GenomicAlignments/DESCRIPTION0000644000175200017520000000540314551577536016731 0ustar00biocbuildbiocbuildPackage: GenomicAlignments Title: Representation and manipulation of short genomic alignments Description: Provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working with the nucleotide content of the alignments. biocViews: Infrastructure, DataImport, Genetics, Sequencing, RNASeq, SNP, Coverage, Alignment, ImmunoOncology URL: https://bioconductor.org/packages/GenomicAlignments Video: https://www.youtube.com/watch?v=2KqBSbkfhRo , https://www.youtube.com/watch?v=3PK_jx44QTs BugReports: https://github.com/Bioconductor/GenomicAlignments/issues Version: 1.38.2 License: Artistic-2.0 Encoding: UTF-8 Authors@R: c( person("Hervé", "Pagès", role=c("aut", "cre"), email="hpages.on.github@gmail.com"), person("Valerie", "Obenchain", role="aut"), person("Martin", "Morgan", role="aut"), person("Fedor", "Bezrukov", role="ctb"), person("Robert", "Castelo", role="ctb")) Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), GenomeInfoDb (>= 1.13.1), GenomicRanges (>= 1.41.5), SummarizedExperiment (>= 1.9.13), Biostrings (>= 2.55.7), Rsamtools (>= 1.31.2) Imports: methods, utils, stats, BiocGenerics, S4Vectors, IRanges, GenomicRanges, Biostrings, Rsamtools, BiocParallel LinkingTo: S4Vectors, IRanges Suggests: ShortRead, rtracklayer, BSgenome, GenomicFeatures, RNAseqData.HNRNPC.bam.chr14, pasillaBamSubset, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Hsapiens.UCSC.hg19, DESeq2, edgeR, RUnit, BiocStyle Collate: utils.R cigar-utils.R GAlignments-class.R GAlignmentPairs-class.R GAlignmentsList-class.R GappedReads-class.R OverlapEncodings-class.R findMateAlignment.R readGAlignments.R junctions-methods.R sequenceLayer.R pileLettersAt.R stackStringsFromGAlignments.R intra-range-methods.R coverage-methods.R setops-methods.R findOverlaps-methods.R coordinate-mapping-methods.R encodeOverlaps-methods.R findCompatibleOverlaps-methods.R summarizeOverlaps-methods.R findSpliceOverlaps-methods.R zzz.R git_url: https://git.bioconductor.org/packages/GenomicAlignments git_branch: RELEASE_3_18 git_last_commit: d740c47 git_last_commit_date: 2024-01-16 Repository: Bioconductor 3.18 Date/Publication: 2024-01-16 NeedsCompilation: yes Packaged: 2024-01-16 22:12:46 UTC; biocbuild Author: Hervé Pagès [aut, cre], Valerie Obenchain [aut], Martin Morgan [aut], Fedor Bezrukov [ctb], Robert Castelo [ctb] Maintainer: Hervé Pagès GenomicAlignments/NAMESPACE0000644000175200017520000001210014516004601016405 0ustar00biocbuildbiocbuilduseDynLib(GenomicAlignments) import(methods) importFrom(utils, read.table) importFrom(stats, setNames) import(BiocGenerics) import(S4Vectors) import(IRanges) import(GenomeInfoDb) import(GenomicRanges) import(SummarizedExperiment) import(Biostrings) import(Rsamtools) importFrom(BiocParallel, bplapply) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Export S4 classes ### exportClasses( GAlignments, GAlignmentPairs, GAlignmentsList, GappedReads, OverlapEncodings ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Export S3 methods ### S3method(as.data.frame, OverlapEncodings) S3method(levels, OverlapEncodings) ### We also export them thru the export() directive so that (a) they can be ### called directly, (b) tab-completion on the name of the generic shows them, ### and (c) methods() doesn't asterisk them. export( as.data.frame.OverlapEncodings, levels.OverlapEncodings ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Export S4 methods for generics not defined in GenomicAlignments ### exportMethods( length, names, "names<-", c, "[[", show, coerce, unlist, order, sort, rank, levels, update, ## Generics defined in BiocGenerics: as.data.frame, is.unsorted, order, sort, rank, start, end, width, strand, "strand<-", invertStrand, updateObject, ## Generics defined in S4Vectors: bindROWS, getListElement, parallel_slot_names, "elementMetadata<-", pcompare, ## Generics defined in IRanges: ranges, rglist, relistToClass, findOverlaps, windows, narrow, coverage, pintersect, ## Generics defined in GenomeInfoDb: seqinfo, "seqinfo<-", seqnames, "seqnames<-", seqlevelsInUse, ## Generics defined in GenomicRanges: granges, grglist, ## Generics defined in Biostrings: encoding, ## Generics defined in S4Vectors: first, second ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Export non-generic functions ### export( validCigar, CIGAR_OPS, explodeCigarOps, explodeCigarOpLengths, cigarToRleList, cigarRangesAlongReferenceSpace, cigarRangesAlongQuerySpace, cigarRangesAlongPairwiseSpace, extractAlignmentRangesOnReference, cigarWidthAlongReferenceSpace, cigarWidthAlongQuerySpace, cigarWidthAlongPairwiseSpace, cigarNarrow, cigarQNarrow, cigarOpTable, queryLoc2refLoc, queryLocs2refLocs, GAlignments, GAlignmentPairs, GAlignmentsList, GappedReads, Lencoding, Rencoding, Lnjunc, Rnjunc, findMateAlignment, makeGAlignmentPairs, getDumpedAlignments, countDumpedAlignments, flushDumpedAlignments, readGAlignments, readGAlignmentPairs, readGAlignmentsList, readGappedReads, NATURAL_INTRON_MOTIFS, summarizeJunctions, readTopHatJunctions, readSTARJunctions, sequenceLayer, pileLettersAt, stackStringsFromGAlignments, stackStringsFromBam, alphabetFrequencyFromBam, encodeOverlaps1, flipQuery, selectEncodingWithCompatibleStrand, extractQueryStartInTranscript, countCompatibleOverlaps, Union, IntersectionNotEmpty, IntersectionStrict ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Export S4 generics defined in GenomicAlignments + export corresponding ### methods ### export( ## GAlignments-class.R: rname, "rname<-", cigar, qwidth, njunc, ## GAlignmentPairs-class.R: last, strandMode, "strandMode<-", isProperPair, ## GappedReads-class.R: qseq, ## OverlapEncodings-class.R: Loffset, Roffset, flippedQuery, encodingHalves, isCompatibleWithSplicing, ## readGAlignments.R: readGAlignments, readGAlignmentPairs, readGAlignmentsList, readGappedReads, ## junctions-methods.R: junctions, ## intra-range-methods.R: qnarrow, ## encodeOverlaps-methods.R: encodeOverlaps, isCompatibleWithSkippedExons, extractSteppedExonRanks, extractSpannedExonRanks, extractSkippedExonRanks, ## findCompatibleOverlaps-methods.R: findCompatibleOverlaps, ## summarizeOverlaps-methods.R: summarizeOverlaps, ## findSpliceOverlaps-methods.R: findSpliceOverlaps, ## coordinateMapping-methods.R: mapToAlignments, pmapToAlignments, mapFromAlignments, pmapFromAlignments ) ### Exactly the same list as above. exportMethods( rname, "rname<-", cigar, qwidth, njunc, first, last, strandMode, "strandMode<-", isProperPair, qseq, Loffset, Roffset, flippedQuery, encodingHalves, isCompatibleWithSplicing, readGAlignments, readGAlignmentPairs, readGAlignmentsList, readGappedReads, junctions, qnarrow, encodeOverlaps, isCompatibleWithSkippedExons, extractSteppedExonRanks, extractSpannedExonRanks, extractSkippedExonRanks, findCompatibleOverlaps, summarizeOverlaps, findSpliceOverlaps, mapToAlignments, pmapToAlignments, mapFromAlignments, pmapFromAlignments ) GenomicAlignments/NEWS0000644000175200017520000002723514516004601015704 0ustar00biocbuildbiocbuildCHANGES IN VERSION 1.38.0 ------------------------- - No changes in this version. CHANGES IN VERSION 1.36.0 ------------------------- NEW FEATURES o Add 'strandMode' argument to readGAlignmentsList() (contributed by Robert Castelo). BUG FIXES o Increase 'cigar_buf' size to reduce risk of buffer overflow in cigar-utils C code. CHANGES IN VERSION 1.34.0 ------------------------- BUG FIXES o Fix buffer overflow in cigarNarrow()/cigarQNarrow() See commit 5d0a29b2795a95dfb006e8e54c7532b12bd5e79c. CHANGES IN VERSION 1.32.0 ------------------------- - No significant changes in this version. CHANGES IN VERSION 1.30.0 ------------------------- - No changes in this version. CHANGES IN VERSION 1.28.0 ------------------------- - No changes in this version. CHANGES IN VERSION 1.26.0 ------------------------- - No changes in this version. CHANGES IN VERSION 1.24.0 ------------------------- - No changes in this version. CHANGES IN VERSION 1.22.0 ------------------------- NEW FEATURES o Add stackStringsFromGAlignments(). Analog to stackStringsFromBam() except that it stacks the read sequences stored in a GAlignments object instead of a BAM file. BUG FIXES o Fix summarizeJunctions() error when no junctions are found and 'genome' is specified. CHANGES IN VERSION 1.20.0 ------------------------- No NEW FEATURES or SIGNIFICANT USER-VISIBLE CHANGES or BUG FIXES since version 1.18.0 CHANGES IN VERSION 1.18.0 ------------------------- No NEW FEATURES or SIGNIFICANT USER-VISIBLE CHANGES or BUG FIXES since version 1.16.0 CHANGES IN VERSION 1.16.0 ------------------------- NEW FEATURES o Add coercion from list to GAlignmentsList. SIGNIFICANT USER-VISIBLE CHANGES o Improve performance of [[<- on GAlignmentsList objects. This is a 100x speedup or more on a big GAlignmentsList object. BUG FIXES o Remove spurious warning in summarizeOverlaps(). CHANGES IN VERSION 1.14.0 ------------------------- SIGNIFICANT USER-VISIBLE CHANGES o makeGAlignmentPairs() no more drops pairs with discordant seqnames. o Change 'maxgap' and 'minoverlap' argument defaults in methods of the findOverlaps() so they adhere to the new argument defaults of the generic defined in IRanges 2.12.0. See NEWS file in the IRanges package for more information about this change. DEPRECATED AND DEFUNCT o Remove 'force' argument from seqinfo() and seqlevels() setters (the argument got deprecated in BioC 3.5 in favor of new and more flexible 'pruning.mode' argument). BUG FIXES o Fix bug in pairing code of readGAlignmentPairs() when one mate in a pair is lost because of user-supplied filtering (e.g. mapqFilter=10). CHANGES IN VERSION 1.12.0 ------------------------- NEW FEATURES o seqlevels() setter now supports "fine" and "tidy" pruning modes on GAlignmentsList objects (in addition to "coarse" mode, which is the default). o Add "pcompare", "is.unsorted", "order", "sort", and "rank" methods for GAlignments objects. o Add encodingHalves(), a low-level overlap encoding utility. SIGNIFICANT USER-VISIBLE CHANGES o Tweak behavior of isCompatibleWithSplicing() on paired-end encodings. o Minor tweaks to low-level encoding utilities Lencoding(), Rencoding(), Lnjunc(), Rnjunc(), and njunc(). All of them now are based on new encodingHalves() low level utility. o Speed up first() and last() on GAlignmentPairs object when the object has no names. Thanks to Pete Hickey for the suggestion. o 2x to 3x speedup to "junctions" methods. DEPRECATED AND DEFUNCT o Argument 'force' of seqinfo() and seqlevels() setters is deprecated in favor of new and more flexible 'pruning.mode' argument. BUG FIXES o Make grglist() work on GAlignmentPairs object with strandMode set to 0. Thanks to Dario Strbenac for catching and reporting this. o Fix bug affecting extractSteppedExonRanks() on certain paired-end encodings. o Fix subsetting of OverlapEncodings objects and modernize their "show" method. CHANGES IN VERSION 1.10.0 ------------------------- NEW FEATURES o The GAlignmentPairs container now supports pairs with discordant strand and/or seqnames. The "granges" and "ranges" methods for GAlignmentPairs objects get new argument 'on.discordant.seqnames' to let the user control how to handle pairs with discordant seqnames. See ?GAlignmentPairs for more information. o Add "invertStrand" method for GAlignmentPairs objects. o Add 'use.names' argument to the "ranges", "granges", "grglist" and "rglist" methods for GAlignments and GAlignmentsList objects. o Add 'use.names' argument to the "granges" and "grglist" methods for GAlignmentPairs objects. o Add "ranges" method for GAlignmentPairs objects. SIGNIFICANT USER-VISIBLE CHANGES o The 'at' argument of pileLettersAt() is now expected to be a GPos object (GRanges still accepted). o 50x speed-up of the granges() extractor for GAlignmentPairs object. The improvement is based on a suggestion by Arne Muller. DEPRECATED AND DEFUNCT o Remove left() and right() generics and methods (were defunct in BioC 3.3). o Remove 'invert.strand' argument from "first" and "last" methods for GAlignmentPairs objects (was defunct in BioC 3.3). o Remove strand() setter for GAlignmentPairs objects (was defunct in BioC 3.3). o Remove 'order.as.in.query' argument from "grglist" method for GAlignmentPairs objects and from "grglist" and "rglist" methods for GAlignmentsList objects (was defunct in BioC 3.3). BUG FIXES o Fix 'use.names=FALSE' in "grglist" and "rglist" methods for GAlignmentsList objects. CHANGES IN VERSION 1.8.0 ------------------------ NEW FEATURES o Add coercion from GAlignments or GAlignmentPairs to DataFrame, and from GAlignmentsList to GAlignmentPairs. SIGNIFICANT USER-VISIBLE CHANGES o Use DESeq2 instead of DESeq in summarizeOverlaps examples (better late than never). DEPRECATED AND DEFUNCT o After being deprecated in BioC 3.2, the left() and right() getters and strand() setter for GAlignmentPairs objects are now defunct. o After being deprecated in BioC 3.2, the 'invert.strand' argument of the first() and last() getters for GAlignmentPairs objects are now defunct. o After being deprecated in BioC 3.2, the 'order.as.in.query' argument of the "grglist" method for GAlignmentPairs objects is now defunct. o After being deprecated in BioC 3.2, the 'order.as.in.query' argument of the "rglist" and "grglist" methods for GAlignmentsList objects are now defunct. o Remove the "mapCoords" and "pmapCoords" methods (were defunct in BioC 3.2). o Remove the readGAlignment*FromBam() functions (were defunct in BioC 3.2). BUG FIXES o seqnames() setter for GAlignments objects is now consistent with seqnames() setter for GRanges objects. CHANGES IN VERSION 1.6.0 ------------------------ NEW FEATURES o Add strandMode() getter and setter for GAlignmentPairs objects in response to the following post: https://support.bioconductor.org/p/65844/ See ?strandMode for more information. o The readGAlignment*() functions now allow repeated seqnames in the BAM header. o Add "coverage" method for GAlignmentsList objects. o The strand setter now works on a GAlignmentsList object in a restricted way (only strand(x) <- "+" or "-" or "*" is supported). SIGNIFICANT USER-VISIBLE CHANGES o summarizeOverlaps() now returns a RangedSummarizedExperiment object (defined in the new SummarizedExperiment package) instead of an "old" SummarizedExperiment object (defined in the GenomicRanges package). o Slightly modify the behavior of junctions() on a GAlignmentPairs object so that the returned ranges now have the "real strand" set on them. See ?junctions and the documentation of the 'real.strand' argument in the man page of GAlignmentPairs objects for more information. o Add 'real.strand' argument to first() and last() getters for GAlignmentPairs objects. DEPRECATED AND DEFUNCT o Deprecate left() and right() getters and strand() setter for GAlignmentPairs objects. o Deprecate 'invert.strand' argument of first() and last() getters for GAlignmentPairs objects. o Deprecate 'order.as.in.query' argument of "grglist" method for GAlignmentPairs objects. o Deprecate 'order.as.in.query' argument in "rglist" method for GAlignmentsList objects (this concept is not defined for these objects in general and the argument was ignored anyway). o After being deprecated in BioC 3.1, the "mapCoords" and "pmapCoords" methods are now defunct. mapToAlignments() should be used instead. o After being deprecated in BioC 3.1, the readGAlignment*FromBam() functions are now defunct. Everybody says "Let's all use the readGAlignment*() functions instead! (no FromBam suffix). Yeah!" BUG FIXES o Various fixes to grglist/granges/rglist/ranges methods for GAlignmentsList objects: - Respect cigar information (as claimed in man page). - Restore 'drop.D.ranges' argument in "grglist" method (mistakenly got deprecated at the beginning of BioC 3.2 devel cycle). - The 'drop.D.ranges' argument in "rglist" method now works (was ignored). - Handle empty list elements. CHANGES IN VERSION 1.4.0 ------------------------ NEW FEATURES o All "findOverlaps" methods now support 'select' equal "last" or "arbitrary" (in addition to "all" and "first"). SIGNIFICANT USER-VISIBLE CHANGES o Add mapToAlignments(), pmapToAlignments(), mapFromAlignments(), and pmapFromAlignments() as replacements for the "mapCoords" and "pmapCoords" methods for GAlignments objects. o Clarify use of 'fragments' in summarizeOverlaps() man page. o Tweak "show" method for GAlignments objects to display a shorter version of long CIGARs. o Add checks and more helpful error message for summarizeOverlaps() when "file does not exist" DEPRECATED AND DEFUNCT o Deprecated readGAlignment*FromBam() functions in favor of readGAlignments(), readGAlignmentPairs(), readGAlignmentsList() and readGappedReads(). o Deprecated "mapCoords" and "pmapCoords" methods. o Removed Lngap(), Rngap(), introns(), and makeGAlignmentsListFromFeatureFragments() functions, and "ngap", "map", "pmap", and "splitAsListReturnedClass" methods (were defunct in GenomicAlignments 1.2.0). BUG FIXES o Fix off-by-one error when processing 'S' in query_locs_to_ref_locs(). CHANGES IN VERSION 1.2.0 ------------------------ NEW FEATURES o Add 'preprocess.reads' argument to "summarizeOverlaps" methods. o Add alphabetFrequencyFromBam(). SIGNIFICANT USER-VISIBLE CHANGES o Export GappedReads() constructor. o coverage,Bamfile-method now returns empty RleList rather than NULL o Add "mapCoords" and "pmapCoords" methods. o Defunct "map" and "pmap" methods (skip deprecation). BUG FIXES o Bug fix in flipQuery(). o Fix issue with "show" method for GAlignments and GAlignmentPairs objects when 'showHeadLines' global option is set to Inf. CHANGES IN VERSION 1.0.0 ------------------------ The first version of GenomicAlignments was included in Bioconductor 2.14. The package was created from existing code in IRanges, ShortRead, Rsamtools and GenomicRanges. NEW FEATURES o coverage,BamFile-method uses \code{yieldSize} to iterate through large files o coverage,character-method calculates coverage from a BAM file GenomicAlignments/R/0000755000175200017520000000000014551551747015416 5ustar00biocbuildbiocbuildGenomicAlignments/R/GAlignmentPairs-class.R0000644000175200017520000006132614516004601021657 0ustar00biocbuildbiocbuild### ========================================================================= ### GAlignmentPairs objects ### ------------------------------------------------------------------------- ### ### "first" and "last" GAlignments must have identical seqinfo. setClass("GAlignmentPairs", contains="List", representation( strandMode="integer", # single integer (0L, 1L, or 2L) first="GAlignments", # of length N, no names, no elt metadata last="GAlignments", # of length N, no names, no elt metadata isProperPair="logical", # of length N NAMES="character_OR_NULL", # R doesn't like @names !! elementMetadata="DataFrame" # N rows ), prototype( strandMode=1L, elementType="GAlignments" ) ) ### Combine the new "parallel slots" with those of the parent class. Make ### sure to put the new parallel slots **first**. See R/Vector-class.R file ### in the S4Vectors package for what slots should or should not be considered ### "parallel". setMethod("parallel_slot_names", "GAlignmentPairs", function(x) c("first", "last", "isProperPair", "NAMES", callNextMethod()) ) ### Formal API: ### strandMode(x) - indicates how to infer the strand of a pair from the ### strand of the first and last alignments in the pair: ### 0: strand of the pair is always *; ### 1: strand of the pair is strand of its first alignment; ### 2: strand of the pair is strand of its last alignment. ### These modes are equivalent to 'strandSpecific' equal 0, 1, ### and 2, respectively, for the featureCounts() function ### defined in the Rsubread package. ### length(x) - single integer N. Nb of pairs in 'x'. ### names(x) - NULL or character vector. ### first(x) - returns "first" slot. ### last(x) - returns "last" slot. ### seqnames(x) - same as 'seqnames(first(x))' or 'seqnames(last(x))'. ### strand(x) - obeys strandMode(x) (see above). ### njunc(x) - same as 'njunc(first(x)) + njunc(last(x))'. ### isProperPair(x) - returns "isProperPair" slot. ### seqinfo(x) - returns 'seqinfo(first(x))' (same as 'seqinfo(last(x))'). ### granges(x) - GRanges object of the same length as 'x'. ### grglist(x) - GRangesList object of the same length as 'x'. ### show(x) - compact display in a data.frame-like fashion. ### GAlignmentPairs(x) - constructor. ### x[i] - GAlignmentPairs object of the same class as 'x' ### (endomorphism). ### setGeneric("strandMode", function(x) standardGeneric("strandMode")) setGeneric("strandMode<-", signature="x", function(x, value) standardGeneric("strandMode<-") ) setGeneric("isProperPair", function(x) standardGeneric("isProperPair")) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### updateObject() ### setMethod("updateObject", "GAlignmentPairs", function(object, ..., verbose=FALSE) { object@first <- updateObject(object@first, ..., verbose=verbose) object@last <- updateObject(object@last, ..., verbose=verbose) callNextMethod() } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Getters. ### setMethod("strandMode", "GAlignmentPairs", function(x) x@strandMode ) setMethod("names", "GAlignmentPairs", function(x) x@NAMES ) .make_it_real <- function(strand, strand_mode) { if (strand_mode == 0L) return(Rle(strand("*"), length(strand))) invertStrand(strand) } .first_strand <- function(x, real.strand=FALSE) { if (!isTRUEorFALSE(real.strand)) stop("'real.strand' must be TRUE or FALSE") ans <- strand(x@first) if (real.strand && strandMode(x) != 1L) ans <- .make_it_real(ans, strandMode(x)) ans } .last_strand <- function(x, real.strand=FALSE) { if (!isTRUEorFALSE(real.strand)) stop("'real.strand' must be TRUE or FALSE") ans <- strand(x@last) if (real.strand && strandMode(x) != 2L) ans <- .make_it_real(ans, strandMode(x)) ans } setMethod("first", "GAlignmentPairs", function(x, real.strand=FALSE) { if (!isTRUEorFALSE(real.strand)) stop("'real.strand' must be TRUE or FALSE") ans <- x@first x_names <- names(x) if (!is.null(x_names)) ans <- setNames(ans, x_names) if (real.strand && strandMode(x) != 1L) strand(ans) <- .first_strand(x, real.strand=TRUE) ans } ) setGeneric("last", function(x, ...) standardGeneric("last")) setMethod("last", "GAlignmentPairs", function(x, ...) second(x, ...)) setMethod("second", "GAlignmentPairs", function(x, real.strand=FALSE) { if (!isTRUEorFALSE(real.strand)) stop("'real.strand' must be TRUE or FALSE") ans <- x@last x_names <- names(x) if (!is.null(x_names)) ans <- setNames(ans, x_names) if (real.strand && strandMode(x) != 2L) strand(ans) <- .last_strand(x, real.strand=TRUE) ans } ) setMethod("seqnames", "GAlignmentPairs", function(x) { ans <- seqnames(x@first) ans[seqnames(x@last) != ans] <- NA ans } ) setMethod("strand", "GAlignmentPairs", function(x) { ans <- .first_strand(x, real.strand=TRUE) x_last_strand <- .last_strand(x, real.strand=TRUE) ans[ans != x_last_strand] <- "*" ans } ) setMethod("njunc", "GAlignmentPairs", function(x) {njunc(x@first) + njunc(x@last)} ) setMethod("isProperPair", "GAlignmentPairs", function(x) x@isProperPair ) setMethod("seqinfo", "GAlignmentPairs", function(x) seqinfo(x@first) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Setters. ### .normarg_strandMode_replace_value <- function(value) { if (!isSingleNumber(value)) stop("invalid strand mode (must be 0, 1, or 2)") if (!is.integer(value)) value <- as.integer(value) if (!(value %in% 0:2)) stop("invalid strand mode (must be 0, 1, or 2)") value } setReplaceMethod("strandMode", "GAlignmentPairs", function(x, value) { x@strandMode <- .normarg_strandMode_replace_value(value) x } ) setMethod("invertStrand", "GAlignmentPairs", function(x) { strand_mode <- strandMode(x) if (strand_mode != 0L) strandMode(x) <- 3L - strand_mode x } ) setReplaceMethod("names", "GAlignmentPairs", function(x, value) { if (!is.null(value)) value <- as.character(value) x@NAMES <- value validObject(x) x } ) setMethod("seqlevelsInUse", "GAlignmentPairs", function(x) { in_use1 <- seqlevelsInUse(x@first) in_use2 <- seqlevelsInUse(x@last) ## We cannot just do union() because we want the returned levels ## to be in the order they appear in 'seqlevels(x)'. intersect(seqlevels(x), union(in_use1, in_use2)) } ) setReplaceMethod("seqinfo", "GAlignmentPairs", function(x, new2old=NULL, pruning.mode=c("error", "coarse", "fine", "tidy"), value) { if (!is(value, "Seqinfo")) stop("the supplied 'seqinfo' must be a Seqinfo object") dangling_seqlevels <- GenomeInfoDb:::getDanglingSeqlevels(x, new2old=new2old, pruning.mode=pruning.mode, seqlevels(value)) if (length(dangling_seqlevels) != 0L) { dropme_in_first <- seqnames(x@first) %in% dangling_seqlevels dropme_in_last <- seqnames(x@last) %in% dangling_seqlevels dropme <- dropme_in_first | dropme_in_last x <- x[!dropme] } seqinfo(x@first, new2old=new2old) <- value seqinfo(x@last, new2old=new2old) <- value x } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Validity. ### .valid.GAlignmentPairs.strandMode <- function(x) { if (!(isSingleInteger(x@strandMode) && x@strandMode %in% 0:2)) return("'x@strandMode' must be 0L, 1L, or 2L") NULL } .valid.GAlignmentPairs.isProperPair <- function(x) { x_isProperPair <- x@isProperPair if (!is.logical(x_isProperPair) || !is.null(attributes(x_isProperPair))) { msg <- c("'x@isProperPair' must be a logical vector ", "with no attributes") return(paste(msg, collapse="")) } if (S4Vectors:::anyMissing(x_isProperPair)) return("'x@isProperPair' cannot contain NAs") NULL } .valid.GAlignmentPairs.seqinfo <- function(x) { if (!identical(seqinfo(x@first), seqinfo(x@last))) return("'seqinfo(x@first)' and 'seqinfo(x@last)' must be identical") NULL } .valid.GAlignmentPairs <- function(x) { c(.valid.GAlignmentPairs.strandMode(x), .valid.GAlignmentPairs.isProperPair(x), .valid.GAlignmentPairs.seqinfo(x)) } setValidity2("GAlignmentPairs", .valid.GAlignmentPairs, where=asNamespace("GenomicAlignments")) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Constructor. ### GAlignmentPairs <- function(first, last, strandMode=1L, isProperPair=TRUE, names=NULL) { if (!(is(first, "GAlignments") && is(last, "GAlignments"))) stop("'first' and 'last' must be GAlignments objects") if (length(first) != length(last)) stop("'first' and 'last' must have the same length") strandMode <- .normarg_strandMode_replace_value(strandMode) if (identical(isProperPair, TRUE)) isProperPair <- rep.int(isProperPair, length(first)) new2("GAlignmentPairs", strandMode=strandMode, NAMES=names, first=first, last=last, isProperPair=isProperPair, elementMetadata=S4Vectors:::make_zero_col_DataFrame(length(first)), check=TRUE) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### List methods. ### ### TODO: Remove the "[[" method below after the definition of the ### GAlignmentPairs class is changed to derive from CompressedList. ### (The "[[" method for CompressedList objects should do just fine i.e. it ### should do something like x@unlistData[x@partitioning[[i]]] and that ### should be optimal.) .GAlignmentPairs.getElement <- function(x, i) { c(x@first[i], x@last[i]) } setMethod("[[", "GAlignmentPairs", function(x, i, j, ... , drop=TRUE) { if (missing(i) || !missing(j) || length(list(...)) > 0L) stop("invalid subsetting") i <- normalizeDoubleBracketSubscript(i, x) .GAlignmentPairs.getElement(x, i) } ) ### TODO: Remove this method after the definition of the GAlignmentPairs ### class is changed to derive from CompressedList. setMethod("unlist", "GAlignmentPairs", function(x, recursive=TRUE, use.names=TRUE) { if (!isTRUEorFALSE(use.names)) stop("'use.names' must be TRUE or FALSE") x_first <- x@first x_last <- x@last collate_subscript <- S4Vectors:::make_XYZxyz_to_XxYyZz_subscript(length(x)) ans <- c(x_first, x_last)[collate_subscript] if (use.names) names(ans) <- rep(names(x), each=2L) ans } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Coercion. ### ### 'caller' must be "ranges" or "granges". .error_on_discordant_seqnames <- function(caller) { if (caller == "ranges") { range_type <- "range" returned_object <- "IRanges" a_returned_object <- "an IRanges" alternate_caller <- "rglist" alternate_returned_object <- "an IRangesList" } else { range_type <- "genomic range" returned_object <- "GRanges" a_returned_object <- "a GRanges" alternate_caller <- "grglist" alternate_returned_object <- "a GRangesList" } wmsg( "For some pairs in 'x', the 2 alignments are not on the same ", "chromosome. Cannot associate a unique ", range_type, " to ", "such pairs. Please call ", caller, "() with ", "'on.discordant.seqnames=\"drop\"' to drop these pairs, ", "or with 'on.discordant.seqnames=\"split\"' to represent ", "each of them with 2 ", range_type, "s in the returned ", returned_object, " object. Note that in both cases the returned ", "object won't be parallel to 'x'. Alternatively, please ", "consider using ", alternate_caller, "() instead of ", caller, "() ", "to turn 'x' into ", alternate_returned_object, " object instead of ", a_returned_object, " object. See ?GAlignmentPairs for more ", "information." ) } .make_split_IRanges_from_GAlignmentPairs <- function(x, use.names=TRUE, use.mcols=FALSE) { x_first_ranges <- ranges(x@first, use.names=FALSE) x_last_ranges <- ranges(x@last, use.names=FALSE) x_len <- length(x) collate_subscript <- S4Vectors:::make_XYZxyz_to_XxYyZz_subscript(x_len) partitioning <- PartitioningByEnd(2L * seq_len(x_len)) range_pairs <- relist(c(x_first_ranges, x_last_ranges)[collate_subscript], partitioning) x_first_seqnames <- seqnames(x@first) x_last_seqnames <- seqnames(x@last) merge_idx <- x_first_seqnames == x_last_seqnames range_pairs[merge_idx] <- range(range_pairs[merge_idx]) if (use.names) names(range_pairs) <- names(x) ans <- unlist(range_pairs, use.names=use.names) if (use.mcols) { i <- rep.int(seq_len(x_len), elementNROWS(range_pairs)) mcols(ans) <- extractROWS(mcols(x, use.names=FALSE), i) } ans } setMethod("ranges", "GAlignmentPairs", function(x, use.names=TRUE, use.mcols=FALSE, on.discordant.seqnames=c("error", "drop", "split")) { if (!isTRUEorFALSE(use.names)) stop("'use.names' must be TRUE or FALSE") if (!isTRUEorFALSE(use.mcols)) stop("'use.mcols' must be TRUE or FALSE") on.discordant.seqnames <- match.arg(on.discordant.seqnames) ans_seqnames <- seqnames(x) is_discordant <- is.na(ans_seqnames) if (any(is_discordant)) { if (on.discordant.seqnames == "error") stop(.error_on_discordant_seqnames("ranges")) if (on.discordant.seqnames == "split") return(.make_split_IRanges_from_GAlignmentPairs(x, use.names=use.names, use.mcols=use.mcols)) ## on.discordant.seqnames == "drop" x <- x[!is_discordant] } x_first_ranges <- ranges(x@first, use.names=FALSE) x_last_ranges <- ranges(x@last, use.names=FALSE) ans <- punion(x_first_ranges, x_last_ranges, fill.gap=TRUE) if (use.names) names(ans) <- names(x) if (use.mcols) mcols(ans) <- mcols(x, use.names=FALSE) ans } ) .make_split_GRanges_from_GAlignmentPairs <- function(x, use.names=TRUE, use.mcols=FALSE) { x_first_seqnames <- seqnames(x@first) x_last_seqnames <- seqnames(x@last) is_discordant <- x_first_seqnames != x_last_seqnames ndiscordant <- sum(is_discordant) collate_subscript <- S4Vectors:::make_XYZxyz_to_XxYyZz_subscript(ndiscordant) partitioning <- PartitioningByEnd(2L * seq_len(ndiscordant)) seqnames1 <- x_first_seqnames[is_discordant] seqnames2 <- x_last_seqnames[is_discordant] discordant_seqnames <- relist(c(seqnames1, seqnames2)[collate_subscript], partitioning) ans_seqnames <- as(x_first_seqnames, "List") ans_seqnames[is_discordant] <- discordant_seqnames ans_ranges <- .make_split_IRanges_from_GAlignmentPairs(x, use.names=use.names, use.mcols=use.mcols) strand1 <- .first_strand(x, real.strand=TRUE)[is_discordant] strand2 <- .last_strand(x, real.strand=TRUE)[is_discordant] discordant_strand <- relist(c(strand1, strand2)[collate_subscript], partitioning) ans_strand <- as(strand(x), "List") ans_strand[is_discordant] <- discordant_strand GRanges(unlist(ans_seqnames, use.names=FALSE), ans_ranges, unlist(ans_strand, use.names=FALSE), seqinfo=seqinfo(x)) } setMethod("granges", "GAlignmentPairs", function(x, use.names=TRUE, use.mcols=FALSE, on.discordant.seqnames=c("error", "drop", "split")) { if (!isTRUEorFALSE(use.mcols)) stop("'use.mcols' must be TRUE or FALSE") on.discordant.seqnames <- match.arg(on.discordant.seqnames) ans_seqnames <- seqnames(x) is_discordant <- is.na(ans_seqnames) if (any(is_discordant)) { if (on.discordant.seqnames == "error") stop(.error_on_discordant_seqnames("granges")) if (on.discordant.seqnames == "split") return(.make_split_GRanges_from_GAlignmentPairs(x, use.names=use.names, use.mcols=use.mcols)) ## on.discordant.seqnames == "drop" x <- x[!is_discordant] ans_seqnames <- seqnames(x) } ans <- GRanges(ans_seqnames, ranges(x, use.names=use.names), strand(x), seqinfo=seqinfo(x)) if (use.mcols) mcols(ans) <- mcols(x, use.names=FALSE) ans } ) ### Shrink CompressedList 'x' (typically a GRangesList) by half by combining ### pairs of consecutive top-level elements. shrinkByHalf <- function(x) { if (length(x) %% 2L != 0L) stop("'x' must have an even length") x_eltNROWS <- elementNROWS(x) if (length(x_eltNROWS) == 0L) { ans_nelt1 <- ans_nelt2 <- integer(0) } else { ans_nelt1 <- x_eltNROWS[c(TRUE, FALSE)] ans_nelt2 <- x_eltNROWS[c(FALSE, TRUE)] } ans_eltNROWS <- ans_nelt1 + ans_nelt2 ans_partitioning <- PartitioningByEnd(cumsum(ans_eltNROWS)) ans <- relist(x@unlistData, ans_partitioning) mcols(ans) <- DataFrame(nelt1=ans_nelt1, nelt2=ans_nelt2) ans } setMethod("grglist", "GAlignmentPairs", function(x, use.names=TRUE, use.mcols=FALSE, drop.D.ranges=FALSE) { if (!isTRUEorFALSE(use.names)) stop("'use.names' must be TRUE or FALSE") if (!isTRUEorFALSE(use.mcols)) stop("'use.mcols' must be TRUE or FALSE") x_mcols <- mcols(x, use.names=FALSE) if (use.mcols && "query.break" %in% colnames(x_mcols)) stop("'mcols(x)' cannot have reserved column \"query.break\"") x_first <- x@first x_last <- x@last if (strandMode(x) == 0L) { x_first <- unstrand(x_first) x_last <- unstrand(x_last) x_unlisted <- c(x_first, x_last) } else if (strandMode(x) == 1L) { x_last <- invertStrand(x@last) x_unlisted <- c(x_first, x_last) } else if (strandMode(x) == 2L) { x_first <- invertStrand(x@first) x_unlisted <- c(x_last, x_first) } else { ## Should never happen. stop("unsupported strandMode: ", strandMode(x)) } ## Not the same as doing 'unlist(x, use.names=FALSE)'. collate_subscript <- S4Vectors:::make_XYZxyz_to_XxYyZz_subscript(length(x)) x_unlisted <- x_unlisted[collate_subscript] grl <- grglist(x_unlisted, order.as.in.query=TRUE, drop.D.ranges=drop.D.ranges) ans <- shrinkByHalf(grl) if (use.names) names(ans) <- names(x) ans_mcols <- DataFrame(query.break=mcols(ans, use.names=FALSE)$nelt1) if (use.mcols) ans_mcols <- cbind(ans_mcols, x_mcols) mcols(ans) <- ans_mcols ans } ) setAs("GAlignmentPairs", "IntegerRanges", function(from) ranges(from, use.names=TRUE, use.mcols=TRUE) ) setAs("GAlignmentPairs", "GRanges", function(from) granges(from, use.names=TRUE, use.mcols=TRUE) ) setAs("GAlignmentPairs", "GRangesList", function(from) grglist(from, use.names=TRUE, use.mcols=TRUE) ) setAs("GAlignmentPairs", "GAlignments", function(from) unlist(from, use.names=TRUE) ) setAs("GAlignmentPairs", "DataFrame", function(from) { firstDF <- as(first(from), "DataFrame") colnames(firstDF) <- paste0(colnames(firstDF), ".first") lastDF <- as(last(from), "DataFrame") colnames(lastDF) <- paste0(colnames(lastDF), ".last") DataFrame(firstDF, lastDF, mcols(from, use.names=FALSE)) }) setMethod("as.data.frame", "GAlignmentPairs", function(x, row.names = NULL, optional = FALSE) { as.data.frame(as(x, "DataFrame"), row.names=row.names, optional=optional) }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### fillJunctionGaps() ### ### Not exported. Used in the SplicingGraphs package. ### fillJunctionGaps <- function(x) { if (!is(x, "GRangesList")) stop("'x' must be a GRangesList object") query.breaks <- mcols(x, use.names=FALSE)$query.break if (is.null(query.breaks)) stop("'x' must be a GRangesList object with a \"query.breaks\" ", "metadata column") idx <- sequence(query.breaks, from=start(x@partitioning)) half1_partitioning <- PartitioningByEnd(cumsum(query.breaks)) half1 <- relist(x@unlistData[idx], half1_partitioning) half1 <- range(half1)@unlistData half2_eltNROWS <- elementNROWS(x) - query.breaks half2_partitioning <- PartitioningByEnd(cumsum(half2_eltNROWS)) half2 <- relist(x@unlistData[-idx], half2_partitioning) half2 <- range(half2)@unlistData collate_subscript <- S4Vectors:::make_XYZxyz_to_XxYyZz_subscript(length(x)) ans_unlistData <- c(half1, half2)[collate_subscript] ans_partitioning <- PartitioningByEnd(2L * seq_along(x), names=names(x)) relist(ans_unlistData, ans_partitioning) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### "show" method. ### .makeNakedMatFromGAlignmentPairs <- function(x) { lx <- length(x) nc <- ncol(mcols(x, use.names=FALSE)) pair_cols <- cbind(seqnames=as.character(seqnames(x)), strand=as.character(strand(x))) x_first <- x@first first_cols <- cbind(ranges=showAsCell(ranges(x_first))) x_last <- x@last last_cols <- cbind(ranges=showAsCell(ranges(x_last))) ans <- cbind(pair_cols, `:`=rep.int(":", lx), first_cols, `--`=rep.int("--", lx), last_cols) if (nc > 0L) { tmp <- do.call(data.frame, lapply(mcols(x, use.names=FALSE), showAsCell)) ans <- cbind(ans, `|`=rep.int("|", lx), as.matrix(tmp)) } ans } showGAlignmentPairs <- function(x, margin="", print.classinfo=FALSE, print.seqinfo=FALSE) { lx <- length(x) nc <- ncol(mcols(x, use.names=FALSE)) cat(class(x), " object with ", lx, " ", ifelse(lx == 1L, "pair", "pairs"), ", strandMode=", strandMode(x), ", and ", nc, " metadata ", ifelse(nc == 1L, "column", "columns"), ":\n", sep="") out <- S4Vectors:::makePrettyMatrixForCompactPrinting(x, .makeNakedMatFromGAlignmentPairs) if (print.classinfo) { .PAIR_COL2CLASS <- c( seqnames="Rle", strand="Rle" ) .HALVES_COL2CLASS <- c( ranges="IRanges" ) .COL2CLASS <- c(.PAIR_COL2CLASS, ":", .HALVES_COL2CLASS, "--", .HALVES_COL2CLASS) classinfo <- S4Vectors:::makeClassinfoRowForCompactPrinting(x, .COL2CLASS) ## A sanity check, but this should never happen! stopifnot(identical(colnames(classinfo), colnames(out))) out <- rbind(classinfo, out) } if (nrow(out) != 0L) rownames(out) <- paste0(margin, rownames(out)) ## We set 'max' to 'length(out)' to avoid the getOption("max.print") ## limit that would typically be reached when 'showHeadLines' global ## option is set to Inf. print(out, quote=FALSE, right=TRUE, max=length(out)) if (print.seqinfo) { cat(margin, "-------\n", sep="") cat(margin, "seqinfo: ", summary(seqinfo(x)), "\n", sep="") } } setMethod("show", "GAlignmentPairs", function(object) showGAlignmentPairs(object, margin=" ", print.classinfo=TRUE, print.seqinfo=TRUE) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Concatenation ### setMethod("bindROWS", "GAlignmentPairs", GenomicRanges:::concatenate_GenomicRanges_objects ) GenomicAlignments/R/GAlignments-class.R0000644000175200017520000005167714516004601021053 0ustar00biocbuildbiocbuild### ========================================================================= ### GAlignments objects ### ------------------------------------------------------------------------- ### setClass("GAlignments", contains="Ranges", representation( seqnames="Rle", # 'factor' Rle start="integer", # POS field in SAM cigar="character", # extended CIGAR (see SAM format specs) strand="Rle", # 'factor' Rle #mismatches="character_OR_NULL", # see MD optional field in SAM format #specs NAMES="character_OR_NULL", # R doesn't like @names !! elementMetadata="DataFrame", seqinfo="Seqinfo" ), prototype( seqnames=Rle(factor()), strand=Rle(strand()), elementMetadata=new("DFrame") ) ) ### Combine the new "parallel slots" with those of the parent class. Make ### sure to put the new parallel slots **first**. See R/Vector-class.R file ### in the S4Vectors package for what slots should or should not be considered ### "parallel". setMethod("parallel_slot_names", "GAlignments", function(x) c("seqnames", "start", "cigar", "strand", "NAMES", callNextMethod()) ) ### Formal API: ### names(x) - NULL or character vector. ### length(x) - single integer. Nb of alignments in 'x'. ### seqnames(x) - 'factor' Rle of the same length as 'x'. ### rname(x) - same as 'seqnames(x)'. ### seqnames(x) <- value - replacement form of 'seqnames(x)'. ### rname(x) <- value - same as 'seqnames(x) <- value'. ### cigar(x) - character vector of the same length as 'x'. ### strand(x) - 'factor' Rle of the same length as 'x' (levels: +, -, *). ### qwidth(x) - integer vector of the same length as 'x'. ### start(x), end(x), width(x) - integer vectors of the same length as 'x'. ### njunc(x) - integer vector of the same length as 'x'. ### grglist(x) - GRangesList object of the same length as 'x'. ### granges(x) - GRanges object of the same length as 'x'. ### rglist(x) - CompressedIRangesList object of the same length as 'x'. ### ranges(x) - IRanges object of the same length as 'x'. ### as.data.frame(x) - data.frame with 1 row per alignment in 'x'. ### show(x) - compact display in a data.frame-like fashion. ### GAlignments(x) - constructor. ### x[i] - GAlignments object of the same class as 'x' (endomorphism). ### ### qnarrow(x, start=NA, end=NA, width=NA) - GAlignments object of the ### same length and class as 'x' (endomorphism). ### ### narrow(x, start=NA, end=NA, width=NA) - GAlignments object of the ### same length and class as 'x' (endomorphism). ### ### findOverlaps(query, subject) - 'query' or 'subject' or both are ### GAlignments objects. Just a convenient wrapper for ### 'findOverlaps(grglist(query), subject, ...)', etc... ### ### countOverlaps(query, subject) - 'query' or 'subject' or both are ### GAlignments objects. Just a convenient wrapper for ### 'countOverlaps(grglist(query), subject, ...)', etc... ### ### subsetByOverlaps(query, subject) - 'query' or 'subject' or both are ### GAlignments objects. ### setGeneric("rname", function(x) standardGeneric("rname")) setGeneric("rname<-", function(x, value) standardGeneric("rname<-")) setGeneric("cigar", function(x) standardGeneric("cigar")) setGeneric("qwidth", function(x) standardGeneric("qwidth")) setGeneric("njunc", function(x) standardGeneric("njunc")) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### updateObject() ### ### Internal representation of GAlignments objects has changed in ### GenomicAlignments 1.15.11 (Bioc 3.7). ### .get_GAlignments_version <- function(object) { if (.hasSlot(object, "elementType")) "current" else "< 1.15.11" } setMethod("updateObject", "GAlignments", function(object, ..., verbose=FALSE) { ## elementType slot. version <- .get_GAlignments_version(object) if (version == "current") { if (verbose) message("[updateObject] Internal representation of ", class(object), " object is current.\n", "[updateObject] Nothing to update.") } else { if (verbose) message("[updateObject] ", class(object), " object uses ", "internal representation from\n", "[updateObject] GenomicAlignments ", version, ". ", "Updating it ...") object@elementType <- new(class(object))@elementType } callNextMethod() } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Getters. ### setMethod("names", "GAlignments", function(x) x@NAMES) setMethod("seqnames", "GAlignments", function(x) x@seqnames) setMethod("rname", "GAlignments", function(x) seqnames(x)) setMethod("cigar", "GAlignments", function(x) x@cigar) setMethod("width", "GAlignments", function(x) cigarWidthAlongReferenceSpace(x@cigar) ) setMethod("start", "GAlignments", function(x, ...) x@start) setMethod("strand", "GAlignments", function(x) x@strand) setMethod("qwidth", "GAlignments", function(x) cigarWidthAlongQuerySpace(x@cigar) ) setMethod("njunc", "GAlignments", function(x) {unname(elementNROWS(rglist(x))) - 1L} ) setMethod("seqinfo", "GAlignments", function(x) x@seqinfo) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Setters. ### setReplaceMethod("names", "GAlignments", function(x, value) { if (!is.null(value)) value <- as.character(value) x@NAMES <- value validObject(x) x } ) setReplaceMethod("seqnames", "GAlignments", function(x, value) { value <- GenomicRanges:::.normalize_seqnames_replacement_value(value, x) BiocGenerics:::replaceSlots(x, seqnames=value) } ) setReplaceMethod("rname", "GAlignments", function(x, value) `seqnames<-`(x, value) ) setReplaceMethod("strand", "GAlignments", function(x, value) { x@strand <- GenomicRanges:::normalize_strand_replacement_value(value, x) x } ) ### Does NOT suppoprt pruning mode "fine". Pruning modes "coarse" and "tidy" ### are equivalent on a GAlignments object. ### FIXME: This repeats most of the code in ### GenomicRanges:::set_GenomicRanges_seqinfo! set_GAlignments_seqinfo <- function(x, new2old=NULL, pruning.mode=c("error", "coarse", "fine", "tidy"), value) { pruning.mode <- match.arg(pruning.mode) if (pruning.mode == "fine") stop(wmsg("\"fine\" pruning mode not supported on ", class(x), " objects")) if (!is(value, "Seqinfo")) stop("the supplied 'seqinfo' must be a Seqinfo object") dangling_seqlevels <- GenomeInfoDb:::getDanglingSeqlevels(x, new2old=new2old, pruning.mode=pruning.mode, seqlevels(value)) if (length(dangling_seqlevels) != 0L) { ## Prune 'x'. non_dangling_range <- !(seqnames(x) %in% dangling_seqlevels) x <- x[non_dangling_range] } old_seqinfo <- seqinfo(x) x@seqnames <- GenomeInfoDb:::makeNewSeqnames(x, new2old, seqlevels(value)) x@seqinfo <- value geom_has_changed <- GenomeInfoDb:::sequenceGeometryHasChanged( seqinfo(x), old_seqinfo, new2old=new2old) if (any(geom_has_changed, na.rm=TRUE)) validObject(x) x } setReplaceMethod("seqinfo", "GAlignments", set_GAlignments_seqinfo) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Validity. ### .valid.GAlignments.start <- function(x) { x_start <- start(x) if (!is.integer(x_start) || !is.null(names(x_start)) || S4Vectors:::anyMissing(x_start)) return("'start(x)' must be an unnamed integer vector with no NAs") NULL } .valid.GAlignments.cigar <- function(x) { x_cigar <- cigar(x) if (!is.character(x_cigar) || !is.null(names(x_cigar)) || any(is.na(x_cigar))) return("'cigar(x)' must be an unnamed character vector with no NAs") tmp <- validCigar(x_cigar) if (!is.null(tmp)) return(paste("in 'cigar(x)':", tmp)) NULL } .valid.GAlignments.strand <- function(x) { x_strand <- strand(x) if (!is(x_strand, "Rle") || !is.factor(runValue(x_strand)) || !identical(levels(runValue(x_strand)), levels(strand())) || !is.null(names(x_strand)) || any(is.na(x_strand))) return("'strand(x)' must be an unnamed 'factor' Rle with no NAs (and with levels +, - and *)") NULL } .valid.GAlignments <- function(x) { c(GenomicRanges:::.valid.GenomicRanges.seqnames(x), .valid.GAlignments.start(x), .valid.GAlignments.cigar(x), .valid.GAlignments.strand(x), GenomicRanges:::valid.GenomicRanges.seqinfo(x)) } setValidity2("GAlignments", .valid.GAlignments, where=asNamespace("GenomicAlignments")) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Constructor. ### .asFactorRle <- function(x) { if (is.character(x)) { x <- Rle(as.factor(x)) } else if (is.factor(x)) { x <- Rle(x) } else if (is(x, "Rle") && is.character(runValue(x))) { runValue(x) <- as.factor(runValue(x)) } else if (!is(x, "Rle") || !is.factor(runValue(x))) { stop("'x' must be a character vector, a factor, ", "a 'character' Rle, or a 'factor' Rle") } x } GAlignments <- function(seqnames=Rle(factor()), pos=integer(0), cigar=character(0), strand=NULL, names=NULL, seqlengths=NULL, ...) { ## Prepare the 'seqnames' slot. seqnames <- .asFactorRle(seqnames) if (any(is.na(seqnames))) stop("'seqnames' cannot have NAs") ## Prepare the 'pos' slot. if (!is.integer(pos) || any(is.na(pos))) stop("'pos' must be an integer vector with no NAs") ## Prepare the 'cigar' slot. if (!is.character(cigar) || any(is.na(cigar))) stop("'cigar' must be a character vector with no NAs") ## Prepare the 'strand' slot. if (is.null(strand)) { if (length(seqnames) != 0L) stop("'strand' must be specified when 'seqnames' is not empty") strand <- Rle(strand()) } else if (is.factor(strand)) { strand <- Rle(strand) } ## Prepare the 'elementMetadata' slot. varlist <- list(...) elementMetadata <- if (0L == length(varlist)) S4Vectors:::make_zero_col_DataFrame(length(seqnames)) else do.call(DataFrame, varlist) ## Prepare the 'seqinfo' slot. if (is.null(seqlengths)) { seqlengths <- rep(NA_integer_, length(levels(seqnames))) names(seqlengths) <- levels(seqnames) } else if (!is.numeric(seqlengths) || is.null(names(seqlengths)) || any(duplicated(names(seqlengths)))) { stop("'seqlengths' must be an integer vector with unique names") } else if (!setequal(names(seqlengths), levels(seqnames))) { stop("'names(seqlengths)' incompatible with 'levels(seqnames)'") } else if (!is.integer(seqlengths)) { storage.mode(seqlengths) <- "integer" } seqinfo <- Seqinfo(seqnames=names(seqlengths), seqlengths=seqlengths) ## Create and return the GAlignments instance. new("GAlignments", NAMES=names, seqnames=seqnames, start=pos, cigar=cigar, strand=strand, elementMetadata=elementMetadata, seqinfo=seqinfo) } setMethod("update", "GAlignments", function(object, ...) { BiocGenerics:::replaceSlots(object, ...) }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Internal helper function used by higher level coercion functions. ### ### NOT exported. ### ### Names are propagated via 'x@partitioning' ('x' is a CompressedIRangesList). make_GRangesList_from_CompressedIRangesList <- function(x, seqnames, strand, seqinfo) { x_eltNROWS <- elementNROWS(x) seqnames <- rep.int(seqnames, x_eltNROWS) strand <- rep.int(strand, x_eltNROWS) unlisted_ans <- GRanges(seqnames=seqnames, ranges=x@unlistData, strand=strand) seqinfo(unlisted_ans) <- seqinfo ans <- relist(unlisted_ans, x) mcols(ans) <- mcols(x, use.names=FALSE) ans } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Coercion. ### setMethod("ranges", "GAlignments", function(x, use.names=TRUE, use.mcols=FALSE) { if (!isTRUEorFALSE(use.names)) stop("'use.names' must be TRUE or FALSE") if (!isTRUEorFALSE(use.mcols)) stop("'use.mcols' must be TRUE or FALSE") if (use.names) { ans_names <- names(x) } else { ans_names <- NULL } ans <- IRanges(start=start(x), width=width(x), names=ans_names) if (use.mcols) mcols(ans) <- mcols(x, use.names=FALSE) ans } ) setMethod("granges", "GAlignments", function(x, use.names=TRUE, use.mcols=FALSE) { if (!isTRUEorFALSE(use.mcols)) stop("'use.mcols' must be TRUE or FALSE") ans <- GRanges(seqnames(x), ranges(x, use.names=use.names), strand(x), seqinfo=seqinfo(x)) if (use.mcols) mcols(ans) <- mcols(x, use.names=FALSE) ans } ) setMethod("grglist", "GAlignments", function(x, use.names=TRUE, use.mcols=FALSE, order.as.in.query=FALSE, drop.D.ranges=FALSE) { rgl <- rglist(x, use.names=use.names, use.mcols=use.mcols, order.as.in.query=order.as.in.query, drop.D.ranges=drop.D.ranges) make_GRangesList_from_CompressedIRangesList(rgl, seqnames(x), strand(x), seqinfo(x)) } ) setMethod("rglist", "GAlignments", function(x, use.names=TRUE, use.mcols=FALSE, order.as.in.query=FALSE, drop.D.ranges=FALSE) { if (!isTRUEorFALSE(use.names)) stop("'use.names' must be TRUE or FALSE") if (!isTRUEorFALSE(use.mcols)) stop("'use.mcols' must be TRUE or FALSE") if (!isTRUEorFALSE(order.as.in.query)) stop("'order.as.in.query' must be TRUE or FALSE") ans <- extractAlignmentRangesOnReference(x@cigar, x@start, drop.D.ranges=drop.D.ranges) if (order.as.in.query) ans <- revElements(ans, strand(x) == "-") if (use.names) names(ans) <- names(x) if (use.mcols) mcols(ans) <- mcols(x, use.names=FALSE) ans } ) setAs("GAlignments", "IntegerRanges", function(from) ranges(from, use.names=TRUE, use.mcols=TRUE) ) setAs("GAlignments", "GRanges", function(from) granges(from, use.names=TRUE, use.mcols=TRUE) ) setAs("GAlignments", "GRangesList", function(from) grglist(from, use.names=TRUE, use.mcols=TRUE) ) setAs("GAlignments", "IntegerRangesList", function(from) rglist(from, use.names=TRUE, use.mcols=TRUE) ) setAs("GAlignments", "DataFrame", function(from) { DataFrame(seqnames=seqnames(from), strand=strand(from), cigar=cigar(from), qwidth=qwidth(from), start=start(from), end=end(from), width=width(from), njunc=njunc(from), mcols(from, use.names=FALSE), row.names=names(from), check.names=FALSE) }) setMethod("as.data.frame", "GAlignments", function(x, row.names=NULL, optional=FALSE, ...) { as.data.frame(as(x, "DataFrame"), row.names=row.names, optional=optional) } ) setAs("GenomicRanges", "GAlignments", function(from) { from_mcols <- mcols(from, use.names=FALSE) ans_cigar <- from_mcols[["cigar"]] if (is.null(ans_cigar)) ans_cigar <- paste0(width(from), "M") ans <- GAlignments(seqnames(from), start(from), ans_cigar, strand(from), if (!is.null(names(from))) names(from) else from_mcols$name, seqlengths(from), from_mcols[setdiff(colnames(from_mcols), c("cigar", "name"))] ) metadata(ans) <- metadata(from) seqinfo(ans) <- seqinfo(from) ans } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Subsetting ### ### Avoid infinite recursion that we would otherwise get: ### GAlignments(Rle(factor("chr1")), 11L, "20M", strand("+"))[[1]] ### # Error: C stack usage 7969700 is too close to the limit setMethod("getListElement", "GAlignments", function(x, i, exact=TRUE) stop(wmsg(class(x), " objects don't support [[, $, as.list(), ", "lapply(), or unlist()")) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### "show" method. ### .from_GAlignments_to_naked_character_matrix_for_display <- function(x) { x_len <- length(x) x_mcols <- mcols(x, use.names=FALSE) x_nmc <- ncol(x_mcols) ans <- cbind(seqnames=as.character(seqnames(x)), strand=as.character(strand(x)), cigar=S4Vectors:::sketchStr(cigar(x), 23L), qwidth=qwidth(x), start=start(x), end=end(x), width=width(x), njunc=njunc(x)) if (x_nmc > 0L) { tmp <- as.data.frame(lapply(x_mcols, showAsCell), optional=TRUE) ans <- cbind(ans, `|`=rep.int("|", x_len), as.matrix(tmp)) } ans } showGAlignments <- function(x, margin="", print.classinfo=FALSE, print.seqinfo=FALSE) { lx <- length(x) nc <- ncol(mcols(x, use.names=FALSE)) cat(class(x), " object with ", lx, " ", ifelse(lx == 1L, "alignment", "alignments"), " and ", nc, " metadata ", ifelse(nc == 1L, "column", "columns"), ":\n", sep="") out <- S4Vectors:::makePrettyMatrixForCompactPrinting(x, .from_GAlignments_to_naked_character_matrix_for_display) if (print.classinfo) { .COL2CLASS <- c( seqnames="Rle", strand="Rle", cigar="character", qwidth="integer", start="integer", end="integer", width="integer", njunc="integer" ) classinfo <- S4Vectors:::makeClassinfoRowForCompactPrinting(x, .COL2CLASS) ## A sanity check, but this should never happen! stopifnot(identical(colnames(classinfo), colnames(out))) out <- rbind(classinfo, out) } if (nrow(out) != 0L) rownames(out) <- paste0(margin, rownames(out)) ## We set 'max' to 'length(out)' to avoid the getOption("max.print") ## limit that would typically be reached when 'showHeadLines' global ## option is set to Inf. print(out, quote=FALSE, right=TRUE, max=length(out)) if (print.seqinfo) { cat(margin, "-------\n", sep="") cat(margin, "seqinfo: ", summary(seqinfo(x)), "\n", sep="") } } setMethod("show", "GAlignments", function(object) showGAlignments(object, margin=" ", print.classinfo=TRUE, print.seqinfo=TRUE) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Concatenation ### setMethod("bindROWS", "GAlignments", GenomicRanges:::concatenate_GenomicRanges_objects ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Comparing/ordering. ### setMethod("pcompare", c("GAlignments", "GAlignments"), function(x, y) { x <- granges(x, use.names=FALSE) y <- granges(y, use.names=FALSE) callGeneric(x, y) } ) setMethod("is.unsorted", "GAlignments", function(x, na.rm=FALSE, strictly=FALSE, ...) { x <- granges(x, use.names=FALSE) callGeneric() } ) setMethod("order", "GAlignments", function(..., na.last=TRUE, decreasing=FALSE, method=c("auto", "shell", "radix")) { args <- list(...) order_args <- c(lapply(args, granges, use.names=FALSE), list(na.last=na.last, decreasing=decreasing, method=method)) do.call(order, order_args) } ) setMethod("sort", "GAlignments", function(x, decreasing=FALSE, ...) { oo <- GenomicRanges:::order_GenomicRanges(x, decreasing=decreasing, ...) extractROWS(x, oo) } ) setMethod("rank", "GAlignments", function(x, na.last=TRUE, ties.method=c("average", "first", "last", "random", "max", "min"), ...) { x <- granges(x, use.names=FALSE) callGeneric() } ) GenomicAlignments/R/GAlignmentsList-class.R0000644000175200017520000002421114516004601021667 0ustar00biocbuildbiocbuild### ========================================================================= ### GAlignmentsList objects ### ------------------------------------------------------------------------- ### setClass("GAlignmentsList", contains="CompressedRangesList", representation( unlistData="GAlignments", elementMetadata="DataFrame" ), prototype( elementType="GAlignments", elementMetadata=new("DFrame") ) ) ### Formal API: ### names(x) - NULL or character vector. ### length(x) - single integer. Nb of alignments in 'x'. ### seqnames(x) - 'factor' Rle of the same length as 'x'. ### rname(x) - same as 'seqnames(x)'. ### seqnames(x) <- value - replacement form of 'seqnames(x)'. ### rname(x) <- value - same as 'seqnames(x) <- value'. ### cigar(x) - character vector of the same length as 'x'. ### strand(x) - 'factor' Rle of the same length as 'x' (levels: +, -, *). ### qwidth(x) - integer vector of the same length as 'x'. ### start(x), end(x), width(x) - integer vectors of the same length as 'x'. ### njunc(x) - integer vector of the same length as 'x'. ### grglist(x) - GRangesList object of the same length as 'x'. ### granges(x) - GRanges object of the same length as 'x'. ### rglist(x) - CompressedIRangesList object of the same length as 'x'. ### ranges(x) - IRanges object of the same length as 'x'. ### show(x) - compact display in a data.frame-like fashion. ### GAlignmentsList(x, ...) - constructor. ### x[i] - GAlignmentsList object of the same class as 'x' ### (endomorphism). ### ### findOverlaps(query, subject) - 'query' or 'subject' or both are ### GAlignments objects. Just a convenient wrapper for ### 'findOverlaps(grglist(query), subject, ...)', etc... ### ### countOverlaps(query, subject) - 'query' or 'subject' or both are ### GAlignments objects. Just a convenient wrapper for ### 'countOverlaps(grglist(query), subject, ...)', etc... ### ### subsetByOverlaps(query, subject) - 'query' or 'subject' or both are ### GAlignments objects. ### ### qnarrow(x, start=NA, end=NA, width=NA) - GAlignmentsList object of the ### same length and class as 'x' (endomorphism). ### ### narrow(x, start=NA, end=NA, width=NA) - GAlignmentsList object of the ### same length and class as 'x' (endomorphism). ### ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Getters. ### setMethod("seqnames", "GAlignmentsList", function(x) relist(seqnames(unlist(x, use.names=FALSE)), x) ) setMethod("rname", "GAlignmentsList", function(x) relist(rname(unlist(x, use.names=FALSE)), x) ) setMethod("cigar", "GAlignmentsList", function(x) relist(cigar(unlist(x, use.names=FALSE)), x) ) setMethod("strand", "GAlignmentsList", function(x) relist(strand(unlist(x, use.names=FALSE)), x) ) setMethod("qwidth", "GAlignmentsList", function(x) relist(qwidth(unlist(x, use.names=FALSE)), x) ) setMethod("njunc", "GAlignmentsList", function(x) relist(njunc(unlist(x, use.names=FALSE)), x) ) setMethod("seqinfo", "GAlignmentsList", function(x) seqinfo(unlist(x, use.names=FALSE)) ) setMethod("elementMetadata", "GAlignmentsList", GenomicRanges:::get_GenomicRangesList_mcols ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Setters. ### setReplaceMethod("rname", "GAlignmentsList", function(x, value) `seqnames<-`(x, value) ) setReplaceMethod("elementMetadata", "GAlignmentsList", GenomicRanges:::set_CompressedGenomicRangesList_mcols ) setReplaceMethod("strand", "GAlignmentsList", GenomicRanges:::set_CompressedGenomicRangesList_strand ) setReplaceMethod("strand", c("GAlignmentsList", "character"), function(x, ..., value) { if (length(value) > 1L) stop("length(value) must be 1") strand(x@unlistData) <- value x } ) setReplaceMethod("seqinfo", "GAlignmentsList", GenomicRanges:::set_CompressedGenomicRangesList_seqinfo ) setReplaceMethod("seqnames", "GAlignmentsList", GenomicRanges:::set_CompressedGenomicRangesList_seqnames ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Validity. ### .valid.GAlignmentsList <- function(x) { ## TDB: Currently known pitfalls are caught by ## GAlignments validity. } setValidity2("GAlignmentsList", .valid.GAlignmentsList) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Constructors. ### GAlignmentsList <- function(...) { listData <- list(...) if (length(listData) == 0L) { unlistData <- GAlignments() } else { if (length(listData) == 1L && is.list(listData[[1L]])) listData <- listData[[1L]] if (!all(sapply(listData, is, "GAlignments"))) stop("all elements in '...' must be GAlignments objects") unlistData <- suppressWarnings(do.call("c", unname(listData))) } relist(unlistData, PartitioningByEnd(listData)) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Coercion. ### setMethod("ranges", "GAlignmentsList", function(x, use.names=TRUE, use.mcols=FALSE) { if (!isTRUEorFALSE(use.names)) stop("'use.names' must be TRUE or FALSE") if (!isTRUEorFALSE(use.mcols)) stop("'use.mcols' must be TRUE or FALSE") ans <- unlist(range(rglist(x)), use.names=FALSE) if (use.names) names(ans) <- names(x) if (use.mcols) mcols(ans) <- mcols(x, use.names=FALSE) ans } ) setMethod("granges", "GAlignmentsList", function(x, use.names=TRUE, use.mcols=FALSE, ignore.strand=FALSE) { if (!isTRUEorFALSE(use.mcols)) stop("'use.mcols' must be TRUE or FALSE") if (ignore.strand) strand(x@unlistData) <- "*" msg <- paste0("For some list elements in 'x', the ranges are ", "not on the same chromosome and strand. ", "Cannot extract a single range for them. ", "As a consequence, the returned GRanges object ", "is not parallel to 'x'.") rg <- range(grglist(x, ignore.strand=ignore.strand)) is_one_to_one <- all(elementNROWS(rg) == 1L) if (!is_one_to_one && all(width(x@partitioning) > 0)) { if (ignore.strand) warning(msg) else warning(paste0(msg, " Consider using 'ignore.strand=TRUE'.")) } ans <- unlist(rg, use.names=use.names) if (is_one_to_one && use.mcols) mcols(ans) <- mcols(x, use.names=FALSE) ans } ) setMethod("grglist", "GAlignmentsList", function(x, use.names=TRUE, use.mcols=FALSE, drop.D.ranges=FALSE, ignore.strand=FALSE) { if (!isTRUEorFALSE(use.names)) stop("'use.names' must be TRUE or FALSE") if (!isTRUEorFALSE(use.mcols)) stop("'use.mcols' must be TRUE or FALSE") if (!isTRUEorFALSE(ignore.strand)) stop("'ignore.strand' must be TRUE or FALSE") if (ignore.strand) strand(x@unlistData) <- "*" unlisted_x <- unlist(x, use.names=FALSE) grl <- grglist(unlisted_x, drop.D.ranges=drop.D.ranges) ans <- IRanges:::regroupBySupergroup(grl, x) if (!use.names) names(ans) <- NULL if (use.mcols) mcols(ans) <- mcols(x, use.names=FALSE) ans } ) setMethod("rglist", "GAlignmentsList", function(x, use.names=TRUE, use.mcols=FALSE, drop.D.ranges=FALSE) { if (!isTRUEorFALSE(use.names)) stop("'use.names' must be TRUE or FALSE") if (!isTRUEorFALSE(use.mcols)) stop("'use.mcols' must be TRUE or FALSE") unlisted_x <- unlist(x, use.names=FALSE) rgl <- rglist(unlisted_x, drop.D.ranges=drop.D.ranges) ans <- IRanges:::regroupBySupergroup(rgl, x) if (!use.names) names(ans) <- NULL if (use.mcols) mcols(ans) <- mcols(x, use.names=FALSE) ans } ) setAs("GAlignmentsList", "IntegerRanges", function(from) ranges(from, use.names=TRUE, use.mcols=TRUE) ) setAs("GAlignmentsList", "GRanges", function(from) granges(from, use.names=TRUE, use.mcols=TRUE) ) setAs("GAlignmentsList", "GRangesList", function(from) grglist(from, use.names=TRUE, use.mcols=TRUE) ) setAs("GAlignmentsList", "IntegerRangesList", function(from) rglist(from, use.names=TRUE, use.mcols=TRUE) ) setAs("GAlignmentPairs", "GAlignmentsList", function(from) { if (length(from) == 0L) pbe <- PartitioningByEnd() else pbe <- PartitioningByEnd(seq(2, 2*length(from), 2), names=names(from)) new("GAlignmentsList", unlistData=unlist(from, use.names=FALSE), partitioning=pbe) } ) setAs("GAlignmentsList", "GAlignmentPairs", function(from) { from_mcols <- mcols(from, use.names=FALSE) ga <- unlist(from[from_mcols$mate_status != "unmated"]) first <- c(TRUE, FALSE) last <- c(FALSE, TRUE) ga_mcols <- mcols(ga, use.names=FALSE) isProperPair <- if (!is.null(ga_mcols$flag)) { bamFlagTest(ga_mcols$flag[first], "isProperPair") } else { TRUE } GAlignmentPairs(ga[first], ga[last], isProperPair=isProperPair, names=names(ga)[first]) } ) setAs("list", "GAlignmentsList", function(from) do.call(GAlignmentsList, from)) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Subsetting. ### ## "[", "[<-" and "[[", "[[<-" from CompressedList ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Going from GAlignments to GAlignmentsList with extractList() and family. ### setMethod("relistToClass", "GAlignments", function(x) "GAlignmentsList" ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### show method. ### setMethod("show", "GAlignmentsList", function(object) GenomicRanges:::show_GenomicRangesList(object) ) GenomicAlignments/R/GappedReads-class.R0000644000175200017520000000517514516004601021012 0ustar00biocbuildbiocbuild### ========================================================================= ### GappedReads objects ### ------------------------------------------------------------------------- ### setClass("GappedReads", contains="GAlignments", representation( qseq="DNAStringSet" ## TODO: Maybe add the read quality? mismatch information? ) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Getters. ### setGeneric("qseq", function(x) standardGeneric("qseq")) setMethod("qseq", "GappedReads", function(x) x@qseq) ### Overriding "qwidth" method for GAlignments objects with a faster ### method. setMethod("qwidth", "GappedReads", function(x) width(qseq(x))) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Validity. ### .valid.GappedReads.qseq <- function(x) { x_qseq <- qseq(x) if (class(x_qseq) != "DNAStringSet" || !is.null(names(x_qseq))) return("'qseq(x)' must be an unnamed DNAStringSet instance") if (length(x_qseq) != length(cigar(x))) return("'qseq(x)' and 'cigar(x)' must have the same length") if (!identical(width(x_qseq), cigarWidthAlongQuerySpace(cigar(x)))) return(paste("'width(qseq(x))' and", "'cigarWidthAlongQuerySpace(cigar(x))'", "must be identical")) NULL } .valid.GappedReads <- function(x) { .valid.GappedReads.qseq(x) } setValidity2("GappedReads", .valid.GappedReads, where=asNamespace("GenomicAlignments")) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Constructor. ### GappedReads <- function(seqnames=Rle(factor()), pos=integer(0), cigar=character(0), strand=NULL, qseq=DNAStringSet(), names=NULL, seqlengths=NULL) { galn <- GAlignments(seqnames=seqnames, pos=pos, cigar=cigar, strand=strand, names=names, seqlengths=seqlengths) new("GappedReads", galn, qseq=qseq) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Subsetting. ### setMethod("extractROWS", "GappedReads", function(x, i) { i <- normalizeSingleBracketSubscript(i, x, as.NSBS=TRUE) x@qseq <- extractROWS(x@qseq, i) callNextMethod() } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### The "show" method. ### ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Combining. ### setMethod("c", "GappedReads", function (x, ..., recursive = FALSE) { stop("coming soon") } ) GenomicAlignments/R/OverlapEncodings-class.R0000644000175200017520000003045614516004601022075 0ustar00biocbuildbiocbuild### ========================================================================= ### OverlapEncodings objects ### ------------------------------------------------------------------------- ### setClass("OverlapEncodings", contains="Vector", representation( Loffset="integer", # no NAs, >= 0 Roffset="integer", # no NAs, >= 0 encoding="factor", # no NAs flippedQuery="logical" # no NAs ) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### parallel_slot_names() ### ### Combine the new "parallel slots" with those of the parent class. Make ### sure to put the new parallel slots **first**. See R/Vector-class.R file ### in the S4Vectors package for what slots should or should not be considered ### "parallel". setMethod("parallel_slot_names", "OverlapEncodings", function(x) c("Loffset", "Roffset", "encoding", "flippedQuery", callNextMethod()) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Getters. ### setGeneric("Loffset", function(x) standardGeneric("Loffset")) setMethod("Loffset", "OverlapEncodings", function(x) x@Loffset) setGeneric("Roffset", function(x) standardGeneric("Roffset")) setMethod("Roffset", "OverlapEncodings", function(x) x@Roffset) ### encoding() generic is defined in Biostrings. setMethod("encoding", "OverlapEncodings", function(x) x@encoding) ### S3/S4 combo for levels.OverlapEncodings levels.OverlapEncodings <- function(x) levels(encoding(x)) setMethod("levels", "OverlapEncodings", levels.OverlapEncodings) setGeneric("flippedQuery", function(x) standardGeneric("flippedQuery")) setMethod("flippedQuery", "OverlapEncodings", function(x) x@flippedQuery) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### The encodingHalves(), Lencoding() and Rencoding() low-level utilities. ### .split_encoding_halves <- function(x, single.end.on.left=FALSE, single.end.on.right=FALSE, as.factors=FALSE) { if (!is.character(x)) stop("'x' must be a character vector") if (!isTRUEorFALSE(single.end.on.left)) stop("'single.end.on.left' must be TRUE or FALSE") if (!isTRUEorFALSE(single.end.on.right)) stop("'single.end.on.right' must be TRUE or FALSE") if (!isTRUEorFALSE(as.factors)) stop("'as.factors' must be TRUE or FALSE") encoding_blocks <- CharacterList(strsplit(x, ":", fixed=TRUE)) unlisted_blocks <- unlist(encoding_blocks, use.names=FALSE) block_halves <- CharacterList(strsplit(unlisted_blocks, "--", fixed=TRUE)) ## Check that the blocks in any given encoding are either all single-end ## or all paired-end. nhalves <- unique(relist(elementNROWS(block_halves), encoding_blocks)) if (any(elementNROWS(nhalves) != 1L)) stop("some encodings are ill-formed") nhalves <- as.integer(nhalves) if (any(nhalves > 2L)) stop("some encodings are ill-formed") halves2encoding <- function(halves, skeleton) { blocks <- relist(as.character(halves), skeleton) encoding <- unstrsplit(blocks, sep=":") if (length(encoding) != 0L) encoding <- setNames(paste0(encoding, ":"), names(encoding)) encoding } Lencoding <- halves2encoding(heads(block_halves, n=1L), encoding_blocks) Rencoding <- halves2encoding(tails(block_halves, n=1L), encoding_blocks) if (!(single.end.on.left && single.end.on.right)) { idx <- which(nhalves == 1L) if (!single.end.on.left) Lencoding[idx] <- NA_character_ if (!single.end.on.right) Rencoding[idx] <- NA_character_ } if (as.factors) { Lencoding <- factor(Lencoding, levels=unique(Lencoding)) Rencoding <- factor(Rencoding, levels=unique(Rencoding)) } list(Lencoding, Rencoding) } setGeneric("encodingHalves", signature="x", function(x, single.end.on.left=FALSE, single.end.on.right=FALSE, as.factors=FALSE) standardGeneric("encodingHalves") ) setMethod("encodingHalves", "character", .split_encoding_halves) setMethod("encodingHalves", "factor", function(x, single.end.on.left=FALSE, single.end.on.right=FALSE, as.factors=FALSE) { levels_halves <- encodingHalves(levels(x), single.end.on.left=single.end.on.left, single.end.on.right=single.end.on.right, as.factors=as.factors) x <- as.integer(x) list(levels_halves[[1L]][x], levels_halves[[2L]][x]) } ) setMethod("encodingHalves", "OverlapEncodings", function(x, single.end.on.left=FALSE, single.end.on.right=FALSE, as.factors=FALSE) { encodingHalves(encoding(x), single.end.on.left=single.end.on.left, single.end.on.right=single.end.on.right, as.factors=as.factors) } ) Lencoding <- function(x, ...) encodingHalves(x, ...)[[1L]] Rencoding <- function(x, ...) encodingHalves(x, ...)[[2L]] ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### The njunc(), Lnjunc(), and Rnjunc() low-level utilities. ### ### 'x' must be a character vector of single-end encodings. .njunc_single_end_encodings <- function(x) { if (!is.character(x)) stop("'x' must be a character vector") old_warn <- getOption("warn") options(warn=2L) on.exit(options(warn=old_warn)) M <- try(as.integer(sub(":.*", "", x)), silent=TRUE) if (inherits(M, "try-error")) stop("some encodings are ill-formed") options(warn=old_warn) if (any(M < 1L, na.rm=TRUE)) warning(wmsg("some encodings start with a value < 1 and that is ", "interpreted as a negative number of junctions)")) setNames(M - 1L, names(x)) } Lnjunc <- function(x, single.end.on.left=FALSE) { Lencoding <- Lencoding(x, single.end.on.left=single.end.on.left, as.factors=TRUE) .njunc_single_end_encodings(levels(Lencoding))[as.integer(Lencoding)] } Rnjunc <- function(x, single.end.on.right=FALSE) { Rencoding <- Rencoding(x, single.end.on.right=single.end.on.right, as.factors=TRUE) .njunc_single_end_encodings(levels(Rencoding))[as.integer(Rencoding)] } ### We make this the default "njunc" method although it will only work on ### objects supported by encodingHalves() (which is called behind the scene), ### that is, for character, factor, and OverlapEncodings objects. So instead ### of defining 3 "njunc" methods (one for each type of object supported by ### encodingHalves()), we define a single one for expediency. Another advantage ### of this approach is that if, in the future, encodingHalves() is extended to ### support more types of objects, then njunc() will work out-of-the-box on ### them i.e. with no need to define additional "njunc" methods. setMethod("njunc", "ANY", function(x) { Lnjunc <- Lnjunc(x, single.end.on.left=TRUE) Rnjunc <- Rnjunc(x) Rnjunc[is.na(Rnjunc)] <- 0L Lnjunc + Rnjunc } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Coercion. ### ### S3/S4 combo for as.data.frame.OverlapEncodings as.data.frame.OverlapEncodings <- function(x, row.names=NULL, optional=FALSE, ...) { if (!(is.null(row.names) || is.character(row.names))) stop("'row.names' must be NULL or a character vector") data.frame(Loffset=Loffset(x), Roffset=Roffset(x), encoding=encoding(x), flippedQuery=flippedQuery(x), row.names=row.names, check.rows=TRUE, check.names=FALSE, stringsAsFactors=FALSE) } setMethod("as.data.frame", "OverlapEncodings", as.data.frame.OverlapEncodings) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Displaying ### .make_naked_matrix_from_OverlapEncodings <- function(x) { x_len <- length(x) x_mcols <- mcols(x, use.names=FALSE) x_nmc <- if (is.null(x_mcols)) 0L else ncol(x_mcols) ans <- cbind(Loffset=as.character(Loffset(x)), Roffset=as.character(Roffset(x)), encoding=as.character(encoding(x)), flippedQuery=as.character(flippedQuery(x))) if (x_nmc > 0L) { tmp <- do.call(data.frame, c(lapply(x_mcols, showAsCell), list(check.names=FALSE))) ans <- cbind(ans, `|`=rep.int("|", x_len), as.matrix(tmp)) } ans } showOverlapEncodings <- function(x, margin="", print.classinfo=FALSE) { x_class <- class(x) x_len <- length(x) x_mcols <- mcols(x, use.names=FALSE) x_nmc <- if (is.null(x_mcols)) 0L else ncol(x_mcols) cat(classNameForDisplay(x), " object of length ", x_len, " with ", x_nmc, " metadata column", ifelse(x_nmc == 1L, "", "s"), ":\n", sep="") out <- S4Vectors:::makePrettyMatrixForCompactPrinting(x, .make_naked_matrix_from_OverlapEncodings) if (print.classinfo) { .COL2CLASS <- c( Loffset="integer", Roffset="integer", encoding="factor", flippedQuery="logical" ) classinfo <- S4Vectors:::makeClassinfoRowForCompactPrinting(x, .COL2CLASS) ## A sanity check, but this should never happen! stopifnot(identical(colnames(classinfo), colnames(out))) out <- rbind(classinfo, out) } if (nrow(out) != 0L) rownames(out) <- paste0(margin, rownames(out)) ## We set 'max' to 'length(out)' to avoid the getOption("max.print") ## limit that would typically be reached when 'showHeadLines' global ## option is set to Inf. print(out, quote=FALSE, right=TRUE, max=length(out)) } setMethod("show", "OverlapEncodings", function(object) showOverlapEncodings(object, margin=" ", print.classinfo=TRUE) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Detection of "splice compatible" overlaps. ### build_compatible_encoding_subpatterns <- function(njunc) { ## Each "atom" must match exactly 1 code in the encoding. ATOM0 <- "[fgij]" if (njunc == 0L) return(ATOM0) #ntimes <- function(atom, n) rep.int(atom, n) ntimes <- function(atom, n) { if (n == 1L) atom else c(atom, "{", n, "}") } LEFT_ATOM <- "[jg]" MIDDLE_ATOM <- "g" RIGHT_ATOM <- "[gf]" WILDCARD_ATOM <- "[^:-]" sapply(seq_len(njunc + 1L), function(i) { if (i == 1L) { atoms <- c(LEFT_ATOM, ntimes(WILDCARD_ATOM, njunc)) } else if (i == njunc + 1L) { atoms <- c(ntimes(WILDCARD_ATOM, njunc), RIGHT_ATOM) } else { atoms <- c(ntimes(WILDCARD_ATOM, i-1L), MIDDLE_ATOM, ntimes(WILDCARD_ATOM, njunc-i+1L)) } paste0(atoms, collapse="") }) } .build_compatible_encoding_pattern <- function(max.njunc) { subpatterns <- sapply(0:max.njunc, function(njunc) paste0(build_compatible_encoding_subpatterns(njunc), collapse=":") ) paste0(":(", paste0(subpatterns, collapse="|"), "):") } ### 'x' must be a character vector of single-end encodings. .is_compatible_with_splicing <- function(x) { if (!is.character(x)) stop("'x' must be a character vector") max.njunc <- max(c(0L, .njunc_single_end_encodings(x))) pattern <- .build_compatible_encoding_pattern(max.njunc) setNames(grepl(pattern, x), names(x)) } setGeneric("isCompatibleWithSplicing", function(x) standardGeneric("isCompatibleWithSplicing") ) setMethod("isCompatibleWithSplicing", "character", function(x) { halves <- encodingHalves(x, single.end.on.left=TRUE, single.end.on.right=TRUE) ans1 <- .is_compatible_with_splicing(halves[[1L]]) ans2 <- .is_compatible_with_splicing(halves[[2L]]) ans1 & ans2 } ) setMethod("isCompatibleWithSplicing", "factor", function(x) isCompatibleWithSplicing(levels(x))[as.integer(x)] ) setMethod("isCompatibleWithSplicing", "OverlapEncodings", function(x) isCompatibleWithSplicing(encoding(x)) ) GenomicAlignments/R/cigar-utils.R0000644000175200017520000002764714516004601017763 0ustar00biocbuildbiocbuild### ========================================================================= ### Low-level CIGAR utilities ### ------------------------------------------------------------------------- ### See p. 4 of the SAM Spec v1.4 at http://samtools.sourceforge.net/ for the ### list of CIGAR operations and their meanings. CIGAR_OPS <- c("M", "I", "D", "N", "S", "H", "P", "=", "X") .normarg_cigar <- function(cigar) { if (is.factor(cigar)) cigar <- as.character(cigar) if (!is.character(cigar)) stop("'cigar' must be a character vector or factor") cigar } .normarg_flag <- function(flag, cigar) { if (!is.null(flag)) { if (!is.numeric(flag)) stop("'flag' must be NULL or a vector of integers") if (!is.integer(flag)) flag <- as.integer(flag) if (length(cigar) != length(flag)) stop("'cigar' and 'flag' must have the same length") } flag } .normarg_pos <- function(pos, cigar) { if (!is.numeric(pos)) stop("'pos' must be a vector of integers") if (!is.integer(pos)) pos <- as.integer(pos) if (length(pos) != 1L && length(pos) != length(cigar)) stop("'pos' must have length 1 or the same length as 'cigar'") pos } .select_reference_space <- function(N.regions.removed) { if (!isTRUEorFALSE(N.regions.removed)) stop("'N.regions.removed' must be TRUE or FALSE") if (N.regions.removed) { space <- 2L # REFERENCE_N_REGIONS_REMOVED } else { space <- 1L # REFERENCE } space } .select_query_space <- function(before.hard.clipping, after.soft.clipping) { if (!isTRUEorFALSE(before.hard.clipping)) stop("'before.hard.clipping' must be TRUE or FALSE") if (!isTRUEorFALSE(after.soft.clipping)) stop("'after.soft.clipping' must be TRUE or FALSE") if (before.hard.clipping) { if (after.soft.clipping) stop("'before.hard.clipping' and 'after.soft.clipping' ", "cannot both be TRUE") space <- 4L # QUERY_BEFORE_HARD_CLIPPING } else if (after.soft.clipping) { space <- 5L # QUERY_AFTER_SOFT_CLIPPING } else { space <- 3L # QUERY } space } .select_pairwise_space <- function(N.regions.removed, dense) { if (!isTRUEorFALSE(N.regions.removed)) stop("'N.regions.removed' must be TRUE or FALSE") if (!isTRUEorFALSE(dense)) stop("'dense' must be TRUE or FALSE") if (N.regions.removed) { if (dense) stop("'N.regions.removed' and 'dense' ", "cannot both be TRUE") space <- 7L # PAIRWISE_N_REGIONS_REMOVED } else if (dense) { space <- 8L # PAIRWISE_DENSE } else { space <- 6L # PAIRWISE } space } .normarg_ops <- function(ops) { if (is.null(ops)) return(ops) if (!is.character(ops)) stop("'ops' must be a character vector") if (any(is.na(ops))) stop("'ops' cannot contain NAs") if (length(ops) == 1L) { ops <- strsplit(ops, NULL, fixed=TRUE)[[1L]] } else if (any(nchar(ops) != 1L)) { stop("when 'length(ops) != 1', all its elements ", "must be single letters") } if (anyDuplicated(ops)) stop("'ops' cannot contain duplicated letters") if (!all(ops %in% CIGAR_OPS)) stop("'ops' contains invalid CIGAR operations") ops } validCigar <- function(cigar) { cigar <- .normarg_cigar(cigar) .Call2("valid_cigar", cigar, 0L, PACKAGE="GenomicAlignments") } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Transform CIGARs into other useful representations ### explodeCigarOps <- function(cigar, ops=CIGAR_OPS) { cigar <- .normarg_cigar(cigar) ops <- .normarg_ops(ops) .Call2("explode_cigar_ops", cigar, ops, PACKAGE="GenomicAlignments") } explodeCigarOpLengths <- function(cigar, ops=CIGAR_OPS) { cigar <- .normarg_cigar(cigar) ops <- .normarg_ops(ops) .Call2("explode_cigar_op_lengths", cigar, ops, PACKAGE="GenomicAlignments") } cigarToRleList <- function(cigar) { cigar_ops <- explodeCigarOps(cigar) cigar_op_lengths <- explodeCigarOpLengths(cigar) if (length(cigar) == 0L) { unlisted_cigar_ops <- character(0) unlisted_cigar_op_lengths <- integer(0) } else { unlisted_cigar_ops <- unlist(cigar_ops, use.names=FALSE) unlisted_cigar_op_lengths <- unlist(cigar_op_lengths, use.names=FALSE) } ## Prepare 'ans_flesh'. ans_flesh <- Rle(unlisted_cigar_ops, unlisted_cigar_op_lengths) ## Prepare 'ans_skeleton'. nops_per_cigar <- elementNROWS(cigar_op_lengths) ans_breakpoints <- cumsum(unlisted_cigar_op_lengths)[cumsum(nops_per_cigar)] ans_skeleton <- PartitioningByEnd(ans_breakpoints) ## Relist. relist(ans_flesh, ans_skeleton) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Summarize CIGARs ### cigarOpTable <- function(cigar) { cigar <- .normarg_cigar(cigar) ans <- .Call2("cigar_op_table", cigar, PACKAGE="GenomicAlignments") stopifnot(identical(CIGAR_OPS, colnames(ans))) # sanity check ans } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### From CIGARs to ranges ### .cigar_ranges <- function(cigar, flag, space, pos, f, ops, drop.empty.ranges, reduce.ranges, with.ops) { cigar <- .normarg_cigar(cigar) flag <- .normarg_flag(flag, cigar) if (!isSingleNumber(space)) stop("'space' must be a single integer") if (!is.integer(space)) space <- as.integer(space) pos <- .normarg_pos(pos, cigar) if (!is.null(f)) { if (!is.factor(f)) stop("'f' must be NULL or a factor") if (length(f) != length(cigar)) stop("'f' must have the same length as 'cigar'") } ops <- .normarg_ops(ops) if (!isTRUEorFALSE(drop.empty.ranges)) stop("'drop.empty.ranges' must be TRUE or FALSE") if (!isTRUEorFALSE(reduce.ranges)) stop("'reduce.ranges' must be TRUE or FALSE") if (!isTRUEorFALSE(with.ops)) stop("'with.ops' must be TRUE or FALSE") .Call2("cigar_ranges", cigar, flag, space, pos, f, ops, drop.empty.ranges, reduce.ranges, with.ops, PACKAGE="GenomicAlignments") } cigarRangesAlongReferenceSpace <- function(cigar, flag=NULL, N.regions.removed=FALSE, pos=1L, f=NULL, ops=CIGAR_OPS, drop.empty.ranges=FALSE, reduce.ranges=FALSE, with.ops=FALSE) { space <- .select_reference_space(N.regions.removed) C_ans <- .cigar_ranges(cigar, flag, space, pos, f, ops, drop.empty.ranges, reduce.ranges, with.ops) if (is.null(f)) return(C_ans) compress <- length(C_ans) >= 200L IRangesList(C_ans, compress=compress) } cigarRangesAlongQuerySpace <- function(cigar, flag=NULL, before.hard.clipping=FALSE, after.soft.clipping=FALSE, ops=CIGAR_OPS, drop.empty.ranges=FALSE, reduce.ranges=FALSE, with.ops=FALSE) { space <- .select_query_space(before.hard.clipping, after.soft.clipping) .cigar_ranges(cigar, flag, space, 1L, NULL, ops, drop.empty.ranges, reduce.ranges, with.ops) } cigarRangesAlongPairwiseSpace <- function(cigar, flag=NULL, N.regions.removed=FALSE, dense=FALSE, ops=CIGAR_OPS, drop.empty.ranges=FALSE, reduce.ranges=FALSE, with.ops=FALSE) { space <- .select_pairwise_space(N.regions.removed, dense) .cigar_ranges(cigar, flag, space, 1L, NULL, ops, drop.empty.ranges, reduce.ranges, with.ops) } ### A convenience wrapper to cigarRangesAlongReferenceSpace(). extractAlignmentRangesOnReference <- function(cigar, pos=1L, drop.D.ranges=FALSE, f=NULL) { if (!isTRUEorFALSE(drop.D.ranges)) stop("'drop.D.ranges' must be TRUE or FALSE") ## Not sure why we include "I" operations here since they don't generate ## coverage on the reference (they always produce zero-width ranges on the ## reference). if (drop.D.ranges) { ops <- c("M", "=", "X", "I") } else { ops <- c("M", "=", "X", "I", "D") } cigarRangesAlongReferenceSpace(cigar, flag=NULL, pos=pos, f=f, ops=ops, drop.empty.ranges=FALSE, reduce.ranges=TRUE) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### From CIGARs to sequence lengths ### .cigar_width <- function(cigar, flag, space) { cigar <- .normarg_cigar(cigar) flag <- .normarg_flag(flag, cigar) if (!isSingleNumber(space)) stop("'space' must be a single integer") if (!is.integer(space)) space <- as.integer(space) .Call2("cigar_width", cigar, flag, space, PACKAGE="GenomicAlignments") } cigarWidthAlongReferenceSpace <- function(cigar, flag=NULL, N.regions.removed=FALSE) { space <- .select_reference_space(N.regions.removed) .cigar_width(cigar, flag, space) } cigarWidthAlongQuerySpace <- function(cigar, flag=NULL, before.hard.clipping=FALSE, after.soft.clipping=FALSE) { space <- .select_query_space(before.hard.clipping, after.soft.clipping) .cigar_width(cigar, flag, space) } cigarWidthAlongPairwiseSpace <- function(cigar, flag=NULL, N.regions.removed=FALSE, dense=FALSE) { space <- .select_pairwise_space(N.regions.removed, dense) .cigar_width(cigar, flag, space) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Narrow CIGARs ### cigarNarrow <- function(cigar, start=NA, end=NA, width=NA) { cigar_width <- cigarWidthAlongReferenceSpace(cigar) cigar_ranges <- IRanges(start=rep.int(1L, length(cigar_width)), width=cigar_width) threeranges <- threebands(cigar_ranges, start=start, end=end, width=width) C_ans <- .Call2("cigar_narrow", cigar, width(threeranges$left), width(threeranges$right), PACKAGE="GenomicAlignments") ans <- C_ans[[1L]] attr(ans, "rshift") <- C_ans[[2L]] ans } cigarQNarrow <- function(cigar, start=NA, end=NA, width=NA) { cigar_qwidth <- cigarWidthAlongQuerySpace(cigar) cigar_qranges <- IRanges(start=rep.int(1L, length(cigar_qwidth)), width=cigar_qwidth) threeranges <- threebands(cigar_qranges, start=start, end=end, width=width) C_ans <- .Call2("cigar_qnarrow", cigar, width(threeranges$left), width(threeranges$right), PACKAGE="GenomicAlignments") ans <- C_ans[[1L]] attr(ans, "rshift") <- C_ans[[2L]] ans } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Translate coordinates between query-based and reference-based ### queryLoc2refLoc <- function(qloc, cigar, pos=1L) { stop(wmsg("Not implemented yet, sorry! Please use mapToAlignments() ", "and family if you need to map coordinates between query reads ", "and reference genome on the basis of the reported CIGAR. ", "See '?mapToAlignments'")) } queryLocs2refLocs <- function(qlocs, cigar, pos=1L, flag=NULL) { stop(wmsg("Not implemented yet, sorry! Please use mapToAlignments() ", "and family if you need to map coordinates between query reads ", "and reference genome on the basis of the reported CIGAR. ", "See '?mapToAlignments'")) } GenomicAlignments/R/coordinate-mapping-methods.R0000644000175200017520000001270014516004601022741 0ustar00biocbuildbiocbuild### ========================================================================= ### coordinate mapping methods ### ------------------------------------------------------------------------- ### ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Generics ### setGeneric("mapToAlignments", signature=c("x", "alignments"), function(x, alignments, ...) standardGeneric("mapToAlignments") ) setGeneric("pmapToAlignments", signature=c("x", "alignments"), function(x, alignments, ...) standardGeneric("pmapToAlignments") ) setGeneric("mapFromAlignments", signature=c("x", "alignments"), function(x, alignments, ...) standardGeneric("mapFromAlignments") ) setGeneric("pmapFromAlignments", signature=c("x", "alignments"), function(x, alignments, ...) standardGeneric("pmapFromAlignments") ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### mapToAlignments() and mapFromAlignments() methods ### .mapFromAlignments <- function(x, alignments) { if (!length(x) && !length(alignments)) return(GRanges(xHits=integer(), transcriptsHits=integer())) if (is.null(xNames <- names(x)) || is.null(alignmentsNames <- names(alignments))) stop ("both 'x' and 'alignments' must have names") ## name matching determines pairs match0 <- match(alignmentsNames, alignmentsNames) match1 <- match(xNames, alignmentsNames) group0 <- splitAsList(seq_along(alignmentsNames), match0) group1 <- group0[match(na.omit(match1), names(group0))] xHits <- rep(which(!is.na(match1)), elementNROWS(group1)) alignmentsHits <- unlist(group1, use.names=FALSE) if (!length(xHits <- na.omit(xHits))) stop ("none of 'names(x)' are in 'names(alignments)'") x <- x[xHits] alignments <- alignments[alignmentsHits] s <- .Call("query_locs_to_ref_locs", start(x), cigar(alignments), start(alignments), FALSE) e <- .Call("query_locs_to_ref_locs", end(x), cigar(alignments), start(alignments), TRUE) e <- pmax(e, s - 1L) ## remove non-hits keep <- !is.na(s) & !is.na(e) seqname <- as.character(seqnames(alignments)) GRanges(Rle(seqname[keep]), IRanges(s[keep], e[keep], names=names(x)[keep]), xHits=xHits[keep], alignmentsHits=alignmentsHits[keep]) } .mapToAlignments <- function(x, alignments) { if (!length(x) && !length(alignments)) return(GRanges(xHits=integer(), transcriptsHits=integer())) if (is.null(names(alignments))) stop ("'alignments' must have names") ## map all possible pairs; returns hits only map <- .Call("map_ref_locs_to_query_locs", start(x), end(x), cigar(alignments), start(alignments)) xHits <- map[[3]] alignmentsHits <- map[[4]] if (length(xHits)) GRanges(Rle(names(alignments)[alignmentsHits]), IRanges(map[[1]], pmax(map[[2]], map[[1]] - 1L), names=names(x)[xHits]), strand="*", xHits, alignmentsHits) else GRanges(xHits=integer(), transcriptsHits=integer()) } setMethod("mapToAlignments", c("IntegerRanges", "GAlignments"), function(x, alignments, ...) ranges(.mapToAlignments(x, alignments)) ) setMethod("mapToAlignments", c("GenomicRanges", "GAlignments"), function(x, alignments, ...) .mapToAlignments(x, alignments) ) setMethod("mapFromAlignments", c("IntegerRanges", "GAlignments"), function(x, alignments, ...) ranges(.mapFromAlignments(x, alignments)) ) setMethod("mapFromAlignments", c("GenomicRanges", "GAlignments"), function(x, alignments, ...) .mapFromAlignments(x, alignments) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### pmapToAlignments() and pmapFromAlignments() methods ### .pmapAlignments <- function(x, alignments, reverse) { if (length(x) && length(alignments)) { if (length(x) != length(alignments)) stop("'x' and 'alignments' must have the same length") if (reverse) { FUN <- "query_locs_to_ref_locs" seqname <- as.character(seqnames(alignments)) } else { if (is.null(names(alignments))) stop ("'alignments' must have names") FUN <- "ref_locs_to_query_locs" seqname <- names(alignments) } s <- .Call(FUN, start(x), cigar(alignments), start(alignments), FALSE) e <- .Call(FUN, end(x), cigar(alignments), start(alignments), TRUE) e <- pmax(e, s - 1L) ## non-hits if (any(skip <- is.na(s) | is.na(e))) { s[skip] <- 0L e[skip] <- -1L seqname[skip] <- "UNMAPPED" } GRanges(Rle(seqname), IRanges(s, e, names=names(x))) } else { GRanges() } } setMethod("pmapToAlignments", c("IntegerRanges", "GAlignments"), function(x, alignments, ...) ranges(.pmapAlignments(x, alignments, FALSE)) ) setMethod("pmapToAlignments", c("GenomicRanges", "GAlignments"), function(x, alignments, ...) .pmapAlignments(ranges(x), alignments, FALSE) ) setMethod("pmapFromAlignments", c("IntegerRanges", "GAlignments"), function(x, alignments, ...) ranges(.pmapAlignments(x, alignments, TRUE)) ) setMethod("pmapFromAlignments", c("GenomicRanges", "GAlignments"), function(x, alignments, ...) .pmapAlignments(ranges(x), alignments, TRUE) ) GenomicAlignments/R/coverage-methods.R0000644000175200017520000000410414516004601020753 0ustar00biocbuildbiocbuild### ========================================================================= ### "coverage" methods ### ------------------------------------------------------------------------- setMethod("coverage", "GAlignments", function(x, shift=0L, width=NULL, weight=1L, method=c("auto", "sort", "hash", "naive"), drop.D.ranges=FALSE) { x <- grglist(x, drop.D.ranges=drop.D.ranges) coverage(x, shift=shift, width=width, weight=weight, method=method) } ) setMethod("coverage", "GAlignmentPairs", function(x, shift=0L, width=NULL, weight=1L, method=c("auto", "sort", "hash", "naive"), drop.D.ranges=FALSE) { x <- grglist(x, drop.D.ranges=drop.D.ranges) ## Should we do this instead? ## See https://support.bioconductor.org/p/123463/#123552 #x <- reduce(grglist(x, drop.D.ranges=drop.D.ranges)) coverage(x, shift=shift, width=width, weight=weight, method=method) } ) setMethod("coverage", "GAlignmentsList", function(x, shift=0L, width=NULL, weight=1L, ...) { x <- unlist(x, use.names=FALSE) callGeneric() } ) setMethod("coverage", "BamFile", function(x, shift=0L, width=NULL, weight=1L, ..., param=ScanBamParam()) { if (!isOpen(x)) { open(x) on.exit(close(x)) } cvg <- NULL repeat { aln <- readGAlignments(x, param=param) if (length(aln) == 0L) { if (is.null(cvg)) cvg <- coverage(aln, shift=shift, width=width, weight=weight, ...) break } cvg0 <- coverage(aln, shift=shift, width=width, weight=weight, ...) if (is.null(cvg)) cvg <- cvg0 else cvg <- cvg + cvg0 } cvg }) setMethod("coverage", "character", function(x, shift=0L, width=NULL, weight=1L, ..., yieldSize=2500000L) { if (!isSingleString(x)) stop("'x' must be a single string for coverage,character-method") bf <- BamFile(x, yieldSize=yieldSize) coverage(bf, shift=shift, width=width, weight=weight, ...) }) GenomicAlignments/R/encodeOverlaps-methods.R0000644000175200017520000007661714551551747022174 0ustar00biocbuildbiocbuild### ========================================================================= ### encodeOverlaps() and related utilities ### ------------------------------------------------------------------------- ### ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### encodeOverlaps1() - A low-level utility. ### ### > query <- IRanges(start=c(7, 15, 22), end=c(9, 19, 23)) ### > subject <- IRanges(start=c(1, 4, 15, 22, 1, 30, 25), ### end=c(2, 9, 19, 25, 10, 38, 25)) ### > encodeOverlaps1(query, subject, as.matrix=TRUE) ### [,1] [,2] [,3] [,4] [,5] [,6] [,7] ### [1,] "m" "j" "a" "a" "i" "a" "a" ### [2,] "m" "m" "g" "a" "m" "a" "a" ### [3,] "m" "m" "m" "f" "m" "a" "a" ### > encodeOverlaps1(query, subject) ### $Loffset ### [1] 1 ### ### $Roffset ### [1] 2 ### ### $encoding ### [1] "3:jmm:agm:aaf:imm:" ### ### > query.space <- c(0, 1, 0) ### > encodeOverlaps1(query, subject, query.space=query.space)$encoding ### [1] "3:mXm:jXm:aXm:aXf:iXm:aXa:aXa:" ### > query.space <- rep(-1, length(query)) ### > subject.space <- rep(-1, length(subject)) ### > encodeOverlaps1(rev(query), rev(subject), ### query.space=query.space, subject.space=subject.space) ### $Loffset ### [1] 2 ### ### $Roffset ### [1] 1 ### ### $encoding ### [1] "3:aai:jmm:agm:aaf:" ### ### > encodeOverlaps1(query, subject, query.break=2)$encoding ### [1] "2--1:jm--m:ag--m:aa--f:im--m:" ### > encodeOverlaps1(rev(query), rev(subject), ### query.space=query.space, subject.space=subject.space, ### query.break=1)$encoding ### [1] "1--2:a--ai:j--mm:a--gm:a--af:" ### 'query.space' must be either an integer vector of the same length as ### 'query', or NULL. If NULL, then it's interpreted as ### 'integer(length(query))' i.e. all the ranges in 'query' are considered to ### be on space 0. encodeOverlaps1 <- function(query, subject, query.space=NULL, subject.space=NULL, query.break=0L, flip.query=FALSE, as.matrix=FALSE, as.raw=FALSE) { if (!is(query, "IntegerRanges")) stop("'query' must be an IntegerRanges object") if (!is(subject, "IntegerRanges")) stop("'subject' must be an IntegerRanges object") if (is.numeric(query.space) && !is.integer(query.space)) query.space <- as.integer(query.space) if (is.numeric(subject.space) && !is.integer(subject.space)) subject.space <- as.integer(subject.space) if (!isSingleNumber(query.break)) stop("'query.break' must be a single integer value") if (!is.integer(query.break)) query.break <- as.integer(query.break) if (!isTRUEorFALSE(flip.query)) stop("'flip.query' must be TRUE or FALSE") if (!isTRUEorFALSE(as.matrix)) stop("'as.matrix' must be TRUE or FALSE") if (!isTRUEorFALSE(as.raw)) stop("'as.raw' must be TRUE or FALSE") .Call2("encode_overlaps1", start(query), width(query), query.space, query.break, flip.query, start(subject), width(subject), subject.space, as.matrix, as.raw, PACKAGE="GenomicAlignments") } ### TODO: Put this in the (upcoming) man page for encodeOverlaps(). ### A simple (but inefficient) implementation of the "findOverlaps" method for ### IntegerRanges objects. Complexity and memory usage is M x N where M and N ### are the lengths of 'query' and 'subject', respectively. findRangesOverlaps <- function(query, subject) { ovenc <- encodeOverlaps1(query, subject, as.matrix=TRUE, as.raw=TRUE) offsets <- which(charToRaw("c") <= ovenc & ovenc <= charToRaw("k")) - 1L q_hits <- offsets %% nrow(ovenc) + 1L s_hits <- offsets %/% nrow(ovenc) + 1L cbind(queryHits=q_hits, subjectHits=s_hits) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### The .RangesList_encodeOverlaps() helper. ### ### This is the power horse behind all the "encodeOverlaps" methods. ### .RangesList_encode_overlaps <- function(query.starts, query.widths, query.spaces, query.breaks, subject.starts, subject.widths, subject.spaces) { .Call2("RangesList_encode_overlaps", query.starts, query.widths, query.spaces, query.breaks, subject.starts, subject.widths, subject.spaces, PACKAGE="GenomicAlignments") } .Hits_encode_overlaps <- function(query.starts, query.widths, query.spaces, query.breaks, subject.starts, subject.widths, subject.spaces, hits, flip.query) { if (queryLength(hits) != length(query.starts) || subjectLength(hits) != length(subject.starts)) stop("'hits' is not compatible with 'query' and 'subject'") .Call2("Hits_encode_overlaps", query.starts, query.widths, query.spaces, query.breaks, subject.starts, subject.widths, subject.spaces, queryHits(hits), subjectHits(hits), flip.query, PACKAGE="GenomicAlignments") } .RangesList_encodeOverlaps <- function(query.starts, query.widths, subject.starts, subject.widths, hits, flip.query=NULL, query.spaces=NULL, subject.spaces=NULL, query.breaks=NULL) { if (is.null(hits)) { C_ans <- .RangesList_encode_overlaps(query.starts, query.widths, query.spaces, query.breaks, subject.starts, subject.widths, subject.spaces) flip.query <- logical(length(C_ans$encoding)) } else { if (!is(hits, "Hits")) stop("'hits' must be a Hits object") if (is.null(flip.query)) { flip.query <- logical(length(hits)) } else { if (!is.logical(flip.query)) stop("'flip.query' must be a logical vector") if (length(flip.query) != length(hits)) stop("'flip.query' must have the same length as 'hits'") } C_ans <- .Hits_encode_overlaps(query.starts, query.widths, query.spaces, query.breaks, subject.starts, subject.widths, subject.spaces, hits, flip.query) } encoding <- as.factor(C_ans$encoding) new2("OverlapEncodings", Loffset=C_ans$Loffset, Roffset=C_ans$Roffset, encoding=encoding, flippedQuery=flip.query, check=FALSE) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### encodeOverlaps() generic and methods for IntegerRangesList objects. ### setGeneric("encodeOverlaps", signature=c("query", "subject"), function(query, subject, hits=NULL, ...) standardGeneric("encodeOverlaps") ) setMethods("encodeOverlaps", list(c("IntegerRangesList", "IntegerRangesList"), c("IntegerRangesList", "IntegerRanges"), c("IntegerRanges", "IntegerRangesList")), function(query, subject, hits=NULL, ...) { .RangesList_encodeOverlaps(as.list(start(query)), as.list(width(query)), as.list(start(subject)), as.list(width(subject)), hits) } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### .isWrongStrand() internal helper. ### .oneValPerTopLevelElt <- function(x, errmsg) { if (!is(x, "RleList")) stop("'x' must be an RleList object") vals <- runValue(x) vals_eltNROWS <- elementNROWS(vals) if (!all(vals_eltNROWS == 1L)) stop(errmsg) unlist(vals, use.names=FALSE) } .isWrongStrand <- function(query, subject, hits) { if (!is(query, "GRangesList") || !is(subject, "GRangesList")) stop("'query' and 'subject' must be GRangesList objects") ## Extract the top-level strand and seqnames of the query. errmsg <- c("some alignments in 'query' have ranges on ", "more than 1 reference sequence (fusion reads?)") query_seqnames <- .oneValPerTopLevelElt(seqnames(query), errmsg) errmsg <- c("some alignments in 'query' have ranges on ", "both strands") query_strand <- .oneValPerTopLevelElt(strand(query), errmsg) ## Extract the top-level strand and seqnames of the subject. errmsg <- c("some transcripts in 'subject' mix exons from ", "different chromosomes (trans-splicing?)") subject_seqnames <- .oneValPerTopLevelElt(seqnames(subject), errmsg) errmsg <- c("some transcripts in 'subject' mix exons from ", "both strands (trans-splicing?)") subject_strand <- .oneValPerTopLevelElt(strand(subject), errmsg) ## Expand the top-level strand and seqnames of the query and subject. if (!is.null(hits)) { if (!is(hits, "Hits")) stop("'hits' must be NULL or a Hits object") if (queryLength(hits) != length(query) || subjectLength(hits) != length(subject)) stop("'hits' is not compatible with 'query' and 'subject' ", "('queryLength(hits)' and 'subjectLength(hits)' don't ", "match the lengths of 'query' and 'subject')") query_seqnames <- query_seqnames[queryHits(hits)] query_strand <- query_strand[queryHits(hits)] subject_seqnames <- subject_seqnames[subjectHits(hits)] subject_strand <- subject_strand[subjectHits(hits)] } ## Should never happen if 'encodeOverlaps(query, subject, hits)' ## was called with 'hits' being the result of a call to ## 'findOverlaps(query, subject)'. if (!all(query_seqnames == subject_seqnames)) stop("cannot use 'flip.query.if.wrong.strand=TRUE' to ", "encode overlaps across chromosomes") query_strand != subject_strand } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### flipQuery() ### flipQuery <- function(x, i) { if (!is(x, "GRangesList")) stop("'x' must be a GRangesList object") i <- normalizeSingleBracketSubscript(i, x, as.NSBS=TRUE) xi <- extractROWS(x, i) x <- replaceROWS(x, i, invertStrand(revElements(xi))) xi_query.break <- mcols(xi, use.names=FALSE)$query.break if (!is.null(xi_query.break)) { revxi_query.break <- elementNROWS(xi) - xi_query.break mcols(x)$query.break <- replaceROWS(mcols(x, use.names=FALSE)$query.break, i, revxi_query.break) } x } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Should we use generic + methods for this? ### .get_GRanges_spaces <- function(x) { ans <- as.integer(seqnames(x)) x_strand <- as.integer(strand(x)) is_minus <- which(x_strand == as.integer(strand("-"))) ans[is_minus] <- - ans[is_minus] ans } .get_GRangesList_spaces <- function(x) { unlisted_ans <- .get_GRanges_spaces(x@unlistData) as.list(relist(unlisted_ans, x)) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### "encodeOverlaps" method for GRangesList objects. ### .GRangesList_encodeOverlaps <- function(query, subject, hits, flip.query.if.wrong.strand) { if (!isTRUEorFALSE(flip.query.if.wrong.strand)) stop("'flip.query.if.wrong.strand' must be TRUE or FALSE") seqinfo <- merge(seqinfo(query), seqinfo(subject)) seqlevels(query) <- seqlevels(subject) <- seqlevels(seqinfo) if (flip.query.if.wrong.strand) { flip.query <- .isWrongStrand(query, subject, hits) } else { flip.query <- NULL } query.breaks <- mcols(query, use.names=FALSE)$query.break .RangesList_encodeOverlaps(as.list(start(query)), as.list(width(query)), as.list(start(subject)), as.list(width(subject)), hits, flip.query, query.spaces=.get_GRangesList_spaces(query), subject.spaces=.get_GRangesList_spaces(subject), query.breaks=query.breaks) } setMethod("encodeOverlaps", c("GRangesList", "GRangesList"), function(query, subject, hits=NULL, flip.query.if.wrong.strand=FALSE) .GRangesList_encodeOverlaps(query, subject, hits, flip.query.if.wrong.strand) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### selectEncodingWithCompatibleStrand(). ### selectEncodingWithCompatibleStrand <- function(ovencA, ovencB, query.strand, subject.strand, hits=NULL) { if (!is(ovencA, "OverlapEncodings")) stop("'ovencA' must be an OverlapEncodings object") if (!is(ovencB, "OverlapEncodings")) stop("'ovencB' must be an OverlapEncodings object") if (!is.null(hits)) { if (!is(hits, "Hits")) stop("'hits' must be a Hits object or NULL") query.strand <- query.strand[queryHits(hits)] subject.strand <- subject.strand[subjectHits(hits)] } ans <- ovencA names(ans) <- NULL mcols(ans) <- NULL is_wrong_strand <- query.strand != subject.strand idx <- which(is_wrong_strand) ans@Loffset[idx] <- ovencB@Loffset[idx] ans@Roffset[idx] <- ovencB@Roffset[idx] ans_encoding <- as.character(ans@encoding) ans_encoding[idx] <- as.character(ovencB@encoding[idx]) ans@encoding <- as.factor(ans_encoding) ans@flippedQuery[is_wrong_strand] <- TRUE ans } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### isCompatibleWithSkippedExons(). ### ### FIXME: Revisit this and make sure it's doing the right thing for ### paired-end encodings. Maybe look at isCompatibleWithSplicing() for some ### inspiration (used to suffer from similar issue on paired-end encodings but ### was refactored). setGeneric("isCompatibleWithSkippedExons", signature="x", function(x, max.skipped.exons=NA) standardGeneric("isCompatibleWithSkippedExons") ) .build_CompatibleWithSkippedExons_pattern0 <- function(max.njunc1, max.Lnjunc, max.Rnjunc, max.skipped.exons=NA) { if (!identical(max.skipped.exons, NA)) stop("only 'max.skipped.exons=NA' is supported for now, sorry") ## Subpattern for single-end reads. skipped_exons_subpatterns <- function(n) paste0(":(", strrep(".", n + 1L), ":)*") subpattern1 <- sapply(0:max.njunc1, function(njunc) paste0(build_compatible_encoding_subpatterns(njunc), collapse=skipped_exons_subpatterns(njunc))) subpattern1 <- paste0(":(", paste0(subpattern1, collapse="|"), "):") ## Subpattern for paired-end reads. Lsubpattern <- sapply(0:max.Lnjunc, function(njunc) paste0(":", build_compatible_encoding_subpatterns(njunc), "-", collapse=".