ensembldb/DESCRIPTION0000644000175400017540000000440713241155731015250 0ustar00biocbuildbiocbuildPackage: ensembldb Type: Package Title: Utilities to create and use Ensembl-based annotation databases Version: 2.2.2 Authors@R: c(person(given = "Johannes", family = "Rainer", email = "johannes.rainer@eurac.edu", role = c("aut", "cre")), person(given = "Tim", family = "Triche", email = "tim.triche@usc.edu", role = "ctb"), person(given = "Christian", family = "Weichenberger", email = "christian.weichenberger@eurac.edu", role = "ctb")) Author: Johannes Rainer with contributions from Tim Triche and Christian Weichenberger. Maintainer: Johannes Rainer URL: https://github.com/jotsetung/ensembldb BugReports: https://github.com/jotsetung/ensembldb/issues Imports: methods, RSQLite (>= 1.1), DBI, Biobase, GenomeInfoDb, AnnotationDbi (>= 1.31.19), rtracklayer, S4Vectors, AnnotationHub, Rsamtools, IRanges (>= 2.11.16), ProtGenerics, Biostrings, curl Depends: BiocGenerics (>= 0.15.10), GenomicRanges (>= 1.23.21), GenomicFeatures (>= 1.29.10), AnnotationFilter (>= 1.1.9) Suggests: BiocStyle, knitr, rmarkdown, EnsDb.Hsapiens.v75 (>= 0.99.8), shiny, testthat, BSgenome.Hsapiens.UCSC.hg19, ggbio (>= 1.24.0), Gviz (>= 1.20.0), magrittr 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 functions-EnsDb.R functions-Filter.R Methods-Filter.R functions-create-EnsDb.R select-methods.R seqname-utils.R Deprecated.R zzz.R biocViews: Genetics, AnnotationData, Sequencing, Coverage License: LGPL RoxygenNote: 6.0.1 NeedsCompilation: no Packaged: 2018-02-15 01:01:13 UTC; biocbuild ensembldb/NAMESPACE0000644000175400017540000001431713175714743014774 0ustar00biocbuildbiocbuild## ensembldb NAMESPACE import(methods) importFrom("utils", "read.table", "str", "download.file") import("BiocGenerics") importFrom("Biobase", "createPackage", "validMsg") importMethodsFrom("ProtGenerics", "proteins") import("S4Vectors") importFrom("DBI", "dbDriver") import("RSQLite") importMethodsFrom("AnnotationDbi", "columns", "dbconn", "keys", "keytypes", "mapIds", "select") importFrom("GenomeInfoDb", "Seqinfo", "isCircular", "genome", "seqlengths", "seqnames", "seqlevels", "keepSeqlevels", "seqlevelsStyle", "seqlevelsStyle<-", "genomeStyles") importFrom("rtracklayer", "import") ## Ranges and stuff importFrom("IRanges", "IRanges", "IRangesList") importMethodsFrom("IRanges", "subsetByOverlaps") import("GenomicRanges") import("GenomicFeatures") ## 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") ## Stuff needed for protein annotations. importClassesFrom("Biostrings", "AAStringSet") importFrom("Biostrings", "AAStringSet") importFrom("curl", "curl") ## AnnotationFilter importClassesFrom("AnnotationFilter", "AnnotationFilter", "CharacterFilter", "IntegerFilter", "ExonIdFilter", "ExonRankFilter", "ExonStartFilter", "ExonEndFilter", "GeneIdFilter", "GenenameFilter", "GeneBiotypeFilter", "GeneStartFilter", "GeneEndFilter", "EntrezFilter", "SymbolFilter", "TxIdFilter", "TxNameFilter", "TxBiotypeFilter", "TxStartFilter", "TxEndFilter", "ProteinIdFilter", "UniprotFilter", "SeqNameFilter", "SeqStrandFilter", "GRangesFilter", "AnnotationFilterList" ) importMethodsFrom("AnnotationFilter", "field", "value", "condition", "supportedFilters", "convertFilter") importFrom("AnnotationFilter", "AnnotationFilter", "ExonIdFilter", "ExonRankFilter", "ExonStartFilter", "ExonEndFilter", "GeneIdFilter", "GenenameFilter", "GeneBiotypeFilter", "GeneStartFilter", "GeneEndFilter", "EntrezFilter", "SymbolFilter", "TxIdFilter", "TxNameFilter", "TxBiotypeFilter", "TxStartFilter", "TxEndFilter", "ProteinIdFilter", "UniprotFilter", "SeqNameFilter", "SeqStrandFilter", "GRangesFilter", "AnnotationFilterList", "feature" ) ## Functions export("ensDbFromAH", "ensDbFromGff", "ensDbFromGRanges", "ensDbFromGtf", "fetchTablesFromEnsembl", "listEnsDbs", "makeEnsemblSQLiteFromTables", "makeEnsembldbPackage", "runEnsDbApp", "filter" ) ## Classes exportClasses( "EnsDb", "ProtDomIdFilter", "UniprotDbFilter", "UniprotMappingTypeFilter", "OnlyCodingTxFilter", "TxSupportLevelFilter" ) ## Methods for EnsFilter exportMethods( "seqnames", "seqlevels", "show", "convertFilter" ) ## Methods for class EnsDb: exportMethods("cdsBy", "dbconn", "disjointExons", "ensemblVersion", "exons", "exonsBy", "exonsByOverlaps", "fiveUTRsByTranscript", "genes", "getGeneRegionTrackForGviz", "getGenomeFaFile", "lengthOf", "listColumns", "listGenebiotypes", "listTxbiotypes", "listTables", "metadata", "organism", "promoters", "returnFilterColumns", "returnFilterColumns<-", "seqinfo", "threeUTRsByTranscript", "toSAF", "transcripts", "transcriptsBy", "transcriptsByOverlaps", "updateEnsDb", "useMySQL", "supportedFilters", "addFilter", "activeFilter", "dropFilter" ) ## Protein data related stuff exportMethods("hasProteinData", "proteins", "listUniprotDbs", "listUniprotMappingTypes") export("listProteinColumns") ## Methods for AnnotationDbi exportMethods("columns", "keytypes", "keys", "select", "mapIds") ## Methods for GenomeInfoDb and related stuff exportMethods("seqlevelsStyle", "seqlevelsStyle<-", "supportedSeqlevelsStyles", "seqlevels") ## Constructor functions: export( "EnsDb", "EntrezidFilter", "ExonidFilter", "ExonrankFilter", "GeneidFilter", "GenebiotypeFilter", "SeqnameFilter", "SeqstrandFilter", "SeqstartFilter", "SeqendFilter", "TxidFilter", "TxbiotypeFilter", "UniprotDbFilter", "UniprotMappingTypeFilter", "OnlyCodingTxFilter", "ProtDomIdFilter", "TxSupportLevelFilter" ) ensembldb/R/0000755000175400017540000000000013241131543013731 5ustar00biocbuildbiocbuildensembldb/R/Classes.R0000644000175400017540000003446713175714743015506 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()) ) #' @title Filters supported by ensembldb #' #' @description \code{ensembldb} supports most of the filters from the #' \code{\link{AnnotationFilter}} package to retrieve specific content from #' \code{\linkS4class{EnsDb}} databases. These filters can be passed to #' the methods such as \code{\link{genes}} with the \code{filter} parameter #' or can be added as a \emph{global} filter to an \code{EnsDb} object #' (see \code{\link{addFilter}} for more details). Use the #' \code{\link{supportedFilters}} to list all filters supported for #' \code{EnsDb} object. #' #' @note For users of \code{ensembldb} version < 2.0: in the #' \code{\link[AnnotationFilter]{GRangesFilter}} from the #' \code{AnnotationFilter} package the \code{condition} parameter was #' renamed to \code{type} (to be consistent with the \code{IRanges} package) #' . In addition, the \code{condition = "overlapping"} is no longer #' recognized. To retrieve all features overlapping the range #' \code{type = "any"} has to be used. #' #' @details \code{ensembldb} supports the following filters from the #' \code{AnnotationFilter} package: #' #' \describe{ #' #' \item{GeneIdFilter}{ #' filter based on the Ensembl gene ID. #' } #' #' \item{GenenameFilter}{ #' filter based on the name of the gene as provided by Ensembl. In most cases #' this will correspond to the official gene symbol. #' } #' #' \item{SymbolFilter}{ #' filter based on the gene names. \code{\linkS4class{EnsDb}} objects don't #' have a dedicated \emph{symbol} column, the filtering is hence based on the #' gene names. #' } #' #' \item{GeneBiotype}{ #' filter based on the biotype of genes (e.g. \code{"protein_coding"}). #' } #' #' \item{GeneStartFilter}{ #' filter based on the genomic start coordinate of genes. #' } #' #' \item{GeneEndFilter}{ #' filter based on the genomic end coordinate of genes. #' } #' #' \item{EntrezidFilter}{ #' filter based on the genes' NCBI Entrezgene ID. #' } #' #' \item{TxIdFilter}{ #' filter based on the Ensembld transcript ID. #' } #' #' \item{TxNameFilter}{ #' filter based on the Ensembld transcript ID; no transcript names are #' provided in \code{\linkS4class{EnsDb}} databases. #' } #' #' \item{TxBiotypeFilter}{ #' filter based on the transcripts' biotype. #' } #' #' \item{TxStartFilter}{ #' filter based on the genomic start coordinate of the transcripts. #' } #' #' \item{TxEndFilter}{ #' filter based on the genonic end coordinates of the transcripts. #' } #' #' \item{ExonIdFilter}{ #' filter based on Ensembl exon IDs. #' } #' #' \item{ExonRankFilter}{ #' filter based on the index/rank of the exon within the transcrips. #' } #' #' \item{ExonStartFilter}{ #' filter based on the genomic start coordinates of the exons. #' } #' #' \item{ExonEndFilter}{ #' filter based on the genomic end coordinates of the exons. #' } #' #' \item{GRangesFilter}{ #' Allows to fetch features within or overlapping specified genomic region(s)/ #' range(s). This filter takes a \code{\link[GenomicRanges]{GRanges}} object #' as input and, if \code{type = "any"} (the default) will restrict #' results to features (genes, transcripts or exons) that are partially #' overlapping the region. Alternatively, by specifying #' \code{condition = "within"} it will return features located within the #' range. In addition, the \code{\link[AnnotationFilter]{GRangesFilter}} #' supports \code{condition = "start"}, \code{condition = "end"} and #' \code{condition = "equal"} filtering for features with the same start or #' end coordinate or that are equal to the \code{GRanges}. #' #' Note that the type of feature on which the filter is applied depends on #' the method that is called, i.e. \code{\link{genes}} will filter on the #' genomic coordinates of genes, \code{\link{transcripts}} on those of #' transcripts and \code{\link{exons}} on exon coordinates. #' #' 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. #' #' If the specified \code{GRanges} object defines multiple regions, 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{SeqNameFilter}{ #' filter based on chromosome names. #' } #' #' \item{SeqStrandFilter}{ #' filter based on the chromosome strand. The strand can be specified with #' \code{value = "+"}, \code{value = "-"}, \code{value = -1} or #' \code{value = 1}. #' } #' #' \item{ProteinIdFilter}{ #' filter based on Ensembl protein IDs. This filter is only supported if the #' \code{\linkS4class{EnsDb}} provides protein annotations; use the #' \code{\link{hasProteinData}} method to evaluate. #' } #' #' \item{UniprotFilter}{ #' filter based on Uniprot IDs. This filter is only supported if the #' \code{\linkS4class{EnsDb}} provides protein annotations; use the #' \code{\link{hasProteinData}} method to evaluate. #' } #' #' } #' #' In addition, the following filters are defined by \code{ensembldb}: #' \describe{ #' #' \item{TxSupportLevel}{ #' allows to filter results using the provided transcript support level. #' Support levels for transcripts are defined by Ensembl based on the #' available evidences for a transcript with 1 being the highest evidence #' grade and 5 the lowest level. This filter is only supported on #' \code{EnsDb} databases with a db schema version higher 2.1. #' } #' #' \item{UniprotDbFilter}{ #' allows to filter results based on the specified Uniprot database name(s). #' } #' #' \item{UniprotMappingTypeFilter}{ #' allows to filter results based on the mapping method/type that was used #' to assign Uniprot IDs to Ensembl protein IDs. #' } #' #' \item{ProtDomIdFilter}{ #' allows to retrieve entries from the database matching the provided filter #' criteria based on their protein domain ID (\emph{protein_domain_id}). #' } #' #' \item{OnlyCodingTxFilter}{ #' allows to retrieve entries only for protein coding transcripts, i.e. #' transcripts with a CDS. This filter does not take any input arguments. #' } #' #' } #' #' @param condition \code{character(1)} specifying the \emph{condition} of the #' filter. For \code{character}-based filters (such as #' \code{\link[AnnotationFilter]{GeneIdFilter}}) \code{"=="}, \code{"!="}, #' \code{"startsWith"} and \code{"endsWith"} are supported. Allowed values #' for \code{integer}-based filters (such as #' \code{\link[AnnotationFilter]{GeneStartFilter}}) are \code{"=="}, #' \code{"!="}, \code{"<"}. \code{"<="}, \code{">"} and \code{">="}. #' #' @param value The value(s) for the filter. For #' \code{\link[AnnotationFilter]{GRangesFilter}} it has to be a #' \code{\link[GenomicRanges]{GRanges}} object. #' #' @note Protein annotation based filters can only be used if the #' \code{\linkS4class{EnsDb}} database contains protein annotations, i.e. #' if \code{\link{hasProteinData}} is \code{TRUE}. Also, only protein coding #' transcripts will have protein annotations available, thus, non-coding #' transcripts/genes will not be returned by the queries using protein #' annotation filters. #' #' @name Filter-classes #' #' @seealso #' \code{\link{supportedFilters}} to list all filters supported for \code{EnsDb} #' objects. #' \code{\link{listUniprotDbs}} and \code{\link{listUniprotMappingTypes}} to #' list all Uniprot database names respectively mapping method types from #' the database. #' #' \code{\link[AnnotationFilter]{GeneIdFilter}} for more details on the #' filter objects. #' #' \code{\link{genes}}, \code{\link{transcripts}}, \code{\link{exons}}, #' \code{\link{listGenebiotypes}}, \code{\link{listTxbiotypes}}. #' #' \code{\link{addFilter}} for globally adding filters to an \code{EnsDb} #' object. #' #' @author Johannes Rainer #' @examples #' #' ## Create a filter that could be used to retrieve all informations for #' ## the respective gene. #' gif <- GeneIdFilter("ENSG00000012817") #' gif #' #' ## Create a filter for a chromosomal end position of a gene #' sef <- GeneEndFilter(10000, condition = ">") #' 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 = "+"), type = "any") #' 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")) #' #' #' ## Create a ProteinIdFilter: #' pf <- ProteinIdFilter("ENSP00000362111") #' pf #' ## Using this filter would retrieve all database entries that are associated #' ## with a protein with the ID "ENSP00000362111" #' if (hasProteinData(edb)) { #' res <- genes(edb, filter = pf) #' res #' } #' #' ## UniprotFilter: #' uf <- UniprotFilter("O60762") #' ## Get the transcripts encoding that protein: #' if (hasProteinData(edb)) { #' transcripts(edb, filter = uf) #' ## The mapping Ensembl protein ID to Uniprot ID can however be 1:n: #' transcripts(edb, filter = TxIdFilter("ENST00000371588"), #' columns = c("protein_id", "uniprot_id")) #' } #' #' ## ProtDomIdFilter: #' pdf <- ProtDomIdFilter("PF00335") #' ## Also here we could get all transcripts related to that protein domain #' if (hasProteinData(edb)) { #' transcripts(edb, filter = pdf, columns = "protein_id") #' } #' NULL ############################################################ ## OnlyCodingTxFilter ## ## That's a special case filter that just returns transcripts ## that have tx_cds_seq_start defined (i.e. not NULL). #' @rdname Filter-classes setClass("OnlyCodingTxFilter", contains = "CharacterFilter", prototype = list( condition = "==", value = character(), field = "empty" )) #' @rdname Filter-classes OnlyCodingTxFilter <- function() { new("OnlyCodingTxFilter") } ############################################################ ## ProtDomIdFilter #' @rdname Filter-classes setClass("ProtDomIdFilter", contains = "CharacterFilter", prototype = list( condition = "==", value = "", field = "prot_dom_id" )) #' @return For \code{ProtDomIdFilter}: A \code{ProtDomIdFilter} object. #' @rdname Filter-classes ProtDomIdFilter <- function(value, condition = "==") { new("ProtDomIdFilter", condition = condition, value = as.character(value)) } ############################################################ ## UniprotDbFilter #' @rdname Filter-classes setClass("UniprotDbFilter", contains = "CharacterFilter", prototype = list( condition = "==", values = "", field = "uniprot_db" )) #' @return For \code{UniprotDbFilter}: A \code{UniprotDbFilter} object. #' @rdname Filter-classes UniprotDbFilter <- function(value, condition = "==") { new("UniprotDbFilter", condition = condition, value = as.character(value)) } ############################################################ ## UniprotMappingTypeFilter #' @rdname Filter-classes setClass("UniprotMappingTypeFilter", contains = "CharacterFilter", prototype = list( condition = "==", values = "", field = "uniprot_mapping_type" )) #' @return For \code{UniprotMappingTypeFilter}: A #' \code{UniprotMappingTypeFilter} object. #' @rdname Filter-classes UniprotMappingTypeFilter <- function(value, condition = "==") { new("UniprotMappingTypeFilter", condition = condition, value = as.character(value)) } #' @rdname Filter-classes setClass("TxSupportLevelFilter", contains = "IntegerFilter", prototype = list( condition = "==", values = 0L, field = "tx_support_level" )) #' @return For \code{TxSupportLevel}: A #' \code{TxSupportLevel} object. #' @rdname Filter-classes TxSupportLevelFilter <- function(value, condition = "==") { if (!is.numeric(value)) stop("Parameter 'value' has to be numeric") new("TxSupportLevelFilter", condition = condition, value = as.integer(value)) } ensembldb/R/Deprecated.R0000644000175400017540000001557513175714743016150 0ustar00biocbuildbiocbuild## Deprecated functions. #' @aliases ensembldb-deprecated #' #' @title Deprecated functionality #' #' @description All functions, methods and classes listed on this page are #' deprecated and might be removed in future releases. #' #' @param value The value for the filter. #' @param condition The condition for the filter. #' #' @name Deprecated NULL #> NULL #' @description \code{GeneidFilter} creates a \code{GeneIdFilter}. Use #' \code{\link[AnnotationFilter]{GeneIdFilter}} instead. #' #' @rdname Deprecated GeneidFilter <- function(value, condition = "==") { .Deprecated("GeneIdFilter") 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), field = "gene_id")) } #' @description \code{GenebiotypeFilter} creates a \code{GeneBiotypeFilter}. Use #' \code{\link[AnnotationFilter]{GeneBiotypeFilter}} instead. #' #' @rdname Deprecated GenebiotypeFilter <- function(value, condition = "=="){ .Deprecated("GeneBiotypeFilter") if(missing(value)) stop("A filter without a value makes no sense!") return(new("GeneBiotypeFilter", condition = condition, value = as.character(value), field = "gene_biotype")) } #' @description \code{EntrezidFilter} creates a \code{EntrezFilter}. Use #' \code{\link[AnnotationFilter]{EntrezFilter}} instead. #' #' @rdname Deprecated EntrezidFilter <- function(value, condition = "=="){ .Deprecated("EntrezFilter") if(missing(value)) stop("A filter without a value makes no sense!") return(new("EntrezFilter", condition = condition, value = as.character(value), field = "entrez")) } #' @description \code{TxidFilter} creates a \code{TxIdFilter}. Use #' \code{\link[AnnotationFilter]{TxIdFilter}} instead. #' #' @rdname Deprecated TxidFilter <- function(value, condition = "=="){ .Deprecated("TxIdFilter") if(missing(value)) stop("A filter without a value makes no sense!") return(new("TxIdFilter", condition = condition, value = as.character(value), field = "tx_id")) } #' @description \code{TxbiotypeFilter} creates a \code{TxBiotypeFilter}. Use #' \code{\link[AnnotationFilter]{TxBiotypeFilter}} instead. #' #' @rdname Deprecated TxbiotypeFilter <- function(value, condition="=="){ .Deprecated("TxBiotypeFilter") if(missing(value)) stop("A filter without a value makes no sense!") return(new("TxBiotypeFilter", condition=condition, value=as.character(value), field = "tx_biotype")) } #' @description \code{ExonidFilter} creates a \code{ExonIdFilter}. Use #' \code{\link[AnnotationFilter]{ExonIdFilter}} instead. #' #' @rdname Deprecated ExonidFilter <- function(value, condition="=="){ .Deprecated("ExonIdFilter") if(missing(value)) stop("A filter without a value makes no sense!") return(new("ExonIdFilter", condition=condition, value=as.character(value), field = "exon_id")) } #' @description \code{ExonrankFilter} creates a \code{ExonRankFilter}. Use #' \code{\link[AnnotationFilter]{ExonRankFilter}} instead. #' #' @rdname Deprecated ExonrankFilter <- function(value, condition="=="){ .Deprecated("ExonRankFilter") if(missing(value)) stop("A filter without a value makes no sense!") return(new("ExonRankFilter", condition=condition, value=as.integer(value), field = "exon_rank")) } #' @description \code{SeqNameFilter} creates a \code{SeqNameFilter}. Use #' \code{\link[AnnotationFilter]{SeqNameFilter}} instead. #' #' @rdname Deprecated SeqnameFilter <- function(value, condition="=="){ .Deprecated("SeqNameFilter") if(missing(value)) stop("A filter without a value makes no sense!") return(new("SeqNameFilter", condition=condition, value=as.character(value), field = "seq_name")) } #' @description \code{SeqstrandFilter} creates a \code{SeqStrandFilter}. Use #' \code{\link[AnnotationFilter]{SeqStrandFilter}} instead. #' #' @rdname Deprecated SeqstrandFilter <- function(value, condition="=="){ .Deprecated("SeqStrandFilter") 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), field = "seq_strand")) } #' @description \code{SeqstartFilter} creates a \code{GeneStartFilter}, #' \code{TxStartFilter} or \code{ExonStartFilter} depending on the value of the #' parameter \code{feature}. Use \code{\link[AnnotationFilter]{GeneStartFilter}}, #' \code{\link[AnnotationFilter]{TxStartFilter}} and #' \code{\link[AnnotationFilter]{ExonStartFilter}} instead. #' #' @param feature For \code{SeqstartFilter} and \code{SeqendFilter}: on what type #' of feature should the filter be applied? Supported are \code{"gene"}, #' \code{"tx"} and \code{"exon"}. #' #' @rdname Deprecated SeqstartFilter <- function(value, condition=">", feature="gene"){ .Deprecated(msg = paste0("The use of 'SeqstartFilter' is deprecated. Use ", "one of 'GeneStartFilter', 'ExonStartFilter'", " or 'TxStartFilter' instead.")) feature <- match.arg(feature, c("gene", "exon", "tx")) return(new(paste0(sub("^([[:alpha:]])", "\\U\\1", feature, perl=TRUE), "StartFilter"), value = as.integer(value), condition = condition, field = paste0(feature, "_start"))) } #' @description \code{SeqendFilter} creates a \code{GeneEndFilter}, #' \code{TxEndFilter} or \code{ExonEndFilter} depending on the value of the #' parameter \code{feature}. Use \code{\link[AnnotationFilter]{GeneEndFilter}}, #' \code{\link[AnnotationFilter]{TxEndFilter}} and #' \code{\link[AnnotationFilter]{ExonEndFilter}} instead. #' #' @rdname Deprecated SeqendFilter <- function(value, condition="<", feature="gene"){ .Deprecated(msg = paste0("The use of 'SeqendFilter' is deprecated. Use ", "one of 'GeneEndFilter', 'ExonEndFilter'", " or 'TxEndFilter' instead.")) feature <- match.arg(feature, c("gene", "exon", "tx")) return(new(paste0(sub("^([[:alpha:]])", "\\U\\1", feature, perl=TRUE)), "EndFilter"), value = as.integer(value), condition = condition, field = paste0(feature, "_end")) } ensembldb/R/Generics.R0000644000175400017540000001010213175714743015624 0ustar00biocbuildbiocbuild##*********************************************************************** ## ## Generic methods ## ##*********************************************************************** ## A setGeneric("activeFilter", function(x, ...) standardGeneric("activeFilter")) setGeneric("addFilter", function(x, ...) standardGeneric("addFilter")) ## B setGeneric("buildQuery", function(x, ...) standardGeneric("buildQuery")) ## C setGeneric("cleanColumns", function(x, columns, ...) starndardGeneric("cleanColumns")) ## D setGeneric("dbSeqlevelsStyle", function(x, ...) standardGeneric("dbSeqlevelsStyle")) setGeneric("dropFilter", function(x, ...) standardGeneric("dropFilter")) ## E setGeneric("ensemblVersion", function(x) standardGeneric("ensemblVersion")) setGeneric("ensDbColumn", function(object, ...) standardGeneric("ensDbColumn")) setGeneric("ensDbQuery", function(object, ...) standardGeneric("ensDbQuery")) ## F ## if (!isGeneric("filter")) ## setGeneric("filter", function(x, ...) ## standardGeneric("filter")) setGeneric("formatSeqnamesForQuery", function(x, sn, ...) standardGeneric("formatSeqnamesForQuery")) setGeneric("formatSeqnamesFromQuery", function(x, sn, ...) standardGeneric("formatSeqnamesFromQuery")) ## G setGeneric("getGeneRegionTrackForGviz", function(x, ...) standardGeneric("getGeneRegionTrackForGviz")) setGeneric("getGenomeFaFile", function(x, ...) standardGeneric("getGenomeFaFile")) setGeneric("getGenomeTwoBitFile", function(x, ...) standardGeneric("getGenomeTwoBitFile")) setGeneric("getMetadataValue", function(x, name) standardGeneric("getMetadataValue")) setGeneric("getProperty", function(x, name=NULL, ...) standardGeneric("getProperty")) setGeneric("getWhat", function(x, ...) standardGeneric("getWhat")) ## H setGeneric("hasProteinData", function(x) standardGeneric("hasProteinData")) ## L setGeneric("lengthOf", function(x, ...) standardGeneric("lengthOf")) setGeneric("listColumns", function(x, ...) standardGeneric("listColumns")) setGeneric("listGenebiotypes", function(x, ...) standardGeneric("listGenebiotypes")) setGeneric("listTables", function(x, ...) standardGeneric("listTables")) setGeneric("listTxbiotypes", function(x, ...) standardGeneric("listTxbiotypes")) setGeneric("listUniprotDbs", function(object, ...) standardGeneric("listUniprotDbs")) setGeneric("listUniprotMappingTypes", function(object, ...) standardGeneric("listUniprotMappingTypes")) ## O setGeneric("orderResultsInR", function(x) standardGeneric("orderResultsInR")) setGeneric("orderResultsInR<-", function(x, value) standardGeneric("orderResultsInR<-")) ## P setGeneric("print", function(x, ...) standardGeneric("print")) setGeneric("properties", function(x, ...) standardGeneric("properties")) ## R setGeneric("requireTable", function(x, db, ...) standardGeneric("requireTable")) setGeneric("returnFilterColumns", function(x) standardGeneric("returnFilterColumns")) setGeneric("returnFilterColumns<-", function(x, value) standardGeneric("returnFilterColumns<-")) ## S setGeneric("seqinfo", function(x) standardGeneric("seqinfo")) setGeneric("setProperty", function(x, value=NULL, ...) standardGeneric("setProperty")) ## setGeneric("show", function(object, ...) ## standardGeneric("show")) setGeneric("supportedSeqlevelsStyles", function(x) standardGeneric("supportedSeqlevelsStyles")) ## T setGeneric("tablesByDegree", function(x, ...) standardGeneric("tablesByDegree")) setGeneric("tablesForColumns", function(x, attributes, ...) standardGeneric("tablesForColumns")) setGeneric("toSAF", function(x, ...) standardGeneric("toSAF")) ## setGeneric("transcriptLengths", function(x, with.cds_len=FALSE, ## with.utr5_len=FALSE, ## with.utr3_len=FALSE, ...) ## standardGeneric("transcriptLengths")) ## U setGeneric("updateEnsDb", function(x, ...) standardGeneric("updateEnsDb")) setGeneric("useMySQL", function(x, host = "localhost", port = 3306, user, pass) standardGeneric("useMySQL")) ensembldb/R/Methods-Filter.R0000644000175400017540000003525613175714743016734 0ustar00biocbuildbiocbuild## Methods for filter classes. #' @description Extract the field/column name from an AnnotationFilter and #' ensure that it matches the correct database column name. Depending on #' whether argument \code{db} is present, the column names are also #' prefixed with the name of the corresponding table. #' #' @param object An \code{AnnotationFilter} object. #' #' @param db An \code{EnsDb} object. #' #' @param with.tables \code{character} specifying the tables that should be #' considered when prefixing the column name. #' #' @noRd setMethod("ensDbColumn", "AnnotationFilter", function(object, db, with.tables = character()) { clmn <- .fieldInEnsDb(object@field) if (missing(db)) return(clmn) if (length(with.tables) == 0) with.tables <- names(listTables(db)) unlist(prefixColumns(db, clmn, with.tables = with.tables), use.names = FALSE) }) setMethod("ensDbColumn", "AnnotationFilterList", function(object, db, with.tables = character()) { if (length(object) == 0) return(character()) unique(unlist(lapply(object, ensDbColumn, db, with.tables = with.tables))) }) #' @title Convert an AnnotationFilter to a SQL WHERE condition for EnsDb #' #' @aliases convertFilter,AnnotationFilter,EnsDb-method #' convertFilter,AnnotationFilterList,EnsDb-method #' #' @description `convertFilter` converts an `AnnotationFilter::AnnotationFilter` #' or `AnnotationFilter::AnnotationFilterList` to an SQL where condition #' for an `EnsDb` database. #' #' @note This function *might* be used in direct SQL queries on the SQLite #' database underlying an `EnsDb` but is more thought to illustrate the #' use of `AnnotationFilter` objects in combination with SQL databases. #' This method is used internally to create the SQL calls to the database. #' #' @param object `AnnotationFilter` or `AnnotationFilterList` objects (or #' objects extending these classes). #' #' @param db `EnsDb` object. #' #' @param with.tables optional `character` vector specifying the names of the #' database tables that are being queried. #' #' @return A `character(1)` with the SQL where condition. #' #' @md #' #' @rdname convertFilter #' #' @author Johannes Rainer #' #' @examples #' #' library(EnsDb.Hsapiens.v75) #' edb <- EnsDb.Hsapiens.v75 #' #' ## Define a filter #' flt <- AnnotationFilter(~ genename == "BCL2") #' #' ## Use the method from the AnnotationFilter package: #' convertFilter(flt) #' #' ## Create a combination of filters #' flt_list <- AnnotationFilter(~ genename %in% c("BCL2", "BCL2L11") & #' tx_biotype == "protein_coding") #' flt_list #' #' convertFilter(flt_list) #' #' ## Use the filters in the context of an EnsDb database: #' convertFilter(flt, edb) #' #' convertFilter(flt_list, edb) setMethod("convertFilter", signature = c(object = "AnnotationFilter", db = "EnsDb"), function(object, db, with.tables = character()) { ensDbQuery(object, db, with.tables) }) #' @rdname convertFilter setMethod("convertFilter", signature = c(object = "AnnotationFilterList", db = "EnsDb"), function(object, db, with.tables = character()) { ensDbQuery(object, db, with.tables) }) #' @description Build the \emph{where} query for an \code{AnnotationFilter} or #' \code{AnnotationFilterList}. #' #' @noRd setMethod("ensDbQuery", "AnnotationFilter", function(object, db, with.tables = character()) { .queryForEnsDbWithTables(object, db, with.tables) }) setMethod("ensDbQuery", "AnnotationFilterList", function(object, db, with.tables = character()) { wq <- NULL if (length(object)) { wq <- ensDbQuery(object[[1]], db, with.tables = with.tables) if (length(object) > 1) { for (i in 2:length(object)) { wq <- paste(wq, .logOp2SQL(object@logOp[(i -1)]), ensDbQuery(object[[i]], db, with.tables = with.tables)) } } ## Encapsule all inside brackets. wq <- paste0("(", wq, ")") } wq }) #' Need an ensDbQuery for SeqNameFilter to support different chromosome naming #' styles #' #' @noRd setMethod("ensDbQuery", "SeqNameFilter", function(object, db, with.tables = character()) { ## val <- sQuote(value(object, db)) ## Doing all the stuff in here: vals <- value(object) clmn <- .fieldInEnsDb(field(object)) if (!missing(db)) { ## o Eventually rename the seqname based on the seqlevelsStyle. vals <- formatSeqnamesForQuery(db, vals) if (length(with.tables) == 0) with.tables <- names(listTables(db)) clmn <- unlist(prefixColumns(db, clmn, with.tables = with.tables)) } ## o Quote the values. vals <- sQuote(vals) ## o Concatenate values. if (length(vals) > 1) vals <- paste0("(", paste0(vals, collapse = ","), ")") paste(clmn, .conditionForEnsDb(object), vals) }) setMethod("ensDbQuery", "SeqStrandFilter", function(object, db, with.tables = character()) { ## We have to ensure that value is converted to +1, -1. val <- strand2num(value(object)) clmn <- .fieldInEnsDb(field(object)) if (!missing(db)) { if (length(with.tables) == 0) with.tables <- names(listTables(db)) clmn <- unlist(prefixColumns(db, clmn, with.tables = with.tables)) } paste(clmn, .conditionForEnsDb(object), val) }) setMethod("start", signature(x="GRangesFilter"), function(x, ...){ start(value(x)) }) setMethod("end", signature(x="GRangesFilter"), function(x, ...){ end(value(x)) }) setMethod("strand", signature(x="GRangesFilter"), function(x, ...){ as.character(strand(value(x))) }) #' @description \code{seqnames}: accessor for the sequence names of the #' \code{GRanges} object within a \code{GRangesFilter} #' @param x For \code{seqnames}, \code{seqlevels}: a \code{GRangesFilter} object. #' #' @rdname Filter-classes setMethod("seqnames", signature(x="GRangesFilter"), function(x){ as.character(seqnames(value(x))) }) #' @description \code{seqnames}: accessor for the \code{seqlevels} of the #' \code{GRanges} object within a \code{GRangesFilter} #' #' @rdname Filter-classes setMethod("seqlevels", signature(x="GRangesFilter"), function(x){ seqlevels(value(x)) }) setMethod("ensDbColumn", "GRangesFilter", function(object, db, with.tables = character(), ...){ 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") if (!missing(db)) { if (length(with.tables) == 0) with.tables <- names(listTables(db)) cols <- unlist(prefixColumns(db, cols, 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" } cols[c("start", "end", "seqname", "strand")] }) setMethod("ensDbQuery", "GRangesFilter", function(object, db, with.tables = character()) { cols <- ensDbColumn(object, db, with.tables) if (missing(db)) db <- NULL buildWhereForGRanges(object, cols, db = db) }) setMethod("ensDbColumn", signature(object = "OnlyCodingTxFilter"), function(object, db, ...) { "tx.tx_cds_seq_start" }) setMethod("ensDbQuery", "OnlyCodingTxFilter", function(object, db, with.tables = character()) { "tx.tx_cds_seq_start is not null" }) setMethod("ensDbColumn", "ProteinIdFilter", function(object, db, with.tables = character(), ...) { if (missing(db)) { return(callNextMethod()) } if (!hasProteinData(db)) stop("The 'EnsDb' database used does not provide", " protein annotations! A 'ProteinIdFilter' can not", " be used.") callNextMethod() }) setMethod("ensDbQuery", "ProteinIdFilter", function(object, db, with.tables = character()) { if (missing(db)) return(callNextMethod()) if (!hasProteinData(db)) stop("The 'EnsDb' database used does not provide", " protein annotations! A 'ProteinIdFilter' can not", " be used.") .queryForEnsDbWithTables(object, db, with.tables) }) setMethod("ensDbColumn", "UniprotFilter", function(object, db, with.tables = character(), ...) { if (missing(db)) return(callNextMethod()) if (!hasProteinData(db)) stop("The 'EnsDb' database used does not provide", " protein annotations! A 'UniprotFilter' can not", " be used.") callNextMethod() }) setMethod("ensDbQuery", "UniprotFilter", function(object, db, with.tables = character()) { if (missing(db)) return(callNextMethod()) if (!hasProteinData(db)) stop("The 'EnsDb' database used does not provide", " protein annotations! A 'UniprotFilter' can not", " be used.") .queryForEnsDbWithTables(object, db, with.tables) }) setMethod("ensDbColumn", "ProtDomIdFilter", function(object, db, with.tables = character(), ...) { if (missing(db)) return(callNextMethod()) if (!hasProteinData(db)) stop("The 'EnsDb' database used does not provide", " protein annotations! A 'ProtDomIdFilter' can not", " be used.") callNextMethod() }) setMethod("ensDbQuery", "ProtDomIdFilter", function(object, db, with.tables = character()) { if (missing(db)) return(callNextMethod()) if (!hasProteinData(db)) stop("The 'EnsDb' database used does not provide", " protein annotations! A 'ProtDomIdFilter' can not", " be used.") .queryForEnsDbWithTables(object, db, with.tables) }) setMethod("ensDbColumn", "UniprotDbFilter", function(object, db, with.tables = character(), ...) { if (missing(db)) return(callNextMethod()) if (!hasProteinData(db)) stop("The 'EnsDb' database used does not provide", " protein annotations! A 'UniprotDbFilter' can not", " be used.") callNextMethod() }) setMethod("ensDbQuery", "UniprotDbFilter", function(object, db, with.tables = character()) { if (missing(db)) return(callNextMethod()) if (!hasProteinData(db)) stop("The 'EnsDb' database used does not provide", " protein annotations! A 'ProteinIdFilter' can not", " be used.") .queryForEnsDbWithTables(object, db, with.tables) }) setMethod("ensDbColumn", "UniprotMappingTypeFilter", function(object, db, with.tables = character(), ...) { if (missing(db)) return(callNextMethod()) if (!hasProteinData(db)) stop("The 'EnsDb' database used does not provide", " protein annotations! A 'UniprotMappingTypeFilter' ", "can not be used.") callNextMethod() }) setMethod("ensDbQuery", "UniprotMappingTypeFilter", function(object, db, with.tables = character()) { if (missing(db)) return(callNextMethod()) if (!hasProteinData(db)) stop("The 'EnsDb' database used does not provide", " protein annotations! A 'UniprotMappingTypeFilter' can not", " be used.") .queryForEnsDbWithTables(object, db, with.tables) }) setMethod("ensDbColumn", "TxSupportLevelFilter", function(object, db, with.tables = character(), ...) { if (missing(db)) return(callNextMethod()) if (!any(listColumns(db) %in% "tx_support_level")) stop("The 'EnsDb' database used does not provide", " transcript support levels! A 'TxSupportLevelFilter' ", "can not be used.") callNextMethod() }) setMethod("ensDbQuery", "TxSupportLevelFilter", function(object, db, with.tables = character()) { if (missing(db)) return(callNextMethod()) if (!any(listColumns(db) %in% "tx_support_level")) stop("The 'EnsDb' database used does not provide", " transcript support levels! A 'TxSupportLevelFilter' ", "can not be used.") .queryForEnsDbWithTables(object, db, with.tables) }) ensembldb/R/Methods.R0000644000175400017540000025273113175714743015510 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")) if (hasProteinData(object)) cat("|Protein data available.\n") flts <- .activeFilter(object) if (is(flts, "AnnotationFilter") | is(flts, "AnnotationFilterList")) { cat("|Active filter(s):\n") show(flts) } } }) ############################################################ ## organism 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) }) ############################################################ ## metadata 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)){ msg <- validMsg(NULL, NULL) OK <- dbHasRequiredTables(object@ensdb) if (is.character(OK)) msg <- validMsg(msg, OK) OK <- dbHasValidTables(object@ensdb) if (is.character(OK)) msg <- validMsg(msg, OK) if (hasProteinData(object)) { OK <- dbHasRequiredTables( object@ensdb, tables = .ensdb_protein_tables(dbSchemaVersion(dbconn(object)))) if (is.character(OK)) msg <- validMsg(msg, OK) OK <- dbHasValidTables( object@ensdb, tables = .ensdb_protein_tables(dbSchemaVersion(dbconn(object)))) if (is.character(OK)) msg <- validMsg(msg, OK) cdsTx <- dbGetQuery(dbconn(object), "select tx_id, tx_cds_seq_start from tx"); if (is.character(cdsTx$tx_cds_seq_start)) { suppressWarnings( cdsTx[, "tx_cds_seq_start"] <- as.numeric(cdsTx$tx_cds_seq_start) ) } cdsTx <- cdsTx[!is.na(cdsTx$tx_cds_seq_start), "tx_id"] protTx <- dbGetQuery(dbconn(object), "select distinct tx_id from protein")$tx_id if (!all(cdsTx %in% protTx)) msg <- validMsg(msg, paste0("Not all transcripts with a CDS ", "are assigned to a protein ID!")) if (!all(protTx %in% cdsTx)) msg <- validMsg(msg, paste0("Not all proteins are assigned to ", "a transcript with a CDS!")) } if (is.null(msg)) TRUE else msg } return(TRUE) } setValidity("EnsDb", validateEnsDb) setMethod("initialize", "EnsDb", function(.Object,...){ OK <- validateEnsDb(.Object) if(class(OK)=="character"){ stop(OK) } callNextMethod(.Object, ...) }) ############################################################ ## dbconn 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)$name==name, "value"]) }) ############################################################ ## seqinfo setMethod("seqinfo", "EnsDb", function(x){ Chrs <- dbGetQuery(dbconn(x), "select * from chromosome") Chr.build <- .getMetaDataValue(dbconn(x), "genome_build") Chrs$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 setMethod("listTables", "EnsDb", function(x, ...){ if(length(x@tables)==0){ tables <- dbListTables(dbconn(x)) ## 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 setMethod("listColumns", "EnsDb", function(x, table, skip.keys=TRUE, ...){ if(length(x@tables)==0){ tables <- dbListTables(dbconn(x)) ## 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 <- unlist(Tab[names(Tab) %in% table], use.names = FALSE) }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(unique(columns)) }) ############################################################ ## listGenebiotypes setMethod("listGenebiotypes", "EnsDb", function(x, ...){ return(dbGetQuery(dbconn(x), "select distinct gene_biotype from gene")[,1]) }) ############################################################ ## listTxbiotypes 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 { dbtabs <- names(listTables(x)) dbtabs <- dbtabs[dbtabs != "metadata"] bm <- columns %in% unlist(listTables(x)[dbtabs]) removed <- columns[!bm] } if(length(removed) > 0){ if (length(removed) == 1) warning("Column ", paste(sQuote(removed), collapse=", "), " is not present in the database and has been removed") else warning("Columns ", paste(sQuote(removed), collapse=", "), " are not present in the database 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) }) ############################################################ ## tablesByDegree ## ## returns the table names ordered by degree, i.e. edges to other tables setMethod("tablesByDegree", "EnsDb", function(x, tab=names(listTables(x)), ...){ Table.order <- c(gene = 1, tx = 2, tx2exon = 3, exon = 4, chromosome = 5, protein = 6, uniprot = 7, protein_domain = 8, entrezgene = 9, metadata = 99) Tab <- tab[ order(Table.order[ tab ]) ] return(Tab) }) ############################################################ ## hasProteinData ## ## Simply check if the database has required tables protein, uniprot ## and protein_domain. #' @title Determine whether protein data is available in the database #' #' @aliases hasProteinData #' #' @description Determines whether the \code{\linkS4class{EnsDb}} #' provides protein annotation data. #' #' @param x The \code{\linkS4class{EnsDb}} object. #' #' @return A logical of length one, \code{TRUE} if protein annotations are #' available and \code{FALSE} otherwise. #' #' @author Johannes Rainer #' #' @seealso \code{\link{listTables}} #' #' @examples #' library(EnsDb.Hsapiens.v75) #' ## Does this database/package have protein annotations? #' hasProteinData(EnsDb.Hsapiens.v75) setMethod("hasProteinData", "EnsDb", function(x) { tabs <- listTables(x) return(all(c("protein", "uniprot", "protein_domain") %in% names(tabs))) }) ############################################################ ## genes ## ## get genes from the database. setMethod("genes", "EnsDb", function(x, columns = c(listColumns(x, "gene"), "entrezid"), filter = AnnotationFilterList(), order.by = "", order.type = "asc", return.type = "GRanges"){ return.type <- match.arg(return.type, c("data.frame", "GRanges", "DataFrame")) columns <- cleanColumns(x, 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"))) } filter <- .processFilterParam(filter, x) 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, startWith = "gene", join = "suggested") ## issue #48: collapse entrezid column if dbschema 2.0 is used. if (as.numeric(dbSchemaVersion(x)) > 1 & any(columns == "entrezid")) Res <- .collapseEntrezidInTable(Res, by = "gene_id") if (return.type=="data.frame" | return.type=="DataFrame") { notThere <- !(retColumns %in% colnames(Res)) if(any(notThere)) warning("Columns ", paste0("'", retColumns[notThere], "'", collapse=", "), " not found in the database!") retColumns <- retColumns[!notThere] Res <- Res[, retColumns, drop = FALSE] 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 = AnnotationFilterList(), order.by = "", order.type = "asc", return.type = "GRanges"){ return.type <- match.arg(return.type, c("data.frame", "GRanges", "DataFrame")) columns <- cleanColumns(x, 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"))) } filter <- .processFilterParam(filter, x) 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, startWith = "tx", join = "suggested") ## issue #48: collapse entrezid column if dbschema 2.0 is used. if (as.numeric(dbSchemaVersion(x)) > 1 & any(columns == "entrezid")) Res <- .collapseEntrezidInTable(Res, by = "tx_id") if(return.type=="data.frame" | return.type=="DataFrame"){ notThere <- !(retColumns %in% colnames(Res)) if(any(notThere)) warning("Columns ", paste0("'", retColumns[notThere], "'", collapse=", "), " not found in the database!") retColumns <- retColumns[!notThere] Res <- Res[, retColumns, drop = FALSE] 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: ## 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 = AnnotationFilterList(), 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 <- cleanColumns(x, 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"))) } filter <- .processFilterParam(filter, x) 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, startWith = "exon", join = "suggested") ## issue #48: collapse entrezid column if dbschema 2.0 is used. if (as.numeric(dbSchemaVersion(x)) > 1 & any(columns == "entrezid")) Res <- .collapseEntrezidInTable(Res, by = "exon_id") if(return.type=="data.frame" | return.type=="DataFrame"){ notThere <- !(retColumns %in% colnames(Res)) if(any(notThere)) warning("Columns ", paste0("'", retColumns[notThere], "'", collapse=", "), " not found in the database!") retColumns <- retColumns[!notThere] Res <- Res[, retColumns, drop = FALSE] 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) } }) ############################################################ ## exonsBy ## ## should return a GRangesList setMethod("exonsBy", "EnsDb", function(x, by = c("tx", "gene"), columns = listColumns(x, "exon"), filter = AnnotationFilterList(), 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" } } filter <- .processFilterParam(filter, x) ## We're applying eventual GRangesFilter to either gene or tx. filter <- setFeatureInGRangesFilter(filter, by) ## Eventually add columns for the filters: columns <- cleanColumns(x, 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, startWith = by, join = "suggested") ## issue #48: collapse entrezid column if dbschema 2.0 is used. if (as.numeric(dbSchemaVersion(x)) > 1 & any(columns == "entrezid")) Res <- .collapseEntrezidInTable(Res, by = "exon_id") ## 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$seq_name))] ## replace exon_idx with exon_rank colnames(Res)[colnames(Res) == "exon_idx"] <- "exon_rank" columns[columns == "exon_idx"] <- "exon_rank" ret_cols[ret_cols == "exon_idx"] <- "exon_rank" notThere <- !(ret_cols %in% colnames(Res)) if (any(notThere)) warning("Columns ", paste0("'", ret_cols[notThere], "'", collapse = ", "), " not found in the database!") ret_cols <- ret_cols[!notThere] columns.metadata <- ret_cols[!(ret_cols %in% c("seq_name", "seq_strand", "exon_seq_start", "exon_seq_end"))] columns.metadata <- match(columns.metadata, colnames(Res)) GR <- GRanges(seqnames = Rle(Res$seq_name), strand = Rle(Res$seq_strand), ranges = IRanges(start = Res$exon_seq_start, end = Res$exon_seq_end), seqinfo = SI, Res[, columns.metadata, drop=FALSE] ) return(split(GR, Res[, paste0(by, bySuff)])) }) ############################################################ ## transcriptsBy ## setMethod("transcriptsBy", "EnsDb", function(x, by = c("gene", "exon"), columns = listColumns(x, "tx"), filter = AnnotationFilterList()) { if (any(by == "cds")) stop("fetching transcripts by cds is not (yet) implemented.") by <- match.arg(by, c("gene", "exon")) byId <- paste0(by, "_id") min.columns <- c(paste0(by, "_id"), "seq_name", "tx_seq_start", "tx_seq_end", "tx_id", "seq_strand") ## can not have exon columns! ex_cols <- c(listColumns(x, "exon"), "exon_idx") ex_cols <- ex_cols[ex_cols != "tx_id"] torem <- columns %in% ex_cols if (any(torem)) warning("Columns ", paste(columns[ torem ], collapse=","), " have been removed as they are not allowed if", " transcripts are fetched.") columns <- columns[!torem] ## Process filters filter <- .processFilterParam(filter, x) ## GRanges filter should be based on either gene or exon coors. filter <- setFeatureInGRangesFilter(filter, by) ## Eventually add columns for the filters: columns <- addFilterColumns(columns, filter, x) ret_cols <- unique(columns) ## define the minimal columns that we need... columns <- cleanColumns(x, unique(c(columns, min.columns))) ## get the seqinfo: suppressWarnings( SI <- seqinfo(x) ) byIdFull <- unlist(prefixColumns(x, columns = byId, clean = FALSE), use.names = FALSE) orderR <- orderResultsInR(x) if (orderR) { order.by <- "" } else { order.by <- paste0(byIdFull , ", case when seq_strand = 1 then tx_seq_start", " when seq_strand = -1 then (tx_seq_end * -1) end") } Res <- getWhat(x, columns=columns, filter=filter, order.by=order.by, skip.order.check=TRUE, startWith = by, join = "suggested") ## issue #48: collapse entrezid column if dbschema 2.0 is used. if (as.numeric(dbSchemaVersion(x)) > 1 & any(columns == "entrezid")) Res <- .collapseEntrezidInTable(Res, by = "tx_id") if (orderR) { startEnd <- (Res$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("Columns ", paste0("'", ret_cols[notThere], "'", collapse=", "), " not found in the database!") 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)) 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])) }) ############################################################ ## lengthOf ## 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=AnnotationFilterList()){ 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 = AnnotationFilterList()){ ## First we're going to fetch the exonsBy. ## Or use getWhat??? ## Dash, have to make two queries! filter <- .processFilterParam(filter, x) 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 = AnnotationFilterList(), use.names = FALSE){ by <- match.arg(by, c("tx", "gene")) filter <- .processFilterParam(filter, x) filter <- setFeatureInGRangesFilter(filter, by) columns <- cleanColumns(x, columns) ## Eventually add columns for the filters: columns <- addFilterColumns(columns, filter, x) ## Add a filter ensuring that only coding transcripts are queried. filter <- AnnotationFilterList(OnlyCodingTxFilter() ,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 <- paste0("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, startWith = by, join = "suggested") ## issue #48: collapse entrezid column if dbschema 2.0 is used. if (as.numeric(dbSchemaVersion(x)) > 1 & any(columns == "entrezid")) Res <- .collapseEntrezidInTable(Res, by = "exon_id") ## 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$seq_strand), ranges=IRanges(start=cdsStarts, end=cdsEnds), seqinfo=SI) } GR <- split(GR, Res[, paste0(by, bySuff)]) ## For "by gene" we reduce the redundant ranges; ## that way we loose however all additional columns! if(by == "gene") GR <- reduce(GR) return(GR) }) ############################################################ ## getUTRsByTranscript ## getUTRsByTranscript <- function(x, what, columns = NULL, filter = AnnotationFilterList()) { filter <- .processFilterParam(filter, x) columns <- cleanColumns(x, columns) filter <- setFeatureInGRangesFilter(filter, "tx") ## Eventually add columns for the filters: columns <- addFilterColumns(columns, filter, x) columns <- unique(c(columns, "exon_id", "exon_idx")) ## Add the filter for coding tx only. filter <- AnnotationFilterList(OnlyCodingTxFilter(), filter) ## what do we need: tx_cds_seq_start, tx_cds_seq_end and exon_idx fetchCols <- unique(c("tx_id", columns, "tx_cds_seq_start", "tx_cds_seq_end", "seq_name", "seq_strand", "exon_seq_start", "exon_seq_end")) order.by <- "tx.tx_id" ## get the seqinfo: suppressWarnings( SI <- seqinfo(x) ) ## Note: doing that with a single query and some coordinate juggling ## is faster than calling exonsBy and GRangesList setdiff etc. Res <- getWhat(x, columns=fetchCols, filter=filter, order.by=order.by, skip.order.check=TRUE, startWith = "tx", join = "suggested") ## issue #48: collapse entrezid column if dbschema 2.0 is used. if (as.numeric(dbSchemaVersion(x)) > 1 & any(columns == "entrezid")) Res <- .collapseEntrezidInTable(Res, by = "exon_id") nas <- is.na(Res$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 = AnnotationFilterList()) { filter <- .processFilterParam(filter, x) getUTRsByTranscript(x = x, what = "three", columns = columns, filter = filter) }) ############################################################ ## fiveUTRsByTranscript ## setMethod("fiveUTRsByTranscript", "EnsDb", function(x, columns = NULL, filter = AnnotationFilterList()) { filter <- .processFilterParam(filter, x) 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)) }) ############################################################ ## buildQuery setMethod("buildQuery", "EnsDb", function(x, columns=c("gene_id", "gene_biotype", "gene_name"), filter = AnnotationFilterList(), 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)) }) ############################################################ ## getWhat ## ## 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. ## Additional parameters: ## o startWith: the name of the database table from which the join should start ## or NULL for the default behaviour (i.e. genes-> tx etc). ## o join: the type of join that should be used; one of "join", ## "left outer join" or "suggested". setMethod("getWhat", "EnsDb", function(x, columns = c("gene_id", "gene_biotype", "gene_name"), filter = AnnotationFilterList(), order.by = "", order.type = "asc", group.by = NULL, skip.order.check = FALSE, startWith = NULL, join = "suggested") { 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, startWith = startWith, join = join) ## Eventually renaming seqnames according to the specified style. if(any(colnames(Res) == "seq_name")) Res$seq_name <- formatSeqnamesFromQuery(x, Res$seq_name) return(Res) }) ############################################################ ## disjointExons ## ## that's similar to the code from the GenomicFeatures package. setMethod("disjointExons", "EnsDb", function(x, aggregateGenes = FALSE, includeTranscripts = TRUE, filter = AnnotationFilterList(), ...){ filter <- .processFilterParam(filter, x) exonsByGene <- exonsBy(x, by = "gene", filter = filter) exonicParts <- disjoin(unlist(exonsByGene, use.names = FALSE)) if (aggregateGenes) { foGG <- findOverlaps(exonsByGene, exonsByGene) aggregateNames <- GenomicFeatures:::.listNames(names(exonsByGene), as.list(foGG)) foEG <- findOverlaps(exonicParts, exonsByGene, select="first") gene_id <- aggregateNames[foEG] pasteNames <- GenomicFeatures:::.pasteNames(names(exonsByGene), as.list(foGG))[foEG] orderByGeneName <- order(pasteNames) exonic_rle <- runLength(Rle(pasteNames[orderByGeneName])) } else { ## drop exonic parts that overlap > 1 gene foEG <- findOverlaps(exonicParts, exonsByGene) idxList <- as.list(foEG) if (any(keep <- countQueryHits(foEG) == 1)) { idxList <- idxList[keep] exonicParts <- exonicParts[keep] } gene_id <- GenomicFeatures:::.listNames(names(exonsByGene), idxList) orderByGeneName <- order(unlist(gene_id, use.names=FALSE)) exonic_rle <- runLength(Rle(unlist(gene_id[orderByGeneName], use.names=FALSE))) } values <- DataFrame(gene_id) if (includeTranscripts) { exonsByTx <- exonsBy(x, by="tx", filter=filter) foET <- findOverlaps(exonicParts, exonsByTx) values$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$exonic_part <- exonic_part return(exonicParts) } ) ############################################################ ## getGeneRegionTrackForGviz ## Fetch data to add as a GeneTrack. ## filter ... Used to filter the result. ## chromosome, start, end ... Either all or none has to be specified. If ## specified, the function first retrieves all ## transcripts that have an exon in the specified ## range and adds them as a TranscriptidFilter to ## the filters. The query to fetch the "real" data ## is performed afterwards. ## featureIs ... Wheter gene_biotype or tx_biotype should be ## mapped to the column feature. setMethod( "getGeneRegionTrackForGviz", "EnsDb", function(x, filter = AnnotationFilterList(), chromosome = NULL, start = NULL, end = NULL, featureIs = "gene_biotype") { featureIs <- match.arg(featureIs, c("gene_biotype", "tx_biotype")) filter <- .processFilterParam(filter, x) if(missing(chromosome)) chromosome <- NULL if(missing(start)) start <- NULL if(missing(end)) end <- NULL ## If only chromosome is specified, create a SeqNameFilter and ## add it to the filter if(is.null(start) & is.null(end) & !is.null(chromosome)){ filter <- AnnotationFilterList(filter, SeqNameFilter(chromosome)) chromosome <- NULL } if(any(c(!is.null(chromosome), !is.null(start), !is.null(end)))){ ## Require however that all are defined!!! if(all(c(!is.null(chromosome), !is.null(start), !is.null(end)))){ ## Fix eventually provided UCSC chromosome names: chromosome <- ucscToEns(chromosome) ## Define a GRangesFilter to include all features that overlap ## that region. grg <- GRangesFilter(GRanges(seqnames = chromosome, ranges = IRanges(start, end)), feature = "tx", type = "any") tids <- transcripts(x, filter = grg, columns = "tx_id")$tx_id filter <- AnnotationFilterList(filter, TxIdFilter(tids)) }else{ stop("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) }) ####============================================================ ## 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) }) #' remove the property with the specified name. #' @noRd dropProperty <- function(x, name) { if (missing(name)) return(x) prps <- x@.properties if (any(names(prps) == name)) prps <- prps[names(prps) != name] x@.properties <- prps 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 = -1L, minoverlap = 0L, type = c("any", "start", "end"), columns = listColumns(x, "tx"), filter = AnnotationFilterList()) { if (missing(ranges)) stop("Parameter 'ranges' is missing!") filter <- .processFilterParam(filter, x) SLs <- unique(as.character(seqnames(ranges))) filter <- AnnotationFilterList(filter, SeqNameFilter(SLs)) columns <- cleanColumns(x, columns) subsetByOverlaps(transcripts(x, columns = columns, filter = filter), ranges, maxgap = maxgap, minoverlap = minoverlap, type = match.arg(type)) }) ####============================================================ ## exonsByOverlaps ## ####------------------------------------------------------------ setMethod("exonsByOverlaps", "EnsDb", function(x, ranges, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end"), columns = listColumns(x, "exon"), filter = AnnotationFilterList()) { if(missing(ranges)) stop("Parameter 'ranges' is missing!") filter <- .processFilterParam(filter, x) SLs <- unique(as.character(seqnames(ranges))) filter <- AnnotationFilterList(filter, SeqNameFilter(SLs)) columns <- cleanColumns(x, columns) 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 should be in the format EnsDb.Hsapiens.v75! sqliteName <- .makePackageName(dbconn(x)) ## 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.") } }) ############################################################ ## proteins ## ## If return type is GRanges, make a seqlevel and seqinfo for each protein, i.e. ## put each protein on its own sequence. #' @title Protein related functionality #' #' @aliases proteins #' #' @description This help page provides information about most of the #' functionality related to protein annotations in \code{ensembldb}. #' #' The \code{proteins} method retrieves protein related annotations from #' an \code{\linkS4class{EnsDb}} database. #' #' @details The \code{proteins} method performs the query starting from the #' \code{protein} tables and can hence return all annotations from the #' database that are related to proteins and transcripts encoding these #' proteins from the database. Since \code{proteins} does thus only query #' annotations for protein coding transcripts, the \code{\link{genes}} or #' \code{\link{transcripts}} methods have to be used to retrieve annotations #' for non-coding transcripts. #' #' @param object The \code{\linkS4class{EnsDb}} object. #' #' @param columns For \code{proteins}: character vector defining the columns to #' be extracted from the database. Can be any column(s) listed by the #' \code{\link{listColumns}} method. #' #' @param filter For \code{proteins}: A filter object extending #' \code{AnnotationFilter} or a list of such objects to select #' specific entries from the database. See \code{\link{Filter-classes}} for #' a documentation of available filters and use #' \code{\link{supportedFilters}} to get the full list of supported filters. #' #' @param order.by For \code{proteins}: a character vector specifying the #' column(s) by which the result should be ordered. #' #' @param order.type For \code{proteins}: if the results should be ordered #' ascending (\code{order.type = "asc"}) or descending #' (\code{order.type = "desc"}) #' #' @param return.type For \code{proteins}: character of lenght one specifying #' the type of the returned object. Can be either \code{"DataFrame"}, #' \code{"data.frame"} or \code{"AAStringSet"}. #' #' @return The \code{proteins} method returns protein related annotations from #' an \code{\linkS4class{EnsDb}} object with its \code{return.type} argument #' allowing to define the type of the returned object. Note that if #' \code{return.type = "AAStringSet"} additional annotation columns are #' stored in a \code{DataFrame} that can be accessed with the \code{mcols} #' method on the returned object. #' #' @rdname ProteinFunctionality #' #' @author Johannes Rainer #' #' @examples #' library(ensembldb) #' library(EnsDb.Hsapiens.v75) #' edb <- EnsDb.Hsapiens.v75 #' ## Get all proteins from tha database for the gene ZBTB16, if protein #' ## annotations are available #' if (hasProteinData(edb)) #' proteins(edb, filter = GenenameFilter("ZBTB16")) setMethod("proteins", "EnsDb", function(object, columns = listColumns(object, "protein"), filter = AnnotationFilterList(), order.by = "", order.type = "asc", return.type = "DataFrame") { if (!hasProteinData(object)) stop("The used EnsDb does not provide protein annotations!", " Thus, 'proteins' can not be used.") return.type <- match.arg(return.type, c("DataFrame", "AAStringSet", "data.frame")) columns <- cleanColumns(object, unique(c(columns, "protein_id"))) filter <- .processFilterParam(filter, object) filter <- setFeatureInGRangesFilter(filter, "tx") ## Eventually add columns for the filters: columns <- addFilterColumns(columns, filter, object) ## Check that we don't have any exon columns here. ex_cols <- unique(listColumns(object, c("exon", "tx2exon"))) ex_cols <- ex_cols[ex_cols != "tx_id"] if (any(columns %in% ex_cols)) { warning("Exon specific columns are not allowed for proteins. Columns ", paste0("'", columns[columns %in% ex_cols], "'", collapse = ", "), " have been removed.") columns <- columns[!(columns %in% ex_cols)] } retColumns <- columns ## Process order.by: ## If not specified we might want to order them by seq_name or tx_seq_start ## if present in parameter columns if (all(order.by == "")) { order.by <- NULL if (any(columns == "seq_name")) order.by <- "seq_name" seq_col_idx <- grep(columns, pattern = "_seq_") if (length(seq_col_idx) > 0) order.by <- c(order.by, columns[seq_col_idx[1]]) if (is.null(order.by)) order.by <- "" } ## If we're going to return a GRanges we need to know the length of the ## peptide sequence. if (return.type == "AAStringSet") { columns <- unique(c(columns, "protein_sequence")) } ## protein_id is *always* required columns <- unique(c(columns), "protein_id") ## Get the data Res <- getWhat(object, columns = columns, filter = filter, order.by = order.by, order.type = order.type, startWith = "protein", join = "suggested") ## issue #48: collapse entrezid column if dbschema 2.0 is used. if (as.numeric(dbSchemaVersion(object)) > 1 & any(columns == "entrezid")) Res <- .collapseEntrezidInTable(Res, by = "protein_id") ## Now process the result. cols_not_found <- !(retColumns %in% colnames(Res)) retColumns <- retColumns[!cols_not_found] if (any(cols_not_found)) warning("Columns ", paste0("'", retColumns[cols_not_found], "'", collapse = ", "), " not found in the database!") if (return.type == "AAStringSet") { aass <- AAStringSet(Res$protein_sequence) names(aass) <- Res$protein_id ## Add the mcols: retColumns <- retColumns[retColumns != "protein_sequence"] if (length(retColumns) > 0) mcols(aass) <- DataFrame(Res[, retColumns, drop = FALSE]) return(aass) } else { Res <- Res[, retColumns, drop = FALSE] if (return.type == "DataFrame") Res <- DataFrame(Res) return(Res) } return(NULL) }) ############################################################ ## listUniprotDbs #' @aliases listUniprotDbs #' #' @description The \code{listUniprotDbs} method lists all Uniprot database #' names in the \code{EnsDb}. #' #' @examples #' #' ## List the names of all Uniprot databases from which Uniprot IDs are #' ## available in the EnsDb #' if (hasProteinData(edb)) #' listUniprotDbs(edb) #' #' @rdname ProteinFunctionality setMethod("listUniprotDbs", "EnsDb", function(object) { if (!hasProteinData(object)) stop("The provided EnsDb database does not provide protein annotations!") res <- dbGetQuery(dbconn(object), "select distinct uniprot_db from uniprot") return(res$uniprot_db) }) ############################################################ ## listUniprotMappingTypes #' @aliases listUniprotMappingTypes #' #' @description The \code{listUniprotMappingTypes} method lists all methods #' that were used for the mapping of Uniprot IDs to Ensembl protein IDs. #' #' @examples #' #' ## List the type of all methods that were used to map Uniprot IDs to Ensembl #' ## protein IDs #' if (hasProteinData(edb)) #' listUniprotMappingTypes(edb) #' #' @rdname ProteinFunctionality setMethod("listUniprotMappingTypes", "EnsDb", function(object) { if (!hasProteinData(object)) stop("The provided EnsDb database does not provide protein annotations!") res <- dbGetQuery(dbconn(object), "select distinct uniprot_mapping_type from uniprot") return(res$uniprot_mapping_type) }) #' @description \code{supportedFilters} returns a \code{data.frame} with the #' names of all filters and the corresponding field supported by the #' \code{EnsDb} object. #' #' @param object For \code{supportedFilters}: an \code{EnsDb} object. #' #' @param ... For \code{supportedFilters}: currently not used. #' #' @return For \code{supportedFilters}: a \code{data.frame} with the names and #' the corresponding field of the supported filter classes. #' #' @rdname Filter-classes setMethod("supportedFilters", "EnsDb", function(object, ...) { .supportedFilters(object) }) #' @title Globally add filters to an EnsDb database #' #' @aliases addFilter addFilter,EnsDb-method #' #' @description These methods allow to set, delete or show globally defined #' filters on an \code{\linkS4class{EnsDb}} object. #' #' \code{addFilter}: adds an annotation filter to the \code{EnsDb} object. #' #' @details Adding a filter to an \code{EnsDb} object causes this filter to be #' permanently active. The filter will be used for all queries to the #' database and is added to all additional filters passed to the methods #' such as \code{\link{genes}}. #' #' @param x The \code{\linkS4class{EnsDb}} object to which the filter should be #' added. #' #' @param filter The filter as an #' \code{\link[AnnotationFilter]{AnnotationFilter}}, #' \code{\link[AnnotationFilter]{AnnotationFilterList}} or filter #' expression. See #' #' @return \code{addFilter} and \code{filter} return an \code{EnsDb} object #' with the specified filter added. #' #' \code{activeFilter} returns an #' \code{\link[AnnotationFilter]{AnnotationFilterList}} object being the #' active global filter or \code{NA} if no filter was added. #' #' \code{dropFilter} returns an \code{EnsDb} object with all eventually #' present global filters removed. #' #' @author Johannes Rainer #' #' @rdname global-filters #' #' @seealso \code{\link{Filter-classes}} for a list of all supported filters. #' #' @examples #' library(EnsDb.Hsapiens.v75) #' edb <- EnsDb.Hsapiens.v75 #' #' ## Add a global SeqNameFilter to the database such that all subsequent #' ## queries will be applied on the filtered database. #' edb_y <- addFilter(edb, SeqNameFilter("Y")) #' #' ## Note: using the filter function is equivalent to a call to addFilter. #' #' ## Each call returns now only features encoded on chromosome Y #' gns <- genes(edb_y) #' #' seqlevels(gns) #' #' ## Get all lincRNA gene transcripts on chromosome Y #' transcripts(edb_y, filter = ~ gene_biotype == "lincRNA") #' #' ## Get the currently active global filter: #' activeFilter(edb_y) #' #' ## Delete this filter again. #' edb_y <- dropFilter(edb_y) #' #' activeFilter(edb_y) setMethod("addFilter", "EnsDb", function(x, filter = AnnotationFilterList()) { .addFilter(x, filter) }) #' @aliases dropFilter dropFilter,EnsDb-method #' #' @description \code{dropFilter} deletes all globally set filters from the #' \code{EnsDb} object. #' #' @rdname global-filters setMethod("dropFilter", "EnsDb", function(x) { .dropFilter(x) }) #' @aliases activeFilter activeFilter,EnsDb-method #' #' @description \code{activeFilter} returns the globally set filter from an #' \code{EnsDb} object. #' #' @rdname global-filters setMethod("activeFilter", "EnsDb", function(x) { .activeFilter(x) }) ensembldb/R/dbhelpers.R0000644000175400017540000010001113175714743016034 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) ## 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") ## Add the db schema version to the properties. EDB <- setProperty(EDB, DBSCHEMAVERSION = .getMetaDataValue(con, "DBSCHEMAVERSION")) ## Setting the default for the returnFilterColumns returnFilterColumns(EDB) <- TRUE ## Defining the default for the ordering orderResultsInR(EDB) <- FALSE ## Check it again... OK <- validateEnsDb(EDB) if (is.character(OK)) stop(OK) EDB } ## loadEnsDb <- function(x) { ## ## con <- ensDb( x ) ## ## EDB <- new( "EnsDb", ensdb=con ) ## return(EnsDb(x)) ## } ## 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]) } ############################################################ ## prefixColumns ## ## Determines which tables (along with the table attributes) are required for ## the join. ## Updated version of prefixColumns: ## o Uses the order of the tables returned by listTables and adds the first ## table in which the column was found. That's different to the previous ## default of trying to join as few tables as possible but avoids problems ## with table joins between e.g. tx and protein in which not all tx_id are ## present in the protein table. 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) } getCols <- columns result <- lapply(Tab, function(z) { if (length(getCols) > 0) { gotIt <- z[z %in% getCols] if (length(gotIt) > 0) { getCols <<- getCols[!(getCols %in% gotIt)] return(gotIt) } else { return(character()) } } }) ## If getCols length > 0 it contains columns not present in the db. result <- result[lengths(result) > 0] if (length(result) == 0) stop("None of the columns could be found in the database!") result <- mapply(result, names(result), FUN = function(z, y) { paste0(y, ".", z) }, SIMPLIFY = FALSE) return(result) } ############################################################ ## 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)]) } ############################################################ ## ** NEW JOIN ENGINE ** ## ## 1: table 1 ## 2: table 2 ## 3: on ## 4: suggested join .JOINS2 <- rbind( c("gene", "tx", "on (gene.gene_id=tx.gene_id)", "join"), c("gene", "chromosome", "on (gene.seq_name=chromosome.seq_name)", "join"), c("tx", "tx2exon", "on (tx.tx_id=tx2exon.tx_id)", "join"), c("tx2exon", "exon", "on (tx2exon.exon_id=exon.exon_id)", "join"), c("tx", "protein", "on (tx.tx_id=protein.tx_id)", "left outer join"), c("gene", "entrezgene", "on (gene.gene_id=entrezgene.gene_id)", "left outer join"), c("protein", "protein_domain", "on (protein.protein_id=protein_domain.protein_id)", "left outer join"), c("protein", "uniprot", "on (protein.protein_id=uniprot.protein_id)", "left outer join"), c("uniprot", "protein_domain", "on (uniprot.protein_id=protein_domain.protein_id)", "left outer join") ) ## Takes the names of two tables, determines how to join them and returns the ## join query row, if found. joinTwoTables <- function(a, b) { gotIt <- which((.JOINS2[, 1] %in% a & .JOINS2[, 2] %in% b) | (.JOINS2[, 2] %in% a & .JOINS2[, 1] %in% b)) if (length(gotIt) == 0) { stop("Table(s) ", paste(a, collapse = ", "), " can not be joined with ", paste(b, collapse = ", "), "!") } else { return(.JOINS2[gotIt[1], ]) } } ## x: EnsDb. ## tab: tables to join. ## join: which type of join should be used? ## startWith: optional table name from which the join should start. That's ## specifically important for a left outer join call. joinQueryOnTables2 <- function(x, tab, join = "suggested", startWith = NULL) { ## join can be join, left join, left outer join or suggested in which case ## the join defined in the .JOINS2 table will be used. join <- match.arg(join, c("join", "left join", "left outer join", "suggested")) ## Order the tables. ## Start with startWith, or with the first one. if (missing(tab)) stop("Argument 'tab' missing! Need some tables to make a join!") if (!is.null(startWith)) { if (!any(tab == startWith)) stop("If provided, 'startWith' has to be the name of one of the", " tables that should be joined!") } ## Add eventually needed tables to link the ones provided. The tables will ## be ordered by degree. tab <- addRequiredTables(x, tab) if (!is.null(startWith)) { alreadyUsed <- startWith tab <- tab[tab != startWith] } else { alreadyUsed <- tab[1] tab <- tab[-1] } Query <- alreadyUsed ## Iteratively build the query. while (length(tab) > 0) { res <- joinTwoTables(a = alreadyUsed, b = tab) newTab <- res[1:2][!(res[1:2] %in% alreadyUsed)] ## Could also use the suggested join which is in element 4. Query <- paste(Query, ifelse(join == "suggested", res[4], join), newTab, res[3]) alreadyUsed <- c(alreadyUsed, newTab) tab <- tab[tab != newTab] } return(Query) } ## 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 ## NOTE: if "startWith" is not NULL, we're adding it to the tables!!!! joinQueryOnColumns2 <- function(x, columns, join = "suggested", startWith = NULL) { 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 <- joinQueryOnTables2(x, tab = c(names(columns.bytable), startWith), join = join, startWith = startWith) 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. 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")) } if (hasProteinData(x)) { ## Resolve the proteins: need tx to map between proteome and genome if (any(tab %in% c("uniprot", "protein_domain", "protein")) & any(tab %in% c("exon", "tx2exon", "gene", "chromosome", "entrezgene"))) tab <- unique(c(tab, "tx")) ## Need protein. if (any(tab %in% c("uniprot", "protein_domain")) & any(tab %in% c("exon", "tx2exon", "tx", "gene", "chromosome", "entrezgene"))) tab <- unique(c(tab, "protein")) } ## entrezgene is only linked via gene if (any(tab == "entrezgene") & length(tab) > 1) tab <- unique(c(tab, "gene")) 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 ## startWith: optional table from which the join should start. .buildQuery <- function(x, columns, filter = AnnotationFilterList(), order.by = "", order.type = "asc", group.by, skip.order.check=FALSE, return.all.columns = TRUE, join = "suggested", startWith = NULL) { 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 (!is(filter, "AnnotationFilterList")) stop("parameter 'filter' has to be an 'AnnotationFilterList'!") if (length(filter) > 0) { ## check filter! ## add the columns needed for the filter filtercolumns <- unlist(lapply(filter, ensDbColumn, 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 <- joinQueryOnColumns2(x, columns=columns, join = join, startWith = startWith) ## b) the filter part of the query if (length(filter) > 0) { ## USE THE ensDbQuery method here!!! filterquery <- paste0(" where ", ensDbQuery(filter, x, with.tables = need.tables)) } else { filterquery <- "" } ## c) the order part of the query if (any(order.by != "")) { if (!skip.order.check) { 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(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 ## join: what type of join should be performed. ## startWith: the name of the table from which the query should be started. .getWhat <- function(x, columns, filter = AnnotationFilterList(), order.by = "", order.type = "asc", group.by = NULL, skip.order.check = FALSE, join = "suggested", startWith = NULL) { ## 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 (!is(filter, "AnnotationFilterList")) stop("parameter 'filter' has to be an 'AnnotationFilterList'!") ## Add also the global filter if present. global_filter <- .activeFilter(x) if (is(global_filter, "AnnotationFilter") | is(global_filter, "AnnotationFilterList")) filter <- AnnotationFilterList(global_filter, filter) ## If any filter is a SymbolFilter, add "symbol" to the return columns. if (length(filter) > 0) { if (any(.anyIs(filter, "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, join = join, startWith = startWith) ## Get the data ## cat("Query: ", Q, "\n") 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, join = join, startWith = startWith) ## Get the data ## cat("Query: ", Q, "\n") 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] Res } ############################################################ ## Check database validity. #' @description Return tables with attributes based on the provided schema. #' #' @noRd .ensdb_tables <- function(version = "1.0") { .ENSDB_TABLES <- list(`1.0` = 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")), `2.0` = list( gene = c("gene_id", "gene_name", "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"), entrezgene = c("gene_id", "entrezid"), metadata = c("name", "value")) ) .ENSDB_TABLES[[version]] } .ensdb_protein_tables <- function(version = "1.0") { .ENSDB_PROTEIN_TABLES <- list(`1.0` = list( protein = c("tx_id", "protein_id", "protein_sequence"), uniprot = c("protein_id", "uniprot_id", "uniprot_db", "uniprot_mapping_type"), protein_domain = c("protein_id", "protein_domain_id", "protein_domain_source", "interpro_accession", "prot_dom_start", "prot_dom_end")), `2.0` = list( protein = c("tx_id", "protein_id", "protein_sequence"), uniprot = c("protein_id", "uniprot_id", "uniprot_db", "uniprot_mapping_type"), protein_domain = c("protein_id", "protein_domain_id", "protein_domain_source", "interpro_accession", "prot_dom_start", "prot_dom_end")) ) .ENSDB_PROTEIN_TABLES[[version]] } #' @description Extract the database schema version if available in the metadata #' database column. #' #' @param x Can be either a connection object or an \code{EnsDb} object. #' #' @noRd dbSchemaVersion <- function(x) { if (is(x, "EnsDb")) { return(getProperty(x, "DBSCHEMAVERSION")) } else { tabs <- dbListTables(x) if (any(tabs == "metadata")) { res <- dbGetQuery(x, "select * from metadata") if (any(res$name == "DBSCHEMAVERSION") & any(colnames(res) == "value")) return(res[res$name == "DBSCHEMAVERSION", "value"]) } } return("1.0") } dbHasRequiredTables <- function(con, returnError = TRUE, tables = .ensdb_tables(dbSchemaVersion(con))) { tabs <- dbListTables(con) if (length(tabs) == 0) { if (returnError) return("Database does not have any tables!") return(FALSE) } not_there <- names(tables)[!(names(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, tables = .ensdb_tables(dbSchemaVersion(con))) { for (tab in names(tables)) { cols <- 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) ## Guess index length on the maximal number of characters of an ID. indexLength <- max(nchar( dbGetQuery(sqlite_con, "select distinct gene_id from gene")$gene_id )) .createEnsDbIndices(mysql, indexLength = paste0("(", indexLength, ")"), proteins = hasProteinData(x)) if (verbose) message("OK") return(TRUE) } ## Small helper function to cfeate all the indices. .createEnsDbIndices <- function(con, indexLength = "", proteins = FALSE) { indexCols <- c(chromosome = "seq_name", gene = "gene_id", gene = "gene_name", gene = "seq_name", tx = "tx_id", tx = "gene_id", exon = "exon_id", tx2exon = "tx_id", tx2exon = "exon_id") if (as.numeric(dbSchemaVersion(con)) > 1) indexCols <- c(indexCols, entrezgene = "gene_id", entrezgene = "entrezid") if (proteins) { indexCols <- c(indexCols, protein = "tx_id", protein = "protein_id", uniprot = "protein_id", uniprot = "uniprot_id", protein_domain = "protein_domain_id", protein_domain = "protein_id") } for (i in 1:length(indexCols)) { tabname <- names(indexCols)[i] colname <- indexCols[i] ## Check if we've got any values at all. if not we're not creating the ## index. ids <- dbGetQuery(con, paste0("select distinct ", colname, " from ", tabname))[, colname] if (length(ids) == 0 | all(is.na(ids))) { ## No need to make an index here! } else { if (indexLength != "") idxL <- paste0("(", min(c(max(nchar(ids)), 20)), ")") else idxL <- "" aff_rows <- dbExecute( con, paste0("create index ", tabname, "_", colname, "_idx ", "on ", tabname, " (",colname, idxL,")")) } } ## Add the one on the numeric index: aff_rows <- dbExecute(con, paste0("create index tx2exon_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.") } } #' Simple helper that "translates" R logical operators to SQL. #' @noRd .logOp2SQL <- function(x) { if (x == "|") return("or") if (x == "&") return("and") return(NULL) } ensembldb/R/functions-EnsDb.R0000644000175400017540000000514213175714743017076 0ustar00biocbuildbiocbuild## Functions related to EnsDb objects. #' @title Globally filter an EnsDb database #' #' @description \code{.addFilter} globally filters the provided #' \code{EnsDb} database, i.e. it returns a \code{EnsDb} object with the #' provided filter permanently set and active. #' #' @details Adding a filter to an \code{EnsDb} object causes this filter to be #' permanently active. The filter will be used for all queries to the #' databases and is added to all additional filters passed to the methods. #' #' @param x An \code{EnsDb} object on which the filter(s) should be set. #' #' @param filter An \code{\link[AnnotationFilter]{AnnotationFilterList}}, #' \code{\link[AnnotationFulter]{AnnotationFilter}} object or a filter #' expression providing the filter(s) to be set. #' #' @return For \code{.addFilter}: the \code{EnsDb} object with the filter #' globally added and enabled. #' #' For \code{.dropFilter}: the \code{EnsDb} object with all filters removed. #' #' For \code{.activeFilter}: an \code{} #' #' @author Johannes Rainer #' #' @noRd .addFilter <- function(x, filter = AnnotationFilterList()) { if (length(filter) == 0) stop("No filter provided") filter <- .processFilterParam(filter, x) ## Now, if there was no error, filter is an AnnotationFilterList got_filter <- getProperty(x, "FILTER") if (is(got_filter, "AnnotationFilter") | is(got_filter, "AnnotationFilterList")) { ## Append the new filter. filter <- AnnotationFilterList(got_filter, filter) } else { if (!is.na(got_filter)) stop("Globally set filter is not an 'AnnotationFilter' or ", "'AnnotationFilterList'") } x@.properties$FILTER <- filter x } #' @description \code{.dropFilter} drops all (globally) added filters from the #' submitted \code{EnsDb} object. #' #' @noRd .dropFilter <- function(x) { dropProperty(x, "FILTER") } #' @description \code{.activeFilter} lists the globally set and active filter(s) #' of an \code{EnsDb} object. #' #' @noRd .activeFilter <- function(x) { getProperty(x, "FILTER") } #' @aliases filter #' #' @description \code{filter} filters an \code{EnsDb} object. \code{filter} is #' an alias for the \code{addFilter} function. #' #' @rdname global-filters filter <- function(x, filter = AnnotationFilterList()) { if (is(x, "EnsDb")) addFilter(x, filter) else stop("ensembldb::filter requires an 'EnsDb' object as input. To call ", "the filter function from the stats or dplyr package use ", "stats::filter and dplyr::filter instead.") } ensembldb/R/functions-Filter.R0000644000175400017540000003217713175714743017340 0ustar00biocbuildbiocbuild## Some utility functions for Filters. ## Vector to map AnnotationFilter fields to actual database columns. ## Format: field name = database column name .ENSDB_FIELDS <- c( ## gene entrez = "entrezid", gene_biotype = "gene_biotype", gene_id = "gene_id", genename = "gene_name", symbol = "gene_name", seq_name = "seq_name", seq_strand = "seq_strand", gene_start = "gene_seq_start", gene_end = "gene_seq_end", description = "description", ## tx tx_id = "tx_id", tx_biotype = "tx_biotype", tx_name = "tx_id", tx_start = "tx_seq_start", tx_end = "tx_seq_end", tx_support_level = "tx_support_level", ## exon exon_id = "exon_id", exon_rank = "exon_idx", exon_start = "exon_seq_start", exon_end = "exon_seq_end", ## protein protein_id = "protein_id", uniprot = "uniprot_id", uniprot_db = "uniprot_db", uniprot_mapping_type = "uniprot_mapping_type", prot_dom_id = "protein_domain_id" ) ## .supportedFilters <- function(x) { ## flts <- c( ## "EntrezFilter", "GeneBiotypeFilter", "GeneIdFilter", "GenenameFilter", ## "SymbolFilter", "SeqNameFilter", "SeqStrandFilter", "GeneStartFilter", ## "GeneEndFilter", "TxIdFilter", "TxBiotypeFilter", "TxNameFilter", ## "TxStartFilter", "TxEndFilter", "ExonIdFilter", "ExonRankFilter", ## "ExonStartFilter", "ExonEndFilter", "GRangesFilter" ## ) ## if (hasProteinData(x)) ## flts <- c(flts, "ProteinIdFilter", "UniprotFilter", "UniprotDbFilter", ## "UniprotMappingTypeFilter", "ProtDomIdFilter") ## if (any(listColumns(x) == "tx_support_level")) ## flts <- c(flts, "TxSupportLevelFilter") ## return(sort(flts)) ## } .supportedFilters <- function(x) { flds <- .filterFields(x) flts <- c(.fieldToClass(flds), "GRangesFilter") flds <- c(flds, NA) idx <- order(flts) data.frame(filter = flts[idx], field = flds[idx], stringsAsFactors = FALSE) } .filterFields <- function(x) { flds <- c("entrez", "gene_biotype", "gene_id", "genename", "symbol", "seq_name", "seq_strand", "gene_start", "gene_end", "tx_id", "tx_biotype", "tx_name", "tx_start", "tx_end", "exon_id", "exon_rank", "exon_start", "exon_end") if (hasProteinData(x)) flds <- c(flds, "protein_id", "uniprot", "uniprot_db", "uniprot_mapping_type", "prot_dom_id") if (any(listColumns(x) == "tx_support_level")) flds <- c(flds, "tx_support_level") sort(flds) } .fieldToClass <- function(field) { class <- gsub("_([[:alpha:]])", "\\U\\1", field, perl=TRUE) class <- sub("^([[:alpha:]])", "\\U\\1", class, perl=TRUE) paste0(class, if (length(class)) "Filter" else character(0)) } #' Utility function to map from the default AnnotationFilters fields to the #' database columns used in ensembldb. #' #' @param x The field name to be \emph{translated}. #' @return The column name in the EnsDb database. #' @noRd .fieldInEnsDb <- function(x) { if (length(x) == 0 || missing(x)) stop("Error in .fieldInEnsDb: got empty input argument!") if (is.na(.ENSDB_FIELDS[x])) stop("Unable to map field '", x, "'!") else .ENSDB_FIELDS[x] } #' Utility function to map the condition of an AnnotationFilter to the SQL #' condition to be used in the EnsDb database. #' #' @param x An \code{AnnotationFilter}. #' #' @return A character representing the condition for the SQL call. #' @noRd .conditionForEnsDb <- function(x) { cond <- condition(x) if (length(unique(value(x))) > 1) { if (cond == "==") cond <- "in" if (cond == "!=") cond <- "not in" } if (cond == "==") cond <- "=" if (cond %in% c("startsWith", "endsWith", "contains")) cond <- "like" cond } #' Single quote character values, paste multiple values and enclose in quotes. #' #' @param x An \code{AnnotationFilter} object. #' @noRd .valueForEnsDb <- function(x) { vals <- unique(value(x)) if (is(x, "CharacterFilter")) { vals <- sQuote(gsub(unique(vals), pattern = "'", replacement = "''")) } if (length(vals) > 1) vals <- paste0("(", paste0(vals, collapse = ","), ")") ## Process the like/startsWith/endsWith if (condition(x) == "startsWith") vals <- paste0("'", unique(x@value), "%'") if (condition(x) == "endsWith") vals <- paste0("'%", unique(x@value), "'") if (condition(x) == "contains") vals <- paste0("'%", unique(x@value), "%'") vals } #' That's to build the standard query from an AnnotationFilter for EnsDb. #' #' @param x An \code{AnnotationFilter}. #' @noRd .queryForEnsDb <- function(x) { paste(.fieldInEnsDb(field(x)), .conditionForEnsDb(x), .valueForEnsDb(x)) } #' This is a slightly more sophisticated function that does also prefix the #' columns. #' @noRd .queryForEnsDbWithTables <- function(x, db, tables = character()) { clmn <- .fieldInEnsDb(field(x)) if (!missing(db)) { if (length(tables) == 0) tables <- names(listTables(db)) clmn <- unlist(prefixColumns(db, clmn, with.tables = tables)) } res <- paste(clmn, .conditionForEnsDb(x), .valueForEnsDb(x)) ## cat(" ", res, "\n") return(res) } #' Simple helper function to convert expressions to AnnotationFilter or #' AnnotationFilterList. #' #' @param x Can be an \code{AnnotationFilter}, an \code{AnnotationFilterList}, #' a \code{list} or a filter \code{expression}. This should NOT be empty! #' #' @return Returns an \code{AnnotationFilterList} with all filters. #' #' @noRd .processFilterParam <- function(x, db) { if (missing(db)) stop("Argument 'db' missing.") ## Check if x is a formula and eventually translate it. if (is(x, "formula")) res <- AnnotationFilter(x) else res <- x if (is(res, "AnnotationFilter")) res <- AnnotationFilterList(res) if (!is(res, "AnnotationFilterList")) { ## Did not get a filter expression, thus checking what we've got. if (is(res, "list")) { if (length(res)) { ## Check that all elements are AnnotationFilter objects! if (!all(unlist(lapply(res, function(z) { inherits(z, "AnnotationFilter") }), use.names = FALSE))) stop("One of more elements in 'filter' are not ", "'AnnotationFilter' objects!") res <- as(res, "AnnotationFilterList") res@logOp <- rep("&", (length(res) - 1)) } else { res <- AnnotationFilterList() } } else { stop("'filter' has to be an 'AnnotationFilter', a list of ", "'AnnotationFilter' object, an 'AnnotationFilterList' ", "or a valid filter expression!") } } supp_filters <- supportedFilters(db)$filter have_filters <- unique(.AnnotationFilterClassNames(res)) if (!all(have_filters %in% supp_filters)) stop("AnnotationFilter classes: ", paste(have_filters[!(have_filters %in% supp_filters)]), " are not supported by EnsDb databases.") res } ############################################################ ## setFeatureInGRangesFilter ## ## 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 if (is(x[[i]], "AnnotationFilterList")) x[[i]] <- setFeatureInGRangesFilter(x[[i]], feature = feature) } x } ############################################################ ## isProteinFilter ##' evaluates whether the filter is a protein annotation related filter. ##' @param x The object that should be evaluated. Can be an AnnotationFilter or ##' an AnnotationFilterList. ##' @return Returns TRUE if 'x' is a filter for protein annotation tables and ##' FALSE otherwise. ##' @noRd isProteinFilter <- function(x) { if (is(x, "AnnotationFilterList")) return(unlist(lapply(x, isProteinFilter))) else return(is(x, "ProteinIdFilter") | is(x, "UniprotFilter") | is(x, "ProtDomIdFilter") | is(x, "UniprotDbFilter") | is(x, "UniprotMappingTypeFilter")) } ## ############################################################ ## ## 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. ## if(!all(unlist(lapply(x, function(z){ ## return((is(z, "AnnotationFilter") | is(z, "GRangesFilter"))) ## }), use.names = FALSE))) ## stop("One of more elements in 'filter' are not filter objects!") ## }else{ ## if(is(x, "AnnotationFilter") | is(x, "GRangesFilter")){ ## x <- list(x) ## }else{ ## stop("'filter' has to be a filter object or a list of", ## " filter objects!") ## } ## } ## return(x) ## } #' build the \emph{where} query for a \code{GRangedFilter}. Supported conditions #' are: \code{"start"}, \code{"end"}, \code{"equal"}, \code{"within"}, #' \code{"any"}. #' #' @param grf \code{GRangesFilter}. #' #' @param columns named character vectors with the column names for start, end, #' strand and seq_name. #' #' @param db An optional \code{EnsDb} instance. Used to \emph{translate} #' seqnames depending on the specified seqlevels style. #' #' @return A character with the corresponding \emph{where} query. #' @noRd buildWhereForGRanges <- function(grf, columns, db = NULL){ condition <- condition(grf) if (!(condition %in% c("start", "end", "within", "equal", "any"))) stop("'condition' ", condition, " not supported. Condition (type) can ", "be one of 'any', 'start', 'end', 'equal', 'within'.") if( is.null(names(columns))) stop("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("'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)) } ## start: start, seqname and strand have to match. if (condition == "start") { query <- paste0(columns["start"], "=", start(z), " and ", columns["seqname"], "='", seqn, "'") } ## end: end, seqname and strand have to match. if (condition == "end") { query <- paste0(columns["end"], "=", end(z), " and ", columns["seqname"], "='", seqn, "'") } ## equal: start, end, seqname and strand have to match. if (condition == "equal") { query <- paste0(columns["start"], "=", start(z), " and ", columns["end"], "=", end(z), " and ", columns["seqname"], "='", seqn, "'") } ## within: start has to be >= start, end <= end, seqname and strand ## have to match. if (condition == "within") { query <- paste0(columns["start"], ">=", start(z), " and ", columns["end"], "<=", end(z), " and ", columns["seqname"], "='", seqn, "'") } ## any: essentially the overlapping. if (condition == "any") { 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, ")") ## Collapse now the queries. query <- paste0(quers, collapse=" or ") paste0("(", query, ")") } #' @description Helper to extract all AnnotationFilter class names from an #' AnnotationFilterList (recursively!) #' #' @param x The \code{AnnotationFilterList}. #' #' @return A \code{character} with the names of the classes. #' @noRd .AnnotationFilterClassNames <- function(x) { classes <- lapply(x, function(z) { if (is(z, "AnnotationFilterList")) return(.AnnotationFilterClassNames(z)) class(z) }) unlist(classes, use.names = FALSE) } #' @description Test if any of the filter(s) is an SymbolFilter. #' #' @noRd .anyIs <- function(x, what = "SymbolFilter") { if (is(x, "AnnotationFilter")) { is(x, what) } else { unlist(lapply(x, .anyIs, what = what)) } } ensembldb/R/functions-create-EnsDb.R0000644000175400017540000022004713241131543020323 0ustar00biocbuildbiocbuild############################################################ ## Functions related to the creation of EnsDb databases. ## 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", "ens_counts.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.") haveCounts <- file.exists(paste0(path, .Platform$file.sep, "ens_counts.txt")) ## read the counts - use these numbers to validate that we did read ## everything if (haveCounts) counts <- read.table(paste0(path, .Platform$file.sep, "ens_counts.txt"), sep = "\t", as.is = TRUE, header = TRUE) ## read information info <- read.table(paste0(path, .Platform$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 message("Processing 'chromosome' table ... ", appendLF = FALSE) tmp <- read.table(paste0(path, .Platform$file.sep ,"ens_chromosome.txt"), sep="\t", as.is=TRUE, header=TRUE) tmp[, "seq_name"] <- as.character(tmp[, "seq_name"]) dbWriteTable(con, name="chromosome", tmp, row.names=FALSE) rm(tmp) message("OK") message("Processing 'gene' table ... ", appendLF = FALSE) ## process genes: some gene names might have fancy names... tmp <- read.table(paste0(path, .Platform$file.sep, "ens_gene.txt"), sep="\t", as.is=TRUE, header=TRUE, quote="", comment.char="" ) OK <- .checkIntegerCols(tmp) ## Check that we have the expected number of rows: if (haveCounts) if (nrow(tmp) != counts[1, "gene"]) stop("The data read from the 'ens_gene.txt' file does not match ", "the expected number of entries.") dbWriteTable(con, name="gene", tmp, row.names=FALSE) ## Check that we can read the correct number of entries if (haveCounts) { res <- dbGetQuery(con, "select count(*) from gene;")[1, 1] if (res != counts[1, "gene"]) stop("The number of rows in the 'gene' database table does not ", "match the expected number.") } rm(tmp) message("OK") if (as.numeric(info[info$name == "DBSCHEMAVERSION", "value"]) > 1) { message("Processing 'entrezgene' table ... ", appendLF = FALSE) ## process genes: some gene names might have fancy names... tmp <- read.table(paste0(path, .Platform$file.sep, "ens_entrezgene.txt"), sep="\t", as.is=TRUE, header=TRUE, quote="", comment.char="" ) dbWriteTable(con, name="entrezgene", tmp, row.names=FALSE) rm(tmp) message("OK") } message("Processing 'trancript' table ... ", appendLF = FALSE) ## process transcripts: tmp <- read.table(paste0(path, .Platform$file.sep, "ens_tx.txt"), sep="\t", as.is=TRUE, header=TRUE) ## Check that we have the expected number of rows: if (haveCounts) if (nrow(tmp) != counts[1, "tx"]) stop("The data read from the 'ens_tx.txt' file does not match the", " expected number of entries.") ## Fix the tx_cds_seq_start and tx_cds_seq_end columns: these should be integer! suppressWarnings( tmp[, "tx_cds_seq_start"] <- as.integer(tmp[, "tx_cds_seq_start"]) ) suppressWarnings( tmp[, "tx_cds_seq_end"] <- as.integer(tmp[, "tx_cds_seq_end"]) ) OK <- .checkIntegerCols(tmp) ## Fix the tx_support_level column to ensure it contains only INTEGER! if (any(colnames(tmp) == "tx_support_level")) { tsl <- strsplit(tmp$tx_support_level, split = " ", fixed = TRUE) tsl <- lapply(tsl, function(z) { if (length(z) > 1) z <- z[1] if (is.na(z)) return(NA_integer_) if (z == "NA" | z == "NULL") z <- NA as.integer(z) }) tmp$tx_support_level <- unlist(tsl, use.names = FALSE) } dbWriteTable(con, name="tx", tmp, row.names=FALSE) rm(tmp) message("OK") ## process exons: message("Processing 'exon' table ... ", appendLF = FALSE) tmp <- read.table(paste0(path, .Platform$file.sep, "ens_exon.txt"), sep = "\t", as.is = TRUE, header = TRUE) if (haveCounts) if (nrow(tmp) != counts[1, "exon"]) stop("The data read from the 'ens_exon.txt' file does not match ", "the expected number of entries.") OK <- .checkIntegerCols(tmp) dbWriteTable(con, name="exon", tmp, row.names=FALSE) rm(tmp) message("OK") message("Processing 'tx2exon' table ... ", appendLF = FALSE) tmp <- read.table(paste0(path, .Platform$file.sep, "ens_tx2exon.txt"), sep = "\t", as.is = TRUE, header = TRUE) OK <- .checkIntegerCols(tmp) dbWriteTable(con, name="tx2exon", tmp, row.names = FALSE) rm(tmp) message("OK") ## process proteins; if available. prot_file <- paste0(path, .Platform$file.sep, "ens_protein.txt") if (file.exists(prot_file)) { message("Processing 'protein' table ... ", appendLF = FALSE) tmp <- read.table(prot_file, sep = "\t", as.is = TRUE, header = TRUE) if (haveCounts) if (nrow(tmp) != counts[1, "protein"]) stop("The data read from the 'ens_protein.txt' file does not ", "match the expected number of entries.") OK <- .checkIntegerCols(tmp) dbWriteTable(con, name = "protein", tmp, row.names = FALSE) message("OK") message("Processing 'uniprot' table ... ", appendLF = FALSE) tmp <- read.table(paste0(path, .Platform$file.sep, "ens_uniprot.txt"), sep = "\t", as.is = TRUE, header = TRUE) OK <- .checkIntegerCols(tmp) dbWriteTable(con, name = "uniprot", tmp, row.names = FALSE) message("OK") message("Processing 'protein_domain' table ... ", appendLF = FALSE) tmp <- read.table(paste0(path, .Platform$file.sep, "ens_protein_domain.txt"), sep = "\t", as.is = TRUE, header = TRUE) OK <- .checkIntegerCols(tmp) dbWriteTable(con, name = "protein_domain", tmp, row.names = FALSE) message("OK") } ## Create indices message("Creating indices ... ", appendLF = FALSE) .createEnsDbIndices(con, proteins = file.exists(prot_file)) message("OK") dbDisconnect(con) ## Check if the data could be loaded. message("Checking validity of the database ... ", appendLF = FALSE) msg <- validObject(EnsDb(dbname)) if (!is.logical(msg)) stop(msg) message("OK") ## 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", "prot_dom_start", "prot_dom_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="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) } #### ## 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 = "") ## Splitting by " " but be careful, if there are more than one " "! hdr <- strsplit(tmp, split = " ", fixed = TRUE) hdr <- lapply(hdr, function(z) { if (length(z) > 2) z[2] <- paste(z[2:length(z)], collapse = " ") z[1:2] }) Header <- do.call(rbind, hdr) colnames(Header) <- c("name", "value") haveHeader <- TRUE } ## Check parameters Parms <- .checkExtractVersions(gtf, organism, genomeVersion, version) ensemblVersion <- Parms["version"] organism <- Parms["organism"] genomeVersion <- Parms["genomeVersion"] ## Fix for issue #75: validate versions I got with versions extracted from ## the GTF header but don't stop if something is wrong, just show a warning if (haveHeader) { header.version <- Header[Header[, "name"] == "genome-version", "value"] if (length(header.version)) { if (genomeVersion != header.version) warning("Genome version extracted from the gtf file name ('", genomeVersion, "') and genome version specified in the", " gtf file ('", header.version, "') do not match. ", "Ensure you pass the correct genome version along ", "with parameter 'genomeVersion'") } } 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$file.sep)) gtfFilename <- gtfFilename[length(gtfFilename)] ## updating the Metadata information... lite <- dbDriver("SQLite") con <- dbConnect(lite, dbname = dbname ) bla <- dbExecute(con, paste0("update metadata set value='", gtfFilename, "' where name='source_file';")) dbDisconnect(con) return(dbname) } ####============================================================ ## fixCDStypeInEnsemblGTF ## ## Takes an GRanges object as input and returns a GRanges object in ## which the feature type stop_codon and start_codon is replaced by ## feature type CDS. This is to fix a potential problem (bug?) in ## GTF files from Ensembl, in which the stop_codon or start_codon for ## some transcripts is outside of the CDS. ####------------------------------------------------------------ fixCDStypeInEnsemblGTF <- function(x){ if(any(unique(x$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 <- dbExecute(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){ ## Check if I can extract the genome-version idx <- grep(tmp, pattern = "^#!genome-version") if (length(idx) > 0) { genFromHeader <- sub(tmp[idx], pattern = "^#!genome-version", replacement = "") genFromHeader <- gsub(genFromHeader, pattern = " ", replacement = "", fixed = TRUE) if (genFromHeader != genFromFile) { warning("Genome version extracted from file name (", genFromFile, ") does not match genome version", " defined within the gff file (", genFromHeader, "). Will use the version defined within the gff.") } } } 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 <- dbExecute(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$transcript_id) areNas <- is.na(idx) idx <- idx[!areNas] cdsStarts <- cdsStarts[!areNas] cdsEnds <- cdsEnds[!areNas] tx[idx, "tx_cds_seq_start"] <- cdsStarts tx[idx, "tx_cds_seq_end"] <- cdsEnds }else{ mess <- paste0(" I can't find type=='CDS'! The resulting database", " will lack CDS information!") message(mess, appendLF = TRUE) warning(mess) } colnames(tx) <- c("tx_seq_start", "tx_seq_end", "tx_id", "gene_id", "tx_biotype", "tx_cds_seq_start", "tx_cds_seq_end") ## rearranging data.frame: tx <- tx[ , c("tx_id", "tx_biotype", "tx_seq_start", "tx_seq_end", "tx_cds_seq_start", "tx_cds_seq_end", "gene_id")] ## write the table. OK <- .checkIntegerCols(tx) dbWriteTable(con, name="tx", tx, overwrite=TRUE, row.names=FALSE) rm(tx) rm(CDS) rm(cdsStarts) rm(cdsEnds) message("OK") ## ---------------------------- ## ## process exons message("Processing exons ... ", appendLF=FALSE) ## Fix for issue #72: seems there are GTFs without an exon_id! if (!any(gotColumns == "exon_id") & any(gotColumns == "exon_number")) { mcols(x)$exon_id <- paste0(x$transcript_id, ":", x$exon_number) warning("No column 'exon_id' present, created artificial exon IDs by ", "concatenating the transcript ID and the exon number.") gotColumns <- c(gotColumns, "exon_id") } reqCols <- c("exon_id", "transcript_id", "exon_number") if(!all(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$seq_name ## Try to get sequence lengths from Ensembl or Ensemblgenomes. sl <- tryGetSeqinfoFromEnsembl(organism, version, seqnames = chroms$seq_name) if (nrow(sl) > 0) { sl <- sl[sl[, "name"] %in% rownames(chroms), ] chroms[sl[, "name"], "seq_length"] <- sl[, "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") return(TRUE) } ############################################################ ##' Fetch chromosome sequence lengths from Ensembl. ##' @param organism The organism. Has to be in the form "Homo sapiens" ##' @param ensemblVersion The Ensembl version. ##' @param seqnames The names of the chromosomes/sequences; optional. ##' @return A matrix with two columns name and seq_length. ##' @noRd tryGetSeqinfoFromEnsembl <- function(organism, ensemblVersion, seqnames, skip = FALSE){ if (skip) return(matrix(nrow = 0, ncol = 2)) message("Fetch seqlengths from ensembl ... ", appendLF=FALSE) tmp <- try( .getSeqlengthsFromMysqlFolder(organism = organism, ensembl = ensemblVersion, seqnames = seqnames) , silent = TRUE) if (is(tmp, "try-error") | is.null(tmp)) { message("FAIL") warning("Unable to retrieve sequence lengths from Ensembl.") return(matrix(nrow = 0, ncol = 2)) } colnames(tmp) <- c("name", "length") return(tmp) } 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", sep = "") 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", sep = "") 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) ## If we've got protein data in one of the two: if (hasProteinData(x) | hasProteinData(y)) { Messages <- c(Messages, protein = "OK") Messages["protein"] <- compareProteins(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" )) ## seqlengths: if (!all.equal(chromX[inboth, "seqlengths"], chromY[inboth, "seqlengths"])) { same <- length(which(chromX[inboth, "seqlengths"] == chromY[inboth, "seqlengths"])) different <- length(inboth) - same } else { same <- length(inboth) different <- 0 } 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) } compareProteins <- function(x, y){ cat("\nComparing protein data:\n") Ret <- "OK" if (!hasProteinData(x) | !hasProteinData(y)) { Ret <- "WARN" cat(paste0("No protein data available for one or both EnsDbs.")) return(Ret) } X <- proteins(x) Y <- proteins(y) inboth <- X$protein_id[X$protein_id %in% Y$protein_id] onlyX <- X$protein_id[!(X$protein_id %in% Y$protein_id)] onlyY <- Y$protein_id[!(Y$protein_id %in% X$protein_id)] if(length(onlyX) > 0 | length(onlyY) > 0) Ret <- "WARN" cat(paste0(" protein IDs: (", length(inboth), ") common, (", length(onlyX), ") only in x, (", length(onlyY), ") only in y.\n")) X <- X[X$protein_id %in% inboth, ] Y <- Y[Y$protein_id %in% inboth, ] ## sorting both by protein_id should be enough. X <- X[order(X$protein_id), ] Y <- Y[order(Y$protein_id), ] ## tx_id same <- length(which(X$tx_id == Y$tx_id)) different <- length(inboth) - same if(different > 0) Ret <- "ERROR" cat(paste0( " Transcript IDs: (",same, ") identical, (", different, ") different.\n" )) ## sequence same <- length(which(X$protein_sequence == Y$protein_sequence)) different <- length(inboth) - same if(different > 0) Ret <- "ERROR" cat(paste0( " Protein sequence: (",same, ") identical, (", different, ") different.\n" )) cat(paste0("Done. Result: ", Ret,"\n")) return(Ret) } compareExons <- function(x, y){ cat("\nComparing exon data:\n") Ret <- "OK" exonX <- exons(x) exonY <- exons(y) inboth <- names(exonX)[names(exonX) %in% names(exonY)] onlyX <- names(exonX)[!(names(exonX) %in% names(exonY))] onlyY <- names(exonY)[!(names(exonY) %in% names(exonX))] if(length(onlyX) > 0 | length(onlyY) > 0) Ret <- "WARN" cat(paste0(" exon IDs: (", length(inboth), ") common, (", length(onlyX), ") only in x, (", length(onlyY), ") only in y.\n")) ## start same <- length( which(start(exonX[inboth]) == start(exonY[inboth])) ) different <- length(inboth) - same if(different > 0) Ret <- "ERROR" cat(paste0( " Exon start coordinates: (",same, ") identical, (", different, ") different.\n" )) ## end same <- length( which(end(exonX[inboth]) == end(exonY[inboth])) ) different <- length(inboth) - same if(different > 0) Ret <- "ERROR" cat(paste0( " Exon end coordinates: (",same, ") identical, (", different, ") different.\n" )) ## now getting also the exon index in tx: exonX <- exons(x, columns=c("exon_id", "tx_id", "exon_idx"), return.type="DataFrame") rownames(exonX) <- paste(exonX$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 <- basename(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 <- basename(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 <- basename(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=".")) } ## 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]) } ############################################################ ## Utilities to fetch sequence lengths from Ensembl's ftp server, more ## specifically from the MySQL tables there. ## These replace the (unexported) functions from GenomicFeatures used thus far. .ENSEMBL_URL <- "ftp://ftp.ensembl.org/pub/" .ENSEMBLGENOMES_URL <- "ftp://ftp.ensemblgenomes.org/pub/" ##' Get the base url containing the mysql database for the specified host, ##' orgnism and Ensembl version. ##' @details The function will first build an approximate database name (without ##' the trailing <_genome version number> as this is not easy to guess). ##' Next all directories in the base MySQL folder will be scanned for the best ##' matching folder. ##' @param type Either "ensembl" or "ensemblgenomes" ##' @param organism Character specifying the organism. Has to be the full name, ##' i.e "homo_sapiens" or "Homo sapiens". ##' @param ensembl The Ensembl version. ##' @param genone The Genome version. ##' @noRd .getEnsemblMysqlUrl <- function(type = "ensembl", organism, ensembl, genome) { type <- match.arg(type, c("ensembl", "ensemblgenomes")) if (type == "ensembl") { my_url <- paste0(.ENSEMBL_URL, "release-", ensembl, "/mysql/") db_name <- .guessDatabaseName(organism, ensembl) ## List folders; GenomicFeatures does it without 'dirlistonly', ## eventually that's what breaks on Windows? ## res <- getURL(my_url, dirlistonly = TRUE) res <- readLines(curl(my_url)) res <- gsub(res, pattern = "\r", replacement = "", fixed = TRUE) if (length(res) > 0) { ## dirs <- unlist(strsplit(res, split = "\n")) ## ## Remove the \r on Windows. ## dirs <- sub(dirs, pattern = "\r", replacement = "", fixed = TRUE) ## idx <- grep(dirs, pattern = db_name) idx <- grep(res, pattern = db_name) if (length(idx) > 1) stop("Found more than one database matching '", db_name, "' in Ensembl's ftp server!") if (length(idx) == 0) stop("No database matching '", db_name, "' found in Ensembl's", " ftp server.") db_dir <- unlist(strsplit(res[idx], split = " ", fixed = TRUE)) db_dir <- db_dir[length(db_dir)] return(paste0(my_url, db_dir)) } } else { ## That's tricky! Have to find out whether the species is in plants, ## fungi, bacteria etc. ## List dirs of bacteria, fungi, metazoa, plants, protists sub_folders <- c("bacteria", "fungi", "metazoa", "plants", "protists") db_name <- .guessDatabaseName(organism, ensembl) for (fold in sub_folders) { my_url <- paste0(.ENSEMBLGENOMES_URL, "release-", ensembl, "/", fold, "/mysql/") res <- try(readLines(curl(my_url))) if (is(res, "try-error")| length(res) == 0) next if (length(res) > 0) { res <- gsub(res, pattern = "\r", replacement = "", fixed = TRUE) idx <- grep(res, pattern = db_name) if (length(idx) > 1) stop("Found more than one database matching '", db_name, "' in Ensemblgenomes' ftp server!") if (length(idx) == 1) { db_dir <- unlist(strsplit(res[idx], split = " ", fixed = TRUE)) db_dir <- db_dir[length(db_dir)] return(paste0(my_url, db_dir)) } } ## ## Catch eventual errors ## res <- try(getURL(my_url, dirlistonly = TRUE), silent = TRUE) ## if (is(res, "try-error") | length(res) == 0) ## next ## if (length(res) > 0) { ## dirs <- unlist(strsplit(res, split = "\n")) ## ## Remove the \r on Windows. ## dirs <- sub(dirs, pattern = "\r", replacement = "", fixed = TRUE) ## idx <- grep(dirs, pattern = db_name) ## if (length(idx) == 1) ## return(paste0(my_url, dirs[idx])) ## if (length(idx) > 1) ## stop("Found more than one database matching '", db_name, ## "' in Ensembl's ftp server!") ## ## Well, then let's go to the next one. ## } } stop("No database matching '", db_name, "' found in Ensembl's", " ftp server") } } ############################################################ ## .guessDatabaseName ##' build the database name from species, ensembl version and genome version. ##' The latter is specifically difficult, as it is not quite clear how Ensembl ##' defines the Genome version number. ##' @param organism Character specifying the organism. Has to be in the format ##' "homo_sapiens" or "Homo sapiens", i.e. the full name. ##' @param ensembl The Ensembl version number. ##' @param genome The Genome version, e.g. GRCh38 (optional!). ##' @return A character representing the guessed database name in Ensembl. ##' @noRd .guessDatabaseName <- function(organism, ensembl, genome) { if (missing(organism) & missing(ensembl)) stop("'organism' and 'ensembl' are required!") ## Organism: all lower case, replace . with _ organism <- tolower(gsub(organism, pattern = ".", replacement = "_", fixed = TRUE)) organism <- gsub(organism, pattern = " ", replacement = "_", fixed = TRUE) dbname <- paste0(organism, "_core_", ensembl) ## Genome: remove all letters and keep just the numbers. if (!missing(genome)) { genome <- gsub(genome, pattern = "[a-zA-Z]", replacement = "") ## replace .0 at the end genome <- gsub(genome, pattern = ".0$", replacement = "") genome <- gsub(genome, pattern = ".", replacement = "", fixed = TRUE) genome <- gsub(genome, pattern = "_", replacement = "", fixed = TRUE) dbname <- paste0(dbname, "_", genome) } return(dbname) } ############################################################ ## .getSeqlengthsFromMysqlFolder ##' Fetch the coord_system.txt.gz and seq_region.txt.gz and extract the ##' seqlengths from there. ##' @noRd .getSeqlengthsFromMysqlFolder <- function(organism, ensembl, seqnames) { ## Test whether we have the database in ensembl mysql_url <- try(.getEnsemblMysqlUrl(type = "ensembl", organism = organism, ensembl = ensembl), silent = TRUE) if (is(mysql_url, "try-error")) { mysql_url <- try(.getEnsemblMysqlUrl(type = "ensemblgenomes", organism = organism, ensembl = ensembl), silent = TRUE) } if (is(mysql_url, "try-error")) { warning("Can not get the sequence lengths from Ensembl or", " Ensemblgenomes. Seqinfo will lack the sequence lengths.") return(NULL) } ## Get the coord_system table coord_syst <- .getReadMysqlTable(mysql_url, "coord_system.txt.gz", colnames = c("coord_system_id", "species_id", "name", "version", "rank", "attrib")) ## Subset to the ones with "default" in "attrib" coord_syst <- coord_syst[grep(coord_syst$attrib, pattern = "default"), , drop = FALSE] rownames(coord_syst) <- as.character(coord_syst$coord_system_id) ## Get the seq_region table seq_region <- .getReadMysqlTable(mysql_url, "seq_region.txt.gz", colnames = c("seq_region_id", "name", "coord_system_id", "length")) ## Sub-set to the ones matching the coord_syst_ids and from these, select ## the one entry with the smallest rank, if more than one present. seq_region <- seq_region[seq_region$coord_system_id %in% coord_syst$coord_system_id, , drop = FALSE] seq_region <- cbind(seq_region, rank = coord_syst[as.character(seq_region$coord_system_id), "rank"]) ## Sub-set to the seqlevels we've got. if (!missing(seqnames)) { seq_region <- seq_region[seq_region$name %in% seqnames, , drop = FALSE] if (!all(seqnames %in% seq_region$name)) warning("Could not determine length for all seqnames.") } sr <- split(seq_region, f = seq_region$name) if (length(sr) == 0) return(NULL) sr <- lapply(sr, function(z) { if (nrow(z) == 1) return(z) z <- z[order(z$rank), ] return(z[1, , drop = FALSE]) }) sr <- do.call(rbind, sr) rownames(sr) <- sr$name return(sr[, c("name", "length")]) } ##' Download and read a table from Ensembl ##' @param base_url the base url to the mysql folder on the server. ##' @param file_name the file name of the table. ##' @param colnames the column names. ##' @return A data.frame with the table's content. ##' @noRd .getReadMysqlTable <- function(base_url, file_name, colnames) { tmp_file <- tempfile() download.file(url = paste0(base_url, "/", file_name), destfile = tmp_file, quiet = TRUE) tmp <- read.table(tmp_file, sep = "\t", quote = "", comment.char = "", as.is = TRUE) colnames(tmp) <- colnames return(tmp) } ensembldb/R/functions-utils.R0000644000175400017540000002127213175714743017245 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 = AnnotationFilterList(), edb) { if (missing(cols)) cols <- NULL gimmeAll <- returnFilterColumns(edb) if (!gimmeAll) return(cols) ## Put filter into an AnnotationFilterList if it's not already one if (is(filter, "AnnotationFilter")) filter <- AnnotationFilterList(filter) ## Or alternatively process the filters and add columns. symFilts <- c("SymbolFilter") addC <- unlist(lapply(filter, function(z) { if(class(z) %in% symFilts) return(z@field) if (is(z, "AnnotationFilterList")) return(addFilterColumns(cols = cols, filter = z, edb)) return(ensDbColumn(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))) } ## 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!") } } ############################################################ ## anyProteinColumns ## ## Check if any of 'x' are protein columns. anyProteinColumns <- function(x){ return(any(x %in% unlist(.ensdb_protein_tables(), use.names = FALSE))) } ############################################################ ## listProteinColumns ## #' @description The \code{listProteinColumns} function allows to conveniently #' extract all database columns containing protein annotations from #' an \code{\linkS4class{EnsDb}} database. #' #' @return The \code{listProteinColumns} function returns a character vector #' with the column names containing protein annotations or throws an error #' if no such annotations are available. #' #' @rdname ProteinFunctionality #' #' @examples #' #' ## List all columns containing protein annotations #' library(EnsDb.Hsapiens.v75) #' edb <- EnsDb.Hsapiens.v75 #' if (hasProteinData(edb)) #' listProteinColumns(edb) listProteinColumns <- function(object) { if (missing(object)) stop("'object' is missing with no default.") if (!is(object, "EnsDb")) stop("'object' has to be an instance of an 'EnsDb' object.") if (!hasProteinData(object)) stop("The provided EnsDb database does not contain protein annotations!") return(listColumns(object, c("protein", "uniprot", "protein_domain"))) } ############################################################ ## .ProteinsFromDataframe #' @param x \code{EnsDb} object. #' #' @param data \code{data.frame} with the results from a call to the #' \code{proteins} method; has to have required columns \code{"protein_id"} #' and \code{"protein_sequence"}. #' #' @noRd .ProteinsFromDataframe <- function(x, data) { if (!all(c("protein_id", "protein_sequence") %in% colnames(data))) stop("Reguired columns 'protein_id' and 'protein_sequence' not in 'data'!") ## Get the column names for uniprot and protein_domain uniprot_cols <- listColumns(x, "uniprot") uniprot_cols <- uniprot_cols[uniprot_cols != "protein_id"] uniprot_cols <- uniprot_cols[uniprot_cols %in% colnames(data)] if (length(uniprot_cols) > 0) warning("Don't know yet how to handle the 1:n mapping between", " protein_id and uniprot_id!") prot_dom_cols <- listColumns(x, "protein_domain") prot_dom_cols <- prot_dom_cols[prot_dom_cols != "protein_id"] prot_dom_cols <- prot_dom_cols[prot_dom_cols %in% colnames(data)] ## Create the protein part of the object, i.e. the AAStringSet. ## Use all columns other than protein_id, protein_sequence prot_cols <- colnames(data) prot_cols <- prot_cols[!(prot_cols %in% c(uniprot_cols, prot_dom_cols))] protein_sub <- unique(data[, prot_cols, drop = FALSE]) aass <- AAStringSet(protein_sub$protein_sequence) names(aass) <- protein_sub$protein_id prot_cols <- prot_cols[!(prot_cols %in% c("protein_id", "protein_sequence"))] if (length(prot_cols) > 0) { mcols(aass) <- DataFrame(protein_sub[, prot_cols, drop = FALSE]) ## drop these columns from data to eventually speed up splits data <- data[, !(colnames(data) %in% prot_cols), drop = FALSE] } ## How to process the Uniprot here??? have a 1:n mapping! ## Create the protein domain part if (length(prot_dom_cols) > 0) { message("Processing protein domains not yet implemented!") ## Split the dataframe by protein_id ## process this list to create the IRangesList. ## pranges should have the same order and the same names } else { pranges <- IRangesList(replicate(length(aass), IRanges())) names(pranges) <- names(aass) } metadata <- list(created = date()) ##return(new("Proteins", aa = aass, pranges = pranges, metadata = metadata)) } ## map chromosome strand... strand2num <- function(x){ if (is.numeric(x)) { if (x >= 0) return(1) else return(-1) } xm <- x if(xm == "+" | xm == "-") xm <- paste0(xm, 1) xm <- as.numeric(xm) if (is.na(xm)) stop("'", x, "' can not be converted to a strand!") return(xm) } num2strand <- function(x){ if(x < 0){ return("-") }else{ return("+") } } #' @description Collapses entries in the \code{"entrezid"} column of a #' \code{data.frame} or \code{DataFrame} making the rest of \code{x} unique. #' #' @param x Either a \code{data.frame} or a \code{DataFrame}. #' #' @param by \code{character(1)} defining the column by which the #' \code{"entrezid"} column should be splitted. #' #' @author Johannes Rainer #' #' @noRd .collapseEntrezidInTable <- function(x, by = "gene_id") { ## Slow version: use unique call. eg_idx <- which(colnames(x) == "entrezid") if (length(eg_idx)) { ## Avoid an additional lapply unique call. tmp <- unique(x[, c(by, "entrezid")]) egs <- split(tmp[, "entrezid"], f = factor(tmp[, by], levels = unique(tmp[, by]))) ## Use a unique call. ## x_sub <- x[match(names(egs), x[, by]), , drop = FALSE] would be much ## faster but does not work e.g. for exons or transcripts. x_sub <- unique(x[, -eg_idx, drop = FALSE]) x_sub$entrezid <- egs[x_sub[, by]] return(x_sub) } x } ensembldb/R/select-methods.R0000644000175400017540000003526713175714743017030 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(withProteins = hasProteinData(x))) } ) ## This just returns some (eventually) usefull names for keys .simpleKeytypes <- function(x){ return(c("GENEID","TXID","TXNAME","EXONID","EXONNAME","CDSID","CDSNAME")) } .filterKeytypes <- function(withProteins = FALSE){ return(names(.keytype2FilterMapping(withProteins = withProteins))) } ## returns a vector mapping keytypes (names of vector) to filter names (elements). .keytype2FilterMapping <- function(withProteins = FALSE){ filters <- c(ENTREZID = "EntrezFilter", GENEID = "GeneIdFilter", GENEBIOTYPE = "GeneBiotypeFilter", GENENAME = "GenenameFilter", TXID = "TxIdFilter", TXBIOTYPE = "TxBiotypeFilter", EXONID = "ExonIdFilter", SEQNAME = "SeqNameFilter", SEQSTRAND = "SeqStrandFilter", TXNAME = "TxIdFilter", SYMBOL = "SymbolFilter") if (withProteins) { filters <- c(filters, PROTEINID = "ProteinIdFilter", UNIPROTID = "UniprotFilter", PROTEINDOMAINID = "ProtDomIdFilter") } return(filters) } filterForKeytype <- function(keytype, x, vals){ if (missing(vals)) vals <- 1 if (!missing(x)) { withProts <- hasProteinData(x) } else { withProts <- FALSE } filters <- .keytype2FilterMapping(withProts) if(any(names(filters) == keytype)){ filt <- do.call(filters[keytype], args = list(value = vals)) ## 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 <- AnnotationFilterList() filter <- .processFilterParam(filter, x) keyt <- keytypes(x) if (length(keytype) > 1) { keytype <- keytype[1] warning("Using only first provided keytype.") } if (!any(keyt == keytype)) stop("keytype '", keytype, "' not supported! ", "Allowed choices are: ", paste0("'", keyt ,"'", collapse = ", "), ".") 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 ## ## We have to be carefull, if the database contains protein annotations too: ## o If the keys are DNA/RNA related, start from a DNA/RNA related table. ## o if keys are protein related: start from a protein column. ## Reason is that we do have only protein annotations for protein coding genes ## and no annotation for the remaining. Thus the type of the join (left join, ## left outer join) is crucial, as well as the table with which we start the ## query! ## What if we provide more than one filter? ## a) GenenameFilter and ProteinidFilter: doesn't really matter from which table ## we start, because the query will only return results with protein ## annotions. -> if there is one DNA/RNA related filter: don't do anything. ## b) Only protein filters: start from the highest protein table. 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, "formula") | is(keys, "AnnotationFilter") | is(keys, "AnnotationFilterList"))) stop("Argument keys should be a character vector, an object", " extending AnnotationFilter, a filter expression", " or an AnnotationFilterList.") 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, x, vals = keys) ## value(keyFilter) <- keys ## keyFilter@value <- keys keys <- list(keyFilter) ## Add also the keytype itself to the columns. if (!any(columns == keytype)) columns <- c(keytype, columns) } ## Check and fix filter. keys <- .processFilterParam(keys, x) } ## 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))) ## TODO @jo: Do we have to check that we are allowed to have protein filters ## or columns? ## OK, now perform the query given the filters we've got. ## Check if keys does only contain protein annotation columns; in that case ## select one of tables "protein", "uniprot", "protein_domain" in that order ## if (all(unlist(lapply(keys, isProteinFilter)))) { if (all(isProteinFilter(keys))) { startWith <- "protein_domain" if (any(unlist(lapply(keys, function(z) is(z, "UniprotFilter"))))) startWith <- "uniprot" if (any(unlist(lapply(keys, function(z) is(z, "ProteinIdFilter"))))) startWith <- "protein" } else { startWith <- NULL } ## Otherwise set startWith to NULL res <- getWhat(x, columns = ensCols, filter = keys, startWith = startWith) ## Order results if length of filters is 1. if (length(keys) == 1) { ## Define the filters on which we could sort. sortFilts <- c("GenenameFilter", "GeneIdFilter", "EntrezFilter", "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 <- ensDbColumn(keys[[1]]) sortCol <- keys[[1]]@field } else { sortCol <- ensDbColumn(keys[[1]]) ## sortCol <- removePrefix(ensDbColumn(keys[[1]], x)) } res <- res[order(match(res[, sortCol], keyvals)), ] } } else { ## Show a mild warning message message(paste0("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) 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, "AnnotationFilter"))) ## stop("Argument keys should be a character vector, an object extending", ## " AnnotationFilter or a list of objects extending AnnotationFilter.") 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) } else { ## Test if we can convert the filter. Returns ALWAYS an ## AnnotationFilterList keys <- .processFilterParam(keys, x) 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] ## Use the first element to determine the keytype... mapping <- .keytype2FilterMapping() columns <- c(names(mapping)[mapping == cn], column) keytype <- NULL } ## if(is(keys, "list") | is(keys, "AnnotationFilter")){ ## 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!") 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.R0000644000175400017540000002416113175714743016666 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.R0000644000175400017540000000076713175714743014742 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/0000755000175400017540000000000013241155731014634 5ustar00biocbuildbiocbuildensembldb/build/vignette.rds0000644000175400017540000000056713241155731017203 0ustar00biocbuildbiocbuildSN0t UAHi"q"iq(`I9!w3Y;]BHXE.>MNIӇl6>EyD 9VUxurEGXp*ON3nT >U]c h2irensembldb/inst/0000755000175400017540000000000013241155731014512 5ustar00biocbuildbiocbuildensembldb/inst/NEWS0000644000175400017540000003710513241131543015212 0ustar00biocbuildbiocbuildCHANGES IN VERSION 2.2.2 ------------------------ - Fix for issue #75. CHANGES IN VERSION 2.2.1 ------------------------ - Fix for issue #72: problem with Ensembl GTF file lacking exon_id field. CHANGES IN VERSION 2.1.12 ------------------------ BUG FIXES: o Use new defaults from the IRanges package for arguments maxgap = -1L, minoverlap = 0L in transcriptsByOverlaps and exonsByOverlaps methods. CHANGES IN VERSION 2.1.12 ------------------------ BUG FIXES: o Remove RSQLite warnings (issue #54). CHANGES IN VERSION 2.1.11 ------------------------ BUG FIXES: o ensDbFromGtf failed to parse header for GTF files with more than one white space. CHANGES IN VERSION 2.1.10 ------------------------ USER VISIBLE CHANGES o supportedFilters returns a data frame with the filter class name and corresponding field (column) name. CHANGES IN VERSION 2.1.9 ------------------------ NEW FEATURES o Support for global filters in an EnsDb object. o Add filter function. CHANGES IN VERSION 2.1.8 ------------------------ NEW FEATURES o New annotations available in EnsDb objects: gene.description and tx.tx_support_level. o New TxSupportLevelFilter object. CHANGES IN VERSION 1.99.13 -------------------------- USER VISIBLE CHANGES: o Most filter classes are now imported from the AnnotationFilter package. o Parameter 'filter' supports now filter expression. o Multiple filters can be combined with & and |. o buildQuery is no longer exported. CHANGES IN VERSION 1.99.11 -------------------------- BUG FIXES: o ensDbFromGtf failed to fetch sequence length for some ensemblgenomes versions. CHANGES IN VERSION 1.99.11 -------------------------- NEW FEATURES o Retrieving also the taxonomy ID from the Ensembl databases and storing this information into the metadata table. CHANGES IN VERSION 1.99.10 -------------------------- BUG FIXES: o Fix problem on Windows systems failing to download files from Ensembl servers. CHANGES IN VERSION 1.99.6 ------------------------- BUG FIXES: o MySQL database name for useMySQL was not created as expected for GTF/GFF based EnsDbs. CHANGES IN VERSION 1.99.5 ------------------------- NEW FEATURES: o OnlyCodingTxFilter is now exported. This filter allows to query for protein coding genes. CHANGES IN VERSION 1.99.3 ------------------------- BUG FIXES: o Add two additional uniprot table columns to internal variable and fix failing unit test. CHANGES IN VERSION 1.99.3 ------------------------- BUG FIXES: o Add two additional uniprot table columns to internal variable and fix failing unit test. CHANGES IN VERSION 1.99.3 ------------------------- NEW FEATURES: o UniprotdbFilter and UniprotmappingtypeFilter. USER VISIBLE CHANGES: o Fetching Uniprot database and the type of mapping method for Uniprot IDs to Ensembl protein IDs: database columns uniprot_db and uniprot_mapping_type. CHANGES IN VERSION 1.99.2 ------------------------- BUG FIXES: o Perl script is no longer failing if no chromosome info is available. CHANGES IN VERSION 1.99.1 ------------------------- BUG FIXES: o No protein table indices were created when inserting an EnsDb with protein data to MySQL. CHANGES IN VERSION 1.99.0 ------------------------- NEW FEATURES: o The perl script to create EnsDb databases fetches also protein annotations. o Added functionality to extract protein annotations from the database (if available) ensuring backward compatibility. o Add proteins vignette. USER VISIBLE CHANGES: o Improved functionality to fetch sequence lengths for chromosomes from Ensembl or ensemblgenomes. 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 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 1.5.2 ------------------------- 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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15429068 15429181 ENSE00003105168 28114876 28114889 ENSE00002994181 28111970 28111984 ENSE00003035759 28111776 28111809 ENSE00003089533 5312577 5312605 ENSE00003100810 5306691 5306703 ENSE00001782073 9345205 9345423 ENSE00001705189 9345442 9345707 ENSE00001641329 9346315 9346392 ENSE00001803456 9346521 9346632 ENSE00001739988 9346734 9346879 ENSE00001717366 9346986 9347067 ENSE00001761223 9347762 9347784 ENSE00003671391 13551375 13551415 ENSE00003551235 13551547 13552752 ENSE00003639402 13491303 13491471 ENSE00003668884 13491863 13493369 ENSE00003649651 27809047 27809373 ENSE00003691087 13263395 13263563 ENSE00003621115 23793569 23793901 ENSE00003657297 13263065 13263272 ENSE00003625313 13262741 13262939 ENSE00003638565 13629856 13629913 ENSE00003555471 13629403 13629733 ENSE00003625865 19576759 19577094 ENSE00003697854 27605054 27605329 ensembldb/inst/chrY/ens_gene.txt0000644000175400017540000010531613175714743017763 0ustar00biocbuildbiocbuildgene_id gene_name entrezid gene_biotype 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.txt0000644000175400017540000000046413175714743020623 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.txt0000644000175400017540000016134513175714743017504 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.txt0000644000175400017540000037207413175714743020463 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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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.Rmd0000644000175400017540000000457113175714743020276 0ustar00biocbuildbiocbuild--- title: "Using a MySQL server backend" author: "Johannes Rainer" package: ensembldb output: BiocStyle::html_document: toc_float: true vignette: > %\VignetteIndexEntry{Using a MySQL server backend} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} %\VignetteDepends{ensembldb,EnsDb.Hsapiens.v75,BiocStyle} --- # 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.html0000644000175400017540000325213713241155654020520 0ustar00biocbuildbiocbuild Using a MySQL server backend

