sequenceLayer {GenomicAlignments} | R Documentation

## Lay read sequences alongside the reference space, using their CIGARs

### Description

`sequenceLayer` can lay strings that belong to a given space (e.g. the `"query"` space) alongside another space (e.g. the `"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 `"query"` space) alongside the `"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

`x` | An XStringSet object containing strings that belong to a given space.
`cigar` | A character vector or factor of the same length as `x` containing the extended CIGAR strings (one per element in `x`).
`from, to` | A single string specifying one of the 8 supported spaces listed in the Details section below. `from` must be the current space (i.e. the space the strings in `x` belong to) and `to` is the space alonside which to lay the strings in `x`.
`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: `"reference"`, `"reference-N-regions-removed"`, `"query"`, `"query-before-hard-clipping"`, `"query-after-soft-clipping"`, `"pairwise"`, `"pairwise-N-regions-removed"`, and `"pairwise-dense"`.

Each space can be characterized by the extended CIGAR operations that are visible in it. A CIGAR operation is said to be 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 visible in all spaces, the D/N operations are visible in the `"reference"` space but not in the `"query"` space, the S operation is visible in the `"query"` space but not in the `"reference"` or in the `"query-after-soft-clipping"` space, etc...

Here are the extended CIGAR operations that are visible in each space:

1. reference: M, D, N, =, X

2. reference-N-regions-removed: M, D, =, X

3. query: M, I, S, =, X

4. query-before-hard-clipping: M, I, S, H, =, X

5. query-after-soft-clipping: M, I, =, X

6. pairwise: M, I, D, N, =, X

7. pairwise-N-regions-removed: M, I, D, =, X

8. pairwise-dense: M, =, X

`sequenceLayer` lays a string that belongs to one space alongside another by (1) removing the substrings associated with operations that are not visible anymore in the new space, and (2) injecting substrings associated with operations that become 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 `*.letter` argument.

For example, when going from the `"query"` space to the `"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 `D.letter`, and the N-substrings with the letter specified in `N.letter`. The other `*.letter` arguments are ignored in that case.

### Value

An XStringSet object of the same class and length as `x`.

### Author(s)

Hervé Pagès

### See Also

- The `stackStringsFromBam` function for stacking the read sequences (or their quality strings) stored in a BAM file on a region of interest.

- The `readGAlignments` function for loading read sequences from a BAM file (via a GAlignments object).

- The `extractAt` and `replaceAt` functions in the Biostrings package for extracting/replacing arbitrary substrings from/in a string or set of strings.

- cigar-utils for the CIGAR utility functions used internally by `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))

[Package GenomicAlignments version 1.26.0 Index]