlaps(txdb, gr) } ensembldb/man/0000755000175400017540000000000013001231247014277 5ustar00biocbuildbiocbuildensembldb/man/EnsDb-AnnotationDbi.Rd0000644000175400017540000001567613001231247020327 0ustar00biocbuildbiocbuild\name{select} \Rdversion{1.1} \alias{select} \alias{select,EnsDb-method} \alias{columns,EnsDb-method} \alias{keys,EnsDb-method} \alias{keytypes,EnsDb-method} \alias{mapIds,EnsDb-method} \title{Integration into the AnnotationDbi framework} \description{ Several of the methods available for \code{AnnotationDbi} objects are also implemented for \code{EnsDb} objects. This enables to extract data from \code{EnsDb} objects in a similar fashion than from objects inheriting from the base annotation package class \code{AnnotationDbi}. In addition to the \emph{standard} usage, the \code{select} and \code{mapIds} for \code{EnsDb} objects support also the filter framework of the ensembdb package and thus allow to perform more fine-grained queries to retrieve data. } \usage{ \S4method{columns}{EnsDb}(x) \S4method{keys}{EnsDb}(x, keytype, filter,...) \S4method{keytypes}{EnsDb}(x) \S4method{mapIds}{EnsDb}(x, keys, column, keytype, ..., multiVals) \S4method{select}{EnsDb}(x, keys, columns, keytype, ...) } \arguments{ (In alphabetic order) \item{column}{ For \code{mapIds}: the column to search on, i.e. from which values should be retrieved. } \item{columns}{ For \code{select}: the columns from which values should be retrieved. Use the \code{columns} method to list all possible columns. } \item{keys}{ The keys/ids for which data should be retrieved from the database. This can be either a character vector of keys/IDs, a single filter object extending \code{\linkS4class{BasicFilter}} or a list of such objects. } \item{keytype}{ For \code{mapIds} and \code{select}: the type (column) that matches the provided keys. This argument does not have to be specified if argument \code{keys} is a filter object extending \code{\linkS4class{BasicFilter}} or a \code{list} of such objects. For \code{keys}: which keys should be returned from the database. } \item{filter}{ For \code{keys}: either a single object extending \code{\linkS4class{BasicFilter}} or a list of such object to retrieve only specific keys from the database. } \item{multiVals}{ What should \code{mapIds} do when there are multiple values that could be returned? Options are: \code{"first"}, \code{"list"}, \code{"filter"}, \code{"asNA"}. See \code{\link[AnnotationDbi]{mapIds}} for a detailed description. } \item{x}{ The \code{EnsDb} object. } \item{...}{ Not used. } } \section{Methods and Functions}{ \describe{ \item{columns}{ List all the columns that can be retrieved by the \code{mapIds} and \code{select} methods. Note that these column names are different from the ones supported by the \code{\link{genes}}, \code{\link{transcripts}} etc. methods that can be listed by the \code{\link{listColumns}} method. Returns a character vector of supported column names. } \item{keys}{ Retrieves all keys from the column name specified with \code{keytype}. By default (if \code{keytype} is not provided) it returns all gene IDs. Note that \code{keytype="TXNAME"} will return transcript ids, since no transcript names are available in the database. Returns a character vector of IDs. } \item{keytypes}{ List all supported key types (column names). Returns a character vector of key types. } \item{mapIds}{ Retrieve the mapped ids for a set of keys that are of a particular keytype. Argument \code{keys} can be either a character vector of keys/IDs, a single filter object extending \code{\linkS4class{BasicFilter}} or a list of such objects. For the latter, the argument \code{keytype} does not have to be specified. Importantly however, if the filtering system is used, the ordering of the results might not represent the ordering of the keys. The method usually returns a named character vector or, depending on the argument \code{multiVals} a named list, with names corresponding to the keys (same ordering is only guaranteed if \code{keys} is a character vector). } \item{select}{ Retrieve the data as a \code{data.frame} based on parameters for selected \code{keys}, \code{columns} and \code{keytype} arguments. Multiple matches of the keys are returned in one row for each possible match. Argument \code{keys} can be either a character vector of keys/IDs, a single filter object extending \code{\linkS4class{BasicFilter}} or a list of such objects. For the latter, the argument \code{keytype} does not have to be specified. Note that values from a column \code{"TXNAME"} will be the same than for a column \code{"TXID"}, since internally no database column \code{"tx_name"} is present and the column is thus mapped to \code{"tx_id"}. Returns a \code{data.frame} with the column names corresponding to the argument \code{columns} and rows with all data matching the criteria specified with \code{keys}. } } } \value{ See method description above. } \author{ Johannes Rainer } \seealso{ \code{\linkS4class{BasicFilter}} \code{\link{listColumns}} \code{\link{transcripts}} } \examples{ library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 ## List all supported keytypes. keytypes(edb) ## List all supported columns for the select and mapIds methods. columns(edb) ## List /real/ database column names. listColumns(edb) ## Retrieve all keys corresponding to transcript ids. txids <- keys(edb, keytype="TXID") length(txids) head(txids) ## Retrieve all keys corresponding to gene names of genes encoded on chromosome X gids <- keys(edb, keytype="GENENAME", filter=SeqnameFilter("X")) length(gids) head(gids) ## Get a mapping of the genes BCL2 and BCL2L11 to all of their ## transcript ids and return the result as list maps <- mapIds(edb, keys=c("BCL2", "BCL2L11"), column="TXID", keytype="GENENAME", multiVals="list") maps ## Perform the same query using a combination of a GenenameFilter and a TxbiotypeFilter ## to just retrieve protein coding transcripts for these two genes. mapIds(edb, keys=list(GenenameFilter(c("BCL2", "BCL2L11")), TxbiotypeFilter("protein_coding")), column="TXID", multiVals="list") ## select: ## Retrieve all transcript and gene related information for the above example. select(edb, keys=list(GenenameFilter(c("BCL2", "BCL2L11")), TxbiotypeFilter("protein_coding")), columns=c("GENEID", "GENENAME", "TXID", "TXBIOTYPE", "TXSEQSTART", "TXSEQEND", "SEQNAME", "SEQSTRAND")) ## Get all data for genes encoded on chromosome Y Y <- select(edb, keys="Y", keytype="SEQNAME") head(Y) nrow(Y) ## Get selected columns for all lincRNAs encoded on chromosome Y Y <- select(edb, keys=list(SeqnameFilter("Y"), GenebiotypeFilter("lincRNA")), columns=c("GENEID", "GENEBIOTYPE", "TXID", "GENENAME")) head(Y) nrow(Y) } \keyword{classes} ensembldb/man/EnsDb-class.Rd0000644000175400017540000002313213001231247016665 0ustar00biocbuildbiocbuild\name{EnsDb-class} \Rdversion{1.1} \docType{class} \alias{EnsDb-class} \alias{buildQuery} \alias{buildQuery,EnsDb-method} \alias{dbconn} \alias{dbconn,EnsDb-method} \alias{ensemblVersion} \alias{ensemblVersion,EnsDb-method} \alias{listColumns} \alias{listColumns,EnsDb-method} \alias{metadata} \alias{metadata,EnsDb-method} \alias{seqinfo} \alias{seqinfo,EnsDb-method} \alias{seqlevels} \alias{seqlevels,EnsDb-method} \alias{organism} \alias{organism,EnsDb-method} \alias{show} \alias{show,EnsDb-method} \alias{listGenebiotypes} \alias{listGenebiotypes,EnsDb-method} \alias{listTxbiotypes} \alias{listTxbiotypes,EnsDb-method} \alias{listTables} \alias{listTables,EnsDb-method} \alias{updateEnsDb} \alias{updateEnsDb,EnsDb-method} \alias{returnFilterColumns} \alias{returnFilterColumns,EnsDb-method} \alias{returnFilterColumns<-} \alias{returnFilterColumns<-,EnsDb-method} \title{Basic usage of an Ensembl based annotation database} \description{ Get some basic information from an Ensembl based annotation package generated with \code{\link{makeEnsembldbPackage}}. } \section{Objects from the Class}{ A connection to the respective annotation database is created upon loading of an annotation package created with the \code{\link{makeEnsembldbPackage}} function. In addition, the \code{\link{EnsDb}} constructor specifying the SQLite database file can be called to generate an instance of the object (see \code{\link{makeEnsemblSQLiteFromTables}} for an example). } \usage{ \S4method{buildQuery}{EnsDb}(x, columns=c("gene_id", "gene_biotype", "gene_name"), filter=list(), order.by, order.type="asc", skip.order.check=FALSE) \S4method{dbconn}{EnsDb}(x) \S4method{ensemblVersion}{EnsDb}(x) \S4method{listColumns}{EnsDb}(x, table, skip.keys=TRUE, ...) \S4method{listGenebiotypes}{EnsDb}(x, ...) \S4method{listTxbiotypes}{EnsDb}(x, ...) \S4method{listTables}{EnsDb}(x, ...) \S4method{metadata}{EnsDb}(x, ...) \S4method{organism}{EnsDb}(object) \S4method{returnFilterColumns}{EnsDb}(x) \S4method{returnFilterColumns}{EnsDb}(x) \S4method{returnFilterColumns}{EnsDb}(x) <- value \S4method{seqinfo}{EnsDb}(x) \S4method{seqlevels}{EnsDb}(x) \S4method{updateEnsDb}{EnsDb}(x, ...) } \arguments{ (in alphabetic order) \item{...}{Additional arguments. Not used. } \item{columns}{ Columns (attributes) to be retrieved from the database tables. Use the \code{listColumns} or \code{listTables} method for a list of supported columns. } \item{filter}{ list of \code{\linkS4class{BasicFilter}} instance(s) to select specific entries from the database (see examples below). } \item{object}{ For \code{organism}: an \code{EnsDb} instance. } \item{order.by}{name of one of the columns above on which the results should be sorted. } \item{order.type}{if the results should be ordered ascending (\code{asc}, default) or descending (\code{desc}). } \item{skip.keys}{ for \code{listColumns}: whether primary and foreign keys (not being e.g. \code{"gene_id"} or alike) should be returned or not. By default these will not be returned. } \item{skip.order.check}{ if paramter \code{order.by} should be checked for allowed column names. If \code{TRUE} the function checks if the provided order criteria orders on columns present in the database tables. } \item{table}{ For \code{listColumns}: optionally specify the table name for which the columns should be returned. } \item{value}{ For \code{returnFilterColumns}: a logical of length one specifying whether columns that are used for eventual filters should also be returned. } \item{x}{ An \code{EnsDb} instance. } } \section{Slots}{ \describe{ \item{ensdb}{ Object of class \code{"DBIConnection"}: the connection to the database. } \item{tables}{ Named list of database table columns with the names being the database table names. The tables are ordered by their degree, i.e. the number of other tables they can be joined with. } \item{.properties}{ Internal list storing user-defined properties. Should not be directly accessed. } } } \section{Methods and Functions}{ \describe{ \item{buildQuery}{ Helper function building the SQL query to be used to retrieve the wanted information. Usually there is no need to call this method. } \item{dbconn}{ Returns the connection to the internal SQL database. } \item{ensemblVersion}{ Returns the Ensembl version on which the package was built. } \item{listColumns}{ Lists all columns of all tables in the database, or, if \code{table} is specified, of the respective table. } \item{listGenebiotypes}{ Lists all gene biotypes defined in the database. } \item{listTxbiotypes}{ Lists all transcript biotypes defined in the database. } \item{listTables}{ Returns a named list of database table columns (names of the list being the database table names). } \item{metadata}{ Returns a \code{data.frame} with the metadata information from the database, i.e. informations about the Ensembl version or Genome build the database was build upon. } \item{organism}{ Returns the organism name (e.g. \code{"homo_sapiens"}). } \item{returnFilterColumns, returnFilterColumns<-}{ Get or set the option which results in columns that are used for eventually specified filters to be added as result columns. The default value is \code{TRUE} (i.e. filter columns are returned). } \item{seqinfo}{ Returns the sequence/chromosome information from the database. } \item{seqlevels}{ Returns the chromosome/sequence names that are available in the database. } \item{show}{ Displays some informations from the database. } \item{updateEnsDb}{ Updates the \code{EnsDb} object to the most recent implementation. } } } \value{ \describe{ \item{For \code{buildQuery}}{ A character string with the SQL query. } \item{For \code{connection}}{ The SQL connection to the RSQLite database. } \item{For \code{EnsDb}}{ An \code{EnsDb} instance. } \item{For \code{lengthOf}}{ A named integer vector with the length of the genes or transcripts. } \item{For \code{listColumns}}{ A character vector with the column names. } \item{For \code{listGenebiotypes}}{ A character vector with the biotypes of the genes in the database. } \item{For \code{listTxbiotypes}}{ A character vector with the biotypes of the transcripts in the database. } \item{For \code{listTables}}{ A list with the names corresponding to the database table names and the elements being the attribute (column) names of the table. } \item{For \code{metadata}}{ A \code{data.frame}. } \item{For \code{organism}}{ A character string. } \item{For \code{returnFilterColumns}}{ A logical of length 1. } \item{For \code{seqinfo}}{ A \code{Seqinfo} class. } \item{For \code{updateEnsDb}}{ A \code{EnsDb} object. } } } \note{ While a column named \code{"tx_name"} is listed by the \code{listTables} and \code{listColumns} method, no such column is present in the database. Transcript names returned by the methods are actually the transcript IDs. This \emph{virtual} column was only introduced to be compliant with \code{TxDb} objects (which provide transcript names). } \author{ Johannes Rainer } \seealso{ \code{\link{EnsDb}}, \code{\link{makeEnsembldbPackage}}, \code{\linkS4class{BasicFilter}}, \code{\link{exonsBy}}, \code{\link{genes}}, \code{\link{transcripts}}, \code{\link{makeEnsemblSQLiteFromTables}} } \examples{ library(EnsDb.Hsapiens.v75) ## Display some information: EnsDb.Hsapiens.v75 ## Show the tables along with its columns listTables(EnsDb.Hsapiens.v75) ## For what species is this database? organism(EnsDb.Hsapiens.v75) ## What Ensembl version if the database based on? ensemblVersion(EnsDb.Hsapiens.v75) ## Get some more information from the database metadata(EnsDb.Hsapiens.v75) ## Get all the sequence names. seqlevels(EnsDb.Hsapiens.v75) ###### buildQuery ## ## Join tables gene and transcript and return gene_id and tx_id buildQuery(EnsDb.Hsapiens.v75, columns=c("gene_id", "tx_id")) ## Get all exon_ids and transcript ids of genes encoded on chromosome Y. buildQuery(EnsDb.Hsapiens.v75, columns=c("exon_id", "tx_id"), filter=list(SeqnameFilter( "Y"))) ## List all available gene biotypes from the database: listGenebiotypes(EnsDb.Hsapiens.v75) ## List all available transcript biotypes: listTxbiotypes(EnsDb.Hsapiens.v75) ## Update the EnsDb; this is in most instances not necessary at all. updateEnsDb(EnsDb.Hsapiens.v75) ###### returnFilterColumns returnFilterColumns(EnsDb.Hsapiens.v75) ## Get protein coding genes on chromosome X, specifying to return ## only columns gene_name as additional column. genes(EnsDb.Hsapiens.v75, filter=list(SeqnameFilter("X"), GenebiotypeFilter("protein_coding")), columns=c("gene_name")) ## By default we get also the gene_biotype column as the data was filtered ## on this column. ## This can be changed using the returnFilterColumns option returnFilterColumns(EnsDb.Hsapiens.v75) <- FALSE genes(EnsDb.Hsapiens.v75, filter=list(SeqnameFilter("X"), GenebiotypeFilter("protein_coding")), columns=c("gene_name")) } \keyword{classes} ensembldb/man/EnsDb-exonsBy.Rd0000644000175400017540000005165313001231247017220 0ustar00biocbuildbiocbuild\name{exonsBy} \Rdversion{1.1} \docType{class} \alias{disjointExons,EnsDb-method} \alias{cdsBy} \alias{cdsBy,EnsDb-method} \alias{fiveUTRsByTranscript,EnsDb-method} \alias{threeUTRsByTranscript,EnsDb-method} \alias{exons} \alias{exons,EnsDb-method} \alias{exonsBy} \alias{exonsBy,EnsDb-method} \alias{exonsByOverlaps,EnsDb-method} \alias{genes} \alias{genes,EnsDb-method} \alias{toSAF} \alias{toSAF,GRangesList-method} \alias{transcripts} \alias{transcripts,EnsDb-method} \alias{transcriptsBy} \alias{transcriptsBy,EnsDb-method} \alias{transcriptsByOverlaps,EnsDb-method} \alias{promoters} \alias{promoters,EnsDb-method} \title{Retrieve annotation data from an Ensembl based package} \description{ Retrieve gene/transcript/exons annotations stored in an Ensembl based database package generated with the \code{\link{makeEnsembldbPackage}} function. } \usage{ \S4method{exons}{EnsDb}(x, columns=listColumns(x,"exon"), filter, order.by, order.type="asc", return.type="GRanges") \S4method{exonsBy}{EnsDb}(x, by=c("tx", "gene"), columns=listColumns(x, "exon"), filter, use.names=FALSE) \S4method{exonsByOverlaps}{EnsDb}(x, ranges, maxgap=0L, minoverlap=1L, type=c("any", "start", "end"), columns=listColumns(x, "exon"), filter) \S4method{transcripts}{EnsDb}(x, columns=listColumns(x, "tx"), filter, order.by, order.type="asc", return.type="GRanges") \S4method{transcriptsBy}{EnsDb}(x, by=c("gene", "exon"), columns=listColumns(x, "tx"), filter) \S4method{transcriptsByOverlaps}{EnsDb}(x, ranges, maxgap=0L, minoverlap=1L, type=c("any", "start", "end"), columns=listColumns(x, "tx"), filter) \S4method{promoters}{EnsDb}(x, upstream=2000, downstream=200, ...) \S4method{genes}{EnsDb}(x, columns=listColumns(x, "gene"), filter, order.by, order.type="asc", return.type="GRanges") \S4method{disjointExons}{EnsDb}(x, aggregateGenes=FALSE, includeTranscripts=TRUE, filter, ...) \S4method{cdsBy}{EnsDb}(x, by=c("tx", "gene"), columns=NULL, filter, use.names=FALSE) \S4method{fiveUTRsByTranscript}{EnsDb}(x, columns=NULL, filter) \S4method{threeUTRsByTranscript}{EnsDb}(x, columns=NULL, filter) \S4method{toSAF}{GRangesList}(x, ...) } \arguments{ (In alphabetic order) \item{...