getSeq-methods {BSgenome} | R Documentation

## getSeq methods for BSgenome and XStringSet objects

### Description

`getSeq` methods for extracting a set of sequences (or subsequences) from a BSgenome or XStringSet object. For XStringSets, there are also convenience methods on `[` that delegate to `getSeq`.

### Usage

    ## S4 method for signature 'BSgenome'
    getSeq(x, names, start=NA, end=NA, width=NA,
                     strand="+", as.character=FALSE)
    ## S4 method for signature 'XStringSet'
    getSeq(x, names)

### Arguments

`x` | A BSgenome or XStringSet object. See the `available.genomes` function for how to install a genome.
`names` | When `x` is a BSgenome, `names` must be a character vector containing the names of the sequences in `x` where to get the subsequences from, or a GRanges object, or a GRangesList object, or a named IntegerRangesList object, or a named IntegerRanges object. The IntegerRangesList or IntegerRanges object must be named according to the sequences in `x` where to get the subsequences from. If `names` is missing, then `seqnames(x)` is used. See `?`BSgenome-class`` for details on how to get the lists of single sequences and multiple sequences (respectively) contained in a BSgenome object. When `x` is a XStringSet object, `names` must be a character vector, GRanges or GRangesList object.
`start, end, width` | Vector of integers (eventually with NAs) specifying the locations of the subsequences to extract. These are not needed (and it's an error to supply them) when `names` is a GRanges, GRangesList, IntegerRangesList, or IntegerRanges object.
`strand` | A vector containing `"+"`s or/and `"-"`s. This is not needed (and it's an error to supply it) when `names` is a GRanges or GRangesList object.
`as.character` | `TRUE` or `FALSE`. Should the extracted sequences be returned in a standard character vector?
`...` | Additional arguments. (Currently ignored.)

### Details

L, the number of sequences to extract, is determined as follow:

- If `names` is a GRanges or IntegerRanges object then L = `length(names)`.

- If `names` is a GRangesList or IntegerRangesList object then L = `length(unlist(names))`.

- Otherwise, L is the length of the longest of `names`, `start`, `end` and `width` and all these arguments are recycled to this length. `NA`s and negative values in these 3 arguments are solved according to the rules of the SEW (Start/End/Width) interface (see `?solveUserSEW` for the details).

If `names` is neither a GRanges or GRangesList object, then the `strand` argument is also recycled to length L.

Here is how the names passed to the `names` argument are matched to the names of the sequences in BSgenome object `x`. For each `name` in `names`:

- (1): If `x` contains a single sequence with that name then this sequence is used for extraction;

- (2): Otherwise the names of all the elements in all the multiple sequences are searched. If the `names` argument is a character vector then `name` is treated as a regular expression and `grep` is used for this search, otherwise (i.e. when the names are supplied via a higher level object like GRanges or GRangesList) then `name` must match exactly the name of the sequence. If exactly 1 sequence is found, then it is used for extraction, otherwise (i.e. if no sequence or more than 1 sequence is found) then an error is raised.

There are convenience methods for extracting sequences from XStringSet objects using a GenomicRanges or GRangesList subscript (character subscripts are implicitly supported). Both methods are simple wrappers around `getSeq`, although the GRangesList method differs from the `getSeq` behavior in that the within-element results are concatenated and returned as an XStringSet, rather than an XStringSetList. See the examples.

### Value

Normally a DNAStringSet object (or character vector if `as.character=TRUE`).

With the 2 following exceptions:

1. A DNAStringSetList object (or CharacterList object if `as.character=TRUE`) of the same shape as `names` if `names` is a GRangesList object.

2. A DNAString object (or single character string if `as.character=TRUE`) if L = 1 and `names` is not a GRanges, GRangesList, IntegerRangesList, or IntegerRanges object.

### Note

Be aware that using `as.character=TRUE` can be very inefficient when extracting a "big" amount of DNA sequences (e.g. millions of short sequences or a small number of very long sequences).

Note that the masks in `x`, if any, are always ignored. In other words, masked regions in the genome are extracted in the same way as unmasked regions (this is achieved by dropping the masks before extraction). See `?`MaskedDNAString-class`` for more information about masked DNA sequences.

### Author(s)

H. Pagès; improvements suggested by Matt Settles and others

### See Also

`getSeq`, `available.genomes`, BSgenome-class, DNAString-class, DNAStringSet-class, MaskedDNAString-class, GRanges-class, GRangesList-class, IntegerRangesList-class, IntegerRanges-class, `grep`

### Examples

    ## ---------------------------------------------------------------------
    ## A. SIMPLE EXAMPLES
    ## ---------------------------------------------------------------------

    ## Load the Caenorhabditis elegans genome (UCSC Release ce2):
    library(BSgenome.Celegans.UCSC.ce2)

    ## Look at the index of sequences:
    Celegans

    ## Get chromosome V as a DNAString object:
    getSeq(Celegans, "chrV")
    ## which is in fact the same as doing:
    Celegans$chrV

    ## Not run:
      ## Never try this:
      getSeq(Celegans, "chrV", as.character=TRUE)
      ## or this (even worse):
      getSeq(Celegans, as.character=TRUE)