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.R0000644000175400017540000004123313241155725017343 0ustar00biocbuildbiocbuild## ----load-libs, 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) ## ----no-network, echo = FALSE, results = "hide"---------------------------- ## Disable code chunks that require network connection - conditionally ## disable this on Windows only. This is to avoid TIMEOUT errors on the ## Bioconductor Windows build maching (issue #47). use_network <- FALSE ## ----filters--------------------------------------------------------------- supportedFilters(edb) ## ----transcripts----------------------------------------------------------- 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$tx_biotype) ## ----transcripts-filter-expression----------------------------------------- ## Use a filter expression to perform the filtering. transcripts(edb, filter = ~ genename == "ZBTB16") ## ----transcripts-filter---------------------------------------------------- library(magrittr) filter(edb, ~ symbol == "BCL2" & tx_biotype != "protein_coding") %>% transcripts ## ----filter-Y-------------------------------------------------------------- edb_y <- addFilter(edb, SeqNameFilter("Y")) ## All subsequent filters on that EnsDb will only work on features encoded on ## chromosome Y genes(edb_y) ## Get all lincRNAs on chromosome Y genes(edb_y, filter = ~ gene_biotype == "lincRNA") ## ----list-columns---------------------------------------------------------- ## list all database tables along with their columns listTables(edb) ## list columns from a specific table listColumns(edb, "tx") ## ----transcripts-example2-------------------------------------------------- Tx <- transcripts(edb, columns = c(listColumns(edb , "tx"), "gene_name"), filter = TxBiotypeFilter("nonsense_mediated_decay"), return.type = "DataFrame") nrow(Tx) Tx ## ----cdsBy----------------------------------------------------------------- yCds <- cdsBy(edb, filter = SeqNameFilter("Y")) yCds ## ----genes-GRangesFilter--------------------------------------------------- ## Define the filter grf <- GRangesFilter(GRanges("11", ranges = IRanges(114000000, 114000050), strand = "+"), type = "any") ## Query genes: gn <- genes(edb, filter = grf) gn ## Next we retrieve all transcripts for that gene so that we can plot them. txs <- transcripts(edb, filter = GenenameFilter(gn$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-GRangesFilter--------------------------------------------- transcripts(edb, filter = grf) ## ----biotypes-------------------------------------------------------------- ## 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) ## ----genes-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 = GenenameFilter("BCL", condition = "startsWith"), return.type = "DataFrame") nrow(BCLs) BCLs ## ----example-AnnotationFilterList------------------------------------------ ## determine the average length of snRNA, snoRNA and rRNA genes encoded on ## chromosomes X and Y. mean(lengthOf(edb, of = "tx", filter = AnnotationFilterList( 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 = ~ gene_biotype == "protein_coding" & seq_name %in% c("X", "Y"))) ## ----example-first-two-exons----------------------------------------------- ## 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 = "<"))) ## ----transcriptsBy-X-Y----------------------------------------------------- TxByGns <- transcriptsBy(edb, by = "gene", filter = SeqNameFilter(c("X", "Y"))) TxByGns ## ----exonsBy-RNAseq, message = FALSE, 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 = AnnotationFilterList( # SeqNameFilter(c(1:22, "X", "Y")), # GeneIdFilter("ENSG", "startsWith"))) # ## ----toSAF-RNAseq, message = FALSE, eval=FALSE----------------------------- # ## Transforming the GRangesList into a data.frame in SAF format # EnsGenes.SAF <- toSAF(EnsGenes) # ## ----disjointExons, message = FALSE, eval=FALSE---------------------------- # ## Create a GRanges of non-overlapping exon parts. # DJE <- disjointExons(edb, filter = AnnotationFilterList( # SeqNameFilter(c(1:22, "X", "Y")), # GeneIdFilter("ENSG%", "startsWith"))) # ## ----transcript-sequence-AnnotationHub, message = FALSE, 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) # ## ----transcript-sequence-extractTranscriptSeqs, message = FALSE, 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) # ## ----seqlevelsStyle, 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 = ~ seq_name == "chrY") ## The seqlevels of the returned GRanges are also in UCSC style seqlevels(genesY) ## ----seqlevelsStyle-2, message = FALSE------------------------------------- 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") ## ----extractTranscriptSeqs-BSGenome, 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-restore------------------------------------------------ seqlevelsStyle(edb) <- "Ensembl" ## ----gviz-plot, message=FALSE, fig.align='center', fig.width=7.5, fig.height=2.3---- ## 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" ## ----pplot-plot, message=FALSE, fig.align='center', fig.width=7.5, fig.height=4---- library(ggbio) ## Create a plot for all transcripts of the gene SKA2 autoplot(edb, ~ genename == "SKA2") ## ----pplot-plot-2, message=FALSE, fig.align='center', fig.width=7.5, fig.height=4---- ## Get the chromosomal region in which the gene is encoded ska2 <- genes(edb, filter = ~ genename == "SKA2") strand(ska2) <- "*" autoplot(edb, GRangesFilter(ska2), names.expr = "gene_name") ## ----AnnotationDbi, message = FALSE---------------------------------------- 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) ## ----select, message = FALSE, 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 = ~ genename %in% c("BCL2", "BCL2L11") & tx_biotype == "protein_coding", columns = c("GENEID", "GENENAME", "TXID", "TXBIOTYPE")) ## ----mapIds, message = FALSE----------------------------------------------- ## 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") ## ----AnnotationHub-query, message = FALSE, eval = use_network-------------- # library(AnnotationHub) # ## Load the annotation resource. # ah <- AnnotationHub() # # ## Query for all available EnsDb databases # query(ah, "EnsDb") # ## ----AnnotationHub-query-2, message = FALSE, eval = use_network------------ # ahDb <- query(ah, pattern = c("Xiphophorus Maculatus", "EnsDb", 87)) # ## What have we got # ahDb # ## ----AnnotationHub-fetch, message = FALSE, eval = FALSE-------------------- # ahEdb <- ahDb[[1]] # # ## retriebe all genes # gns <- genes(ahEdb) # ## ----edb-from-ensembl, message = FALSE, 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") # ## ----gtf-gff-edb, message = FALSE, 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"]] # ## ----EnsDb-from-Y-GRanges, message = FALSE, eval = use_network------------- # ## Generate a sqlite database from a GRanges object specifying # ## genes encoded on chromosome Y # load(system.file("YGRanges.RData", package = "ensembldb")) # Y # # ## Create the EnsDb database file # DB <- ensDbFromGRanges(Y, path = tempdir(), version = 75, # organism = "Homo_sapiens") # # ## Load the database # edb <- EnsDb(DB) # edb # ## ----EnsDb-from-GTF, message = FALSE, 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.Rmd0000644000175400017540000014012513175714743017673 0ustar00biocbuildbiocbuild--- title: "Generating an using Ensembl based annotation packages" author: "Johannes Rainer" graphics: yes package: ensembldb output: BiocStyle::html_document: toc_float: true vignette: > %\VignetteIndexEntry{Generating an using Ensembl based annotation packages} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} %\VignetteDepends{ensembldb,EnsDb.Hsapiens.v75,BiocStyle,AnnotationHub,ggbio,Gviz,magrittr} --- # 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. From version 1.7 on, `EnsDb` databases created by the `ensembldb` package contain also protein annotation data (see Section [11](#org5bd9a97) for the database layout and an overview of available attributes/columns). For more information on the use of the protein annotations refer to the *proteins* vignette. 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 `EnsDb` object providing access to the underlying SQLite database is bound to the variable name `EnsDb.Hsapiens.v75`. ```{r load-libs, 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) ``` ```{r no-network, echo = FALSE, results = "hide" } ## Disable code chunks that require network connection - conditionally ## disable this on Windows only. This is to avoid TIMEOUT errors on the ## Bioconductor Windows build maching (issue #47). use_network <- FALSE ``` # Using `ensembldb` annotation packages to retrieve specific annotations One of the strengths of the `ensembldb` package and the related `EnsDb` databases is its implementation of a filter framework that enables to efficiently extract data sub-sets from the databases. The `ensembldb` package supports most of the filters defined in the `AnnotationFilter` Bioconductor package and defines some additional filters specific to the data stored in `EnsDb` databases. Filters can be passed directly to all methods extracting data from an `EnsDb` (such as `genes`, `transcripts` or `exons`). Alternatively it is possible with the `addFilter` or `filter` functions to add a filter directly to an `EnsDb` which will then be used in all queries on that object. The `supportedFilters` method can be used to get an overview over all supported filter classes, each of them (except the `GRangesFilter`) working on a single column/field in the database. ```{r filters } supportedFilters(edb) ``` These filters can be divided into 3 main filter types: - `IntegerFilter`: filter classes extending this basic object can take a single numeric value as input and support the conditions `=, !`, >, <, >= and <=. All filters that work on chromosomal coordinates, such as the `GeneEndFilter` extend `IntegerFilter`. - `CharacterFilter`: filter classes extending this object can take a single or multiple character values as input and allow conditions: `=, !`, "startsWith" and "endsWith". All filters working on IDs extend this class. - `GRangesFilter`: takes a `GRanges` object as input and supports all conditions that `findOverlaps` from the `IRanges` package supports ("any", "start", "end", "within", "equal"). Note that these have to be passed using the parameter `type` to the constructor function. The supported filters are: - `EntrezFilter`: allows to filter results based on NCBI Entrezgene identifiers of the genes. - `ExonEndFilter`: filter using the chromosomal end coordinate of exons. - `ExonIdFilter`: filter based on the (Ensembl) exon identifiers. - `ExonRankFilter`: filter based 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. - `ExonStartFilter`: filter using the chromosomal start coordinate of exons. - `GeneBiotypeFilter`: filter using the gene biotypes defined in the Ensembl database; use the `listGenebiotypes` method to list all available biotypes. - `GeneEndFilter`: filter using the chromosomal end coordinate of gene. - `GeneIdFilter`: filter based on the Ensembl gene IDs. - `GenenameFilter`: filter based on the names (symbols) of the genes. - `GeneStartFilter`: filter using the chromosomal start coordinate of gene. - `GRangesFilter`: allows to retrieve all features (genes, transcripts or exons) that are either within (setting parameter `type` to "within") or partially overlapping (setting `type` to "any") 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. - `SeqNameFilter`: filter by the name of the chromosomes the genes are encoded on. - `SeqStrandFilter`: filter for the chromosome strand on which the genes are encoded. - `SymbolFilter`: 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. - `TxBiotypeFilter`: filter on the transcript biotype defined in Ensembl; use the `listTxbiotypes` method to list all available biotypes. - `TxEndFilter`: filter using the chromosomal end coordinate of transcripts. - `TxIdFilter`: filter on the Ensembl transcript identifiers. - `TxNameFilter`: filter on the Ensembl transcript names (currently identical to the transcript IDs). - `TxStartFilter`: filter using the chromosomal start coordinate of transcripts. In addition to the above listed *DNA-RNA-based* filters, *protein-specific* filters are also available: - `ProtDomIdFilter`: filter by the protein domain ID. - `ProteinIdFilter`: filter by Ensembl protein ID filters. - `UniprotDbFilter`: filter by the name of the Uniprot database. - `UniprotFilter`: filter by the Uniprot ID. - `UniprotMappingTypeFilter`: filter by the mapping type of Ensembl protein IDs to Uniprot IDs. These can however only be used on `EnsDb` databases that provide protein annotations, i.e. for which a call to `hasProteinData` returns `TRUE`. `EnsDb` databases for more recent Ensembl versions (starting from Ensembl 87) provide also evidence levels for individual transcripts in the `tx_support_level` database column. Such databases support also a `TxSupportLevelFilter` filter to use this columns for filtering. A simple use case for the filter framework 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` 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 instead of the default `GRanges`. ```{r transcripts } 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 extractor methods (such as `exons`, `genes` or `transcripts)` 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. Instead of passing a filter *object* to the method it is also possible to provide a filter *expression* written as a `formula`. The `formula` has to be written in the form `~ ` with `` being the field (database column) in the database, `` the condition for the filter object and `` its value. Use the `supportedFilter` method to get the field names corresponding to each filter class. ```{r transcripts-filter-expression } ## Use a filter expression to perform the filtering. transcripts(edb, filter = ~ genename == "ZBTB16") ``` Filter expression have to be written as a formula (i.e. starting with a `~`) in the form *column name* followed by the logical condition. Alternatively, `EnsDb` objects can be filtered directly using the `filter` function. In the example below we use the `filter` function to filter the `EnsDb` object and pass that filtered database to the `transcripts` method using the `%>%` from the `magrittr` package. ```{r transcripts-filter } library(magrittr) filter(edb, ~ symbol == "BCL2" & tx_biotype != "protein_coding") %>% transcripts ``` Adding a filter to an `EnsDb` enables this filter (globally) on all subsequent queries on that object. We could thus filter an `EnsDb` to (virtually) contain only features encoded on chromosome Y. ```{r filter-Y } edb_y <- addFilter(edb, SeqNameFilter("Y")) ## All subsequent filters on that EnsDb will only work on features encoded on ## chromosome Y genes(edb_y) ## Get all lincRNAs on chromosome Y genes(edb_y, filter = ~ gene_biotype == "lincRNA") ``` 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-columns } ## 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 transcripts-example2 } 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 cdsBy } 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 genes-GRangesFilter } ## Define the filter grf <- GRangesFilter(GRanges("11", ranges = IRanges(114000000, 114000050), strand = "+"), type = "any") ## 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-GRangesFilter } 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. The functions `listGenebiotypes` and `listTxbiotypes` can be used to get an overview of allowed/available gene and transcript biotype ```{r biotypes } ## 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 genes-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 = GenenameFilter("BCL", condition = "startsWith"), 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. For the first query we combine two `AnnotationFilter` objects using an `AnnotationFilterList` object, in the second we define the query using a filter expression. ```{r example-AnnotationFilterList } ## determine the average length of snRNA, snoRNA and rRNA genes encoded on ## chromosomes X and Y. mean(lengthOf(edb, of = "tx", filter = AnnotationFilterList( 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 = ~ gene_biotype == "protein_coding" & seq_name %in% 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 example-first-two-exons } ## 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 transcriptsBy-X-Y } TxByGns <- transcriptsBy(edb, by = "gene", filter = 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 exonsBy-RNAseq, message = FALSE, 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 = AnnotationFilterList( SeqNameFilter(c(1:22, "X", "Y")), GeneIdFilter("ENSG", "startsWith"))) ``` 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 toSAF-RNAseq, message = FALSE, 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 disjointExons, message = FALSE, eval=FALSE } ## Create a GRanges of non-overlapping exon parts. DJE <- disjointExons(edb, filter = AnnotationFilterList( SeqNameFilter(c(1:22, "X", "Y")), GeneIdFilter("ENSG%", "startsWith"))) ``` # 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 transcript-sequence-AnnotationHub, message = FALSE, 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 transcript-sequence-extractTranscriptSeqs, message = FALSE, 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 seqlevelsStyle, 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 = ~ seq_name == "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-2, message = FALSE } 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 extractTranscriptSeqs-BSGenome, 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-restore } 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` and `ggbio` 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=2.3 } ## 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" ``` Alternatively, we can also use `ggbio` for plotting. For `ggplot` we can directly pass the `EnsDb` object along with optional filters (or as in the example below a filter expression as a `formula`). ```{r pplot-plot, message=FALSE, fig.align='center', fig.width=7.5, fig.height=4 } library(ggbio) ## Create a plot for all transcripts of the gene SKA2 autoplot(edb, ~ genename == "SKA2") ``` To plot the genomic region and plot genes from both strands we can use a `GRangesFilter`. ```{r pplot-plot-2, message=FALSE, fig.align='center', fig.width=7.5, fig.height=4 } ## Get the chromosomal region in which the gene is encoded ska2 <- genes(edb, filter = ~ genename == "SKA2") strand(ska2) <- "*" autoplot(edb, GRangesFilter(ska2), names.expr = "gene_name") ``` # 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 AnnotationDbi, message = FALSE } 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 select, message = FALSE, 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 = ~ genename %in% c("BCL2", "BCL2L11") & tx_biotype == "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 mapIds, message = FALSE } ## 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. - Since a single Ensembl gene ID might be mapped to multiple NCBI Entrezgene IDs methods such as `genes`, `transcripts` etc return a `list` in the `"entrezid"` column of the resulting result object. # Getting or building `EnsDb` databases/packages Some of 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. ## Getting `EnsDb` databases Some `EnsDb` databases are available as `R` packages from Bioconductor and can be simply installed with the `biocLite` function from the `BiocInstaller` package. The name of such annotation packages starts with *EnsDb* followed by the abbreviation of the organism and the Ensembl version on which the annotation bases. `EnsDb.Hsapiens.v86` provides thus an `EnsDb` database for homo sapiens with annotations from Ensembl version 86. Since Bioconductor version 3.5 `EnsDb` databases can also be retrieved directly from `AnnotationHub`. ```{r AnnotationHub-query, message = FALSE, eval = use_network } library(AnnotationHub) ## Load the annotation resource. ah <- AnnotationHub() ## Query for all available EnsDb databases query(ah, "EnsDb") ``` We can simply fetch one of the databases. ```{r AnnotationHub-query-2, message = FALSE, eval = use_network } ahDb <- query(ah, pattern = c("Xiphophorus Maculatus", "EnsDb", 87)) ## What have we got ahDb ``` Fetch the `EnsDb` and use it. ```{r AnnotationHub-fetch, message = FALSE, eval = FALSE } ahEdb <- ahDb[[1]] ## retriebe all genes gns <- genes(ahEdb) ``` We could even make an annotation package from this `EnsDb` object using the `makeEnsembldbPackage` and passing `dbfile(dbconn(ahEdb))` as `ensdb` argument. ## Building annotation packages ### Directly from Ensembl databases The `fetchTablesFromEnsembl` function 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 this functionality to build databases, the Ensembl Perl API needs to be installed (see 5 for details). Below we create an `EnsDb` database by fetching the required data directly from the Ensembl core databases. The `makeEnsembldbPackage` function is then used to create an annotation package from this `EnsDb` containing all human genes for Ensembl version 75. ```{r edb-from-ensembl, message = FALSE, 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". ### From a GTF or GFF file 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. Also note that protein annotations are usually not available in GTF or GFF files, thus, such annotations will not be included in the generated `EnsDb` database - protein annotations are only available in `EnsDb` databases created with the Ensembl Perl API (such as the ones provided through `AnnotationHub` or as Bioconductor packages). 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 gtf-gff-edb, message = FALSE, 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 EnsDb-from-Y-GRanges, message = FALSE, eval = use_network } ## Generate a sqlite database from a GRanges object specifying ## genes encoded on chromosome Y load(system.file("YGRanges.RData", package = "ensembldb")) Y ## Create the EnsDb database file DB <- ensDbFromGRanges(Y, path = tempdir(), version = 75, organism = "Homo_sapiens") ## Load the database edb <- EnsDb(DB) edb ``` Alternatively we can build the annotation database using the `ensDbFromGtf` `ensDbFromGff` functions, that extract 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, the NCBI Entrezgene IDs and protein annotations 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 EnsDb-from-GTF, message = FALSE, 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 [165](#orgfd622d5)). Note that the protein-specific annotations might not be available in all `EnsDB` databases (e.g. such ones created with `ensembldb` version < 1.7 or created from GTF or GFF files). - **gene**: all gene specific annotations. - `gene_id`: the Ensembl ID of the gene. - `gene_name`: the name (symbol) of the gene. - `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. - `description`: the description of the gene. - **entrezgene**: mapping of Ensembl genes to NCBI Entrezgene identifiers. Note that this mapping can be a one-to-many mapping. - `gene_id`: the Ensembl gene ID. - `entrezid`: the NCBI Entrezgene ID. - **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. `EnsDb` databases for more recent Ensembl releases have also a column `tx_support_level` providing the evidence level for a transcript (1 high evidence, 5 low evidence, NA no evidence calculated). - **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. - **protein**: provides protein annotation for a (coding) transcript. - `protein_id`: the Ensembl protein ID. - `tx_id`: the transcript ID which CDS encodes the protein. - `protein_sequence`: the peptide sequence of the protein (translated from the transcript's coding sequence after applying eventual RNA editing). - **uniprot**: provides the mapping from Ensembl protein ID(s) to Uniprot ID(s). Not all Ensembl proteins are annotated to Uniprot IDs, also, each Ensembl protein might be mapped to multiple Uniprot IDs. - `protein_id`: the Ensembl protein ID. - `uniprot_id`: the Uniprot ID. - `uniprot_db`: the Uniprot database in which the ID is defined. - `uniprot_mapping_type`: the type of the mapping method that was used to assign the Uniprot ID to an Ensembl protein ID. - **protein\_domain**: provides protein domain annotations and mapping to proteins. - `protein_id`: the Ensembl protein ID on which the protein domain is present. - `protein_domain_id`: the ID of the protein domain (from the protein domain source). - `protein_domain_source`: the source/analysis method in/by which the protein domain was defined (such as pfam etc). - `interpro_accession`: the Interpro accession ID of the protein domain. - `prot_dom_start`: the start position of the protein domain within the protein's sequence. - `prot_dom_end`: the end position of the protein domain within the protein's sequence. - **metadata**: some additional, internal, informations (Genome build, Ensembl version etc). - `name` - `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. The database layout: as already described above, protein related annotations (green) might not be available in each `EnsDb` database. ![img](images/dblayout.png "Database layout.") # Footnotes 1 2 3 4 5 ensembldb/inst/doc/ensembldb.html0000644000175400017540000645164513241155730020124 0ustar00biocbuildbiocbuild Generating an using Ensembl based annotation packages