*")) Lsubpattern <- paste0("(", paste0(Lsubpattern, collapse="|"), ")") Rsubpattern <- sapply(0:max.Rnjunc, function(njunc) paste0("-", build_compatible_encoding_subpatterns(njunc), ":", collapse=".*")) Rsubpattern <- paste0("(", paste0(Rsubpattern, collapse="|"), ")") LRsubpattern <- paste0(Lsubpattern, ".*", Rsubpattern) ## Final pattern. paste0("(", subpattern1, "|", LRsubpattern, ")") } .build_CompatibleWithSkippedExons_pattern <- function(x, max.skipped.exons=NA) { njunc <- njunc(x) Lnjunc <- Lnjunc(x) Rnjunc <- Rnjunc(x) max.njunc1 <- max(c(0L, njunc[is.na(Lnjunc)])) max.Lnjunc <- max(c(0L, Lnjunc), na.rm=TRUE) max.Rnjunc <- max(c(0L, Rnjunc), na.rm=TRUE) .build_CompatibleWithSkippedExons_pattern0(max.njunc1, max.Lnjunc, max.Rnjunc, max.skipped.exons=max.skipped.exons) } .isCompatibleWithSkippedExons <- function(x, max.skipped.exons=NA) { if (!is.character(x)) stop("'x' must be a character vector") pattern1 <- .build_CompatibleWithSkippedExons_pattern(x, max.skipped.exons) grepl(pattern1, x) & !isCompatibleWithSplicing(x) } setMethod("isCompatibleWithSkippedExons", "character", .isCompatibleWithSkippedExons ) setMethod("isCompatibleWithSkippedExons", "factor", function(x, max.skipped.exons=NA) { ok <- isCompatibleWithSkippedExons(levels(x), max.skipped.exons=max.skipped.exons) ok[as.integer(x)] } ) setMethod("isCompatibleWithSkippedExons", "OverlapEncodings", function(x, max.skipped.exons=NA) isCompatibleWithSkippedExons(encoding(x), max.skipped.exons=max.skipped.exons) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### extractSteppedExonRanks(). ### .extract_njunc_from_encoding <- function(x) { as.integer(unlist(strsplit(sub(":.*", "", x), "--", fixed=TRUE), use.names=FALSE)) - 1L } .extractSteppedExonRanksFromEncodingBlocks <- function(encoding_blocks, encoding_patterns) { patterns <- paste0("^", encoding_patterns, "$") ii <- lapply(patterns, grep, encoding_blocks) ii_eltNROWS <- elementNROWS(ii) if (any(ii_eltNROWS == 0L)) return(integer(0)) if (any(ii_eltNROWS != 1L)) stop("cannot unambiguously extract stepped exon ranks from ", "encoding \"", paste0(encoding_blocks, collapse=":"), "\"") ans <- unlist(ii, use.names=FALSE) diff_ans <- diff(ans) if (any(diff_ans <= 0L)) return(integer(0)) ans } ### 'encoding' must be a single encoding. ### Returns an integer vector. If the encoding is single-end then the vector ### is unnamed and strictly sorted. If it's paired-end then it's made of the ### concatenation of the 2 strictly sorted integer vectors that correspond to ### each end. In that case the vector is named. .extractSteppedExonRanks <- function(encoding, for.query.right.end=FALSE) { if (!isTRUEorFALSE(for.query.right.end)) stop("'for.query.right.end' must be TRUE or FALSE") encoding_blocks <- strsplit(encoding, ":", fixed=TRUE)[[1L]] njunc <- .extract_njunc_from_encoding(encoding_blocks[1L]) encoding_blocks <- encoding_blocks[-1L] if (length(njunc) == 1L) { ## Single-end read. if (for.query.right.end) stop("cannot use 'for.query.right.end=TRUE' ", "on single-end encoding: ", encoding) encoding_patterns <- build_compatible_encoding_subpatterns(njunc) return(.extractSteppedExonRanksFromEncodingBlocks(encoding_blocks, encoding_patterns)) } if (length(njunc) != 2L) # should never happen stop(encoding, ": invalid encoding") ## Paired-end read. encoding_blocks <- strsplit(encoding_blocks, "--", fixed=TRUE) if (!all(elementNROWS(encoding_blocks) == 2L)) # should never happen stop(encoding, ": invalid encoding") encoding_blocks <- matrix(unlist(encoding_blocks, use.names=FALSE), nrow=2L) Lencoding_patterns <- build_compatible_encoding_subpatterns(njunc[1L]) Lranks <- .extractSteppedExonRanksFromEncodingBlocks(encoding_blocks[1L, ], Lencoding_patterns) Rencoding_patterns <- build_compatible_encoding_subpatterns(njunc[2L]) Rranks <- .extractSteppedExonRanksFromEncodingBlocks(encoding_blocks[2L, ], Rencoding_patterns) if (for.query.right.end) return(Rranks) # unnamed! (like for a single-end read) names(Rranks) <- rep.int("R", length(Rranks)) names(Lranks) <- rep.int("L", length(Lranks)) c(Lranks, Rranks) } setGeneric("extractSteppedExonRanks", function(x, for.query.right.end=FALSE) standardGeneric("extractSteppedExonRanks") ) setMethod("extractSteppedExonRanks", "character", function(x, for.query.right.end=FALSE) { lapply(x, .extractSteppedExonRanks, for.query.right.end) } ) setMethod("extractSteppedExonRanks", "factor", function(x, for.query.right.end=FALSE) { if (length(x) == 0L) return(list()) ranks <- extractSteppedExonRanks(levels(x), for.query.right.end=for.query.right.end) ranks[as.integer(x)] } ) setMethod("extractSteppedExonRanks", "OverlapEncodings", function(x, for.query.right.end=FALSE) { ranks <- extractSteppedExonRanks(encoding(x), for.query.right.end=for.query.right.end) ranks_eltNROWS <- elementNROWS(ranks) tmp <- unlist(unname(ranks), use.names=TRUE) # we want the inner names tmp <- tmp + rep.int(Loffset(x), ranks_eltNROWS) flevels <- seq_len(length(ranks)) f <- factor(rep.int(flevels, ranks_eltNROWS), levels=flevels) unname(split(tmp, f)) } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### extractSpannedExonRanks(). ### setGeneric("extractSpannedExonRanks", function(x, for.query.right.end=FALSE) standardGeneric("extractSpannedExonRanks") ) setMethod("extractSpannedExonRanks", "character", function(x, for.query.right.end=FALSE) { .extractRanks <- function(encoding) { ranks <- .extractSteppedExonRanks(encoding, for.query.right.end=for.query.right.end) if (length(ranks) == 0L) return(c(NA_integer_, NA_integer_)) c(ranks[1L], ranks[length(ranks)]) } ranks <- lapply(x, .extractRanks) if (length(ranks) == 0L) { firstSpannedExonRank <- lastSpannedExonRank <- integer(0) } else { ranks <- unlist(ranks, use.names=FALSE) firstSpannedExonRank <- ranks[c(TRUE, FALSE)] lastSpannedExonRank <- ranks[c(FALSE, TRUE)] } data.frame(firstSpannedExonRank=firstSpannedExonRank, lastSpannedExonRank=lastSpannedExonRank, check.names=FALSE, stringsAsFactors=FALSE) } ) setMethod("extractSpannedExonRanks", "factor", function(x, for.query.right.end=FALSE) { if (length(x) == 0L) return(list()) ranks <- extractSpannedExonRanks(levels(x), for.query.right.end=for.query.right.end) ans <- ranks[as.integer(x), , drop=FALSE] rownames(ans) <- NULL ans } ) setMethod("extractSpannedExonRanks", "OverlapEncodings", function(x, for.query.right.end=FALSE) { ranks <- extractSpannedExonRanks(encoding(x), for.query.right.end=for.query.right.end) ranks$firstSpannedExonRank <- ranks$firstSpannedExonRank + Loffset(x) ranks$lastSpannedExonRank <- ranks$lastSpannedExonRank + Loffset(x) ranks } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### extractSkippedExonRanks(). ### setGeneric("extractSkippedExonRanks", function(x, for.query.right.end=FALSE) standardGeneric("extractSkippedExonRanks") ) setMethod("extractSkippedExonRanks", "character", function(x, for.query.right.end=FALSE) { .extractRanks <- function(encoding) { ranks <- .extractSteppedExonRanks(encoding, for.query.right.end=for.query.right.end) if (length(ranks) == 0L) return(ranks) ranks_names <- names(ranks) if (is.null(ranks_names)) # single-end read return(setdiff(ranks[1L]:ranks[length(ranks)], ranks)) ## Paired-end read. ranks <- split(unname(ranks), ranks_names) Lranks <- ranks$L Lranks <- setdiff(Lranks[1L]:Lranks[length(Lranks)], Lranks) Rranks <- ranks$R Rranks <- setdiff(Rranks[1L]:Rranks[length(Rranks)], Rranks) names(Lranks) <- rep.int("L", length(Lranks)) names(Rranks) <- rep.int("R", length(Rranks)) c(Lranks, Rranks) } lapply(x, .extractRanks) } ) setMethod("extractSkippedExonRanks", "factor", function(x, for.query.right.end=FALSE) { if (length(x) == 0L) return(list()) ranks <- extractSkippedExonRanks(levels(x), for.query.right.end=for.query.right.end) ranks[as.integer(x)] } ) setMethod("extractSkippedExonRanks", "OverlapEncodings", function(x, for.query.right.end=FALSE) { ranks <- extractSkippedExonRanks(encoding(x), for.query.right.end=for.query.right.end) ranks_eltNROWS <- elementNROWS(ranks) tmp <- unlist(unname(ranks), use.names=TRUE) # we want the inner names tmp <- tmp + rep.int(Loffset(x), ranks_eltNROWS) flevels <- seq_len(length(ranks)) f <- factor(rep.int(flevels, ranks_eltNROWS), levels=flevels) unname(split(tmp, f)) } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### extractQueryStartInTranscript(). ### ### TODO: Maybe put this in IRanges and rename it setElementNROWS, or even ### better introduce an "elementNROWS<-" generic and make this the method ### for CompressedList objects? .setElementNROWS <- function(x, eltNROWS) { if (!is(x, "CompressedList")) stop("'x' must be a CompressedList object") if (!is.numeric(eltNROWS) || length(eltNROWS) != length(x)) stop("'eltNROWS' must be an integer vector of the same length as 'x'") if (!is.integer(eltNROWS)) eltNROWS <- as.integer(eltNROWS) if (S4Vectors:::anyMissingOrOutside(eltNROWS, lower=0L)) stop("'eltNROWS' cannot contain NAs or negative values") x_eltNROWS <- elementNROWS(x) if (!all(eltNROWS <= x_eltNROWS)) stop("'all(eltNROWS <= elementNROWS(x))' must be TRUE") from <- cumsum(c(1L, x_eltNROWS[-length(x_eltNROWS)])) ii <- sequence(eltNROWS, from=from) x@unlistData <- x@unlistData[ii] x@partitioning@end <- unname(cumsum(eltNROWS)) x } ### Returns a data.frame with 1 row per overlap, and 3 integer columns: ### 1. startInTranscript ### 2. firstSpannedExonRank ### 3. startInFirstSpannedExon ### Rows for overlaps that are not "compatible" or "almost compatible" ### contain NAs. extractQueryStartInTranscript <- function(query, subject, hits=NULL, ovenc=NULL, flip.query.if.wrong.strand=FALSE, for.query.right.end=FALSE) { if (!is(query, "GRangesList") || !is(subject, "GRangesList")) stop("'query' and 'subject' must be GRangesList objects") seqinfo <- merge(seqinfo(query), seqinfo(subject)) seqlevels(query) <- seqlevels(subject) <- seqlevels(seqinfo) if (is.null(hits)) { if (length(query) != length(subject)) stop("'query' and 'subject' must have the same length") } else { if (!is(hits, "Hits")) stop("'hits' must be a Hits object or NULL") if (queryLength(hits) != length(query) || subjectLength(hits) != length(subject)) stop("'hits' is not compatible with 'query' and 'subject' ", "('queryLength(hits)' and 'subjectLength(hits)' don't ", "match the lengths of 'query' and 'subject')") query <- query[queryHits(hits)] subject <- subject[subjectHits(hits)] } if (is.null(ovenc)) { ovenc <- encodeOverlaps(query, subject, flip.query.if.wrong.strand=flip.query.if.wrong.strand) } else { if (!is(ovenc, "OverlapEncodings")) stop("'ovenc' must be an OverlapEncodings object") if (length(ovenc) != length(query)) stop("when not NULL, 'ovenc' must have the same length ", "as 'hits', if specified, otherwise as 'query'") } if (!isTRUEorFALSE(for.query.right.end)) stop("'for.query.right.end' must be TRUE or FALSE") query <- flipQuery(query, flippedQuery(ovenc)) ## Extract first range from each list element of 'query'. if (for.query.right.end) { query.break <- mcols(query, use.names=FALSE)$query.break if (is.null(query.break)) stop("using 'for.query.right.end=TRUE' requires that ", "'mcols(query)' has a \"query.break\" column ", "indicating for each paired-end read the position of the ", "break between the ranges coming from one end and those ", "coming from the other end") query <- tails(query, n=-query.break) } query1 <- unlist(heads(query, n=1L), use.names=FALSE) ## A sanity check. if (length(query1) != length(query)) stop("some list elements in 'query' are empty") query_start1 <- start(query1) query_end1 <- end(query1) query_strand1 <- as.factor(strand(query1)) ## Extract start/end/strand of the first spanned exon ## in each top-level element of 'subject'. exrank <- extractSpannedExonRanks(ovenc, for.query.right.end=for.query.right.end)$firstSpannedExonRank sii1 <- start(subject@partitioning) + exrank - 1L subject_start1 <- start(subject@unlistData)[sii1] subject_end1 <- end(subject@unlistData)[sii1] subject_strand1 <- as.factor(strand(subject@unlistData))[sii1] ## A sanity check. if (any(!is.na(exrank) & (query_strand1 != subject_strand1))) { ## TODO: Error message needs to take into account whether 'hits' ## and/or 'ovenc' was supplied or not. stop("'ovenc' is incompatible with the supplied 'query' ", "and/or 'subject' and/or 'hits'") } ## Compute the "query start in first spanned exon". startInFirstSpannedExon <- rep.int(NA_integer_, length(query)) is_on_plus <- query_strand1 == "+" idx <- which(!is.na(exrank) & is_on_plus) startInFirstSpannedExon[idx] <- query_start1[idx] - subject_start1[idx] + 1L idx <- which(!is.na(exrank) & !is_on_plus) startInFirstSpannedExon[idx] <- subject_end1[idx] - query_end1[idx] + 1L ## Truncate each transcript in 'subject' right before the first spanned ## exon and compute the cumulated width of the truncated object. subject2_eltNROWS <- exrank - 1L subject2_eltNROWS[is.na(exrank)] <- 0L subject2 <- .setElementNROWS(subject, subject2_eltNROWS) subject2_cumwidth <- unname(sum(width(subject2))) subject2_cumwidth[is.na(exrank)] <- NA_integer_ ## Compute the "query start in transcript". startInTranscript <- subject2_cumwidth + startInFirstSpannedExon data.frame(startInTranscript=startInTranscript, firstSpannedExonRank=exrank, startInFirstSpannedExon=startInFirstSpannedExon, check.names=FALSE, stringsAsFactors=FALSE) } GenomicAlignments/R/findCompatibleOverlaps-methods.R0000644000175200017520000000264314516004601023622 0ustar00biocbuildbiocbuild### ========================================================================= ### findCompatibleOverlaps ### ------------------------------------------------------------------------- ### setGeneric("findCompatibleOverlaps", function(query, subject) standardGeneric("findCompatibleOverlaps") ) .GAlignmentsORGAlignmentPairs.findCompatibleOverlaps <- function(query, subject) { ## Starting with BioC 3.2, the 'order.as.in.query' argument is not ## supported anymore for GAlignmentPairs objects. if (is(query, "GAlignmentPairs")) grl <- grglist(query) else grl <- grglist(query, order.as.in.query=TRUE) ## TODO: Use 'type="within"' when it's supported for circular ## sequences like the mitochondrial chromosome. ov <- findOverlaps(grl, subject, ignore.strand=TRUE) ovenc <- encodeOverlaps(grl, subject, hits=ov, flip.query.if.wrong.strand=TRUE) ov_is_compat <- isCompatibleWithSplicing(ovenc) ov[ov_is_compat] } setMethod("findCompatibleOverlaps", c("GAlignments", "GRangesList"), .GAlignmentsORGAlignmentPairs.findCompatibleOverlaps ) setMethod("findCompatibleOverlaps", c("GAlignmentPairs", "GRangesList"), .GAlignmentsORGAlignmentPairs.findCompatibleOverlaps ) countCompatibleOverlaps <- function(query, subject) { compatov <- findCompatibleOverlaps(query, subject) tabulate(queryHits(compatov), nbins=queryLength(compatov)) } GenomicAlignments/R/findMateAlignment.R0000644000175200017520000003034714516004601021115 0ustar00biocbuildbiocbuild### ========================================================================= ### findMateAlignment() ### ------------------------------------------------------------------------- ### ### For each element in GAlignments object 'x', finds its mate in GAlignments ### object 'y'. ### ### Alignments 'x[i1]' and 'y[i2]' are considered mates iff they pass all the ### following tests: ### ### (A) names(x[i1]) == names(y[i2]) ### ### (B) mcols(x[i1])$mrnm == seqnames(y[i2]) & ### mcols(y[i2])$mrnm == seqnames(x[i1]) ### ### (C) mcols(x[i1])$mpos == start(y[i2]) & ### mcols(y[i2])$mpos == start(x[i1]) ### ### (D) isMateMinusStrand(x[i1]) == isMinusStrand(y[i2]) & ### isMateMinusStrand(y[i2]) == isMinusStrand(x[i1]) ### ### (E) isFirstSegment(x[i1]) & isLastSegment(y[i2]) | ### isFirstSegment(y[i2]) & isLastSegment(x[i1]) ### ### (F) isProperPair(x[i1]) == isProperPair(y[i2]) ### ### (G) isSecondaryAlignment(x[i1]) == isSecondaryAlignment(y[i2]) .checkMetadatacols <- function(arg, argname) { if (!is(arg, "GAlignments")) stop("'", argname, "' must be a GAlignments object") if (is.null(names(arg))) stop("'", argname, "' must have names") arg_mcols <- mcols(arg) REQUIRED_COLNAMES <- c("flag", "mrnm", "mpos") if (!all(REQUIRED_COLNAMES %in% colnames(arg_mcols))) { colnames_in1string <- paste0("\"", REQUIRED_COLNAMES, "\"", collapse=", ") stop("required columns in 'mcols(", argname, ")': ", colnames_in1string) } if (!is.integer(arg_mcols$flag)) stop("'mcols(", argname, ")$flag' must be an integer vector") if (!is.factor(arg_mcols$mrnm)) stop("'mcols(", argname, ")$mrnm' must be a factor") if (!identical(levels(arg_mcols$mrnm), levels(seqnames(arg)))) stop("'mcols(", argname, ")$mrnm' and 'seqnames(", argname, ")' ", "must have exactly the same levels in the same order") if (!is.integer(arg_mcols$mpos)) stop("'mcols(", argname, ")$mpos' must be an integer vector") arg_mcols } ### 'names', 'flagbits', 'mrnm', and 'mpos', must all come from the same ### GAlignments object x. ### 'names': names(x). ### 'flagbits': integer matrix (of 0's and 1's) obtained with ### bamFlagAsBitMatrix(mcols(x)$flag, bitnames=.MATING_FLAG_BITNAMES) ### 'mrnm': factor obtained with mcols(x)$mrnm ### 'mpos': integer vector obtained with mcols(x)$mpos ### Returns 'names' with NAs injected at positions corresponding to alignments ### that satisfy at least one of following conditions: ### 1. Bit 0x1 (isPaired) is 0 ### 2. Read is neither first or last mate ### 3. Bit 0x8 (hasUnmappedMate) is 1 ### 4. 'mrnm' is NA (i.e. RNEXT = '*') ### 5. 'mpos' is NA (i.e. PNEXT = 0) ### My understanding of the SAM Spec is that 3., 4. and 5. should happen ### simultaneously even though the Spec don't clearly state this. .MATING_FLAG_BITNAMES <- c("isPaired", "hasUnmappedMate", "isFirstMateRead", "isSecondMateRead") .makeGAlignmentsGNames <- function(names, flagbits, mrnm, mpos) { is_paired <- flagbits[ , "isPaired"] is_first <- flagbits[ , "isFirstMateRead"] is_last <- flagbits[ , "isSecondMateRead"] has_unmappedmate <- flagbits[ , "hasUnmappedMate"] alter_idx <- which(!is_paired | is_first == is_last | has_unmappedmate | is.na(mrnm) | is.na(mpos)) names[alter_idx] <- NA_integer_ names } ### Puts NAs last. .getCharacterOrderAndGroupSizes <- function(x) { x2 <- match(x, x, nomatch=.Machine$integer.max, incomparables=NA_character_) xo <- base::order(x2) ox2 <- Rle(x2[xo]) group.sizes <- runLength(ox2) ngroup <- length(group.sizes) if (ngroup != 0L && runValue(ox2)[ngroup] == .Machine$integer.max) group.sizes <- group.sizes[-ngroup] list(xo=xo, group.sizes=group.sizes) } ### Should return the same as: ### args <- as.list(setNames(rep(TRUE, length(bitnames)), bitnames)) ### tmp <- do.call(scanBamFlag, args) ### tmp[[2L]] - tmp[[1L]] .makeFlagBitmask <- function(bitnames) { bitpos <- match(bitnames, FLAG_BITNAMES) sum(as.integer(2L ^ (bitpos-1L))) } ### 'x_hits' and 'y_hits' must be 2 integer vectors of the same length N ### representing the N edges of a bipartite graph between the [1, x_len] and ### [1, y_len] intervals (the i-th edge being represented by (x[i], y[i])). ### Returns an integer vector F of length 'x_len' where F[k] is defined by: ### - If there is no occurence of k in 'x', then F[k] = NA. ### - If there is more than 1 occurence of k in 'x', then F[k] = 0. ### - If there is exactly 1 occurence of k in 'x', at index i_k, then ### F[k] = y[i_k]. ### In addition, if more than 1 value of index k is associated to F[k], then ### F[k] is replaced by -F[k]. .makeMateIdx2 <- function(x_hits, y_hits, x_len) { idx1 <- which(has_duplicates(y_hits)) y_hits[idx1] <- - y_hits[idx1] idx2 <- which(has_duplicates(x_hits)) y_hits[idx2] <- 0L ans <- rep.int(NA_integer_, x_len) ans[x_hits] <- y_hits ans } .showGAlignmentsEltsWithMoreThan1Mate <- function(x, idx) { if (length(idx) == 0L) return() cat("\n!! Found more than 1 mate for the following elements in 'x': ", paste(idx, collapse=", "), ".\n!! Details:\n!! ", sep="") showGAlignments(x[idx], margin="!! ", print.classinfo=TRUE, print.seqinfo=FALSE) cat("!! ==> won't assign a mate to them!\n") } .dump_envir <- new.env(hash=TRUE, parent=emptyenv()) .dumpEnvir <- function() .dump_envir flushDumpedAlignments <- function() { objnames <- ls(envir=.dumpEnvir()) rm(list=objnames, envir=.dumpEnvir()) } dumpAlignments <- function(gal) { objnames <- ls(envir=.dumpEnvir()) nobj <- length(objnames) if (nobj == 0L) { new_objname <- 1L } else { new_objname <- as.integer(objnames[nobj]) + 1L } new_objname <- sprintf("%08d", new_objname) assign(new_objname, gal, envir=.dumpEnvir()) } countDumpedAlignments <- function() { sum(unlist(eapply(.dumpEnvir(), length, USE.NAMES=FALSE))) } getDumpedAlignments <- function() { objnames <- ls(envir=.dumpEnvir()) args <- unname(mget(objnames, envir=.dumpEnvir())) do.call(c, args) } ### Takes about 2.3 s and 170MB of RAM to mate 1 million alignments, ### and about 13 s and 909MB of RAM to mate 5 million alignments. findMateAlignment <- function(x) { x_names <- names(x) if (is.null(x_names)) stop("'x' must have names") x_mcols <- .checkMetadatacols(x, "x") ## flushDumpedAlignments() must be placed *after* the first reference to ## 'x', otherwise, when doing 'findMateAlignment(getDumpedAlignments())', ## the flushing would happen before 'x' is evaluated, causing 'x' to be ## evaluated to NULL. flushDumpedAlignments() x_flag <- x_mcols$flag bitnames <- c(.MATING_FLAG_BITNAMES, "isMinusStrand", "isMateMinusStrand") x_flagbits <- bamFlagAsBitMatrix(x_flag, bitnames=bitnames) x_mrnm <- x_mcols$mrnm x_mpos <- x_mcols$mpos x_gnames <- .makeGAlignmentsGNames(x_names, x_flagbits, x_mrnm, x_mpos) x_seqnames <- as.factor(seqnames(x)) x_start <- start(x) xo_and_GS <- .getCharacterOrderAndGroupSizes(x_gnames) xo <- xo_and_GS$xo group.sizes <- xo_and_GS$group.sizes ans <- Rsamtools:::.findMateWithinGroups(group.sizes, x_flag[xo], x_seqnames[xo], x_start[xo], x_mrnm[xo], x_mpos[xo]) dumpme_idx <- which(ans <= 0L) if (length(dumpme_idx) != 0L) { dumpAlignments(x[xo[dumpme_idx]]) ans[dumpme_idx] <- NA_integer_ } ans[xo] <- xo[ans] # isn't that cute! dump_count <- countDumpedAlignments() if (dump_count != 0L) warning(" ", dump_count, " alignments with ambiguous pairing ", "were dumped.\n Use 'getDumpedAlignments()' to retrieve ", "them from the dump environment.") ans } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### makeGAlignmentPairs(). ### ### TODO: Make isFirstSegment() an S4 generic function with methods for ### matrices, integer vectors, and GAlignments objects. Put this with the ### flag utils in Rsamtools. .isFirstSegment.matrix <- function(x) { is_paired <- as.logical(x[ , "isPaired"]) is_first0 <- as.logical(x[ , "isFirstMateRead"]) is_last0 <- as.logical(x[ , "isSecondMateRead"]) ## According to SAM Spec, bits 0x40 (isFirstMateRead) and 0x80 ## (isSecondMateRead) can both be set or unset, even when bit 0x1 ## (isPaired) is set. However we are not interested in those situations ## (which have a special meaning). is_paired & is_first0 & (!is_last0) } .isFirstSegment.integer <- function(flag) { bitnames <- c("isPaired", "isFirstMateRead", "isSecondMateRead") .isFirstSegment.matrix(bamFlagAsBitMatrix(flag, bitnames=bitnames)) } .isFirstSegment.GAlignments <- function(x) .isFirstSegment.integer(mcols(x, use.names=FALSE)$flag) ### TODO: Make isLastSegment() an S4 generic function with methods for ### matrices, integer vectors, and GAlignments objects. Put this with the ### flag utils in Rsamtools. .isLastSegment.matrix <- function(x) { is_paired <- as.logical(x[ , "isPaired"]) is_first0 <- as.logical(x[ , "isFirstMateRead"]) is_last0 <- as.logical(x[ , "isSecondMateRead"]) ## According to SAM Spec, bits 0x40 (isFirstMateRead) and 0x80 ## (isSecondMateRead) can both be set or unset, even when bit 0x1 ## (isPaired) is set. However we are not interested in those situations ## (which have a special meaning). is_paired & is_last0 & (!is_first0) } .isLastSegment.integer <- function(flag) { bitnames <- c("isPaired", "isFirstMateRead", "isSecondMateRead") .isLastSegment.matrix(bamFlagAsBitMatrix(flag, bitnames=bitnames)) } .isLastSegment.GAlignments <- function(x) .isLastSegment.integer(mcols(x, use.names=FALSE)$flag) ### 'x' must be a GAlignments objects. makeGAlignmentPairs <- function(x, use.names=FALSE, use.mcols=FALSE, strandMode=1) { if (!isTRUEorFALSE(use.names)) stop("'use.names' must be TRUE or FALSE") if (!isTRUEorFALSE(use.mcols)) { if (!is.character(use.mcols)) stop("'use.mcols' must be TRUE or FALSE or a character vector ", "specifying the metadata columns to propagate") if (!all(use.mcols %in% colnames(mcols(x, use.names=FALSE)))) stop("'use.mcols' must be a subset of 'colnames(mcols(x))'") } mate <- findMateAlignment(x) x_is_first <- .isFirstSegment.GAlignments(x) x_is_last <- .isLastSegment.GAlignments(x) first_idx <- which(!is.na(mate) & x_is_first) last_idx <- mate[first_idx] ## Fundamental property of the 'mate' vector: it's a permutation of order ## 2 and with no fixed point on the set of indices for which 'mate' is ## not NA. ## Check there are no fixed points. if (!all(first_idx != last_idx)) stop("findMateAlignment() returned an invalid 'mate' vector") ## Check order 2 (i.e. permuting a 2nd time brings back the original ## set of indices). if (!identical(mate[last_idx], first_idx)) stop("findMateAlignment() returned an invalid 'mate' vector") ## One more sanity check. if (!all(x_is_last[last_idx])) stop("findMateAlignment() returned an invalid 'mate' vector") ## Check the 0x2 bit (isProperPair). x_is_proper <- as.logical(bamFlagAsBitMatrix(mcols(x, use.names=FALSE)$flag, bitnames="isProperPair")) ans_is_proper <- x_is_proper[first_idx] ## The big split! ans_first <- x[first_idx] ans_last <- x[last_idx] ans_names <- NULL if (use.names) ans_names <- names(ans_first) names(ans_first) <- names(ans_last) <- NULL if (is.character(use.mcols)) { mcols(ans_first) <- mcols(ans_first, use.names=FALSE)[use.mcols] mcols(ans_last) <- mcols(ans_last, use.names=FALSE)[use.mcols] } else if (!use.mcols) { mcols(ans_first) <- mcols(ans_last) <- NULL } GAlignmentPairs(ans_first, ans_last, strandMode=strandMode, isProperPair=ans_is_proper, names=ans_names) } GenomicAlignments/R/findOverlaps-methods.R0000644000175200017520000001471114516004601021621 0ustar00biocbuildbiocbuild### ========================================================================= ### findOverlaps methods ### ------------------------------------------------------------------------- setMethod("findOverlaps", c("GAlignments", "Vector"), function(query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within"), select = c("all", "first", "last", "arbitrary"), ignore.strand = FALSE) { findOverlaps(grglist(query), subject, maxgap = maxgap, minoverlap = minoverlap, type = match.arg(type), select = match.arg(select), ignore.strand = ignore.strand) } ) setMethod("findOverlaps", c("Vector", "GAlignments"), function(query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within"), select = c("all", "first", "last", "arbitrary"), ignore.strand = FALSE) { findOverlaps(query, grglist(subject), maxgap = maxgap, minoverlap = minoverlap, type = match.arg(type), select = match.arg(select), ignore.strand = ignore.strand) } ) ### Not strictly needed! Defining the above 2 methods covers that case but ### with the following note: ### > findOverlaps(al1, al0) ### Note: Method with signature "GAlignments#ANY" chosen for ### function "findOverlaps", target signature ### "GAlignments#GAlignments". ### "ANY#GAlignments" would also be valid setMethod("findOverlaps", c("GAlignments", "GAlignments"), function(query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within"), select = c("all", "first", "last", "arbitrary"), ignore.strand = FALSE) { findOverlaps(grglist(query), grglist(subject), maxgap = maxgap, minoverlap = minoverlap, type = match.arg(type), select = match.arg(select), ignore.strand = ignore.strand) } ) setMethod("findOverlaps", c("GAlignmentPairs", "Vector"), function(query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within"), select = c("all", "first", "last", "arbitrary"), ignore.strand = FALSE) { findOverlaps(grglist(query), subject, maxgap = maxgap, minoverlap = minoverlap, type = match.arg(type), select = match.arg(select), ignore.strand = ignore.strand) } ) setMethod("findOverlaps", c("Vector", "GAlignmentPairs"), function(query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within"), select = c("all", "first", "last", "arbitrary"), ignore.strand = FALSE) { findOverlaps(query, grglist(subject), maxgap = maxgap, minoverlap = minoverlap, type = match.arg(type), select = match.arg(select), ignore.strand = ignore.strand) } ) setMethod("findOverlaps", c("GAlignmentPairs", "GAlignmentPairs"), function(query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within"), select = c("all", "first", "last", "arbitrary"), ignore.strand = FALSE) { findOverlaps(grglist(query), grglist(subject), maxgap = maxgap, minoverlap = minoverlap, type = match.arg(type), select = match.arg(select), ignore.strand = ignore.strand) } ) setMethod("findOverlaps", c("GAlignmentsList", "Vector"), function(query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within"), select = c("all", "first", "last", "arbitrary"), ignore.strand = FALSE) { hits <- findOverlaps(grglist(unlist(query, use.names = FALSE)), subject, maxgap = maxgap, minoverlap = minoverlap, type = match.arg(type), select = match.arg(select), ignore.strand = ignore.strand) ## TODO: Replace 'factor(togroup(PartitioningByWidth(...)))' with ## 'as.factor(PartitioningByWidth(...))' when "as.factor" method for ## ManyToOneGrouping objects becomes available. query_map <- factor(togroup(PartitioningByWidth(query))) remapHits(hits, Lnodes.remapping=query_map) } ) setMethod("findOverlaps", c("Vector", "GAlignmentsList"), function(query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within"), select = c("all", "first", "last", "arbitrary"), ignore.strand = FALSE) { hits <- findOverlaps(query, grglist(unlist(subject, use.names = FALSE)), maxgap = maxgap, minoverlap = minoverlap, type = match.arg(type), select = match.arg(select), ignore.strand = ignore.strand) ## TODO: Replace 'factor(togroup(PartitioningByWidth(...)))' with ## 'as.factor(PartitioningByWidth(...))' when "as.factor" method for ## ManyToOneGrouping objects becomes available. subject_map <- factor(togroup(PartitioningByWidth(subject))) remapHits(hits, Rnodes.remapping=subject_map) } ) setMethod("findOverlaps", c("GAlignmentsList", "GAlignmentsList"), function(query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within"), select = c("all", "first", "last", "arbitrary"), ignore.strand = FALSE) { hits <- findOverlaps(grglist(unlist(query, use.names = FALSE)), grglist(unlist(subject, use.names = FALSE)), maxgap = maxgap, minoverlap = minoverlap, type = match.arg(type), select = match.arg(select), ignore.strand = ignore.strand) ## TODO: Replace 'factor(togroup(PartitioningByWidth(...)))' with ## 'as.factor(PartitioningByWidth(...))' when "as.factor" method for ## ManyToOneGrouping objects becomes available. query_map <- factor(togroup(PartitioningByWidth(query))) subject_map <- factor(togroup(PartitioningByWidth(subject))) remapHits(hits, Lnodes.remapping=query_map, Rnodes.remapping=subject_map) } ) GenomicAlignments/R/findSpliceOverlaps-methods.R0000644000175200017520000003031614516004601022760 0ustar00biocbuildbiocbuild### ========================================================================= ### "findSpliceOverlaps" methods ### ------------------------------------------------------------------------- ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Low-level helper functions ### .rangeForSorted <- function(x) { part <- PartitioningByWidth(x) xflat <- unlist(x, use.names=FALSE) IRanges(start(xflat)[start(part)], end(xflat)[end(part)]) } .compatibleTranscription <- function(query, subject, splice, clip=0L) { qrng <- ranges(query) srng <- ranges(subject) sprng <- ranges(splice) ## FIXME : should clip be a modifiable parameter? ## Or we could consider taking it out entirely... the aligner should clip if (clip != 0L) { bounds <- .rangeForSorted(qrng) - clip qrange <- restrict(qrng, start(bounds), end(bounds), keep.all.ranges = TRUE) } bnds <- elementNROWS(setdiff(qrng, srng)) == 0L splc <- elementNROWS(intersect(srng, sprng)) == 0L bnds & splc } .novelBounds <- function(query, subject, qhits) { qrange <- range(query) qstrand <- as.character(strand(qrange)) qrange <- unlist(qrange, use.names=FALSE) srange <- unlist(range(subject), use.names=FALSE) ## bounds violation for each match lviol <- start(qrange) < start(srange) rviol <- end(qrange) > end(srange) TSSviol <- as.logical(ifelse(qstrand != "-", lviol, rviol)) TSEviol <- as.logical(ifelse(qstrand != "+", lviol, rviol)) ## bounds violation across all subjects hit (grouped) lviol <- start(qrange) == start(srange) rviol <- end(qrange) == end(srange) TSSmatch <- as.logical(ifelse(qstrand != "-", lviol, rviol)) TSEmatch <- as.logical(ifelse(qstrand != "+", lviol, rviol)) TSSgroup <- (rowsum(as.integer(TSSmatch), qhits)[,1] > 0L)[factor(qhits)] TSEgroup <- (rowsum(as.integer(TSEmatch), qhits)[,1] > 0L)[factor(qhits)] TSS <- TSSviol & !TSSgroup TSE <- TSEviol & !TSEgroup DataFrame(TSS=TSS, TSE=TSE) } .allMatch <- function(x, idx) { (rowsum(as.integer(x), idx)[,1] == table(idx))[idx] } .oneMatch <- function(x, idx) { xcnt <- rowsum(as.integer(x), idx)[,1] oneMatch <- rep((xcnt == 1L), table(idx)) unname(x & oneMatch) } .novelExon <- function(splice, intronRegion) { splice_eltNROWS <- elementNROWS(splice) if (sum(splice_eltNROWS) == 0L) return(logical(length(splice))) ## subset on elements with splices ans <- logical(length(splice)) idx <- splice_eltNROWS > 0L splice <- splice[idx] internal <- unlist(.gaps(splice), use.names=FALSE) if (sum(length(internal)) == 0L) return(ans) ## FIXME : not competely "within" hits <- findOverlaps(internal, intronRegion, ignore.strand=TRUE) if (length(hits) > 0L) { ans0 <- logical(length(splice)) togroup <- togroup(PartitioningByWidth(splice), j=queryHits(hits)) ne <- table(togroup) == 1L ans0[unique(togroup)] <- ne ans[idx] <- ans0 ans } ans } .novelSpliceEvent <- function(splice, intron) { splice_eltNROWS <- elementNROWS(splice) intron_eltNROWS <- elementNROWS(intron) if (sum(splice_eltNROWS) == 0L | sum(intron_eltNROWS) == 0L) DataFrame(Site=rep.int(FALSE, length(splice)), Junction=rep.int(FALSE, length(splice))) ## subset on elements with splices site <- junction <- rep.int(FALSE, length(splice)) idx <- splice_eltNROWS > 0L splice <- splice[idx] intron <- intron[idx] iflat <- unlist(intron, use.names=FALSE) sflat <- unlist(splice, use.names=FALSE) site[idx] <- .spliceEvent("site", iflat, sflat, splice) junction[idx] <- .spliceEvent("junction", iflat, sflat, splice) DataFrame(Site=site, Junction=junction) } .spliceEvent <- function(type, iflat, sflat, splice) { elt <- togroup(PartitioningByWidth(splice)) if (type == "site") { combiner <- c elt <- rep(elt, each=2) } else if (type == "junction") { combiner <- paste } ikeys <- paste(seqnames(iflat), combiner(start(iflat), end(iflat)), strand(iflat), sep = ":") skeys <- paste(seqnames(sflat), combiner(start(sflat), end(sflat)), strand(sflat), sep = ":") novel <- !(skeys %in% ikeys) rowsum(as.integer(novel), elt) > 0L } .novelRetention <- function(query, intronRegion) { ans <- logical(length(query)) if (length(query) == 0L) return(ans) hits <- findOverlaps(unlist(query, use.names=FALSE), intronRegion, ignore.strand=TRUE) if (length(hits) > 0L) { togroup <- togroup(PartitioningByWidth(query), j=queryHits(hits)) ans[unique(togroup)] <- TRUE } ans } .intronicRegions <- function(tx, intron) { txflt <- unlist(tx, use.names = FALSE) intronflt <- unlist(intron, use.names = FALSE) regions <- setdiff(intronflt, txflt, ignore.strand = TRUE) #map <- findOverlaps(regions, intronflt) #mcols(regions)$tx_id <- # splitAsList(names(tx)[togroup(introns)][subjectHits(intronic_to_tx)], # queryHits(intronic_to_tx)) regions } .result <- function(hits, nc=NULL, compatible=NULL, unique=NULL, coding=NULL, strandSpecific=NULL, novelTSS=NULL, novelTSE=NULL, novelSite=NULL, novelJunction=NULL, novelExon=NULL, novelRetention=NULL) { nms <- c("compatible", "unique", "coding", "strandSpecific", "novelTSS", "novelTSE", "novelSite", "novelJunction", "novelExon", "novelRetention") ## full result if (!is.null(nc)) { mcols(hits) <- DataFrame(compatible, unique, coding, strandSpecific, novelTSS, novelTSE, novelSite, novelJunction, novelExon, novelRetention) hits ## no overlaps } else if (is.null(compatible)) { mat <- matrix(logical(0), length(hits), length(nms)) mcols(hits) <- DataFrame(mat) names(mcols(hits)) <- nms hits ## no compatible overlaps } else { mat <- matrix(FALSE, length(hits), length(nms)) mcols(hits) <- DataFrame(cbind(compatible, unique, coding, strandSpecific, mat)) names(mcols(hits)) <- nms hits } } .insertGaps <- function(reads) { query.break <- mcols(reads, use.names=FALSE)$query.break if (is.null(query.break)) stop("missing 'query.break' metadata variable: reads not paired?") reads_flat <- unlist(reads, use.names = FALSE) reads_part <- PartitioningByWidth(reads) left_end <- start(reads_part) + query.break - 1L right_start <- left_end + 1L start <- end(reads_flat)[left_end] end <- pmax(start(reads_flat)[right_start], start - 1L) if (any(seqnames(reads_flat)[left_end] != seqnames(reads_flat)[right_start])) stop("reads are on different chromosomes") GRanges(seqnames(reads_flat)[left_end], IRanges(start, end), strand(reads_flat)[left_end]) } ## Until we have the formal 'gaps' method for GRangeList .isNumericOrNAs <- S4Vectors:::isNumericOrNAs .gaps <- function(x, start=NA, end=NA) { if (!.isNumericOrNAs(start)) stop("'start' must be an integer vector or NA") if (!is.integer(start)) start <- as.integer(start) if (!.isNumericOrNAs(end)) stop("'end' must be an integer vector or NA") if (!is.integer(end)) end <- as.integer(end) ## seqname and strand consistent in list elements if (all(elementNROWS(runValue(seqnames(x))) == 1L) && all(elementNROWS(runValue(strand(x))) == 1L)) { flat <- unlist(x, use.names=FALSE) gaps <- gaps(ranges(x), start, end) ### FIXME: this makes this function more of an 'introns' than a .gaps. ### FIXME: this breaks when the GRangesList is not ordered by position if (!is.null(mcols(x, use.names=FALSE)$query.break)) { insert_gaps <- as(ranges(.insertGaps(x)), "CompressedIRangesList") gaps <- setdiff(gaps, insert_gaps) } idx <- elementNROWS(gaps) != 0 ## FIXME : can't handle lists with empty elements ## 'start' and 'end' not quite right here firstseg <- start(PartitioningByWidth(x)) seqnms <- rep(seqnames(flat)[firstseg], elementNROWS(gaps)) strand <- rep(strand(flat)[firstseg], elementNROWS(gaps)) gr <- relist(GRanges(seqnms, unlist(gaps, use.names=FALSE), strand), gaps) gr } else { ### FIXME: does not handle query.break column yet setdiff(range(x), x) } } .findSpliceOverlaps <- function(query, subject, ignore.strand=FALSE, cds=NULL) { ## adjust strand based on 'XS' if (!is.null(xs <- mcols(query, use.names=FALSE)$XS)) { strand <- ifelse(!is.na(xs), xs, "*") strand(query) <- relist(Rle(strand, elementNROWS(query)), query) } ## NOTE: this misses reads completely within an intron, but this ## is intentional: a read is only assigned to a transcript if it ## hits an exon. Otherwise, it could be from another gene inside ## an intron (happens frequently). olap <- findOverlaps(query, subject, ignore.strand=ignore.strand) if (length(olap) == 0L) return(.result(olap)) if (!is.null(cds)) { coding <- logical(length(olap)) hits <- findOverlaps(query, cds, ignore.strand=ignore.strand) coding[queryHits(olap) %in% queryHits(hits)] <- TRUE } else { coding <- rep.int(NA, length(olap)) } query <- query[queryHits(olap)] subject <- subject[subjectHits(olap)] splice <- .gaps(query) compatible <- .compatibleTranscription(query, subject, splice) unique <- .oneMatch(compatible, queryHits(olap)) strandSpecific <- all(strand(query) != "*") mcols(olap) <- DataFrame(compatible, unique, coding, strandSpecific) olap } setGeneric("findSpliceOverlaps", signature=c("query", "subject"), function(query, subject, ignore.strand=FALSE, ...) standardGeneric("findSpliceOverlaps") ) setMethod("findSpliceOverlaps", c("GRangesList", "GRangesList"), function(query, subject, ignore.strand=FALSE, ..., cds=NULL) { .findSpliceOverlaps(query, subject, ignore.strand, cds=cds) }) setMethod("findSpliceOverlaps", c("GAlignments", "GRangesList"), function(query, subject, ignore.strand=FALSE, ..., cds=NULL) { findSpliceOverlaps(grglist(query, order.as.in.query=TRUE), subject, ignore.strand, ..., cds=cds) }) setMethod("findSpliceOverlaps", c("GAlignmentPairs", "GRangesList"), function(query, subject, ignore.strand=FALSE, ..., cds=NULL) { ### FIXME: ### instead of relying on query.break column, maybe we should add a ### 'splice = .gaps(query)' argument to .findSpliceOverlaps that we ### set to junctions(query) here. The downside is that a GRangesList ### derived from GAlignmentPairs will no longer work. findSpliceOverlaps(grglist(query), subject, ignore.strand, ..., cds=cds) }) setMethod("findSpliceOverlaps", c("character", "ANY"), function(query, subject, ignore.strand=FALSE, ..., param=ScanBamParam(), singleEnd=TRUE) { findSpliceOverlaps(BamFile(query), subject, ignore.strand, ..., param=param, singleEnd=singleEnd) }) setMethod("findSpliceOverlaps", c("BamFile", "ANY"), function(query, subject, ignore.strand=FALSE, ..., param=ScanBamParam(), singleEnd=TRUE) { findSpliceOverlaps(.readRanges(query, param, singleEnd), subject, ignore.strand, ...) }) .readRanges <- function(bam, param, singleEnd) { if (!"XS" %in% bamTag(param)) bamTag(param) <- c(bamTag(param), "XS") if (singleEnd) reads <- readGAlignments(bam, param=param) else { reads <- readGAlignmentPairs(path(bam), param=param) first_xs <- mcols(first(reads), use.names=FALSE)$XS last_xs <- mcols(last(reads), use.names=FALSE)$XS if (!is.null(first_xs) && !is.null(last_xs)) { xs <- first_xs xs[is.na(xs)] <- last_xs[is.na(xs)] mcols(reads)$XS <- xs } } metadata(reads)$bamfile <- bam reads } GenomicAlignments/R/intra-range-methods.R0000644000175200017520000000641614516004601021377 0ustar00biocbuildbiocbuild### ========================================================================= ### Intra-range methods ### ------------------------------------------------------------------------- ### ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### updateCigarAndStart() -- NOT exported ### setGeneric("updateCigarAndStart", function(x, cigar=NULL, start=NULL) standardGeneric("updateCigarAndStart") ) setMethod("updateCigarAndStart", "GAlignments", function(x, cigar=NULL, start=NULL) { if (is.null(cigar)) { cigar <- cigar(x) } else { if (!is.character(cigar) || length(cigar) != length(x)) stop("when not NULL, 'cigar' must be a character vector ", "of the same length as 'x'") ## There might be an "rshift" attribute on 'cigar', typically. ## We want to get rid of it as well as any other potential ## attribute like names, dim, dimnames etc... attributes(cigar) <- NULL } if (is.null(start)) start <- start(x) else if (!is.integer(start) || length(start) != length(x)) stop("when not NULL, 'start' must be an integer vector ", "of the same length as 'x'") x@cigar <- cigar x@start <- start x } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### narrow() ### ### The default "narrow" method calls windows() so we only need to implement ### a "windows" method for GAlignments objects to make narrow() work on these ### objects. setMethod("windows", "GAlignments", function(x, start=NA, end=NA, width=NA) .narrowGAlignments(x, cigarNarrow, start, end, width) ) setMethod("narrow", "GappedReads", function(x, start=NA, end=NA, width=NA, use.names=TRUE) { stop("coming soon") ## ans_cigar <- cigarNarrow(cigar(x), ## start=start, end=end, width=width) ## ans_start <- start(x) + attr(ans_cigar, "rshift") ## updateCigarAndStart(x, cigar=ans_cigar, start=ans_start) } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### qnarrow() ### setGeneric("qnarrow", signature="x", function(x, start=NA, end=NA, width=NA) standardGeneric("qnarrow") ) .narrowGAlignments <- function(x, CIGAR_CUTTER, start, end, width) { ans_cigar <- CIGAR_CUTTER(cigar(x), start=start, end=end, width=width) ans_start <- start(x) + attr(ans_cigar, "rshift") updateCigarAndStart(x, cigar=ans_cigar, start=ans_start) } setMethod("qnarrow", "GAlignments", function(x, start=NA, end=NA, width=NA) .narrowGAlignments(x, cigarQNarrow, start, end, width) ) setMethod("qnarrow", "GAlignmentsList", function(x, start=NA, end=NA, width=NA) { gal <- qnarrow(x@unlistData, start=start, end=end, width=width) relist(gal, x@partitioning) } ) setMethod("qnarrow", "GappedReads", function(x, start=NA, end=NA, width=NA) { stop("coming soon") ## ans_cigar <- cigarQNarrow(cigar(x), ## start=start, end=end, width=width) ## ans_start <- start(x) + attr(ans_cigar, "rshift") ## updateCigarAndStart(x, cigar=ans_cigar, start=ans_start) } ) GenomicAlignments/R/junctions-methods.R0000644000175200017520000003355014516004601021203 0ustar00biocbuildbiocbuild### ========================================================================= ### Extract junctions from genomic alignments ### ------------------------------------------------------------------------- ### ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### junctions() generic and methods. ### setGeneric("junctions", signature="x", function(x, use.mcols=FALSE, ...) standardGeneric("junctions") ) setMethod("junctions", "GAlignments", function(x, use.mcols=FALSE) { if (!isTRUEorFALSE(use.mcols)) stop("'use.mcols' must be TRUE or FALSE") rgl <- cigarRangesAlongReferenceSpace(cigar(x), pos=start(x), ops="N") names(rgl) <- names(x) ans <- make_GRangesList_from_CompressedIRangesList(rgl, seqnames(x), strand(x), seqinfo(x)) if (use.mcols) mcols(ans) <- mcols(x, use.names=FALSE) ans } ) setMethod("junctions", "GAlignmentPairs", function(x, use.mcols=FALSE) { if (!isTRUEorFALSE(use.mcols)) stop("'use.mcols' must be TRUE or FALSE") first_junctions <- junctions(first(x, real.strand=TRUE)) last_junctions <- junctions(last(x, real.strand=TRUE)) ## pc() is a fast "parallel c()" for list-like objects. ## In the case below, it's equivalent to (but faster than) doing ## 'mendoapply(c, first_junctions, last_junctions)'. ans <- pc(first_junctions, last_junctions) if (use.mcols) { mcols(ans) <- mcols(x, use.names=FALSE) } else { mcols(ans) <- NULL } ans } ) setMethod("junctions", "GAlignmentsList", function(x, use.mcols=FALSE, ignore.strand=FALSE) { if (!isTRUEorFALSE(use.mcols)) stop("'use.mcols' must be TRUE or FALSE") if (!isTRUEorFALSE(ignore.strand)) stop("'ignore.strand' must be TRUE or FALSE") if (ignore.strand) strand(x@unlistData) <- "*" grl <- junctions(x@unlistData) ans_breakpoints <- end(grl@partitioning)[end(x@partitioning)] ans_partitioning <- PartitioningByEnd(ans_breakpoints, names=names(x)) ans <- relist(grl@unlistData, ans_partitioning) if (use.mcols) mcols(ans) <- mcols(x, use.names=FALSE) ans } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Natural intron motifs taken from: ### http://www.ncbi.nlm.nih.gov/pmc/articles/PMC84117/ NATURAL_INTRON_MOTIFS <- c("GT-AG", "GC-AG", "AT-AC", "AT-AA", "AT-AG") ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### summarizeJunctions() ### .extract_unoriented_intron_motif <- function(genome, junctions) { mcols(junctions) <- NULL junctions_len <- length(junctions) Ldinucl_gr <- Rdinucl_gr <- junctions end(Ldinucl_gr) <- start(Ldinucl_gr) + 1L start(Rdinucl_gr) <- end(Rdinucl_gr) - 1L all_dinucl <- getSeq(genome, c(Ldinucl_gr, Rdinucl_gr)) Ldinucl <- head(all_dinucl, n=junctions_len) Rdinucl <- tail(all_dinucl, n=junctions_len) xscat(Ldinucl, rep.int("-", junctions_len), Rdinucl) } .infer_intron_strand <- function(unoriented_intron_motif) { natural_intron_motifs <- DNAStringSet(NATURAL_INTRON_MOTIFS) intron_strand <- rep.int(NA, length(unoriented_intron_motif)) idx <- which(unoriented_intron_motif %in% natural_intron_motifs) intron_strand[idx] <- FALSE idx <- which(unoriented_intron_motif %in% reverseComplement(natural_intron_motifs)) intron_strand[idx] <- TRUE if (any(is.na(intron_strand))) warning("For some junctions, the dinucleotides found at the intron ", "boundaries don't\n match any of the natural intron motifs ", "stored in predefined character vector\n ", "'NATURAL_INTRON_MOTIFS'. For these junctions, the ", "intron_motif and\n intron_strand metadata columns ", "were set to NA and *, respectively.") strand(intron_strand) } .orient_intron_motif <- function(unoriented_intron_motif, intron_strand) { ans <- unoriented_intron_motif idx <- which(intron_strand == "-") ans[idx] <- reverseComplement(ans[idx]) ans <- factor(as.character(ans), levels=NATURAL_INTRON_MOTIFS) } summarizeJunctions <- function(x, with.revmap=FALSE, genome=NULL) { if (!isTRUEorFALSE(with.revmap)) stop("'with.revmap' must be TRUE or FALSE") if (!is.null(genome)) { if (!requireNamespace("BSgenome", quietly=TRUE)) stop(wmsg("Couldn't load the BSgenome package. Please install ", "the BSgenome package in order to use the 'genome' ", "argument.")) genome <- BSgenome::getBSgenome(genome) } x_junctions <- junctions(x) if (!is.null(genome)) { si1 <- seqinfo(x_junctions) si2 <- seqinfo(genome) seqinfo(x_junctions) <- suppressWarnings(merge(si1, si2)) if (length(intersect(seqlevels(si1), seqlevels(si2))) == 0L) warning(wmsg("the genome specified via the 'genome' argument ", "doesn't seem to be the same as the reference ", "genome used for the alignments in 'x'")) } unlisted_junctions <- unlist(x_junctions, use.names=FALSE) unstranded_unlisted_junctions <- unstrand(unlisted_junctions) ans <- sort(unique(unstranded_unlisted_junctions)) unq2dups <- as(findMatches(ans, unstranded_unlisted_junctions), "List") ans_score <- elementNROWS(unq2dups) tmp <- extractList(strand(unlisted_junctions), unq2dups) ans_plus_score <- sum(tmp == "+") ans_minus_score <- sum(tmp == "-") ans_mcols <- DataFrame(score=ans_score, plus_score=ans_plus_score, minus_score=ans_minus_score) if (with.revmap) { crossed_by <- togroup(PartitioningByWidth(x_junctions)) ans_revmap <- extractList(crossed_by, unq2dups) ## 'ans_revmap' should never contain duplicates when 'x' is a ## GAlignments object, because a given junction can show up at most ## once per SAM/BAM record (i.e. per element in 'x', or per alignment). ## This doesn't hold anymore if the elements in 'x' consist of more ## than 1 SAM/BAM record (or alignment) e.g. if 'x' is a ## GAlignmentPairs or GAlignmentsList object, because, in that case, ## the same junction can show up more than once per element in 'x'. if (!is(x, "GAlignments")) ans_revmap <- unique(ans_revmap) ans_mcols$revmap <- ans_revmap } if (!is.null(genome)) { unoriented_intron_motif <- .extract_unoriented_intron_motif(genome, ans) ans_intron_strand <- .infer_intron_strand(unoriented_intron_motif) ans_intron_motif <- .orient_intron_motif(unoriented_intron_motif, ans_intron_strand) ans_mcols <- cbind(ans_mcols, DataFrame(intron_motif=ans_intron_motif, intron_strand=ans_intron_strand)) } mcols(ans) <- ans_mcols ans } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### readTopHatJunctions() ### ### Read splice junctions file (junctions.bed) generated by the TopHat ### aligner into a GRanges object. ### Usage: ### readTopHatJunctions("junctions.bed") ### ### Comparing with output of bed_to_juncs script (assuming the ### 'new_list.juncs' file was obtained by passing 'junctions.bed' thru ### bed_to_juncs): ### junctions1 <- readTopHatJunctions("junctions.bed") ### junctions2 <- readTopHatJunctions("new_list.juncs", ### file.is.raw.juncs=TRUE) ### stopifnot(all(junctions1 == junctions2)) ### .bed_to_Juncs <- function(x) { if (!is(x, "GRanges")) stop("'x' must be a GRanges object") if (!identical(mcols(x, use.names=FALSE)$thick, ranges(x))) stop("this BED file doesn't look like the junctions.bed file ", "generated by TopHat") blocks <- mcols(x, use.names=FALSE)$blocks stopifnot(all(elementNROWS(blocks) == 2L)) unlisted_blocks <- unlist(blocks, use.names=FALSE) even_idx <- 2L * seq_along(x) odd_idx <- even_idx - 1L ans_start <- start(x) + end(unlisted_blocks)[odd_idx] ans_end <- start(x) + start(unlisted_blocks)[even_idx] - 2L ans <- GRanges(seqnames(x), IRanges(ans_start, ans_end), strand=strand(x)) mcols(ans) <- DataFrame(name=mcols(x, use.names=FALSE)$name, score=as.integer(mcols(x, use.names=FALSE)$score)) ans } ### 'file' must be the path or a connection object to a junctions.bed file as ### generated by TopHat, or to a tab-delimited file obtained by running ### TopHat's bed_to_juncs script on a junctions.bed file. ### Returns the junctions in a GRanges object. ### IMPORTANT NOTE: readTopHatJunctions() does NOT follow the convention used ### by TopHat that describes a junction by the position of the nucleotide ### immediately before and after the intron. In the GRanges object returned ### by readTopHatJunctions(), a junction is considered to start at the ### left-most and to end at the right-most nucleotide of the intron. readTopHatJunctions <- function(file, file.is.raw.juncs=FALSE) { if (!isTRUEorFALSE(file.is.raw.juncs)) stop("'file.is.raw.juncs' must be TRUE or FALSE") if (is.character(file)) { if (!isSingleString(file)) stop("'file' must be a single string") file_ext0 <- ".bed" file_ext <- substr(file, start=nchar(file) - nchar(file_ext0) + 1L, stop=nchar(file)) if (file.is.raw.juncs) { if (file_ext == file_ext0) stop("'file.is.raw.juncs=TRUE' is not aimed to be ", "used on a file\n with the .bed extension") df <- read.table(file, stringsAsFactors=FALSE) ## The 2nd and 3rd columns in 'new_list.juncs' are the left and ## right positions of the junctions, respectively. The convention ## used by TopHat is that these are NOT the positions of the ## left-most and right-most nucleotides of the intron, but rather ## the positions immediately before and after, respectively, that ## is, the last and the first positions of the flanking exons. ## Also these positions are *both* 0-based. ans_ranges <- IRanges(df[[2L]] + 2L, df[[3L]]) ans <- GRanges(df[[1L]], ans_ranges, strand=df[[4L]]) return(ans) } if (file_ext != file_ext0) warning("'file' has no .bed extension, suggesting it may not ", "be a junctions.bed\n file as generated by TopHat. ", "I will assume it is this file anyway (or a BED\n file ", "with similar content). If 'file' is a tab-delimited ", "file obtained\n by running TopHat's bed_to_juncs script ", "on a junctions.bed file, you\n should use ", "'file.is.raw.juncs=TRUE'") } junctions_bed <- rtracklayer::import(file) .bed_to_Juncs(junctions_bed) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### readSTARJunctions() ### ### Read splice junctions file (SJ.out.tab) generated by the STAR aligner ### into a GRanges object. ### Usage: ### readSTARJunctions("SJ.out.tab") ### .STAR_INTRON_MOTIFS <- c("GT-AG", "CT-AC", "GC-AG", "CT-GC", "AT-AC", "GT-AT") .get_STAR_intron_motif_levels <- function() { ans <- .STAR_INTRON_MOTIFS[(1:3)*2L - 1L] stopifnot(all(ans == NATURAL_INTRON_MOTIFS[1:3])) rev_motifs <- .STAR_INTRON_MOTIFS[(1:3)*2L] stopifnot(all(rev_motifs == reverseComplement(DNAStringSet(ans)))) ans } readSTARJunctions <- function(file) { motif123 <- .get_STAR_intron_motif_levels() df <- read.table(file, stringsAsFactors=FALSE) ans_seqnames <- df[[1L]] ans_start <- df[[2L]] ans_end <- df[[3L]] ans_strand <- strand(df[[4L]] == 2L) STAR_intron_motif_code <- df[[5L]] if (!is.integer(ans_start) || !is.integer(ans_end) || !is.integer(STAR_intron_motif_code) || S4Vectors:::anyMissingOrOutside(STAR_intron_motif_code, lower=0L, upper=6L)) stop("'file' does not look like a junction file generated ", "by the STAR aligner (normally the SJ.out.tab file)") STAR_intron_motif_code[STAR_intron_motif_code == 0L] <- NA_integer_ code1 <- STAR_intron_motif_code + 1L ans_intron_motif <- factor(motif123[code1 %/% 2L], levels=motif123) ans_intron_strand <- strand(as.logical(code1 %% 2L)) has_code_zero <- is.na(ans_intron_motif) stopifnot(identical(has_code_zero, ans_intron_strand == "*")) idx0 <- which(!has_code_zero) if (!identical(ans_strand[idx0], ans_intron_strand[idx0])) warning("For some junctions, the strand reported in the motif_strand ", "metadata column\n (which was inferred from the STAR intron ", "motif code stored in column 5 of\n 'file') is conflicting ", "with the strand of the junction reported in column 4\n ", "of 'file'. Bug in STAR? Obscure feature? Or corrupted file? ", "Please ask on the\n STAR general user mailing list ", "(https://groups.google.com/d/forum/rna-star)\n for ", "clarifications about this (only if you're confident that ", "your SJ.out.tab\n file is not corrupted though).") GRanges(ans_seqnames, IRanges(ans_start, ans_end), strand=ans_strand, intron_motif=ans_intron_motif, intron_strand=ans_intron_strand, um_reads=df[[7L]], mm_reads=df[[8L]]) } GenomicAlignments/R/pileLettersAt.R0000644000175200017520000001105114516004601020277 0ustar00biocbuildbiocbuild### ========================================================================= ### pileLettersAt() ### ------------------------------------------------------------------------- ### .pileLettersOnSingleRefAt() is the workhorse behind pileLettersAt(). ### 'x', 'pos', 'cigar': 3 parallel vectors describing N strings aligned ### to the same reference sequence. 'x' must be an XStringSet (typically ### DNAStringSet) object containing the unaligned strings (a.k.a. the ### query sequences) reported with respect to the + strand. 'pos' must ### be an integer vector where 'pos[i]' is the 1-based position on the ### reference sequence of the first aligned letter in 'x[[i]]'. 'cigar' ### must be a character vector containing the extended CIGAR strings. ### 'at': must be an integer vector containing the positions of interest ### with respect to the reference sequence. ### Returns an XStringSet (typically DNAStringSet) object parallel to ### 'at' (i.e. with 1 string per position of interest). .pileLettersOnSingleRefAt <- function(x, pos, cigar, at) { stopifnot(is(x, "XStringSet")) N <- length(x) # nb of alignments stopifnot(is.integer(pos) && length(pos) == N) stopifnot(is.character(cigar) && length(cigar) == N) stopifnot(is.integer(at)) ops <- c("M", "=", "X") ranges_on_ref <- cigarRangesAlongReferenceSpace(cigar, pos=pos, ops=ops) ranges_on_query <- cigarRangesAlongQuerySpace(cigar, ops=ops) ## 'ranges_on_ref' and 'ranges_on_query' are IRangesList objects parallel ## to 'x', 'pos', and 'cigar'. In addition, the 2 IRangesList objects ## have the same "shape" (i.e. same elementNROWS()), so, after ## unlisting, the 2 unlisted objects are parallel IRanges objects. unlisted_ranges_on_ref <- unlist(ranges_on_ref, use.names=FALSE) unlisted_ranges_on_query <- unlist(ranges_on_query, use.names=FALSE) ## 2 integer vectors parallel to IRanges objects 'unlisted_ranges_on_ref' ## and 'unlisted_ranges_on_query' above. range_group <- togroup(PartitioningByWidth(ranges_on_ref)) query2ref_shift <- start(unlisted_ranges_on_ref) - start(unlisted_ranges_on_query) hits <- findOverlaps(at, unlisted_ranges_on_ref) hits_at_in_x <- at[queryHits(hits)] - query2ref_shift[subjectHits(hits)] hits_group <- range_group[subjectHits(hits)] unlisted_piles <- subseq(x[hits_group], start=hits_at_in_x, width=1L) piles_skeleton <- PartitioningByEnd(queryHits(hits), NG=length(at), names=names(at)) piles <- relist(unlisted_piles, piles_skeleton) unstrsplit(piles) } ### 'x', 'seqnames', 'pos', 'cigar': 4 parallel vectors describing N ### aligned strings. 'x', 'pos', and 'cigar' as above. 'seqnames' must ### be a factor-Rle where 'seqnames[i]' is the name of the reference ### sequence of the i-th alignment. ### 'at': must be a GPos object containing the genomic positions of ### interest. 'seqlevels(at)' must be identical to 'levels(seqnames)'. ### Returns an XStringSet (typically DNAStringSet) object parallel to ### 'at' (i.e. with 1 string per genomic position of interest). pileLettersAt <- function(x, seqnames, pos, cigar, at) { stopifnot(is(x, "XStringSet")) N <- length(x) # nb of alignments stopifnot(is(seqnames, "Rle")) stopifnot(is.factor(runValue(seqnames))) stopifnot(length(seqnames) == N) stopifnot(is.integer(pos) && length(pos) == N) stopifnot(is.character(cigar) && length(cigar) == N) if (!is(at, "GPos")) at <- GPos(at) stopifnot(identical(seqlevels(at), levels(seqnames))) ## We process 1 chromosome at a time. So we start by splitting ## 'x', 'pos', 'cigar', and 'start(at)' by chromosome. The 4 ## resulting list-like objects have 1 list element per chromosome ## in 'seqlevels(at)' (or in 'levels(seqnames)', which is identical ## to 'seqlevels(at)'). x_by_chrom <- split(x, seqnames) # XStringSetList pos_by_chrom <- split(pos, seqnames) # IntegerList cigar_by_chrom <- split(cigar, seqnames) # CharacterList at_by_chrom <- split(start(at), seqnames(at)) # IntegerList ## Unsplit index. split_idx <- unlist(split(seq_along(at), seqnames(at)), use.names=FALSE) unsplit_idx <- integer(length(at)) unsplit_idx[split_idx] <- seq_along(at) do.call("c", lapply(seq_along(seqlevels(at)), function(i) .pileLettersOnSingleRefAt( x_by_chrom[[i]], pos_by_chrom[[i]], cigar_by_chrom[[i]], at_by_chrom[[i]])))[unsplit_idx] } GenomicAlignments/R/readGAlignments.R0000644000175200017520000004163514516004601020575 0ustar00biocbuildbiocbuild### ========================================================================= ### readGAlignments() and related functions ### ------------------------------------------------------------------------- ### A "flag filter" is represented as a 'flag' vector of length 2 with names ### keep0 and keep1. The .combineBamFlagFilters() function performs a logical ### AND between 2 "flag filters". It returns a "flag filter". .combineBamFlagFilters <- function(flagfilterA, flagfilterB) { if (!identical(names(flagfilterA), c("keep0", "keep1")) || !identical(names(flagfilterB), c("keep0", "keep1"))) stop("input must be BAM flag filters") ans <- bamFlagAND(flagfilterA, flagfilterB) if (!all(bamFlagAsBitMatrix(ans[["keep0"]]) | bamFlagAsBitMatrix(ans[["keep1"]]))) stop("BAM flag filters to combine are incompatible") ans } .normargParam <- function(param, flag0, what0) { if (is.null(param)) param <- ScanBamParam() bamFlag(param) <- .combineBamFlagFilters(bamFlag(param, asInteger=TRUE), flag0) bamWhat(param) <- union(bamWhat(param), what0) param } ### 'x' must be a GAlignments object. .bindExtraData <- function(x, use.names, param, bamcols, with.which_label=FALSE) { if (use.names) names(x) <- bamcols$qname if (is.null(param)) return(x) colnames <- c(bamWhat(param), bamTag(param)) if (with.which_label) colnames <- c(colnames, "which_label") if (length(colnames) != 0L) { df <- do.call(DataFrame, bamcols[colnames]) ## Sadly, the DataFrame() constructor is mangling the duplicated ## colnames to make them unique. Since we of course don't want this, ## we need to fix them. colnames(df) <- colnames mcols(x) <- df } x } .load_bamcols_from_BamFile <- function(file, param, what0, with.which_label=FALSE) { flag0 <- scanBamFlag(isUnmappedQuery=FALSE) param <- .normargParam(param, flag0, what0) res <- scanBam(file, param=param) if (length(res) == 0L) # should never happen stop("scanBam() returned a list of length zero") Rsamtools:::.load_bamcols_from_scanBam_res(res, param, with.which_label=with.which_label) } .load_seqlengths_from_BamFile <- function(file, seqlevels) { seqlengths <- seqlengths(file) if (is.null(seqlengths)) return(NULL) bad <- setdiff(seqlevels, names(seqlengths)) if (length(bad) == 0L) return(seqlengths) bad <- paste(bad, collapse="' '") msg <- sprintf("'rname' lengths not in BamFile header; seqlengths not used\n file: %s\n missing rname(s): '%s'", path(file), bad) warning(msg) NULL } .open_BamFile <- function(file, index=file, asMates=FALSE, param=NULL) { if ((missing(index) || identical(index, file)) && (is.null(param) || length(bamWhich(param)) == 0L)) index <- character(0) open(BamFile(file, index=index, asMates=asMates), "rb") } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### readGAlignments() ### setGeneric("readGAlignments", signature="file", function(file, index=file, use.names=FALSE, param=NULL, with.which_label=FALSE) standardGeneric("readGAlignments") ) .readGAlignments.BamFile <- function(file, index=file, use.names=FALSE, param=NULL, with.which_label=FALSE) { if (!isTRUEorFALSE(use.names)) stop("'use.names' must be TRUE or FALSE") if (is.null(param)) param <- ScanBamParam() if (!asMates(file)) bamWhat(param) <- setdiff(bamWhat(param), c("groupid", "mate_status")) what0 <- c("rname", "strand", "pos", "cigar") if (use.names) what0 <- c(what0, "qname") bamcols <- .load_bamcols_from_BamFile(file, param, what0, with.which_label=with.which_label) seqlengths <- .load_seqlengths_from_BamFile(file, levels(bamcols[["rname"]])) ans <- GAlignments(seqnames=bamcols$rname, pos=bamcols$pos, cigar=bamcols$cigar, strand=bamcols$strand, seqlengths=seqlengths) .bindExtraData(ans, use.names, param, bamcols, with.which_label=with.which_label) } setMethod("readGAlignments", "BamFile", .readGAlignments.BamFile) setMethod("readGAlignments", "character", function(file, index=file, use.names=FALSE, param=NULL, with.which_label=FALSE) { bam <- .open_BamFile(file, index=index, param=param) on.exit(close(bam)) readGAlignments(bam, character(0), use.names=use.names, param=param, with.which_label=with.which_label) } ) setMethod("readGAlignments", "BamViews", function(file, index=file, use.names=FALSE, param=NULL, with.which_label=FALSE) { if (missing(index)) index <- bamIndicies(file) if (is.null(param)) { param <- ScanBamParam(which=bamRanges(file)) } else if (!identical(bamRanges(file), bamWhich(param))) { warning("'bamRanges(file)' and 'bamWhich(param)' differ; using 'bamRanges(file)'") bamWhich(param) <- bamRanges(file) } fun <- function(i, bamViews, verbose) readGAlignments(file=bamPaths(bamViews)[i], index=bamIndicies(bamViews)[i], use.names=use.names, param=param, with.which_label=with.which_label) ### Rsamtools:::.BamViews_delegate requires the ShortRead package! Rsamtools:::.BamViews_delegate("readGAlignments", file, fun) } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### readGAlignmentPairs() ### setGeneric("readGAlignmentPairs", signature="file", function(file, index=file, use.names=FALSE, param=NULL, with.which_label=FALSE, strandMode=1) standardGeneric("readGAlignmentPairs") ) ### 'use.mcols' can be TRUE, FALSE, or a character vector specifying the ### *inner* metadata columns to return, i.e., the metadata columns to set on ### the 2 halves of the returned GAlignmentPairs object. .make_GAlignmentPairs_from_GAlignments <- function(gal, strandMode=1L, use.mcols=FALSE) { groupid <- Rle(mcols(gal, use.names=FALSE)[ , "groupid"]) stopifnot(isStrictlySorted(runValue(groupid))) mate_status <- Rle(mcols(gal, use.names=FALSE)[ , "mate_status"]) ## Dump alignments with "ambiguous" mate status. flushDumpedAlignments() dumped_gal <- gal[mate_status == "ambiguous"] dumped_count <- length(dumped_gal) if (dumped_count != 0L) { dumpAlignments(dumped_gal) warning(" ", dumped_count, " alignments with ambiguous pairing ", "were dumped.\n Use 'getDumpedAlignments()' to retrieve ", "them from the dump environment.") } ## Keep alignments that (1) have a mate status set to "mated" and ## (2) belong to a group of size 2. ok <- mate_status == "mated" & Rle(runLength(groupid) == 2L, runLength(groupid)) if (!all(ok)) gal <- gal[ok] ## Check flag bits 0x40 and 0x80. flag <- mcols(gal, use.names=FALSE)[ , "flag"] is_first_mate <- as.logical(bamFlagAsBitMatrix(flag, bitnames="isFirstMateRead")) is_last_mate <- as.logical(bamFlagAsBitMatrix(flag, bitnames="isSecondMateRead")) bits_0x40_0x80_are_ok <- is_first_mate != is_last_mate stopifnot(all(bits_0x40_0x80_are_ok)) ## Split and order the pairs by ascending start position of the first mate. idx1 <- which(is_first_mate) idx2 <- which(is_last_mate) oo1 <- orderIntegerPairs(as.integer(gal@seqnames)[idx1], gal@start[idx1]) idx1 <- idx1[oo1] idx2 <- idx2[oo1] ans_first <- gal[idx1] ans_last <- gal[idx2] groupid1 <- mcols(ans_first, use.names=FALSE)[ , "groupid"] groupid2 <- mcols(ans_last, use.names=FALSE)[ , "groupid"] stopifnot(identical(groupid1, groupid2)) ## Drop the names. ans_names <- names(ans_first) names(ans_first) <- names(ans_last) <- NULL ## Check isProperPair (0x2) and isSecondaryAlignment (0x100) flag bits. flag1 <- mcols(ans_first, use.names=FALSE)[ , "flag"] flag2 <- mcols(ans_last, use.names=FALSE)[ , "flag"] is_proper1 <- bamFlagAsBitMatrix(flag1, bitnames="isProperPair") is_proper2 <- bamFlagAsBitMatrix(flag2, bitnames="isProperPair") stopifnot(identical(is_proper1, is_proper2)) is_secondary1 <- bamFlagAsBitMatrix(flag1, bitnames="isSecondaryAlignment") is_secondary2 <- bamFlagAsBitMatrix(flag2, bitnames="isSecondaryAlignment") stopifnot(identical(is_secondary1, is_secondary2)) ## Make the GAlignmentPairs object and return it. if (is.character(use.mcols)) { mcols(ans_first) <- mcols(ans_first, use.names=FALSE)[use.mcols] mcols(ans_last) <- mcols(ans_last, use.names=FALSE)[use.mcols] } else if (!use.mcols) { mcols(ans_first) <- mcols(ans_last) <- NULL } GAlignmentPairs(ans_first, ans_last, strandMode=strandMode, isProperPair=as.logical(is_proper1), names=ans_names) } .readGAlignmentPairs.BamFile <- function(file, index=file, use.names=FALSE, param=NULL, with.which_label=FALSE, strandMode=1) { if (!asMates(file)) { asMates(file) <- TRUE ## This is required because BamFile objects have a pass-by-address ## semantic. on.exit(asMates(file) <- FALSE) } if (is.null(param)) param <- ScanBamParam() flag0 <- scanBamFlag(isPaired=TRUE, hasUnmappedMate=FALSE) what0 <- c("flag", "groupid", "mate_status") param2 <- .normargParam(param, flag0, what0) gal <- readGAlignments(file, use.names=use.names, param=param2, with.which_label=with.which_label) use.mcols <- c(bamWhat(param), bamTag(param)) if (with.which_label) use.mcols <- c(use.mcols, "which_label") .make_GAlignmentPairs_from_GAlignments(gal, strandMode=strandMode, use.mcols=use.mcols) } setMethod("readGAlignmentPairs", "BamFile", .readGAlignmentPairs.BamFile) setMethod("readGAlignmentPairs", "character", function(file, index=file, use.names=FALSE, param=NULL, with.which_label=FALSE, strandMode=1) { bam <- .open_BamFile(file, index=index, asMates=TRUE, param=param) on.exit(close(bam)) readGAlignmentPairs(bam, character(0), use.names=use.names, param=param, with.which_label=with.which_label, strandMode=strandMode) } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### readGAlignmentsList() ### setGeneric("readGAlignmentsList", signature="file", function(file, index=file, use.names=FALSE, param=ScanBamParam(), with.which_label=FALSE, strandMode=NA) standardGeneric("readGAlignmentsList") ) .setRealStrand <- function(gal, param, strandMode) { if (strandMode == 0L) strand(gal) <- Rle(strand("*"), length(gal)) else { gal_mcols <- mcols(gal, use.names=FALSE) if (is.null(gal_mcols$flag)) warning("Flag information missing in GAlignmentsList object. Strand information might not be accurate.") else { mask_first_mate <- bamFlagTest(gal_mcols$flag, "isFirstMateRead") if (strandMode == 1L) strand(gal[!mask_first_mate]) <- invertStrand(strand(gal[!mask_first_mate])) else if (strandMode == 2L) strand(gal[mask_first_mate]) <- invertStrand(strand(gal[mask_first_mate])) else stop("strandMode should be either 0, 1 or 2.") } } ## if the user didn't request the 'flag' info ## then remove it to reduce memory footprint if (!"flag" %in% bamWhat(param)) mcols(gal)$flag <- NULL gal } .matesFromBam <- function(file, use.names, param, what0, with.which_label, strandMode) { bamcols <- .load_bamcols_from_BamFile(file, param, what0, with.which_label=with.which_label) seqlengths <- .load_seqlengths_from_BamFile(file, levels(bamcols$rname)) gal <- GAlignments(seqnames=bamcols$rname, pos=bamcols$pos, cigar=bamcols$cigar, strand=bamcols$strand, seqlengths=seqlengths) if (!is.na(strandMode)) { flag0 <- scanBamFlag() what0 <- "flag" param2 <- .normargParam(param, flag0, what0) gal <- .bindExtraData(gal, use.names=FALSE, param2, bamcols, with.which_label=with.which_label) gal <- .setRealStrand(gal, param, strandMode) } else gal <- .bindExtraData(gal, use.names=FALSE, param, bamcols, with.which_label=with.which_label) if (asMates(file)) { f <- factor(bamcols$groupid) gal <- unname(split(gal, f)) mcols(gal)$mate_status <- bamcols$mate_status[match(levels(f), bamcols$groupid)] } else { ## groupid=NULL when asMates=FALSE gal <- unname(split(gal, seq_along(gal))) } if (use.names) names(gal) <- unique(splitAsList(bamcols$qname, bamcols$groupid)) gal } .readGAlignmentsList.BamFile <- function(file, index=file, use.names=FALSE, param=ScanBamParam(), with.which_label=FALSE, strandMode=NA) { if (!isTRUEorFALSE(use.names)) stop("'use.names' must be TRUE or FALSE") if (!asMates(file)) bamWhat(param) <- setdiff(bamWhat(param), c("groupid", "mate_status")) what0 <- c("rname", "strand", "pos", "cigar", "groupid", "mate_status") if (!is.na(strandMode)) what0 <- c(what0, "flag") if (use.names) what0 <- c(what0, "qname") .matesFromBam(file, use.names, param, what0, with.which_label, strandMode) } setMethod("readGAlignmentsList", "BamFile", .readGAlignmentsList.BamFile) setMethod("readGAlignmentsList", "character", function(file, index=file, use.names=FALSE, param=ScanBamParam(), with.which_label=FALSE, strandMode=NA) { bam <- .open_BamFile(file, index=index, asMates=TRUE, param=param) on.exit(close(bam)) readGAlignmentsList(bam, character(0), use.names=use.names, param=param, with.which_label=with.which_label, strandMode=strandMode) } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### readGappedReads() ### setGeneric("readGappedReads", signature="file", function(file, index=file, use.names=FALSE, param=NULL, with.which_label=FALSE) standardGeneric("readGappedReads") ) .readGappedReads.BamFile <- function(file, index=file, use.names=FALSE, param=NULL, with.which_label=FALSE) { if (!isTRUEorFALSE(use.names)) stop("'use.names' must be TRUE or FALSE") if (is.null(param)) param <- ScanBamParam() if (!asMates(file)) bamWhat(param) <- setdiff(bamWhat(param), c("groupid", "mate_status")) what0 <- c("rname", "strand", "pos", "cigar", "seq") if (use.names) what0 <- c(what0, "qname") bamcols <- .load_bamcols_from_BamFile(file, param, what0, with.which_label=with.which_label) seqlengths <- .load_seqlengths_from_BamFile(file, levels(bamcols[["rname"]])) ans <- GappedReads(seqnames=bamcols$rname, pos=bamcols$pos, cigar=bamcols$cigar, strand=bamcols$strand, qseq=bamcols$seq, seqlengths=seqlengths) .bindExtraData(ans, use.names, param, bamcols, with.which_label=with.which_label) } setMethod("readGappedReads", "BamFile", .readGappedReads.BamFile) setMethod("readGappedReads", "character", function(file, index=file, use.names=FALSE, param=NULL, with.which_label=FALSE) { bam <- .open_BamFile(file, index=index, param=param) on.exit(close(bam)) readGappedReads(bam, character(0), use.names=use.names, param=param, with.which_label=with.which_label) } ) GenomicAlignments/R/sequenceLayer.R0000644000175200017520000006005314516004601020331 0ustar00biocbuildbiocbuild### ========================================================================= ### sequenceLayer() ### ------------------------------------------------------------------------- .make_empty_sequences <- function(skeleton, class="BStringSet") { if (is.null(skeleton)) return(NULL) skeleton <- PartitioningByEnd(skeleton) skeleton_len <- length(skeleton) if (skeleton_len == 0L) { unlisted_len <- 0L } else { unlisted_len <- end(skeleton)[skeleton_len] } unlisted_ans <- rep.int(as("", class), unlisted_len) relist(unlisted_ans, skeleton) } ### 'filler_width' must be an integer vector, and 'letter' an XString object ### of length 1. .make_sequence_fillers_from_widths <- function(filler_width, letter) { if (length(filler_width) == 0L) { max_width <- 0L at <- IRanges() } else { max_width <- max(filler_width) at <- IRanges(1L, filler_width) } biggest_filler <- rep.int(letter, max_width) extractAt(biggest_filler, at) } ### 'filler_widths' must be an IntegerList object (or list of integers). .make_sequence_fillers_from_list_of_widths <- function(filler_widths, letter) { unlisted_widths <- unlist(filler_widths, use.names=FALSE) unlisted_ans <- .make_sequence_fillers_from_widths(unlisted_widths, letter) relist(unlisted_ans, filler_widths) } ### Parallel combine. .pcombine <- function(x, y) { if (is.null(x)) return(y) if (is.null(y)) return(x) if (length(x) != length(y)) stop("'x' and 'y' must have the same length") xy <- c(x, y) collate_subscript <- S4Vectors:::make_XYZxyz_to_XxYyZz_subscript(length(x)) ans_flesh <- unlist(xy[collate_subscript], use.names=FALSE) ans_breakpoints <- end(PartitioningByEnd(x)) + end(PartitioningByEnd(y)) ans_skeleton <- PartitioningByEnd(ans_breakpoints) relist(ans_flesh, ans_skeleton) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### sequenceLayer() ### .make_fillers <- function(cigar, ops, letter) { ops_width <- explodeCigarOpLengths(cigar, ops=ops) .make_sequence_fillers_from_list_of_widths(ops_width, letter) } .D_ranges_on_query_space <- function(cigar, before.hard.clipping=FALSE, after.soft.clipping=FALSE) { cigarRangesAlongQuerySpace(cigar, before.hard.clipping=before.hard.clipping, after.soft.clipping=after.soft.clipping, ops="D") } .N_ranges_on_query_space <- function(cigar, before.hard.clipping=FALSE, after.soft.clipping=FALSE) { cigarRangesAlongQuerySpace(cigar, before.hard.clipping=before.hard.clipping, after.soft.clipping=after.soft.clipping, ops="N") } .D_and_N_ranges_on_query_space <- function(cigar, before.hard.clipping=FALSE, after.soft.clipping=FALSE) { D_ranges <- .D_ranges_on_query_space(cigar, before.hard.clipping=before.hard.clipping, after.soft.clipping=after.soft.clipping) N_ranges <- .N_ranges_on_query_space(cigar, before.hard.clipping=before.hard.clipping, after.soft.clipping=after.soft.clipping) .pcombine(D_ranges, N_ranges) } .make_D_and_N_fillers <- function(cigar, D.letter, N.letter) { D_fillers <- .make_fillers(cigar, "D", D.letter) N_fillers <- .make_fillers(cigar, "N", N.letter) .pcombine(D_fillers, N_fillers) } sequenceLayer <- function(x, cigar, from="query", to="reference", D.letter="-", N.letter=".", I.letter="-", S.letter="+", H.letter="+") { if (!is(x, "XStringSet")) stop("'x' must be an XStringSet object") ## The 8 spaces below are also defined at the top of the src/cigar_utils.c ## file in the GenomicAlignments package. SPACES <- c("reference", "reference-N-regions-removed", "query", "query-before-hard-clipping", "query-after-soft-clipping", "pairwise", "pairwise-N-regions-removed", "pairwise-dense") from <- match.arg(from, SPACES) to <- match.arg(to, SPACES) D.letter <- Biostrings:::.normarg_padding.letter(D.letter, seqtype(x)) N.letter <- Biostrings:::.normarg_padding.letter(N.letter, seqtype(x)) I.letter <- Biostrings:::.normarg_padding.letter(I.letter, seqtype(x)) S.letter <- Biostrings:::.normarg_padding.letter(S.letter, seqtype(x)) H.letter <- Biostrings:::.normarg_padding.letter(H.letter, seqtype(x)) if (from == to) return(x) ## Right now, the way 'S.letter' and 'H.letter' are injected in 'x' when ## 'to' is "query-before-hard-clipping" can result in padding in the ## wrong order (i.e. padding with 'H.letter' followed by padding with ## 'S.letter') so we temporarily work around this by enforcing 'S.letter' ## and 'H.letter' to be the same. if (from != "query" && to == "query-before-hard-clipping" && as.character(S.letter) != as.character(H.letter)) stop("'H.letter' must be the same as 'S.letter' ", "when 'from' is not \"query\" and 'to' ", "is \"query-before-hard-clipping\"") ## TODO: What follows is a big ugly piece of stinking code (350 lines!). ## There is of course a better way... inject_at <- ops_to_remove <- NULL if (from == "reference") { getCigarRanges <- function(cigar, ops) cigarRangesAlongReferenceSpace(cigar, ops=ops) if (to == "reference-N-regions-removed") { ## "reference" -> "reference-N-regions-removed" ops_to_remove <- "N" } else if (to == "query") { ## "reference" -> "query" ops_to_remove <- c("D", "N") I_inject_at <- getCigarRanges(cigar, "I") I_fillers <- .make_fillers(cigar, "I", I.letter) S_inject_at <- getCigarRanges(cigar, "S") S_fillers <- .make_fillers(cigar, "S", S.letter) inject_at <- .pcombine(I_inject_at, S_inject_at) fillers <- .pcombine(I_fillers, S_fillers) } else if (to == "query-before-hard-clipping") { ## "reference" -> "query-before-hard-clipping" ops_to_remove <- c("D", "N") I_inject_at <- getCigarRanges(cigar, "I") I_fillers <- .make_fillers(cigar, "I", I.letter) S_inject_at <- getCigarRanges(cigar, "S") S_fillers <- .make_fillers(cigar, "S", S.letter) H_inject_at <- getCigarRanges(cigar, "H") H_fillers <- .make_fillers(cigar, "H", H.letter) inject_at <- .pcombine(I_inject_at, S_inject_at) inject_at <- .pcombine(inject_at, H_inject_at) fillers <- .pcombine(I_fillers, S_fillers) fillers <- .pcombine(fillers, H_fillers) } else if (to == "query-after-soft-clipping") { ## "reference" -> "query-after-soft-clipping" ops_to_remove <- c("D", "N") ops_to_inject <- "I" inject_at <- getCigarRanges(cigar, ops_to_inject) fillers <- .make_fillers(cigar, ops_to_inject, I.letter) } else if (to == "pairwise") { ## "reference" -> "pairwise" ops_to_inject <- "I" inject_at <- getCigarRanges(cigar, ops_to_inject) fillers <- .make_fillers(cigar, ops_to_inject, I.letter) } else if (to == "pairwise-N-regions-removed") { ## "reference" -> "pairwise-N-regions-removed" ops_to_remove <- "N" ops_to_inject <- "I" inject_at <- getCigarRanges(cigar, ops_to_inject) fillers <- .make_fillers(cigar, ops_to_inject, I.letter) } else if (to == "pairwise-dense") { ## "reference" -> "pairwise-dense" ops_to_remove <- c("D", "N") } } else if (from == "reference-N-regions-removed") { getCigarRanges <- function(cigar, ops) cigarRangesAlongReferenceSpace(cigar, N.regions.removed=TRUE, ops=ops) if (to == "reference") { ## "reference-N-regions-removed" -> "reference" ops_to_inject <- "N" inject_at <- getCigarRanges(cigar, ops_to_inject) fillers <- .make_fillers(cigar, ops_to_inject, N.letter) } else if (to == "query") { ## "reference-N-regions-removed" -> "query" ops_to_remove <- "D" I_inject_at <- getCigarRanges(cigar, "I") I_fillers <- .make_fillers(cigar, "I", I.letter) S_inject_at <- getCigarRanges(cigar, "S") S_fillers <- .make_fillers(cigar, "S", S.letter) inject_at <- .pcombine(I_inject_at, S_inject_at) fillers <- .pcombine(I_fillers, S_fillers) } else if (to == "query-before-hard-clipping") { ## "reference-N-regions-removed" -> "query-before-hard-clipping" ops_to_remove <- "D" I_inject_at <- getCigarRanges(cigar, "I") I_fillers <- .make_fillers(cigar, "I", I.letter) S_inject_at <- getCigarRanges(cigar, "S") S_fillers <- .make_fillers(cigar, "S", S.letter) H_inject_at <- getCigarRanges(cigar, "H") H_fillers <- .make_fillers(cigar, "H", H.letter) inject_at <- .pcombine(I_inject_at, S_inject_at) inject_at <- .pcombine(inject_at, H_inject_at) fillers <- .pcombine(I_fillers, S_fillers) fillers <- .pcombine(fillers, H_fillers) } else if (to == "query-after-soft-clipping") { ## "reference-N-regions-removed" -> "query-after-soft-clipping" ops_to_remove <- "D" ops_to_inject <- "I" inject_at <- getCigarRanges(cigar, ops_to_inject) fillers <- .make_fillers(cigar, ops_to_inject, I.letter) } else if (to == "pairwise") { ## "reference-N-regions-removed" -> "pairwise" I_inject_at <- getCigarRanges(cigar, "I") I_fillers <- .make_fillers(cigar, "I", I.letter) N_inject_at <- getCigarRanges(cigar, "N") N_fillers <- .make_fillers(cigar, "N", N.letter) inject_at <- .pcombine(I_inject_at, N_inject_at) fillers <- .pcombine(I_fillers, N_fillers) } else if (to == "pairwise-N-regions-removed") { ## "reference-N-regions-removed" -> "pairwise-N-regions-removed" ops_to_inject <- "I" inject_at <- getCigarRanges(cigar, ops_to_inject) fillers <- .make_fillers(cigar, ops_to_inject, I.letter) } else if (to == "pairwise-dense") { ## "reference-N-regions-removed" -> "pairwise-dense" ops_to_remove <- "D" } } else if (from == "query") { getCigarRanges <- function(cigar, ops) cigarRangesAlongQuerySpace(cigar, ops=ops) if (to == "reference") { ## "query" -> "reference" ops_to_remove <- c("I", "S") inject_at <- .D_and_N_ranges_on_query_space(cigar) fillers <- .make_D_and_N_fillers(cigar, D.letter, N.letter) } else if (to == "reference-N-regions-removed") { ## "query" -> "reference-N-regions-removed" ops_to_remove <- c("S", "I") ops_to_inject <- "D" inject_at <- getCigarRanges(cigar, ops_to_inject) fillers <- .make_fillers(cigar, ops_to_inject, D.letter) } else if (to == "query-before-hard-clipping") { ## "query" -> "query-before-hard-clipping" ops_to_inject <- "H" inject_at <- getCigarRanges(cigar, ops_to_inject) fillers <- .make_fillers(cigar, ops_to_inject, H.letter) } else if (to == "query-after-soft-clipping") { ## "query" -> "query-after-soft-clipping" ops_to_remove <- "S" } else if (to == "pairwise") { ## "query" -> "pairwise" ops_to_remove <- "S" inject_at <- .D_and_N_ranges_on_query_space(cigar) fillers <- .make_D_and_N_fillers(cigar, D.letter, N.letter) } else if (to == "pairwise-N-regions-removed") { ## "query" -> "pairwise-N-regions-removed" ops_to_remove <- "S" ops_to_inject <- "D" inject_at <- getCigarRanges(cigar, ops_to_inject) fillers <- .make_fillers(cigar, ops_to_inject, D.letter) } else if (to == "pairwise-dense") { ## "query" -> "pairwise-dense" ops_to_remove <- c("I", "S") } } else if (from == "query-before-hard-clipping") { getCigarRanges <- function(cigar, ops) cigarRangesAlongQuerySpace(cigar, before.hard.clipping=TRUE, ops=ops) if (to == "reference") { ## "query-before-hard-clipping" -> "reference" ops_to_remove <- c("H", "S", "I") inject_at <- .D_and_N_ranges_on_query_space(cigar, before.hard.clipping=TRUE) fillers <- .make_D_and_N_fillers(cigar, D.letter, N.letter) } else if (to == "reference-N-regions-removed") { ## "query-before-hard-clipping" -> "reference-N-regions-removed" ops_to_remove <- c("H", "S", "I") ops_to_inject <- "D" inject_at <- getCigarRanges(cigar, ops_to_inject) fillers <- .make_fillers(cigar, ops_to_inject, D.letter) } else if (to == "query") { ## "query-before-hard-clipping" -> "query" ops_to_remove <- "H" } else if (to == "query-after-soft-clipping") { ## "query-before-hard-clipping" -> "query-after-soft-clipping" ops_to_remove <- c("H", "S") } else if (to == "pairwise") { ## "query-before-hard-clipping" -> "pairwise" ops_to_remove <- c("H", "S") inject_at <- .D_and_N_ranges_on_query_space(cigar, before.hard.clipping=TRUE) fillers <- .make_D_and_N_fillers(cigar, D.letter, N.letter) } else if (to == "pairwise-N-regions-removed") { ## "query-before-hard-clipping" -> "pairwise-N-regions-removed" ops_to_remove <- c("H", "S") ops_to_inject <- "D" inject_at <- getCigarRanges(cigar, ops_to_inject) fillers <- .make_fillers(cigar, ops_to_inject, D.letter) } else if (to == "pairwise-dense") { ## "query-before-hard-clipping" -> "pairwise-dense" ops_to_remove <- c("I", "S", "H") } } else if (from == "query-after-soft-clipping") { getCigarRanges <- function(cigar, ops) cigarRangesAlongQuerySpace(cigar, after.soft.clipping=TRUE, ops=ops) if (to == "reference") { ## "query-after-soft-clipping" -> "reference" ops_to_remove <- "I" inject_at <- .D_and_N_ranges_on_query_space(cigar, after.soft.clipping=TRUE) fillers <- .make_D_and_N_fillers(cigar, D.letter, N.letter) } else if (to == "reference-N-regions-removed") { ## "query-after-soft-clipping" -> "reference-N-regions-removed" ops_to_remove <- "I" ops_to_inject <- "D" inject_at <- getCigarRanges(cigar, ops_to_inject) fillers <- .make_fillers(cigar, ops_to_inject, D.letter) } else if (to == "query") { ## "query-after-soft-clipping" -> "query" ops_to_inject <- "S" inject_at <- getCigarRanges(cigar, ops_to_inject) fillers <- .make_fillers(cigar, ops_to_inject, S.letter) } else if (to == "query-before-hard-clipping") { ## "query-after-soft-clipping" -> "query-before-hard-clipping" S_inject_at <- getCigarRanges(cigar, "S") S_fillers <- .make_fillers(cigar, "S", S.letter) H_inject_at <- getCigarRanges(cigar, "H") H_fillers <- .make_fillers(cigar, "H", H.letter) inject_at <- .pcombine(S_inject_at, H_inject_at) fillers <- .pcombine(S_fillers, H_fillers) } else if (to == "pairwise") { ## "query-after-soft-clipping" -> "pairwise" inject_at <- .D_and_N_ranges_on_query_space(cigar, after.soft.clipping=TRUE) fillers <- .make_D_and_N_fillers(cigar, D.letter, N.letter) } else if (to == "pairwise-N-regions-removed") { ## "query-after-soft-clipping" -> "pairwise-N-regions-removed" ops_to_inject <- "D" inject_at <- getCigarRanges(cigar, ops_to_inject) fillers <- .make_fillers(cigar, ops_to_inject, D.letter) } else if (to == "pairwise-dense") { ## "query-after-soft-clipping" -> "pairwise-dense" ops_to_remove <- "I" } } else if (from == "pairwise") { getCigarRanges <- function(cigar, ops) cigarRangesAlongPairwiseSpace(cigar, ops=ops) if (to == "reference") { ## "pairwise" -> "reference" ops_to_remove <- "I" } else if (to == "reference-N-regions-removed") { ## "pairwise" -> "reference-N-regions-removed" ops_to_remove <- c("I", "N") } else if (to == "query") { ## "pairwise" -> "query" ops_to_remove <- c("D", "N") ops_to_inject <- "S" inject_at <- getCigarRanges(cigar, ops_to_inject) fillers <- .make_fillers(cigar, ops_to_inject, S.letter) } else if (to == "query-before-hard-clipping") { ## "pairwise" -> "query-before-hard-clipping" ops_to_remove <- c("D", "N") S_inject_at <- getCigarRanges(cigar, "S") S_fillers <- .make_fillers(cigar, "S", S.letter) H_inject_at <- getCigarRanges(cigar, "H") H_fillers <- .make_fillers(cigar, "H", H.letter) inject_at <- .pcombine(S_inject_at, H_inject_at) fillers <- .pcombine(S_fillers, H_fillers) } else if (to == "query-after-soft-clipping") { ## "pairwise" -> "query-after-soft-clipping" ops_to_remove <- c("D", "N") } else if (to == "pairwise-N-regions-removed") { ## "pairwise" -> "pairwise-N-regions-removed" ops_to_remove <- "N" } else if (to == "pairwise-dense") { ## "pairwise" -> "pairwise-dense" ops_to_remove <- c("I", "D", "N") } } else if (from == "pairwise-N-regions-removed") { getCigarRanges <- function(cigar, ops) cigarRangesAlongPairwiseSpace(cigar, N.regions.removed=TRUE, ops=ops) if (to == "reference") { ## "pairwise-N-regions-removed" -> "reference" ops_to_remove <- "I" ops_to_inject <- "N" inject_at <- getCigarRanges(cigar, ops_to_inject) fillers <- .make_fillers(cigar, ops_to_inject, N.letter) } else if (to == "reference-N-regions-removed") { ## "pairwise-N-regions-removed" -> "reference-N-regions-removed" ops_to_remove <- "I" } else if (to == "query") { ## "pairwise-N-regions-removed" -> "query" ops_to_remove <- "D" ops_to_inject <- "S" inject_at <- getCigarRanges(cigar, ops_to_inject) fillers <- .make_fillers(cigar, ops_to_inject, S.letter) } else if (to == "query-before-hard-clipping") { ## "pairwise-N-regions-removed" -> "query-before-hard-clipping" ops_to_remove <- "D" S_inject_at <- getCigarRanges(cigar, "S") S_fillers <- .make_fillers(cigar, "S", S.letter) H_inject_at <- getCigarRanges(cigar, "H") H_fillers <- .make_fillers(cigar, "H", H.letter) inject_at <- .pcombine(S_inject_at, H_inject_at) fillers <- .pcombine(S_fillers, H_fillers) } else if (to == "query-after-soft-clipping") { ## "pairwise-N-regions-removed" -> "query-after-soft-clipping" ops_to_remove <- "D" } else if (to == "pairwise") { ## "pairwise-N-regions-removed" -> "pairwise" ops_to_inject <- "N" inject_at <- getCigarRanges(cigar, ops_to_inject) fillers <- .make_fillers(cigar, ops_to_inject, N.letter) } else if (to == "pairwise-dense") { ## "pairwise-N-regions-removed" -> "pairwise-dense" ops_to_remove <- c("I", "D") } } else if (from == "pairwise-dense") { getCigarRanges <- function(cigar, ops) cigarRangesAlongPairwiseSpace(cigar, dense=TRUE, ops=ops) if (to == "reference") { ## "pairwise-dense" -> "reference" D_inject_at <- getCigarRanges(cigar, "D") D_fillers <- .make_fillers(cigar, "D", D.letter) N_inject_at <- getCigarRanges(cigar, "N") N_fillers <- .make_fillers(cigar, "N", N.letter) inject_at <- .pcombine(D_inject_at, N_inject_at) fillers <- .pcombine(D_fillers, N_fillers) } else if (to == "reference-N-regions-removed") { ## "pairwise-dense" -> "reference-N-regions-removed" ops_to_inject <- "D" inject_at <- getCigarRanges(cigar, ops_to_inject) fillers <- .make_fillers(cigar, ops_to_inject, D.letter) } else if (to == "query") { ## "pairwise-dense" -> "query" I_inject_at <- getCigarRanges(cigar, "I") I_fillers <- .make_fillers(cigar, "I", I.letter) S_inject_at <- getCigarRanges(cigar, "S") S_fillers <- .make_fillers(cigar, "S", S.letter) inject_at <- .pcombine(I_inject_at, S_inject_at) fillers <- .pcombine(I_fillers, S_fillers) } else if (to == "query-before-hard-clipping") { ## "pairwise-dense" -> "query-before-hard-clipping" I_inject_at <- getCigarRanges(cigar, "I") I_fillers <- .make_fillers(cigar, "I", I.letter) S_inject_at <- getCigarRanges(cigar, "S") S_fillers <- .make_fillers(cigar, "S", S.letter) H_inject_at <- getCigarRanges(cigar, "H") H_fillers <- .make_fillers(cigar, "H", H.letter) inject_at <- .pcombine(.pcombine(I_inject_at, S_inject_at), H_inject_at) fillers <- .pcombine(.pcombine(I_fillers, S_fillers), H_fillers) } else if (to == "query-after-soft-clipping") { ## "pairwise-dense" -> "query-after-soft-clipping" ops_to_inject <- "I" inject_at <- getCigarRanges(cigar, ops_to_inject) fillers <- .make_fillers(cigar, ops_to_inject, I.letter) } else if (to == "pairwise") { ## "pairwise-dense" -> "pairwise" I_inject_at <- getCigarRanges(cigar, "I") I_fillers <- .make_fillers(cigar, "I", I.letter) D_inject_at <- getCigarRanges(cigar, "D") D_fillers <- .make_fillers(cigar, "D", D.letter) N_inject_at <- getCigarRanges(cigar, "N") N_fillers <- .make_fillers(cigar, "N", N.letter) inject_at <- .pcombine(.pcombine(I_inject_at, D_inject_at), N_inject_at) fillers <- .pcombine(.pcombine(I_fillers, D_fillers), N_fillers) } else if (to == "pairwise-N-regions-removed") { ## "pairwise-dense" -> "pairwise-N-regions-removed" I_inject_at <- getCigarRanges(cigar, "I") I_fillers <- .make_fillers(cigar, "I", I.letter) D_inject_at <- getCigarRanges(cigar, "D") D_fillers <- .make_fillers(cigar, "D", D.letter) inject_at <- .pcombine(I_inject_at, D_inject_at) fillers <- .pcombine(I_fillers, D_fillers) } } at <- inject_at if (is.null(inject_at)) { value <- NULL } else { value <- fillers } if (length(ops_to_remove) != 0L) { at2 <- getCigarRanges(cigar, ops_to_remove) value2 <- .make_empty_sequences(at2, class=class(x)) at <- .pcombine(at, at2) value <- .pcombine(value, value2) } replaceAt(x, at, value=value) } GenomicAlignments/R/setops-methods.R0000644000175200017520000000162314516004601020500 0ustar00biocbuildbiocbuild### ========================================================================= ### Set operations ### ------------------------------------------------------------------------- ### ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### pintersect() ### ### TODO: Revisit this method (seems to do strange things). setMethod("pintersect", c("GAlignments", "GRanges"), function(x, y, ...) { bounds <- try(callGeneric(granges(x), y), silent=TRUE) if (inherits(bounds, "try-error")) stop("CIGAR is empty after intersection") start <- start(bounds) - start(x) + 1L start[which(start < 1L)] <- 1L end <- end(bounds) - end(x) - 1L end[which(end > -1L)] <- -1L narrow(x, start=start, end=end) } ) setMethod("pintersect", c("GRanges", "GAlignments"), function(x, y, ...) { callGeneric(y, x) } ) GenomicAlignments/R/stackStringsFromGAlignments.R0000644000175200017520000001547114516004601023164 0ustar00biocbuildbiocbuild### ========================================================================= ### stackStringsFromGAlignments() & related ### ------------------------------------------------------------------------- ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### stackStringsFromGAlignments() ### ### All the alignments in GAlignments object 'x' must be on the **same** ### chromosome. This is NOT checked! .stack_reads <- function(x, from, to, what="seq", D.letter="-", N.letter=".", Lpadding.letter="+", Rpadding.letter="+") { x_mcols <- mcols(x, use.names=FALSE) what_col_idx <- match(what, colnames(x_mcols)) if (is.na(what_col_idx)) stop(wmsg("'x' does not have a \"", what, "\" metadata column")) what_col <- x_mcols[[what_col_idx]] if (what == "qual") what_col <- BStringSet(what_col) layed_seq <- sequenceLayer(what_col, cigar(x), D.letter=D.letter, N.letter=N.letter) ans <- stackStrings(layed_seq, from, to, shift=start(x)-1L, Lpadding.letter=Lpadding.letter, Rpadding.letter=Rpadding.letter) names(ans) <- names(x) mcols(ans) <- x_mcols ans } stackStringsFromGAlignments <- function(x, region, what="seq", D.letter="-", N.letter=".", Lpadding.letter="+", Rpadding.letter="+") { if (!is(x, "GAlignments")) stop(wmsg("'x' must be a GAlignments object")) if (!is(region, "GRanges")) region <- as(region, "GRanges") if (length(region) != 1L) stop(wmsg("'region' must contain a single genomic range")) region_seqname <- seqlevelsInUse(region) if (!