stackStringsFromBam {GenomicAlignments} | R Documentation

## Stack the read sequences stored in a GAlignments object or a BAM file

### Description

`stackStringsFromGAlignments` stacks the read sequences (or their quality strings) stored in a GAlignments object over a user-specified region.

`stackStringsFromBam` stacks the read sequences (or their quality strings) stored in a BAM file over a user-specified region.

`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 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

`x` | A GAlignments object with the read sequences in the `"seq"` metadata column (if `what` is set to `"seq"`), or with the the read quality strings in the `"qual"` metadata column (if `what` is set to `"qual"`). Such an object is typically obtained by specifying `param=ScanBamParam(what=c("seq", "qual"))` when reading a BAM file with calling `readGAlignments()`.
`region` | A GRanges object with exactly 1 genomic range. The read sequences (or read quality strings) will be stacked over that region.
`what` | A single string. Either `"seq"` or `"qual"`. If `"seq"` (the default), the read sequences will be stacked. If `"qual"`, the read quality strings will be stacked.
`D.letter, N.letter` | A single letter used as a filler for injections. The 2 arguments are passed down to the `sequenceLayer` function. See `?sequenceLayer` for more details.
`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 `stackStrings` function defined in the Biostrings package. See `?stackStrings` in the Biostrings package for more details.
`file, index` | The path to the BAM file containing the reads, and to its index file, respectively. The latter is given without the '.bai' extension. See `scanBam` for more information.
`param` | A ScanBamParam object containing exactly 1 genomic region (i.e. `unlist(bamWhich(param))` must have length 1). Alternatively, `param` can be a GRanges or IntegerRangesList object containing exactly 1 genomic region (the strand will be ignored in case of a GRanges object), or a character string specifying a single genomic region (in the `"chr14:5201-5300"` format).
`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.
`...` | Further arguments to be passed to alphabetFrequency.

### Details

`stackStringsFromGAlignments` performs the 3 following steps:

1. Subset GAlignments object `x` to keep only the alignments that overlap with the specified region.

2. Lay the sequences in `x` alongside the reference space, using their CIGARs. This is done with the `sequenceLayer` function.

3. Stack them on the specified region. This is done with the `stackStrings` function defined in the Biostrings package.

`stackStringsFromBam` performs the 3 following steps:

1. 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 `readGAlignments` function. Note that if the file contains paired-end reads, the pairing is ignored.

2. Same as `stackStringsFromGAlignments`.

3. Same as `stackStringsFromGAlignments`.

`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 `stackStringsFromBam`: A rectangular (i.e. constant-width) DNAStringSet object (if `what` is `"seq"`) or BStringSet object (if `what` is `"qual"`).

For `alphabetFrequencyFromBam`: By default a matrix like one returned by `alphabetFrequency`. The matrix has 1 row per nucleotide position in the specified region.

### Note

TWO IMPORTANT CAVEATS ABOUT `stackStringsFromGAlignments` AND `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 `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(s)

Hervé Pagès

### See Also

- The `pileLettersAt` function for piling the letters of a set of aligned reads on top of a set of genomic positions.

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

- The `sequenceLayer` function for laying read sequences alongside the reference space, using their CIGARs.

- The `stackStrings` function in the Biostrings package for stacking an arbitrary XStringSet object.

- The `alphabetFrequency` function in the Biostrings package.

- The SAMtools mpileup command available at 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.

[Package GenomicAlignments version 1.24.0 Index]