}{ For \code{promoters}: additional arguments to be passed to the \code{transcripts} method. } \item{aggregateGenes}{ For \code{disjointExons}: When \code{FALSE} (default) exon fragments that overlap multiple genes are dropped. When \code{TRUE}, all fragments are kept and the \code{gene_id} metadata column includes all gene IDs that overlap the exon fragment. } \item{by}{ For \code{exonsBy}: wheter exons sould be fetched by genes or by transcripts; as in the corresponding function of the \code{GenomicFeatures} package. For \code{transcriptsBy}: whether transcripts should be fetched by genes or by exons; fetching transcripts by cds as supported by the \code{\link[GenomicFeatures]{transcriptsBy}} method in the \code{GenomicFeatures} package is currently not implemented. For \code{cdsBy}: whether cds should be fetched by transcript of by gene. } \item{columns}{ Columns to be retrieved from the database tables. Default values for \code{genes} are all columns from the \code{gene} database table, for \code{exons} and \code{exonsBy} the column names of the \code{exon} database table table and for \code{transcript} and \code{transcriptBy} the columns of the \code{tx} data base table (see details below for more information). Note that any of the column names of the database tables can be submitted to any of the methods (use \code{\link{listTables}} or \code{\link{listColumns}} methods for a complete list of allowed column names). For \code{cdsBy}: this argument is only supported for for \code{by="tx"}. } \item{downstream}{ For method \code{promoters}: the number of nucleotides downstream of the transcription start site that should be included in the promoter region. } \item{filter}{ A filter object extending \code{\linkS4class{BasicFilter}} or a list of such object(s) to select specific entries from the database (see examples below). } \item{includeTranscripts}{ For \code{disjointExons}: When \code{TRUE} (default) a \code{tx_name} metadata column is included that lists all transcript IDs that overlap the exon fragment. Note: this is different to the \code{\link[GenomicFeatures]{disjointExons}} function in the \code{GenomicFeatures} package, that lists the transcript names, not IDs. } \item{maxgap}{ For \code{exonsByOverlaps} and \code{transcriptsByOverlaps}: see \code{\link[GenomicFeatures]{exonsByOverlaps}} help page in the \code{GenomicFeatures} package. } \item{minoverlap}{ For \code{exonsByOverlaps} and \code{transcriptsByOverlaps}: see \code{\link[GenomicFeatures]{exonsByOverlaps}} help page in the \code{GenomicFeatures} package. } \item{order.by}{ Name of one of the columns above on which the results should be sorted. } \item{order.type}{ If the results should be ordered ascending (\code{asc}, default) or descending (\code{desc}). } \item{ranges}{ For \code{exonsByOverlaps} and \code{transcriptsByOverlaps}: a \code{GRanges} object specifying the genomic regions. } \item{return.type}{ Type of the returned object. Can be either \code{"data.frame"}, \code{"DataFrame"} or \code{"GRanges"}. In the latter case the return object will be a \code{GRanges} object with the GRanges specifying the chromosomal start and end coordinates of the feature (gene, transcript or exon, depending whether \code{genes}, \code{transcripts} or \code{exons} was called). All additional columns are added as metadata columns to the GRanges object. } \item{type}{ For \code{exonsByOverlaps} and \code{transcriptsByOverlaps}: see \code{\link[GenomicFeatures]{exonsByOverlaps}} help page in the \code{GenomicFeatures} package. } \item{upstream}{ For method \code{promoters}: the number of nucleotides upstream of the transcription start site that should be included in the promoter region. } \item{use.names}{ For \code{cdsBy} and \code{exonsBy}: only for \code{by="gene"}: use the names of the genes instead of their IDs as names of the resulting \code{GRangesList}. } \item{x}{ For \code{toSAF} a \code{GRangesList} object. For all other methods an \code{EnsDb} instance. } } \section{Methods and Functions}{ \describe{ \item{exons}{ Retrieve exon information from the database. Additional columns from transcripts or genes associated with the exons can be specified and are added to the respective exon annotation. } \item{exonsBy}{ Retrieve exons grouped by transcript or by gene. This function returns a \code{GRangesList} as does the analogous function in the \code{GenomicFeatures} package. Using the \code{columns} parameter it is possible to determine which additional values should be retrieved from the database. These will be included in the \code{GRanges} object for the exons as metadata columns. The exons in the inner \code{GRanges} are ordered by the exon index within the transcript (if \code{by="tx"}), or increasingly by the chromosomal start position of the exon or decreasingly by the chromosomal end position of the exon depending whether the gene is encoded on the + or - strand (for \code{by="gene"}). The \code{GRanges} in the \code{GRangesList} will be ordered by the name of the gene or transcript. } \item{exonsByOverlaps}{ Retrieve exons overlapping specified genomic ranges. For more information see \code{\link[GenomicFeatures]{exonsByOverlaps}} method in the \code{GenomicFeatures} package. The functionality is to some extent similar and redundant to the \code{exons} method in combination with \code{\link{GRangesFilter}} filter. } \item{transcripts}{ Retrieve transcript information from the database. Additional columns from genes or exons associated with the transcripts can be specified and are added to the respective transcript annotation. } \item{transcriptsBy}{ Retrieve transcripts grouped by gene or exon. This function returns a \code{GRangesList} as does the analogous function in the \code{GenomicFeatures} package. Using the \code{columns} parameter it is possible to determine which additional values should be retrieved from the database. These will be included in the \code{GRanges} object for the transcripts as metadata columns. The transcripts in the inner \code{GRanges} are ordered increasingly by the chromosomal start position of the transcript for genes encoded on the + strand and in a decreasing manner by the chromosomal end position of the transcript for genes encoded on the - strand. The \code{GRanges} in the \code{GRangesList} will be ordered by the name of the gene or exon. } \item{transcriptsByOverlaps}{ Retrieve transcripts overlapping specified genomic ranges. For more information see \code{\link[GenomicFeatures]{transcriptsByOverlaps}} method in the \code{GenomicFeatures} package. The functionality is to some extent similar and redundant to the \code{transcripts} method in combination with \code{\link{GRangesFilter}} filter. } \item{promoters}{ Retrieve promoter information from the database. Additional columns from genes or exons associated with the promoters can be specified and are added to the respective promoter annotation. } \item{genes}{ Retrieve gene information from the database. Additional columns from transcripts or exons associated with the genes can be specified and are added to the respective gene annotation. } \item{disjointExons}{ This method is identical to \code{\link[GenomicFeatures]{disjointExons}} defined in the \code{GenomicFeatures} package. It creates a \code{GRanges} of non-overlapping exon parts with metadata columns of \code{gene_id} and \code{exonic_part}. Exon parts that overlap more than one gene can be dropped with \code{aggregateGenes=FALSE}. } \item{cdsBy}{ Returns the coding region grouped either by transcript or by gene. Each element in the \code{GRangesList} represents the cds for one transcript or gene, with the individual ranges corresponding to the coding part of its exons. For \code{by="tx"} additional annotation columns can be added to the individual \code{GRanges} (in addition to the default columns \code{exon_id} and \code{exon_rank}). Note that the \code{GRangesList} is sorted by its names. } \item{fiveUTRsByTranscript}{ Returns the 5' untranslated region for protein coding transcripts. } \item{threeUTRsByTranscript}{ Returns the 3' untranslated region for protein coding transcripts. } \item{toSAF}{ Reformats a \code{GRangesList} object into a \code{data.frame} corresponding to a standard SAF (Simplified Annotation Format) file (i.e. with column names \code{"GeneID"}, \code{"Chr"}, \code{"Start"}, \code{"End"} and \code{"Strand"}). Note: this method makes only sense on a \code{GRangesList} that groups features (exons, transcripts) by gene. } } } \details{ A detailed description of all database tables and the associated attributes/column names is also given in the vignette of this package. An overview of the columns is given below: \describe{ \item{gene_id}{the Ensembl gene ID of the gene.} \item{gene_name}{the name of the gene (in most cases its official symbol).} \item{entrezid}{the NCBI Entrezgene ID of the gene; note that this can also be a \code{";"} separated list of IDs for Ensembl genes mapped to more than one Entrezgene.} \item{gene_biotype}{the biotype of the gene.} \item{gene_seq_start}{the start coordinate of the gene on the sequence (usually a chromosome).} \item{gene_seq_end}{the end coordinate of the gene.} \item{seq_name}{the name of the sequence the gene is encoded (usually a chromosome).} \item{seq_strand}{the strand on which the gene is encoded} \item{seq_coord_system}{the coordinate system of the sequence.} \item{tx_id}{the Ensembl transcript ID.} \item{tx_biotype}{the biotype of the transcript.} \item{tx_seq_start}{the chromosomal start coordinate of the transcript.} \item{tx_seq_end}{the chromosomal end coordinate of the transcript.} \item{tx_cds_seq_start}{the start coordinate of the coding region of the transcript (NULL for non-coding transcripts).} \item{tx_cds_seq_end}{the end coordinate of the coding region.} \item{exon_id}{the ID of the exon. In Ensembl, each exon specified by a unique chromosomal start and end position has its own ID. Thus, the same exon might be part of several transcripts.} \item{exon_seq_start}{the chromosomal start coordinate of the exon.} \item{exon_seq_end}{the chromosomal end coordinate of the exon.} \item{exon_idx}{the index of the exon in the transcript model. As noted above, an exon can be part of several transcripts and thus its position inside these transcript might differ.} } Also, the vignette provides examples on how to retrieve sequences for genes/transcripts/exons. } \note{ Ensembl defines genes not only on standard chromosomes, but also on patched chromosomes and chromosome variants. Thus it might be advisable to restrict the queries to just those chromosomes of interest (e.g. by specifying a \code{SeqnameFilter(c(1:22, "X", "Y"))}). In addition, also so called LRG genes (Locus Reference Genomic) are defined in Ensembl. Their gene id starts with LRG instead of ENS for Ensembl genes, thus, a filter can be applied to specifically select those genes or exclude those genes (see examples below). Depending on the value of the global option \code{"ucscChromosomeNames"} (use \code{getOption(ucscChromosomeNames, FALSE)} to get its value or \code{option(ucscChromosomeNames=TRUE)} to change its value) the sequence/chromosome names of the returned \code{GRanges} objects or provided in the returned \code{data.frame} or \code{DataFrame} correspond to Ensembl chromosome names (if value is \code{FALSE}) or UCSC chromosome names (if \code{TRUE}). This ensures a better integration with the \code{Gviz} package, in which this option is set by default to \code{TRUE}. } \value{ For \code{exons}, \code{transcripts} and \code{genes}, a \code{data.frame}, \code{DataFrame} or a \code{GRanges}, depending on the value of the \code{return.type} parameter. The result is ordered as specified by the parameter \code{order.by} or, if not provided, by \code{seq_name} and chromosomal start coordinate, but NOT by any ordering of values in eventually submitted filter objects. For \code{exonsBy}, \code{transcriptsBy}: a \code{GRangesList}, depending on the value of the \code{return.type} parameter. The results are ordered by the value of the \code{by} parameter. For \code{exonsByOverlaps} and \code{transcriptsByOverlaps}: a \code{GRanges} with the exons or transcripts overlapping the specified regions. For \code{toSAF}: a \code{data.frame} with column names \code{"GeneID"} (the group name from the \code{GRangesList}, i.e. the ID by which the \code{GRanges} are split), \code{"Chr"} (the seqnames from the \code{GRanges}), \code{"Start"} (the start coordinate), \code{"End"} (the end coordinate) and \code{"Strand"} (the strand). For \code{disjointExons}: a \code{GRanges} of non-overlapping exon parts. For \code{cdsBy}: a \code{GRangesList} with \code{GRanges} per either transcript or exon specifying the start and end coordinates of the coding region of the transcript or gene. For \code{fiveUTRsByTranscript}: a \code{GRangesList} with \code{GRanges} for each protein coding transcript representing the start and end coordinates of full or partial exons that constitute the 5' untranslated region of the transcript. For \code{threeUTRsByTranscript}: a \code{GRangesList} with \code{GRanges} for each protein coding transcript representing the start and end coordinates of full or partial exons that constitute the 3' untranslated region of the transcript. } \note{ While it is possible to request values from a column \code{"tx_name"} (with the \code{columns} argument), no such column is present in the database. The returned values correspond to the ID of the transcripts. } \author{ Johannes Rainer, Tim Triche } \seealso{ \code{\link{makeEnsembldbPackage}}, \code{\linkS4class{BasicFilter}}, \code{\link{listColumns}}, \code{\link{lengthOf}} } \examples{ library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 ###### genes ## ## get all genes endcoded on chromosome Y AllY <- genes(edb, filter=SeqnameFilter("Y")) AllY ## return result as DataFrame. AllY.granges <- genes(edb, filter=SeqnameFilter("Y"), return.type="DataFrame") AllY.granges ## include all transcripts of the gene and their chromosomal ## coordinates, sort by chrom start of transcripts and return as ## GRanges. AllY.granges.tx <- genes(edb, filter=SeqnameFilter("Y"), columns=c("gene_id", "seq_name", "seq_strand", "tx_id", "tx_biotype", "tx_seq_start", "tx_seq_end"), order.by="tx_seq_start") AllY.granges.tx ###### transcripts ## ## get all transcripts of a gene Tx <- transcripts(edb, filter=GeneidFilter("ENSG00000184895"), order.by="tx_seq_start") Tx ## get all transcripts of two genes along with some information on the ## gene and transcript Tx <- transcripts(edb, filter=GeneidFilter(c("ENSG00000184895", "ENSG00000092377")), columns=c("gene_id", "gene_seq_start", "gene_seq_end", "gene_biotype", "tx_biotype")) Tx ###### promoters ## ## get the bona-fide promoters (2k up- to 200nt downstream of TSS) promoters(edb, filter=GeneidFilter(c("ENSG00000184895", "ENSG00000092377"))) ###### exons ## ## get all exons of the provided genes Exon <- exons(edb, filter=GeneidFilter(c("ENSG00000184895", "ENSG00000092377")), order.by="exon_seq_start", columns=c( "gene_id", "gene_seq_start", "gene_seq_end", "gene_biotype")) Exon ##### exonsBy ## ## get all exons for transcripts encoded on chromosomes X and Y. ETx <- exonsBy(edb, by="tx", filter=SeqnameFilter(c("X", "Y"))) ETx ## get all exons for genes encoded on chromosome 1 to 22, X and Y and ## include additional annotation columns in the result EGenes <- exonsBy(edb, by="gene", filter=SeqnameFilter(c("X", "Y")), columns=c("gene_biotype", "gene_name")) EGenes ## Note that this might also contain "LRG" genes. length(grep(names(EGenes), pattern="LRG")) ## to fetch just Ensemblgenes, use an GeneidFilter with value ## "ENS%" and condition "like" ##### transcriptsBy ## TGenes <- transcriptsBy(edb, by="gene", filter=SeqnameFilter(c("X", "Y"))) TGenes ## convert this to a SAF formatted data.frame that can be used by the ## featureCounts function from the Rsubreader package. head(toSAF(TGenes)) ##### transcriptsByOverlaps ## ir <- IRanges(start=c(2654890, 2709520, 28111770), end=c(2654900, 2709550, 28111790)) gr <- GRanges(rep("Y", length(ir)), ir) ## Retrieve all transcripts overlapping any of the regions. txs <- transcriptsByOverlaps(edb, gr) txs ## Alternatively, use a GRangesFilter grf <- GRangesFilter(gr, condition="overlapping") txs <- transcripts(edb, filter=grf) txs #### cdsBy ## Get the coding region for all transcripts on chromosome Y. ## Specifying also additional annotation columns (in addition to the default ## exon_id and exon_rank). cds <- cdsBy(edb, by="tx", filter=SeqnameFilter("Y"), columns=c("tx_biotype", "gene_name")) #### the 5' untranslated regions: fUTRs <- fiveUTRsByTranscript(edb, filter=SeqnameFilter("Y")) #### the 3' untranslated regions with additional column gene_name. tUTRs <- threeUTRsByTranscript(edb, filter=SeqnameFilter("Y"), columns="gene_name") } \keyword{classes} ensembldb/man/EnsDb-lengths.Rd0000644000175400017540000000525613001231247017233 0ustar00biocbuildbiocbuild\name{lengthOf} \Rdversion{1.1} \alias{lengthOf} \alias{lengthOf,GRangesList-method} \alias{lengthOf,EnsDb-method} %\alias{transcriptLengths} %\alias{transcriptLengths,EnsDb-method} %\alias{transcriptLengths,TxDb-method} \title{Calculating lengths of features} \description{ These methods allow to calculate the lengths of features (transcripts, genes, CDS, 3' or 5' UTRs) defined in an \code{EnsDb} object or database. } \usage{ \S4method{lengthOf}{EnsDb}(x, of="gene", filter=list()) } \arguments{ (In alphabetic order) \item{filter}{ list of \code{\linkS4class{BasicFilter}} instance(s) to select specific entries from the database (see examples below). } \item{of}{ for \code{lengthOf}: whether the length of genes or transcripts should be retrieved from the database. } \item{x}{ For \code{lengthOf}: either an \code{EnsDb} or a \code{GRangesList} object. For all other methods an \code{EnsDb} instance. } } \section{Methods and Functions}{ \describe{ \item{lengthOf}{ Retrieve the length of genes or transcripts from the database. The length is the sum of the lengths of all exons of a transcript or a gene. In the latter case the exons are first reduced so that the length corresponds to the part of the genomic sequence covered by the exons. Note: in addition to this method, also the \code{\link[GenomicFeatures]{transcriptLengths}} function in the \code{GenomicFeatures} package can be used. } } } \value{ For \code{lengthOf}: see method description above. } \author{ Johannes Rainer } \seealso{ \code{\link{exonsBy}} \code{\link{transcripts}} \code{\link[GenomicFeatures]{transcriptLengths}} } \examples{ library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 ##### lengthOf ## ## length of a specific gene. lengthOf(edb, filter=list(GeneidFilter("ENSG00000000003"))) ## length of a transcript lengthOf(edb, of="tx", filter=list(TxidFilter("ENST00000494424"))) ## average length of all protein coding genes encoded on chromosomes X ## and Y mean(lengthOf(edb, of="gene", filter=list(GenebiotypeFilter("protein_coding"), SeqnameFilter(c("X", "Y"))))) ## average length of all snoRNAs mean(lengthOf(edb, of="gene", filter=list(GenebiotypeFilter("snoRNA"), SeqnameFilter(c("X", "Y"))))) ##### transcriptLengths ## ## Calculate the length of transcripts encoded on chromosome Y, including ## length of the CDS, 5' and 3' UTR. ##len <- transcriptLengths(edb, with.cds_len=TRUE, with.utr5_len=TRUE, ## with.utr3_len=TRUE, filter=SeqnameFilter("Y")) ##head(len) } \keyword{classes} ensembldb/man/EnsDb-seqlevels.Rd0000644000175400017540000001037013001231247017563 0ustar00biocbuildbiocbuild\name{seqlevelsStyle} \Rdversion{1.1} \alias{seqlevelsStyle} \alias{seqlevelsStyle,EnsDb-method} \alias{seqlevelsStyle<-} \alias{seqlevelsStyle<-,EnsDb-method} \alias{supportedSeqlevelsStyles} \alias{supportedSeqlevelsStyles,EnsDb-method} \title{Support for other than Ensembl seqlevel style} \description{ The methods and functions on this help page allow to integrate \code{EnsDb} objects and the annotations they provide with other Bioconductor annotation packages that base on chromosome names (seqlevels) that are different from those defined by Ensembl. } \usage{ \S4method{seqlevelsStyle}{EnsDb}(x) \S4method{seqlevelsStyle}{EnsDb}(x) <- value \S4method{supportedSeqlevelsStyles}{EnsDb}(x) } \arguments{ (In alphabetic order) \item{value}{ For \code{seqlevelsStyle<-}: a character string specifying the seqlevels style that should be set. Use the \code{supportedSeqlevelsStyle} to list all available and supported seqlevel styles. } \item{x}{ An \code{EnsDb} instance. } } \section{Methods and Functions}{ \describe{ \item{seqlevelsStyle}{ Get the style of the seqlevels in which results returned from the \code{EnsDb} object are encoded. By default, and internally, seqnames as provided by Ensembl are used. The method returns a character string specifying the currently used seqlevelstyle. } \item{seqlevelsStyle<-}{ Change the style of the seqlevels in which results returned from the \code{EnsDb} object are encoded. Changing the seqlevels helps integrating annotations from \code{EnsDb} objects e.g. with annotations from packages that base on UCSC annotations. } \item{supportedSeqlevelsStyles}{ Lists all seqlevel styles for which mappings between seqlevel styles are available in the \code{GenomeInfoDb} package. The method returns a character vector with supported seqlevel styles for the organism of the \code{EnsDb} object. } } } \note{ The mapping between different seqname styles is performed based on data provided by the \code{GenomeInfoDb} package. Note that in most instances no mapping is provided for seqnames other than for primary chromosomes. By default functions from the \code{ensembldb} package return the \emph{original} seqname is in such cases. This behaviour can be changed with the \code{ensembldb.seqnameNotFound} global option. For the special keyword \code{"ORIGINAL"} (the default), the original seqnames are returned, for \code{"MISSING"} an error is thrown if a seqname can not be mapped. In all other cases, the value of the option is returned as seqname if no mapping is available (e.g. setting \code{options(ensembldb.seqnameNotFound=NA)} returns an \code{NA} if the seqname is not mappable). } \value{ For \code{seqlevelsStyle}: see method description above. For \code{supportedSeqlevelsStyles}: see method description above. } \author{ Johannes Rainer } \seealso{ \code{\linkS4class{EnsDb}} \code{\link{transcripts}} } \examples{ library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 ## Get the internal, default seqlevel style. seqlevelsStyle(edb) ## Get the seqlevels from the database. seqlevels(edb) ## Get all supported mappings for the organism of the EnsDb. supportedSeqlevelsStyles(edb) ## Change the seqlevels to UCSC style. seqlevelsStyle(edb) <- "UCSC" seqlevels(edb) ## Change the option ensembldb.seqnameNotFound to return NA in case ## the seqname can not be mapped form Ensembl to UCSC. options(ensembldb.seqnameNotFound=NA) seqlevels(edb) ## Restoring the original setting. options(ensembldb.seqnameNotFound="ORIGINAL") ## Integrate Ensembl based annotations with a BSgenome package that is based on ## UCSC style seqnames. library(BSgenome.Hsapiens.UCSC.hg19) bsg <- BSgenome.Hsapiens.UCSC.hg19 ## Get the genome version unique(genome(bsg)) unique(genome(edb)) ## Although differently named, both represent genome build GRCh37. ## Extract the full transcript sequences of all lincRNAs encoded on chromsome Y. yTxSeqs <- extractTranscriptSeqs(bsg, exonsBy(edb, "tx", filter=list(SeqnameFilter("chrY"), GenebiotypeFilter("lincRNA")))) yTxSeqs } \keyword{classes} ensembldb/man/EnsDb-sequences.Rd0000644000175400017540000000707613001231247017564 0ustar00biocbuildbiocbuild\name{getGenomeFaFile} \Rdversion{1.1} %\alias{extractTranscriptSeqs} %\alias{extractTranscriptSeqs,ANY-method} %\alias{extractTranscriptSeqs,ANY,ANY} %\alias{extractTranscriptSeqs,ANY,EnsDb-method} \alias{getGenomeFaFile} \alias{getGenomeFaFile,EnsDb-method} \title{Functionality related to DNA/RNA sequences} \description{ Utility functions related to RNA/DNA sequences, such as extracting RNA/DNA sequences for features defined in \code{Ensb}. } \usage{ \S4method{getGenomeFaFile}{EnsDb}(x, pattern="dna.toplevel.fa") %\S4method{extractTranscriptSeqs}{ANY,EnsDb}(x, transcripts, filter) } \arguments{ (In alphabetic order) %% \item{filter}{ %% A filter object extending \code{\linkS4class{BasicFilter}} or a list %% of such object(s) to select specific entries from the database (see %% examples below). %% } \item{pattern}{ For method \code{getGenomeFaFile}: the pattern to be used to identify the fasta file representing genomic DNA sequence. } %% \item{transcripts}{ %% For \code{extractTranscriptSeqs}: the \code{EnsDb} object from which %% the transcript definitions should be extracted. %% } \item{x}{ %% For \code{extractTranscriptSeqs}: An object representing a single %% chromosome or a collection of chromosomes. Refer to the help of the %% \code{\link[GenomicFeatures]{extractTranscriptSeqs}} method in %% \code{GenomicFeatures} package for more information. For all other methods an \code{EnsDb} instance. } } \section{Methods and Functions}{ \describe{ %% \item{extractTranscriptSeqs}{ %% Extract transcript sequences. This method adapts the %% \code{\link[GenomicFeatures]{extractTranscriptSeqs}} from the %% \code{GenomicFeatures} package to allow the usage of filters to %% specify the transcripts from which the sequence should be %% extracted. %% } \item{getGenomeFaFile}{ Returns a \code{\link[Rsamtools]{FaFile-class}} (defined in \code{Rsamtools}) with the genomic sequence of the genome build matching the Ensembl version of the \code{EnsDb} object. The file is retrieved using the \code{AnnotationHub} package, thus, at least for the first invocation, an internet connection is required to locate and download the file; subsequent calls will load the cached file instead. If no fasta file for the actual Ensembl version is available the function tries to identify a file matchint the species and genome build version of the closest Ensembl release and returns that instead. See the vignette for an example to work with such files. } } } \value{ For \code{getGenomeFaFile}: a \code{\link[Rsamtools]{FaFile-class}} object with the genomic DNA sequence. %% For \code{extractTranscriptSeqs}: A \code{DNAStringSet} object %% parallel to \code{transcripts} (i.e. the i-th element in it is the %% sequence of the i-th transcript in \code{transcripts}). } \author{ Johannes Rainer } \seealso{ \code{\linkS4class{BasicFilter}} \code{\link{transcripts}} \code{\link{exonsBy}} } \examples{ ## Loading an EnsDb for Ensembl version 75 (genome GRCh37): library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 \dontrun{ ## Retrieve a FaFile with the gneomic DNA sequence matching the organism, ## genome release version and, if possible, the Ensembl version of the ## EnsDb object. Dna <- getGenomeFaFile(edb) ## Extract the transcript sequence for all transcripts encoded on chromosome ## Y. ##extractTranscriptSeqs(Dna, edb, filter=SeqnameFilter("Y")) } } \keyword{classes} ensembldb/man/EnsDb-utils.Rd0000644000175400017540000000732513001231247016726 0ustar00biocbuildbiocbuild\name{getGeneRegionTrackForGviz} \Rdversion{1.1} \alias{getGeneRegionTrackForGviz} \alias{getGeneRegionTrackForGviz,EnsDb-method} \title{Utility functions} \description{ Utility functions integrating \code{EnsDb} objects with other Bioconductor packages. } \usage{ \S4method{getGeneRegionTrackForGviz}{EnsDb}(x, filter=list(), chromosome=NULL, start=NULL, end=NULL, featureIs="gene_biotype") } \arguments{ (In alphabetic order) \item{chromosome}{ For \code{getGeneRegionTrackForGviz}: optional chromosome name to restrict the returned entry to a specific chromosome. } \item{end}{ For \code{getGeneRegionTrackForGviz}: optional chromosomal end coordinate specifying, together with \code{start}, the chromosomal region from which features should be retrieved. } \item{featureIs}{ For \code{getGeneRegionTrackForGviz}: whether the gene (\code{"gene_biotype"}) or the transcript biotype (\code{"tx_biotype"}) should be returned in column \code{"feature"}. } \item{filter}{ A filter object extending \code{\linkS4class{BasicFilter}} or a list of such object(s) to select specific entries from the database (see examples below). } \item{start}{ For \code{getGeneRegionTrackForGviz}: optional chromosomal start coordinate specifying, together with \code{end}, the chromosomal region from which features should be retrieved. } \item{x}{ For \code{toSAF} a \code{GRangesList} object. For all other methods an \code{EnsDb} instance. } } \section{Methods and Functions}{ \describe{ \item{getGeneRegionTrackForGviz}{ Retrieve a \code{GRanges} object with transcript features from the \code{EnsDb} that can be used directly in the \code{Gviz} package to create a \code{GeneRegionTrack}. Using the \code{filter}, \code{chromosome}, \code{start} and \code{end} arguments it is possible to fetch specific features (e.g. lincRNAs) from the database. If \code{chromosome}, \code{start} and \code{end} is provided the function internally first retrieves all transcripts that have an exon or an intron in the specified chromosomal region and subsequently fetch all of these transcripts. This ensures that all transcripts of the region are returned, even those that have \emph{only} an intron in the region. The function returns a \code{GRanges} object with additional annotation columns \code{"feature"}, \code{"gene"}, \code{"exon"}, \code{"exon_rank"}, \code{"trancript"}, \code{"symbol"} specifying the feature type (either gene or transcript biotype), the (Ensembl) gene ID, the exon ID, the rank/index of the exon in the transcript, the transcript ID and the gene symbol/name. } } } \value{ For \code{getGeneRegionTrackForGviz}: see method description above. } \author{ Johannes Rainer } \seealso{ \code{\linkS4class{BasicFilter}} \code{\link{transcripts}} } \examples{ library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 ###### getGeneRegionTrackForGviz ## ## Get all genes encoded on chromosome Y in the specifyed region. AllY <- getGeneRegionTrackForGviz(edb, chromosome="Y", start=5000000, end=7000000) ## We could plot this now using plotTracks(GeneRegionTrack(AllY)) ## We can also use filters to further restrict the query to e.g. ## all lincRNA genes encoded in that region. lincsY <- getGeneRegionTrackForGviz(edb, chromosome="Y", start=5000000, end=7000000, filter=GenebiotypeFilter("lincRNA")) } \keyword{classes} ensembldb/man/EnsDb.Rd0000644000175400017540000000303513001231247015562 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/dbhelpers.R \name{EnsDb} \alias{EnsDb} \title{Connect to an EnsDb object} \usage{ EnsDb(x) } \arguments{ \item{x}{Either a character specifying the \emph{SQLite} database file, or a \code{DBIConnection} to e.g. a MySQL database.