(region_seqname %in% seqlevels(x))) stop(wmsg("seqlevel not in 'x': ", region_seqname)) what <- match.arg(what, c("seq", "qual")) x <- x[overlapsAny(granges(x), region)] .stack_reads(x, start(region), end(region), what=what, D.letter=D.letter, N.letter=N.letter, Lpadding.letter=Lpadding.letter, Rpadding.letter=Rpadding.letter) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### stackStringsFromBam() ### ### Should always return a ScanBamParam object containing exactly 1 genomic ### region. .normarg_param <- function(param) { if (isSingleString(param)) { tmp1 <- strsplit(param, ":", fixed=TRUE)[[1L]] if (length(tmp1) != 2L) stop(wmsg("when a character string, 'param' must be ", "of the form \"chr14:5201-5300\"")) tmp2 <- as.integer(strsplit(tmp1[2L], "-", fixed=TRUE)[[1L]]) if (length(tmp2) != 2L || any(is.na(tmp2))) stop(wmsg("when a character string, 'param' must be ", "of the form \"chr14:5201-5300\"")) param <- GRanges(tmp1[1L], IRanges(tmp2[1L], tmp2[2L])) } if (is(param, "GenomicRanges")) { if (length(param) != 1L) stop(wmsg("when a GRanges object, 'param' must have length 1")) seqlevels(param) <- seqlevelsInUse(param) param <- ScanBamParam(which=param) return(param) } if (is(param, "IntegerRangesList")) { ## We support IntegerRangesList just because ScanBamParam() supports ## it too and also because that's what's returned by bamWhich(). param <- param[elementNROWS(param) != 0L] if (length(unlist(param, use.names=FALSE)) != 1L) stop(wmsg("when an IntegerRangesList object, 'param' must contain ", "exactly 1 genomic region (i.e. 'unlist(param)' must ", "have length 1)")) param <- ScanBamParam(which=param) return(param) } if (!is(param, "ScanBamParam")) stop(wmsg("'param' must be either a ScanBamParam or IntegerRangesList ", "object containing exactly 1 genomic region, or a GRanges ", "object of length 1, or a character string specifying a ", "singe genomic region (in the \"chr14:5201-5300\" format)")) param_which <- bamWhich(param) param_which <- param_which[elementNROWS(param_which) != 0L] if (length(unlist(param_which, use.names=FALSE)) != 1L) stop(wmsg("when a ScanBamParam object, 'param' must contain exactly ", "1 genomic region (i.e. 'unlist(bamWhich(param))' must ", "have length 1)")) bamWhich(param) <- param_which param } stackStringsFromBam <- function(file, index=file, param, what="seq", use.names=FALSE, D.letter="-", N.letter=".", Lpadding.letter="+", Rpadding.letter="+") { param <- .normarg_param(param) region_range <- unlist(bamWhich(param), use.names=FALSE) what <- match.arg(what, c("seq", "qual")) param_what <- bamWhat(param) if (!(what %in% param_what)) bamWhat(param) <- c(param_what, what) gal <- readGAlignments(file, index=index, use.names=use.names, param=param) ans <- .stack_reads(gal, start(region_range), end(region_range), what=what, D.letter=D.letter, N.letter=N.letter, Lpadding.letter=Lpadding.letter, Rpadding.letter=Rpadding.letter) if (!(what %in% param_what)) { ## Remove the what metadata column. ans_mcols <- mcols(ans, use.names=FALSE) what_col_idx <- match(what, colnames(ans_mcols)) ans_mcols <- ans_mcols[ , -what_col_idx, drop=FALSE] ## Sadly, subsetting a DataFrame will mangle the colnames of the ## returned DataFrame if it has duplicated colnames. Since we of ## course don't want this, we fix them. colnames(ans_mcols) <- param_what mcols(ans) <- ans_mcols } ans } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### alphabetFrequencyFromBam() ### alphabetFrequencyFromBam <- function(file, index=file, param, what="seq", ...) { param <- .normarg_param(param) region_range <- unlist(bamWhich(param), use.names=FALSE) region_seqname <- names(bamWhich(param)) what <- match.arg(what, c("seq", "qual")) bamWhat(param) <- what gal <- readGAlignments(file, index=index, param=param) seqlevels(gal) <- region_seqname what_col <- mcols(gal, use.names=FALSE)[ , what] if (what == "qual") what_col <- BStringSet(what_col) at <- start(region_range) - 1L + seq_len(width(region_range)) at <- GRanges(region_seqname, IRanges(at, width=1L)) piles <- pileLettersAt(what_col, seqnames(gal), start(gal), cigar(gal), at) alphabetFrequency(piles, ...) } GenomicAlignments/R/summarizeOverlaps-methods.R0000644000175200017520000003031114516004601022707 0ustar00biocbuildbiocbuild### ========================================================================= ### summarizeOverlaps() generic and methods ### ------------------------------------------------------------------------- setGeneric("summarizeOverlaps", signature=c("features", "reads"), function(features, reads, mode=Union, ignore.strand=FALSE, ...) standardGeneric("summarizeOverlaps") ) ### ------------------------------------------------------------------------- ### Methods for GAlignments, GAlignmentsList and GAlignmentPairs objects ### .dispatchOverlaps <- function(features, reads, mode, ignore.strand, inter.feature, preprocess.reads, ...) { if (ignore.strand) { if (is(features, "GRangesList")) { r <- unlist(features) strand(r) <- "*" features@unlistData <- r } else { strand(features) <- "*" } } if (!is.null(preprocess.reads)) reads <- preprocess.reads(reads, ...) mode(features, reads, ignore.strand=ignore.strand, inter.feature=inter.feature) } .summarizeOverlaps <- function(features, reads, mode=Union, ignore.strand=FALSE, inter.feature=TRUE, preprocess.reads=NULL, ...) { if (!isTRUEorFALSE(ignore.strand)) stop("'ignore.strand' must be TRUE or FALSE") if (!ignore.strand) { reads_strand <- strand(reads) if (is(reads_strand, "List")) reads_strand <- unlist(reads_strand, use.names=FALSE) if (all(reads_strand == "*")) ignore.strand <- TRUE } mode <- match.fun(mode) counts <- .dispatchOverlaps(features, reads, mode, ignore.strand, inter.feature, preprocess.reads, ...) colData <- DataFrame(object=class(reads), records=length(reads), row.names="reads") SummarizedExperiment(assays=SimpleList(counts=as.matrix(counts)), rowRanges=features, colData=colData) } setMethod("summarizeOverlaps", c("GRanges", "GAlignments"), .summarizeOverlaps ) setMethod("summarizeOverlaps", c("GRangesList", "GAlignments"), .summarizeOverlaps ) setMethod("summarizeOverlaps", c("GRanges", "GAlignmentsList"), .summarizeOverlaps ) setMethod("summarizeOverlaps", c("GRangesList", "GAlignmentsList"), .summarizeOverlaps ) setMethod("summarizeOverlaps", c("GRanges", "GAlignmentPairs"), .summarizeOverlaps ) setMethod("summarizeOverlaps", c("GRangesList", "GAlignmentPairs"), .summarizeOverlaps ) setMethod("summarizeOverlaps", c("GRanges", "GRanges"), .summarizeOverlaps ) setMethod("summarizeOverlaps", c("GRangesList", "GRanges"), .summarizeOverlaps ) setMethod("summarizeOverlaps", c("GRanges", "GRangesList"), .summarizeOverlaps ) setMethod("summarizeOverlaps", c("GRangesList", "GRangesList"), .summarizeOverlaps ) ### ------------------------------------------------------------------------- ### 'mode' functions ### Union <- function(features, reads, ignore.strand=FALSE, inter.feature=TRUE) { ov <- findOverlaps(features, reads, ignore.strand=ignore.strand) if (inter.feature) { ## Remove ambigous reads. reads_to_keep <- which(countSubjectHits(ov) == 1L) ov <- ov[subjectHits(ov) %in% reads_to_keep] } countQueryHits(ov) } IntersectionStrict <- function(features, reads, ignore.strand=FALSE, inter.feature=TRUE) { ov <- findOverlaps(reads, features, type="within", ignore.strand=ignore.strand) if (inter.feature) { ## Remove ambigous reads. reads_to_keep <- which(countQueryHits(ov) == 1L) ov <- ov[queryHits(ov) %in% reads_to_keep] } countSubjectHits(ov) } .removeSharedRegions <- function(features, ignore.strand=FALSE) { if (is(features, "GRanges")) { regions <- disjoin(features, ignore.strand=ignore.strand) } else if (is(features, "GRangesList")) { regions <- disjoin(features@unlistData, ignore.strand=ignore.strand) } else { stop("internal error") # should never happen } ov <- findOverlaps(features, regions, ignore.strand=ignore.strand) regions_to_keep <- which(countSubjectHits(ov) == 1L) ov <- ov[subjectHits(ov) %in% regions_to_keep] unlisted_ans <- regions[subjectHits(ov)] ans_partitioning <- as(ov, "PartitioningByEnd") relist(unlisted_ans, ans_partitioning) } IntersectionNotEmpty <- function(features, reads, ignore.strand=FALSE, inter.feature=TRUE) { features <- .removeSharedRegions(features, ignore.strand=ignore.strand) Union(features, reads, ignore.strand=ignore.strand, inter.feature=inter.feature) } ### ------------------------------------------------------------------------- ### Methods for BamFiles and BamViews objects ### .checkArgs <- function(bam, singleEnd, fragments) { if (singleEnd) { if (all(isTRUE(asMates(bam)))) stop("cannot specify both 'singleEnd=TRUE' and 'asMates=TRUE'") if (fragments) stop("when 'fragments=TRUE', 'singleEnd' should be FALSE") ## all paired-end reading now goes through new C algo } else { asMates(bam) <- TRUE } } .getReadFunction <- function(singleEnd, fragments) { if (singleEnd) { FUN <- readGAlignments } else { if (fragments) FUN <- readGAlignmentsList else FUN <- readGAlignmentPairs } FUN } .countWithYieldSize <- function(FUN, features, bf, mode, ignore.strand, inter.feature, param, preprocess.reads, ...) { if (is.na(yieldSize(bf))) { x <- FUN(bf, param=param, ...) .dispatchOverlaps(features, x, mode, ignore.strand, inter.feature, preprocess.reads, ...) } else { if (!isOpen(bf)) { open(bf) on.exit(close(bf)) } ct <- integer(length(features)) while (length(x <- FUN(bf, param=param, ...))) { ct <- ct + .dispatchOverlaps(features, x, mode, ignore.strand, inter.feature, preprocess.reads, ...) } ct } } .dispatchBamFiles <- function(features, reads, mode, ignore.strand, count.mapped.reads=FALSE, inter.feature=TRUE, singleEnd=TRUE, fragments=FALSE, param=ScanBamParam(), preprocess.reads=NULL, ...) { exist <- sapply(reads, function(bf) file.exists(path(bf))) if (!all(exist)) stop(paste0("file(s): ", paste(path(reads)[!exist], collapse=","), " do not exist")) FUN <- .getReadFunction(singleEnd, fragments) cts <- bplapply(setNames(seq_along(reads), names(reads)), function(i, FUN, reads, features, mode, ignore.strand, inter.feature, param, preprocess.reads, ...) { bf <- reads[[i]] .countWithYieldSize(FUN, features, bf, mode, ignore.strand, inter.feature, param, preprocess.reads, ...) }, FUN, reads, features, mode=match.fun(mode), ignore.strand=ignore.strand, inter.feature=inter.feature, param=param, preprocess.reads=preprocess.reads, ... ) counts <- as.matrix(do.call(cbind, cts)) if (count.mapped.reads) { countBam <- countBam(reads) flag <- scanBamFlag(isUnmappedQuery=FALSE) param <- ScanBamParam(flag=flag, what="seq") colData <- DataFrame(countBam[c("records", "nucleotides")], mapped=countBam(reads, param=param)$records, row.names=colnames(counts)) } else { colData <- DataFrame(row.names=colnames(counts)) } SummarizedExperiment(assays=SimpleList(counts=counts), rowRanges=features, colData=colData) } setMethod("summarizeOverlaps", c("GRanges", "BamFile"), function(features, reads, mode=Union, ignore.strand=FALSE, inter.feature=TRUE, singleEnd=TRUE, fragments=FALSE, param=ScanBamParam(), preprocess.reads=NULL, ...) { .checkArgs(reads, singleEnd, fragments) .dispatchBamFiles(features, BamFileList(reads), mode, ignore.strand, inter.feature=inter.feature, singleEnd=singleEnd, fragments=fragments, param=param, preprocess.reads=preprocess.reads, ...) }) setMethod("summarizeOverlaps", c("GRangesList", "BamFile"), function(features, reads, mode=Union, ignore.strand=FALSE, inter.feature=TRUE, singleEnd=TRUE, fragments=FALSE, param=ScanBamParam(), preprocess.reads=NULL, ...) { .checkArgs(reads, singleEnd, fragments) .dispatchBamFiles(features, BamFileList(reads), mode, ignore.strand, inter.feature=inter.feature, singleEnd=singleEnd, fragments=fragments, param=param, preprocess.reads=preprocess.reads, ...) }) .summarizeOverlaps_character <- function(features, reads, mode=Union, ignore.strand=FALSE, yieldSize=1000000L, inter.feature=TRUE, singleEnd=TRUE, fragments=FALSE, param=ScanBamParam(), preprocess.reads=NULL, ...) { if (!all(file.exists(reads))) stop("file(s) do not exist:\n ", paste(reads[!file.exists(reads)], collapse="\n ")) if (is.null(names(reads))) { if (any(duplicated(reads))) stop("duplicate 'reads' paths not allowed; use distinct names()") } else if (any(duplicated(names(reads)))) stop("duplicate 'names(reads)' file paths not allowed") reads <- BamFileList(reads, yieldSize=yieldSize, obeyQname=FALSE, asMates=!singleEnd) summarizeOverlaps(features, reads, mode, ignore.strand=ignore.strand, inter.feature=inter.feature, singleEnd=singleEnd, fragments=fragments, param=param, preprocess.reads=preprocess.reads, ...) } setMethod("summarizeOverlaps", c("GRanges", "character"), .summarizeOverlaps_character ) setMethod("summarizeOverlaps", c("GRangesList", "character"), .summarizeOverlaps_character ) .summarizeOverlaps_BamFileList <- function(features, reads, mode=Union, ignore.strand=FALSE, inter.feature=TRUE, singleEnd=TRUE, fragments=FALSE, param=ScanBamParam(), preprocess.reads=NULL, ...) { if (any(duplicated(names(reads)))) stop("duplicate 'names(reads)' not allowed") .checkArgs(reads, singleEnd, fragments) .dispatchBamFiles(features, reads, mode, ignore.strand, inter.feature=inter.feature, singleEnd=singleEnd, fragments=fragments, param=param, preprocess.reads=preprocess.reads, ...) } setMethod("summarizeOverlaps", c("GRanges", "BamFileList"), .summarizeOverlaps_BamFileList ) setMethod("summarizeOverlaps", c("GRangesList", "BamFileList"), .summarizeOverlaps_BamFileList ) setMethod("summarizeOverlaps", c("BamViews", "missing"), function(features, reads, mode=Union, ignore.strand=FALSE, inter.feature=TRUE, singleEnd=TRUE, fragments=FALSE, param=ScanBamParam(), preprocess.reads=NULL, ...) { se <- callGeneric(bamRanges(features), BamFileList(bamPaths(features)), mode=mode, ignore.strand=ignore.strand, inter.feature=inter.feature, singleEnd=singleEnd, fragments=fragments, param=param, preprocess.reads=preprocess.reads, ...) colData(se)$bamSamples <- bamSamples(features) colData(se)$bamIndices <- bamIndicies(features) metadata(se)$bamExperiment <- bamExperiment(features) se }) GenomicAlignments/R/utils.R0000644000175200017520000000137514516004601016666 0ustar00biocbuildbiocbuild### ========================================================================= ### Some low-level (non exported) utility functions. ### ------------------------------------------------------------------------- ### 3 equivalent implementations for this: ### (a) x %in% x[duplicated(x)] ### (b) duplicated(x) | duplicated(x, fromLast=TRUE) ### (c) xx <- match(x, x); ans <- xx != seq_along(xx); ans[xx] <- ans; ans ### Comparing the 3 implementations on an integer vector of length 12 millions: ### (a) is the most memory efficient; ### (b) is a little bit faster than (a) (by only 8%) but uses between 12-14% ### more memory; ### (c) is as fast as (a) but uses about 30% more memory. has_duplicates <- function(x) { x %in% x[duplicated(x)] } GenomicAlignments/R/zzz.R0000644000175200017520000000024014516004601016351 0ustar00biocbuildbiocbuild.onUnload <- function(libpath) { library.dynam.unload("GenomicAlignments", libpath) } .test <- function() BiocGenerics:::testPackage("GenomicAlignments") GenomicAlignments/README.md0000644000175200017520000000072714516004601016461 0ustar00biocbuildbiocbuild[](https://bioconductor.org/) **GenomicAlignments** is an R/Bioconductor package for representing and manipulating short genomic alignments (typically aligned reads). See https://bioconductor.org/packages/GenomicAlignments for more information including how to install the release version of the package (please refrain from installing directly from GitHub). GenomicAlignments/build/0000755000175200017520000000000014551577536016320 5ustar00biocbuildbiocbuildGenomicAlignments/build/vignette.rds0000644000175200017520000000104014551577536020652 0ustar00biocbuildbiocbuildUo00MC]ueGJ:Mb:uG,b;?|s E)g+?R\xR3ޑy}h\0s\\KAHSZw(H/x$ZZBNL<^%(4%N|2@_h-ǒ>.c}&IoSχ@ZȸΉ}K~f+ s𱤓Z펺'R,1PjM86F kV xi.w'80&E J ~tO>ȕIVaHVx7;XYAuK Rj^ǧ+hyɃ S>roC*E‹!~*Vy.45j23G)kڱه.˹ڑr`lҥKVz9a~8S"'7| I6jH1 b/of6E ($77+^UGenomicAlignments/inst/0000755000175200017520000000000014551577536016176 5ustar00biocbuildbiocbuildGenomicAlignments/inst/CITATION0000644000175200017520000000165414516004601017314 0ustar00biocbuildbiocbuildcitEntry(entry="article", title = "Software for Computing and Annotating Genomic Ranges", author = personList( as.person("Michael Lawrence" ), as.person("Wolfgang Huber" ), as.person("Herv\\'e Pag\\`es" ), as.person("Patrick Aboyoun" ), as.person("Marc Carlson" ), as.person("Robert Gentleman" ), as.person("Martin Morgan" ), as.person("Vincent Carey" )), year = 2013, journal = "{PLoS} Computational Biology", volume = "9", issue = "8", doi = "10.1371/journal.pcbi.1003118", url = "http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003118", textVersion = "Lawrence M, Huber W, Pag\\`es H, Aboyoun P, Carlson M, et al. (2013) Software for Computing and Annotating Genomic Ranges. PLoS Comput Biol 9(8): e1003118. doi:10.1371/journal.pcbi.1003118" ) GenomicAlignments/inst/doc/0000755000175200017520000000000014551577536016743 5ustar00biocbuildbiocbuildGenomicAlignments/inst/doc/GenomicAlignmentsIntroduction.R0000644000175200017520000000261514551577455025077 0ustar00biocbuildbiocbuild### R code from vignette source 'GenomicAlignmentsIntroduction.Rnw' ################################################### ### code chunk number 1: options ################################################### options(width=72) ################################################### ### code chunk number 2: install (eval = FALSE) ################################################### ## if (!require("BiocManager")) ## install.packages("BiocManager") ## BiocManager::install("GenomicAlignments") ################################################### ### code chunk number 3: initialize ################################################### library(GenomicAlignments) ################################################### ### code chunk number 4: readGAlignments ################################################### library(GenomicAlignments) aln1_file <- system.file("extdata", "ex1.bam", package="Rsamtools") aln1 <- readGAlignments(aln1_file) aln1 length(aln1) ################################################### ### code chunk number 5: accessors ################################################### head(seqnames(aln1)) seqlevels(aln1) head(strand(aln1)) head(cigar(aln1)) head(qwidth(aln1)) head(start(aln1)) head(end(aln1)) head(width(aln1)) head(njunc(aln1)) ################################################### ### code chunk number 6: SessionInfo ################################################### sessionInfo() GenomicAlignments/inst/doc/GenomicAlignmentsIntroduction.Rnw0000644000175200017520000001302614516004601025415 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{An Introduction to the GenomicAlignments Package} %\VignetteDepends{Rsamtools} %\VignetteKeywords{sequence, sequencing, alignments} %\VignettePackage{GenomicAlignments} \documentclass[10pt]{article} \usepackage{times} \usepackage{hyperref} \textwidth=6.5in \textheight=8.5in %\parskip=.3cm \oddsidemargin=-.1in \evensidemargin=-.1in \headheight=-.3in \newcommand{\Rfunction}[1]{{\texttt{#1}}} \newcommand{\Robject}[1]{{\texttt{#1}}} \newcommand{\Rpackage}[1]{{\textit{#1}}} \newcommand{\Rmethod}[1]{{\texttt{#1}}} \newcommand{\Rfunarg}[1]{{\texttt{#1}}} \newcommand{\Rclass}[1]{{\textit{#1}}} \newcommand{\Rcode}[1]{{\texttt{#1}}} \newcommand{\software}[1]{\textsf{#1}} \newcommand{\R}{\software{R}} \newcommand{\Bioconductor}{\software{Bioconductor}} \newcommand{\GenomicRanges}{\Rpackage{GenomicRanges}} \title{An Introduction to the GenomicAlignments Package} \author{Herv\'{e} Pag\`{e}s} \date{\today} \begin{document} \maketitle <>= options(width=72) @ \tableofcontents \section{Introduction} The \Rpackage{GenomicAlignments} package serves as the foundation for representing genomic alignments within the \software{Bioconductor} project. In the \software{Bioconductor} package hierarchy, it builds upon the \Rpackage{GenomicRanges} (infrastructure) package and provides support for many \software{Bioconductor} packages. This package defines three classes: \Rclass{GAlignments}, \Rclass{GAlignmentPairs}, and \Rclass{GAlignmentsList}), which are used to represent genomic alignments, pairs of genomic alignments, and groups of genomic alignments. The \Rpackage{GenomicAlignments} package is available at bioconductor.org and can be downloaded via \Rfunction{BiocManager::install}: <>= if (!require("BiocManager")) install.packages("BiocManager") BiocManager::install("GenomicAlignments") @ <>= library(GenomicAlignments) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{\Rclass{GAlignments}: Genomic Alignments} The \Rclass{GAlignments} class which is a container for storing a set of genomic alignments. The class is intended to support alignments in general, not only those coming from a 'Binary Alignment Map' or 'BAM' files. Also alignments with gaps in the reference sequence (a.k.a. \emph{gapped alignments}) are supported which, for example, makes the class suited for storing junction reads from an RNA-seq experiment. More precisely, a \Rclass{GAlignments} object is a vector-like object where each element describes an \emph{alignment}, that is, how a given sequence (called \emph{query} or \emph{read}, typically short) aligns to a reference sequence (typically long). As shown later in this document, a \Rclass{GAlignments} object can be created from a 'BAM' file. In that case, each element in the resulting object will correspond to a record in the file. One important thing to note though is that not all the information present in the BAM/SAM records is stored in the object. In particular, for now, we discard the query sequences (SEQ field), the query ids (QNAME field), the query qualities (QUAL), the mapping qualities (MAPQ) and any other information that is not needed in order to support the basic set of operations described in this document. This also means that multi-reads (i.e. reads with multiple hits in the reference) don't receive any special treatment i.e. the various SAM/BAM records corresponding to a multi-read will show up in the \Rclass{GAlignments} object as if they were coming from different/unrelated queries. Also paired-end reads will be treated as single-end reads and the pairing information will be lost. This might change in the future. \subsection{Load a `BAM' file into a \Rclass{GAlignments} object} First we use the \Rfunction{readGAlignments} function from the \Rpackage{GenomicAlignments} package to load a toy `BAM' file into a \Rclass{GAlignments} object: <>= library(GenomicAlignments) aln1_file <- system.file("extdata", "ex1.bam", package="Rsamtools") aln1 <- readGAlignments(aln1_file) aln1 length(aln1) @ 3271 `BAM' records were loaded into the object. Note that \Rfunction{readGAlignments} would have discarded any `BAM' record describing an unaligned query (see description of the field in the SAM Format Specification \footnote{\url{http://samtools.sourceforge.net/SAM1.pdf}} for more information). The reader interested in tracking down these events can always use the \Rfunction{scanBam} function but this goes beyond the scope of this document. \subsection{Simple accessor methods} There is one accessor per field displayed by the \Rmethod{show} method and it has the same name as the field. All of them return a vector or factor of the same length as the object: <>= head(seqnames(aln1)) seqlevels(aln1) head(strand(aln1)) head(cigar(aln1)) head(qwidth(aln1)) head(start(aln1)) head(end(aln1)) head(width(aln1)) head(njunc(aln1)) @ \subsection{More accessor methods} [coming soon...] %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{\Rclass{GAlignmentPairs}: Pairs of Genomic Alignments} [coming soon...] %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{\Rclass{GAlignmentsList}: Groups of Genomic Alignments} [coming soon...] %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Session Information} All of the output in this vignette was produced under the following conditions: \begin{small} <>= sessionInfo() @ \end{small} \end{document} GenomicAlignments/inst/doc/GenomicAlignmentsIntroduction.pdf0000644000175200017520000027066514551577455025463 0ustar00biocbuildbiocbuild%PDF-1.5 % 45 0 obj << /Length 2010 /Filter /FlateDecode >> stream xYK6Whs1Uk!oz7dnV#Ab%)οn48d h4oϙJn+2+- +YV%7z#T|_uM;o;_GIW[W~{SCu类%k`nhU DXUqjBЪtS -Xm`~21! ζy)\ +.Y&uXv tHTou+8!ZH0)eM2:c)70' Fҍ_B~ۉCR כ QJ>aԳ6h %?vG,=~Tr%}OSQNWu/`k10'g_?%7Ks HLJ8YZ@J@kH*I ͥoO65307Ua'}EtYEo_oj)B]XsTBWA[R!Z-1'l)3Mxr dK+ϢwmǵJZ%@T&ßerir宥S}e,).Cak}ol5s@iA!ws6EU8J̗~bөn:V8qHsHs>bd&.\ܲ mjc>t9$bUHtԼqmiֵ. 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ahfhD!]*gBC;l6dUiЧZ!#*m4LD=[MYyH,RvbIjYjEhlZWM|Ymfs: oҫpP (BI >?E$d endstream endobj startxref 94110 %%EOF GenomicAlignments/inst/doc/OverlapEncodings.R0000644000175200017520000007642714551577513022343 0ustar00biocbuildbiocbuild### R code from vignette source 'OverlapEncodings.Rnw' ################################################### ### code chunk number 1: style ################################################### BiocStyle::latex() ################################################### ### code chunk number 2: options ################################################### options(width=100) .precomputed_results_dir <- "precomputed_results" .loadPrecomputed <- function(objname) { filename <- paste0(objname, ".rda") path <- file.path(.precomputed_results_dir, filename) tempenv <- new.env(parent=emptyenv()) load(path, envir=tempenv) updateObject(get(objname, envir=tempenv)) } .checkIdenticalToPrecomputed <- function(obj, objname, ignore.metadata=FALSE) { precomputed_obj <- .loadPrecomputed(objname) if (ignore.metadata) metadata(obj) <- metadata(precomputed_obj) <- list() ## Replace NAs with FALSE in circularity flag (because having the flag set ## to NA instead of FALSE (or vice-versa) is not considered a significant ## difference between the 2 objects). isCircular(obj) <- isCircular(obj) %in% TRUE isCircular(precomputed_obj) <- isCircular(precomputed_obj) %in% TRUE if (!identical(obj, precomputed_obj)) stop("'", objname, "' is not identical to precomputed version") } ################################################### ### code chunk number 3: untreated1_chr4 ################################################### library(pasillaBamSubset) untreated1_chr4() ################################################### ### code chunk number 4: readGAlignments ################################################### library(GenomicAlignments) flag0 <- scanBamFlag(isDuplicate=FALSE, isNotPassingQualityControls=FALSE) param0 <- ScanBamParam(flag=flag0) U1.GAL <- readGAlignments(untreated1_chr4(), use.names=TRUE, param=param0) head(U1.GAL) ################################################### ### code chunk number 5: U1.GAL_names_is_dup ################################################### U1.GAL_names_is_dup <- duplicated(names(U1.GAL)) table(U1.GAL_names_is_dup) ################################################### ### code chunk number 6: U1.GAL_qnames ################################################### U1.uqnames <- unique(names(U1.GAL)) U1.GAL_qnames <- factor(names(U1.GAL), levels=U1.uqnames) ################################################### ### code chunk number 7: U1.GAL_dup2unq ################################################### U1.GAL_dup2unq <- match(U1.GAL_qnames, U1.GAL_qnames) ################################################### ### code chunk number 8: skipped-regions-in-U1.GAL ################################################### head(unique(cigar(U1.GAL))) table(njunc(U1.GAL)) ################################################### ### code chunk number 9: no-indels-in-U1.GAL ################################################### colSums(cigarOpTable(cigar(U1.GAL))) ################################################### ### code chunk number 10: readGAlignmentPairs ################################################### U3.galp <- readGAlignmentPairs(untreated3_chr4(), use.names=TRUE, param=param0) head(U3.galp) ################################################### ### code chunk number 11: first-and-last-U3.galp ################################################### head(first(U3.galp)) head(last(U3.galp)) ################################################### ### code chunk number 12: isProperPair ################################################### table(isProperPair(U3.galp)) ################################################### ### code chunk number 13: keep-only-proper-pairs ################################################### U3.GALP <- U3.galp[isProperPair(U3.galp)] ################################################### ### code chunk number 14: U3.GALP_names_is_dup ################################################### U3.GALP_names_is_dup <- duplicated(names(U3.GALP)) table(U3.GALP_names_is_dup) ################################################### ### code chunk number 15: U3.GALP_qnames ################################################### U3.uqnames <- unique(names(U3.GALP)) U3.GALP_qnames <- factor(names(U3.GALP), levels=U3.uqnames) ################################################### ### code chunk number 16: U3.GALP_dup2unq ################################################### U3.GALP_dup2unq <- match(U3.GALP_qnames, U3.GALP_qnames) ################################################### ### code chunk number 17: skipped-region-in-U3.GALP ################################################### head(unique(cigar(first(U3.GALP)))) head(unique(cigar(last(U3.GALP)))) table(njunc(first(U3.GALP)), njunc(last(U3.GALP))) ################################################### ### code chunk number 18: no-indels-in-U3.GALP ################################################### colSums(cigarOpTable(cigar(first(U3.GALP)))) colSums(cigarOpTable(cigar(last(U3.GALP)))) ################################################### ### code chunk number 19: txdb ################################################### library(TxDb.Dmelanogaster.UCSC.dm3.ensGene) TxDb.Dmelanogaster.UCSC.dm3.ensGene txdb <- TxDb.Dmelanogaster.UCSC.dm3.ensGene ################################################### ### code chunk number 20: exbytx ################################################### exbytx <- exonsBy(txdb, by="tx", use.names=TRUE) length(exbytx) # nb of transcripts ################################################### ### code chunk number 21: CHECK_exbytx ################################################### .checkIdenticalToPrecomputed(exbytx, "exbytx", ignore.metadata=TRUE) ################################################### ### code chunk number 22: check-for-trans-splicing-in-exbytx ################################################### table(elementNROWS(runLength(seqnames(exbytx)))) table(elementNROWS(runLength(strand(exbytx)))) ################################################### ### code chunk number 23: exbytx_strand ################################################### exbytx_strand <- unlist(runValue(strand(exbytx)), use.names=FALSE) ################################################### ### code chunk number 24: exbytx2gene ################################################### tx <- transcripts(txdb, columns=c("tx_name", "gene_id")) head(tx) df <- mcols(tx) exbytx2gene <- as.character(df$gene_id) exbytx2gene <- factor(exbytx2gene, levels=unique(exbytx2gene)) names(exbytx2gene) <- df$tx_name exbytx2gene <- exbytx2gene[names(exbytx)] head(exbytx2gene) nlevels(exbytx2gene) # nb of genes ################################################### ### code chunk number 25: U1.OV00 ################################################### U1.OV00 <- findOverlaps(U1.GAL, exbytx, ignore.strand=TRUE) ################################################### ### code chunk number 26: length-of-U1.OV00 ################################################### length(U1.OV00) ################################################### ### code chunk number 27: U1.GAL_ntx ################################################### U1.GAL_ntx <- countQueryHits(U1.OV00) mcols(U1.GAL)$ntx <- U1.GAL_ntx head(U1.GAL) table(U1.GAL_ntx) mean(U1.GAL_ntx >= 1) ################################################### ### code chunk number 28: U1.GAL_ntx_again (eval = FALSE) ################################################### ## U1.GAL_ntx_again <- countOverlaps(U1.GAL, exbytx, ignore.strand=TRUE) ## stopifnot(identical(unname(U1.GAL_ntx_again), U1.GAL_ntx)) ################################################### ### code chunk number 29: U1.uqnames_ntx ################################################### U1.OV10 <- remapHits(U1.OV00, Lnodes.remapping=U1.GAL_qnames) U1.uqnames_ntx <- countQueryHits(U1.OV10) names(U1.uqnames_ntx) <- U1.uqnames table(U1.uqnames_ntx) mean(U1.uqnames_ntx >= 1) ################################################### ### code chunk number 30: U1.exbytx_nOV10 ################################################### U1.exbytx_nOV10 <- countSubjectHits(U1.OV10) names(U1.exbytx_nOV10) <- names(exbytx) mean(U1.exbytx_nOV10 >= 50) ################################################### ### code chunk number 31: top-10-transcripts-based-on-U1.exbytx_nOV10 ################################################### head(sort(U1.exbytx_nOV10, decreasing=TRUE), n=10) ################################################### ### code chunk number 32: U3.OV00 ################################################### U3.OV00 <- findOverlaps(U3.GALP, exbytx, ignore.strand=TRUE) ################################################### ### code chunk number 33: length-of-U3.OV00 ################################################### length(U3.OV00) ################################################### ### code chunk number 34: U3.GALP_ntx ################################################### U3.GALP_ntx <- countQueryHits(U3.OV00) mcols(U3.GALP)$ntx <- U3.GALP_ntx head(U3.GALP) table(U3.GALP_ntx) mean(U3.GALP_ntx >= 1) ################################################### ### code chunk number 35: U3.GALP_ntx_again (eval = FALSE) ################################################### ## U3.GALP_ntx_again <- countOverlaps(U3.GALP, exbytx, ignore.strand=TRUE) ## stopifnot(identical(unname(U3.GALP_ntx_again), U3.GALP_ntx)) ################################################### ### code chunk number 36: U3.uqnames_ntx ################################################### U3.OV10 <- remapHits(U3.OV00, Lnodes.remapping=U3.GALP_qnames) U3.uqnames_ntx <- countQueryHits(U3.OV10) names(U3.uqnames_ntx) <- U3.uqnames table(U3.uqnames_ntx) mean(U3.uqnames_ntx >= 1) ################################################### ### code chunk number 37: U3.exbytx_nOV10 ################################################### U3.exbytx_nOV10 <- countSubjectHits(U3.OV10) names(U3.exbytx_nOV10) <- names(exbytx) mean(U3.exbytx_nOV10 >= 50) ################################################### ### code chunk number 38: top-10-transcripts-based-on-U3.exbytx_nOV10 ################################################### head(sort(U3.exbytx_nOV10, decreasing=TRUE), n=10) ################################################### ### code chunk number 39: U1.grl_and_U1.grlf ################################################### U1.grl <- grglist(U1.GAL, order.as.in.query=TRUE) U1.grlf <- flipQuery(U1.grl) # flipped ################################################### ### code chunk number 40: U1.ovencAB ################################################### U1.ovencA <- encodeOverlaps(U1.grl, exbytx, hits=U1.OV00) U1.ovencB <- encodeOverlaps(U1.grlf, exbytx, hits=U1.OV00) ################################################### ### code chunk number 41: U1.ovenc ################################################### U1.grl_strand <- unlist(runValue(strand(U1.grl)), use.names=FALSE) U1.ovenc <- selectEncodingWithCompatibleStrand(U1.ovencA, U1.ovencB, U1.grl_strand, exbytx_strand, hits=U1.OV00) U1.ovenc ################################################### ### code chunk number 42: U1.ovenc_again ################################################### U1.ovenc_again <- encodeOverlaps(U1.grl, exbytx, hits=U1.OV00, flip.query.if.wrong.strand=TRUE) stopifnot(identical(U1.ovenc_again, U1.ovenc)) ################################################### ### code chunk number 43: U1.ovenc_table ################################################### U1.unique_encodings <- levels(U1.ovenc) length(U1.unique_encodings) head(U1.unique_encodings) U1.ovenc_table <- table(encoding(U1.ovenc)) tail(sort(U1.ovenc_table)) ################################################### ### code chunk number 44: U3.ovenc ################################################### U3.grl <- grglist(U3.GALP) U3.ovenc <- encodeOverlaps(U3.grl, exbytx, hits=U3.OV00, flip.query.if.wrong.strand=TRUE) U3.ovenc ################################################### ### code chunk number 45: U3.ovenc_table ################################################### U3.unique_encodings <- levels(U3.ovenc) length(U3.unique_encodings) head(U3.unique_encodings) U3.ovenc_table <- table(encoding(U3.ovenc)) tail(sort(U3.ovenc_table)) ################################################### ### code chunk number 46: U1-unique-compatible-encodings ################################################### sort(U1.ovenc_table[isCompatibleWithSplicing(U1.unique_encodings)]) ################################################### ### code chunk number 47: U1.OV00_is_comp ################################################### U1.OV00_is_comp <- isCompatibleWithSplicing(U1.ovenc) table(U1.OV00_is_comp) # 531797 "splice compatible" overlaps ################################################### ### code chunk number 48: U1.compOV00 ################################################### U1.compOV00 <- U1.OV00[U1.OV00_is_comp] ################################################### ### code chunk number 49: U1.compOV00_again (eval = FALSE) ################################################### ## U1.compOV00_again <- findCompatibleOverlaps(U1.GAL, exbytx) ## stopifnot(identical(U1.compOV00_again, U1.compOV00)) ################################################### ### code chunk number 50: U1.GAL_ncomptx ################################################### U1.GAL_ncomptx <- countQueryHits(U1.compOV00) mcols(U1.GAL)$ncomptx <- U1.GAL_ncomptx head(U1.GAL) table(U1.GAL_ncomptx) mean(U1.GAL_ncomptx >= 1) ################################################### ### code chunk number 51: U1.GAL_ncomptx_again (eval = FALSE) ################################################### ## U1.GAL_ncomptx_again <- countCompatibleOverlaps(U1.GAL, exbytx) ## stopifnot(identical(U1.GAL_ncomptx_again, U1.GAL_ncomptx)) ################################################### ### code chunk number 52: U1.uqnames_ncomptx ################################################### U1.compOV10 <- remapHits(U1.compOV00, Lnodes.remapping=U1.GAL_qnames) U1.uqnames_ncomptx <- countQueryHits(U1.compOV10) names(U1.uqnames_ncomptx) <- U1.uqnames table(U1.uqnames_ncomptx) mean(U1.uqnames_ncomptx >= 1) ################################################### ### code chunk number 53: U1.exbytx_ncompOV10 ################################################### U1.exbytx_ncompOV10 <- countSubjectHits(U1.compOV10) names(U1.exbytx_ncompOV10) <- names(exbytx) mean(U1.exbytx_ncompOV10 >= 50) ################################################### ### code chunk number 54: top-10-transcripts-based-on-U1.exbytx_ncompOV10 ################################################### head(sort(U1.exbytx_ncompOV10, decreasing=TRUE), n=10) ################################################### ### code chunk number 55: U3-unique-compatible-encodings ################################################### sort(U3.ovenc_table[isCompatibleWithSplicing(U3.unique_encodings)]) ################################################### ### code chunk number 56: U3.OV00_is_comp ################################################### U3.OV00_is_comp <- isCompatibleWithSplicing(U3.ovenc) table(U3.OV00_is_comp) # 106835 "splice compatible" paired-end overlaps ################################################### ### code chunk number 57: U3.compOV00 ################################################### U3.compOV00 <- U3.OV00[U3.OV00_is_comp] ################################################### ### code chunk number 58: U3.compOV00_again (eval = FALSE) ################################################### ## U3.compOV00_again <- findCompatibleOverlaps(U3.GALP, exbytx) ## stopifnot(identical(U3.compOV00_again, U3.compOV00)) ################################################### ### code chunk number 59: U3.GALP_ncomptx ################################################### U3.GALP_ncomptx <- countQueryHits(U3.compOV00) mcols(U3.GALP)$ncomptx <- U3.GALP_ncomptx head(U3.GALP) table(U3.GALP_ncomptx) mean(U3.GALP_ncomptx >= 1) ################################################### ### code chunk number 60: U3.GALP_ncomptx_again (eval = FALSE) ################################################### ## U3.GALP_ncomptx_again <- countCompatibleOverlaps(U3.GALP, exbytx) ## stopifnot(identical(U3.GALP_ncomptx_again, U3.GALP_ncomptx)) ################################################### ### code chunk number 61: U3.uqnames_ncomptx ################################################### U3.compOV10 <- remapHits(U3.compOV00, Lnodes.remapping=U3.GALP_qnames) U3.uqnames_ncomptx <- countQueryHits(U3.compOV10) names(U3.uqnames_ncomptx) <- U3.uqnames table(U3.uqnames_ncomptx) mean(U3.uqnames_ncomptx >= 1) ################################################### ### code chunk number 62: U3.exbytx_ncompOV10 ################################################### U3.exbytx_ncompOV10 <- countSubjectHits(U3.compOV10) names(U3.exbytx_ncompOV10) <- names(exbytx) mean(U3.exbytx_ncompOV10 >= 50) ################################################### ### code chunk number 63: top-10-transcripts-based-on-U3.exbytx_ncompOV10 ################################################### head(sort(U3.exbytx_ncompOV10, decreasing=TRUE), n=10) ################################################### ### code chunk number 64: Dmelanogaster ################################################### library(BSgenome.Dmelanogaster.UCSC.dm3) Dmelanogaster ################################################### ### code chunk number 65: U1-reference-query-sequences ################################################### library(GenomicFeatures) U1.GAL_rqseq <- extractTranscriptSeqs(Dmelanogaster, U1.grl) head(U1.GAL_rqseq) ################################################### ### code chunk number 66: U3.grl_first-and-U3.grl_last ################################################### U3.grl_first <- grglist(first(U3.GALP, real.strand=TRUE), order.as.in.query=TRUE) U3.grl_last <- grglist(last(U3.GALP, real.strand=TRUE), order.as.in.query=TRUE) ################################################### ### code chunk number 67: U3-reference-query-sequences ################################################### U3.GALP_rqseq1 <- extractTranscriptSeqs(Dmelanogaster, U3.grl_first) U3.GALP_rqseq2 <- extractTranscriptSeqs(Dmelanogaster, U3.grl_last) ################################################### ### code chunk number 68: U1.OV00_qstart ################################################### U1.OV00_qstart <- extractQueryStartInTranscript(U1.grl, exbytx, hits=U1.OV00, ovenc=U1.ovenc) head(subset(U1.OV00_qstart, U1.OV00_is_comp)) ################################################### ### code chunk number 69: txseq ################################################### txseq <- extractTranscriptSeqs(Dmelanogaster, exbytx) ################################################### ### code chunk number 70: U1.OV00_rqseq-vs-U1.OV00_txseq ################################################### U1.OV00_rqseq <- U1.GAL_rqseq[queryHits(U1.OV00)] U1.OV00_rqseq[flippedQuery(U1.ovenc)] <- reverseComplement(U1.OV00_rqseq[flippedQuery(U1.ovenc)]) U1.OV00_txseq <- txseq[subjectHits(U1.OV00)] stopifnot(all( U1.OV00_rqseq[U1.OV00_is_comp] == narrow(U1.OV00_txseq[U1.OV00_is_comp], start=U1.OV00_qstart$startInTranscript[U1.OV00_is_comp], width=width(U1.OV00_rqseq)[U1.OV00_is_comp]) )) ################################################### ### code chunk number 71: U3.OV00_Lqstart ################################################### U3.OV00_Lqstart <- extractQueryStartInTranscript(U3.grl, exbytx, hits=U3.OV00, ovenc=U3.ovenc) head(subset(U3.OV00_Lqstart, U3.OV00_is_comp)) ################################################### ### code chunk number 72: U3.OV00_Rqstart ################################################### U3.OV00_Rqstart <- extractQueryStartInTranscript(U3.grl, exbytx, hits=U3.OV00, ovenc=U3.ovenc, for.query.right.end=TRUE) head(subset(U3.OV00_Rqstart, U3.OV00_is_comp)) ################################################### ### code chunk number 73: U3.OV00_Lrqseq_and_Rrqseq ################################################### U3.OV00_Lrqseq <- U3.GALP_rqseq1[queryHits(U3.OV00)] U3.OV00_Rrqseq <- U3.GALP_rqseq2[queryHits(U3.OV00)] ################################################### ### code chunk number 74: U3.OV00_Lrqseq_and_Rrqseq ################################################### flip_idx <- which(flippedQuery(U3.ovenc)) tmp <- U3.OV00_Lrqseq[flip_idx] U3.OV00_Lrqseq[flip_idx] <- reverseComplement(U3.OV00_Rrqseq[flip_idx]) U3.OV00_Rrqseq[flip_idx] <- reverseComplement(tmp) ################################################### ### code chunk number 75: U3.OV00_txseq ################################################### U3.OV00_txseq <- txseq[subjectHits(U3.OV00)] ################################################### ### code chunk number 76: U3.OV00_Lrqseq-vs-U3.OV00_txseq ################################################### stopifnot(all( U3.OV00_Lrqseq[U3.OV00_is_comp] == narrow(U3.OV00_txseq[U3.OV00_is_comp], start=U3.OV00_Lqstart$startInTranscript[U3.OV00_is_comp], width=width(U3.OV00_Lrqseq)[U3.OV00_is_comp]) )) ################################################### ### code chunk number 77: U3.OV00_Rrqseq-vs-U3.OV00_txseq ################################################### stopifnot(all( U3.OV00_Rrqseq[U3.OV00_is_comp] == narrow(U3.OV00_txseq[U3.OV00_is_comp], start=U3.OV00_Rqstart$startInTranscript[U3.OV00_is_comp], width=width(U3.OV00_Rrqseq)[U3.OV00_is_comp]) )) ################################################### ### code chunk number 78: findSequenceHits ################################################### ### A wrapper to vwhichPDict() that supports IUPAC ambiguity codes in 'qseq' ### and 'txseq', and treats them as such. findSequenceHits <- function(qseq, txseq, which.txseq=NULL, max.mismatch=0) { .asHits <- function(x, pattern_length) { query_hits <- unlist(x) if (is.null(query_hits)) query_hits <- integer(0) subject_hits <- rep.int(seq_len(length(x)), elementNROWS(x)) Hits(query_hits, subject_hits, pattern_length, length(x), sort.by.query=TRUE) } .isHitInTranscriptBounds <- function(hits, qseq, txseq) { sapply(seq_len(length(hits)), function(i) { pattern <- qseq[[queryHits(hits)[i]]] subject <- txseq[[subjectHits(hits)[i]]] v <- matchPattern(pattern, subject, max.mismatch=max.mismatch, fixed=FALSE) any(1L <= start(v) & end(v) <= length(subject)) }) } if (!is.null(which.txseq)) { txseq0 <- txseq txseq <- txseq[which.txseq] } names(qseq) <- NULL other <- alphabetFrequency(qseq, baseOnly=TRUE)[ , "other"] is_clean <- other == 0L # "clean" means "no IUPAC ambiguity code" ## Find hits for "clean" original queries. qseq0 <- qseq[is_clean] pdict0 <- PDict(qseq0, max.mismatch=max.mismatch) m0 <- vwhichPDict(pdict0, txseq, max.mismatch=max.mismatch, fixed="pattern") hits0 <- .asHits(m0, length(qseq0)) hits0@nLnode <- length(qseq) hits0@from <- which(is_clean)[hits0@from] ## Find hits for non "clean" original queries. qseq1 <- qseq[!is_clean] m1 <- vwhichPDict(qseq1, txseq, max.mismatch=max.mismatch, fixed=FALSE) hits1 <- .asHits(m1, length(qseq1)) hits1@nLnode <- length(qseq) hits1@from <- which(!is_clean)[hits1@from] ## Combine the hits. query_hits <- c(queryHits(hits0), queryHits(hits1)) subject_hits <- c(subjectHits(hits0), subjectHits(hits1)) if (!is.null(which.txseq)) { ## Remap the hits. txseq <- txseq0 subject_hits <- which.txseq[subject_hits] hits0@nRnode <- length(txseq) } ## Order the hits. oo <- orderIntegerPairs(query_hits, subject_hits) hits0@from <- query_hits[oo] hits0@to <- subject_hits[oo] if (max.mismatch != 0L) { ## Keep only "in bounds" hits. is_in_bounds <- .isHitInTranscriptBounds(hits0, qseq, txseq) hits0 <- hits0[is_in_bounds] } hits0 } ################################################### ### code chunk number 79: which.txseq (eval = FALSE) ################################################### ## chr4tx <- transcripts(txdb, vals=list(tx_chrom="chr4")) ## chr4txnames <- mcols(chr4tx)$tx_name ## which.txseq <- match(chr4txnames, names(txseq)) ################################################### ### code chunk number 80: U1.sbcompHITS (eval = FALSE) ################################################### ## U1.sbcompHITSa <- findSequenceHits(U1.oqseq, txseq, ## which.txseq=which.txseq, max.mismatch=6) ## U1.sbcompHITSb <- findSequenceHits(reverseComplement(U1.oqseq), txseq, ## which.txseq=which.txseq, max.mismatch=6) ## U1.sbcompHITS <- union(U1.sbcompHITSa, U1.sbcompHITSb) ################################################### ### code chunk number 81: LOAD_U1.sbcompHITS ################################################### U1.sbcompHITSa <- .loadPrecomputed("U1.sbcompHITSa") U1.sbcompHITSb <- .loadPrecomputed("U1.sbcompHITSb") U1.sbcompHITS <- union(U1.sbcompHITSa, U1.sbcompHITSb) ################################################### ### code chunk number 82: U1.uqnames_nsbcomptx ################################################### U1.uqnames_nsbcomptx <- countQueryHits(U1.sbcompHITS) names(U1.uqnames_nsbcomptx) <- U1.uqnames table(U1.uqnames_nsbcomptx) mean(U1.uqnames_nsbcomptx >= 1) ################################################### ### code chunk number 83: U1.exbytx_nsbcompHITS ################################################### U1.exbytx_nsbcompHITS <- countSubjectHits(U1.sbcompHITS) names(U1.exbytx_nsbcompHITS) <- names(exbytx) mean(U1.exbytx_nsbcompHITS >= 50) ################################################### ### code chunk number 84: top-10-transcripts-based-on-U1.exbytx_nsbcompHITS ################################################### head(sort(U1.exbytx_nsbcompHITS, decreasing=TRUE), n=10) ################################################### ### code chunk number 85: encoding-based-compatible-implies-string-based-compatible ################################################### stopifnot(length(setdiff(U1.compOV10, U1.sbcompHITS)) == 0) ################################################### ### code chunk number 86: string-based-compatible-does-NOT-imply-encoding-based-compatible ################################################### length(setdiff(U1.sbcompHITS, U1.compOV10)) ################################################### ### code chunk number 87: U1-unique-almost-compatible-encodings ################################################### sort(U1.ovenc_table[isCompatibleWithSkippedExons(U1.unique_encodings)]) ################################################### ### code chunk number 88: U1.OV00_is_acomp ################################################### U1.OV00_is_acomp <- isCompatibleWithSkippedExons(U1.ovenc) table(U1.OV00_is_acomp) # 1202 "almost splice compatible" overlaps ################################################### ### code chunk number 89: U1.acompOV00 ################################################### U1.acompOV00 <- U1.OV00[U1.OV00_is_acomp] ################################################### ### code chunk number 90: U1.GAL_nacomptx ################################################### U1.GAL_nacomptx <- countQueryHits(U1.acompOV00) mcols(U1.GAL)$nacomptx <- U1.GAL_nacomptx head(U1.GAL) table(U1.GAL_nacomptx) mean(U1.GAL_nacomptx >= 1) ################################################### ### code chunk number 91: U1.exbytx_nacompOV00 ################################################### U1.exbytx_nacompOV00 <- countSubjectHits(U1.acompOV00) names(U1.exbytx_nacompOV00) <- names(exbytx) table(U1.exbytx_nacompOV00) mean(U1.exbytx_nacompOV00 >= 50) ################################################### ### code chunk number 92: U1.OV00_qstart ################################################### head(subset(U1.OV00_qstart, U1.OV00_is_acomp)) ################################################### ### code chunk number 93: U3-unique-almost-compatible-encodings ################################################### sort(U3.ovenc_table[isCompatibleWithSkippedExons(U3.unique_encodings)]) ################################################### ### code chunk number 94: U3.OV00_is_acomp ################################################### U3.OV00_is_acomp <- isCompatibleWithSkippedExons(U3.ovenc) table(U3.OV00_is_acomp) # 141 "almost splice compatible" paired-end overlaps ################################################### ### code chunk number 95: U3.acompOV00 ################################################### U3.acompOV00 <- U3.OV00[U3.OV00_is_acomp] ################################################### ### code chunk number 96: U3.GALP_nacomptx ################################################### U3.GALP_nacomptx <- countQueryHits(U3.acompOV00) mcols(U3.GALP)$nacomptx <- U3.GALP_nacomptx head(U3.GALP) table(U3.GALP_nacomptx) mean(U3.GALP_nacomptx >= 1) ################################################### ### code chunk number 97: U3.exbytx_nacompOV00 ################################################### U3.exbytx_nacompOV00 <- countSubjectHits(U3.acompOV00) names(U3.exbytx_nacompOV00) <- names(exbytx) table(U3.exbytx_nacompOV00) mean(U3.exbytx_nacompOV00 >= 50) ################################################### ### code chunk number 98: U3.OV00_Lqstart-and-U3.OV00_Rqstart ################################################### head(subset(U3.OV00_Lqstart, U3.OV00_is_acomp)) head(subset(U3.OV00_Rqstart, U3.OV00_is_acomp)) ################################################### ### code chunk number 99: U1.GAL_is_nsj ################################################### U1.GAL_is_nsj <- U1.GAL_nacomptx != 0L & U1.GAL_ncomptx == 0L head(which(U1.GAL_is_nsj)) ################################################### ### code chunk number 100: U1.OV00_is_nsj ################################################### U1.OV00_is_nsj <- queryHits(U1.OV00) %in% which(U1.GAL_is_nsj) ################################################### ### code chunk number 101: narrow-U1.OV00_is_nsj ################################################### U1.OV00_is_nsj <- U1.OV00_is_nsj & U1.OV00_is_acomp U1.nsjOV00 <- U1.OV00[U1.OV00_is_nsj] ################################################### ### code chunk number 102: U1.nsjOV00_skippedex ################################################### U1.nsjOV00_skippedex <- extractSkippedExonRanks(U1.ovenc)[U1.OV00_is_nsj] names(U1.nsjOV00_skippedex) <- queryHits(U1.nsjOV00) table(elementNROWS(U1.nsjOV00_skippedex)) ################################################### ### code chunk number 103: U1.exbytx_skippedex ################################################### f <- factor(names(exbytx)[subjectHits(U1.nsjOV00)], levels=names(exbytx)) U1.exbytx_skippedex <- split(U1.nsjOV00_skippedex, f) ################################################### ### code chunk number 104: names-of-U1.exbytx_skippedex ################################################### head(names(U1.exbytx_skippedex)) # transcript names ################################################### ### code chunk number 105: FBtr0089124-skipped-exons ################################################### U1.exbytx_skippedex$FBtr0089124 ################################################### ### code chunk number 106: FBtr0089147-skipped-exons ################################################### U1.exbytx_skippedex$FBtr0089147 ################################################### ### code chunk number 107: sessionInfo ################################################### sessionInfo() GenomicAlignments/inst/doc/OverlapEncodings.Rnw0000644000175200017520000016240314516004601022656 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{Overlap encodings} %\VignetteDepends{pasillaBamSubset, GenomicAlignments, GenomicFeatures, BSgenome.Dmelanogaster.UCSC.dm3, TxDb.Dmelanogaster.UCSC.dm3.ensGene} %\VignetteKeywords{sequence, sequencing, alignments} %\VignettePackage{GenomicAlignments} \documentclass{article} <>= BiocStyle::latex() @ \title{Overlap encodings} \author{Herv\'e Pag\`es} \date{Last modified: December 2016; Compiled: \today} \begin{document} \maketitle <>= options(width=100) .precomputed_results_dir <- "precomputed_results" .loadPrecomputed <- function(objname) { filename <- paste0(objname, ".rda") path <- file.path(.precomputed_results_dir, filename) tempenv <- new.env(parent=emptyenv()) load(path, envir=tempenv) updateObject(get(objname, envir=tempenv)) } .checkIdenticalToPrecomputed <- function(obj, objname, ignore.metadata=FALSE) { precomputed_obj <- .loadPrecomputed(objname) if (ignore.metadata) metadata(obj) <- metadata(precomputed_obj) <- list() ## Replace NAs with FALSE in circularity flag (because having the flag set ## to NA instead of FALSE (or vice-versa) is not considered a significant ## difference between the 2 objects). isCircular(obj) <- isCircular(obj) %in% TRUE isCircular(precomputed_obj) <- isCircular(precomputed_obj) %in% TRUE if (!identical(obj, precomputed_obj)) stop("'", objname, "' is not identical to precomputed version") } @ \tableofcontents %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Introduction} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% In the context of an RNA-seq experiment, encoding the overlaps between the aligned reads and the transcripts can be used for detecting those overlaps that are ``splice compatible'', that is, compatible with the splicing of the transcript. Various tools are provided in the \Rpackage{GenomicAlignments} package for working with {\it overlap encodings}. In this vignette, we illustrate the use of these tools on the single-end and paired-end reads of an RNA-seq experiment. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Load reads from a BAM file} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Load single-end reads from a BAM file} BAM file {\tt untreated1\_chr4.bam} (located in the \Rpackage{pasillaBamSubset} data package) contains single-end reads from the ``Pasilla'' experiment and aligned against the dm3 genome (see \Rcode{?untreated1\_chr4} in the \Rpackage{pasillaBamSubset} package for more information about those reads): <>= library(pasillaBamSubset) untreated1_chr4() @ We use the \Rfunction{readGAlignments} function defined in the \Rpackage{GenomicAlignments} package to load the reads into a \Rclass{GAlignments} object. It's probably a good idea to get rid of the PCR or optical duplicates (flag bit 0x400 in the SAM format, see the SAM Spec \footnote{\url{http://samtools.sourceforge.net/}} for the details), as well as reads not passing quality controls (flag bit 0x200 in the SAM format). We do this by creating a \Rclass{ScanBamParam} object that we pass to \Rcode{readGAlignments} (see \Rcode{?ScanBamParam} in the \Rpackage{Rsamtools} package for the details). Note that we also use \Rcode{use.names=TRUE} in order to load the {\it query names} (aka {\it query template names}, see QNAME field in the SAM Spec) from the BAM file (\Rcode{readGAlignments} will use them to set the names of the returned object): <>= library(GenomicAlignments) flag0 <- scanBamFlag(isDuplicate=FALSE, isNotPassingQualityControls=FALSE) param0 <- ScanBamParam(flag=flag0) U1.GAL <- readGAlignments(untreated1_chr4(), use.names=TRUE, param=param0) head(U1.GAL) @ Because the aligner used to align those reads can report more than 1 alignment per {\it original query} (i.e. per read stored in the input file, typically a FASTQ file), we shouldn't expect the names of \Rcode{U1.GAL} to be unique: <>= U1.GAL_names_is_dup <- duplicated(names(U1.GAL)) table(U1.GAL_names_is_dup) @ Storing the {\it query names} in a factor will be useful as we will see later in this document: <>= U1.uqnames <- unique(names(U1.GAL)) U1.GAL_qnames <- factor(names(U1.GAL), levels=U1.uqnames) @ Note that we explicitely provide the levels of the factor to enforce their order. Otherwise \Rcode{factor()} would put them in lexicographic order which is not advisable because it depends on the locale in use. Another object that will be useful to keep near at hand is the mapping between each {\it query name} and its first occurence in \Rcode{U1.GAL\_qnames}: <>= U1.GAL_dup2unq <- match(U1.GAL_qnames, U1.GAL_qnames) @ Our reads can have up to 2 {\it skipped regions} (a {\it skipped region} corresponds to an N operation in the CIGAR): <>= head(unique(cigar(U1.GAL))) table(njunc(U1.GAL)) @ Also, the following table indicates that indels were not allowed/supported during the alignment process (no I or D CIGAR operations): <>= colSums(cigarOpTable(cigar(U1.GAL))) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Load paired-end reads from a BAM file} BAM file {\tt untreated3\_chr4.bam} (located in the \Rpackage{pasillaBamSubset} data package) contains paired-end reads from the ``Pasilla'' experiment and aligned against the dm3 genome (see \Rcode{?untreated3\_chr4} in the \Rpackage{pasillaBamSubset} package for more information about those reads). We use the \Rfunction{readGAlignmentPairs} function to load them into a \Rclass{GAlignmentPairs} object: <>= U3.galp <- readGAlignmentPairs(untreated3_chr4(), use.names=TRUE, param=param0) head(U3.galp) @ The \Rcode{show} method for \Rclass{GAlignmentPairs} objects displays two {\tt ranges} columns, one for the {\it first} alignment in the pair (the left column), and one for the {\it last} alignment in the pair (the right column). The {\tt strand} column corresponds to the strand of the {\it first} alignment. <>= head(first(U3.galp)) head(last(U3.galp)) @ According to the SAM format specifications, the aligner is expected to mark each alignment pair as {\it proper} or not (flag bit 0x2 in the SAM format). The SAM Spec only says that a pair is {\it proper} if the {\it first} and {\it last} alignments in the pair are ``properly aligned according to the aligner''. So the exact criteria used for setting this flag is left to the aligner. We use \Rcode{isProperPair} to extract this flag from the \Rclass{GAlignmentPairs} object: <>= table(isProperPair(U3.galp)) @ Even though we could do {\it overlap encodings} with the full object, we keep only the {\it proper} pairs for our downstream analysis: <>= U3.GALP <- U3.galp[isProperPair(U3.galp)] @ Because the aligner used to align those reads can report more than 1 alignment per {\it original query template} (i.e. per pair of sequences stored in the input files, typically 1 FASTQ file for the {\it first} ends and 1 FASTQ file for the {\it last} ends), we shouldn't expect the names of \Rcode{U3.GALP} to be unique: <>= U3.GALP_names_is_dup <- duplicated(names(U3.GALP)) table(U3.GALP_names_is_dup) @ Storing the {\it query template names} in a factor will be useful: <>= U3.uqnames <- unique(names(U3.GALP)) U3.GALP_qnames <- factor(names(U3.GALP), levels=U3.uqnames) @ as well as having the mapping between each {\it query template name} and its first occurence in \Rcode{U3.GALP\_qnames}: <>= U3.GALP_dup2unq <- match(U3.GALP_qnames, U3.GALP_qnames) @ Our reads can have up to 1 {\it skipped region} per end: <>= head(unique(cigar(first(U3.GALP)))) head(unique(cigar(last(U3.GALP)))) table(njunc(first(U3.GALP)), njunc(last(U3.GALP))) @ Like for our single-end reads, the following tables indicate that indels were not allowed/supported during the alignment process: <>= colSums(cigarOpTable(cigar(first(U3.GALP)))) colSums(cigarOpTable(cigar(last(U3.GALP)))) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Find all the overlaps between the reads and transcripts} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Load the transcripts from a \Rclass{TxDb} object} In order to compute overlaps between reads and transcripts, we need access to the genomic positions of a set of known transcripts and their exons. It is essential that the reference genome of this set of transcripts and exons be {\bf exactly} the same as the reference genome used to align the reads. We could use the \Rfunction{makeTxDbFromUCSC} function defined in the \Rpackage{GenomicFeatures} package to make a \Rclass{TxDb} object containing the dm3 transcripts and their exons retrieved from the UCSC Genome Browser\footnote{\url{http://genome.ucsc.edu/cgi-bin/hgGateway}}. The Bioconductor project however provides a few annotation packages containing \Rclass{TxDb} objects for the most commonly studied organisms (those data packages are sometimes called the {\it TxDb} packages). One of them is the \Rpackage{TxDb.Dmelanogaster.\-UCSC.\-dm3.ensGene} package. It contains a \Rclass{TxDb} object that was made by pointing the \Rfunction{makeTxDbFromUCSC} function to the dm3 genome and {\it Ensembl Genes} track \footnote{See \url{http://genome.ucsc.edu/cgi-bin/hgTrackUi?hgsid=276880911&g=ensGene} for a description of this track.}. We can use it here: <>= library(TxDb.Dmelanogaster.UCSC.dm3.ensGene) TxDb.Dmelanogaster.UCSC.dm3.ensGene txdb <- TxDb.Dmelanogaster.UCSC.dm3.ensGene @ We extract the exons grouped by transcript in a \Rclass{GRangesList} object: <>= exbytx <- exonsBy(txdb, by="tx", use.names=TRUE) length(exbytx) # nb of transcripts @ <>= .checkIdenticalToPrecomputed(exbytx, "exbytx", ignore.metadata=TRUE) @ We check that all the exons in any given transcript belong to the same chromosome and strand. Knowing that our set of transcripts is free of this sort of trans-splicing events typically allows some significant simplifications during the downstream analysis \footnote{Dealing with trans-splicing events is not covered in this document.}. A quick and easy way to check this is to take advantage of the fact that \Rcode{seqnames} and \Rcode{strand} return \Rclass{RleList} objects. So we can extract the number of Rle runs for each transcript and make sure it's always 1: <>= table(elementNROWS(runLength(seqnames(exbytx)))) table(elementNROWS(runLength(strand(exbytx)))) @ Therefore the strand of any given transcript is unambiguously defined and can be extracted with: <>= exbytx_strand <- unlist(runValue(strand(exbytx)), use.names=FALSE) @ We will also need the mapping between the transcripts and their gene. We start by using \Rfunction{transcripts} to extract this information from our \Rclass{TxDb} object \Rcode{txdb}, and then we construct a named factor that represents the mapping: <>= tx <- transcripts(txdb, columns=c("tx_name", "gene_id")) head(tx) df <- mcols(tx) exbytx2gene <- as.character(df$gene_id) exbytx2gene <- factor(exbytx2gene, levels=unique(exbytx2gene)) names(exbytx2gene) <- df$tx_name exbytx2gene <- exbytx2gene[names(exbytx)] head(exbytx2gene) nlevels(exbytx2gene) # nb of genes @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Single-end overlaps} \subsubsection{Find the single-end overlaps} We are ready to compute the overlaps with the \Rfunction{findOverlaps} function. Note that the strand of the queries produced by the RNA-seq experiment is typically unknown so we use \Rcode{ignore.strand=TRUE}: <>= U1.OV00 <- findOverlaps(U1.GAL, exbytx, ignore.strand=TRUE) @ \Rcode{U1.OV00} is a \Rclass{Hits} object that contains 1 element per overlap. Its length gives the number of overlaps: <>= length(U1.OV00) @ \subsubsection{Tabulate the single-end overlaps} We will repeatedly use the 2 following little helper functions to ``tabulate'' the overlaps in a given \Rclass{Hits} object (e.g. \Rcode{U1.OV00}), i.e. to count the number of overlaps for each element in the query or for each element in the subject: Number of transcripts for each alignment in \Rcode{U1.GAL}: <>= U1.GAL_ntx <- countQueryHits(U1.OV00) mcols(U1.GAL)$ntx <- U1.GAL_ntx head(U1.GAL) table(U1.GAL_ntx) mean(U1.GAL_ntx >= 1) @ 76\% of the alignments in \Rcode{U1.GAL} have an overlap with at least 1 transcript in \Rcode{exbytx}. Note that \Rfunction{countOverlaps} can be used directly on \Rcode{U1.GAL} and \Rcode{exbytx} for computing \Rcode{U1.GAL\_ntx}: <>= U1.GAL_ntx_again <- countOverlaps(U1.GAL, exbytx, ignore.strand=TRUE) stopifnot(identical(unname(U1.GAL_ntx_again), U1.GAL_ntx)) @ Because \Rcode{U1.GAL} can (and actually does) contain more than 1 alignment per {\it original query} (aka read), we also count the number of transcripts for each read: <>= U1.OV10 <- remapHits(U1.OV00, Lnodes.remapping=U1.GAL_qnames) U1.uqnames_ntx <- countQueryHits(U1.OV10) names(U1.uqnames_ntx) <- U1.uqnames table(U1.uqnames_ntx) mean(U1.uqnames_ntx >= 1) @ 78.4\% of the reads have an overlap with at least 1 transcript in \Rcode{exbytx}. Number of reads for each transcript: <>= U1.exbytx_nOV10 <- countSubjectHits(U1.OV10) names(U1.exbytx_nOV10) <- names(exbytx) mean(U1.exbytx_nOV10 >= 50) @ Only 0.869\% of the transcripts in \Rcode{exbytx} have an overlap with at least 50 reads. Top 10 transcripts: <>= head(sort(U1.exbytx_nOV10, decreasing=TRUE), n=10) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Paired-end overlaps} \subsubsection{Find the paired-end overlaps} Like with our single-end overlaps, we call \Rfunction{findOverlaps} with \Rcode{ignore.strand=TRUE}: <>= U3.OV00 <- findOverlaps(U3.GALP, exbytx, ignore.strand=TRUE) @ Like \Rcode{U1.OV00}, \Rcode{U3.OV00} is a \Rclass{Hits} object. Its length gives the number of paired-end overlaps: <>= length(U3.OV00) @ \subsubsection{Tabulate the paired-end overlaps} Number of transcripts for each alignment pair in \Rcode{U3.GALP}: <>= U3.GALP_ntx <- countQueryHits(U3.OV00) mcols(U3.GALP)$ntx <- U3.GALP_ntx head(U3.GALP) table(U3.GALP_ntx) mean(U3.GALP_ntx >= 1) @ 71\% of the alignment pairs in \Rcode{U3.GALP} have an overlap with at least 1 transcript in \Rcode{exbytx}. Note that \Rfunction{countOverlaps} can be used directly on \Rcode{U3.GALP} and \Rcode{exbytx} for computing \Rcode{U3.GALP\_ntx}: <>= U3.GALP_ntx_again <- countOverlaps(U3.GALP, exbytx, ignore.strand=TRUE) stopifnot(identical(unname(U3.GALP_ntx_again), U3.GALP_ntx)) @ Because \Rcode{U3.GALP} can (and actually does) contain more than 1 alignment pair per {\it original query template}, we also count the number of transcripts for each template: <>= U3.OV10 <- remapHits(U3.OV00, Lnodes.remapping=U3.GALP_qnames) U3.uqnames_ntx <- countQueryHits(U3.OV10) names(U3.uqnames_ntx) <- U3.uqnames table(U3.uqnames_ntx) mean(U3.uqnames_ntx >= 1) @ 72.3\% of the templates have an overlap with at least 1 transcript in \Rcode{exbytx}. Number of templates for each transcript: <>= U3.exbytx_nOV10 <- countSubjectHits(U3.OV10) names(U3.exbytx_nOV10) <- names(exbytx) mean(U3.exbytx_nOV10 >= 50) @ Only 0.756\% of the transcripts in \Rcode{exbytx} have an overlap with at least 50 templates. Top 10 transcripts: <>= head(sort(U3.exbytx_nOV10, decreasing=TRUE), n=10) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Encode the overlaps between the reads and transcripts} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Single-end encodings} The {\it overlap encodings} are strand sensitive so we will compute them twice, once for the ``original alignments'' (i.e. the alignments of the {\it original queries}), and once again for the ``flipped alignments'' (i.e. the alignments of the ``flipped {\it original queries}''). We extract the ranges of the ``original'' and ``flipped'' alignments in 2 \Rclass{GRangesList} objects with: <>= U1.grl <- grglist(U1.GAL, order.as.in.query=TRUE) U1.grlf <- flipQuery(U1.grl) # flipped @ and encode their overlaps with the transcripts: <>= U1.ovencA <- encodeOverlaps(U1.grl, exbytx, hits=U1.OV00) U1.ovencB <- encodeOverlaps(U1.grlf, exbytx, hits=U1.OV00) @ \Rcode{U1.ovencA} and \Rcode{U1.ovencB} are 2 \Rclass{OverlapsEncodings} objects of the same length as \Rclass{Hits} object \Rcode{U1.OV00}. For each hit in \Rcode{U1.OV00}, we have 2 corresponding encodings, one in \Rcode{U1.ovencA} and one in \Rcode{U1.ovencB}, but only one of them encodes a hit between alignment ranges and exon ranges that are on the same strand. We use the \Rfunction{selectEncodingWithCompatibleStrand} function to merge them into a single \Rclass{OverlapsEncodings} of the same length. For each hit in \Rcode{U1.OV00}, this selects the encoding corresponding to alignment ranges and exon ranges with compatible strand: <>= U1.grl_strand <- unlist(runValue(strand(U1.grl)), use.names=FALSE) U1.ovenc <- selectEncodingWithCompatibleStrand(U1.ovencA, U1.ovencB, U1.grl_strand, exbytx_strand, hits=U1.OV00) U1.ovenc @ As a convenience, the 2 above calls to \Rfunction{encodeOverlaps} + merging step can be replaced by a single call to \Rfunction{encodeOverlaps} on \Rcode{U1.grl} (or \Rcode{U1.grlf}) with \Rcode{flip.query.if.wrong.strand=TRUE}: <>= U1.ovenc_again <- encodeOverlaps(U1.grl, exbytx, hits=U1.OV00, flip.query.if.wrong.strand=TRUE) stopifnot(identical(U1.ovenc_again, U1.ovenc)) @ Unique encodings in \Rcode{U1.ovenc}: <>= U1.unique_encodings <- levels(U1.ovenc) length(U1.unique_encodings) head(U1.unique_encodings) U1.ovenc_table <- table(encoding(U1.ovenc)) tail(sort(U1.ovenc_table)) @ Encodings are sort of cryptic but utilities are provided to extract specific meaning from them. Use of these utilities is covered later in this document. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Paired-end encodings} Let's encode the overlaps in \Rcode{U3.OV00}: <>= U3.grl <- grglist(U3.GALP) U3.ovenc <- encodeOverlaps(U3.grl, exbytx, hits=U3.OV00, flip.query.if.wrong.strand=TRUE) U3.ovenc @ Unique encodings in \Rcode{U3.ovenc}: <>= U3.unique_encodings <- levels(U3.ovenc) length(U3.unique_encodings) head(U3.unique_encodings) U3.ovenc_table <- table(encoding(U3.ovenc)) tail(sort(U3.ovenc_table)) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Detect ``splice compatible'' overlaps} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% We are interested in a particular type of overlap where the read overlaps the transcript in a ``splice compatible'' way, that is, in a way that is compatible with the splicing of the transcript. The \Rfunction{isCompatibleWithSplicing} function can be used on an \Rclass{OverlapEncodings} object to detect this type of overlap. Note that \Rfunction{isCompatibleWithSplicing} can also be used on a character vector or factor. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Detect ``splice compatible'' single-end overlaps} \subsubsection{``Splice compatible'' single-end encodings} \Rcode{U1.ovenc} contains 7 unique encodings compatible with the splicing of the transcript: <>= sort(U1.ovenc_table[isCompatibleWithSplicing(U1.unique_encodings)]) @ Encodings \Rcode{"1:i:"} (455176 occurences in \Rcode{U1.ovenc}), \Rcode{"2:jm:af:"} (72929 occurences in \Rcode{U1.ovenc}), and \Rcode{"3:jmm:agm:aaf:"} (488 occurences in \Rcode{U1.ovenc}), correspond to the following overlaps: \begin{itemize} \item \Rcode{"1:i:"} \begin{verbatim} - read (no skipped region): oooooooo - transcript: ... >>>>>>>>>>>>>> ... \end{verbatim} \item \Rcode{"2:jm:af:"} \begin{verbatim} - read (1 skipped region): ooooo---ooo - transcript: ... >>>>>>>>> >>>>>>>>> ... \end{verbatim} \item \Rcode{"3:jmm:agm:aaf:"} \begin{verbatim} - read (2 skipped regions): oo---ooooo---o - transcript: ... >>>>>>>> >>>>> >>>>>>> ... \end{verbatim} \end{itemize} For clarity, only the exons involved in the overlap are represented. The transcript can of course have more upstream and downstream exons, which is denoted by the ... on the left side (5' end) and right side (3' end) of each drawing. Note that the exons represented in the 2nd and 3rd drawings are consecutive and adjacent in the processed transcript. Encodings \Rcode{"1:f:"} and \Rcode{"1:j:"} are variations of the situation described by encoding \Rcode{"1:i:"}. For \Rcode{"1:f:"}, the first aligned base of the read (or ``flipped'' read) is aligned with the first base of the exon. For \Rcode{"1:j:"}, the last aligned base of the read (or ``flipped'' read) is aligned with the last base of the exon: \begin{itemize} \item \Rcode{"1:f:"} \begin{verbatim} - read (no skipped region): oooooooo - transcript: ... >>>>>>>>>>>>>> ... \end{verbatim} \item \Rcode{"1:j:"} \begin{verbatim} - read (no skipped region): oooooooo - transcript: ... >>>>>>>>>>>>>> ... \end{verbatim} \end{itemize} <>= U1.OV00_is_comp <- isCompatibleWithSplicing(U1.ovenc) table(U1.OV00_is_comp) # 531797 "splice compatible" overlaps @ Finally, let's extract the ``splice compatible'' overlaps from \Rcode{U1.OV00}: <>= U1.compOV00 <- U1.OV00[U1.OV00_is_comp] @ Note that high-level convenience wrapper \Rfunction{findCompatibleOverlaps} can be used for computing the ``splice compatible'' overlaps directly between a \Rclass{GAlignments} object (containing reads) and a \Rclass{GRangesList} object (containing transcripts): <>= U1.compOV00_again <- findCompatibleOverlaps(U1.GAL, exbytx) stopifnot(identical(U1.compOV00_again, U1.compOV00)) @ \subsubsection{Tabulate the ``splice compatible'' single-end overlaps} Number of ``splice compatible'' transcripts for each alignment in \Rcode{U1.GAL}: <>= U1.GAL_ncomptx <- countQueryHits(U1.compOV00) mcols(U1.GAL)$ncomptx <- U1.GAL_ncomptx head(U1.GAL) table(U1.GAL_ncomptx) mean(U1.GAL_ncomptx >= 1) @ 75\% of the alignments in \Rcode{U1.GAL} are ``splice compatible'' with at least 1 transcript in \Rcode{exbytx}. Note that high-level convenience wrapper \Rfunction{countCompatibleOverlaps} can be used directly on \Rcode{U1.GAL} and \Rcode{exbytx} for computing \Rcode{U1.GAL\_ncomptx}: <>= U1.GAL_ncomptx_again <- countCompatibleOverlaps(U1.GAL, exbytx) stopifnot(identical(U1.GAL_ncomptx_again, U1.GAL_ncomptx)) @ Number of ``splice compatible'' transcripts for each read: <>= U1.compOV10 <- remapHits(U1.compOV00, Lnodes.remapping=U1.GAL_qnames) U1.uqnames_ncomptx <- countQueryHits(U1.compOV10) names(U1.uqnames_ncomptx) <- U1.uqnames table(U1.uqnames_ncomptx) mean(U1.uqnames_ncomptx >= 1) @ 77.5\% of the reads are ``splice compatible'' with at least 1 transcript in \Rcode{exbytx}. Number of ``splice compatible'' reads for each transcript: <>= U1.exbytx_ncompOV10 <- countSubjectHits(U1.compOV10) names(U1.exbytx_ncompOV10) <- names(exbytx) mean(U1.exbytx_ncompOV10 >= 50) @ Only 0.87\% of the transcripts in \Rcode{exbytx} are ``splice compatible'' with at least 50 reads. Top 10 transcripts: <>= head(sort(U1.exbytx_ncompOV10, decreasing=TRUE), n=10) @ Note that this ``top 10'' is slightly different from the ``top 10'' we obtained earlier when we counted {\bf all} the overlaps. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Detect ``splice compatible'' paired-end overlaps} \subsubsection{``Splice compatible'' paired-end encodings} WARNING: For paired-end encodings, \Rcode{isCompatibleWithSplicing} considers that the encoding is ``splice compatible'' if its 2 halves are ``splice compatible''. This can produce false positives if for example the right end of the alignment is located upstream of the left end in transcript space. The paired-end read could not come from this transcript. To eliminate these false positives, one would need to look at the position of the left and right ends in transcript space. This can be done with \Rcode{extractQueryStartInTranscript}. \Rcode{U3.ovenc} contains 13 unique paired-end encodings compatible with the splicing of the transcript: <>= sort(U3.ovenc_table[isCompatibleWithSplicing(U3.unique_encodings)]) @ Paired-end encodings \Rcode{"1{-}{-}1:i{-}{-}i:"} (100084 occurences in \Rcode{U3.ovenc}), \Rcode{"2{-}{-}1:jm{-}{-}m:af{-}{-}i:"} (2700 occurences in \Rcode{U3.ovenc}), \Rcode{"1{-}{-}2:i{-}{-}jm:a{-}{-}af:"} (2480 occurences in \Rcode{U3.ovenc}), \Rcode{"1{-}{-}1:i{-}{-}m:a{-}{-}i:"} (287 occurences in \Rcode{U3.ovenc}), and \Rcode{"2{-}{-}2:jm{-}{-}mm:af{-}{-}jm:aa{-}{-}af:"} (153 occurences in \Rcode{U3.ovenc}), correspond to the following paired-end overlaps: \begin{itemize} \item \Rcode{"1{-}{-}1:i{-}{-}i:"} \begin{verbatim} - paired-end read (no skipped region on the first end, no skipped region on the last end): oooo oooo - transcript: ... >>>>>>>>>>>>>>>> ... \end{verbatim} \item \Rcode{"2{-}{-}1:jm{-}{-}m:af{-}{-}i:"} \begin{verbatim} - paired-end read (1 skipped region on the first end, no skipped region on the last end): ooo---o oooo - transcript: ... >>>>>>>> >>>>>>>>>>> ... \end{verbatim} \item \Rcode{"1{-}{-}2:i{-}{-}jm:a{-}{-}af:"} \begin{verbatim} - paired-end read (no skipped region on the first end, 1 skipped region on the last end): oooo oo---oo - transcript: ... >>>>>>>>>>>>>> >>>>>>>>> ... \end{verbatim} \item \Rcode{"1{-}{-}1:i{-}{-}m:a{-}{-}i:"} \begin{verbatim} - paired-end read (no skipped region on the first end, no skipped region on the last end): oooo oooo - transcript: ... >>>>>>>>> >>>>>>> ... \end{verbatim} \item \Rcode{"2{-}{-}2:jm{-}{-}mm:af{-}{-}jm:aa{-}{-}af:"} \begin{verbatim} - paired-end read (1 skipped region on the first end, 1 skipped region on the last end): ooo---o oo---oo - transcript: ... >>>>>> >>>>>>> >>>>> ... \end{verbatim} \end{itemize} Note: switch use of ``first'' and ``last'' above if the read was ``flipped''. <>= U3.OV00_is_comp <- isCompatibleWithSplicing(U3.ovenc) table(U3.OV00_is_comp) # 106835 "splice compatible" paired-end overlaps @ Finally, let's extract the ``splice compatible'' paired-end overlaps from \Rcode{U3.OV00}: <>= U3.compOV00 <- U3.OV00[U3.OV00_is_comp] @ Note that, like with our single-end reads, high-level convenience wrapper \Rfunction{findCompatibleOverlaps} can be used for computing the ``splice compatible'' paired-end overlaps directly between a \Rclass{GAlignmentPairs} object (containing paired-end reads) and a \Rclass{GRangesList} object (containing transcripts): <>= U3.compOV00_again <- findCompatibleOverlaps(U3.GALP, exbytx) stopifnot(identical(U3.compOV00_again, U3.compOV00)) @ \subsubsection{Tabulate the ``splice compatible'' paired-end overlaps} Number of ``splice compatible'' transcripts for each alignment pair in \Rcode{U3.GALP}: <>= U3.GALP_ncomptx <- countQueryHits(U3.compOV00) mcols(U3.GALP)$ncomptx <- U3.GALP_ncomptx head(U3.GALP) table(U3.GALP_ncomptx) mean(U3.GALP_ncomptx >= 1) @ 69.7\% of the alignment pairs in \Rcode{U3.GALP} are ``splice compatible'' with at least 1 transcript in \Rcode{exbytx}. Note that high-level convenience wrapper \Rfunction{countCompatibleOverlaps} can be used directly on \Rcode{U3.GALP} and \Rcode{exbytx} for computing \Rcode{U3.GALP\_ncomptx}: <>= U3.GALP_ncomptx_again <- countCompatibleOverlaps(U3.GALP, exbytx) stopifnot(identical(U3.GALP_ncomptx_again, U3.GALP_ncomptx)) @ Number of ``splice compatible'' transcripts for each template: <>= U3.compOV10 <- remapHits(U3.compOV00, Lnodes.remapping=U3.GALP_qnames) U3.uqnames_ncomptx <- countQueryHits(U3.compOV10) names(U3.uqnames_ncomptx) <- U3.uqnames table(U3.uqnames_ncomptx) mean(U3.uqnames_ncomptx >= 1) @ 70.7\% of the templates are ``splice compatible'' with at least 1 transcript in \Rcode{exbytx}. Number of ``splice compatible'' templates for each transcript: <>= U3.exbytx_ncompOV10 <- countSubjectHits(U3.compOV10) names(U3.exbytx_ncompOV10) <- names(exbytx) mean(U3.exbytx_ncompOV10 >= 50) @ Only 0.7\% of the transcripts in \Rcode{exbytx} are ``splice compatible'' with at least 50 templates. Top 10 transcripts: <>= head(sort(U3.exbytx_ncompOV10, decreasing=TRUE), n=10) @ Note that this ``top 10'' is slightly different from the ``top 10'' we obtained earlier when we counted {\bf all} the paired-end overlaps. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Compute the {\it reference query sequences} and project them on the transcriptome} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Compute the {\it reference query sequences}} The {\it reference query sequences} are the query sequences {\bf after} alignment, by opposition to the {\it original query sequences} (aka ``true'' or ``real'' query sequences) which are the query sequences {\bf before} alignment. The {\it reference query sequences} can easily be computed by extracting the nucleotides mapped to each read from the reference genome. This of course requires that we have access to the reference genome used by the aligner. In Bioconductor, the full genome sequence for the dm3 assembly is stored in the \Rpackage{BSgenome.Dmelanogaster.UCSC.dm3} data package \footnote{See \url{http://bioconductor.org/packages/release/data/annotation/} for the full list of annotation packages available in the current release of Bioconductor.