    ## End(Not run)

    ## Get the first 20 bases of each chromosome:
    getSeq(Celegans, end=20)

    ## Get the last 20 bases of each chromosome:
    getSeq(Celegans, start=-20)

    ## ---------------------------------------------------------------------
    ## B. EXTRACTING SMALL SEQUENCES FROM DIFFERENT CHROMOSOMES
    ## ---------------------------------------------------------------------

    myseqs <- data.frame(
      chr=c("chrI", "chrX", "chrM", "chrM", "chrX", "chrI", "chrM", "chrI"),
      start=c(NA, -40, 8510, 301, 30001, 9220500, -2804, -30),
      end=c(50, NA, 8522, 324, 30011, 9220555, -2801, -11),
      strand=c("+", "-", "+", "+", "-", "-", "+", "-")
    )
    getSeq(Celegans, myseqs$chr,
           start=myseqs$start, end=myseqs$end)
    getSeq(Celegans, myseqs$chr,
           start=myseqs$start, end=myseqs$end, strand=myseqs$strand)

    ## ---------------------------------------------------------------------
    ## C. USING A GRanges OBJECT
    ## ---------------------------------------------------------------------

    gr1 <- GRanges(seqnames=c("chrI", "chrI", "chrM"),
                   ranges=IRanges(start=101:103, width=9))
    gr1  # all strand values are "*"
    getSeq(Celegans, gr1)  # treats strand values as if they were "+"

    strand(gr1)[] <- "-"
    getSeq(Celegans, gr1)

    strand(gr1)[1] <- "+"
    getSeq(Celegans, gr1)

    strand(gr1)[2] <- "*"
    if (interactive())
      getSeq(Celegans, gr1)  # Error: cannot mix "*" with other strand values

    gr2 <- GRanges(seqnames=c("chrM", "NM_058280_up_1000"),
                   ranges=IRanges(start=103:102, width=9))
    gr2
    if (interactive()) {
      ## Because the sequence names are supplied via a GRanges object, they
      ## are not treated as regular expressions:
      getSeq(Celegans, gr2)  # Error: sequence NM_058280_up_1000 not found
    }

    ## ---------------------------------------------------------------------
    ## D. USING A GRangesList OBJECT
    ## ---------------------------------------------------------------------

    gr1 <- GRanges(seqnames=c("chrI", "chrII", "chrM", "chrII"),
                   ranges=IRanges(start=101:104, width=12),
                   strand="+")
    gr2 <- shift(gr1, 5)
    gr3 <- gr2
    strand(gr3) <- "-"

    grl <- GRangesList(gr1, gr2, gr3)
    getSeq(Celegans, grl)

    ## ---------------------------------------------------------------------
    ## E. EXTRACTING A HIGH NUMBER OF RANDOM 40-MERS FROM A GENOME
    ## ---------------------------------------------------------------------

    extractRandomReads <- function(x, density, readlength)
    {
        if (!is.integer(readlength))
            readlength <- as.integer(readlength)
        start <- lapply(seqnames(x),
                        function(name)
                        {
                          seqlength <- seqlengths(x)[name]
                          sample(seqlength - readlength + 1L,
                                 seqlength * density,
                                 replace=TRUE)
                        })
        names <- rep.int(seqnames(x), elementNROWS(start))
        ranges <- IRanges(start=unlist(start), width=readlength)
        strand <- strand(sample(c("+", "-"), length(names), replace=TRUE))
        gr <- GRanges(seqnames=names, ranges=ranges, strand=strand)
        getSeq(x, gr)
    }

    ## With a density of 1 read every 100 genome bases, the total number of
    ## extracted 40-mers is about 1 million:
    rndreads <- extractRandomReads(Celegans, 0.01, 40)

    ## Notes:
    ## - The short sequences in 'rndreads' can be seen as the result of a
    ##   simulated high-throughput sequencing experiment. A non-realistic
    ##   one though because:
    ##     (a) It assumes that the underlying technology is perfect (the
    ##         generated reads have no technology induced errors).
    ##     (b) It assumes that the sequenced genome is exactly the same as
    ##         the reference genome.
    ##     (c) The simulated reads can contain IUPAC ambiguity letters only
    ##         because the reference genome contains them. In a real
    ##         high-throughput sequencing experiment, the sequenced genome
    ##         of course doesn't contain those letters, but the sequencer
    ##         can introduce them in the generated reads to indicate
    ##         ambiguous base-calling.
    ## - Those reads are coming from the plus and minus strands of the
    ##   chromosomes.
    ## - With a density of 0.01 and the reads being only 40-base long, the
    ##   average coverage of the genome is only 0.4 which is low. The total
    ##   number of reads is about 1 million and it takes less than 10 sec.
    ##   to generate them.
    ## - A higher coverage can be achieved by using a higher density and/or
    ##   longer reads. For example, with a density of 0.1 and 100-base reads
    ##   the average coverage is 10. The total number of reads is about 10
    ##   millions and it takes less than 1 minute to generate them.
    ## - Those reads could easily be mapped back to the reference by using
    ##   an efficient matching tool like matchPDict() for performing exact
    ##   matching (see ?matchPDict for more information). Typically, a
    ##   small percentage of the reads (4 to 5% in our case) will hit the
    ##   reference at multiple locations. This is especially true for such
    ##   short reads, and, in a lower proportion, is still true for longer
    ##   reads, even for reads as long as 300 bases.

    ## ---------------------------------------------------------------------
    ## F. SEE THE BSgenome CACHE IN ACTION
    ## ---------------------------------------------------------------------

    options(verbose=TRUE)
    first20 <- getSeq(Celegans, end=20)
    first20
    gc()
    stopifnot(length(ls(Celegans@.seqs_cache)) == 0L)
    ## One more gc() call is needed in order to see the amount of memory in
    ## used after all the chromosomes have been removed from the cache:
    gc()

    ## ---------------------------------------------------------------------
    ## G. USING '[' FOR CONVENIENT EXTRACTION
    ## ---------------------------------------------------------------------

    seqs <- getSeq(Celegans)
    seqs[gr1]
    seqs[grl]

[Package BSgenome version 1.52.0 Index]