} } \value{ A \code{\linkS4class{EnsDb}} object. } \description{ The \code{EnsDb} constructor function connects to the database specified with argument \code{x} and returns a corresponding \code{\linkS4class{EnsDb}} object. } \details{ By providing the connection to a MySQL database, it is possible to use MySQL as the database backend and queries will be performed on that database. Note however that this requires the package \code{RMySQL} to be installed. In addition, the user needs to have access to a MySQL server providing already an EnsDb database, or must have write privileges on a MySQL server, in which case the \code{\link{useMySQL}} method can be used to insert the annotations from an EnsDB package into a MySQL database. } \examples{ ## "Standard" way to create an EnsDb object: library(EnsDb.Hsapiens.v75) EnsDb.Hsapiens.v75 ## Alternatively, provide the full file name of a SQLite database file dbfile <- system.file("extdata/EnsDb.Hsapiens.v75.sqlite", package = "EnsDb.Hsapiens.v75") edb <- EnsDb(dbfile) edb ## Third way: connect to a MySQL database \dontrun{ library(RMySQL) dbcon <- dbConnect(MySQL(), user = my_user, pass = my_pass, host = my_host, dbname = "ensdb_hsapiens_v75") edb <- EnsDb(dbcon) } } \author{ Johannes Rainer } ensembldb/man/GeneidFilter-class.Rd0000644000175400017540000004147313001231247020243 0ustar00biocbuildbiocbuild\name{GeneidFilter-class} \Rdversion{1.1} \docType{class} \alias{BasicFilter-class} \alias{EntrezidFilter-class} \alias{GeneidFilter-class} \alias{GenebiotypeFilter-class} \alias{GenenameFilter-class} \alias{TxidFilter-class} \alias{TxbiotypeFilter-class} \alias{ExonidFilter-class} \alias{SeqnameFilter-class} \alias{SeqstrandFilter-class} \alias{SeqstartFilter-class} \alias{SeqendFilter-class} \alias{GRangesFilter-class} \alias{ExonrankFilter-class} \alias{column,EntrezidFilter,missing,missing-method} \alias{column,GeneidFilter,missing,missing-method} \alias{column,GenenameFilter,missing,missing-method} \alias{column,GenebiotypeFilter,missing,missing-method} \alias{column,TxidFilter,missing,missing-method} \alias{column,TxbiotypeFilter,missing,missing-method} \alias{column,ExonidFilter,missing,missing-method} \alias{column,ExonrankFilter,missing,missing-method} \alias{column,SeqnameFilter,missing,missing-method} \alias{column,SeqstrandFilter,missing,missing-method} \alias{column,SeqstartFilter,missing,missing-method} \alias{column,SeqendFilter,missing,missing-method} \alias{column,GRangesFilter,missing,missing-method} \alias{where,EntrezidFilter,missing,missing-method} \alias{where,GeneidFilter,missing,missing-method} \alias{where,GenenameFilter,missing,missing-method} \alias{where,GenebiotypeFilter,missing,missing-method} \alias{where,TxidFilter,missing,missing-method} \alias{where,TxbiotypeFilter,missing,missing-method} \alias{where,ExonidFilter,missing,missing-method} \alias{where,ExonrankFilter,missing,missing-method} \alias{where,SeqnameFilter,missing,missing-method} \alias{where,SeqstrandFilter,missing,missing-method} \alias{where,SeqstartFilter,missing,missing-method} \alias{where,SeqendFilter,missing,missing-method} \alias{where,GRangesFilter,missing,missing-method} % EnsDb, missing \alias{column,EntrezidFilter,EnsDb,missing-method} \alias{column,GeneidFilter,EnsDb,missing-method} \alias{column,GenenameFilter,EnsDb,missing-method} \alias{column,GenebiotypeFilter,EnsDb,missing-method} \alias{column,TxidFilter,EnsDb,missing-method} \alias{column,TxbiotypeFilter,EnsDb,missing-method} \alias{column,ExonidFilter,EnsDb,missing-method} \alias{column,ExonrankFilter,EnsDb,missing-method} \alias{column,SeqnameFilter,EnsDb,missing-method} \alias{column,SeqstrandFilter,EnsDb,missing-method} \alias{column,SeqstartFilter,EnsDb,missing-method} \alias{column,SeqendFilter,EnsDb,missing-method} \alias{column,GRangesFilter,EnsDb,missing-method} \alias{column,OnlyCodingTx,EnsDb,missing-method} \alias{where,EntrezidFilter,EnsDb,missing-method} \alias{where,GeneidFilter,EnsDb,missing-method} \alias{where,GenenameFilter,EnsDb,missing-method} \alias{where,GenebiotypeFilter,EnsDb,missing-method} \alias{where,TxidFilter,EnsDb,missing-method} \alias{where,TxbiotypeFilter,EnsDb,missing-method} \alias{where,ExonidFilter,EnsDb,missing-method} \alias{where,ExonrankFilter,EnsDb,missing-method} \alias{where,SeqnameFilter,EnsDb,missing-method} \alias{where,SeqstrandFilter,EnsDb,missing-method} \alias{where,SeqstartFilter,EnsDb,missing-method} \alias{where,SeqendFilter,EnsDb,missing-method} \alias{where,GRangesFilter,EnsDb,missing-method} \alias{where,OnlyCodingTx,EnsDb,missing-method} % EnsDb, character \alias{column,EntrezidFilter,EnsDb,character-method} \alias{column,GeneidFilter,EnsDb,character-method} \alias{column,GenenameFilter,EnsDb,character-method} \alias{column,GenebiotypeFilter,EnsDb,character-method} \alias{column,TxidFilter,EnsDb,character-method} \alias{column,TxbiotypeFilter,EnsDb,character-method} \alias{column,ExonidFilter,EnsDb,character-method} \alias{column,ExonrankFilter,EnsDb,character-method} \alias{column,SeqnameFilter,EnsDb,character-method} \alias{column,SeqstrandFilter,EnsDb,character-method} \alias{column,SeqstartFilter,EnsDb,character-method} \alias{column,SeqendFilter,EnsDb,character-method} \alias{column,GRangesFilter,EnsDb,character-method} \alias{column,OnlyCodingTx,EnsDb,character-method} \alias{where,EntrezidFilter,EnsDb,character-method} \alias{where,GeneidFilter,EnsDb,character-method} \alias{where,GenenameFilter,EnsDb,character-method} \alias{where,GenebiotypeFilter,EnsDb,character-method} \alias{where,TxidFilter,EnsDb,character-method} \alias{where,TxbiotypeFilter,EnsDb,character-method} \alias{where,ExonidFilter,EnsDb,character-method} \alias{where,ExonrankFilter,EnsDb,character-method} \alias{where,SeqnameFilter,EnsDb,character-method} \alias{where,SeqstrandFilter,EnsDb,character-method} \alias{where,SeqstartFilter,EnsDb,character-method} \alias{where,SeqendFilter,EnsDb,character-method} \alias{where,GRangesFilter,EnsDb,character-method} \alias{where,OnlyCodingTx,EnsDb,character-method} % \alias{condition,BasicFilter-method} \alias{condition<-,BasicFilter-method} \alias{condition<-} \alias{condition,GRangesFilter-method} \alias{condition<-,GRangesFilter-method} \alias{show,BasicFilter-method} \alias{show,GRangesFilter-method} \alias{print,BasicFilter-method} \alias{where,BasicFilter,missing,missing-method} \alias{where,BasicFilter,EnsDb,missing-method} \alias{where,BasicFilter,EnsDb,character-method} \alias{where,list,EnsDb,character-method} \alias{where,list,EnsDb,missing-method} \alias{where,list,missing,missing-method} \alias{value,BasicFilter,missing-method} \alias{value<-} \alias{value<-,BasicFilter-method} \alias{value<-,ExonrankFilter-method} \alias{value,BasicFilter,EnsDb-method} \alias{value,GRangesFilter,missing-method} \alias{value,GRangesFilter,EnsDb-method} \alias{value,SeqnameFilter,EnsDb-method} \alias{condition} \alias{value} \alias{column} \alias{where} % Additional GRangesFilter stuff \alias{end,GRangesFilter-method} \alias{seqlevels,GRangesFilter-method} \alias{seqnames,GRangesFilter-method} \alias{start,GRangesFilter-method} \alias{strand,GRangesFilter-method} % SymbolFilter \alias{SymbolFilter-class} \alias{column,SymbolFilter,missing,missing-method} \alias{column,SymbolFilter,EnsDb,missing-method} \alias{column,SymbolFilter,EnsDb,character-method} \alias{where,SymbolFilter,missing,missing-method} \alias{where,SymbolFilter,EnsDb,missing-method} \alias{where,SymbolFilter,EnsDb,character-method} \title{Filter results fetched from the Ensembl database} \description{ These classes allow to specify which entries (i.e. genes, transcripts or exons) should be retrieved from the database. } \section{Objects from the Class}{ While objects can be created by calls e.g. of the form \code{new("GeneidFilter", ...)} users are strongly encouraged to use the specific functions: \code{\link{GeneidFilter}}, \code{\link{EntrezidFilter}}, \code{\link{GenenameFilter}}, \code{\link{GenebiotypeFilter}}, \code{\link{GRangesFilter}}, \code{\link{SymbolFilter}}, \code{\link{TxidFilter}}, \code{\link{TxbiotypeFilter}}, \code{\link{ExonidFilter}}, \code{\link{ExonrankFilter}}, \code{\link{SeqnameFilter}}, \code{\link{SeqstrandFilter}}, \code{\link{SeqstartFilter}} and \code{\link{SeqendFilter}}. See examples below for usage. } \section{Slots}{ \describe{ \item{\code{condition}:}{ Object of class \code{"character"}: can be either \code{"="}, \code{"in"} or \code{"like"} to filter on character values (e.g. gene id, gene biotype, seqname etc), or \code{"="}, \code{">"} or \code{"<"} for numerical values (chromosome/seq coordinates). Note that for \code{"like"} \code{value} should be a SQL pattern (e.g. \code{"ENS\%"}). } \item{\code{value}:}{ Object of class \code{"character"}: the value to be used for filtering. } } } \section{Extends}{ Class \code{\linkS4class{BasicFilter}}, directly. } \section{Methods for all \code{BasicFilter} objects}{ \describe{ Note: these methods are applicable to all classes extending the \code{BasicFilter} class. \item{column}{\code{signature(object = "GeneidFilter", db = "EnsDb", with.tables = "character")}: returns the column (attribute name) to be used for the filtering. Submitting the \code{db} parameter ensures that returned column is valid in the corresponding database schema. The optional argument \code{with.tables} allows to specify which in which database table the function should look for the attribute/column name. By default the method will check all database tables. } \item{column}{\code{signature(object = "GeneidFilter", db = "EnsDb", with.tables = "missing")}: returns the column (attribute name) to be used for the filtering. Submitting the \code{db} parameter ensures that returned column is valid in the corresponding database schema. } \item{column}{\code{signature(object = "GeneidFilter", db = "missing", with.tables = "missing")}: returns the column (table column name) to be used for the filtering. } \item{condition}{\code{signature(x = "BasicFilter")}: returns the value for the \code{condition} slot. } \item{condition<-}{ setter method for condition. } \item{value}{\code{signature(x = "BasicFilter", db = "EnsDb")}: returns the value of the \code{value} slot of the filter object. } \item{value<-}{ setter method for value. } \item{where}{\code{signature(object = "GeneidFilter", db = "EnsDb", with.tables = "character")}: returns the where condition for the SQL call. Submitting also the \code{db} parameter ensures that the columns are valid in the corresponding database schema. The optional argument \code{with.tables} allows to specify which in which database table the function should look for the attribute/column name. By default the method will check all database tables. } \item{where}{\code{signature(object = "GeneidFilter", db = "EnsDb", with.tables = "missing")}: returns the where condition for the SQL call. Submitting also the \code{db} parameter ensures that the columns are valid in the corresponding database schema. } \item{where}{\code{signature(object = "GeneidFilter", db = "missing", with.tables = "missing")}: returns the where condition for the SQL call. } } } \section{Methods for \code{GRangesFilter} objects}{ \describe{ \item{start, end, strand}{ Get the start and end coordinate and the strand from the \code{GRanges} within the filter. } \item{seqlevels, seqnames}{ Get the names of the sequences from the \code{GRanges} of the filter. } } } \details{ \describe{ \item{\code{ExonidFilter}}{ Allows to filter based on the (Ensembl) exon identifier. } \item{\code{ExonrankFilter}}{ Allows to filter based on the rank (index) of the exon within the transcript model. Exons are always numbered 5' to 3' end of the transcript, thus, also on the reverse strand, the exon 1 is the most 5' exon of the transcript. } \item{\code{EntrezidFilter}}{ Filter results based on the NCBI Entrezgene identifierts of the genes. Use the \code{\link{listGenebiotypes}} method to get a complete list of all available gene biotypes. } \item{\code{GenebiotypeFilter}}{ Filter results based on the gene biotype as defined in the Ensembl database. } \item{\code{GeneidFilter}}{ Filter results based on the Ensembl gene identifiers. } \item{\code{GenenameFilter}}{ Allows to filter on the gene names (symbols) of the genes. } \item{\code{SymbolFilter}}{ Filter on gene symbols. Note that since no such database column is available in an \code{EnsDb} database the gene names are used to filter. These do however correspond all to the official gene symbols. } \item{\code{GRangesFilter}}{ Allows to fetch features within or overlapping specified genomic region(s)/range(s). This filter takes a \code{GRanges} object as input and, if \code{condition="within"} (the default) will restrict results to features (genes, transcripts or exons) that are completely within the region. Alternatively, by specifying \code{condition="overlapping"} it will return all features (i.e. genes for a call to \code{\link{genes}}, transcripts for a call to \code{\link{transcripts}} and exons for a call to \code{\link{exons}}) that are partially overlapping with the region, i.e. which start coordinate is smaller than the end coordinate of the region and which end coordinate is larger than the start coordinate of the region. Thus, genes and transcripts that have an intron overlapping the region will also be returned. Calls to the methods \code{\link{exonsBy}}, \code{\link{cdsBy}} and \code{\link{transcriptsBy}} use the start and end coordinates of the feature type specified with argument \code{by} (i.e. \code{"gene"}, \code{"transcript"} or \code{"exon"}) for the filtering. Note: if the specified \code{GRanges} object defines multiple region, all features within (or overlapping) any of these regions are returned. Chromosome names/seqnames can be provided in UCSC format (e.g. \code{"chrX"}) or Ensembl format (e.g. \code{"X"}); see \code{\link{seqlevelsStyle}} for more information. } \item{\code{SeqendFilter}}{ Filter based on the chromosomal end coordinate of the exons, transcripts or genes. } \item{\code{SeqnameFilter}}{ Filter on the sequence name on which the features are encoded (mostly the chromosome names). Supports UCSC chromosome names (e.g. \code{"chrX"}) and Ensembl chromosome names (e.g. \code{"X"}). } \item{\code{SeqstartFilter}}{ Filter based on the chromosomal start coordinates of the exons, transcripts or genes. } \item{\code{SeqstrandFilter}}{ Filter based on the strand on which the features are encoded. } \item{\code{TxbiotypeFilter}}{ Filter on the transcript biotype defined in Ensembl. Use the \code{\link{listTxbiotypes}} method to get a complete list of all available transcript biotypes. } \item{\code{TxidFilter}}{ Filter on the Ensembl transcript identifiers. } } } \note{ The \code{column} and \code{where} methods should be always called along with the \code{EnsDb} object, as this ensures that the returned column names are valid for the database schema. The optional argument \code{with.tables} should on the other hand only be used rarely as it is more intended for internal use. Note that the database column \code{"entrezid"} queried for \code{EntrezidFilter} classes can contain multiple, \code{";"} separated, Entrezgene IDs, thus, using this filter at present might not return all entries from the database. Also, the database does not provide a column with the official gene symbols and a \code{SymbolFilter} queries the gene names instead. } \author{ Johannes Rainer } \seealso{ \code{\link{genes}}, \code{\link{transcripts}}, \code{\link{exons}}, \code{\link{listGenebiotypes}}, \code{\link{listTxbiotypes}} } \examples{ ## create a filter that could be used to retrieve all informations for ## the respective gene. Gif <- GeneidFilter("ENSG00000012817") Gif ## returns the where condition of the SQL querys where(Gif) ## create a filter for a chromosomal end position of a gene Sef <- SeqendFilter(10000, condition=">", "gene") Sef ## for additional examples see the help page of "genes" ## Example for GRangesFilter: ## retrieve all genes overlapping the specified region grf <- GRangesFilter(GRanges("11", ranges=IRanges(114000000, 114000050), strand="+"), condition="overlapping") library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 genes(edb, filter=grf) ## Get also all transcripts overlapping that region transcripts(edb, filter=grf) ## Retrieve all transcripts for the above gene gn <- genes(edb, filter=grf) txs <- transcripts(edb, filter=GenenameFilter(gn$gene_name)) ## Next we simply plot their start and end coordinates. plot(3, 3, pch=NA, xlim=c(start(gn), end(gn)), ylim=c(0, length(txs)), yaxt="n", ylab="") ## Highlight the GRangesFilter region rect(xleft=start(grf), xright=end(grf), ybottom=0, ytop=length(txs), col="red", border="red") for(i in 1:length(txs)){ current <- txs[i] rect(xleft=start(current), xright=end(current), ybottom=i-0.975, ytop=i-0.125, border="grey") text(start(current), y=i-0.5,pos=4, cex=0.75, labels=current$tx_id) } ## Thus, we can see that only 4 transcripts of that gene are