}: <>= library(BSgenome.Dmelanogaster.UCSC.dm3) Dmelanogaster @ To extract the portions of the reference genome corresponding to the ranges in \Rcode{U1.grl}, we can use the \Rfunction{extractTranscriptSeqs} function defined in the \Rpackage{GenomicFeatures} package: <>= library(GenomicFeatures) U1.GAL_rqseq <- extractTranscriptSeqs(Dmelanogaster, U1.grl) head(U1.GAL_rqseq) @ When reads are paired-end, we need to extract separately the ranges corresponding to their {\it first} ends (aka {\it first} segments in BAM jargon) and those corresponding to their {\it last} ends (aka {\it last} segments in BAM jargon): <>= U3.grl_first <- grglist(first(U3.GALP, real.strand=TRUE), order.as.in.query=TRUE) U3.grl_last <- grglist(last(U3.GALP, real.strand=TRUE), order.as.in.query=TRUE) @ Then we extract the portions of the reference genome corresponding to the ranges in \Rclass{GRangesList} objects \Rcode{U3.grl\_first} and \Rcode{U3.grl\_last}: <>= U3.GALP_rqseq1 <- extractTranscriptSeqs(Dmelanogaster, U3.grl_first) U3.GALP_rqseq2 <- extractTranscriptSeqs(Dmelanogaster, U3.grl_last) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Project the single-end alignments on the transcriptome} The \Rfunction{extractQueryStartInTranscript} function computes for each overlap the position of the {\it query start} in the transcript: <>= U1.OV00_qstart <- extractQueryStartInTranscript(U1.grl, exbytx, hits=U1.OV00, ovenc=U1.ovenc) head(subset(U1.OV00_qstart, U1.OV00_is_comp)) @ \Rcode{U1.OV00\_qstart} is a data frame with 1 row per overlap and 3 columns: \begin{enumerate} \item \Rcode{startInTranscript}: the 1-based start position of the read with respect to the transcript. Position 1 always corresponds to the first base on the 5' end of the transcript sequence. \item \Rcode{firstSpannedExonRank}: the rank of the first exon spanned by the read, that is, the rank of the exon found at position \Rcode{startInTranscript} in the transcript. \item \Rcode{startInFirstSpannedExon}: the 1-based start position of the read with respect to the first exon spanned by the read. \end{enumerate} Having this information allows us for example to compare the read and transcript nucleotide sequences for each ``splice compatible'' overlap. If we use the {\it reference query sequence} instead of the {\it original query sequence} for this comparison, then it should match {\bf exactly} the sequence found at the {\it query start} in the transcript. Let's start by using \Rfunction{extractTranscriptSeqs} again to extract the transcript sequences (aka transcriptome) from the dm3 reference genome: <>= txseq <- extractTranscriptSeqs(Dmelanogaster, exbytx) @ For each ``splice compatible'' overlap, the read sequence in \Rcode{U1.GAL\_rqseq} must be an {\it exact} substring of the transcript sequence in \Rcode{exbytx\_seq}: <>= U1.OV00_rqseq <- U1.GAL_rqseq[queryHits(U1.OV00)] U1.OV00_rqseq[flippedQuery(U1.ovenc)] <- reverseComplement(U1.OV00_rqseq[flippedQuery(U1.ovenc)]) U1.OV00_txseq <- txseq[subjectHits(U1.OV00)] stopifnot(all( U1.OV00_rqseq[U1.OV00_is_comp] == narrow(U1.OV00_txseq[U1.OV00_is_comp], start=U1.OV00_qstart$startInTranscript[U1.OV00_is_comp], width=width(U1.OV00_rqseq)[U1.OV00_is_comp]) )) @ Because of this relationship between the {\it reference query sequence} and the transcript sequence of a ``splice compatible'' overlap, and because of the relationship between the {\it original query sequences} and the {\it reference query sequences}, then the edit distance reported in the NM tag is actually the edit distance between the {\it original query} and the transcript of a ``splice compatible'' overlap. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Project the paired-end alignments on the transcriptome} For a paired-end read, the {\it query start} is the start of its ``left end''. <>= U3.OV00_Lqstart <- extractQueryStartInTranscript(U3.grl, exbytx, hits=U3.OV00, ovenc=U3.ovenc) head(subset(U3.OV00_Lqstart, U3.OV00_is_comp)) @ Note that \Rfunction{extractQueryStartInTranscript} can be called with \Rcode{for.query.right.end=TRUE} if we want this information for the ``right ends'' of the reads: <>= U3.OV00_Rqstart <- extractQueryStartInTranscript(U3.grl, exbytx, hits=U3.OV00, ovenc=U3.ovenc, for.query.right.end=TRUE) head(subset(U3.OV00_Rqstart, U3.OV00_is_comp)) @ Like with single-end reads, having this information allows us for example to compare the read and transcript nucleotide sequences for each ``splice compatible'' overlap. If we use the {\it reference query sequence} instead of the {\it original query sequence} for this comparison, then it should match {\bf exactly} the sequences of the ``left'' and ``right'' ends of the read in the transcript. Let's assign the ``left and right reference query sequences'' to each overlap: <>= U3.OV00_Lrqseq <- U3.GALP_rqseq1[queryHits(U3.OV00)] U3.OV00_Rrqseq <- U3.GALP_rqseq2[queryHits(U3.OV00)] @ For the single-end reads, the sequence associated with a ``flipped query'' just needed to be ``reverse complemented''. For paired-end reads, we also need to swap the 2 sequences in the pair: <>= flip_idx <- which(flippedQuery(U3.ovenc)) tmp <- U3.OV00_Lrqseq[flip_idx] U3.OV00_Lrqseq[flip_idx] <- reverseComplement(U3.OV00_Rrqseq[flip_idx]) U3.OV00_Rrqseq[flip_idx] <- reverseComplement(tmp) @ Let's assign the transcript sequence to each overlap: <>= U3.OV00_txseq <- txseq[subjectHits(U3.OV00)] @ For each ``splice compatible'' overlap, we expect the ``left and right reference query sequences'' of the read to be {\it exact} substrings of the transcript sequence. Let's check the ``left reference query sequences'': <>= stopifnot(all( U3.OV00_Lrqseq[U3.OV00_is_comp] == narrow(U3.OV00_txseq[U3.OV00_is_comp], start=U3.OV00_Lqstart$startInTranscript[U3.OV00_is_comp], width=width(U3.OV00_Lrqseq)[U3.OV00_is_comp]) )) @ and the ``right reference query sequences'': <>= stopifnot(all( U3.OV00_Rrqseq[U3.OV00_is_comp] == narrow(U3.OV00_txseq[U3.OV00_is_comp], start=U3.OV00_Rqstart$startInTranscript[U3.OV00_is_comp], width=width(U3.OV00_Rrqseq)[U3.OV00_is_comp]) )) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Align the reads to the transcriptome} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Aligning the reads to the reference genome is not the most efficient nor accurate way to count the number of ``splice compatible'' overlaps per {\it original query}. Supporting junction reads (i.e. reads that align with at least 1 skipped region in their CIGAR) introduces a significant computational cost during the alignment process. Then, as we've seen in the previous sections, each alignment produced by the aligner needs to be broken into a set of ranges (based on its CIGAR) and those ranges compared to the ranges of the exons grouped by transcript. A more straightforward and accurate approach is to align the reads directly to the transcriptome, and without allowing the typical skipped region that the aligner needs to introduce when aligning a junction read to the reference genome. With this approach, a ``hit'' between a read and a transcript is necessarily compatible with the splicing of the transcript. In case of a ``hit'', we'll say that the read and the transcript are ``string-based compatible'' (to differentiate from our previous notion of ``splice compatible'' overlaps that we will call ``encoding-based compatible'' in this section). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Align the single-end reads to the transcriptome} \subsubsection{Find the ``hits''} The single-end reads are in \Rcode{U1.oqseq}, the transcriptome is in \Rcode{exbytx\_seq}. Since indels were not allowed/supported during the alignment of the reads to the reference genome, we don't need to allow/support them either for aligning the reads to the transcriptome. Also since our goal is to find (and count) ``splice compatible'' overlaps between reads and transcripts, we don't need to keep track of the details of the alignments between the reads and the transcripts. Finally, since BAM file {\tt untreated1\_chr4.bam} is not the full output of the aligner but the subset obtained by keeping only the alignments located on chr4, we don't need to align \Rcode{U1.oqseq} to the full transcriptome, but only to the subset of \Rcode{exbytx\_seq} made of the transcripts located on chr4. With those simplifications in mind, we write the following function that we will use to find the ``hits'' between the reads and the transcriptome: <>= ### A wrapper to vwhichPDict() that supports IUPAC ambiguity codes in 'qseq' ### and 'txseq', and treats them as such. findSequenceHits <- function(qseq, txseq, which.txseq=NULL, max.mismatch=0) { .asHits <- function(x, pattern_length) { query_hits <- unlist(x) if (is.null(query_hits)) query_hits <- integer(0) subject_hits <- rep.int(seq_len(length(x)), elementNROWS(x)) Hits(query_hits, subject_hits, pattern_length, length(x), sort.by.query=TRUE) } .isHitInTranscriptBounds <- function(hits, qseq, txseq) { sapply(seq_len(length(hits)), function(i) { pattern <- qseq[[queryHits(hits)[i]]] subject <- txseq[[subjectHits(hits)[i]]] v <- matchPattern(pattern, subject, max.mismatch=max.mismatch, fixed=FALSE) any(1L <= start(v) & end(v) <= length(subject)) }) } if (!is.null(which.txseq)) { txseq0 <- txseq txseq <- txseq[which.txseq] } names(qseq) <- NULL other <- alphabetFrequency(qseq, baseOnly=TRUE)[ , "other"] is_clean <- other == 0L # "clean" means "no IUPAC ambiguity code" ## Find hits for "clean" original queries. qseq0 <- qseq[is_clean] pdict0 <- PDict(qseq0, max.mismatch=max.mismatch) m0 <- vwhichPDict(pdict0, txseq, max.mismatch=max.mismatch, fixed="pattern") hits0 <- .asHits(m0, length(qseq0)) hits0@nLnode <- length(qseq) hits0@from <- which(is_clean)[hits0@from] ## Find hits for non "clean" original queries. qseq1 <- qseq[!is_clean] m1 <- vwhichPDict(qseq1, txseq, max.mismatch=max.mismatch, fixed=FALSE) hits1 <- .asHits(m1, length(qseq1)) hits1@nLnode <- length(qseq) hits1@from <- which(!is_clean)[hits1@from] ## Combine the hits. query_hits <- c(queryHits(hits0), queryHits(hits1)) subject_hits <- c(subjectHits(hits0), subjectHits(hits1)) if (!is.null(which.txseq)) { ## Remap the hits. txseq <- txseq0 subject_hits <- which.txseq[subject_hits] hits0@nRnode <- length(txseq) } ## Order the hits. oo <- orderIntegerPairs(query_hits, subject_hits) hits0@from <- query_hits[oo] hits0@to <- subject_hits[oo] if (max.mismatch != 0L) { ## Keep only "in bounds" hits. is_in_bounds <- .isHitInTranscriptBounds(hits0, qseq, txseq) hits0 <- hits0[is_in_bounds] } hits0 } @ Let's compute the index of the transcripts in \Rcode{exbytx\_seq} located on chr4 (\Rfunction{findSequenceHits} will restrict the search to those transcripts): <>= chr4tx <- transcripts(txdb, vals=list(tx_chrom="chr4")) chr4txnames <- mcols(chr4tx)$tx_name which.txseq <- match(chr4txnames, names(txseq)) @ We know that the aligner tolerated up to 6 mismatches per read. The 3 following commands find the ``hits'' for each {\it original query}, then find the ``hits'' for each ``flipped {\it original query}'', and finally merge all the ``hits'' (note that the 3 commands take about 1 hour to complete on a modern laptop): <>= U1.sbcompHITSa <- findSequenceHits(U1.oqseq, txseq, which.txseq=which.txseq, max.mismatch=6) U1.sbcompHITSb <- findSequenceHits(reverseComplement(U1.oqseq), txseq, which.txseq=which.txseq, max.mismatch=6) U1.sbcompHITS <- union(U1.sbcompHITSa, U1.sbcompHITSb) @ <>= U1.sbcompHITSa <- .loadPrecomputed("U1.sbcompHITSa") U1.sbcompHITSb <- .loadPrecomputed("U1.sbcompHITSb") U1.sbcompHITS <- union(U1.sbcompHITSa, U1.sbcompHITSb) @ \subsubsection{Tabulate the ``hits''} Number of ``string-based compatible'' transcripts for each read: <>= U1.uqnames_nsbcomptx <- countQueryHits(U1.sbcompHITS) names(U1.uqnames_nsbcomptx) <- U1.uqnames table(U1.uqnames_nsbcomptx) mean(U1.uqnames_nsbcomptx >= 1) @ 77.7\% of the reads are ``string-based compatible'' with at least 1 transcript in \Rcode{exbytx}. Number of ``string-based compatible'' reads for each transcript: <>= U1.exbytx_nsbcompHITS <- countSubjectHits(U1.sbcompHITS) names(U1.exbytx_nsbcompHITS) <- names(exbytx) mean(U1.exbytx_nsbcompHITS >= 50) @ Only 0.865\% of the transcripts in \Rcode{exbytx} are ``string-based compatible'' with at least 50 reads. Top 10 transcripts: <>= head(sort(U1.exbytx_nsbcompHITS, decreasing=TRUE), n=10) @ \subsubsection{A closer look at the ``hits''} [WORK IN PROGRESS, might be removed or replaced soon...] Any ``encoding-based compatible'' overlap is of course ``string-based compatible'': <>= stopifnot(length(setdiff(U1.compOV10, U1.sbcompHITS)) == 0) @ but the reverse is not true: <>= length(setdiff(U1.sbcompHITS, U1.compOV10)) @ %To understand why the {\it overlap encodings} approach doesn't find all %the ``string-based compatible'' hits, let's look at the second hit in %\Rcode{setdiff(U1.sbcompHITS, U1.compOV10)}. This is a perfect hit between %read SRR031728.4692406 and transcript 18924: % %<<>>= %matchPattern(U1.oqseq[[6306]], txseq[[18924]]) %U1.GAL_idx <- which(U1.GAL_qnames == "SRR031728.4692406") %U1.GAL[U1.GAL_idx] %U1.GAL_idx %in% queryHits(U1.OV00) %U1.GAL[12636] %which(queryHits(U1.OV00) == 12636) %U1.OV00[305] %as.character(encoding(U1.ovenc)[305]) %@ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Align the paired-end reads to the transcriptome} [COMING SOON...] %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Detect ``almost splice compatible'' overlaps} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% In many aspects, ``splice compatible'' overlaps can be seen as perfect. We are now insterested in a less perfect type of overlap where the read overlaps the transcript in a way that {\it would} be ``splice compatible'' if 1 or more exons were removed from the transcript. In that case we say that the overlap is ``almost splice compatible'' with the transcript. The \Rfunction{isCompatibleWithSkippedExons} function can be used on an \Rclass{OverlapEncodings} object to detect this type of overlap. Note that \Rfunction{isCompatibleWithSkippedExons} can also be used on a character vector of factor. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Detect ``almost splice compatible'' single-end overlaps} \subsubsection{``Almost splice compatible'' single-end encodings} \Rcode{U1.ovenc} contains 7 unique encodings ``almost splice compatible'' with the splicing of the transcript: <>= sort(U1.ovenc_table[isCompatibleWithSkippedExons(U1.unique_encodings)]) @ Encodings \Rcode{"2:jm:am:af:"} (1015 occurences in \Rcode{U1.ovenc}), \Rcode{"2:jm:am:am:af:"} (144 occurences in \Rcode{U1.ovenc}), and \Rcode{"3:jmm:agm:aam:aaf:"} (21 occurences in \Rcode{U1.ovenc}), correspond to the following overlaps: \begin{itemize} \item \Rcode{"2:jm:am:af:"} \begin{verbatim} - read (1 skipped region): ooooo----------ooo - transcript: ... >>>>>>> >>>> >>>>>>>> ... \end{verbatim} \item \Rcode{"2:jm:am:am:af:"} \begin{verbatim} - read (1 skipped region): ooooo------------------ooo - transcript: ... >>>>>>> >>>> >>>>> >>>>>>>> ... \end{verbatim} \item \Rcode{"3:jmm:agm:aam:aaf:"} \begin{verbatim} - read (2 skipped regions): oo---oooo-----------oo - transcript: ... >>>>>>> >>>> >>>>> >>>>>>>> ... \end{verbatim} \end{itemize} <>= U1.OV00_is_acomp <- isCompatibleWithSkippedExons(U1.ovenc) table(U1.OV00_is_acomp) # 1202 "almost splice compatible" overlaps @ Finally, let's extract the ``almost splice compatible'' overlaps from \Rcode{U1.OV00}: <>= U1.acompOV00 <- U1.OV00[U1.OV00_is_acomp] @ \subsubsection{Tabulate the ``almost splice compatible'' single-end overlaps} Number of ``almost splice compatible'' transcripts for each alignment in \Rcode{U1.GAL}: <>= U1.GAL_nacomptx <- countQueryHits(U1.acompOV00) mcols(U1.GAL)$nacomptx <- U1.GAL_nacomptx head(U1.GAL) table(U1.GAL_nacomptx) mean(U1.GAL_nacomptx >= 1) @ Only 0.27\% of the alignments in \Rcode{U1.GAL} are ``almost splice compatible'' with at least 1 transcript in \Rcode{exbytx}. Number of ``almost splice compatible'' alignments for each transcript: <>= U1.exbytx_nacompOV00 <- countSubjectHits(U1.acompOV00) names(U1.exbytx_nacompOV00) <- names(exbytx) table(U1.exbytx_nacompOV00) mean(U1.exbytx_nacompOV00 >= 50) @ Only 0.017\% of the transcripts in \Rcode{exbytx} are ``almost splice compatible'' with at least 50 alignments in \Rcode{U1.GAL}. Finally note that the ``query start in transcript'' values returned by \Rfunction{extractQueryStartInTranscript} are also defined for ``almost splice compatible'' overlaps: <>= head(subset(U1.OV00_qstart, U1.OV00_is_acomp)) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Detect ``almost splice compatible'' paired-end overlaps} \subsubsection{``Almost splice compatible'' paired-end encodings} \Rcode{U3.ovenc} contains 5 unique paired-end encodings ``almost splice compatible'' with the splicing of the transcript: <>= sort(U3.ovenc_table[isCompatibleWithSkippedExons(U3.unique_encodings)]) @ Paired-end encodings \Rcode{"2{-}{-}1:jm{-}{-}m:am{-}{-}m:af{-}{-}i:"} (73 occurences in \Rcode{U3.ovenc}), \Rcode{"1{-}{-}2:i{-}{-}jm:a{-}{-}am:a{-}{-}af:"} (53 occurences in \Rcode{U3.ovenc}), and \Rcode{"2{-}{-}2:jm{-}{-}mm:am{-}{-}mm:af{-}{-}jm:aa{-}{-}af:"} (9 occurences in \Rcode{U3.ovenc}), correspond to the following paired-end overlaps: \begin{itemize} \item \Rcode{"2{-}{-}1:jm{-}{-}m:am{-}{-}m:af{-}{-}i:"} \begin{verbatim} - paired-end read (1 skipped region on the first end, no skipped region on the last end): ooo----------o oooo - transcript: ... >>>>> >>>> >>>>>>>>> ... \end{verbatim} \item \Rcode{"1{-}{-}2:i{-}{-}jm:a{-}{-}am:a{-}{-}af:"} \begin{verbatim} - paired-end read (no skipped region on the first end, 1 skipped region on the last end): oooo oo---------oo - transcript: ... >>>>>>>>>>> >>> >>>>>> ... \end{verbatim} \item \Rcode{"2{-}{-}2:jm{-}{-}mm:am{-}{-}mm:af{-}{-}jm:aa{-}{-}af:"} \begin{verbatim} - paired-end read (1 skipped region on the first end, 1 skipped region on the last end): o----------ooo oo---oo - transcript: ... >>>>> >>>> >>>>>>>> >>>>>> ... \end{verbatim} \end{itemize} Note: switch use of ``first'' and ``last'' above if the read was ``flipped''. <>= U3.OV00_is_acomp <- isCompatibleWithSkippedExons(U3.ovenc) table(U3.OV00_is_acomp) # 141 "almost splice compatible" paired-end overlaps @ Finally, let's extract the ``almost splice compatible'' paired-end overlaps from \Rcode{U3.OV00}: <>= U3.acompOV00 <- U3.OV00[U3.OV00_is_acomp] @ \subsubsection{Tabulate the ``almost splice compatible'' paired-end overlaps} Number of ``almost splice compatible'' transcripts for each alignment pair in \Rcode{U3.GALP}: <>= U3.GALP_nacomptx <- countQueryHits(U3.acompOV00) mcols(U3.GALP)$nacomptx <- U3.GALP_nacomptx head(U3.GALP) table(U3.GALP_nacomptx) mean(U3.GALP_nacomptx >= 1) @ Only 0.2\% of the alignment pairs in \Rcode{U3.GALP} are ``almost splice compatible'' with at least 1 transcript in \Rcode{exbytx}. Number of ``almost splice compatible'' alignment pairs for each transcript: <>= U3.exbytx_nacompOV00 <- countSubjectHits(U3.acompOV00) names(U3.exbytx_nacompOV00) <- names(exbytx) table(U3.exbytx_nacompOV00) mean(U3.exbytx_nacompOV00 >= 50) @ Only 0.0034\% of the transcripts in \Rcode{exbytx} are ``almost splice compatible'' with at least 50 alignment pairs in \Rcode{U3.GALP}. Finally note that the ``query start in transcript'' values returned by \Rfunction{extractQueryStartInTranscript} are also defined for ``almost splice compatible'' paired-end overlaps: <>= head(subset(U3.OV00_Lqstart, U3.OV00_is_acomp)) head(subset(U3.OV00_Rqstart, U3.OV00_is_acomp)) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Detect novel splice junctions} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{By looking at single-end overlaps} An alignment in \Rcode{U1.GAL} with ``almost splice compatible'' overlaps but no ``splice compatible'' overlaps suggests the presence of one or more transcripts that are not in our annotations. First we extract the index of those alignments ({\it nsj} here stands for ``{\bf n}ovel {\bf s}plice {\bf j}unction''): <>= U1.GAL_is_nsj <- U1.GAL_nacomptx != 0L & U1.GAL_ncomptx == 0L head(which(U1.GAL_is_nsj)) @ We make this an index into \Rcode{U1.OV00}: <>= U1.OV00_is_nsj <- queryHits(U1.OV00) %in% which(U1.GAL_is_nsj) @ We intersect with \Rcode{U1.OV00\_is\_acomp} and then subset \Rcode{U1.OV00} to keep only the overlaps that suggest novel splicing: <>= U1.OV00_is_nsj <- U1.OV00_is_nsj & U1.OV00_is_acomp U1.nsjOV00 <- U1.OV00[U1.OV00_is_nsj] @ For each overlap in \Rcode{U1.nsjOV00}, we extract the ranks of the skipped exons (we use a list for this as there might be more than 1 skipped exon per overlap): <>= U1.nsjOV00_skippedex <- extractSkippedExonRanks(U1.ovenc)[U1.OV00_is_nsj] names(U1.nsjOV00_skippedex) <- queryHits(U1.nsjOV00) table(elementNROWS(U1.nsjOV00_skippedex)) @ Finally, we split \Rcode{U1.nsjOV00\_skippedex} by transcript names: <>= f <- factor(names(exbytx)[subjectHits(U1.nsjOV00)], levels=names(exbytx)) U1.exbytx_skippedex <- split(U1.nsjOV00_skippedex, f) @ \Rcode{U1.exbytx\_skippedex} is a named list of named lists of integer vectors. The first level of names (outer names) are transcript names and the second level of names (inner names) are alignment indices into \Rcode{U1.GAL}: <>= head(names(U1.exbytx_skippedex)) # transcript names @ Transcript FBtr0089124 receives 7 hits. All of them skip exons 9 and 10: <>= U1.exbytx_skippedex$FBtr0089124 @ Transcript FBtr0089147 receives 4 hits. Two of them skip exon 2, one of them skips exons 2 to 6, and one of them skips exon 10: <>= U1.exbytx_skippedex$FBtr0089147 @ A few words about the interpretation of \Rcode{U1.exbytx\_skippedex}: Because of how we've conducted this analysis, the aligments reported in \Rcode{U1.exbytx\_skippedex} are guaranteed to not have any ``splice compatible'' overlaps with other known transcripts. All we can say, for example in the case of transcript FBtr0089124, is that the 7 reported hits that skip exons 9 and 10 show evidence of one or more unknown transcripts with a splice junction that corresponds to the gap between exons 8 and 11. But without further analysis, we can't make any assumption about the exons structure of those unknown transcripts. In particular, we cannot assume the existence of an unknown transcript made of the same exons as transcript FBtr0089124 minus exons 9 and 10! %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{By looking at paired-end overlaps} [COMING SOON...] %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{\Rcode{sessionInfo()}} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% <>= sessionInfo() @ \end{document} GenomicAlignments/inst/doc/OverlapEncodings.pdf0000644000175200017520000110163514551577513022702 0ustar00biocbuildbiocbuild%PDF-1.5 % 232 0 obj << /Length 1348 /Filter /FlateDecode >> stream x\Ms6WHT 8nIF8Z]u$Rh]$-*jZDAh. 46WB`P"1^cE8Q_mjzJ(Q d0 $%ZC$,΃6_&hm:n}m⊫ …DB00dф8|q&wxV=a:`)h00o])\UWה ;M6ᥝ JXh 2k:[˶$O"$UeW({68&`1&ERzZxL^giaӢ;I7b&5' `e9&+ʕwyOpM),p֍Ŋh.ygl6 BLbkhIضAf $`"B=0 u 5@x!pC1Rԅ@ _+dVx@V,/u [1Z4Ut]e$\?uT(3tOj<הjJR[\3%iUbbah ɤ$LCNq:/m7;+؆/_iը |!pwm±Z}2+P@E>ž}jusd[P=aaDA yDiyMˤ^zPwL9[H J22 $U2:6ɇ#+TJ2LhOJ/) \Ag!2kKVPMն2T=hMY"zFw6\uĈ6hNj^ZB^xX"ڥxirφq7bVYZ'ŢZh--JI(-ųw5pk ~ 6f\f\E7U7LKO!X%IY*NxcjLq(񭥃i b\)3Qy}O %w-~aTY^)(g9>z]s_P;o?QJT[+I~o&XM{i0D{CyVx/N[CnJ‰'<+K홁za&hV@5;'JU/6>YGtÉrZX,v,@9EΏw~r aٗdKW9j~P4řU)6'p;F90WvG^(qFz endstream endobj 2 0 obj << /Type /ObjStm /N 100 /First 862 /Length 1921 /Filter /FlateDecode >> stream x[]oH}uH }J6&) ~uI\;iR33g={ -DFDAB& ))(`P$BR+cB7&*БU;&gEZfo㠗HKQa4:uDJ@mЕ`P;@1^:x''"xG.@wΒ$4H{D '' BeDDžHPETUçU'O%N^iUT\.“/4]OdV8xLK_q-f)da0K)d$Yk ppokG\| S8)LkQmTOkYAڮ⊄6V9۹ձQ{7O6+iՍsf4ZRƂqsGU7G?mSw,WtP^[&i4y|Zo밪6j36XK.qP aQT̒g ȠRʤ:OS.$#SCϽ !]X#~59~"?%mG+>EƦi'~mZ~PUlMzWKre*"??Hen, KZΏģGYd(zYx43bˬz8_LC>D^ON>l:ṛi4=Fy3/gb.ї}Fc xEuY۲.BiKX.vNI*k]ֶ}YvTQiGvTکΔ*Ui.폳 텿Yݼt9폧'iA<{{&;fMsdVxK$؜Q[crMv{͆#&W |nq%ۑ"& |E)Sd`KY8/s~J> stream x\Y~_K*q8ᇸ$R$J$U $AbE:}g Rw63_&W M]qO|ھ^}tϹf%ßvvQ:a$sLwV?ډ _TQ4D٤Gn`>u FŻ ?iKP‹d `ĹVb"K./uE+Fh)IW^i0iUyy " ;O@ә>OI ƔMϔ>`>S%uquY¿)̂Dz sヱX^C8S2x0.:W;&u|Pw[MAx QX-M |e""DIDh߽wC 0X-O!׉y1}sy~?O]x>H=lU?}ݷ,C2\,QeU].k7lxS nHΖU#+O׌𰩲85g=aֲXP`+k }W'PN'E-Qx!2ܼ̄ G80T}+l<:ۭ -YCqay:|"hixnyÇPyXkXpS!(g٪\s"OtjbhFܤ_ӫNᨯ]: /o'-8$[MuY$] ky}[`1[㗗VޣрG`Y#ظL} Yq4l '?!Hw{ ܪkcv#Rjk*y<є|Nן'פaS3]+eo7דrn3Ízfo׀Y^gXLgjh'X5}1:D}Ӵow'W-wLjaE9rxC_ LUpuwKEU (&_6k܇?$glY< 7نJ#=θ$3TP㬍p?Ѩw5  >z~#7=)H KUP;@.n)_)XDmJTlrK6rb(X)RiY:+>5Tϕ8jRSai̷ېWVG\ߣ_^]u& j0!M3 n`0 gCG2JB,C() ŐQq1/1$q#t`?et!cʂrĘI-<: ְ@y (*ACuĄ_QI.@qU֡_<ۀ3B 됥b/Џo,] aXs8µe34?5 S04J1j?iU<]q{M #JDfoCw/f=K_28H܁RbɌ"u Pt#%UR]oWd@]A ^.Cu[~~M^@><- endstream endobj 288 0 obj << /Length 2500 /Filter /FlateDecode >> stream xZ[s۶~s"7DӓxΙ&iltPLRq.DYv8/p.-`"8N#ˬ&bTFL[]ow\[eL҉(Ywx kihbMuR.HYzk&J`Hb+{e餩:>oX0}#bd볲~`SmӻaRӬc`VdUPk9ɘ4趡SXeݝ_V8: #DdU%|q&a1W$Qˣ_Go fc.Q(Ƶ`U `up݋__eh8n8IDS,BTMVOp=aHD8,J T8-zISg-ClgrV_)>_ySu]-1;wKzrZBYiZtru]YYۇI_:C(c?U%ņ?|{eݻcґq@i'Eq%iMG뀦r$!ThS+C5}2g ~VءeՃYJo^KgvRL$`*Si`jf(CqpCN$kZ܋ e={T=F Q"[,]3L11x?` ;.H`(aA0\~xXY< Z+P4qA2(d*$=G XWTϔΪPʄ âfڱ*.GJ:b ?*FR> 9/߱\$؏%z y >xFǩ_^#by_{N62!Fo[(,kRP؀! }"Di&L , i֕d|c'@j.ʞV*NTDZh EA$mC1 f1X-(~c4ABN>}omƹ~:rALz6$bVk84*jxzweK;˄;ko(bppk0@Uy_Li +!%xH*81gcCL i<CVV2ofmf9Rh/0¶s c󰴣oY!P$0pĨ.$RɄT ޹_AƹoA Fpo>k8lOc+$hmrX笘Wk 7n E\R0T}j-WߩCN25g61j6OKmׇ2mj]΢m 63J'0,I# *:~Z#[=iiĻF 7k`rU_"N%#?7vu"FJw+\<]߹ 'G-hc L{gB2"2+z_a V1zËp DKf^Nu-oj}#ࠂikQxjjdOOΞλLW8XC&B%,j՝M-ߧn"{sMAt%g'T>y9MPg> stream xZ۶?r&u'Nzڙӡ%H%R&n=D.zwMlx@tO47v>2RDQe FSckts&ڟf&9QDhT8&?gLPӐ6.YQȧMjzyh^_ Or-r BVlOtwKaa`S:~lA.:N&?010O$u`0J[1@+_Š &%x)Hy@ATv6y\sS#, l00<w-?66_ML;V ^dn.؋O2D.T.C#CvoН`mn0Pt }󢄏j(ȆKo6ɰV;>u@ A* {#s0"~|D)Ĩ!F2O'isQ[گS3.Kr%$$SDppIEm"0 P>mGaI9mIX5LmܙY=7z6E6Q+S€[I<¤ \":iIm=D1` c#|sl^af >fRf>I'kƅ' Y>}CjjN:sٶGBoЮ<0^c˲Gܟa<ߐL -_Ih#P'.D+> 2W" 1A!c 6N!2;9> Ewqpv% %0GeU)E /gGKqLjyJLBv t'B6m P]5-^vxhX6X0cux L5C#n>H eT_\W ##IoW{(O500]&,2~xYÎEyC(zLf L)-\3VYzNP0뾂̜F9SȞl x#{t Уb8W4OM#5ʇP <̈́sʥvf$c?cS*Ԣ1ŗmQN: RpkfSLe *,c9X+\|/YƵX?@=0ӠLD/ JfQݞP!.B/@Ҩ{#)E"4tlV9 Ƒ6I.=#v(gҪ,J‡8RYYwBXިͼ;D\5 LtYu7*f>1 x=3ۿcuhw׶G`&(|F,Me5%Fh# v,LS&NC{#3Aˑ$ ` ճ.v`򴸘k/v W |y_53,~ D)9 "/)SxuG?zjI[{#T*|1C<`MQXK]ˇLe׳t)==O{;gӺ׃S }98w:z37_Rs08qAc6l endstream endobj 296 0 obj << /Length 2354 /Filter /FlateDecode >> stream xZ[o~ r( Rp$ACQI*n}go)Ӊۍ\.wf8Y$8yu/N 2rqÓY6SQdJN*jL_02>Er~YȯP_;ϩ@ĨPI]$߿˾K2S+, "&RKDVS\Uٺh&SiڴuV&SN Or Fuzu]d0+۰lG׮ʭ:|OUɔP)_fuE'ʪ-}\L H`93H0M ˚PN'SN: k"anΨ#I{n?vd9R$$Xn`-49NtE p7n(8Y|EIfO|=#AyCRau7NWRLP^(Sol "`yYQVjE^b'EY>X+^$ M! 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'ǧM@ pWBV&~b0@VYEQp?FN?.g`5!ˠИ~1Df/9pr?]p1T q@c|фK& D0*3B`~$d2܊@?FF!.'|h\*4~Sx[ {PԈS] gM FF Գ OH|# J:f\*C{OWHJ`g9Fҗ6Tttt9#}VW(M9[ XzԨ{AgͮZtuf0\hrkI}Ŗ6/[l:5ss i߱G5tVw[5;xF&ZFA//UjxǍKŘ]t G35e48΋Zw%#-@Wk.pN%5yoj{c]:DjH/{Cj9^;DZ~6VKmXkNG_>U_'\{1/Ecf7T/UxyAhQ )&je?uB.U:h҆ivqvˢ*x?dmuzVg eGW坻4{4G/ղ"Tz溒=i #vjZ`E5gќy:O([JW EΜu6Q*agǁ:y*yyf` &,[ph=Cz 1J \-kjAﭷJuPɽ؟>m7{Soz Tn2&gfezse OoKԞ^[,s:ko6hkLeְqXӥBMPsM L9K9b?a؈qj*ZמZ,Wh\y'4$p6H|Z}]'ϟ8Z]1Ds7k'*̳ :7\pVЩ0m`f3N0Uu[ת:ҷ@-x{%/=LbZЩqSv{5+ m"3]սEݚhkBaFHabj%[Ĕ4ԞSg=ZnXtNÅun=+M>x@497#yFjvg_~4Lqt#Gul4ut|2ne m ;]\hL6c8Ai`*8*#e/xe[O]kq^dbȋt8"2MM_; T6=S>H03EwޥO={0?-q7֪PXE)mu2æ> stream xmUMo0+J! ᫊"RVmk N7R$ݪ70W?g_,ɍehܬ=WWU\;;׺v7MOtҺ=po>fv8 | G՗_n}w̭][GL2sQ擾ݾk^!00jYV%H~~v}\; C}h{ϗC`Rރѩc~^ON6[7ݛ ZԲW/{FR^ww?U4H6!L@@B@q\s *G|F/+>㹴3Z~Z83f3[:٭ ߬Lg3t33 ~!>CO!>S 33>IY ?BXIAup*Çq G潪N$p|eO_:q;:'dE_kCvW endstream endobj 482 0 obj << /Length 842 /Filter /FlateDecode >> stream xmUn0CƆ"RVmk N7R$L̛O3 /~\k4~VzhO{|wޝn8O.oN?'uRG]>3dX;ҷ*נ_~vC̵:}W {1Esgq]ߍG@]dbڣH~z~ohTǰ9wxΏU]~NÛ Ju~*6{y~?xڰvtش~>ZjR˦YE3=sׁpuRA)`*R2$!`8li9UEХGSj043`4`4Ý(?Q  rt\e #q5p眛[q>x \iEܰpNMk l4\? 皞c:gN5^ ELOup3%M6`^ۘ1ل150ym 1F}3&ԗ0 bKl+֌>oRa Oѷ`)w`)?\֟agYg ֙P.L(ulgYˉx/N|N|&ٝ N|N'>cv'>7'>S} ~)>_Sϔ+>cR|&L|'a9i0K)cR{XTG5;)NͽRPs> stream xuUMo@Wla_BZXʡIW ld!fm웙7շĶM[؟McpuUӃsk/zfN꺼Ɠfn݅R^w}9qdMoXj_v}EQ>>pø;en>ڲ?`1&5vaj UkNAm<}\MxHM0}Z7WuI]ǽBnz/_ N{y;:ڰox\7nXw.kP^k3^Kյ u/A )`JbD>`2$`TY'``9&Dkx+0*NXXQQ3c w"]j~1F60aG+gıcW c rn q9Qܗ8% DMq.5Sh]`4$a]~9Vk ]8 IncT5obY:socsOPcYB?9Os֙3\Q.4ٰX3Z9#>^Z} ?L[ V|V|oV|3[: } B|)W|L| ,Y a!SMV,鸞:?8C8G潪N$ĸ<ޏ< Nuν_B,u7zl endstream endobj 484 0 obj << /Length 846 /Filter /FlateDecode >> stream xuUMo@Wla_BZXʡMW ldiof<ۻW_W7nzrc7)U7Nߜk]{7+wR}uN7|5s. )裮ݏk&8n~iyQqE0N[,g IM/*D@f`B9xczOpm`>W'9WRzL E]PwWqD`PދoSφ}= imX]ӷn<7̵^y]/׵Il/ܥ: ل0%1 " 0Z{q́0R0r0QK5<T`,if,1L.S5?׃[#M cL#F3X1+N978Nsk`q KpN8q )q4ϮEp O.5Ypc.Y7ь1O*ezl,d mY%0ymȋ,aYʘ8 xA} 3/Y1<*T71މf 97g19w(g1?\֟`g Yg 9LsQ.(ulgYˊx/V|V|&٭ V|N+>cv+>7+>S} ~!>_Sϔ+>cB|&LOr`B,&+jwRP{xᇣI^U E'b\o|s C:].cDܛX=oNܙ endstream endobj 485 0 obj << /Length 845 /Filter /FlateDecode >> stream xuUMo@+H.ȲrhQիԒ ؇6jo73o{q3mfѭVOn/Cf)rtskzf꺼Ɠpi?p>fv8coJ?< a9(})suזÌ\$qATh L}s6G 7o],jotuþ{UןtptZ|MÏѩNN6[7ݫ ZԲWO&suB`ilB =@ )U 9yI(ѥ S*043``MSiv|kiCXc, pDˆzA:x0)ljsn l9u}SrI4"nXCA8%&ٵ6AI cMϱXS_S/w"': fyRy(#c^g!ch"ƨ-kC^d cRx~h K^| МQV14Nd5cY9Y?C9돡'g ?%>O:ShYggΈrYgDg>[bghX|&^V|{ig33qgng3tZ[Yog,g-g B|B|\3gg3?f)O5[TT+&GUP#a#7a/c?w:'dEgtdbP2ڂ endstream endobj 486 0 obj << /Length 845 /Filter /FlateDecode >> stream xuUMo@+H.ȲrhQիԒ ؇6jo73o{q3mfѭVOn/Cf)rtskzf꺼Ɠpi?p>fv8coBy>x sXQO7SVk-#RInߵR/P,+m$oƳ;w>X. C}?77 D`RއۣS/&}g= imX[ӷnx\i"EܰpJMkl4\?ǚc:7^EHOupQF^odž1BЖEQ?[0^׆ƨАԗ0 9+ãbLi~jЙ}s~zrCOe fYJ|֟uМ8gΈrYφ}ŊϘъ1LҊkgigϘ݊og3f3|3ߊY[3 =L3f/gxg ,; f)Rx jb&'W *.lMr0߫j١hGF}o.:_hW~tOȢή*ڌ endstream endobj 487 0 obj << /Length 845 /Filter /FlateDecode >> stream xuUMo@+H.ȲrhQիԒ ؇6jo73o{q3mfѭVOn/Cf)rtskzf꺼Ɠpi?p>fv8coRy>x sXQO7SVk-#RInߵR/P,+m$oƳ;w>X. 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style ################################################### BiocStyle::latex() ################################################### ### code chunk number 2: bamfiles ################################################### library(RNAseqData.HNRNPC.bam.chr14) bamfiles <- RNAseqData.HNRNPC.bam.chr14_BAMFILES names(bamfiles) # the names of the runs ################################################### ### code chunk number 3: quickBamFlagSummary ################################################### library(Rsamtools) quickBamFlagSummary(bamfiles[1], main.groups.only=TRUE) ################################################### ### code chunk number 4: ScanBamParam ################################################### flag1 <- scanBamFlag(isFirstMateRead=TRUE, isSecondMateRead=FALSE, isDuplicate=FALSE, isNotPassingQualityControls=FALSE) param1 <- ScanBamParam(flag=flag1, what="seq") ################################################### ### code chunk number 5: readGAlignments ################################################### library(GenomicAlignments) gal1 <- readGAlignments(bamfiles[1], use.names=TRUE, param=param1) ################################################### ### code chunk number 6: read_sequences ################################################### mcols(gal1)$seq ################################################### ### code chunk number 7: original-query-sequences ################################################### oqseq1 <- mcols(gal1)$seq is_on_minus <- as.logical(strand(gal1) == "-") oqseq1[is_on_minus] <- reverseComplement(oqseq1[is_on_minus]) ################################################### ### code chunk number 8: is_dup ################################################### is_dup <- duplicated(names(gal1)) table(is_dup) ################################################### ### code chunk number 9: same-name-implies-same-seq-in-U1-oqseq ################################################### dup2unq <- match(names(gal1), names(gal1)) stopifnot(all(oqseq1 == oqseq1[dup2unq])) ################################################### ### code chunk number 10: oqseq1 ################################################### oqseq1 <- oqseq1[!is_dup] ################################################### ### code chunk number 11: most_frequent_cigars ################################################### head(sort(table(cigar(gal1)), decreasing=TRUE)) ################################################### ### code chunk number 12: cigarOpTable ################################################### colSums(cigarOpTable(cigar(gal1))) ################################################### ### code chunk number 13: table_njunc ################################################### table(njunc(gal1)) ################################################### ### code chunk number 14: readGAlignmentPairs ################################################### library(pasillaBamSubset) flag0 <- scanBamFlag(isDuplicate=FALSE, isNotPassingQualityControls=FALSE) param0 <- ScanBamParam(flag=flag0) U3.galp <- readGAlignmentPairs(untreated3_chr4(), use.names=TRUE, param=param0) head(U3.galp) ################################################### ### code chunk number 15: first-and-last-U3.galp ################################################### head(first(U3.galp)) head(last(U3.galp)) ################################################### ### code chunk number 16: isProperPair ################################################### table(isProperPair(U3.galp)) ################################################### ### code chunk number 17: keep-only-proper-pairs ################################################### U3.GALP <- U3.galp[isProperPair(U3.galp)] ################################################### ### code chunk number 18: U3.GALP_names_is_dup ################################################### U3.GALP_names_is_dup <- duplicated(names(U3.GALP)) table(U3.GALP_names_is_dup) ################################################### ### code chunk number 19: U3.GALP_qnames ################################################### U3.uqnames <- unique(names(U3.GALP)) U3.GALP_qnames <- factor(names(U3.GALP), levels=U3.uqnames) ################################################### ### code chunk number 20: U3.GALP_dup2unq ################################################### U3.GALP_dup2unq <- match(U3.GALP_qnames, U3.GALP_qnames) ################################################### ### code chunk number 21: gaps-in-U3.GALP ################################################### head(unique(cigar(first(U3.GALP)))) head(unique(cigar(last(U3.GALP)))) table(njunc(first(U3.GALP)), njunc(last(U3.GALP))) ################################################### ### code chunk number 22: no-indels-in-U3.GALP ################################################### colSums(cigarOpTable(cigar(first(U3.GALP)))) colSums(cigarOpTable(cigar(last(U3.GALP)))) ################################################### ### code chunk number 23: sessionInfo ################################################### sessionInfo() GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.Rnw0000644000175200017520000003411614516004601025352 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{Working with aligned nucleotides} %\VignetteDepends{GenomicAlignments, RNAseqData.HNRNPC.bam.chr14, pasillaBamSubset, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Dmelanogaster.UCSC.dm3, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene} %\VignetteKeywords{sequence, sequencing, alignments} %\VignettePackage{GenomicAlignments} \documentclass{article} <>= BiocStyle::latex() @ \title{Working with aligned nucleotides (WORK-IN-PROGRESS!)} \author{Herv\'e Pag\`es} \date{Last modified: January 2014; Compiled: \today} \begin{document} \maketitle \tableofcontents %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Introduction} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% This vignette belongs to the \Rpackage{GenomicAlignments} package. It illustrates how to use the package for working with the nucleotide content of aligned reads. After the reads generated by a high-throughput sequencing experiment have been aligned to a reference genome, the questions that are being asked about these alignments typically fall in two broad categories: {\bf positional only} and {\bf nucleotide-related}. {\bf Positional only} questions are about the position of the alignments with respect to the reference genome. Note that the position of an alignment is actually better described in terms of genomic ranges (1 range for an alignment with no gaps, 2 or more ranges for an alignment with gaps). Knowing the ranges of the alignments is sufficient to perform common tasks like {\it read counting} or for {\it computing the coverage}. {\it Read counting} is the process of counting the number of aligned reads per gene or exon and is typically performed in the context of a differential analysis. This task can be accomplished with the \Rfunction{summarizeOverlaps} function provided in the \Rpackage{GenomicAlignments} package and is explained in details in the ``Counting reads with summarizeOverlaps'' vignette (also located in this package). {\it Computing the coverage} is often the preliminary step to peak detection (ChIP-seq analysis) or to a copy number analysis. It can be accomplished with the \Rfunction{coverage} function. See \Rcode{?\`{}coverage-methods\`{}} for more information. {\bf Nucleotide-related} questions are about the nucleotide content of the alignments. In particular how this content compares to the corresponding nucleotides in the reference genome. These questions typically arise in the context of small genetic variation detection between one or more samples and a reference genome. The \Rpackage{GenomicAlignments} package provides a suite of low- to mid-level tools for dealing with {\bf nucleotide-related} questions about the alignments. In this vignette we illustrate their use on the single-end and paired-end reads of an RNA-seq experiment. Note that these tools do NOT constitute a complete variant toolbox. If this is what you're looking for, other \Bioconductor{} packages might be more appropriate. See the GeneticVariability and SNP views at this URL \url{http://bioconductor.org/packages/release/BiocViews.html#___AssayDomains} for a complete list of packages that deal with small genetic variations. Most of them provide tools of higher level than the tools described in this vignette. See for example the \Rpackage{VariantTools} and \Rpackage{VariantAnnotation} packages for a complete variant toolbox (including variant calling capabilities). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Load the aligned reads and their sequences from a BAM file} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% In this section, we illustrate how aligned reads and their sequences can be loaded from a BAM file. The reads we're going to use for this are paired-end reads from a published study by Zarnack et al., 2012. A subset of these reads are stored in the BAM files located in the \Rpackage{RNAseqData.HNRNPC.bam.chr14} data package. The package contains 8 BAM files, 1 per sequencing run: <>= library(RNAseqData.HNRNPC.bam.chr14) bamfiles <- RNAseqData.HNRNPC.bam.chr14_BAMFILES names(bamfiles) # the names of the runs @ Each BAM file was obtained by (1) aligning the reads (paired-end) to the full hg19 genome with TopHat2, and then (2) subsetting to keep only alignments on chr14. See \Rcode{?RNAseqData.HNRNPC.bam.chr14} for more information about this data set. As a preliminary step, we check whether the BAM files contain single- or paired-end alignments. This can be done with the \Rfunction{quickBamFlagSummary} utility from the \Rpackage{Rsamtools} package: <>= library(Rsamtools) quickBamFlagSummary(bamfiles[1], main.groups.only=TRUE) @ This confirms that all the alignments in the 1st BAM file (run ERR127306) are paired-end. This means that we should preferably load them with the \Rfunction{readGAlignmentPairs} function from the \Rpackage{GenomicAlignments} package. However for the purpose of keeping things simple, we will ignore the pairing for now and load only the alignments corresponding to the first segment of the pairs. We will use the \Rfunction{readGAlignments} function from the \Rpackage{GenomicAlignments} package for this, together with a \Rclass{ScanBamParam} object for the filtering. See \Rcode{?ScanBamParam} in the \Rpackage{Rsamtools} package for the details. Furthermore, while preparing the \Rclass{ScanBamParam} object to perform the filtering, we'll also get rid of the PCR or optical duplicates (flag bit 0x400 in the SAM format, see the SAM Spec \footnote{\url{http://samtools.sourceforge.net/}} for the details), as well as reads not passing quality controls (flag bit 0x200 in the SAM format). Finally we also request the read sequences (a.k.a. the {\it segment sequences} in the SAM Spec, stored in the SEQ field) via the \Rclass{ScanBamParam} object: <>= flag1 <- scanBamFlag(isFirstMateRead=TRUE, isSecondMateRead=FALSE, isDuplicate=FALSE, isNotPassingQualityControls=FALSE) param1 <- ScanBamParam(flag=flag1, what="seq") @ We're now ready to load the alignments and their sequences. Note that we use \Rcode{use.names=TRUE} in order to also load the {\it query names} (a.k.a. the {\it query template names} in the SAM Spec, stored in the QNAME field) from the BAM file. \Rfunction{readGAlignments} will use them to set the names of the returned object: <>= library(GenomicAlignments) gal1 <- readGAlignments(bamfiles[1], use.names=TRUE, param=param1) @ This returns a \Rclass{GAlignments} object. The read sequences are stored in the \Rcode{seq} metadata column of the object: <>= mcols(gal1)$seq @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Compute the {\it original query sequences}} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Because the BAM format imposes that the read sequence is ``reverse complemented'' when a read aligns to the minus strand, we need to ``reverse complement'' it again to restore the {\it original query sequences} (i.e. the sequences before alignment, that is, as they can be seen in the FASTQ file assuming that the aligner didn't perform any hard-clipping on them): <>= oqseq1 <- mcols(gal1)$seq is_on_minus <- as.logical(strand(gal1) == "-") oqseq1[is_on_minus] <- reverseComplement(oqseq1[is_on_minus]) @ Because the aligner used to align the reads can report more than 1 alignment per read (i.e. per sequence stored in the FASTQ file), we shouldn't expect the names of \Rcode{gal1} to be unique: <>= is_dup <- duplicated(names(gal1)) table(is_dup) @ However, sequences with the same {\it query name} should correspond to the same {\it original query} and therefore should be the same. Let's do a quick sanity check: <>= dup2unq <- match(names(gal1), names(gal1)) stopifnot(all(oqseq1 == oqseq1[dup2unq])) @ Finally, let's reduce \Rcode{oqseq1} to one {\it original query sequence} per unique {\it query name} (like in the FASTQ file containing the 1st end of the unaligned reads): <>= oqseq1 <- oqseq1[!is_dup] @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Mismatches, indels, and gaps} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Because the aligner possibly tolerated a small number of mismatches, indels, and/or gaps during the alignment process, the sequences in \Rcode{mcols(gal1)\$seq} gnerally don't match exactly the reference genome. The information of where indels and/or gaps occur in the alignments is represented in the CIGAR strings. Let's have a look at these string. First the most frequent cigars: <>= head(sort(table(cigar(gal1)), decreasing=TRUE)) @ Then a summary of the total number of insertions (I), deletions (D), and gaps (N): <>= colSums(cigarOpTable(cigar(gal1))) @ This tells us that the aligner that was used supports indels (I/D) and junction reads (N). Finally we count and summarize the number of gaps per alignment: <>= table(njunc(gal1)) @ Some reads contain up to 3 gaps (i.e. span 3 splice junctions). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Put the read sequences and reference sequences ``side by side''} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% TODO (with \Rfunction{sequenceLayer}) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{OLD STUFF (needs to be recycled/updated)} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Load paired-end reads from a BAM file} BAM file {\tt untreated3\_chr4.bam} (located in the \Rpackage{pasillaBamSubset} data package) contains paired-end reads from the ``Pasilla'' experiment and aligned against the dm3 genome (see \Rcode{?untreated3\_chr4} in the \Rpackage{pasillaBamSubset} package for more information about those reads). We use the \Rfunction{readGAlignmentPairs} function to load them into a \Rclass{GAlignmentPairs} object: <>= library(pasillaBamSubset) flag0 <- scanBamFlag(isDuplicate=FALSE, isNotPassingQualityControls=FALSE) param0 <- ScanBamParam(flag=flag0) U3.galp <- readGAlignmentPairs(untreated3_chr4(), use.names=TRUE, param=param0) head(U3.galp) @ The \Rcode{show} method for \Rclass{GAlignmentPairs} objects displays two {\tt ranges} columns, one for the {\it first} alignment in the pair (the left column), and one for the {\it last} alignment in the pair (the right column). The {\tt strand} column corresponds to the strand of the {\it first} alignment. <>= head(first(U3.galp)) head(last(U3.galp)) @ According to the SAM format specifications, the aligner is expected to mark each alignment pair as {\it proper} or not (flag bit 0x2 in the SAM format). The SAM Spec only says that a pair is {\it proper} if the {\it first} and {\it last} alignments in the pair are ``properly aligned according to the aligner''. So the exact criteria used for setting this flag is left to the aligner. We use \Rcode{isProperPair} to extract this flag from the \Rclass{GAlignmentPairs} object: <>= table(isProperPair(U3.galp)) @ Even though we could do {\it overlap encodings} with the full object, we keep only the {\it proper} pairs for our downstream analysis: <>= U3.GALP <- U3.galp[isProperPair(U3.galp)] @ Because the aligner used to align those reads can report more than 1 alignment per {\it original query template} (i.e. per pair of sequences stored in the input files, typically 1 FASTQ file for the {\it first} ends and 1 FASTQ file for the {\it last} ends), we shouldn't expect the names of \Rcode{U3.GALP} to be unique: <>= U3.GALP_names_is_dup <- duplicated(names(U3.GALP)) table(U3.GALP_names_is_dup) @ Storing the {\it query template names} in a factor will be useful: <>= U3.uqnames <- unique(names(U3.GALP)) U3.GALP_qnames <- factor(names(U3.GALP), levels=U3.uqnames) @ as well as having the mapping between each {\it query template name} and its first occurence in \Rcode{U3.GALP\_qnames}: <>= U3.GALP_dup2unq <- match(U3.GALP_qnames, U3.GALP_qnames) @ Our reads can have up to 1 gap per end: <>= head(unique(cigar(first(U3.GALP)))) head(unique(cigar(last(U3.GALP)))) table(njunc(first(U3.GALP)), njunc(last(U3.GALP))) @ Like for our single-end reads, the following tables indicate that indels were not allowed/supported during the alignment process: <>= colSums(cigarOpTable(cigar(first(U3.GALP)))) colSums(cigarOpTable(cigar(last(U3.GALP)))) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{\Rcode{sessionInfo()}} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% <>= sessionInfo() @ \end{document} GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.pdf0000644000175200017520000066737114551577523025415 0ustar00biocbuildbiocbuild%PDF-1.5 % 53 0 obj << /Length 2389 /Filter /FlateDecode >> stream x˒6>_QU7^;N''4,SBR'_hP5JŒj.6ݍn ]L qfIM,O;GO $gJ]#YȦ 3gѻq~(d ?-tU݌Dx]L$+u4_i:x{l,O媬*w4PrG\'=D¤0YMQ "lʓ]wcG-elAf_|n*Rau6uїM}r[OYf߫2`J(Jt^:/_ @):,fMùG#~y$[Ggď.ȲfXpaȼma0gjp.+w>t8ԿoY˯G3V'6uֻSLd墬 bic*`,zN66n-]Y\ ؘE:AJ KKtxugǤRXS#!=0[7ٵI}6Ru,tǺ* bN &Hi/&y& z L&rdW/E_>Mkĵor⮨z5{7ladLg_rW2dD2w1llu]EF]TrB"V3Y6ʓchC~]~' wng?D#/׷%,W=^iL!NcW5"c Gfjgm!SզͲ,=rxòGNLlqCpl2MS? iYUo!EHihԺsF .a/)PPLJx ANփ6R ExEСfNcں!oa)LYC DP@#d9\!3|Q;rq;of]aJ|"a咈řG7,;LW#s*ox~wv3^\Rf7nϼ0*Pݴ0Yջ03!@%[n~Uф94qR$}© >!Y,m0WqA_ j[7]ck㦮a&bI)+٪uהL05.p *QX7ѝ(0;֙JXgkIKrV$]H Bl-W{ꀥ!;TȓA1Iˤ` Lo|@A%H i7ܣc)6SPHD` ׅnhE+fYV e8P-"֙8_hFyfC}FUBR,2{OeX&k1a7P ֒;+4b}] r0w]١!KD| HOiFxlP[vTewH"zVń4:+])G)0ZpBJ?T4KAaPB})1je"8JzyJ3md əI -`-=|'ӽcv* ' >yBɩ<FUB,Mz*S 㬈lG"1Жb <"[aDP/C#@ReOyYu.xrϴT &%ŨA_*^H+(+CUWSMޔ0zs -0 Y8Z3Ov|_-^)t>` endstream endobj 69 0 obj << /Length 3284 /Filter /FlateDecode >> stream xZmsܶ_qu>Qx#ffDvƊ#)LLJQ:yIL|w(On;v`qlv=cO/ElI,,b1bUfØU&/B6oWTJ̖T)9 C"|5+/?/Nl_vsD=I"%Hʄq"(uUUTlWT9:"_@O j 6nFE_ d.6[yUrH׫MeZ5yc&2azjJEN]_n5r 9("!T=KEZb!\EyB8:CaXӬ)=CMxq6gžuuC7)9ɂfX-nNVu/EvTfԢ-wX ܛʼ9 9YdY} ԝMAPP7tyQlN۬[5m2gDH ЍRxyNk :[3_tT5 QnD؟sw:GN'LDRR7pAFwUU$یo趗[BǢ¼ó:xqIgī̟ٔnuw7ҫPSqPiJ-2=i`r]6R"]L}Ţ H3\J7& r!fi; N~N>g=e^@%@xI]x% .(MZ_&]x0,]G`bFnLwܘvN++oIG:irw"xcMb X)PiɈĶSN2 끔ڶ"G׮"Ŵ6oܜн!TlL3h1QKnwS\ %?i\ 5d-}"cq015Ə! 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'summarizeOverlaps.Rnw' ################################################### ### code chunk number 1: style ################################################### BiocStyle::latex() ################################################### ### code chunk number 2: options ################################################### options(width=72) options("showHeadLines" = 3) options("showTailLines" = 3) ################################################### ### code chunk number 3: firstExample ################################################### library(GenomicAlignments) library(DESeq2) library(edgeR) fls <- list.files(system.file("extdata", package="GenomicAlignments"), recursive=TRUE, pattern="*bam$", full=TRUE) features <- GRanges( seqnames = c(rep("chr2L", 4), rep("chr2R", 5), rep("chr3L", 2)), ranges = IRanges(c(1000, 3000, 4000, 7000, 2000, 3000, 3600, 4000, 7500, 5000, 5400), width=c(rep(500, 3), 600, 900, 500, 300, 900, 300, 500, 500)), "-", group_id=c(rep("A", 4), rep("B", 5), rep("C", 2))) olap <- summarizeOverlaps(features, fls) deseq <- DESeqDataSet(olap, design= ~ 1) edger <- DGEList(assay(olap), group=rownames(colData(olap))) ################################################### ### code chunk number 4: simple ################################################### rd <- GAlignments("a", seqnames = Rle("chr1"), pos = as.integer(100), cigar = "300M", strand = strand("+")) gr1 <- GRanges("chr1", IRanges(start=50, width=150), strand="+") gr2 <- GRanges("chr1", IRanges(start=350, width=150), strand="+") ################################################### ### code chunk number 5: simpleGRanges ################################################### gr <- c(gr1, gr2) data.frame(union = assay(summarizeOverlaps(gr, rd)), intStrict = assay(summarizeOverlaps(gr, rd, mode="IntersectionStrict")), intNotEmpty = assay(summarizeOverlaps(gr, rd, mode="IntersectionNotEmpty"))) ################################################### ### code chunk number 6: simpleGRangesList ################################################### grl <- GRangesList(c(gr1, gr2)) data.frame(union = assay(summarizeOverlaps(grl, rd)), intStrict = assay(summarizeOverlaps(grl, rd, mode="IntersectionStrict")), intNotEmpty = assay(summarizeOverlaps(grl, rd, mode="IntersectionNotEmpty"))) ################################################### ### code chunk number 7: data ################################################### group_id <- c("A", "B", "C", "C", "D", "D", "E", "F", "G", "G", "H", "H") features <- GRanges( seqnames = Rle(c("chr1", "chr2", "chr1", "chr1", "chr2", "chr2", "chr1", "chr1", "chr2", "chr2", "chr1", "chr1")), strand = strand(rep("+", length(group_id))), ranges = IRanges( start=c(1000, 2000, 3000, 3600, 7000, 7500, 4000, 4000, 3000, 3350, 5000, 5400), width=c(500, 900, 500, 300, 600, 300, 500, 900, 150, 200, 500, 500)), DataFrame(group_id) ) reads <- GAlignments( names = c("a","b","c","d","e","f","g"), seqnames = Rle(c(rep(c("chr1", "chr2"), 3), "chr1")), pos = as.integer(c(1400, 2700, 3400, 7100, 4000, 3100, 5200)), cigar = c("500M", "100M", "300M", "500M", "300M", "50M200N50M", "50M150N50M"), strand = strand(rep.int("+", 7L))) ################################################### ### code chunk number 8: GRanges ################################################### data.frame(union = assay(summarizeOverlaps(features, reads)), intStrict = assay(summarizeOverlaps(features, reads, mode="IntersectionStrict")), intNotEmpty = assay(summarizeOverlaps(features, reads, mode="IntersectionNotEmpty"))) ################################################### ### code chunk number 9: lst ################################################### lst <- split(features, mcols(features)[["group_id"]]) length(lst) ################################################### ### code chunk number 10: GRangesList ################################################### data.frame(union = assay(summarizeOverlaps(lst, reads)), intStrict = assay(summarizeOverlaps(lst, reads, mode="IntersectionStrict")), intNotEmpty = assay(summarizeOverlaps(lst, reads, mode="IntersectionNotEmpty"))) ################################################### ### code chunk number 11: gff (eval = FALSE) ################################################### ## library(rtracklayer) ## fl <- paste0("ftp://ftp.ensembl.org/pub/release-62/", ## "gtf/drosophila_melanogaster/", ## "Drosophila_melanogaster.BDGP5.25.62.gtf.gz") ## gffFile <- file.path(tempdir(), basename(fl)) ## download.file(fl, gffFile) ## gff0 <- import(gffFile) ################################################### ### code chunk number 12: gff_parse (eval = FALSE) ################################################### ## idx <- mcols(gff0)$source == "protein_coding" & ## mcols(gff0)$type == "exon" & ## seqnames(gff0) == "4" ## gff <- gff0[idx] ## ## adjust seqnames to match Bam files ## seqlevels(gff) <- paste("chr", seqlevels(gff), sep="") ## chr4genes <- split(gff, mcols(gff)$gene_id) ################################################### ### code chunk number 13: pasilla_param ################################################### param <- ScanBamParam( what='qual', which=GRanges("chr4", IRanges(1, 1e6)), flag=scanBamFlag(isUnmappedQuery=FALSE, isPaired=NA), tag="NH") ################################################### ### code chunk number 14: pasilla_count (eval = FALSE) ################################################### ## fls <- c("treated1.bam", "untreated1.bam", "untreated2.bam") ## path <- "pathToBAMFiles" ## bamlst <- BamFileList(fls) ## genehits <- summarizeOverlaps(chr4genes, bamlst, mode="Union") ################################################### ### code chunk number 15: pasilla_exoncountset (eval = FALSE) ################################################### ## expdata <- MIAME( ## name="pasilla knockdown", ## lab="Genetics and Developmental Biology, University of ## Connecticut Health Center", ## contact="Dr. Brenton Graveley", ## title="modENCODE Drosophila pasilla RNA Binding Protein RNAi ## knockdown RNA-Seq Studies", ## pubMedIds="20921232", ## url="http://www.ncbi.nlm.nih.gov/projects/geo/query/acc.cgi?acc=GSE18508", ## abstract="RNA-seq of 3 biological replicates of from the Drosophila ## melanogaster S2-DRSC cells that have been RNAi depleted of mRNAs ## encoding pasilla, a mRNA binding protein and 4 biological replicates ## of the the untreated cell line.") ## ## design <- data.frame( ## condition=c("treated", "untreated", "untreated"), ## replicate=c(1,1,2), ## type=rep("single-read", 3), ## countfiles=path(colData(genehits)[,1]), stringsAsFactors=TRUE) ## ## geneCDS <- DESeqDataSet(genehits, design=design, metadata=list(expdata=expdata)) ################################################### ### code chunk number 16: pasilla_genes (eval = FALSE) ################################################### ## chr4tx <- split(gff, mcols(gff)$transcript_id) ## txhits <- summarizeOverlaps(chr4tx, bamlst) ## txCDS <- DESeqDataSet(txhits, design=design, metadata=list(expdata=expdata)) GenomicAlignments/inst/doc/summarizeOverlaps.Rnw0000644000175200017520000003243314516004601023143 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{Counting reads with summarizeOverlaps} %\VignetteDepends{GenomicAlignments, DESeq2, edgeR, rtracklayer} %\VignetteKeywords{sequence, sequencing, alignments} %\VignettePackage{GenomicAlignments} \documentclass{article} <>= BiocStyle::latex() @ \title{Counting reads with \Rfunction{summarizeOverlaps}} \author{Valerie Obenchain} \date{Edited: April 2016; Compiled: \today} \begin{document} \maketitle \tableofcontents <>= options(width=72) options("showHeadLines" = 3) options("showTailLines" = 3) @ \section{Introduction} This vignette illustrates how reads mapped to a genome can be counted with \Rfunction{summarizeOverlaps}. Different "modes" of counting are provided to resolve reads that overlap multiple features. The built-in count modes are fashioned after the "Union", "IntersectionStrict", and "IntersectionNotEmpty" methods found in the HTSeq package by Simon Anders (see references). \section{A First Example} In this example reads are counted from a list of BAM files and returned in a \Robject{matrix} for use in further analysis such as those offered in \Biocpkg{DESeq2} and \Biocpkg{edgeR}. <>= library(GenomicAlignments) library(DESeq2) library(edgeR) fls <- list.files(system.file("extdata", package="GenomicAlignments"), recursive=TRUE, pattern="*bam$", full=TRUE) features <- GRanges( seqnames = c(rep("chr2L", 4), rep("chr2R", 5), rep("chr3L", 2)), ranges = IRanges(c(1000, 3000, 4000, 7000, 2000, 3000, 3600, 4000, 7500, 5000, 5400), width=c(rep(500, 3), 600, 900, 500, 300, 900, 300, 500, 500)), "-", group_id=c(rep("A", 4), rep("B", 5), rep("C", 2))) olap <- summarizeOverlaps(features, fls) deseq <- DESeqDataSet(olap, design= ~ 1) edger <- DGEList(assay(olap), group=rownames(colData(olap))) @ %% By default, the \Rfunction{summarizeOverlaps} function iterates through files in `chunks' and with files processed in parallel. For finer-grain control over memory consumption, use the \Rfunction{BamFileList} function and specify the \Rcode{yieldSize} argument (e.g., \Rcode{yieldSize=1000000}) to determine the size of each `chunk' (smaller chunks consume less memory, but are a little less efficient to process). For controlling the number of processors in use, use \Rfunction{BiocParallel::register} to use an appropriate back-end, e.g., in linux or Mac to process on 6 cores of a single machine use \Rcode{register(MulticoreParam(workers=6))}; see the \Biocpkg{BiocParallel} vignette for further details. \section{Counting Modes} The modes of "Union", "IntersectionStrict" and "IntersectionNotEmpty" provide different approaches to resolving reads that overlap multiple features. Figure~\ref{fig-summarizeOverlaps-modes} illustrates how both simple and gapped reads are handled by the modes. Note that a read is counted a maximum of once; there is no double counting. For additional detail on the counting modes see the \Rfunction{summarizeOverlaps} man page. \begin{figure}[!h] \begin{center} \includegraphics{summarizeOverlaps-modes.pdf} \caption{Counting Modes} \label{fig-summarizeOverlaps-modes} \end{center} \end{figure} \newpage \section{Counting Features} Features can be exons, transcripts, genes or any region of interest. The number of ranges that define a single feature is specified in the \Rcode{features} argument. When annotation regions of interest are defined by a single range a \Rclass{GRanges} should be used as the \Rcode{features} argument. With a \Rclass{GRanges} it is assumed that each row (i.e., each range) represents a distinct feature. If \Rcode{features} was a \Rclass{GRanges} of exons, the result would be counts per exon. When the region of interest is defined by one or more ranges the \Rcode{features} argument should be a \Rclass{GRangesList}. In practice this could be a list of exons by gene or transcripts by gene or other similar relationships. The count result will be the same length as the \Rclass{GRangesList}. For a list of exons by genes, the result would be counts per gene. The combination of defining the features as either\Rclass{GRanges} or \Rclass{GRangesList} and choosing a counting mode controls how \Rfunction{summarizeOverlaps} assigns hits. Regardless of the mode chosen, each read is assigned to at most a single feature. These options are intended to provide flexibility in defining different biological problems. This next example demonstrates how the same read can be counted differently depending on how the \Rcode{features} argument is specified. We use a single read that overlaps two ranges, gr1 and gr2. <>= rd <- GAlignments("a", seqnames = Rle("chr1"), pos = as.integer(100), cigar = "300M", strand = strand("+")) gr1 <- GRanges("chr1", IRanges(start=50, width=150), strand="+") gr2 <- GRanges("chr1", IRanges(start=350, width=150), strand="+") @ \noindent When provided as a \Rclass{GRanges} both gr1 and gr2 are considered distinct features. In this case none of the modes count the read as a hit. Mode \Rcode{Union} discards the read becasue more than 1 feature is overlapped. \Rcode{IntersectionStrict} requires the read to fall completely within a feature which is not the case for either gr1 or gr2. \Rcode{IntersetctionNotEmpty} requires the read to overlap a single unique disjoint region of the \Rcode{features}. In this case gr1 and gr2 do not overlap so each range is considered a unique disjoint region. However, the read overlaps both gr1 and gr2 so a decision cannot be made and the read is discarded. <>= gr <- c(gr1, gr2) data.frame(union = assay(summarizeOverlaps(gr, rd)), intStrict = assay(summarizeOverlaps(gr, rd, mode="IntersectionStrict")), intNotEmpty = assay(summarizeOverlaps(gr, rd, mode="IntersectionNotEmpty"))) @ \noindent Next we count with \Rcode{features} as a \Rclass{GRangesList}; this is list of length 1 with 2 elements. Modes \Rcode{Union} and \Rcode{IntersectionNotEmpty} both count the read for the single feature. <>= grl <- GRangesList(c(gr1, gr2)) data.frame(union = assay(summarizeOverlaps(grl, rd)), intStrict = assay(summarizeOverlaps(grl, rd, mode="IntersectionStrict")), intNotEmpty = assay(summarizeOverlaps(grl, rd, mode="IntersectionNotEmpty"))) @ In this more complicated example we have 7 reads, 5 are simple and 2 have gaps in the CIGAR. There are 12 ranges that will serve as the \Robject{features}. <>= group_id <- c("A", "B", "C", "C", "D", "D", "E", "F", "G", "G", "H", "H") features <- GRanges( seqnames = Rle(c("chr1", "chr2", "chr1", "chr1", "chr2", "chr2", "chr1", "chr1", "chr2", "chr2", "chr1", "chr1")), strand = strand(rep("+", length(group_id))), ranges = IRanges( start=c(1000, 2000, 3000, 3600, 7000, 7500, 4000, 4000, 3000, 3350, 5000, 5400), width=c(500, 900, 500, 300, 600, 300, 500, 900, 150, 200, 500, 500)), DataFrame(group_id) ) reads <- GAlignments( names = c("a","b","c","d","e","f","g"), seqnames = Rle(c(rep(c("chr1", "chr2"), 3), "chr1")), pos = as.integer(c(1400, 2700, 3400, 7100, 4000, 3100, 5200)), cigar = c("500M", "100M", "300M", "500M", "300M", "50M200N50M", "50M150N50M"), strand = strand(rep.int("+", 7L))) @ \noindent Using a \Rclass{GRanges} as the \Rcode{features} all 12 ranges are considered to be different features and counts are produced for each row, <>= data.frame(union = assay(summarizeOverlaps(features, reads)), intStrict = assay(summarizeOverlaps(features, reads, mode="IntersectionStrict")), intNotEmpty = assay(summarizeOverlaps(features, reads, mode="IntersectionNotEmpty"))) @ \noindent When the data are split by group to create a \Rclass{GRangesList} the highest list-levels are treated as different features and the multiple list elements are considered part of the same features. Counts are returned for each group. <>= lst <- split(features, mcols(features)[["group_id"]]) length(lst) @ <>= data.frame(union = assay(summarizeOverlaps(lst, reads)), intStrict = assay(summarizeOverlaps(lst, reads, mode="IntersectionStrict")), intNotEmpty = assay(summarizeOverlaps(lst, reads, mode="IntersectionNotEmpty"))) @ If desired, users can supply their own counting function as the \Rcode{mode} argument and take advantage of the infrastructure for counting over multiple BAM files and parsing the results into a \Rclass{RangedSummarizedExperiment} object. See \Rcode{?'BamViews-class'} or \Rcode{?'BamFile-class'} in the \Biocpkg{Rsamtools} package. \section{\Rcode{pasilla} Data} In this excercise we count the \Biocpkg{pasilla} data by gene and by transcript then create a \Rclass{DESeqDataSet}. This object can be used in differential expression methods offered in the \Biocpkg{DESeq2} package. \subsection{source files} Files are available through NCBI Gene Expression Omnibus (GEO), accession number GSE18508. \url{http://www.ncbi.nlm.nih.gov/projects/geo/query/acc.cgi?acc=GSE18508}. SAM files can be converted to BAM with the \Rfunction{asBam} function in the \Biocpkg{Rsamtools} package. Of the seven files available, 3 are single-reads and 4 are paired-end. Smaller versions of untreated1 (single-end) and untreated2 (paired-end) have been made available in the \Biocpkg{pasillaBamSubset} package. This subset includes chromosome 4 only. \Rfunction{summarizeOverlaps} is capable of counting paired-end reads in both a \Rcode{BamFile}-method (set argument \Rcode{singleEnd=TRUE}) or a \Rcode{GAlignmentPairs}-method. For this example, we use the 3 single-end read files, \begin{itemize} \item treated1.bam \item untreated1.bam \item untreated2.bam \end{itemize} Annotations are retrieved as a GTF file from the ENSEMBL web site. We download the file our local disk, then use \Biocpkg{Rtracklayer}'s \Rfunction{import} function to parse the file to a \Rclass{GRanges} instance. <>= library(rtracklayer) fl <- paste0("ftp://ftp.ensembl.org/pub/release-62/", "gtf/drosophila_melanogaster/", "Drosophila_melanogaster.BDGP5.25.62.gtf.gz") gffFile <- file.path(tempdir(), basename(fl)) download.file(fl, gffFile) gff0 <- import(gffFile) @ Subset on the protein-coding, exon regions of chromosome 4 and split by gene id. <>= idx <- mcols(gff0)$source == "protein_coding" & mcols(gff0)$type == "exon" & seqnames(gff0) == "4" gff <- gff0[idx] ## adjust seqnames to match Bam files seqlevels(gff) <- paste("chr", seqlevels(gff), sep="") chr4genes <- split(gff, mcols(gff)$gene_id) @ \subsection{counting} The \Rcode{param} argument can be used to subset the reads in the bam file on characteristics such as position, unmapped or paired-end reads. Quality scores or the "NH" tag, which identifies reads with multiple mappings, can be included as metadata columns for further subsetting. See \Rcode{?ScanBamParam} for details about specifying the \Rcode{param} argument. <>= param <- ScanBamParam( what='qual', which=GRanges("chr4", IRanges(1, 1e6)), flag=scanBamFlag(isUnmappedQuery=FALSE, isPaired=NA), tag="NH") @ We use \Rfunction{summarizeOverlaps} to count with the default mode of "Union". If a \Rcode{param} argument is not included all reads from the BAM file are counted. <>= fls <- c("treated1.bam", "untreated1.bam", "untreated2.bam") path <- "pathToBAMFiles" bamlst <- BamFileList(fls) genehits <- summarizeOverlaps(chr4genes, bamlst, mode="Union") @ \noindent A \Rcode{CountDataSet} is constructed from the counts and experiment data in \Rclass{pasilla}. <>= expdata <- MIAME( name="pasilla knockdown", lab="Genetics and Developmental Biology, University of Connecticut Health Center", contact="Dr. Brenton Graveley", title="modENCODE Drosophila pasilla RNA Binding Protein RNAi knockdown RNA-Seq Studies", pubMedIds="20921232", url="http://www.ncbi.nlm.nih.gov/projects/geo/query/acc.cgi?acc=GSE18508", abstract="RNA-seq of 3 biological replicates of from the Drosophila melanogaster S2-DRSC cells that have been RNAi depleted of mRNAs encoding pasilla, a mRNA binding protein and 4 biological replicates of the the untreated cell line.") design <- data.frame( condition=c("treated", "untreated", "untreated"), replicate=c(1,1,2), type=rep("single-read", 3), countfiles=path(colData(genehits)[,1]), stringsAsFactors=TRUE) geneCDS <- DESeqDataSet(genehits, design=design, metadata=list(expdata=expdata)) @ If the primary interest is to count by transcript instead of by gene, the annotation file can be split on transcript id. <>= chr4tx <- split(gff, mcols(gff)$transcript_id) txhits <- summarizeOverlaps(chr4tx, bamlst) txCDS <- DESeqDataSet(txhits, design=design, metadata=list(expdata=expdata)) @ \section{References} \url{http://www-huber.embl.de/users/anders/HTSeq/doc/overview.html} \noindent\url{http://www-huber.embl.de/users/anders/HTSeq/doc/count.html} \end{document} GenomicAlignments/inst/doc/summarizeOverlaps.pdf0000644000175200017520000067670314551577536023211 0ustar00biocbuildbiocbuild%PDF-1.5 % 55 0 obj << /Length 2021 /Filter /FlateDecode >> stream x[K6ϯ`@VMcWOrsHЈ|$5OAII툊 Kl@h-8xq`$P1q@\|'HJ*E 0RR nj]Yy7hZUgr+ xsP%鏓R0 Jy _̊ C`QPR$j,.D!%WJQ!e{FjŞ)Ft_)X%&ٔ2WH҉a$: J:NfR%Y_:^׃if fg &fyjV`ɗY=sE4PIqx"dL \4"FL[(UΖiVYY!,3D!3[.WWIx2<,7b"vob囎rھNʝ W0; H(ܚ,zD,FVmӟfSHHo0ro"_m]׳6v߀ %RZ[%Hɍ HS:GH|RBI0,fE#uBYݴz!-V>8`#q$ q{.p2c52!h$-eZS>8<2]gFlC!wˆ ĸȇ K;8iyB Ip(!|f4ƆQۛѧnFy d6b"( (z<=7s ˾K|̘0H`Xct%ȏQ̘Gzt6պi;Ř Z>H:  DDY{69HRc0nծޤպ3$FHpK q?S f-!H.Il{w>1ͯ8a ۞E)ub.^Mu2-ynYn403;n.ѰgiyL~WE7+ffTW`eMD( w`vM+Lk r`=Om5VѾL ;#1_ka) endstream endobj 73 0 obj << /Length 2188 /Filter /FlateDecode >> stream xZs۸~_S kә.\ig)w$sB I>o>*n<văUgoξiT:Y$#9Q,n,6IXlQχg2tiby+L׏o{`԰Ĩ77g0 )4+ KRgCD 0Z g}")*OPޭG;|`P$θ9Dht:hM&dBiro$:8iH0P"^֮d`gZ ncdX}2hpgE!LI|D3C)%Q_^4C|?%?qՇ|ܽWvKŧQGi֎r؋zg8Tđʯ|vYxbў]:{@?dqTG::n߱)Ŏ.w3_HYg4fͮucYy~.kkМrO6|_XH޾S^ɳ;.r*6:eAwWZWq߻CJ[xQ/]\R- ;N]BEU HSO*, Hs zMDrN4x"MBY3駑,.={MʻE,S9:>S/wyoyc5$]?q!'o1m>_ZYnF94Tb Aq{#3($ֵq* *#Ȋb&E^QK= qjnؼ4UYJyo/xm~溥Mt1w8X0(o8B)K*ۿ&3XS?4PrQzzl1@1'a6|[>iO8a\:1wM D\QL&{D%q2҂eߡ Ns[,CY@(;L22]y< H':ڭjCAe+v""KH3.4DdсLH%RH&A'hȫOiJⴚw!MQdaƶTM\49ӚrP ,Tf;}WBWɩV;r=b;wۼ&'JӐLdxIKR^mW)FSaUz;s{$_9N!0l(٭o=4nkPվE8( PT J(PeHI,A0r>`6"0!}~&HՁDK׈cj@,\Cj'c`"JO7Tv؎éVGvp ,),]/pT0۶'fvUxP߻G%|澟МƘigѾ:Sخw.Wk@\. 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Drop gal <- gal0 seqlevels(gal, pruning.mode="coarse") <- "chr2" checkIdentical("chr2", seqlevels(gal)) ## Rename gal <- gal0 seqlevels(gal)[seqlevels(gal) == "chr2"] <- "2" checkIdentical(c("chr1", "2"), seqlevels(gal)) } test_GAlignments_combine <- function() { galn <- GAlignments(seqnames=factor("A"), pos=1L, cigar="1M", strand=strand("-")) galn_c <- GAlignments(seqnames=rep(factor("A"), 2), pos=rep(1L, 2), cigar=rep("1M", 2), strand=rep(strand("-"), 2)) checkIdentical(galn_c, c(galn, galn)) } GenomicAlignments/inst/unitTests/test_GAlignmentsList-class.R0000644000175200017520000001166614516004601025516 0ustar00biocbuildbiocbuild.noGaps <- GAlignments( Rle(factor(c("chr1", "chr2", "chr1", "chr3")), c(1, 3, 2, 4)), pos=1:10, cigar=paste0(10:1, "M"), strand=Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), names=head(letters, 10), score=1:10) .Gaps <- GAlignments( Rle(factor(c("chr2", "chr4")), c(3, 4)), pos=1:7, cigar=c("5M", "3M2N3M2N3M", "5M", "10M", "5M1N4M", "8M2N1M", "5M"), strand=Rle(strand(c("-", "+")), c(4, 3)), names=tail(letters, 7), score=1:7) GAList <- GAlignmentsList(a=.noGaps, b=.Gaps) quiet <- suppressWarnings test_GAlignmentsList_construction <- function() { checkTrue(validObject(GAlignmentsList())) checkTrue(validObject(new("GAlignmentsList"))) checkTrue(validObject(GAlignmentsList(.noGaps, .Gaps))) checkTrue(validObject(GAlignmentsList(GAlignments()))) checkTrue(validObject(GAlignmentsList(a=GAlignments()))) checkException(GAlignmentsList(GRanges()), silent = TRUE) } test_GAlignmentsList_coercion <- function() { galist <- GAlignmentsList(a=.noGaps[seqnames(.noGaps) == "chr3"], b=.Gaps[seqnames(.Gaps) == "chr4"]) ## IRangesList rgl <- rglist(galist) checkIdentical(length(galist), length(rgl)) for (i in seq_along(galist)) { target <- unlist(rglist(galist[[i]]), use.names=FALSE) checkIdentical(target, rgl[[i]]) } ## GRangesList grl <- grglist(galist) checkIdentical(length(galist), length(grl)) for (i in seq_along(galist)) { target <- unlist(grglist(galist[[i]]), use.names=FALSE) checkIdentical(target, grl[[i]]) } ## IntegerRanges checkIdentical(length(ranges(galist)), length(ranges(galist[1])) + length(ranges(galist[2]))) checkIdentical(length(quiet(granges(galist))), length(quiet(granges(galist[1]))) + length(quiet(granges(galist[2])))) checkIdentical(length(granges(galist, ignore.strand=TRUE)), length(granges(galist[1], ignore.strand=TRUE)) + length(granges(galist[2], ignore.strand=TRUE))) gr <- granges(galist, ignore.strand=TRUE) ir <- ranges(galist) checkIdentical(length(gr), length(ir)) gr <- quiet(granges(galist, ignore.strand=FALSE)) checkTrue(length(gr) == 4L) ## data.frame galist <- GAlignmentsList(a=.noGaps[1:2], b=.Gaps[1:2]) df_group <- togroup(PartitioningByWidth(galist)) df <- data.frame(group=df_group, group_name=names(galist)[df_group], seqnames=factor(c("chr1", rep("chr2", 3)), seqlevels(galist)), strand=strand(c("-", "+", "-", "-")), cigar=c("10M", "9M", "5M", "3M2N3M2N3M"), qwidth=c(10L, 9L, 5L, 9L), start=c(1L, 2L, 1L, 2L), end=c(10L, 10L, 5L, 14L), width=c(10L, 9L, 5L, 13L), njunc=c(0L, 0L, 0L, 2L), score=c(1L, 2L, 1L, 2L), row.names=c("a", "b", "t", "u"), stringsAsFactors=FALSE) checkTrue(all.equal(as.data.frame(galist), df)) ## introns galist <- GAList grl <- junctions(galist) checkIdentical(names(galist), names(grl)) checkTrue(length(galist) == length(grl)) checkTrue(length(grl[[1]]) == 0L) checkTrue(length(grl[[2]]) == 4L) ## empty ranges galist <- GAlignmentsList( GAlignments("chr1", 20L, "10M", strand("+")), GAlignments()) checkTrue(length(ranges(galist)) == 1L) checkTrue(length(rglist(galist)) == 2L) checkTrue(length(granges(galist)) == 1L) checkTrue(length(grglist(galist)) == 2L) } test_GAlignmentsList_accessors <- function() { galist <- GAlignmentsList(.noGaps, .Gaps) target <- RleList(lapply(GAList, seqnames), compress=TRUE) checkIdentical(seqnames(GAList), target) target <- RleList(lapply(GAList, rname), compress=TRUE) checkIdentical(rname(GAList), target) target <- CharacterList(lapply(GAList, cigar), compress=TRUE) checkIdentical(cigar(GAList), target) target <- RleList(lapply(GAList, strand), compress=TRUE) checkIdentical(strand(GAList), target) target <- IntegerList(lapply(GAList, width)) checkIdentical(width(GAList), target) target <- SplitDataFrameList(lapply(GAList, mcols)) checkIdentical(mcols(GAList, level="within"), target) } test_GAlignmentsList_subset_combine <- function() { galist <- GAList score <- seq_len(nobj(PartitioningByWidth(galist))) meta <- DataFrame(score=score, more=score+10) mcols(galist@unlistData) <- meta ## 'c' checkIdentical(GAlignmentsList(), c(GAlignmentsList(), GAlignmentsList())) checkIdentical(GAlignmentsList(.noGaps, .Gaps), quiet(c(GAlignmentsList(.noGaps), GAlignmentsList(.Gaps)))) ## '[' checkIdentical(galist, galist[]) checkIdentical(galist, galist[Rle(TRUE)]) checkIdentical(galist[c(TRUE, FALSE),], galist[1]) } GenomicAlignments/inst/unitTests/test_cigar-utils.R0000644000175200017520000001042614516004601023563 0ustar00biocbuildbiocbuild### test_cigarRangesAlongReferenceSpace <- function() { cigar <- c("30M5000N10M", "50M4S", "90=10X5I50M10D40M", "18M10I22M", "99I") pos <- c(101, 201, 1001, 301, 2001) seqlevels <- c("chr2", "chr6") rname <- factor(c("chr6", "chr6", "chr2", "chr6", "chr2"), levels=seqlevels) ops <- c("M", "=", "X", "I", "D") current <- as.list(cigarRangesAlongReferenceSpace(cigar, pos=pos, f=rname, ops=ops)) ir2 <- IRanges(start=c(1001, 1091, 1101, 1101, 1151, 1161, 2001), end=c(1090, 1100, 1100, 1150, 1160, 1200, 2000)) ir6 <- IRanges(start=c(101, 5131, 201, 301, 319, 319), end=c(130, 5140, 250, 318, 318, 340)) target <- list(chr2=ir2, chr6=ir6) checkIdentical(target, current) current <- as.list(cigarRangesAlongReferenceSpace(cigar, pos=pos, f=rname, ops=ops, reduce.ranges=TRUE)) ir2b <- c(reduce(ir2[1:6]), reduce(ir2[7])) ir6b <- c(reduce(ir6[1:2]), reduce(ir6[3]), reduce(ir6[4:6])) target <- list(chr2=ir2b, chr6=ir6b) checkIdentical(target, current) current <- as.list(extractAlignmentRangesOnReference(cigar, pos=pos, f=rname)) checkIdentical(target, current) current <- as.list(cigarRangesAlongReferenceSpace(cigar, pos=pos, f=rname, ops=setdiff(ops, "D"))) ir2 <- ir2[-5] target <- list(chr2=ir2, chr6=ir6) checkIdentical(target, current) current <- as.list(cigarRangesAlongReferenceSpace(cigar, pos=pos, f=rname, ops=setdiff(ops, "D"), reduce.ranges=TRUE)) ir2 <- c(reduce(ir2[1:5]), reduce(ir2[6])) ir6 <- c(reduce(ir6[1:2]), reduce(ir6[3]), reduce(ir6[4:6])) target <- list(chr2=ir2, chr6=ir6) checkIdentical(target, current) current <- as.list(extractAlignmentRangesOnReference(cigar, pos=pos, drop.D.ranges=TRUE, f=rname)) checkIdentical(target, current) } test_cigarQNarrow <- function() { cigar <- c("25M4D10M", "6S17M6I3M3S") ans <- cigarQNarrow(cigar) ans0 <- cigar attr(ans0, "rshift") <- c(0L, 0L) checkIdentical(ans, ans0) ans <- cigarQNarrow(cigar, start=3, end=-3) ans0 <- c("23M4D8M", "4S17M6I3M1S") attr(ans0, "rshift") <- c(2L, 0L) checkIdentical(ans, ans0) ans <- cigarQNarrow(cigar, start=7, end=-4) ans0 <- c("19M4D7M", "17M6I3M") attr(ans0, "rshift") <- c(6L, 0L) checkIdentical(ans, ans0) ans <- cigarQNarrow(cigar, start=8, end=-5) ans0 <- c("18M4D6M", "16M6I2M") attr(ans0, "rshift") <- c(7L, 1L) checkIdentical(ans, ans0) ans <- cigarQNarrow(cigar, start=25) ans0 <- c("1M4D10M", "5I3M3S") attr(ans0, "rshift") <- c(24L, 17L) checkIdentical(ans, ans0) ans <- cigarQNarrow(cigar, start=26) ans0 <- c("10M", "4I3M3S") attr(ans0, "rshift") <- c(29L, 17L) checkIdentical(ans, ans0) ans <- cigarQNarrow(cigar, start=26, end=-8) ans0 <- c("3M", "3I") attr(ans0, "rshift") <- c(29L, 17L) checkIdentical(ans, ans0) ans <- cigarQNarrow(cigar, start=26, end=-10) ans0 <- c("1M", "1I") attr(ans0, "rshift") <- c(29L, 17L) checkIdentical(ans, ans0) } test_cigarNarrow <- function() { cigar <- c("25M4D10M", "6S17M6I3M3S") ans <- cigarNarrow(cigar) ans0 <- c("25M4D10M", "17M6I3M") attr(ans0, "rshift") <- c(0L, 0L) checkIdentical(ans, ans0) ans <- cigarNarrow(cigar, start=3, end=-3) ans0 <- c("23M4D8M", "15M6I1M") attr(ans0, "rshift") <- c(2L, 2L) checkIdentical(ans, ans0) ans <- cigarNarrow(cigar, start=7, end=-4) ans0 <- c("19M4D7M", "11M") attr(ans0, "rshift") <- c(6L, 6L) checkIdentical(ans, ans0) ans <- cigarNarrow(cigar, start=8, end=-5) ans0 <- c("18M4D6M", "9M") attr(ans0, "rshift") <- c(7L, 7L) checkIdentical(ans, ans0) ans <- cigarNarrow(cigar[1], start=26, end=-10) ans0 <- "1M" attr(ans0, "rshift") <- 29L checkIdentical(ans, ans0) } GenomicAlignments/inst/unitTests/test_coordinate-mapping-methods.R0000644000175200017520000001431614547043765026605 0ustar00biocbuildbiocbuild## CIGAR ops M, =, X x1 <- GRanges("chr1", IRanges(c(5, 10, 20, 25), width=2, names=LETTERS[1:4])) align1 <- GAlignments(rep("chr1", 3), rep(10L, 3), c("11M", "11=", "11X"), strand(rep("+", 3)), names=letters[1:3]) ## CIGAR ops S, N, D, I, H, P x2 <- GRanges("chr1", IRanges(c(1, 20), width=6, names=LETTERS[1:2])) cigar <- c("1S6M1S", "3M2N3M", "3M2D3M", "3M2I3M", "1H6M1H", "1P6M1P") align2 <- GAlignments(rep("chr1", 6), rep(10L, 6), cigar, strand(rep("+", 6))) align3 <- GAlignments(rep("chr1", 6), rep(20L, 6), cigar, strand(rep("+", 6))) names(align2) <- names(align3) <- letters[1:6] x2bis <- GRanges("chr1", IRanges(1:6, width=1, names=LETTERS[1:6])) align2bis <- GAlignments("chr1", 2L, cigar = "1S2M1S", strand("+")) names(align2bis) = letters[1] test_mapToAlignments <- function() { ans <- mapToAlignments(x1, align1) checkIdentical(start(ans), rep(1L, 3)) checkIdentical(end(ans), rep(2L, 3)) checkIdentical(mcols(ans)$xHits, c(rep(2L, 3))) checkIdentical(mcols(ans)$alignmentsHits, c(1L, 2L, 3L)) checkIdentical(names(ans), rep("B", 3)) checkIdentical(seqlevels(ans), letters[1:3]) ans <- mapToAlignments(x2, align3) checkIdentical(end(ans), c(7L, 4L, 4L, 8L, 6L, 6L)) checkIdentical(mcols(ans)$alignmentsHits, as.integer(1:6)) ans <- mapToAlignments(x2bis, align2bis) checkIdentical(start(ans), c(2L, 3L)) checkIdentical(end(ans), c(2L, 3L)) checkIdentical(mcols(ans)$xHits, c(2L, 3L)) } test_mapFromAlignments <- function() { x <- x1 names(x) <- rep("all", length(x)) align <- align1 names(align) <- rep("all", length(align)) ans <- mapFromAlignments(x, align) checkIdentical(start(ans), c(14L, 14L, 14L, 19L, 19L, 19L)) checkIdentical(mcols(ans)$xHits, c(rep(1L, 3), rep(2L, 3))) checkIdentical(mcols(ans)$alignmentsHits, rep(1:3, 2)) checkIdentical(seqlevels(ans), "chr1") checkIdentical(names(ans), rep("all", 6)) names(x) <- c("hit", "hit", "blank", "blank") names(align) <- c("BLANK", "hit", "BLANK") ans <- mapFromAlignments(x, align) checkIdentical(names(ans), c("hit", "hit")) checkIdentical(seqlevels(ans), "chr1") x <- x2 names(x) <- rep("all", length(x)) align <- align2 names(align) <- rep("all", length(align)) ans <- mapFromAlignments(x, align) checkIdentical(start(ans), rep(10L, 6)) checkIdentical(end(ans), c(15L, 17L, 17L, 13L, 15L, 15L)) checkIdentical(mcols(ans)$alignmentsHits, as.integer(1:6)) } test_pmapToAlignments <- function() { x <- x1 align <- rep(align1[1], length(x1)) ans <- pmapToAlignments(x, align) checkIdentical(length(ans), length(x)) checkIdentical(width(ans), c(0L, 2L, 0L, 0L)) checkIdentical(start(ans), c(0L, 1L, 0L, 0L)) checkIdentical(end(ans), c(-1L, 2L, -1L, -1L)) checkIdentical(names(ans), names(x)) checkTrue(all(seqlevels(ans) %in% c("a", "UNMAPPED"))) x <- rep(x2[2], length(align3)) align <- align3 ans <- pmapToAlignments(x, align) checkIdentical(width(ans), c(6L, 4L, 4L, 8L, 6L, 6L)) checkIdentical(start(ans), c(2L, rep(1L, 5))) checkIdentical(end(ans), c(7L, 4L, 4L, 8L, 6L, 6L)) } test_pmapFromAlignments <- function() { x <- x1 names(x) <- rep("all", length(x)) align <- rep(align1[1], length(x1)) names(align) <- rep("all", length(align)) ans <- pmapFromAlignments(x, align) checkTrue(ncol(mcols(ans)) == 0L) checkTrue(length(ans) == length(x1)) checkIdentical(width(ans), c(2L, 2L, 0L, 0L)) checkIdentical(start(ans), c(14L, 19L, 0L, 0L)) checkIdentical(names(ans), names(x)) checkTrue(all(seqlevels(ans) %in% c("chr1", "UNMAPPED"))) x <- rep(x2[1], length(align2)) names(x) <- LETTERS[seq_along(x)] align <- align2 ans <- pmapFromAlignments(x, align) checkIdentical(width(ans), c(6L, 8L, 8L, 4L, 6L, 6L)) checkIdentical(end(ans), c(15L, 17L, 17L, 13L, 15L, 15L)) checkIdentical(names(ans), names(x)) checkTrue(all(seqlevels(ans) %in% "chr1")) } test_ref_locs_to_query_locs <- function() { cigar <- "66S42M2I20M8I18D15M43243N5M1D38M1D85M1D115M139S" pos <- 525842L ref <- 43425L + pos - 1L query <- 238L ans <- .Call("ref_locs_to_query_locs", ref, cigar, pos, FALSE, PACKAGE="GenomicAlignments") checkIdentical(ans, query) ## out of bounds ans_s <- .Call("ref_locs_to_query_locs", start(x1[1]), cigar(align1[1]), start(align1[1]), FALSE, PACKAGE="GenomicAlignments") ans_e <- .Call("ref_locs_to_query_locs", end(x1[1]), cigar(align1[1]), start(align1[1]), TRUE, PACKAGE="GenomicAlignments") checkIdentical(ans_s, NA_integer_) checkIdentical(ans_e, NA_integer_) } test_query_locs_to_ref_locs <- function() { ## out of bounds ans_s <- .Call("query_locs_to_ref_locs", start(x1[4]), cigar(align1[1]), start(align1[1]), FALSE, PACKAGE="GenomicAlignments") ans_e <- .Call("query_locs_to_ref_locs", end(x1[4]), cigar(align1[1]), start(align1[1]), TRUE, PACKAGE="GenomicAlignments") checkIdentical(ans_s, NA_integer_) checkIdentical(ans_e, NA_integer_) } test_map_ref_locs_to_query_locs <- function() { ## hit map <- .Call("map_ref_locs_to_query_locs", 12L, 16L, "11M", 10L) checkIdentical(unlist(map), c(3L, 7L, 1L, 1L)) ## first record out of bounds map <- .Call("map_ref_locs_to_query_locs", c(5L, 12L), c(16L, 16L), "11M", 10L) checkIdentical(unlist(map), c(3L, 7L, 2L, 1L)) ## second record out of bounds map <- .Call("map_ref_locs_to_query_locs", c(12L, 5L), c(16L, 16L), "11M", 10L) checkIdentical(unlist(map), c(3L, 7L, 1L, 1L)) ## first alignment out of bounds map <- .Call("map_ref_locs_to_query_locs", 12L, 16L, c("11M", "11M"), c(20L, 10L)) checkIdentical(unlist(map), c(3L, 7L, 1L, 2L)) ## second alignment out of bounds map <- .Call("map_ref_locs_to_query_locs", 12L, 16L, c("11M", "11M"), c(10L, 20L)) checkIdentical(unlist(map), c(3L, 7L, 1L, 1L)) } GenomicAlignments/inst/unitTests/test_findSpliceOverlaps-methods.R0000644000175200017520000002333614516004601026601 0ustar00biocbuildbiocbuild## TODO: Add tests for the following "findSpliceOverlaps" methods: ## findSpliceOverlaps,GAlignments,GRangesList ## findSpliceOverlaps,GAlignmentPairs,GRangesList ## findSpliceOverlaps,character,ANY ## findSpliceOverlaps,BamFile,ANY .extract <- function(x, col) as.logical(mcols(x)[[col]]) test_findSpliceOverlaps_compatible <- function() { genes <- GRangesList( GRanges("chr1", IRanges(5, 15), "+"), GRanges("chr1", IRanges(5, 15), "-")) reads <- GRangesList(GRanges("chr1", IRanges(3, 13), "+")) res <- findSpliceOverlaps(reads, genes[1]) checkIdentical(FALSE, .extract(res, "compatible")) res <- findSpliceOverlaps(reads, genes[2]) checkIdentical(logical(0), .extract(res, "compatible")) ## exact match to intron boundaries ## (no overlap between 'reads' and 'genes') genes <- GRangesList( GRanges("chr1", IRanges(c(5, 20), c(10, 25)), "+")) reads <- GRangesList( GRanges("chr1", IRanges(11, 19), "+")) res <- findSpliceOverlaps(reads, genes) checkIdentical(logical(0), .extract(res, "compatible")) ## overlap and span intron boundaries genes <- GRangesList( GRanges("chr1", IRanges(c(5, 20, 30), c(10, 25, 35)), "+")) reads <- GRangesList( GRanges("chr1", IRanges(5, 12), "+"), GRanges("chr1", IRanges(18, 23), "+"), GRanges("chr1", IRanges(c(4, 30), c(26, 36)), "+")) res <- findSpliceOverlaps(reads[1], genes) ## not a junction read checkIdentical(FALSE, .extract(res, "compatible")) res <- findSpliceOverlaps(reads[2], genes) ## not a junction read checkIdentical(FALSE, .extract(res, "compatible")) res <- findSpliceOverlaps(reads[3], genes) ## junction read checkIdentical(FALSE, .extract(res, "compatible")) } #test_findSpliceOverlaps_novelTSS <- function() #{ # ## strand # genes <- GRangesList( # GRanges("chr1", IRanges(5, 15), "+"), # GRanges("chr1", IRanges(5, 15), "-"), # GRanges("chr1", IRanges(5, 15), "*")) # reads <- GRangesList(GRanges("chr1", IRanges(3, 13), "+")) # res <- findSpliceOverlaps(reads, genes[1]) # checkIdentical(TRUE, .extract(res, "novelTSS")) # res <- suppressWarnings(findSpliceOverlaps(reads, genes[2])) # checkIdentical(logical(0), .extract(res, "novelTSS")) # res <- findSpliceOverlaps(reads, genes[3]) # checkIdentical(TRUE, .extract(res, "novelTSS")) # # ## multiple matches # genes <- GRangesList( # GRanges("chr1", IRanges(15, 20), "+"), # GRanges("chr1", IRanges(10, 20), "+"), # GRanges("chr1", IRanges(5, 20), "+")) # reads <- GRangesList(GRanges("chr1", IRanges(5, 20), "+")) # res <- findSpliceOverlaps(reads, genes[1]) # checkIdentical(TRUE, .extract(res, "novelTSS")) # res <- findSpliceOverlaps(reads, genes[1:2]) # checkIdentical(c(TRUE, TRUE), .extract(res, "novelTSS")) # res <- findSpliceOverlaps(reads, genes) # checkIdentical(c(FALSE, FALSE, FALSE), .extract(res, "novelTSS")) # # ## junctions # genes <- GRangesList( # GRanges("chr1", IRanges(c(5, 15), c(10, 20)), "+")) # reads <- GRangesList( # GRanges("chr1", IRanges(12, 23), "+"), # GRanges("chr1", IRanges(3, 18), "+")) # res <- findSpliceOverlaps(reads[1], genes) # checkIdentical(FALSE, .extract(res, "novelTSS")) # res <- findSpliceOverlaps(reads[2], genes) # checkIdentical(TRUE, .extract(res, "novelTSS")) #} #test_findSpliceOverlaps_novelTSE <- function() #{ # ## strand # genes <- GRangesList( # GRanges("chr1", IRanges(5, 15), "+"), # GRanges("chr1", IRanges(5, 15), "-"), # GRanges("chr1", IRanges(5, 15), "*")) # reads <- GRangesList(GRanges("chr1", IRanges(12, 18), "+")) # res <- findSpliceOverlaps(reads, genes[1]) # checkIdentical(TRUE, .extract(res, "novelTSE")) # res <- findSpliceOverlaps(reads, genes[2]) # checkIdentical(logical(0), .extract(res, "novelTSE")) # res <- findSpliceOverlaps(reads, genes[3]) # checkIdentical(TRUE, .extract(res, "novelTSE")) # # ## multiple matches # genes <- GRangesList( # GRanges("chr1", IRanges(5, 15), "+"), # GRanges("chr1", IRanges(5, 20), "+"), # GRanges("chr1", IRanges(5, 25), "+")) # reads <- GRangesList(GRanges("chr1", IRanges(5, 25), "+")) # res <- findSpliceOverlaps(reads, genes[1]) # checkIdentical(TRUE, .extract(res, "novelTSE")) # res <- findSpliceOverlaps(reads, genes[1:2]) # checkIdentical(c(TRUE, TRUE), .extract(res, "novelTSE")) # res <- findSpliceOverlaps(reads, genes) # checkIdentical(c(FALSE, FALSE, FALSE), .extract(res, "novelTSE")) # # ## junctions # genes <- GRangesList( # GRanges("chr1", IRanges(c(5, 15), c(10, 20)), "+")) # reads <- GRangesList( # GRanges("chr1", IRanges(2, 12), "+"), # GRanges("chr1", IRanges(18, 23), "+")) # res <- findSpliceOverlaps(reads[1], genes) # checkIdentical(FALSE, .extract(res, "novelTSE")) # res <- findSpliceOverlaps(reads[2], genes) # checkIdentical(TRUE, .extract(res, "novelTSE")) #} #test_findSpliceOverlaps_novelExon <- function() #{ # genes <- GRangesList( # GRanges("chr1", IRanges(c(5, 20), c(10, 25)), "+")) # ## 'within' intron boundaries # reads <- GRangesList( # GRanges("chr1", IRanges(c(7, 12, 20), c(10, 18, 23)), "+")) # res <- findSpliceOverlaps(reads, genes) # checkIdentical(TRUE, .extract(res, "novelExon")) # # ## FIXME :currently TRUE # ## Do we want a novel exon to be completely w/in? # reads <- GRangesList( # GRanges("chr1", IRanges(c(5, 9, 20), c(7, 12, 23)), "+"), # GRanges("chr1", IRanges(c(7, 15, 23), c(10, 21, 25)), "+")) # res <- findSpliceOverlaps(reads[1], genes) # checkIdentical(TRUE, .extract(res, "novelExon")) # res <- findSpliceOverlaps(reads[2], genes) # checkIdentical(TRUE, .extract(res, "novelExon")) # # ## region not 'intronic' in all transcripts # genes <- GRangesList( # GRanges("chr1", IRanges(c(5, 20), c(10, 25)), "+"), # GRanges("chr1", IRanges(c(5, 20), c(15, 25)), "+")) # reads <- GRangesList( # GRanges("chr1", IRanges(c(7, 12, 20), c(10, 18, 23)), "+")) # res <- findSpliceOverlaps(reads, genes) # checkIdentical(c(FALSE, FALSE), .extract(res, "novelExon")) #} #test_findSpliceOverlaps_novelRetention <- function() #{ # genes <- GRangesList( # GRanges("chr1", IRanges(c(5, 20), c(10, 25)), "+")) # ## 'within' intron boundaries # reads <- GRangesList( # GRanges("chr1", IRanges(c(7, 12, 20), c(10, 18, 23)), "+")) # res <- findSpliceOverlaps(reads, genes) # checkIdentical(TRUE, .extract(res, "novelRetention")) # # ## exact match to intron boundaries # ## (no overlap between 'reads' and 'genes') # reads <- GRangesList( # GRanges("chr1", IRanges(11, 19), "+")) # res <- findSpliceOverlaps(reads, genes) # checkIdentical(logical(0), .extract(res, "compatible")) # # ## overlap and span intron boundaries # genes <- GRangesList( # GRanges("chr1", IRanges(c(5, 20, 30), c(10, 25, 35)), "+")) # reads <- GRangesList( # GRanges("chr1", IRanges(5, 12), "+"), # GRanges("chr1", IRanges(18, 23), "+"), # GRanges("chr1", IRanges(c(4, 30), c(26, 36)), "+")) # res <- findSpliceOverlaps(reads[1], genes) ## not a junction read # checkIdentical(TRUE, .extract(res, "novelRetention")) # res <- findSpliceOverlaps(reads[3], genes) ## junction read # checkIdentical(TRUE, .extract(res, "novelRetention")) # # ## FIXME : hits a portion of the intronic region # ## but is not completely 'within' # ## region is not 'intronic' in all transcripts # genes <- GRangesList( # GRanges("chr1", IRanges(c(5, 20), c(10, 25)), "+"), # GRanges("chr1", IRanges(c(5, 20), c(15, 25)), "+")) # reads <- GRangesList( # GRanges("chr1", IRanges(4, 26), "+")) # res <- findSpliceOverlaps(reads[1], genes) # checkIdentical(c(TRUE, TRUE), .extract(res, "novelRetention")) #} # #test_findSpliceOverlaps_novelSite <- function() #{ # genes <- GRangesList( # GRanges("chr1", IRanges(c(5, 15), c(10, 20)), "+")) # ## single novel site, novel junction # reads <- GRangesList( # GRanges("chr1", IRanges(c(5, 15), c(7, 20)), "+")) # res <- findSpliceOverlaps(reads, genes) # checkIdentical(TRUE, .extract(res, "novelSite")) # checkIdentical(TRUE, .extract(res, "novelJunction")) # # ## two novel sites, novel junction # reads <- GRangesList( # GRanges("chr1", IRanges(c(5, 17), c(7, 20)), "+")) # res <- findSpliceOverlaps(reads, genes) # checkIdentical(TRUE, .extract(res, "novelSite")) # checkIdentical(TRUE, .extract(res, "novelJunction")) #} # #test_findSpliceOverlaps_novelJunction <- function() #{ # ## novel junction, no novel sites # genes <- GRangesList( # GRanges("chr1", IRanges(c(5, 20), c(10, 25)), "+"), # GRanges("chr1", IRanges(c(5, 22), c(15, 25)), "+")) # # ## query = GRanges # reads <- GRangesList( # GRanges("chr1", IRanges(c(5, 20), c(15, 25)), "+")) # GRres <- findSpliceOverlaps(reads, genes) # checkIdentical(c(TRUE, TRUE), .extract(GRres, "novelJunction")) # checkIdentical(c(FALSE, FALSE), .extract(GRres, "novelSite")) # # ## query = GAlignments # gal <- GAlignments("chr1", 5L, "11M4N6M", strand("+")) # GALres <- findSpliceOverlaps(gal, genes) # checkIdentical(c(TRUE, TRUE), .extract(GALres, "novelJunction")) # checkIdentical(c(FALSE, FALSE), .extract(GALres, "novelSite")) # # ## query = GAlignmentPairs # gal1 <- GAlignments("chr1", 5L, "11M4N6M", strand("+")) # gal2 <- GAlignments("chr1", 50L, "6M", strand("-")) # galp <- GAlignmentPairs(gal1, gal2) # GALPres <- findSpliceOverlaps(galp, genes) # checkIdentical(c(TRUE, TRUE), .extract(GALPres, "novelJunction")) # checkIdentical(c(FALSE, FALSE), .extract(GALPres, "novelSite")) #} GenomicAlignments/inst/unitTests/test_intra-range-methods.R0000644000175200017520000000315214516004601025206 0ustar00biocbuildbiocbuild.noGaps <- GAlignments( Rle(factor(c("chr1", "chr2", "chr1", "chr3")), c(1, 3, 2, 4)), pos=1:10, cigar=paste0(10:1, "M"), strand=Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), names=head(letters, 10), score=1:10) .Gaps <- GAlignments( Rle(factor(c("chr2", "chr4")), c(3, 4)), pos=1:7, cigar=c("5M", "3M2N3M2N3M", "5M", "10M", "5M1N4M", "8M2N1M", "5M"), strand=Rle(strand(c("-", "+")), c(4, 3)), names=tail(letters, 7), score=1:7) test_GAlignments_qnarrow <- function() { gal <- GAlignments(seqnames=rep(factor("A"), 8), pos=10:17, cigar=c("5M", "5X", "3M2I3M", "3M2D3M", "3M2N3M", "3M2S3M", "3M2H3M", "3M2P3M"), strand=Rle(strand(rep("+", 8)))) n1 <- narrow(gal, start=3) q1 <- qnarrow(gal, start=3) checkIdentical(qwidth(n1), qwidth(q1)) checkIdentical(width(n1), width(q1)) n2 <- narrow(gal, start=4) q2 <- qnarrow(gal, start=4) checkIdentical(width(n2), width(q2)) ## M and X checkIdentical(qwidth(n2[1:2]), qwidth(q2[1:2])) ## I checkIdentical(qwidth(q2[3]), width(q2[3]) + 2L) ## D, N and P checkIdentical(qwidth(q2[c(4,5,8)]), width(q2[c(4,5,8)])) ## S and H checkIdentical(qwidth(q2[6]), width(q2[6]) + 2L) checkIdentical(qwidth(q2[7]), width(q2[7])) } test_GAlignmentsList_qnarrow <- function() { galist <- GAlignmentsList(.noGaps[1:6], .Gaps) qn <- qnarrow(galist, end=-4) checkIdentical(qnarrow(galist[[1]], end=-4), qn[[1]]) checkIdentical(qnarrow(galist[[2]], end=-4), qn[[2]]) } GenomicAlignments/inst/unitTests/test_readGAlignmentPairs.R0000644000175200017520000002774514516004601025234 0ustar00biocbuildbiocbuild### test_readGAlignmentPairs.R # Flag bits summary # ----------------- # 0x1: template having multiple segments in sequencing # 0x2: each segment properly aligned according to the aligner # 0x4: segment unmapped # 0x8: next segment in the template unmapped # 0x10: SEQ being reverse complemented # 0x20: SEQ of the next segment in the template being reversed # 0x40: the first segment in the template # 0x80: the last segment in the template # 0x100: secondary alignment # 0x200: not passing quality controls # 0x400: PCR or optical duplicate make_samline <- function(QNAME, mapped=0, RNAME="*", POS=0, strand="+", primary=1, flagbits=0, RNEXT="*", PNEXT=0, proper=0) { FLAG <- 0x4 * (!mapped) + 0x10 * (strand == "-") + 0x100 * (!primary) + 0x2 * proper + flagbits if (mapped) { CIGAR <- "14M" } else { CIGAR <- "*" } paste(QNAME, FLAG, RNAME, POS, "255", CIGAR, RNEXT, PNEXT, "0", "*", "*", sep="\t") } make_mapped_pair <- function(QNAME, RNAME1, POS1, CIGAR1, strand1, RNAME2, POS2, CIGAR2, strand2, primary, proper, pair_id=NA) { flag0 <- 0x1 + 0x2 * proper + 0x100 * (!primary) FLAG1 <- flag0 + 0x40 + 0x10 * (strand1 == "-") + 0x20 * (strand2 == "-") FLAG2 <- flag0 + 0x80 + 0x10 * (strand2 == "-") + 0x20 * (strand1 == "-") line1 <- paste(QNAME, FLAG1, RNAME1, POS1, "255", CIGAR1, RNAME2, POS2, "0", "*", "*", sep="\t") line2 <- paste(QNAME, FLAG2, RNAME2, POS2, "255", CIGAR2, RNAME1, POS1, "0", "*", "*", sep="\t") if (!identical(pair_id, NA)) { pair_id_tag <- paste0("pi:Z:", pair_id) line1 <- paste(line1, pair_id_tag, sep="\t") line2 <- paste(line2, pair_id_tag, sep="\t") } c(line1, line2) } make_mapped_pairs <- function(mapped_pair_table) { if (!is.data.frame(mapped_pair_table)) { if (is.character(mapped_pair_table)) mapped_pair_table <- textConnection(mapped_pair_table) mapped_pair_table <- read.table(mapped_pair_table, header=TRUE, stringsAsFactors=FALSE) } row_groups <- unname(split(seq_len(nrow(mapped_pair_table)), mapped_pair_table$QNAME)) unlist(lapply(row_groups, function(row_group) { lines <- unlist(lapply(row_group, function(i) do.call("make_mapped_pair", mapped_pair_table[i, ]))) if (length(row_group) == 3L) { lines[c(2L, 4L, 6L)] <- lines[c(6L, 2L, 4L)] } else { lines[c(FALSE, TRUE)] <- rev(lines[c(FALSE, TRUE)]) } lines })) } make_toy_bamfile <- function(mapped_pair_table, filename) { lines0 <- c("@HD\tVN:1.3", "@SQ\tSN:chr1\tLN:2450", "@SQ\tSN:chr2\tLN:1882", "@SQ\tSN:chrX\tLN:999") ## Single end reads lines1 <- c( ## s001: 1 primary alignment make_samline("s001", mapped=1, RNAME="chr1", POS=10, strand="+", primary=1), ## s002: 1 primary alignment + 3 secondary alignments make_samline("s002", mapped=1, RNAME="chr1", POS=20, strand="+", primary=1), make_samline("s002", mapped=1, RNAME="chr1", POS=21, strand="+", primary=0), make_samline("s002", mapped=1, RNAME="chr1", POS=22, strand="+", primary=0), make_samline("s002", mapped=1, RNAME="chr1", POS=20, strand="+", primary=0), ## s003: unmapped make_samline("s003") ) ## Paired end reads lines2 <- c( ## Mapped pairs make_mapped_pairs(mapped_pair_table), ## p991: 1 pair with a missing mate (can happen if file was