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. From version 1.7 on, EnsDb databases created by the ensembldb package contain also protein annotation data (see Section 11 for the database layout and an overview of available attributes/columns). For more information on the use of the protein annotations refer to the proteins vignette.

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 EnsDb object providing access to the underlying SQLite database is bound to the variable name 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: 0.3.0
## |Creation time: Thu May 18 09:15:45 2017
## |ensembl_version: 75
## |ensembl_host: localhost
## |Organism: homo_sapiens
## |taxonomy_id: 9606
## |genome_build: GRCh37
## |DBSCHEMAVERSION: 2.0
## | No. of genes: 64102.
## | No. of transcripts: 215647.
## |Protein data available.
## For what organism was the database generated?
organism(edb)
## [1] "Homo sapiens"

2 Using ensembldb annotation packages to retrieve specific annotations

One of the strengths of the ensembldb package and the related EnsDb databases is its implementation of a filter framework that enables to efficiently extract data sub-sets from the databases. The ensembldb package supports most of the filters defined in the AnnotationFilter Bioconductor package and defines some additional filters specific to the data stored in EnsDb databases. Filters can be passed directly to all methods extracting data from an EnsDb (such as genes, transcripts or exons). Alternatively it is possible with the addFilter or filter functions to add a filter directly to an EnsDb which will then be used in all queries on that object.

The supportedFilters method can be used to get an overview over all supported filter classes, each of them (except the GRangesFilter) working on a single column/field in the database.

supportedFilters(edb)
##                      filter                field
## 1              EntrezFilter               entrez
## 2             ExonEndFilter             exon_end
## 3              ExonIdFilter              exon_id
## 4            ExonRankFilter            exon_rank
## 5           ExonStartFilter           exon_start
## 6             GRangesFilter                 <NA>
## 7         GeneBiotypeFilter         gene_biotype
## 8             GeneEndFilter             gene_end
## 9              GeneIdFilter              gene_id
## 10          GeneStartFilter           gene_start
## 11           GenenameFilter             genename
## 12          ProtDomIdFilter          prot_dom_id
## 13          ProteinIdFilter           protein_id
## 14            SeqNameFilter             seq_name
## 15          SeqStrandFilter           seq_strand
## 16             SymbolFilter               symbol
## 17          TxBiotypeFilter           tx_biotype
## 18              TxEndFilter               tx_end
## 19               TxIdFilter                tx_id
## 20             TxNameFilter              tx_name
## 21            TxStartFilter             tx_start
## 22          UniprotDbFilter           uniprot_db
## 23            UniprotFilter              uniprot
## 24 UniprotMappingTypeFilter uniprot_mapping_type

These filters can be divided into 3 main filter types:

  • IntegerFilter: filter classes extending this basic object can take a single numeric value as input and support the conditions =, !, >, <, >= and <=. All filters that work on chromosomal coordinates, such as the GeneEndFilter extend IntegerFilter.
  • CharacterFilter: filter classes extending this object can take a single or multiple character values as input and allow conditions: =, !, “startsWith” and “endsWith”. All filters working on IDs extend this class.
  • GRangesFilter: takes a GRanges object as input and supports all conditions that findOverlaps from the IRanges package supports (“any”, “start”, “end”, “within”, “equal”). Note that these have to be passed using the parameter type to the constructor function.

The supported filters are:

  • EntrezFilter: allows to filter results based on NCBI Entrezgene identifiers of the genes.
  • ExonEndFilter: filter using the chromosomal end coordinate of exons.
  • ExonIdFilter: filter based on the (Ensembl) exon identifiers.
  • ExonRankFilter: filter based 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.
  • ExonStartFilter: filter using the chromosomal start coordinate of exons.
  • GeneBiotypeFilter: filter using the gene biotypes defined in the Ensembl database; use the listGenebiotypes method to list all available biotypes.
  • GeneEndFilter: filter using the chromosomal end coordinate of gene.
  • GeneIdFilter: filter based on the Ensembl gene IDs.
  • GenenameFilter: filter based on the names (symbols) of the genes.
  • GeneStartFilter: filter using the chromosomal start coordinate of gene.
  • GRangesFilter: allows to retrieve all features (genes, transcripts or exons) that are either within (setting parameter type to “within”) or partially overlapping (setting type to “any”) 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.
  • SeqNameFilter: filter by the name of the chromosomes the genes are encoded on.
  • SeqStrandFilter: filter for the chromosome strand on which the genes are encoded.
  • SymbolFilter: 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.
  • TxBiotypeFilter: filter on the transcript biotype defined in Ensembl; use the listTxbiotypes method to list all available biotypes.
  • TxEndFilter: filter using the chromosomal end coordinate of transcripts.
  • TxIdFilter: filter on the Ensembl transcript identifiers.
  • TxNameFilter: filter on the Ensembl transcript names (currently identical to the transcript IDs).
  • TxStartFilter: filter using the chromosomal start coordinate of transcripts.

In addition to the above listed DNA-RNA-based filters, protein-specific filters are also available:

  • ProtDomIdFilter: filter by the protein domain ID.
  • ProteinIdFilter: filter by Ensembl protein ID filters.
  • UniprotDbFilter: filter by the name of the Uniprot database.
  • UniprotFilter: filter by the Uniprot ID.
  • UniprotMappingTypeFilter: filter by the mapping type of Ensembl protein IDs to Uniprot IDs.

These can however only be used on EnsDb databases that provide protein annotations, i.e. for which a call to hasProteinData returns TRUE.

EnsDb databases for more recent Ensembl versions (starting from Ensembl 87) provide also evidence levels for individual transcripts in the tx_support_level database column. Such databases support also a TxSupportLevelFilter filter to use this columns for filtering.

A simple use case for the filter framework 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 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 instead of the default GRanges.

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 extractor methods (such as exons, genes or transcripts) 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.

Instead of passing a filter object to the method it is also possible to provide a filter expression written as a formula. The formula has to be written in the form ~ <field> <condition> <value> with <field> being the field (database column) in the database, <condition> the condition for the filter object and <value> its value. Use the supportedFilter method to get the field names corresponding to each filter class.

## Use a filter expression to perform the filtering.
transcripts(edb, filter = ~ genename == "ZBTB16")
## GRanges object with 9 ranges and 7 metadata columns:
##                   seqnames                 ranges strand |           tx_id
##                      <Rle>              <IRanges>  <Rle> |     <character>
##   ENST00000335953       11 [113930315, 114121398]      + | ENST00000335953
##   ENST00000541602       11 [113930447, 114060486]      + | ENST00000541602
##   ENST00000544220       11 [113930459, 113934368]      + | ENST00000544220
##   ENST00000535700       11 [113930979, 113934466]      + | ENST00000535700
##   ENST00000392996       11 [113931229, 114121374]      + | ENST00000392996
##   ENST00000539918       11 [113935134, 114118066]      + | ENST00000539918
##   ENST00000545851       11 [114051488, 114118018]      + | ENST00000545851
##   ENST00000535379       11 [114107929, 114121279]      + | ENST00000535379
##   ENST00000535509       11 [114117512, 114121198]      + | ENST00000535509
##                                tx_biotype tx_cds_seq_start tx_cds_seq_end
##                               <character>        <integer>      <integer>
##   ENST00000335953          protein_coding        113934023      114121277
##   ENST00000541602         retained_intron             <NA>           <NA>
##   ENST00000544220          protein_coding        113934023      113934368
##   ENST00000535700          protein_coding        113934023      113934466
##   ENST00000392996          protein_coding        113934023      114121277
##   ENST00000539918 nonsense_mediated_decay        113935134      113992549
##   ENST00000545851    processed_transcript             <NA>           <NA>
##   ENST00000535379    processed_transcript             <NA>           <NA>
##   ENST00000535509         retained_intron             <NA>           <NA>
##                           gene_id         tx_name   gene_name
##                       <character>     <character> <character>
##   ENST00000335953 ENSG00000109906 ENST00000335953      ZBTB16
##   ENST00000541602 ENSG00000109906 ENST00000541602      ZBTB16
##   ENST00000544220 ENSG00000109906 ENST00000544220      ZBTB16
##   ENST00000535700 ENSG00000109906 ENST00000535700      ZBTB16
##   ENST00000392996 ENSG00000109906 ENST00000392996      ZBTB16
##   ENST00000539918 ENSG00000109906 ENST00000539918      ZBTB16
##   ENST00000545851 ENSG00000109906 ENST00000545851      ZBTB16
##   ENST00000535379 ENSG00000109906 ENST00000535379      ZBTB16
##   ENST00000535509 ENSG00000109906 ENST00000535509      ZBTB16
##   -------
##   seqinfo: 1 sequence from GRCh37 genome

Filter expression have to be written as a formula (i.e. starting with a ~) in the form column name followed by the logical condition.

Alternatively, EnsDb objects can be filtered directly using the filter function. In the example below we use the filter function to filter the EnsDb object and pass that filtered database to the transcripts method using the %>% from the magrittr package.

library(magrittr)
## 
## Attaching package: 'magrittr'
## The following object is masked from 'package:AnnotationFilter':
## 
##     not
filter(edb, ~ symbol == "BCL2" & tx_biotype != "protein_coding") %>% transcripts
## GRanges object with 1 range and 6 metadata columns:
##                   seqnames               ranges strand |           tx_id
##                      <Rle>            <IRanges>  <Rle> |     <character>
##   ENST00000590515       18 [60795445, 60829102]      - | ENST00000590515
##                             tx_biotype tx_cds_seq_start tx_cds_seq_end
##                            <character>        <integer>      <integer>
##   ENST00000590515 processed_transcript             <NA>           <NA>
##                           gene_id         tx_name
##                       <character>     <character>
##   ENST00000590515 ENSG00000171791 ENST00000590515
##   -------
##   seqinfo: 1 sequence from GRCh37 genome

Adding a filter to an EnsDb enables this filter (globally) on all subsequent queries on that object. We could thus filter an EnsDb to (virtually) contain only features encoded on chromosome Y.

edb_y <- addFilter(edb, SeqNameFilter("Y"))

## All subsequent filters on that EnsDb will only work on features encoded on
## chromosome Y
genes(edb_y)
## GRanges object with 495 ranges and 6 metadata columns:
##                   seqnames               ranges strand |         gene_id
##                      <Rle>            <IRanges>  <Rle> |     <character>
##   ENSG00000251841        Y   [2652790, 2652894]      + | ENSG00000251841
##   ENSG00000184895        Y   [2654896, 2655740]      - | ENSG00000184895
##   ENSG00000237659        Y   [2657868, 2658369]      + | ENSG00000237659
##   ENSG00000232195        Y   [2696023, 2696259]      + | ENSG00000232195
##   ENSG00000129824        Y   [2709527, 2800041]      + | ENSG00000129824
##               ...      ...                  ...    ... .             ...
##   ENSG00000224240        Y [28695572, 28695890]      + | ENSG00000224240
##   ENSG00000227629        Y [28732789, 28737748]      - | ENSG00000227629
##   ENSG00000237917        Y [28740998, 28780799]      - | ENSG00000237917
##   ENSG00000231514        Y [28772667, 28773306]      - | ENSG00000231514
##   ENSG00000235857        Y [59001391, 59001635]      + | ENSG00000235857
##                     gene_name   gene_biotype seq_coord_system      symbol
##                   <character>    <character>      <character> <character>
##   ENSG00000251841  RNU6-1334P          snRNA       chromosome  RNU6-1334P
##   ENSG00000184895         SRY protein_coding       chromosome         SRY
##   ENSG00000237659  RNASEH2CP1     pseudogene       chromosome  RNASEH2CP1
##   ENSG00000232195    TOMM22P2     pseudogene       chromosome    TOMM22P2
##   ENSG00000129824      RPS4Y1 protein_coding       chromosome      RPS4Y1
##               ...         ...            ...              ...         ...
##   ENSG00000224240     CYCSP49     pseudogene       chromosome     CYCSP49
##   ENSG00000227629  SLC25A15P1     pseudogene       chromosome  SLC25A15P1
##   ENSG00000237917     PARP4P1     pseudogene       chromosome     PARP4P1
##   ENSG00000231514     FAM58CP     pseudogene       chromosome     FAM58CP
##   ENSG00000235857     CTBP2P1     pseudogene       chromosome     CTBP2P1
##                   entrezid
##                     <list>
##   ENSG00000251841       NA
##   ENSG00000184895     6736
##   ENSG00000237659       NA
##   ENSG00000232195       NA
##   ENSG00000129824     6192
##               ...      ...
##   ENSG00000224240       NA
##   ENSG00000227629       NA
##   ENSG00000237917       NA
##   ENSG00000231514       NA
##   ENSG00000235857       NA
##   -------
##   seqinfo: 1 sequence from GRCh37 genome
## Get all lincRNAs on chromosome Y
genes(edb_y, filter = ~ gene_biotype == "lincRNA")
## GRanges object with 48 ranges and 6 metadata columns:
##                   seqnames               ranges strand |         gene_id
##                      <Rle>            <IRanges>  <Rle> |     <character>
##   ENSG00000231535        Y   [2870953, 2970313]      + | ENSG00000231535
##   ENSG00000229308        Y   [3904538, 3968361]      + | ENSG00000229308
##   ENSG00000237069        Y   [6110487, 6111651]      - | ENSG00000237069
##   ENSG00000229643        Y   [6225260, 6229454]      - | ENSG00000229643
##   ENSG00000129816        Y   [6258472, 6279605]      + | ENSG00000129816
##               ...      ...                  ...    ... .             ...
##   ENSG00000228296        Y [27209230, 27246039]      - | ENSG00000228296
##   ENSG00000223641        Y [27329790, 27330920]      - | ENSG00000223641
##   ENSG00000228786        Y [27524447, 27540866]      - | ENSG00000228786
##   ENSG00000240450        Y [27629055, 27632852]      + | ENSG00000240450
##   ENSG00000231141        Y [27874637, 27879535]      + | ENSG00000231141
##                      gene_name gene_biotype seq_coord_system       symbol
##                    <character>  <character>      <character>  <character>
##   ENSG00000231535    LINC00278      lincRNA       chromosome    LINC00278
##   ENSG00000229308   AC010084.1      lincRNA       chromosome   AC010084.1
##   ENSG00000237069      TTTY23B      lincRNA       chromosome      TTTY23B
##   ENSG00000229643    LINC00280      lincRNA       chromosome    LINC00280
##   ENSG00000129816       TTTY1B      lincRNA       chromosome       TTTY1B
##               ...          ...          ...              ...          ...
##   ENSG00000228296       TTTY4C      lincRNA       chromosome       TTTY4C
##   ENSG00000223641      TTTY17C      lincRNA       chromosome      TTTY17C
##   ENSG00000228786 LINC00266-4P      lincRNA       chromosome LINC00266-4P
##   ENSG00000240450     CSPG4P1Y      lincRNA       chromosome     CSPG4P1Y
##   ENSG00000231141        TTTY3      lincRNA       chromosome        TTTY3
##                               entrezid
##                                 <list>
##   ENSG00000231535            100873962
##   ENSG00000229308                   NA
##   ENSG00000237069     100101121,252955
##   ENSG00000229643                   NA
##   ENSG00000129816      100101116,50858
##               ...                  ...
##   ENSG00000228296 474150,474149,114761
##   ENSG00000223641 474152,474151,252949
##   ENSG00000228786                   NA
##   ENSG00000240450               114758
##   ENSG00000231141        474148,114760
##   -------
##   seqinfo: 1 sequence from GRCh37 genome

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"        "gene_biotype"    
## [4] "gene_seq_start"   "gene_seq_end"     "seq_name"        
## [7] "seq_strand"       "seq_coord_system" "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"
## 
## $protein
## [1] "tx_id"            "protein_id"       "protein_sequence"
## 
## $uniprot
## [1] "protein_id"           "uniprot_id"           "uniprot_db"          
## [4] "uniprot_mapping_type"
## 
## $protein_domain
## [1] "protein_id"            "protein_domain_id"     "protein_domain_source"
## [4] "interpro_accession"    "prot_dom_start"        "prot_dom_end"         
## 
## $entrezgene
## [1] "gene_id"  "entrezid"
## 
## $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 = "+"), type = "any")

## 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   gene_biotype seq_coord_system      symbol
##                   <character>    <character>      <character> <character>
##   ENSG00000109906      ZBTB16 protein_coding       chromosome      ZBTB16
##                   entrezid
##                     <list>
##   ENSG00000109906     7704
##   -------
##   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.