indeed overlapping the region. ## No exon is overlapping that region, thus we're not getting anything exons(edb, filter=grf) ## Example for ExonrankFilter ## Extract all exons 1 and (if present) 2 for all genes encoded on the ## Y chromosome exons(edb, columns=c("tx_id", "exon_idx"), filter=list(SeqnameFilter("Y"), ExonrankFilter(3, condition="<"))) ## Get all transcripts for the gene SKA2 transcripts(edb, filter=GenenameFilter("SKA2")) ## Which is the same as using a SymbolFilter transcripts(edb, filter=SymbolFilter("SKA2")) } \keyword{classes} ensembldb/man/SeqendFilter.Rd0000644000175400017540000001673713001231247017171 0ustar00biocbuildbiocbuild\name{SeqendFilter} \alias{EntrezidFilter} \alias{GeneidFilter} \alias{GenenameFilter} \alias{GenebiotypeFilter} \alias{TxidFilter} \alias{TxbiotypeFilter} \alias{ExonidFilter} \alias{ExonrankFilter} \alias{SeqnameFilter} \alias{SeqstrandFilter} \alias{SeqstartFilter} \alias{SeqendFilter} \alias{GRangesFilter} \alias{SymbolFilter} \title{ Constructor functions for filter objects } \description{ These functions allow to create filter objects that can be used to retrieve specific elements from the annotation database. } \usage{ EntrezidFilter(value, condition = "=") GeneidFilter(value, condition = "=") GenenameFilter(value, condition = "=") GenebiotypeFilter(value, condition = "=") GRangesFilter(value, condition="within", feature="gene") TxidFilter(value, condition = "=") TxbiotypeFilter(value, condition = "=") ExonidFilter(value, condition = "=") ExonrankFilter(value, condition = "=") SeqnameFilter(value, condition = "=") SeqstrandFilter(value, condition = "=") SeqstartFilter(value, condition = "=", feature = "gene") SeqendFilter(value, condition = "=", feature = "gene") SymbolFilter(value, condition = "=") } %- maybe also 'usage' for other objects documented here. \arguments{ \item{value}{ The filter value, e.g., for \code{GeneidFilter} the id of the gene for which the data should be retrieved. For character values (all filters except \code{SeqstartFilter} and \code{SeqendFilter}) also a character vector of values is allowed. Allowed values for \code{SeqstrandFilter} are: \code{"+"}, \code{"-"}, \code{"1"} or \code{"-1"}. For \code{GRangeFilter} this has to be a \code{GRanges} object. } \item{condition}{ The condition to be used in the comparison. For character values \code{"="}, \code{"in"} and \code{"like"} are allowed, for numeric values (\code{SeqstartFilter} and \code{SeqendFilter}) \code{"="}, \code{">"}, \code{">="}, \code{"<"} and \code{"<="}. Note that for \code{"like"} \code{value} should be a SQL pattern (e.g. \code{"ENS\%"}). For \code{GRangesFilter}, \code{"within"} and \code{"overlapping"} are allowed. See below for details. } \item{feature}{ For \code{SeqstartFilter} and \code{SeqendFilter}: the chromosomal position of which features should be used in the filter (either \code{"gene"}, \code{"transcript"} or \code{"exon"}). For \code{GRangesFilter}: the submitted value is overwritten internally depending on the called method, i.e. calling \code{genes} will set feature to \code{"gene"}, \code{transcripts} to \code{"tx"} and \code{exons} to \code{"exon"}. } } \details{ \describe{ \item{EntrezidFilter}{ Filter results based on the NCBI Entrezgene ID of the genes. } \item{GeneidFilter}{ Filter results based on Ensembl gene IDs. } \item{GenenameFilter}{ Filter results based on gene names (gene symbols). } \item{GenebiotypeFilter}{ Filter results based on the biotype of the genes. For a complete list of available gene biotypes use the \code{\link{listGenebiotypes}} method. } \item{GRangesFilter}{ Allows to fetch features within or overlapping the specified genomic region(s)/range(s). This filter takes a \code{GRanges} object as input and, if \code{condition="within"} (the default) will restrict results to features (genes, transcripts or exons) that are completely within the region. Alternatively, by specifying \code{condition="overlapping"} it will return all features that are partially overlapping with the region, i.e. which start coordinate is smaller than the end coordinate of the region and which end coordinate is larger than the start coordinate of the region. Thus, genes and transcripts that have an intron overlapping the region will also be returned. Note: if the specified \code{GRanges} object defines multiple region, all features within (or overlapping) any of these regions are returned. See \code{\linkS4class{GRangesFilter}} for more details. } \item{TxidFilter}{ Filter results based on the Ensembl transcript IDs. } \item{TxbiotypeFilter}{ Filter results based on the biotype of the transcripts. For a complete list of available transcript biotypes use the \code{\link{listTxbiotypes}} method. } \item{ExonidFilter}{ Filter based on the Ensembl exon ID. } \item{ExonrankFilter}{ Filter results based on exon ranks (indices) of exons within transcripts. } \item{SeqnameFilter}{ Filter results based on the name of the sequence the features are encoded. } \item{SeqstrandFilter}{ Filter results based on the strand on which the features are encoded. } \item{SeqstartFilter}{ Filter results based on the (chromosomal) start coordinate of the features (exons, genes or transcripts). } \item{SeqendFilter}{ Filter results based on the (chromosomal) end coordinates. } \item{SymbolFilter}{ Filter results based on the gene names. The database does not provide an explicit \emph{symbol} column, thus this filter uses the gene name instead (which in many cases corresponds to the official gene name). } } } \value{ Depending on the function called an instance of: \code{\linkS4class{EntrezidFilter}}, \code{\linkS4class{GeneidFilter}}, \code{\linkS4class{GenenameFilter}}, \code{\linkS4class{GenebiotypeFilter}}, \code{\linkS4class{GRangesFilter}}, \code{\linkS4class{TxidFilter}}, \code{\linkS4class{TxbiotypeFilter}}, \code{\linkS4class{ExonidFilter}}, \code{\linkS4class{ExonrankFilter}}, \code{\linkS4class{SeqnameFilter}}, \code{\linkS4class{SeqstrandFilter}}, \code{\linkS4class{SeqstartFilter}}, \code{\linkS4class{SeqendFilter}}, \code{\linkS4class{SymbolFilter}} } \author{ Johannes Rainer } \seealso{ \code{\linkS4class{EntrezidFilter}}, \code{\linkS4class{GeneidFilter}}, \code{\linkS4class{GenenameFilter}}, \code{\linkS4class{GenebiotypeFilter}}, \code{\linkS4class{GRangesFilter}}, \code{\linkS4class{TxidFilter}}, \code{\linkS4class{TxbiotypeFilter}}, \code{\linkS4class{ExonidFilter}}, \code{\linkS4class{ExonrankFilter}}, \code{\linkS4class{SeqnameFilter}}, \code{\linkS4class{SeqstrandFilter}}, \code{\linkS4class{SeqstartFilter}}, \code{\linkS4class{SeqendFilter}}, \code{\linkS4class{SymbolFilter}} } \examples{ ## create a filter that could be used to retrieve all informations for ## the respective gene. Gif <- GeneidFilter("ENSG00000012817") Gif ## returns the where condition of the SQL querys where(Gif) ## create a filter for a chromosomal end position of a gene Sef <- SeqendFilter(100000, condition="<", "gene") Sef ## To find genes within a certain chromosomal position filters should be ## combined: Ssf <- SeqstartFilter(10000, condition=">", "gene") Snf <- SeqnameFilter("2") ## combine the filters Filter <- list(Ssf, Sef, Snf) Filter ## generate the where SQL call for these filters: where(Filter) ## Create a GRangesFilter GRangesFilter(GRanges("X", IRanges(123, 5454))) ## Create a GRangesFilter with multiple ranges grf <- GRangesFilter(GRanges(c("X", "Y"), IRanges(start=c(123, 900), end=c(5454, 910)))) ## Evaluate the 'where' SQL condition that would be applied. where(grf) ## Change the "condition" of the filter and evaluate the ## 'where' condition again. condition(grf) <- "overlapping" where(grf) } \keyword{data} ensembldb/man/listEnsDbs.Rd0000644000175400017540000000300713001231247016640 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/dbhelpers.R \name{listEnsDbs} \alias{listEnsDbs} \title{List EnsDb databases in a MySQL server} \usage{ listEnsDbs(dbcon, host, port, user, pass) } \arguments{ \item{dbcon}{A \code{DBIConnection} object providing access to a MySQL database. Either \code{dbcon} or all of the other arguments have to be specified.} \item{host}{Character specifying the host on which the MySQL server is running.} \item{port}{The port of the MySQL server (usually \code{3306}).} \item{user}{The username for the MySQL server.} \item{pass}{The password for the MySQL server.} } \value{ A \code{data.frame} listing the database names, organism name and Ensembl version of the EnsDb databases found on the server. } \description{ The \code{listEnsDbs} function lists EnsDb databases in a MySQL server. } \details{ The use of this function requires that the \code{RMySQL} package is installed and that the user has either access to a MySQL server with already installed EnsDb databases, or write access to a MySQL server in which case EnsDb databases could be added with the \code{\link{useMySQL}} method. EnsDb databases follow the same naming conventions than the EnsDb packages, with the exception that the name is all lower case and that \code{"."} is replaced by \code{"_"}. } \examples{ \dontrun{ library(RMySQL) dbcon <- dbConnect(MySQL(), host = "localhost", user = my_user, pass = my_pass) listEnsDbs(dbcon) } } \author{ Johannes Rainer } \seealso{ \code{\link{useMySQL}} } ensembldb/man/makeEnsemblDbPackage.Rd0000644000175400017540000002324013001231247020534 0ustar00biocbuildbiocbuild\name{makeEnsembldbPackage} \alias{ensDbFromAH} \alias{ensDbFromGRanges} \alias{ensDbFromGtf} \alias{ensDbFromGff} \alias{makeEnsembldbPackage} \alias{fetchTablesFromEnsembl} \alias{makeEnsemblSQLiteFromTables} \title{ Generating a Ensembl annotation package from Ensembl } \description{ The functions described on this page allow to build \code{EnsDb} annotation objects/databases from Ensembl annotations. The most complete set of annotations, which include also the NCBI Entrezgene identifiers for each gene, can be retrieved by the functions using the Ensembl Perl API (i.e. functions \code{fetchTablesFromEnsembl}, \code{makeEnsemblSQLiteFromTables}). Alternatively the functions \code{ensDbFromAH}, \code{ensDbFromGRanges}, \code{ensDbFromGff} and \code{ensDbFromGtf} can be used to build \code{EnsDb} objects using GFF or GTF files from Ensembl, which can be either manually downloaded from the Ensembl ftp server, or directly form within R using \code{AnnotationHub}. The generated SQLite database can be packaged into an R package using the \code{makeEnsembldbPackage}. } \usage{ ensDbFromAH(ah, outfile, path, organism, genomeVersion, version) ensDbFromGRanges(x, outfile, path, organism, genomeVersion, version) ensDbFromGff(gff, outfile, path, organism, genomeVersion, version) ensDbFromGtf(gtf, outfile, path, organism, genomeVersion, version) fetchTablesFromEnsembl(version, ensemblapi, user="anonymous", host="ensembldb.ensembl.org", pass="", port=5306, species="human") makeEnsemblSQLiteFromTables(path=".", dbname) makeEnsembldbPackage(ensdb, version, maintainer, author, destDir=".", license="Artistic-2.0") } \arguments{ (in alphabetical order) \item{ah}{ For \code{ensDbFromAH}: an \code{AnnotationHub} object representing a single resource (i.e. GTF file from Ensembl) from \code{AnnotationHub}. } \item{author}{ The author of the package. } \item{dbname}{ The name for the database (optional). By default a name based on the species and Ensembl version will be automatically generated (and returned by the function). } \item{destDir}{ Where the package should be saved to. } \item{ensdb}{ The file name of the SQLite database generated by \code{makeEnsemblSQLiteFromTables}. } \item{ ensemblapi }{ The path to the Ensembl perl API installed locally on the system. The Ensembl perl API version has to fit the version. } \item{genomeVersion}{ For \code{ensDbFromAH}, \code{ensDbFromGtf} and \code{ensDbFromGff}: the version of the genome (e.g. \code{"GRCh37"}). If not provided the function will try to guess it from the file name (assuming file name convention of Ensembl GTF files). } \item{gff}{ The GFF file to import. } \item{gtf}{ The GTF file name. } \item{host}{ The hostname to access the Ensembl database. } \item{license}{ The license of the package. } \item{maintainer}{ The maintainer of the package. } \item{organism}{ For \code{ensDbFromAH}, \code{ensDbFromGff} and \code{ensDbFromGtf}: the organism name (e.g. \code{"Homo_sapiens"}). If not provided the function will try to guess it from the file name (assuming file name convention of Ensembl GTF files). } \item{outfile}{ The desired file name of the SQLite file. If not provided the name of the GTF file will be used. } \item{pass}{ The password for the Ensembl database. } \item{path}{ The directory in which the tables retrieved by \code{fetchTablesFromEnsembl} or the SQLite database file generated by \code{ensDbFromGtf} are stored. } \item{port}{ The port to be used to connect to the Ensembl database. } \item{species}{ The species for which the annotations should be retrieved. } \item{user}{ The username for the Ensembl database. } \item{version}{ For \code{fetchTablesFromEnsembl}, \code{ensDbFromGRanges} and \code{ensDbFromGtf}: the Ensembl version for which the annotation should be retrieved (e.g. 75). The \code{ensDbFromGtf} function will try to guess the Ensembl version from the GTF file name if not provided. For \code{makeEnsemblDbPackage}: the version for the package. } \item{x}{ For \code{ensDbFromGRanges}: the \code{GRanges} object. } } \section{Functions}{ \describe{ \item{ensDbFromAH}{ Create an \code{EnsDb} (SQLite) database from a GTF file provided by \code{AnnotationHub}. The function returns the file name of the generated database file. For usage see the examples below. } \item{ensDbFromGff}{ Create an \code{EnsDb} (SQLite) database from a GFF file from Ensembl. The function returns the file name of the generated database file. For usage see the examples below. } \item{ensDbFromGtf}{ Create an \code{EnsDb} (SQLite) database from a GTF file from Ensembl. The function returns the file name of the generated database file. For usage see the examplesbelow. } \item{ensDbFromGRanges}{ Create an \code{EnsDb} (SQLite) database from a GRanges object (e.g. from \code{AnnotationHub}). The function returns the file name of the generated database file. For usage see the examples below. } \item{fetchTablesFromEnsembl}{ Uses the Ensembl Perl API to fetch all required data from an Ensembl database server and stores them locally to text files (that can be used as input for the \code{makeEnsembldbSQLiteFromTables} function). } \item{makeEnsemblSQLiteFromTables}{ Creates the SQLite \code{EnsDb} database from the tables generated by the \code{fetchTablesFromEnsembl}. } \item{makeEnsembldbPackage}{ Creates an R package containing the \code{EnsDb} database from a \code{EnsDb} SQLite database created by any of the above functions \code{ensDbFromAH}, \code{ensDbFromGff}, \code{ensDbFromGtf} or \code{makeEnsemblSQLiteFromTables}. } } } \details{ The \code{fetchTablesFromEnsembl} function internally calls the perl script \code{get_gene_transcript_exon_tables.pl} to retrieve all required information from the Ensembl database using the Ensembl perl API. As an alternative way, a EnsDb database file can be generated by the \code{ensDbFromGtf} or \code{ensDbFromGff} from a GTF or GFF file downloaded from the Ensembl ftp server or using the \code{ensDbFromAH} to build a database directly from corresponding resources from the AnnotationHub. The returned database file name can then be used as an input to the \code{makeEnsembldbPackage} or it can be directly loaded and used by the \code{EnsDb} constructor. } \note{ A local installation of the Ensembl perl API is required for the \code{fetchTablesFromEnsembl}. See \url{http://www.ensembl.org/info/docs/api/api_installation.html} for installation inscructions. A database generated from a GTF/GFF files lacks some features as they are not available in the GTF files from Ensembl. These are: NCBI Entrezgene IDs. } \value{ \code{makeEnsemblSQLiteFromTables}, \code{ensDbFromAH}, \code{ensDbFromGRanges} and \code{ensDbFromGtf}: the name of the SQLite file. } \seealso{ \code{\link{EnsDb}}, \code{\link{genes}} } \author{ Johannes Rainer } \examples{ \dontrun{ ## get all human gene/transcript/exon annotations from Ensembl (75) ## the resulting tables will be stored by default to the current working ## directory; if the correct Ensembl api (version 75) is defined in the ## PERL5LIB environment variable, the ensemblapi parameter can also be omitted. fetchTablesFromEnsembl(75, ensemblapi="/home/bioinfo/ensembl/75/API/ensembl/modules", species="human") ## These tables can then be processed to generate a SQLite database ## containing the annotations DBFile <- makeEnsemblSQLiteFromTables() ## and finally we can generate the package makeEnsembldbPackage(ensdb=DBFile, version="0.0.1", maintainer="Johannes Rainer ", author="J Rainer") ## Build an annotation database form a GFF file from Ensembl. ## ftp://ftp.ensembl.org/pub/release-83/gff3/rattus_norvegicus gff <- "Rattus_norvegicus.Rnor_6.0.83.gff3.gz" DB <- ensDbFromGff(gff=gff) edb <- EnsDb(DB) edb ## Build an annotation file from a GTF file. ## the GTF file can be downloaded from ## ftp://ftp.ensembl.org/pub/release-75/gtf/homo_sapiens/ gtffile <- "Homo_sapiens.GRCh37.75.gtf.gz" ## generate the SQLite database file DB <- ensDbFromGtf(gtf=paste0(ensemblhost, gtffile)) ## load the DB file directly EDB <- EnsDb(DB) ## Alternatively, we could fetch a GTF file directly from AnnotationHub ## and build the database from that: library(AnnotationHub) ah <- AnnotationHub() ## Query for all GTF files from Ensembl for Ensembl version 81 query(ah, c("Ensembl", "release-81", "GTF")) ## We could get the one from e.g. Bos taurus: DB <- ensDbFromAH(ah["AH47941"]) edb <- EnsDb(DB) edb } ## Generate a sqlite database for genes encoded on chromosome Y chrY <- system.file("chrY", package="ensembldb") DBFile <- makeEnsemblSQLiteFromTables(path=chrY ,dbname=tempfile()) ## load this database: edb <- EnsDb(DBFile) edb ## Generate a sqlite database from a GRanges object specifying ## genes encoded on chromosome Y load(system.file("YGRanges.RData", package="ensembldb")) Y DB <- ensDbFromGRanges(Y, path=tempdir(), version=75, organism="Homo_sapiens") edb <- EnsDb(DB) } \keyword{ data } ensembldb/man/runEnsDbApp.Rd0000644000175400017540000000161613001231247016753 0ustar00biocbuildbiocbuild\name{runEnsDbApp} \alias{runEnsDbApp} \title{ Search annotations interactively } \description{ This function starts the interactive \code{EnsDb} shiny web application that allows to look up gene/transcript/exon annotations from an \code{EnsDb} annotation package installed locally. } \usage{ runEnsDbApp(...) } \arguments{ \item{...