subsetted ## with e.g. filterBam) make_mapped_pair("p991", "chr2", 150, "18M", "+", "chr2", 199, "18M", "-", primary=1, proper=1)[2L], ## p992: 1 pair with 1st mate unmapped and 2nd mate mapped make_samline("p992", flagbits=0x1 + 0x40, RNEXT="chr2", PNEXT=150), make_samline("p992", mapped=1, RNAME="chr2", POS=150, strand="+", primary=1, flagbits=0x1 + 0x8 + 0x80), ## p993: 1 pair with both mates unmapped make_samline("p993", flagbits=0x1 + 0x8 + 0x40), make_samline("p993", flagbits=0x1 + 0x8 + 0x80) ) ## Reads with multiple segments lines3 <- c( ## m001: 3 segments in the template (index of each segment in template ## is known) make_samline("m001", mapped=1,, RNAME="chrX", POS=10, strand="+", flagbits=0x1 + 0x40, RNEXT="chrX", PNEXT=20, proper=1), make_samline("m001", mapped=1, RNAME="chrX", POS=20, strand="+", flagbits=0x1 + 0x40 + 0x80, RNEXT="chrX", PNEXT=30, proper=1), make_samline("m001", mapped=1, RNAME="chrX", POS=30, strand="+", flagbits=0x1 + 0x80, RNEXT="chrX", PNEXT=10, proper=1), ## m002: 3 segments in the template (index of each segment in template ## was lost) make_samline("m002", mapped=1, RNAME="chrX", POS=10, strand="+", flagbits=0x1, RNEXT="chrX", PNEXT=20, proper=1), make_samline("m002", mapped=1, RNAME="chrX", POS=20, strand="+", flagbits=0x1, RNEXT="chrX", PNEXT=30, proper=1), make_samline("m002", mapped=1, RNAME="chrX", POS=30, strand="+", flagbits=0x1, RNEXT="chrX", PNEXT=10, proper=1) ) samfile <- paste0(filename, ".sam") cat(c(lines0, lines1, lines2, lines3), file=samfile, sep="\n") bamfile <- asBam(samfile, filename, overwrite=TRUE) ## Should never happen. if (bamfile != paste0(filename, ".bam")) stop("asBam() returned an unexpected path") bamfile } ### 1 line per mapped pair. Each line will generate 2 lines/records in the ### SAM file. The pair_id field will be stored in the SAM/BAM file as a user ### defined tag ("pi" tag). mapped_pair_table <- " QNAME RNAME1 POS1 CIGAR1 strand1 RNAME2 POS2 CIGAR2 strand2 primary proper pair_id # p001: 1 primary proper pair p001 chr2 10 18M + chr2 110 18M - 1 1 p001 # p002: 1 primary non proper pair p002 chr2 20 18M + chr2 120 18M - 1 0 p002 # p003: 2 proper pairs: 1 primary + 1 secondary p003 chr2 30 18M + chr2 130 18M - 1 1 p003a p003 chr2 31 18M + chr2 131 18M - 0 1 p003b # p004: 2 non proper pairs: 1 primary + 1 secondary p004 chr2 40 18M + chr2 140 18M - 1 0 p004a p004 chr2 41 18M + chr2 141 18M - 0 0 p004b # p005: 2 primary pairs (some aligners seem to produce that, even though they # probably shouldn't) p005 chr2 50 18M + chr2 150 18M - 1 1 p005a p005 chr2 51 18M + chr2 151 18M - 1 1 p005b # p006: 3 pairs: 1 primary proper + 1 secondary proper + 1 secondary non # proper p006 chr2 60 18M + chr2 160 18M - 1 1 p006a p006 chr2 61 18M + chr2 161 18M - 0 1 p006b p006 chr2 62 18M + chr2 60 18M - 0 0 p006c # p007: 2 pairs mapped to the same position: 1 primary proper + 1 secondary # proper p007 chr2 70 9M1D9M + chr2 170 18M - 1 1 p007a p007 chr2 70 18M + chr2 170 7M2I9M - 0 1 p007b # p008: 3 pairs mapped to the same position: 1 primary proper + 1 secondary # proper + 1 secondary non proper p008 chr2 80 18M + chr2 180 18M - 1 1 p008a p008 chr2 80 9M2D9M + chr2 180 7M2I9M - 0 1 p008b p008 chr2 80 6M3I9M + chr2 180 9M3D9M - 0 0 p008c # p009: 3 pairs mapped to the same position: 1 primary proper + 2 secondary # proper. The secondary pairs can NOT be disambiguated. p009 chr2 90 18M + chr2 190 18M - 1 1 p009a p009 chr2 90 9M2D9M + chr2 190 7M2I9M - 0 1 p009b p009 chr2 90 6M3I9M + chr2 190 9M3D9M - 0 1 p009c " toy_bamfile <- make_toy_bamfile(mapped_pair_table, tempfile()) test_readGAlignmentPairs <- function() { param <- ScanBamParam(tag="pi") galp <- suppressWarnings( readGAlignmentPairs(toy_bamfile, use.names=TRUE, param=param) ) ## Check the dumped alignments dumped_gal <- getDumpedAlignments() checkTrue(validObject(dumped_gal, complete=TRUE)) pi_target <- rep(c("p009b", "p009c"), each=2) checkIdentical(pi_target, sort(mcols(dumped_gal)$pi)) ## Check 'galp' checkTrue(validObject(galp, complete=TRUE)) pi_target <- c("p001", "p002", "p003a", "p003b", "p004a", "p004b", "p005a", "p005b", "p006a", "p006b", "p006c", "p007a", "p007b", "p008a", "p008b", "p008c", "p009a") checkIdentical(pi_target, mcols(first(galp))$pi) checkIdentical(pi_target, mcols(last(galp))$pi) } ### Starting with BioC 2.14, readGAlignmentPairs() behavior changed when ### using the 'which' argument. Old behavior: the same pair was returned once ### per each range in 'which' that had an overlap with the *two* segments in ### the pair. New behavior: the same pair is returned once per each range in ### 'which' that has an overlap with *any* of the 2 segments in the pair. ### The new behavior is a consequence of using ### scanBam(BamFile(asMates=TRUE), ...) ### behind the scene instead of ### findMateAlignment() ### for the pairing. ### The new behavior breaks the test below so I'm turning it off for now. if (FALSE) { test_readGAlignmentPairs_which <- function() { ## 4 non-overlapping regions of interest: first two regions only overlap ## with first p001 mate and last two regions only with last p001 mate. my_ROI <- GRanges("chr2", IRanges(c(10, 15, 110, 115), width=1)) my_ROI_labels <- c("chr2:10-10", "chr2:15-15", "chr2:110-110", "chr2:115-115") param <- ScanBamParam(tag="pi", which=my_ROI[c(1, 4)]) target1 <- readGAlignmentPairs(toy_bamfile, use.names=TRUE, param=param, with.which_label=TRUE) checkTrue(validObject(target1, complete=TRUE)) checkIdentical(1L, length(target1)) checkIdentical(Rle(factor(my_ROI_labels[1], levels=my_ROI_labels[c(1, 4)])), mcols(first(target1))$which_label) checkIdentical(Rle(factor(my_ROI_labels[4], levels=my_ROI_labels[c(1, 4)])), mcols(last(target1))$which_label) mcols(target1@first)$which_label <- mcols(target1@last)$which_label <- NULL ## Checking all possible combinations of ranges in 'which'. check_my_ROI_subsets <- function(subset) { check_my_ROI_subset <- function(i) { #print(i) param <- ScanBamParam(tag="pi", which=my_ROI[i]) current <- suppressWarnings( readGAlignmentPairs(toy_bamfile, use.names=TRUE, param=param) ) if (sum(i <= 2L) == 1L && sum(i >= 3L) == 1L) { checkIdentical(target1, current) } else { checkIdentical(0L, length(current)) } } check_my_ROI_subset(subset) if (length(subset) >= 2L) { check_my_ROI_subset(rev(subset)) if (length(subset) >= 4L) check_my_ROI_subset(c(4L, 1:3)) } TRUE } for (m in 1:length(my_ROI)) combn(length(my_ROI), m, FUN=check_my_ROI_subsets) } } GenomicAlignments/inst/unitTests/test_readGAlignments.R0000644000175200017520000000431114516004601024400 0ustar00biocbuildbiocbuildtest_readGAlignments <- function() { fl <- system.file("extdata", "ex1.bam", package="Rsamtools") which <- IRangesList(seq1=IRanges(1, width=100)) param <- ScanBamParam(which=which) result <- readGAlignments(fl, param=param) checkTrue(validObject(result)) checkIdentical(c(seq1=1575L, seq2=1584L), seqlengths(result)) } test_readGAlignments_missing_param <- function() { fl <- system.file("unitTests", "cases", "ex1_noindex.bam", package="Rsamtools") result0 <- readGAlignments(fl) checkTrue(validObject(result0)) bf <- open(BamFile(fl, character())) result1 <- readGAlignments(bf) checkIdentical(result1, result0) } test_readGAlignments_length0 <- function() { fl <- system.file("extdata", "ex1.bam", package="Rsamtools") which <- IRangesList(seq1=IRanges(100000, width=100)) param <- ScanBamParam(which=which) result <- readGAlignments(fl, param=param) checkTrue(validObject(result)) which <- IRangesList(seq1=IRanges(c(1, 100000), width=100)) param <- ScanBamParam(which=which) result <- readGAlignments(fl, param=param) checkTrue(validObject(result)) } test_readGAlignments_tag <- function() { fl <- system.file("extdata", "ex1.bam", package="Rsamtools") ## valid param <- ScanBamParam(tag=("NM")) gal <- readGAlignments(fl, param=param) checkIdentical(924L, sum(mcols(gal)[["NM"]])) ## empty param <- ScanBamParam(tag=("FO")) gal <- readGAlignments(fl, param=param) checkIdentical(rep.int(NA, length(gal)), mcols(gal)[["FO"]]) } test_readGAlignments_BamViews <- function() { checkTrue(validObject(readGAlignments(BamViews()))) src <- system.file("unitTests", "cases", package="Rsamtools") fl <- c(system.file("extdata", "ex1.bam", package="Rsamtools"), file.path(src, "ex1_shuf1000.bam")) bv <- BamViews(fl, auto.range=TRUE) rng <- bamRanges(bv) aln <- readGAlignments(bv) checkEquals(length(bamPaths(bv)), length(aln)) fl <- c(fl, tempfile()) bv <- BamViews(fl, bamRanges=rng) current <- suppressWarnings({ tryCatch({ aln <- readGAlignments(bv) }, error=identity) }) checkTrue(is(current, "bperror")) } GenomicAlignments/inst/unitTests/test_readGAlignmentsList.R0000644000175200017520000001766114516004601025250 0ustar00biocbuildbiocbuildlibrary(pasillaBamSubset) chr4 <- untreated3_chr4() test_readGAlignmentsList_construction <- function() { fl <- system.file("extdata", "ex1.bam", package="Rsamtools") bf <- BamFile(fl, asMates=TRUE) galist <- readGAlignmentsList(fl) checkTrue(is.null(names(galist))) galist <- readGAlignmentsList(fl, use.names=TRUE) target <- c("EAS54_61:4:143:69:578", "EAS219_FC30151:7:51:1429:1043") checkIdentical(names(galist)[1:2], target) ## first segment first param <- ScanBamParam(what="flag") galist <- readGAlignmentsList(fl, param=param) mates <- galist[mcols(galist)$mate_status == "mated"] flagBit <- bamFlagAsBitMatrix(mcols(unlist(mates))$flag, bitnames="isFirstMateRead") m <- matrix(flagBit, nrow=2) checkIdentical(c(1572L, 0), rowSums(m)) } test_readGAlignmentsList_noYieldSize <- function() { fl <- system.file("extdata", "ex1.bam", package="Rsamtools") bf <- BamFile(fl, asMates=TRUE) galist <- readGAlignmentsList(fl) checkTrue(validObject(galist)) } test_readGAlignmentsList_yieldSize <- function() { bf <- BamFile(chr4, asMates=TRUE, yieldSize=1) scn1 <- scanBam(bf) galist1 <- readGAlignmentsList(bf) checkTrue(length(scn1[[1]]$qname) == 2) checkTrue(length(unique(scn1[[1]]$qname)) == 1) checkTrue(length(unique(scn1[[1]]$qname)) == length(galist1)) bf <- BamFile(chr4, asMates=TRUE, yieldSize=2) scn2 <- scanBam(bf) galist2 <- readGAlignmentsList(bf) checkTrue(length(scn2[[1]]$qname) == 4) checkTrue(length(unique(scn2[[1]]$qname)) == 2) checkTrue(length(unique(scn2[[1]]$qname)) == length(galist2)) } test_readGAlignmentsList_mcols <- function() { bf <- BamFile(chr4, asMates=TRUE, yieldSize=100) param <- ScanBamParam(tag=("NM")) galist <- readGAlignmentsList(bf, param=param) checkIdentical(colnames(mcols(unlist(galist))), "NM") checkTrue(names(mcols(galist)) == "mate_status") param <- ScanBamParam(tag=("FO")) galist <- readGAlignmentsList(bf, param=param) checkIdentical(rep.int(NA, length(unlist(galist))), mcols(unlist(galist))[["FO"]]) } test_readGAlignmentsList_compare_pairs <- function() { bamfile <- BamFile(untreated3_chr4(), asMates=TRUE) galist <- readGAlignmentsList(bamfile) mates <- galist[mcols(galist)$mate_status == "mated"] galp <- readGAlignmentPairs(bamfile) checkIdentical(length(galp), 75409L) tbl <- table(mcols(galist)) checkIdentical(tbl[["mated"]], 75409L) checkIdentical(tbl[["ambiguous"]], 0L) checkIdentical(tbl[["unmated"]], 21227L) } test_readGAlignmentsList_flags <- function() { bamfile <- BamFile(untreated3_chr4(), asMates=TRUE) param <- ScanBamParam(flag=scanBamFlag(isProperPair=TRUE)) galist <- readGAlignmentsList(bamfile, param=param) status <- table(mcols(galist)$mate_status) checkIdentical(status[["mated"]], 45828L) checkIdentical(status[["ambiguous"]], 0L) checkIdentical(status[["unmated"]], 0L) } ## toy_bamfile read summary: ## -------------------------- ## single-end ## s001: 1 primary alignment ## s002: 1 primary alignment + 3 secondary alignments ## s003: unmapped ## paired-end ## p991: 1 pair with a missing mate (can happen if file was subsetted) ## p992: 1 pair with 1st mate unmapped and 2nd mate mapped ## p993: 1 pair with both mates unmapped ## multi-segments ## m001: 3 segments in the template (index of each segment is known) ## m002: 3 segments in the template (index of each segment was lost) ## mapped pairs ('pi' tag only exists for these mapped pairs) ## p001: 1 primary proper pair ## p002: 1 primary non proper pair ## p003: 2 proper pairs: 1 primary + 1 secondary ## p004: 2 non proper pairs: 1 primary + 1 secondary ## p005: 2 primary pairs ## p006: 3 pairs: 1 primary proper + 1 secondary proper + ## 1 secondary non proper ## p007: 2 pairs mapped to the same position: ## 1 primary proper + 1 secondary proper ## p008: 3 pairs mapped to the same position: 1 primary proper + ## 1 secondary proper + 1 secondary non proper ## p009: 3 pairs mapped to the same position: ## 1 primary proper + 2 secondary proper. source(system.file("unitTests", "test_readGAlignmentPairs.R", package="GenomicAlignments")) bf <- BamFile(toy_bamfile, asMates=TRUE) test_readGAlignmentsList_toybamfile <- function() { param <- ScanBamParam(tag="pi") galp <- readGAlignmentPairs(toy_bamfile, use.names=TRUE, param=param) galist <- readGAlignmentsList(bf, use.names=TRUE, param=param) ## 'mated' mated_galist <- unlist(galist[mcols(galist)$mate_status == "mated"]) pi_target <- c("p001", "p002", "p003a", "p003b", "p004a", "p004b", "p005a", "p005b", "p006a", "p006b", "p006c", "p007a", "p007b", "p008a", "p008b", "p008c", "p009a") checkTrue(all(mcols(mated_galist)$pi %in% pi_target)) ## 'ambiguous' GAList match 'dumped' GAPairs ambig_galist <- unlist(galist[mcols(galist)$mate_status == "ambiguous"]) dumped_galp <- getDumpedAlignments() pi_target <- rep(c("p009b", "p009c"), each=2) checkIdentical(pi_target, sort(mcols(dumped_galp)$pi)) checkIdentical(pi_target, sort(mcols(ambig_galist)$pi)) ## 'unmated': unmated_galist <- unlist(galist[mcols(galist)$mate_status == "unmated"]) ## unmated single-end, paired-end or multi-segment (no pi tags) name_target <- c("m001", "m002", "p991", "p992", "s001", "s002") unmated <- names(unmated_galist)[is.na(mcols(unmated_galist)$pi)] checkTrue(all(unmated %in% name_target)) ## non-proper mapped-pairs (have pi tags) pi_target <- c("p002", "p004a", "p004b", "p006c", "p008c") unmated <- na.omit(unique(mcols(unmated_galist)$pi)) checkTrue(all(unmated %in% pi_target)) ## Reads of this type cannot be filtered out wrt readGAlignmentsList. ## They are always returned by readGAlignmentsList but never returned by ## readGAlignmentPairs. bamFlag(param) <- scanBamFlag(isProperPair=TRUE, hasUnmappedMate=FALSE, isUnmappedQuery=FALSE, isPaired=TRUE) galist <- readGAlignmentsList(bf, use.names=TRUE, param=param) unmated_galist <- unlist(galist[mcols(galist)$mate_status == "unmated"]) unmated <- names(unmated_galist)[is.na(mcols(unmated_galist)$pi)] name_target <- c("m001", "m002", "p991") checkTrue(all(unmated %in% name_target)) } test_readGAlignmentsList_which <- function() { ## 4 non-overlapping regions of interest: first two regions only overlap ## with first p001 mate and last two regions only with last p001 mate. my_ROI <- GRanges("chr2", IRanges(c(10, 15, 110, 115), width=1)) my_ROI_labels <- c("chr2:10-10", "chr2:15-15", "chr2:110-110", "chr2:115-115") param <- ScanBamParam(tag="pi", which=my_ROI[c(1, 4)]) target1 <- readGAlignmentsList(toy_bamfile, use.names=TRUE, param=param, with.which_label=TRUE) ## Duplicate results with distinct 'which_label' checkIdentical(2L, length(target1)) checkIdentical(as.vector(mcols(target1)$mate_status), c("mated", "mated")) rng1 <- as.vector(mcols(unlist(target1[1]))$which_label) checkTrue(all(rng1 %in% my_ROI_labels[1])) rng2 <- as.vector(mcols(unlist(target1[2]))$which_label) checkTrue(all(rng2 %in% my_ROI_labels[4])) } text_readGAlignmentsList_findOverlaps <- function() { fl <- system.file("extdata", "ex1.bam", package="Rsamtools") bf <- BamFile(fl, asMates=TRUE) galist <- readGAlignmentsList(bf, strandMode=1L) f <- GRanges(seqnames="seq1", IRanges(30, 250), strand="-") ov <- findOverlaps(galist[1], f, ignore.strand=FALSE) checkIdentical(0L, length(ov)) galist <- readGAlignmentsList(bf, strandMode=2L) f <- GRanges(seqnames="seq1", IRanges(30, 250), strand="-") ov <- findOverlaps(galist[1], f, ignore.strand=FALSE) checkIdentical(1L, length(ov)) } GenomicAlignments/inst/unitTests/test_summarizeOverlaps-methods.R0000644000175200017520000003042014516004601026525 0ustar00biocbuildbiocbuild.getCounts <- function(res) { as.integer(assays(res)$counts) } quiet <- suppressMessages gr <- GRanges(c(rep("chr1", 7), rep("chr2", 4)), IRanges(c(1000, 3000, 3600, 4000, 4000, 5000, 5400, 2000, 3000, 7000, 7500), width = c(500, 500, 300, 500, 900, 500, 500, 900, 500, 600, 300)), "+", group = c("A", "B", "C", "C", "D", "D", "E", "F", "G", "H", "H")) rds <- GAlignments(c(rep(c("chr1", "chr2"), 3), "chr1"), as.integer(c(1400, 2700, 3400, 7100, 4000, 3100, 5200)), c("500M", "100M", "300M", "500M", "300M", "50M200N50M", "50M150N50M"), strand(rep("+", 7))) test_summarizeOverlaps_Union_single <- function() { ## single-end no junctions mode <- "Union" ga <- GAlignments("chr1", 20L, "11M", strand("+")) ann <- GRanges("chr1", IRanges(c(1, 10, 25, 22), c(50, 25, 40, 26)), "+") res <- summarizeOverlaps(ann[1], ga, mode) checkIdentical(1L, .getCounts(res)) res <- summarizeOverlaps(ann[2], ga, mode) checkIdentical(1L, .getCounts(res)) res <- summarizeOverlaps(ann[3], ga, mode) checkIdentical(1L, .getCounts(res)) res <- summarizeOverlaps(ann[4], ga, mode) checkIdentical(1L, .getCounts(res)) ## >1 feature res <- summarizeOverlaps(ann, ga, mode) checkIdentical(c(0L, 0L, 0L, 0L), .getCounts(res)) } test_summarizeOverlaps_Union_paired <- function() { ## single-end with a junction (behaves like paired-end) mode <- "Union" ga <- GAlignments("chr1", 1L, "10M4N11M", strand("+")) ga1 <- GAlignments("chr1", 1L, "10M", strand("+")) ga2 <- GAlignments("chr1", 15L, "11M", strand("-")) galp <- GAlignmentPairs(ga1, ga2) ann <- GRanges("chr1", IRanges(c(1, 5, 12, 20), c(25, 20, 14, 30)), "+") res_ga <- summarizeOverlaps(ann[1], ga, mode) res_galp <- summarizeOverlaps(ann[1], galp, mode) checkIdentical(1L, .getCounts(res_ga)) checkIdentical(1L, .getCounts(res_galp)) res_ga <- summarizeOverlaps(ann[2], ga, mode) res_galp <- summarizeOverlaps(ann[2], galp, mode) checkIdentical(1L, .getCounts(res_ga)) checkIdentical(1L, .getCounts(res_galp)) res_ga <- summarizeOverlaps(ann[3], ga, mode) res_galp <- summarizeOverlaps(ann[3], galp, mode) checkIdentical(0L, .getCounts(res_ga)) checkIdentical(0L, .getCounts(res_galp)) res_ga <- summarizeOverlaps(ann[4], ga, mode) res_galp <- summarizeOverlaps(ann[4], galp, mode) checkIdentical(1L, .getCounts(res_ga)) checkIdentical(1L, .getCounts(res_galp)) ## >1 feature res_ga <- summarizeOverlaps(ann, ga, mode) res_galp <- summarizeOverlaps(ann, ga, mode) checkIdentical(c(0L, 0L, 0L, 0L), .getCounts(res_ga)) checkIdentical(c(0L, 0L, 0L, 0L), .getCounts(res_galp)) } test_summarizeOverlaps_IntersectionStrict_single <- function() { ## single-end, no junctions mode <- "IntersectionStrict" ga <- GAlignments("chr1", 7L, "6M", strand("+")) ann <- GRanges("chr1", IRanges(c(1, 5, 10), width=10), "+") res <- summarizeOverlaps(ann[1], ga, mode) checkIdentical(0L, .getCounts(res)) res <- summarizeOverlaps(ann[2], ga, mode) checkIdentical(1L, .getCounts(res)) res <- summarizeOverlaps(ann[3], ga, mode) checkIdentical(0L, .getCounts(res)) ## >1 feature ann <- GRanges("chr1", IRanges(c(5, 6, 10), c(15, 16, 15)), "+") res <- summarizeOverlaps(ann[1:2], ga, mode) checkIdentical(c(0L, 0L), .getCounts(res)) res <- summarizeOverlaps(ann[c(1,3)], ga, mode) checkIdentical(c(1L, 0L), .getCounts(res)) } test_summarizeOverlaps_IntersectionStrict_paired <- function() { ## single-end with a junction (behaves like paired-end) mode <- "IntersectionStrict" ga <- GAlignments("chr1", 10L, "6M4N6M", strand("+")) ga1 <- GAlignments("chr1", 10L, "6M", strand("+")) ga2 <- GAlignments("chr1", 20L, "6M", strand("-")) galp <- GAlignmentPairs(ga1, ga2) ann <- GRanges("chr1", IRanges(c(1, 1, 20), c(30, 15, 30)), "+") res_ga <- summarizeOverlaps(ann[1], ga, mode) res_galp <- summarizeOverlaps(ann[1], galp, mode) checkIdentical(1L, .getCounts(res_ga)) checkIdentical(1L, .getCounts(res_galp)) res_ga <- summarizeOverlaps(ann[2], ga, mode) res_galp <- summarizeOverlaps(ann[2], galp, mode) checkIdentical(0L, .getCounts(res_ga)) checkIdentical(0L, .getCounts(res_galp)) res_ga <- summarizeOverlaps(ann[3], ga, mode) res_galp <- summarizeOverlaps(ann[3], galp, mode) checkIdentical(0L, .getCounts(res_ga)) checkIdentical(0L, .getCounts(res_galp)) ## >1 feature res_ga <- summarizeOverlaps(ann, ga, mode) res_galp <- summarizeOverlaps(ann, galp, mode) checkIdentical(c(1L, 0L, 0L), .getCounts(res_ga)) checkIdentical(c(1L, 0L, 0L), .getCounts(res_galp)) } test_summarizeOverlaps_IntersectionNotEmpty_single <- function() { ## single-end, no junctions mode <- "IntersectionNotEmpty" ga <- GAlignments("chr1", 10L, "11M", strand("+")) ann <- GRanges("chr1", IRanges(c(1, 5, 12), c(15, 30, 15)), "+") res <- summarizeOverlaps(ann[1], ga, mode) checkIdentical(1L, .getCounts(res)) res <- summarizeOverlaps(ann[2], ga, mode) checkIdentical(1L, .getCounts(res)) res <- summarizeOverlaps(ann[3], ga, mode) checkIdentical(1L, .getCounts(res)) res <- summarizeOverlaps(ann, ga, mode) checkIdentical(c(0L, 1L, 0L), .getCounts(res)) ## >1 feature ann <- GRanges("chr1", IRanges(c(5, 15), c(20, 25)), "+") res <- summarizeOverlaps(ann, ga, mode) checkIdentical(c(1L, 0L), .getCounts(res)) ann <- GRanges("chr1", IRanges(c(5, 12), c(18, 25)), "+") res <- summarizeOverlaps(ann, ga, mode) checkIdentical(c(0L, 0L), .getCounts(res)) ann <- GRanges(rep("chr1", 3), IRanges(c(1L, 20L, 20L), width=c(50, 11, 11)), c("+", "+", "-")) ga <- GAlignments("chr1", 23L, "5M", strand("*")) res <- summarizeOverlaps(ann, ga, mode) checkIdentical(c(0L, 0L, 0L), .getCounts(res)) strand(ga) <- "-" res <- summarizeOverlaps(ann, ga, mode) checkIdentical(c(0L, 0L, 1L), .getCounts(res)) ga <- GAlignments("chr1", 28L, "5M", strand("+")) res <- summarizeOverlaps(ann, ga, mode) checkIdentical(c(1L, 0L, 0L), .getCounts(res)) ## ignore.strand ann <- GRanges("chr1", IRanges(c(5, 1), end=c(10, 6)), strand=c("+", "-")) reads <- GRanges("chr1", IRanges(2, 2), strand="+") checkIdentical(c(0L, 1L), IntersectionNotEmpty(ann, reads, ignore.strand=TRUE)) } test_summarizeOverlaps_IntersectionNotEmpty_paired <- function() { ## single-end with a junction (behaves like paired-end) mode <- "IntersectionNotEmpty" ga <- GAlignments("chr1", 10L, "6M4N6M", strand("+")) ga1 <- GAlignments("chr1", 10L, "6M", strand("+")) ga2 <- GAlignments("chr1", 20L, "6M", strand("-")) galp <- GAlignmentPairs(ga1, ga2) ann <- GRanges("chr1", IRanges(c(1, 1, 20), c(30, 15, 30)), "+") ## single-end, junctions res_ga <- summarizeOverlaps(ann[1], ga, mode) res_galp <- summarizeOverlaps(ann[1], galp, mode) checkIdentical(1L, .getCounts(res_ga)) checkIdentical(1L, .getCounts(res_galp)) res_ga <- summarizeOverlaps(ann[2], ga, mode) res_galp <- summarizeOverlaps(ann[2], galp, mode) checkIdentical(1L, .getCounts(res_ga)) checkIdentical(1L, .getCounts(res_galp)) res_ga <- summarizeOverlaps(ann[3], ga, mode) res_galp <- summarizeOverlaps(ann[3], galp, mode) checkIdentical(1L, .getCounts(res_ga)) checkIdentical(1L, .getCounts(res_galp)) ## > 1 feature res_ga <- summarizeOverlaps(ann, ga, mode) res_galp <- summarizeOverlaps(ann, galp, mode) checkIdentical(c(0L, 0L, 0L), .getCounts(res_ga)) checkIdentical(c(0L, 0L, 0L), .getCounts(res_galp)) ann <- GRanges("chr1", IRanges(c(1, 2), c(23, 23)), "+") res_ga <- summarizeOverlaps(ann, ga, mode) res_galp <- summarizeOverlaps(ann, galp, mode) checkIdentical(c(0L, 0L), .getCounts(res_ga)) checkIdentical(c(0L, 0L), .getCounts(res_galp)) ann <- GRanges("chr1", IRanges(c(1, 21), c(23, 30)), "+") res_ga <- summarizeOverlaps(ann, ga, mode) res_galp <- summarizeOverlaps(ann, galp, mode) checkIdentical(c(0L, 0L), .getCounts(res_ga)) checkIdentical(c(0L, 0L), .getCounts(res_galp)) ann <- GRanges("chr1", IRanges(c(1, 1), c(23, 21)), "+") res_ga <- summarizeOverlaps(ann, ga, mode) res_galp <- summarizeOverlaps(ann, galp, mode) checkIdentical(c(1L, 0L), .getCounts(res_ga)) checkIdentical(c(1L, 0L), .getCounts(res_galp)) } test_summarizeOverlaps_inter.feature_GRanges <- function() { ## rows 5,6,7 from figure in vignette ft <- gr[10:11] rd <- GAlignments(rep("chr2", 3), as.integer(c(7100, 7100, 7500)), c("300M", "500M", "50M"), strand(rep("+", 3))) mode <- "Union" res <- summarizeOverlaps(ft, rd[1], mode, inter.feature=TRUE) checkIdentical(c(1L, 0L), .getCounts(res)) res <- summarizeOverlaps(ft, rd[1], mode, inter.feature=FALSE) checkIdentical(c(1L, 0L), .getCounts(res)) res <- summarizeOverlaps(ft, rd[2], mode, inter.feature=TRUE) checkIdentical(c(0L, 0L), .getCounts(res)) res <- summarizeOverlaps(ft, rd[2], mode, inter.feature=FALSE) checkIdentical(c(1L, 1L), .getCounts(res)) res <- summarizeOverlaps(ft, rd[3], mode, inter.feature=TRUE) checkIdentical(c(0L, 0L), .getCounts(res)) res <- summarizeOverlaps(ft, rd[3], mode, inter.feature=FALSE) checkIdentical(c(1L, 1L), .getCounts(res)) mode <- "IntersectionStrict" res <- summarizeOverlaps(ft, rd[1], mode, inter.feature=TRUE) checkIdentical(c(1L, 0L), .getCounts(res)) res <- summarizeOverlaps(ft, rd[1], mode, inter.feature=FALSE) checkIdentical(c(1L, 0L), .getCounts(res)) res <- summarizeOverlaps(ft, rd[2], mode, inter.feature=TRUE) checkIdentical(c(1L, 0L), .getCounts(res)) res <- summarizeOverlaps(ft, rd[2], mode, inter.feature=FALSE) checkIdentical(c(1L, 0L), .getCounts(res)) res <- summarizeOverlaps(ft, rd[3], mode, inter.feature=TRUE) checkIdentical(c(0L, 0L), .getCounts(res)) res <- summarizeOverlaps(ft, rd[3], mode, inter.feature=FALSE) checkIdentical(c(1L, 1L), .getCounts(res)) mode <- "IntersectionNotEmpty" res <- summarizeOverlaps(ft, rd[1], mode, inter.feature=TRUE) checkIdentical(c(1L, 0L), .getCounts(res)) res <- summarizeOverlaps(ft, rd[1], mode, inter.feature=FALSE) checkIdentical(c(1L, 0L), .getCounts(res)) res <- summarizeOverlaps(ft, rd[2], mode, inter.feature=TRUE) checkIdentical(c(1L, 0L), .getCounts(res)) res <- summarizeOverlaps(ft, rd[2], mode, inter.feature=FALSE) checkIdentical(c(1L, 0L), .getCounts(res)) res <- summarizeOverlaps(ft, rd[3], mode, inter.feature=TRUE) checkIdentical(c(0L, 0L), .getCounts(res)) res <- summarizeOverlaps(ft, rd[3], mode, inter.feature=FALSE) checkIdentical(c(0L, 0L), .getCounts(res)) ## read spans both features rd <- GAlignments("chr2", 7000L, "750M", strand("+")) res <- summarizeOverlaps(ft, rd, mode, inter.feature=TRUE) checkIdentical(c(0L, 0L), .getCounts(res)) res <- summarizeOverlaps(ft, rd, mode, inter.feature=FALSE) checkIdentical(c(1L, 1L), .getCounts(res)) } test_summarizeOverlaps_inter.feature_GRangesList <- function() { grl <- split(gr, mcols(gr)[["group"]]) mode <- "Union" res <- quiet(summarizeOverlaps(grl, rds, mode)) checkIdentical(c(1L, rep(0L, 4), rep(1L, 3)), .getCounts(res)) res <- quiet(summarizeOverlaps(grl, rds, mode, inter.feature=FALSE)) checkIdentical(c(1L, 1L, 2L, 2L, rep(1L, 4)), .getCounts(res)) co <- countOverlaps(grl, rds, type="any") checkIdentical(unname(co), .getCounts(res)) mode <- "IntersectionStrict" res <- quiet(summarizeOverlaps(grl, rds, mode)) checkIdentical(c(0L, 0L, 0L, 1L, 0L, rep(1L, 3)), .getCounts(res)) res <- quiet(summarizeOverlaps(grl, rds, mode, inter.feature=FALSE)) checkIdentical(c(0L, 0L, 1L, 2L, 0L, rep(1L, 3)), .getCounts(res)) co <- countSubjectHits(findOverlaps(rds, grl, type="within")) checkIdentical(unname(co), .getCounts(res)) mode <- "IntersectionNotEmpty" rd <- rds[c(5,7)] ft <- grl[3:5] res <- quiet(summarizeOverlaps(ft, rd, mode)) checkIdentical(c(0L, 1L, 0L), .getCounts(res)) res <- quiet(summarizeOverlaps(ft, rd, mode, inter.feature=FALSE)) checkIdentical(c(0L, 1L, 0L), .getCounts(res)) } GenomicAlignments/man/0000755000175200017520000000000014547043765015771 5ustar00biocbuildbiocbuildGenomicAlignments/man/GAlignmentPairs-class.Rd0000644000175200017520000004040014547043765022405 0ustar00biocbuildbiocbuild\name{GAlignmentPairs-class} \docType{class} % Class: \alias{class:GAlignmentPairs} \alias{GAlignmentPairs-class} \alias{parallel_slot_names,GAlignmentPairs-method} \alias{updateObject,GAlignmentPairs-method} % Constructors: \alias{GAlignmentPairs} % Accessors: \alias{strandMode} \alias{strandMode,GAlignmentPairs-method} \alias{strandMode<-} \alias{strandMode<-,GAlignmentPairs-method} \alias{invertStrand,GAlignmentPairs-method} \alias{names,GAlignmentPairs-method} \alias{names<-,GAlignmentPairs-method} \alias{first} \alias{first,GAlignmentPairs-method} \alias{second} \alias{second,GAlignmentPairs-method} \alias{last} \alias{last,GAlignmentPairs-method} \alias{seqnames,GAlignmentPairs-method} \alias{strand,GAlignmentPairs-method} \alias{njunc,GAlignmentPairs-method} \alias{isProperPair} \alias{isProperPair,GAlignmentPairs-method} \alias{seqinfo,GAlignmentPairs-method} \alias{seqlevelsInUse,GAlignmentPairs-method} \alias{seqinfo<-,GAlignmentPairs-method} % List methods: \alias{[[,GAlignmentPairs,ANY,ANY-method} \alias{unlist,GAlignmentPairs-method} % Coercion: \alias{ranges,GAlignmentPairs-method} \alias{granges,GAlignmentPairs-method} \alias{grglist,GAlignmentPairs-method} \alias{coerce,GAlignmentPairs,IntegerRanges-method} \alias{coerce,GAlignmentPairs,GRanges-method} \alias{coerce,GAlignmentPairs,GRangesList-method} \alias{coerce,GAlignmentPairs,GAlignments-method} \alias{coerce,GAlignmentPairs,DataFrame-method} \alias{as.data.frame,GAlignmentPairs-method} % "show" method: \alias{show,GAlignmentPairs-method} % Concatenation: \alias{bindROWS,GAlignmentPairs-method} \title{GAlignmentPairs objects} \description{ The GAlignmentPairs class is a container for storing \emph{pairs of genomic alignments}. These pairs are typically obtained by aligning paired-end reads to a reference genome or transcriptome. } \details{ A GAlignmentPairs object is a list-like object where each list element represents a pair of genomic alignment. An \emph{alignment pair} is made of a "first" and a "last"/"second" alignment, and is formally represented by a \link{GAlignments} object of length 2. In most applications, an \emph{alignment pair} will represent an aligned paired-end read. In that case, the "first" member of the pair represents the alignment of the first end of the read (aka "first segment in the template", using SAM Spec terminology), and the "last" member of the pair represents the alignment of the second end of the read (aka "last segment in the template", using SAM Spec terminology). In general, a GAlignmentPairs object will be created by loading records from a BAM (or SAM) file containing aligned paired-end reads, using the \code{readGAlignmentPairs} function (see below). Each element in the returned object will be obtained by pairing 2 records. } \section{Constructor}{ \describe{ \item{\code{GAlignmentPairs(first, last, strandMode=1, isProperPair=TRUE, names=NULL)}:}{ Low-level GAlignmentPairs constructor. Generally not used directly. } } } \section{Accessors}{ In the code snippets below, \code{x} is a GAlignmentPairs object. \describe{ \item{\code{strandMode(x)}, \code{strandMode(x) <- value}:}{ The \emph{strand mode} is a per-object switch on GAlignmentPairs objects that controls the behavior of the \code{strand} getter. More precisely, it indicates how the strand of a pair should be inferred from the strand of the first and last alignments in the pair: \itemize{ \item 0: strand of the pair is always *. \item 1: strand of the pair is strand of its first alignment. This mode should be used when the paired-end data was generated using one of the following stranded protocols: Directional Illumina (Ligation), Standard SOLiD. \item 2: strand of the pair is strand of its last alignment. This mode should be used when the paired-end data was generated using one of the following stranded protocols: dUTP, NSR, NNSR, Illumina stranded TruSeq PE protocol. } These modes are equivalent to \code{strandSpecific} equal 0, 1, and 2, respectively, for the \code{featureCounts} function defined in the \pkg{Rsubread} package. Note that, by default, the \code{\link{readGAlignmentPairs}} function sets the \emph{strand mode} to 1 on the returned GAlignmentPairs object. The function has a \code{strandMode} argument to let the user set a different \emph{strand mode}. The \emph{strand mode} can also be changed any time with the \code{strandMode} setter or with \code{invertStrand}. Also note that 3rd party programs TopHat2 and Cufflinks have a \code{--library-type} option to let the user specify which protocol was used. Please refer to the documentation of these programs for more information. } \item{\code{length(x)}:}{ Return the number of alignment pairs in \code{x}. } \item{\code{names(x)}, \code{names(x) <- value}:}{ Get or set the names on \code{x}. See \code{\link{readGAlignmentPairs}} for how to automatically extract and set the names when reading the alignments from a file. } \item{\code{first(x, real.strand=FALSE)}, \code{last(x, real.strand=FALSE)}, \code{second(x, real.strand=FALSE)}:}{ Get the "first" or "last"/"second" alignment for each alignment pair in \code{x}. The result is a \link{GAlignments} object of the same length as \code{x}. If \code{real.strand=TRUE}, then the strand is inverted on-the-fly according to the \emph{strand mode} currently set on the object (see \code{strandMode(x)} above). More precisely, if \code{strandMode(x)} is 0, then the strand is set to * for the \link{GAlignments} object returned by both, \code{first()} and \code{last()}. If \code{strandMode(x)} is 1, then the strand of the object returned by \code{last()} is inverted. If \code{strandMode(x)} is 2, then the strand of the object returned by \code{first()} is inverted. } \item{\code{seqnames(x)}:}{ Get the sequence names of the pairs in \code{x} i.e. the name of the reference sequence for each alignment pair in \code{x}. The sequence name of a pair is the sequence name of the 2 alignments in the pair if they are the same (\emph{concordant seqnames}), or NA if they differ (\emph{discordant seqnames}). The sequence names are returned in a factor-\link[S4Vectors]{Rle} object that is \emph{parallel} to \code{x}, i.e. the i-th element in the returned object is the sequence name of the i-th pair in \code{x}. } \item{\code{strand(x)}:}{ Get the strand for each alignment pair in \code{x}. Obey \code{strandMode(x)} above to infer the strand of a pair. Return \code{*} for pairs with \emph{discordant strand}, or for all pairs if \code{strandMode(x)} is 0. } \item{\code{njunc(x)}:}{ Equivalent to \code{njunc(first(x)) + njunc(last(x))}. } \item{\code{isProperPair(x)}:}{ Get the "isProperPair" flag bit (bit 0x2 in SAM Spec) set by the aligner for each alignment pair in \code{x}. } \item{\code{seqinfo(x)}, \code{seqinfo(x) <- value}:}{ Get or set the information about the underlying sequences. \code{value} must be a \link[GenomeInfoDb]{Seqinfo} object. } \item{\code{seqlevels(x)}, \code{seqlevels(x) <- value}:}{ Get or set the sequence levels. \code{seqlevels(x)} is equivalent to \code{seqlevels(seqinfo(x))} or to \code{levels(seqnames(x))}, those 2 expressions being guaranteed to return identical character vectors on a GAlignmentPairs object. \code{value} must be a character vector with no NAs. See \code{?\link{seqlevels}} for more information. } \item{\code{seqlengths(x)}, \code{seqlengths(x) <- value}:}{ Get or set the sequence lengths. \code{seqlengths(x)} is equivalent to \code{seqlengths(seqinfo(x))}. \code{value} can be a named non-negative integer or numeric vector eventually with NAs. } \item{\code{isCircular(x)}, \code{isCircular(x) <- value}:}{ Get or set the circularity flags. \code{isCircular(x)} is equivalent to \code{isCircular(seqinfo(x))}. \code{value} must be a named logical vector eventually with NAs. } \item{\code{genome(x)}, \code{genome(x) <- value}:}{ Get or set the genome identifier or assembly name for each sequence. \code{genome(x)} is equivalent to \code{genome(seqinfo(x))}. \code{value} must be a named character vector eventually with NAs. } \item{\code{seqnameStyle(x)}:}{ Get or set the seqname style for \code{x}. Note that this information is not stored in \code{x} but inferred by looking up \code{seqnames(x)} against a seqname style database stored in the seqnames.db metadata package (required). \code{seqnameStyle(x)} is equivalent to \code{seqnameStyle(seqinfo(x))} and can return more than 1 seqname style (with a warning) in case the style cannot be determined unambiguously. } } } \section{Vector methods}{ In the code snippets below, \code{x} is a GAlignmentPairs object. \describe{ \item{\code{x[i]}:}{ Return a new GAlignmentPairs object made of the selected alignment pairs. } } } \section{List methods}{ In the code snippets below, \code{x} is a GAlignmentPairs object. \describe{ \item{\code{x[[i]]}:}{ Extract the i-th alignment pair as a \link{GAlignments} object of length 2. As expected \code{x[[i]][1]} and \code{x[[i]][2]} are respectively the "first" and "last" alignments in the pair. } \item{\code{unlist(x, use.names=TRUE)}:}{ Return the \link{GAlignments} object conceptually defined by \code{c(x[[1]], x[[2]], ..., x[[length(x)]])}. \code{use.names} determines whether \code{x} names should be propagated to the result or not. } } } \section{Coercion}{ In the code snippets below, \code{x} is a GAlignmentPairs object. \describe{ \item{\code{granges(x, use.names=TRUE, use.mcols=FALSE, on.discordant.seqnames=c("error", "drop", "split"))}, \code{ranges(x, use.names=TRUE, use.mcols=FALSE, on.discordant.seqnames=c("error", "drop", "split"))}:}{ Return a \link{GRanges} object (for \code{granges()}) or \link[IRanges]{IRanges}) object (for \code{ranges()}). If \code{x} contains no pairs with \emph{discordant seqnames}, the operation is guaranteed to be successful and to return an object \emph{parallel} to \code{x}, that is, an object where the i-th element is the range of the genomic region spanned by the i-th alignment in \code{x} (all gaps in the region are ignored). If \code{x} contains pairs with discordant seqnames, then an error is raised, unless the \code{on.discordant.seqnames} argument is set to \code{"drop"} or \code{"split"}, in which case the pairs with discordant seqnames are either dropped or represented with 2 genomic ranges (or 2 ranges for \code{ranges()}) in the returned object. In that case, the returned object is NOT \emph{parallel} to \code{x}. If \code{use.names} is TRUE, then the names on \code{x} (if any) are propagated to the returned object. If \code{use.mcols} is TRUE, then the metadata columns on \code{x} (if any) are propagated to the returned object. } \item{\code{grglist(x, use.mcols=FALSE, drop.D.ranges=FALSE)}:}{ Return a \link{GRangesList} object of length \code{length(x)} where the i-th element represents the ranges (with respect to the reference) of the i-th alignment pair in \code{x}. The strand of the returned ranges obeys the \emph{strand mode} currently set on the object (see \code{strandMode(x)} above). More precisely, if \code{grl1} and \code{grl2} are \code{grglist(first(x, real.strand=TRUE), order.as.in.query=TRUE)} and \code{grglist(last(x, real.strand=TRUE), order.as.in.query=TRUE)}, respectively, then the i-th element in the returned \link{GRangesList} object is \code{c(grl1[[i]], grl2[[i]])} if \code{strandMode(x)} is 0 or 1, or \code{c(grl2[[i]], grl1[[i]])} if \code{strandMode(x)} is 2. Note that, if \code{strandMode(x)} is 1 or 2, this results in the ranges being in consistent order with the original "query template", that is, being in the order defined by walking the "query template" from the beginning to the end. If \code{use.names} is TRUE, then the names on \code{x} (if any) are propagated to the returned object. If \code{use.mcols} is TRUE, then the metadata columns on \code{x} (if any) are propagated to the returned object. If \code{drop.D.ranges} is TRUE, then deletions (Ds in the CIGAR) are treated like junctions (Ns in the CIGAR), that is, the ranges corresponding to deletions are dropped. } \item{\code{as(x, "GRanges")}, \code{as(x, "IntegerRanges")}, \code{as(x, "GRangesList")}:}{ Alternate ways of doing \code{granges(x, use.names=TRUE, use.mcols=TRUE)}, \code{ranges(x, use.names=TRUE, use.mcols=TRUE)}, and \code{grglist(x, use.names=TRUE, use.mcols=TRUE)}, respectively. } \item{\code{as(x, "GAlignments")}:}{ Equivalent of \code{unlist(x, use.names=TRUE)}. } } } \section{Other methods}{ In the code snippets below, \code{x} is a GAlignmentPairs object. \describe{ \item{\code{show(x)}:}{ By default the \code{show} method displays 5 head and 5 tail elements. This can be changed by setting the global options \code{showHeadLines} and \code{showTailLines}. If the object length is less than (or equal to) the sum of these 2 options plus 1, then the full object is displayed. Note that these options also affect the display of \link{GRanges} and \link{GAlignments} objects, as well as other \link[S4Vectors]{Vector} derivatives defined in the \pkg{IRanges} and \pkg{Biostrings} packages (e.g. \link[IRanges]{IRanges} and \link[Biostrings]{XStringSet} objects). } } } \author{Hervé Pagès} \seealso{ \itemize{ \item \code{\link{readGAlignmentPairs}} for reading aligned paired-end reads from a file (typically a BAM file) into a GAlignmentPairs object. \item \link{GAlignments} objects for handling aligned single-end reads. \item \code{\link{makeGAlignmentPairs}} for pairing the elements of a \link{GAlignments} object into a GAlignmentPairs object. \item \link{junctions-methods} for extracting and summarizing junctions from a GAlignmentPairs object. \item \link[GenomicAlignments]{coverage-methods} for computing the coverage of a GAlignmentPairs object. \item \link[GenomicAlignments]{findOverlaps-methods} for finding range overlaps between a GAlignmentPairs object and another range-based object. \item \code{\link[GenomeInfoDb]{seqinfo}} in the \pkg{GenomeInfoDb} package for getting/setting/modifying the sequence information stored in an object. \item The \link[GenomicRanges]{GRanges} and \link[GenomicRanges]{GRangesList} classes defined and documented in the \pkg{GenomicRanges} package. } } \examples{ library(Rsamtools) # for the ex1.bam file ex1_file <- system.file("extdata", "ex1.bam", package="Rsamtools") galp <- readGAlignmentPairs(ex1_file, use.names=TRUE, strandMode=1) galp length(galp) head(galp) head(names(galp)) first(galp) last(galp) # or second(galp) strandMode(galp) first(galp, real.strand=TRUE) last(galp, real.strand=TRUE) strand(galp) strandMode(galp) <- 2 first(galp, real.strand=TRUE) last(galp, real.strand=TRUE) strand(galp) seqnames(galp) head(njunc(galp)) table(isProperPair(galp)) seqlevels(galp) ## Rename the reference sequences: seqlevels(galp) <- sub("seq", "chr", seqlevels(galp)) seqlevels(galp) galp[[1]] unlist(galp) grglist(galp) # a GRangesList object strandMode(galp) <- 1 grglist(galp) ## Alternatively the strand mode can be changed with invertStrand(): invertStrand(galp) stopifnot(identical(unname(elementNROWS(grglist(galp))), njunc(galp) + 2L)) granges(galp) # a GRanges object } \keyword{methods} \keyword{classes} GenomicAlignments/man/GAlignments-class.Rd0000644000175200017520000004545714547043765021612 0ustar00biocbuildbiocbuild\name{GAlignments-class} \docType{class} % Class: \alias{class:GAlignments} \alias{GAlignments-class} \alias{parallel_slot_names,GAlignments-method} \alias{updateObject,GAlignments-method} % Accessors: \alias{names,GAlignments-method} \alias{seqnames,GAlignments-method} \alias{rname} \alias{rname,GAlignments-method} \alias{strand,GAlignments-method} \alias{names<-,GAlignments-method} \alias{seqnames<-,GAlignments-method} \alias{rname<-} \alias{rname<-,GAlignments-method} \alias{strand<-,GAlignments,ANY-method} \alias{cigar} \alias{cigar,GAlignments-method} \alias{qwidth} \alias{qwidth,GAlignments-method} \alias{start,GAlignments-method} \alias{width,GAlignments-method} \alias{njunc} \alias{njunc,GAlignments-method} \alias{seqinfo,GAlignments-method} \alias{seqinfo<-,GAlignments-method} % Constructor: \alias{GAlignments} \alias{update,GAlignments-method} % Coercion: \alias{ranges,GAlignments-method} \alias{granges,GAlignments-method} \alias{grglist,GAlignments-method} \alias{rglist,GAlignments-method} \alias{coerce,GAlignments,IntegerRanges-method} \alias{coerce,GAlignments,GRanges-method} \alias{coerce,GAlignments,GRangesList-method} \alias{coerce,GAlignments,IntegerRangesList-method} \alias{coerce,GAlignments,DataFrame-method} \alias{as.data.frame,GAlignments-method} \alias{coerce,GenomicRanges,GAlignments-method} % subsetting \alias{getListElement,GAlignments-method} % "show" method: \alias{show,GAlignments-method} % Concatenation: \alias{bindROWS,GAlignments-method} % Comparison/ordering: \alias{pcompare,GAlignments,GAlignments-method} \alias{is.unsorted,GAlignments-method} \alias{order,GAlignments-method} \alias{sort,GAlignments-method} \alias{rank,GAlignments-method} \title{GAlignments objects} \description{ The GAlignments class is a simple container which purpose is to store a set of genomic alignments that will hold just enough information for supporting the operations described below. } \details{ A GAlignments object is a vector-like object where each element describes a genomic alignment i.e. how a given sequence (called "query" or "read", typically short) aligns to a reference sequence (typically long). Typically, a GAlignments object will be created by loading records from a BAM (or SAM) file and each element in the resulting object will correspond to a record. BAM/SAM records generally contain a lot of information but only part of that information is loaded in the GAlignments object. In particular, we discard the query sequences (SEQ field), the query qualities (QUAL), the mapping qualities (MAPQ) and any other information that is not needed in order to support the operations or methods described below. This means that multi-reads (i.e. reads with multiple hits in the reference) won't receive any special treatment i.e. the various SAM/BAM records corresponding to a multi-read will show up in the GAlignments object as if they were coming from different/unrelated queries. Also paired-end reads will be treated as single-end reads and the pairing information will be lost (see \code{?\link{GAlignmentPairs}} for how to handle aligned paired-end reads). Each element of a GAlignments object consists of: \itemize{ \item The name of the reference sequence. (This is the RNAME field in a SAM/BAM record.) \item The strand in the reference sequence to which the query is aligned. (This information is stored in the FLAG field in a SAM/BAM record.) \item The CIGAR string in the "Extended CIGAR format" (see the SAM Format Specifications for the details). \item The 1-based leftmost position/coordinate of the clipped query relative to the reference sequence. We will refer to it as the "start" of the query. (This is the POS field in a SAM/BAM record.) \item The 1-based rightmost position/coordinate of the clipped query relative to the reference sequence. We will refer to it as the "end" of the query. (This is NOT explicitly stored in a SAM/BAM record but can be inferred from the POS and CIGAR fields.) Note that all positions/coordinates are always relative to the first base at the 5' end of the plus strand of the reference sequence, even when the query is aligned to the minus strand. \item The genomic intervals between the "start" and "end" of the query that are "covered" by the alignment. Saying that the full [start,end] interval is covered is the same as saying that the alignment contains no junction (no N in the CIGAR). It is then considered to be a simple alignment. Note that a simple alignment can have mismatches or deletions (in the reference). In other words, a deletion (encoded with a D in the CIGAR) is NOT considered to introduce a gap in the coverage, but a junction is. } Note that the last 2 items are not expicitly stored in the GAlignments object: they are inferred on-the-fly from the CIGAR and the "start". Optionally, a GAlignments object can have names (accessed thru the \code{\link[base]{names}} generic function) which will be coming from the QNAME field of the SAM/BAM records. The rest of this man page will focus on describing how to: \itemize{ \item Access the information stored in a GAlignments object in a way that is independent from how the data are actually stored internally. \item How to create and manipulate a GAlignments object. } } \section{Constructor}{ \describe{ \item{\code{GAlignments(seqnames=Rle(factor()), pos=integer(0), cigar=character(0), strand=NULL, names=NULL, seqlengths=NULL, ...)}:}{ Low-level GAlignments constructor. Generally not used directly. Named arguments in \code{...} are used as metadata columns. } } } \section{Accessors}{ In the code snippets below, \code{x} is a GAlignments object. \describe{ \item{\code{length(x)}:}{ Return the number of alignments in \code{x}. } \item{\code{names(x)}, \code{names(x) <- value}:}{ Get or set the names on \code{x}. See \code{\link{readGAlignments}} for how to automatically extract and set the names when reading the alignments from a file. } \item{\code{seqnames(x)}, \code{seqnames(x) <- value}:}{ Get or set the name of the reference sequence for each alignment in \code{x} (see Details section above for more information about the RNAME field of a SAM/BAM file). \code{value} can be a factor, or a 'factor' \link[S4Vectors]{Rle}, or a character vector. } \item{\code{rname(x)}, \code{rname(x) <- value}:}{ Same as \code{seqnames(x)} and \code{seqnames(x) <- value}. } \item{\code{strand(x)}, \code{strand(x) <- value}:}{ Get or set the strand for each alignment in \code{x} (see Details section above for more information about the strand of an alignment). \code{value} can be a factor (with levels +, - and *), or a 'factor' \link[S4Vectors]{Rle}, or a character vector. } \item{\code{cigar(x)}:}{ Returns a character vector of length \code{length(x)} containing the CIGAR string for each alignment. } \item{\code{qwidth(x)}:}{ Returns an integer vector of length \code{length(x)} containing the length of the query *after* hard clipping (i.e. the length of the query sequence that is stored in the corresponding SAM/BAM record). } \item{\code{start(x)}, \code{end(x)}:}{ Returns an integer vector of length \code{length(x)} containing the "start" and "end" (respectively) of the query for each alignment. See Details section above for the exact definitions of the "start" and "end" of a query. Note that \code{start(x)} and \code{end(x)} are equivalent to \code{start(granges(x))} and \code{end(granges(x))}, respectively (or, alternatively, to \code{min(rglist(x))} and \code{max(rglist(x))}, respectively). } \item{\code{width(x)}:}{ Equivalent to \code{width(granges(x))} (or, alternatively, to \code{end(x) - start(x) + 1L}). Note that this is generally different from \code{qwidth(x)} except for alignments with a trivial CIGAR string (i.e. a string of the form \code{"M"} where is a number). } \item{\code{njunc(x)}:}{ Returns an integer vector of the same length as \code{x} containing the number of junctions (i.e. N operations in the CIGAR) in each alignment. Equivalent to \code{unname(elementNROWS(rglist(x))) - 1L}. } \item{\code{seqinfo(x)}, \code{seqinfo(x) <- value}:}{ Get or set the information about the underlying sequences. \code{value} must be a \link[GenomeInfoDb]{Seqinfo} object. } \item{\code{seqlevels(x)}, \code{seqlevels(x) <- value}:}{ Get or set the sequence levels. \code{seqlevels(x)} is equivalent to \code{seqlevels(seqinfo(x))} or to \code{levels(seqnames(x))}, those 2 expressions being guaranteed to return identical character vectors on a GAlignments object. \code{value} must be a character vector with no NAs. See \code{?\link{seqlevels}} for more information. } \item{\code{seqlengths(x)}, \code{seqlengths(x) <- value}:}{ Get or set the sequence lengths. \code{seqlengths(x)} is equivalent to \code{seqlengths(seqinfo(x))}. \code{value} can be a named non-negative integer or numeric vector eventually with NAs. } \item{\code{isCircular(x)}, \code{isCircular(x) <- value}:}{ Get or set the circularity flags. \code{isCircular(x)} is equivalent to \code{isCircular(seqinfo(x))}. \code{value} must be a named logical vector eventually with NAs. } \item{\code{genome(x)}, \code{genome(x) <- value}:}{ Get or set the genome identifier or assembly name for each sequence. \code{genome(x)} is equivalent to \code{genome(seqinfo(x))}. \code{value} must be a named character vector eventually with NAs. } \item{\code{seqnameStyle(x)}:}{ Get or set the seqname style for \code{x}. Note that this information is not stored in \code{x} but inferred by looking up \code{seqnames(x)} against a seqname style database stored in the \pkg{seqnames.db} metadata package (required). \code{seqnameStyle(x)} is equivalent to \code{seqnameStyle(seqinfo(x))} and can return more than 1 seqname style (with a warning) in case the style cannot be determined unambiguously. } } } \section{Coercion}{ In the code snippets below, \code{x} is a GAlignments object. \describe{ \item{\code{granges(x, use.names=TRUE, use.mcols=FALSE)}, \code{ranges(x, use.names=TRUE, use.mcols=FALSE)}:}{ Return a \link{GRanges} object (for \code{granges()}) or \link[IRanges]{IRanges}) object (for \code{ranges()}) \emph{parallel} to \code{x} where the i-th element is the range of the genomic region spanned by the i-th alignment in \code{x}. All gaps in the region are ignored. If \code{use.names} is TRUE, then the names on \code{x} (if any) are propagated to the returned object. If \code{use.mcols} is TRUE, then the metadata columns on \code{x} (if any) are propagated to the returned object. } \item{\code{grglist(x, use.names=TRUE, use.mcols=FALSE, order.as.in.query=FALSE, drop.D.ranges=FALSE)}, \code{rglist(x, use.names=TRUE, use.mcols=FALSE, order.as.in.query=FALSE, drop.D.ranges=FALSE)}:}{ Return either a \link{GRangesList} or a \link[IRanges]{IntegerRangesList} object of length \code{length(x)} where the i-th element represents the ranges (with respect to the reference) of the i-th alignment in \code{x}. More precisely, the \link[IRanges]{IntegerRangesList} object returned by \code{rglist(x)} is a \link[IRanges]{CompressedIRangesList} object. If \code{use.names} is TRUE, then the names on \code{x} (if any) are propagated to the returned object. If \code{use.mcols} is TRUE, then the metadata columns on \code{x} (if any) are propagated to the returned object. The \code{order.as.in.query} toggle affects the order of the ranges \emph{within} each top-level element of the returned object. If FALSE (the default), then the ranges are ordered from 5' to 3' in elements associated with the plus strand (i.e. corresponding to alignments located on the plus strand), and from 3' to 5' in elements associated with the minus strand. So, whatever the strand is, the ranges are in ascending order (i.e. left-to-right). If TRUE, then the order of the ranges in elements associated with the \emph{minus} strand is reversed. So they end up being ordered from 5' to 3' too, which means that they are now in decending order (i.e. right-to-left). It also means that, when \code{order.as.in.query=TRUE} is used, the ranges are \emph{always} ordered consistently with the original "query template", that is, in the order defined by walking the "query template" from the beginning to the end. If \code{drop.D.ranges} is TRUE, then deletions (D operations in the CIGAR) are treated like junctions (N operations in the CIGAR), that is, the ranges corresponding to deletions are dropped. See Details section above for more information. } \item{\code{as(x, "GRanges")}, \code{as(x, "IntegerRanges")}, \code{as(x, "GRangesList")}, \code{as(x, "IntegerRangesList")}:}{ Alternate ways of doing \code{granges(x, use.names=TRUE, use.mcols=TRUE)}, \code{ranges(x, use.names=TRUE, use.mcols=TRUE)}, \code{grglist(x, use.names=TRUE, use.mcols=TRUE)}, and \code{rglist(x, use.names=TRUE, use.mcols=TRUE)}, respectively. } } In the code snippet below, \code{x} is a \link[GenomicRanges]{GRanges} object. \describe{ \item{\code{as(from, "GAlignments")}:}{ Creates a GAlignments object from a \link[GenomicRanges]{GRanges} object. The metadata columns are propagated. cigar values are created from the sequence width unless a "cigar" metadata column already exists in \code{from}. } } } \section{Subsetting and related operations}{ In the code snippets below, \code{x} is a GAlignments object. \describe{ \item{\code{x[i]}:}{ Return a new GAlignments object made of the selected alignments. \code{i} can be a numeric or logical vector. } } } \section{Concatenation}{ \describe{ \item{\code{c(x, ..., ignore.mcols=FALSE)}:}{ Concatenate GAlignments object \code{x} and the GAlignments objects in \code{...} together. See \code{?\link[S4Vectors]{c}} in the \pkg{S4Vectors} package for more information about concatenating Vector derivatives. } } } \section{Other methods}{ \describe{ \item{\code{show(x)}:}{ By default the \code{show} method displays 5 head and 5 tail elements. This can be changed by setting the global options \code{showHeadLines} and \code{showTailLines}. If the object length is less than (or equal to) the sum of these 2 options plus 1, then the full object is displayed. Note that these options also affect the display of \link{GRanges} and \link{GAlignmentPairs} objects, as well as other objects defined in the \pkg{IRanges} and \pkg{Biostrings} packages (e.g. \link[IRanges]{IntegerRanges} and \link[Biostrings]{DNAStringSet} objects). } } } \references{ \url{http://samtools.sourceforge.net/} } \author{Hervé Pagès and P. Aboyoun} \seealso{ \itemize{ \item \code{\link{readGAlignments}} for reading genomic alignments from a file (typically a BAM file) into a GAlignments object. \item \link{GAlignmentPairs} objects for handling aligned paired-end reads. \item \link{junctions-methods} for extracting and summarizing junctions from a GAlignments object. \item \link[GenomicAlignments]{coverage-methods} for computing the coverage of a GAlignments object. \item \link[GenomicAlignments]{findOverlaps-methods} for finding overlapping genomic alignments. \item \code{\link[GenomeInfoDb]{seqinfo}} in the \pkg{GenomeInfoDb} package for getting/setting/modifying the sequence information stored in an object. \item The \link[GenomicRanges]{GRanges} and \link[GenomicRanges]{GRangesList} classes defined and documented in the \pkg{GenomicRanges} package. \item The \link[IRanges]{CompressedIRangesList} class defined and documented in the \pkg{IRanges} package. } } \examples{ library(Rsamtools) # for the ex1.bam file ex1_file <- system.file("extdata", "ex1.bam", package="Rsamtools") gal <- readGAlignments(ex1_file, param=ScanBamParam(what="flag")) gal ## --------------------------------------------------------------------- ## A. BASIC MANIPULATION ## --------------------------------------------------------------------- length(gal) head(gal) names(gal) # no names by default seqnames(gal) strand(gal) head(cigar(gal)) head(qwidth(gal)) table(qwidth(gal)) head(start(gal)) head(end(gal)) head(width(gal)) head(njunc(gal)) seqlevels(gal) ## Invert the strand: invertStrand(gal) ## Rename the reference sequences: seqlevels(gal) <- sub("seq", "chr", seqlevels(gal)) seqlevels(gal) grglist(gal) # a GRangesList object stopifnot(identical(unname(elementNROWS(grglist(gal))), njunc(gal) + 1L)) granges(gal) # a GRanges object rglist(gal) # a CompressedIRangesList object stopifnot(identical(unname(elementNROWS(rglist(gal))), njunc(gal) + 1L)) ranges(gal) # an IRanges object ## Modify the number of lines in 'show' options(showHeadLines=3) options(showTailLines=2) gal ## Revert to default options(showHeadLines=NULL) options(showTailLines=NULL) ## --------------------------------------------------------------------- ## B. SUBSETTING ## --------------------------------------------------------------------- gal[strand(gal) == "-"] gal[grep("I", cigar(gal), fixed=TRUE)] gal[grep("N", cigar(gal), fixed=TRUE)] # no junctions ## A confirmation that none of the alignments contains junctions (in ## other words, each alignment can be represented by a single genomic ## range on the reference): stopifnot(all(njunc(gal) == 0)) ## Different ways to subset: gal[6] # a GAlignments object of length 1 grglist(gal)[[6]] # a GRanges object of length 1 rglist(gal)[[6]] # a NormalIRanges object of length 1 ## Unlike N operations, D operations don't introduce gaps: ii <- grep("D", cigar(gal), fixed=TRUE) gal[ii] njunc(gal[ii]) grglist(gal[ii]) ## qwidth() vs width(): gal[qwidth(gal) != width(gal)] ## This MUST return an empty object: gal[cigar(gal) == "35M" & qwidth(gal) != 35] ## but this doesn't have too: gal[cigar(gal) != "35M" & qwidth(gal) == 35] } \keyword{methods} \keyword{classes} GenomicAlignments/man/GAlignmentsList-class.Rd0000644000175200017520000003312214547043765022430 0ustar00biocbuildbiocbuild\name{GAlignmentsList-class} \docType{class} % Class \alias{class:GAlignmentsList} \alias{GAlignmentsList-class} \alias{GAlignmentsList} % Constructors: \alias{GAlignmentsList} % Accessors: \alias{names,GAlignmentsList-method} \alias{names<-,GAlignmentsList-method} \alias{seqnames,GAlignmentsList-method} \alias{seqnames<-,GAlignmentsList-method} \alias{rname,GAlignmentsList-method} \alias{rname<-,GAlignmentsList-method} \alias{strand,GAlignmentsList-method} \alias{strand<-,GAlignmentsList-method} \alias{strand<-,GAlignmentsList,character-method} \alias{cigar,GAlignmentsList-method} \alias{qwidth,GAlignmentsList-method} \alias{njunc,GAlignmentsList-method} \alias{elementMetadata,GAlignmentsList-method} \alias{elementMetadata<-,GAlignmentsList-method} \alias{seqinfo,GAlignmentsList-method} \alias{seqinfo<-,GAlignmentsList-method} % Coercion: \alias{ranges,GAlignmentsList-method} \alias{granges,GAlignmentsList-method} \alias{grglist,GAlignmentsList-method} \alias{rglist,GAlignmentsList-method} \alias{coerce,GAlignmentsList,IntegerRanges-method} \alias{coerce,GAlignmentsList,GRanges-method} \alias{coerce,GAlignmentsList,GRangesList-method} \alias{coerce,GAlignmentsList,IntegerRangesList-method} \alias{coerce,GAlignmentPairs,GAlignmentsList-method} \alias{coerce,GAlignmentsList,GAlignmentPairs-method} \alias{coerce,list,GAlignmentsList-method} % extractList() and family: \alias{relistToClass,GAlignments-method} % show: \alias{show,GAlignmentsList-method} \title{GAlignmentsList objects} \description{ The GAlignmentsList class is a container for storing a collection of \link{GAlignments} objects. } \details{ A GAlignmentsList object contains a list of \link{GAlignments} objects. The majority of operations on this page are described in more detail on the GAlignments man page, see ?\code{GAlignments}. } \section{Constructor}{ \describe{ \item{\code{GAlignmentsList(...)}:}{ Creates a GAlignmentsList from a list of \link{GAlignments} objects. } } } \section{Accessors}{ In the code snippets below, \code{x} is a GAlignmentsList object. \describe{ \item{\code{length(x)}:}{ Return the number of elements in \code{x}. } \item{\code{names(x)}, \code{names(x) <- value}:}{ Get or set the names of the elements of \code{x}. } \item{\code{seqnames(x)}, \code{seqnames(x) <- value}:}{ Get or set the name of the reference sequences of the alignments in each element of \code{x}. } \item{\code{rname(x)}, \code{rname(x) <- value}:}{ Same as \code{seqnames(x)} and \code{seqnames(x) <- value}. } \item{\code{strand(x)}, \code{strand(x) <- value}:}{ Get or set the strand of the alignments in each element of \code{x}. } \item{\code{cigar(x)}:}{ Returns a character list of length \code{length(x)} containing the CIGAR string for the alignments in each element of \code{x}. } \item{\code{qwidth(x)}:}{ Returns an integer list of length \code{length(x)} containing the length of the alignments in each element of \code{x} *after* hard clipping (i.e. the length of the query sequence that is stored in the corresponding SAM/BAM record). } \item{\code{start(x)}, \code{end(x)}:}{ Returns an integer list of length \code{length(x)} containing the "start" and "end" (respectively) of the alignments in each element of \code{x}. } \item{\code{width(x)}:}{ Returns an integer list of length \code{length(x)} containing the "width" of the alignments in each element of \code{x}. } \item{\code{njunc(x)}:}{ Returns an integer list of length \code{x} containing the number of junctions (i.e. N operations in the CIGAR) for the alignments in each element of \code{x}. } \item{\code{seqinfo(x)}, \code{seqinfo(x) <- value}:}{ Get or set the information about the underlying sequences. \code{value} must be a \link[GenomeInfoDb]{Seqinfo} object. } \item{\code{seqlevels(x)}, \code{seqlevels(x) <- value}:}{ Get or set the sequence levels of the alignments in each element of \code{x}. } \item{\code{seqlengths(x)}, \code{seqlengths(x) <- value}:}{ Get or set the sequence lengths for each element of \code{x}. \code{seqlengths(x)} is equivalent to \code{seqlengths(seqinfo(x))}. \code{value} can be a named non-negative integer or numeric vector eventually with NAs. } \item{\code{isCircular(x)}, \code{isCircular(x) <- value}:}{ Get or set the circularity flags for the alignments in each element in \code{x}. \code{value} must be a named logical list eventually with NAs. } \item{\code{genome(x)}, \code{genome(x) <- value}:}{ Get or set the genome identifier or assembly name for the alignments in each element of \code{x}. \code{value} must be a named character list eventually with NAs. } \item{\code{seqnameStyle(x)}:}{ Get or set the seqname style for alignments in each element of \code{x}. } } } \section{Coercion}{ In the code snippets below, \code{x} is a GAlignmentsList object. \describe{ \item{\code{granges(x, use.names=TRUE, use.mcols=FALSE, ignore.strand=FALSE)}, \code{ranges(x, use.names=TRUE, use.mcols=FALSE)}:}{ Return either a \link{GRanges} or a \link[IRanges]{IRanges} object of length \code{length(x)}. Note this coercion IGNORES the cigar information. The resulting ranges span the entire range, including any junctions or spaces between paired-end reads. If \code{use.names} is TRUE, then the names on \code{x} (if any) are propagated to the returned object. If \code{use.mcols} is TRUE, then the metadata columns on \code{x} (if any) are propagated to the returned object. \code{granges} coercion supports \code{ignore.strand} to allow ranges of opposite strand to be combined (see examples). All ranges in the resulting GRanges will have strand \sQuote{*}. } \item{\code{grglist(x, use.names=TRUE, use.mcols=FALSE, ignore.strand=FALSE)}, \code{rglist(x, use.names=TRUE, use.mcols=FALSE)}:}{ Return either a \link{GRangesList} or an \link[IRanges]{IRangesList} object of length \code{length(x)}. This coercion RESPECTS the cigar information. The resulting ranges are fragments of the original ranges that do not include junctions or spaces between paired-end reads. If \code{use.names} is TRUE, then the names on \code{x} (if any) are propagated to the returned object. If \code{use.mcols} is TRUE, then the metadata columns on \code{x} (if any) are propagated to the returned object. \code{grglist} coercion supports \code{ignore.strand} to allow ranges of opposite strand to be combined (see examples). When \code{ignore.strand} is TRUE all ranges in the resulting GRangesList have strand \sQuote{*}. } \item{\code{as(x, "GRanges")}, \code{as(x, "IntegerRanges")}, \code{as(x, "GRangesList")}, \code{as(x, "IntegerRangesList")}:}{ Alternate ways of doing \code{granges(x, use.names=TRUE, use.mcols=TRUE)}, \code{ranges(x, use.names=TRUE, use.mcols=TRUE)}, \code{grglist(x, use.names=TRUE, use.mcols=TRUE)}, and \code{rglist(x, use.names=TRUE, use.mcols=TRUE)}, respectively. } \item{\code{as.data.frame(x, row.names = NULL, optional = FALSE, ..., value.name = "value", use.outer.mcols = FALSE, group_name.as.factor = FALSE)}:}{ Coerces \code{x} to a \code{data.frame}. See as.data.frame on the \code{List} man page for details (?\code{List}). } \item{\code{as(x, "GALignmentsList")}:}{ Here \code{x} is a \link{GAlignmentPairs} object. Return a GAlignmentsList object of length \code{length(x)} where the i-th list element represents the ranges of the i-th alignment pair in \code{x}. } } } \section{Subsetting and related operations}{ In the code snippets below, \code{x} is a GAlignmentsList object. \describe{ \item{\code{x[i]}, \code{x[i] <- value}:}{ Get or set list elements \code{i}. \code{i} can be a numeric or logical vector. \code{value} must be a GAlignments. } \item{\code{x[[i]]}, \code{x[[i]] <- value}:}{ Same as \code{x[i]}, \code{x[i] <- value}. } \item{\code{x[i, j]}, \code{x[i, j] <- value}:}{ Get or set list elements \code{i} with optional metadata columns \code{j}. \code{i} can be a numeric, logical or missing. \code{value} must be a GAlignments. } } } \section{Concatenation}{ \describe{ \item{\code{c(x, ..., ignore.mcols=FALSE)}:}{ Concatenate GAlignmentsList object \code{x} and the GAlignmentsList objects in \code{...} together. See \code{?\link[S4Vectors]{c}} in the \pkg{S4Vectors} package for more information about concatenating Vector derivatives. } } } \references{ \url{http://samtools.sourceforge.net/} } \author{Valerie Obenchain} \seealso{ \itemize{ \item \code{\link{readGAlignmentsList}} for reading genomic alignments from a file (typically a BAM file) into a GAlignmentsList object. \item \link{GAlignments} and \link{GAlignmentPairs} objects for handling aligned single- and paired-end reads, respectively. \item \link{junctions-methods} for extracting and summarizing junctions from a GAlignmentsList object. \item \link[GenomicAlignments]{findOverlaps-methods} for finding range overlaps between a GAlignmentsList object and another range-based object. \item \code{\link[GenomeInfoDb]{seqinfo}} in the \pkg{GenomeInfoDb} package for getting/setting/modifying the sequence information stored in an object. \item The \link[GenomicRanges]{GRanges} and \link[GenomicRanges]{GRangesList} classes defined and documented in the \pkg{GenomicRanges} package. } } \examples{ gal1 <- GAlignments( seqnames=Rle(factor(c("chr1", "chr2", "chr1", "chr3")), c(1, 3, 2, 4)), pos=1:10, cigar=paste0(10:1, "M"), strand=Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), names=head(letters, 10), score=1:10) gal2 <- GAlignments( seqnames=Rle(factor(c("chr2", "chr4")), c(3, 4)), pos=1:7, cigar=c("5M", "3M2N3M2N3M", "5M", "10M", "5M1N4M", "8M2N1M", "5M"), strand=Rle(strand(c("-", "+")), c(4, 3)), names=tail(letters, 7), score=1:7) galist <- GAlignmentsList(noGaps=gal1, Gaps=gal2) ## --------------------------------------------------------------------- ## A. BASIC MANIPULATION ## --------------------------------------------------------------------- length(galist) names(galist) seqnames(galist) strand(galist) head(cigar(galist)) head(qwidth(galist)) head(start(galist)) head(end(galist)) head(width(galist)) head(njunc(galist)) seqlevels(galist) ## Rename the reference sequences: seqlevels(galist) <- sub("chr", "seq", seqlevels(galist)) seqlevels(galist) grglist(galist) # a GRangesList object rglist(galist) # an IRangesList object ## --------------------------------------------------------------------- ## B. SUBSETTING ## --------------------------------------------------------------------- galist[strand(galist) == "-"] has_junctions <- sapply(galist, function(x) any(grepl("N", cigar(x), fixed=TRUE))) galist[has_junctions] ## Different ways to subset: galist[2] # a GAlignments object of length 1 galist[[2]] # a GAlignments object of length 1 grglist(galist[2]) # a GRangesList object of length 1 rglist(galist[2]) # a NormalIRangesList object of length 1 ## --------------------------------------------------------------------- ## C. mcols()/elementMetadata() ## --------------------------------------------------------------------- ## Metadata can be defined on the individual GAlignment elements ## and the overall GAlignmentsList object. By default, 'level=between' ## extracts the GALignmentsList metadata. Using 'level=within' ## will extract the metadata on the individual GAlignments objects. mcols(galist) ## no metadata on the GAlignmentsList object mcols(galist, level="within") ## --------------------------------------------------------------------- ## D. readGAlignmentsList() ## --------------------------------------------------------------------- library(pasillaBamSubset) ## 'file' as character. fl <- untreated3_chr4() galist1 <- readGAlignmentsList(fl) galist1[1:3] length(galist1) table(elementNROWS(galist1)) ## When 'file' is a BamFile, 'asMates' must be TRUE. If FALSE, ## the data are treated as single-end and each list element of the ## GAlignmentsList will be of length 1. For single-end data ## use readGAlignments() instead of readGAlignmentsList(). bf <- BamFile(fl, yieldSize=3, asMates=TRUE) readGAlignmentsList(bf) ## Use a 'param' to fine tune the results. param <- ScanBamParam(flag=scanBamFlag(isProperPair=TRUE)) galist2 <- readGAlignmentsList(fl, param=param) length(galist2) ## --------------------------------------------------------------------- ## E. COERCION ## --------------------------------------------------------------------- ## The granges() and grlist() coercions support 'ignore.strand' to ## allow ranges from different strands to be combined. In this example ## paired-end reads aligned to opposite strands were read into a ## GAlignmentsList. If the desired operation is to combine these ranges, ## regardless of junctions or the space between pairs, 'ignore.strand' ## must be TRUE. granges(galist[1]) granges(galist[1], ignore.strand=TRUE) ## grglist() galist <- GAlignmentsList(noGaps=gal1, Gaps=gal2) grglist(galist) grglist(galist, ignore.strand=TRUE) } \keyword{methods} \keyword{classes} GenomicAlignments/man/GappedReads-class.Rd0000644000175200017520000000253014547043765021542 0ustar00biocbuildbiocbuild\name{GappedReads-class} \docType{class} % Class: \alias{class:GappedReads} \alias{GappedReads-class} % Constructor: \alias{GappedReads} % Accessors: \alias{qseq} \alias{qseq,GappedReads-method} \alias{qwidth,GappedReads-method} % Combining: \alias{c,GappedReads-method} \title{(Legacy) GappedReads objects} \description{ The GappedReads class extends the \link{GAlignments} class. A GappedReads object contains all the information contained in a \link{GAlignments} object plus the sequences of the queries. Those sequences can be accessed via the \code{qseq} accessor. } \section{Constructor}{ GappedReads objects are typically created when reading a file containing aligned reads with the \code{\link{readGappedReads}} function. } \section{Accessors}{ In the code snippets below, \code{x} is a GappedReads object. \describe{ \item{\code{qseq(x)}:}{ Extracts the sequences of the queries as a \code{\link[Biostrings]{DNAStringSet}} object. } } } \references{ \url{http://samtools.sourceforge.net/} } \author{Hervé Pagès} \seealso{ \itemize{ \item \link{GAlignments} objects. \item \code{\link{readGappedReads}}. } } \examples{ greads_file <- system.file("extdata", "ex1.bam", package="Rsamtools") greads <- readGappedReads(greads_file) greads qseq(greads) } \keyword{methods} \keyword{classes} GenomicAlignments/man/OverlapEncodings-class.Rd0000644000175200017520000003060614547043765022632 0ustar00biocbuildbiocbuild\name{OverlapEncodings-class} \docType{class} \alias{class:OverlapEncodings} \alias{OverlapEncodings-class} \alias{OverlapEncodings} \alias{parallel_slot_names,OverlapEncodings-method} \alias{Loffset} \alias{Loffset,OverlapEncodings-method} \alias{Roffset} \alias{Roffset,OverlapEncodings-method} \alias{encoding,OverlapEncodings-method} \alias{levels,OverlapEncodings-method} \alias{levels.OverlapEncodings} \alias{flippedQuery} \alias{flippedQuery,OverlapEncodings-method} \alias{encodingHalves} \alias{encodingHalves,character-method} \alias{encodingHalves,factor-method} \alias{encodingHalves,OverlapEncodings-method} \alias{Lencoding} \alias{Rencoding} \alias{Lnjunc} \alias{Rnjunc} \alias{njunc,ANY-method} \alias{as.data.frame.OverlapEncodings} \alias{as.data.frame,OverlapEncodings-method} \alias{show,OverlapEncodings-method} \alias{isCompatibleWithSplicing} \alias{isCompatibleWithSplicing,character-method} \alias{isCompatibleWithSplicing,factor-method} \alias{isCompatibleWithSplicing,OverlapEncodings-method} \title{OverlapEncodings objects} \description{ The OverlapEncodings class is a container for storing the "overlap encodings" returned by the \code{\link{encodeOverlaps}} function. } \usage{ ## -=-= OverlapEncodings getters =-=- \S4method{Loffset}{OverlapEncodings}(x) \S4method{Roffset}{OverlapEncodings}(x) \S4method{encoding}{OverlapEncodings}(x) \S4method{levels}{OverlapEncodings}(x) \S4method{flippedQuery}{OverlapEncodings}(x) ## -=-= Coercing an OverlapEncodings object =-=- \S4method{as.data.frame}{OverlapEncodings}(x, row.names=NULL, optional=FALSE, ...) ## -=-= Low-level encoding utilities =-=- encodingHalves(x, single.end.on.left=FALSE, single.end.on.right=FALSE, as.factors=FALSE) Lencoding(x, ...) Rencoding(x, ...) \S4method{njunc}{ANY}(x) Lnjunc(x, single.end.on.left=FALSE) Rnjunc(x, single.end.on.right=FALSE) isCompatibleWithSplicing(x) } \arguments{ \item{x}{ An OverlapEncodings object. For the low-level encoding utilities, \code{x} can also be a character vector or factor containing encodings. } \item{row.names}{ \code{NULL} or a character vector. } \item{optional}{ Ignored. } \item{...}{ Extra arguments passed to the \code{as.data.frame} method for OverlapEncodings objects are ignored. Extra arguments passed to \code{Lencoding} or \code{Rencoding} are passed down to \code{encodingHalves}. } \item{single.end.on.left, single.end.on.right}{ By default the 2 halves of a single-end encoding are considered to be NAs. If \code{single.end.on.left} (resp. \code{single.end.on.right}) is \code{TRUE}, then the left (resp. right) half of a single-end encoding is considered to be the unmodified encoding. } \item{as.factors}{ By default \code{encodingHalves} returns the 2 encoding halves as a list of 2 character vectors parallel to the input. If \code{as.factors} is \code{TRUE}, then it returns them as a list of 2 factors parallel to the input. } } \details{ Given a \code{query} and a \code{subject} of the same length, both list-like objects with top-level elements typically containing multiple ranges (e.g. \link[IRanges]{IntegerRangesList} objects), the "overlap encoding" of the i-th element in \code{query} and i-th element in \code{subject} is a character string describing how the ranges in \code{query[[i]]} are \emph{qualitatively} positioned relatively to the ranges in \code{subject[[i]]}. The \code{\link{encodeOverlaps}} function computes those overlap encodings and returns them in an OverlapEncodings object of the same length as \code{query} and \code{subject}. The topic of working with overlap encodings is covered in details in the "OverlapEncodings" vignette located this package (\pkg{GenomicAlignments}) and accessible with \code{vignette("OverlapEncodings")}. } \section{OverlapEncodings getters}{ In the following code snippets, \code{x} is an OverlapEncodings object typically obtained by a call to \code{\link{encodeOverlaps}(query, subject)}. \describe{ \item{\code{length(x)}:}{ Get the number of elements (i.e. encodings) in \code{x}. This is equal to \code{length(query)} and \code{length(subject)}. } \item{\code{Loffset(x)}, \code{Roffset(x)}:}{ Get the "left offsets" and "right offsets" of the encodings, respectively. Both are integer vectors of the same length as \code{x}. Let's denote \code{Qi = query[[i]]}, \code{Si = subject[[i]]}, and [q1,q2] the range covered by \code{Qi} i.e. \code{q1 = min(start(Qi))} and \code{q2 = max(end(Qi))}, then \code{Loffset(x)[i]} is the number \code{L} of ranges at the \emph{head} of \code{Si} that are strictly to the left of all the ranges in \code{Qi} i.e. \code{L} is the greatest value such that \code{end(Si)[k] < q1 - 1} for all \code{k} in \code{seq_len(L)}. Similarly, \code{Roffset(x)[i]} is the number \code{R} of ranges at the \emph{tail} of \code{Si} that are strictly to the right of all the ranges in \code{Qi} i.e. \code{R} is the greatest value such that \code{start(Si)[length(Si) + 1 - k] > q2 + 1} for all \code{k} in \code{seq_len(L)}. } \item{\code{encoding(x)}:}{ Factor of the same length as \code{x} where the i-th element is the encoding obtained by comparing each range in \code{Qi} with all the ranges in \code{tSi = Si[(1+L):(length(Si)-R)]} (\code{tSi} stands for "trimmed Si"). More precisely, here is how this encoding is obtained: \enumerate{ \item All the ranges in \code{Qi} are compared with \code{tSi[1]}, then with \code{tSi[2]}, etc... At each step (one step per range in \code{tSi}), comparing all the ranges in \code{Qi} with \code{tSi[k]} is done with \code{rangeComparisonCodeToLetter(compare(Qi, tSi[k]))}. So at each step, we end up with a vector of \code{M} single letters (where \code{M} is \code{length(Qi)}). \item Each vector obtained previously (1 vector per range in \code{tSi}, all of them of length \code{M}) is turned into a single string (called "encoding block") by pasting its individual letters together. \item All the encoding blocks (1 per range in \code{tSi}) are pasted together into a single long string and separated by colons (\code{":"}). An additional colon is prepended to the long string and another one appended to it. \item Finally, a special block containing the value of \code{M} is prepended to the long string. The final string is the encoding. } } \item{\code{levels(x)}:}{ Equivalent to \code{levels(encoding(x))}. } \item{\code{flippedQuery(x)}:}{ Whether or not the top-level element in query used for computing the encoding was "flipped" before the encoding was computed. Note that this flipping generally affects the "left offset", "right offset", in addition to the encoding itself. } } } \section{Coercing an OverlapEncodings object}{ In the following code snippets, \code{x} is an OverlapEncodings object. \describe{ \item{\code{as.data.frame(x)}:}{ Return \code{x} as a data frame with columns \code{"Loffset"}, \code{"Roffset"} and \code{"encoding"}. } } } \section{Low-level encoding utilities}{ In the following code snippets, \code{x} can be an OverlapEncodings object, or a character vector or factor containing encodings. \describe{ \item{\code{encodingHalves(x, single.end.on.left=FALSE, single.end.on.right=FALSE, as.factors=FALSE)}:}{ Extract the 2 halves of paired-end encodings and return them as a list of 2 character vectors (or 2 factors) parallel to the input. Paired-end encodings are obtained by encoding paired-end overlaps i.e. overlaps between paired-end reads and transcripts (typically). The difference between a single-end encoding and a paired-end encoding is that all the blocks in the latter contain a \code{"--"} separator to mark the separation between the "left encoding" and the "right encoding". See examples below and the "Overlap encodings" vignette located in this package for examples of paired-end encodings. } \item{\code{Lencoding(x, ...)}, \code{Rencoding(x, ...)}:}{ Extract the "left encodings" and "right encodings" of paired-end encodings. Equivalent to \code{encodingHalves(x, ...)[[1]]} and \code{encodingHalves(x, ...)[[2]]}, respectively. } \item{\code{njunc(x)}, \code{Lnjunc(x, single.end.on.left=FALSE)}, \code{Rnjunc(x, single.end.on.right=FALSE)}:}{ Extract the number of junctions in each encoding by looking at their first block (aka special block). If an element \code{xi} in \code{x} is a paired-end encoding, then \code{Lnjunc(xi)}, \code{Rnjunc(xi)}, and \code{njunc(xi)}, return \code{njunc(Lencoding(xi))}, \code{njunc(Rencoding(xi))}, and \code{Lnjunc(xi) + Rnjunc(xi)}, respectively. } \item{\code{isCompatibleWithSplicing(x)}:}{ Returns a logical vector \emph{parallel} to \code{x} indicating whether the corresponding encoding describes a \emph{splice compatible} overlap i.e. an overlap that is compatible with the splicing of the transcript. WARNING: For paired-end encodings, \code{isCompatibleWithSplicing} considers that the encoding is \emph{splice compatible} if its 2 halves are \emph{splice compatible}. This can produce false positives if for example the right end of the alignment is located upstream of the left end in transcript space. The paired-end read could not come from this transcript. To eliminate these false positives, one would need to have access and look at the position of the left and right ends in transcript space. This can be done with \code{\link{extractQueryStartInTranscript}}. } } } \author{Hervé Pagès} \seealso{ \itemize{ \item The "OverlapEncodings" vignette in this package. \item The \code{\link{encodeOverlaps}} function for computing "overlap encodings". \item The \code{\link[IRanges]{pcompare}} function in the \pkg{IRanges} package for the interpretation of the strings returned by \code{encoding}. \item The \link[GenomicRanges]{GRangesList} class defined and documented in the \pkg{GenomicRanges} package. } } \examples{ ## --------------------------------------------------------------------- ## A. BASIC MANIPULATION OF AN OverlapEncodings OBJECT ## --------------------------------------------------------------------- example(encodeOverlaps) # to generate the 'ovenc' object length(ovenc) Loffset(ovenc) Roffset(ovenc) encoding(ovenc) levels(ovenc) nlevels(ovenc) flippedQuery(ovenc) njunc(ovenc) as.data.frame(ovenc) njunc(levels(ovenc)) ## --------------------------------------------------------------------- ## B. WORKING WITH PAIRED-END ENCODINGS (POSSIBLY MIXED WITH SINGLE-END ## ENCODINGS) ## --------------------------------------------------------------------- encodings <- c("4:jmmm:agmm:aagm:aaaf:", "3--1:jmm--b:agm--i:") encodingHalves(encodings) encodingHalves(encodings, single.end.on.left=TRUE) encodingHalves(encodings, single.end.on.right=TRUE) encodingHalves(encodings, single.end.on.left=TRUE, single.end.on.right=TRUE) Lencoding(encodings) Lencoding(encodings, single.end.on.left=TRUE) Rencoding(encodings) Rencoding(encodings, single.end.on.right=TRUE) njunc(encodings) Lnjunc(encodings) Lnjunc(encodings, single.end.on.left=TRUE) Rnjunc(encodings) Rnjunc(encodings, single.end.on.right=TRUE) ## --------------------------------------------------------------------- ## C. DETECTION OF "SPLICE COMPATIBLE" OVERLAPS ## --------------------------------------------------------------------- ## Reads that are compatible with the splicing of the transcript can ## be detected with a regular expression (the regular expression below ## assumes that reads have at most 2 junctions): regex0 <- "(:[fgij]:|:[jg].:.[gf]:|:[jg]..:.g.:..[gf]:)" grepl(regex0, encoding(ovenc)) # read4 is NOT "compatible" ## This was for illustration purpose only. In practise you don't need ## (and should not) use this regular expression, but use instead the ## isCompatibleWithSplicing() utility function: isCompatibleWithSplicing(ovenc) } \keyword{methods} \keyword{classes} GenomicAlignments/man/cigar-utils.Rd0000644000175200017520000004420314516004601020464 0ustar00biocbuildbiocbuild\name{cigar-utils} \alias{cigar-utils} \alias{validCigar} \alias{CIGAR_OPS} \alias{explodeCigarOps} \alias{explodeCigarOpLengths} \alias{cigarToRleList} \alias{cigarOpTable} \alias{cigarRangesAlongReferenceSpace} \alias{cigarRangesAlongQuerySpace} \alias{cigarRangesAlongPairwiseSpace} \alias{extractAlignmentRangesOnReference} \alias{cigarWidthAlongReferenceSpace} \alias{cigarWidthAlongQuerySpace} \alias{cigarWidthAlongPairwiseSpace} \alias{cigarNarrow} \alias{cigarQNarrow} \alias{queryLoc2refLoc} \alias{queryLocs2refLocs} \title{ CIGAR utility functions } \description{ Utility functions for low-level CIGAR manipulation. } \usage{ ## -=-= Supported CIGAR operations =-=- CIGAR_OPS ## -=-= Transform CIGARs into other useful representations =-=- explodeCigarOps(cigar, ops=CIGAR_OPS) explodeCigarOpLengths(cigar, ops=CIGAR_OPS) cigarToRleList(cigar) ## -=-= Summarize CIGARs =-=- cigarOpTable(cigar) ## -=-= From CIGARs to ranges =-=- cigarRangesAlongReferenceSpace(cigar, flag=NULL, N.regions.removed=FALSE, pos=1L, f=NULL, ops=CIGAR_OPS, drop.empty.ranges=FALSE, reduce.ranges=FALSE, with.ops=FALSE) cigarRangesAlongQuerySpace(cigar, flag=NULL, before.hard.clipping=FALSE, after.soft.clipping=FALSE, ops=CIGAR_OPS, drop.empty.ranges=FALSE, reduce.ranges=FALSE, with.ops=FALSE) cigarRangesAlongPairwiseSpace(cigar, flag=NULL, N.regions.removed=FALSE, dense=FALSE, ops=CIGAR_OPS, drop.empty.ranges=FALSE, reduce.ranges=FALSE, with.ops=FALSE) extractAlignmentRangesOnReference(cigar, pos=1L, drop.D.ranges=FALSE, f=NULL) ## -=-= From CIGARs to sequence lengths =-=- cigarWidthAlongReferenceSpace(cigar, flag=NULL, N.regions.removed=FALSE) cigarWidthAlongQuerySpace(cigar, flag=NULL, before.hard.clipping=FALSE, after.soft.clipping=FALSE) cigarWidthAlongPairwiseSpace(cigar, flag=NULL, N.regions.removed=FALSE, dense=FALSE) ## -=-= Narrow CIGARs =-=- cigarNarrow(cigar, start=NA, end=NA, width=NA) cigarQNarrow(cigar, start=NA, end=NA, width=NA) ## -=-= Translate coordinates between query and reference spaces =-=- queryLoc2refLoc(qloc, cigar, pos=1L) queryLocs2refLocs(qlocs, cigar, pos=1L, flag=NULL) } \arguments{ \item{cigar}{ A character vector or factor containing the extended CIGAR strings. It can be of arbitrary length except for \code{queryLoc2refLoc} which only accepts a single CIGAR (as a character vector or factor of length 1). } \item{ops}{ Character vector containing the extended CIGAR operations to actually consider. Zero-length operations or operations not listed \code{ops} are ignored. } \item{flag}{ \code{NULL} or an integer vector containing the SAM flag for each read. According to the SAM Spec v1.4, flag bit 0x4 is the only reliable place to tell whether a segment (or read) is mapped (bit is 0) or not (bit is 1). If \code{flag} is supplied, then \code{cigarRangesAlongReferenceSpace}, \code{cigarRangesAlongQuerySpace}, \code{cigarRangesAlongPairwiseSpace}, and \code{extractAlignmentRangesOnReference} don't produce any range for unmapped reads i.e. they treat them as if their CIGAR was empty (independently of what their CIGAR is). If \code{flag} is supplied, then \code{cigarWidthAlongReferenceSpace}, \code{cigarWidthAlongQuerySpace}, and \code{cigarWidthAlongPairwiseSpace} return \code{NA}s for unmapped reads. } \item{N.regions.removed}{ \code{TRUE} or \code{FALSE}. If \code{TRUE}, then \code{cigarRangesAlongReferenceSpace} and \code{cigarWidthAlongReferenceSpace} report ranges/widths with respect to the "reference" space from which the N regions have been removed, and \code{cigarRangesAlongPairwiseSpace} and \code{cigarWidthAlongPairwiseSpace} report them with respect to the "pairwise" space from which the N regions have been removed. } \item{pos}{ An integer vector containing the 1-based leftmost position/coordinate for each (eventually clipped) read sequence. Must have length 1 (in which case it's recycled to the length of \code{cigar}), or the same length as \code{cigar}. } \item{f}{ \code{NULL} or a factor of length \code{cigar}. If \code{NULL}, then the ranges are grouped by alignment i.e. the returned \link[IRanges]{IRangesList} object has 1 list element per element in \code{cigar}. Otherwise they are grouped by factor level i.e. the returned \link[IRanges]{IRangesList} object has 1 list element per level in \code{f} and is named with those levels. For example, if \code{f} is a factor containing the chromosome for each read, then the returned \link[IRanges]{IRangesList} object will have 1 list element per chromosome and each list element will contain all the ranges on that chromosome. } \item{drop.empty.ranges}{ Should empty ranges be dropped? } \item{reduce.ranges}{ Should adjacent ranges coming from the same cigar be merged or not? Using \code{TRUE} can significantly reduce the size of the returned object. } \item{with.ops}{ \code{TRUE} or \code{FALSE} indicating whether the returned ranges should be named with their corresponding CIGAR operation. } \item{before.hard.clipping}{ \code{TRUE} or \code{FALSE}. If \code{TRUE}, then \code{cigarRangesAlongQuerySpace} and \code{cigarWidthAlongQuerySpace} report ranges/widths with respect to the "query" space to which the H regions have been added. \code{before.hard.clipping} and \code{after.soft.clipping} cannot both be \code{TRUE}. } \item{after.soft.clipping}{ \code{TRUE} or \code{FALSE}. If \code{TRUE}, then \code{cigarRangesAlongQuerySpace} and \code{cigarWidthAlongQuerySpace} report ranges/widths with respect to the "query" space from which the S regions have been removed. \code{before.hard.clipping} and \code{after.soft.clipping} cannot both be \code{TRUE}. } \item{dense}{ \code{TRUE} or \code{FALSE}. If \code{TRUE}, then \code{cigarRangesAlongPairwiseSpace} and \code{cigarWidthAlongPairwiseSpace} report ranges/widths with respect to the "pairwise" space from which the I, D, and N regions have been removed. \code{N.regions.removed} and \code{dense} cannot both be \code{TRUE}. } \item{drop.D.ranges}{ Should the ranges corresponding to a deletion from the reference (encoded with a D in the CIGAR) be dropped? By default we keep them to be consistent with the pileup tool from SAMtools. Note that, when \code{drop.D.ranges} is \code{TRUE}, then Ds and Ns in the CIGAR are equivalent. } \item{start,end,width}{ Vectors of integers. NAs and negative values are accepted and "solved" according to the rules of the SEW (Start/End/Width) interface (see \code{?