The functions listGenebiotypes and listTxbiotypes can be used to get an overview of allowed/available gene and transcript biotype

## 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 = GenenameFilter("BCL", condition = "startsWith"),
          return.type = "DataFrame")
nrow(BCLs)
## [1] 25
BCLs
## DataFrame with 25 rows and 4 columns
##       gene_name entrezid   gene_biotype         gene_id
##     <character>   <list>    <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. For the first query we combine two AnnotationFilter objects using an AnnotationFilterList object, in the second we define the query using a filter expression.

## determine the average length of snRNA, snoRNA and rRNA genes encoded on
## chromosomes X and Y.
mean(lengthOf(edb, of = "tx", filter = AnnotationFilterList(
                                  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 = ~ gene_biotype == "protein_coding" &
                                  seq_name %in% 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 = 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 = AnnotationFilterList(
                                          SeqNameFilter(c(1:22, "X", "Y")),
                                          GeneIdFilter("ENSG", "startsWith")))

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 = AnnotationFilterList(
                  SeqNameFilter(c(1:22, "X", "Y")),
                  GeneIdFilter("ENSG%", "startsWith")))

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 = ~ seq_name == "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" "chr17"
## [10] "chr18" "chr19" "chr2"  "chr20" "chr21" "chr22" "chr3"  "chr4"  "chr5" 
## [19] "chr6"  "chr7"  "chr8"  "chr9"  NA      NA      NA      NA      NA     
## [28] 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 GCCTAGTGCGCGCGCAGTAAC...TAAATGTTTACTTGTATATG ENST00000155093
##   [2]  4023 ATGTTTAGGGTTGGCTTCTTA...TGGAAACACATCCCTTGTAA ENST00000215473
##   [3]   802 AGAGGACCAAGCCTCCCTGTG...ATAAAATGTTTTAAAAATCA ENST00000215479
##   [4]   910 TGTCTGTCAGAGCTGTCAGCC...AACACTGGTATATTTCTGTT ENST00000250776
##   [5]  1305 TTCCAGGATATGAACTCTACA...AATCCTGTGGCTGTAGGAAA ENST00000250784
##   ...   ... ...
## [727]   333 ATGGATGAAGAAGAGAAAACC...GTGAACTTTCTAGATTGCAT ENST00000604924
## [728]  1247 CATGGCGGGGTTCCTGCCTTC...CTTTGGAGTAATGTCTTAGT ENST00000605584
## [729]   199 CAGTTCTCGCTCCTGTGCAGC...TGGTCTGGGTGGCTTCTGGA ENST00000605663
## [730]   276 GCCCCAGGAGGAAAGGGGGAC...AAATAAAGAACAGCGCATTC ENST00000606439
## [731]   444 ATGGGAGCCACTGGGCTTGGC...ACGTTCATGAAGAAGACTAA 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 ATGGATGAAGATGAATTTGAA...AAGAAGTTGGTCTGCCCTAA ENST00000155093
##   [2]  4023 ATGTTTAGGGTTGGCTTCTTA...TGGAAACACATCCCTTGTAA ENST00000215473
##   [3]   579 ATGGGGACCTGGATTTTGTTT...AGCAGGAGGAAGTGGATTAA ENST00000215479
##   [4]   792 ATGGCCCGGGGCCCCAAGAAG...CCAAACAGAGCAGTGGCTAA ENST00000250784
##   [5]   378 ATGAGTCCAAAGCCGAGAGCC...CTACTCCCCTATCTCCCTGA ENST00000250823
##   ...   ... ...
## [156]    63 CGCAAGGATTTAAAAGAGATG...CACCCTGTTGGCCAGGCTAG 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 and ggbio

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)
## Warning in .formatSeqnameByStyleForQuery(x, sn, ifNotFound): Seqnames:
## Y could not be mapped to the seqlevels style of the database (Ensembl)!
## Returning the orginal seqnames for these.
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"

Alternatively, we can also use ggbio for plotting. For ggplot we can directly pass the EnsDb object along with optional filters (or as in the example below a filter expression as a formula).

library(ggbio)

## Create a plot for all transcripts of the gene SKA2
autoplot(edb, ~ genename == "SKA2")

To plot the genomic region and plot genes from both strands we can use a GRangesFilter.

## Get the chromosomal region in which the gene is encoded
ska2 <- genes(edb, filter = ~ genename == "SKA2")
strand(ska2) <- "*"
autoplot(edb, GRangesFilter(ska2), names.expr = "gene_name")

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"            
##  [4] "EXONSEQEND"          "EXONSEQSTART"        "GENEBIOTYPE"        
##  [7] "GENEID"              "GENENAME"            "GENESEQEND"         
## [10] "GENESEQSTART"        "INTERPROACCESSION"   "ISCIRCULAR"         
## [13] "PROTDOMEND"          "PROTDOMSTART"        "PROTEINDOMAINID"    
## [16] "PROTEINDOMAINSOURCE" "PROTEINID"           "PROTEINSEQUENCE"    
## [19] "SEQCOORDSYSTEM"      "SEQLENGTH"           "SEQNAME"            
## [22] "SEQSTRAND"           "SYMBOL"              "TXBIOTYPE"          
## [25] "TXCDSSEQEND"         "TXCDSSEQSTART"       "TXID"               
## [28] "TXNAME"              "TXSEQEND"            "TXSEQSTART"         
## [31] "UNIPROTDB"           "UNIPROTID"           "UNIPROTMAPPINGTYPE"
## 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] "gene_id"               "entrezid"              "exon_id"              
##  [7] "exon_seq_start"        "exon_seq_end"          "gene_name"            
## [10] "gene_biotype"          "gene_seq_start"        "gene_seq_end"         
## [13] "seq_strand"            "seq_coord_system"      "symbol"               
## [16] "name"                  "value"                 "tx_id"                
## [19] "protein_id"            "protein_sequence"      "protein_domain_id"    
## [22] "protein_domain_source" "interpro_accession"    "prot_dom_start"       
## [25] "prot_dom_end"          "tx_biotype"            "tx_seq_start"         
## [28] "tx_seq_end"            "tx_cds_seq_start"      "tx_cds_seq_end"       
## [31] "tx_name"               "exon_idx"              "uniprot_id"           
## [34] "uniprot_db"            "uniprot_mapping_type"
## List all of the supported key types.
keytypes(edb)
##  [1] "ENTREZID"        "EXONID"          "GENEBIOTYPE"     "GENEID"         
##  [5] "GENENAME"        "PROTEINDOMAINID" "PROTEINID"       "SEQNAME"        
##  [9] "SEQSTRAND"       "SYMBOL"          "TXBIOTYPE"       "TXID"           
## [13] "TXNAME"          "UNIPROTID"
## 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 = ~ genename %in% c("BCL2", "BCL2L11") &
                tx_biotype == "protein_coding",
       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 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" "ENST00000393252"
##  [5] "ENST00000433098" "ENST00000405953" "ENST00000415458" "ENST00000436733"
##  [9] "ENST00000437029" "ENST00000452231" "ENST00000361493" "ENST00000431217"
## [13] "ENST00000439718" "ENST00000438054" "ENST00000357757" "ENST00000393253"
## [17] "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!
## $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.

  • Since a single Ensembl gene ID might be mapped to multiple NCBI Entrezgene IDs methods such as genes, transcripts etc return a list in the "entrezid" column of the resulting result object.

10 Getting or building EnsDb databases/packages

Some of 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 Getting EnsDb databases

Some EnsDb databases are available as R packages from Bioconductor and can be simply installed with the biocLite function from the BiocInstaller package. The name of such annotation packages starts with EnsDb followed by the abbreviation of the organism and the Ensembl version on which the annotation bases. EnsDb.Hsapiens.v86 provides thus an EnsDb database for homo sapiens with annotations from Ensembl version 86.

Since Bioconductor version 3.5 EnsDb databases can also be retrieved directly from AnnotationHub.

library(AnnotationHub)
## Load the annotation resource.
ah <- AnnotationHub()

## Query for all available EnsDb databases
query(ah, "EnsDb")

We can simply fetch one of the databases.

ahDb <- query(ah, pattern = c("Xiphophorus Maculatus", "EnsDb", 87))
## What have we got
ahDb

Fetch the EnsDb and use it.

ahEdb <- ahDb[[1]]

## retriebe all genes
gns <- genes(ahEdb)

We could even make an annotation package from this EnsDb object using the makeEnsembldbPackage and passing dbfile(dbconn(ahEdb)) as ensdb argument.

10.2 Building annotation packages

10.2.1 Directly from Ensembl databases

The fetchTablesFromEnsembl function 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 this functionality to build databases, the Ensembl Perl API needs to be installed (see 5 for details).

Below we create an EnsDb database by fetching the required data directly from the Ensembl core databases. The makeEnsembldbPackage function is then used to create an annotation package from this EnsDb containing all human genes for Ensembl version 75.

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”.

10.2.2 From a GTF or GFF file

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. Also note that protein annotations are usually not available in GTF or GFF files, thus, such annotations will not be included in the generated EnsDb database - protein annotations are only available in EnsDb databases created with the Ensembl Perl API (such as the ones provided through AnnotationHub or as Bioconductor packages).

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

## Create the EnsDb database file
DB <- ensDbFromGRanges(Y, path = tempdir(), version = 75,
               organism = "Homo_sapiens")

## Load the database
edb <- EnsDb(DB)
edb

Alternatively we can build the annotation database using the ensDbFromGtf ensDbFromGff functions, that extract 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, the NCBI Entrezgene IDs and protein annotations 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 165). Note that the protein-specific annotations might not be available in all EnsDB databases (e.g. such ones created with ensembldb version < 1.7 or created from GTF or GFF files).

  • gene: all gene specific annotations.
    • gene_id: the Ensembl ID of the gene.
    • gene_name: the name (symbol) of the gene.
    • 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.
    • description: the description of the gene.
  • entrezgene: mapping of Ensembl genes to NCBI Entrezgene identifiers. Note that this mapping can be a one-to-many mapping.
    • gene_id: the Ensembl gene ID.
    • entrezid: the NCBI Entrezgene ID.

  • 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.

    EnsDb databases for more recent Ensembl releases have also a column tx_support_level providing the evidence level for a transcript (1 high evidence, 5 low evidence, NA no evidence calculated).

  • 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.
  • protein: provides protein annotation for a (coding) transcript.
    • protein_id: the Ensembl protein ID.
    • tx_id: the transcript ID which CDS encodes the protein.
    • protein_sequence: the peptide sequence of the protein (translated from the transcript’s coding sequence after applying eventual RNA editing).
  • uniprot: provides the mapping from Ensembl protein ID(s) to Uniprot ID(s). Not all Ensembl proteins are annotated to Uniprot IDs, also, each Ensembl protein might be mapped to multiple Uniprot IDs.
    • protein_id: the Ensembl protein ID.
    • uniprot_id: the Uniprot ID.
    • uniprot_db: the Uniprot database in which the ID is defined.
    • uniprot_mapping_type: the type of the mapping method that was used to assign the Uniprot ID to an Ensembl protein ID.
  • protein_domain: provides protein domain annotations and mapping to proteins.
    • protein_id: the Ensembl protein ID on which the protein domain is present.
    • protein_domain_id: the ID of the protein domain (from the protein domain source).
    • protein_domain_source: the source/analysis method in/by which the protein domain was defined (such as pfam etc).
    • interpro_accession: the Interpro accession ID of the protein domain.
    • prot_dom_start: the start position of the protein domain within the protein’s sequence.
    • prot_dom_end: the end position of the protein domain within the protein’s sequence.
  • metadata: some additional, internal, informations (Genome build, Ensembl version etc).
    • name
    • 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.

The database layout: as already described above, protein related annotations (green) might not be available in each EnsDb database.

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ensembldb/inst/doc/proteins.R0000644000175400017540000000755213241155730017255 0ustar00biocbuildbiocbuild## ----doeval, echo = FALSE, results = "hide"-------------------------------- ## Globally switch off execution of code chunks evalMe <- FALSE haveProt <- FALSE ## ----loadlib, message = FALSE, eval = evalMe------------------------------- # library(ensembldb) # library(EnsDb.Hsapiens.v75) # edb <- EnsDb.Hsapiens.v75 # ## Evaluate whether we have protein annotation available # hasProteinData(edb) # ## ----listCols, message = FALSE, eval = evalMe------------------------------ # listTables(edb) # ## ----haveprot, echo = FALSE, results = "hide", eval = evalMe--------------- # ## Use this to conditionally disable eval on following chunks # haveProt <- hasProteinData(edb) & evalMe # ## ----a_transcripts, eval = haveProt---------------------------------------- # ## Get also protein information for ZBTB16 transcripts # txs <- transcripts(edb, filter = GenenameFilter("ZBTB16"), # columns = c("protein_id", "uniprot_id", "tx_biotype")) # txs # ## ----a_transcripts_coding_noncoding, eval = haveProt----------------------- # ## Subset to transcripts with tx_biotype other than protein_coding. # txs[txs$tx_biotype != "protein_coding", c("uniprot_id", "tx_biotype", # "protein_id")] # ## ----a_transcripts_coding, eval = haveProt--------------------------------- # ## List the protein IDs and uniprot IDs for the coding transcripts # mcols(txs[txs$tx_biotype == "protein_coding", # c("tx_id", "protein_id", "uniprot_id")]) # ## ----a_transcripts_coding_up, eval = haveProt------------------------------ # ## List all uniprot mapping types in the database. # listUniprotMappingTypes(edb) # # ## Get all protein_coding transcripts of ZBTB16 along with their protein_id # ## and Uniprot IDs, restricting to protein_id to uniprot_id mappings based # ## on "DIRECT" mapping methods. # txs <- transcripts(edb, filter = list(GenenameFilter("ZBTB16"), # UniprotMappingTypeFilter("DIRECT")), # columns = c("protein_id", "uniprot_id", "uniprot_db")) # mcols(txs) # ## ----a_genes_protdomid_filter, eval = haveProt----------------------------- # ## Get all genes that encode a transcript encoding for a protein that contains # ## a certain protein domain. # gns <- genes(edb, filter = ProtDomIdFilter("PS50097")) # length(gns) # # sort(gns$gene_name) # ## ----a_2_annotationdbi, message = FALSE, eval = haveProt------------------- # ## Show all columns that are provided by the database # columns(edb) # # ## Show all key types/filters that are supported # keytypes(edb) # ## ----a_2_select, message = FALSE, eval = haveProt-------------------------- # select(edb, keys = "ZBTB16", keytype = "GENENAME", # columns = "UNIPROTID") # ## ----a_2_select_nmd, message = FALSE, eval = haveProt---------------------- # ## Call select, this time providing a GenenameFilter. # select(edb, keys = GenenameFilter("ZBTB16"), # columns = c("TXBIOTYPE", "UNIPROTID", "PROTEINID")) # ## ----b_proteins, message = FALSE, eval = haveProt-------------------------- # ## Get all proteins and return them as an AAStringSet # prts <- proteins(edb, filter = GenenameFilter("ZBTB16"), # return.type = "AAStringSet") # prts # ## ----b_proteins_mcols, message = FALSE, eval = haveProt-------------------- # mcols(prts) # ## ----b_proteins_prot_doms, message = FALSE, eval = haveProt---------------- # ## Get also protein domain annotations in addition to the protein annotations. # pd <- proteins(edb, filter = GenenameFilter("ZBTB16"), # columns = c("tx_id", listColumns(edb, "protein_domain")), # return.type = "AAStringSet") # pd # ## ----b_proteins_prot_doms_2, message = FALSE, eval = haveProt-------------- # ## The number of protein domains per protein: # table(names(pd)) # # ## The mcols # mcols(pd) # ensembldb/inst/doc/proteins.Rmd0000644000175400017540000002655713175714743017617 0ustar00biocbuildbiocbuild--- title: "Querying protein features" author: "Johannes Rainer" graphics: yes package: ensembldb output: BiocStyle::html_document: toc_float: true vignette: > %\VignetteIndexEntry{Querying protein features} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} %\VignetteDepends{ensembldb,EnsDb.Hsapiens.v75,BiocStyle} --- From Bioconductor release 3.5 on, `EnsDb` databases/packages created by the `ensembldb` package contain also, for transcripts with a coding regions, mappings between transcripts and proteins. Thus, in addition to the RNA/DNA-based features also the following protein related information is available: - `protein_id`: the Ensembl protein ID. This is the primary ID for the proteins defined in Ensembl and each (protein coding) Ensembl transcript has one protein ID assigned to it. - `protein_sequence`: the amino acid sequence of a protein. - `uniprot_id`: the Uniprot ID for a protein. Note that not every Ensembl `protein_id` has an Uniprot ID, and each `protein_id` might be mapped to several `uniprot_id`. Also, the same Uniprot ID might be mapped to different `protein_id`. - `uniprot_db`: the name of the Uniprot database in which the feature is annotated. Can be either *SPTREMBL* or *SWISSPROT*. - `uniprot_mapping_type`: the type of the mapping method that was used to assign the Uniprot ID to the Ensembl protein ID. - `protein_domain_id`: the ID of the protein domain according to the source/analysis in/by which is was defined. - `protein_domain_source`: the source of the protein domain information, one of *pfscan*, *scanprosite*, *superfamily*, *pfam*, *prints*, *smart*, *pirsf* or *tigrfam*. - `interpro_accession`: the Interpro accession ID of the protein domain (if available). - `prot_dom_start`: the start of the protein domain within the sequence of the protein. - `prot_dom_start`: the end position of the protein domain within the sequence of the protein. Thus, for protein coding transcripts, these annotations can be fetched from the database too, given that protein annotations are available. Note that only `EnsDb` databases created through the Ensembl Perl API contain protein annotation, while databases created using `ensDbFromAH`, `ensDbFromGff`, `ensDbFromGRanges` and `ensDbFromGtf` don't. ```{r doeval, echo = FALSE, results = "hide" } ## Globally switch off execution of code chunks evalMe <- FALSE haveProt <- FALSE ``` ```{r loadlib, message = FALSE, eval = evalMe } library(ensembldb) library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 ## Evaluate whether we have protein annotation available hasProteinData(edb) ``` If protein annotation is available, the additional tables and columns are also listed by the `listTables` and `listColumns` methods. ```{r listCols, message = FALSE, eval = evalMe } listTables(edb) ``` In the following sections we show examples how to 1) fetch protein annotations as additional columns to gene/transcript annotations, 2) fetch protein annotation data and 3) map proteins to the genome. ```{r haveprot, echo = FALSE, results = "hide", eval = evalMe } ## Use this to conditionally disable eval on following chunks haveProt <- hasProteinData(edb) & evalMe ``` # Fetch protein annotation for genes and transcripts Protein annotations for (protein coding) transcripts can be retrieved by simply adding the desired annotation columns to the `columns` parameter of the e.g. `genes` or `transcripts` methods. ```{r a_transcripts, eval = haveProt } ## Get also protein information for ZBTB16 transcripts txs <- transcripts(edb, filter = GenenameFilter("ZBTB16"), columns = c("protein_id", "uniprot_id", "tx_biotype")) txs ``` The gene ZBTB16 has protein coding and non-coding transcripts, thus, we get the protein ID for the coding- and `NA` for the non-coding transcripts. Note also that we have a transcript targeted for nonsense mediated mRNA-decay with a protein ID associated with it, but no Uniprot ID. ```{r a_transcripts_coding_noncoding, eval = haveProt } ## Subset to transcripts with tx_biotype other than protein_coding. txs[txs$tx_biotype != "protein_coding", c("uniprot_id", "tx_biotype", "protein_id")] ``` While the mapping from a protein coding transcript to a Ensembl protein ID (column `protein_id`) is 1:1, the mapping between `protein_id` and `uniprot_id` can be n:m, i.e. each Ensembl protein ID can be mapped to 1 or more Uniprot IDs and each Uniprot ID can be mapped to more than one `protein_id` (and hence `tx_id`). This should be kept in mind if querying transcripts from the database fetching Uniprot related additional columns or even protein ID features, as in such cases a redundant list of transcripts is returned. ```{r a_transcripts_coding, eval = haveProt } ## List the protein IDs and uniprot IDs for the coding transcripts mcols(txs[txs$tx_biotype == "protein_coding", c("tx_id", "protein_id", "uniprot_id")]) ``` Some of the n:m mappings for Uniprot IDs can be resolved by restricting either to entries from one Uniprot database (*SPTREMBL* or *SWISSPROT*) or to mappings of a certain type of mapping method. The corresponding filters are the `UniprotDbFilter` and the `UniprotMappingTypeFilter` (using the `uniprot_db` and `uniprot_mapping_type` columns of the `uniprot` database table). In the example below we restrict the result to Uniprot IDs with the mapping type *DIRECT*. ```{r a_transcripts_coding_up, eval = haveProt } ## List all uniprot mapping types in the database. listUniprotMappingTypes(edb) ## Get all protein_coding transcripts of ZBTB16 along with their protein_id ## and Uniprot IDs, restricting to protein_id to uniprot_id mappings based ## on "DIRECT" mapping methods. txs <- transcripts(edb, filter = list(GenenameFilter("ZBTB16"), UniprotMappingTypeFilter("DIRECT")), columns = c("protein_id", "uniprot_id", "uniprot_db")) mcols(txs) ``` For this example the use of the `UniprotMappingTypeFilter` resolved the multiple mapping of Uniprot IDs to Ensembl protein IDs, but the Uniprot ID *Q05516* is still assigned to the two Ensembl protein IDs *ENSP00000338157* and *ENSP00000376721*. All protein annotations can also be added as *metadata columns* to the results of the `genes`, `exons`, `exonsBy`, `transcriptsBy`, `cdsBy`, `fiveUTRsByTranscript` and `threeUTRsByTranscript` methods by specifying the desired column names with the `columns` parameter. For non coding transcripts `NA` will be reported in the protein annotation columns. In addition to retrieve protein annotations from the database, we can also use protein data to filter the results. In the example below we fetch for example all genes from the database that have a certain protein domain in the protein encoded by any of its transcripts. ```{r a_genes_protdomid_filter, eval = haveProt } ## Get all genes that encode a transcript encoding for a protein that contains ## a certain protein domain. gns <- genes(edb, filter = ProtDomIdFilter("PS50097")) length(gns) sort(gns$gene_name) ``` So, in total we got 152 genes with that protein domain. In addition to the `ProtDomIdFilter`, also the `ProteinidFilter` and the `UniprotidFilter` can be used to query the database for entries matching conditions on their protein ID or Uniprot ID. # Use methods from the `AnnotationDbi` package to query protein annotation The `select`, `keys` and `mapIds` methods from the `AnnotationDbi` package can also be used to query `EnsDb` objects for protein annotations. Supported columns and key types are returned by the `columns` and `keytypes` methods. ```{r a_2_annotationdbi, message = FALSE, eval = haveProt } ## Show all columns that are provided by the database columns(edb) ## Show all key types/filters that are supported keytypes(edb) ``` Below we fetch all Uniprot IDs annotated to the gene *ZBTB16*. ```{r a_2_select, message = FALSE, eval = haveProt } select(edb, keys = "ZBTB16", keytype = "GENENAME", columns = "UNIPROTID") ``` This returns us all Uniprot IDs of all proteins encoded by the gene's transcripts. One of the transcripts from ZBTB16, while having a CDS and being annotated to a protein, does not have an Uniprot ID assigned (thus `NA` is returned by the above call). As we see below, this transcript is targeted for non sense mediated mRNA decay. ```{r a_2_select_nmd, message = FALSE, eval = haveProt } ## Call select, this time providing a GenenameFilter. select(edb, keys = GenenameFilter("ZBTB16"), columns = c("TXBIOTYPE", "UNIPROTID", "PROTEINID")) ``` Note also that we passed this time a `GenenameFilter` with the `keys` parameter. # Retrieve proteins from the database Proteins can be fetched using the dedicated `proteins` method that returns, unlike DNA/RNA-based methods like `genes` or `transcripts`, not a `GRanges` object by default, but a `DataFrame` object. Alternatively, results can be returned as a `data.frame` or as an `AAStringSet` object from the `Biobase` package. Note that this might change in future releases if a more appropriate object to represent protein annotations becomes available. In the code chunk below we fetch all protein annotations for the gene *ZBTB16*. ```{r b_proteins, message = FALSE, eval = haveProt } ## Get all proteins and return them as an AAStringSet prts <- proteins(edb, filter = GenenameFilter("ZBTB16"), return.type = "AAStringSet") prts ``` Besides the amino acid sequence, the `prts` contains also additional annotations that can be accessed with the `mcols` method (metadata columns). All additional columns provided with the parameter `columns` are also added to the `mcols` `DataFrame`. ```{r b_proteins_mcols, message = FALSE, eval = haveProt } mcols(prts) ``` Note that the `proteins` method will retrieve only gene/transcript annotations of transcripts encoding a protein. Thus annotations for the non-coding transcripts of the gene *ZBTB16*, that were returned by calls to `genes` or `transcripts` in the previous section are not fetched. Querying in addition Uniprot identifiers or protein domain data will result at present in a redundant list of proteins as shown in the code block below. ```{r b_proteins_prot_doms, message = FALSE, eval = haveProt } ## Get also protein domain annotations in addition to the protein annotations. pd <- proteins(edb, filter = GenenameFilter("ZBTB16"), columns = c("tx_id", listColumns(edb, "protein_domain")), return.type = "AAStringSet") pd ``` The result contains one row/element for each protein domain in each of the proteins. The number of protein domains per protein and the `mcols` are shown below. ```{r b_proteins_prot_doms_2, message = FALSE, eval = haveProt } ## The number of protein domains per protein: table(names(pd)) ## The mcols mcols(pd) ``` As we can see each protein can have several protein domains with the start and end coordinates within the amino acid sequence being reported in columns `prot_dom_start` and `prot_dom_end`. Also, not all Ensembl protein IDs, like `protein_id` *ENSP00000445047* are mapped to an Uniprot ID or have protein domains. # Map peptide features within proteins to the genome Functionality to map peptide features (i.e. ranges within the amino acid sequence of the protein) to genomic coordinates are provided by the `Pbase` Bioconductor package. These rely in part on the protein annotations provided by `EnsDb` databases. See the corresponding vignette *Pbase-with-ensembldb* in that package. ensembldb/inst/doc/proteins.html0000644000175400017540000327602613241155731020030 0ustar00biocbuildbiocbuild Querying protein features

From Bioconductor release 3.5 on, EnsDb databases/packages created by the ensembldb package contain also, for transcripts with a coding regions, mappings between transcripts and proteins. Thus, in addition to the RNA/DNA-based features also the following protein related information is available:

  • protein_id: the Ensembl protein ID. This is the primary ID for the proteins defined in Ensembl and each (protein coding) Ensembl transcript has one protein ID assigned to it.
  • protein_sequence: the amino acid sequence of a protein.
  • uniprot_id: the Uniprot ID for a protein. Note that not every Ensembl protein_id has an Uniprot ID, and each protein_id might be mapped to several uniprot_id. Also, the same Uniprot ID might be mapped to different protein_id.
  • uniprot_db: the name of the Uniprot database in which the feature is annotated. Can be either SPTREMBL or SWISSPROT.
  • uniprot_mapping_type: the type of the mapping method that was used to assign the Uniprot ID to the Ensembl protein ID.
  • protein_domain_id: the ID of the protein domain according to the source/analysis in/by which is was defined.
  • protein_domain_source: the source of the protein domain information, one of pfscan, scanprosite, superfamily, pfam, prints, smart, pirsf or tigrfam.
  • interpro_accession: the Interpro accession ID of the protein domain (if available).
  • prot_dom_start: the start of the protein domain within the sequence of the protein.
  • prot_dom_start: the end position of the protein domain within the sequence of the protein.

Thus, for protein coding transcripts, these annotations can be fetched from the database too, given that protein annotations are available. Note that only EnsDb databases created through the Ensembl Perl API contain protein annotation, while databases created using ensDbFromAH, ensDbFromGff, ensDbFromGRanges and ensDbFromGtf don’t.

library(ensembldb)
library(EnsDb.Hsapiens.v75)
edb <- EnsDb.Hsapiens.v75
## Evaluate whether we have protein annotation available
hasProteinData(edb)

If protein annotation is available, the additional tables and columns are also listed by the listTables and listColumns methods.

listTables(edb)

In the following sections we show examples how to 1) fetch protein annotations as additional columns to gene/transcript annotations, 2) fetch protein annotation data and 3) map proteins to the genome.

1 Fetch protein annotation for genes and transcripts

Protein annotations for (protein coding) transcripts can be retrieved by simply adding the desired annotation columns to the columns parameter of the e.g. genes or transcripts methods.

## Get also protein information for ZBTB16 transcripts
txs <- transcripts(edb, filter = GenenameFilter("ZBTB16"),
                   columns = c("protein_id", "uniprot_id", "tx_biotype"))
txs

The gene ZBTB16 has protein coding and non-coding transcripts, thus, we get the protein ID for the coding- and NA for the non-coding transcripts. Note also that we have a transcript targeted for nonsense mediated mRNA-decay with a protein ID associated with it, but no Uniprot ID.

## Subset to transcripts with tx_biotype other than protein_coding.
txs[txs$tx_biotype != "protein_coding", c("uniprot_id", "tx_biotype",
                                          "protein_id")]

While the mapping from a protein coding transcript to a Ensembl protein ID (column protein_id) is 1:1, the mapping between protein_id and uniprot_id can be n:m, i.e. each Ensembl protein ID can be mapped to 1 or more Uniprot IDs and each Uniprot ID can be mapped to more than one protein_id (and hence tx_id). This should be kept in mind if querying transcripts from the database fetching Uniprot related additional columns or even protein ID features, as in such cases a redundant list of transcripts is returned.

## List the protein IDs and uniprot IDs for the coding transcripts
mcols(txs[txs$tx_biotype == "protein_coding",
          c("tx_id", "protein_id", "uniprot_id")])

Some of the n:m mappings for Uniprot IDs can be resolved by restricting either to entries from one Uniprot database (SPTREMBL or SWISSPROT) or to mappings of a certain type of mapping method. The corresponding filters are the UniprotDbFilter and the UniprotMappingTypeFilter (using the uniprot_db and uniprot_mapping_type columns of the uniprot database table). In the example below we restrict the result to Uniprot IDs with the mapping type DIRECT.

## List all uniprot mapping types in the database.
listUniprotMappingTypes(edb)

## Get all protein_coding transcripts of ZBTB16 along with their protein_id
## and Uniprot IDs, restricting to protein_id to uniprot_id mappings based
## on "DIRECT" mapping methods.
txs <- transcripts(edb, filter = list(GenenameFilter("ZBTB16"),
                      UniprotMappingTypeFilter("DIRECT")),
                   columns = c("protein_id", "uniprot_id", "uniprot_db"))
mcols(txs)

For this example the use of the UniprotMappingTypeFilter resolved the multiple mapping of Uniprot IDs to Ensembl protein IDs, but the Uniprot ID Q05516 is still assigned to the two Ensembl protein IDs ENSP00000338157 and ENSP00000376721.

All protein annotations can also be added as metadata columns to the results of the genes, exons, exonsBy, transcriptsBy, cdsBy, fiveUTRsByTranscript and threeUTRsByTranscript methods by specifying the desired column names with the columns parameter. For non coding transcripts NA will be reported in the protein annotation columns.

In addition to retrieve protein annotations from the database, we can also use protein data to filter the results. In the example below we fetch for example all genes from the database that have a certain protein domain in the protein encoded by any of its transcripts.

## Get all genes that encode a transcript encoding for a protein that contains
## a certain protein domain.
gns <- genes(edb, filter = ProtDomIdFilter("PS50097"))
length(gns)

sort(gns$gene_name)

So, in total we got 152 genes with that protein domain. In addition to the ProtDomIdFilter, also the ProteinidFilter and the UniprotidFilter can be used to query the database for entries matching conditions on their protein ID or Uniprot ID.

2 Use methods from the AnnotationDbi package to query protein annotation

The select, keys and mapIds methods from the AnnotationDbi package can also be used to query EnsDb objects for protein annotations. Supported columns and key types are returned by the columns and keytypes methods.

## Show all columns that are provided by the database
columns(edb)

## Show all key types/filters that are supported
keytypes(edb)

Below we fetch all Uniprot IDs annotated to the gene ZBTB16.

select(edb, keys = "ZBTB16", keytype = "GENENAME",
       columns = "UNIPROTID")

This returns us all Uniprot IDs of all proteins encoded by the gene’s transcripts. One of the transcripts from ZBTB16, while having a CDS and being annotated to a protein, does not have an Uniprot ID assigned (thus NA is returned by the above call). As we see below, this transcript is targeted for non sense mediated mRNA decay.

## Call select, this time providing a GenenameFilter.
select(edb, keys = GenenameFilter("ZBTB16"),
       columns = c("TXBIOTYPE", "UNIPROTID", "PROTEINID"))

Note also that we passed this time a GenenameFilter with the keys parameter.

3 Retrieve proteins from the database

Proteins can be fetched using the dedicated proteins method that returns, unlike DNA/RNA-based methods like genes or transcripts, not a GRanges object by default, but a DataFrame object. Alternatively, results can be returned as a data.frame or as an AAStringSet object from the Biobase package. Note that this might change in future releases if a more appropriate object to represent protein annotations becomes available.

In the code chunk below we fetch all protein annotations for the gene ZBTB16.

## Get all proteins and return them as an AAStringSet
prts <- proteins(edb, filter = GenenameFilter("ZBTB16"),
                 return.type = "AAStringSet")
prts

Besides the amino acid sequence, the prts contains also additional annotations that can be accessed with the mcols method (metadata columns). All additional columns provided with the parameter columns are also added to the mcols DataFrame.

mcols(prts)

Note that the proteins method will retrieve only gene/transcript annotations of transcripts encoding a protein. Thus annotations for the non-coding transcripts of the gene ZBTB16, that were returned by calls to genes or transcripts in the previous section are not fetched.

Querying in addition Uniprot identifiers or protein domain data will result at present in a redundant list of proteins as shown in the code block below.

## Get also protein domain annotations in addition to the protein annotations.
pd <- proteins(edb, filter = GenenameFilter("ZBTB16"),
               columns = c("tx_id", listColumns(edb, "protein_domain")),
               return.type = "AAStringSet")
pd

The result contains one row/element for each protein domain in each of the proteins. The number of protein domains per protein and the mcols are shown below.

## The number of protein domains per protein:
table(names(pd))

## The mcols
mcols(pd)

As we can see each protein can have several protein domains with the start and end coordinates within the amino acid sequence being reported in columns prot_dom_start and prot_dom_end. Also, not all Ensembl protein IDs, like protein_id ENSP00000445047 are mapped to an Uniprot ID or have protein domains.

4 Map peptide features within proteins to the genome

Functionality to map peptide features (i.e. ranges within the amino acid sequence of the protein) to genomic coordinates are provided by the Pbase Bioconductor package. These rely in part on the protein annotations provided by EnsDb databases. See the corresponding vignette Pbase-with-ensembldb in that package.