}{ Additional arguments passed to the \code{\link[shiny]{runApp}} function from the \code{shiny} package. } } \details{ The \code{shiny} based web application allows to look up any annotation available in any of the locally installed \code{EnsDb} annotation packages. } \value{ If the button \emph{Return & close} is clicked, the function returns the results of the present query either as \code{data.frame} or as \code{GRanges} object. } \seealso{ \code{\link{EnsDb}}, \code{\link{genes}} } \author{ Johannes Rainer } \keyword{data} \keyword{shiny} ensembldb/man/useMySQL-EnsDb-method.Rd0000644000175400017540000000327013001231247020521 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Methods.R \docType{methods} \name{useMySQL,EnsDb-method} \alias{useMySQL} \alias{useMySQL,EnsDb-method} \title{Use a MySQL backend} \usage{ \S4method{useMySQL}{EnsDb}(x, host = "localhost", port = 3306, user, pass) } \arguments{ \item{x}{The \code{\linkS4class{EnsDb}} object.} \item{host}{Character vector specifying the host on which the MySQL server runs.} \item{port}{The port on which the MySQL server can be accessed.} \item{user}{The user name for the MySQL server.} \item{pass}{The password for the MySQL server.} } \value{ A \code{\linkS4class{EnsDb}} object providing access to the data stored in the MySQL backend. } \description{ Change the SQL backend from \emph{SQLite} to \emph{MySQL}. When first called on an \code{\linkS4class{EnsDb}} object, the function tries to create and save all of the data into a MySQL database. All subsequent calls will connect to the already existing MySQL database. } \details{ This functionality requires that the \code{RMySQL} package is installed and that the user has (write) access to a running MySQL server. If the corresponding database does already exist users without write access can use this functionality. } \note{ At present the function does not evaluate whether the versions between the SQLite and MySQL database differ. } \examples{ ## Load the EnsDb database (SQLite backend). library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 ## Now change the backend to MySQL; my_user and my_pass should ## be the user name and password to access the MySQL server. \dontrun{ edb_mysql <- useMySQL(edb, host = "localhost", user = my_user, pass = my_pass) } } \author{ Johannes Rainer } ensembldb/tests/0000755000175400017540000000000013001231246014665 5ustar00biocbuildbiocbuildensembldb/tests/runTests.R0000644000175400017540000000005013001231246016632 0ustar00biocbuildbiocbuildBiocGenerics:::testPackage("ensembldb") ensembldb/vignettes/0000755000175400017540000000000013013177517015550 5ustar00biocbuildbiocbuildensembldb/vignettes/MySQL-backend.Rmd0000644000175400017540000000501413001231247020532 0ustar00biocbuildbiocbuild--- title: "Using a MySQL server backend" graphics: yes output: BiocStyle::html_document2 vignette: > %\VignetteIndexEntry{Using a MySQL server backend} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} %\VignetteDepends{ensembldb,EnsDb.Hsapiens.v75,BiocStyle} %\VignettePackage{ensembldb} %\VignetteKeywords{annotation,database} --- **Package**: `r BiocStyle::Biocpkg("ensembldb")`
**Authors**: `r packageDescription("ensembldb")$Author`
**Modified**: 20 September, 2016
**Compiled**: `r date()` # Introduction `ensembldb` uses by default, similar to other annotation packages in Bioconductor, a SQLite database backend, i.e. annotations are retrieved from file-based SQLite databases that are provided *via* packages, such as the `EnsDb.Hsapiens.v75` package. In addition, `ensembldb` allows to switch the backend from SQLite to MySQL and thus to retrieve annotations from a MySQL server instead. Such a setup might be useful for a lab running a well-configured MySQL server that would require installation of EnsDb databases only on the database server and not on the individual clients. **Note** the code in this document is not executed during vignette generation as this would require access to a MySQL server. # Using `ensembldb` with a MySQL server Installation of `EnsDb` databases in a MySQL server is straight forward - given that the user has write access to the server: ```{r eval=FALSE} library(ensembldb) ## Load the EnsDb package that should be installed on the MySQL server library(EnsDb.Hsapiens.v75) ## Call the useMySQL method providing the required credentials to create ## databases and inserting data on the MySQL server edb_mysql <- useMySQL(EnsDb.Hsapiens.v75, host = "localhost", user = "userwrite", pass = "userpass") ## Use this EnsDb object genes(edb_mysql) ``` To use an `EnsDb` in a MySQL server without the need to install the corresponding R-package, the connection to the database can be passed to the `EnsDb` constructor function. With the resulting `EnsDb` object annotations can be retrieved from the MySQL database. ```{r eval=FALSE} library(ensembldb) library(RMySQL) ## Connect to the MySQL database to list the databases. dbcon <- dbConnect(MySQL(), host = "localhost", user = "readonly", pass = "readonly") ## List the available databases listEnsDbs(dbcon) ## Connect to one of the databases and use that one. dbcon <- dbConnect(MySQL(), host = "localhost", user = "readonly", pass = "readonly", dbname = "ensdb_hsapiens_v75") edb <- EnsDb(dbcon) edb ``` ensembldb/vignettes/MySQL-backend.org0000644000175400017540000000560413001231247020604 0ustar00biocbuildbiocbuild#+TITLE: Using a MySQL server backend #+AUTHOR: Johannes Rainer #+EMAIL: johannes.rainer@eurac.edu #+OPTIONS: ^:{} toc:nil #+PROPERTY: exports code #+PROPERTY: session *R* #+BEGIN_html --- title: "Using a MySQL server backend" graphics: yes output: BiocStyle::html_document2 vignette: > %\VignetteIndexEntry{Using a MySQL server backend} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} %\VignetteDepends{ensembldb,EnsDb.Hsapiens.v75,BiocStyle} %\VignettePackage{ensembldb} %\VignetteKeywords{annotation,database} --- #+END_html # #+BEGIN_EXPORT html #+BEGIN_html **Package**: `r BiocStyle::Biocpkg("ensembldb")`
**Authors**: `r packageDescription("ensembldb")$Author`
**Modified**: 20 September, 2016
**Compiled**: `r date()` #+END_html ** Introduction =ensembldb= uses by default, similar to other annotation packages in Bioconductor, a SQLite database backend, i.e. annotations are retrieved from file-based SQLite databases that are provided /via/ packages, such as the =EnsDb.Hsapiens.v75= package. In addition, =ensembldb= allows to switch the backend from SQLite to MySQL and thus to retrieve annotations from a MySQL server instead. Such a setup might be useful for a lab running a well-configured MySQL server that would require installation of EnsDb databases only on the database server and not on the individual clients. *Note* the code in this document is not executed during vignette generation as this would require access to a MySQL server. ** Using =ensembldb= with a MySQL server Installation of =EnsDb= databases in a MySQL server is straight forward - given that the user has write access to the server: #+BEGIN_SRC R :ravel eval=FALSE library(ensembldb) ## Load the EnsDb package that should be installed on the MySQL server library(EnsDb.Hsapiens.v75) ## Call the useMySQL method providing the required credentials to create ## databases and inserting data on the MySQL server edb_mysql <- useMySQL(EnsDb.Hsapiens.v75, host = "localhost", user = "userwrite", pass = "userpass") ## Use this EnsDb object genes(edb_mysql) #+END_SRC To use an =EnsDb= in a MySQL server without the need to install the corresponding R-package, the connection to the database can be passed to the =EnsDb= constructor function. With the resulting =EnsDb= object annotations can be retrieved from the MySQL database. #+BEGIN_SRC R :ravel eval=FALSE library(ensembldb) library(RMySQL) ## Connect to the MySQL database to list the databases. dbcon <- dbConnect(MySQL(), host = "localhost", user = "readonly", pass = "readonly") ## List the available databases listEnsDbs(dbcon) ## Connect to one of the databases and use that one. dbcon <- dbConnect(MySQL(), host = "localhost", user = "readonly", pass = "readonly", dbname = "ensdb_hsapiens_v75") edb <- EnsDb(dbcon) edb #+END_SRC ensembldb/vignettes/ensembldb.Rmd0000644000175400017540000011541513001231247020142 0ustar00biocbuildbiocbuild--- title: "Generating an using Ensembl based annotation packages" graphics: yes output: BiocStyle::html_document2 vignette: > %\VignetteIndexEntry{Generating an using Ensembl based annotation packages} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} %\VignetteDepends{ensembldb,EnsDb.Hsapiens.v75,Gviz,BiocStyle} %\VignettePackage{ensembldb} %\VignetteKeywords{annotation,database} --- **Package**: `r BiocStyle::Biocpkg("ensembldb")`
**Authors**: `r packageDescription("ensembldb")$Author`
**Modified**: 12 September, 2016
**Compiled**: `r date()` # Introduction The `ensembldb` package provides functions to create and use transcript centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl 1 using their Perl API. The functionality and data is similar to that of the `TxDb` packages from the `GenomicFeatures` package, but, in addition to retrieve all gene/transcript models and annotations from the database, the `ensembldb` package provides also a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes. In the databases, along with the gene and transcript models and their chromosomal coordinates, additional annotations including the gene name (symbol) and NCBI Entrezgene identifiers as well as the gene and transcript biotypes are stored too (see Section [11](#orgtarget1) for the database layout and an overview of available attributes/columns). Another main goal of this package is to generate *versioned* annotation packages, i.e. annotation packages that are build for a specific Ensembl release, and are also named according to that (e.g. `EnsDb.Hsapiens.v75` for human gene definitions of the Ensembl code database version 75). This ensures reproducibility, as it allows to load annotations from a specific Ensembl release also if newer versions of annotation packages/releases are available. It also allows to load multiple annotation packages at the same time in order to e.g. compare gene models between Ensembl releases. In the example below we load an Ensembl based annotation package for Homo sapiens, Ensembl version 75. The connection to the database is bound to the variable `EnsDb.Hsapiens.v75`. ```{r warning=FALSE, message=FALSE} library(EnsDb.Hsapiens.v75) ## Making a "short cut" edb <- EnsDb.Hsapiens.v75 ## print some informations for this package edb ## for what organism was the database generated? organism(edb) ``` # Using `ensembldb` annotation packages to retrieve specific annotations The `ensembldb` package provides a set of filter objects allowing to specify which entries should be fetched from the database. The complete list of filters, which can be used individually or can be combined, is shown below (in alphabetical order): - `ExonidFilter`: allows to filter the result based on the (Ensembl) exon identifiers. - `ExonrankFilter`: filter results on the rank (index) of an exon within the transcript model. Exons are always numbered from 5' to 3' end of the transcript, thus, also on the reverse strand, the exon 1 is the most 5' exon of the transcript. - `EntrezidFilter`: allows to filter results based on NCBI Entrezgene identifiers of the genes. - `GenebiotypeFilter`: allows to filter for the gene biotypes defined in the Ensembl database; use the `listGenebiotypes` method to list all available biotypes. - `GeneidFilter`: allows to filter based on the Ensembl gene IDs. - `GenenameFilter`: allows to filter based on the names (symbols) of the genes. - `SymbolFilter`: allows to filter on gene symbols; note that no database columns *symbol* is available in an `EnsDb` database and hence the gene name is used for filtering. - `GRangesFilter`: allows to retrieve all features (genes, transcripts or exons) that are either within (setting `condition` to "within") or partially overlapping (setting `condition` to "overlapping") the defined genomic region/range. Note that, depending on the called method (`genes`, `transcripts` or `exons`) the start and end coordinates of either the genes, transcripts or exons are used for the filter. For methods `exonsBy`, `cdsBy` and `txBy` the coordinates of `by` are used. - `SeqendFilter`: filter based on the chromosomal end coordinate of the exons, transcripts or genes (correspondingly set =feature = "exon"=, =feature = "tx"= or =feature = "gene"=). - `SeqnameFilter`: filter by the name of the chromosomes the genes are encoded on. - `SeqstartFilter`: filter based on the chromosomal start coordinates of the exons, transcripts or genes (correspondingly set =feature = "exon"=, =feature = "tx"= or =feature = "gene"=). - `SeqstrandFilter`: filter for the chromosome strand on which the genes are encoded. - `TxbiotypeFilter`: filter on the transcript biotype defined in Ensembl; use the `listTxbiotypes` method to list all available biotypes. - `TxidFilter`: filter on the Ensembl transcript identifiers. Each of the filter classes can take a single value or a vector of values (with the exception of the `SeqendFilter` and `SeqstartFilter`) for comparison. In addition, it is possible to specify the *condition* for the filter, e.g. setting `condition` to = to retrieve all entries matching the filter value, to != to negate the filter or setting `condition = "like"= to allow partial matching. The =condition` parameter for `SeqendFilter` and `SeqendFilter` can take the values = , >, >=, < and <= (since these filters base on numeric values). A simple example would be to get all transcripts for the gene *BCL2L11*. To this end we specify a `GenenameFilter` with the value *BCL2L11*. As a result we get a `GRanges` object with `start`, `end`, `strand` and `seqname` of the `GRanges` object being the start coordinate, end coordinate, chromosome name and strand for the respective transcripts. All additional annotations are available as metadata columns. Alternatively, by setting `return.type` to "DataFrame", or "data.frame" the method would return a `DataFrame` or `data.frame` object. ```{r } Tx <- transcripts(edb, filter = list(GenenameFilter("BCL2L11"))) Tx ## as this is a GRanges object we can access e.g. the start coordinates with head(start(Tx)) ## or extract the biotype with head(Tx$tx_biotype) ``` The parameter `columns` of the `exons`, `genes` and `transcripts` method allows to specify which database attributes (columns) should be retrieved. The `exons` method returns by default all exon-related columns, the `transcripts` all columns from the transcript database table and the `genes` all from the gene table. Note however that in the example above we got also a column `gene_name` although this column is not present in the transcript database table. By default the methods return also all columns that are used by any of the filters submitted with the `filter` argument (thus, because a `GenenameFilter` was used, the column `gene_name` is also returned). Setting `returnFilterColumns(edb) <- FALSE` disables this option and only the columns specified by the `columns` parameter are retrieved. To get an overview of database tables and available columns the function `listTables` can be used. The method `listColumns` on the other hand lists columns for the specified database table. ```{r } ## list all database tables along with their columns listTables(edb) ## list columns from a specific table listColumns(edb, "tx") ``` Thus, we could retrieve all transcripts of the biotype *nonsense\_mediated\_decay* (which, according to the definitions by Ensembl are transcribed, but most likely not translated in a protein, but rather degraded after transcription) along with the name of the gene for each transcript. Note that we are changing here the `return.type` to `DataFrame`, so the method will return a `DataFrame` with the results instead of the default `GRanges`. ```{r } Tx <- transcripts(edb, columns = c(listColumns(edb , "tx"), "gene_name"), filter = TxbiotypeFilter("nonsense_mediated_decay"), return.type = "DataFrame") nrow(Tx) Tx ``` For protein coding transcripts, we can also specifically extract their coding region. In the example below we extract the CDS for all transcripts encoded on chromosome Y. ```{r } yCds <- cdsBy(edb, filter = SeqnameFilter("Y")) yCds ``` Using a `GRangesFilter` we can retrieve all features from the database that are either within or overlapping the specified genomic region. In the example below we query all genes that are partially overlapping with a small region on chromosome 11. The filter restricts to all genes for which either an exon or an intron is partially overlapping with the region. ```{r } ## Define the filter grf <- GRangesFilter(GRanges("11", ranges = IRanges(114000000, 114000050), strand = "+"), condition = "overlapping") ## Query genes: gn <- genes(edb, filter = grf) gn ## Next we retrieve all transcripts for that gene so that we can plot them. txs <- transcripts(edb, filter = GenenameFilter(gn$gene_name)) ``` ```{r tx-for-zbtb16, message=FALSE, fig.align='center', fig.width=7.5, fig.height=5} plot(3, 3, pch = NA, xlim = c(start(gn), end(gn)), ylim = c(0, length(txs)), yaxt = "n", ylab = "") ## Highlight the GRangesFilter region rect(xleft = start(grf), xright = end(grf), ybottom = 0, ytop = length(txs), col = "red", border = "red") for(i in 1:length(txs)) { current <- txs[i] rect(xleft = start(current), xright = end(current), ybottom = i-0.975, ytop = i-0.125, border = "grey") text(start(current), y = i-0.5, pos = 4, cex = 0.75, labels = current$tx_id) } ``` As we can see, 4 transcripts of the gene ZBTB16 are also overlapping the region. Below we fetch these 4 transcripts. Note, that a call to `exons` will not return any features from the database, as no exon is overlapping with the region. ```{r } transcripts(edb, filter = grf) ``` The `GRangesFilter` supports also `GRanges` defining multiple regions and a query will return all features overlapping any of these regions. Besides using the `GRangesFilter` it is also possible to search for transcripts or exons overlapping genomic regions using the `exonsByOverlaps` or `transcriptsByOverlaps` known from the `GenomicFeatures` package. Note that the implementation of these methods for `EnsDb` objects supports also to use filters to further fine-tune the query. To get an overview of allowed/available gene and transcript biotype the functions `listGenebiotypes` and `listTxbiotypes` can be used. ```{r } ## Get all gene biotypes from the database. The GenebiotypeFilter ## allows to filter on these values. listGenebiotypes(edb) ## Get all transcript biotypes from the database. listTxbiotypes(edb) ``` Data can be fetched in an analogous way using the `exons` and `genes` methods. In the example below we retrieve `gene_name`, `entrezid` and the `gene_biotype` of all genes in the database which names start with "BCL2". ```{r } ## We're going to fetch all genes which names start with BCL. To this end ## we define a GenenameFilter with partial matching, i.e. condition "like" ## and a % for any character/string. BCLs <- genes(edb, columns = c("gene_name", "entrezid", "gene_biotype"), filter = list(GenenameFilter("BCL%", condition = "like")), return.type = "DataFrame") nrow(BCLs) BCLs ``` Sometimes it might be useful to know the length of genes or transcripts (i.e. the total sum of nucleotides covered by their exons). Below we calculate the mean length of transcripts from protein coding genes on chromosomes X and Y as well as the average length of snoRNA, snRNA and rRNA transcripts encoded on these chromosomes. ```{r } ## determine the average length of snRNA, snoRNA and rRNA genes encoded on ## chromosomes X and Y. mean(lengthOf(edb, of = "tx", filter = list(GenebiotypeFilter(c("snRNA", "snoRNA", "rRNA")), SeqnameFilter(c("X", "Y"))))) ## determine the average length of protein coding genes encoded on the same ## chromosomes. mean(lengthOf(edb, of = "tx", filter = list(GenebiotypeFilter("protein_coding"), SeqnameFilter(c("X", "Y"))))) ``` Not unexpectedly, transcripts of protein coding genes are longer than those of snRNA, snoRNA or rRNA genes. At last we extract the first two exons of each transcript model from the database. ```{r } ## Extract all exons 1 and (if present) 2 for all genes encoded on the ## Y chromosome exons(edb, columns = c("tx_id", "exon_idx"), filter = list(SeqnameFilter("Y"), ExonrankFilter(3, condition = "<"))) ``` # Extracting gene/transcript/exon models for RNASeq feature counting For the feature counting step of an RNAseq experiment, the gene or transcript models (defined by the chromosomal start and end positions of their exons) have to be known. To extract these from an Ensembl based annotation package, the `exonsBy`, `genesBy` and `transcriptsBy` methods can be used in an analogous way as in `TxDb` packages generated by the `GenomicFeatures` package. However, the `transcriptsBy` method does not, in contrast to the method in the `GenomicFeatures` package, allow to return transcripts by "cds". While the annotation packages built by the `ensembldb` contain the chromosomal start and end coordinates of the coding region (for protein coding genes) they do not assign an ID to each CDS. A simple use case is to retrieve all genes encoded on chromosomes X and Y from the database. ```{r } TxByGns <- transcriptsBy(edb, by = "gene", filter = list(SeqnameFilter(c("X", "Y"))) ) TxByGns ``` Since Ensembl contains also definitions of genes that are on chromosome variants (supercontigs), it is advisable to specify the chromosome names for which the gene models should be returned. In a real use case, we might thus want to retrieve all genes encoded on the *standard* chromosomes. In addition it is advisable to use a `GeneidFilter` to restrict to Ensembl genes only, as also *LRG* (Locus Reference Genomic) genes2 are defined in the database, which are partially redundant with Ensembl genes. ```{r eval=FALSE} ## will just get exons for all genes on chromosomes 1 to 22, X and Y. ## Note: want to get rid of the "LRG" genes!!! EnsGenes <- exonsBy(edb, by = "gene", filter = list(SeqnameFilter(c(1:22, "X", "Y")), GeneidFilter("ENSG%", "like"))) ``` The code above returns a `GRangesList` that can be used directly as an input for the `summarizeOverlaps` function from the `GenomicAlignments` package 3. Alternatively, the above `GRangesList` can be transformed to a `data.frame` in *SAF* format that can be used as an input to the `featureCounts` function of the `Rsubread` package 4. ```{r eval=FALSE} ## Transforming the GRangesList into a data.frame in SAF format EnsGenes.SAF <- toSAF(EnsGenes) ``` Note that the ID by which the `GRangesList` is split is used in the SAF formatted `data.frame` as the `GeneID`. In the example below this would be the Ensembl gene IDs, while the start, end coordinates (along with the strand and chromosomes) are those of the the exons. In addition, the `disjointExons` function (similar to the one defined in `GenomicFeatures`) can be used to generate a `GRanges` of non-overlapping exon parts which can be used in the `DEXSeq` package. ```{r eval=FALSE} ## Create a GRanges of non-overlapping exon parts. DJE <- disjointExons(edb, filter = list(SeqnameFilter(c(1:22, "X", "Y")), GeneidFilter("ENSG%", "like"))) ``` # Retrieving sequences for gene/transcript/exon models The methods to retrieve exons, transcripts and genes (i.e. `exons`, `transcripts` and `genes`) return by default `GRanges` objects that can be used to retrieve sequences using the `getSeq` method e.g. from BSgenome packages. The basic workflow is thus identical to the one for `TxDb` packages, however, it is not straight forward to identify the BSgenome package with the matching genomic sequence. Most BSgenome packages are named according to the genome build identifier used in UCSC which does not (always) match the genome build name used by Ensembl. Using the Ensembl version provided by the `EnsDb`, the correct genomic sequence can however be retrieved easily from the `AnnotationHub` using the `getGenomeFaFile`. If no Fasta file matching the Ensembl version is available, the function tries to identify a Fasta file with the correct genome build from the *closest* Ensembl release and returns that instead. In the code block below we retrieve first the `FaFile` with the genomic DNA sequence, extract the genomic start and end coordinates for all genes defined in the package, subset to genes encoded on sequences available in the `FaFile` and extract all of their sequences. Note: these sequences represent the sequence between the chromosomal start and end coordinates of the gene. ```{r eval=FALSE} library(EnsDb.Hsapiens.v75) library(Rsamtools) edb <- EnsDb.Hsapiens.v75 ## Get the FaFile with the genomic sequence matching the Ensembl version ## using the AnnotationHub package. Dna <- getGenomeFaFile(edb) ## Get start/end coordinates of all genes. genes <- genes(edb) ## Subset to all genes that are encoded on chromosomes for which ## we do have DNA sequence available. genes <- genes[seqnames(genes) %in% seqnames(seqinfo(Dna))] ## Get the gene sequences, i.e. the sequence including the sequence of ## all of the gene's exons and introns. geneSeqs <- getSeq(Dna, genes) ``` To retrieve the (exonic) sequence of transcripts (i.e. without introns) we can use directly the `extractTranscriptSeqs` method defined in the `GenomicFeatures` on the `EnsDb` object, eventually using a filter to restrict the query. ```{r eval=FALSE} ## get all exons of all transcripts encoded on chromosome Y yTx <- exonsBy(edb, filter = SeqnameFilter("Y")) ## Retrieve the sequences for these transcripts from the FaFile. library(GenomicFeatures) yTxSeqs <- extractTranscriptSeqs(Dna, yTx) yTxSeqs ## Extract the sequences of all transcripts encoded on chromosome Y. yTx <- extractTranscriptSeqs(Dna, edb, filter = SeqnameFilter("Y")) ## Along these lines, we could use the method also to retrieve the coding sequence ## of all transcripts on the Y chromosome. cdsY <- cdsBy(edb, filter = SeqnameFilter("Y")) extractTranscriptSeqs(Dna, cdsY) ``` Note: in the next section we describe how transcript sequences can be retrieved from a `BSgenome` package that is based on UCSC, not Ensembl. # Integrating annotations from Ensembl based `EnsDb` packages with UCSC based annotations Sometimes it might be useful to combine (Ensembl based) annotations from `EnsDb` packages/objects with annotations from other Bioconductor packages, that might base on UCSC annotations. To support such an integration of annotations, the `ensembldb` packages implements the `seqlevelsStyle` and `seqlevelsStyle<-` from the `GenomeInfoDb` package that allow to change the style of chromosome naming. Thus, sequence/chromosome names other than those used by Ensembl can be used in, and are returned by, the queries to `EnsDb` objects as long as a mapping for them is provided by the `GenomeInfoDb` package (which provides a mapping mostly between UCSC, NCBI and Ensembl chromosome names for the *main* chromosomes). In the example below we change the seqnames style to UCSC. ```{r message=FALSE} ## Change the seqlevels style form Ensembl (default) to UCSC: seqlevelsStyle(edb) <- "UCSC" ## Now we can use UCSC style seqnames in SeqnameFilters or GRangesFilter: genesY <- genes(edb, filter = SeqnameFilter("chrY")) ## The seqlevels of the returned GRanges are also in UCSC style seqlevels(genesY) ``` Note that in most instances no mapping is available for sequences not corresponding to the main chromosomes (i.e. contigs, patched chromosomes etc). What is returned in cases in which no mapping is available can be specified with the global `ensembldb.seqnameNotFound` option. By default (with `ensembldb.seqnameNotFound` set to "ORIGINAL"), the original seqnames (i.e. the ones from Ensembl) are returned. With `ensembldb.seqnameNotFound` "MISSING" each time a seqname can not be found an error is thrown. For all other cases (e.g. `ensembldb.seqnameNotFound = NA`) the value of the option is returned. ```{r } seqlevelsStyle(edb) <- "UCSC" ## Getting the default option: getOption("ensembldb.seqnameNotFound") ## Listing all seqlevels in the database. seqlevels(edb)[1:30] ## Setting the option to NA, thus, for each seqname for which no mapping is available, ## NA is returned. options(ensembldb.seqnameNotFound=NA) seqlevels(edb)[1:30] ## Resetting the option. options(ensembldb.seqnameNotFound = "ORIGINAL") ``` Next we retrieve transcript sequences from genes encoded on chromosome Y using the `BSGenome` package for the human genome from UCSC. The specified version `hg19` matches the genome build of Ensembl version 75, i.e. `GRCh37`. Note that while we changed the style of the seqnames to UCSC we did not change the naming of the genome release. ```{r warning=FALSE, message=FALSE} library(BSgenome.Hsapiens.UCSC.hg19) bsg <- BSgenome.Hsapiens.UCSC.hg19 ## Get the genome version unique(genome(bsg)) unique(genome(edb)) ## Although differently named, both represent genome build GRCh37. ## Extract the full transcript sequences. yTxSeqs <- extractTranscriptSeqs(bsg, exonsBy(edb, "tx", filter = SeqnameFilter("chrY"))) yTxSeqs ## Extract just the CDS Test <- cdsBy(edb, "tx", filter = SeqnameFilter("chrY")) yTxCds <- extractTranscriptSeqs(bsg, cdsBy(edb, "tx", filter = SeqnameFilter("chrY"))) yTxCds ``` At last changing the seqname style to the default value ="Ensembl"=. ```{r } seqlevelsStyle(edb) <- "Ensembl" ``` # Interactive annotation lookup using the `shiny` web app In addition to the `genes`, `transcripts` and `exons` methods it is possibly to search interactively for gene/transcript/exon annotations using the internal, `shiny` based, web application. The application can be started with the `runEnsDbApp()` function. The search results from this app can also be returned to the R workspace either as a `data.frame` or `GRanges` object. # Plotting gene/transcript features using `ensembldb` and `Gviz` The `Gviz` package provides functions to plot genes and transcripts along with other data on a genomic scale. Gene models can be provided either as a `data.frame`, `GRanges`, `TxDB` database, can be fetched from biomart and can also be retrieved from `ensembldb`. Below we generate a `GeneRegionTrack` fetching all transcripts from a certain region on chromosome Y. Note that if we want in addition to work also with BAM files that were aligned against DNA sequences retrieved from Ensembl or FASTA files representing genomic DNA sequences from Ensembl we should change the `ucscChromosomeNames` option from `Gviz` to `FALSE` (i.e. by calling `options(ucscChromosomeNames = FALSE)`). This is not necessary if we