\link[IRanges]{solveUserSEW}} for the details). } \item{qloc}{ An integer vector containing "query-based locations" i.e. 1-based locations relative to the query sequence stored in the SAM/BAM file. } \item{qlocs}{ A list of the same length as \code{cigar} where each element is an integer vector containing "query-based locations" i.e. 1-based locations relative to the corresponding query sequence stored in the SAM/BAM file. } } \value{ \code{CIGAR_OPS} is a predefined character vector containing the supported extended CIGAR operations: M, I, D, N, S, H, P, =, X. See p. 4 of the SAM Spec v1.4 at \url{http://samtools.sourceforge.net/} for the list of extended CIGAR operations and their meanings. For \code{explodeCigarOps} and \code{explodeCigarOpLengths}: Both functions return a list of the same length as \code{cigar} where each list element is a character vector (for \code{explodeCigarOps}) or an integer vector (for \code{explodeCigarOpLengths}). The 2 lists have the same shape, that is, same \code{length()} and same \code{elementNROWS()}. The i-th character vector in the list returned by \code{explodeCigarOps} contains one single-letter string per CIGAR operation in \code{cigar[i]}. The i-th integer vector in the list returned by \code{explodeCigarOpLengths} contains the corresponding CIGAR operation lengths. Zero-length operations or operations not listed in \code{ops} are ignored. For \code{cigarToRleList}: A \link[IRanges]{CompressedRleList} object. For \code{cigarOpTable}: An integer matrix with number of rows equal to the length of \code{cigar} and nine columns, one for each extended CIGAR operation. For \code{cigarRangesAlongReferenceSpace}, \code{cigarRangesAlongQuerySpace}, \code{cigarRangesAlongPairwiseSpace}, and \code{extractAlignmentRangesOnReference}: An \link[IRanges]{IRangesList} object (more precisely a \link[IRanges]{CompressedIRangesList} object) with 1 list element per element in \code{cigar}. However, if \code{f} is a factor, then the returned \link[IRanges]{IRangesList} object can be a \link[IRanges]{SimpleIRangesList} object (instead of \link[IRanges]{CompressedIRangesList}), and in that case, has 1 list element per level in \code{f} and is named with those levels. For \code{cigarWidthAlongReferenceSpace} and \code{cigarWidthAlongPairwiseSpace}: An integer vector of the same length as \code{cigar} where each element is the width of the alignment with respect to the "reference" and "pairwise" space, respectively. More precisely, for \code{cigarWidthAlongReferenceSpace}, the returned widths are the lengths of the alignments on the reference, N gaps included (except if \code{N.regions.removed} is \code{TRUE}). NAs or \code{"*"} in \code{cigar} will produce NAs in the returned vector. For \code{cigarWidthAlongQuerySpace}: An integer vector of the same length as \code{cigar} where each element is the length of the corresponding query sequence as inferred from the CIGAR string. Note that, by default (i.e. if \code{before.hard.clipping} and \code{after.soft.clipping} are \code{FALSE}), this is the length of the query sequence stored in the SAM/BAM file. If \code{before.hard.clipping} or \code{after.soft.clipping} is \code{TRUE}, the returned widths are the lengths of the query sequences before hard clipping or after soft clipping. NAs or \code{"*"} in \code{cigar} will produce NAs in the returned vector. For \code{cigarNarrow} and \code{cigarQNarrow}: A character vector of the same length as \code{cigar} containing the narrowed cigars. In addition the vector has an "rshift" attribute which is an integer vector of the same length as \code{cigar}. It contains the values that would need to be added to the POS field of a SAM/BAM file as a consequence of this cigar narrowing. For \code{queryLoc2refLoc}: An integer vector of the same length as \code{qloc} containing the "reference-based locations" (i.e. the 1-based locations relative to the reference sequence) corresponding to the "query-based locations" passed in \code{qloc}. For \code{queryLocs2refLocs}: A list of the same length as \code{qlocs} where each element is an integer vector containing the "reference-based locations" corresponding to the "query-based locations" passed in the corresponding element in \code{qlocs}. } \references{ \url{http://samtools.sourceforge.net/} } \author{Hervé Pagès & P. Aboyoun} \seealso{ \itemize{ \item The \link[GenomicAlignments]{sequenceLayer} function in the \pkg{GenomicAlignments} package for laying the query sequences alongside the "reference" or "pairwise" spaces. \item The \link{GAlignments} container for storing a set of genomic alignments. \item The \link[IRanges]{IRanges}, \link[IRanges]{IRangesList}, and \link[IRanges]{RleList} classes in the \pkg{IRanges} package. \item The \code{\link[IRanges]{coverage}} generic and methods for computing the coverage across a set of ranges or genomic ranges. } } \examples{ ## --------------------------------------------------------------------- ## A. CIGAR_OPS, explodeCigarOps(), explodeCigarOpLengths(), ## cigarToRleList(), and cigarOpTable() ## --------------------------------------------------------------------- ## Supported CIGAR operations: CIGAR_OPS ## Transform CIGARs into other useful representations: cigar1 <- "3H15M55N4M2I6M2D5M6S" cigar2 <- c("40M2I9M", cigar1, "2S10M2000N15M", "3H33M5H") explodeCigarOps(cigar2) explodeCigarOpLengths(cigar2) explodeCigarOpLengths(cigar2, ops=c("I", "S")) cigarToRleList(cigar2) ## Summarize CIGARs: cigarOpTable(cigar2) ## --------------------------------------------------------------------- ## B. From CIGARs to ranges and to sequence lengths ## --------------------------------------------------------------------- ## CIGAR ranges along the "reference" space: cigarRangesAlongReferenceSpace(cigar1, with.ops=TRUE)[[1]] cigarRangesAlongReferenceSpace(cigar1, reduce.ranges=TRUE, with.ops=TRUE)[[1]] ops <- setdiff(CIGAR_OPS, "N") cigarRangesAlongReferenceSpace(cigar1, ops=ops, with.ops=TRUE)[[1]] cigarRangesAlongReferenceSpace(cigar1, ops=ops, reduce.ranges=TRUE, with.ops=TRUE)[[1]] ops <- setdiff(CIGAR_OPS, c("D", "N")) cigarRangesAlongReferenceSpace(cigar1, ops=ops, with.ops=TRUE)[[1]] cigarWidthAlongReferenceSpace(cigar1) pos2 <- c(1, 1001, 1, 351) cigarRangesAlongReferenceSpace(cigar2, pos=pos2, with.ops=TRUE) res1a <- extractAlignmentRangesOnReference(cigar2, pos=pos2) res1b <- cigarRangesAlongReferenceSpace(cigar2, pos=pos2, ops=setdiff(CIGAR_OPS, "N"), reduce.ranges=TRUE) stopifnot(identical(res1a, res1b)) res2a <- extractAlignmentRangesOnReference(cigar2, pos=pos2, drop.D.ranges=TRUE) res2b <- cigarRangesAlongReferenceSpace(cigar2, pos=pos2, ops=setdiff(CIGAR_OPS, c("D", "N")), reduce.ranges=TRUE) stopifnot(identical(res2a, res2b)) seqnames <- factor(c("chr6", "chr6", "chr2", "chr6"), levels=c("chr2", "chr6")) extractAlignmentRangesOnReference(cigar2, pos=pos2, f=seqnames) ## CIGAR ranges along the "query" space: cigarRangesAlongQuerySpace(cigar2, with.ops=TRUE) cigarWidthAlongQuerySpace(cigar1) cigarWidthAlongQuerySpace(cigar1, before.hard.clipping=TRUE) ## CIGAR ranges along the "pairwise" space: cigarRangesAlongPairwiseSpace(cigar2, with.ops=TRUE) cigarRangesAlongPairwiseSpace(cigar2, dense=TRUE, with.ops=TRUE) ## --------------------------------------------------------------------- ## C. COMPUTE THE COVERAGE OF THE READS STORED IN A BAM FILE ## --------------------------------------------------------------------- ## The information stored in a BAM file can be used to compute the ## "coverage" of the mapped reads i.e. the number of reads that hit any ## given position in the reference genome. ## The following function takes the path to a BAM file and returns an ## object representing the coverage of the mapped reads that are stored ## in the file. The returned object is an RleList object named with the ## names of the reference sequences that actually receive some coverage. flag0 <- scanBamFlag(isUnmappedQuery=FALSE, isDuplicate=FALSE) extractCoverageFromBAM <- function(bamfile) { stopifnot(is(bamfile, "BamFile")) ## This ScanBamParam object allows us to load only the necessary ## information from the file. param <- ScanBamParam(flag=flag0, what=c("rname", "pos", "cigar")) bam <- scanBam(bamfile, param=param)[[1]] ## Note that unmapped reads and reads that are PCR/optical duplicates ## have already been filtered out by using the ScanBamParam object ## above. f <- factor(bam$rname, levels=seqlevels(bamfile)) irl <- extractAlignmentRangesOnReference(bam$cigar, pos=bam$pos, f=f) coverage(irl, width=seqlengths(bamfile)) } library(Rsamtools) f1 <- system.file("extdata", "ex1.bam", package="Rsamtools") cvg <- extractCoverageFromBAM(BamFile(f1)) ## extractCoverageFromBAM() is equivalent but slightly more efficient ## than loading a GAlignments object and computing its coverage: cvg2 <- coverage(readGAlignments(f1, param=ScanBamParam(flag=flag0))) stopifnot(identical(cvg, cvg2)) ## --------------------------------------------------------------------- ## D. cigarNarrow() and cigarQNarrow() ## --------------------------------------------------------------------- ## cigarNarrow(): cigarNarrow(cigar1) # only drops the soft/hard clipping cigarNarrow(cigar1, start=10) cigarNarrow(cigar1, start=15) cigarNarrow(cigar1, start=15, width=57) cigarNarrow(cigar1, start=16) #cigarNarrow(cigar1, start=16, width=55) # ERROR! (empty cigar) cigarNarrow(cigar1, start=71) cigarNarrow(cigar1, start=72) cigarNarrow(cigar1, start=75) ## cigarQNarrow(): cigarQNarrow(cigar1, start=4, end=-3) cigarQNarrow(cigar1, start=10) cigarQNarrow(cigar1, start=19) cigarQNarrow(cigar1, start=24) ## --------------------------------------------------------------------- ## E. PERFORMANCE ## --------------------------------------------------------------------- if (interactive()) { ## We simulate 20 millions aligned reads, all 40-mers. 95% of them ## align with no indels. 5% align with a big deletion in the ## reference. In the context of an RNAseq experiment, those 5% would ## be suspected to be "junction reads". set.seed(123) nreads <- 20000000L njunctionreads <- nreads * 5L / 100L cigar3 <- character(nreads) cigar3[] <- "40M" junctioncigars <- paste( paste(10:30, "M", sep=""), paste(sample(80:8000, njunctionreads, replace=TRUE), "N", sep=""), paste(30:10, "M", sep=""), sep="") cigar3[sample(nreads, njunctionreads)] <- junctioncigars some_fake_rnames <- paste("chr", c(1:6, "X"), sep="") rname <- factor(sample(some_fake_rnames, nreads, replace=TRUE), levels=some_fake_rnames) pos <- sample(80000000L, nreads, replace=TRUE) ## The following takes < 3 sec. to complete: system.time(irl1 <- extractAlignmentRangesOnReference(cigar3, pos=pos)) ## The following takes < 4 sec. to complete: system.time(irl2 <- extractAlignmentRangesOnReference(cigar3, pos=pos, f=rname)) ## The sizes of the resulting objects are about 240M and 160M, ## respectively: object.size(irl1) object.size(irl2) } } \keyword{manip} GenomicAlignments/man/coordinate-mapping-methods.Rd0000644000175200017520000002707314516004601023470 0ustar00biocbuildbiocbuild\name{mapToAlignments} \alias{coordinate-mapping-methods} \alias{mapToAlignments} \alias{mapToAlignments,IntegerRanges,GAlignments-method} \alias{mapToAlignments,GenomicRanges,GAlignments-method} \alias{pmapToAlignments} \alias{pmapToAlignments,IntegerRanges,GAlignments-method} \alias{pmapToAlignments,GenomicRanges,GAlignments-method} \alias{mapFromAlignments} \alias{mapFromAlignments,IntegerRanges,GAlignments-method} \alias{mapFromAlignments,GenomicRanges,GAlignments-method} \alias{pmapFromAlignments} \alias{pmapFromAlignments,IntegerRanges,GAlignments-method} \alias{pmapFromAlignments,GenomicRanges,GAlignments-method} \title{Map range coordinates between reads and genome space using CIGAR alignments} \description{ Map range coordinates between reads (local) and genome (reference) space using the CIGAR in a \code{GAlignments} object. See \code{?\link[GenomicFeatures]{mapToTranscripts}} in the \pkg{GenomicRanges} package for mapping coordinates between features in the transcriptome and genome space. } \usage{ \S4method{mapToAlignments}{GenomicRanges,GAlignments}(x, alignments, ...) \S4method{pmapToAlignments}{GenomicRanges,GAlignments}(x, alignments, ...) \S4method{mapFromAlignments}{GenomicRanges,GAlignments}(x, alignments, ...) \S4method{pmapFromAlignments}{GenomicRanges,GAlignments}(x, alignments, ...) } \arguments{ \item{x}{ \code{\linkS4class{GenomicRanges}} object of positions to be mapped. \code{x} must have names when mapping to the genome. } \item{alignments}{ A \code{\linkS4class{GAlignments}} object that represents the alignment of \code{x} to the genome. The \code{aligments} object must have names. When mapping to the genome names are used to determine mapping pairs and in the reverse direction they are used as the seqlevels of the output object. } \item{\dots}{ Arguments passed to other methods. } } \details{ These methods use a \code{GAlignments} object to represent the alignment between the ranges in \code{x} and the output. The following CIGAR operations in the "Extended CIGAR format" are used in the mapping algorithm: \itemize{ \item{M, X, =} Sequence match or mismatch \item{I} Insertion to the reference \item{D} Deletion from the reference \item{N} Skipped region from the reference \item{S} Soft clip on the read \item{H} Hard clip on the read \item{P} Silent deletion from the padded reference } \itemize{ \item{\code{mapToAlignments}, \code{pmapToAlignments}}{ The CIGAR is used to map the genomic (reference) position \code{x} to local coordinates. The mapped position starts at \preformatted{ start(x) - start(alignments) + 1 } and is incremented or decremented as the algorithm walks the length of the CIGAR. A successful mapping in this direction requires that \code{x} fall within \code{alignments}. The seqlevels of the return object are taken from the \code{alignments} object and will be a name descriptive of the read or aligned region. In this direction, mapping is attempted between all elements of \code{x} and all elements of \code{alignments}. } \item{\code{mapFromAlignments}, \code{pmapFromAlignments}}{ The CIGAR is used to map the local position \code{x} to genomic (reference) coordinates. The mapped position starts at \preformatted{ start(x) + start(alignments) - 1 } and is incremented or decremented as the algorithm walks the length of the CIGAR. A successful mapping in this direction requires that the width of \code{alignments} is <= the width of \code{x}. When mapping to the genome, name matching is used to determine the mapping pairs (vs attempting to match all possible pairs). Ranges in \code{x} are only mapped to ranges in \code{alignments} with the same name. Name matching is motivated by use cases such as differentially expressed regions where the expressed regions in \code{x} would only be related to a subset of regions in \code{alignments}, which may contains gene or transcript ranges. } \item{element-wise versions}{ \code{pmapToAlignments} and \code{pmapFromAlignments} are element-wise (aka `parallel`) versions of \code{mapToAlignments} and \code{mapFromAlignments}. The i-th range in \code{x} is mapped to the i-th range in \code{alignments}; \code{x} and \code{alignments} must have the same length. Ranges in \code{x} that do not map (out of bounds) are returned as zero-width ranges starting at 0. These ranges are given the special seqname of "UNMAPPED". Note the non-parallel methods do not return unmapped ranges so the "UNMAPPED" seqname is unique to \code{pmapToAlignments} and \code{pmapFromAlignments}. } \item{strand}{ By SAM convention, the CIGAR string is reported for mapped reads on the forward genomic strand. There is no need to consider strand in these methods. The output of these methods will always be unstranded (i.e., "*"). } } } \value{ An object the same class as \code{x}. Parallel methods return an object the same shape as \code{x}. Ranges that cannot be mapped (out of bounds) are returned as zero-width ranges starting at 0 with a seqname of "UNMAPPED". Non-parallel methods return an object that varies in length similar to a Hits object. The result only contains mapped records, out of bound ranges are not returned. \code{xHits} and \code{alignmentsHits} metadata columns indicate the elements of \code{x} and \code{alignments} used in the mapping. When present, names from \code{x} are propagated to the output. When mapping locally, the seqlevels of the output are the names on the \code{alignment} object. When mapping globally, the output seqlevels are the seqlevels of \code{alignment} which are usually chromosome names. } \seealso{ \itemize{ \item \code{?\link[GenomicFeatures]{mapToTranscripts}} in the in the \pkg{GenomicFeatures} package for methods mapping between transcriptome and genome space. \item \url{http://samtools.sourceforge.net/} for a description of the Extended CIGAR format. } } \author{V. Obenchain, M. Lawrence and H. Pagès} \examples{ ## --------------------------------------------------------------------- ## A. Basic use ## --------------------------------------------------------------------- ## 1. Map to local space with mapToAlignments() ## --------------------------------------------------------------------- ## Mapping to local coordinates requires 'x' to be within 'alignments'. ## In this 'x', the second range is too long and can't be mapped. alignments <- GAlignments("chr1", 10L, "11M", strand("*"), names="read_A") x <- GRanges("chr1", IRanges(c(12, 12), width=c(6, 20))) mapToAlignments(x, alignments) ## The element-wise version of the function returns unmapped ranges ## as zero-width ranges with a seqlevel of "UNMAPPED": pmapToAlignments(x, c(alignments, alignments)) ## Mapping the same range through different alignments demonstrates ## how the CIGAR operations affect the outcome. ops <- c("no-op", "junction", "insertion", "deletion") x <- GRanges(rep("chr1", 4), IRanges(rep(12, 4), width=rep(6, 4), names=ops)) alignments <- GAlignments(rep("chr1", 4), rep(10L, 4), cigar = c("11M", "5M2N4M", "5M2I4M", "5M2D4M"), strand = strand(rep("*", 4)), names = paste0("region_", 1:4)) pmapToAlignments(x, alignments) ## 2. Map to genome space with mapFromAlignments() ## --------------------------------------------------------------------- ## One of the criteria when mapping to genomic coordinates is that the ## shifted 'x' range falls within 'alignments'. Here the first 'x' ## range has a shifted start value of 14 (5 + 10 - 1 = 14) with a width of ## 2 and so is successfully mapped. The second has a shifted start of 29 ## (20 + 10 - 1 = 29) which is outside the range of 'alignments'. x <- GRanges("chr1", IRanges(c(5, 20), width=2, names=rep("region_A", 2))) alignments <- GAlignments("chr1", 10L, "11M", strand("*"), names="region_A") mapFromAlignments(x, alignments) ## Another characteristic of mapping this direction is the name matching ## used to determine pairs. Mapping is only attempted between ranges in 'x' ## and 'alignments' with the same name. If we change the name of the first 'x' ## range, only the second will be mapped to 'alignment'. We know the second ## range fails to map so we get an empty result. names(x) <- c("region_B", "region_A") mapFromAlignments(x, alignments) ## CIGAR operations: insertions reduce the width of the output while ## junctions and deletions increase it. ops <- c("no-op", "junction", "insertion", "deletion") x <- GRanges(rep("chr1", 4), IRanges(rep(3, 4), width=rep(5, 4), names=ops)) alignments <- GAlignments(rep("chr1", 4), rep(10L, 4), cigar = c("11M", "5M2N4M", "5M2I4M", "5M2D4M"), strand = strand(rep("*", 4))) pmapFromAlignments(x, alignments) ## --------------------------------------------------------------------- ## B. TATA box motif: mapping from read -> genome -> transcript ## --------------------------------------------------------------------- ## The TATA box motif is a conserved DNA sequence in the core promoter ## region. Many eukaryotic genes have a TATA box located approximately ## 25-35 base pairs upstream of the transcription start site. The motif is ## the binding site of general transcription factors or histones and ## plays a key role in transcription. ## In this example, the position of the TATA box motif (if present) is ## located in the DNA sequence corresponding to read ranges. The local ## motif positions are mapped to genome coordinates and then mapped ## to gene features such as promoters regions. ## Load reads from chromosome 4 of D. melanogaster (dm3): library(pasillaBamSubset) fl <- untreated1_chr4() gal <- readGAlignments(fl) ## Extract DNA sequences corresponding to the read ranges: library(GenomicFeatures) library(BSgenome.Dmelanogaster.UCSC.dm3) dna <- extractTranscriptSeqs(BSgenome.Dmelanogaster.UCSC.dm3, grglist(gal)) ## Search for the consensus motif TATAAA in the sequences: box <- vmatchPattern("TATAAA", dna) ## Some sequences had more than one match: table(elementNROWS(box)) ## The element-wise function we'll use for mapping to genome coordinates ## requires the two input argument to have the same length. We need to ## replicate the read ranges to match the number of motifs found. ## Expand the read ranges to match motifs found: motif <- elementNROWS(box) != 0 alignments <- rep(gal[motif], elementNROWS(box)[motif]) ## We make the IRanges into a GRanges object so the seqlevels can ## propagate to the output. Seqlevels are needed in the last mapping step. readCoords <- GRanges(seqnames(alignments), unlist(box, use.names=FALSE)) ## Map the local position of the motif to genome coordinates: genomeCoords <- pmapFromAlignments(readCoords, alignments) genomeCoords ## We are interested in the location of the TATA box motifs in the ## promoter regions. To perform the mapping we need the promoter ranges ## as a GRanges or GRangesList. ## Extract promoter regions 50 bp upstream from the transcription start site: library(TxDb.Dmelanogaster.UCSC.dm3.ensGene) txdb <- TxDb.Dmelanogaster.UCSC.dm3.ensGene promoters <- promoters(txdb, upstream=50, downstream=0) ## Map the genome coordinates to the promoters: names(promoters) <- mcols(promoters)$tx_name ## must be named mapToTranscripts(genomeCoords, promoters) } \keyword{methods} \keyword{utilities} GenomicAlignments/man/coverage-methods.Rd0000644000175200017520000001277514516004601021506 0ustar00biocbuildbiocbuild\name{coverage-methods} \alias{coverage-methods} \alias{coverage} \alias{coverage,GAlignments-method} \alias{coverage,GAlignmentPairs-method} \alias{coverage,GAlignmentsList-method} \alias{coverage,BamFile-method} \alias{coverage,character-method} \title{Coverage of a GAlignments, GAlignmentPairs, or GAlignmentsList object} \description{ \code{\link[IRanges]{coverage}} methods for \link{GAlignments}, \link{GAlignmentPairs}, \link{GAlignmentsList}, and \link[Rsamtools]{BamFile} objects. NOTE: The \code{\link[IRanges]{coverage}} generic function and methods for \link[IRanges]{IntegerRanges} and \link[IRanges]{IntegerRangesList} objects are defined and documented in the \pkg{IRanges} package. Methods for \link[GenomicRanges]{GRanges} and \link[GenomicRanges]{GRangesList} objects are defined and documented in the \pkg{GenomicRanges} package. } \usage{ \S4method{coverage}{GAlignments}(x, shift=0L, width=NULL, weight=1L, method=c("auto", "sort", "hash", "naive"), drop.D.ranges=FALSE) \S4method{coverage}{GAlignmentPairs}(x, shift=0L, width=NULL, weight=1L, method=c("auto", "sort", "hash", "naive"), drop.D.ranges=FALSE) \S4method{coverage}{GAlignmentsList}(x, shift=0L, width=NULL, weight=1L, ...) \S4method{coverage}{BamFile}(x, shift=0L, width=NULL, weight=1L, ..., param=ScanBamParam()) \S4method{coverage}{character}(x, shift=0L, width=NULL, weight=1L, ..., yieldSize=2500000L) } \arguments{ \item{x}{ A \link{GAlignments}, \link{GAlignmentPairs}, \link{GAlignmentsList}, or \link[Rsamtools]{BamFile} object, or the path to a BAM file. } \item{shift, width, weight}{ See \code{coverage} method for \link[GenomicRanges]{GRanges} objects in the \pkg{GenomicRanges} package. } \item{method}{ See \code{?\link[IRanges]{coverage}} in the \pkg{IRanges} package for a description of this argument. } \item{drop.D.ranges}{ Whether the coverage calculation should ignore ranges corresponding to D (deletion) in the CIGAR string. } \item{...}{ Additional arguments passed to the \code{coverage} method for \link{GAlignments} objects. } \item{param}{ An optional \link[Rsamtools]{ScanBamParam} object passed to \code{\link{readGAlignments}}. } \item{yieldSize}{ An optional argument controlling how many records are input when iterating through a \link[Rsamtools]{BamFile}. } } \details{ The methods for \link{GAlignments} and \link{GAlignmentPairs} objects do: \preformatted{ coverage(grglist(x, drop.D.ranges=drop.D.ranges), ...) } The method for \link{GAlignmentsList} objects does: \preformatted{ coverage(unlist(x), ...) } The method for \link[Rsamtools]{BamFile} objects iterates through a BAM file, reading \code{yieldSize(x)} records (or all records, if \code{is.na(yieldSize(x))}) and calculating: \preformatted{ gal <- readGAlignments(x, param=param) coverage(gal, shift=shift, width=width, weight=weight, ...) } The method for \code{character} vectors of length 1 creates a \link[Rsamtools]{BamFile} object from \code{x} and performs the calculation for \code{coverage,BamFile-method}. } \value{ A named \link[IRanges]{RleList} object with one coverage vector per seqlevel in \code{x}. } \seealso{ \itemize{ \item \code{\link[IRanges]{coverage}} in the \pkg{IRanges} package. \item \link[GenomicRanges]{coverage-methods} in the \pkg{GenomicRanges} package. \item \link[IRanges]{RleList} objects in the \pkg{IRanges} package. \item \link{GAlignments} and \link{GAlignmentPairs} objects. \item \code{\link{readGAlignments}}. \item \link[Rsamtools]{BamFile} objects in the \pkg{Rsamtools} package. } } \examples{ ## --------------------------------------------------------------------- ## A. EXAMPLE WITH TOY DATA ## --------------------------------------------------------------------- ex1_file <- system.file("extdata", "ex1.bam", package="Rsamtools") ## Coverage of a GAlignments object: gal <- readGAlignments(ex1_file) cvg1 <- coverage(gal) cvg1 ## Coverage of a GAlignmentPairs object: galp <- readGAlignmentPairs(ex1_file) cvg2 <- coverage(galp) cvg2 ## Coverage of a GAlignmentsList object: galist <- readGAlignmentsList(ex1_file) cvg3 <- coverage(galist) cvg3 table(mcols(galist)$mate_status) mated_idx <- which(mcols(galist)$mate_status == "mated") mated_galist <- galist[mated_idx] mated_cvg3 <- coverage(mated_galist) mated_cvg3 ## Sanity checks: stopifnot(identical(cvg1, cvg3)) stopifnot(identical( cvg2, mated_cvg3)) ## --------------------------------------------------------------------- ## B. EXAMPLE WITH REAL DATA ## --------------------------------------------------------------------- library(pasillaBamSubset) ## See '?pasillaBamSubset' for more information about the 2 BAM files ## included in this package. reads <- readGAlignments(untreated3_chr4()) table(njunc(reads)) # data contains junction reads ## Junctions do NOT contribute to the coverage: read1 <- reads[which(njunc(reads) != 0L)[1]] # 1st read with a junction read1 # cigar shows a "skipped region" of length 15306 grglist(read1)[[1]] # the junction is between pos 4500 and 19807 coverage(read1)$chr4 # junction is not covered ## Sanity checks: cvg <- coverage(reads) read_chunks <- unlist(grglist(reads), use.names=FALSE) read_chunks_per_chrom <- split(read_chunks, seqnames(read_chunks)) stopifnot(identical(sum(cvg), sum(width(read_chunks_per_chrom)))) galist <- readGAlignmentsList(untreated3_chr4()) stopifnot(identical(cvg, coverage(galist))) } \keyword{methods} \keyword{utilities} GenomicAlignments/man/encodeOverlaps-methods.Rd0000644000175200017520000002135414516004601022655 0ustar00biocbuildbiocbuild\name{encodeOverlaps-methods} \alias{encodeOverlaps-methods} \alias{encodeOverlaps} \alias{encodeOverlaps,IntegerRangesList,IntegerRangesList-method} \alias{encodeOverlaps,IntegerRangesList,IntegerRanges-method} \alias{encodeOverlaps,IntegerRanges,IntegerRangesList-method} \alias{encodeOverlaps1} \alias{flipQuery} \alias{encodeOverlaps,GRangesList,GRangesList-method} \alias{selectEncodingWithCompatibleStrand} \alias{isCompatibleWithSkippedExons} \alias{isCompatibleWithSkippedExons,character-method} \alias{isCompatibleWithSkippedExons,factor-method} \alias{isCompatibleWithSkippedExons,OverlapEncodings-method} \alias{extractSteppedExonRanks} \alias{extractSteppedExonRanks,character-method} \alias{extractSteppedExonRanks,factor-method} \alias{extractSteppedExonRanks,OverlapEncodings-method} \alias{extractSpannedExonRanks} \alias{extractSpannedExonRanks,character-method} \alias{extractSpannedExonRanks,factor-method} \alias{extractSpannedExonRanks,OverlapEncodings-method} \alias{extractSkippedExonRanks} \alias{extractSkippedExonRanks,character-method} \alias{extractSkippedExonRanks,factor-method} \alias{extractSkippedExonRanks,OverlapEncodings-method} \alias{extractQueryStartInTranscript} \title{Encode the overlaps between RNA-seq reads and the transcripts of a gene model} \description{ In the context of an RNA-seq experiment, encoding the overlaps between the aligned reads and the transcripts of a given gene model can be used for detecting those overlaps that are \emph{compatible} with the splicing of the transcript. The central tool for this is the \code{encodeOverlaps} method for \link[GenomicRanges]{GRangesList} objects, which computes the "overlap encodings" between a \code{query} and a \code{subject}, both list-like objects with list elements containing multiple ranges. Other related utilities are also documented in this man page. } \usage{ encodeOverlaps(query, subject, hits=NULL, ...) \S4method{encodeOverlaps}{GRangesList,GRangesList}(query, subject, hits=NULL, flip.query.if.wrong.strand=FALSE) ## Related utilities: flipQuery(x, i) selectEncodingWithCompatibleStrand(ovencA, ovencB, query.strand, subject.strand, hits=NULL) isCompatibleWithSkippedExons(x, max.skipped.exons=NA) extractSteppedExonRanks(x, for.query.right.end=FALSE) extractSpannedExonRanks(x, for.query.right.end=FALSE) extractSkippedExonRanks(x, for.query.right.end=FALSE) extractQueryStartInTranscript(query, subject, hits=NULL, ovenc=NULL, flip.query.if.wrong.strand=FALSE, for.query.right.end=FALSE) } \arguments{ \item{query, subject}{ Typically \link[GenomicRanges]{GRangesList} objects representing the the aligned reads and the transcripts of a given gene model, respectively. If the 2 objects don't have the same length, and if the \code{hits} argument is not supplied, then the shortest is recycled to the length of the longest (the standard recycling rules apply). More generally speaking, \code{query} and \code{subject} must be list-like objects with list elements containing multiple ranges e.g. \link[IRanges]{IntegerRangesList} or \link[GenomicRanges]{GRangesList} objects. } \item{hits}{ An optional \link[S4Vectors]{Hits} object typically obtained from a previous call to \code{\link[IRanges]{findOverlaps}(query, subject)}. Strictly speaking, \code{hits} only needs to be compatible with \code{query} and \code{subject}, that is, \code{\link[S4Vectors]{queryLength}(hits)} and \code{\link[S4Vectors]{subjectLength}(hits)} must be equal to \code{length(query)} and \code{length(subject)}, respectively. Supplying \code{hits} is a convenient way to do \code{encodeOverlaps(query[queryHits(hits)], subject[subjectHits(hits)])}, that is, calling \code{encodeOverlaps(query, subject, hits)} is equivalent to the above, but is much more efficient, especially when \code{query} and/or \code{subject} are big. Of course, when \code{hits} is supplied, \code{query} and \code{subject} are not expected to have the same length anymore. } \item{...}{ Additional arguments for methods. } \item{flip.query.if.wrong.strand}{ See the "OverlapEncodings" vignette located in this package (\pkg{GenomicAlignments}). } \item{x}{ For \code{flipQuery}: a \link[GenomicRanges]{GRangesList} object. For \code{isCompatibleWithSkippedExons}, \code{extractSteppedExonRanks}, \code{extractSpannedExonRanks}, and \code{extractSkippedExonRanks}: an \link{OverlapEncodings} object, a factor, or a character vector. } \item{i}{ Subscript specifying the elements in \code{x} to flip. If missing, all the elements are flipped. } \item{ovencA, ovencB, ovenc}{ \link{OverlapEncodings} objects. } \item{query.strand, subject.strand}{ Vector-like objects containing the strand of the query and subject, respectively. } \item{max.skipped.exons}{ Not supported yet. If \code{NA} (the default), the number of skipped exons must be 1 or more (there is no max). } \item{for.query.right.end}{ If \code{TRUE}, then the information reported in the output is for the right ends of the paired-end reads. Using \code{for.query.right.end=TRUE} with single-end reads is an error. } } \details{ See \code{?OverlapEncodings} for a short introduction to "overlap encodings". The topic of working with overlap encodings is covered in details in the "OverlapEncodings" vignette located this package (\pkg{GenomicAlignments}) and accessible with \code{vignette("OverlapEncodings")}. } \value{ For \code{encodeOverlaps}: An \link{OverlapEncodings} object. If \code{hits} is not supplied, this object is \emph{parallel} to the longest of \code{query} and \code{subject}, that is, it has the length of the longest and the i-th encoding in it corresponds to the i-th element in the longest. If \code{hits} is supplied, then the returned object is \emph{parallel} to it, that is, it has one encoding per hit. For \code{flipQuery}: TODO For \code{selectEncodingWithCompatibleStrand}: TODO For \code{isCompatibleWithSkippedExons}: A logical vector \emph{parallel} to \code{x}. For \code{extractSteppedExonRanks}, \code{extractSpannedExonRanks}, and \code{extractSkippedExonRanks}: TODO For \code{extractQueryStartInTranscript}: TODO } \author{Hervé Pagès} \seealso{ \itemize{ \item The \link{OverlapEncodings} class for a brief introduction to "overlap encodings". \item The \link[S4Vectors]{Hits} class defined and documented in the \pkg{S4Vectors} package. \item The "OverlapEncodings" vignette in this package. \item \code{\link{findCompatibleOverlaps}} for a specialized version of \code{\link[IRanges]{findOverlaps}} that uses \code{encodeOverlaps} internally to keep only the hits where the junctions in the aligned read are \emph{compatible} with the splicing of the annotated transcript. \item The \link[GenomicRanges]{GRangesList} class defined and documented in the \pkg{GenomicRanges} package. \item The \code{\link[IRanges]{findOverlaps}} generic function defined in the \pkg{IRanges} package. } } \examples{ ## --------------------------------------------------------------------- ## A. BETWEEN 2 IntegerRangesList OBJECTS ## --------------------------------------------------------------------- ## In the context of an RNA-seq experiment, encoding the overlaps ## between 2 GRangesList objects, one containing the reads (the query), ## and one containing the transcripts (the subject), can be used for ## detecting hits between reads and transcripts that are "compatible" ## with the splicing of the transcript. Here we illustrate this with 2 ## IntegerRangesList objects, in order to keep things simple: ## 4 aligned reads in the query: read1 <- IRanges(c(7, 15, 22), c(9, 19, 23)) # 2 junctions read2 <- IRanges(c(5, 15), c(9, 17)) # 1 junction read3 <- IRanges(c(16, 22), c(19, 24)) # 1 junction read4 <- IRanges(c(16, 23), c(19, 24)) # 1 junction query <- IRangesList(read1, read2, read3, read4) ## 1 transcript in the subject: tx <- IRanges(c(1, 4, 15, 22, 38), c(2, 9, 19, 25, 47)) # 5 exons subject <- IRangesList(tx) ## Encode the overlaps: ovenc <- encodeOverlaps(query, subject) ovenc encoding(ovenc) ## --------------------------------------------------------------------- ## B. BETWEEN 2 GRangesList OBJECTS ## --------------------------------------------------------------------- ## With real RNA-seq data, the reads and transcripts will typically be ## stored in GRangesList objects. Please refer to the "OverlapEncodings" ## vignette in this package for realistic examples. } \keyword{methods} \keyword{utilities} GenomicAlignments/man/findCompatibleOverlaps-methods.Rd0000644000175200017520000000756514516004601024350 0ustar00biocbuildbiocbuild\name{findCompatibleOverlaps-methods} \alias{findCompatibleOverlaps-methods} \alias{findCompatibleOverlaps} \alias{findCompatibleOverlaps,GAlignments,GRangesList-method} \alias{findCompatibleOverlaps,GAlignmentPairs,GRangesList-method} \alias{countCompatibleOverlaps} \title{Finding hits between reads and transcripts that are \emph{compatible} with the splicing of the transcript} \description{ In the context of an RNA-seq experiment, \code{findCompatibleOverlaps} (or \code{countCompatibleOverlaps}) can be used for finding (or counting) hits between reads and transcripts that are \emph{compatible} with the splicing of the transcript. } \usage{ findCompatibleOverlaps(query, subject) countCompatibleOverlaps(query, subject) } \arguments{ \item{query}{ A \link{GAlignments} or \link{GAlignmentPairs} object representing the aligned reads. } \item{subject}{ A \link{GRangesList} object representing the transcripts. } } \details{ \code{findCompatibleOverlaps} is a specialized version of \code{\link[IRanges]{findOverlaps}} that uses \code{\link{encodeOverlaps}} internally to keep only the hits where the junctions in the aligned read are \emph{compatible} with the splicing of the annotated transcript. The topic of working with overlap encodings is covered in details in the "OverlapEncodings" vignette located this package (\pkg{GenomicAlignments}) and accessible with \code{vignette("OverlapEncodings")}. } \value{ A \link[S4Vectors]{Hits} object for \code{findCompatibleOverlaps}. An integer vector \emph{parallel} to (i.e. same length as) \code{query} for \code{countCompatibleOverlaps}. } \author{Hervé Pagès} \seealso{ \itemize{ \item The \code{\link[IRanges]{findOverlaps}} generic function defined in the \pkg{IRanges} package. \item The \code{\link{encodeOverlaps}} generic function and \link{OverlapEncodings} class. \item The "OverlapEncodings" vignette in this package. \item \link{GAlignments} and \link{GAlignmentPairs} objects. \item \link[GenomicRanges]{GRangesList} objects in the \pkg{GenomicRanges} package. } } \examples{ ## Here we only show a simple example illustrating the use of ## countCompatibleOverlaps() on a very small data set. Please ## refer to the "OverlapEncodings" vignette in the GenomicAlignments ## package for a comprehensive presentation of "overlap ## encodings" and related tools/concepts (e.g. "compatible" ## overlaps, "almost compatible" overlaps etc...), and for more ## examples. ## sm_treated1.bam contains a small subset of treated1.bam, a BAM ## file containing single-end reads from the "Pasilla" experiment ## (RNA-seq, Fly, see the pasilla data package for the details) ## and aligned to reference genome BDGP Release 5 (aka dm3 genome on ## the UCSC Genome Browser): sm_treated1 <- system.file("extdata", "sm_treated1.bam", package="GenomicAlignments", mustWork=TRUE) ## Load the alignments: flag0 <- scanBamFlag(isDuplicate=FALSE, isNotPassingQualityControls=FALSE) param0 <- ScanBamParam(flag=flag0) gal <- readGAlignments(sm_treated1, use.names=TRUE, param=param0) ## Load the transcripts (IMPORTANT: Like always, the reference genome ## of the transcripts must be *exactly* the same as the reference ## genome used to align the reads): library(TxDb.Dmelanogaster.UCSC.dm3.ensGene) txdb <- TxDb.Dmelanogaster.UCSC.dm3.ensGene exbytx <- exonsBy(txdb, by="tx", use.names=TRUE) ## Number of "compatible" transcripts per alignment in 'gal': gal_ncomptx <- countCompatibleOverlaps(gal, exbytx) mcols(gal)$ncomptx <- gal_ncomptx table(gal_ncomptx) mean(gal_ncomptx >= 1) ## --> 33% of the alignments in 'gal' are "compatible" with at least ## 1 transcript in 'exbytx'. ## Keep only alignments compatible with at least 1 transcript in ## 'exbytx': compgal <- gal[gal_ncomptx >= 1] head(compgal) } \keyword{methods} \keyword{utilities} GenomicAlignments/man/findMateAlignment.Rd0000644000175200017520000002615314516004601021633 0ustar00biocbuildbiocbuild\name{findMateAlignment} \alias{findMateAlignment} \alias{makeGAlignmentPairs} \alias{getDumpedAlignments} \alias{countDumpedAlignments} \alias{flushDumpedAlignments} \title{Pairing the elements of a GAlignments object} \description{ Utilities for pairing the elements of a \link{GAlignments} object. NOTE: Until BioC 2.13, \code{findMateAlignment} was the power horse used by \code{\link{readGAlignmentPairs}} for pairing the records loaded from a BAM file containing aligned paired-end reads. Starting with BioC 2.14, \code{\link{readGAlignmentPairs}} relies on \code{\link[Rsamtools]{scanBam}(BamFile(asMates=TRUE), ...)} for the pairing. } \usage{ findMateAlignment(x) makeGAlignmentPairs(x, use.names=FALSE, use.mcols=FALSE, strandMode=1) ## Related low-level utilities: getDumpedAlignments() countDumpedAlignments() flushDumpedAlignments() } \arguments{ \item{x}{ A named \link{GAlignments} object with metadata columns \code{flag}, \code{mrnm}, and \code{mpos}. Typically obtained by loading aligned paired-end reads from a BAM file with: \preformatted{ param <- ScanBamParam(what=c("flag", "mrnm", "mpos")) x <- readGAlignments(..., use.names=TRUE, param=param) } } \item{use.names}{ Whether the names on the input object should be propagated to the returned object or not. } \item{use.mcols}{ Names of the metadata columns to propagate to the returned \link{GAlignmentPairs} object. } \item{strandMode}{ Strand mode to set on the returned \link{GAlignmentPairs} object. See \code{?\link{strandMode}} for more information. } } \details{ \subsection{Pairing algorithm used by findMateAlignment}{ \code{findMateAlignment} is the power horse used by \code{makeGAlignmentPairs} for pairing the records loaded from a BAM file containing aligned paired-end reads. It implements the following pairing algorithm: \itemize{ \item First, only records with flag bit 0x1 (multiple segments) set to 1, flag bit 0x4 (segment unmapped) set to 0, and flag bit 0x8 (next segment in the template unmapped) set to 0, are candidates for pairing (see the SAM Spec for a description of flag bits and fields). \code{findMateAlignment} will ignore any other record. That is, records that correspond to single-end reads, or records that correspond to paired-end reads where one or both ends are unmapped, are discarded. \item Then the algorithm looks at the following fields and flag bits: \itemize{ \item (A) QNAME \item (B) RNAME, RNEXT \item (C) POS, PNEXT \item (D) Flag bits Ox10 (segment aligned to minus strand) and 0x20 (next segment aligned to minus strand) \item (E) Flag bits 0x40 (first segment in template) and 0x80 (last segment in template) \item (F) Flag bit 0x2 (proper pair) \item (G) Flag bit 0x100 (secondary alignment) } 2 records rec1 and rec2 are considered mates iff all the following conditions are satisfied: \itemize{ \item (A) QNAME(rec1) == QNAME(rec2) \item (B) RNEXT(rec1) == RNAME(rec2) and RNEXT(rec2) == RNAME(rec1) \item (C) PNEXT(rec1) == POS(rec2) and PNEXT(rec2) == POS(rec1) \item (D) Flag bit 0x20 of rec1 == Flag bit 0x10 of rec2 and Flag bit 0x20 of rec2 == Flag bit 0x10 of rec1 \item (E) rec1 corresponds to the first segment in the template and rec2 corresponds to the last segment in the template, OR, rec2 corresponds to the first segment in the template and rec1 corresponds to the last segment in the template \item (F) rec1 and rec2 have same flag bit 0x2 \item (G) rec1 and rec2 have same flag bit 0x100 } } } \subsection{Timing and memory requirement of the pairing algorithm}{ The estimated timings and memory requirements on a modern Linux system are (those numbers may vary depending on your hardware and OS): \preformatted{ nb of alignments | time | required memory -----------------+--------------+---------------- 8 millions | 28 sec | 1.4 GB 16 millions | 58 sec | 2.8 GB 32 millions | 2 min | 5.6 GB 64 millions | 4 min 30 sec | 11.2 GB } This is for a \link{GAlignments} object coming from a file with an "average nb of records per unique QNAME" of 2.04. A value of 2 (which means the file contains only primary reads) is optimal for the pairing algorithm. A greater value, say > 3, will significantly degrade its performance. An easy way to avoid this degradation is to load only primary alignments by setting the \code{isSecondaryAlignment} flag to \code{FALSE} in ScanBamParam(). See examples in \code{?\link{readGAlignmentPairs}} for how to do this. } \subsection{Ambiguous pairing}{ The above algorithm will find almost all pairs unambiguously, even when the same pair of reads maps to several places in the genome. Note that, when a given pair maps to a single place in the genome, looking at (A) is enough to pair the 2 corresponding records. The additional conditions (B), (C), (D), (E), (F), and (G), are only here to help in the situation where more than 2 records share the same QNAME. And that works most of the times. Unfortunately there are still situations where this is not enough to solve the pairing problem unambiguously. For example, here are 4 records (loaded in a GAlignments object) that cannot be paired with the above algorithm: Showing the 4 records as a GAlignments object of length 4: \preformatted{ GAlignments with 4 alignments and 2 metadata columns: seqnames strand cigar qwidth start end SRR031714.2658602 chr2R + 21M384N16M 37 6983850 6984270 SRR031714.2658602 chr2R + 21M384N16M 37 6983850 6984270 SRR031714.2658602 chr2R - 13M372N24M 37 6983858 6984266 SRR031714.2658602 chr2R - 13M378N24M 37 6983858 6984272 width njunc | mrnm mpos | SRR031714.2658602 421 1 | chr2R 6983858 SRR031714.2658602 421 1 | chr2R 6983858 SRR031714.2658602 409 1 | chr2R 6983850 SRR031714.2658602 415 1 | chr2R 6983850 } Note that the BAM fields show up in the following columns: \itemize{ \item QNAME: the names of the GAlignments object (unnamed col) \item RNAME: the seqnames col \item POS: the start col \item RNEXT: the mrnm col \item PNEXT: the mpos col } As you can see, the aligner has aligned the same pair to the same location twice! The only difference between the 2 aligned pairs is in the CIGAR i.e. one end of the pair is aligned twice to the same location with exactly the same CIGAR while the other end of the pair is aligned twice to the same location but with slightly different CIGARs. Now showing the corresponding flag bits: \preformatted{ isPaired isProperPair isUnmappedQuery hasUnmappedMate isMinusStrand [1,] 1 1 0 0 0 [2,] 1 1 0 0 0 [3,] 1 1 0 0 1 [4,] 1 1 0 0 1 isMateMinusStrand isFirstMateRead isSecondMateRead isSecondaryAlignment [1,] 1 0 1 0 [2,] 1 0 1 0 [3,] 0 1 0 0 [4,] 0 1 0 0 isNotPassingQualityControls isDuplicate [1,] 0 0 [2,] 0 0 [3,] 0 0 [4,] 0 0 } As you can see, rec(1) and rec(2) are second mates, rec(3) and rec(4) are both first mates. But looking at (A), (B), (C), (D), (E), (F), and (G), the pairs could be rec(1) <-> rec(3) and rec(2) <-> rec(4), or they could be rec(1) <-> rec(4) and rec(2) <-> rec(3). There is no way to disambiguate! So \code{findMateAlignment} is just ignoring (with a warning) those alignments with ambiguous pairing, and dumping them in a place from which they can be retrieved later (i.e. after \code{findMateAlignment} has returned) for further examination (see "Dumped alignments" subsection below for the details). In other words, alignments that cannot be paired unambiguously are not paired at all. Concretely, this means that \code{\link{readGAlignmentPairs}} is guaranteed to return a \link{GAlignmentPairs} object where every pair was formed in an non-ambiguous way. Note that, in practice, this approach doesn't seem to leave aside a lot of records because ambiguous pairing events seem pretty rare. } \subsection{Dumped alignments}{ Alignments with ambiguous pairing are dumped in a place ("the dump environment") from which they can be retrieved with \code{getDumpedAlignments()} after \code{findMateAlignment} has returned. Two additional utilities are provided for manipulation of the dumped alignments: \code{countDumpedAlignments} for counting them (a fast equivalent to \code{length(getDumpedAlignments())}), and \code{flushDumpedAlignments} to flush "the dump environment". Note that "the dump environment" is automatically flushed at the beginning of a call to \code{findMateAlignment}. } } \value{ For \code{findMateAlignment}: An integer vector of the same length as \code{x}, containing only positive or NA values, where the i-th element is interpreted as follow: \itemize{ \item An NA value means that no mate or more than 1 mate was found for \code{x[i]}. \item A non-NA value j gives the index in \code{x} of \code{x[i]}'s mate. } For \code{makeGAlignmentPairs}: A \link{GAlignmentPairs} object where the pairs are formed internally by calling \code{findMateAlignment} on \code{x}. For \code{getDumpedAlignments}: \code{NULL} or a \link{GAlignments} object containing the dumped alignments. See "Dumped alignments" subsection in the "Details" section above for the details. For \code{countDumpedAlignments}: The number of dumped alignments. Nothing for \code{flushDumpedAlignments}. } \author{Hervé Pagès} \seealso{ \itemize{ \item \link{GAlignments} and \link{GAlignmentPairs} objects. \item \code{\link{readGAlignments}} and \code{\link{readGAlignmentPairs}}. } } \examples{ bamfile <- system.file("extdata", "ex1.bam", package="Rsamtools", mustWork=TRUE) param <- ScanBamParam(what=c("flag", "mrnm", "mpos")) x <- readGAlignments(bamfile, use.names=TRUE, param=param) mate <- findMateAlignment(x) head(mate) table(is.na(mate)) galp0 <- makeGAlignmentPairs(x) galp <- makeGAlignmentPairs(x, use.name=TRUE, use.mcols="flag") galp colnames(mcols(galp)) colnames(mcols(first(galp))) colnames(mcols(last(galp))) } \keyword{manip} GenomicAlignments/man/findOverlaps-methods.Rd0000644000175200017520000001021114516004601022326 0ustar00biocbuildbiocbuild\name{findOverlaps-methods} \alias{findOverlaps-methods} \alias{findOverlaps} \alias{findOverlaps,GAlignments,Vector-method} \alias{findOverlaps,Vector,GAlignments-method} \alias{findOverlaps,GAlignments,GAlignments-method} \alias{findOverlaps,GAlignmentPairs,Vector-method} \alias{findOverlaps,Vector,GAlignmentPairs-method} \alias{findOverlaps,GAlignmentPairs,GAlignmentPairs-method} \alias{findOverlaps,GAlignmentsList,Vector-method} \alias{findOverlaps,Vector,GAlignmentsList-method} \alias{findOverlaps,GAlignmentsList,GAlignmentsList-method} \title{Finding overlapping genomic alignments} \description{ Finds range overlaps between a \link{GAlignments}, \link{GAlignmentPairs}, or \link{GAlignmentsList} object, and another range-based object. NOTE: The \code{\link[IRanges]{findOverlaps}} generic function and methods for \link[IRanges]{IntegerRanges} and \link[IRanges]{IntegerRangesList} objects are defined and documented in the \pkg{IRanges} package. The methods for \link[GenomicRanges]{GRanges} and \link[GenomicRanges]{GRangesList} objects are defined and documented in the \pkg{GenomicRanges} package. \link{GAlignments}, \link{GAlignmentPairs}, and \link{GAlignmentsList} objects also support \code{countOverlaps}, \code{overlapsAny}, and \code{subsetByOverlaps} thanks to the default methods defined in the \pkg{IRanges} package and to the \code{findOverlaps} method defined in this package and documented below. } \usage{ \S4method{findOverlaps}{GAlignments,GAlignments}(query, subject, maxgap=-1L, minoverlap=0L, type=c("any", "start", "end", "within"), select=c("all", "first", "last", "arbitrary"), ignore.strand=FALSE) } \arguments{ \item{query, subject}{ A \link{GAlignments}, \link{GAlignmentPairs}, or \link{GAlignmentsList} object for either \code{query} or \code{subject}. A vector-like object containing ranges for the other one. } \item{maxgap, minoverlap, type, select}{ See \code{?\link[IRanges]{findOverlaps}} in the \pkg{IRanges} package for a description of these arguments. } \item{ignore.strand}{ When set to \code{TRUE}, the strand information is ignored in the overlap calculations. } } \details{ When the query or the subject (or both) is a \link{GAlignments} object, it is first turned into a \link{GRangesList} object (with \code{as( , "GRangesList")}) and then the rules described previously apply. \link{GAlignmentsList} objects are coerced to \link{GAlignments} then to a \link{GRangesList}. Feature indices are mapped back to the original \link{GAlignmentsList} list elements. When the query is a \link{GAlignmentPairs} object, it is first turned into a \link{GRangesList} object (with \code{as( , "GRangesList")}) and then the rules described previously apply. } \value{ A \link[S4Vectors]{Hits} object when \code{select="all"} or an integer vector otherwise. } \seealso{ \itemize{ \item \code{\link[IRanges]{findOverlaps}}. \item \link[S4Vectors]{Hits-class}. \item \link{GRanges-class}. \item \link{GRangesList-class}. \item \link{GAlignments-class}. \item \link{GAlignmentPairs-class}. \item \link{GAlignmentsList-class}. } } \examples{ ex1_file <- system.file("extdata", "ex1.bam", package="Rsamtools") galn <- readGAlignments(ex1_file) subject <- granges(galn)[1] ## Note the absence of query no. 9 (i.e. 'galn[9]') in this result: as.matrix(findOverlaps(galn, subject)) ## This is because, by default, findOverlaps()/countOverlaps() are ## strand specific: galn[8:10] countOverlaps(galn[8:10], subject) countOverlaps(galn[8:10], subject, ignore.strand=TRUE) ## Count alignments in 'galn' that DO overlap with 'subject' vs those ## that do NOT: table(overlapsAny(galn, subject)) ## Extract those that DO: subsetByOverlaps(galn, subject) ## GAlignmentsList galist <- GAlignmentsList(galn[8:10], galn[3000:3002]) gr <- GRanges(c("seq1", "seq1", "seq2"), IRanges(c(15, 18, 1233), width=1), strand=c("-", "+", "+")) countOverlaps(galist, gr) countOverlaps(galist, gr, ignore.strand=TRUE) findOverlaps(galist, gr) findOverlaps(galist, gr, ignore.strand=TRUE) } \keyword{methods} \keyword{utilities} GenomicAlignments/man/findSpliceOverlaps-methods.Rd0000644000175200017520000001523014516004601023474 0ustar00biocbuildbiocbuild\name{findSpliceOverlaps-methods} \alias{findSpliceOverlaps-methods} \alias{findSpliceOverlaps} \alias{findSpliceOverlaps,GRangesList,GRangesList-method} \alias{findSpliceOverlaps,GAlignments,GRangesList-method} \alias{findSpliceOverlaps,GAlignmentPairs,GRangesList-method} \alias{findSpliceOverlaps,character,ANY-method} \alias{findSpliceOverlaps,BamFile,ANY-method} \title{Classify ranges (reads) as compatible with existing genomic annotations or as having novel splice events} \description{ The \code{findSpliceOverlaps} function identifies ranges (reads) that are compatible with a specific transcript isoform. The non-compatible ranges are analyzed for the presence of novel splice events. } \usage{ findSpliceOverlaps(query, subject, ignore.strand=FALSE, ...) \S4method{findSpliceOverlaps}{GRangesList,GRangesList}(query, subject, ignore.strand=FALSE, ..., cds=NULL) \S4method{findSpliceOverlaps}{GAlignments,GRangesList}(query, subject, ignore.strand=FALSE, ..., cds=NULL) \S4method{findSpliceOverlaps}{GAlignmentPairs,GRangesList}(query, subject, ignore.strand=FALSE, ..., cds=NULL) \S4method{findSpliceOverlaps}{BamFile,ANY}(query, subject, ignore.strand=FALSE, ..., param=ScanBamParam(), singleEnd=TRUE) } \arguments{ \item{query}{ A \link[GenomicRanges]{GRangesList}, \link{GAlignments}, \link{GAlignmentPairs}, or \link[Rsamtools]{BamFile} object containing the reads. Can also be a single string containing the path to a BAM file. Single or paired-end reads are specified with the \code{singleEnd} argument (default FALSE). Paired-end reads can be supplied in a BAM file or \link{GAlignmentPairs} object. Single-end are expected to be in a BAM file, \link{GAlignments} or \link[GenomicRanges]{GRanges} object. } \item{subject}{ A \link[GenomicRanges]{GRangesList} containing the annotations. This list is expected to contain exons grouped by transcripts. } \item{ignore.strand}{ When set to \code{TRUE}, strand information is ignored in the overlap calculations. } \item{...}{ Additional arguments such as \code{param} and \code{singleEnd} used in the method for \link[Rsamtools]{BamFile} objects. See below. } \item{cds}{ Optional \link[GenomicRanges]{GRangesList} of coding regions for each transcript in the \code{subject}. If provided, the "coding" output column will be a \code{logical} vector indicating if the read falls in a coding region. When not provided, the "coding" output is \code{NA}. } \item{param}{ An optional \code{\link[Rsamtools]{ScanBamParam}} instance to further influence scanning, counting, or filtering. } \item{singleEnd}{ A logical value indicating if reads are single or paired-end. See \code{\link{summarizeOverlaps}} for more information. } } \details{ When a read maps compatibly and uniquely to a transcript isoform we can quantify the expression and look for shifts in the balance of isoform expression. If a read does not map in compatible way, novel splice events such as splice junctions, novel exons or retentions can be quantified and compared across samples. \code{findSpliceOverlaps} detects which reads (query) match to transcripts (subject) in a compatible fashion. Compatibility is based on both the transcript bounds and splicing pattern. Assessing the splicing pattern involves comparision of the read splices (i.e., the N operations in the CIGAR) with the transcript introns. For paired-end reads, the inter-read gap is not considered a splice junction. The analysis of non-compatible reads for novel splice events is under construction. } \value{ The output is a \link[S4Vectors]{Hits} object with the metadata columns defined below. Each column is a \code{logical} indicating if the read (query) met the criteria. \itemize{ \item{compatible: }{Every splice (N) in a read alignment matches an intron in an annotated transcript. The read does not extend into an intron or outside the transcript bounds. } \item{unique: }{The read is compatible with only one annotated transcript. } \item{strandSpecific: }{The query (read) was stranded. } } } \note{ WARNING: The current implementation of \code{findSpliceOverlaps} doesn't work properly on paired-end reads where the 2 ends overlap! } \author{Michael Lawrence and Valerie Obenchain} \seealso{ \itemize{ \item \link[GenomicRanges]{GRangesList} objects in the \pkg{GenomicRanges} package. \item \link{GAlignments} and \link{GAlignmentPairs} objects. \item \link[Rsamtools]{BamFile} objects in the \pkg{Rsamtools} package. } } \examples{ ## ----------------------------------------------------------------------- ## Isoform expression : ## ----------------------------------------------------------------------- ## findSpliceOverlaps() can assist in quantifying isoform expression ## by identifying reads that map compatibly and uniquely to a ## transcript isoform. library(TxDb.Dmelanogaster.UCSC.dm3.ensGene) library(pasillaBamSubset) se <- untreated1_chr4() ## single-end reads txdb <- TxDb.Dmelanogaster.UCSC.dm3.ensGene exbytx <- exonsBy(txdb, "tx") cdsbytx <- cdsBy(txdb, "tx") param <- ScanBamParam(which=GRanges("chr4", IRanges(1e5,3e5))) sehits <- findSpliceOverlaps(se, exbytx, cds=cdsbytx, param=param) ## Tally the reads by category to get an idea of read distribution. lst <- lapply(mcols(sehits), table) nms <- names(lst) %in% c("compatible", "unique") tbl <- do.call(rbind, lst[nms]) tbl ## Reads compatible with one or more transcript isoforms. rnms <- rownames(tbl) tbl[rnms == "compatible","TRUE"]/sum(tbl[rnms == "compatible",]) ## Reads compatible with a single isoform. tbl[rnms == "unique","TRUE"]/sum(tbl[rnms == "unique",]) ## All reads fall in a coding region as defined by ## the txdb annotation. lst[["coding"]] ## Check : Total number of reads should be the same across categories. lapply(lst, sum) ## ----------------------------------------------------------------------- ## Paired-end reads : ## ----------------------------------------------------------------------- ## 'singleEnd' is set to FALSE for a BAM file with paired-end reads. pe <- untreated3_chr4() hits2 <- findSpliceOverlaps(pe, exbytx, singleEnd=FALSE, param=param) ## In addition to BAM files, paired-end reads can be supplied in a ## GAlignmentPairs object. genes <- GRangesList( GRanges("chr1", IRanges(c(5, 20), c(10, 25)), "+"), GRanges("chr1", IRanges(c(5, 22), c(15, 25)), "+")) galp <- GAlignmentPairs( GAlignments("chr1", 5L, "11M4N6M", strand("+")), GAlignments("chr1", 50L, "6M", strand("-"))) findSpliceOverlaps(galp, genes) } \keyword{methods} \keyword{utilities} GenomicAlignments/man/intra-range-methods.Rd0000644000175200017520000001053514516004601022112 0ustar00biocbuildbiocbuild\name{intra-range-methods} \alias{intra-range-methods} \alias{windows,GAlignments-method} \alias{narrow,GappedReads-method} \alias{qnarrow} \alias{qnarrow,GAlignments-method} \alias{qnarrow,GAlignmentsList-method} \alias{qnarrow,GappedReads-method} \title{Intra range transformations of a GAlignments or GAlignmentsList object} \description{ This man page documents intra range transformations of a \link{GAlignments} or \link{GAlignmentsList} object. See \code{?`\link[IRanges]{intra-range-methods}`} and \code{?`\link[IRanges]{inter-range-methods}`} in the \pkg{IRanges} package for a quick introduction to intra range and inter range transformations. Intra range methods for \link{GRanges} and \link{GRangesList} objects are defined and documented in the \pkg{GenomicRanges} package. } \usage{ \S4method{qnarrow}{GAlignments}(x, start=NA, end=NA, width=NA) \S4method{qnarrow}{GAlignmentsList}(x, start=NA, end=NA, width=NA) } \arguments{ \item{x}{ A \link{GAlignments} or \link{GAlignmentsList} object. } \item{start, end, width}{ Vectors of integers. NAs and negative values are accepted and "solved" according to the rules of the SEW (Start/End/Width) interface (see \code{?\link[IRanges]{solveUserSEW}} for more information about the SEW interface). See \code{?`\link[IRanges]{intra-range-methods}`} for more information about the \code{start}, \code{end}, and \code{width} arguments. } } \details{ \itemize{ \item(){ \code{qnarrow} on a \link{GAlignments} object behaves like \code{narrow} except that the \code{start}/\code{end}/\code{width} arguments here specify the narrowing with respect to the query sequences instead of the reference sequences. \code{qnarrow} on a \link{GAlignmentsList} object returns a \link{GAlignmentsList} object. } } } \value{ An object of the same class as -- and \emph{parallel} to (i.e. same length and names as) -- the original object \code{x}. } \note{ There is no difference between \code{narrow} and \code{qnarrow} when all the alignments have a simple CIGAR (i.e. no indels or junctions). } \author{Hervé Pagès and Valerie Obenchain} \seealso{ \itemize{ \item \link{GAlignments} and \link{GAlignmentsList} objects. \item The \link[IRanges]{intra-range-methods} man page in the \pkg{IRanges} package. \item The \link[GenomicRanges]{intra-range-methods} man page in the \pkg{GenomicRanges} package. } } \examples{ ## --------------------------------------------------------------------- ## A. ON A GAlignments OBJECT ## --------------------------------------------------------------------- ex1_file <- system.file("extdata", "ex1.bam", package="Rsamtools") param <- ScanBamParam(what=c("seq", "qual")) gal <- readGAlignments(ex1_file, param=param) gal ## This trims 3 nucleotides on the left and 5 nucleotides on the right ## of each alignment: gal2 <- qnarrow(gal, start=4, end=-6) gal2 ## Note that the 'start' and 'end' values are relative to the query ## sequences and specify the query substring that must be kept for each ## alignment. Negative values are relative to the right end of the query ## sequence. ## Also note that the metadata columns on 'gal' are propagated as-is so ## the "seq" and "qual" matadata columns must be adjusted "by hand" with ## narrow(); mcols(gal2)$seq <- narrow(mcols(gal)$seq, start=4, end=-6) mcols(gal2)$qual <- narrow(mcols(gal)$qual, start=4, end=-6) gal2 ## Sanity checks: stopifnot(identical(qwidth(gal2), width(mcols(gal2)$seq))) stopifnot(identical(qwidth(gal2), width(mcols(gal2)$qual))) ## --------------------------------------------------------------------- ## B. ON A GAlignmentsList OBJECT ## --------------------------------------------------------------------- gal1 <- GAlignments( seqnames=Rle(factor(c("chr1", "chr2", "chr1", "chr3")), c(1, 3, 2, 4)), pos=1:10, cigar=paste0(10:1, "M"), strand=Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), names=head(letters, 10), score=1:10) gal2 <- GAlignments( seqnames=Rle(factor(c("chr2", "chr4")), c(3, 4)), pos=1:7, cigar=c("5M", "3M2N3M2N3M", "5M", "10M", "5M1N4M", "8M2N1M", "5M"), strand=Rle(strand(c("-", "+")), c(4, 3)), names=tail(letters, 7), score=1:7) galist <- GAlignmentsList(noGaps=gal1, Gaps=gal2) galist qnarrow(galist) } \keyword{methods} \keyword{utilities} GenomicAlignments/man/junctions-methods.Rd0000644000175200017520000004245714516004601021727 0ustar00biocbuildbiocbuild\name{junctions-methods} \alias{junctions-methods} \alias{junctions} \alias{junctions,GAlignments-method} \alias{junctions,GAlignmentPairs-method} \alias{junctions,GAlignmentsList-method} \alias{NATURAL_INTRON_MOTIFS} \alias{summarizeJunctions} \alias{readTopHatJunctions} \alias{readSTARJunctions} \title{Extract junctions from genomic alignments} \description{ Given an object \code{x} containing genomic alignments (e.g. a \link{GAlignments}, \link{GAlignmentPairs}, or \link{GAlignmentsList} object), \code{junctions(x)} extracts the junctions from it and \code{summarizeJunctions(x)} extracts and summarizes them. \code{readTopHatJunctions} and \code{readSTARJunctions} are utilities for importing the junction file generated by the TopHat and STAR aligners, respectively. } \usage{ ## junctions() generic and methods ## ------------------------------- junctions(x, use.mcols=FALSE, ...) \S4method{junctions}{GAlignments}(x, use.mcols=FALSE) \S4method{junctions}{GAlignmentPairs}(x, use.mcols=FALSE) \S4method{junctions}{GAlignmentsList}(x, use.mcols=FALSE, ignore.strand=FALSE) ## summarizeJunctions() and NATURAL_INTRON_MOTIFS ## ---------------------------------------------- summarizeJunctions(x, with.revmap=FALSE, genome=NULL) NATURAL_INTRON_MOTIFS ## Utilities for importing the junction file generated by some aligners ## -------------------------------------------------------------------- readTopHatJunctions(file, file.is.raw.juncs=FALSE) readSTARJunctions(file) } \arguments{ \item{x}{ A \link{GAlignments}, \link{GAlignmentPairs}, or \link{GAlignmentsList} object. } \item{use.mcols}{ \code{TRUE} or \code{FALSE} (the default). Whether the metadata columns on \code{x} (accessible with \code{mcols(x)}) should be propagated to the returned object or not. } \item{...}{ Additional arguments, for use in specific methods. } \item{ignore.strand}{ \code{TRUE} or \code{FALSE} (the default). If set to \code{TRUE}, then the strand of \code{x} is set to \code{"*"} prior to any computation. } \item{with.revmap}{ \code{TRUE} or \code{FALSE} (the default). If set to \code{TRUE}, then a \code{revmap} metadata column is added to the output of \code{summarizeJunctions}. This metadata column is an \link[IRanges]{IntegerList} object representing the mapping from each element in the ouput (i.e. each junction) to the corresponding elements in the input \code{x}. } \item{genome}{ \code{NULL} (the default), or a \link[BSgenome]{BSgenome} object containing the sequences of the reference genome that was used to align the reads, or the name of this reference genome specified in a way that is accepted by the \code{\link[BSgenome]{getBSgenome}} function defined in the \pkg{BSgenome} software package. In that case the corresponding BSgenome data package needs to be already installed (see \code{?\link[BSgenome]{getBSgenome}} in the \pkg{BSgenome} package for the details). If \code{genome} is supplied, then the \code{intron_motif} and \code{intron_strand} metadata columns are computed (based on the dinucleotides found at the intron boundaries) and added to the output of \code{summarizeJunctions}. See the Value section below for a description of these metadata columns. } \item{file}{ The path (or a connection) to the junction file generated by the aligner. This file should be the \emph{junctions.bed} or \emph{new_list.juncs} file for \code{readTopHatJunctions}, and the \emph{SJ.out.tab} file for \code{readSTARJunctions}. } \item{file.is.raw.juncs}{ \code{TRUE} or \code{FALSE} (the default). If set to \code{TRUE}, then the input file is assumed to be a TopHat \emph{.juncs} file instead of the \emph{junctions.bed} file generated by TopHat. A TopHat \emph{.juncs} file can be obtained by passing the \emph{junctions.bed} file thru TopHat's \emph{bed_to_juncs} script. See the TopHat manual at \url{http://tophat.cbcb.umd.edu/manual.shtml} for more information. } } \details{ An N operation in the CIGAR of a genomic alignment is interpreted as a junction. \code{junctions(x)} will return the genomic ranges of all junctions found in \code{x}. More precisely, on a \link{GAlignments} object \code{x}, \code{junctions(x)} is equivalent to: \preformatted{ psetdiff(granges(x), grglist(x, order.as.in.query=TRUE)) } On a \link{GAlignmentPairs} object \code{x}, it's equivalent to (but faster than): \preformatted{ mendoapply(c, junctions(first(x, real.strand=TRUE)), junctions(last(x, real.strand=TRUE))) } Note that starting with BioC 3.2, the behavior of \code{junctions} on a \link{GAlignmentPairs} object has been slightly modified so that the returned ranges now have the \emph{real strand} set on them. See the documentation of the \code{real.strand} argument in the man page of \link{GAlignmentPairs} objects for more information. \code{NATURAL_INTRON_MOTIFS} is a predefined character vector containing the 5 natural intron motifs described at \url{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC84117/}. } \value{ \code{junctions(x)} returns the genomic ranges of the junctions in a \link[GenomicRanges]{GRangesList} object \emph{parallel} to \code{x} (i.e. with 1 list element per element in \code{x}). If \code{x} has names on it, they're propagated to the returned object. If \code{use.mcols} is TRUE and \code{x} has metadata columns on it (accessible with \code{mcols(x)}), they're propagated to the returned object. \code{summarizeJunctions} returns the genomic ranges of the unique junctions in \code{x} in an unstranded \link[GenomicRanges]{GRanges} object with the following metadata columns: \itemize{ \item \code{score}: The total number of alignments crossing each junction, i.e. that have the junction encoded in their CIGAR. \item \code{plus_score} and \code{minus_score}: The strand-specific number of alignments crossing each junction. \item \code{revmap}: [Only if \code{with.revmap} was set to \code{TRUE}.] An \link[IRanges]{IntegerList} object representing the mapping from each element in the ouput (i.e. each junction) to the corresponding elements in input \code{x}. \item \code{intron_motif} and \code{intron_strand}: [Only if \code{genome} was supplied.] The intron motif and strand for each junction, based on the dinucleotides found in the genome sequences at the intron boundaries. The \code{intron_motif} metadata column is a factor whose levels are the 5 natural intron motifs stored in predefined character vector \code{NATURAL_INTRON_MOTIFS}. If the dinucleotides found at the intron boundaries don't match any of these natural intron motifs, then \code{intron_motif} and \code{intron_strand} are set to \code{NA} and \code{*}, respectively. } \code{readTopHatJunctions} and \code{readSTARJunctions} return the junctions reported in the input file in a stranded \link[GenomicRanges]{GRanges} object. With the following metadata columns for \code{readTopHatJunctions} (when reading in the \emph{junctions.bed} file): \itemize{ \item \code{name}: An id assigned by TopHat to each junction. This id is of the form JUNC00000017 and is unique within the \emph{junctions.bed} file. \item \code{score}: The total number of alignments crossing each junction. } With the following metadata columns for \code{readSTARJunctions}: \itemize{ \item \code{intron_motif} and \code{intron_strand}: The intron motif and strand for each junction, based on the code found in the input file (0: non-canonical, 1: GT/AG, 2: CT/AC, 3: GC/AG, 4: CT/GC, 5: AT/AC, 6: GT/AT). Note that of the 5 natural intron motifs stored in predefined character vector \code{NATURAL_INTRON_MOTIFS}, only the first 3 are assigned codes by the STAR software (2 codes per motif, one if the intron is on the plus strand and one if it's on the minus strand). Thus the \code{intron_motif} metadata column is a factor with only 3 levels. If code is 0, then \code{intron_motif} and \code{intron_strand} are set to \code{NA} and \code{*}, respectively. \item \code{um_reads}: The number of uniquely mapping reads crossing the junction (a pair where the 2 alignments cross the same junction is counted only once). \item \code{mm_reads}: The number of multi-mapping reads crossing the junction (a pair where the 2 alignments cross the same junction is counted only once). } See STAR manual at \url{https://code.google.com/p/rna-star/} for more information. } \author{Hervé Pagès} \references{ \url{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC84117/} for the 5 natural intron motifs stored in predefined character vector \code{NATURAL_INTRON_MOTIFS}. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions \itemize{ \item TopHat2 paper: \url{http://genomebiology.com/2013/14/4/r36} \item TopHat2 software and manual: \url{http://tophat.cbcb.umd.edu/} } STAR: ultrafast universal RNA-seq aligner \itemize{ \item STAR paper: \url{http://bioinformatics.oxfordjournals.org/content/early/2012/10/25/bioinformatics.bts635} \item STAR software and manual: \url{https://code.google.com/p/rna-star/} } } \seealso{ \itemize{ \item The \code{\link{readGAlignments}} and \code{\link{readGAlignmentPairs}} functions for reading genomic alignments from a BAM file. \item \link{GAlignments}, \link{GAlignmentPairs}, and \link{GAlignmentsList} objects. \item \link[GenomicRanges]{GRanges} and \link[GenomicRanges]{GRangesList} objects implemented and documented in the \pkg{GenomicRanges} package. \item \link[IRanges]{IntegerList} objects implemented and documented in the \pkg{IRanges} package. \item The \code{\link[BSgenome]{getBSgenome}} function in the \pkg{BSgenome} package, for searching the installed BSgenome data packages for the specified genome and returning it as a \link[BSgenome]{BSgenome} object. \item The \code{\link[IRanges]{extractList}} function in the \pkg{IRanges} package, for extracting groups of elements from a vector-like object and returning them into a \link[S4Vectors]{List} object. } } \examples{ library(RNAseqData.HNRNPC.bam.chr14) bamfile <- RNAseqData.HNRNPC.bam.chr14_BAMFILES[1] ## --------------------------------------------------------------------- ## A. junctions() ## --------------------------------------------------------------------- gal <- readGAlignments(bamfile) table(njunc(gal)) # some alignments have 3 junctions! juncs <- junctions(gal) juncs stopifnot(identical(unname(elementNROWS(juncs)), njunc(gal))) galp <- readGAlignmentPairs(bamfile) juncs <- junctions(galp) juncs stopifnot(identical(unname(elementNROWS(juncs)), njunc(galp))) ## --------------------------------------------------------------------- ## B. summarizeJunctions() ## --------------------------------------------------------------------- ## By default, only the "score", "plus_score", and "minus_score" ## metadata columns are returned: junc_summary <- summarizeJunctions(gal) junc_summary ## The "score" metadata column reports the total number of alignments ## crossing each junction, i.e., that have the junction encoded in their ## CIGAR: median(mcols(junc_summary)$score) ## The "plus_score" and "minus_score" metadata columns report the ## strand-specific number of alignments crossing each junction: stopifnot(identical(mcols(junc_summary)$score, mcols(junc_summary)$plus_score + mcols(junc_summary)$minus_score)) ## If 'with.revmap' is TRUE, the "revmap" metadata column is added to ## the output. This metadata column is an IntegerList object represen- ## ting the mapping from each element in the ouput (i.e. a junction) to ## the corresponding elements in the input 'x'. Here we're going to use ## this to compute a 'score2' for each junction. We obtain this score ## by summing the mapping qualities of the alignments crossing the ## junction: gal <- readGAlignments(bamfile, param=ScanBamParam(what="mapq")) junc_summary <- summarizeJunctions(gal, with.revmap=TRUE) junc_score2 <- sum(extractList(mcols(gal)$mapq, mcols(junc_summary)$revmap)) mcols(junc_summary)$score2 <- junc_score2 ## If the name of the reference genome is specified thru the 'genome' ## argument (in which case the corresponding BSgenome data package needs ## to be installed), then summarizeJunctions() returns the intron motif ## and strand for each junction. ## Since the reads in RNAseqData.HNRNPC.bam.chr14 were aligned to ## the hg19 genome, the following requires that you have ## BSgenome.Hsapiens.UCSC.hg19 installed: junc_summary <- summarizeJunctions(gal, with.revmap=TRUE, genome="hg19") mcols(junc_summary)$score2 <- junc_score2 # putting 'score2' back ## The "intron_motif" metadata column is a factor whose levels are the ## 5 natural intron motifs stored in predefined character vector ## 'NATURAL_INTRON_MOTIFS': table(mcols(junc_summary)$intron_motif) ## --------------------------------------------------------------------- ## C. STRANDED RNA-seq PROTOCOL ## --------------------------------------------------------------------- ## Here is a simple test for checking whether the RNA-seq protocol was ## stranded or not: strandedTest <- function(plus_score, minus_score) (sum(plus_score ^ 2) + sum(minus_score ^ 2)) / sum((plus_score + minus_score) ^ 2) ## The result of this test is guaranteed to be >= 0.5 and <= 1. ## If, for each junction, the strand of the crossing alignments looks ## random (i.e. "plus_score" and "minus_score" are close), then ## strandedTest() will return a value close to 0.5. If it doesn't look ## random (i.e. for each junction, one of "plus_score" and "minus_score" ## is much bigger than the other), then strandedTest() will return a ## value close to 1. ## If the reads are single-end, the test is meaningful when applied ## directly on 'junc_summary'. However, for the test to be meaningful ## on paired-end reads, it needs to be applied on the first and last ## alignments separately: junc_summary1 <- summarizeJunctions(first(galp)) junc_summary2 <- summarizeJunctions(last(galp)) strandedTest(mcols(junc_summary1)$plus_score, mcols(junc_summary1)$minus_score) strandedTest(mcols(junc_summary2)$plus_score, mcols(junc_summary2)$minus_score) ## Both values are close to 0.5 which suggests that the RNA-seq protocol ## used for this experiment was not stranded. ## --------------------------------------------------------------------- ## D. UTILITIES FOR IMPORTING THE JUNCTION FILE GENERATED BY SOME ## ALIGNERS ## --------------------------------------------------------------------- ## The TopHat aligner generates a junctions.bed file where it reports ## all the junctions satisfying some "quality" criteria (see the TopHat ## manual at http://tophat.cbcb.umd.edu/manual.shtml for more ## information). This file can be loaded with readTopHatJunctions(): runname <- names(RNAseqData.HNRNPC.bam.chr14_BAMFILES)[1] junctions_file <- system.file("extdata", "tophat2_out", runname, "junctions.bed", package="RNAseqData.HNRNPC.bam.chr14") th_junctions <- readTopHatJunctions(junctions_file) ## Comparing the "TopHat junctions" with the result of ## summarizeJunctions(): th_junctions14 <- th_junctions seqlevels(th_junctions14, pruning.mode="coarse") <- "chr14" mcols(th_junctions14)$intron_strand <- strand(th_junctions14) strand(th_junctions14) <- "*" ## All the "TopHat junctions" are in 'junc_summary': stopifnot(all(th_junctions14 \%in\% junc_summary)) ## But not all the junctions in 'junc_summary' are reported by TopHat ## (that's because TopHat reports only junctions that satisfy some ## "quality" criteria): is_in_th_junctions14 <- junc_summary \%in\% th_junctions14 table(is_in_th_junctions14) # 32 junctions are not in TopHat's # junctions.bed file junc_summary2 <- junc_summary[is_in_th_junctions14] ## 'junc_summary2' and 'th_junctions14' contain the same junctions in ## the same order: stopifnot(all(junc_summary2 == th_junctions14)) ## Let's merge their metadata columns. We use our own version of ## merge() for this, which is stricter (it checks that the common ## columns are the same in the 2 data frames to merge) and also ## simpler: merge2 <- function(df1, df2) { common_colnames <- intersect(colnames(df1), colnames(df2)) lapply(common_colnames, function(colname) stopifnot(all(df1[ , colname] == df2[ , colname]))) extra_mcolnames <- setdiff(colnames(df2), colnames(df1)) cbind(df1, df2[ , extra_mcolnames, drop=FALSE]) } mcols(th_junctions14) <- merge2(mcols(th_junctions14), mcols(junc_summary2)) ## Here is a peculiar junction reported by TopHat: idx0 <- which(mcols(th_junctions14)$score2 == 0L) th_junctions14[idx0] gal[mcols(th_junctions14)$revmap[[idx0]]] ## The junction is crossed by 5 alignments (score is 5), all of which ## have a mapping quality of 0! } \keyword{methods} \keyword{manip} GenomicAlignments/man/pileLettersAt.Rd0000644000175200017520000001312114516004601021015 0ustar00biocbuildbiocbuild\name{pileLettersAt} \alias{pileLettersAt} \title{Pile the letters of a set of aligned reads on top of a set of genomic positions} \description{ \code{pileLettersAt} extracts the letters/nucleotides of a set of reads that align to a set of genomic positions of interest. The extracted letters are returned as "piles of letters" (one per genomic position of interest) stored in an \link[Biostrings]{XStringSet} (typically \link[Biostrings]{DNAStringSet}) object. } \usage{ pileLettersAt(x, seqnames, pos, cigar, at) } \arguments{ \item{x}{ An \link[Biostrings]{XStringSet} (typically \link[Biostrings]{DNAStringSet}) object containing N \emph{unaligned} read sequences (a.k.a. the query sequences) reported with respect to the + strand. } \item{seqnames}{ A factor-\link[S4Vectors]{Rle} \emph{parallel} to \code{x}. For each \code{i}, \code{seqnames[i]} must be the name of the reference sequence of the i-th alignment. } \item{pos}{ An integer vector \emph{parallel} to \code{x}. For each \code{i}, \code{pos[i]} must be the 1-based position on the reference sequence of the first aligned letter in \code{x[[i]]}. } \item{cigar}{ A character vector \emph{parallel} to \code{x}. Contains the extended CIGAR strings of the alignments. } \item{at}{ A \link[GenomicRanges]{GPos} object containing the genomic positions of interest. \code{seqlevels(at)} must be identical to \code{levels(seqnames)}. If \code{at} is not a \link[GenomicRanges]{GPos} object, \code{pileLettersAt} will first try to turn it into one by calling the \code{\link[GenomicRanges]{GPos}()} constructor function on it. So for example \code{at} can be a \link[GenomicRanges]{GRanges} object (or any other \link[GenomicRanges]{GenomicRanges} derivative), and, in that case, each range in it will be interpreted as a run of adjacent genomic positions. See \code{?\link[GenomicRanges]{GPos}} in the \pkg{GenomicRanges} package for more information. } } \details{ \code{x}, \code{seqnames}, \code{pos}, \code{cigar} must be 4 \emph{parallel} vectors describing N aligned reads. } \value{ An \link[Biostrings]{XStringSet} (typically \link[Biostrings]{DNAStringSet}) object \emph{parallel} to \code{at} (i.e. with 1 string per genomic position). } \author{Hervé Pagès} \seealso{ \itemize{ \item The \code{pileup} and \code{applyPileups} functions defined in the \pkg{Rsamtools} package, as well as the SAMtools mpileup command (available at \url{http://samtools.sourceforge.net/} as part of the SAMtools project), for more powerful flexible alternatives. \item The \code{\link{stackStringsFromGAlignments}} function for stacking the read sequences (or their quality strings) stored in a \link{GAlignments} object or a BAM file. \item \link[Biostrings]{DNAStringSet} objects in the \pkg{Biostrings} package. \item \link[GenomicRanges]{GPos} objects in the \pkg{GenomicRanges} package. \item \link{GAlignments} objects. \item \link{cigar-utils} for the CIGAR utility functions used internally by \code{pileLettersAt}. } } \examples{ ## Input ## - A BAM file: bamfile <- BamFile(system.file("extdata", "ex1.bam", package="Rsamtools")) seqinfo(bamfile) # to see the seqlevels and seqlengths stackStringsFromBam(bamfile, param="seq1:1-21") # a quick look at # the reads ## - A GPos object containing Genomic Positions Of Interest: my_GPOI <- GPos(c("seq1:1-5", "seq1:21-21", "seq1:1575-1575", "seq2:1513-1514")) ## Some preliminary massage on 'my_GPOI' seqinfo(my_GPOI) <- merge(seqinfo(my_GPOI), seqinfo(bamfile)) seqlevels(my_GPOI) <- seqlevelsInUse(my_GPOI) ## Load the BAM file in a GAlignments object. Note that we load only ## the reads aligned to the sequences in 'seqlevels(my_GPOI)'. Also, ## in order to be consistent with applyPileups() and SAMtools (m)pileup, ## we filter out the following BAM records: ## - secondary alignments (flag bit 0x100); ## - reads not passing quality controls (flag bit 0x200); ## - PCR or optical duplicates (flag bit 0x400). ## See ?ScanBamParam and the SAM Spec for more information. which <- as(seqinfo(my_GPOI), "GRanges") flag <- scanBamFlag(isSecondaryAlignment=FALSE, isNotPassingQualityControls=FALSE, isDuplicate=FALSE) what <- c("seq", "qual") param <- ScanBamParam(flag=flag, what=c("seq", "qual"), which=which) gal <- readGAlignments(bamfile, param=param) seqlevels(gal) <- seqlevels(my_GPOI) ## Extract the read sequences (a.k.a. query sequences) and quality ## strings. Both are reported with respect to the + strand. qseq <- mcols(gal)$seq qual <- mcols(gal)$qual nucl_piles <- pileLettersAt(qseq, seqnames(gal), start(gal), cigar(gal), my_GPOI) qual_piles <- pileLettersAt(qual, seqnames(gal), start(gal), cigar(gal), my_GPOI) mcols(my_GPOI)$nucl_piles <- nucl_piles mcols(my_GPOI)$qual_piles <- qual_piles my_GPOI ## Finally, to summarize A/C/G/T frequencies at each position: alphabetFrequency(nucl_piles, baseOnly=TRUE) ## Note that the pileup() function defined in the Rsamtools package ## can be used to obtain a similar result: scanbam_param <- ScanBamParam(flag=flag, which=my_GPOI) pileup_param <- PileupParam(max_depth=5000, min_base_quality=0, distinguish_strands=FALSE) pileup(bamfile, scanBamParam=scanbam_param, pileupParam=pileup_param) } \keyword{methods} \keyword{manip} GenomicAlignments/man/readGAlignments.Rd0000644000175200017520000005065614516004601021316 0ustar00biocbuildbiocbuild\name{readGAlignments} \alias{readGAlignments} \alias{readGAlignments,BamFile-method} \alias{readGAlignments,character-method} \alias{readGAlignments,BamViews-method} \alias{readGAlignmentPairs} \alias{readGAlignmentPairs,BamFile-method} \alias{readGAlignmentPairs,character-method} \alias{readGAlignmentsList} \alias{readGAlignmentsList,BamFile-method} \alias{readGAlignmentsList,character-method} \alias{readGappedReads} \alias{readGappedReads,BamFile-method} \alias{readGappedReads,character-method} \title{Reading genomic alignments from a file} \description{ Read genomic alignments from a file (typically a BAM file) into a \link{GAlignments}, \link{GAlignmentPairs}, \link{GAlignmentsList}, or \link{GappedReads} object. } \usage{ readGAlignments(file, index=file, use.names=FALSE, param=NULL, with.which_label=FALSE) readGAlignmentPairs(file, index=file, use.names=FALSE, param=NULL, with.which_label=FALSE, strandMode=1) readGAlignmentsList(file, index=file, use.names=FALSE, param=ScanBamParam(), with.which_label=FALSE, strandMode=NA) readGappedReads(file, index=file, use.names=FALSE, param=NULL, with.which_label=FALSE) } \arguments{ \item{file}{ The path to the file to read or a \link[Rsamtools]{BamFile} object. Can also be a \link[Rsamtools]{BamViews} object for \code{readGAlignments}. } \item{index}{ The path to the index file of the BAM file to read. Must be given \emph{without} the '.bai' extension. See \code{\link[Rsamtools]{scanBam}} in the \pkg{Rsamtools} packages for more information. } \item{use.names}{ \code{TRUE} or \code{FALSE}. By default (i.e. \code{use.names=FALSE}), the resulting object has no names. If \code{use.names} is \code{TRUE}, then the names are constructed from the query template names (QNAME field in a SAM/BAM file). Note that the 2 records in a pair (when using \code{readGAlignmentPairs} or the records in a group (when using \code{readGAlignmentsList}) have the same QNAME. } \item{param}{\code{NULL} or a \link[Rsamtools]{ScanBamParam} object. Like for \code{\link[Rsamtools]{scanBam}}, this influences what fields and which records are imported. However, note that the fields specified thru this \link[Rsamtools]{ScanBamParam} object will be loaded \emph{in addition} to any field required for generating the returned object (\link{GAlignments}, \link{GAlignmentPairs}, or \link{GappedReads} object), but only the fields requested by the user will actually be kept as metadata columns of the object. By default (i.e. \code{param=NULL} or \code{param=ScanBamParam()}), no additional field is loaded. The flag used is \code{scanBamFlag(isUnmappedQuery=FALSE)} for \code{readGAlignments}, \code{readGAlignmentsList}, and \code{readGappedReads}. (i.e. only records corresponding to mapped reads are loaded), and \code{scanBamFlag(isUnmappedQuery=FALSE, isPaired=TRUE, hasUnmappedMate=FALSE)} for \code{readGAlignmentPairs} (i.e. only records corresponding to paired-end reads with both ends mapped are loaded). } \item{with.which_label}{\code{TRUE} or \code{FALSE} (the default). If \code{TRUE} and if \code{param} has a \code{which} component, a \code{"which_label"} metadata column is added to the returned \link{GAlignments} or \link{GappedReads} object, or to the \code{\link{first}} and \code{\link{last}} components of the returned \link{GAlignmentPairs} object. In the case of \code{readGAlignmentsList}, it's added as an \emph{inner} metadata column, that is, the metadata column is placed on the \link{GAlignments} object obtained by unlisting the returned \link{GAlignmentsList} object. The purpose of this metadata column is to unambiguously identify the range in \code{which} where each element in the returned object originates from. The labels used to identify the ranges are normally of the form \code{"seq1:12250-246500"}, that is, they're the same as the names found on the outer list that \code{\link{scanBam}} would return if called with the same \code{param} argument. If some ranges are duplicated, then the labels are made unique by appending a unique suffix to all of them. The \code{"which_label"} metadata column is represented as a factor-\link[S4Vectors]{Rle}. } \item{strandMode}{ Strand mode to set on the returned \link{GAlignmentPairs} or \link{GAlignmentsList} object. Note that the default value for this parameter is different for \code{readGAlignmentPairs()} and \code{readGAlignmentsList()}. See details below on \code{readGAlignmentsList()} and \code{?\link{strandMode}} for more information. } } \details{ \itemize{ \item \code{readGAlignments} reads a file containing aligned reads as a \link{GAlignments} object. See \code{?\link{GAlignments}} for a description of \link{GAlignments} objects. When \code{file} is a \link[Rsamtools]{BamViews} object, \code{readGAlignments} visits each path in \code{bamPaths(file)}, returning the result of \code{readGAlignments} applied to the specified path. When \code{index} is missing, it is set equal to \code{bamIndicies(file)}. Only reads in \code{bamRanges(file)} are returned (if \code{param} is supplied, \code{bamRanges(file)} takes precedence over \code{bamWhich(param)}). The return value is a \link[S4Vectors]{SimpleList} object, with elements of the list corresponding to each path. \code{bamSamples(file)} is available as metadata columns (accessed with \code{mcols}) of the returned \link[S4Vectors]{SimpleList} object. \item \code{readGAlignmentPairs} reads a file containing aligned paired-end reads as a \link{GAlignmentPairs} object. See \code{?\link{GAlignmentPairs}} for a description of \link{GAlignmentPairs} objects. \item \code{readGAlignmentsList} reads a file containing aligned reads as a \link{GAlignmentsList} object. See \code{?\link{GAlignmentsList}} for a description of \link{GAlignmentsList} objects. \code{readGAlignmentsList} pairs records into mates according to the pairing criteria described below. The 1st mate will always be 1st in the \link{GAlignmentsList} list elements that have mate_status set to \code{"mated"}, and the 2nd mate will always be 2nd. A \code{GAlignmentsList} is returned with a \sQuote{mate_status} metadata column on the outer list elements. \code{mate_status} is a factor with 3 levels indicating mate status, \sQuote{mated}, \sQuote{ambiguous} or \sQuote{unmated}: \itemize{ \item{mated:} primary or non-primary pairs \item{ambiguous:} multiple segments matching to the same location (indistinguishable) \item{unmated:} mate does not exist or is unmapped } When the \sQuote{file} argument is a BamFile, \sQuote{asMates=TRUE} must be set, otherwise the data are treated as single-end reads. See the \sQuote{asMates} section of \code{?\link[Rsamtools]{BamFile}} in the \pkg{Rsamtools} package for details. Note that, by default, \code{strandMode=NA}, which is different to the default value in \code{readGAlignmentPairs()} and which implies that, by default, the strand values in the returned \code{GAlignmentsList} object correspond to the original strand of the reads. \item \code{readGappedReads} reads a file containing aligned reads as a \link{GappedReads} object. See \code{?