ensembldb/inst/extended_tests/0000755000175400017540000000000013175714743017546 5ustar00biocbuildbiocbuildensembldb/inst/extended_tests/extended_tests.R0000644000175400017540000010304413175714743022715 0ustar00biocbuildbiocbuild## This script comprises extended tests. ##***************************************************************** ## 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$end) myGeneModels <- getGeneRegionTrackForGviz(edb, chromosome=chr, start=start, end=end) ## chromosome has to be the same.... gtrack <- GenomeAxisTrack() gvizTrack <- GeneRegionTrack(geneModels, name="Gviz") ensdbTrack <- GeneRegionTrack(myGeneModels, name="ensdb") plotTracks(list(gtrack, gvizTrack, ensdbTrack)) plotTracks(list(gtrack, gvizTrack, ensdbTrack), from=26700000, to=26780000) ## Looks very nice... } ## Put the stuff below into the vignette: ## Next we get all lincRNAs on chromosome Y Lncs <- getGeneRegionTrackForGviz(edb, filter=list(SeqNameFilter("Y"), GeneBiotypeFilter("lincRNA"))) Prots <- getGeneRegionTrackForGviz(edb, filter=list(SeqNameFilter("Y"), GeneBiotypeFilter("protein_coding"))) if(do.plot){ plotTracks(list(gtrack, GeneRegionTrack(Lncs, name="lincRNAs"), GeneRegionTrack(Prots, name="proteins"))) plotTracks(list(gtrack, GeneRegionTrack(Lncs, name="lincRNAs"), GeneRegionTrack(Prots, name="proteins")), from=5000000, to=7000000, transcriptAnnotation="symbol") } ## is that the same than: TestL <- getGeneRegionTrackForGviz(edb, filter=list(GeneBiotypeFilter("lincRNA")), chromosome="Y", start=5000000, end=7000000) TestP <- getGeneRegionTrackForGviz(edb, filter=list(GeneBiotypeFilter("protein_coding")), chromosome="Y", start=5000000, end=7000000) if(do.plot){ plotTracks(list(gtrack, GeneRegionTrack(Lncs, name="lincRNAs"), GeneRegionTrack(Prots, name="proteins"), GeneRegionTrack(TestL, name="compareL"), GeneRegionTrack(TestP, name="compareP")), from=5000000, to=7000000, transcriptAnnotation="symbol") } expect_true(all(TestL$exon %in% Lncs$exon)) expect_true(all(TestP$exon %in% Prots$exon)) ## Crazy amazing stuff ## system.time( ## All <- getGeneRegionTrackForGviz(edb) ## ) } notrun_test_getSeqlengthsFromMysqlFolder <- function() { ## Test this for some more seqlengths. library(EnsDb.Rnorvegicus.v79) db <- EnsDb.Rnorvegicus.v79 seq_info <- seqinfo(db) seq_lengths <- ensembldb:::.getSeqlengthsFromMysqlFolder( organism = "Rattus norvegicus", ensembl = 79, seqnames = seqlevels(seq_info)) sl <- seqlengths(seq_info) sl_2 <- seq_lengths$length names(sl_2) <- rownames(seq_lengths) checkEquals(sl, sl_2) ## Mus musculus } notrun_test_ensDbFromGtf_Gff_AH <- function() { gtf <- paste0("/Users/jo/Projects/EnsDbs/80/caenorhabditis_elegans/", "Caenorhabditis_elegans.WBcel235.80.gtf.gz") outf <- tempfile() db <- ensDbFromGtf(gtf = gtf, outfile = outf) ## use Gff gff <- paste0("/Users/jo/Projects/EnsDbs/84/canis_familiaris/gff3/", "Canis_familiaris.CanFam3.1.84.gff3.gz") outf <- tempfile() db <- ensDbFromGff(gff, outfile = outf) ## Checking one from ensemblgenomes: gtf <- paste0("/Users/jo/Projects/EnsDbs/ensemblgenomes/30/", "solanum_lycopersicum/", "Solanum_lycopersicum.GCA_000188115.2.30.chr.gtf.gz" ) outf <- tempfile() db <- ensDbFromGtf(gtf = gtf, outfile = outf) gtf <- paste0("/Users/jo/Projects/EnsDbs/ensemblgenomes/30/", "solanum_lycopersicum/", "Solanum_lycopersicum.GCA_000188115.2.30.gtf.gz" ) outf <- tempfile() db <- ensDbFromGtf(gtf = gtf, outfile = outf) ## AH library(AnnotationHub) ah <- AnnotationHub() query(ah, c("release-83", "gtf")) ah_1 <- ah["AH50418"] db <- ensDbFromAH(ah_1, outfile = outf) ah_2 <- ah["AH50352"] db <- ensDbFromAH(ah_2, outfile = outf) } notrun_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) db <- 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. } ############################################################ ## 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) } notrun_compareEnsDbs <- function() { res <- ensembldb:::compareEnsDbs(edb, edb) } ############################################################ ## Massive test validating the cds: ## compare the length of the CDS with the length of the encoded protein. ## Get the CDS sequence, translate that and compare to protein sequence. notrun_massive_cds_test <- function() { ## Get all CDS: tx_cds <- cdsBy(edb, by = "tx", filter = SeqNameFilter(c(1:22, "X", "Y"))) prots <- proteins(edb, filter = TxIdFilter(names(tx_cds)), return.type = "AAStringSet") checkTrue(all(names(tx_cds) %in% mcols(prots)$tx_id)) tx_cds <- tx_cds[mcols(prots)$tx_id] ## Check that the length of the protein sequence is length of CDS/3 diff_width <- sum(width(tx_cds)) != width(prots) * 3 ## Why??? I've got some many differences here??? sum(diff_width) ## Check some of them manually in Ensembl ## 1st: - strand. tx_1 <- tx_cds[diff_width][1] ## Protein: 245aa prots[diff_width][1] ## OK. ## Tx 2206bp: exns <- exonsBy(edb, filter = TxIdFilter(names(tx_1))) sum(width(exns)) ## OK. ## Now to the CDS: cds_ex1 <- "ATGGCGTCCCCGTCTCGGAGACTGCAGACTAAACCAGTCATTACTTGTTTCAAGAGCGTTCTGCTAATCTACACTTTTATTTTCTGG" cds_ex2 <- "ATCACTGGCGTTATCCTTCTTGCAGTTGGCATTTGGGGCAAGGTGAGCCTGGAGAATTACTTTTCTCTTTTAAATGAGAAGGCCACCAATGTCCCCTTCGTGCTCATTGCTACTGGTACCGTCATTATTCTTTTGGGCACCTTTGGTTGTTTTGCTACCTGCCGAGCTTCTGCATGGATGCTAAAACTG" cds_ex3 <- "TATGCAATGTTTCTGACTCTCGTTTTTTTGGTCGAACTGGTCGCTGCCATCGTAGGATTTGTTTTCAGACATGAG" cds_ex4 <- "ATTAAGAACAGCTTTAAGAATAATTATGAGAAGGCTTTGAAGCAGTATAACTCTACAGGAGATTATAGAAGCCATGCAGTAGACAAGATCCAAAATACG" cds_ex5 <- "TTGCATTGTTGTGGTGTCACCGATTATAGAGATTGGACAGATACTAATTATTACTCAGAAAAAGGATTTCCTAAGAGTTGCTGTAAACTTGAAGATTGTACTCCACAGAGAGATGCAGACAAAGTAAACAATGAA" cds_ex6 <- "GGTTGTTTTATAAAGGTGATGACCATTATAGAGTCAGAAATGGGAGTCGTTGCAGGAATTTCCTTTGGAGTTGCTTGCTTCCAA" cds_ex7 <- "CTGATTGGAATCTTTCTCGCCTACTGCCTCTCTCGTGCCATAACAAATAACCAGTATGAGATAGTGTAA" cds_seq <- c(cds_ex1, cds_ex2, cds_ex3, cds_ex4, cds_ex5, cds_ex6, cds_ex7) nchar(cds_seq) width(tx_1) ## The length should be identical: checkEquals(sum(nchar(cds_seq)), sum(width(tx_1)), checkNames = FALSE) ## OK; so WHAT??? sum(width(tx_1)) / 3 ## So, start codon is encoded into a methionine. ## Stop codon is either a TAA, TGA or TAG. UAG can be encoded into Sec (U), UAG into Pyl (O) library(Biostrings) dna_s <- DNAString(paste0(cds_ex1, cds_ex2, cds_ex3, cds_ex4, cds_ex5, cds_ex6, cds_ex7)) translate(dna_s) ## Look at that! translate(DNAString("TAA")) ## -> translates into * translate(DNAString("TGA")) ## -> translates into * translate(DNAString("TAG")) ## -> translates into * translate(DNAString("ATG")) ## -> translates into M ## Assumption: ## If the mRNA ends with a TAA, the protein sequence will be 1aa shorter than ## length(CDS)/3. ## If the mRNA ends with a TAG, UAG the AA length is length(CDS)/3 ## Check one of the mRNA where it fits: tx_2 <- tx_cds[!diff_width][1] prots[!diff_width][1] ## AA is 137 long, ends with I. sum(width(tx_2)) / 3 ## OK ## Check Ensembl: tx_2_1 <- "ATGCTAAAACTG" tx_2_2 <- "TATGCAATGTTTCTGACTCTCGTTTTTTTGGTCGAACTGGTCGCTGCCATCGTAGGATTTGTTTTCAGACATGAG" tx_2_3 <- "ATTAAGAACAGCTTTAAGAATAATTATGAGAAGGCTTTGAAGCAGTATAACTCTACAGGAGATTATAGAAGCCATGCAGTAGACAAGATCCAAAATACG" tx_2_4 <- "TTGCATTGTTGTGGTGTCACCGATTATAGAGATTGGACAGATACTAATTATTACTCAGAAAAAGGATTTCCTAAGAGTTGCTGTAAACTTGAAGATTGTACTCCACAGAGAGATGCAGACAAAGTAAACAATGAA" tx_2_5 <- "GGTTGTTTTATAAAGGTGATGACCATTATAGAGTCAGAAATGGGAGTCGTTGCAGGAATTTCCTTTGGAGTTGCTTGCTTCCAA" tx_2_6 <- "GACATT" tx_2_cds <- paste0(tx_2_1, tx_2_2, tx_2_3, tx_2_4, tx_2_5, tx_2_6) nchar(tx_2_cds) sum(width(tx_2)) ## OK. translate(DNAString(tx_2_cds)) ## Next assumption: ## If we don't have a 3' UTR the AA sequence corresponds to length(CDS)/3 tx_cds <- cdsBy(edb, by = "tx", filter = SeqNameFilter(c(1:22, "X", "Y")), columns = c("tx_seq_start", "tx_seq_end", "tx_cds_seq_start", "tx_cds_seq_end")) prots <- proteins(edb, filter = TxIdFilter(names(tx_cds)), return.type = "AAStringSet") checkTrue(all(names(tx_cds) %in% mcols(prots)$tx_id)) tx_cds <- tx_cds[mcols(prots)$tx_id] ## Calculate the CDS width. tx_cds_width <- sum(width(tx_cds)) txs <- transcripts(edb, filter = TxIdFilter(names(tx_cds))) txs <- txs[names(tx_cds)] ## Subtract 3 from the width if we've got an 3'UTR. to_subtract <- rep(3, length(tx_cds_width)) to_subtract[((end(txs) == txs$tx_cds_seq_end) & as.logical(strand(txs) == "+")) | ((start(txs) == txs$tx_cds_seq_start) & as.logical(strand(txs) == "-"))] <- 0 tx_cds_width <- tx_cds_width - to_subtract ## Check that the length of the protein sequence is length of CDS/3 diff_width <- tx_cds_width != width(prots) * 3 ## Why??? I've got some many differences here??? sum(diff_width) length(diff_width) ## AAAA, still have some that don't fit!!! tx_3 <- tx_cds[diff_width][1] prots[diff_width][1] ## AA is 259aa long, ends with T., TX is: ENST00000371584 ## WTF, we've got no START CODON!!! sum(width(tx_3)) / 3 ## OMG!!! ## Now, exclude those without a 5' UTR: no_five <- ((start(txs) == txs$tx_cds_seq_start) & as.logical(strand(txs) == "+")) | ((end(txs) == txs$tx_cds_seq_end) & as.logical(strand(txs) == "-")) still_prot <- (diff_width & !no_five) } 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"]] } 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! } 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)) } ## 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. notrun_test_protein_domains <- function() { res <- ensembldb:::getWhat(edb, columns = c("protein_id", "tx_id", "gene_id", "gene_name"), filter = list(ProtDomIdFilter("PF00096"))) } 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/extended_tests/performance_tests.R0000644000175400017540000001720013175714743023414 0ustar00biocbuildbiocbuild############################################################ ## 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) } ## 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) } ## Compare the performance of inner join with left outer join. dontrun_test_outer_join_performance <- function() { Q_1 <- ensembldb:::joinQueryOnTables2(edb, tab = c("gene", "exon")) Q_2 <- ensembldb:::joinQueryOnTables2(edb, tab = c("gene", "exon"), startWith = "exon") Q_3 <- ensembldb:::joinQueryOnTables2(edb, tab = c("gene", "exon"), startWith = "exon", join = "left outer join") library(microbenchmark) library(RSQLite) microbenchmark(dbGetQuery(dbconn(edb), paste0("select * from ", Q_1)), dbGetQuery(dbconn(edb), paste0("select * from ", Q_2)), dbGetQuery(dbconn(edb), paste0("select * from ", Q_3)), times = 10) ## Result: Q_1 is a second faster (13 instead of 14). ## Check performance joining tx and genes. 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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_entrezgene.txt: contains mapping between ensembl gene_id and entrezgene ID.\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_protein.txt: the mapping between (protein coding) transcripts and protein IDs including also the peptide sequence.\n"); print("- ens_protein_domain.txt: provides for each protein all annotated protein domains along with their start and end coordinates on the protein sequence."); print("- ens_uniprot.txt: provides the mapping between Ensembl protein IDs and Uniprot IDs (if available). The mapping can be 1: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."; } my $ensembl_version_num = $ensembl_version + 0; print "Connecting to ".$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"); my $meta_container = $registry->get_adaptor($species, $ensembl_database, 'MetaContainer' ); ## determine the species: my $species_id = $gene_adaptor->db->species_id; my $species_ens = $gene_adaptor->db->species; ## Determine the taxonomy ID: my $taxonomy_id = 0; $taxonomy_id = $meta_container->get_taxonomy_id(); 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\tgene_biotype\tgene_seq_start\tgene_seq_end\tseq_name\tseq_strand\tseq_coord_system\tdescription\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\ttx_support_level\n"; open(EXON , ">ens_exon.txt"); print EXON "exon_id\texon_seq_start\texon_seq_end\n"; open(ENTREZGENE, ">ens_entrezgene.txt"); print ENTREZGENE "gene_id\tentrezid\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(PROTEIN, ">ens_protein.txt"); ## print PROTEIN "tx_id\tprotein_id\tuniprot_id\tprotein_sequence\n"; print PROTEIN "tx_id\tprotein_id\tprotein_sequence\n"; open(UNIPROT, ">ens_uniprot.txt"); print UNIPROT "protein_id\tuniprot_id\tuniprot_db\tuniprot_mapping_type\n"; open(PROTDOM, ">ens_protein_domain.txt"); print PROTDOM "protein_id\tprotein_domain_id\tprotein_domain_source\tinterpro_accession\tprot_dom_start\tprot_dom_end\n"; open(CHR , ">ens_chromosome.txt"); print CHR "seq_name\tseq_length\tis_circular\n"; open(COUNTS, ">ens_counts.txt"); print COUNTS "gene\ttx\texon\tprotein\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; my $count_gene = 0; my $count_tx = 0; my $count_exon = 0; my $count_protein = 0; @gene_ids = @{$gene_adaptor->list_stable_ids()}; foreach my $gene_id (@gene_ids){ $counta++; $count_gene++; if(($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; ## Instead of transforming to chromosome we transform to 'toplevel', ## for genes encoded on chromosome this should be the chromosome, for others ## the most "top" level sequence. ## my $gene = $orig_gene->transform("chromosome"); my $gene = $orig_gene->transform("toplevel"); 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 $tmp_version = $chr_slice->coord_system()->version(); if (defined $tmp_version and length $tmp_version) { $coord_system_version = $tmp_version; } # 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(); # } } ## 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; my $description = $gene->description; if(!defined($description)){ $description = "NULL"; } ## get entrezgene ID, if any... my $all_entries = $gene->get_all_DBLinks("EntrezGene"); foreach my $dbe (@{$all_entries}){ print ENTREZGENE "$gene_id\t".$dbe->primary_id."\n"; } # 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$gene_external_name\t$gene_biotype\t$gene_seq_start\t$gene_seq_end\t$chrom\t$strand\t$coord_system\t$description\n"; ## process transcript(s) my @transcripts = @{ $gene->get_all_Transcripts }; ## ok looping through the transcripts foreach my $transcript (@transcripts){ $count_tx++; if($do_transform==1){ ## just to be shure that we have the transcript in chromosomal coordinations. ## $transcript = $transcript->transform("chromosome"); $transcript = $transcript->transform("toplevel"); } ##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; my $tx_tsl = "NULL"; if ($ensembl_version_num >= $min_tsl_version) { $tx_tsl = $transcript->tsl; if (!defined($tx_tsl)) { $tx_tsl = "NULL"; } } my $tx_description = $transcript->description; # if (!defined($tx_description)) { # $tx_description = "NULL"; # } ## write info. print TRANSCRIPT "$tx_id\t$tx_biotype\t$tx_seq_start\t$tx_seq_end\t$tx_cds_start\t$tx_cds_end\t$gene_id\t$tx_tsl\n"; ## print G2T "$gene_id\t$tx_id\n"; ## Process proteins/translations (if possible) my $transl = $transcript->translation(); if (defined($transl)) { $count_protein++; my $transl_id = $transl->stable_id(); my $prot_seq = $transl->seq(); ## Check if we could get UNIPROT ID(s): my @unip = @{ $transl->get_all_DBLinks('Uniprot%') }; if (scalar(@unip) > 0) { foreach my $uniprot (@unip) { my $unip_id = $uniprot->display_id(); my $dbn = $uniprot->dbname(); $dbn =~ s/Uniprot\///g; my $maptype = $uniprot->info_type(); print UNIPROT "$transl_id\t$unip_id\t$dbn\t$maptype\n"; ## print PROTEIN "$tx_id\t$transl_id\t$unip_id\t$prot_seq\n"; } } print PROTEIN "$tx_id\t$transl_id\t$prot_seq\n"; my $prot_doms = $transl->get_all_DomainFeatures; while ( my $prot_dom = shift @{$prot_doms}) { my $logic_name = $prot_dom->analysis()->logic_name(); my $prot_dom_id = $prot_dom->display_id(); my $interpro_acc = $prot_dom->interpro_ac(); my $prot_start = $prot_dom->start(); my $prot_end = $prot_dom->end(); print PROTDOM "$transl_id\t$prot_dom_id\t$logic_name\t$interpro_acc\t$prot_start\t$prot_end\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"); $exon->transform("toplevel"); } 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; $count_exon++; print EXON "$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$species_ens\n"; print INFO "taxonomy_id\t$taxonomy_id\n"; print INFO "genome_build\t$coord_system_version\n"; print INFO "DBSCHEMAVERSION\t2.1\n"; print COUNTS "$count_gene\t$count_tx\t$count_exon\t$count_protein\n"; close(INFO); close(GENE); close(TRANSCRIPT); close(EXON); close(ENTREZGENE); ##close(G2T); close(T2E); close(CHR); close(PROTEIN); close(PROTDOM); close(UNIPROT); close(COUNTS); ensembldb/inst/perl/test_script.pl0000644000175400017540000000474513175714743020400 0ustar00biocbuildbiocbuild## 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 = "0.2.2"; ## 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 $gene_id = "ENSG00000109906"; $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"); my $current_gene = $gene_adaptor->fetch_by_stable_id($gene_id); print "Current gene: ".$current_gene->display_id()."\n"; my @transcripts = @{ $current_gene->get_all_Transcripts }; foreach my $transcript (@transcripts){ print "Current tx: ".$transcript->display_id()."\n"; my $transl = $transcript->translation(); if (defined($transl)) { my $transl_id = $transl->stable_id(); print "Current translation ".$transl_id."\n"; my $attr = $transl->get_all_Attributes(); foreach my $a (@{$attr}) { print "\tName: ", $a->name(), "\n"; print "\tCode: ", $a->code(), "\n"; print "\tDesc: ", $a->description(), "\n"; print "\tValu: ", $a->value(), "\n"; } my @unip = @{ $transl->get_all_DBLinks('Uniprot%') }; if (scalar(@unip) > 0) { foreach my $uniprot (@unip) { my $unip_id = $uniprot->display_id(); ##print UNIPROT "$transl_id\t$unip_id\n"; ## OK, add also ## o uniprot_db: $uniprot->dbname(); ## o uniprot_info: $uniprot->info_text(); my $dbn = $uniprot->dbname(); $dbn =~ s/Uniprot\///g; my $descr = $uniprot->description(); my $infot = $uniprot->info_text(); my $stat = $uniprot->status(); my $infotype = $uniprot->info_type(); ## mapping type. print "uniprot: ".$unip_id."\n"; print " dbname: ".$dbn."\n"; print " info_text ".$infot."\n"; ## Defines the method by which this ID was mapped (Uniprot ID was ## matched to the Ensembl protein ID). print " info_type ".$infotype."\n"; } } } } ensembldb/inst/pkg-template/0000755000175400017540000000000013175714743017116 5ustar00biocbuildbiocbuildensembldb/inst/pkg-template/DESCRIPTION0000644000175400017540000000056113175714743020626 0ustar00biocbuildbiocbuildPackage: @PKGNAME@ Title: @PKGTITLE@ Description: @PKGDESCRIPTION@ Version: @PKGVERSION@ Author: @AUTHOR@ Maintainer: @MAINTAINER@ Depends: ensembldb License: @LIC@ organism: @ORGANISM@ species: @SPECIES@ provider: @PROVIDER@ provider_version: @PROVIDERVERSION@ release_date: @RELEASEDATE@ resource_url: @SOURCEURL@ biocViews: AnnotationData, EnsDb, @ORGANISMBIOCVIEW@ ensembldb/inst/pkg-template/NAMESPACE0000644000175400017540000000035313175714743020336 0ustar00biocbuildbiocbuild##import(AnnotationDbi) #import(GenomicFeatures) import(ensembldb) ### Don't export @TXDBOBJNAME@ (the object defined in this ### package): it is created and dynamically exported at load time (refer ### to R/zzz.R for the details). ensembldb/inst/pkg-template/R/0000755000175400017540000000000013175714743017317 5ustar00biocbuildbiocbuildensembldb/inst/pkg-template/R/zzz.R0000644000175400017540000000074113175714743020301 0ustar00biocbuildbiocbuild### ### Load any db objects whenever the package is loaded. ### .onLoad <- function(libname, pkgname) { ns <- asNamespace(pkgname) path <- system.file("extdata", package=pkgname, lib.loc=libname) files <- dir(path) for(i in seq_len(length(files))){ db <- EnsDb(system.file("extdata", files[[i]], package=pkgname, lib.loc=libname)) objname <- sub(".sqlite$","",files[[i]]) assign(objname, db, envir=ns) namespaceExport(ns, objname) } } ensembldb/inst/pkg-template/man/0000755000175400017540000000000013175714743017671 5ustar00biocbuildbiocbuildensembldb/inst/pkg-template/man/package.Rd0000644000175400017540000000135713175714743021561 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/scripts/0000755000175400017540000000000013175714743016213 5ustar00biocbuildbiocbuildensembldb/inst/scripts/checkEnsDbs.R0000644000175400017540000000131713175714743020514 0ustar00biocbuildbiocbuild#' @description Check EnsDb sqlite files found in the specified folder. #' #' @param x \code{character(1)} with the folder in which we're looking for EnsDb #' objects. #' #' @author Johannes Rainer #' #' @noRd #' #' @examples #' dir <- "/Users/jo/tmp/ensdb_20" checkEnsDbs <- function(x) { edbs <- dir(x, pattern = ".sqlite$", full.names = TRUE) for (i in 1:length(edbs)) { message("\nChecking EnsDb: ", basename(edbs[i])) edb <- EnsDb(edbs[i]) ensembldb:::validateEnsDb(edb) ensembldb:::checkValidEnsDb(edb) ## Now check also some query calls: gns <- genes(edb) message(" version: ", ensembldb:::dbSchemaVersion(edb)) message(" OK") } } ensembldb/inst/scripts/generate-EnsDBs.R0000644000175400017540000003060713175714743021252 0ustar00biocbuildbiocbuild## Functions related to create EnsDbs by downloading and installing MySQL ## databases from Ensembl. library(RCurl) library(RMySQL) library(ensembldb) #' @description Get core database names from the specified folder. #' #' @param ftp_folder The ftp url to the per-species mysql folders. #' #' @author Johannes Rainer #' #' @noRd listCoreDbsInFolder <- function(ftp_folder) { if (missing(ftp_folder)) stop("Argument 'ftp_folder' missing!") folders <- unlist(strsplit(getURL(ftp_folder, dirlistonly = TRUE), split = "\n")) res <- t(sapply(folders, function(z) { tmp <- unlist(strsplit(z, split = "_")) return(c(folder = z, organism = paste0(tmp[1:2], collapse = "_"), type = tmp[3], version = paste0(tmp[4:length(tmp)], collapse = "_"))) })) return(res[which(res[, "type"] == "core"), ]) } #' @description Creates an EnsDb for the specified species by first downloading #' the corresponding MySQL database from Ensembl, installing it and #' subsequently creating the EnsDb database from it. #' #' @param ftp_folder The ftp url to the per-species mysql folders. If not #' provided it will use the default Ensembl ftp: #' \code{ftp://ftp.ensembl.org/pub/release-/mysql/}. #' #' @param ens_version The Ensembl version (version of the Ensembl Perl API). #' #' @param species The name of the species (e.g. "homo_sapiens"). #' #' @param user The user name for the MySQL database (write access). #' #' @param host The host on which the MySQL database is running. #' #' @param pass The password for the MySQL database. #' #' @param port The port of the MySQL database. #' #' @param local_tmp Local directory that will be used to temporarily store the #' downloaded MySQL database files. #' #' @param dropDb Whether the Ensembl core database should be deleted once the #' EnsDb has been created. #' #' @author Johannes Rainer #' #' @examples #' #' ## For Ensemblgenomes: #' ftp_folder <- "ftp://ftp.ensemblgenomes.org/pub/release-33/fungi/mysql/" #' @noRd createEnsDbForSpecies <- function(ftp_folder, ens_version = 86, species, user, host, pass, port = 3306, local_tmp = tempdir(), sub_dir = "", dropDb = TRUE) { ## if ftp_folder is missing use the default one: base_url = "ftp://ftp.ensembl.org/pub" ## (1) Get all directories from Ensembl if (missing(ftp_folder)) ftp_folder <- paste0(base_url, "/release-", ens_version, "/mysql/") res <- listCoreDbsInFolder(ftp_folder) folders <- unlist(strsplit(getURL(ftp_folder, dirlistonly = TRUE), split = "\n")) res <- t(sapply(folders, function(z) { tmp <- unlist(strsplit(z, split = "_")) return(c(folder = z, organism = paste0(tmp[1:2], collapse = "_"), type = tmp[3], version = paste0(tmp[4:length(tmp)], collapse = "_"))) })) res <- res[which(res[, "type"] == "core"), ] if (nrow(res) == 0) stop("No directories found!") if (missing(species)) species <- res[, "organism"] rownames(res) <- res[, "organism"] ## Check if we've got the species available got_specs <- species %in% rownames(res) if (!all(got_specs)) warning("No core database for species ", paste0(species[!got_specs], collapse = ", "), " found.") species <- species[got_specs] res <- res[species, , drop = FALSE] if (length(species) == 0) stop("No database for any provided species found!") ## (2) Process each species message("Going to process ", nrow(res), " species.") for (i in 1:nrow(res)) { message("Processing species: ", res[i, "organism"], " (", i, " of ", nrow(res), ")") processOneSpecies(ftp_folder = paste0(ftp_folder, res[i, "folder"]), ens_version = ens_version, species = species[i], user = user, host = host, pass = pass, port = port, local_tmp = local_tmp, dropDb = dropDb) message("Done with species: ", res[i, "organism"], ", ", nrow(res) - i, " left.") } } #' @description This function performs the actual tasks of downloading the #' database files, installing them, deleting the download, creating the #' EnsDb and deleting the database. #' #' @details While the location of the downloaded temporary MySQL database file #' can be specified, the final SQLite file as well as all intermediate files #' will be placed in the current working directory. #' #' @param ftp_folder The folder on Ensembl's ftp server containing the mysql #' database files. Has to be the full path to these files. #' #' @param ens_version The Ensembl version (version of the Ensembl Perl API). #' #' @param species The name of the species (e.g. "homo_sapiens"). #' #' @param user The user name for the MySQL database (write access). #' #' @param host The host on which the MySQL database is running. #' #' @param pass The password for the MySQL database. #' #' @param port The port of the MySQL database. #' #' @param local_tmp Local directory that will be used to temporarily store the #' downloaded MySQL database files. #' #' @param dropDb Whether the Ensembl core database should be deleted once the #' EnsDb has been created. #' #' @author Johannes Rainer #' #' @noRd processOneSpecies <- function(ftp_folder, ens_version = 86, species, user, host = "localhost", pass, port = 3306, local_tmp = tempdir(), dropDb = TRUE) { if (missing(ftp_folder)) stop("'ftp_folder' has to be specified!") if (missing(user)) stop("'user' has to be specified!") if (missing(species)) stop("'species' has to be specified!") ## (1) Download database files. res <- downloadFilesFromFtpFolder(url = ftp_folder, dest = local_tmp) ## (2) Install database. db_name <- basename(ftp_folder) res <- installEnsemblDb(dir = local_tmp, host = host, dbname = db_name, user = user, pass = pass, port = port) ## (3) Delete the downloads. fls <- dir(local_tmp, full.names = TRUE) res <- sapply(fls, unlink) ## (4) Create the EnsDb (requires the correct Ensembl API) ## They are created in the local directory. fetchTablesFromEnsembl(version = ens_version, species = species, user = user, host = host, pass = pass, port = port) DBFile <- makeEnsemblSQLiteFromTables() unlink("*.txt") ## (5) Delete the database. if (dropDb) { con <- dbConnect(MySQL(), host = host, user = user, pass = pass, port = port, dbname = "mysql") res <- dbGetQuery(con, paste("drop database ", db_name)) dbDisconnect(con) } } #' #' @description Download all files from an ftp directory to a local directory. #' #' @param url A character string specifying the url of the directory. #' #' @param dest A character string specifying the local directory. #' #' @return A character string with the path of the local directory. #' #' @author Johannes Rainer #' #' @noRd #' #' @examples #' #' ftp_dir <- "ftp://ftp.ensembl.org/pub/release-88/mysql/homo_sapiens_core_88_38" #' local_dir <- downloadFilesFromFtpFolder(ftp_dir) downloadFilesFromFtpFolder <- function(url, dest = tempdir()) { fls <- getURL(paste0(url, "/"), dirlistonly = TRUE) fls <- unlist(strsplit(fls, split = "\n")) message("Downloading ", length(fls), " files ... ", appendLF = FALSE) for (i in 1:length(fls)) { download.file(url = paste0(url, "/", fls[i]), destfile = paste0(dest, "/", fls[i]), quiet = TRUE) } message("OK") return(dest) } #' @description Install an Ensembl MySQL database downloaded from the Ensembl #' ftp server (e.g. using \link{downloadFilesFromFtpFolder}). #' #' @note The local directory is expected to correspond to the name of the #' database, i.e. \code{basename(dir)} will be used as the database name if #' argument \code{dbname} is missing. #' #' @param dir The path to the local directory where the database files are. #' #' @param host The host running the MySQL database. #' #' @param dbname The name of the database. If not provided the name of the #' provided directory will be used instead. #' #' @param user The user name for the MySQL database (rw access). #' #' @param pass The password for the MySQL database. #' #' @param port The port of the MySQL database. #' #' @author Johannes Rainer #' #' @noRd #' #' @examples #' user <- "user" #' pass <- "pass" #' dbname <- "homo_sapiens_core_88_38" #' ## set to directory returned by the downloadFilesFromFtpFolder #' dir <- local_dir #' #' installEnsemblDb(dir = dir, dbname = dbname, user = user, pass = pass) installEnsemblDb <- function(dir, host = "localhost", dbname, user, pass, port = 3306) { if (missing(dir)) stop("Argument 'dir' missing!") if (missing(dbname)) dbname <- basename(dir) if (missing(user)) stop("Argument 'user' missing!") ## Eventually unzip the files... tmp <- system(paste0("gunzip ", dir, "/*.gz")) ## Create the database con <- dbConnect(MySQL(), host = host, user = user, pass = pass, port = port, dbname = "mysql") res <- dbGetQuery(con, paste0("create database ", dbname)) dbDisconnect(con) ## Now create the tables and stuff. tmp <- system(paste0("mysql -h ", host, " -u ", user, " --password=", pass, " -P ", port, " ", dbname, " < ", dir, "/", dbname, ".sql")) ## Importing the data. cmd <- paste0("mysqlimport -h ", host, " -u ", user, " --password=", pass, " -P ", port, " ", dbname, " -L ", dir, "/*.txt") tmp <- system(cmd) } #' @description Creates EnsDb packages from all sqlite database files found in #' the directory specified with parameter \code{dir}. #' @param dir The path to the directory where the SQLite files can be found. #' @param author The author of the package. #' @param maintainer The maintainer of the package. #' @param version The version of the package. #' @noRd createPackagesFromSQLite <- function(dir = ".", author, maintainer, version) { if (missing(author) | missing(maintainer) | missing(version)) stop("Parameter 'author', 'maintainer' and 'version' are required!") edbs <- dir(dir, full.names = TRUE, pattern = ".sqlite") if (length(edbs) == 0) stop("Found no SQLite database files in the specified directory!") message("Processing ", length(edbs), " packages.") for (i in 1:length(edbs)) { message("Processing ", basename(edbs[i]), " (", i, " of ", length(edbs), ")", appendLF = FALSE) makeEnsembldbPackage(ensdb = edbs[i], version = version, author = author, maintainer = maintainer) message("OK") } } ## ftpf <- paste0("ftp://ftp.ensembl.org/pub/release-86/mysql/", ## "anas_platyrhynchos_core_86_1") ## local_dir <- tempdir() ## downloadFilesFromFtpFolder(ftpf, dest = local_dir) ## installEnsemblDb(dir = local_dir, host = "localhost", user = "jo", ## pass = "jo123", dbname = "anas_platyrhynchos_core_86_1") ## fls <- dir(local_dir, full.names = TRUE) ## res <- sapply(fls, unlink) ## fetchTablesFromEnsembl(86, species = "anas_platyrhynchos", user = "jo", ## host = "localhost", pass = "jo123", port = 3306) ## DBFile <- makeEnsemblSQLiteFromTables() ## unlink("*.txt") ## system.time(fetchTablesFromEnsembl(86, species = "anas_platyrhynchos")) ## ftpf <- paste0("ftp://ftp.ensembl.org/pub/release-86/mysql/", ## "homo_sapiens_core_86_38") ## local_dir <- tempdir() ## processOneSpecies(ftp_folder = ftpf, version = 86, ## species = "homo_sapiens", user = "jo", ## host = "localhost", ## pass = "jo123", port = 3306, local_tmp = local_dir, ## dropDb = FALSE) ## Add an issue: ## + Fix problem of non-defined sequence type "chromosome" in anas platyrhynchos ## database. -> update to the perl script. ## + Compare Hsapiens EnsDb created with new script and the "original" one. ensembldb/inst/shinyHappyPeople/0000755000175400017540000000000013175714743020025 5ustar00biocbuildbiocbuildensembldb/inst/shinyHappyPeople/server.R0000644000175400017540000002004313175714743021455 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.R0000644000175400017540000001330613175714743020570 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="") ## 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For \code{UniprotDbFilter}: A \code{UniprotDbFilter} object. For \code{UniprotMappingTypeFilter}: A \code{UniprotMappingTypeFilter} object. For \code{TxSupportLevel}: A \code{TxSupportLevel} object. For \code{supportedFilters}: a \code{data.frame} with the names and the corresponding field of the supported filter classes. } \description{ \code{ensembldb} supports most of the filters from the \code{\link{AnnotationFilter}} package to retrieve specific content from \code{\linkS4class{EnsDb}} databases. These filters can be passed to the methods such as \code{\link{genes}} with the \code{filter} parameter or can be added as a \emph{global} filter to an \code{EnsDb} object (see \code{\link{addFilter}} for more details). Use the \code{\link{supportedFilters}} to list all filters supported for \code{EnsDb} object. \code{supportedFilters} returns a \code{data.frame} with the names of all filters and the corresponding field supported by the \code{EnsDb} object. \code{seqnames}: accessor for the sequence names of the \code{GRanges} object within a \code{GRangesFilter} \code{seqnames}: accessor for the \code{seqlevels} of the \code{GRanges} object within a \code{GRangesFilter} } \details{ \code{ensembldb} supports the following filters from the \code{AnnotationFilter} package: \describe{ \item{GeneIdFilter}{ filter based on the Ensembl gene ID. } \item{GenenameFilter}{ filter based on the name of the gene as provided by Ensembl. In most cases this will correspond to the official gene symbol. } \item{SymbolFilter}{ filter based on the gene names. \code{\linkS4class{EnsDb}} objects don't have a dedicated \emph{symbol} column, the filtering is hence based on the gene names. } \item{GeneBiotype}{ filter based on the biotype of genes (e.g. \code{"protein_coding"}). } \item{GeneStartFilter}{ filter based on the genomic start coordinate of genes. } \item{GeneEndFilter}{ filter based on the genomic end coordinate of genes. } \item{EntrezidFilter}{ filter based on the genes' NCBI Entrezgene ID. } \item{TxIdFilter}{ filter based on the Ensembld transcript ID. } \item{TxNameFilter}{ filter based on the Ensembld transcript ID; no transcript names are provided in \code{\linkS4class{EnsDb}} databases. } \item{TxBiotypeFilter}{ filter based on the transcripts' biotype. } \item{TxStartFilter}{ filter based on the genomic start coordinate of the transcripts. } \item{TxEndFilter}{ filter based on the genonic end coordinates of the transcripts. } \item{ExonIdFilter}{ filter based on Ensembl exon IDs. } \item{ExonRankFilter}{ filter based on the index/rank of the exon within the transcrips. } \item{ExonStartFilter}{ filter based on the genomic start coordinates of the exons. } \item{ExonEndFilter}{ filter based on the genomic end coordinates of the exons. } \item{GRangesFilter}{ Allows to fetch features within or overlapping specified genomic region(s)/ range(s). This filter takes a \code{\link[GenomicRanges]{GRanges}} object as input and, if \code{type = "any"} (the default) will restrict results to features (genes, transcripts or exons) that are partially overlapping the region. Alternatively, by specifying \code{condition = "within"} it will return features located within the range. In addition, the \code{\link[AnnotationFilter]{GRangesFilter}} supports \code{condition = "start"}, \code{condition = "end"} and \code{condition = "equal"} filtering for features with the same start or end coordinate or that are equal to the \code{GRanges}. Note that the type of feature on which the filter is applied depends on the method that is called, i.e. \code{\link{genes}} will filter on the genomic coordinates of genes, \code{\link{transcripts}} on those of transcripts and \code{\link{exons}} on exon coordinates. 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. If the specified \code{GRanges} object defines multiple regions, 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{SeqNameFilter}{ filter based on chromosome names. } \item{SeqStrandFilter}{ filter based on the chromosome strand. The strand can be specified with \code{value = "+"}, \code{value = "-"}, \code{value = -1} or \code{value = 1}. } \item{ProteinIdFilter}{ filter based on Ensembl protein IDs. This filter is only supported if the \code{\linkS4class{EnsDb}} provides protein annotations; use the \code{\link{hasProteinData}} method to evaluate. } \item{UniprotFilter}{ filter based on Uniprot IDs. This filter is only supported if the \code{\linkS4class{EnsDb}} provides protein annotations; use the \code{\link{hasProteinData}} method to evaluate. } } In addition, the following filters are defined by \code{ensembldb}: \describe{ \item{TxSupportLevel}{ allows to filter results using the provided transcript support level. Support levels for transcripts are defined by Ensembl based on the available evidences for a transcript with 1 being the highest evidence grade and 5 the lowest level. This filter is only supported on \code{EnsDb} databases with a db schema version higher 2.1. } \item{UniprotDbFilter}{ allows to filter results based on the specified Uniprot database name(s). } \item{UniprotMappingTypeFilter}{ allows to filter results based on the mapping method/type that was used to assign Uniprot IDs to Ensembl protein IDs. } \item{ProtDomIdFilter}{ allows to retrieve entries from the database matching the provided filter criteria based on their protein domain ID (\emph{protein_domain_id}). } \item{OnlyCodingTxFilter}{ allows to retrieve entries only for protein coding transcripts, i.e. transcripts with a CDS. This filter does not take any input arguments. } } } \note{ For users of \code{ensembldb} version < 2.0: in the \code{\link[AnnotationFilter]{GRangesFilter}} from the \code{AnnotationFilter} package the \code{condition} parameter was renamed to \code{type} (to be consistent with the \code{IRanges} package) . In addition, the \code{condition = "overlapping"} is no longer recognized. To retrieve all features overlapping the range \code{type = "any"} has to be used. Protein annotation based filters can only be used if the \code{\linkS4class{EnsDb}} database contains protein annotations, i.e. if \code{\link{hasProteinData}} is \code{TRUE}. Also, only protein coding transcripts will have protein annotations available, thus, non-coding transcripts/genes will not be returned by the queries using protein annotation filters. } \examples{ ## Create a filter that could be used to retrieve all informations for ## the respective gene. gif <- GeneIdFilter("ENSG00000012817") gif ## Create a filter for a chromosomal end position of a gene sef <- GeneEndFilter(10000, condition = ">") 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 = "+"), type = "any") 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")) ## Create a ProteinIdFilter: pf <- ProteinIdFilter("ENSP00000362111") pf ## Using this filter would retrieve all database entries that are associated ## with a protein with the ID "ENSP00000362111" if (hasProteinData(edb)) { res <- genes(edb, filter = pf) res } ## UniprotFilter: uf <- UniprotFilter("O60762") ## Get the transcripts encoding that protein: if (hasProteinData(edb)) { transcripts(edb, filter = uf) ## The mapping Ensembl protein ID to Uniprot ID can however be 1:n: transcripts(edb, filter = TxIdFilter("ENST00000371588"), columns = c("protein_id", "uniprot_id")) } ## ProtDomIdFilter: pdf <- ProtDomIdFilter("PF00335") ## Also here we could get all transcripts related to that protein domain if (hasProteinData(edb)) { transcripts(edb, filter = pdf, columns = "protein_id") } } \seealso{ \code{\link{supportedFilters}} to list all filters supported for \code{EnsDb} objects. \code{\link{listUniprotDbs}} and \code{\link{listUniprotMappingTypes}} to list all Uniprot database names respectively mapping method types from the database. \code{\link[AnnotationFilter]{GeneIdFilter}} for more details on the filter objects. \code{\link{genes}}, \code{\link{transcripts}}, \code{\link{exons}}, \code{\link{listGenebiotypes}}, \code{\link{listTxbiotypes}}. \code{\link{addFilter}} for globally adding filters to an \code{EnsDb} object. } \author{ Johannes Rainer } ensembldb/man/ProteinFunctionality.Rd0000644000175400017540000001017613175714743021007 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/functions-utils.R, R/Methods.R \docType{methods} \name{listProteinColumns} \alias{listProteinColumns} \alias{proteins,EnsDb-method} \alias{proteins} \alias{listUniprotDbs,EnsDb-method} \alias{listUniprotDbs} \alias{listUniprotMappingTypes,EnsDb-method} \alias{listUniprotMappingTypes} \title{Protein related functionality} \usage{ listProteinColumns(object) \S4method{proteins}{EnsDb}(object, columns = listColumns(object, "protein"), filter = AnnotationFilterList(), order.by = "", order.type = "asc", return.type = "DataFrame") \S4method{listUniprotDbs}{EnsDb}(object) \S4method{listUniprotMappingTypes}{EnsDb}(object) } \arguments{ \item{object}{The \code{\linkS4class{EnsDb}} object.} \item{columns}{For \code{proteins}: character vector defining the columns to be extracted from the database. Can be any column(s) listed by the \code{\link{listColumns}} method.} \item{filter}{For \code{proteins}: A filter object extending \code{AnnotationFilter} or a list of such objects to select specific entries from the database. See \code{\link{Filter-classes}} for a documentation of available filters and use \code{\link{supportedFilters}} to get the full list of supported filters.} \item{order.by}{For \code{proteins}: a character vector specifying the column(s) by which the result should be ordered.} \item{order.type}{For \code{proteins}: if the results should be ordered ascending (\code{order.type = "asc"}) or descending (\code{order.type = "desc"})} \item{return.type}{For \code{proteins}: character of lenght one specifying the type of the returned object. Can be either \code{"DataFrame"}, \code{"data.frame"} or \code{"AAStringSet"}.} } \value{ The \code{listProteinColumns} function returns a character vector with the column names containing protein annotations or throws an error if no such annotations are available. The \code{proteins} method returns protein related annotations from an \code{\linkS4class{EnsDb}} object with its \code{return.type} argument allowing to define the type of the returned object. Note that if \code{return.type = "AAStringSet"} additional annotation columns are stored in a \code{DataFrame} that can be accessed with the \code{mcols} method on the returned object. } \description{ The \code{listProteinColumns} function allows to conveniently extract all database columns containing protein annotations from an \code{\linkS4class{EnsDb}} database. This help page provides information about most of the functionality related to protein annotations in \code{ensembldb}. The \code{proteins} method retrieves protein related annotations from an \code{\linkS4class{EnsDb}} database. The \code{listUniprotDbs} method lists all Uniprot database names in the \code{EnsDb}. The \code{listUniprotMappingTypes} method lists all methods that were used for the mapping of Uniprot IDs to Ensembl protein IDs. } \details{ The \code{proteins} method performs the query starting from the \code{protein} tables and can hence return all annotations from the database that are related to proteins and transcripts encoding these proteins from the database. Since \code{proteins} does thus only query annotations for protein coding transcripts, the \code{\link{genes}} or \code{\link{transcripts}} methods have to be used to retrieve annotations for non-coding transcripts. } \examples{ ## List all columns containing protein annotations library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 if (hasProteinData(edb)) listProteinColumns(edb) library(ensembldb) library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 ## Get all proteins from tha database for the gene ZBTB16, if protein ## annotations are available if (hasProteinData(edb)) proteins(edb, filter = GenenameFilter("ZBTB16")) ## List the names of all Uniprot databases from which Uniprot IDs are ## available in the EnsDb if (hasProteinData(edb)) listUniprotDbs(edb) ## List the type of all methods that were used to map Uniprot IDs to Ensembl ## protein IDs if (hasProteinData(edb)) listUniprotMappingTypes(edb) } \author{ Johannes Rainer } ensembldb/man/convertFilter.Rd0000644000175400017540000000364613175714743017450 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Methods-Filter.R \docType{methods} \name{convertFilter,AnnotationFilter,EnsDb-method} \alias{convertFilter,AnnotationFilter,EnsDb-method} \alias{convertFilter,AnnotationFilterList,EnsDb-method} \alias{convertFilter,AnnotationFilterList,EnsDb-method} \title{Convert an AnnotationFilter to a SQL WHERE condition for EnsDb} \usage{ \S4method{convertFilter}{AnnotationFilter,EnsDb}(object, db, with.tables = character()) \S4method{convertFilter}{AnnotationFilterList,EnsDb}(object, db, with.tables = character()) } \arguments{ \item{object}{\code{AnnotationFilter} or \code{AnnotationFilterList} objects (or objects extending these classes).} \item{db}{\code{EnsDb} object.} \item{with.tables}{optional \code{character} vector specifying the names of the database tables that are being queried.} } \value{ A \code{character(1)} with the SQL where condition. } \description{ \code{convertFilter} converts an \code{AnnotationFilter::AnnotationFilter} or \code{AnnotationFilter::AnnotationFilterList} to an SQL where condition for an \code{EnsDb} database. } \note{ This function \emph{might} be used in direct SQL queries on the SQLite database underlying an \code{EnsDb} but is more thought to illustrate the use of \code{AnnotationFilter} objects in combination with SQL databases. This method is used internally to create the SQL calls to the database. } \examples{ library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 ## Define a filter flt <- AnnotationFilter(~ genename == "BCL2") ## Use the method from the AnnotationFilter package: convertFilter(flt) ## Create a combination of filters flt_list <- AnnotationFilter(~ genename \%in\% c("BCL2", "BCL2L11") & tx_biotype == "protein_coding") flt_list convertFilter(flt_list) ## Use the filters in the context of an EnsDb database: convertFilter(flt, edb) convertFilter(flt_list, edb) } \author{ Johannes Rainer } ensembldb/man/global-filters.Rd0000644000175400017540000000525413175714743017525 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Methods.R, R/functions-EnsDb.R \docType{methods} \name{addFilter,EnsDb-method} \alias{addFilter,EnsDb-method} \alias{addFilter} \alias{dropFilter,EnsDb-method} \alias{dropFilter} \alias{activeFilter,EnsDb-method} \alias{activeFilter} \alias{filter} \title{Globally add filters to an EnsDb database} \usage{ \S4method{addFilter}{EnsDb}(x, filter = AnnotationFilterList()) \S4method{dropFilter}{EnsDb}(x) \S4method{activeFilter}{EnsDb}(x) filter(x, filter = AnnotationFilterList()) } \arguments{ \item{x}{The \code{\linkS4class{EnsDb}} object to which the filter should be added.} \item{filter}{The filter as an \code{\link[AnnotationFilter]{AnnotationFilter}}, \code{\link[AnnotationFilter]{AnnotationFilterList}} or filter expression. See} } \value{ \code{addFilter} and \code{filter} return an \code{EnsDb} object with the specified filter added. \code{activeFilter} returns an \code{\link[AnnotationFilter]{AnnotationFilterList}} object being the active global filter or \code{NA} if no filter was added. \code{dropFilter} returns an \code{EnsDb} object with all eventually present global filters removed. } \description{ These methods allow to set, delete or show globally defined filters on an \code{\linkS4class{EnsDb}} object. \code{addFilter}: adds an annotation filter to the \code{EnsDb} object. \code{dropFilter} deletes all globally set filters from the \code{EnsDb} object. \code{activeFilter} returns the globally set filter from an \code{EnsDb} object. \code{filter} filters an \code{EnsDb} object. \code{filter} is an alias for the \code{addFilter} function. } \details{ Adding a filter to an \code{EnsDb} object causes this filter to be permanently active. The filter will be used for all queries to the database and is added to all additional filters passed to the methods such as \code{\link{genes}}. } \examples{ library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 ## Add a global SeqNameFilter to the database such that all subsequent ## queries will be applied on the filtered database. edb_y <- addFilter(edb, SeqNameFilter("Y")) ## Note: using the filter function is equivalent to a call to addFilter. ## Each call returns now only features encoded on chromosome Y gns <- genes(edb_y) seqlevels(gns) ## Get all lincRNA gene transcripts on chromosome Y transcripts(edb_y, filter = ~ gene_biotype == "lincRNA") ## Get the currently active global filter: activeFilter(edb_y) ## Delete this filter again. edb_y <- dropFilter(edb_y) activeFilter(edb_y) } \seealso{ \code{\link{Filter-classes}} for a list of all supported filters. } \author{ Johannes Rainer } ensembldb/man/hasProteinData-EnsDb-method.Rd0000644000175400017540000000143113175714743021765 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Methods.R \docType{methods} \name{hasProteinData,EnsDb-method} \alias{hasProteinData,EnsDb-method} \alias{hasProteinData} \title{Determine whether protein data is available in the database} \usage{ \S4method{hasProteinData}{EnsDb}(x) } \arguments{ \item{x}{The \code{\linkS4class{EnsDb}} object.} } \value{ A logical of length one, \code{TRUE} if protein annotations are available and \code{FALSE} otherwise. } \description{ Determines whether the \code{\linkS4class{EnsDb}} provides protein annotation data. } \examples{ library(EnsDb.Hsapiens.v75) ## Does this database/package have protein annotations? hasProteinData(EnsDb.Hsapiens.v75) } \seealso{ \code{\link{listTables}} } \author{ Johannes Rainer } ensembldb/man/listEnsDbs.Rd0000644000175400017540000000304613175714743016666 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) } } \seealso{ \code{\link{useMySQL}} } \author{ Johannes Rainer } ensembldb/man/makeEnsemblDbPackage.Rd0000644000175400017540000002333213175714743020561 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. } \item{...}{ Currently not used. } } \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.Rd0000644000175400017540000000161613175714743016776 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.Rd0000644000175400017540000000333013175714743020541 0ustar00biocbuildbiocbuild% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Methods.R \docType{methods} \name{useMySQL,EnsDb-method} \alias{useMySQL,EnsDb-method} \alias{useMySQL} \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/readme.md0000644000175400017540000000211113175714743015321 0ustar00biocbuildbiocbuild

