cigar-utils {GenomicAlignments} | R Documentation

## 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

`cigar` | A character vector or factor containing the extended CIGAR strings. It can be of arbitrary length except for `queryLoc2refLoc` which only accepts a single CIGAR (as a character vector or factor of length 1).
`ops` | Character vector containing the extended CIGAR operations to actually consider. Zero-length operations or operations not listed `ops` are ignored.
`flag` | `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 `flag` is supplied, then `cigarRangesAlongReferenceSpace`, `cigarRangesAlongQuerySpace`, `cigarRangesAlongPairwiseSpace`, and `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 `flag` is supplied, then `cigarWidthAlongReferenceSpace`, `cigarWidthAlongQuerySpace`, and `cigarWidthAlongPairwiseSpace` return `NA`s for unmapped reads.
`N.regions.removed` | `TRUE` or `FALSE`. If `TRUE`, then `cigarRangesAlongReferenceSpace` and `cigarWidthAlongReferenceSpace` report ranges/widths with respect to the "reference" space from which the N regions have been removed, and `cigarRangesAlongPairwiseSpace` and `cigarWidthAlongPairwiseSpace` report them with respect to the "pairwise" space from which the N regions have been removed.
`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 `cigar`), or the same length as `cigar`.
`f` | `NULL` or a factor of length `cigar`. If `NULL`, then the ranges are grouped by alignment i.e. the returned IRangesList object has 1 list element per element in `cigar`. Otherwise they are grouped by factor level i.e. the returned IRangesList object has 1 list element per level in `f` and is named with those levels. For example, if `f` is a factor containing the chromosome for each read, then the returned IRangesList object will have 1 list element per chromosome and each list element will contain all the ranges on that chromosome.
`drop.empty.ranges` | Should empty ranges be dropped?
`reduce.ranges` | Should adjacent ranges coming from the same cigar be merged or not? Using `TRUE` can significantly reduce the size of the returned object.
`with.ops` | `TRUE` or `FALSE` indicating whether the returned ranges should be named with their corresponding CIGAR operation.
`before.hard.clipping` | `TRUE` or `FALSE`. If `TRUE`, then `cigarRangesAlongQuerySpace` and `cigarWidthAlongQuerySpace` report ranges/widths with respect to the "query" space to which the H regions have been added. `before.hard.clipping` and `after.soft.clipping` cannot both be `TRUE`.
`after.soft.clipping` | `TRUE` or `FALSE`. If `TRUE`, then `cigarRangesAlongQuerySpace` and `cigarWidthAlongQuerySpace` report ranges/widths with respect to the "query" space from which the S regions have been removed. `before.hard.clipping` and `after.soft.clipping` cannot both be `TRUE`.
`dense` | `TRUE` or `FALSE`. If `TRUE`, then `cigarRangesAlongPairwiseSpace` and `cigarWidthAlongPairwiseSpace` report ranges/widths with respect to the "pairwise" space from which the I, D, and N regions have been removed. `N.regions.removed` and `dense` cannot both be `TRUE`.
`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 `drop.D.ranges` is `TRUE`, then Ds and Ns in the CIGAR are equivalent.
`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 `?solveUserSEW` for the details).
`qloc` | An integer vector containing "query-based locations" i.e. 1-based locations relative to the query sequence stored in the SAM/BAM file.
`qlocs` | A list of the same length as `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

`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 http://samtools.sourceforge.net/ for the list of extended CIGAR operations and their meanings.

For `explodeCigarOps` and `explodeCigarOpLengths`: Both functions return a list of the same length as `cigar` where each list element is a character vector (for `explodeCigarOps`) or an integer vector (for `explodeCigarOpLengths`). The 2 lists have the same shape, that is, same `length()` and same `elementNROWS()`. The i-th character vector in the list returned by `explodeCigarOps` contains one single-letter string per CIGAR operation in `cigar[i]`. The i-th integer vector in the list returned by `explodeCigarOpLengths` contains the corresponding CIGAR operation lengths. Zero-length operations or operations not listed in `ops` are ignored.

For `cigarToRleList`: A CompressedRleList object.

For `cigarOpTable`: An integer matrix with number of rows equal to the length of `cigar` and nine columns, one for each extended CIGAR operation.

For `cigarRangesAlongReferenceSpace`, `cigarRangesAlongQuerySpace`, `cigarRangesAlongPairwiseSpace`, and `extractAlignmentRangesOnReference`: An IRangesList object (more precisely a CompressedIRangesList object) with 1 list element per element in `cigar`. However, if `f` is a factor, then the returned IRangesList object can be a SimpleIRangesList object (instead of CompressedIRangesList), and in that case, has 1 list element per level in `f` and is named with those levels.

For `cigarWidthAlongReferenceSpace` and `cigarWidthAlongPairwiseSpace`: An integer vector of the same length as `cigar` where each element is the width of the alignment with respect to the "reference" and "pairwise" space, respectively. More precisely, for `cigarWidthAlongReferenceSpace`, the returned widths are the lengths of the alignments on the reference, N gaps included (except if `N.regions.removed` is `TRUE`). NAs or `"*"` in `cigar` will produce NAs in the returned vector.

For `cigarWidthAlongQuerySpace`: An integer vector of the same length as `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 `before.hard.clipping` and `after.soft.clipping` are `FALSE`), this is the length of the query sequence stored in the SAM/BAM file. If `before.hard.clipping` or `after.soft.clipping` is `TRUE`, the returned widths are the lengths of the query sequences before hard clipping or after soft clipping. NAs or `"*"` in `cigar` will produce NAs in the returned vector.

For `cigarNarrow` and `cigarQNarrow`: A character vector of the same length as `cigar` containing the narrowed cigars. In addition the vector has an "rshift" attribute which is an integer vector of the same length as `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 `queryLoc2refLoc`: An integer vector of the same length as `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 `qloc`.

For `queryLocs2refLocs`: A list of the same length as `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 `qlocs`.

### Author(s)

Hervé Pagès & P. Aboyoun

### References

http://samtools.sourceforge.net/

### See Also

- The sequenceLayer function in the GenomicAlignments package for laying the query sequences alongside the "reference" or "pairwise" spaces.

- The GAlignments container for storing a set of genomic alignments.

- The IRanges, IRangesList, and RleList classes in the IRanges package.

- The `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)
    }

[Package GenomicAlignments version 1.24.0 Index]