\link{GappedReads}} for a description of \link{GappedReads} objects. } For all these functions, flags, tags and ranges may be specified in the supplied \link[Rsamtools]{ScanBamParam} object for fine tuning of results. } \section{Pairing criteria}{ This section describes the pairing criteria used by \code{readGAlignmentsList} and \code{readGAlignmentPairs}. \itemize{ \item First, only records with flag bit 0x1 (multiple segments) set to 1, flag bit 0x4 (segment unmapped) set to 0, and flag bit 0x8 (next segment in the template unmapped) set to 0, are candidates for pairing (see the SAM Spec for a description of flag bits and fields). Records that correspond to single-end reads, or records that correspond to paired-end reads where one or both ends are unmapped, will remain unmated. \item Then the following fields and flag bits are considered: \itemize{ \item (A) QNAME \item (B) RNAME, RNEXT \item (C) POS, PNEXT \item (D) Flag bits Ox10 (segment aligned to minus strand) and 0x20 (next segment aligned to minus strand) \item (E) Flag bits 0x40 (first segment in template) and 0x80 (last segment in template) \item (F) Flag bit 0x2 (proper pair) \item (G) Flag bit 0x100 (secondary alignment) } 2 records rec1 and rec2 are considered mates iff all the following conditions are satisfied: \itemize{ \item (A) QNAME(rec1) == QNAME(rec2) \item (B) RNEXT(rec1) == RNAME(rec2) and RNEXT(rec2) == RNAME(rec1) \item (C) PNEXT(rec1) == POS(rec2) and PNEXT(rec2) == POS(rec1) \item (D) Flag bit 0x20 of rec1 == Flag bit 0x10 of rec2 and Flag bit 0x20 of rec2 == Flag bit 0x10 of rec1 \item (E) rec1 corresponds to the first segment in the template and rec2 corresponds to the last segment in the template, OR, rec2 corresponds to the first segment in the template and rec1 corresponds to the last segment in the template \item (F) rec1 and rec2 have same flag bit 0x2 \item (G) rec1 and rec2 have same flag bit 0x100 } } Note that this is actually the pairing criteria used by \code{\link[Rsamtools]{scanBam}} (when the \link[Rsamtools]{BamFile} passed to it has the \code{asMates} toggle set to \code{TRUE}), which \code{readGAlignmentsList} and \code{readGAlignmentPairs} call behind the scene. It is also the pairing criteria used by \code{\link{findMateAlignment}}. } \value{ A \link{GAlignments} object for \code{readGAlignments}. A \link{GAlignmentPairs} object for \code{readGAlignmentPairs}. Note that a BAM (or SAM) file can in theory contain a mix of single-end and paired-end reads, but in practise it seems that single-end and paired-end are not mixed. In other words, the value of flag bit 0x1 (\code{isPaired}) is the same for all the records in a file. So if \code{readGAlignmentPairs} returns a \link{GAlignmentPairs} object of length zero, this almost always means that the BAM (or SAM) file contains alignments for single-end reads (although it could also mean that the user-supplied \code{\linkS4class{ScanBamParam}} is filtering out everything, or that the file is empty, or that all the records in the file correspond to unmapped reads). A \link{GAlignmentsList} object for \code{readGAlignmentsList}. When the list contains paired-end reads a metadata data column of \code{mate_status} is added to the object. See details in the `Bam specific back-ends' section on this man page. A \link{GappedReads} object for \code{readGappedReads}. } \note{ BAM records corresponding to unmapped reads are always ignored. Starting with Rsamtools 1.7.1 (BioC 2.10), PCR or optical duplicates are loaded by default (use \code{scanBamFlag(isDuplicate=FALSE)} to drop them). } \author{Hervé Pagès and Valerie Obenchain} \seealso{ \itemize{ \item \code{\link[Rsamtools]{scanBam}} and \code{\link[Rsamtools]{ScanBamParam}} in the \pkg{Rsamtools} package. \item \link{GAlignments}, \link{GAlignmentPairs}, \link{GAlignmentsList}, and \link{GappedReads} objects. \item \link[IRanges]{IRangesList} objects (used in the examples below to specify the \code{which} regions) in the \pkg{IRanges} package. } } \examples{ ## --------------------------------------------------------------------- ## A. readGAlignments() ## --------------------------------------------------------------------- ## Simple use: bamfile <- system.file("extdata", "ex1.bam", package="Rsamtools", mustWork=TRUE) gal1 <- readGAlignments(bamfile) gal1 names(gal1) ## Using the 'use.names' arg: gal2 <- readGAlignments(bamfile, use.names=TRUE) gal2 head(names(gal2)) ## Using the 'param' arg to drop PCR or optical duplicates as well as ## secondary alignments, and to load additional BAM fields: param <- ScanBamParam(flag=scanBamFlag(isDuplicate=FALSE, isSecondaryAlignment=FALSE), what=c("qual", "flag")) gal3 <- readGAlignments(bamfile, param=param) gal3 mcols(gal3) ## Using the 'param' arg to load alignments from particular regions. which <- IRangesList(seq1=IRanges(1000, 1100), seq2=IRanges(c(1546, 1555, 1567), width=10)) param <- ScanBamParam(which=which) gal4 <- readGAlignments(bamfile, use.names=TRUE, param=param) gal4 ## IMPORTANT NOTE: A given record is loaded one time for each region ## it overlaps with. We call this "duplicated record selection" (this ## is a scanBam() feature, readGAlignments() is based on scanBam()): which <- IRangesList(seq2=IRanges(c(1555, 1567), width=10)) param <- ScanBamParam(which=which) gal5 <- readGAlignments(bamfile, use.names=TRUE, param=param) gal5 # record EAS114_26:7:37:79:581 was loaded twice ## This becomes clearer if we use 'with.which_label=TRUE' to identify ## the region in 'which' where each element in 'gal5' originates from. gal5 <- readGAlignments(bamfile, use.names=TRUE, param=param, with.which_label=TRUE) gal5 ## Not surprisingly, we also get "duplicated record selection" when ## 'which' contains repeated or overlapping regions. Using the same ## regions as we did for 'gal4' above, except that now we're ## repeating the region on seq1: which <- IRangesList(seq1=rep(IRanges(1000, 1100), 2), seq2=IRanges(c(1546, 1555, 1567), width=10)) param <- ScanBamParam(which=which) gal4b <- readGAlignments(bamfile, use.names=TRUE, param=param) length(gal4b) # > length(gal4), because all the records overlapping # with bases 1000 to 1100 on seq1 are now duplicated ## The "duplicated record selection" will artificially increase the ## coverage or affect other downstream results. It can be mitigated ## (but not completely eliminated) by first "reducing" the set of ## regions: which <- reduce(which) which param <- ScanBamParam(which=which) gal4c <- readGAlignments(bamfile, use.names=TRUE, param=param) length(gal4c) # < length(gal4), because the 2 first original regions # on seq2 were merged into a single one ## Note that reducing the set of regions didn't completely eliminate ## "duplicated record selection". Records that overlap the 2 reduced ## regions on seq2 (which$seq2) are loaded twice (like for 'gal5' ## above). See example D. below for how to completely eliminate ## "duplicated record selection". ## Using the 'param' arg to load tags. Except for MF and Aq, the tags ## specified below are predefined tags (see the SAM Spec for the list ## of predefined tags and their meaning). param <- ScanBamParam(tag=c("MF", "Aq", "NM", "UQ", "H0", "H1"), what="isize") gal6 <- readGAlignments(bamfile, param=param) mcols(gal6) # "tag" cols always after "what" cols ## With a BamViews object: fls <- system.file("extdata", "ex1.bam", package="Rsamtools", mustWork=TRUE) bv <- BamViews(fls, bamSamples=DataFrame(info="test", row.names="ex1"), auto.range=TRUE) ## Note that the "readGAlignments" method for BamViews objects ## requires the ShortRead package to be installed. aln <- readGAlignments(bv) aln aln[[1]] aln[colnames(bv)] mcols(aln) ## --------------------------------------------------------------------- ## B. readGAlignmentPairs() ## --------------------------------------------------------------------- galp1 <- readGAlignmentPairs(bamfile) head(galp1) names(galp1) ## Here we use the 'param' arg to filter by proper pair, drop PCR / ## optical duplicates, and drop secondary alignments. Filtering by ## proper pair and dropping secondary alignments can help make the ## pairing algorithm run significantly faster: param <- ScanBamParam(flag=scanBamFlag(isProperPair=TRUE, isDuplicate=FALSE, isSecondaryAlignment=FALSE)) galp2 <- readGAlignmentPairs(bamfile, use.names=TRUE, param=param) galp2 head(galp2) head(names(galp2)) ## --------------------------------------------------------------------- ## C. readGAlignmentsList() ## --------------------------------------------------------------------- library(pasillaBamSubset) ## 'file' as character. bam <- untreated3_chr4() galist1 <- readGAlignmentsList(bam) galist1[1:3] length(galist1) table(elementNROWS(galist1)) ## When 'file' is a BamFile, 'asMates' must be TRUE. If FALSE, ## the data are treated as single-end and each list element of the ## GAlignmentsList will be of length 1. For single-end data ## use readGAlignments(). bamfile <- BamFile(bam, yieldSize=3, asMates=TRUE) readGAlignmentsList(bamfile) ## Use a 'param' to fine tune the results. param <- ScanBamParam(flag=scanBamFlag(isProperPair=TRUE)) galist2 <- readGAlignmentsList(bam, param=param) galist2[1:3] length(galist2) table(elementNROWS(galist2)) ## For paired-end data, we can set the 'strandMode' parameter to ## infer the strand of a pair from the strand of the first and ## last alignments in the pair galist3 <- readGAlignmentsList(bam, param=param, strandMode=0) galist3[1:3] galist4 <- readGAlignmentsList(bam, param=param, strandMode=1) galist4[1:3] galist5 <- readGAlignmentsList(bam, param=param, strandMode=2) galist5[1:3] ## --------------------------------------------------------------------- ## D. COMPARING 4 STRATEGIES FOR LOADING THE ALIGNMENTS THAT OVERLAP ## WITH THE EXONIC REGIONS ON FLY CHROMMOSOME 4 ## --------------------------------------------------------------------- library(pasillaBamSubset) bam <- untreated1_chr4() library(TxDb.Dmelanogaster.UCSC.dm3.ensGene) txdb <- TxDb.Dmelanogaster.UCSC.dm3.ensGene ex <- exons(txdb) seqlevels(ex, pruning.mode="coarse") <- "chr4" length(ex) ## Some of the exons overlap with each other: isDisjoint(ex) # FALSE exonic_regions <- reduce(ex) isDisjoint(exonic_regions) # no more overlaps length(exonic_regions) ## Strategy #1: slow and loads a lot of records more than once (see ## "duplicated record selection" in example A. above). param1 <- ScanBamParam(which=ex) gal1 <- readGAlignments(bam, param=param1) length(gal1) # many "duplicated records" ## Strategy #2: faster and generates less duplicated records but ## doesn't eliminate them. param2 <- ScanBamParam(which=exonic_regions) gal2 <- readGAlignments(bam, param=param2) length(gal2) # less "duplicated records" ## Strategy #3: fast and completely eliminates duplicated records. gal0 <- readGAlignments(bam) gal3 <- subsetByOverlaps(gal0, exonic_regions, ignore.strand=TRUE) length(gal3) # no "duplicated records" ## Note that, in this case using 'exonic_regions' or 'ex' makes no ## difference: gal3b <- subsetByOverlaps(gal0, ex, ignore.strand=TRUE) stopifnot(identical(gal3, gal3b)) ## Strategy #4: strategy #3 however can require a lot of memory if the ## file is big because we load all the alignments into memory before we ## select those that overlap with the exonic regions. Strategy #4 ## addresses this by loading the file by chunks. bamfile <- BamFile(bam, yieldSize=50000) open(bamfile) while (length(chunk0 <- readGAlignments(bamfile))) { chunk <- subsetByOverlaps(chunk0, ex, ignore.strand=TRUE) cat("chunk0:", length(chunk0), "- chunk:", length(chunk), "\n") ## ... do something with 'chunk' ... } close(bamfile) ## --------------------------------------------------------------------- ## E. readGappedReads() ## --------------------------------------------------------------------- greads1 <- readGappedReads(bamfile) greads1 names(greads1) qseq(greads1) greads2 <- readGappedReads(bamfile, use.names=TRUE) head(greads2) head(names(greads2)) } \keyword{manip} GenomicAlignments/man/sequenceLayer.Rd0000644000175200017520000003312714516004601021051 0ustar00biocbuildbiocbuild\name{sequenceLayer} \alias{sequenceLayer} \title{Lay read sequences alongside the reference space, using their CIGARs} \description{ \code{sequenceLayer} can lay strings that belong to a given space (e.g. the \code{"query"} space) alongside another space (e.g. the \code{"reference"} space) by removing/injecting substrings from/into them, using the supplied CIGARs. Its primary use case is to lay the read sequences stored in a BAM file (which are considered to belong to the \code{"query"} space) alongside the \code{"reference"} space. It can also be used to remove the parts of the read sequences that correspond to soft-clipping. More generally it can lay strings that belong to any supported space alongside any other supported space. See the Details section below for the list of supported spaces. } \usage{ sequenceLayer(x, cigar, from="query", to="reference", D.letter="-", N.letter=".", I.letter="-", S.letter="+", H.letter="+") } \arguments{ \item{x}{ An \link{XStringSet} object containing strings that belong to a given space. } \item{cigar}{ A character vector or factor of the same length as \code{x} containing the extended CIGAR strings (one per element in \code{x}). } \item{from, to}{ A single string specifying one of the 8 supported spaces listed in the Details section below. \code{from} must be the current space (i.e. the space the strings in \code{x} belong to) and \code{to} is the space alonside which to lay the strings in \code{x}. } \item{D.letter, N.letter, I.letter, S.letter, H.letter}{ A single letter used as a filler for injections. More on this in the Details section below. } } \details{ The 8 supported spaces are: \code{"reference"}, \code{"reference-N-regions-removed"}, \code{"query"}, \code{"query-before-hard-clipping"}, \code{"query-after-soft-clipping"}, \code{"pairwise"}, \code{"pairwise-N-regions-removed"}, and \code{"pairwise-dense"}. Each space can be characterized by the extended CIGAR operations that are \emph{visible} in it. A CIGAR operation is said to be \emph{visible} in a given space if it "runs along it", that is, if it's associated with a block of contiguous positions in that space (the size of the block being the length of the operation). For example, the M/=/X operations are \emph{visible} in all spaces, the D/N operations are \emph{visible} in the \code{"reference"} space but not in the \code{"query"} space, the S operation is \emph{visible} in the \code{"query"} space but not in the \code{"reference"} or in the \code{"query-after-soft-clipping"} space, etc... Here are the extended CIGAR operations that are \emph{visible} in each space: \enumerate{ \item reference: M, D, N, =, X \item reference-N-regions-removed: M, D, =, X \item query: M, I, S, =, X \item query-before-hard-clipping: M, I, S, H, =, X \item query-after-soft-clipping: M, I, =, X \item pairwise: M, I, D, N, =, X \item pairwise-N-regions-removed: M, I, D, =, X \item pairwise-dense: M, =, X } \code{sequenceLayer} lays a string that belongs to one space alongside another by (1) removing the substrings associated with operations that are not \emph{visible} anymore in the new space, and (2) injecting substrings associated with operations that become \emph{visible} in the new space. Each injected substring has the length of the operation associated with it, and its content is controlled via the corresponding \code{*.letter} argument. For example, when going from the \code{"query"} space to the \code{"reference"} space (the default), the I- and S-substrings (i.e. the substrings associated with I/S operations) are removed, and substrings associated with D/N operations are injected. More precisely, the D-substrings are filled with the letter specified in \code{D.letter}, and the N-substrings with the letter specified in \code{N.letter}. The other \code{*.letter} arguments are ignored in that case. } \value{ An \link{XStringSet} object of the same class and length as \code{x}. } \author{Hervé Pagès} \seealso{ \itemize{ \item The \code{\link{stackStringsFromBam}} function for stacking the read sequences (or their quality strings) stored in a BAM file on a region of interest. \item The \code{\link{readGAlignments}} function for loading read sequences from a BAM file (via a \link{GAlignments} object). \item The \code{\link[Biostrings]{extractAt}} and \code{\link[Biostrings]{replaceAt}} functions in the \pkg{Biostrings} package for extracting/replacing arbitrary substrings from/in a string or set of strings. \item \link{cigar-utils} for the CIGAR utility functions used internally by \code{sequenceLayer}. } } \examples{ ## --------------------------------------------------------------------- ## A. FROM "query" TO "reference" SPACE ## --------------------------------------------------------------------- ## Load read sequences from a BAM file (they will be returned in a ## GAlignments object): bamfile <- system.file("extdata", "ex1.bam", package="Rsamtools") param <- ScanBamParam(what="seq") gal <- readGAlignments(bamfile, param=param) qseq <- mcols(gal)$seq # the read sequences (aka query sequences) ## Lay the query sequences alongside the reference space. This will ## remove the substrings associated with insertions to the reference ## (I operations) and soft clipping (S operations), and will inject new ## substrings (filled with "-") where deletions from the reference (D ## operations) and skipped regions from the reference (N operations) ## occurred during the alignment process: qseq_on_ref <- sequenceLayer(qseq, cigar(gal)) ## A typical use case for doing the above is to compute 1 consensus ## sequence per chromosome. The code below shows how this can be done ## in 2 extra steps. ## Step 1: Compute one consensus matrix per chromosome. qseq_on_ref_by_chrom <- splitAsList(qseq_on_ref, seqnames(gal)) pos_by_chrom <- splitAsList(start(gal), seqnames(gal)) cm_by_chrom <- lapply(names(pos_by_chrom), function(seqname) consensusMatrix(qseq_on_ref_by_chrom[[seqname]], as.prob=TRUE, shift=pos_by_chrom[[seqname]]-1, width=seqlengths(gal)[[seqname]])) names(cm_by_chrom) <- names(pos_by_chrom) ## 'cm_by_chrom' is a list of consensus matrices. Each matrix has 17 ## rows (1 per letter in the DNA alphabet) and 1 column per chromosome ## position. ## Step 2: Compute the consensus string from each consensus matrix. ## We'll put "+" in the strings wherever there is no coverage for that ## position, and "N" where there is coverage but no consensus. cs_by_chrom <- lapply(cm_by_chrom, function(cm) { ## Because consensusString() doesn't like consensus matrices ## with columns that contain only zeroes (and you will have ## columns like that for chromosome positions that don't ## receive any coverage), we need to "fix" 'cm' first. idx <- colSums(cm) == 0 cm["+", idx] <- 1 DNAString(consensusString(cm, ambiguityMap="N")) }) ## consensusString() provides some flexibility to let you extract ## the consensus in different ways. See '?consensusString' in the ## Biostrings package for the details. ## Finally, note that the read quality strings can also be used as ## input for sequenceLayer(): param <- ScanBamParam(what="qual") gal <- readGAlignments(bamfile, param=param) qual <- mcols(gal)$qual # the read quality strings qual_on_ref <- sequenceLayer(qual, cigar(gal)) ## Note that since the "-" letter is a valid quality code, there is ## no way to distinguish it from the "-" letters inserted by ## sequenceLayer(). ## --------------------------------------------------------------------- ## B. FROM "query" TO "query-after-soft-clipping" SPACE ## --------------------------------------------------------------------- ## Going from "query" to "query-after-soft-clipping" simply removes ## the substrings associated with soft clipping (S operations): qseq <- DNAStringSet(c("AAAGTTCGAA", "TTACGATTAN", "GGATAATTTT")) cigar <- c("3H10M", "2S7M1S2H", "2M1I1M3D2M4S") clipped_qseq <- sequenceLayer(qseq, cigar, from="query", to="query-after-soft-clipping") sequenceLayer(clipped_qseq, cigar, from="query-after-soft-clipping", to="query") sequenceLayer(clipped_qseq, cigar, from="query-after-soft-clipping", to="query", S.letter="-") ## --------------------------------------------------------------------- ## C. BRING QUERY AND REFERENCE SEQUENCES TO THE "pairwise" or ## "pairwise-dense" SPACE ## --------------------------------------------------------------------- ## Load read sequences from a BAM file: library(RNAseqData.HNRNPC.bam.chr14) bamfile <- RNAseqData.HNRNPC.bam.chr14_BAMFILES[1] param <- ScanBamParam(what="seq", which=GRanges("chr14", IRanges(1, 25000000))) gal <- readGAlignments(bamfile, param=param) qseq <- mcols(gal)$seq # the read sequences (aka query sequences) ## Load the corresponding reference sequences from the appropriate ## BSgenome package (the reads in RNAseqData.HNRNPC.bam.chr14 were ## aligned to hg19): library(BSgenome.Hsapiens.UCSC.hg19) rseq <- getSeq(Hsapiens, as(gal, "GRanges")) # the reference sequences ## Bring 'qseq' and 'rseq' to the "pairwise" space. ## For 'qseq', this will remove the substrings associated with soft ## clipping (S operations) and inject substrings (filled with "-") ## associated with deletions from the reference (D operations) and ## skipped regions from the reference (N operations). For 'rseq', this ## will inject substrings (filled with "-") associated with insertions ## to the reference (I operations). qseq2 <- sequenceLayer(qseq, cigar(gal), from="query", to="pairwise") rseq2 <- sequenceLayer(rseq, cigar(gal), from="reference", to="pairwise") ## Sanity check: 'qseq2' and 'rseq2' should have the same shape. stopifnot(identical(elementNROWS(qseq2), elementNROWS(rseq2))) ## A closer look at reads with insertions and deletions: cigar_op_table <- cigarOpTable(cigar(gal)) head(cigar_op_table) I_idx <- which(cigar_op_table[ , "I"] >= 2) # at least 2 insertions qseq2[I_idx] rseq2[I_idx] D_idx <- which(cigar_op_table[ , "D"] >= 2) # at least 2 deletions qseq2[D_idx] rseq2[D_idx] ## A closer look at reads with skipped regions: N_idx <- which(cigar_op_table[ , "N"] != 0) qseq2[N_idx] rseq2[N_idx] ## A variant of the "pairwise" space is the "pairwise-dense" space. ## In that space, all indels and skipped regions are removed from 'qseq' ## and 'rseq'. qseq3 <- sequenceLayer(qseq, cigar(gal), from="query", to="pairwise-dense") rseq3 <- sequenceLayer(rseq, cigar(gal), from="reference", to="pairwise-dense") ## Sanity check: 'qseq3' and 'rseq3' should have the same shape. stopifnot(identical(elementNROWS(qseq3), elementNROWS(rseq3))) ## Insertions were removed: qseq3[I_idx] rseq3[I_idx] ## Deletions were removed: qseq3[D_idx] rseq3[D_idx] ## Skipped regions were removed: qseq3[N_idx] rseq3[N_idx] ## --------------------------------------------------------------------- ## D. SANITY CHECKS ## --------------------------------------------------------------------- SPACES <- c("reference", "reference-N-regions-removed", "query", "query-before-hard-clipping", "query-after-soft-clipping", "pairwise", "pairwise-N-regions-removed", "pairwise-dense") cigarWidth <- list( function(cigar) cigarWidthAlongReferenceSpace(cigar), function(cigar) cigarWidthAlongReferenceSpace(cigar, N.regions.removed=TRUE), function(cigar) cigarWidthAlongQuerySpace(cigar), function(cigar) cigarWidthAlongQuerySpace(cigar, before.hard.clipping=TRUE), function(cigar) cigarWidthAlongQuerySpace(cigar, after.soft.clipping=TRUE), function(cigar) cigarWidthAlongPairwiseSpace(cigar), function(cigar) cigarWidthAlongPairwiseSpace(cigar, N.regions.removed=TRUE), function(cigar) cigarWidthAlongPairwiseSpace(cigar, dense=TRUE) ) cigar <- c("3H2S4M1D2M2I1M5N3M6H", "5M1I3M2D4M2S") seq <- list( BStringSet(c(A="AAAA-BBC.....DDD", B="AAAAABBB--CCCC")), BStringSet(c(A="AAAA-BBCDDD", B="AAAAABBB--CCCC")), BStringSet(c(A="++AAAABBiiCDDD", B="AAAAAiBBBCCCC++")), BStringSet(c(A="+++++AAAABBiiCDDD++++++", B="AAAAAiBBBCCCC++")), BStringSet(c(A="AAAABBiiCDDD", B="AAAAAiBBBCCCC")), BStringSet(c(A="AAAA-BBiiC.....DDD", B="AAAAAiBBB--CCCC")), BStringSet(c(A="AAAA-BBiiCDDD", B="AAAAAiBBB--CCCC")), BStringSet(c(A="AAAABBCDDD", B="AAAAABBBCCCC")) ) stopifnot(all(sapply(1:8, function(i) identical(width(seq[[i]]), cigarWidth[[i]](cigar)) ))) sequenceLayer2 <- function(x, cigar, from, to) sequenceLayer(x, cigar, from=from, to=to, I.letter="i") identical_XStringSet <- function(target, current) { ok1 <- identical(class(target), class(current)) ok2 <- identical(names(target), names(current)) ok3 <- all(target == current) ok1 && ok2 && ok3 } res <- sapply(1:8, function(i) { sapply(1:8, function(j) { target <- seq[[j]] current <- sequenceLayer2(seq[[i]], cigar, from=SPACES[i], to=SPACES[j]) identical_XStringSet(target, current) }) }) stopifnot(all(res)) } \keyword{methods} \keyword{manip} GenomicAlignments/man/setops-methods.Rd0000644000175200017520000000273614516004601021224 0ustar00biocbuildbiocbuild\name{setops-methods} \alias{setops-methods} \alias{pintersect} \alias{pintersect,GAlignments,GRanges-method} \alias{pintersect,GRanges,GAlignments-method} \title{Set operations on GAlignments objects} \description{ Performs set operations on \link{GAlignments} objects. NOTE: The \code{\link[IRanges]{pintersect}} generic function and method for \link[IRanges]{IntegerRanges} objects is defined and documented in the \pkg{IRanges} package. Methods for \link[GenomicRanges]{GRanges} and \link[GenomicRanges]{GRangesList} objects are defined and documented in the \pkg{GenomicRanges} package. } \usage{ \S4method{pintersect}{GAlignments,GRanges}(x, y, ...) \S4method{pintersect}{GRanges,GAlignments}(x, y, ...) } \arguments{ \item{x, y}{ A \link{GAlignments} object and a \link[GenomicRanges]{GRanges} object. They must have the same length. } \item{...}{ Further arguments to be passed to or from other methods. } } \value{ A \link{GAlignments} object \emph{parallel} to (i.e. same length as) \code{x} and \code{y}. } \seealso{ \itemize{ \item The \link{GAlignments} class. \item The \link[GenomicRanges]{setops-methods} man page in the \pkg{GenomicRanges} package. } } \examples{ ## Parallel intersection of a GAlignments and a GRanges object: bamfile <- system.file("extdata", "ex1.bam", package="Rsamtools") gal <- readGAlignments(bamfile) pintersect(gal, shift(as(gal, "GRanges"), 6L)) } \keyword{methods} \keyword{utilities} GenomicAlignments/man/stackStringsFromGAlignments.Rd0000644000175200017520000002473214516004601023702 0ustar00biocbuildbiocbuild\name{stackStringsFromBam} \alias{stackStringsFromGAlignments} \alias{stackStringsFromBam} \alias{alphabetFrequencyFromBam} \title{Stack the read sequences stored in a GAlignments object or a BAM file} \description{ \code{stackStringsFromGAlignments} stacks the read sequences (or their quality strings) stored in a \link{GAlignments} object over a user-specified region. \code{stackStringsFromBam} stacks the read sequences (or their quality strings) stored in a BAM file over a user-specified region. \code{alphabetFrequencyFromBam} computes the alphabet frequency of the reads over a user-specified region. All these functions take into account the CIGAR of each read to \emph{lay} the read sequence (or its quality string) alongside the reference space. This step ensures that each nucleotide in a read is associated with the correct position on the reference sequence. } \usage{ stackStringsFromGAlignments(x, region, what="seq", D.letter="-", N.letter=".", Lpadding.letter="+", Rpadding.letter="+") stackStringsFromBam(file, index=file, param, what="seq", use.names=FALSE, D.letter="-", N.letter=".", Lpadding.letter="+", Rpadding.letter="+") alphabetFrequencyFromBam(file, index=file, param, what="seq", ...) } \arguments{ \item{x}{ A \link{GAlignments} object with the read sequences in the \code{"seq"} metadata column (if \code{what} is set to \code{"seq"}), or with the the read quality strings in the \code{"qual"} metadata column (if \code{what} is set to \code{"qual"}). Such an object is typically obtained by specifying \code{param=\link[Rsamtools]{ScanBamParam}(what=c("seq", "qual"))} when reading a BAM file with calling \code{\link{readGAlignments}()}. } \item{region}{ A \link[GenomicRanges]{GRanges} object with exactly 1 genomic range. The read sequences (or read quality strings) will be stacked over that region. } \item{what}{ A single string. Either \code{"seq"} or \code{"qual"}. If \code{"seq"} (the default), the read sequences will be stacked. If \code{"qual"}, the read quality strings will be stacked. } \item{D.letter, N.letter}{ A single letter used as a filler for injections. The 2 arguments are passed down to the \code{\link{sequenceLayer}} function. See \code{?\link{sequenceLayer}} for more details. } \item{Lpadding.letter, Rpadding.letter}{ A single letter to use for padding the sequences on the left, and another one to use for padding on the right. The 2 arguments are passed down to the \code{\link[Biostrings]{stackStrings}} function defined in the \pkg{Biostrings} package. See \code{?\link[Biostrings]{stackStrings}} in the \pkg{Biostrings} package for more details. } \item{file, index}{ The path to the BAM file containing the reads, and to its index file, respectively. The latter is given \emph{without} the '.bai' extension. See \code{\link{scanBam}} for more information. } \item{param}{ A \link{ScanBamParam} object containing exactly 1 genomic region (i.e. \code{unlist(bamWhich(param))} must have length 1). Alternatively, \code{param} can be a \link[GenomicRanges]{GRanges} or \link[IRanges]{IntegerRangesList} object containing exactly 1 genomic region (the strand will be ignored in case of a \link[GenomicRanges]{GRanges} object), or a character string specifying a single genomic region (in the \code{"chr14:5201-5300"} format). } \item{use.names}{ Use the query template names (QNAME field) as the names of the returned object? If not (the default), then the returned object has no names. } \item{...}{ Further arguments to be passed to \link[Biostrings]{alphabetFrequency}. } } \details{ \code{stackStringsFromGAlignments} performs the 3 following steps: \enumerate{ \item Subset \link{GAlignments} object \code{x} to keep only the alignments that overlap with the specified region. \item Lay the sequences in \code{x} alongside the reference space, using their CIGARs. This is done with the \code{\link{sequenceLayer}} function. \item Stack them on the specified region. This is done with the \code{\link[Biostrings]{stackStrings}} function defined in the \pkg{Biostrings} package. } \code{stackStringsFromBam} performs the 3 following steps: \enumerate{ \item Load the read sequences (or their quality strings) from the BAM file. Only the read sequences that overlap with the specified region are loaded. This is done with the \code{\link{readGAlignments}} function. Note that if the file contains paired-end reads, the pairing is ignored. \item Same as \code{stackStringsFromGAlignments}. \item Same as \code{stackStringsFromGAlignments}. } \code{alphabetFrequencyFromBam} also performs steps 1. and 2. but, instead of stacking the sequences at step 3., it computes the nucleotide frequencies for each genomic position in the specified region. } \value{ For \code{stackStringsFromBam}: A rectangular (i.e. constant-width) \link[Biostrings]{DNAStringSet} object (if \code{what} is \code{"seq"}) or \link[Biostrings]{BStringSet} object (if \code{what} is \code{"qual"}). For \code{alphabetFrequencyFromBam}: By default a matrix like one returned by \code{\link[Biostrings]{alphabetFrequency}}. The matrix has 1 row per nucleotide position in the specified region. } \note{ TWO IMPORTANT CAVEATS ABOUT \code{stackStringsFromGAlignments} AND \code{stackStringsFromBam}: Specifying a big genomic region, say >= 100000 bp, can require a lot of memory (especially with high coverage reads) so is not recommended. See the \code{\link{pileLettersAt}} function for piling the read letters on top of a set of genomic positions, which is more flexible and more memory efficient. Paired-end reads are treated as single-end reads (i.e. they're not paired). } \author{Hervé Pagès} \seealso{ \itemize{ \item The \code{\link{pileLettersAt}} function for piling the letters of a set of aligned reads on top of a set of genomic positions. \item The \code{\link{readGAlignments}} function for loading read sequences (or their quality strings) from a BAM file (via a \link{GAlignments} object). \item The \code{\link{sequenceLayer}} function for laying read sequences alongside the reference space, using their CIGARs. \item The \code{\link[Biostrings]{stackStrings}} function in the \pkg{Biostrings} package for stacking an arbitrary \link[Biostrings]{XStringSet} object. \item The \code{\link[Biostrings]{alphabetFrequency}} function in the \pkg{Biostrings} package. \item The SAMtools mpileup command available at \url{http://samtools.sourceforge.net/} as part of the SAMtools project. } } \examples{ ## --------------------------------------------------------------------- ## A. EXAMPLE WITH TOY DATA ## --------------------------------------------------------------------- bamfile1 <- BamFile(system.file("extdata", "ex1.bam", package="Rsamtools")) region1 <- GRanges("seq1", IRanges(1, 60)) # region of interest ## Stack the read sequences directly from the BAM file: stackStringsFromBam(bamfile1, param=region1, use.names=TRUE) ## or, alternatively, from a GAlignments object: gal1 <- readGAlignments(bamfile1, param=ScanBamParam(what="seq"), use.names=TRUE) stackStringsFromGAlignments(gal1, region1) ## Compute the "consensus matrix" (1 column per nucleotide position ## in the region of interest): af <- alphabetFrequencyFromBam(bamfile1, param=region1, baseOnly=TRUE) cm1a <- t(af[ , DNA_BASES]) cm1a ## Stack their quality strings: stackStringsFromBam(bamfile1, param=region1, what="qual") ## Control the number of reads to display: options(showHeadLines=18) options(showTailLines=6) stackStringsFromBam(bamfile1, param=GRanges("seq1", IRanges(61, 120))) stacked_qseq <- stackStringsFromBam(bamfile1, param="seq2:1509-1519") stacked_qseq # deletion in read 13 af <- alphabetFrequencyFromBam(bamfile1, param="seq2:1509-1519", baseOnly=TRUE) cm1b <- t(af[ , DNA_BASES]) # consensus matrix cm1b ## Sanity check: stopifnot(identical(consensusMatrix(stacked_qseq)[DNA_BASES, ], cm1b)) stackStringsFromBam(bamfile1, param="seq2:1509-1519", what="qual") ## --------------------------------------------------------------------- ## B. EXAMPLE WITH REAL DATA ## --------------------------------------------------------------------- library(RNAseqData.HNRNPC.bam.chr14) bamfile2 <- BamFile(RNAseqData.HNRNPC.bam.chr14_BAMFILES[1]) ## Region of interest: region2 <- GRanges("chr14", IRanges(19650095, 19650159)) ## Stack the read sequences directly from the BAM file: stackStringsFromBam(bamfile2, param=region2) ## or, alternatively, from a GAlignments object: gal2 <- readGAlignments(bamfile2, param=ScanBamParam(what="seq")) stackStringsFromGAlignments(gal2, region2) af <- alphabetFrequencyFromBam(bamfile2, param=region2, baseOnly=TRUE) cm2 <- t(af[ , DNA_BASES]) # consensus matrix cm2 ## --------------------------------------------------------------------- ## C. COMPUTE READ CONSENSUS SEQUENCE FOR REGION OF INTEREST ## --------------------------------------------------------------------- ## Let's write our own little naive function to go from consensus matrix ## to consensus sequence. For each nucleotide position in the region of ## interest (i.e. each column in the matrix), we select the letter with ## highest frequency. We also use special letter "*" at positions where ## there is a tie, and special letter "." at positions where all the ## frequencies are 0 (a particular type of tie): cm_to_cs <- function(cm) { stopifnot(is.matrix(cm)) nr <- nrow(cm) rnames <- rownames(cm) stopifnot(!is.null(rnames) && all(nchar(rnames) == 1L)) selection <- apply(cm, 2, function(x) { i <- which.max(x) if (x[i] == 0L) return(nr + 1L) if (sum(x == x[i]) != 1L) return(nr + 2L) i }) paste0(c(rnames, ".", "*")[selection], collapse="") } cm_to_cs(cm1a) cm_to_cs(cm1b) cm_to_cs(cm2) ## Note that the consensus sequences we obtain are relative to the ## plus strand of the reference sequence. } \keyword{methods} \keyword{manip} GenomicAlignments/man/summarizeOverlaps-methods.Rd0000644000175200017520000006031714547043765023460 0ustar00biocbuildbiocbuild\name{summarizeOverlaps-methods} \alias{summarizeOverlaps-methods} \alias{summarizeOverlaps} \alias{summarizeOverlaps,GRanges,GAlignments-method} \alias{summarizeOverlaps,GRangesList,GAlignments-method} \alias{summarizeOverlaps,GRanges,GAlignmentsList-method} \alias{summarizeOverlaps,GRangesList,GAlignmentsList-method} \alias{summarizeOverlaps,GRanges,GAlignmentPairs-method} \alias{summarizeOverlaps,GRangesList,GAlignmentPairs-method} \alias{summarizeOverlaps,GRanges,GRanges-method} \alias{summarizeOverlaps,GRangesList,GRanges-method} \alias{summarizeOverlaps,GRanges,GRangesList-method} \alias{summarizeOverlaps,GRangesList,GRangesList-method} \alias{Union} \alias{IntersectionStrict} \alias{IntersectionNotEmpty} \alias{summarizeOverlaps,GRanges,BamFile-method} \alias{summarizeOverlaps,GRangesList,BamFile-method} \alias{summarizeOverlaps,GRanges,character-method} \alias{summarizeOverlaps,GRangesList,character-method} \alias{summarizeOverlaps,GRanges,BamFileList-method} \alias{summarizeOverlaps,GRangesList,BamFileList-method} \alias{summarizeOverlaps,BamViews,missing-method} \title{Perform overlap queries between reads and genomic features} \description{ \code{summarizeOverlaps} extends \code{findOverlaps} by providing options to resolve reads that overlap multiple features. } \usage{ \S4method{summarizeOverlaps}{GRanges,GAlignments}( features, reads, mode=Union, ignore.strand=FALSE, inter.feature=TRUE, preprocess.reads=NULL, ...) \S4method{summarizeOverlaps}{GRangesList,GAlignments}( features, reads, mode=Union, ignore.strand=FALSE, inter.feature=TRUE, preprocess.reads=NULL, ...) \S4method{summarizeOverlaps}{GRanges,GRanges}( features, reads, mode=Union, ignore.strand=FALSE, inter.feature=TRUE, preprocess.reads=NULL, ...) \S4method{summarizeOverlaps}{GRangesList,GRanges}( features, reads, mode=Union, ignore.strand=FALSE, inter.feature=TRUE, preprocess.reads=NULL, ...) \S4method{summarizeOverlaps}{GRanges,GAlignmentPairs}( features, reads, mode=Union, ignore.strand=FALSE, inter.feature=TRUE, preprocess.reads=NULL, ...) \S4method{summarizeOverlaps}{GRangesList,GAlignmentPairs}( features, reads, mode=Union, ignore.strand=FALSE, inter.feature=TRUE, preprocess.reads=NULL, ...) ## mode funtions Union(features, reads, ignore.strand=FALSE, inter.feature=TRUE) IntersectionStrict(features, reads, ignore.strand=FALSE, inter.feature=TRUE) IntersectionNotEmpty(features, reads, ignore.strand=FALSE, inter.feature=TRUE) \S4method{summarizeOverlaps}{GRanges,BamFile}( features, reads, mode=Union, ignore.strand=FALSE, inter.feature=TRUE, singleEnd=TRUE, fragments=FALSE, param=ScanBamParam(), preprocess.reads=NULL, ...) \S4method{summarizeOverlaps}{BamViews,missing}( features, reads, mode=Union, ignore.strand=FALSE, inter.feature=TRUE, singleEnd=TRUE, fragments=FALSE, param=ScanBamParam(), preprocess.reads=NULL, ...) } \arguments{ \item{features}{ A \link[GenomicRanges]{GRanges} or a \link[GenomicRanges]{GRangesList} object of genomic regions of interest. When a \link[GenomicRanges]{GRanges} is supplied, each row is considered a feature. When a \link[GenomicRanges]{GRangesList} is supplied, each higher list-level is considered a feature. This distinction is important when defining overlaps. When \code{features} is a \link[Rsamtools]{BamViews} the \code{reads} argument is missing. Features are extracted from the \code{bamRanges} and the \code{reads} from \code{bamPaths}. Metadata from \code{bamPaths} and \code{bamSamples} are stored in the \code{colData} of the resulting \link[SummarizedExperiment]{RangedSummarizedExperiment} object. \code{bamExperiment} metadata are stored in the \code{metadata} slot. } \item{reads}{ A \link[GenomicRanges]{GRanges}, \link[GenomicRanges]{GRangesList} \link{GAlignments}, \link{GAlignmentsList}, \link{GAlignmentPairs}, \link[Rsamtools]{BamViews} or \link[Rsamtools]{BamFileList} object that represents the data to be counted by \code{summarizeOverlaps}. \code{reads} is missing when a \link[Rsamtools]{BamViews} object is the only argument supplied to \code{summarizeOverlaps}. \code{reads} are the files specified in \code{bamPaths} of the \link[Rsamtools]{BamViews} object. } \item{mode}{ \code{mode} can be one of the pre-defined count methods such as "Union", "IntersectionStrict", or "IntersectionNotEmpty" or it a user supplied count function. For a custom count function, the input arguments must match those of the pre-defined options and the function must return a vector of counts the same length as the annotation ('features' argument). See examples for details. The pre-defined options are designed after the counting modes available in the HTSeq package by Simon Anders (see references). \itemize{ \item "Union" : (Default) Reads that overlap any portion of exactly one feature are counted. Reads that overlap multiple features are discarded. This is the most conservative of the 3 modes. \item "IntersectionStrict" : A read must fall completely "within" the feature to be counted. If a read overlaps multiple features but falls "within" only one, the read is counted for that feature. If the read is "within" multiple features, the read is discarded. \item "IntersectionNotEmpty" : A read must fall in a unique disjoint region of a feature to be counted. When a read overlaps multiple features, the features are partitioned into disjoint intervals. Regions that are shared between the features are discarded leaving only the unique disjoint regions. If the read overlaps one of these remaining regions, it is assigned to the feature the unique disjoint region came from. \item user supplied function : A function can be supplied as the \code{mode} argument. It must (1) have arguments that correspond to \code{features}, \code{reads}, \code{ignore.strand} and \code{inter.feature} arguments (as in the defined mode functions) and (2) return a vector of counts the same length as \code{features}. } } \item{ignore.strand}{ A logical indicating if strand should be considered when matching. } \item{inter.feature}{ (Default TRUE) A logical indicating if the counting \code{mode} should be aware of overlapping features. When TRUE (default), reads mapping to multiple features are dropped (i.e., not counted). When FALSE, these reads are retained and a count is assigned to each feature they map to. There are 6 possible combinations of the \code{mode} and \code{inter.feature} arguments. When \code{inter.feature=FALSE} the behavior of modes \sQuote{Union} and \sQuote{IntersectionStrict} are essentially \sQuote{countOverlaps} with \sQuote{type=any} and \code{type=within}, respectively. \sQuote{IntersectionNotEmpty} does not reduce to a simple countOverlaps because common (shared) regions of the annotation are removed before counting. } \item{preprocess.reads}{ A function applied to the reads before counting. The first argument should be \code{reads} and the return value should be an object compatible with the \code{reads} argument to the counting modes, Union, IntersectionStrict and IntersectionNotEmpty. The distinction between a user-defined 'mode' and user-defined 'preprocess.reads' function is that in the first case the user defines how to count; in the second case the reads are preprocessed before counting with a pre-defined mode. See examples. } \item{...}{ Additional arguments passed to functions or methods called from within \code{summarizeOverlaps}. For BAM file methods arguments may include \code{singleEnd}, \code{fragments} or \code{param} which apply to reading records from a file (see below). Providing \code{count.mapped.reads=TRUE} include additional passes through the BAM file to collect statistics similar to those from \code{countBam}. A \code{BPPARAM} argument can be passed down to the \code{bplapply} called by \code{summarizeOverlaps}. The argument can be MulticoreParam(), SnowParam(), BatchJobsParam() or DoparParam(). See the BiocParallel package for details in specifying the params. } \item{singleEnd}{ (Default TRUE) A logical indicating if reads are single or paired-end. In Bioconductor > 2.12 it is not necessary to sort paired-end BAM files by \code{qname}. When counting with \code{summarizeOverlaps}, setting \code{singleEnd=FALSE} will trigger paired-end reading and counting. It is fine to also set \code{asMates=TRUE} in the \code{BamFile} but is not necessary when \code{singleEnd=FALSE}. } \item{fragments}{ (Default FALSE) A logical; applied to paired-end data only. \code{fragments} controls which function is used to read the data which subsequently affects which records are included in counting. When \code{fragments=FALSE}, data are read with \code{\link{readGAlignmentPairs}} and returned in a \code{GAlignmentPairs} class. In this case, singletons, reads with unmapped pairs, and other fragments, are dropped. When \code{fragments=TRUE}, data are read with \code{\link{readGAlignmentsList}} and returned in a \code{GAlignmentsList} class. This class holds \sQuote{mated pairs} as well as same-strand pairs, singletons, reads with unmapped pairs and other fragments. Because more records are kept, generally counts will be higher when \code{fragments=TRUE}. The term \sQuote{mated pairs} refers to records paired with the algorithm described on the \code{?\link{readGAlignmentsList}} man page. } \item{param}{An optional \code{\link[Rsamtools]{ScanBamParam}} instance to further influence scanning, counting, or filtering. See \code{?\link{BamFile}} for details of how records are returned when both \code{yieldSize} is specified in a \code{\link{BamFile}} and \code{which} is defined in a \code{\link{ScanBamParam}}. } } \details{ \describe{ \item{\code{summarizeOverlaps}:}{ offers counting modes to resolve reads that overlap multiple features. The \code{mode} argument defines a set of rules to resolve the read to a single feature such that each read is counted a maximum of once. New to GenomicRanges >= 1.13.9 is the \code{inter.feature} argument which allows reads to be counted for each feature they overlap. When \code{inter.feature=TRUE} the counting modes are aware of feature overlap; reads that overlap multiple features are dropped and not counted. When \code{inter.feature=FALSE} multiple feature overlap is ignored and reads are counted once for each feature they map to. This essentially reduces modes \sQuote{Union} and \sQuote{IntersectionStrict} to \code{countOverlaps} with \code{type="any"}, and \code{type="within"}, respectively. \sQuote{IntersectionNotEmpty} is not reduced to a derivative of \code{countOverlaps} because the shared regions are removed before counting. The \code{BamViews}, \code{BamFile} and \code{BamFileList} methods summarize overlaps across one or several files. The latter uses \code{bplapply}; control parallel evaluation using the \code{\link{register}} interface in the \pkg{BiocParallel} package. } \item{features :}{ A \sQuote{feature} can be any portion of a genomic region such as a gene, transcript, exon etc. When the \code{features} argument is a \link[GenomicRanges]{GRanges} the rows define the features. The result will be the same length as the \link[GenomicRanges]{GRanges}. When \code{features} is a \link[GenomicRanges]{GRangesList} the highest list-level defines the features and the result will be the same length as the \link[GenomicRanges]{GRangesList}. When \code{inter.feature=TRUE}, each count \code{mode} attempts to assign a read that overlaps multiple features to a single feature. If there are ranges that should be considered together (e.g., exons by transcript or cds regions by gene) the \link[GenomicRanges]{GRangesList} would be appropriate. If there is no grouping in the data then a \link[GenomicRanges]{GRanges} would be appropriate. } \item{paired-end reads :}{ Paired-end reads are counted as a single hit if one or both parts of the pair are overlapped. Paired-end records can be counted in a \link{GAlignmentPairs} container or BAM file. Counting pairs in BAM files: \itemize{ \item{The \code{singleEnd} argument should be FALSE.} \item{When \code{reads} are supplied as a BamFile or BamFileList, the \code{asMates} argument to the BamFile should be TRUE.} \item{When \code{fragments} is FALSE, a \code{GAlignmentPairs} object is used in counting (pairs only).} \item{When \code{fragments} is TRUE, a \code{GAlignmentsList} object is used in counting (pairs, singletons, unmapped mates, etc.)} } } } } \value{ A \link[SummarizedExperiment]{RangedSummarizedExperiment} object. The \code{assays} slot holds the counts, \code{rowRanges} holds the annotation from \code{features}. When \code{reads} is a \code{BamFile} or \code{BamFileList} \code{colData} is an empty DataFrame with a single row named \sQuote{counts}. If \code{count.mapped.reads=TRUE}, \code{colData} holds the output of \code{countBam} in 3 columns named \sQuote{records} (total records), \sQuote{nucleotides} and \sQuote{mapped} (mapped records). When \code{features} is a \code{BamViews} \code{colData} includes 2 columns named \code{bamSamples} and \code{bamIndices}. In all other cases, \code{colData} has columns of \sQuote{object} (class of reads) and \sQuote{records} (length of \code{reads}). } \references{ HTSeq : \url{http://www-huber.embl.de/users/anders/HTSeq/doc/overview.html} htseq-count : \url{http://www-huber.embl.de/users/anders/HTSeq/doc/count.html} } \author{Valerie Obenchain} \seealso{ \itemize{ \item The \pkg{DESeq2}, \pkg{DEXSeq} and \pkg{edgeR} packages. \item The \link[SummarizedExperiment]{RangedSummarizedExperiment} class defined in the \pkg{SummarizedExperiment} package. \item The \link{GAlignments} and \link{GAlignmentPairs} classes. \item The \link[Rsamtools]{BamFileList} and \link[Rsamtools]{BamViews} classes in the \pkg{Rsamtools} package. \item The \link{readGAlignments} and \link{readGAlignmentPairs} functions. } } \examples{ reads <- GAlignments( names = c("a","b","c","d","e","f","g"), seqnames = Rle(c(rep(c("chr1", "chr2"), 3), "chr1")), pos = as.integer(c(1400, 2700, 3400, 7100, 4000, 3100, 5200)), cigar = c("500M", "100M", "300M", "500M", "300M", "50M200N50M", "50M150N50M"), strand = strand(rep("+", 7))) gr <- GRanges( seqnames = c(rep("chr1", 7), rep("chr2", 4)), strand = "+", ranges = IRanges(c(1000, 3000, 3600, 4000, 4000, 5000, 5400, 2000, 3000, 7000, 7500), width = c(500, 500, 300, 500, 900, 500, 500, 900, 500, 600, 300), names=c("A", "B", "C1", "C2", "D1", "D2", "E", "F", "G", "H1", "H2"))) groups <- factor(c(1,2,3,3,4,4,5,6,7,8,8)) grl <- splitAsList(gr, groups) names(grl) <- LETTERS[seq_along(grl)] ## --------------------------------------------------------------------- ## Counting modes. ## --------------------------------------------------------------------- ## First count with a GRanges as the 'features'. 'Union' is the ## most conservative counting mode followed by 'IntersectionStrict' ## then 'IntersectionNotEmpty'. counts1 <- data.frame(union=assays(summarizeOverlaps(gr, reads))$counts, intStrict=assays(summarizeOverlaps(gr, reads, mode="IntersectionStrict"))$counts, intNotEmpty=assays(summarizeOverlaps(gr, reads, mode="IntersectionNotEmpty"))$counts) colSums(counts1) ## Split the 'features' into a GRangesList and count again. counts2 <- data.frame(union=assays(summarizeOverlaps(grl, reads))$counts, intStrict=assays(summarizeOverlaps(grl, reads, mode="IntersectionStrict"))$counts, intNotEmpty=assays(summarizeOverlaps(grl, reads, mode="IntersectionNotEmpty"))$counts) colSums(counts2) ## The GRangesList ('grl' object) has 8 features whereas the GRanges ## ('gr' object) has 11. The affect on counting can be seen by looking ## at feature 'H' with mode 'Union'. In the GRanges this feature is ## represented by ranges 'H1' and 'H2', gr[c("H1", "H2")] ## and by list element 'H' in the GRangesList, grl["H"] ## Read "d" hits both 'H1' and 'H2'. This is considered a multi-hit when ## using a GRanges (each range is a separate feature) so the read was ## dropped and not counted. counts1[c("H1", "H2"), ] ## When using a GRangesList, each list element is considered a feature. ## The read hits multiple ranges within list element 'H' but only one ## list element. This is not considered a multi-hit so the read is counted. counts2["H", ] ## --------------------------------------------------------------------- ## Counting multi-hit reads. ## --------------------------------------------------------------------- ## The goal of the counting modes is to provide a set of rules that ## resolve reads hitting multiple features so each read is counted ## a maximum of once. However, sometimes it may be desirable to count ## a read for each feature it overlaps. This can be accomplished by ## setting 'inter.feature' to FALSE. ## When 'inter.feature=FALSE', modes 'Union' and 'IntersectionStrict' ## essentially reduce to countOverlaps() with type="any" and ## type="within", respectively. ## When 'inter.feature=TRUE' only features "A", "F" and "G" have counts. se1 <- summarizeOverlaps(gr, reads, mode="Union", inter.feature=TRUE) assays(se1)$counts ## When 'inter.feature=FALSE' all 11 features have a count. There are ## 7 total reads so one or more reads were counted more than once. se2 <- summarizeOverlaps(gr, reads, mode="Union", inter.feature=FALSE) assays(se2)$counts ## --------------------------------------------------------------------- ## Counting BAM files. ## --------------------------------------------------------------------- library(pasillaBamSubset) library(TxDb.Dmelanogaster.UCSC.dm3.ensGene) exbygene <- exonsBy(TxDb.Dmelanogaster.UCSC.dm3.ensGene, "gene") ## (i) Single-end : ## Large files can be iterated over in chunks by setting a ## 'yieldSize' on the BamFile. bf_s <- BamFile(untreated1_chr4(), yieldSize=50000) se_s <- summarizeOverlaps(exbygene, bf_s, singleEnd=TRUE) table(assays(se_s)$counts > 0) ## When a character (file name) is provided as 'reads' instead ## of a BamFile object summarizeOverlaps() will create a BamFile ## and set a reasonable default 'yieldSize'. ## (ii) Paired-end : ## A paired-end file may contain singletons, reads with unmapped ## pairs or reads with more than two fragments. When 'fragments=FALSE' ## only reads paired by the algorithm are included in the counting. nofrag <- summarizeOverlaps(exbygene, untreated3_chr4(), singleEnd=FALSE, fragments=FALSE) table(assays(nofrag)$counts > 0) ## When 'fragments=TRUE' all singletons, reads with unmapped pairs ## and other fragments will be included in the counting. bf <- BamFile(untreated3_chr4(), asMates=TRUE) frag <- summarizeOverlaps(exbygene, bf, singleEnd=FALSE, fragments=TRUE) table(assays(frag)$counts > 0) ## As expected, using 'fragments=TRUE' results in a larger number ## of total counts because singletons, unmapped pairs etc. are ## included in the counting. ## Total reads in the file: countBam(untreated3_chr4()) ## Reads counted with 'fragments=FALSE': sum(assays(nofrag)$counts) ## Reads counted with 'fragments=TRUE': sum(assays(frag)$counts) ## --------------------------------------------------------------------- ## Use ouput of summarizeOverlaps() for differential expression analysis ## with DESeq2 or edgeR. ## --------------------------------------------------------------------- fls <- list.files(system.file("extdata", package="GenomicAlignments"), recursive=TRUE, pattern="*bam$", full=TRUE) names(fls) <- basename(fls) bf <- BamFileList(fls, index=character(), yieldSize=1000) genes <- GRanges( seqnames = c(rep("chr2L", 4), rep("chr2R", 5), rep("chr3L", 2)), ranges = IRanges(c(1000, 3000, 4000, 7000, 2000, 3000, 3600, 4000, 7500, 5000, 5400), width=c(rep(500, 3), 600, 900, 500, 300, 900, 300, 500, 500))) se <- summarizeOverlaps(genes, bf) ## When the reads are BAM files, the 'colData' contains summary ## information from a call to countBam(). colData(se) ## Start differential expression analysis with the DESeq2 or edgeR ## package: library(DESeq2) deseq <- DESeqDataSet(se, design= ~ 1) library(edgeR) edger <- DGEList(assays(se)$counts, group=rownames(colData(se))) ## --------------------------------------------------------------------- ## Filter records by map quality before counting. ## (user-supplied 'mode' function) ## --------------------------------------------------------------------- ## The 'mode' argument can take a custom count function whose ## arguments are the same as those in the current counting modes ## (i.e., Union, IntersectionNotEmpty, IntersectionStrict). ## In this example records are filtered by map quality before counting. mapq_filter <- function(features, reads, ignore.strand, inter.feature) { require(GenomicAlignments) # needed for parallel evaluation Union(features, reads[mcols(reads)$mapq >= 20], ignore.strand, inter.feature) } genes <- GRanges("seq1", IRanges(seq(1, 1500, by=200), width=100)) param <- ScanBamParam(what="mapq") fl <- system.file("extdata", "ex1.bam", package="Rsamtools") se <- summarizeOverlaps(genes, fl, mode=mapq_filter, param=param) assays(se)$counts ## The count function can be completely custom (i.e., not use the ## pre-defined count functions at all). Requirements are that ## the input arguments match the pre-defined modes and the output ## is a vector of counts the same length as 'features'. my_count <- function(features, reads, ignore.strand, inter.feature) { ## perform filtering, or subsetting etc. require(GenomicAlignments) # needed for parallel evaluation countOverlaps(features, reads) } ## --------------------------------------------------------------------- ## Preprocessing reads before counting with a standard count mode. ## (user-supplied 'preprocess.reads' function) ## --------------------------------------------------------------------- ## The 'preprocess.reads' argument takes a function that is ## applied to the reads before counting with a pre-defined mode. ResizeReads <- function(reads, width=1, fix="start", ...) { reads <- as(reads, "GRanges") stopifnot(all(strand(reads) != "*")) resize(reads, width=width, fix=fix, ...) } ## By default ResizeReads() counts reads that overlap on the 5' end: summarizeOverlaps(grl, reads, mode=Union, preprocess.reads=ResizeReads) ## Count reads that overlap on the 3' end by passing new values ## for 'width' and 'fix': summarizeOverlaps(grl, reads, mode=Union, preprocess.reads=ResizeReads, width=1, fix="end") ## --------------------------------------------------------------------- ## summarizeOverlaps() with BamViews. ## --------------------------------------------------------------------- ## bamSamples and bamPaths metadata are included in the colData. ## bamExperiment metadata is put into the metadata slot. fl <- system.file("extdata", "ex1.bam", package="Rsamtools", mustWork=TRUE) rngs <- GRanges(c("seq1", "seq2"), IRanges(1, c(1575, 1584))) samp <- DataFrame(info="test", row.names="ex1") view <- BamViews(fl, bamSamples=samp, bamRanges=rngs) se <- summarizeOverlaps(view, mode=Union, ignore.strand=TRUE) colData(se) metadata(se) } \keyword{methods} \keyword{utilities} GenomicAlignments/src/0000755000175200017520000000000014551577536016010 5ustar00biocbuildbiocbuildGenomicAlignments/src/GenomicAlignments.h0000644000175200017520000000335514516004601021545 0ustar00biocbuildbiocbuild#include /* cigar_utils.c */ const char *_get_cigar_parsing_error(); int _next_cigar_OP( const char *cigar_string, int offset, char *OP, int *OPL ); SEXP valid_cigar( SEXP cigar, SEXP ans_type ); SEXP explode_cigar_ops( SEXP cigar, SEXP ops ); SEXP explode_cigar_op_lengths( SEXP cigar, SEXP ops ); SEXP cigar_op_table(SEXP cigar); SEXP cigar_ranges( SEXP cigar, SEXP flag, SEXP space, SEXP pos, SEXP f, SEXP ops, SEXP drop_empty_ranges, SEXP reduce_ranges, SEXP with_ops ); SEXP cigar_width( SEXP cigar, SEXP flag, SEXP space ); SEXP cigar_narrow( SEXP cigar, SEXP left_width, SEXP right_width ); SEXP cigar_qnarrow( SEXP cigar, SEXP left_qwidth, SEXP right_qwidth ); /* coordinate_mapping_methods.c */ SEXP map_query_locs_to_ref_locs( SEXP start, SEXP end, SEXP cigar, SEXP pos ); SEXP map_ref_locs_to_query_locs( SEXP start, SEXP end, SEXP cigar, SEXP pos ); SEXP ref_locs_to_query_locs( SEXP ref_locs, SEXP cigar, SEXP pos, SEXP narrow_left ); SEXP query_locs_to_ref_locs( SEXP query_locs, SEXP cigar, SEXP pos, SEXP narrow_left ); /* encodeOverlaps_methods.c */ SEXP encode_overlaps1( SEXP query_start, SEXP query_width, SEXP query_space, SEXP query_break, SEXP flip_query, SEXP subject_start, SEXP subject_width, SEXP subject_space, SEXP as_matrix, SEXP as_raw ); SEXP RangesList_encode_overlaps( SEXP query_starts, SEXP query_widths, SEXP query_spaces, SEXP query_breaks, SEXP subject_starts, SEXP subject_widths, SEXP subject_spaces ); SEXP Hits_encode_overlaps( SEXP query_starts, SEXP query_widths, SEXP query_spaces, SEXP query_breaks, SEXP subject_starts, SEXP subject_widths, SEXP subject_spaces, SEXP query_hits, SEXP subject_hits, SEXP flip_query ); GenomicAlignments/src/IRanges_stubs.c0000644000175200017520000000003414516004601020674 0ustar00biocbuildbiocbuild#include "_IRanges_stubs.c" GenomicAlignments/src/R_init_GenomicAlignments.c0000644000175200017520000000221414516004601023035 0ustar00biocbuildbiocbuild#include "GenomicAlignments.h" #include #define CALLMETHOD_DEF(fun, numArgs) {#fun, (DL_FUNC) &fun, numArgs} #define REGISTER_CCALLABLE(fun) \ R_RegisterCCallable("GenomicAlignments", #fun, (DL_FUNC) &fun) static const R_CallMethodDef callMethods[] = { /* cigar_utils.c */ CALLMETHOD_DEF(valid_cigar, 2), CALLMETHOD_DEF(explode_cigar_ops, 2), CALLMETHOD_DEF(explode_cigar_op_lengths, 2), CALLMETHOD_DEF(cigar_op_table, 1), CALLMETHOD_DEF(cigar_ranges, 9), CALLMETHOD_DEF(cigar_width, 3), CALLMETHOD_DEF(cigar_narrow, 3), CALLMETHOD_DEF(cigar_qnarrow, 3), /* mapping_methods.c */ CALLMETHOD_DEF(map_query_locs_to_ref_locs, 4), CALLMETHOD_DEF(map_ref_locs_to_query_locs, 4), CALLMETHOD_DEF(ref_locs_to_query_locs, 4), CALLMETHOD_DEF(query_locs_to_ref_locs, 4), /* encodeOverlaps_methods.c */ CALLMETHOD_DEF(encode_overlaps1, 10), CALLMETHOD_DEF(RangesList_encode_overlaps, 7), CALLMETHOD_DEF(Hits_encode_overlaps, 10), {NULL, NULL, 0} }; void R_init_GenomicAlignments(DllInfo *info) { R_registerRoutines(info, NULL, callMethods, NULL, NULL); /* cigar_utils.c */ REGISTER_CCALLABLE(_next_cigar_OP); return; } GenomicAlignments/src/S4Vectors_stubs.c0000644000175200017520000000003614516004601021202 0ustar00biocbuildbiocbuild#include "_S4Vectors_stubs.c" GenomicAlignments/src/cigar_utils.c0000644000175200017520000007564614516004601020456 0ustar00biocbuildbiocbuild#include "GenomicAlignments.h" #include "IRanges_interface.h" #include "S4Vectors_interface.h" #include /* for isdigit() */ /* The 8 supported spaces. */ #define REFERENCE 1 #define REFERENCE_N_REGIONS_REMOVED 2 #define QUERY 3 #define QUERY_BEFORE_HARD_CLIPPING 4 #define QUERY_AFTER_SOFT_CLIPPING 5 #define PAIRWISE 6 #define PAIRWISE_N_REGIONS_REMOVED 7 #define PAIRWISE_DENSE 8 static char errmsg_buf[200]; const char *_get_cigar_parsing_error() { return errmsg_buf; } /* Return the number of chars that was read, or 0 if there is no more char to read (i.e. cigar_string[offset] is '\0'), or -1 in case of a parse error (in which case _get_cigar_parsing_error() can be used to get a pointer to the error message). Zero-length operations are ignored. */ int _next_cigar_OP(const char *cigar_string, int offset, char *OP, int *OPL) { char c; int offset0, opl; if (!cigar_string[offset]) return 0; offset0 = offset; do { /* Extract *OPL */ opl = 0; while (isdigit(c = cigar_string[offset])) { offset++; opl *= 10; opl += c - '0'; } /* Extract *OP */ if (!(*OP = cigar_string[offset])) { snprintf(errmsg_buf, sizeof(errmsg_buf), "unexpected CIGAR end after char %d", offset); return -1; } offset++; } while (opl == 0); *OPL = opl; return offset - offset0; } /* Return the number of chars that was read, or 0 if there is no more char to read (i.e. offset is 0), or -1 in case of a parse error. Zero-length operations are ignored. */ static int prev_cigar_OP(const char *cigar_string, int offset, char *OP, int *OPL) { char c; int offset0, opl, powof10; if (offset == 0) return 0; offset0 = offset; do { /* Extract *OP */ offset--; *OP = cigar_string[offset]; /* Extract *OPL */ if (offset == 0) { snprintf(errmsg_buf, sizeof(errmsg_buf), "no CIGAR operation length before char %d", offset + 1); return -1; } offset--; opl = 0; powof10 = 1; while (offset >= 0 && isdigit(c = cigar_string[offset])) { opl += (c - '0') * powof10; powof10 *= 10; offset--; } offset++; } while (opl == 0); *OPL = opl; return offset0 - offset; } static int ops_lkup_table[256]; static void init_ops_lkup_table(SEXP ops) { int ops_len, i; SEXP ops_elt; char OP; if (ops == R_NilValue) { for (i = 0; i < 256; i++) ops_lkup_table[i] = 1; return; } for (i = 0; i < 256; i++) ops_lkup_table[i] = 0; ops_len = LENGTH(ops); for (i = 0; i < ops_len; i++) { ops_elt = STRING_ELT(ops, i); if (ops_elt == NA_STRING || LENGTH(ops_elt) == 0) error("'ops' contains NAs and/or empty strings"); OP = CHAR(ops_elt)[0]; ops_lkup_table[(unsigned char) OP] = 1; } return; } static int is_in_ops(char OP) { return ops_lkup_table[(unsigned char) OP]; } static int is_visible_in_space(char OP, int space) { if (OP == 'M') return 1; switch (space) { case QUERY_BEFORE_HARD_CLIPPING: if (OP == 'H') return 1; /* fall through */ case QUERY: if (OP == 'S') return 1; /* fall through */ case QUERY_AFTER_SOFT_CLIPPING: if (OP == 'I') return 1; break; case PAIRWISE: if (OP == 'I') return 1; /* fall through */ case REFERENCE: if (OP == 'D' || OP == 'N') return 1; break; case PAIRWISE_N_REGIONS_REMOVED: if (OP == 'I') return 1; /* fall through */ case REFERENCE_N_REGIONS_REMOVED: if (OP == 'D') return 1; } if (OP == '=' || OP == 'X') return 1; return 0; } static void drop_or_append_or_merge_range(int start, int width, int drop_empty_range, int merge_range, int nelt0, IntPairAE *range_buf, const char *OP, CharAEAE *OP_buf) { int buf_nelt, buf_nelt_minus_1, prev_end_plus_1; CharAE *OP_buf_new_elt, *OP_buf_prev_elt; if (drop_empty_range && width == 0) /* Drop. */ return; buf_nelt = IntPairAE_get_nelt(range_buf); if (merge_range && buf_nelt > nelt0) { /* The incoming range should never overlap with the previous incoming range i.e. 'start' should always be > the end of the previous incoming range. */ buf_nelt_minus_1 = buf_nelt - 1; prev_end_plus_1 = range_buf->a->elts[buf_nelt_minus_1] + range_buf->b->elts[buf_nelt_minus_1]; if (start == prev_end_plus_1) { /* Merge. */ range_buf->b->elts[buf_nelt_minus_1] += width; if (OP_buf != NULL) { OP_buf_prev_elt = OP_buf->elts[buf_nelt_minus_1]; CharAE_insert_at(OP_buf_prev_elt, CharAE_get_nelt(OP_buf_prev_elt), *OP); } return; } } /* Append. */ IntPairAE_insert_at(range_buf, buf_nelt, start, width); if (OP_buf != NULL) { OP_buf_new_elt = new_CharAE(1); CharAE_insert_at(OP_buf_new_elt, 0, *OP); CharAEAE_insert_at(OP_buf, buf_nelt, OP_buf_new_elt); } return; } /* Make sure init_ops_lkup_table() is called before parse_cigar_ranges(). */ static const char *parse_cigar_ranges(const char *cigar_string, int space, int pos, int drop_empty_ranges, int reduce_ranges, IntPairAE *range_buf, CharAEAE *OP_buf) { int buf_nelt0, cigar_offset, n, OPL /* Operation Length */, start, width; char OP /* Operation */; buf_nelt0 = IntPairAE_get_nelt(range_buf); cigar_offset = 0; start = pos; while ((n = _next_cigar_OP(cigar_string, cigar_offset, &OP, &OPL))) { if (n == -1) return _get_cigar_parsing_error(); width = is_visible_in_space(OP, space) ? OPL : 0; if (is_in_ops(OP)) drop_or_append_or_merge_range(start, width, drop_empty_ranges, reduce_ranges, buf_nelt0, range_buf, &OP, OP_buf); start += width; cigar_offset += n; } return NULL; } static const char *parse_cigar_width(const char *cigar_string, int space, int *width) { int cigar_offset, n, OPL /* Operation Length */; char OP /* Operation */; *width = cigar_offset = 0; while ((n = _next_cigar_OP(cigar_string, cigar_offset, &OP, &OPL))) { if (n == -1) return _get_cigar_parsing_error(); if (is_visible_in_space(OP, space)) *width += OPL; cigar_offset += n; } return NULL; } /**************************************************************************** * --- .Call ENTRY POINT --- * Args: * cigar: character vector containing the extended CIGAR string for each * read; * ans_type: a single integer specifying the type of answer to return: * 0: 'ans' is a string describing the first validity failure or NULL; * 1: 'ans' is logical vector with TRUE values for valid elements * in 'cigar'. */ SEXP valid_cigar(SEXP cigar, SEXP ans_type) { SEXP ans, cigar_elt; int cigar_len, ans_type0, i, width; const char *cigar_string, *errmsg; char string_buf[200]; cigar_len = LENGTH(cigar); ans_type0 = INTEGER(ans_type)[0]; if (ans_type0 == 1) PROTECT(ans = NEW_LOGICAL(cigar_len)); else ans = R_NilValue; for (i = 0; i < cigar_len; i++) { cigar_elt = STRING_ELT(cigar, i); if (cigar_elt == NA_STRING) { if (ans_type0 == 1) LOGICAL(ans)[i] = 1; continue; } cigar_string = CHAR(cigar_elt); if (strcmp(cigar_string, "*") == 0) { if (ans_type0 == 1) LOGICAL(ans)[i] = 1; continue; } /* We use parse_cigar_width() here just for its ability to parse and detect ill-formed CIGAR strings */ errmsg = parse_cigar_width(cigar_string, 0L, &width); if (ans_type0 == 1) { LOGICAL(ans)[i] = errmsg == NULL; continue; } if (errmsg != NULL) { snprintf(string_buf, sizeof(string_buf), "element %d is invalid (%s)", i + 1, errmsg); return mkString(string_buf); } } if (ans_type0 == 1) UNPROTECT(1); return ans; } /**************************************************************************** * explode_cigar_ops() and explode_cigar_op_lengths() */ /* Make sure init_ops_lkup_table() is called before split_cigar_string(). */ static const char *split_cigar_string(const char *cigar_string, CharAE *OPbuf, IntAE *OPLbuf) { int offset, n, OPL /* Operation Length */; char OP /* Operation */; offset = 0; while ((n = _next_cigar_OP(cigar_string, offset, &OP, &OPL))) { if (n == -1) return _get_cigar_parsing_error(); if (is_in_ops(OP)) { if (OPbuf != NULL) CharAE_insert_at(OPbuf, CharAE_get_nelt(OPbuf), OP); if (OPLbuf != NULL) IntAE_insert_at(OPLbuf, IntAE_get_nelt(OPLbuf), OPL); } offset += n; } return NULL; } /* --- .Call ENTRY POINTS --- * - explode_cigar_ops() * - explode_cigar_op_lengths() * Args: * cigar: character vector containing the extended CIGAR strings to * explode. * ops: NULL or a character vector containing the CIGAR operations to * actually consider. If NULL, then all CIGAR operations are * considered. * Both functions return a list of the same length as 'cigar' where each * list element is a character vector (for explode_cigar_ops()) or an integer * vector (for explode_cigar_op_lengths()). The 2 lists have the same shape, * that is, same length() and same elementNROWS(). The i-th character vector * in the list returned by explode_cigar_ops() contains one single-letter * string per CIGAR operation in 'cigar[i]'. The i-th integer vector in the * list returned by explode_cigar_op_lengths() contains the corresponding * CIGAR operation lengths. Zero-length operations or operations not listed * in 'ops' are ignored. */ SEXP explode_cigar_ops(SEXP cigar, SEXP ops) { SEXP ans, cigar_elt, ans_elt, ans_elt_elt; int cigar_len, ans_elt_len, i, j; CharAE *OPbuf; const char *cigar_string, *errmsg; cigar_len = LENGTH(cigar); init_ops_lkup_table(ops); PROTECT(ans = NEW_LIST(cigar_len)); OPbuf = new_CharAE(0); for (i = 0; i < cigar_len; i++) { cigar_elt = STRING_ELT(cigar, i); if (cigar_elt == NA_STRING) { UNPROTECT(1); error("'cigar[%d]' is NA", i + 1); } cigar_string = CHAR(cigar_elt); if (strcmp(cigar_string, "*") == 0) { UNPROTECT(1); error("'cigar[%d]' is \"*\"", i + 1); } CharAE_set_nelt(OPbuf, 0); errmsg = split_cigar_string(cigar_string, OPbuf, NULL); if (errmsg != NULL) { UNPROTECT(1); error("in 'cigar[%d]': %s", i + 1, errmsg); } ans_elt_len = CharAE_get_nelt(OPbuf); PROTECT(ans_elt = NEW_CHARACTER(ans_elt_len)); for (j = 0; j < ans_elt_len; j++) { PROTECT(ans_elt_elt = mkCharLen(OPbuf->elts + j, 1)); SET_STRING_ELT(ans_elt, j, ans_elt_elt); UNPROTECT(1); } SET_VECTOR_ELT(ans, i, ans_elt); UNPROTECT(1); } UNPROTECT(1); return ans; } SEXP explode_cigar_op_lengths(SEXP cigar, SEXP ops) { SEXP ans, cigar_elt, ans_elt; int cigar_len, i; IntAE *OPLbuf; const char *cigar_string, *errmsg; cigar_len = LENGTH(cigar); init_ops_lkup_table(ops); PROTECT(ans = NEW_LIST(cigar_len)); OPLbuf = new_IntAE(0, 0, 0); for (i = 0; i < cigar_len; i++) { cigar_elt = STRING_ELT(cigar, i); if (cigar_elt == NA_STRING) { UNPROTECT(1); error("'cigar[%d]' is NA", i + 1); } cigar_string = CHAR(cigar_elt); if (strcmp(cigar_string, "*") == 0) { UNPROTECT(1); error("'cigar[%d]' is \"*\"", i + 1); } IntAE_set_nelt(OPLbuf, 0); errmsg = split_cigar_string(cigar_string, NULL, OPLbuf); if (errmsg != NULL) { UNPROTECT(1); error("in 'cigar[%d]': %s", i + 1, errmsg); } PROTECT(ans_elt = new_INTEGER_from_IntAE(OPLbuf)); SET_VECTOR_ELT(ans, i, ans_elt); UNPROTECT(1); } UNPROTECT(1); return ans; } /**************************************************************************** * cigar_op_table() */ static const char *cigar_string_op_table(SEXP cigar_string, const char *allOPs, int *table_row, int table_nrow) { const char *cig0, *tmp; int offset, n, OPL /* Operation Length */; char OP /* Operation */; if (cigar_string == NA_STRING) return "CIGAR string is NA"; if (LENGTH(cigar_string) == 0) return "CIGAR string is empty"; cig0 = CHAR(cigar_string); offset = 0; while ((n = _next_cigar_OP(cig0, offset, &OP, &OPL))) { if (n == -1) return _get_cigar_parsing_error(); tmp = strchr(allOPs, (int) OP); if (tmp == NULL) { snprintf(errmsg_buf, sizeof(errmsg_buf), "unknown CIGAR operation '%c' at char %d", OP, offset + 1); return errmsg_buf; } *(table_row + (tmp - allOPs) * table_nrow) += OPL; offset += n; } return NULL; } /* --- .Call ENTRY POINT --- * Args: * cigar: character vector containing the extended CIGAR string for each * read; * Return an integer matrix with the number of rows equal to the length of * 'cigar' and 9 columns, one for each extended CIGAR operation containing * a frequency count for the operations for each element of 'cigar'. */ SEXP cigar_op_table(SEXP cigar) { SEXP cigar_string, ans, ans_dimnames, ans_colnames; int cigar_len, allOPs_len, i, j, *ans_row; const char *allOPs = "MIDNSHP=X", *errmsg; char OPstrbuf[2]; cigar_len = LENGTH(cigar); allOPs_len = strlen(allOPs); PROTECT(ans = allocMatrix(INTSXP, cigar_len, allOPs_len)); memset(INTEGER(ans), 0, LENGTH(ans) * sizeof(int)); ans_row = INTEGER(ans); for (i = 0, ans_row = INTEGER(ans); i < cigar_len; i++, ans_row++) { cigar_string = STRING_ELT(cigar, i); if (cigar_string == NA_STRING) { INTEGER(ans)[i] = NA_INTEGER; continue; } errmsg = cigar_string_op_table(cigar_string, allOPs, ans_row, cigar_len); if (errmsg != NULL) { UNPROTECT(1); error("in 'cigar[%d]': %s", i + 1, errmsg); } } PROTECT(ans_colnames = NEW_CHARACTER(allOPs_len)); OPstrbuf[1] = '\0'; for (j = 0; j < allOPs_len; j++) { OPstrbuf[0] = allOPs[j]; SET_STRING_ELT(ans_colnames, j, mkChar(OPstrbuf)); } PROTECT(ans_dimnames = NEW_LIST(2)); SET_ELEMENT(ans_dimnames, 0, R_NilValue); SET_ELEMENT(ans_dimnames, 1, ans_colnames); SET_DIMNAMES(ans, ans_dimnames); UNPROTECT(3); return ans; } /**************************************************************************** * cigar_ranges() */ static SEXP make_list_of_IRanges(const IntPairAEAE *range_buf, SEXP names) { SEXP ans, ans_names; PROTECT(ans = new_list_of_IRanges_from_IntPairAEAE("IRanges", range_buf)); PROTECT(ans_names = duplicate(names)); SET_NAMES(ans, ans_names); UNPROTECT(2); return ans; } static SEXP make_CompressedIRangesList(const IntPairAE *range_buf, const CharAEAE *OP_buf, SEXP breakpoints) { SEXP ans, ans_unlistData, ans_unlistData_names, ans_partitioning; PROTECT(ans_unlistData = new_IRanges_from_IntPairAE("IRanges", range_buf)); if (OP_buf != NULL) { PROTECT(ans_unlistData_names = new_CHARACTER_from_CharAEAE(OP_buf)); set_IRanges_names(ans_unlistData, ans_unlistData_names); UNPROTECT(1); } PROTECT(ans_partitioning = new_PartitioningByEnd("PartitioningByEnd", breakpoints, NULL)); PROTECT(ans = new_CompressedList( "CompressedIRangesList", ans_unlistData, ans_partitioning)); UNPROTECT(3); return ans; } /* --- .Call ENTRY POINT --- * Args: * cigar: character vector containing extended CIGAR strings. * flag: NULL or an integer vector of the same length as 'cigar' * containing the SAM flag for each read. Serves only as a way to * indicate whether a read is mapped or not. According to the SAM * Spec v1.4, flag bit 0x4 is the only reliable place to tell * whether a segment (or read) is mapped (bit is 0) or not (bit is 1). * space: single integer indicating one of the 8 supported spaces (defined * at the top of this file). * pos: integer vector of the same length as 'cigar' (or of length 1) * containing the 1-based leftmost position/coordinate of the * clipped read sequences. * f: NULL or a factor of length 'cigar'. If NULL, then the ranges are * grouped by alignment and stored in a CompressedIRangesList object * with 1 list element per element in 'cigar'. If a factor, then they * are grouped by factor level and stored in an ordinary list of * IRanges objects with 1 list element per level in 'f' and named * with those levels. * ops: NULL or a character vector containing the CIGAR operations to * translate to ranges. If NULL, then all CIGAR operations are * translated. * drop_empty_ranges: TRUE or FALSE. * reduce_ranges: TRUE or FALSE. * with_ops: TRUE or FALSE indicating whether the returned ranges should be * named with their corresponding CIGAR operation. * * Returns either a CompressedIRangesList object of the same length as 'cigar' * (if 'f' is NULL) or an ordinary list of IRanges objects with 1 list element * per level in 'f' (if 'f' is a factor). This list is then turned into a * SimpleIRangesList object in R. */ SEXP cigar_ranges(SEXP cigar, SEXP flag, SEXP space, SEXP pos, SEXP f, SEXP ops, SEXP drop_empty_ranges, SEXP reduce_ranges, SEXP with_ops) { SEXP ans, ans_breakpoints, f_levels, cigar_elt; int cigar_len, space0, pos_len, f_is_NULL, ans_len, *breakpoint, drop_empty_ranges0, reduce_ranges0, with_ops0, i; IntPairAE *range_buf1; IntPairAEAE *range_buf2; CharAEAE *OP_buf; const int *flag_elt, *pos_elt, *f_elt; const char *cigar_string, *errmsg; cigar_len = LENGTH(cigar); if (flag != R_NilValue) flag_elt = INTEGER(flag); init_ops_lkup_table(ops); space0 = INTEGER(space)[0]; pos_len = LENGTH(pos); pos_elt = INTEGER(pos); f_is_NULL = f == R_NilValue; if (f_is_NULL) { ans_len = cigar_len; /* We will typically generate at least 'cigar_len' ranges. */ range_buf1 = new_IntPairAE(ans_len, 0); PROTECT(ans_breakpoints = NEW_INTEGER(ans_len)); breakpoint = INTEGER(ans_breakpoints); } else { f_levels = GET_LEVELS(f); ans_len = LENGTH(f_levels); range_buf2 = new_IntPairAEAE(ans_len, ans_len); f_elt = INTEGER(f); } drop_empty_ranges0 = LOGICAL(drop_empty_ranges)[0]; reduce_ranges0 = LOGICAL(reduce_ranges)[0]; with_ops0 = LOGICAL(with_ops)[0]; if (with_ops0 && f_is_NULL) { OP_buf = new_CharAEAE(cigar_len, 0); } else { OP_buf = NULL; } for (i = 0; i < cigar_len; i++) { if (flag != R_NilValue) { if (*flag_elt == NA_INTEGER) { if (f_is_NULL) UNPROTECT(1); error("'flag' contains NAs"); } if (*flag_elt & 0x004) { /* The CIGAR of an unmapped read doesn't produce any range i.e. it's treated as an empty CIGAR. */ goto for_tail; } } cigar_elt = STRING_ELT(cigar, i); if (cigar_elt == NA_STRING) { if (f_is_NULL) UNPROTECT(1); error("'cigar[%d]' is NA", i + 1); } cigar_string = CHAR(cigar_elt); if (strcmp(cigar_string, "*") == 0) { if (f_is_NULL) UNPROTECT(1); error("'cigar[%d]' is \"*\"", i + 1); } if (*pos_elt == NA_INTEGER || *pos_elt == 0) { if (f_is_NULL) UNPROTECT(1); error("'pos[%d]' is NA or 0", i + 1); } if (!f_is_NULL) { if (*f_elt == NA_INTEGER) error("'f[%d]' is NA", i + 1); range_buf1 = range_buf2->elts[*f_elt - 1]; } errmsg = parse_cigar_ranges(cigar_string, space0, *pos_elt, drop_empty_ranges0, reduce_ranges0, range_buf1, OP_buf); if (errmsg != NULL) { if (f_is_NULL) UNPROTECT(1); error("in 'cigar[%d]': %s", i + 1, errmsg); } for_tail: if (flag != R_NilValue) flag_elt++; if (pos_len != 1) pos_elt++; if (f_is_NULL) *(breakpoint++) = IntPairAE_get_nelt(range_buf1); else f_elt++; } if (!f_is_NULL) return make_list_of_IRanges(range_buf2, f_levels); PROTECT(ans = make_CompressedIRangesList(range_buf1, OP_buf, ans_breakpoints)); UNPROTECT(2); return ans; } /**************************************************************************** * --- .Call ENTRY POINT --- * Args: * cigar, flag, space: see cigar_ranges() function above. * Return an integer vector of the same length as 'cigar' containing the * widths of the alignments as inferred from the cigar information. */ SEXP cigar_width(SEXP cigar, SEXP flag, SEXP space) { SEXP ans, cigar_elt; int cigar_len, space0, i, *ans_elt; const int *flag_elt; const char *cigar_string, *errmsg; cigar_len = LENGTH(cigar); if (flag != R_NilValue) flag_elt = INTEGER(flag); space0 = INTEGER(space)[0]; PROTECT(ans = NEW_INTEGER(cigar_len)); for (i = 0, ans_elt = INTEGER(ans); i < cigar_len; i++, ans_elt++) { if (flag != R_NilValue) { if (*flag_elt == NA_INTEGER) { UNPROTECT(1); error("'flag' contains NAs"); } if (*flag_elt & 0x004) { *ans_elt = NA_INTEGER; goto for_tail; } } cigar_elt = STRING_ELT(cigar, i); if (cigar_elt == NA_STRING) { *ans_elt = NA_INTEGER; goto for_tail; } cigar_string = CHAR(cigar_elt); if (strcmp(cigar_string, "*") == 0) { *ans_elt = NA_INTEGER; goto for_tail; } errmsg = parse_cigar_width(cigar_string, space0, ans_elt); if (errmsg != NULL) { UNPROTECT(1); error("in 'cigar[%d]': %s", i + 1, errmsg); } for_tail: if (flag != R_NilValue) flag_elt++; } UNPROTECT(1); return ans; } /**************************************************************************** * cigar_narrow() */ static const char *Lnarrow_cigar_string(SEXP cigar_string, int *Lwidth, int *Loffset, int *rshift) { const char *cig0; int offset, n, OPL /* Operation Length */; char OP /* Operation */; if (cigar_string == NA_STRING) return "CIGAR string is NA"; if (LENGTH(cigar_string) == 0) return "CIGAR string is empty"; cig0 = CHAR(cigar_string); *rshift = offset = 0; while ((n = _next_cigar_OP(cig0, offset, &OP, &OPL))) { if (n == -1) return _get_cigar_parsing_error(); switch (OP) { /* Alignment match (can be a sequence match or mismatch) */ case 'M': case '=': case 'X': if (*Lwidth < OPL) { *Loffset = offset; *rshift += *Lwidth; return NULL; } *Lwidth -= OPL; *rshift += OPL; break; /* Insertion to the reference or soft/hard clip on the read */ case 'I': case 'S': case 'H': break; /* Deletion (or skipped region) from the reference */ case 'D': case 'N': if (*Lwidth < OPL) *Lwidth = 0; else *Lwidth -= OPL; *rshift += OPL; break; /* Silent deletion from the padded reference */ case 'P': break; default: snprintf(errmsg_buf, sizeof(errmsg_buf), "unknown CIGAR operation '%c' at char %d", OP, offset + 1); return errmsg_buf; } offset += n; } snprintf(errmsg_buf, sizeof(errmsg_buf), "CIGAR is empty after narrowing"); return errmsg_buf; } static const char *Rnarrow_cigar_string(SEXP cigar_string, int *Rwidth, int *Roffset) { const char *cig0; int offset, n, OPL /* Operation Length */; char OP /* Operation */; if (cigar_string == NA_STRING) return "CIGAR string is NA"; if (LENGTH(cigar_string) == 0) return "CIGAR string is empty"; cig0 = CHAR(cigar_string); offset = LENGTH(cigar_string); while ((n = prev_cigar_OP(cig0, offset, &OP, &OPL))) { if (n == -1) return _get_cigar_parsing_error(); offset -= n; switch (OP) { /* Alignment match (can be a sequence match or mismatch) */ case 'M': case '=': case 'X': if (*Rwidth < OPL) { *Roffset = offset; return NULL; } *Rwidth -= OPL; break; /* Insertion to the reference or soft/hard clip on the read */ case 'I': case 'S': case 'H': break; /* Deletion (or skipped region) from the reference */ case 'D': case 'N': if (*Rwidth < OPL) *Rwidth = 0; else *Rwidth -= OPL; break; /* Silent deletion from the padded reference */ case 'P': break; default: snprintf(errmsg_buf, sizeof(errmsg_buf), "unknown CIGAR operation '%c' at char %d", OP, offset + 1); return errmsg_buf; } } snprintf(errmsg_buf, sizeof(errmsg_buf), "CIGAR is empty after narrowing"); return errmsg_buf; } #define CIGAR_BUF_LENGTH 250000 /* FIXME: 'cigar_buf' is under the risk of a buffer overflow! */ static const char *narrow_cigar_string(SEXP cigar_string, int Lwidth, int Rwidth, char *cigar_buf, int *rshift) { int Loffset, Roffset, buf_offset; const char *cig0; int offset, n, OPL /* Operation Length */, ret; char OP /* Operation */; const char *errmsg; size_t size; //Rprintf("narrow_cigar_string():\n"); errmsg = Lnarrow_cigar_string(cigar_string, &Lwidth, &Loffset, rshift); if (errmsg != NULL) return errmsg; //Rprintf(" Lwidth=%d Loffset=%d *rshift=%d\n", // Lwidth, Loffset, *rshift); errmsg = Rnarrow_cigar_string(cigar_string, &Rwidth, &Roffset); if (errmsg != NULL) return errmsg; //Rprintf(" Rwidth=%d Roffset=%d\n", Rwidth, Roffset); if (Roffset < Loffset) { snprintf(errmsg_buf, sizeof(errmsg_buf), "CIGAR is empty after narrowing"); return errmsg_buf; } buf_offset = 0; cig0 = CHAR(cigar_string); for (offset = Loffset; offset <= Roffset; offset += n) { n = _next_cigar_OP(cig0, offset, &OP, &OPL); if (offset == Loffset) OPL -= Lwidth; if (offset == Roffset) OPL -= Rwidth; if (OPL <= 0) { snprintf(errmsg_buf, sizeof(errmsg_buf), "CIGAR is empty after narrowing"); return errmsg_buf; } size = CIGAR_BUF_LENGTH - buf_offset; ret = snprintf(cigar_buf + buf_offset, size, "%d%c", OPL, OP); if (ret >= size) { snprintf(errmsg_buf, sizeof(errmsg_buf), "'cigar_buf' overflow"); return errmsg_buf; } buf_offset += ret; } return NULL; } /* --- .Call ENTRY POINT --- */ SEXP cigar_narrow(SEXP cigar, SEXP left_width, SEXP right_width) { SEXP ans, ans_cigar, ans_cigar_string, ans_rshift, cigar_string; int cigar_len, i; static char cigar_buf[CIGAR_BUF_LENGTH]; const char *errmsg; cigar_len = LENGTH(cigar); PROTECT(ans_cigar = NEW_CHARACTER(cigar_len)); PROTECT(ans_rshift = NEW_INTEGER(cigar_len)); for (i = 0; i < cigar_len; i++) { cigar_string = STRING_ELT(cigar, i); if (cigar_string == NA_STRING) { SET_STRING_ELT(ans_cigar, i, NA_STRING); INTEGER(ans_rshift)[i] = NA_INTEGER; continue; } errmsg = narrow_cigar_string(cigar_string, INTEGER(left_width)[i], INTEGER(right_width)[i], cigar_buf, INTEGER(ans_rshift) + i); if (errmsg != NULL) { UNPROTECT(2); error("in 'cigar[%d]': %s", i + 1, errmsg); } PROTECT(ans_cigar_string = mkChar(cigar_buf)); SET_STRING_ELT(ans_cigar, i, ans_cigar_string); UNPROTECT(1); } PROTECT(ans = NEW_LIST(2)); SET_VECTOR_ELT(ans, 0, ans_cigar); SET_VECTOR_ELT(ans, 1, ans_rshift); UNPROTECT(3); return ans; } /**************************************************************************** * cigar_qnarrow() */ static const char *Lqnarrow_cigar_string(SEXP cigar_string, int *Lqwidth, int *Loffset, int *rshift) { const char *cig0; int offset, n, OPL /* Operation Length */; char OP /* Operation */; if (cigar_string == NA_STRING) return "CIGAR string is NA"; if (LENGTH(cigar_string) == 0) return "CIGAR string is empty"; cig0 = CHAR(cigar_string); *rshift = offset = 0; while ((n = _next_cigar_OP(cig0, offset, &OP, &OPL))) { if (n == -1) return _get_cigar_parsing_error(); switch (OP) { /* Alignment match (can be a sequence match or mismatch) */ case 'M': case '=': case 'X': if (*Lqwidth < OPL) { *Loffset = offset; *rshift += *Lqwidth; return NULL; } *Lqwidth -= OPL; *rshift += OPL; break; /* Insertion to the reference or soft/hard clip on the read */ case 'I': case 'S': case 'H': if (*Lqwidth < OPL) { *Loffset = offset; return NULL; } *Lqwidth -= OPL; break; /* Deletion (or skipped region) from the reference */ case 'D': case 'N': *rshift += OPL; break; /* Silent deletion from the padded reference */ case 'P': break; default: snprintf(errmsg_buf, sizeof(errmsg_buf), "unknown CIGAR operation '%c' at char %d", OP, offset + 1); return errmsg_buf; } offset += n; } snprintf(errmsg_buf, sizeof(errmsg_buf), "CIGAR is empty after qnarrowing"); return errmsg_buf; } static const char *Rqnarrow_cigar_string(SEXP cigar_string, int *Rqwidth, int *Roffset) { const char *cig0; int offset, n, OPL /* Operation Length */; char OP /* Operation */; if (cigar_string == NA_STRING) return "CIGAR string is NA"; if (LENGTH(cigar_string) == 0) return "CIGAR string is empty"; cig0 = CHAR(cigar_string); offset = LENGTH(cigar_string); while ((n = prev_cigar_OP(cig0, offset, &OP, &OPL))) { if (n == -1) return _get_cigar_parsing_error(); offset -= n; switch (OP) { /* M, =, X, I, S, H */ case 'M': case '=': case 'X': case 'I': case 'S': case 'H': if (*Rqwidth < OPL) { *Roffset = offset; return NULL; } *Rqwidth -= OPL; break; /* Deletion (or skipped region) from the reference, or silent deletion from the padded reference */ case 'D': case 'N': case 'P': break; default: snprintf(errmsg_buf, sizeof(errmsg_buf), "unknown CIGAR operation '%c' at char %d", OP, offset + 1); return errmsg_buf; } } snprintf(errmsg_buf, sizeof(errmsg_buf), "CIGAR is empty after qnarrowing"); return errmsg_buf; } /* FIXME: 'cigar_buf' is under the risk of a buffer overflow! */ static const char *qnarrow_cigar_string(SEXP cigar_string, int Lqwidth, int Rqwidth, char *cigar_buf, int *rshift) { int Loffset, Roffset, buf_offset; const char *cig0; int offset, n, OPL /* Operation Length */, ret; char OP /* Operation */; const char *errmsg; size_t size; //Rprintf("qnarrow_cigar_string():\n"); errmsg = Lqnarrow_cigar_string(cigar_string, &Lqwidth, &Loffset, rshift); if (errmsg != NULL) return errmsg; //Rprintf(" Lqwidth=%d Loffset=%d *rshift=%d\n", // Lqwidth, Loffset, *rshift); errmsg = Rqnarrow_cigar_string(cigar_string, &Rqwidth, &Roffset); if (errmsg != NULL) return errmsg; //Rprintf(" Rqwidth=%d Roffset=%d\n", Rqwidth, Roffset); if (Roffset < Loffset) { snprintf(errmsg_buf, sizeof(errmsg_buf), "CIGAR is empty after qnarrowing"); return errmsg_buf; } buf_offset = 0; cig0 = CHAR(cigar_string); for (offset = Loffset; offset <= Roffset; offset += n) { n = _next_cigar_OP(cig0, offset, &OP, &OPL); if (offset == Loffset) OPL -= Lqwidth; if (offset == Roffset) OPL -= Rqwidth; if (OPL <= 0) { snprintf(errmsg_buf, sizeof(errmsg_buf), "CIGAR is empty after qnarrowing"); return errmsg_buf; } size = CIGAR_BUF_LENGTH - buf_offset; ret = snprintf(cigar_buf + buf_offset, size, "%d%c", OPL, OP); if (ret >= size) { snprintf(errmsg_buf, sizeof(errmsg_buf), "'cigar_buf' overflow"); return errmsg_buf; } buf_offset += ret; } return NULL; } /* --- .Call ENTRY POINT --- * Return a list of 2 elements: 1st elt is the narrowed cigar vector, 2nd elt * is the 'rshift' vector i.e. the integer vector of the same length as 'cigar' * that would need to be added to the 'pos' field of a SAM/BAM file as a * consequence of this narrowing. */ SEXP cigar_qnarrow(SEXP cigar, SEXP left_qwidth, SEXP right_qwidth) { SEXP ans, ans_cigar, ans_cigar_string, ans_rshift, cigar_string; int cigar_len, i; static char cigar_buf[CIGAR_BUF_LENGTH]; const char *errmsg; cigar_len = LENGTH(cigar); PROTECT(ans_cigar = NEW_CHARACTER(cigar_len)); PROTECT(ans_rshift = NEW_INTEGER(cigar_len)); for (i = 0; i < cigar_len; i++) { cigar_string = STRING_ELT(cigar, i); if (cigar_string == NA_STRING) { SET_STRING_ELT(ans_cigar, i, NA_STRING); INTEGER(ans_rshift)[i] = NA_INTEGER; continue; } errmsg = qnarrow_cigar_string(cigar_string, INTEGER(left_qwidth)[i], INTEGER(right_qwidth)[i], cigar_buf, INTEGER(ans_rshift) + i); if (errmsg != NULL) { UNPROTECT(2); error("in 'cigar[%d]': %s", i + 1, errmsg); } PROTECT(ans_cigar_string = mkChar(cigar_buf)); SET_STRING_ELT(ans_cigar, i, ans_cigar_string); UNPROTECT(1); } PROTECT(ans = NEW_LIST(2)); SET_VECTOR_ELT(ans, 0, ans_cigar); SET_VECTOR_ELT(ans, 1, ans_rshift); UNPROTECT(3); return ans; } GenomicAlignments/src/coordinate_mapping_methods.c0000644000175200017520000002537014547043765023545 0ustar00biocbuildbiocbuild#include "GenomicAlignments.h" #include "IRanges_interface.h" #include "S4Vectors_interface.h" /**************************************************************************** * Mapping from genome (reference) to local space. */ /* Returns integer position of 'ref_loc' mapped to local space. * If 'ref_loc' cannot be mapped NA is returned. */ int to_query(int ref_loc, const char *cig0, int pos, Rboolean narrow_left) { int query_loc = ref_loc - pos + 1; int n, offset = 0, OPL, query_consumed = 0; char OP; while (query_consumed < query_loc && (n = _next_cigar_OP(cig0, offset, &OP, &OPL))) { switch (OP) { /* Alignment match (can be a sequence match or mismatch) */ case 'M': case '=': case 'X': query_consumed += OPL; break; /* Insertion to the reference */ case 'I': /* Soft clip on the read */ case 'S': query_loc += OPL; query_consumed += OPL; break; /* Deletion from the reference */ case 'D': /* Skipped region from reference; narrow to query */ case 'N': { Rboolean query_loc_past_gap = query_loc - query_consumed > OPL; if (query_loc_past_gap) { query_loc -= OPL; } else { if (narrow_left) { query_loc = query_consumed; } else { query_loc = query_consumed + 1; } } } break; /* Hard clip on the read */ case 'H': break; /* Silent deletion from the padded reference */ case 'P': break; default: break; } offset += n; } if (query_loc <= 0 || n == 0) query_loc = NA_INTEGER; return query_loc; } /**************************************************************************** * --- .Call ENTRY POINT --- * Args: * ref_locs : global positions in the reference to map * cigar : character string containing the extended CIGAR; * pos : reference position at which the query alignment begins * (after clip) * narrow_left: whether to narrow to the left (or right) side of a gap * * Returns an integer vector of local query positions. This assumes the * reference positions actually occur in the read alignment region, * outside of any deletions or insertions. */ SEXP ref_locs_to_query_locs(SEXP ref_locs, SEXP cigar, SEXP pos, SEXP narrow_left) { int nlocs, i; SEXP query_locs; nlocs = LENGTH(ref_locs); PROTECT(query_locs = allocVector(INTSXP, nlocs)); for (i = 0; i < nlocs; i++) { const char *cig_i = CHAR(STRING_ELT(cigar, i)); INTEGER(query_locs)[i] = to_query(INTEGER(ref_locs)[i], cig_i, INTEGER(pos)[i], asLogical(narrow_left)); } UNPROTECT(1); return query_locs; } /**************************************************************************** * --- .Call ENTRY POINT --- * Args: * ref_locs : global positions in the reference to map * cigar : character string containing the extended CIGAR; * pos : reference position at which the query alignment begins * (after clip) * narrow_left: whether to narrow to the left (or right) side of a gap * * Returns a list of length 4: * - start of local query position * - end of local query position * - index of 'start' used in match ('from_hits') * - index of 'pos' used in match ('to_hits') * All list elements are integer vectors. This assumes that the reference * positions actually occur in the read alignment region, outside of * any deletions or insertions. */ SEXP map_ref_locs_to_query_locs(SEXP start, SEXP end, SEXP cigar, SEXP pos) { SEXP ans, ans_start, ans_end, ans_qhits, ans_shits; IntAE *sbuf, *ebuf, *qhbuf, *shbuf; int i, j, s, e; int nlocs = LENGTH(start); int ncigar = LENGTH(cigar); sbuf = new_IntAE(0, 0, 0); ebuf = new_IntAE(0, 0, 0); qhbuf = new_IntAE(0, 0, 0); shbuf = new_IntAE(0, 0, 0); for (i = 0; i < nlocs; i++) { for (j = 0; j < ncigar; j++) { const char *cig_j = CHAR(STRING_ELT(cigar, j)); int pos_j = INTEGER(pos)[j]; s = to_query(INTEGER(start)[i], cig_j, pos_j, FALSE); if (s == NA_INTEGER) continue; e = to_query(INTEGER(end)[i], cig_j, pos_j, TRUE); if (e == NA_INTEGER) continue; IntAE_insert_at(sbuf, IntAE_get_nelt(sbuf), s); IntAE_insert_at(ebuf, IntAE_get_nelt(ebuf), e); IntAE_insert_at(qhbuf, IntAE_get_nelt(qhbuf), i + 1); IntAE_insert_at(shbuf, IntAE_get_nelt(shbuf), j + 1); } } PROTECT(ans = NEW_LIST(4)); PROTECT(ans_start = new_INTEGER_from_IntAE(sbuf)); PROTECT(ans_end = new_INTEGER_from_IntAE(ebuf)); PROTECT(ans_qhits = new_INTEGER_from_IntAE(qhbuf)); PROTECT(ans_shits = new_INTEGER_from_IntAE(shbuf)); SET_VECTOR_ELT(ans, 0, ans_start); SET_VECTOR_ELT(ans, 1, ans_end); SET_VECTOR_ELT(ans, 2, ans_qhits); SET_VECTOR_ELT(ans, 3, ans_shits); UNPROTECT(5); return ans; } /**************************************************************************** * Mapping from local to genome (reference) space. */ /* Returns integer position of 'query_loc' mapped to genome-based space. * If 'query_loc' cannot be mapped NA is returned. */ int to_ref(int query_loc, const char *cig0, int pos, Rboolean narrow_left) { int ref_loc = query_loc + pos - 1; int n, offset = 0, OPL, query_consumed = 0; char OP; while (query_consumed < query_loc && (n = _next_cigar_OP(cig0, offset, &OP, &OPL))) { switch (OP) { /* Alignment match (can be a sequence match or mismatch) */ case 'M': case '=': case 'X': query_consumed += OPL; break; /* Insertion to the reference */ case 'I': { int width_from_insertion_start = query_loc - query_consumed; Rboolean query_loc_past_insertion = width_from_insertion_start > OPL; if (query_loc_past_insertion) { ref_loc -= OPL; } else { ref_loc -= width_from_insertion_start; if (!narrow_left) { ref_loc += 1; } } query_consumed += OPL; break; } /* Soft clip on the read */ case 'S': query_consumed += OPL; break; /* Deletion from the reference */ case 'D': case 'N': /* Skipped region from reference; narrow to query */ ref_loc += OPL; break; /* Hard clip on the read */ case 'H': break; /* Silent deletion from the padded reference */ case 'P': break; default: break; } offset += n; } if (n == 0) ref_loc = NA_INTEGER; return ref_loc; } /**************************************************************************** * --- .Call ENTRY POINT --- * Args: * query_locs: local positions in the read that we will map * cigar: character string containing the extended CIGAR; * pos: reference position at which the query alignment begins * (after clip) * narrow_left: whether to narrow to the left (or right) side of a gap * Returns an integer vector of local query positions. This assumes * that the reference positions actually occur in the read alignment region, * outside of any deletions or insertions. */ SEXP query_locs_to_ref_locs(SEXP query_locs, SEXP cigar, SEXP pos, SEXP narrow_left) { int nlocs, i; SEXP ref_locs; nlocs = LENGTH(query_locs); PROTECT(ref_locs = allocVector(INTSXP, nlocs)); for (i = 0; i < nlocs; i++) { const char *cig_i = CHAR(STRING_ELT(cigar, i)); INTEGER(ref_locs)[i] = to_ref(INTEGER(query_locs)[i], cig_i, INTEGER(pos)[i], asLogical(narrow_left)); } UNPROTECT(1); return ref_locs; } /**************************************************************************** * --- .Call ENTRY POINT --- * Args: * query_locs: local positions in the read that we will map * cigar: character string containing the extended CIGAR; * pos: reference position at which the query alignment begins * (after clip) * narrow_left: whether to narrow to the left (or right) side of a gap * Returns a list of length 4: * - start of local query position * - end of local query position * - index of 'start' used in match ('from_hits') * - index of 'pos' used in match ('to_hits') * All list elements are integer vectors. This assumes that the reference * positions actually occur in the read alignment region, outside of * any deletions or insertions. */ SEXP map_query_locs_to_ref_locs(SEXP start, SEXP end, SEXP cigar, SEXP pos) { SEXP ans, ans_start, ans_end, ans_qhits, ans_shits; IntAE *sbuf, *ebuf, *qhbuf, *shbuf; int i, j, s, e, nlocs, ncigar; nlocs = LENGTH(start); ncigar = LENGTH(cigar); sbuf = new_IntAE(0, 0, 0); ebuf = new_IntAE(0, 0, 0); qhbuf = new_IntAE(0, 0, 0); shbuf = new_IntAE(0, 0, 0); for (i = 0; i < nlocs; i++) { for (j = 0; j < ncigar; j++) { const char *cig_j = CHAR(STRING_ELT(cigar, j)); int pos_j = INTEGER(pos)[j]; s = to_ref(INTEGER(start)[i], cig_j, pos_j, FALSE); if (s == NA_INTEGER) break; e = to_ref(INTEGER(end)[i], cig_j, pos_j, TRUE); if (e == NA_INTEGER) break; IntAE_insert_at(sbuf, IntAE_get_nelt(sbuf), s); IntAE_insert_at(ebuf, IntAE_get_nelt(ebuf), e); IntAE_insert_at(qhbuf, IntAE_get_nelt(qhbuf), i + 1); IntAE_insert_at(shbuf, IntAE_get_nelt(shbuf), j + 1); } } PROTECT(ans = NEW_LIST(4)); PROTECT(ans_start = new_INTEGER_from_IntAE(sbuf)); PROTECT(ans_end = new_INTEGER_from_IntAE(ebuf)); PROTECT(ans_qhits = new_INTEGER_from_IntAE(qhbuf)); PROTECT(ans_shits = new_INTEGER_from_IntAE(shbuf)); SET_VECTOR_ELT(ans, 0, ans_start); SET_VECTOR_ELT(ans, 1, ans_end); SET_VECTOR_ELT(ans, 2, ans_qhits); SET_VECTOR_ELT(ans, 3, ans_shits); UNPROTECT(5); return ans; } GenomicAlignments/src/encodeOverlaps_methods.c0000644000175200017520000003701014516004601022624 0ustar00biocbuildbiocbuild/**************************************************************************** * Encode overlaps * * Author: Herve Pages * ****************************************************************************/ #include "GenomicAlignments.h" #include "IRanges_interface.h" #include "S4Vectors_interface.h" /* * A low-level helper for "superficial" checking of the 'space' vector * associated with an IntegerRanges object. */ static const int *check_ranges_space(SEXP space, int len, const char *what) { if (space == R_NilValue) return NULL; if (!IS_INTEGER(space)) error("'%s_space' must be an integer vector or NULL", what); if (LENGTH(space) != len) error("when not NULL, '%s_space' must have " "the same length as 'start(%s)'", what, what); return INTEGER(space); } static void CharAE_append_char(CharAE *char_ae, char c, int times) { int i; for (i = 0; i < times; i++) CharAE_insert_at(char_ae, CharAE_get_nelt(char_ae), c); return; } static void CharAE_append_int(CharAE *char_ae, int d) { static char buf[12]; /* should be enough for 32-bit ints */ int ret; ret = snprintf(buf, sizeof(buf), "%d", d); if (ret < 0) /* should never happen */ error("GenomicAlignments internal error " "in CharAE_append_int(): " "snprintf() returned value < 0"); if (ret >= sizeof(buf)) /* could happen with ints > 32-bit */ error("GenomicAlignments internal error " "in CharAE_append_int(): " "output of snprintf() was truncated"); CharAE_append_string(char_ae, buf); return; } /* * A special 1-letter code 'X' is used for ranges that are not on the same * space. */ static char overlap_letter(int x_start, int x_width, int x_space, int y_start, int y_width, int y_space) { int code; if (x_space != y_space) return 'X'; code = overlap_code(x_start, x_width, y_start, y_width); if (x_space < 0) code = invert_overlap_code(code); return 'g' + code; } /* * q_start, q_width: int arrays of length q_len. No NAs. * q_space: NULL or an int array of length q_len. No NAs. * q_len: nb of ranges in the query. * q_break: 0 if all the ranges in the query are coming from the same * segment (single-end read), or, an int >= 1 and < q_len specifying * the position of the break between the ranges coming from one * segment and the ranges coming from the other if the query is a * paired-end read. * flip_query: if non-zero, then the query is "flipped" before the encoding is * computed. * s_start, s_width: int arrays of length s_len. No NAs. * s_space: NULL or an int array of length s_len. No NAs. * s_len: nb of ranges in the subject. * as_matrix, Loffset, Roffset: if as_matrix, then the full matrix of codes * is returned and the returned values for Loffset and Roffset are * undefined. Otherwise, the matrix is trimmed and the returned values * for Loffset and Roffset are the number of cols removed on the left * and right sides of the matrix, respectively. * out: character array containing the matrix of codes (possibly trimmed) */ static void unsafe_overlap_encoding( const int *q_start, const int *q_width, const int *q_space, int q_len, int q_break, int flip_query, const int *s_start, const int *s_width, const int *s_space, int s_len, int as_matrix, int *Loffset, int *Roffset, CharAE *out) { int out_nelt0, i, starti, widthi, spacei, j, startj, widthj, spacej, j1, j2, nrow; char letter; if (!as_matrix) { if (q_break != 0) { if (flip_query) { CharAE_append_int(out, q_len - q_break); CharAE_append_char(out, '-', 2); CharAE_append_int(out, q_break); } else { CharAE_append_int(out, q_break); CharAE_append_char(out, '-', 2); CharAE_append_int(out, q_len - q_break); } } else { CharAE_append_int(out, q_len); } CharAE_append_char(out, ':', 1); out_nelt0 = CharAE_get_nelt(out); } /* j1: 0-based index of first (i.e. leftmost) col with a non-"m", or 's_len' if there is no such col. j2: 0-based index of last (i.e. rightmost) col with a non-"a", or -1 if there is no such col. */ j1 = s_len; j2 = -1; /* Walk col by col. */ for (j = 0; j < s_len; j++) { startj = s_start[j]; widthj = s_width[j]; spacej = s_space == NULL ? 