[![Years in Bioconductor](http://www.bioconductor.org/shields/years-in-bioc/ensembldb.svg)](http://www.bioconductor.org/packages/release/bioc/html/ensembldb.html) [![Bioconductor release build status](http://www.bioconductor.org/shields/build/release/bioc/ensembldb.svg)](http://www.bioconductor.org/packages/release/bioc/html/ensembldb.html) [![Bioconductor devel build status](http://www.bioconductor.org/shields/build/devel/bioc/ensembldb.svg)](http://www.bioconductor.org/checkResults/deve/bioc-LATEST/ensembldb) [![Travis build result](https://travis-ci.org/jotsetung/ensembldb.svg?branch=master)](https://travis-ci.org/jotsetung/ensembldb) [![codecov result](https://codecov.io/github/jotsetung/ensembldb/coverage.svg?branch=master)](https://codecov.io/github/jotsetung/ensembldb?branch=master) # `ensembldb`: build and use Ensembl-based annotation packages For more information please refer to the [vignettes/ensembldb.org](vignettes/ensembldb.org) file. ensembldb/tests/0000755000175400017540000000000013175714743014711 5ustar00biocbuildbiocbuildensembldb/tests/testthat/0000755000175400017540000000000013241155731016537 5ustar00biocbuildbiocbuildensembldb/tests/testthat.R0000644000175400017540000000016413175714743016675 0ustar00biocbuildbiocbuildlibrary(testthat) library(ensembldb) library(EnsDb.Hsapiens.v75) edb <- EnsDb.Hsapiens.v75 test_check("ensembldb") ensembldb/tests/testthat/test_Classes.R0000644000175400017540000000731113175714743021332 0ustar00biocbuildbiocbuildtest_that("OnlyCodingTxFilter constructor works", { fl <- OnlyCodingTxFilter() expect_true(is(fl, "OnlyCodingTxFilter")) }) test_that("ProtDomIdFilter constructor works", { fl <- ProtDomIdFilter("a") expect_true(is(fl, "ProtDomIdFilter")) fl <- AnnotationFilter(~ prot_dom_id %in% 1:4) expect_true(is(fl, "ProtDomIdFilter")) expect_equal(value(fl), c("1", "2", "3", "4")) expect_equal(condition(fl), "==") }) test_that("UniprotDbFilter constructor works", { fl <- UniprotDbFilter("a") expect_true(is(fl, "UniprotDbFilter")) fl <- AnnotationFilter(~ uniprot_db != "4") expect_true(is(fl, "UniprotDbFilter")) expect_equal(value(fl), "4") expect_equal(condition(fl), "!=") }) test_that("UniprotMappingTypeFilter constructor works", { fl <- UniprotMappingTypeFilter("a") expect_true(is(fl, "UniprotMappingTypeFilter")) fl <- AnnotationFilter(~ uniprot_mapping_type != "4") expect_true(is(fl, "UniprotMappingTypeFilter")) expect_equal(value(fl), "4") expect_equal(condition(fl), "!=") }) test_that("GRangesFilter works for EnsDb", { ## Testing slots gr <- GRanges("X", ranges = IRanges(123, 234), strand = "-") grf <- GRangesFilter(gr, type = "within") ## Now check some stuff expect_equal(start(grf), start(gr)) expect_equal(end(grf), end(gr)) expect_equal(as.character(strand(gr)), strand(grf)) expect_equal(as.character(seqnames(gr)), seqnames(grf)) ## Test column: ## filter alone. exp <- c(start = "gene_seq_start", end = "gene_seq_end", seqname = "seq_name", strand = "seq_strand") expect_equal(ensembldb:::ensDbColumn(grf), exp) grf@feature <- "tx" exp <- c(start = "tx_seq_start", end = "tx_seq_end", seqname = "seq_name", strand = "seq_strand") expect_equal(ensembldb:::ensDbColumn(grf), exp) grf@feature <- "exon" exp <- c(start = "exon_seq_start", end = "exon_seq_end", seqname = "seq_name", strand = "seq_strand") expect_equal(ensembldb:::ensDbColumn(grf), exp) ## filter and ensdb. exp <- c(start = "exon.exon_seq_start", end = "exon.exon_seq_end", seqname = "gene.seq_name", strand = "gene.seq_strand") expect_equal(ensembldb:::ensDbColumn(grf, edb), exp) grf@feature <- "tx" exp <- c(start = "tx.tx_seq_start", end = "tx.tx_seq_end", seqname = "gene.seq_name", strand = "gene.seq_strand") expect_equal(ensembldb:::ensDbColumn(grf, edb), exp) grf@feature <- "gene" exp <- c(start = "gene.gene_seq_start", end = "gene.gene_seq_end", seqname = "gene.seq_name", strand = "gene.seq_strand") expect_equal(ensembldb:::ensDbColumn(grf, edb), exp) exp <- paste0("(gene_seq_start>=123 and gene_seq_end<=234 and", " seq_name='X' and seq_strand = -1)") expect_equal(ensembldb:::ensDbQuery(grf), exp) ## what if we set strand to * grf2 <- GRangesFilter(GRanges("1", IRanges(123, 234)), type = "within") exp <- paste0("(gene.gene_seq_start>=123 and gene.gene_seq_end<=234", " and gene.seq_name='1')") expect_equal(ensembldb:::ensDbQuery(grf2, edb), exp) ## Now, using overlapping. grf2 <- GRangesFilter(GRanges("X", IRanges(123, 234), strand = "-"), type = "any", feature = "transcript") exp <- paste0("(tx.tx_seq_start<=234 and tx.tx_seq_end>=123 and", " gene.seq_name='X' and gene.seq_strand = -1)") expect_equal(ensembldb:::ensDbQuery(grf2, edb), exp) }) test_that("TxSupportLevelFilter works for EnsDb", { fl <- TxSupportLevelFilter(3) expect_true(is(fl, "TxSupportLevelFilter")) fl <- AnnotationFilter(~ tx_support_level == 3) expect_true(is(fl, "TxSupportLevelFilter")) }) ensembldb/tests/testthat/test_Methods-Filter.R0000644000175400017540000007001313175714743022562 0ustar00biocbuildbiocbuild test_that("ensDbColumn works", { smb <- SymbolFilter("a") expect_equal(unname(ensembldb:::ensDbColumn(smb)), "gene_name") expect_equal(unname(ensembldb:::ensDbColumn(smb, edb)), "gene.gene_name") expect_error(unname(ensembldb:::ensDbColumn(smb, edb, "tx"))) expect_equal(unname(ensembldb:::ensDbColumn(smb, edb, "gene")), "gene.gene_name") ## fl <- OnlyCodingTxFilter() expect_equal(ensembldb:::ensDbColumn(fl), "tx.tx_cds_seq_start") ## gene filters: fl <- GeneIdFilter("a") expect_equal(unname(ensembldb:::ensDbColumn(fl)), "gene_id") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb)), "gene.gene_id") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb, "tx")), "tx.gene_id") fl <- GenenameFilter("a") expect_equal(unname(ensembldb:::ensDbColumn(fl)), "gene_name") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb)), "gene.gene_name") expect_error(unname(ensembldb:::ensDbColumn(fl, edb, "tx"))) fl <- GeneStartFilter(123) expect_equal(unname(ensembldb:::ensDbColumn(fl)), "gene_seq_start") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb)), "gene.gene_seq_start") expect_error(unname(ensembldb:::ensDbColumn(fl, edb, "tx"))) fl <- GeneEndFilter(123) expect_equal(unname(ensembldb:::ensDbColumn(fl)), "gene_seq_end") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb)), "gene.gene_seq_end") expect_error(unname(ensembldb:::ensDbColumn(fl, edb, "tx"))) fl <- EntrezFilter(123) expect_equal(unname(ensembldb:::ensDbColumn(fl)), "entrezid") if (as.numeric(ensembldb:::dbSchemaVersion(edb)) > 1) { expect_equal(unname(ensembldb:::ensDbColumn(fl, edb)), "entrezgene.entrezid") } else { expect_equal(unname(ensembldb:::ensDbColumn(fl, edb)), "gene.entrezid") } expect_error(unname(ensembldb:::ensDbColumn(fl, edb, "tx"))) fl <- SeqNameFilter("a") expect_equal(unname(ensembldb:::ensDbColumn(fl)), "seq_name") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb)), "gene.seq_name") expect_error(unname(ensembldb:::ensDbColumn(fl, edb, "tx"))) fl <- SeqStrandFilter("a") expect_equal(unname(ensembldb:::ensDbColumn(fl)), "seq_strand") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb)), "gene.seq_strand") expect_error(unname(ensembldb:::ensDbColumn(fl, edb, "tx"))) ## tx filters: fl <- TxIdFilter("a") expect_equal(unname(ensembldb:::ensDbColumn(fl)), "tx_id") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb)), "tx.tx_id") expect_error(unname(ensembldb:::ensDbColumn(fl, edb, "gene"))) expect_equal(unname(ensembldb:::ensDbColumn(fl, edb, "protein")), "protein.tx_id") fl <- TxNameFilter("a") expect_equal(unname(ensembldb:::ensDbColumn(fl)), "tx_id") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb)), "tx.tx_id") expect_error(unname(ensembldb:::ensDbColumn(fl, edb, "gene"))) fl <- TxBiotypeFilter("a") expect_equal(unname(ensembldb:::ensDbColumn(fl)), "tx_biotype") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb)), "tx.tx_biotype") expect_error(unname(ensembldb:::ensDbColumn(fl, edb, "gene"))) fl <- TxStartFilter(123) expect_equal(unname(ensembldb:::ensDbColumn(fl)), "tx_seq_start") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb)), "tx.tx_seq_start") expect_error(unname(ensembldb:::ensDbColumn(fl, edb, "gene"))) fl <- TxEndFilter(123) expect_equal(unname(ensembldb:::ensDbColumn(fl)), "tx_seq_end") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb)), "tx.tx_seq_end") expect_error(unname(ensembldb:::ensDbColumn(fl, edb, "gene"))) ## exon filters: fl <- ExonIdFilter(123) expect_equal(unname(ensembldb:::ensDbColumn(fl)), "exon_id") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb)), "tx2exon.exon_id") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb, "tx2exon")), "tx2exon.exon_id") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb, "exon")), "exon.exon_id") expect_error(unname(ensembldb:::ensDbColumn(fl, edb, "gene"))) fl <- ExonEndFilter(123) expect_equal(unname(ensembldb:::ensDbColumn(fl)), "exon_seq_end") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb)), "exon.exon_seq_end") expect_error(unname(ensembldb:::ensDbColumn(fl, edb, "gene"))) fl <- ExonStartFilter(123) expect_equal(unname(ensembldb:::ensDbColumn(fl)), "exon_seq_start") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb)), "exon.exon_seq_start") expect_error(unname(ensembldb:::ensDbColumn(fl, edb, "gene"))) fl <- ExonRankFilter(123) expect_equal(unname(ensembldb:::ensDbColumn(fl)), "exon_idx") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb)), "tx2exon.exon_idx") expect_error(unname(ensembldb:::ensDbColumn(fl, edb, "gene"))) ## protein filters: fl <- ProteinIdFilter("a") expect_equal(unname(ensembldb:::ensDbColumn(fl)), "protein_id") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb)), "protein.protein_id") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb, "uniprot")), "uniprot.protein_id") expect_error(unname(ensembldb:::ensDbColumn(fl, edb, "gene"))) fl <- UniprotFilter("a") expect_equal(unname(ensembldb:::ensDbColumn(fl)), "uniprot_id") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb)), "uniprot.uniprot_id") expect_error(unname(ensembldb:::ensDbColumn(fl, edb, "gene"))) fl <- ProtDomIdFilter("a") expect_equal(unname(ensembldb:::ensDbColumn(fl)), "protein_domain_id") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb)), "protein_domain.protein_domain_id") expect_error(unname(ensembldb:::ensDbColumn(fl, edb, "gene"))) fl <- UniprotDbFilter("a") expect_equal(unname(ensembldb:::ensDbColumn(fl)), "uniprot_db") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb)), "uniprot.uniprot_db") expect_error(unname(ensembldb:::ensDbColumn(fl, edb, "gene"))) fl <- UniprotMappingTypeFilter("a") expect_equal(unname(ensembldb:::ensDbColumn(fl)), "uniprot_mapping_type") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb)), "uniprot.uniprot_mapping_type") expect_error(unname(ensembldb:::ensDbColumn(fl, edb, "gene"))) }) test_that("ensDbQuery works", { smb <- SymbolFilter("I'm a gene") expect_equal(ensembldb:::ensDbQuery(smb), "gene_name = 'I''m a gene'") expect_equal(ensembldb:::ensDbQuery(smb, edb), "gene.gene_name = 'I''m a gene'") expect_equal(ensembldb:::ensDbQuery(smb, edb, c("gene", "tx")), "gene.gene_name = 'I''m a gene'") expect_error(ensembldb:::ensDbQuery(smb, edb, "tx")) smb <- SymbolFilter(c("a", "x"), condition = "!=") expect_equal(ensembldb:::ensDbQuery(smb), "gene_name not in ('a','x')") smb <- SymbolFilter(c("a", "x"), condition = "!=") expect_equal(ensembldb:::ensDbQuery(smb, edb), "gene.gene_name not in ('a','x')") ## gene_id with tx table. fl <- GeneIdFilter("a") expect_equal(ensembldb:::ensDbQuery(fl), "gene_id = 'a'") expect_equal(ensembldb:::ensDbQuery(fl, edb), "gene.gene_id = 'a'") expect_equal(ensembldb:::ensDbQuery(fl, edb, "tx"), "tx.gene_id = 'a'") ## numeric filter(s) fl <- ExonRankFilter(21) expect_equal(ensembldb:::ensDbQuery(fl), "exon_idx = 21") expect_equal(ensembldb:::ensDbQuery(fl, edb), "tx2exon.exon_idx = 21") ## fl <- OnlyCodingTxFilter() expect_equal(ensembldb:::ensDbQuery(fl), "tx.tx_cds_seq_start is not null") ## fL <- AnnotationFilterList(GeneIdFilter("a"), TxBiotypeFilter("coding", condition = "!="), GeneStartFilter(123, condition = "<")) res <- ensembldb:::ensDbQuery(fL) expect_equal(res, paste0("(gene_id = 'a' and tx_biotype != 'coding' ", "and gene_seq_start < 123)")) res <- ensembldb:::ensDbQuery(fL, edb) expect_equal(res, paste0("(gene.gene_id = 'a' and tx.tx_biotype != ", "'coding' and gene.gene_seq_start < 123)")) fL <- AnnotationFilterList(GeneIdFilter("a"), TxBiotypeFilter("coding", condition = "!="), TxStartFilter(123, condition = "<")) res <- ensembldb:::ensDbQuery(fL, edb) expect_equal(res, paste0("(gene.gene_id = 'a' and tx.tx_biotype != ", "'coding' and tx.tx_seq_start < 123)")) res <- ensembldb:::ensDbQuery(fL, edb, c("tx", "gene", "exon")) expect_equal(res, paste0("(tx.gene_id = 'a' and tx.tx_biotype != ", "'coding' and tx.tx_seq_start < 123)")) fl <- ProteinIdFilter("a") expect_equal(unname(ensembldb:::ensDbQuery(fl)), "protein_id = 'a'") expect_equal(unname(ensembldb:::ensDbQuery(fl, edb)), "protein.protein_id = 'a'") expect_equal(unname(ensembldb:::ensDbQuery(fl, edb, "uniprot")), "uniprot.protein_id = 'a'") fl <- ProtDomIdFilter("a") expect_equal(ensembldb:::ensDbQuery(fl), "protein_domain_id = 'a'") expect_equal(ensembldb:::ensDbQuery(fl, edb), "protein_domain.protein_domain_id = 'a'") fl <- UniprotDbFilter("a") expect_equal(ensembldb:::ensDbQuery(fl), "uniprot_db = 'a'") expect_equal(ensembldb:::ensDbQuery(fl, edb), "uniprot.uniprot_db = 'a'") fl <- UniprotMappingTypeFilter("a") expect_equal(ensembldb:::ensDbQuery(fl), "uniprot_mapping_type = 'a'") expect_equal(ensembldb:::ensDbQuery(fl, edb), "uniprot.uniprot_mapping_type = 'a'") ## Seq name and seq strand. fl <- SeqNameFilter("3") expect_equal(ensembldb:::ensDbQuery(fl), "seq_name = '3'") expect_equal(ensembldb:::ensDbQuery(fl, edb), "gene.seq_name = '3'") fl <- SeqNameFilter("chr3") expect_equal(ensembldb:::ensDbQuery(fl), "seq_name = 'chr3'") expect_equal(ensembldb:::ensDbQuery(fl, edb), "gene.seq_name = 'chr3'") seqlevelsStyle(edb) <- "UCSC" expect_equal(ensembldb:::ensDbQuery(fl), "seq_name = 'chr3'") expect_equal(ensembldb:::ensDbQuery(fl, edb), "gene.seq_name = '3'") seqlevelsStyle(edb) <- "Ensembl" fl <- SeqStrandFilter("+") expect_equal(ensembldb:::ensDbQuery(fl), "seq_strand = 1") expect_equal(ensembldb:::ensDbQuery(fl, edb), "gene.seq_strand = 1") fl <- SeqStrandFilter("+1") expect_equal(ensembldb:::ensDbQuery(fl), "seq_strand = 1") expect_equal(ensembldb:::ensDbQuery(fl, edb), "gene.seq_strand = 1") fl <- SeqStrandFilter("-") expect_equal(ensembldb:::ensDbQuery(fl), "seq_strand = -1") expect_equal(ensembldb:::ensDbQuery(fl, edb), "gene.seq_strand = -1") fl <- SeqStrandFilter("-1") expect_equal(ensembldb:::ensDbQuery(fl), "seq_strand = -1") expect_equal(ensembldb:::ensDbQuery(fl, edb), "gene.seq_strand = -1") ## GRangesFilter: see test_ensDb_for_GRangesFilter }) test_that("ensDbQuery works for AnnotationFilterList", { gnf <- GenenameFilter("BCL2", condition = "!=") snf <- SeqNameFilter(4) ssf <- SeqStrandFilter("+") afl <- AnnotationFilterList(gnf, snf, ssf, logOp = c("|", "&")) Q <- ensembldb:::ensDbQuery(afl) expect_equal(Q, "(gene_name != 'BCL2' or seq_name = '4' and seq_strand = 1)") ## Nested AnnotationFilterLists. afl1 <- AnnotationFilterList(GenenameFilter("BCL2"), GenenameFilter("BCL2L11"), logOp = "|") afl2 <- AnnotationFilterList(afl1, SeqNameFilter(18)) Q <- ensembldb:::ensDbQuery(afl2, db = edb) expect_equal(Q, paste0("((gene.gene_name = 'BCL2' or gene.gene_name = ", "'BCL2L11') and gene.seq_name = '18')")) library(RSQLite) res <- dbGetQuery(dbconn(edb), paste0("select distinct gene_name from gene", " where ", Q)) expect_equal(res$gene_name, "BCL2") res2 <- genes(edb, filter = AnnotationFilterList(GenenameFilter(c("BCL2L11", "BCL2")), SeqNameFilter(18))) expect_equal(res$gene_name, res2$gene_name) ## Same with a GRangesFilter. grf <- GRangesFilter(GRanges(18, IRanges(60790600, 60790700))) afl2 <- AnnotationFilterList(afl1, grf) Q <- ensembldb:::ensDbQuery(afl2, db = edb) expect_equal(Q, paste0("((gene.gene_name = 'BCL2' or gene.gene_name = ", "'BCL2L11') and (gene.gene_seq_start<=60790700", " and gene.gene_seq_end>=60790600 and gene.seq_name", "='18'))")) res <- dbGetQuery(dbconn(edb), paste0("select distinct gene_name from gene", " where ", Q)) expect_equal(res$gene_name, "BCL2") }) test_that("ensDbQuery works for SeqNameFilter", { fl <- SeqNameFilter("3") res <- ensembldb:::ensDbQuery(fl) expect_equal(res, "seq_name = '3'") res <- ensembldb:::ensDbQuery(fl, edb) expect_equal(res, "gene.seq_name = '3'") fl <- SeqNameFilter("chr3") res <- ensembldb:::ensDbQuery(fl) expect_equal(res, "seq_name = 'chr3'") res <- ensembldb:::ensDbQuery(fl, edb) expect_equal(res, "gene.seq_name = 'chr3'") seqlevelsStyle(edb) <- "UCSC" res <- ensembldb:::ensDbQuery(fl) expect_equal(res, "seq_name = 'chr3'") res <- ensembldb:::ensDbQuery(fl, edb) expect_equal(res, "gene.seq_name = '3'") seqlevelsStyle(edb) <- "Ensembl" }) test_that("ensDbQuery works for GRangesFilter", { gr <- GRanges(seqnames = "a", ranges = IRanges(start = 1, end = 5)) F <- GRangesFilter(value = gr, type = "within") expect_equal(unname(ensembldb:::ensDbColumn(F)), c("gene_seq_start", "gene_seq_end", "seq_name", "seq_strand")) expect_equal(unname(ensembldb:::ensDbColumn(F, edb)), c("gene.gene_seq_start", "gene.gene_seq_end", "gene.seq_name", "gene.seq_strand")) expect_equal(ensembldb:::ensDbQuery(F), paste0("(gene_seq_start>=1 and gene_seq_end", "<=5 and seq_name='a')")) expect_equal(ensembldb:::ensDbQuery(F, edb), paste0("(gene.gene_seq_start>=1 and gene.gene_seq_end", "<=5 and gene.seq_name='a')")) F <- GRangesFilter(value = gr, type = "any") expect_equal(ensembldb:::ensDbQuery(F), paste0("(gene_seq_start<=5 and gene_seq_end", ">=1 and seq_name='a')")) expect_equal(ensembldb:::ensDbQuery(F, edb), paste0("(gene.gene_seq_start<=5 and gene.gene_seq_end", ">=1 and gene.seq_name='a')")) ## tx F <- GRangesFilter(value = gr, feature = "tx", type = "within") expect_equal(unname(ensembldb:::ensDbColumn(F)), c("tx_seq_start", "tx_seq_end", "seq_name", "seq_strand")) expect_equal(unname(ensembldb:::ensDbColumn(F, edb)), c("tx.tx_seq_start", "tx.tx_seq_end", "gene.seq_name", "gene.seq_strand")) ## exon F <- GRangesFilter(value = gr, feature = "exon", type = "within") expect_equal(unname(ensembldb:::ensDbColumn(F)), c("exon_seq_start", "exon_seq_end", "seq_name", "seq_strand")) expect_equal(unname(ensembldb:::ensDbColumn(F, edb)), c("exon.exon_seq_start", "exon.exon_seq_end", "gene.seq_name", "gene.seq_strand")) ## Check the buildWhere res <- ensembldb:::buildWhereForGRanges(F, columns = ensembldb:::ensDbColumn(F)) expect_equal(res,"(exon_seq_start>=1 and exon_seq_end<=5 and seq_name='a')") res <- ensembldb:::ensDbQuery(F) expect_equal(res,"(exon_seq_start>=1 and exon_seq_end<=5 and seq_name='a')") res <- ensembldb:::ensDbQuery(F, edb) expect_equal(res, paste0("(exon.exon_seq_start>=1 and exon.exon_seq_end", "<=5 and gene.seq_name='a')")) }) test_that("buildWhereForGRanges works", { grng <- GRanges(seqname = "X", IRanges(start = 10, end = 100)) ## start flt <- GRangesFilter(grng, type = "start") res <- ensembldb:::buildWhereForGRanges( flt, columns = ensembldb:::ensDbColumn(flt)) expect_equal(res, "(gene_seq_start=10 and seq_name='X')") res <- ensembldb:::buildWhereForGRanges( flt, columns = ensembldb:::ensDbColumn(flt, db = edb)) expect_equal(res, "(gene.gene_seq_start=10 and gene.seq_name='X')") ## end flt <- GRangesFilter(grng, type = "end") res <- ensembldb:::buildWhereForGRanges( flt, columns = ensembldb:::ensDbColumn(flt)) expect_equal(res, "(gene_seq_end=100 and seq_name='X')") ## equal flt <- GRangesFilter(grng, type = "equal") res <- ensembldb:::buildWhereForGRanges( flt, columns = ensembldb:::ensDbColumn(flt)) expect_equal(res, "(gene_seq_start=10 and gene_seq_end=100 and seq_name='X')") ## within flt <- GRangesFilter(grng, type = "within") res <- ensembldb:::buildWhereForGRanges( flt, columns = ensembldb:::ensDbColumn(flt)) expect_equal(res, "(gene_seq_start>=10 and gene_seq_end<=100 and seq_name='X')") ## any flt <- GRangesFilter(grng, type = "any") res <- ensembldb:::buildWhereForGRanges( flt, columns = ensembldb:::ensDbColumn(flt)) expect_equal(res, "(gene_seq_start<=100 and gene_seq_end>=10 and seq_name='X')") ## Same with a strand specified. grng <- GRanges(seqname = "X", IRanges(start = 10, end = 100), strand = "-") ## start flt <- GRangesFilter(grng, type = "start") res <- ensembldb:::buildWhereForGRanges( flt, columns = ensembldb:::ensDbColumn(flt)) expect_equal(res, "(gene_seq_start=10 and seq_name='X' and seq_strand = -1)") ## end flt <- GRangesFilter(grng, type = "end") res <- ensembldb:::buildWhereForGRanges( flt, columns = ensembldb:::ensDbColumn(flt)) expect_equal(res, "(gene_seq_end=100 and seq_name='X' and seq_strand = -1)") ## equal flt <- GRangesFilter(grng, type = "equal") res <- ensembldb:::buildWhereForGRanges( flt, columns = ensembldb:::ensDbColumn(flt)) expect_equal(res, paste0("(gene_seq_start=10 and gene_seq_end=100 and ", "seq_name='X' and seq_strand = -1)")) ## within flt <- GRangesFilter(grng, type = "within") res <- ensembldb:::buildWhereForGRanges( flt, columns = ensembldb:::ensDbColumn(flt)) expect_equal(res, paste0("(gene_seq_start>=10 and gene_seq_end<=100 and ", "seq_name='X' and seq_strand = -1)")) ## any flt <- GRangesFilter(grng, type = "any") res <- ensembldb:::buildWhereForGRanges( flt, columns = ensembldb:::ensDbColumn(flt)) expect_equal(res, paste0("(gene_seq_start<=100 and gene_seq_end>=10 and ", "seq_name='X' and seq_strand = -1)")) }) test_that("ensDbColumn works with AnnotationFilterList", { afl <- AnnotationFilterList(GeneIdFilter(123), SeqNameFilter(3)) afl2 <- AnnotationFilterList(afl, SeqNameFilter(5)) res <- ensembldb:::ensDbColumn(afl2) expect_equal(res, c("gene_id", "seq_name")) }) ############################################################ ## Using protein data based filters. test_that("ProteinIdFilter works", { pf <- ProteinIdFilter("ABC") expect_equal(value(pf), "ABC") expect_equal(field(pf), "protein_id") expect_equal(ensembldb:::ensDbQuery(pf), "protein_id = 'ABC'") if (hasProteinData(edb)) { expect_equal(ensembldb:::ensDbColumn(pf, edb), "protein.protein_id") expect_equal(ensembldb:::ensDbColumn(pf, edb, with.tables = "protein_domain"), "protein_domain.protein_id") expect_equal(ensembldb:::ensDbColumn(pf, edb, with.tables = "uniprot"), "uniprot.protein_id") expect_equal(ensembldb:::ensDbQuery(pf, edb), "protein.protein_id = 'ABC'") expect_equal(ensembldb:::ensDbQuery(pf, edb, with.tables = "uniprot"), "uniprot.protein_id = 'ABC'") } else { expect_error(ensembldb:::ensDbColumn(pf, edb)) expect_error(ensembldb:::ensDbQuery(pf, edb)) expect_error(ensembldb:::ensDbColumn(pf, edb, with.tables = "uniprot")) expect_error(ensembldb:::ensDbQuery(pf, edb, with.tables = "uniprot")) } pf <- ProteinIdFilter(c("A", "B")) expect_equal(ensembldb:::ensDbQuery(pf), "protein_id in ('A','B')") expect_error(ProteinIdFilter("B", condition = ">")) }) test_that("UniprotFilter works", { pf <- UniprotFilter("ABC") expect_equal(value(pf), "ABC") expect_equal(field(pf), "uniprot") expect_equal(unname(ensembldb:::ensDbColumn(pf)), "uniprot_id") expect_equal(ensembldb:::ensDbQuery(pf), "uniprot_id = 'ABC'") if (hasProteinData(edb)) { expect_equal(ensembldb:::ensDbColumn(pf, edb), "uniprot.uniprot_id") expect_equal(ensembldb:::ensDbColumn(pf, edb, with.tables = "uniprot"), "uniprot.uniprot_id") expect_equal(ensembldb:::ensDbQuery(pf, edb), "uniprot.uniprot_id = 'ABC'") expect_equal(ensembldb:::ensDbQuery(pf, edb, with.tables = "uniprot"), "uniprot.uniprot_id = 'ABC'") } else { expect_error(ensembldb:::ensDbColumn(pf, edb)) expect_error(ensembldb:::ensDbQuery(pf, edb)) expect_error(ensembldb:::ensDbColumn(pf, edb, with.tables = "uniprot")) expect_error(ensembldb:::ensDbQuery(pf, edb, with.tables = "uniprot")) } pf <- UniprotFilter(c("A", "B")) expect_equal(ensembldb:::ensDbQuery(pf), "uniprot_id in ('A','B')") expect_error(UniprotFilter("B", condition = ">")) }) test_that("ProtDomIdFilter works", { pf <- ProtDomIdFilter("ABC") expect_equal(value(pf), "ABC") expect_equal(field(pf), "prot_dom_id") expect_equal(unname(ensembldb:::ensDbColumn(pf)), "protein_domain_id") expect_equal(ensembldb:::ensDbQuery(pf), "protein_domain_id = 'ABC'") if (hasProteinData(edb)) { expect_equal(ensembldb:::ensDbColumn(pf, edb), "protein_domain.protein_domain_id") expect_equal(ensembldb:::ensDbColumn(pf, edb, with.tables = "protein_domain"), "protein_domain.protein_domain_id") expect_equal(ensembldb:::ensDbQuery(pf, edb), "protein_domain.protein_domain_id = 'ABC'") expect_equal(ensembldb:::ensDbQuery(pf, edb, with.tables = "protein_domain"), "protein_domain.protein_domain_id = 'ABC'") } else { expect_error(ensembldb:::ensDbColumn(pf, edb)) expect_error(ensembldb:::ensDbQuery(pf, edb)) expect_error(ensembldb:::ensDbColumn(pf, edb, with.tables = "protein_domain")) expect_error(ensembldb:::ensDbQuery(pf, edb, with.tables = "protein_domain")) } pf <- ProtDomIdFilter(c("A", "B")) expect_equal(ensembldb:::ensDbQuery(pf), "protein_domain_id in ('A','B')") expect_error(ProtDomIdFilter("B", condition = ">")) }) test_that("UniprotDbFilter works", { pf <- UniprotDbFilter("ABC") expect_equal(value(pf), "ABC") expect_equal(field(pf), "uniprot_db") expect_equal(ensembldb:::ensDbQuery(pf), "uniprot_db = 'ABC'") if (hasProteinData(edb)) { expect_equal(ensembldb:::ensDbColumn(pf, edb), "uniprot.uniprot_db") expect_equal(ensembldb:::ensDbColumn(pf, edb, with.tables = "uniprot"), "uniprot.uniprot_db") expect_equal(ensembldb:::ensDbQuery(pf, edb), "uniprot.uniprot_db = 'ABC'") expect_equal(ensembldb:::ensDbQuery(pf, edb, with.tables = "uniprot"), "uniprot.uniprot_db = 'ABC'") } else { expect_error(ensembldb:::ensDbColumn(pf, edb)) expect_error(ensembldb:::ensDbQuery(pf, edb)) expect_error(ensembldb:::ensDbColumn(pf, edb, with.tables = "uniprot")) expect_error(ensembldb:::ensDbQuery(pf, edb, with.tables = "uniprot")) } pf <- UniprotDbFilter(c("A", "B")) expect_equal(ensembldb:::ensDbQuery(pf), "uniprot_db in ('A','B')") expect_error(UniprotDbFilter("B", condition = ">")) }) test_that("UniprotMappingTypeFilter works", { pf <- UniprotMappingTypeFilter("ABC") expect_equal(value(pf), "ABC") expect_equal(field(pf), "uniprot_mapping_type") expect_equal(ensembldb:::ensDbQuery(pf), "uniprot_mapping_type = 'ABC'") expect_equal(unname(ensembldb:::ensDbColumn(pf)), "uniprot_mapping_type") if (hasProteinData(edb)) { expect_equal(ensembldb:::ensDbColumn(pf, edb), "uniprot.uniprot_mapping_type") expect_equal(ensembldb:::ensDbColumn(pf, edb, with.tables = "uniprot"), "uniprot.uniprot_mapping_type") expect_equal(ensembldb:::ensDbQuery(pf, edb), "uniprot.uniprot_mapping_type = 'ABC'") expect_equal(ensembldb:::ensDbQuery(pf, edb, with.tables = "uniprot"), "uniprot.uniprot_mapping_type = 'ABC'") } else { expect_error(ensembldb:::ensDbColumn(pf, edb)) expect_error(ensembldb:::ensDbQuery(pf, edb)) expect_error(ensembldb:::ensDbColumn(pf, edb, with.tables = "uniprot")) expect_error(ensembldb:::ensDbQuery(pf, edb, with.tables = "uniprot")) } pf <- UniprotMappingTypeFilter(c("A", "B")) expect_equal(ensembldb:::ensDbQuery(pf), "uniprot_mapping_type in ('A','B')") expect_error(UniprotMappingTypeFilter("B", condition = ">")) }) test_that("TxSupportLevelFilter works", { fl <- TxSupportLevelFilter(3) expect_equal(value(fl), 3) expect_equal(ensembldb:::ensDbQuery(fl), "tx_support_level = 3") expect_equal(unname(ensembldb:::ensDbColumn(fl)), "tx_support_level") supportsTsl <- any(supportedFilters(edb)$filter == "TxSupportLevelFilter") if (supportsTsl) { expect_equal(unname(ensembldb:::ensDbColumn(fl, edb)), "tx.tx_support_level") expect_equal(unname(ensembldb:::ensDbColumn(fl, edb, with.tables = "tx")), "tx.tx_support_level") expect_equal(unname(ensembldb:::ensDbQuery(fl, edb)), "tx.tx_support_level = 3") expect_equal(unname(ensembldb:::ensDbQuery(fl, edb, with.tables = "tx")), "tx.tx_support_level = 3") expect_error(ensembldb:::ensDbQuery(fl, edb, with.tables = "gene")) } else { expect_error(ensembldb:::ensDbColumn(fl, edb)) expect_error(ensembldb:::ensDbColumn(fl, edb, with.tables = "tx")) expect_error(ensembldb:::ensDbQuery(fl, edb)) expect_error(ensembldb:::ensDbQuery(fl, edb, with.tables = "tx")) } }) test_that("convertFilter works", { flt <- AnnotationFilter(~ genename == "BCL2") expect_equal(convertFilter(flt), "genename == 'BCL2'") expect_equal(convertFilter(flt, edb), "gene.gene_name = 'BCL2'") flt_list <- AnnotationFilter(~ genename %in% c("BCL2", "BCL2L11") & tx_biotype == "protein_coding") expect_equal( convertFilter(flt_list), "genename %in% c('BCL2', 'BCL2L11') & tx_biotype == 'protein_coding'") expect_equal( convertFilter(flt_list, edb), "(gene.gene_name in ('BCL2','BCL2L11') and tx.tx_biotype = 'protein_coding')") }) ensembldb/tests/testthat/test_Methods-with-returnFilterColumns.R0000644000175400017540000003035613175714743026342 0ustar00biocbuildbiocbuildtest_that("set returnFilterColumns works", { orig <- returnFilterColumns(edb) returnFilterColumns(edb) <- TRUE expect_equal(TRUE, returnFilterColumns(edb)) returnFilterColumns(edb) <- FALSE expect_equal(FALSE, returnFilterColumns(edb)) expect_error(returnFilterColumns(edb) <- "d") expect_error(returnFilterColumns(edb) <- c(TRUE, FALSE)) ## Restore the "original" setting returnFilterColumns(edb) <- orig }) test_that("returnFilterColumns works with_genes", { 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)), type = "within") res <- genes(edb, filter = grf) expect_equal(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 expect_equal(colnames(res), cols) returnFilterColumns(edb) <- TRUE res <- genes(edb, filter = grf, return.type = "data.frame", columns = cols) ## Now I expect also the gene coords. expect_equal(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) expect_equal(res$gene_name, "SKA2") expect_equal(colnames(res), c(cols, "gene_biotype", "gene_seq_start", "gene_seq_end", "seq_name", "seq_strand")) returnFilterColumns(edb) <- FALSE res <- genes(edb, filter = list(gbt, grf), return.type = "data.frame", columns = cols) expect_equal(colnames(res), cols) returnFilterColumns(edb) <- orig }) test_that("returnFilterColumns works with_tx", { orig <- returnFilterColumns(edb) returnFilterColumns(edb) <- FALSE ## What happens if we use a GRangesFilter with return filter cols FALSE? grf <- GRangesFilter(GRanges(17, IRanges(57180000, 57233000)), type = "within") res <- transcripts(edb, filter = grf) cols <- c("tx_id", "gene_name") res <- transcripts(edb, filter = grf, return.type = "data.frame", columns = cols) ## Expect only the columns expect_equal(colnames(res), cols) returnFilterColumns(edb) <- TRUE res <- transcripts(edb, filter = grf, return.type = "data.frame", columns = cols) ## Now I expect also the gene coords. expect_equal(colnames(res), c(cols, "tx_seq_start", "tx_seq_end", "seq_name", "seq_strand")) ## Use a gene biotype filter gbt <- GeneBiotypeFilter("protein_coding") returnFilterColumns(edb) <- TRUE res <- transcripts(edb, filter = list(gbt, grf), return.type = "data.frame", columns = cols) expect_equal(unique(res$gene_name), "SKA2") expect_equal(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) expect_equal(colnames(res), cols) returnFilterColumns(edb) <- orig }) test_that("returnFilterColumns works with exons", { 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)), type = "within") 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 expect_equal(colnames(res), cols) returnFilterColumns(edb) <- TRUE res <- exons(edb, filter = grf, return.type = "data.frame", columns = cols) ## Now I expect also the gene coords. expect_equal(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) expect_equal(unique(res$gene_name), c("TRIM37", "SKA2")) expect_equal(colnames(res), c(cols, "gene_biotype", "exon_seq_start", "exon_seq_end", "seq_name", "seq_strand")) returnFilterColumns(edb) <- FALSE res <- exons(edb, filter = list(gbt, grf), return.type = "data.frame", columns = cols) expect_equal(colnames(res), cols) returnFilterColumns(edb) <- orig }) test_that("returnFilterColumns works with exonsBy", { orig <- returnFilterColumns(edb) returnFilterColumns(edb) <- FALSE ## What happens if we use a GRangesFilter with return filter cols FALSE? grf <- GRangesFilter(GRanges(17, IRanges(57180000, 57233000)), type = "within") ## By genes cols <- c("exon_id", "gene_name") res <- exonsBy(edb, by = "gene", filter = grf, columns = cols) res <- unlist(res) ## Expect only the columns expect_equal(colnames(mcols(res)), cols) returnFilterColumns(edb) <- TRUE res <- exonsBy(edb, by = "gene", filter = grf, columns = cols) res <- unlist(res) ## Now I expect also the gene coords, but not the seq_name and seq_strand, ## as these are redundant with data which is in the GRanges! expect_equal(colnames(mcols(res)), c(cols, "gene_seq_start", "gene_seq_end")) ## Use a gene biotype filter gbt <- GeneBiotypeFilter("protein_coding") returnFilterColumns(edb) <- TRUE res <- unlist(exonsBy(edb, by = "gene", filter = list(gbt, grf), columns = cols)) expect_equal(unique(res$gene_name), c("SKA2")) expect_equal(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)) expect_equal(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 expect_equal(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. expect_equal(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)) expect_equal(unique(res$gene_name), c("SKA2")) expect_equal(colnames(mcols(res)), c(cols, "gene_biotype", "tx_seq_start", "tx_seq_end", "exon_rank")) returnFilterColumns(edb) <- FALSE res <- unlist(exonsBy(edb, by = "tx", filter = list(gbt, grf), columns = cols)) expect_equal(colnames(mcols(res)), c(cols, "exon_rank")) returnFilterColumns(edb) <- orig }) test_that("returnFilterColumns works with transcriptsBy", { orig <- returnFilterColumns(edb) returnFilterColumns(edb) <- FALSE ## What happens if we use a GRangesFilter with return filter cols FALSE? grf <- GRangesFilter(GRanges(17, IRanges(57180000, 57233000)), type = "within") ## By genes cols <- c("tx_id", "gene_name") res <- transcriptsBy(edb, by = "gene", filter = grf, columns = cols) res <- unlist(res) ## Expect only the columns expect_equal(colnames(mcols(res)), cols) returnFilterColumns(edb) <- TRUE res <- transcriptsBy(edb, by = "gene", filter = grf, columns = cols) res <- unlist(res) ## Now I expect also the gene coords. expect_equal(colnames(mcols(res)), c(cols, "gene_seq_start", "gene_seq_end")) ## Use a gene biotype filter gbt <- GeneBiotypeFilter("protein_coding") returnFilterColumns(edb) <- TRUE res <- unlist(transcriptsBy(edb, by = "gene", filter = list(gbt, grf), columns = cols)) expect_equal(unique(res$gene_name), c("SKA2")) expect_equal(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)) expect_equal(colnames(mcols(res)), cols) returnFilterColumns(edb) <- orig }) test_that("returnFilterColumns works with_cdsBy", { orig <- returnFilterColumns(edb) grf <- GRangesFilter(GRanges(17, IRanges(57180000, 57233000)), type = "within") ## 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 expect_equal(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. expect_equal(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)) expect_equal(unique(res$gene_name), c("SKA2")) expect_equal(colnames(mcols(res)), c(cols, "gene_biotype", "tx_seq_start", "tx_seq_end", "seq_name", "seq_strand", "exon_id", "exon_rank")) returnFilterColumns(edb) <- FALSE res <- unlist(cdsBy(edb, by = "tx", filter = list(gbt, grf), columns = cols)) expect_equal(colnames(mcols(res)), c(cols, "exon_id", "exon_rank")) returnFilterColumns(edb) <- orig }) test_that("returnFilterColumns works with threeUTRsByTranscript", { orig <- returnFilterColumns(edb) grf <- GRangesFilter(GRanges(17, IRanges(57180000, 57233000)), type = "within") ## By tx returnFilterColumns(edb) <- FALSE cols <- c("gene_id", "gene_name") res <- threeUTRsByTranscript(edb, filter = grf, columns = cols) res <- unlist(res) ## Expect only the columns expect_equal(colnames(mcols(res)), c(cols, "exon_id", "exon_rank")) returnFilterColumns(edb) <- TRUE res <- threeUTRsByTranscript(edb, filter = grf, columns = cols) res <- unlist(res) ## Now I expect also the gene coords. expect_equal(colnames(mcols(res)), c(cols, "tx_seq_start", "tx_seq_end", "seq_name", "seq_strand", "exon_id", "exon_rank")) ## Use a gene biotype filter gbt <- GeneBiotypeFilter("protein_coding") returnFilterColumns(edb) <- TRUE res <- unlist(threeUTRsByTranscript(edb, filter = list(gbt, grf), columns = cols)) expect_equal(unique(res$gene_name), c("SKA2")) expect_equal(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)) expect_equal(colnames(mcols(res)), c(cols, "exon_id", "exon_rank")) returnFilterColumns(edb) <- orig }) ensembldb/tests/testthat/test_Methods.R0000644000175400017540000011665313175714743021352 0ustar00biocbuildbiocbuild ## testing genes method. test_that("genes method works", { Gns <- genes(edb, filter = ~ genename == "BCL2") expect_identical(Gns$gene_name, "BCL2") Gns <- genes(edb, filter = SeqNameFilter("Y"), return.type = "DataFrame") expect_identical(sort(colnames(Gns)), sort(unique(c(listColumns(edb, "gene"), "entrezid")))) Gns <- genes(edb, filter = ~ seq_name == "Y" & gene_id == "ENSG00000012817", return.type = "DataFrame", columns = c("gene_id", "tx_name")) expect_identical(colnames(Gns), c("gene_id", "tx_name", "seq_name")) expect_true(all(Gns$seq_name == "Y")) expect_true(all(Gns$gene_id == "ENSG00000012817")) Gns <- genes(edb, filter = AnnotationFilterList(SeqNameFilter("Y"), GeneIdFilter("ENSG00000012817")), columns = c("gene_id", "gene_name")) ## Here we don't need the seqnames in mcols! expect_identical(colnames(mcols(Gns)), c("gene_id", "gene_name")) expect_true(all(Gns$seq_name == "Y")) expect_true(all(Gns$gene_id == "ENSG00000012817")) Gns <- genes(edb, filter = ~ seq_name == "Y" | genename == "BCL2", return.type = "DataFrame") expect_true(all(Gns$seq_name %in% c("18", "Y"))) Gns <- genes(edb, filter = AnnotationFilterList(SeqNameFilter("Y"), GenenameFilter("BCL2")), return.type = "DataFrame") expect_true(nrow(Gns) == 0) Gns <- genes(edb, filter = AnnotationFilterList(SeqNameFilter("Y"), GenenameFilter("BCL2"), logOp = "|"), return.type = "DataFrame") expect_true(all(Gns$seq_name %in% c("18", "Y"))) afl <- AnnotationFilterList(GenenameFilter(c("BCL2", "BCL2L11")), SeqNameFilter(18), logOp = "&") afl2 <- AnnotationFilterList(SeqNameFilter("Y"), afl, logOp = "|") Gns <- genes(edb, filter = afl2, columns = "gene_name", return.type = "DataFrame") expect_identical(colnames(Gns), c("gene_name", "gene_id", "seq_name")) expect_true(!any(Gns$gene_name == "BCL2L11")) expect_true(any(Gns$gene_name == "BCL2")) expect_true(all(Gns$seq_name %in% c("Y", "18"))) }) test_that("transcripts method works", { Tns <- transcripts(edb, filter = SeqNameFilter("Y"), return.type = "DataFrame") expect_identical(sort(colnames(Tns)), sort(c(listColumns(edb, "tx"), "seq_name"))) Tns <- transcripts(edb, columns = c("tx_id", "tx_name"), filter = list(SeqNameFilter("Y"), TxIdFilter("ENST00000435741"))) expect_identical(sort(colnames(mcols(Tns))), sort(c("tx_id", "tx_name"))) expect_true(all(Tns$seq_name == "Y")) expect_true(all(Tns$tx_id == "ENST00000435741")) ## Check the default ordering. Tns <- transcripts(edb, filter = list(TxBiotypeFilter("protein_coding"), SeqNameFilter("X")), return.type = "data.frame", columns = c("seq_name", listColumns(edb, "tx"))) expect_identical(order(Tns$seq_name, method = "radix"), 1:nrow(Tns)) }) test_that("promoters works", { res <- promoters(edb, filter = ~ genename == "ZBTB16") res_2 <- transcripts(edb, filter = GenenameFilter("ZBTB16")) expect_identical(length(res), length(res_2)) expect_true(all(width(res) == 2200)) }) test_that("transcriptsBy works", { ## Expect results on the forward strand to be ordered by tx_seq_start res <- transcriptsBy(edb, filter = ~ seq_name == "Y" & seq_strand == "-", by = "gene") fw <- res[[3]] expect_identical(order(start(fw)), 1:length(fw)) ## Expect results on the reverse strand to be ordered by -tx_seq_end res <- transcriptsBy(edb, filter = list(SeqNameFilter("Y"), SeqStrandFilter("-")), by = "gene") rv <- res[[3]] expect_identical(order(start(rv), decreasing = TRUE), 1:length(rv)) }) test_that("exons works", { Exns <- exons(edb, filter = SeqNameFilter("Y"), return.type = "DataFrame") expect_identical(sort(colnames(Exns)), sort(c(listColumns(edb, "exon"), "seq_name"))) ## Check correct ordering. Exns <- exons(edb, return.type = "data.frame", filter = SeqNameFilter(20:22)) expect_identical(order(Exns$seq_name, method = "radix"), 1:nrow(Exns)) }) test_that("exonsBy works", { ##ExnsBy <- exonsBy(edb, filter=list(SeqNameFilter("X")), by="tx") ExnsBy <- exonsBy(edb, filter = list(SeqNameFilter("Y")), by = "tx", columns = c("tx_name")) expect_identical(sort(colnames(mcols(ExnsBy[[1]]))), sort(c("exon_id", "exon_rank", "tx_name"))) suppressWarnings( ExnsBy <- exonsBy(edb, filter = list(SeqNameFilter("Y")), by = "tx", columns = c("tx_name"), use.names = TRUE) ) expect_identical(sort(colnames(mcols(ExnsBy[[1]]))), sort(c("exon_id", "exon_rank", "tx_name"))) ## Check what happens if we specify tx_id. ExnsBy <- exonsBy(edb, filter=list(SeqNameFilter("Y")), by="tx", columns=c("tx_id")) expect_identical(sort(colnames(mcols(ExnsBy[[1]]))), sort(c("exon_id", "exon_rank", "tx_id"))) ExnsBy <- exonsBy(edb, filter=list(SeqNameFilter("Y"), SeqStrandFilter("+")), by="gene") ## Check that ordering is on start on the forward strand. fw <- ExnsBy[[3]] expect_identical(order(start(fw)), 1:length(fw)) ## ExnsBy <- exonsBy(edb, filter=list(SeqNameFilter("Y"), SeqStrandFilter("-")), by="gene") ## Check that ordering is on start on the forward strand. rv <- ExnsBy[[3]] expect_identical(order(end(rv), decreasing = TRUE), 1:length(rv)) }) test_that("listGenebiotypes works", { GBT <- listGenebiotypes(edb) TBT <- listTxbiotypes(edb) }) ## test if we get the expected exceptions if we're not submitting ## correct filter objects test_that("Filter errors work in methods", { expect_error(genes(edb, filter="d")) expect_error(genes(edb, filter=list(SeqNameFilter("X"), "z"))) expect_error(transcripts(edb, filter="d")) expect_error(transcripts(edb, filter=list(SeqNameFilter("X"), "z"))) expect_error(exons(edb, filter="d")) expect_error(exons(edb, filter=list(SeqNameFilter("X"), "z"))) expect_error(exonsBy(edb, filter="d")) expect_error(exonsBy(edb, filter=list(SeqNameFilter("X"), "z"))) expect_error(transcriptsBy(edb, filter="d")) expect_error(transcriptsBy(edb, filter=list(SeqNameFilter("X"), "z"))) expect_error(transcripts(edb, filter = ~ other_filter == "b")) }) test_that("genes returns correct columns", { cols <- c("gene_name", "tx_id") Resu <- genes(edb, filter=SeqNameFilter("Y"), columns=cols, return.type = "data.frame") expect_identical(sort(c(cols, "seq_name", "gene_id")), sort(colnames(Resu))) Resu <- genes(edb, filter=SeqNameFilter("Y"), columns=cols, return.type = "DataFrame") expect_identical(sort(c(cols, "seq_name", "gene_id")), sort(colnames(Resu))) Resu <- genes(edb, filter=SeqNameFilter("Y"), columns=cols) expect_identical(sort(c(cols, "gene_id")), sort(colnames(mcols(Resu)))) }) test_that("transcripts return correct columns", { cols <- c("tx_id", "exon_id", "tx_biotype") Resu <- transcripts(edb, filter=SeqNameFilter("Y"), columns=cols, return.type = "data.frame") expect_identical(sort(c(cols, "seq_name")), sort(colnames(Resu))) Resu <- transcripts(edb, filter=SeqNameFilter("Y"), columns=cols, return.type = "DataFrame") expect_identical(sort(c(cols, "seq_name")), sort(colnames(Resu))) Resu <- transcripts(edb, filter=SeqNameFilter("Y"), columns=cols) expect_identical(sort(cols), sort(colnames(mcols(Resu)))) }) test_that("exons returns correct columns", { cols <- c("tx_id", "exon_id", "tx_biotype") Resu <- exons(edb, filter=SeqNameFilter("Y"), columns=cols, return.type = "data.frame") expect_identical(sort(c(cols, "seq_name")), sort(colnames(Resu))) Resu <- exons(edb, filter=SeqNameFilter("Y"), columns=cols, return.type = "DataFrame") expect_identical(sort(c(cols, "seq_name")), sort(colnames(Resu))) Resu <- exons(edb, filter=SeqNameFilter("Y"), columns=cols) expect_identical(sort(cols), sort(colnames(mcols(Resu)))) }) test_that("cdsBy works", { 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. expect_true(all(start(cds) >= min(tx$tx_cds_seq_start))) expect_true(all(end(cds) <= max(tx$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. expect_true(all(start(cds)[-1] == tx$exon_seq_start[-1])) expect_true(all(end(cds)[-nrow(tx)] == tx$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") } } ## Just checking if we get also tx_name cs <- cdsBy(edb, filter = SeqNameFilter("Y"), column="tx_name") expect_true(any(colnames(mcols(cs[[1]])) == "tx_name")) do.plot <- FALSE ## By tx cs <- cdsBy(edb, filter=list(SeqNameFilter("Y"), SeqStrandFilter("+"))) tx <- exonsBy(edb, filter=list(SeqNameFilter("Y"), SeqStrandFilter("+"))) ## Check for the first if it makes sense: whichTx <- names(cs)[1] whichCs <- cs[[1]] tx <- transcripts(edb, 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(edb, 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(edb, filter=list(SeqNameFilter("Y"), SeqStrandFilter("-"))) whichTx <- names(cs)[1] whichCs <- cs[[1]] tx <- transcripts(edb, 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(edb, 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(edb, filter=list(SeqNameFilter("Y")), columns=c("gene_biotype", "gene_name")) }) test_that("cdsBy with gene works", { checkSingleGene <- function(whichCs, gene, do.plot=FALSE){ tx <- transcripts(edb, 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$tx_cds_seq_end, ybottom = rep((i-1+0.1), nrow(current)), ytop = rep((i-0.1), nrow(current)), border = "blue") } rect(xleft=start(whichCs), xright=end(whichCs), ybottom=rep(length(tx)+0.1, length(whichCs)), ytop=rep(length(tx)+0.9, length(whichCs)), border="red") } } do.plot <- FALSE ## By gene. cs <- cdsBy(edb, filter=list(SeqNameFilter("Y"), SeqStrandFilter("+")), by="gene", columns=NULL) checkSingleGene(cs[[1]], gene=names(cs)[[1]], do.plot=do.plot) checkSingleGene(cs[[2]], gene=names(cs)[[2]], do.plot=do.plot) ## - strand cs <- cdsBy(edb, filter=list(SeqNameFilter("Y"), SeqStrandFilter("-")), by="gene", columns=NULL) checkSingleGene(cs[[1]], gene=names(cs)[[1]], do.plot=do.plot) checkSingleGene(cs[[2]], gene=names(cs)[[2]], do.plot=do.plot) ## looks good! cs2 <- cdsBy(edb, filter=list(SeqNameFilter("Y"), SeqStrandFilter("+")), by="gene", use.names=TRUE) }) test_that("UTRs work", { checkGeneUTRs <- function(f, t, c, tx, do.plot=FALSE){ if(any(strand(c) == "+")){ ## End of five UTR has to be smaller than any start of cds expect_true(max(end(f)) < min(start(c))) ## 3' expect_true(min(start(t)) > max(end(c))) }else{ ## 5' expect_true(min(start(f)) > max(end(c))) ## 3' expect_true(max(end(t)) < min(start(c))) } ## just plot... if(do.plot){ tx <- transcripts(edb, filter=TxIdFilter(tx), columns=c("exon_seq_start", "exon_seq_end"), return.type="data.frame") XL <- range(c(start(f), start(c), start(t), end(f), end(c), end(t))) YL <- c(0, 4) plot(4, 4, pch=NA, xlim=XL, ylim=YL, yaxt="n", ylab="", xlab="") ## five UTR rect(xleft=start(f), xright=end(f), ybottom=0.1, ytop=0.9, col="blue") ## cds rect(xleft=start(c), xright=end(c), ybottom=1.1, ytop=1.9) ## three UTR rect(xleft=start(t), xright=end(t), ybottom=2.1, ytop=2.9, col="red") ## all exons rect(xleft=tx$exon_seq_start, xright=tx$exon_seq_end, ybottom=3.1, ytop=3.9) } } ## check presence of tx_name fUTRs <- fiveUTRsByTranscript(edb, filter = TxIdFilter("ENST00000155093"), column = "tx_name") expect_true(any(colnames(mcols(fUTRs[[1]])) == "tx_name")) do.plot <- FALSE fUTRs <- fiveUTRsByTranscript(edb, filter = list(SeqNameFilter("Y"), SeqStrandFilter("+"))) tUTRs <- threeUTRsByTranscript(edb, filter = list(SeqNameFilter("Y"), SeqStrandFilter("+"))) cds <- cdsBy(edb, "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(edb, filter = list(SeqNameFilter("Y"), SeqStrandFilter("-"))) tUTRs <- threeUTRsByTranscript(edb, filter = list(SeqNameFilter("Y"), SeqStrandFilter("-"))) cds <- cdsBy(edb, "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) res_1 <- ensembldb:::getUTRsByTranscript(edb, what = "five", filter = TxIdFilter("ENST00000335953")) res_2 <- fiveUTRsByTranscript(edb, filter = TxIdFilter("ENST00000335953")) expect_identical(res_1, res_2) res_1 <- ensembldb:::getUTRsByTranscript(edb, what = "three", filter = TxIdFilter("ENST00000335953")) res_2 <- threeUTRsByTranscript(edb, filter = TxIdFilter("ENST00000335953")) expect_identical(res_1, res_2) }) test_that("lengthOf works", { system.time( lenY <- lengthOf(edb, "tx", filter=SeqNameFilter("Y")) ) ## Check what would happen if we do it ourselfs... system.time( lenY2 <- sum(width(reduce(exonsBy(edb, "tx", filter=SeqNameFilter("Y"))))) ) expect_identical(lenY, lenY2) ## Same for genes. system.time( lenY <- lengthOf(edb, "gene", filter= ~ seq_name == "Y") ) ## Check what would happen if we do it ourselfs... system.time( lenY2 <- sum(width(reduce(exonsBy(edb, "gene", filter=SeqNameFilter("Y"))))) ) expect_identical(lenY, lenY2) ## Just using the transcriptLengths res <- ensembldb:::.transcriptLengths(edb, filter = GenenameFilter("ZBTB16")) res_2 <- lengthOf(edb, "tx", filter = GenenameFilter("ZBTB16")) expect_identical(sort(res$tx_len), unname(sort(res_2))) ## also cds lengths etc. res <- ensembldb:::.transcriptLengths(edb, filter = GenenameFilter("ZBTB16"), with.cds_len = TRUE, with.utr5_len = TRUE, with.utr3_len = TRUE) expect_identical(colnames(res), c("tx_id", "gene_id", "nexon", "tx_len", "cds_len", "utr5_len", "utr3_len")) tx <- transcripts(edb, filter = list(GenenameFilter("ZBTB16"), TxBiotypeFilter("protein_coding"))) expect_true(all(!is.na(res[names(tx), "cds_len"]))) expect_equal(unname(res[names(tx), "tx_len"]), unname(rowSums(res[names(tx), c("utr5_len", "cds_len", "utr3_len")]))) }) ####============================================================ ## ExonRankFilter ## ####------------------------------------------------------------ test_that("ExonRankFilter works with methods", { txs <- transcripts(edb, columns=c("exon_id", "exon_idx"), filter=SeqNameFilter(c("Y"))) txs <- txs[order(names(txs))] txs2 <- transcripts(edb, columns=c("exon_id"), filter=list(SeqNameFilter(c("Y")), ExonRankFilter(3))) txs2 <- txs[order(names(txs2))] ## hm, that's weird somehow. exns <- exons(edb, columns=c("tx_id", "exon_idx"), filter=list(SeqNameFilter("Y"), ExonRankFilter(3))) expect_true(all(exns$exon_idx == 3)) exns <- exons(edb, columns=c("tx_id", "exon_idx"), filter=list(SeqNameFilter("Y"), ExonRankFilter(3, condition="<"))) expect_true(all(exns$exon_idx < 3)) }) test_that("buildQuery and getWhat works", { library(RSQLite) Q <- buildQuery(edb, columns = c("gene_name", "gene_id")) expect_identical(Q, "select distinct gene.gene_name,gene.gene_id from gene") gf <- GeneIdFilter("ENSG00000000005") Q <- buildQuery(edb, columns = c("gene_name", "exon_idx"), filter = AnnotationFilterList(gf)) res <- dbGetQuery(dbconn(edb), Q) Q_2 <- paste0("select * from gene join tx on (gene.gene_id=tx.gene_id)", " join tx2exon on (tx.tx_id=tx2exon.tx_id) where", " gene.gene_id = 'ENSG00000000005'") res_2 <- dbGetQuery(dbconn(edb), Q_2) expect_identical(res, unique(res_2[, colnames(res)])) res_3 <- ensembldb:::getWhat(edb, columns = c("gene_name", "exon_idx"), filter = AnnotationFilterList(gf)) expect_identical(res_3, unique(res_2[, colnames(res_3)])) }) test_that("toSaf works", { txs <- transcriptsBy(edb, filter = GenenameFilter("ZBTB16")) saf <- ensembldb:::.toSaf(txs) expect_identical(nrow(saf), sum(lengths(txs))) saf2 <- toSAF(txs) expect_identical(saf2, saf) }) test_that("disjointExons works", { dje <- disjointExons(edb, filter = GenenameFilter("ZBTB16")) exns <- exons(edb, filter = GenenameFilter("ZBTB16")) ## Expect that dje is shorter than exns, since overlapping exon parts have ## been fused. expect_true(length(dje) < length(exns)) dje <- disjointExons(edb, filter = GenenameFilter("ZBTB16"), aggregateGenes = TRUE) expect_true(length(dje) < length(exns)) }) test_that("getGeneRegionTrackForGviz works", { res <- getGeneRegionTrackForGviz(edb, filter = GenenameFilter("ZBTB16")) expect_true(all(res$feature %in% c("protein_coding", "utr5", "utr3"))) ## Do the same without a filter: ## LLLLL res2 <- getGeneRegionTrackForGviz(edb, chromosome = "11", start = 113930000, end = 113935000) expect_true(all(res2$symbol == "ZBTB16")) }) test_that("filter columns are correctly added in methods", { filtList <- AnnotationFilterList(GenenameFilter("a"), ExonStartFilter(123), SymbolFilter("b"), TxIdFilter("c")) res <- ensembldb:::addFilterColumns(cols = c("a"), filter = filtList, edb = edb) expect_identical(res, c("a", "gene_name", "exon_seq_start", "symbol", "tx_id")) res <- ensembldb:::addFilterColumns(filter = filtList, edb = edb) expect_identical(res, c("gene_name", "exon_seq_start", "symbol", "tx_id")) ## New filts filtList <- AnnotationFilterList(GenenameFilter("a"), ExonStartFilter(123), SymbolFilter("b"), TxIdFilter("c")) res <- ensembldb:::addFilterColumns(cols = c("a"), filter = filtList, edb = edb) expect_identical(res, c("a", "gene_name", "exon_seq_start", "symbol", "tx_id")) res <- ensembldb:::addFilterColumns(filter = filtList, edb = edb) expect_identical(res, c("gene_name", "exon_seq_start", "symbol", "tx_id")) }) test_that("supportedFilters works", { res <- ensembldb:::.supportedFilters(edb) if (!hasProteinData(edb)) expect_equal(nrow(res), 19) else expect_equal(nrow(res), 24) res <- supportedFilters(edb) if (!hasProteinData(edb)) expect_equal(nrow(res), 19) else expect_equal(nrow(res), 24) }) ## Here we check if we fetch what we expect from the database. test_that("GRangesFilter works in queries", { 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)) expect_equal(txs$tx_id, txs2$tx_id) ## Exons: exs <- exons(edb, filter = GenenameFilter("ZBTB16")) exs2 <- exons(edb, filter = GRangesFilter(zbtb)) expect_equal(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, type = "any")) expect_equal(gns$gene_name, "ZBTB16") ## txs <- transcripts(edb, filter = GRangesFilter(intr, type = "any")) expect_equal(sort(txs$tx_id), sort(c("ENST00000335953", "ENST00000541602", "ENST00000392996", "ENST00000539918"))) 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), type = "any") Test <- transcripts(edb, filter = grf2) expect_equal(names(Test), c("ENST00000383070", "ENST00000250784", "ENST00000598545")) }) test_that("show works", { res <- capture.output(show(edb)) expect_equal(res[1], "EnsDb for Ensembl:") expect_equal(res[9], "|ensembl_version: 75") }) test_that("organism method works", { res <- organism(edb) expect_equal(res, "Homo sapiens") }) test_that("metadata method works", { res <- metadata(edb) expect_equal(res, dbGetQuery(dbconn(edb), "select * from metadata")) }) test_that("ensemblVersion works", { expect_equal(ensemblVersion(edb), "75") }) test_that("getMetadataValue works", { expect_error(ensembldb:::getMetadataValue(edb)) }) test_that("seqinfo and seqlevels work", { si <- seqinfo(edb) expect_true(is(si, "Seqinfo")) sl <- seqlevels(edb) library(RSQLite) chrs <- dbGetQuery(dbconn(edb), "select seq_name from chromosome")[, 1] expect_true(all(sl %in% chrs)) expect_true(all(seqlevels(si) %in% chrs)) }) test_that("ensVersionFromSourceUrl works", { res <- ensembldb:::.ensVersionFromSourceUrl( "ftp://ftp.ensembl.org/release-85/gtf") expect_equal(res, 85) }) test_that("listBiotypes works", { res <- listTxbiotypes(edb) library(RSQLite) res_2 <- dbGetQuery(dbconn(edb), "select distinct tx_biotype from tx")[, 1] expect_true(all(res %in% res_2)) res <- listGenebiotypes(edb) res_2 <- dbGetQuery(dbconn(edb), "select distinct gene_biotype from gene")[, 1] expect_true(all(res %in% res_2)) }) test_that("listTables works", { res <- listTables(edb) schema_version <- ensembldb:::dbSchemaVersion(edb) if (!hasProteinData(edb)) { expect_equal(names(res), names(ensembldb:::.ensdb_tables(schema_version))) } else { expect_equal( sort(names(res)), sort(unique(c(names(ensembldb:::.ensdb_tables(schema_version)), names(ensembldb:::.ensdb_protein_tables( schema_version)))))) } ## Repeat with deleting the cached tables edb@tables <- list() res <- listTables(edb) if (!hasProteinData(edb)) { expect_equal(names(res), names(ensembldb:::.ensdb_tables(schema_version))) } else { expect_equal( sort(names(res)), sort(unique(c(names(ensembldb:::.ensdb_tables(schema_version)), names(ensembldb:::.ensdb_protein_tables( schema_version)))))) } }) test_that("listColumns works", { res <- listColumns(edb, table = "gene") expect_equal(res, c(ensembldb:::.ensdb_tables(ensembldb:::dbSchemaVersion(dbconn(edb)))$gene, "symbol")) res <- listColumns(edb, table = "tx") expect_equal(res, c(ensembldb:::.ensdb_tables(ensembldb:::dbSchemaVersion(dbconn(edb)))$tx, "tx_name")) res <- listColumns(edb, table = "exon") expect_equal(res, c(ensembldb:::.ensdb_tables(ensembldb:::dbSchemaVersion(dbconn(edb)))$exon)) res <- listColumns(edb, table = "chromosome") expect_equal(res, c(ensembldb:::.ensdb_tables(ensembldb:::dbSchemaVersion(dbconn(edb)))$chromosome)) res <- listColumns(edb, table = "tx2exon") expect_equal(res, c(ensembldb:::.ensdb_tables(ensembldb:::dbSchemaVersion(dbconn(edb)))$tx2exon)) if (hasProteinData(edb)) { res <- listColumns(edb, table = "protein") expect_equal(res, ensembldb:::.ensdb_protein_tables(ensembldb:::dbSchemaVersion(dbconn(edb)))$protein) res <- listColumns(edb, table = "uniprot") expect_equal(res, ensembldb:::.ensdb_protein_tables(ensembldb:::dbSchemaVersion(dbconn(edb)))$uniprot) res <- listColumns(edb, table = "protein_domain") expect_equal(res, ensembldb:::.ensdb_protein_tables(ensembldb:::dbSchemaVersion(dbconn(edb)))$protein_domain) } ## Repeat with deleting the cached tables edb@tables <- list() res <- listColumns(edb, table = "gene") expect_equal(res, c(ensembldb:::.ensdb_tables(ensembldb:::dbSchemaVersion(dbconn(edb)))$gene, "symbol")) res <- listColumns(edb, table = "tx") expect_equal(res, c(ensembldb:::.ensdb_tables(ensembldb:::dbSchemaVersion(dbconn(edb)))$tx, "tx_name")) res <- listColumns(edb, table = "exon") expect_equal(res, c(ensembldb:::.ensdb_tables(ensembldb:::dbSchemaVersion(dbconn(edb)))$exon)) res <- listColumns(edb, table = "chromosome") expect_equal(res, c(ensembldb:::.ensdb_tables(ensembldb:::dbSchemaVersion(dbconn(edb)))$chromosome)) res <- listColumns(edb, table = "tx2exon") expect_equal(res, c(ensembldb:::.ensdb_tables(ensembldb:::dbSchemaVersion(dbconn(edb)))$tx2exon)) if (hasProteinData(edb)) { res <- listColumns(edb, table = "protein") expect_equal(res, ensembldb:::.ensdb_protein_tables(ensembldb:::dbSchemaVersion(dbconn(edb)))$protein) res <- listColumns(edb, table = "uniprot") expect_equal(res, ensembldb:::.ensdb_protein_tables(ensembldb:::dbSchemaVersion(dbconn(edb)))$uniprot) res <- listColumns(edb, table = "protein_domain") expect_equal(res, ensembldb:::.ensdb_protein_tables(ensembldb:::dbSchemaVersion(dbconn(edb)))$protein_domain) } }) test_that("cleanColumns works", { cols <- c("gene_id", "tx_id", "tx_name") res <- ensembldb:::cleanColumns(edb, cols) expect_equal(cols, res) cols <- c(cols, "not there") suppressWarnings( res <- ensembldb:::cleanColumns(edb, cols) ) expect_equal(cols[1:3], res) cols <- c("gene_id", "protein_id", "tx_id", "protein_sequence") suppressWarnings( res <- ensembldb:::cleanColumns(edb, cols) ) if (hasProteinData(edb)) { expect_equal(res, cols) } else { expect_equal(res, cols[c(1, 3)]) } ## with full names: cols <- c("gene.gene_id", "protein.protein_id", "tx.tx_id", "protein.protein_sequence") suppressWarnings( res <- ensembldb:::cleanColumns(edb, cols) ) if (hasProteinData(edb)) { expect_equal(res, cols) } else { expect_equal(res, cols[c(1, 3)]) } }) test_that("tablesForColumns works", { expect_error(ensembldb:::tablesForColumns(edb)) res <- ensembldb:::tablesForColumns(edb, columns = "tx_id") if (hasProteinData(edb)) expect_equal(res, c("tx", "tx2exon", "protein")) else expect_equal(res, c("tx", "tx2exon")) res <- ensembldb:::tablesForColumns(edb, columns = "seq_name") expect_equal(res, c("gene", "chromosome")) if (hasProteinData(edb)) { res <- ensembldb:::tablesForColumns(edb, columns = "protein_id") expect_equal(res, c("protein", "uniprot", "protein_domain")) } }) test_that("tablesByDegree works", { res <- ensembldb:::tablesByDegree(edb, tab = c("chromosome", "gene", "tx")) expect_equal(res, c("gene", "tx", "chromosome")) }) test_that("updateEnsDb works", { edb2 <- updateEnsDb(edb) expect_equal(edb2@tables, edb@tables) expect_true(.hasSlot(edb2, ".properties")) }) test_that("properties work", { origProps <- ensembldb:::properties(edb) expect_equal(ensembldb:::getProperty(edb, "foo"), NA) expect_error(ensembldb:::setProperty(edb, "foo")) edb <- ensembldb:::setProperty(edb, foo="bar") expect_equal(ensembldb:::getProperty(edb, "foo"), "bar") expect_equal(length(ensembldb:::properties(edb)), length(origProps) + 1) expect_true(any(names(ensembldb:::properties(edb)) == "foo")) edb <- ensembldb:::dropProperty(edb, "foo") expect_true(all(names(ensembldb:::properties(edb)) != "foo")) }) ## Compare the results for genes call with and without ordering in R test_that("ordering works in genes calls", { 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 expect_equal(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 expect_equal(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")) expect_equal(res_sql, res_r) ensembldb:::orderResultsInR(edb) <- orig }) test_that("transcriptLengths works",{ ## With filter. daFilt <- SeqNameFilter("Y") allTxY <- transcripts(edb, filter = daFilt) txLenY <- transcriptLengths(edb, filter = daFilt) expect_equal(names(allTxY), txLenY$tx_id) rownames(txLenY) <- txLenY$tx_id ## Check if lengths are OK: txLenY2 <- lengthOf(edb, "tx", filter = daFilt) expect_equal(unname(txLenY2[txLenY$tx_id]), txLenY$tx_len) ## Include the cds, 3' and 5' UTR txLenY <- transcriptLengths(edb, with.cds_len = TRUE, with.utr5_len = TRUE, with.utr3_len = TRUE, filter=daFilt) ## sum of 5' CDS and 3' has to match tx_len: txLen <- rowSums(txLenY[, c("cds_len", "utr5_len", "utr3_len")]) expect_equal(txLenY[txLenY$cds_len > 0, "tx_len"], unname(txLen[txLenY$cds_len > 0])) ## just to be sure... expect_equal(txLenY[txLenY$utr3_len > 0, "tx_len"], unname(txLen[txLenY$utr3_len > 0])) ## Seems to be OK. ## Next check the 5' UTR lengths: that also verifies the fiveUTR call. futr <- fiveUTRsByTranscript(edb, filter = daFilt) futrLen <- sum(width(futr)) rownames(txLenY) <- txLenY$tx_id expect_equal(unname(futrLen), txLenY[names(futrLen), "utr5_len"]) ## 3' tutr <- threeUTRsByTranscript(edb, filter=daFilt) tutrLen <- sum(width(tutr)) expect_equal(unname(tutrLen), txLenY[names(tutrLen), "utr3_len"]) }) test_that("transcriptsByOverlaps works", { ir2 <- IRanges(start = c(2654890, 2709520, 28111770), end = c(2654900, 2709550, 28111790)) gr2 <- GRanges(rep("Y", length(ir2)), ir2) grf2 <- GRangesFilter(gr2, type = "any") Test <- transcripts(edb, filter = grf2) Test2 <- transcriptsByOverlaps(edb, gr2) expect_equal(names(Test), names(Test2)) ## on one strand. gr2 <- GRanges(rep("Y", length(ir2)), ir2, strand = rep("-", length(ir2))) grf2 <- GRangesFilter(gr2, type = "any") Test <- transcripts(edb, filter = grf2) Test2 <- transcriptsByOverlaps(edb, gr2) expect_equal(names(Test), names(Test2)) ## Combine with filter... gr2 <- GRanges(rep("Y", length(ir2)), ir2) Test3 <- transcriptsByOverlaps(edb, gr2, filter = SeqStrandFilter("-")) expect_equal(names(Test), names(Test3)) }) test_that("exonsByOverlaps works", { ir2 <- IRanges(start=c(2654890, 2709520, 28111770), end=c(2654900, 2709550, 28111790)) gr2 <- GRanges(rep("Y", length(ir2)), ir2) grf2 <- GRangesFilter(gr2, type = "any") Test <- exons(edb, filter = grf2) Test2 <- exonsByOverlaps(edb, gr2) expect_equal(names(Test), names(Test2)) ## on one strand. gr2 <- GRanges(rep("Y", length(ir2)), ir2, strand=rep("-", length(ir2))) grf2 <- GRangesFilter(gr2, type = "any") Test <- exons(edb, filter = grf2) Test2 <- exonsByOverlaps(edb, gr2) expect_equal(names(Test), names(Test2)) ## Combine with filter... gr2 <- GRanges(rep("Y", length(ir2)), ir2) Test3 <- exonsByOverlaps(edb, gr2, filter=SeqStrandFilter("-")) expect_equal(names(Test), names(Test3)) }) test_that("methods work with global filter", { g_f <- SeqNameFilter(18) edb_2 <- ensembldb:::.addFilter(edb, g_f) ## gns gns <- genes(edb_2) expect_equal(gns, genes(edb, filter = g_f)) edb_2 <- ensembldb:::.addFilter(edb_2, TxBiotypeFilter("protein_coding")) gns_2 <- genes(edb_2) expect_true(all(seqlevels(gns_2) == "18")) expect_true(length(gns) > length(gns_2)) ## Combine with additional filter: gns_2 <- genes(edb_2, filter = SeqStrandFilter("+")) expect_true(all(as.character(strand(gns_2)) == "+")) gns <- genes(edb_2, filter = GenenameFilter("ZBTB16")) expect_true(length(gns) == 0) gns <- genes(edb_2, filter = GenenameFilter("BCL2")) expect_true(all(gns$symbol == "BCL2")) ## transcripts txs <- transcripts(edb_2, filter = ~ genename == "BCL2") expect_true(all(txs$genename == "BCL2")) expect_true(all(txs$tx_biotype == "protein_coding")) ## exonsBy exs <- exonsBy(edb_2, "gene") }) ensembldb/tests/testthat/test_Protein-related-tests.R0000644000175400017540000002673013175714743024141 0ustar00biocbuildbiocbuild ############################################################ ## Getting protein data in other methods. test_that("genes works with proteins", { if (hasProteinData(edb)) { res <- genes(edb, columns = c("gene_name", "gene_id", "protein_id", "uniprot_id", "tx_id", "tx_biotype"), filter = GenenameFilter("ZBTB16"), return.type = "data.frame") ## have a 1:n mapping of protein_id to uniprot id: expect_true(length(unique(res$protein_id)) < nrow(res)) expect_equal(colnames(res), c("gene_name", "gene_id", "protein_id", "uniprot_id", "tx_id", "tx_biotype")) ## All protein_coding have an uniprot_id expect_true(all(!is.na(res[res$tx_biotype == "protein_coding", "uniprot_id"]))) ## combine with cdsBy: cds <- cdsBy(edb, columns = c("tx_biotype", "protein_id"), filter = GenenameFilter("ZBTB16")) codingTx <- unique(res[!is.na(res$protein_id), "tx_id"]) expect_equal(sort(names(cds)), sort(codingTx)) ## Next one fetching also protein domain data. res <- genes(edb, columns = c("gene_name", "tx_id", "protein_id", "protein_domain_id"), filter = GenenameFilter("ZBTB16"), return.type = "data.frame") expect_equal(colnames(res), c("gene_name", "tx_id", "protein_id", "protein_domain_id", "gene_id")) expect_true(nrow(res) > length(unique(res$protein_id))) expect_true(nrow(res) > length(unique(res$tx_id))) } }) test_that("transcripts works with proteins", { if (hasProteinData(edb)) { res <- transcripts(edb, columns = c("tx_biotype", "protein_id", "uniprot_id"), filter = TxIdFilter("ENST00000335953"), return.type = "data.frame") ## 1:1 mapping for tx_id <-> protein_id expect_true(nrow(unique(res[, c("tx_id", "protein_id")])) == 1) ## Mapping tx_id -> uniprot_id is (0,1):n expect_true(nrow(res) > length(unique(res$tx_id))) ## Add protein domains. res <- transcripts(edb, columns = c("tx_biotype", "protein_id", "uniprot_id", "protein_domain_id"), filter = TxIdFilter("ENST00000335953"), return.type = "data.frame") resL <- split(res, f = res$uniprot_id) ## All have the same protein domains: resM <- do.call(rbind, lapply(resL, function(z) z$protein_domain_id)) expect_equal(nrow(unique(resM)), 1) } }) ## exons test_that("exons works with proteins", { if (hasProteinData(edb)) { ## Check if a call that includes a protein_id returns same data than one ## without. exns <- exons(edb, filter = GenenameFilter("BCL2L11"), return.type = "data.frame") exns_2 <- exons(edb, filter = GenenameFilter("BCL2L11"), return.type = "data.frame", columns = c("exon_id", "protein_id")) expect_equal(sort(unique(exns$exon_id)), sort(unique(exns_2$exon_id))) ## ZBTB16 exns <- exons(edb, filter = GenenameFilter("ZBTB16"), return.type = "data.frame") exns_2 <- exons(edb, filter = GenenameFilter("ZBTB16"), return.type = "data.frame", columns = c("exon_id", "protein_id")) expect_equal(sort(unique(exns$exon_id)), sort(unique(exns_2$exon_id))) } }) ## exonsBy test_that("exonsBy works with proteins", { if (hasProteinData(edb)) { exns <- exonsBy(edb, filter = GenenameFilter("ZBTB16"), columns = "tx_biotype") exns_2 <- exonsBy(edb, filter = GenenameFilter("ZBTB16"), columns = c("protein_id", "tx_biotype")) expect_equal(names(exns), names(exns_2)) exns <- unlist(exns) exns_2 <- unlist(exns_2) expect_true(any(is.na(exns_2$protein_id))) expect_equal(exns$exon_id, exns_2$exon_id) } }) ## transcriptsBy test_that("transcriptsBy works with proteins", { if (hasProteinData(edb)) { txs <- transcriptsBy(edb, filter = GenenameFilter("ZBTB16"), columns = "gene_biotype") txs_2 <- transcriptsBy(edb, filter = GenenameFilter("ZBTB16"), columns = c("protein_id", "gene_biotype")) expect_equal(names(txs), names(txs_2)) txs <- unlist(txs) txs_2 <- unlist(txs_2) expect_true(any(is.na(txs_2$protein_id))) expect_equal(start(txs), start(txs_2)) } }) ## cdsBy test_that("cdsBy works with proteins", { if (hasProteinData(edb)) { cds <- cdsBy(edb, filter = GenenameFilter("ZBTB16"), columns = "gene_biotype") cds_2 <- cdsBy(edb, filter = GenenameFilter("ZBTB16"), columns = c("protein_id", "gene_biotype")) expect_equal(names(cds), names(cds_2)) cds <- unlist(cds) cds_2 <- unlist(cds_2) expect_true(all(!is.na(cds_2$protein_id))) expect_equal(start(cds), start(cds_2)) } }) ## fiveUTRsByTranscript test_that("fiveUTRsByTranscript works with proteins", { if (hasProteinData(edb)) { utrs <- fiveUTRsByTranscript(edb, filter = GenenameFilter("ZBTB16"), columns = "tx_biotype") utrs_2 <- fiveUTRsByTranscript(edb, filter = GenenameFilter("ZBTB16"), columns = c("protein_id", "gene_biotype")) expect_equal(names(utrs), names(utrs_2)) utrs <- unlist(utrs) utrs_2 <- unlist(utrs_2) expect_true(all(!is.na(utrs_2$protein_id))) expect_equal(start(utrs), start(utrs_2)) } }) test_that("genes works with protein filters", { ## o ProteinIdFilter pif <- ProteinIdFilter("ENSP00000376721") if (hasProteinData(edb)) { gns <- genes(edb, filter = pif, return.type = "data.frame") expect_equal(gns$gene_name, "ZBTB16") } ## o UniprotFilter uif <- UniprotFilter("Q71UL7_HUMAN") if (hasProteinData(edb)) { gns <- genes(edb, filter = uif, return.type = "data.frame", columns = c("protein_id", "gene_name", "tx_id")) expect_true("ENSP00000376721" %in% gns$protein_id) expect_true(nrow(gns) == 2) } ## o ProtDomIdFilter pdif <- ProtDomIdFilter("PF00096") if (hasProteinData(edb)) { gns <- genes(edb, filter = list(pdif, GenenameFilter("ZBTB%", "startsWith")), return.type = "data.frame", column = c("gene_name", "gene_biotype")) expect_true(all(gns$gene_biotype == "protein_coding")) } }) test_that("proteins works", { if (hasProteinData(edb)) { ## Check return type. prts_DF <- proteins(edb, filter = GenenameFilter("ZBTB16")) expect_true(is(prts_DF, "DataFrame")) prts_df <- proteins(edb, filter = GenenameFilter("ZBTB16"), return.type = "data.frame") expect_true(is(prts_df, "data.frame")) prts_aa <- proteins(edb, filter = GenenameFilter("ZBTB16"), return.type = "AAStringSet") expect_true(is(prts_aa, "AAStringSet")) ## Check content. library(RSQLite) res_q <- dbGetQuery( dbconn(edb), paste0("select tx.tx_id, protein_id, gene_name from ", "protein left outer join tx on (protein.tx_id=", "tx.tx_id) join gene on (gene.gene_id=", "tx.gene_id) where gene_name = 'ZBTB16'")) expect_equal(res_q$tx_id, prts_df$tx_id) expect_equal(res_q$protein_id, prts_df$protein_id) expect_equal(prts_df$protein_id, names(prts_aa)) ## Add protein domain information to the proteins. prts_df <- proteins(edb, filter = ProteinIdFilter(c("ENSP00000338157", "ENSP00000443013")), columns = c("protein_id", "protein_domain_id", "uniprot_id"), return.type = "data.frame") ## Check if we have all data that we expect: uniprots <- dbGetQuery(dbconn(edb), paste0("select uniprot_id from uniprot where", " protein_id in ('ENSP00000338157',", "'ENSP00000443013')"))$uniprot_id expect_true(all(uniprots %in% prts_df$uniprot_id)) protdoms <- dbGetQuery(dbconn(edb), paste0("select protein_domain_id from", " protein_domain where protein_id", " in ('ENSP00000338157',", "'ENSP00000443013')"))$protein_domain_id expect_true(all(protdoms %in% prts_df$protein_domain_id)) } }) test_that("proteins works with uniprot mapping", { ## ZBTB16 and the mapping of 1 protein to two Uniprot IDs, one with DIRECT ## mapping type. if (hasProteinData(edb)) { prts <- proteins(edb, filter = GenenameFilter("ZBTB16"), columns = c("uniprot_id", "uniprot_db", "uniprot_mapping_type"), return.type = "DataFrame") ## NOTE: this is true for Ensembl 86, but might not be the case for 75! ## Here we have the n:m mapping: ## Q05516 is assigned to ENSP00000338157 and ENSP00000376721, ## Each of the two proteins is however also annotated to a second ## Uniprot ID: A0A024R3C6 ## If we use the UniprotMappingTypeFilter with only DIRECT mapping ## we expect to reduce it to the 1:n mapping between Uniprot and Ensembl prts <- proteins(edb, filter = list(GenenameFilter("ZBTB16"), UniprotMappingTypeFilter("DIRECT")), columns = c("uniprot_id", "uniprot_db", "uniprot_mapping_type"), return.type = "DataFrame") expect_true(all(prts$uniprot_mapping_type == "DIRECT")) ## Check the UniprotDbFilter prts <- proteins(edb, filter = list(GenenameFilter("ZBTB16"), UniprotDbFilter("SPTREMBL")), columns = c("uniprot_id", "uniprot_db", "protein_id"), return.type = "DataFrame") expect_true(all(prts$uniprot_db == "SPTREMBL")) } }) test_that("isProteinFilter works", { ## TRUE expect_true(ensembldb:::isProteinFilter(ProteinIdFilter("a"))) expect_true(ensembldb:::isProteinFilter(UniprotFilter("a"))) expect_true(ensembldb:::isProteinFilter(ProtDomIdFilter("a"))) expect_true(ensembldb:::isProteinFilter(UniprotDbFilter("a"))) expect_true(ensembldb:::isProteinFilter(UniprotMappingTypeFilter("a"))) ## FALSE expect_true(!ensembldb:::isProteinFilter(GeneIdFilter("a"))) expect_true(!ensembldb:::isProteinFilter(SymbolFilter("a"))) expect_true(!ensembldb:::isProteinFilter(3)) expect_true(!ensembldb:::isProteinFilter("dfdf")) }) ensembldb/tests/testthat/test_SymbolFilter.R0000644000175400017540000000771313175714743022356 0ustar00biocbuildbiocbuild test_that("SymbolFilter works for gene", { sf <- SymbolFilter("SKA2") gnf <- GenenameFilter("SKA2") returnFilterColumns(edb) <- FALSE gns_sf <- genes(edb, filter = sf) gns_gnf <- genes(edb, filter = gnf) expect_equal(gns_sf, gns_gnf) returnFilterColumns(edb) <- TRUE gns_sf <- genes(edb, filter=sf) expect_equal(gns_sf$gene_name, gns_sf$symbol) ## Hm, what happens if we use both? gns <- genes(edb, filter=list(sf, gnf)) ## All fine. }) test_that("SymbolFilter works for tx", { sf <- SymbolFilter("SKA2") gnf <- GenenameFilter("SKA2") returnFilterColumns(edb) <- FALSE tx_sf <- transcripts(edb, filter=sf) tx_gnf <- transcripts(edb, filter=gnf) expect_equal(tx_sf, tx_gnf) returnFilterColumns(edb) <- TRUE tx_sf <- transcripts(edb, filter=sf, columns=c("gene_name")) expect_equal(tx_sf$gene_name, tx_sf$symbol) }) test_that("SymbolFilter works for exons", { sf <- SymbolFilter("SKA2") gnf <- GenenameFilter("SKA2") returnFilterColumns(edb) <- FALSE ex_sf <- exons(edb, filter=sf) ex_gnf <- exons(edb, filter=gnf) expect_equal(ex_sf, ex_gnf) returnFilterColumns(edb) <- TRUE ex_sf <- exons(edb, filter=sf, columns=c("gene_name")) expect_equal(ex_sf$gene_name, ex_sf$symbol) }) test_that("SymbolFilter works", { sf <- SymbolFilter("SKA2") res <- genes(edb, filter = sf, return.type = "data.frame") expect_equal(res$gene_id, "ENSG00000182628") ## We need now also a column "symbol"! expect_equal(res$symbol, res$gene_name) ## Asking explicitely for symbol res <- genes(edb, filter = sf, return.type = "data.frame", columns = c("symbol", "gene_id")) expect_equal(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")) expect_equal(colnames(res), c("gene_name", "symbol", "gene_id")) res <- genes(edb, filter = sf, return.type = "data.frame", columns = c("gene_id", "gene_name", "symbol")) expect_equal(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")) expect_equal(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") expect_equal(colnames(res), c("tx_name", "symbol", "gene_id")) expect_true(all(res$symbol == "SKA2")) ## Test for transcripts res <- transcripts(edb, filter=sf, return.type="data.frame") expect_true(all(res$symbol == "SKA2")) res <- transcripts(edb, filter = sf, return.type = "data.frame", columns = c("symbol", "tx_id", "gene_name")) expect_true(all(res$symbol == "SKA2")) expect_equal(res$symbol, res$gene_name) expect_equal(colnames(res), c("symbol", "tx_id", "gene_name")) ## Test for exons res <- exons(edb, filter=sf, return.type="data.frame") expect_true(all(res$symbol == "SKA2")) res <- exons(edb, filter = c(sf, TxBiotypeFilter("nonsense_mediated_decay")), return.type = "data.frame", columns = c("symbol", "tx_id", "gene_name")) expect_true(all(res$symbol == "SKA2")) expect_equal(res$symbol, res$gene_name) expect_equal(colnames(res), c("symbol", "tx_id", "gene_name", "exon_id", "tx_biotype")) ## Test for exonsBy res <- exonsBy(edb, filter=sf) expect_true(all(unlist(res)$symbol == "SKA2")) res <- exonsBy(edb, filter = c(sf, TxBiotypeFilter("nonsense_mediated_decay")), columns = c("symbol", "tx_id", "gene_name")) expect_true(all(unlist(res)$symbol == "SKA2")) expect_equal(unlist(res)$symbol, unlist(res)$gene_name) }) ensembldb/tests/testthat/test_dbhelpers.R0000644000175400017540000005116713175714743021715 0ustar00biocbuildbiocbuild test_that("prefixColumns works", { res <- ensembldb:::prefixColumns(edb, columns = "a") expect_true(is.null(res)) expect_error(ensembldb:::prefixColumns(edb, columns = "a", clean = FALSE)) res <- ensembldb:::prefixColumns(edb, columns = c("gene_id", "a"), clean = FALSE) expect_equal(names(res), "gene") expect_equal(res$gene, "gene.gene_id") ## The "new" prefixColumns function ALWAYS returns the first table in which ## a column was found; tables are ordered as in listTables res <- ensembldb:::prefixColumns(edb, columns = c("tx_id", "gene_id", "tx_biotype")) want <- list(gene = "gene.gene_id", tx = c("tx.tx_id", "tx.tx_biotype")) expect_equal(res, want) ## res <- ensembldb:::prefixColumns(edb, columns = c("exon_idx", "seq_name", "gene_id")) want <- list(gene = c("gene.gene_id", "gene.seq_name"), tx2exon = "tx2exon.exon_idx") expect_equal(res, want) ## res <- ensembldb:::prefixColumns(edb, columns = c("exon_idx", "seq_name", "gene_id", "exon_id")) want <- list(gene = c("gene.gene_id", "gene.seq_name"), tx2exon = c("tx2exon.exon_id", "tx2exon.exon_idx")) expect_equal(res, want) if (hasProteinData(edb)) { res <- ensembldb:::prefixColumns(edb, columns = c("tx_id", "protein_id")) want <- list(tx = "tx.tx_id", protein = "protein.protein_id") expect_equal(res, want) ## res <- ensembldb:::prefixColumns(edb, columns = c("uniprot_id", "protein_domain_id")) want <- list(uniprot = "uniprot.uniprot_id", protein_domain = "protein_domain.protein_domain_id") expect_equal(res, want) ## res <- ensembldb:::prefixColumns(edb, columns = c("uniprot_id", "protein_domain_id", "protein_id", "tx_id")) want = list(tx = "tx.tx_id", protein = "protein.protein_id", uniprot = "uniprot.uniprot_id", protein_domain = "protein_domain.protein_domain_id") expect_equal(res, want) } }) ############################################################ ## Test the new join engine. ## o use the startWith argument. ## o change the join argument. test_that("joinTwoTables works", { ## Check errors: expect_error(ensembldb:::joinTwoTables(a = "gene", b = "dont exist")) expect_error(ensembldb:::joinTwoTables(a = c("a", "b"), b = "gene")) ## Working example: res <- ensembldb:::joinTwoTables(a = c("a", "gene"), b = "tx") expect_equal(sort(res[1:2]), c("gene", "tx")) expect_equal(res[3], "on (gene.gene_id=tx.gene_id)") ## Error expect_error(ensembldb:::joinTwoTables(a = "tx", b = "exon")) ## Working example: res <- ensembldb:::joinTwoTables(a = c("tx"), b = c("exon", "tx2exon")) expect_equal(sort(res[1:2]), c("tx", "tx2exon")) expect_equal(res[3], "on (tx.tx_id=tx2exon.tx_id)") res <- ensembldb:::joinTwoTables(a = c("chromosome", "gene", "tx"), b = c("exon", "protein", "tx2exon")) expect_equal(sort(res[1:2]), c("tx", "tx2exon")) expect_equal(res[3], "on (tx.tx_id=tx2exon.tx_id)") }) test_that("joinQueryOnTables2 and joinQueryOnColumns2 work", { ## exceptions expect_error(ensembldb:::joinQueryOnTables2(edb, tab = c("a", "exon"))) res <- ensembldb:::joinQueryOnTables2(edb, tab = c("gene", "exon")) want <- paste0("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)") ## The "default" order is gene->tx->tx2exon->exon expect_equal(res, want) res <- ensembldb:::joinQueryOnColumns2(edb, columns = c("exon_seq_start", "gene_name")) expect_equal(res, want) ## Same but in the order: exon->tx2exon->tx->gene res <- ensembldb:::joinQueryOnTables2(edb, tab = c("gene", "exon"), startWith = "exon") want <- paste0("exon join tx2exon on (tx2exon.exon_id=exon.exon_id)", " join tx on (tx.tx_id=tx2exon.tx_id) join", " gene on (gene.gene_id=tx.gene_id)") expect_equal(res, want) res <- ensembldb:::joinQueryOnColumns2(edb, columns = c("exon_seq_start", "gene_name"), startWith = "exon") expect_equal(res, want) ## That would be less expensive, but with "startWith" we force it to start ## from table exon, instead of just using tx2exon and tx. res <- ensembldb:::joinQueryOnColumns2(edb, columns = c("exon_id", "gene_id"), startWith = "exon") expect_equal(res, want) ## Check proteins too. if (hasProteinData(edb)) { res <- ensembldb:::joinQueryOnTables2(edb, tab = c("protein", "gene", "exon")) ## That should be: gene->tx->tx2exon->exon->protein want <- paste0("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) left outer join", " protein on (tx.tx_id=protein.tx_id)") expect_equal(res, want) res <- ensembldb:::joinQueryOnColumns2(edb, columns = c("protein_id", "gene_name", "exon_seq_start")) expect_equal(res, want) res <- ensembldb:::joinQueryOnTables2(edb, tab = c("protein", "gene"), startWith = "protein") want <- paste0("protein left outer join tx on (tx.tx_id=protein.tx_id)", " join gene on (gene.gene_id=tx.gene_id)") expect_equal(res, want) res <- ensembldb:::joinQueryOnColumns2(edb, columns = c("protein_id", "gene_name"), startWith = "protein") expect_equal(res, want) } }) test_that("addRequiredTables works", { have <- c("exon", "gene") need <- c("exon", "gene", "tx2exon", "tx") expect_equal(sort(need), sort(ensembldb:::addRequiredTables(edb, have))) have <- c("exon", "chromosome") need <- c("exon", "tx2exon", "tx", "gene", "chromosome") expect_equal(sort(need), sort(ensembldb:::addRequiredTables(edb, have))) have <- c("chromosome", "tx") need <- c("chromosome", "tx", "gene") expect_equal(sort(need), sort(ensembldb:::addRequiredTables(edb, have))) if (hasProteinData(edb)) { have <- c("uniprot", "exon") need <- c("uniprot", "exon", "protein", "tx", "tx2exon") expect_equal(sort(need), sort(ensembldb:::addRequiredTables(edb, have))) have <- c("uniprot", "chromosome") need <- c("uniprot", "chromosome", "protein", "tx", "gene") expect_equal(sort(need), sort(ensembldb:::addRequiredTables(edb, have))) have <- c("protein_domain", "gene") need <- c("protein_domain", "gene", "protein", "tx") expect_equal(sort(need), sort(ensembldb:::addRequiredTables(edb, have))) have <- c("protein", "exon") need <- c("protein", "exon", "tx", "tx2exon") expect_equal(sort(need), sort(ensembldb:::addRequiredTables(edb, have))) } }) test_that(".buildQuery with filter works", { columns <- c("gene_id", "gene_name", "exon_id") gnf <- GenenameFilter("BCL2") Q <- ensembldb:::.buildQuery(edb, columns = columns, filter = AnnotationFilterList(gnf)) want <- paste0("select distinct gene.gene_id,gene.gene_name,", "tx2exon.exon_id from gene join tx on (gene.gene_id", "=tx.gene_id) join tx2exon on (tx.tx_id=tx2exon.tx_id)", " where (gene.gene_name = 'BCL2')") expect_equal(Q, want) library(RSQLite) res <- dbGetQuery(dbconn(edb), Q) expect_equal(unique(res$gene_name), "BCL2") ## Two GeneNameFilters combined with or gnf2 <- GenenameFilter("BCL2L11") columns <- c("gene_id", "gene_name", "exon_id") Q <- ensembldb:::.buildQuery(edb, columns = columns, filter = AnnotationFilterList(gnf, gnf2, logOp = "|")) want <- paste0("select distinct gene.gene_id,gene.gene_name,", "tx2exon.exon_id from gene join tx on (gene.gene_id", "=tx.gene_id) join tx2exon on (tx.tx_id=tx2exon.tx_id)", " where (gene.gene_name = 'BCL2' or gene.gene_name = ", "'BCL2L11')") expect_equal(Q, want) res <- dbGetQuery(dbconn(edb), Q) expect_true(all(res$gene_name %in% c("BCL2", "BCL2L11"))) ## Combine with a SeqnameFilter. snf <- SeqNameFilter(2) flt <- AnnotationFilterList(gnf, gnf2, snf, logOp = c("|", "&")) Q <- ensembldb:::.buildQuery(edb, columns = columns, filter = flt) want <- paste0("select distinct gene.gene_id,gene.gene_name,", "tx2exon.exon_id,gene.seq_name from gene join tx on (", "gene.gene_id=tx.gene_id) join tx2exon on (tx.tx_id=", "tx2exon.tx_id) where (gene.gene_name = 'BCL2' or ", "gene.gene_name = 'BCL2L11' and gene.seq_name = '2')") expect_equal(Q, want) res <- dbGetQuery(dbconn(edb), Q) expect_true(all(res$gene_name %in% c("BCL2", "BCL2L11"))) ## now with a nested AnnotationFilterList: flt <- AnnotationFilterList(AnnotationFilterList(gnf, gnf2, logOp = "|"), snf, logOp = "&") Q <- ensembldb:::.buildQuery(edb, columns = columns, filter = flt) want <- paste0("select distinct gene.gene_id,gene.gene_name,", "tx2exon.exon_id,gene.seq_name from gene join tx on (", "gene.gene_id=tx.gene_id) join tx2exon on (tx.tx_id=", "tx2exon.tx_id) where ((gene.gene_name = 'BCL2' or ", "gene.gene_name = 'BCL2L11') and gene.seq_name = '2')") expect_equal(Q, want) res <- dbGetQuery(dbconn(edb), Q) expect_true(all(res$gene_name %in% c("BCL2L11"))) ## If we only want to get BCL2L11 back: flt <- AnnotationFilterList(GenenameFilter(c("BCL2", "BCL2L11")), snf, logOp = "&") Q <- ensembldb:::.buildQuery(edb, columns = columns, filter = flt) want <- paste0("select distinct gene.gene_id,gene.gene_name,", "tx2exon.exon_id,gene.seq_name from gene join tx on (", "gene.gene_id=tx.gene_id) join tx2exon on (tx.tx_id=", "tx2exon.tx_id) where (gene.gene_name in ('BCL2','BCL2L11'", ") and gene.seq_name = '2')") expect_equal(Q, want) res <- dbGetQuery(dbconn(edb), Q) expect_true(all(res$gene_name == "BCL2L11")) ## Check with a GRangesFilter. grf <- GRangesFilter(GRanges(seqnames = 18, IRanges(60790600, 60790700))) flt <- AnnotationFilterList(grf) Q <- ensembldb:::.buildQuery(edb, columns = columns, filter = flt) want <- paste0("select distinct gene.gene_id,gene.gene_name,tx2exon.", "exon_id,gene.gene_seq_start,gene.gene_seq_end,gene.seq_name", ",gene.seq_strand from gene join tx on (gene.gene_id", "=tx.gene_id) join tx2exon on (tx.tx_id=tx2exon.tx_id) ", "where ((gene.gene_seq_start<=60790700 and gene.gene_seq", "_end>=60790600 and gene.seq_name='18'))") expect_equal(Q, want) res <- dbGetQuery(dbconn(edb), Q) expect_true(all(res$gene_name == "BCL2")) }) test_that("buildQuery with startWith works", { columns <- c("gene_id", "gene_name", "exon_id") Q <- ensembldb:::.buildQuery(edb, columns = columns) want <- paste0("select distinct gene.gene_id,gene.gene_name,", "tx2exon.exon_id from gene join tx on (gene.gene_id", "=tx.gene_id) join tx2exon on (tx.tx_id=tx2exon.tx_id)") expect_equal(Q, want) ## Different if we use startWith = exon Q <- ensembldb:::.buildQuery(edb, columns = columns, startWith = "exon") want <- paste0("select distinct gene.gene_id,gene.gene_name,", "tx2exon.exon_id from exon join tx2exon on (tx2exon.exon_id", "=exon.exon_id) join tx on (tx.tx_id=tx2exon.tx_id)", " join gene on (gene.gene_id=tx.gene_id)") expect_equal(Q, want) Q <- ensembldb:::.buildQuery(edb, columns = c("gene_id", "tx_biotype")) want <- paste0("select distinct gene.gene_id,tx.tx_biotype from gene ", "join tx on (gene.gene_id=tx.gene_id)") expect_equal(Q, want) Q <- ensembldb:::.buildQuery(edb, columns = c("gene_id", "tx_biotype"), startWith = "exon") want <- paste0("select distinct gene.gene_id,tx.tx_biotype from exon ", "join tx2exon on (tx2exon.exon_id=exon.exon_id) join ", "tx on (tx.tx_id=tx2exon.tx_id) join ", "gene on (gene.gene_id=tx.gene_id)") expect_equal(Q, want) if (hasProteinData(edb)) { ## Protein columns. Q <- ensembldb:::.buildQuery(edb, columns = c("protein_id", "uniprot_id", "protein_domain_id")) want <- paste0("select distinct protein.protein_id,uniprot.uniprot_id,", "protein_domain.protein_domain_id from protein left ", "outer join protein_domain on (protein.protein_id=", "protein_domain.protein_id) left outer join ", "uniprot on (protein.protein_id=uniprot.protein_id)") expect_equal(Q, want) ## start at protein Q <- ensembldb:::.buildQuery(edb, columns = c("protein_id", "uniprot_id", "protein_domain_id"), startWith = "protein") want <- paste0("select distinct protein.protein_id,uniprot.uniprot_id,", "protein_domain.protein_domain_id from protein left ", "outer join protein_domain on (protein.protein_id=", "protein_domain.protein_id) left outer join ", "uniprot on (protein.protein_id=uniprot.protein_id)") expect_equal(Q, want) ## start at uniprot. Q <- ensembldb:::.buildQuery(edb, columns = c("protein_id", "uniprot_id", "protein_domain_id"), startWith = "uniprot") want <- paste0("select distinct protein.protein_id,uniprot.uniprot_id,", "protein_domain.protein_domain_id from uniprot left ", "outer join protein on (protein.protein_id=", "uniprot.protein_id) left outer join", " protein_domain on (protein.protein_id=", "protein_domain.protein_id)") expect_equal(Q, want) ## join with tx. Q <- ensembldb:::.buildQuery(edb, columns = c("tx_id", "protein_id", "uniprot_id", "gene_id")) want <- paste0("select distinct tx.tx_id,protein.protein_id,", "uniprot.uniprot_id,gene.gene_id from gene join ", "tx on (gene.gene_id=tx.gene_id) left outer join protein", " on (tx.tx_id=protein.tx_id) left outer join uniprot on", " (protein.protein_id=uniprot.protein_id)") expect_equal(Q, want) ## if we started from protein: Q <- ensembldb:::.buildQuery(edb, columns = c("tx_id", "protein_id", "uniprot_id", "gene_id"), startWith = "protein") want <- paste0("select distinct tx.tx_id,protein.protein_id,", "uniprot.uniprot_id,gene.gene_id from protein left outer", " join tx on (tx.tx_id=protein.tx_id) join gene on", " (gene.gene_id=tx.gene_id) left outer join uniprot on", " (protein.protein_id=uniprot.protein_id)") expect_equal(Q, want) } }) ## This test is an important one as it checks that we don't miss any entries ## from the database, e.g. if we query gene and join with protein that we don't ## miss any non-coding transcripts, or if we join protein with uniprot or ## protein_domain that we don't miss any values. test_that("query is valid", { ## Check RNA/DNA tables; shouldn't be a problem there, though. Ygns <- genes(edb, filter = SeqNameFilter("Y"), return.type = "data.frame") Ytxs <- transcripts(edb, filter = SeqNameFilter("Y"), return.type = "data.frame", columns = c("gene_id", "tx_id", "tx_biotype")) Yexns <- exons(edb, filter = SeqNameFilter("Y"), return.type = "data.frame", columns = c("exon_id", "gene_id")) expect_true(all(unique(Ygns$gene_id) %in% unique(Yexns$gene_id))) expect_true(all(unique(Ygns$gene_id) %in% unique(Ytxs$gene_id))) ## Check gene with protein if (hasProteinData(edb)) { library(RSQLite) ## Simulate what a simple join would do: gns_f <- dbGetQuery(dbconn(edb), paste0("select gene.gene_id, tx.tx_id, tx_biotype, ", "protein_id from gene join tx on ", "(gene.gene_id=tx.gene_id) join protein on ", "(tx.tx_id=protein.tx_id) ", "where seq_name = 'Y'")) ## We expect that gns_f is smaller, but that all protein_coding tx are ## there. expect_true(length(unique(gns_f$gene_id)) < length(unique(Ygns$gene_id))) expect_true(all(unique(Ytxs[Ytxs$tx_biotype == "protein_coding", "tx_id"]) %in% unique(gns_f$tx_id))) ## Now test the "real" query: Ygns_2 <- genes(edb, filter = SeqNameFilter("Y"), return.type = "data.frame", columns = c("gene_id", "tx_id", "tx_biotype", "protein_id")) ## We expect that ALL genes are present and ALL tx: expect_true(all(unique(Ygns$gene_id) %in% unique(Ygns_2$gene_id))) expect_true(all(unique(Ygns$tx_id) %in% unique(Ygns_2$tx_id))) ## Get all the tx with protein_id txs <- transcripts(edb, columns = c("tx_id", "protein_id"), return.type = "data.frame") txids <- dbGetQuery(dbconn(edb), "select tx_id from tx;")[, "tx_id"] protids <- dbGetQuery(dbconn(edb), "select protein_id from protein;")[, "protein_id"] expect_true(all(txids %in% txs$tx_id)) expect_true(all(protids %in% txs$protein_id)) ## Check protein with uniprot uniprotids <- dbGetQuery(dbconn(edb), "select uniprot_id from uniprot")$uniprot_id ## Check protein with protein domain ## Check protein_domain with uniprot } }) test_that(".getWhat works", { library(RSQLite) Q_2 <- paste0("select * from gene join tx on (gene.gene_id=tx.gene_id)", " join tx2exon on (tx.tx_id=tx2exon.tx_id) where", " gene.gene_id = 'ENSG00000000005'") res_2 <- dbGetQuery(dbconn(edb), Q_2) gf <- GeneIdFilter("ENSG00000000005") res_3 <- ensembldb:::.getWhat(edb, columns = c("gene_name", "exon_idx"), filter = AnnotationFilterList(gf)) expect_identical(res_3, unique(res_2[, colnames(res_3)])) }) test_that(".logOp2SQL works", { expect_equal(ensembldb:::.logOp2SQL("|"), "or") expect_equal(ensembldb:::.logOp2SQL("&"), "and") expect_equal(ensembldb:::.logOp2SQL("dfdf"), NULL) }) ensembldb/tests/testthat/test_extractTranscriptSeqs.R0000644000175400017540000000552313175714743024320 0ustar00biocbuildbiocbuild test_that("extractTranscriptSeqs works with BSGenome", { 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 expect_equal(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) expect_equal(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) expect_equal(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) expect_equal(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)) expect_equal(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) expect_equal(unname(as.character(cDNA)), unname(as.character(Seqs[grep(names(Seqs), pattern="cdna")]))) strand(exns) <- "+" cDNA <- extractTranscriptSeqs(bsg, exns) expect_true(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) expect_equal(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) expect_equal(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) expect_equal(unname(as.character(UTR)), unname(as.character(Seqs[grep(names(Seqs), pattern="utr3")]))) }) ensembldb/tests/testthat/test_functions-EnsDb.R0000644000175400017540000000476013175714743022743 0ustar00biocbuildbiocbuildtest_that(".addFilter .dropFilter and .activeFilter work", { gf <- GenenameFilter("BCL2") ## .addFilter and .activeFilter edb_2 <- ensembldb:::.addFilter(edb, filter = gf) expect_equal(AnnotationFilterList(gf), ensembldb:::getProperty(edb_2, "FILTER")) expect_equal(AnnotationFilterList(gf), ensembldb:::.activeFilter(edb_2)) edb_2 <- ensembldb:::.addFilter(edb_2, filter = gf) expect_equal(AnnotationFilterList(AnnotationFilterList(gf), AnnotationFilterList(gf)), ensembldb:::getProperty(edb_2, "FILTER")) edb_2 <- ensembldb:::.addFilter(edb, filter = ~ tx_id == 3 & tx_biotype == "protein_coding") flts <- ensembldb:::.activeFilter(edb_2) expect_equal(flts, ensembldb:::getProperty(edb_2, "FILTER")) expect_equal(flts, AnnotationFilter(~ tx_id == 3 & tx_biotype == "protein_coding")) ## Errors expect_error(ensembldb:::.addFilter(edb, "blabla")) expect_error(ensembldb:::filter(edb, "blabla")) expect_error(ensembldb:::.addFilter(edb)) ## .dropFilter edb_2 <- ensembldb:::.dropFilter(edb_2) expect_equal(ensembldb:::.activeFilter(edb_2), NA) ## Same but with the methods. gf <- GenenameFilter("BCL2") ## .addFilter and .activeFilter edb_2 <- addFilter(edb, filter = gf) expect_equal(AnnotationFilterList(gf), ensembldb:::getProperty(edb_2, "FILTER")) edb_2 <- filter(edb, filter = gf) expect_equal(AnnotationFilterList(gf), ensembldb:::getProperty(edb_2, "FILTER")) expect_equal(AnnotationFilterList(gf), activeFilter(edb_2)) edb_2 <- addFilter(edb_2, filter = gf) expect_equal(AnnotationFilterList(AnnotationFilterList(gf), AnnotationFilterList(gf)), ensembldb:::getProperty(edb_2, "FILTER")) edb_2 <- addFilter(edb, filter = ~ tx_id == 3 & tx_biotype == "protein_coding") flts <- activeFilter(edb_2) expect_equal(flts, ensembldb:::getProperty(edb_2, "FILTER")) expect_equal(flts, AnnotationFilter(~ tx_id == 3 & tx_biotype == "protein_coding")) ## Errors expect_error(addFilter(edb, "blabla")) expect_error(addFilter(edb)) ## .dropFilter edb_2 <- dropFilter(edb_2) expect_equal(activeFilter(edb_2), NA) }) ensembldb/tests/testthat/test_functions-Filter.R0000644000175400017540000003024513175714743023172 0ustar00biocbuildbiocbuild test_that(".fieldInEnsDb works", { expect_equal(unname(ensembldb:::.fieldInEnsDb("symbol")), "gene_name") expect_equal(unname(ensembldb:::.fieldInEnsDb("gene_biotype")), "gene_biotype") expect_equal(unname(ensembldb:::.fieldInEnsDb("entrez")), "entrezid") expect_equal(unname(ensembldb:::.fieldInEnsDb("gene_id")), "gene_id") expect_equal(unname(ensembldb:::.fieldInEnsDb("genename")), "gene_name") expect_equal(unname(ensembldb:::.fieldInEnsDb("seq_name")), "seq_name") expect_equal(unname(ensembldb:::.fieldInEnsDb("tx_id")), "tx_id") expect_equal(unname(ensembldb:::.fieldInEnsDb("tx_biotype")), "tx_biotype") expect_equal(unname(ensembldb:::.fieldInEnsDb("tx_name")), "tx_id") expect_equal(unname(ensembldb:::.fieldInEnsDb("exon_id")), "exon_id") expect_equal(unname(ensembldb:::.fieldInEnsDb("exon_rank")), "exon_idx") expect_equal(unname(ensembldb:::.fieldInEnsDb("protein_id")), "protein_id") expect_equal(unname(ensembldb:::.fieldInEnsDb("uniprot")), "uniprot_id") expect_equal(unname(ensembldb:::.fieldInEnsDb("uniprot_db")), "uniprot_db") expect_equal(unname(ensembldb:::.fieldInEnsDb("uniprot_mapping_type")), "uniprot_mapping_type") expect_equal(unname(ensembldb:::.fieldInEnsDb("prot_dom_id")), "protein_domain_id") expect_equal(unname(ensembldb:::.fieldInEnsDb("description")), "description") expect_equal(unname(ensembldb:::.fieldInEnsDb("tx_support_level")), "tx_support_level") expect_error(ensembldb:::.fieldInEnsDb("aaa")) }) test_that(".conditionForEnsDb works", { smb <- SymbolFilter("a") expect_equal(condition(smb), "==") expect_equal(ensembldb:::.conditionForEnsDb(smb), "=") smb <- SymbolFilter(c("a", "b", "c")) expect_equal(ensembldb:::.conditionForEnsDb(smb), "in") smb <- SymbolFilter(c("a", "b", "c"), condition = "!=") expect_equal(ensembldb:::.conditionForEnsDb(smb), "not in") smb <- SymbolFilter(c("a"), condition = "!=") expect_equal(ensembldb:::.conditionForEnsDb(smb), "!=") smb <- SymbolFilter(c("a"), condition = "startsWith") expect_equal(ensembldb:::.conditionForEnsDb(smb), "like") smb <- SymbolFilter(c("a"), condition = "endsWith") expect_equal(ensembldb:::.conditionForEnsDb(smb), "like") ## Tests for numeric filters fl <- GeneStartFilter(4) expect_equal(ensembldb:::.conditionForEnsDb(fl), "=") fl <- GeneStartFilter(4, condition = ">") expect_equal(ensembldb:::.conditionForEnsDb(fl), ">") fl <- GeneStartFilter(4, condition = ">=") expect_equal(ensembldb:::.conditionForEnsDb(fl), ">=") fl <- GeneStartFilter(4, condition = "<") expect_equal(ensembldb:::.conditionForEnsDb(fl), "<") fl <- GeneStartFilter(4, condition = "<=") expect_equal(ensembldb:::.conditionForEnsDb(fl), "<=") fl <- TxSupportLevelFilter(4, condition = "<=") expect_equal(ensembldb:::.conditionForEnsDb(fl), "<=") }) test_that(".valueForEnsDb works", { smb <- SymbolFilter("a") expect_equal(ensembldb:::.valueForEnsDb(smb), "'a'") smb <- SymbolFilter(c("a", "b", "b", "c")) expect_equal(ensembldb:::.valueForEnsDb(smb), "('a','b','c')") smb <- SymbolFilter("a", condition = "startsWith") expect_equal(ensembldb:::.valueForEnsDb(smb), "'a%'") smb <- SymbolFilter("a", condition = "endsWith") expect_equal(ensembldb:::.valueForEnsDb(smb), "'%a'") ## Tests for numeric filters fl <- GeneStartFilter(4) expect_equal(ensembldb:::.valueForEnsDb(fl), 4) }) test_that(".queryForEnsDb works", { smb <- SymbolFilter("a") expect_equal(ensembldb:::.queryForEnsDb(smb), "gene_name = 'a'") smb <- SymbolFilter(c("a", "x"), condition = "!=") expect_equal(ensembldb:::.queryForEnsDb(smb), "gene_name not in ('a','x')") ## Tests for numeric filters fl <- GeneStartFilter(5, condition = "<=") expect_equal(ensembldb:::.queryForEnsDb(fl), "gene_seq_start <= 5") fl <- TxSupportLevelFilter(5, condition = "<=") expect_equal(ensembldb:::.queryForEnsDb(fl), "tx_support_level <= 5") }) test_that(".queryForEnsDbWithTables works", { smb <- SymbolFilter("a") expect_equal(ensembldb:::.queryForEnsDbWithTables(smb), "gene_name = 'a'") smb <- SymbolFilter(c("a", "x"), condition = "!=") expect_equal(ensembldb:::.queryForEnsDbWithTables(smb), "gene_name not in ('a','x')") ## With edb smb <- SymbolFilter("a") expect_equal(ensembldb:::.queryForEnsDbWithTables(smb, edb), "gene.gene_name = 'a'") smb <- SymbolFilter(c("a", "x"), condition = "!=") expect_equal(ensembldb:::.queryForEnsDbWithTables(smb, edb), "gene.gene_name not in ('a','x')") ## With edb, tables smb <- SymbolFilter("a") expect_equal(ensembldb:::.queryForEnsDbWithTables(smb, edb, c("gene", "tx")), "gene.gene_name = 'a'") fl <- GeneIdFilter("b") expect_equal(ensembldb:::.queryForEnsDbWithTables(fl, edb), "gene.gene_id = 'b'") expect_equal(ensembldb:::.queryForEnsDbWithTables(fl, edb, c("tx", "gene")), "tx.gene_id = 'b'") fl <- GeneIdFilter("b", condition = "contains") expect_equal(ensembldb:::.queryForEnsDbWithTables(fl, edb, c("tx", "gene")), "tx.gene_id like '%b%'") ## Entrez if (as.numeric(ensembldb:::dbSchemaVersion(edb)) > 1) { fl <- EntrezFilter("g") expect_equal(ensembldb:::.queryForEnsDbWithTables(fl, edb), "entrezgene.entrezid = 'g'") fl <- EntrezFilter("g", condition = "endsWith") expect_equal(ensembldb:::.queryForEnsDbWithTables(fl, edb), "entrezgene.entrezid like '%g'") } else { fl <- EntrezFilter("g") expect_equal(ensembldb:::.queryForEnsDbWithTables(fl, edb), "gene.entrezid = 'g'") fl <- EntrezFilter("g", condition = "endsWith") expect_equal(ensembldb:::.queryForEnsDbWithTables(fl, edb), "gene.entrezid like '%g'") } ## Numeric filters fl <- TxStartFilter(123) expect_equal(ensembldb:::.queryForEnsDbWithTables(fl, edb), "tx.tx_seq_start = 123") expect_error(ensembldb:::.queryForEnsDbWithTables(fl, edb, "gene")) if (any(listColumns(edb) == "tx_support_level")) { fl <- TxSupportLevelFilter(3) expect_equal(ensembldb:::.queryForEnsDbWithTables(fl, edb), "tx.tx_support_level = 3") } }) test_that(".processFilterParam works", { ## Check that the processFilterParam does what we expect. Check input and ## return ALWAYS an AnnotationFilterList object. snf <- SeqNameFilter(c("Y", 9)) res <- ensembldb:::.processFilterParam(snf, db = edb) expect_true(is(res, "AnnotationFilterList")) expect_equal(res[[1]], snf) ## - single filter gif <- GeneIdFilter("BCL2", condition = "!=") res <- ensembldb:::.processFilterParam(gif, db = edb) expect_true(is(res, "AnnotationFilterList")) expect_equal(res[[1]], gif) ## - list of filters snf <- SeqNameFilter("X") res <- ensembldb:::.processFilterParam(list(gif, snf), edb) expect_true(is(res, "AnnotationFilterList")) expect_true(length(res) == 2) expect_equal(res[[1]], gif) expect_equal(res[[2]], snf) expect_equal(res@logOp, "&") ## - AnnotationFilterList afl <- AnnotationFilterList(gif, snf, logOp = "|") res <- ensembldb:::.processFilterParam(afl, edb) expect_true(is(res, "AnnotationFilterList")) expect_equal(afl, res) afl <- AnnotationFilterList(gif, snf, logOp = "&") res <- ensembldb:::.processFilterParam(afl, edb) expect_true(is(res, "AnnotationFilterList")) expect_equal(afl, res) ## - filter expression res <- ensembldb:::.processFilterParam(~ gene_id != "BCL2" | seq_name == "X", edb) expect_true(is(res, "AnnotationFilterList")) expect_equal(res, AnnotationFilterList(gif, snf, logOp = "|")) flt <- ~ gene_id != "BCL2" | seq_name == "X" res <- ensembldb:::.processFilterParam(flt, edb) expect_true(is(res, "AnnotationFilterList")) expect_equal(res, AnnotationFilterList(gif, snf, logOp = "|")) res <- ensembldb:::.processFilterParam(~ gene_id != "BCL2", edb) expect_true(is(res, "AnnotationFilterList")) expect_equal(res, AnnotationFilterList(gif)) ## - Errors expect_error(ensembldb:::.processFilterParam(db = edb)) expect_error(ensembldb:::.processFilterParam(4, edb)) expect_error(ensembldb:::.processFilterParam(list(afl, "a"), edb)) expect_error(ensembldb:::.processFilterParam("a", edb)) expect_error(ensembldb:::.processFilterParam(~ gene_bla == "14", edb)) ## Errors for filters that are not supported. expect_error(ensembldb:::.processFilterParam(CdsEndFilter(123), edb)) ## Same with calls from within a function. testFun <- function(filter = AnnotationFilterList()) { ensembldb:::.processFilterParam(filter, db = edb) } res <- testFun() expect_true(is(res, "AnnotationFilterList")) expect_true(length(res) == 0) res <- testFun(filter = ~ gene_id == 4) expect_true(is(res, "AnnotationFilterList")) expect_equal(res[[1]], GeneIdFilter(4)) res <- testFun(filter = GenenameFilter("BCL2")) expect_true(is(res, "AnnotationFilterList")) expect_equal(res[[1]], GenenameFilter("BCL2")) res <- testFun(filter = AnnotationFilterList(GenenameFilter("BCL2"))) expect_true(is(res, "AnnotationFilterList")) expect_equal(res[[1]], GenenameFilter("BCL2")) gene <- "ZBTB16" otherFun <- function(gn) { testFun(filter = GenenameFilter(gn)) } res <- otherFun(gene) }) test_that("setFeatureInGRangesFilter works", { afl <- AnnotationFilterList(GeneIdFilter(123), SeqNameFilter(3), GRangesFilter(GRanges())) afl2 <- AnnotationFilterList(afl, GRangesFilter(GRanges())) res <- ensembldb:::setFeatureInGRangesFilter(afl, feature = "tx") expect_equal(res[[3]]@feature, "tx") res <- ensembldb:::setFeatureInGRangesFilter(afl2, feature = "tx") expect_equal(res[[2]]@feature, "tx") expect_equal(res[[1]][[3]]@feature, "tx") }) test_that(".AnnottionFilterClassNames works", { afl1 <- AnnotationFilter(~ genename == 3 & seq_name != 5) expect_equal(.AnnotationFilterClassNames(afl1), c("GenenameFilter", "SeqNameFilter")) afl2 <- AnnotationFilter(~ gene_start > 13 | seq_strand == "+") expect_equal(.AnnotationFilterClassNames(afl2), c("GeneStartFilter", "SeqStrandFilter")) afl3 <- AnnotationFilterList(afl1, SymbolFilter(4)) expect_equal(.AnnotationFilterClassNames(afl3), c("GenenameFilter", "SeqNameFilter", "SymbolFilter")) afl4 <- AnnotationFilterList(afl2, afl3) expect_equal(.AnnotationFilterClassNames(afl4), c("GeneStartFilter", "SeqStrandFilter", "GenenameFilter", "SeqNameFilter", "SymbolFilter")) }) test_that(".anyIs works", { sf <- SymbolFilter("d") expect_true(ensembldb:::.anyIs(sf, "SymbolFilter")) expect_false(ensembldb:::.anyIs(GenenameFilter(3), "SymbolFilter")) flts <- AnnotationFilterList(sf, TxIdFilter("b")) expect_true(any(ensembldb:::.anyIs(flts, "SymbolFilter"))) expect_false(any(ensembldb:::.anyIs(flts, "BLa"))) ## Additional nesting. flts <- AnnotationFilterList(flts, GenenameFilter("2")) expect_true(any(ensembldb:::.anyIs(flts, "SymbolFilter"))) expect_true(any(ensembldb:::.anyIs(flts, "GenenameFilter"))) expect_true(any(ensembldb:::.anyIs(flts, "TxIdFilter"))) }) test_that(".fieldToClass works", { expect_equal(ensembldb:::.fieldToClass("gene_id"), "GeneIdFilter") }) test_that(".filterFields works", { res <- ensembldb:::.filterFields(edb) if (hasProteinData(edb)) { expect_true(any(res == "uniprot")) } expect_true(!any(res == "g_ranges")) }) test_that(".supportedFilters works", { res <- ensembldb:::.supportedFilters(edb) expect_true(class(res) == "data.frame") expect_equal(res[res$filter == "GRangesFilter", "field"], as.character(NA)) expect_equal(res[res$filter == "ExonIdFilter", "field"], "exon_id") expect_equal(res[res$filter == "GenenameFilter", "field"], "genename") }) ensembldb/tests/testthat/test_functions-create-EnsDb.R0000644000175400017540000002370313175714743024202 0ustar00biocbuildbiocbuild test_that(".organismName, .abbrevOrganismName and .makePackageName works", { res <- ensembldb:::.organismName("homo_sapiens") expect_equal(res, "Homo_sapiens") res <- ensembldb:::.abbrevOrganismName("homo_sapiens") expect_equal(res, "hsapiens") res <- ensembldb:::.makePackageName(dbconn(edb)) expect_equal(res, "EnsDb.Hsapiens.v75") }) test_that("ensDbFromGRanges works", { load(system.file("YGRanges.RData", package="ensembldb")) suppressWarnings( DB <- ensDbFromGRanges(Y, path=tempdir(), version=75, organism="Homo_sapiens", skip = TRUE) ) db <- EnsDb(DB) expect_equal(unname(genome(db)), "GRCh37") Test <- makeEnsembldbPackage(DB, destDir = tempdir(), version = "0.0.1", author = "J Rainer", maintainer = "") expect_true(ensembldb:::checkValidEnsDb(db)) }) test_that("ensDbFromGtf and Gff works", { gff <- system.file("gff/Devosia_geojensis.ASM96941v1.32.gff3.gz", package="ensembldb") gtf <- system.file("gtf/Devosia_geojensis.ASM96941v1.32.gtf.gz", package="ensembldb") suppressWarnings( db_gff <- EnsDb(ensDbFromGff(gff, outfile = tempfile(), skip = TRUE)) ) suppressWarnings( db_gtf <- EnsDb(ensDbFromGtf(gtf, outfile = tempfile(), skip = TRUE)) ) expect_equal(ensemblVersion(db_gtf), "32") expect_equal(ensemblVersion(db_gff), "32") res <- ensembldb:::compareChromosomes(db_gtf, db_gff) expect_equal(res, "OK") res <- ensembldb:::compareGenes(db_gtf, db_gff) expect_equal(res, "WARN") ## differences in gene names and Entrezid. res <- ensembldb:::compareTx(db_gtf, db_gff) expect_equal(res, "OK") res <- ensembldb:::compareExons(db_gtf, db_gff) expect_equal(res, "OK") ## Compare them all in one call res <- ensembldb:::compareEnsDbs(db_gtf, db_gff) expect_equal(unname(res["metadata"]), "NOTE") expect_equal(unname(res["chromosome"]), "OK") expect_equal(unname(res["transcript"]), "OK") expect_equal(unname(res["exon"]), "OK") }) test_that("isEnsemblFileName", { res <- ensembldb:::isEnsemblFileName("Caenorhabditis_elegans.WS210.60.gtf.gz") expect_true(res) res <- ensembldb:::isEnsemblFileName("Caenorhabditis_elegans_fdf.60.dfd.gtf.gz") expect_true(!res) fn <- "Caenorhabditis_elegans.WS210.60.gtf.gz" res <- ensembldb:::ensemblVersionFromGtfFileName(fn) expect_equal(res, "60") res <- ensembldb:::organismFromGtfFileName(fn) expect_equal(res, "Caenorhabditis_elegans") res <- ensembldb:::genomeVersionFromGtfFileName(fn) expect_equal(res, "WS210") res <- ensembldb:::elementFromEnsemblFilename(fn, which = 1) expect_equal(res, "Caenorhabditis_elegans") res <- ensembldb:::elementFromEnsemblFilename(fn, which = 2) expect_equal(res, "WS210") res <- ensembldb:::elementFromEnsemblFilename(fn, which = 3) expect_equal(res, "60") res <- ensembldb:::elementFromEnsemblFilename(fn, which = 4) expect_equal(res, "gtf") }) test_that("processEnsemblFileNames works", { Test <- "Homo_sapiens.GRCh38.83.gtf.gz" expect_true(ensembldb:::isEnsemblFileName(Test)) expect_equal(ensembldb:::organismFromGtfFileName(Test), "Homo_sapiens") expect_equal(ensembldb:::genomeVersionFromGtfFileName(Test), "GRCh38") expect_equal(ensembldb:::ensemblVersionFromGtfFileName(Test), "83") Test <- "Homo_sapiens.GRCh38.83.chr.gff3.gz" expect_true(ensembldb:::isEnsemblFileName(Test)) expect_equal(ensembldb:::organismFromGtfFileName(Test), "Homo_sapiens") expect_equal(ensembldb:::genomeVersionFromGtfFileName(Test), "GRCh38") expect_equal(ensembldb:::ensemblVersionFromGtfFileName(Test), "83") Test <- "Gadus_morhua.gadMor1.83.gff3.gz" expect_true(ensembldb:::isEnsemblFileName(Test)) expect_equal(ensembldb:::organismFromGtfFileName(Test), "Gadus_morhua") expect_equal(ensembldb:::genomeVersionFromGtfFileName(Test), "gadMor1") expect_equal(ensembldb:::ensemblVersionFromGtfFileName(Test), "83") Test <- "Solanum_lycopersicum.GCA_000188115.2.30.chr.gtf.gz" expect_true(ensembldb:::isEnsemblFileName(Test)) expect_equal(ensembldb:::organismFromGtfFileName(Test), "Solanum_lycopersicum") expect_equal(ensembldb:::genomeVersionFromGtfFileName(Test), "GCA_000188115.2") expect_equal(ensembldb:::ensemblVersionFromGtfFileName(Test), "30") Test <- "ref_GRCh38.p2_top_level.gff3.gz" expect_equal(ensembldb:::isEnsemblFileName(Test), FALSE) ensembldb:::organismFromGtfFileName(Test) expect_error(ensembldb:::genomeVersionFromGtfFileName(Test)) ##checkException(ensembldb:::ensemblVersionFromGtfFileName(Test)) }) test_that("checkExtractVersions works", { fn <- "Devosia_geojensis.ASM96941v1.32.gff3.gz" res <- ensembldb:::.checkExtractVersions(fn) expect_equal(unname(res["organism"]), "Devosia_geojensis") expect_equal(unname(res["genomeVersion"]), "ASM96941v1") expect_equal(unname(res["version"]), "32") suppressWarnings( res <- ensembldb:::.checkExtractVersions(fn, organism = "Homo_sapiens") ) expect_equal(unname(res["organism"]), "Homo_sapiens") expect_error(ensembldb:::.checkExtractVersions("afdfhjd")) }) test_that("buildMetadata works", { res <- ensembldb:::buildMetadata(organism = "Mus_musculus", ensemblVersion = "88", genomeVersion = "38") expect_equal(colnames(res), c("name", "value")) expect_equal(res[res$name == "Organism", "value"], "Mus_musculus") }) test_that("guessDatabaseName works", { ## Testing real case examples. genome <- "Rnor_5.0" organism <- "Rattus_norvegicus" ensembl <- "75" res <- ensembldb:::.guessDatabaseName(organism, ensembl) expect <- "rattus_norvegicus_core_75" expect_equal(res, expect) genome <- "GRCm38" organism <- "Mus_musculus" expect <- "mus_musculus_core_75_38" res <- ensembldb:::.guessDatabaseName(organism, ensembl, genome = genome) expect_equal(expect, res) }) test_that("getEnsemblMysqlUrl works", { check_getReadMysqlTable <- function(url) { res <- ensembldb:::.getReadMysqlTable(url, "coord_system.txt.gz", colnames = c("coord_system_id", "species_id", "name", "version", "rank", "attrib")) expect_true(nrow(res) > 0) } ## Only run this if we have access to Ensembl. tmp <- try( RCurl::getURL(ensembldb:::.ENSEMBL_URL, dirlistonly = TRUE, .opts = list(timeout = 5, maxredirs = 2)) ) if (!is(tmp, "try-error")) { res <- ensembldb:::.getEnsemblMysqlUrl(type = "ensembl", organism = "macaca mulatta", ensembl = 85) expect_equal(res, paste0(ensembldb:::.ENSEMBL_URL, "release-85/", "mysql/macaca_mulatta_core_85_10")) check_getReadMysqlTable(res) ## Next. res <- ensembldb:::.getEnsemblMysqlUrl(type = "ensembl", organism = "Bos taurus", ensembl = 61) expect_equal(res, paste0(ensembldb:::.ENSEMBL_URL, "release-61/", "mysql/bos_taurus_core_61_4j")) check_getReadMysqlTable(res) ## Next res <- ensembldb:::.getEnsemblMysqlUrl(type = "ensembl", organism = "Ficedula albicollis", ensembl = 77) expect_equal(res, paste0(ensembldb:::.ENSEMBL_URL, "release-77/", "mysql/ficedula_albicollis_core_77_1")) } ## ensemblgenomes tmp <- try( RCurl::getURL(ensembldb:::.ENSEMBLGENOMES_URL, dirlistonly = TRUE, .opts = list(timeout = 5, maxredirs = 2)) ) if (!is(tmp, "try-error")) { ## check fungi res <- ensembldb:::.getEnsemblMysqlUrl(type = "ensemblgenomes", organism = "fusarium_oxysporum", ensembl = 21) db_name <- "fusarium_oxysporum_core_21_74_2" expect_equal(res, paste0(ensembldb:::.ENSEMBLGENOMES_URL, "release-21/", "fungi/mysql/", db_name)) check_getReadMysqlTable(res) ## Next one db_name <- "solanum_lycopersicum_core_28_81_250" res <- ensembldb:::.getEnsemblMysqlUrl(type = "ensemblgenomes", organism = "solanum_lycopersicum", ensembl = 28) expect_equal(res, paste0(ensembldb:::.ENSEMBLGENOMES_URL, "release-28/", "plants/mysql/", db_name)) check_getReadMysqlTable(res) } }) test_that("getSeqlengthsFromMysqlFolder works", { library(curl) ch <- new_handle(timeout = 5) handle_setopt(ch, timeout = 5) tmp <- try( ## RCurl::getURL(ensembldb:::.ENSEMBL_URL, dirlistonly = TRUE, ## .opts = list(timeout = 5, maxredirs = 2)) readLines(curl(ensembldb:::.ENSEMBL_URL, handle = ch)) ) if (!is(tmp, "try-error")) { ## Compare seqlengths we've in EnsDb.Hsapiens.v75 with the expected ## ones. seq_info <- seqinfo(edb) seq_lengths <- ensembldb:::.getSeqlengthsFromMysqlFolder( organism = "Homo sapiens", ensembl = 75, seqnames = seqlevels(seq_info)) sl <- seqlengths(seq_info) sl_2 <- seq_lengths$length names(sl_2) <- rownames(seq_lengths) expect_true(all(names(sl) %in% names(sl_2))) expect_equal(sl, sl_2[names(sl)]) } }) ensembldb/tests/testthat/test_functions-utils.R0000644000175400017540000001407713175714743023112 0ustar00biocbuildbiocbuild test_that("orderDataFrameBy works", { res <- exons(edb, filter = GenenameFilter("ZBTB16"), return.type = "DataFrame") ## Order by end res_2 <- ensembldb:::orderDataFrameBy(res, by = "exon_seq_end") idx <- order(res_2$exon_seq_end) expect_equal(idx, 1:nrow(res_2)) }) test_that("addFilterColumns works for AnnotationFilterList", { afl <- AnnotationFilterList(GenenameFilter(2), SymbolFilter(23)) afl2 <- AnnotationFilterList(SeqNameFilter(4), afl) res <- ensembldb:::addFilterColumns(cols = "gene_biotype", filter = afl, edb) expect_equal(res, c("gene_biotype", "gene_name", "symbol")) res <- ensembldb:::addFilterColumns(cols = "gene_biotype", filter = afl2, edb) expect_equal(res, c("gene_biotype", "seq_name", "gene_name", "symbol")) }) test_that("functions work for encapsuled AnnotationFilterLists", { fl <- AnnotationFilterList(GenenameFilter("a"), AnnotationFilterList(TxIdFilter("b"))) res <- ensembldb:::.processFilterParam(fl, edb) expect_equal(res, fl) res <- ensembldb:::setFeatureInGRangesFilter(fl, "gene") expect_equal(res, fl) res <- ensembldb:::addFilterColumns("z", fl, edb) expect_equal(res, c("z", "gene_name", "tx_id")) res <- ensembldb:::getWhat(edb, filter = fl) ## Check if content is the same flts1 <- ~ genename == "BCL2" & tx_biotype == "protein_coding" res1 <- transcripts(edb, filter = flts1) flts2 <- AnnotationFilterList( AnnotationFilterList(GenenameFilter("BCL2"), AnnotationFilterList( TxBiotypeFilter("protein_coding")) )) res2 <- transcripts(edb, filter = flts2) expect_equal(res1, res2) }) ## Here we want to test if we get always also the filter columns back. test_that("multiFilterReturnCols works also with symbolic filters", { cols <- ensembldb:::addFilterColumns(edb, cols = c("exon_id"), filter = SymbolFilter("SKA2")) expect_equal(cols, c("exon_id", "symbol")) ## Two filter cols <- ensembldb:::addFilterColumns(edb, cols = c("exon_id"), filter = list(SymbolFilter("SKA2"), GenenameFilter("SKA2"))) expect_equal(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) )))) expect_equal(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"))) expect_equal(cols, c("exon_id", "symbol", "gene_name", "exon_seq_start", "exon_seq_end", "seq_name", "seq_strand")) ## SeqStartFilter and GRangesFilter ssf <- TxStartFilter(123) cols <- ensembldb:::addFilterColumns(edb, cols = c("exon_id"), filter = list(SymbolFilter("SKA2"), GenenameFilter("SKA2"), GRangesFilter( GRanges("3", IRanges(3, 5) ), feature = "exon"), ssf)) expect_equal(cols, c("exon_id", "symbol", "gene_name", "exon_seq_start", "exon_seq_end", "seq_name", "seq_strand", "tx_seq_start")) }) test_that("SQLiteName2MySQL works", { have <- "EnsDb.Hsapiens.v75" want <- "ensdb_hsapiens_v75" expect_equal(ensembldb:::SQLiteName2MySQL(have), want) }) test_that("anyProteinColumns works", { expect_true(ensembldb:::anyProteinColumns(c("gene_id", "protein_id"))) expect_true(!ensembldb:::anyProteinColumns(c("gene_id", "exon_id"))) }) test_that("listProteinColumns works", { if (hasProteinData(edb)) { res <- listProteinColumns(edb) expect_true(any(res == "protein_id")) expect_true(any(res == "uniprot_id")) expect_true(any(res == "protein_domain_id")) ## That's new columns fetched for Uniprot: expect_true(any(res == "uniprot_db")) expect_true(any(res == "uniprot_mapping_type")) } else { expect_error(listProteinColumns(edb)) } }) test_that("strand2num works", { expect_equal(ensembldb:::strand2num("+"), 1) expect_equal(ensembldb:::strand2num("+1"), 1) expect_equal(ensembldb:::strand2num("-"), -1) expect_equal(ensembldb:::strand2num("-1"), -1) expect_equal(ensembldb:::strand2num(1), 1) expect_equal(ensembldb:::strand2num(5), 1) expect_equal(ensembldb:::strand2num(-1), -1) expect_equal(ensembldb:::strand2num(-5), -1) expect_error(ensembldb:::strand2num("a")) }) test_that("num2strand works", { expect_equal(ensembldb:::num2strand(1), "+") expect_equal(ensembldb:::num2strand(-1), "-") }) ensembldb/tests/testthat/test_select-methods.R0000644000175400017540000004330413175714743022657 0ustar00biocbuildbiocbuild test_that("columns works", { cols <- columns(edb) ## Don't expect to see any _ there... expect_equal(length(grep(cols, pattern="_")), 0) }) test_that("keytypes works", { keyt <- keytypes(edb) expect_equal(all(c("GENEID", "EXONID", "TXID") %in% keyt), TRUE) }) test_that("ensDbColumnForColumn works", { Test <- ensembldb:::ensDbColumnForColumn(edb, "GENEID") expect_equal(unname(Test), "gene_id") Test <- ensembldb:::ensDbColumnForColumn(edb, c("GENEID", "TXID")) expect_equal(unname(Test), c("gene_id", "tx_id")) suppressWarnings( Test <- ensembldb:::ensDbColumnForColumn(edb, c("GENEID", "TXID", "bla")) ) expect_equal(unname(Test), c("gene_id", "tx_id")) }) test_that("keys works", { ## get all gene ids ids <- keys(edb, "GENEID") expect_true(length(ids) > 0) expect_equal(length(ids), length(unique(ids))) ## get all tx ids ids <- keys(edb, "TXID") expect_true(length(ids) > 0) ## Get the TXNAME... nms <- keys(edb, "TXNAME") expect_equal(nms, ids) expect_equal(length(ids), length(unique(ids))) ## get all gene names ids <- keys(edb, "GENENAME") expect_true(length(ids) > 0) expect_equal(length(ids), length(unique(ids))) ## get all seq names ids <- keys(edb, "SEQNAME") expect_true(length(ids) > 0) expect_equal(length(ids), length(unique(ids))) ## get all seq strands ids <- keys(edb, "SEQSTRAND") expect_true(length(ids) > 0) expect_equal(length(ids), length(unique(ids))) ## get all gene biotypes ids <- keys(edb, "GENEBIOTYPE") expect_true(length(ids) > 0) expect_equal(ids, listGenebiotypes(edb)) ## Now with protein data. if (hasProteinData(edb)) { library(RSQLite) ls <- keys(edb, "PROTEINID") ls_2 <- dbGetQuery(dbconn(edb), "select distinct protein_id from protein")$protein_id expect_equal(sort(ls), sort(ls_2)) ## ks <- keys(edb, "UNIPROTID") ks_2 <- dbGetQuery(dbconn(edb), "select distinct uniprot_id from uniprot")$uniprot_id expect_equal(sort(ks), sort(ks_2)) ## ks <- keys(edb, "PROTEINDOMAINID") ks_2 <- dbGetQuery(dbconn(edb), paste0("select distinct protein_domain_id from", " protein_domain"))$protein_domain_id expect_equal(sort(ks), sort(ks_2)) } ## keys with filter: res <- keys(edb, "GENENAME", filter = ~ genename == "BCL2") expect_equal(res, "BCL2") }) test_that("select method works", { .comprehensiveCheckForGene <- function(x) { ## Check if we've got all of the transcripts. txs <- dbGetQuery( dbconn(edb), paste0("select tx_id from tx where gene_id = '", x$GENEID[1], "';")) expect_equal(sort(txs$tx_id), sort(unique(x$TXID))) ## Check if we've got all exons. exs <- dbGetQuery( dbconn(edb), paste0("select exon_id from tx2exon where tx_id in (", paste0("'", txs$tx_id, "'", collapse = ", "),")")) a <- sort(unique(exs$exon_id)) b <- sort(unique(x$EXONID)) a <- a[!is.na(a)] b <- b[!is.na(b)] expect_equal(a, b) if (hasProteinData(edb)) { ## Check if we've got all proteins prt <- dbGetQuery( dbconn(edb), paste0("select protein_id from protein where tx_id in (", paste0("'", txs$tx_id, "'", collapse = ", "), ")")) a <- sort(prt$protein_id) b <- sort(unique(x$PROTEINID)) a <- a[!is.na(a)] b <- b[!is.na(b)] expect_equal(a, b) ## Check if we've got all uniprots. res <- dbGetQuery( dbconn(edb), paste0("select uniprot_id from uniprot where ", "protein_id in (", paste0("'", prt$protein_id, "'", collapse = ", ") ,")")) a <- sort(unique(res$uniprot_id)) b <- sort(unique(x$UNIPROTID)) a <- a[!is.na(a)] b <- b[!is.na(b)] expect_equal(a, b) ## Check if we've got all protein_domains. res <- dbGetQuery( dbconn(edb), paste0("select protein_domain_id from protein_domain ", "where protein_id in (", paste0("'", prt$protein_id, "'", collapse = ", "), ")")) a <- sort(unique(res$protein_domain_id)) b <- sort(unique(x$PROTEINDOMAINID)) a <- a[!is.na(a)] b <- b[!is.na(b)] expect_equal(a, b) } } library(RSQLite) ## 1) Test: ## Provide GenenameFilter. gf <- GenenameFilter("BCL2") Test <- select(edb, keys = gf) expect_true(all(Test$GENENAME == "BCL2")) .comprehensiveCheckForGene(Test) Test2 <- select(edb, keys = ~ symbol == "BCL2") expect_equal(Test, Test2) ## ZBTB16 tmp <- select(edb, keys = GenenameFilter("ZBTB16")) .comprehensiveCheckForGene(tmp) ## BCL2L11 tmp <- select(edb, keys = GenenameFilter("BCL2L11")) .comprehensiveCheckForGene(tmp) ## NR3C1 tmp <- select(edb, keys = GenenameFilter("NR3C1")) .comprehensiveCheckForGene(tmp) ## Combine GenenameFilter and TxBiotypeFilter. Test2 <- select(edb, keys = ~ symbol == "BCL2" & tx_biotype == "protein_coding") expect_equal(Test$EXONID[Test$TXBIOTYPE == "protein_coding"], Test2$EXONID) ## Choose selected columns. Test3 <- select(edb, keys = gf, columns = c("GENEID", "GENENAME", "SEQNAME")) expect_equal(unique(Test[, c("GENEID", "GENENAME", "SEQNAME")]), Test3) ## Provide keys. Test4 <- select(edb, keys = "BCL2", keytype = "GENENAME") expect_equal(Test[, colnames(Test4)], Test4) gns <- keys(edb, "GENEID") ## Just get stuff from the tx table; should be faster. Test <- select(edb, keys = gns, columns = c("GENEID", "SEQNAME"), keytype = "GENEID") expect_equal(all(Test$GENEID == gns), 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")) expect_equal(Test[, colnames(Test2)], Test2) ## All on chromosome 21 Test <- select(edb, keys = "21", columns = c("GENEID", "GENEBIOTYPE", "GENENAME"), keytype = "SEQNAME") Test2 <- select(edb, keys = ~ seq_name == "21", columns = c("GENEID", "GENEBIOTYPE", "GENENAME")) expect_equal(Test[, colnames(Test2)], Test2) ## What if we can't find it? Test <- select(edb, keys = "bla", columns = c("GENEID", "GENENAME"), keytype = "GENENAME") expect_equal(colnames(Test), c("GENEID", "GENENAME")) expect_true(nrow(Test) == 0) ## TXNAME Test <- select(edb, keys = "ENST00000000233", columns = c("GENEID", "GENENAME"), keytype = "TXNAME") expect_equal(Test$TXNAME, "ENST00000000233") ## Check what happens if we just add TXNAME and also TXID. Test2 <- select(edb, keys = list(gf, TxBiotypeFilter("protein_coding")), columns = c("TXID", "TXNAME", "GENENAME", "GENEID")) expect_equal(colnames(Test2), c("TXID", "TXNAME", "GENENAME", "GENEID", "TXBIOTYPE")) ## Protein stuff. if (hasProteinData(edb)) { ## Test: ## o if we're fetching with PROTEINID keys we're just getting protein ## coding tx, i.e. those with a tx_cds_seq_start not NULL AND we get ## also those with a uniprot ID null. pids <- c("ENSP00000338157", "ENSP00000437716", "ENSP00000443013", "ENSP00000376721", "ENSP00000445047") res <- select(edb, keys = pids, keytype = "PROTEINID", columns = c("TXID", "TXCDSSEQSTART", "TXBIOTYPE", "PROTEINID", "UNIPROTID", "PROTEINDOMAINID")) expect_equal(sort(pids), sort(unique(res$PROTEINID))) res_2 <- select(edb, keys = ProteinIdFilter(pids), columns = c("TXID", "TXCDSSEQSTART", "TXBIOTYPE", "PROTEINID", "UNIPROTID", "PROTEINDOMAINID")) expect_equal(sort(pids), sort(unique(res_2$PROTEINID))) expect_equal(res, res_2) expect_true(all(!is.na(res$TXCDSSEQSTART))) ## Do we have all of the uniprot ids? tmp <- dbGetQuery(dbconn(edb), paste0("select uniprot_id from uniprot where ", "protein_id in (", paste0("'", pids,"'", collapse = ", "),")")) a <- sort(unique(res$UNIPROTID)) b <- sort(unique(tmp$uniprot_id)) a <- a[!is.na(a)] b <- b[!is.na(b)] expect_equal(a, b) ## Do we have all protein domain ids? tmp <- dbGetQuery(dbconn(edb), paste0("select protein_domain_id from protein_domain ", "where protein_id in (", paste0("'", pids,"'", collapse = ", "),")")) a <- sort(unique(res$PROTEINDOMAINID)) b <- sort(unique(tmp$protein_domain_id)) a <- a[!is.na(a)] b <- b[!is.na(b)] expect_equal(a, b) ## o if we're fetching with uniprot and protein id filter we get all ## even if they don't have a protein domain. upids <- c("ZBT16_HUMAN", "Q71UL7_HUMAN", "Q71UL6_HUMAN", "Q71UL5_HUMAN") res <- select(edb, keys = upids, keytype = "UNIPROTID", columns = c("PROTEINID", "UNIPROTID", "PROTEINDOMAINID")) } }) test_that("mapIds works", { ## Simple... map gene ids to gene names allgenes <- keys(edb, keytype = "GENEID") randordergenes <- allgenes[sample(1:length(allgenes), 100)] mi <- mapIds(edb, keys = allgenes, keytype = "GENEID", column = "GENENAME") expect_equal(allgenes, names(mi)) ## Ordering should always match the ordering of the input: mi <- mapIds(edb, keys = randordergenes, keytype = "GENEID", column = "GENENAME") expect_equal(randordergenes, names(mi)) ## Handle multi mappings. ## o first first <- mapIds(edb, keys = randordergenes, keytype = "GENEID", column = "TXID") expect_equal(names(first), randordergenes) ## o list lis <- mapIds(edb, keys = randordergenes, keytype = "GENEID", column = "TXID", multiVals = "list") expect_equal(names(lis), randordergenes) Test <- lapply(lis, function(z){return(z[1])}) expect_equal(first, unlist(Test)) ## o filter filt <- mapIds(edb, keys = randordergenes, keytype = "GENEID", column = "TXID", multiVals = "filter") expect_equal(filt, unlist(lis[unlist(lapply(lis, length)) == 1])) ## o 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") expect_equal(names(Test), c("BCL2", "BCL2L11", "BCL2")) expect_true(length(unique(Test)) == 2) ## Submit Filter: Test <- mapIds(edb, keys = SeqNameFilter("Y"), column = "GENEID", multiVals = "list") TestS <- select(edb, keys = Test[[1]], columns = "SEQNAME", keytype = "GENEID") expect_equal(unique(TestS$SEQNAME), "Y") ## Submit 2 filter.LLLLL Test <- mapIds(edb, keys = ~ seq_name == "Y" & seq_strand == "-", multiVals = "list", column = "GENEID") TestS <- select(edb, keys = Test[[1]], keytype = "GENEID", columns = c("SEQNAME", "SEQSTRAND")) expect_true(all(TestS$SEQNAME == "Y")) expect_true(all(TestS$SEQSTRAND == -1)) ## Now using protein annotations: if (hasProteinData(edb)) { library(RSQLite) txids <- keys(edb, keytype = "TXID", filter = GenenameFilter("ZBTB16")) mapd <- mapIds(edb, keys = txids, keytype = "TXID", column = "GENENAME") expect_equal(names(mapd), txids) expect_true(all(mapd == "ZBTB16")) ## Map to protein ids. mapd <- mapIds(edb, keys = txids, keytype = "TXID", column = "PROTEINID") res <- dbGetQuery(dbconn(edb), paste0("select protein_id from protein where tx_id in", " (", paste0("'", txids,"'", collapse = ", "), ")")) pids <- mapd[!is.na(mapd)] expect_true(all(pids %in% res$protein_id)) ## multi-mapping: ## proteins and uniprot. mapd <- mapIds(edb, keys = pids, keytype = "PROTEINID", column = "UNIPROTID", multiVals = "list") mapd <- mapd[!is.na(mapd)] res <- dbGetQuery(dbconn(edb), paste0("select protein_id, uniprot_id from uniprot ", "where protein_id in (", paste0("'", pids, "'", collapse = ", "), ")")) res <- split(res$uniprot_id, res$protein_id) expect_equal(mapd, res[names(mapd)]) ## Just to ensure: tmp <- proteins(edb, filter = ProteinIdFilter(pids), columns = c("uniprot_id", "protein_id")) upids <- tmp$uniprot_id[!is.na(tmp$uniprot_id)] expect_true(all(res$uniprot_id %in% upids)) ## map protein ids to gene name mapd <- mapIds(edb, keys = pids, keytype = "PROTEINID", column = "GENENAME") expect_true(all(mapd == "ZBTB16")) } }) ## Test if the results are properly sorted if we submit a single filter or just keys. test_that("select results are properly sorted", { ks <- c("ZBTB16", "BCL2", "SKA2", "BCL2L11") ## gene_id res <- select(edb, keys = ks, keytype = "GENENAME") expect_equal(unique(res$GENENAME), ks) res <- select(edb, keys = GenenameFilter(ks)) expect_equal(unique(res$GENENAME), ks) ## Using two filters; res <- select(edb, keys = list(GenenameFilter(ks), TxBiotypeFilter("nonsense_mediated_decay"))) ## We don't expect same sorting here! expect_true(!all(unique(res$GENENAME) == ks[ks %in% unique(res$GENENAME)])) res2 <- select(edb, keys = ~ genename == ks & tx_biotype == "nonsense_mediated_decay") expect_equal(res, res2) ## symbol res <- select(edb, keys = ks, keytype = "SYMBOL", columns = c("GENENAME", "SYMBOL", "SEQNAME")) expect_equal(res$SYMBOL, ks) expect_equal(res$GENENAME, ks) ## tx_biotype ks <- c("retained_intron", "nonsense_mediated_decay") res <- select(edb, keys = ks, keytype = "TXBIOTYPE", columns = c("GENENAME", "TXBIOTYPE")) expect_equal(unique(res$TXBIOTYPE), ks) res <- select(edb, keys = TxBiotypeFilter(ks), keytype = "TXBIOTYPE", columns = c("GENENAME", "TXBIOTYPE")) expect_equal(unique(res$TXBIOTYPE), ks) }) test_that("select works with symbol as keytype", { ks <- c("ZBTB16", "BCL2", "SKA2", "BCL2L11") res <- select(edb, keys = ks, keytype = "GENENAME") res2 <- select(edb, keys = ks, keytype = "SYMBOL") expect_equal(res, res2) res <- select(edb, keys = GenenameFilter(ks), columns = c("TXNAME", "SYMBOL", "GENEID")) expect_equal(colnames(res), c("TXNAME", "SYMBOL", "GENEID", "GENENAME")) res <- select(edb, keys = ~ symbol == ks, columns=c("GENEID")) expect_equal(colnames(res), c("GENEID", "SYMBOL")) expect_equal(res$SYMBOL, ks) res <- select(edb, keys = list(SeqNameFilter("Y"), GeneBiotypeFilter("lincRNA")), columns = c("GENEID", "SYMBOL")) expect_equal(colnames(res), c("GENEID", "SYMBOL", "SEQNAME", "GENEBIOTYPE")) expect_true(all(res$SEQNAME == "Y")) }) test_that("select works with txname", { ## TXNAME as a column ks <- c("ZBTB16", "BCL2", "SKA2") res <- select(edb, keys = ks, keytype = "GENENAME", columns = c("TXNAME")) expect_equal(colnames(res), c("GENENAME", "TXNAME")) }) test_that(".keytype2FilterMapping works", { ## Check whether or not we're getting protein columns. res <- ensembldb:::.keytype2FilterMapping() expect_equal(names(res), c("ENTREZID", "GENEID", "GENEBIOTYPE", "GENENAME", "TXID", "TXBIOTYPE", "EXONID", "SEQNAME", "SEQSTRAND", "TXNAME", "SYMBOL")) res <- ensembldb:::.keytype2FilterMapping(TRUE) expect_true(all(c("PROTEINID", "UNIPROTID", "PROTEINDOMAINID") %in% names(res))) }) test_that("keytypes works", { keyt <- c("ENTREZID", "GENEID", "GENEBIOTYPE", "GENENAME", "TXID", "TXBIOTYPE", "EXONID", "SEQNAME", "SEQSTRAND", "TXNAME", "SYMBOL") res <- keytypes(edb) if (hasProteinData(edb)) { expect_equal(res, sort(c(keyt, "PROTEINID", "UNIPROTID", "PROTEINDOMAINID"))) } else { expect_equal(res, sort(keyt)) } }) test_that("filterForKeytype works", { res <- ensembldb:::filterForKeytype("SYMBOL") expect_true(is(res, "SymbolFilter")) if (hasProteinData(edb)) { res <- ensembldb:::filterForKeytype("PROTEINDOMAINID", edb) expect_true(is(res, "ProtDomIdFilter")) } res <- ensembldb:::filterForKeytype("TXID") expect_true(is(res, "TxIdFilter")) }) ensembldb/tests/testthat/test_seqLevelStyle.R0000644000175400017540000004324113175714743022540 0ustar00biocbuildbiocbuild## Tests related to setting seqLevelsStyle test_that("seqlevelsStyle works", { 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" suppressWarnings( SL2 <- seqlevels(edb) ) expect_equal(sort(ucscs), sort(SL2[!is.na(SL2)])) ## Check if we throw an error message options(ensembldb.seqnameNotFound = "MISSING") expect_error(seqlevels(edb)) ## Check if returning original names works. options(ensembldb.seqnameNotFound = "ORIGINAL") suppressWarnings( SL3 <- seqlevels(edb) ) idx <- which(SL3 %in% ucscs) expect_equal(sort(SL[-idx]), sort(SL3[-idx])) options(ensembldb.seqnameNotFound=orig) }) test_that("seqinfo works with seqlevelsStyle", { edb <- EnsDb.Hsapiens.v75 orig <- getOption("ensembldb.seqnameNotFound") options(ensembldb.seqnameNotFound="MISSING") seqlevelsStyle(edb) <- "UCSC" expect_error(seqinfo(edb)) options(ensembldb.seqnameNotFound="ORIGINAL") suppressWarnings( si <- seqinfo(edb) ) options(ensembldb.seqnameNotFound=orig) }) test_that("getWhat works with seqlevelsStyle", { orig <- getOption("ensembldb.seqnameNotFound") edb <- EnsDb.Hsapiens.v75 seqlevelsStyle(edb) <- "Ensembl" ensRes <- ensembldb:::getWhat(edb, columns=c("seq_name", "seq_strand")) seqlevelsStyle(edb) <- "UCSC" suppressWarnings( ucscRes <- ensembldb:::getWhat(edb, columns=c("seq_name", "seq_strand")) ) seqlevelsStyle(edb) <- "NCBI" suppressWarnings( ncbiRes <- ensembldb:::getWhat(edb, columns=c("seq_name", "seq_strand")) ) options(ensembldb.seqnameNotFound=orig) }) test_that("SeqNameFilter works with seqlevelsStyle", { 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" expect_equal(value(snf), "chrX") ## That makes no sense for a query though. expect_equal(value(snf), "chrX") expect_equal(value(snfEns), c("X", "Y")) seqlevelsStyle(edb) <- "UCSC" expect_equal(ensembldb:::ensDbQuery(snf, edb), "gene.seq_name = 'X'") expect_error(ensembldb:::ensDbQuery(snfEns, edb)) expect_error(ensembldb:::ensDbQuery(snfNo, edb)) expect_error(ensembldb:::ensDbQuery(snfSomeNo, edb)) ## Setting the options to "ORIGINAL" options(ensembldb.seqnameNotFound="ORIGINAL") expect_equal(ensembldb:::ensDbQuery(snf, edb), "gene.seq_name = 'X'") suppressWarnings( expect_equal(ensembldb:::ensDbQuery(snfEns, edb), "gene.seq_name in ('X','Y')") ) suppressWarnings( expect_equal(ensembldb:::ensDbQuery(snfNo, edb), "gene.seq_name in ('bla','blu')") ) suppressWarnings( expect_equal(ensembldb:::ensDbQuery(snfSomeNo, edb), "gene.seq_name in ('bla','X')") ) ## snf <- SeqNameFilter(c("chrX", "Y")) suppressWarnings( expect_equal(ensembldb:::ensDbQuery(snf, edb), "gene.seq_name in ('X','Y')") ) options(ensembldb.seqnameNotFound=orig) }) test_that("genes works with seqlevelsStyles", { 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"))) expect_equal(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)) expect_equal(seqlevels(ensY), "Y") expect_equal(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"))) expect_equal(sort(seqlevels(ucsc21Y)), c("chr21", "chrY")) expect_equal(sort(names(ens21Y)), sort(names(ucsc21Y))) ## GRangesFilter. gr <- GRanges(seqnames="chrY", ranges=IRanges(start=1, end=59373566), strand="+") ucscY <- genes(edb, filter=GRangesFilter(gr)) expect_equal(seqlevels(ucscY), "chrY") expect_equal(unique(as.character(strand(ucscY))), "+") expect_equal(sort(names(ensY)), sort(names(ucscY))) options(ensembldb.seqnameNotFound=orig) }) test_that("transcripts works with seqlevelsStyle", { orig <- getOption("ensembldb.seqnameNotFound") edb <- EnsDb.Hsapiens.v75 seqlevelsStyle(edb) <- "Ensembl" ens21Y <- transcripts(edb, filter = SeqNameFilter(c("Y", "21"))) expect_equal(sort(seqlevels(ens21Y)), c("21", "Y")) gr <- GRanges(seqnames="Y", ranges=IRanges(start=1, end=59373566), strand="+") ensY <- transcripts(edb, filter=GRangesFilter(gr)) expect_equal(seqlevels(ensY), "Y") expect_equal(unique(as.character(strand(ensY))), "+") ## Check UCSC stuff seqlevelsStyle(edb) <- "UCSC" options(ensembldb.seqnameNotFound="ORIGINAL") ucsc21Y <- transcripts(edb, filter=SeqNameFilter(c("chrY", "chr21"))) expect_equal(sort(seqlevels(ucsc21Y)), c("chr21", "chrY")) expect_equal(sort(names(ens21Y)), sort(names(ucsc21Y))) ## GRangesFilter. gr <- GRanges(seqnames="chrY", ranges=IRanges(start=1, end=59373566), strand="+") ucscY <- transcripts(edb, filter=GRangesFilter(gr)) expect_equal(seqlevels(ucscY), "chrY") expect_equal(unique(as.character(strand(ucscY))), "+") expect_equal(sort(names(ensY)), sort(names(ucscY))) options(ensembldb.seqnameNotFound=orig) }) test_that("transcriptsBy works with seqlevelsStyle", { orig <- getOption("ensembldb.seqnameNotFound") edb <- EnsDb.Hsapiens.v75 seqlevelsStyle(edb) <- "Ensembl" ens21Y <- transcriptsBy(edb, filter=SeqNameFilter(c("Y", "21"))) expect_equal(sort(seqlevels(ens21Y)), c("21", "Y")) gr <- GRanges(seqnames="Y", ranges=IRanges(start=1, end=59373566), strand="+") ensY <- transcriptsBy(edb, filter=GRangesFilter(gr)) expect_equal(seqlevels(ensY), "Y") suppressWarnings( expect_equal(unique(as.character(unlist(strand(ensY)))), "+") ) ## Check UCSC stuff seqlevelsStyle(edb) <- "UCSC" options(ensembldb.seqnameNotFound="ORIGINAL") ucsc21Y <- transcriptsBy(edb, filter=SeqNameFilter(c("chrY", "chr21"))) expect_equal(sort(seqlevels(ucsc21Y)), c("chr21", "chrY")) expect_equal(sort(names(ens21Y)), sort(names(ucsc21Y))) ## GRangesFilter. gr <- GRanges(seqnames="chrY", ranges=IRanges(start=1, end=59373566), strand="+") ucscY <- transcriptsBy(edb, filter=GRangesFilter(gr)) expect_equal(seqlevels(ucscY), "chrY") suppressWarnings( expect_equal(unique(as.character(unlist(strand(ucscY)))), "+") ) expect_equal(sort(names(ensY)), sort(names(ucscY))) options(ensembldb.seqnameNotFound=orig) }) test_that("exons works with seqlevelsStyle", { orig <- getOption("ensembldb.seqnameNotFound") edb <- EnsDb.Hsapiens.v75 seqlevelsStyle(edb) <- "Ensembl" ens21Y <- exons(edb, filter=SeqNameFilter(c("Y", "21"))) expect_equal(sort(seqlevels(ens21Y)), c("21", "Y")) gr <- GRanges(seqnames="Y", ranges=IRanges(start=1, end=59373566), strand="+") ensY <- exons(edb, filter=GRangesFilter(gr)) expect_equal(seqlevels(ensY), "Y") expect_equal(unique(as.character(strand(ensY))), "+") ## Check UCSC stuff seqlevelsStyle(edb) <- "UCSC" options(ensembldb.seqnameNotFound="ORIGINAL") ucsc21Y <- exons(edb, filter=SeqNameFilter(c("chrY", "chr21"))) expect_equal(sort(seqlevels(ucsc21Y)), c("chr21", "chrY")) expect_equal(sort(names(ens21Y)), sort(names(ucsc21Y))) ## GRangesFilter. gr <- GRanges(seqnames="chrY", ranges=IRanges(start=1, end=59373566), strand="+") ucscY <- exons(edb, filter=GRangesFilter(gr)) expect_equal(seqlevels(ucscY), "chrY") expect_equal(unique(as.character(strand(ucscY))), "+") expect_equal(sort(names(ensY)), sort(names(ucscY))) options(ensembldb.seqnameNotFound=orig) }) test_that("exonsBy works with seqlevelsStyle", { orig <- getOption("ensembldb.seqnameNotFound") edb <- EnsDb.Hsapiens.v75 seqlevelsStyle(edb) <- "Ensembl" ens21Y <- exonsBy(edb, filter=SeqNameFilter(c("Y", "21"))) expect_equal(sort(seqlevels(ens21Y)), c("21", "Y")) gr <- GRanges(seqnames="Y", ranges=IRanges(start=1, end=59373566), strand="+") ensY <- exonsBy(edb, filter=GRangesFilter(gr)) expect_equal(seqlevels(ensY), "Y") suppressWarnings( expect_equal(unique(as.character(unlist(strand(ensY)))), "+") ) ## Check UCSC stuff seqlevelsStyle(edb) <- "UCSC" options(ensembldb.seqnameNotFound="ORIGINAL") ucsc21Y <- exonsBy(edb, filter=SeqNameFilter(c("chrY", "chr21"))) expect_equal(sort(seqlevels(ucsc21Y)), c("chr21", "chrY")) expect_equal(sort(names(ens21Y)), sort(names(ucsc21Y))) ## GRangesFilter. gr <- GRanges(seqnames="chrY", ranges=IRanges(start=1, end=59373566), strand="+") ucscY <- exonsBy(edb, filter=GRangesFilter(gr)) expect_equal(seqlevels(ucscY), "chrY") suppressWarnings( expect_equal(unique(as.character(unlist(strand(ucscY)))), "+") ) expect_equal(sort(names(ensY)), sort(names(ucscY))) options(ensembldb.seqnameNotFound=orig) }) test_that("cdsBy works with seqlevelsStyle", { orig <- getOption("ensembldb.seqnameNotFound") edb <- EnsDb.Hsapiens.v75 seqlevelsStyle(edb) <- "Ensembl" ens21Y <- cdsBy(edb, filter=SeqNameFilter(c("Y", "21"))) expect_equal(sort(seqlevels(ens21Y)), c("21", "Y")) gr <- GRanges(seqnames="Y", ranges=IRanges(start=1, end=59373566), strand="+") ensY <- cdsBy(edb, filter=GRangesFilter(gr)) expect_equal(seqlevels(ensY), "Y") suppressWarnings( expect_equal(unique(as.character(unlist(strand(ensY)))), "+") ) ## Check UCSC stuff seqlevelsStyle(edb) <- "UCSC" options(ensembldb.seqnameNotFound="ORIGINAL") ucsc21Y <- cdsBy(edb, filter=SeqNameFilter(c("chrY", "chr21"))) expect_equal(sort(seqlevels(ucsc21Y)), c("chr21", "chrY")) expect_equal(sort(names(ens21Y)), sort(names(ucsc21Y))) ## GRangesFilter. gr <- GRanges(seqnames="chrY", ranges=IRanges(start=1, end=59373566), strand="+") ucscY <- cdsBy(edb, filter=GRangesFilter(gr)) expect_equal(seqlevels(ucscY), "chrY") suppressWarnings( expect_equal(unique(as.character(unlist(strand(ucscY)))), "+") ) expect_equal(sort(names(ensY)), sort(names(ucscY))) options(ensembldb.seqnameNotFound=orig) }) test_that("threeUTRsByTranscript works with seqlevelsStyle", { orig <- getOption("ensembldb.seqnameNotFound") edb <- EnsDb.Hsapiens.v75 seqlevelsStyle(edb) <- "Ensembl" ens21Y <- threeUTRsByTranscript(edb, filter=SeqNameFilter(c("Y", "21"))) expect_equal(sort(seqlevels(ens21Y)), c("21", "Y")) gr <- GRanges(seqnames="Y", ranges=IRanges(start=1, end=59373566), strand="+") ensY <- threeUTRsByTranscript(edb, filter=GRangesFilter(gr)) expect_equal(seqlevels(ensY), "Y") suppressWarnings( expect_equal(unique(as.character(unlist(strand(ensY)))), "+") ) ## Check UCSC stuff seqlevelsStyle(edb) <- "UCSC" options(ensembldb.seqnameNotFound="ORIGINAL") ucsc21Y <- threeUTRsByTranscript(edb, filter=SeqNameFilter(c("chrY", "chr21"))) expect_equal(sort(seqlevels(ucsc21Y)), c("chr21", "chrY")) expect_equal(sort(names(ens21Y)), sort(names(ucsc21Y))) ## GRangesFilter. gr <- GRanges(seqnames="chrY", ranges=IRanges(start=1, end=59373566), strand="+") ucscY <- threeUTRsByTranscript(edb, filter=GRangesFilter(gr)) expect_equal(seqlevels(ucscY), "chrY") suppressWarnings( expect_equal(unique(as.character(unlist(strand(ucscY)))), "+") ) expect_equal(sort(names(ensY)), sort(names(ucscY))) options(ensembldb.seqnameNotFound=orig) }) test_that("fiveUTRsByTranscript works with seqlevelsStyle", { orig <- getOption("ensembldb.seqnameNotFound") edb <- EnsDb.Hsapiens.v75 seqlevelsStyle(edb) <- "Ensembl" ens21Y <- fiveUTRsByTranscript(edb, filter=SeqNameFilter(c("Y", "21"))) expect_equal(sort(seqlevels(ens21Y)), c("21", "Y")) gr <- GRanges(seqnames="Y", ranges=IRanges(start=1, end=59373566), strand="+") ensY <- fiveUTRsByTranscript(edb, filter=GRangesFilter(gr)) expect_equal(seqlevels(ensY), "Y") suppressWarnings( expect_equal(unique(as.character(unlist(strand(ensY)))), "+") ) ## Check UCSC stuff seqlevelsStyle(edb) <- "UCSC" options(ensembldb.seqnameNotFound="ORIGINAL") ucsc21Y <- fiveUTRsByTranscript(edb, filter=SeqNameFilter(c("chrY", "chr21"))) expect_equal(sort(seqlevels(ucsc21Y)), c("chr21", "chrY")) expect_equal(sort(names(ens21Y)), sort(names(ucsc21Y))) ## GRangesFilter. gr <- GRanges(seqnames="chrY", ranges=IRanges(start=1, end=59373566), strand="+") ucscY <- fiveUTRsByTranscript(edb, filter=GRangesFilter(gr)) expect_equal(seqlevels(ucscY), "chrY") suppressWarnings( expect_equal(unique(as.character(unlist(strand(ucscY)))), "+") ) expect_equal(sort(names(ensY)), sort(names(ucscY))) options(ensembldb.seqnameNotFound=orig) }) test_that("seting and getting seqlevelsStyle works", { edb <- EnsDb.Hsapiens.v75 ## Testing the getter/setter for the seqlevelsStyle. expect_equal(seqlevelsStyle(edb), "Ensembl") expect_equal(NA, ensembldb:::getProperty(edb, "seqlevelsStyle")) seqlevelsStyle(edb) <- "Ensembl" expect_equal(seqlevelsStyle(edb), "Ensembl") expect_equal("Ensembl", ensembldb:::getProperty(edb, "seqlevelsStyle")) ## Try NCBI. seqlevelsStyle(edb) <- "NCBI" expect_equal(seqlevelsStyle(edb), "NCBI") ## Try UCSC. seqlevelsStyle(edb) <- "UCSC" expect_equal(seqlevelsStyle(edb), "UCSC") ## Error checking: expect_error(seqlevelsStyle(edb) <- "bla") }) test_that("formatting seqnames for query works with seqlevelsStyle", { ## 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) expect_equal(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") expect_error(ensembldb:::formatSeqnamesForQuery(edb, enses)) ## With specifying ifNotFound suppressWarnings( got <- ensembldb:::formatSeqnamesForQuery(edb, enses, ifNotFound=NA) ) expect_equal(all(is.na(got)), TRUE) ## Same by setting the option options(ensembldb.seqnameNotFound=NA) suppressWarnings( got <- ensembldb:::formatSeqnamesForQuery(edb, enses) ) expect_equal(all(is.na(got)), TRUE) ## Now the working example: got <- ensembldb:::formatSeqnamesForQuery(edb, ucscs) expect_equal(got, enses) ## What if one is not mappable: suppressWarnings( got <- ensembldb:::formatSeqnamesForQuery(edb, c(ucscs, "asdfd"), ifNotFound=NA) ) expect_equal(got, c(enses, NA)) }) test_that("formating seqnames from query works with seqlevelsStyle", { 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) expect_equal(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") expect_error(ensembldb:::formatSeqnamesFromQuery(edb, ucsc)) ## With specifying ifNotFound suppressWarnings( got <- ensembldb:::formatSeqnamesFromQuery(edb, ucscs, ifNotFound=NA) ) expect_equal(all(is.na(got)), TRUE) ## Same using options options(ensembldb.seqnameNotFound=NA) suppressWarnings( got <- ensembldb:::formatSeqnamesFromQuery(edb, ucscs, ifNotFound=NA) ) expect_equal(all(is.na(got)), TRUE) ## Now the working example: got <- ensembldb:::formatSeqnamesFromQuery(edb, enses) expect_equal(got, ucscs) ## What if one is not mappable: suppressWarnings( got <- ensembldb:::formatSeqnamesFromQuery(edb, c(enses, "asdfd"), ifNotFound=NA) ) expect_equal(got, c(ucscs, NA)) suppressWarnings( got <- ensembldb:::formatSeqnamesFromQuery(edb, c(enses, "asdfd")) ) expect_equal(got, c(ucscs, NA)) }) test_that("prefixChromName works", { res <- ensembldb:::ucscToEns("chrY") expect_equal(res, "Y") res <- ensembldb:::prefixChromName("Y") expect_equal(res, "Y") useU <- getOption("ucscChromosomeNames", default = FALSE) options(ucscChromosomeNames = TRUE) res <- ensembldb:::prefixChromName("Y") expect_equal(res, "chrY") options(ucscChromosomeNames = useU) }) ensembldb/tests/testthat/test_validity.R0000644000175400017540000000072113175714743021560 0ustar00biocbuildbiocbuild test_that("validity functions work", { OK <- ensembldb:::dbHasRequiredTables(dbconn(edb)) expect_true(OK) ## Check the tables OK <- ensembldb:::dbHasValidTables(dbconn(edb)) expect_true(OK) }) test_that("validateEnsDb works", { expect_true(ensembldb:::validateEnsDb(edb)) }) test_that("compareProteins works", { if (hasProteinData(edb)) { res <- ensembldb:::compareProteins(edb, edb) expect_equal(res, "OK") } }) ensembldb/vignettes/0000755000175400017540000000000013241155731015545 5ustar00biocbuildbiocbuildensembldb/vignettes/MySQL-backend.Rmd0000644000175400017540000000457113175714743020564 0ustar00biocbuildbiocbuild--- title: "Using a MySQL server backend" author: "Johannes Rainer" package: ensembldb output: BiocStyle::html_document: toc_float: true vignette: > %\VignetteIndexEntry{Using a MySQL server backend} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} %\VignetteDepends{ensembldb,EnsDb.Hsapiens.v75,BiocStyle} --- # 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.org0000644000175400017540000000526313175714743020630 0ustar00biocbuildbiocbuild#+TITLE: Using a MySQL server backend #+AUTHOR: Johannes Rainer #+EMAIL: johannes.rainer@eurac.edu #+OPTIONS: ^:{} toc:nil #+PROPERTY: header-args :exports code #+PROPERTY: header-args :session *R* #+BEGIN_EXPORT html --- title: "Using a MySQL server backend" author: "Johannes Rainer" package: ensembldb output: BiocStyle::html_document: toc_float: true vignette: > %\VignetteIndexEntry{Using a MySQL server backend} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} %\VignetteDepends{ensembldb,EnsDb.Hsapiens.v75,BiocStyle} --- #+END_EXPORT ** 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.Rmd0000644000175400017540000014012513175714743020161 0ustar00biocbuildbiocbuild--- title: "Generating an using Ensembl based annotation packages" author: "Johannes Rainer" graphics: yes package: ensembldb output: BiocStyle::html_document: toc_float: true vignette: > %\VignetteIndexEntry{Generating an using Ensembl based annotation packages} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} %\VignetteDepends{ensembldb,EnsDb.Hsapiens.v75,BiocStyle,AnnotationHub,ggbio,Gviz,magrittr} --- # 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. From version 1.7 on, `EnsDb` databases created by the `ensembldb` package contain also protein annotation data (see Section [11](#org5bd9a97) for the database layout and an overview of available attributes/columns). For more information on the use of the protein annotations refer to the *proteins* vignette. 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 `EnsDb` object providing access to the underlying SQLite database is bound to the variable name `EnsDb.Hsapiens.v75`. ```{r load-libs, 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) ``` ```{r no-network, echo = FALSE, results = "hide" } ## Disable code chunks that require network connection - conditionally ## disable this on Windows only. This is to avoid TIMEOUT errors on the ## Bioconductor Windows build maching (issue #47). use_network <- FALSE ``` # Using `ensembldb` annotation packages to retrieve specific annotations One of the strengths of the `ensembldb` package and the related `EnsDb` databases is its implementation of a filter framework that enables to efficiently extract data sub-sets from the databases. The `ensembldb` package supports most of the filters defined in the `AnnotationFilter` Bioconductor package and defines some additional filters specific to the data stored in `EnsDb` databases. Filters can be passed directly to all methods extracting data from an `EnsDb` (such as `genes`, `transcripts` or `exons`). Alternatively it is possible with the `addFilter` or `filter` functions to add a filter directly to an `EnsDb` which will then be used in all queries on that object. The `supportedFilters` method can be used to get an overview over all supported filter classes, each of them (except the `GRangesFilter`) working on a single column/field in the database. ```{r filters } supportedFilters(edb) ``` These filters can be divided into 3 main filter types: - `IntegerFilter`: filter classes extending this basic object can take a single numeric value as input and support the conditions `=, !`, >, <, >= and <=. All filters that work on chromosomal coordinates, such as the `GeneEndFilter` extend `IntegerFilter`. - `CharacterFilter`: filter classes extending this object can take a single or multiple character values as input and allow conditions: `=, !`, "startsWith" and "endsWith". All filters working on IDs extend this class. - `GRangesFilter`: takes a `GRanges` object as input and supports all conditions that `findOverlaps` from the `IRanges` package supports ("any", "start", "end", "within", "equal"). Note that these have to be passed using the parameter `type` to the constructor function. The supported filters are: - `EntrezFilter`: allows to filter results based on NCBI Entrezgene identifiers of the genes. - `ExonEndFilter`: filter using the chromosomal end coordinate of exons. - `ExonIdFilter`: filter based on the (Ensembl) exon identifiers. - `ExonRankFilter`: filter based 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. - `ExonStartFilter`: filter using the chromosomal start coordinate of exons. - `GeneBiotypeFilter`: filter using the gene biotypes defined in the Ensembl database; use the `listGenebiotypes` method to list all available biotypes. - `GeneEndFilter`: filter using the chromosomal end coordinate of gene. - `GeneIdFilter`: filter based on the Ensembl gene IDs. - `GenenameFilter`: filter based on the names (symbols) of the genes. - `GeneStartFilter`: filter using the chromosomal start coordinate of gene. - `GRangesFilter`: allows to retrieve all features (genes, transcripts or exons) that are either within (setting parameter `type` to "within") or partially overlapping (setting `type` to "any") 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. - `SeqNameFilter`: filter by the name of the chromosomes the genes are encoded on. - `SeqStrandFilter`: filter for the chromosome strand on which the genes are encoded. - `SymbolFilter`: 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. - `TxBiotypeFilter`: filter on the transcript biotype defined in Ensembl; use the `listTxbiotypes` method to list all available biotypes. - `TxEndFilter`: filter using the chromosomal end coordinate of transcripts. - `TxIdFilter`: filter on the Ensembl transcript identifiers. - `TxNameFilter`: filter on the Ensembl transcript names (currently identical to the transcript IDs). - `TxStartFilter`: filter using the chromosomal start coordinate of transcripts. In addition to the above listed *DNA-RNA-based* filters, *protein-specific* filters are also available: - `ProtDomIdFilter`: filter by the protein domain ID. - `ProteinIdFilter`: filter by Ensembl protein ID filters. - `UniprotDbFilter`: filter by the name of the Uniprot database. - `UniprotFilter`: filter by the Uniprot ID. - `UniprotMappingTypeFilter`: filter by the mapping type of Ensembl protein IDs to Uniprot IDs. These can however only be used on `EnsDb` databases that provide protein annotations, i.e. for which a call to `hasProteinData` returns `TRUE`. `EnsDb` databases for more recent Ensembl versions (starting from Ensembl 87) provide also evidence levels for individual transcripts in the `tx_support_level` database column. Such databases support also a `TxSupportLevelFilter` filter to use this columns for filtering. A simple use case for the filter framework 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` 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 instead of the default `GRanges`. ```{r transcripts } 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 extractor methods (such as `exons`, `genes` or `transcripts)` 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. Instead of passing a filter *object* to the method it is also possible to provide a filter *expression* written as a `formula`. The `formula` has to be written in the form `~ ` with `` being the field (database column) in the database, `` the condition for the filter object and `` its value. Use the `supportedFilter` method to get the field names corresponding to each filter class. ```{r transcripts-filter-expression } ## Use a filter expression to perform the filtering. transcripts(edb, filter = ~ genename == "ZBTB16") ``` Filter expression have to be written as a formula (i.e. starting with a `~`) in the form *column name* followed by the logical condition. Alternatively, `EnsDb` objects can be filtered directly using the `filter` function. In the example below we use the `filter` function to filter the `EnsDb` object and pass that filtered database to the `transcripts` method using the `%>%` from the `magrittr` package. ```{r transcripts-filter } library(magrittr) filter(edb, ~ symbol == "BCL2" & tx_biotype != "protein_coding") %>% transcripts ``` Adding a filter to an `EnsDb` enables this filter (globally) on all subsequent queries on that object. We could thus filter an `EnsDb` to (virtually) contain only features encoded on chromosome Y. ```{r filter-Y } edb_y <- addFilter(edb, SeqNameFilter("Y")) ## All subsequent filters on that EnsDb will only work on features encoded on ## chromosome Y genes(edb_y) ## Get all lincRNAs on chromosome Y genes(edb_y, filter = ~ gene_biotype == "lincRNA") ``` 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-columns } ## 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 transcripts-example2 } 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 cdsBy } 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 genes-GRangesFilter } ## Define the filter grf <- GRangesFilter(GRanges("11", ranges = IRanges(114000000, 114000050), strand = "+"), type = "any") ## 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-GRangesFilter } 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. The functions `listGenebiotypes` and `listTxbiotypes` can be used to get an overview of allowed/available gene and transcript biotype ```{r biotypes } ## 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 genes-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 = GenenameFilter("BCL", condition = "startsWith"), 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. For the first query we combine two `AnnotationFilter` objects using an `AnnotationFilterList` object, in the second we define the query using a filter expression. ```{r example-AnnotationFilterList } ## determine the average length of snRNA, snoRNA and rRNA genes encoded on ## chromosomes X and Y. mean(lengthOf(edb, of = "tx", filter = AnnotationFilterList( 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 = ~ gene_biotype == "protein_coding" & seq_name %in% 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 example-first-two-exons } ## 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 transcriptsBy-X-Y } TxByGns <- transcriptsBy(edb, by = "gene", filter = 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 exonsBy-RNAseq, message = FALSE, 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 = AnnotationFilterList( SeqNameFilter(c(1:22, "X", "Y")), GeneIdFilter("ENSG", "startsWith"))) ``` 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 toSAF-RNAseq, message = FALSE, 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 disjointExons, message = FALSE, eval=FALSE } ## Create a GRanges of non-overlapping exon parts. DJE <- disjointExons(edb, filter = AnnotationFilterList( SeqNameFilter(c(1:22, "X", "Y")), GeneIdFilter("ENSG%", "startsWith"))) ``` # 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 transcript-sequence-AnnotationHub, message = FALSE, 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 transcript-sequence-extractTranscriptSeqs, message = FALSE, 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 seqlevelsStyle, 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 = ~ seq_name == "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-2, message = FALSE } 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 extractTranscriptSeqs-BSGenome, 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-restore } 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` and `ggbio` 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=2.3 } ## 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" ``` Alternatively, we can also use `ggbio` for plotting. For `ggplot` we can directly pass the `EnsDb` object along with optional filters (or as in the example below a filter expression as a `formula`). ```{r pplot-plot, message=FALSE, fig.align='center', fig.width=7.5, fig.height=4 } library(ggbio) ## Create a plot for all transcripts of the gene SKA2 autoplot(edb, ~ genename == "SKA2") ``` To plot the genomic region and plot genes from both strands we can use a `GRangesFilter`. ```{r pplot-plot-2, message=FALSE, fig.align='center', fig.width=7.5, fig.height=4 } ## Get the chromosomal region in which the gene is encoded ska2 <- genes(edb, filter = ~ genename == "SKA2") strand(ska2) <- "*" autoplot(edb, GRangesFilter(ska2), names.expr = "gene_name") ``` # 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 AnnotationDbi, message = FALSE } 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 select, message = FALSE, 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 = ~ genename %in% c("BCL2", "BCL2L11") & tx_biotype == "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 mapIds, message = FALSE } ## 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. - Since a single Ensembl gene ID might be mapped to multiple NCBI Entrezgene IDs methods such as `genes`, `transcripts` etc return a `list` in the `"entrezid"` column of the resulting result object. # Getting or building `EnsDb` databases/packages Some of 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. ## Getting `EnsDb` databases Some `EnsDb` databases are available as `R` packages from Bioconductor and can be simply installed with the `biocLite` function from the `BiocInstaller` package. The name of such annotation packages starts with *EnsDb* followed by the abbreviation of the organism and the Ensembl version on which the annotation bases. `EnsDb.Hsapiens.v86` provides thus an `EnsDb` database for homo sapiens with annotations from Ensembl version 86. Since Bioconductor version 3.5 `EnsDb` databases can also be retrieved directly from `AnnotationHub`. ```{r AnnotationHub-query, message = FALSE, eval = use_network } library(AnnotationHub) ## Load the annotation resource. ah <- AnnotationHub() ## Query for all available EnsDb databases query(ah, "EnsDb") ``` We can simply fetch one of the databases. ```{r AnnotationHub-query-2, message = FALSE, eval = use_network } ahDb <- query(ah, pattern = c("Xiphophorus Maculatus", "EnsDb", 87)) ## What have we got ahDb ``` Fetch the `EnsDb` and use it. ```{r AnnotationHub-fetch, message = FALSE, eval = FALSE } ahEdb <- ahDb[[1]] ## retriebe all genes gns <- genes(ahEdb) ``` We could even make an annotation package from this `EnsDb` object using the `makeEnsembldbPackage` and passing `dbfile(dbconn(ahEdb))` as `ensdb` argument. ## Building annotation packages ### Directly from Ensembl databases The `fetchTablesFromEnsembl` function 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 this functionality to build databases, the Ensembl Perl API needs to be installed (see 5 for details). Below we create an `EnsDb` database by fetching the required data directly from the Ensembl core databases. The `makeEnsembldbPackage` function is then used to create an annotation package from this `EnsDb` containing all human genes for Ensembl version 75. ```{r edb-from-ensembl, message = FALSE, 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". ### From a GTF or GFF file 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. Also note that protein annotations are usually not available in GTF or GFF files, thus, such annotations will not be included in the generated `EnsDb` database - protein annotations are only available in `EnsDb` databases created with the Ensembl Perl API (such as the ones provided through `AnnotationHub` or as Bioconductor packages). 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 gtf-gff-edb, message = FALSE, 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 EnsDb-from-Y-GRanges, message = FALSE, eval = use_network } ## Generate a sqlite database from a GRanges object specifying ## genes encoded on chromosome Y load(system.file("YGRanges.RData", package = "ensembldb")) Y ## Create the EnsDb database file DB <- ensDbFromGRanges(Y, path = tempdir(), version = 75, organism = "Homo_sapiens") ## Load the database edb <- EnsDb(DB) edb ``` Alternatively we can build the annotation database using the `ensDbFromGtf` `ensDbFromGff` functions, that extract 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, the NCBI Entrezgene IDs and protein annotations 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 EnsDb-from-GTF, message = FALSE, 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 [165](#orgfd622d5)). Note that the protein-specific annotations might not be available in all `EnsDB` databases (e.g. such ones created with `ensembldb` version < 1.7 or created from GTF or GFF files). - **gene**: all gene specific annotations. - `gene_id`: the Ensembl ID of the gene. - `gene_name`: the name (symbol) of the gene. - `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. - `description`: the description of the gene. - **entrezgene**: mapping of Ensembl genes to NCBI Entrezgene identifiers. Note that this mapping can be a one-to-many mapping. - `gene_id`: the Ensembl gene ID. - `entrezid`: the NCBI Entrezgene ID. - **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. `EnsDb` databases for more recent Ensembl releases have also a column `tx_support_level` providing the evidence level for a transcript (1 high evidence, 5 low evidence, NA no evidence calculated). - **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. - **protein**: provides protein annotation for a (coding) transcript. - `protein_id`: the Ensembl protein ID. - `tx_id`: the transcript ID which CDS encodes the protein. - `protein_sequence`: the peptide sequence of the protein (translated from the transcript's coding sequence after applying eventual RNA editing). - **uniprot**: provides the mapping from Ensembl protein ID(s) to Uniprot ID(s). Not all Ensembl proteins are annotated to Uniprot IDs, also, each Ensembl protein might be mapped to multiple Uniprot IDs. - `protein_id`: the Ensembl protein ID. - `uniprot_id`: the Uniprot ID. - `uniprot_db`: the Uniprot database in which the ID is defined. - `uniprot_mapping_type`: the type of the mapping method that was used to assign the Uniprot ID to an Ensembl protein ID. - **protein\_domain**: provides protein domain annotations and mapping to proteins. - `protein_id`: the Ensembl protein ID on which the protein domain is present. - `protein_domain_id`: the ID of the protein domain (from the protein domain source). - `protein_domain_source`: the source/analysis method in/by which the protein domain was defined (such as pfam etc). - `interpro_accession`: the Interpro accession ID of the protein domain. - `prot_dom_start`: the start position of the protein domain within the protein's sequence. - `prot_dom_end`: the end position of the protein domain within the protein's sequence. - **metadata**: some additional, internal, informations (Genome build, Ensembl version etc). - `name` - `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. The database layout: as already described above, protein related annotations (green) might not be available in each `EnsDb` database. ![img](images/dblayout.png "Database layout.") # Footnotes 1 2 3 4 5 ensembldb/vignettes/ensembldb.org0000644000175400017540000024515213175714743020234 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: header-args :exports code #+PROPERTY: header-args:R :session *R* #+EXPORT_SELECT_TAGS: export #+EXPORT_EXCLUDE_TAGS: noexport #+BEGIN_EXPORT html --- title: "Generating an using Ensembl based annotation packages" author: "Johannes Rainer" graphics: yes package: ensembldb output: BiocStyle::html_document: toc_float: true vignette: > %\VignetteIndexEntry{Generating an using Ensembl based annotation packages} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} %\VignetteDepends{ensembldb,EnsDb.Hsapiens.v75,BiocStyle,AnnotationHub,ggbio,Gviz,magrittr} --- #+END_EXPORT * 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 =<