just want to retrieve gene models from an `EnsDb` object, as the `ensembldb` package internally checks the `ucscChromosomeNames` option and, depending on that, maps Ensembl chromosome names to UCSC chromosome names. ```{r gviz-plot, message=FALSE, fig.align='center', fig.width=7.5, fig.height=2.25} ## Loading the Gviz library library(Gviz) library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 ## Retrieving a Gviz compatible GRanges object with all genes ## encoded on chromosome Y. gr <- getGeneRegionTrackForGviz(edb, chromosome = "Y", start = 20400000, end = 21400000) ## Define a genome axis track gat <- GenomeAxisTrack() ## We have to change the ucscChromosomeNames option to FALSE to enable Gviz usage ## with non-UCSC chromosome names. options(ucscChromosomeNames = FALSE) plotTracks(list(gat, GeneRegionTrack(gr))) options(ucscChromosomeNames = TRUE) ``` Above we had to change the option `ucscChromosomeNames` to `FALSE` in order to use it with non-UCSC chromosome names. Alternatively, we could however also change the `seqnamesStyle` of the `EnsDb` object to `UCSC`. Note that we have to use now also chromosome names in the *UCSC style* in the `SeqnameFilter` (i.e. "chrY" instead of `Y`). ```{r message=FALSE} seqlevelsStyle(edb) <- "UCSC" ## Retrieving the GRanges objects with seqnames corresponding to UCSC chromosome names. gr <- getGeneRegionTrackForGviz(edb, chromosome = "chrY", start = 20400000, end = 21400000) seqnames(gr) ## Define a genome axis track gat <- GenomeAxisTrack() plotTracks(list(gat, GeneRegionTrack(gr))) ``` We can also use the filters from the `ensembldb` package to further refine what transcripts are fetched, like in the example below, in which we create two different gene region tracks, one for protein coding genes and one for lincRNAs. ```{r gviz-separate-tracks, message=FALSE, warning=FALSE, fig.align='center', fig.width=7.5, fig.height=2.25} protCod <- getGeneRegionTrackForGviz(edb, chromosome = "chrY", start = 20400000, end = 21400000, filter = GenebiotypeFilter("protein_coding")) lincs <- getGeneRegionTrackForGviz(edb, chromosome = "chrY", start = 20400000, end = 21400000, filter = GenebiotypeFilter("lincRNA")) plotTracks(list(gat, GeneRegionTrack(protCod, name = "protein coding"), GeneRegionTrack(lincs, name = "lincRNAs")), transcriptAnnotation = "symbol") ## At last we change the seqlevels style again to Ensembl seqlevelsStyle <- "Ensembl" ``` # Using `EnsDb` objects in the `AnnotationDbi` framework Most of the methods defined for objects extending the basic annotation package class `AnnotationDbi` are also defined for `EnsDb` objects (i.e. methods `columns`, `keytypes`, `keys`, `mapIds` and `select`). While these methods can be used analogously to basic annotation packages, the implementation for `EnsDb` objects also support the filtering framework of the `ensembldb` package. In the example below we first evaluate all the available columns and keytypes in the database and extract then the gene names for all genes encoded on chromosome X. ```{r } library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 ## List all available columns in the database. columns(edb) ## Note that these do *not* correspond to the actual column names ## of the database that can be passed to methods like exons, genes, ## transcripts etc. These column names can be listed with the listColumns ## method. listColumns(edb) ## List all of the supported key types. keytypes(edb) ## Get all gene ids from the database. gids <- keys(edb, keytype = "GENEID") length(gids) ## Get all gene names for genes encoded on chromosome Y. gnames <- keys(edb, keytype = "GENENAME", filter = SeqnameFilter("Y")) head(gnames) ``` In the next example we retrieve specific information from the database using the `select` method. First we fetch all transcripts for the genes *BCL2* and *BCL2L11*. In the first call we provide the gene names, while in the second call we employ the filtering system to perform a more fine-grained query to fetch only the protein coding transcripts for these genes. ```{r warning=FALSE} ## Use the /standard/ way to fetch data. select(edb, keys = c("BCL2", "BCL2L11"), keytype = "GENENAME", columns = c("GENEID", "GENENAME", "TXID", "TXBIOTYPE")) ## Use the filtering system of ensembldb select(edb, keys = list(GenenameFilter(c("BCL2", "BCL2L11")), TxbiotypeFilter("protein_coding")), columns = c("GENEID", "GENENAME", "TXID", "TXBIOTYPE")) ``` Finally, we use the `mapIds` method to establish a mapping between ids and values. In the example below we fetch transcript ids for the two genes from the example above. ```{r } ## Use the default method, which just returns the first value for multi mappings. mapIds(edb, keys = c("BCL2", "BCL2L11"), column = "TXID", keytype = "GENENAME") ## Alternatively, specify multiVals="list" to return all mappings. mapIds(edb, keys = c("BCL2", "BCL2L11"), column = "TXID", keytype = "GENENAME", multiVals = "list") ## And, just like before, we can use filters to map only to protein coding transcripts. mapIds(edb, keys = list(GenenameFilter(c("BCL2", "BCL2L11")), TxbiotypeFilter("protein_coding")), column = "TXID", multiVals = "list") ``` Note that, if the filters are used, the ordering of the result does no longer match the ordering of the genes. # Important notes These notes might explain eventually unexpected results (and, more importantly, help avoiding them): - The ordering of the results returned by the `genes`, `exons`, `transcripts` methods can be specified with the `order.by` parameter. The ordering of the results does however **not** correspond to the ordering of values in submitted filter objects. The exception is the `select` method. If a character vector of values or a single filter is passed with argument `keys` the ordering of results of this method matches the ordering of the key values or the values of the filter. - Results of `exonsBy`, `transcriptsBy` are always ordered by the `by` argument. - The CDS provided by `EnsDb` objects **always** includes both, the start and the stop codon. - Transcripts with multiple CDS are at present not supported by `EnsDb`. - At present, `EnsDb` support only genes/transcripts for which all of their exons are encoded on the same chromosome and the same strand. # Building an transcript-centric database package based on Ensembl annotation The code in this section is not supposed to be automatically executed when the vignette is built, as this would require a working installation of the Ensembl Perl API, which is not expected to be available on each system. Also, building `EnsDb` from alternative sources, like GFF or GTF files takes some time and thus also these examples are not directly executed when the vignette is build. ## Requirements The `fetchTablesFromEnsembl` function of the package uses the Ensembl Perl API to retrieve the required annotations from an Ensembl database (e.g. from the main site *ensembldb.ensembl.org*). Thus, to use the functionality to built databases, the Ensembl Perl API needs to be installed (see 5 for details). Alternatively, the `ensDbFromAH`, `ensDbFromGff`, `ensDbFromGRanges` and `ensDbFromGtf` functions allow to build EnsDb SQLite files from a `GRanges` object or GFF/GTF files from Ensembl (either provided as files or *via* `AnnotationHub`). These functions do not depend on the Ensembl Perl API, but require a working internet connection to fetch the chromosome lengths from Ensembl as these are not provided within GTF or GFF files. ## Building annotation packages The functions below use the Ensembl Perl API to fetch the required data directly from the Ensembl core databases. Thus, the path to the Perl API specific for the desired Ensembl version needs to be added to the `PERL5LIB` environment variable. An annotation package containing all human genes for Ensembl version 75 can be created using the code in the block below. ```{r eval=FALSE} library(ensembldb) ## get all human gene/transcript/exon annotations from Ensembl (75) ## the resulting tables will be stored by default to the current working ## directory fetchTablesFromEnsembl(75, species = "human") ## These tables can then be processed to generate a SQLite database ## containing the annotations (again, the function assumes the required ## txt files to be present in the current working directory) DBFile <- makeEnsemblSQLiteFromTables() ## and finally we can generate the package makeEnsembldbPackage(ensdb = DBFile, version = "0.99.12", maintainer = "Johannes Rainer ", author = "J Rainer") ``` The generated package can then be build using `R CMD build EnsDb.Hsapiens.v75` and installed with `R CMD INSTALL EnsDb.Hsapiens.v75*`. Note that we could directly generate an `EnsDb` instance by loading the database file, i.e. by calling `edb <- EnsDb(DBFile)` and work with that annotation object. To fetch and build annotation packages for plant genomes (e.g. arabidopsis thaliana), the *Ensembl genomes* should be specified as a host, i.e. setting `host` to "mysql-eg-publicsql.ebi.ac.uk", `port` to `4157` and `species` to e.g. "arabidopsis thaliana". In the next example we create an `EnsDb` database using the `AnnotationHub` package and load also the corresponding genomic DNA sequence matching the Ensembl version. We thus first query the `AnnotationHub` package for all resources available for `Mus musculus` and the Ensembl release 77. Next we create the `EnsDb` object from the appropriate `AnnotationHub` resource. We then use the `getGenomeFaFile` method on the `EnsDb` to directly look up and retrieve the correct or best matching `FaFile` with the genomic DNA sequence. At last we retrieve the sequences of all exons using the `getSeq` method. ```{r eval=FALSE} ## Load the AnnotationHub data. library(AnnotationHub) ah <- AnnotationHub() ## Query all available files for Ensembl release 77 for ## Mus musculus. query(ah, c("Mus musculus", "release-77")) ## Get the resource for the gtf file with the gene/transcript definitions. Gtf <- ah["AH28822"] ## Create a EnsDb database file from this. DbFile <- ensDbFromAH(Gtf) ## We can either generate a database package, or directly load the data edb <- EnsDb(DbFile) ## Identify and get the FaFile object with the genomic DNA sequence matching ## the EnsDb annotation. Dna <- getGenomeFaFile(edb) library(Rsamtools) ## We next retrieve the sequence of all exons on chromosome Y. exons <- exons(edb, filter = SeqnameFilter("Y")) exonSeq <- getSeq(Dna, exons) ## Alternatively, look up and retrieve the toplevel DNA sequence manually. Dna <- ah[["AH22042"]] ``` In the example below we load a `GRanges` containing gene definitions for genes encoded on chromosome Y and generate a EnsDb SQLite database from that information. ```{r message=FALSE} ## Generate a sqlite database from a GRanges object specifying ## genes encoded on chromosome Y load(system.file("YGRanges.RData", package = "ensembldb")) Y DB <- ensDbFromGRanges(Y, path = tempdir(), version = 75, organism = "Homo_sapiens") edb <- EnsDb(DB) edb ## As shown in the example below, we could make an EnsDb package on ## this DB object using the makeEnsembldbPackage function. ``` Alternatively we can build the annotation database using the `ensDbFromGtf` `ensDbFromGff` functions, that extracts most of the required data from a GTF respectively GFF (version 3) file which can be downloaded from Ensembl (e.g. from for human gene definitions from Ensembl version 75; for plant genomes etc files can be retrieved from ). All information except the chromosome lengths and the NCBI Entrezgene IDs can be extracted from these GTF files. The function also tries to retrieve chromosome length information automatically from Ensembl. Below we create the annotation from a gtf file that we fetch directly from Ensembl. ```{r eval=FALSE} library(ensembldb) ## the GTF file can be downloaded from ## ftp://ftp.ensembl.org/pub/release-75/gtf/homo_sapiens/ gtffile <- "Homo_sapiens.GRCh37.75.gtf.gz" ## generate the SQLite database file DB <- ensDbFromGtf(gtf = gtffile) ## load the DB file directly EDB <- EnsDb(DB) ## alternatively, build the annotation package ## and finally we can generate the package makeEnsembldbPackage(ensdb = DB, version = "0.99.12", maintainer = "Johannes Rainer ", author = "J Rainer") ``` # Database layout The database consists of the following tables and attributes (the layout is also shown in Figure [115](#orgparagraph1)): - **gene**: all gene specific annotations. - `gene_id`: the Ensembl ID of the gene. - `gene_name`: the name (symbol) of the gene. - `entrezid`: the NCBI Entrezgene ID(s) of the gene. Note that this can be a `;` separated list of IDs for genes that are mapped to more than one Entrezgene. - `gene_biotype`: the biotype of the gene. - `gene_seq_start`: the start coordinate of the gene on the sequence (usually a chromosome). - `gene_seq_end`: the end coordinate of the gene on the sequence. - `seq_name`: the name of the sequence (usually the chromosome name). - `seq_strand`: the strand on which the gene is encoded. - `seq_coord_system`: the coordinate system of the sequence. - **tx**: all transcript related annotations. Note that while no `tx_name` column is available in this database column, all methods to retrieve data from the database support also this column. The returned values are however the ID of the transcripts. - `tx_id`: the Ensembl transcript ID. - `tx_biotype`: the biotype of the transcript. - `tx_seq_start`: the start coordinate of the transcript. - `tx_seq_end`: the end coordinate of the transcript. - `tx_cds_seq_start`: the start coordinate of the coding region of the transcript (NULL for non-coding transcripts). - `tx_cds_seq_end`: the end coordinate of the coding region of the transcript. - `gene_id`: the gene to which the transcript belongs. - **exon**: all exon related annotation. - `exon_id`: the Ensembl exon ID. - `exon_seq_start`: the start coordinate of the exon. - `exon_seq_end`: the end coordinate of the exon. - **tx2exon**: provides the n:m mapping between transcripts and exons. - `tx_id`: the Ensembl transcript ID. - `exon_id`: the Ensembl exon ID. - `exon_idx`: the index of the exon in the corresponding transcript, always from 5' to 3' of the transcript. - **chromosome**: provides some information about the chromosomes. - `seq_name`: the name of the sequence/chromosome. - `seq_length`: the length of the sequence. - `is_circular`: whether the sequence in circular. - **information**: some additional, internal, informations (Genome build, Ensembl version etc). - `key` - `value` - *virtual* columns: - `symbol`: the database does not have such a database column, but it is still possible to use it in the `columns` parameter. This column is *symlinked* to the `gene_name` column. - `tx_name`: similar to the `symbol` column, this column is *symlinked* to the `tx_id` column. ![img](images/dblayout.png "Database layout.")

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ensembldb/vignettes/ensembldb.org0000644000175400017540000016175113001231247020213 0ustar00biocbuildbiocbuild#+TITLE: Generating and using Ensembl based annotation packages #+AUTHOR: Johannes Rainer #+EMAIL: johannes.rainer@eurac.edu #+DESCRIPTION: #+KEYWORDS: #+LANGUAGE: en #+OPTIONS: ^:{} toc:nil #+PROPERTY: exports code #+PROPERTY: session *R* #+EXPORT_SELECT_TAGS: export #+EXPORT_EXCLUDE_TAGS: noexport #+latex: %\VignetteIndexEntry{Generating an using Ensembl based annotation packages} #+latex: %\VignetteKeywords{annotation, database} #+latex: %\VignetteDepends{ensembldb,EnsDb.Hsapiens.v75,BSgenome.Hsapiens.UCSC.hg19} #+latex: %\VignettePackage{ensembldb} #+latex: %\VignetteEngine{knitr::rmarkdown} #+BEGIN_html --- title: "Generating an using Ensembl based annotation packages" graphics: yes output: BiocStyle::html_document2 vignette: > %\VignetteIndexEntry{Generating an using Ensembl based annotation packages} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} %\VignetteDepends{ensembldb,EnsDb.Hsapiens.v75,Gviz,BiocStyle} %\VignettePackage{ensembldb} %\VignetteKeywords{annotation,database} --- #+END_html # #+BEGIN_EXPORT html #+BEGIN_html **Package**: `r BiocStyle::Biocpkg("ensembldb")`
**Authors**: `r packageDescription("ensembldb")$Author`
**Modified**: 12 September, 2016
**Compiled**: `r date()` #+END_html * How to export this to a =Rnw= vignette :noexport: Use =ox-ravel= from the =orgmode-accessories= package to export this file to a =Rnw= file. After export edit the generated =Rnw= in the following way: 1) Delete all =\usepackage= commands. 2) Move the =<