0 : s_space[j]; if (flip_query) { for (i = q_len - 1; i >= 0; i--) { starti = q_start[i]; widthi = q_width[i]; spacei = q_space == NULL ? 0 : - q_space[i]; letter = overlap_letter(starti, widthi, spacei, startj, widthj, spacej); CharAE_append_char(out, letter, 1); if (j1 == s_len && letter != 'm') j1 = j; if (letter != 'a') j2 = j; if (q_break != 0 && i == q_break) CharAE_append_char(out, '-', 2); } } else { for (i = 0; i < q_len; i++) { if (q_break != 0 && i == q_break) CharAE_append_char(out, '-', 2); starti = q_start[i]; widthi = q_width[i]; spacei = q_space == NULL ? 0 : q_space[i]; letter = overlap_letter(starti, widthi, spacei, startj, widthj, spacej); CharAE_append_char(out, letter, 1); if (j1 == s_len && letter != 'm') j1 = j; if (letter != 'a') j2 = j; } } } if (as_matrix) return; /* By making 'j2' a 1-based index we will then have 0 <= j1 <= j2 <= s_len, which will simplify further arithmetic/logic. */ if (q_len == 0) { /* A 0-row matrix needs special treatment. */ j2 = s_len; } else { j2++; } *Loffset = j1; *Roffset = s_len - j2; nrow = q_len; if (q_break != 0) nrow += 2; /* Remove "a"-cols on the right. */ CharAE_set_nelt(out, out_nelt0 + j2 * nrow); /* Remove "m"-cols on the left. */ CharAE_delete_at(out, out_nelt0, j1 * nrow); /* Insert ":" at the end of each remaining col. */ for (j = j2 - j1; j >= 1; j--) CharAE_insert_at(out, out_nelt0 + j * nrow, ':'); return; } static void overlap_encoding( SEXP query_start, SEXP query_width, SEXP query_space, int query_break, int flip_query, SEXP subject_start, SEXP subject_width, SEXP subject_space, int as_matrix, int *Loffset, int *Roffset, CharAE *out) { int q_len, s_len; const int *q_start, *q_width, *q_space, *s_start, *s_width, *s_space; q_len = check_integer_pairs(query_start, query_width, &q_start, &q_width, "start(query)", "width(query)"); if (query_break != 0 && (query_break < 1 || query_break >= q_len)) error("the position of the break in the query " "must be >= 1 and < length(query)"); q_space = check_ranges_space(query_space, q_len, "query"); s_len = check_integer_pairs(subject_start, subject_width, &s_start, &s_width, "start(subject)", "width(subject)"); s_space = check_ranges_space(subject_space, s_len, "subject"); unsafe_overlap_encoding(q_start, q_width, q_space, q_len, query_break, flip_query, s_start, s_width, s_space, s_len, as_matrix, Loffset, Roffset, out); return; } /* type: 0=CHARSXP, 1=STRSXP, 2=RAWSXP as_matrix: 0 or 1, ignored when type is 0 q_len, q_break, s_len: ignored when type is 0 */ static SEXP make_encoding_from_CharAE(const CharAE *buf, int type, int as_matrix, int q_len, int q_break, int s_len) { SEXP ans, ans_elt, ans_dim; int buf_nelt, i, nrow; buf_nelt = CharAE_get_nelt(buf); if (type == 0 || (type == 1 && !as_matrix)) { PROTECT(ans = mkCharLen(buf->elts, buf_nelt)); if (type == 1) { PROTECT(ans = ScalarString(ans)); UNPROTECT(1); } UNPROTECT(1); return ans; } if (type == 1) { PROTECT(ans = NEW_CHARACTER(buf_nelt)); for (i = 0; i < buf_nelt; i++) { PROTECT(ans_elt = mkCharLen(buf->elts + i, 1)); SET_STRING_ELT(ans, i, ans_elt); UNPROTECT(1); } } else { PROTECT(ans = new_RAW_from_CharAE(buf)); } if (as_matrix) { nrow = q_len; if (q_break != 0) nrow += 2; PROTECT(ans_dim = NEW_INTEGER(2)); INTEGER(ans_dim)[0] = nrow; INTEGER(ans_dim)[1] = s_len; SET_DIM(ans, ans_dim); UNPROTECT(1); } UNPROTECT(1); return ans; } static SEXP make_LIST_from_ovenc_parts(SEXP Loffset, SEXP Roffset, SEXP encoding) { SEXP ans, ans_names, ans_names_elt; PROTECT(ans = NEW_LIST(3)); PROTECT(ans_names = NEW_CHARACTER(3)); PROTECT(ans_names_elt = mkChar("Loffset")); SET_STRING_ELT(ans_names, 0, ans_names_elt); UNPROTECT(1); PROTECT(ans_names_elt = mkChar("Roffset")); SET_STRING_ELT(ans_names, 1, ans_names_elt); UNPROTECT(1); PROTECT(ans_names_elt = mkChar("encoding")); SET_STRING_ELT(ans_names, 2, ans_names_elt); UNPROTECT(1); SET_NAMES(ans, ans_names); UNPROTECT(1); SET_VECTOR_ELT(ans, 0, Loffset); SET_VECTOR_ELT(ans, 1, Roffset); SET_VECTOR_ELT(ans, 2, encoding); UNPROTECT(1); return ans; } /* --- .Call ENTRY POINT --- * 'query_start', 'query_width', 'query_space': integer vectors of the same * length M (or NULL for 'query_space'). * 'query_break': single integer. * 'subject_start', 'subject_width', 'subject_space': integer vectors of the * same length N (or NULL for 'subject_space'). * Integer vectors 'query_start', 'query_width', 'subject_start' and * 'subject_width' are assumed to be NA free. 'query_width' and 'subject_width' * are assumed to contain non-negative values. For efficiency reasons, those * assumptions are not checked. * Return the matrix of 1-letter codes (if 'as_matrix' is TRUE), otherwise a * named list with the 3 following components: * 1. Loffset: single integer; * 2. Roffset: single integer; * 3. encoding: the compact encoding as a single string (if 'as_raw' is * FALSE) or a raw vector (if 'as_raw' is TRUE). */ SEXP encode_overlaps1(SEXP query_start, SEXP query_width, SEXP query_space, SEXP query_break, SEXP flip_query, SEXP subject_start, SEXP subject_width, SEXP subject_space, SEXP as_matrix, SEXP as_raw) { int query_break0, flip_query0, as_matrix0, as_raw0, Loffset, Roffset; CharAE *buf; SEXP encoding, ans_Loffset, ans_Roffset, ans; query_break0 = INTEGER(query_break)[0]; flip_query0 = LOGICAL(flip_query)[0]; as_matrix0 = as_matrix != R_NilValue && LOGICAL(as_matrix)[0]; as_raw0 = as_raw != R_NilValue && LOGICAL(as_raw)[0]; buf = new_CharAE(0); overlap_encoding( query_start, query_width, query_space, query_break0, flip_query0, subject_start, subject_width, subject_space, as_matrix0, &Loffset, &Roffset, buf); PROTECT(encoding = make_encoding_from_CharAE(buf, as_raw0 ? 2 : 1, as_matrix0, LENGTH(query_start), query_break0, LENGTH(subject_start))); if (as_matrix0) { UNPROTECT(1); return encoding; } PROTECT(ans_Loffset = ScalarInteger(Loffset)); PROTECT(ans_Roffset = ScalarInteger(Roffset)); PROTECT(ans = make_LIST_from_ovenc_parts(ans_Loffset, ans_Roffset, encoding)); UNPROTECT(4); return ans; } static SEXP RangesList_encode_overlaps_ij( SEXP query_starts, SEXP query_widths, SEXP query_spaces, SEXP query_breaks, SEXP subject_starts, SEXP subject_widths, SEXP subject_spaces, int i, int j, int flip_query, int *Loffset, int *Roffset, CharAE *buf) { SEXP query_start, query_width, query_space, subject_start, subject_width, subject_space; int query_break; query_start = VECTOR_ELT(query_starts, i); query_width = VECTOR_ELT(query_widths, i); if (query_spaces == R_NilValue) query_space = R_NilValue; else query_space = VECTOR_ELT(query_spaces, i); if (query_breaks == R_NilValue) query_break = 0; else query_break = INTEGER(query_breaks)[i]; subject_start = VECTOR_ELT(subject_starts, j); subject_width = VECTOR_ELT(subject_widths, j); if (subject_spaces == R_NilValue) subject_space = R_NilValue; else subject_space = VECTOR_ELT(subject_spaces, j); overlap_encoding( query_start, query_width, query_space, query_break, flip_query, subject_start, subject_width, subject_space, 0, Loffset, Roffset, buf); return make_encoding_from_CharAE(buf, 0, 0, 0, 0, 0); } /* --- .Call ENTRY POINT ---/ * 'query_starts', 'query_widths', 'query_spaces': lists of integer vectors. * The 3 lists are assumed to have the same length (M) and shape. * 'query_breaks': NULL or integer vector of length M. * 'subject_starts', 'subject_widths', 'subject_spaces': lists of integer * vectors. The 3 lists are assumed to have the same length (N) and shape. * Return a named list with the 3 following components (all of the same * length): * 1. Loffset: integer vector; * 2. Roffset: integer vector; * 3. encoding: character vector containing the compact encodings (type * II). */ SEXP RangesList_encode_overlaps(SEXP query_starts, SEXP query_widths, SEXP query_spaces, SEXP query_breaks, SEXP subject_starts, SEXP subject_widths, SEXP subject_spaces) { int q_len, s_len, ans_len, i, j, k; SEXP ans_Loffset, ans_Roffset, ans_encoding, ans_encoding_elt, ans; CharAE *buf; /* TODO: Add some basic checking of the input values. */ q_len = LENGTH(query_starts); s_len = LENGTH(subject_starts); if (q_len == 0 || s_len == 0) ans_len = 0; else ans_len = q_len >= s_len ? q_len : s_len; PROTECT(ans_Loffset = NEW_INTEGER(ans_len)); PROTECT(ans_Roffset = NEW_INTEGER(ans_len)); PROTECT(ans_encoding = NEW_CHARACTER(ans_len)); buf = new_CharAE(0); for (i = j = k = 0; k < ans_len; i++, j++, k++) { if (i >= q_len) i = 0; /* recycle i */ if (j >= s_len) j = 0; /* recycle j */ PROTECT(ans_encoding_elt = RangesList_encode_overlaps_ij( query_starts, query_widths, query_spaces, query_breaks, subject_starts, subject_widths, subject_spaces, i, j, 0, INTEGER(ans_Loffset) + k, INTEGER(ans_Roffset) + k, buf)); SET_STRING_ELT(ans_encoding, k, ans_encoding_elt); UNPROTECT(1); CharAE_set_nelt(buf, 0); } if (ans_len != 0 && (i != q_len || j != s_len)) warning("longer object length is not a multiple " "of shorter object length"); PROTECT(ans = make_LIST_from_ovenc_parts(ans_Loffset, ans_Roffset, ans_encoding)); UNPROTECT(4); return ans; } /* --- .Call ENTRY POINT ---/ * Same arguments as RangesList_encode_overlaps() plus: * 'query_hits', 'subject_hits': integer vectors of the same length. * 'flip_query': logical vector of the same length as 'query_hits'. */ SEXP Hits_encode_overlaps(SEXP query_starts, SEXP query_widths, SEXP query_spaces, SEXP query_breaks, SEXP subject_starts, SEXP subject_widths, SEXP subject_spaces, SEXP query_hits, SEXP subject_hits, SEXP flip_query) { int q_len, s_len, ans_len, i, j, k; const int *q_hits, *s_hits; SEXP ans_Loffset, ans_Roffset, ans_encoding, ans_encoding_elt, ans; CharAE *buf; /* TODO: Add some basic checking of the input values. */ q_len = LENGTH(query_starts); s_len = LENGTH(subject_starts); ans_len = check_integer_pairs(query_hits, subject_hits, &q_hits, &s_hits, "queryHits(hits)", "subjectHits(hits)"); PROTECT(ans_Loffset = NEW_INTEGER(ans_len)); PROTECT(ans_Roffset = NEW_INTEGER(ans_len)); PROTECT(ans_encoding = NEW_CHARACTER(ans_len)); buf = new_CharAE(0); for (k = 0; k < ans_len; k++) { i = q_hits[k]; j = s_hits[k]; if (i == NA_INTEGER || i < 1 || i > q_len || j == NA_INTEGER || j < 1 || j > s_len) { UNPROTECT(3); error("'queryHits(hits)' or 'subjectHits(hits)' " "contain invalid indices"); } i--; j--; PROTECT(ans_encoding_elt = RangesList_encode_overlaps_ij( query_starts, query_widths, query_spaces, query_breaks, subject_starts, subject_widths, subject_spaces, i, j, LOGICAL(flip_query)[k], INTEGER(ans_Loffset) + k, INTEGER(ans_Roffset) + k, buf)); SET_STRING_ELT(ans_encoding, k, ans_encoding_elt); UNPROTECT(1); CharAE_set_nelt(buf, 0); } PROTECT(ans = make_LIST_from_ovenc_parts(ans_Loffset, ans_Roffset, ans_encoding)); UNPROTECT(4); return ans; } GenomicAlignments/tests/0000755000175200017520000000000014516004601016336 5ustar00biocbuildbiocbuildGenomicAlignments/tests/run_unitTests.R0000644000175200017520000000015114516004601021344 0ustar00biocbuildbiocbuildrequire("GenomicAlignments") || stop("unable to load GenomicRanges package") GenomicAlignments:::.test() GenomicAlignments/vignettes/0000755000175200017520000000000014551577536017231 5ustar00biocbuildbiocbuildGenomicAlignments/vignettes/GenomicAlignmentsIntroduction.Rnw0000644000175200017520000001302614516004601025703 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{An Introduction to the GenomicAlignments Package} %\VignetteDepends{Rsamtools} %\VignetteKeywords{sequence, sequencing, alignments} %\VignettePackage{GenomicAlignments} \documentclass[10pt]{article} \usepackage{times} \usepackage{hyperref} \textwidth=6.5in \textheight=8.5in %\parskip=.3cm \oddsidemargin=-.1in \evensidemargin=-.1in \headheight=-.3in \newcommand{\Rfunction}[1]{{\texttt{#1}}} \newcommand{\Robject}[1]{{\texttt{#1}}} \newcommand{\Rpackage}[1]{{\textit{#1}}} \newcommand{\Rmethod}[1]{{\texttt{#1}}} \newcommand{\Rfunarg}[1]{{\texttt{#1}}} \newcommand{\Rclass}[1]{{\textit{#1}}} \newcommand{\Rcode}[1]{{\texttt{#1}}} \newcommand{\software}[1]{\textsf{#1}} \newcommand{\R}{\software{R}} \newcommand{\Bioconductor}{\software{Bioconductor}} \newcommand{\GenomicRanges}{\Rpackage{GenomicRanges}} \title{An Introduction to the GenomicAlignments Package} \author{Herv\'{e} Pag\`{e}s} \date{\today} \begin{document} \maketitle <>= options(width=72) @ \tableofcontents \section{Introduction} The \Rpackage{GenomicAlignments} package serves as the foundation for representing genomic alignments within the \software{Bioconductor} project. In the \software{Bioconductor} package hierarchy, it builds upon the \Rpackage{GenomicRanges} (infrastructure) package and provides support for many \software{Bioconductor} packages. This package defines three classes: \Rclass{GAlignments}, \Rclass{GAlignmentPairs}, and \Rclass{GAlignmentsList}), which are used to represent genomic alignments, pairs of genomic alignments, and groups of genomic alignments. The \Rpackage{GenomicAlignments} package is available at bioconductor.org and can be downloaded via \Rfunction{BiocManager::install}: <>= if (!require("BiocManager")) install.packages("BiocManager") BiocManager::install("GenomicAlignments") @ <>= library(GenomicAlignments) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{\Rclass{GAlignments}: Genomic Alignments} The \Rclass{GAlignments} class which is a container for storing a set of genomic alignments. The class is intended to support alignments in general, not only those coming from a 'Binary Alignment Map' or 'BAM' files. Also alignments with gaps in the reference sequence (a.k.a. \emph{gapped alignments}) are supported which, for example, makes the class suited for storing junction reads from an RNA-seq experiment. More precisely, a \Rclass{GAlignments} object is a vector-like object where each element describes an \emph{alignment}, that is, how a given sequence (called \emph{query} or \emph{read}, typically short) aligns to a reference sequence (typically long). As shown later in this document, a \Rclass{GAlignments} object can be created from a 'BAM' file. In that case, each element in the resulting object will correspond to a record in the file. One important thing to note though is that not all the information present in the BAM/SAM records is stored in the object. In particular, for now, we discard the query sequences (SEQ field), the query ids (QNAME field), the query qualities (QUAL), the mapping qualities (MAPQ) and any other information that is not needed in order to support the basic set of operations described in this document. This also means that multi-reads (i.e. reads with multiple hits in the reference) don't receive any special treatment i.e. the various SAM/BAM records corresponding to a multi-read will show up in the \Rclass{GAlignments} object as if they were coming from different/unrelated queries. Also paired-end reads will be treated as single-end reads and the pairing information will be lost. This might change in the future. \subsection{Load a `BAM' file into a \Rclass{GAlignments} object} First we use the \Rfunction{readGAlignments} function from the \Rpackage{GenomicAlignments} package to load a toy `BAM' file into a \Rclass{GAlignments} object: <>= library(GenomicAlignments) aln1_file <- system.file("extdata", "ex1.bam", package="Rsamtools") aln1 <- readGAlignments(aln1_file) aln1 length(aln1) @ 3271 `BAM' records were loaded into the object. Note that \Rfunction{readGAlignments} would have discarded any `BAM' record describing an unaligned query (see description of the field in the SAM Format Specification \footnote{\url{http://samtools.sourceforge.net/SAM1.pdf}} for more information). The reader interested in tracking down these events can always use the \Rfunction{scanBam} function but this goes beyond the scope of this document. \subsection{Simple accessor methods} There is one accessor per field displayed by the \Rmethod{show} method and it has the same name as the field. All of them return a vector or factor of the same length as the object: <>= head(seqnames(aln1)) seqlevels(aln1) head(strand(aln1)) head(cigar(aln1)) head(qwidth(aln1)) head(start(aln1)) head(end(aln1)) head(width(aln1)) head(njunc(aln1)) @ \subsection{More accessor methods} [coming soon...] %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{\Rclass{GAlignmentPairs}: Pairs of Genomic Alignments} [coming soon...] %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{\Rclass{GAlignmentsList}: Groups of Genomic Alignments} [coming soon...] %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Session Information} All of the output in this vignette was produced under the following conditions: \begin{small} <>= sessionInfo() @ \end{small} \end{document} GenomicAlignments/vignettes/OverlapEncodings.Rnw0000644000175200017520000016240314516004601023144 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{Overlap encodings} %\VignetteDepends{pasillaBamSubset, GenomicAlignments, GenomicFeatures, BSgenome.Dmelanogaster.UCSC.dm3, TxDb.Dmelanogaster.UCSC.dm3.ensGene} %\VignetteKeywords{sequence, sequencing, alignments} %\VignettePackage{GenomicAlignments} \documentclass{article} <>= BiocStyle::latex() @ \title{Overlap encodings} \author{Herv\'e Pag\`es} \date{Last modified: December 2016; Compiled: \today} \begin{document} \maketitle <>= options(width=100) .precomputed_results_dir <- "precomputed_results" .loadPrecomputed <- function(objname) { filename <- paste0(objname, ".rda") path <- file.path(.precomputed_results_dir, filename) tempenv <- new.env(parent=emptyenv()) load(path, envir=tempenv) updateObject(get(objname, envir=tempenv)) } .checkIdenticalToPrecomputed <- function(obj, objname, ignore.metadata=FALSE) { precomputed_obj <- .loadPrecomputed(objname) if (ignore.metadata) metadata(obj) <- metadata(precomputed_obj) <- list() ## Replace NAs with FALSE in circularity flag (because having the flag set ## to NA instead of FALSE (or vice-versa) is not considered a significant ## difference between the 2 objects). isCircular(obj) <- isCircular(obj) %in% TRUE isCircular(precomputed_obj) <- isCircular(precomputed_obj) %in% TRUE if (!identical(obj, precomputed_obj)) stop("'", objname, "' is not identical to precomputed version") } @ \tableofcontents %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Introduction} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% In the context of an RNA-seq experiment, encoding the overlaps between the aligned reads and the transcripts can be used for detecting those overlaps that are ``splice compatible'', that is, compatible with the splicing of the transcript. Various tools are provided in the \Rpackage{GenomicAlignments} package for working with {\it overlap encodings}. In this vignette, we illustrate the use of these tools on the single-end and paired-end reads of an RNA-seq experiment. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Load reads from a BAM file} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Load single-end reads from a BAM file} BAM file {\tt untreated1\_chr4.bam} (located in the \Rpackage{pasillaBamSubset} data package) contains single-end reads from the ``Pasilla'' experiment and aligned against the dm3 genome (see \Rcode{?untreated1\_chr4} in the \Rpackage{pasillaBamSubset} package for more information about those reads): <>= library(pasillaBamSubset) untreated1_chr4() @ We use the \Rfunction{readGAlignments} function defined in the \Rpackage{GenomicAlignments} package to load the reads into a \Rclass{GAlignments} object. It's probably a good idea to get rid of the PCR or optical duplicates (flag bit 0x400 in the SAM format, see the SAM Spec \footnote{\url{http://samtools.sourceforge.net/}} for the details), as well as reads not passing quality controls (flag bit 0x200 in the SAM format). We do this by creating a \Rclass{ScanBamParam} object that we pass to \Rcode{readGAlignments} (see \Rcode{?ScanBamParam} in the \Rpackage{Rsamtools} package for the details). Note that we also use \Rcode{use.names=TRUE} in order to load the {\it query names} (aka {\it query template names}, see QNAME field in the SAM Spec) from the BAM file (\Rcode{readGAlignments} will use them to set the names of the returned object): <>= library(GenomicAlignments) flag0 <- scanBamFlag(isDuplicate=FALSE, isNotPassingQualityControls=FALSE) param0 <- ScanBamParam(flag=flag0) U1.GAL <- readGAlignments(untreated1_chr4(), use.names=TRUE, param=param0) head(U1.GAL) @ Because the aligner used to align those reads can report more than 1 alignment per {\it original query} (i.e. per read stored in the input file, typically a FASTQ file), we shouldn't expect the names of \Rcode{U1.GAL} to be unique: <>= U1.GAL_names_is_dup <- duplicated(names(U1.GAL)) table(U1.GAL_names_is_dup) @ Storing the {\it query names} in a factor will be useful as we will see later in this document: <>= U1.uqnames <- unique(names(U1.GAL)) U1.GAL_qnames <- factor(names(U1.GAL), levels=U1.uqnames) @ Note that we explicitely provide the levels of the factor to enforce their order. Otherwise \Rcode{factor()} would put them in lexicographic order which is not advisable because it depends on the locale in use. Another object that will be useful to keep near at hand is the mapping between each {\it query name} and its first occurence in \Rcode{U1.GAL\_qnames}: <>= U1.GAL_dup2unq <- match(U1.GAL_qnames, U1.GAL_qnames) @ Our reads can have up to 2 {\it skipped regions} (a {\it skipped region} corresponds to an N operation in the CIGAR): <>= head(unique(cigar(U1.GAL))) table(njunc(U1.GAL)) @ Also, the following table indicates that indels were not allowed/supported during the alignment process (no I or D CIGAR operations): <>= colSums(cigarOpTable(cigar(U1.GAL))) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Load paired-end reads from a BAM file} BAM file {\tt untreated3\_chr4.bam} (located in the \Rpackage{pasillaBamSubset} data package) contains paired-end reads from the ``Pasilla'' experiment and aligned against the dm3 genome (see \Rcode{?untreated3\_chr4} in the \Rpackage{pasillaBamSubset} package for more information about those reads). We use the \Rfunction{readGAlignmentPairs} function to load them into a \Rclass{GAlignmentPairs} object: <>= U3.galp <- readGAlignmentPairs(untreated3_chr4(), use.names=TRUE, param=param0) head(U3.galp) @ The \Rcode{show} method for \Rclass{GAlignmentPairs} objects displays two {\tt ranges} columns, one for the {\it first} alignment in the pair (the left column), and one for the {\it last} alignment in the pair (the right column). The {\tt strand} column corresponds to the strand of the {\it first} alignment. <>= head(first(U3.galp)) head(last(U3.galp)) @ According to the SAM format specifications, the aligner is expected to mark each alignment pair as {\it proper} or not (flag bit 0x2 in the SAM format). The SAM Spec only says that a pair is {\it proper} if the {\it first} and {\it last} alignments in the pair are ``properly aligned according to the aligner''. So the exact criteria used for setting this flag is left to the aligner. We use \Rcode{isProperPair} to extract this flag from the \Rclass{GAlignmentPairs} object: <>= table(isProperPair(U3.galp)) @ Even though we could do {\it overlap encodings} with the full object, we keep only the {\it proper} pairs for our downstream analysis: <>= U3.GALP <- U3.galp[isProperPair(U3.galp)] @ Because the aligner used to align those reads can report more than 1 alignment per {\it original query template} (i.e. per pair of sequences stored in the input files, typically 1 FASTQ file for the {\it first} ends and 1 FASTQ file for the {\it last} ends), we shouldn't expect the names of \Rcode{U3.GALP} to be unique: <>= U3.GALP_names_is_dup <- duplicated(names(U3.GALP)) table(U3.GALP_names_is_dup) @ Storing the {\it query template names} in a factor will be useful: <>= U3.uqnames <- unique(names(U3.GALP)) U3.GALP_qnames <- factor(names(U3.GALP), levels=U3.uqnames) @ as well as having the mapping between each {\it query template name} and its first occurence in \Rcode{U3.GALP\_qnames}: <>= U3.GALP_dup2unq <- match(U3.GALP_qnames, U3.GALP_qnames) @ Our reads can have up to 1 {\it skipped region} per end: <>= head(unique(cigar(first(U3.GALP)))) head(unique(cigar(last(U3.GALP)))) table(njunc(first(U3.GALP)), njunc(last(U3.GALP))) @ Like for our single-end reads, the following tables indicate that indels were not allowed/supported during the alignment process: <>= colSums(cigarOpTable(cigar(first(U3.GALP)))) colSums(cigarOpTable(cigar(last(U3.GALP)))) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Find all the overlaps between the reads and transcripts} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Load the transcripts from a \Rclass{TxDb} object} In order to compute overlaps between reads and transcripts, we need access to the genomic positions of a set of known transcripts and their exons. It is essential that the reference genome of this set of transcripts and exons be {\bf exactly} the same as the reference genome used to align the reads. We could use the \Rfunction{makeTxDbFromUCSC} function defined in the \Rpackage{GenomicFeatures} package to make a \Rclass{TxDb} object containing the dm3 transcripts and their exons retrieved from the UCSC Genome Browser\footnote{\url{http://genome.ucsc.edu/cgi-bin/hgGateway}}. The Bioconductor project however provides a few annotation packages containing \Rclass{TxDb} objects for the most commonly studied organisms (those data packages are sometimes called the {\it TxDb} packages). One of them is the \Rpackage{TxDb.Dmelanogaster.\-UCSC.\-dm3.ensGene} package. It contains a \Rclass{TxDb} object that was made by pointing the \Rfunction{makeTxDbFromUCSC} function to the dm3 genome and {\it Ensembl Genes} track \footnote{See \url{http://genome.ucsc.edu/cgi-bin/hgTrackUi?hgsid=276880911&g=ensGene} for a description of this track.}. We can use it here: <>= library(TxDb.Dmelanogaster.UCSC.dm3.ensGene) TxDb.Dmelanogaster.UCSC.dm3.ensGene txdb <- TxDb.Dmelanogaster.UCSC.dm3.ensGene @ We extract the exons grouped by transcript in a \Rclass{GRangesList} object: <>= exbytx <- exonsBy(txdb, by="tx", use.names=TRUE) length(exbytx) # nb of transcripts @ <>= .checkIdenticalToPrecomputed(exbytx, "exbytx", ignore.metadata=TRUE) @ We check that all the exons in any given transcript belong to the same chromosome and strand. Knowing that our set of transcripts is free of this sort of trans-splicing events typically allows some significant simplifications during the downstream analysis \footnote{Dealing with trans-splicing events is not covered in this document.}. A quick and easy way to check this is to take advantage of the fact that \Rcode{seqnames} and \Rcode{strand} return \Rclass{RleList} objects. So we can extract the number of Rle runs for each transcript and make sure it's always 1: <>= table(elementNROWS(runLength(seqnames(exbytx)))) table(elementNROWS(runLength(strand(exbytx)))) @ Therefore the strand of any given transcript is unambiguously defined and can be extracted with: <>= exbytx_strand <- unlist(runValue(strand(exbytx)), use.names=FALSE) @ We will also need the mapping between the transcripts and their gene. We start by using \Rfunction{transcripts} to extract this information from our \Rclass{TxDb} object \Rcode{txdb}, and then we construct a named factor that represents the mapping: <>= tx <- transcripts(txdb, columns=c("tx_name", "gene_id")) head(tx) df <- mcols(tx) exbytx2gene <- as.character(df$gene_id) exbytx2gene <- factor(exbytx2gene, levels=unique(exbytx2gene)) names(exbytx2gene) <- df$tx_name exbytx2gene <- exbytx2gene[names(exbytx)] head(exbytx2gene) nlevels(exbytx2gene) # nb of genes @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Single-end overlaps} \subsubsection{Find the single-end overlaps} We are ready to compute the overlaps with the \Rfunction{findOverlaps} function. Note that the strand of the queries produced by the RNA-seq experiment is typically unknown so we use \Rcode{ignore.strand=TRUE}: <>= U1.OV00 <- findOverlaps(U1.GAL, exbytx, ignore.strand=TRUE) @ \Rcode{U1.OV00} is a \Rclass{Hits} object that contains 1 element per overlap. Its length gives the number of overlaps: <>= length(U1.OV00) @ \subsubsection{Tabulate the single-end overlaps} We will repeatedly use the 2 following little helper functions to ``tabulate'' the overlaps in a given \Rclass{Hits} object (e.g. \Rcode{U1.OV00}), i.e. to count the number of overlaps for each element in the query or for each element in the subject: Number of transcripts for each alignment in \Rcode{U1.GAL}: <>= U1.GAL_ntx <- countQueryHits(U1.OV00) mcols(U1.GAL)$ntx <- U1.GAL_ntx head(U1.GAL) table(U1.GAL_ntx) mean(U1.GAL_ntx >= 1) @ 76\% of the alignments in \Rcode{U1.GAL} have an overlap with at least 1 transcript in \Rcode{exbytx}. Note that \Rfunction{countOverlaps} can be used directly on \Rcode{U1.GAL} and \Rcode{exbytx} for computing \Rcode{U1.GAL\_ntx}: <>= U1.GAL_ntx_again <- countOverlaps(U1.GAL, exbytx, ignore.strand=TRUE) stopifnot(identical(unname(U1.GAL_ntx_again), U1.GAL_ntx)) @ Because \Rcode{U1.GAL} can (and actually does) contain more than 1 alignment per {\it original query} (aka read), we also count the number of transcripts for each read: <>= U1.OV10 <- remapHits(U1.OV00, Lnodes.remapping=U1.GAL_qnames) U1.uqnames_ntx <- countQueryHits(U1.OV10) names(U1.uqnames_ntx) <- U1.uqnames table(U1.uqnames_ntx) mean(U1.uqnames_ntx >= 1) @ 78.4\% of the reads have an overlap with at least 1 transcript in \Rcode{exbytx}. Number of reads for each transcript: <>= U1.exbytx_nOV10 <- countSubjectHits(U1.OV10) names(U1.exbytx_nOV10) <- names(exbytx) mean(U1.exbytx_nOV10 >= 50) @ Only 0.869\% of the transcripts in \Rcode{exbytx} have an overlap with at least 50 reads. Top 10 transcripts: <>= head(sort(U1.exbytx_nOV10, decreasing=TRUE), n=10) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Paired-end overlaps} \subsubsection{Find the paired-end overlaps} Like with our single-end overlaps, we call \Rfunction{findOverlaps} with \Rcode{ignore.strand=TRUE}: <>= U3.OV00 <- findOverlaps(U3.GALP, exbytx, ignore.strand=TRUE) @ Like \Rcode{U1.OV00}, \Rcode{U3.OV00} is a \Rclass{Hits} object. Its length gives the number of paired-end overlaps: <>= length(U3.OV00) @ \subsubsection{Tabulate the paired-end overlaps} Number of transcripts for each alignment pair in \Rcode{U3.GALP}: <>= U3.GALP_ntx <- countQueryHits(U3.OV00) mcols(U3.GALP)$ntx <- U3.GALP_ntx head(U3.GALP) table(U3.GALP_ntx) mean(U3.GALP_ntx >= 1) @ 71\% of the alignment pairs in \Rcode{U3.GALP} have an overlap with at least 1 transcript in \Rcode{exbytx}. Note that \Rfunction{countOverlaps} can be used directly on \Rcode{U3.GALP} and \Rcode{exbytx} for computing \Rcode{U3.GALP\_ntx}: <>= U3.GALP_ntx_again <- countOverlaps(U3.GALP, exbytx, ignore.strand=TRUE) stopifnot(identical(unname(U3.GALP_ntx_again), U3.GALP_ntx)) @ Because \Rcode{U3.GALP} can (and actually does) contain more than 1 alignment pair per {\it original query template}, we also count the number of transcripts for each template: <>= U3.OV10 <- remapHits(U3.OV00, Lnodes.remapping=U3.GALP_qnames) U3.uqnames_ntx <- countQueryHits(U3.OV10) names(U3.uqnames_ntx) <- U3.uqnames table(U3.uqnames_ntx) mean(U3.uqnames_ntx >= 1) @ 72.3\% of the templates have an overlap with at least 1 transcript in \Rcode{exbytx}. Number of templates for each transcript: <>= U3.exbytx_nOV10 <- countSubjectHits(U3.OV10) names(U3.exbytx_nOV10) <- names(exbytx) mean(U3.exbytx_nOV10 >= 50) @ Only 0.756\% of the transcripts in \Rcode{exbytx} have an overlap with at least 50 templates. Top 10 transcripts: <>= head(sort(U3.exbytx_nOV10, decreasing=TRUE), n=10) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Encode the overlaps between the reads and transcripts} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Single-end encodings} The {\it overlap encodings} are strand sensitive so we will compute them twice, once for the ``original alignments'' (i.e. the alignments of the {\it original queries}), and once again for the ``flipped alignments'' (i.e. the alignments of the ``flipped {\it original queries}''). We extract the ranges of the ``original'' and ``flipped'' alignments in 2 \Rclass{GRangesList} objects with: <>= U1.grl <- grglist(U1.GAL, order.as.in.query=TRUE) U1.grlf <- flipQuery(U1.grl) # flipped @ and encode their overlaps with the transcripts: <>= U1.ovencA <- encodeOverlaps(U1.grl, exbytx, hits=U1.OV00) U1.ovencB <- encodeOverlaps(U1.grlf, exbytx, hits=U1.OV00) @ \Rcode{U1.ovencA} and \Rcode{U1.ovencB} are 2 \Rclass{OverlapsEncodings} objects of the same length as \Rclass{Hits} object \Rcode{U1.OV00}. For each hit in \Rcode{U1.OV00}, we have 2 corresponding encodings, one in \Rcode{U1.ovencA} and one in \Rcode{U1.ovencB}, but only one of them encodes a hit between alignment ranges and exon ranges that are on the same strand. We use the \Rfunction{selectEncodingWithCompatibleStrand} function to merge them into a single \Rclass{OverlapsEncodings} of the same length. For each hit in \Rcode{U1.OV00}, this selects the encoding corresponding to alignment ranges and exon ranges with compatible strand: <>= U1.grl_strand <- unlist(runValue(strand(U1.grl)), use.names=FALSE) U1.ovenc <- selectEncodingWithCompatibleStrand(U1.ovencA, U1.ovencB, U1.grl_strand, exbytx_strand, hits=U1.OV00) U1.ovenc @ As a convenience, the 2 above calls to \Rfunction{encodeOverlaps} + merging step can be replaced by a single call to \Rfunction{encodeOverlaps} on \Rcode{U1.grl} (or \Rcode{U1.grlf}) with \Rcode{flip.query.if.wrong.strand=TRUE}: <>= U1.ovenc_again <- encodeOverlaps(U1.grl, exbytx, hits=U1.OV00, flip.query.if.wrong.strand=TRUE) stopifnot(identical(U1.ovenc_again, U1.ovenc)) @ Unique encodings in \Rcode{U1.ovenc}: <>= U1.unique_encodings <- levels(U1.ovenc) length(U1.unique_encodings) head(U1.unique_encodings) U1.ovenc_table <- table(encoding(U1.ovenc)) tail(sort(U1.ovenc_table)) @ Encodings are sort of cryptic but utilities are provided to extract specific meaning from them. Use of these utilities is covered later in this document. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Paired-end encodings} Let's encode the overlaps in \Rcode{U3.OV00}: <>= U3.grl <- grglist(U3.GALP) U3.ovenc <- encodeOverlaps(U3.grl, exbytx, hits=U3.OV00, flip.query.if.wrong.strand=TRUE) U3.ovenc @ Unique encodings in \Rcode{U3.ovenc}: <>= U3.unique_encodings <- levels(U3.ovenc) length(U3.unique_encodings) head(U3.unique_encodings) U3.ovenc_table <- table(encoding(U3.ovenc)) tail(sort(U3.ovenc_table)) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Detect ``splice compatible'' overlaps} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% We are interested in a particular type of overlap where the read overlaps the transcript in a ``splice compatible'' way, that is, in a way that is compatible with the splicing of the transcript. The \Rfunction{isCompatibleWithSplicing} function can be used on an \Rclass{OverlapEncodings} object to detect this type of overlap. Note that \Rfunction{isCompatibleWithSplicing} can also be used on a character vector or factor. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Detect ``splice compatible'' single-end overlaps} \subsubsection{``Splice compatible'' single-end encodings} \Rcode{U1.ovenc} contains 7 unique encodings compatible with the splicing of the transcript: <>= sort(U1.ovenc_table[isCompatibleWithSplicing(U1.unique_encodings)]) @ Encodings \Rcode{"1:i:"} (455176 occurences in \Rcode{U1.ovenc}), \Rcode{"2:jm:af:"} (72929 occurences in \Rcode{U1.ovenc}), and \Rcode{"3:jmm:agm:aaf:"} (488 occurences in \Rcode{U1.ovenc}), correspond to the following overlaps: \begin{itemize} \item \Rcode{"1:i:"} \begin{verbatim} - read (no skipped region): oooooooo - transcript: ... >>>>>>>>>>>>>> ... \end{verbatim} \item \Rcode{"2:jm:af:"} \begin{verbatim} - read (1 skipped region): ooooo---ooo - transcript: ... >>>>>>>>> >>>>>>>>> ... \end{verbatim} \item \Rcode{"3:jmm:agm:aaf:"} \begin{verbatim} - read (2 skipped regions): oo---ooooo---o - transcript: ... >>>>>>>> >>>>> >>>>>>> ... \end{verbatim} \end{itemize} For clarity, only the exons involved in the overlap are represented. The transcript can of course have more upstream and downstream exons, which is denoted by the ... on the left side (5' end) and right side (3' end) of each drawing. Note that the exons represented in the 2nd and 3rd drawings are consecutive and adjacent in the processed transcript. Encodings \Rcode{"1:f:"} and \Rcode{"1:j:"} are variations of the situation described by encoding \Rcode{"1:i:"}. For \Rcode{"1:f:"}, the first aligned base of the read (or ``flipped'' read) is aligned with the first base of the exon. For \Rcode{"1:j:"}, the last aligned base of the read (or ``flipped'' read) is aligned with the last base of the exon: \begin{itemize} \item \Rcode{"1:f:"} \begin{verbatim} - read (no skipped region): oooooooo - transcript: ... >>>>>>>>>>>>>> ... \end{verbatim} \item \Rcode{"1:j:"} \begin{verbatim} - read (no skipped region): oooooooo - transcript: ... >>>>>>>>>>>>>> ... \end{verbatim} \end{itemize} <>= U1.OV00_is_comp <- isCompatibleWithSplicing(U1.ovenc) table(U1.OV00_is_comp) # 531797 "splice compatible" overlaps @ Finally, let's extract the ``splice compatible'' overlaps from \Rcode{U1.OV00}: <>= U1.compOV00 <- U1.OV00[U1.OV00_is_comp] @ Note that high-level convenience wrapper \Rfunction{findCompatibleOverlaps} can be used for computing the ``splice compatible'' overlaps directly between a \Rclass{GAlignments} object (containing reads) and a \Rclass{GRangesList} object (containing transcripts): <>= U1.compOV00_again <- findCompatibleOverlaps(U1.GAL, exbytx) stopifnot(identical(U1.compOV00_again, U1.compOV00)) @ \subsubsection{Tabulate the ``splice compatible'' single-end overlaps} Number of ``splice compatible'' transcripts for each alignment in \Rcode{U1.GAL}: <>= U1.GAL_ncomptx <- countQueryHits(U1.compOV00) mcols(U1.GAL)$ncomptx <- U1.GAL_ncomptx head(U1.GAL) table(U1.GAL_ncomptx) mean(U1.GAL_ncomptx >= 1) @ 75\% of the alignments in \Rcode{U1.GAL} are ``splice compatible'' with at least 1 transcript in \Rcode{exbytx}. Note that high-level convenience wrapper \Rfunction{countCompatibleOverlaps} can be used directly on \Rcode{U1.GAL} and \Rcode{exbytx} for computing \Rcode{U1.GAL\_ncomptx}: <>= U1.GAL_ncomptx_again <- countCompatibleOverlaps(U1.GAL, exbytx) stopifnot(identical(U1.GAL_ncomptx_again, U1.GAL_ncomptx)) @ Number of ``splice compatible'' transcripts for each read: <>= U1.compOV10 <- remapHits(U1.compOV00, Lnodes.remapping=U1.GAL_qnames) U1.uqnames_ncomptx <- countQueryHits(U1.compOV10) names(U1.uqnames_ncomptx) <- U1.uqnames table(U1.uqnames_ncomptx) mean(U1.uqnames_ncomptx >= 1) @ 77.5\% of the reads are ``splice compatible'' with at least 1 transcript in \Rcode{exbytx}. Number of ``splice compatible'' reads for each transcript: <>= U1.exbytx_ncompOV10 <- countSubjectHits(U1.compOV10) names(U1.exbytx_ncompOV10) <- names(exbytx) mean(U1.exbytx_ncompOV10 >= 50) @ Only 0.87\% of the transcripts in \Rcode{exbytx} are ``splice compatible'' with at least 50 reads. Top 10 transcripts: <>= head(sort(U1.exbytx_ncompOV10, decreasing=TRUE), n=10) @ Note that this ``top 10'' is slightly different from the ``top 10'' we obtained earlier when we counted {\bf all} the overlaps. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Detect ``splice compatible'' paired-end overlaps} \subsubsection{``Splice compatible'' paired-end encodings} WARNING: For paired-end encodings, \Rcode{isCompatibleWithSplicing} considers that the encoding is ``splice compatible'' if its 2 halves are ``splice compatible''. This can produce false positives if for example the right end of the alignment is located upstream of the left end in transcript space. The paired-end read could not come from this transcript. To eliminate these false positives, one would need to look at the position of the left and right ends in transcript space. This can be done with \Rcode{extractQueryStartInTranscript}. \Rcode{U3.ovenc} contains 13 unique paired-end encodings compatible with the splicing of the transcript: <>= sort(U3.ovenc_table[isCompatibleWithSplicing(U3.unique_encodings)]) @ Paired-end encodings \Rcode{"1{-}{-}1:i{-}{-}i:"} (100084 occurences in \Rcode{U3.ovenc}), \Rcode{"2{-}{-}1:jm{-}{-}m:af{-}{-}i:"} (2700 occurences in \Rcode{U3.ovenc}), \Rcode{"1{-}{-}2:i{-}{-}jm:a{-}{-}af:"} (2480 occurences in \Rcode{U3.ovenc}), \Rcode{"1{-}{-}1:i{-}{-}m:a{-}{-}i:"} (287 occurences in \Rcode{U3.ovenc}), and \Rcode{"2{-}{-}2:jm{-}{-}mm:af{-}{-}jm:aa{-}{-}af:"} (153 occurences in \Rcode{U3.ovenc}), correspond to the following paired-end overlaps: \begin{itemize} \item \Rcode{"1{-}{-}1:i{-}{-}i:"} \begin{verbatim} - paired-end read (no skipped region on the first end, no skipped region on the last end): oooo oooo - transcript: ... >>>>>>>>>>>>>>>> ... \end{verbatim} \item \Rcode{"2{-}{-}1:jm{-}{-}m:af{-}{-}i:"} \begin{verbatim} - paired-end read (1 skipped region on the first end, no skipped region on the last end): ooo---o oooo - transcript: ... >>>>>>>> >>>>>>>>>>> ... \end{verbatim} \item \Rcode{"1{-}{-}2:i{-}{-}jm:a{-}{-}af:"} \begin{verbatim} - paired-end read (no skipped region on the first end, 1 skipped region on the last end): oooo oo---oo - transcript: ... >>>>>>>>>>>>>> >>>>>>>>> ... \end{verbatim} \item \Rcode{"1{-}{-}1:i{-}{-}m:a{-}{-}i:"} \begin{verbatim} - paired-end read (no skipped region on the first end, no skipped region on the last end): oooo oooo - transcript: ... >>>>>>>>> >>>>>>> ... \end{verbatim} \item \Rcode{"2{-}{-}2:jm{-}{-}mm:af{-}{-}jm:aa{-}{-}af:"} \begin{verbatim} - paired-end read (1 skipped region on the first end, 1 skipped region on the last end): ooo---o oo---oo - transcript: ... >>>>>> >>>>>>> >>>>> ... \end{verbatim} \end{itemize} Note: switch use of ``first'' and ``last'' above if the read was ``flipped''. <>= U3.OV00_is_comp <- isCompatibleWithSplicing(U3.ovenc) table(U3.OV00_is_comp) # 106835 "splice compatible" paired-end overlaps @ Finally, let's extract the ``splice compatible'' paired-end overlaps from \Rcode{U3.OV00}: <>= U3.compOV00 <- U3.OV00[U3.OV00_is_comp] @ Note that, like with our single-end reads, high-level convenience wrapper \Rfunction{findCompatibleOverlaps} can be used for computing the ``splice compatible'' paired-end overlaps directly between a \Rclass{GAlignmentPairs} object (containing paired-end reads) and a \Rclass{GRangesList} object (containing transcripts): <>= U3.compOV00_again <- findCompatibleOverlaps(U3.GALP, exbytx) stopifnot(identical(U3.compOV00_again, U3.compOV00)) @ \subsubsection{Tabulate the ``splice compatible'' paired-end overlaps} Number of ``splice compatible'' transcripts for each alignment pair in \Rcode{U3.GALP}: <>= U3.GALP_ncomptx <- countQueryHits(U3.compOV00) mcols(U3.GALP)$ncomptx <- U3.GALP_ncomptx head(U3.GALP) table(U3.GALP_ncomptx) mean(U3.GALP_ncomptx >= 1) @ 69.7\% of the alignment pairs in \Rcode{U3.GALP} are ``splice compatible'' with at least 1 transcript in \Rcode{exbytx}. Note that high-level convenience wrapper \Rfunction{countCompatibleOverlaps} can be used directly on \Rcode{U3.GALP} and \Rcode{exbytx} for computing \Rcode{U3.GALP\_ncomptx}: <>= U3.GALP_ncomptx_again <- countCompatibleOverlaps(U3.GALP, exbytx) stopifnot(identical(U3.GALP_ncomptx_again, U3.GALP_ncomptx)) @ Number of ``splice compatible'' transcripts for each template: <>= U3.compOV10 <- remapHits(U3.compOV00, Lnodes.remapping=U3.GALP_qnames) U3.uqnames_ncomptx <- countQueryHits(U3.compOV10) names(U3.uqnames_ncomptx) <- U3.uqnames table(U3.uqnames_ncomptx) mean(U3.uqnames_ncomptx >= 1) @ 70.7\% of the templates are ``splice compatible'' with at least 1 transcript in \Rcode{exbytx}. Number of ``splice compatible'' templates for each transcript: <>= U3.exbytx_ncompOV10 <- countSubjectHits(U3.compOV10) names(U3.exbytx_ncompOV10) <- names(exbytx) mean(U3.exbytx_ncompOV10 >= 50) @ Only 0.7\% of the transcripts in \Rcode{exbytx} are ``splice compatible'' with at least 50 templates. Top 10 transcripts: <>= head(sort(U3.exbytx_ncompOV10, decreasing=TRUE), n=10) @ Note that this ``top 10'' is slightly different from the ``top 10'' we obtained earlier when we counted {\bf all} the paired-end overlaps. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Compute the {\it reference query sequences} and project them on the transcriptome} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Compute the {\it reference query sequences}} The {\it reference query sequences} are the query sequences {\bf after} alignment, by opposition to the {\it original query sequences} (aka ``true'' or ``real'' query sequences) which are the query sequences {\bf before} alignment. The {\it reference query sequences} can easily be computed by extracting the nucleotides mapped to each read from the reference genome. This of course requires that we have access to the reference genome used by the aligner. In Bioconductor, the full genome sequence for the dm3 assembly is stored in the \Rpackage{BSgenome.Dmelanogaster.UCSC.dm3} data package \footnote{See \url{http://bioconductor.org/packages/release/data/annotation/} for the full list of annotation packages available in the current release of Bioconductor.}: <>= library(BSgenome.Dmelanogaster.UCSC.dm3) Dmelanogaster @ To extract the portions of the reference genome corresponding to the ranges in \Rcode{U1.grl}, we can use the \Rfunction{extractTranscriptSeqs} function defined in the \Rpackage{GenomicFeatures} package: <>= library(GenomicFeatures) U1.GAL_rqseq <- extractTranscriptSeqs(Dmelanogaster, U1.grl) head(U1.GAL_rqseq) @ When reads are paired-end, we need to extract separately the ranges corresponding to their {\it first} ends (aka {\it first} segments in BAM jargon) and those corresponding to their {\it last} ends (aka {\it last} segments in BAM jargon): <>= U3.grl_first <- grglist(first(U3.GALP, real.strand=TRUE), order.as.in.query=TRUE) U3.grl_last <- grglist(last(U3.GALP, real.strand=TRUE), order.as.in.query=TRUE) @ Then we extract the portions of the reference genome corresponding to the ranges in \Rclass{GRangesList} objects \Rcode{U3.grl\_first} and \Rcode{U3.grl\_last}: <>= U3.GALP_rqseq1 <- extractTranscriptSeqs(Dmelanogaster, U3.grl_first) U3.GALP_rqseq2 <- extractTranscriptSeqs(Dmelanogaster, U3.grl_last) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Project the single-end alignments on the transcriptome} The \Rfunction{extractQueryStartInTranscript} function computes for each overlap the position of the {\it query start} in the transcript: <>= U1.OV00_qstart <- extractQueryStartInTranscript(U1.grl, exbytx, hits=U1.OV00, ovenc=U1.ovenc) head(subset(U1.OV00_qstart, U1.OV00_is_comp)) @ \Rcode{U1.OV00\_qstart} is a data frame with 1 row per overlap and 3 columns: \begin{enumerate} \item \Rcode{startInTranscript}: the 1-based start position of the read with respect to the transcript. Position 1 always corresponds to the first base on the 5' end of the transcript sequence. \item \Rcode{firstSpannedExonRank}: the rank of the first exon spanned by the read, that is, the rank of the exon found at position \Rcode{startInTranscript} in the transcript. \item \Rcode{startInFirstSpannedExon}: the 1-based start position of the read with respect to the first exon spanned by the read. \end{enumerate} Having this information allows us for example to compare the read and transcript nucleotide sequences for each ``splice compatible'' overlap. If we use the {\it reference query sequence} instead of the {\it original query sequence} for this comparison, then it should match {\bf exactly} the sequence found at the {\it query start} in the transcript. Let's start by using \Rfunction{extractTranscriptSeqs} again to extract the transcript sequences (aka transcriptome) from the dm3 reference genome: <>= txseq <- extractTranscriptSeqs(Dmelanogaster, exbytx) @ For each ``splice compatible'' overlap, the read sequence in \Rcode{U1.GAL\_rqseq} must be an {\it exact} substring of the transcript sequence in \Rcode{exbytx\_seq}: <>= U1.OV00_rqseq <- U1.GAL_rqseq[queryHits(U1.OV00)] U1.OV00_rqseq[flippedQuery(U1.ovenc)] <- reverseComplement(U1.OV00_rqseq[flippedQuery(U1.ovenc)]) U1.OV00_txseq <- txseq[subjectHits(U1.OV00)] stopifnot(all( U1.OV00_rqseq[U1.OV00_is_comp] == narrow(U1.OV00_txseq[U1.OV00_is_comp], start=U1.OV00_qstart$startInTranscript[U1.OV00_is_comp], width=width(U1.OV00_rqseq)[U1.OV00_is_comp]) )) @ Because of this relationship between the {\it reference query sequence} and the transcript sequence of a ``splice compatible'' overlap, and because of the relationship between the {\it original query sequences} and the {\it reference query sequences}, then the edit distance reported in the NM tag is actually the edit distance between the {\it original query} and the transcript of a ``splice compatible'' overlap. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Project the paired-end alignments on the transcriptome} For a paired-end read, the {\it query start} is the start of its ``left end''. <>= U3.OV00_Lqstart <- extractQueryStartInTranscript(U3.grl, exbytx, hits=U3.OV00, ovenc=U3.ovenc) head(subset(U3.OV00_Lqstart, U3.OV00_is_comp)) @ Note that \Rfunction{extractQueryStartInTranscript} can be called with \Rcode{for.query.right.end=TRUE} if we want this information for the ``right ends'' of the reads: <>= U3.OV00_Rqstart <- extractQueryStartInTranscript(U3.grl, exbytx, hits=U3.OV00, ovenc=U3.ovenc, for.query.right.end=TRUE) head(subset(U3.OV00_Rqstart, U3.OV00_is_comp)) @ Like with single-end reads, having this information allows us for example to compare the read and transcript nucleotide sequences for each ``splice compatible'' overlap. If we use the {\it reference query sequence} instead of the {\it original query sequence} for this comparison, then it should match {\bf exactly} the sequences of the ``left'' and ``right'' ends of the read in the transcript. Let's assign the ``left and right reference query sequences'' to each overlap: <>= U3.OV00_Lrqseq <- U3.GALP_rqseq1[queryHits(U3.OV00)] U3.OV00_Rrqseq <- U3.GALP_rqseq2[queryHits(U3.OV00)] @ For the single-end reads, the sequence associated with a ``flipped query'' just needed to be ``reverse complemented''. For paired-end reads, we also need to swap the 2 sequences in the pair: <>= flip_idx <- which(flippedQuery(U3.ovenc)) tmp <- U3.OV00_Lrqseq[flip_idx] U3.OV00_Lrqseq[flip_idx] <- reverseComplement(U3.OV00_Rrqseq[flip_idx]) U3.OV00_Rrqseq[flip_idx] <- reverseComplement(tmp) @ Let's assign the transcript sequence to each overlap: <>= U3.OV00_txseq <- txseq[subjectHits(U3.OV00)] @ For each ``splice compatible'' overlap, we expect the ``left and right reference query sequences'' of the read to be {\it exact} substrings of the transcript sequence. Let's check the ``left reference query sequences'': <>= stopifnot(all( U3.OV00_Lrqseq[U3.OV00_is_comp] == narrow(U3.OV00_txseq[U3.OV00_is_comp], start=U3.OV00_Lqstart$startInTranscript[U3.OV00_is_comp], width=width(U3.OV00_Lrqseq)[U3.OV00_is_comp]) )) @ and the ``right reference query sequences'': <>= stopifnot(all( U3.OV00_Rrqseq[U3.OV00_is_comp] == narrow(U3.OV00_txseq[U3.OV00_is_comp], start=U3.OV00_Rqstart$startInTranscript[U3.OV00_is_comp], width=width(U3.OV00_Rrqseq)[U3.OV00_is_comp]) )) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Align the reads to the transcriptome} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Aligning the reads to the reference genome is not the most efficient nor accurate way to count the number of ``splice compatible'' overlaps per {\it original query}. Supporting junction reads (i.e. reads that align with at least 1 skipped region in their CIGAR) introduces a significant computational cost during the alignment process. Then, as we've seen in the previous sections, each alignment produced by the aligner needs to be broken into a set of ranges (based on its CIGAR) and those ranges compared to the ranges of the exons grouped by transcript. A more straightforward and accurate approach is to align the reads directly to the transcriptome, and without allowing the typical skipped region that the aligner needs to introduce when aligning a junction read to the reference genome. With this approach, a ``hit'' between a read and a transcript is necessarily compatible with the splicing of the transcript. In case of a ``hit'', we'll say that the read and the transcript are ``string-based compatible'' (to differentiate from our previous notion of ``splice compatible'' overlaps that we will call ``encoding-based compatible'' in this section). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Align the single-end reads to the transcriptome} \subsubsection{Find the ``hits''} The single-end reads are in \Rcode{U1.oqseq}, the transcriptome is in \Rcode{exbytx\_seq}. Since indels were not allowed/supported during the alignment of the reads to the reference genome, we don't need to allow/support them either for aligning the reads to the transcriptome. Also since our goal is to find (and count) ``splice compatible'' overlaps between reads and transcripts, we don't need to keep track of the details of the alignments between the reads and the transcripts. Finally, since BAM file {\tt untreated1\_chr4.bam} is not the full output of the aligner but the subset obtained by keeping only the alignments located on chr4, we don't need to align \Rcode{U1.oqseq} to the full transcriptome, but only to the subset of \Rcode{exbytx\_seq} made of the transcripts located on chr4. With those simplifications in mind, we write the following function that we will use to find the ``hits'' between the reads and the transcriptome: <>= ### A wrapper to vwhichPDict() that supports IUPAC ambiguity codes in 'qseq' ### and 'txseq', and treats them as such. findSequenceHits <- function(qseq, txseq, which.txseq=NULL, max.mismatch=0) { .asHits <- function(x, pattern_length) { query_hits <- unlist(x) if (is.null(query_hits)) query_hits <- integer(0) subject_hits <- rep.int(seq_len(length(x)), elementNROWS(x)) Hits(query_hits, subject_hits, pattern_length, length(x), sort.by.query=TRUE) } .isHitInTranscriptBounds <- function(hits, qseq, txseq) { sapply(seq_len(length(hits)), function(i) { pattern <- qseq[[queryHits(hits)[i]]] subject <- txseq[[subjectHits(hits)[i]]] v <- matchPattern(pattern, subject, max.mismatch=max.mismatch, fixed=FALSE) any(1L <= start(v) & end(v) <= length(subject)) }) } if (!is.null(which.txseq)) { txseq0 <- txseq txseq <- txseq[which.txseq] } names(qseq) <- NULL other <- alphabetFrequency(qseq, baseOnly=TRUE)[ , "other"] is_clean <- other == 0L # "clean" means "no IUPAC ambiguity code" ## Find hits for "clean" original queries. qseq0 <- qseq[is_clean] pdict0 <- PDict(qseq0, max.mismatch=max.mismatch) m0 <- vwhichPDict(pdict0, txseq, max.mismatch=max.mismatch, fixed="pattern") hits0 <- .asHits(m0, length(qseq0)) hits0@nLnode <- length(qseq) hits0@from <- which(is_clean)[hits0@from] ## Find hits for non "clean" original queries. qseq1 <- qseq[!is_clean] m1 <- vwhichPDict(qseq1, txseq, max.mismatch=max.mismatch, fixed=FALSE) hits1 <- .asHits(m1, length(qseq1)) hits1@nLnode <- length(qseq) hits1@from <- which(!is_clean)[hits1@from] ## Combine the hits. query_hits <- c(queryHits(hits0), queryHits(hits1)) subject_hits <- c(subjectHits(hits0), subjectHits(hits1)) if (!is.null(which.txseq)) { ## Remap the hits. txseq <- txseq0 subject_hits <- which.txseq[subject_hits] hits0@nRnode <- length(txseq) } ## Order the hits. oo <- orderIntegerPairs(query_hits, subject_hits) hits0@from <- query_hits[oo] hits0@to <- subject_hits[oo] if (max.mismatch != 0L) { ## Keep only "in bounds" hits. is_in_bounds <- .isHitInTranscriptBounds(hits0, qseq, txseq) hits0 <- hits0[is_in_bounds] } hits0 } @ Let's compute the index of the transcripts in \Rcode{exbytx\_seq} located on chr4 (\Rfunction{findSequenceHits} will restrict the search to those transcripts): <>= chr4tx <- transcripts(txdb, vals=list(tx_chrom="chr4")) chr4txnames <- mcols(chr4tx)$tx_name which.txseq <- match(chr4txnames, names(txseq)) @ We know that the aligner tolerated up to 6 mismatches per read. The 3 following commands find the ``hits'' for each {\it original query}, then find the ``hits'' for each ``flipped {\it original query}'', and finally merge all the ``hits'' (note that the 3 commands take about 1 hour to complete on a modern laptop): <>= U1.sbcompHITSa <- findSequenceHits(U1.oqseq, txseq, which.txseq=which.txseq, max.mismatch=6) U1.sbcompHITSb <- findSequenceHits(reverseComplement(U1.oqseq), txseq, which.txseq=which.txseq, max.mismatch=6) U1.sbcompHITS <- union(U1.sbcompHITSa, U1.sbcompHITSb) @ <>= U1.sbcompHITSa <- .loadPrecomputed("U1.sbcompHITSa") U1.sbcompHITSb <- .loadPrecomputed("U1.sbcompHITSb") U1.sbcompHITS <- union(U1.sbcompHITSa, U1.sbcompHITSb) @ \subsubsection{Tabulate the ``hits''} Number of ``string-based compatible'' transcripts for each read: <>= U1.uqnames_nsbcomptx <- countQueryHits(U1.sbcompHITS) names(U1.uqnames_nsbcomptx) <- U1.uqnames table(U1.uqnames_nsbcomptx) mean(U1.uqnames_nsbcomptx >= 1) @ 77.7\% of the reads are ``string-based compatible'' with at least 1 transcript in \Rcode{exbytx}. Number of ``string-based compatible'' reads for each transcript: <>= U1.exbytx_nsbcompHITS <- countSubjectHits(U1.sbcompHITS) names(U1.exbytx_nsbcompHITS) <- names(exbytx) mean(U1.exbytx_nsbcompHITS >= 50) @ Only 0.865\% of the transcripts in \Rcode{exbytx} are ``string-based compatible'' with at least 50 reads. Top 10 transcripts: <>= head(sort(U1.exbytx_nsbcompHITS, decreasing=TRUE), n=10) @ \subsubsection{A closer look at the ``hits''} [WORK IN PROGRESS, might be removed or replaced soon...] Any ``encoding-based compatible'' overlap is of course ``string-based compatible'': <>= stopifnot(length(setdiff(U1.compOV10, U1.sbcompHITS)) == 0) @ but the reverse is not true: <>= length(setdiff(U1.sbcompHITS, U1.compOV10)) @ %To understand why the {\it overlap encodings} approach doesn't find all %the ``string-based compatible'' hits, let's look at the second hit in %\Rcode{setdiff(U1.sbcompHITS, U1.compOV10)}. This is a perfect hit between %read SRR031728.4692406 and transcript 18924: % %<<>>= %matchPattern(U1.oqseq[[6306]], txseq[[18924]]) %U1.GAL_idx <- which(U1.GAL_qnames == "SRR031728.4692406") %U1.GAL[U1.GAL_idx] %U1.GAL_idx %in% queryHits(U1.OV00) %U1.GAL[12636] %which(queryHits(U1.OV00) == 12636) %U1.OV00[305] %as.character(encoding(U1.ovenc)[305]) %@ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Align the paired-end reads to the transcriptome} [COMING SOON...] %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Detect ``almost splice compatible'' overlaps} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% In many aspects, ``splice compatible'' overlaps can be seen as perfect. We are now insterested in a less perfect type of overlap where the read overlaps the transcript in a way that {\it would} be ``splice compatible'' if 1 or more exons were removed from the transcript. In that case we say that the overlap is ``almost splice compatible'' with the transcript. The \Rfunction{isCompatibleWithSkippedExons} function can be used on an \Rclass{OverlapEncodings} object to detect this type of overlap. Note that \Rfunction{isCompatibleWithSkippedExons} can also be used on a character vector of factor. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Detect ``almost splice compatible'' single-end overlaps} \subsubsection{``Almost splice compatible'' single-end encodings} \Rcode{U1.ovenc} contains 7 unique encodings ``almost splice compatible'' with the splicing of the transcript: <>= sort(U1.ovenc_table[isCompatibleWithSkippedExons(U1.unique_encodings)]) @ Encodings \Rcode{"2:jm:am:af:"} (1015 occurences in \Rcode{U1.ovenc}), \Rcode{"2:jm:am:am:af:"} (144 occurences in \Rcode{U1.ovenc}), and \Rcode{"3:jmm:agm:aam:aaf:"} (21 occurences in \Rcode{U1.ovenc}), correspond to the following overlaps: \begin{itemize} \item \Rcode{"2:jm:am:af:"} \begin{verbatim} - read (1 skipped region): ooooo----------ooo - transcript: ... >>>>>>> >>>> >>>>>>>> ... \end{verbatim} \item \Rcode{"2:jm:am:am:af:"} \begin{verbatim} - read (1 skipped region): ooooo------------------ooo - transcript: ... >>>>>>> >>>> >>>>> >>>>>>>> ... \end{verbatim} \item \Rcode{"3:jmm:agm:aam:aaf:"} \begin{verbatim} - read (2 skipped regions): oo---oooo-----------oo - transcript: ... >>>>>>> >>>> >>>>> >>>>>>>> ... \end{verbatim} \end{itemize} <>= U1.OV00_is_acomp <- isCompatibleWithSkippedExons(U1.ovenc) table(U1.OV00_is_acomp) # 1202 "almost splice compatible" overlaps @ Finally, let's extract the ``almost splice compatible'' overlaps from \Rcode{U1.OV00}: <>= U1.acompOV00 <- U1.OV00[U1.OV00_is_acomp] @ \subsubsection{Tabulate the ``almost splice compatible'' single-end overlaps} Number of ``almost splice compatible'' transcripts for each alignment in \Rcode{U1.GAL}: <>= U1.GAL_nacomptx <- countQueryHits(U1.acompOV00) mcols(U1.GAL)$nacomptx <- U1.GAL_nacomptx head(U1.GAL) table(U1.GAL_nacomptx) mean(U1.GAL_nacomptx >= 1) @ Only 0.27\% of the alignments in \Rcode{U1.GAL} are ``almost splice compatible'' with at least 1 transcript in \Rcode{exbytx}. Number of ``almost splice compatible'' alignments for each transcript: <>= U1.exbytx_nacompOV00 <- countSubjectHits(U1.acompOV00) names(U1.exbytx_nacompOV00) <- names(exbytx) table(U1.exbytx_nacompOV00) mean(U1.exbytx_nacompOV00 >= 50) @ Only 0.017\% of the transcripts in \Rcode{exbytx} are ``almost splice compatible'' with at least 50 alignments in \Rcode{U1.GAL}. Finally note that the ``query start in transcript'' values returned by \Rfunction{extractQueryStartInTranscript} are also defined for ``almost splice compatible'' overlaps: <>= head(subset(U1.OV00_qstart, U1.OV00_is_acomp)) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Detect ``almost splice compatible'' paired-end overlaps} \subsubsection{``Almost splice compatible'' paired-end encodings} \Rcode{U3.ovenc} contains 5 unique paired-end encodings ``almost splice compatible'' with the splicing of the transcript: <>= sort(U3.ovenc_table[isCompatibleWithSkippedExons(U3.unique_encodings)]) @ Paired-end encodings \Rcode{"2{-}{-}1:jm{-}{-}m:am{-}{-}m:af{-}{-}i:"} (73 occurences in \Rcode{U3.ovenc}), \Rcode{"1{-}{-}2:i{-}{-}jm:a{-}{-}am:a{-}{-}af:"} (53 occurences in \Rcode{U3.ovenc}), and \Rcode{"2{-}{-}2:jm{-}{-}mm:am{-}{-}mm:af{-}{-}jm:aa{-}{-}af:"} (9 occurences in \Rcode{U3.ovenc}), correspond to the following paired-end overlaps: \begin{itemize} \item \Rcode{"2{-}{-}1:jm{-}{-}m:am{-}{-}m:af{-}{-}i:"} \begin{verbatim} - paired-end read (1 skipped region on the first end, no skipped region on the last end): ooo----------o oooo - transcript: ... >>>>> >>>> >>>>>>>>> ... \end{verbatim} \item \Rcode{"1{-}{-}2:i{-}{-}jm:a{-}{-}am:a{-}{-}af:"} \begin{verbatim} - paired-end read (no skipped region on the first end, 1 skipped region on the last end): oooo oo---------oo - transcript: ... >>>>>>>>>>> >>> >>>>>> ... \end{verbatim} \item \Rcode{"2{-}{-}2:jm{-}{-}mm:am{-}{-}mm:af{-}{-}jm:aa{-}{-}af:"} \begin{verbatim} - paired-end read (1 skipped region on the first end, 1 skipped region on the last end): o----------ooo oo---oo - transcript: ... >>>>> >>>> >>>>>>>> >>>>>> ... \end{verbatim} \end{itemize} Note: switch use of ``first'' and ``last'' above if the read was ``flipped''. <>= U3.OV00_is_acomp <- isCompatibleWithSkippedExons(U3.ovenc) table(U3.OV00_is_acomp) # 141 "almost splice compatible" paired-end overlaps @ Finally, let's extract the ``almost splice compatible'' paired-end overlaps from \Rcode{U3.OV00}: <>= U3.acompOV00 <- U3.OV00[U3.OV00_is_acomp] @ \subsubsection{Tabulate the ``almost splice compatible'' paired-end overlaps} Number of ``almost splice compatible'' transcripts for each alignment pair in \Rcode{U3.GALP}: <>= U3.GALP_nacomptx <- countQueryHits(U3.acompOV00) mcols(U3.GALP)$nacomptx <- U3.GALP_nacomptx head(U3.GALP) table(U3.GALP_nacomptx) mean(U3.GALP_nacomptx >= 1) @ Only 0.2\% of the alignment pairs in \Rcode{U3.GALP} are ``almost splice compatible'' with at least 1 transcript in \Rcode{exbytx}. Number of ``almost splice compatible'' alignment pairs for each transcript: <>= U3.exbytx_nacompOV00 <- countSubjectHits(U3.acompOV00) names(U3.exbytx_nacompOV00) <- names(exbytx) table(U3.exbytx_nacompOV00) mean(U3.exbytx_nacompOV00 >= 50) @ Only 0.0034\% of the transcripts in \Rcode{exbytx} are ``almost splice compatible'' with at least 50 alignment pairs in \Rcode{U3.GALP}. Finally note that the ``query start in transcript'' values returned by \Rfunction{extractQueryStartInTranscript} are also defined for ``almost splice compatible'' paired-end overlaps: <>= head(subset(U3.OV00_Lqstart, U3.OV00_is_acomp)) head(subset(U3.OV00_Rqstart, U3.OV00_is_acomp)) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Detect novel splice junctions} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{By looking at single-end overlaps} An alignment in \Rcode{U1.GAL} with ``almost splice compatible'' overlaps but no ``splice compatible'' overlaps suggests the presence of one or more transcripts that are not in our annotations. First we extract the index of those alignments ({\it nsj} here stands for ``{\bf n}ovel {\bf s}plice {\bf j}unction''): <>= U1.GAL_is_nsj <- U1.GAL_nacomptx != 0L & U1.GAL_ncomptx == 0L head(which(U1.GAL_is_nsj)) @ We make this an index into \Rcode{U1.OV00}: <>= U1.OV00_is_nsj <- queryHits(U1.OV00) %in% which(U1.GAL_is_nsj) @ We intersect with \Rcode{U1.OV00\_is\_acomp} and then subset \Rcode{U1.OV00} to keep only the overlaps that suggest novel splicing: <>= U1.OV00_is_nsj <- U1.OV00_is_nsj & U1.OV00_is_acomp U1.nsjOV00 <- U1.OV00[U1.OV00_is_nsj] @ For each overlap in \Rcode{U1.nsjOV00}, we extract the ranks of the skipped exons (we use a list for this as there might be more than 1 skipped exon per overlap): <>= U1.nsjOV00_skippedex <- extractSkippedExonRanks(U1.ovenc)[U1.OV00_is_nsj] names(U1.nsjOV00_skippedex) <- queryHits(U1.nsjOV00) table(elementNROWS(U1.nsjOV00_skippedex)) @ Finally, we split \Rcode{U1.nsjOV00\_skippedex} by transcript names: <>= f <- factor(names(exbytx)[subjectHits(U1.nsjOV00)], levels=names(exbytx)) U1.exbytx_skippedex <- split(U1.nsjOV00_skippedex, f) @ \Rcode{U1.exbytx\_skippedex} is a named list of named lists of integer vectors. The first level of names (outer names) are transcript names and the second level of names (inner names) are alignment indices into \Rcode{U1.GAL}: <>= head(names(U1.exbytx_skippedex)) # transcript names @ Transcript FBtr0089124 receives 7 hits. All of them skip exons 9 and 10: <>= U1.exbytx_skippedex$FBtr0089124 @ Transcript FBtr0089147 receives 4 hits. Two of them skip exon 2, one of them skips exons 2 to 6, and one of them skips exon 10: <>= U1.exbytx_skippedex$FBtr0089147 @ A few words about the interpretation of \Rcode{U1.exbytx\_skippedex}: Because of how we've conducted this analysis, the aligments reported in \Rcode{U1.exbytx\_skippedex} are guaranteed to not have any ``splice compatible'' overlaps with other known transcripts. All we can say, for example in the case of transcript FBtr0089124, is that the 7 reported hits that skip exons 9 and 10 show evidence of one or more unknown transcripts with a splice junction that corresponds to the gap between exons 8 and 11. But without further analysis, we can't make any assumption about the exons structure of those unknown transcripts. In particular, we cannot assume the existence of an unknown transcript made of the same exons as transcript FBtr0089124 minus exons 9 and 10! %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{By looking at paired-end overlaps} [COMING SOON...] %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{\Rcode{sessionInfo()}} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% <>= sessionInfo() @ \end{document} GenomicAlignments/vignettes/WorkingWithAlignedNucleotides.Rnw0000644000175200017520000003411614516004601025640 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{Working with aligned nucleotides} %\VignetteDepends{GenomicAlignments, RNAseqData.HNRNPC.bam.chr14, pasillaBamSubset, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Dmelanogaster.UCSC.dm3, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene} %\VignetteKeywords{sequence, sequencing, alignments} %\VignettePackage{GenomicAlignments} \documentclass{article} <>= BiocStyle::latex() @ \title{Working with aligned nucleotides (WORK-IN-PROGRESS!)} \author{Herv\'e Pag\`es} \date{Last modified: January 2014; Compiled: \today} \begin{document} \maketitle \tableofcontents %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Introduction} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% This vignette belongs to the \Rpackage{GenomicAlignments} package. It illustrates how to use the package for working with the nucleotide content of aligned reads. After the reads generated by a high-throughput sequencing experiment have been aligned to a reference genome, the questions that are being asked about these alignments typically fall in two broad categories: {\bf positional only} and {\bf nucleotide-related}. {\bf Positional only} questions are about the position of the alignments with respect to the reference genome. Note that the position of an alignment is actually better described in terms of genomic ranges (1 range for an alignment with no gaps, 2 or more ranges for an alignment with gaps). Knowing the ranges of the alignments is sufficient to perform common tasks like {\it read counting} or for {\it computing the coverage}. {\it Read counting} is the process of counting the number of aligned reads per gene or exon and is typically performed in the context of a differential analysis. This task can be accomplished with the \Rfunction{summarizeOverlaps} function provided in the \Rpackage{GenomicAlignments} package and is explained in details in the ``Counting reads with summarizeOverlaps'' vignette (also located in this package). {\it Computing the coverage} is often the preliminary step to peak detection (ChIP-seq analysis) or to a copy number analysis. It can be accomplished with the \Rfunction{coverage} function. See \Rcode{?\`{}coverage-methods\`{}} for more information. {\bf Nucleotide-related} questions are about the nucleotide content of the alignments. In particular how this content compares to the corresponding nucleotides in the reference genome. These questions typically arise in the context of small genetic variation detection between one or more samples and a reference genome. The \Rpackage{GenomicAlignments} package provides a suite of low- to mid-level tools for dealing with {\bf nucleotide-related} questions about the alignments. In this vignette we illustrate their use on the single-end and paired-end reads of an RNA-seq experiment. Note that these tools do NOT constitute a complete variant toolbox. If this is what you're looking for, other \Bioconductor{} packages might be more appropriate. See the GeneticVariability and SNP views at this URL \url{http://bioconductor.org/packages/release/BiocViews.html#___AssayDomains} for a complete list of packages that deal with small genetic variations. Most of them provide tools of higher level than the tools described in this vignette. See for example the \Rpackage{VariantTools} and \Rpackage{VariantAnnotation} packages for a complete variant toolbox (including variant calling capabilities). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Load the aligned reads and their sequences from a BAM file} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% In this section, we illustrate how aligned reads and their sequences can be loaded from a BAM file. The reads we're going to use for this are paired-end reads from a published study by Zarnack et al., 2012. A subset of these reads are stored in the BAM files located in the \Rpackage{RNAseqData.HNRNPC.bam.chr14} data package. The package contains 8 BAM files, 1 per sequencing run: <>= library(RNAseqData.HNRNPC.bam.chr14) bamfiles <- RNAseqData.HNRNPC.bam.chr14_BAMFILES names(bamfiles) # the names of the runs @ Each BAM file was obtained by (1) aligning the reads (paired-end) to the full hg19 genome with TopHat2, and then (2) subsetting to keep only alignments on chr14. See \Rcode{?RNAseqData.HNRNPC.bam.chr14} for more information about this data set. As a preliminary step, we check whether the BAM files contain single- or paired-end alignments. This can be done with the \Rfunction{quickBamFlagSummary} utility from the \Rpackage{Rsamtools} package: <>= library(Rsamtools) quickBamFlagSummary(bamfiles[1], main.groups.only=TRUE) @ This confirms that all the alignments in the 1st BAM file (run ERR127306) are paired-end. This means that we should preferably load them with the \Rfunction{readGAlignmentPairs} function from the \Rpackage{GenomicAlignments} package. However for the purpose of keeping things simple, we will ignore the pairing for now and load only the alignments corresponding to the first segment of the pairs. We will use the \Rfunction{readGAlignments} function from the \Rpackage{GenomicAlignments} package for this, together with a \Rclass{ScanBamParam} object for the filtering. See \Rcode{?ScanBamParam} in the \Rpackage{Rsamtools} package for the details. Furthermore, while preparing the \Rclass{ScanBamParam} object to perform the filtering, we'll also get rid of the PCR or optical duplicates (flag bit 0x400 in the SAM format, see the SAM Spec \footnote{\url{http://samtools.sourceforge.net/}} for the details), as well as reads not passing quality controls (flag bit 0x200 in the SAM format). Finally we also request the read sequences (a.k.a. the {\it segment sequences} in the SAM Spec, stored in the SEQ field) via the \Rclass{ScanBamParam} object: <>= flag1 <- scanBamFlag(isFirstMateRead=TRUE, isSecondMateRead=FALSE, isDuplicate=FALSE, isNotPassingQualityControls=FALSE) param1 <- ScanBamParam(flag=flag1, what="seq") @ We're now ready to load the alignments and their sequences. Note that we use \Rcode{use.names=TRUE} in order to also load the {\it query names} (a.k.a. the {\it query template names} in the SAM Spec, stored in the QNAME field) from the BAM file. \Rfunction{readGAlignments} will use them to set the names of the returned object: <>= library(GenomicAlignments) gal1 <- readGAlignments(bamfiles[1], use.names=TRUE, param=param1) @ This returns a \Rclass{GAlignments} object. The read sequences are stored in the \Rcode{seq} metadata column of the object: <>= mcols(gal1)$seq @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Compute the {\it original query sequences}} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Because the BAM format imposes that the read sequence is ``reverse complemented'' when a read aligns to the minus strand, we need to ``reverse complement'' it again to restore the {\it original query sequences} (i.e. the sequences before alignment, that is, as they can be seen in the FASTQ file assuming that the aligner didn't perform any hard-clipping on them): <>= oqseq1 <- mcols(gal1)$seq is_on_minus <- as.logical(strand(gal1) == "-") oqseq1[is_on_minus] <- reverseComplement(oqseq1[is_on_minus]) @ Because the aligner used to align the reads can report more than 1 alignment per read (i.e. per sequence stored in the FASTQ file), we shouldn't expect the names of \Rcode{gal1} to be unique: <>= is_dup <- duplicated(names(gal1)) table(is_dup) @ However, sequences with the same {\it query name} should correspond to the same {\it original query} and therefore should be the same. Let's do a quick sanity check: <>= dup2unq <- match(names(gal1), names(gal1)) stopifnot(all(oqseq1 == oqseq1[dup2unq])) @ Finally, let's reduce \Rcode{oqseq1} to one {\it original query sequence} per unique {\it query name} (like in the FASTQ file containing the 1st end of the unaligned reads): <>= oqseq1 <- oqseq1[!is_dup] @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Mismatches, indels, and gaps} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Because the aligner possibly tolerated a small number of mismatches, indels, and/or gaps during the alignment process, the sequences in \Rcode{mcols(gal1)\$seq} gnerally don't match exactly the reference genome. The information of where indels and/or gaps occur in the alignments is represented in the CIGAR strings. Let's have a look at these string. First the most frequent cigars: <>= head(sort(table(cigar(gal1)), decreasing=TRUE)) @ Then a summary of the total number of insertions (I), deletions (D), and gaps (N): <>= colSums(cigarOpTable(cigar(gal1))) @ This tells us that the aligner that was used supports indels (I/D) and junction reads (N). Finally we count and summarize the number of gaps per alignment: <>= table(njunc(gal1)) @ Some reads contain up to 3 gaps (i.e. span 3 splice junctions). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Put the read sequences and reference sequences ``side by side''} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% TODO (with \Rfunction{sequenceLayer}) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{OLD STUFF (needs to be recycled/updated)} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Load paired-end reads from a BAM file} BAM file {\tt untreated3\_chr4.bam} (located in the \Rpackage{pasillaBamSubset} data package) contains paired-end reads from the ``Pasilla'' experiment and aligned against the dm3 genome (see \Rcode{?untreated3\_chr4} in the \Rpackage{pasillaBamSubset} package for more information about those reads). We use the \Rfunction{readGAlignmentPairs} function to load them into a \Rclass{GAlignmentPairs} object: <>= library(pasillaBamSubset) flag0 <- scanBamFlag(isDuplicate=FALSE, isNotPassingQualityControls=FALSE) param0 <- ScanBamParam(flag=flag0) U3.galp <- readGAlignmentPairs(untreated3_chr4(), use.names=TRUE, param=param0) head(U3.galp) @ The \Rcode{show} method for \Rclass{GAlignmentPairs} objects displays two {\tt ranges} columns, one for the {\it first} alignment in the pair (the left column), and one for the {\it last} alignment in the pair (the right column). The {\tt strand} column corresponds to the strand of the {\it first} alignment. <>= head(first(U3.galp)) head(last(U3.galp)) @ According to the SAM format specifications, the aligner is expected to mark each alignment pair as {\it proper} or not (flag bit 0x2 in the SAM format). The SAM Spec only says that a pair is {\it proper} if the {\it first} and {\it last} alignments in the pair are ``properly aligned according to the aligner''. So the exact criteria used for setting this flag is left to the aligner. We use \Rcode{isProperPair} to extract this flag from the \Rclass{GAlignmentPairs} object: <>= table(isProperPair(U3.galp)) @ Even though we could do {\it overlap encodings} with the full object, we keep only the {\it proper} pairs for our downstream analysis: <>= U3.GALP <- U3.galp[isProperPair(U3.galp)] @ Because the aligner used to align those reads can report more than 1 alignment per {\it original query template} (i.e. per pair of sequences stored in the input files, typically 1 FASTQ file for the {\it first} ends and 1 FASTQ file for the {\it last} ends), we shouldn't expect the names of \Rcode{U3.GALP} to be unique: <>= U3.GALP_names_is_dup <- duplicated(names(U3.GALP)) table(U3.GALP_names_is_dup) @ Storing the {\it query template names} in a factor will be useful: <>= U3.uqnames <- unique(names(U3.GALP)) U3.GALP_qnames <- factor(names(U3.GALP), levels=U3.uqnames) @ as well as having the mapping between each {\it query template name} and its first occurence in \Rcode{U3.GALP\_qnames}: <>= U3.GALP_dup2unq <- match(U3.GALP_qnames, U3.GALP_qnames) @ Our reads can have up to 1 gap per end: <>= head(unique(cigar(first(U3.GALP)))) head(unique(cigar(last(U3.GALP)))) table(njunc(first(U3.GALP)), njunc(last(U3.GALP))) @ Like for our single-end reads, the following tables indicate that indels were not allowed/supported during the alignment process: <>= colSums(cigarOpTable(cigar(first(U3.GALP)))) colSums(cigarOpTable(cigar(last(U3.GALP)))) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{\Rcode{sessionInfo()}} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% <>= sessionInfo() @ \end{document} GenomicAlignments/vignettes/precomputed_results/0000755000175200017520000000000014516004601023314 5ustar00biocbuildbiocbuildGenomicAlignments/vignettes/precomputed_results/U1.sbcompHITSa.rda0000644000175200017520000044215714516004601026421 0ustar00biocbuildbiocbuildee?nP$SDTKiDQ ))AC nx;5q}8={fykߪU~фO+3ĎbOI)ةEOwTN#VE6~>g]t?i::t?t3J(7J(bF!fbN!XbY!VXbM!b!8p#!8D!x6v!ⅈ"A!x>D/H"U!҄HB%DBd3DyC Q K!x9ī!J(ox3DBQ)DBCQ3DC Q/DMC4"DB&D0G!ڇ8D]B|߆{!zO!!D?)CC 1<B1&B1!BSBL 1-3BG! 0ĢC, "B&B!C3ĮC 7ľCq0đBq&8bC\ q=č7C q/ăB<{B DB$$DB bt!҇"cL!^ Q"D(|wB nB40DC4 ,D-C &DB|߅[!zC 1$İC12ĨCL s_B{y!Xbqe!VXbuM!6b[!vbo}!8h!N8bC\ q3ĭC q7ĽC<0ģ +D C$4!x!DC >DB 7DC Q8DECXCJBQ:[!*R!Z!jv:!ꆨC4 (DMB ~v!>qOBt iN!> y/Bt%ķ! 5DC|G^!z?bT!sC, $kB 1Ħ[Bl W!8p!8D!N8Bq!Ŀ!z!a!x[!b"iB<"y!^ .DBd )DYBd -DB'DB Q$D/x9Dx5k!Q9DohmC I/Bte!z_A!bha!bdQ!Fblq!&bRCLk!fbv9!=ļC C, 8ĒKC,"Bl1;Bbg]!vb_!8p#!8T!΅C\ q=ĭC B<$2 +DqB DB$ 4DC *DiBd)DYBd =D9C ;DB Q8+!^ Q"DBCL!*J!x;;!jN!hY!Zh0'!:]B|kn!>DC 7D?B 14Ĉ#C 16ĸCL11!bz!X C,`qK"XVD*`}"vE;@#8.Ep9+\z7"nGp7{܏AocG'ď A"x.#x1d k#ArG7|`E"(K^D%#(A*DP1JTj"x'Zԍ^F,V>}G1O#g|tDU_GMF-|AD7#D04`tc"&EsD09_#G0#̍`~Dg #XD4e`e"@#8Fp,d"8Gp!\j"Dp/D/H"u4!҆x1DCd9DYCd jɚ3JԞ5jɚ'J(Q{L֨%kEB(%BTQ%D-B *DmB AC|}C|COCt YC|߆!bha!Fbt1!ƆbRCbr_CL 1-3B 1'b~?B, ,Cl W!vbo!8tLl|Bb jgz%(Q{G֨!k~-*g%^Q2zQEI%I|-*g{.JTΖ2JT6oEelQ94[(Del٣DlQ.[y#Q? 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Different "modes" of counting are provided to resolve reads that overlap multiple features. The built-in count modes are fashioned after the "Union", "IntersectionStrict", and "IntersectionNotEmpty" methods found in the HTSeq package by Simon Anders (see references). \section{A First Example} In this example reads are counted from a list of BAM files and returned in a \Robject{matrix} for use in further analysis such as those offered in \Biocpkg{DESeq2} and \Biocpkg{edgeR}. <>= library(GenomicAlignments) library(DESeq2) library(edgeR) fls <- list.files(system.file("extdata", package="GenomicAlignments"), recursive=TRUE, pattern="*bam$", full=TRUE) features <- GRanges( seqnames = c(rep("chr2L", 4), rep("chr2R", 5), rep("chr3L", 2)), ranges = IRanges(c(1000, 3000, 4000, 7000, 2000, 3000, 3600, 4000, 7500, 5000, 5400), width=c(rep(500, 3), 600, 900, 500, 300, 900, 300, 500, 500)), "-", group_id=c(rep("A", 4), rep("B", 5), rep("C", 2))) olap <- summarizeOverlaps(features, fls) deseq <- DESeqDataSet(olap, design= ~ 1) edger <- DGEList(assay(olap), group=rownames(colData(olap))) @ %% By default, the \Rfunction{summarizeOverlaps} function iterates through files in `chunks' and with files processed in parallel. For finer-grain control over memory consumption, use the \Rfunction{BamFileList} function and specify the \Rcode{yieldSize} argument (e.g., \Rcode{yieldSize=1000000}) to determine the size of each `chunk' (smaller chunks consume less memory, but are a little less efficient to process). For controlling the number of processors in use, use \Rfunction{BiocParallel::register} to use an appropriate back-end, e.g., in linux or Mac to process on 6 cores of a single machine use \Rcode{register(MulticoreParam(workers=6))}; see the \Biocpkg{BiocParallel} vignette for further details. \section{Counting Modes} The modes of "Union", "IntersectionStrict" and "IntersectionNotEmpty" provide different approaches to resolving reads that overlap multiple features. Figure~\ref{fig-summarizeOverlaps-modes} illustrates how both simple and gapped reads are handled by the modes. Note that a read is counted a maximum of once; there is no double counting. For additional detail on the counting modes see the \Rfunction{summarizeOverlaps} man page. \begin{figure}[!h] \begin{center} \includegraphics{summarizeOverlaps-modes.pdf} \caption{Counting Modes} \label{fig-summarizeOverlaps-modes} \end{center} \end{figure} \newpage \section{Counting Features} Features can be exons, transcripts, genes or any region of interest. The number of ranges that define a single feature is specified in the \Rcode{features} argument. When annotation regions of interest are defined by a single range a \Rclass{GRanges} should be used as the \Rcode{features} argument. With a \Rclass{GRanges} it is assumed that each row (i.e., each range) represents a distinct feature. If \Rcode{features} was a \Rclass{GRanges} of exons, the result would be counts per exon. When the region of interest is defined by one or more ranges the \Rcode{features} argument should be a \Rclass{GRangesList}. In practice this could be a list of exons by gene or transcripts by gene or other similar relationships. The count result will be the same length as the \Rclass{GRangesList}. For a list of exons by genes, the result would be counts per gene. The combination of defining the features as either\Rclass{GRanges} or \Rclass{GRangesList} and choosing a counting mode controls how \Rfunction{summarizeOverlaps} assigns hits. Regardless of the mode chosen, each read is assigned to at most a single feature. These options are intended to provide flexibility in defining different biological problems. This next example demonstrates how the same read can be counted differently depending on how the \Rcode{features} argument is specified. We use a single read that overlaps two ranges, gr1 and gr2. <>= rd <- GAlignments("a", seqnames = Rle("chr1"), pos = as.integer(100), cigar = "300M", strand = strand("+")) gr1 <- GRanges("chr1", IRanges(start=50, width=150), strand="+") gr2 <- GRanges("chr1", IRanges(start=350, width=150), strand="+") @ \noindent When provided as a \Rclass{GRanges} both gr1 and gr2 are considered distinct features. In this case none of the modes count the read as a hit. Mode \Rcode{Union} discards the read becasue more than 1 feature is overlapped. \Rcode{IntersectionStrict} requires the read to fall completely within a feature which is not the case for either gr1 or gr2. \Rcode{IntersetctionNotEmpty} requires the read to overlap a single unique disjoint region of the \Rcode{features}. In this case gr1 and gr2 do not overlap so each range is considered a unique disjoint region. However, the read overlaps both gr1 and gr2 so a decision cannot be made and the read is discarded. <>= gr <- c(gr1, gr2) data.frame(union = assay(summarizeOverlaps(gr, rd)), intStrict = assay(summarizeOverlaps(gr, rd, mode="IntersectionStrict")), intNotEmpty = assay(summarizeOverlaps(gr, rd, mode="IntersectionNotEmpty"))) @ \noindent Next we count with \Rcode{features} as a \Rclass{GRangesList}; this is list of length 1 with 2 elements. Modes \Rcode{Union} and \Rcode{IntersectionNotEmpty} both count the read for the single feature. <>= grl <- GRangesList(c(gr1, gr2)) data.frame(union = assay(summarizeOverlaps(grl, rd)), intStrict = assay(summarizeOverlaps(grl, rd, mode="IntersectionStrict")), intNotEmpty = assay(summarizeOverlaps(grl, rd, mode="IntersectionNotEmpty"))) @ In this more complicated example we have 7 reads, 5 are simple and 2 have gaps in the CIGAR. There are 12 ranges that will serve as the \Robject{features}. <>= group_id <- c("A", "B", "C", "C", "D", "D", "E", "F", "G", "G", "H", "H") features <- GRanges( seqnames = Rle(c("chr1", "chr2", "chr1", "chr1", "chr2", "chr2", "chr1", "chr1", "chr2", "chr2", "chr1", "chr1")), strand = strand(rep("+", length(group_id))), ranges = IRanges( start=c(1000, 2000, 3000, 3600, 7000, 7500, 4000, 4000, 3000, 3350, 5000, 5400), width=c(500, 900, 500, 300, 600, 300, 500, 900, 150, 200, 500, 500)), DataFrame(group_id) ) reads <- GAlignments( names = c("a","b","c","d","e","f","g"), seqnames = Rle(c(rep(c("chr1", "chr2"), 3), "chr1")), pos = as.integer(c(1400, 2700, 3400, 7100, 4000, 3100, 5200)), cigar = c("500M", "100M", "300M", "500M", "300M", "50M200N50M", "50M150N50M"), strand = strand(rep.int("+", 7L))) @ \noindent Using a \Rclass{GRanges} as the \Rcode{features} all 12 ranges are considered to be different features and counts are produced for each row, <>= data.frame(union = assay(summarizeOverlaps(features, reads)), intStrict = assay(summarizeOverlaps(features, reads, mode="IntersectionStrict")), intNotEmpty = assay(summarizeOverlaps(features, reads, mode="IntersectionNotEmpty"))) @ \noindent When the data are split by group to create a \Rclass{GRangesList} the highest list-levels are treated as different features and the multiple list elements are considered part of the same features. Counts are returned for each group. <>= lst <- split(features, mcols(features)[["group_id"]]) length(lst) @ <>= data.frame(union = assay(summarizeOverlaps(lst, reads)), intStrict = assay(summarizeOverlaps(lst, reads, mode="IntersectionStrict")), intNotEmpty = assay(summarizeOverlaps(lst, reads, mode="IntersectionNotEmpty"))) @ If desired, users can supply their own counting function as the \Rcode{mode} argument and take advantage of the infrastructure for counting over multiple BAM files and parsing the results into a \Rclass{RangedSummarizedExperiment} object. See \Rcode{?'BamViews-class'} or \Rcode{?'BamFile-class'} in the \Biocpkg{Rsamtools} package. \section{\Rcode{pasilla} Data} In this excercise we count the \Biocpkg{pasilla} data by gene and by transcript then create a \Rclass{DESeqDataSet}. This object can be used in differential expression methods offered in the \Biocpkg{DESeq2} package. \subsection{source files} Files are available through NCBI Gene Expression Omnibus (GEO), accession number GSE18508. \url{http://www.ncbi.nlm.nih.gov/projects/geo/query/acc.cgi?acc=GSE18508}. SAM files can be converted to BAM with the \Rfunction{asBam} function in the \Biocpkg{Rsamtools} package. Of the seven files available, 3 are single-reads and 4 are paired-end. Smaller versions of untreated1 (single-end) and untreated2 (paired-end) have been made available in the \Biocpkg{pasillaBamSubset} package. This subset includes chromosome 4 only. \Rfunction{summarizeOverlaps} is capable of counting paired-end reads in both a \Rcode{BamFile}-method (set argument \Rcode{singleEnd=TRUE}) or a \Rcode{GAlignmentPairs}-method. For this example, we use the 3 single-end read files, \begin{itemize} \item treated1.bam \item untreated1.bam \item untreated2.bam \end{itemize} Annotations are retrieved as a GTF file from the ENSEMBL web site. We download the file our local disk, then use \Biocpkg{Rtracklayer}'s \Rfunction{import} function to parse the file to a \Rclass{GRanges} instance. <>= library(rtracklayer) fl <- paste0("ftp://ftp.ensembl.org/pub/release-62/", "gtf/drosophila_melanogaster/", "Drosophila_melanogaster.BDGP5.25.62.gtf.gz") gffFile <- file.path(tempdir(), basename(fl)) download.file(fl, gffFile) gff0 <- import(gffFile) @ Subset on the protein-coding, exon regions of chromosome 4 and split by gene id. <>= idx <- mcols(gff0)$source == "protein_coding" & mcols(gff0)$type == "exon" & seqnames(gff0) == "4" gff <- gff0[idx] ## adjust seqnames to match Bam files seqlevels(gff) <- paste("chr", seqlevels(gff), sep="") chr4genes <- split(gff, mcols(gff)$gene_id) @ \subsection{counting} The \Rcode{param} argument can be used to subset the reads in the bam file on characteristics such as position, unmapped or paired-end reads. Quality scores or the "NH" tag, which identifies reads with multiple mappings, can be included as metadata columns for further subsetting. See \Rcode{?ScanBamParam} for details about specifying the \Rcode{param} argument. <>= param <- ScanBamParam( what='qual', which=GRanges("chr4", IRanges(1, 1e6)), flag=scanBamFlag(isUnmappedQuery=FALSE, isPaired=NA), tag="NH") @ We use \Rfunction{summarizeOverlaps} to count with the default mode of "Union". If a \Rcode{param} argument is not included all reads from the BAM file are counted. <>= fls <- c("treated1.bam", "untreated1.bam", "untreated2.bam") path <- "pathToBAMFiles" bamlst <- BamFileList(fls) genehits <- summarizeOverlaps(chr4genes, bamlst, mode="Union") @ \noindent A \Rcode{CountDataSet} is constructed from the counts and experiment data in \Rclass{pasilla}. <>= expdata <- MIAME( name="pasilla knockdown", lab="Genetics and Developmental Biology, University of Connecticut Health Center", contact="Dr. Brenton Graveley", title="modENCODE Drosophila pasilla RNA Binding Protein RNAi knockdown RNA-Seq Studies", pubMedIds="20921232", url="http://www.ncbi.nlm.nih.gov/projects/geo/query/acc.cgi?acc=GSE18508", abstract="RNA-seq of 3 biological replicates of from the Drosophila melanogaster S2-DRSC cells that have been RNAi depleted of mRNAs encoding pasilla, a mRNA binding protein and 4 biological replicates of the the untreated cell line.") design <- data.frame( condition=c("treated", "untreated", "untreated"), replicate=c(1,1,2), type=rep("single-read", 3), countfiles=path(colData(genehits)[,1]), stringsAsFactors=TRUE) geneCDS <- DESeqDataSet(genehits, design=design, metadata=list(expdata=expdata)) @ If the primary interest is to count by transcript instead of by gene, the annotation file can be split on transcript id. <>= chr4tx <- split(gff, mcols(gff)$transcript_id) txhits <- summarizeOverlaps(chr4tx, bamlst) txCDS <- DESeqDataSet(txhits, design=design, metadata=list(expdata=expdata)) @ \section{References} \url{http://www-huber.embl.de/users/anders/HTSeq/doc/overview.html} \noindent\url{http://www-huber.embl.de/users/anders/HTSeq/doc/count.html} \end{document}