matchPattern {Biostrings} | R Documentation

## String searching functions

### Description

A set of functions for finding all the occurrences (aka "matches" or "hits") of a given pattern (typically short) in a (typically long) reference sequence or set of reference sequences (aka the subject)

### Usage

    matchPattern(pattern, subject,
                 max.mismatch=0, min.mismatch=0,
                 with.indels=FALSE, fixed=TRUE,
                 algorithm="auto")

    countPattern(pattern, subject,
                 max.mismatch=0, min.mismatch=0,
                 with.indels=FALSE, fixed=TRUE,
                 algorithm="auto")

    vmatchPattern(pattern, subject,
                  max.mismatch=0, min.mismatch=0,
                  with.indels=FALSE, fixed=TRUE,
                  algorithm="auto", ...)

    vcountPattern(pattern, subject,
                  max.mismatch=0, min.mismatch=0,
                  with.indels=FALSE, fixed=TRUE,
                  algorithm="auto", ...)

### Arguments

`pattern` | The pattern string.
`subject` | An XString, XStringViews or MaskedXString object for `matchPattern` and `countPattern`. An XStringSet or XStringViews object for `vmatchPattern` and `vcountPattern`.
`max.mismatch, min.mismatch` | The maximum and minimum number of mismatching letters allowed (see `?`lowlevel-matching`` for the details). If non-zero, an algorithm that supports inexact matching is used.
`with.indels` | If `TRUE` then indels are allowed. In that case, `min.mismatch` must be `0` and `max.mismatch` is interpreted as the maximum "edit distance" allowed between the pattern and a match. Note that in order to avoid pollution by redundant matches, only the "best local matches" are returned. Roughly speaking, a "best local match" is a match that is locally both the closest (to the pattern P) and the shortest. More precisely, a substring S' of the subject S is a "best local match" iff:
           (a) nedit(P, S') <= max.mismatch
           (b) for every substring S1 of S':
                   nedit(P, S1) > nedit(P, S')
           (c) for every substring S2 of S that contains S':
                   nedit(P, S2) >= nedit(P, S')
One nice property of "best local matches" is that their first and last letters are guaranteed to match the letters in P that they align with.
`fixed` | If `TRUE` (the default), an IUPAC ambiguity code in the pattern can only match the same code in the subject, and vice versa. If `FALSE`, an IUPAC ambiguity code in the pattern can match any letter in the subject that is associated with the code, and vice versa. See `?`lowlevel-matching`` for more information.
`algorithm` | One of the following: `"auto"`, `"naive-exact"`, `"naive-inexact"`, `"boyer-moore"`, `"shift-or"` or `"indels"`.
`...` | Additional arguments for methods.

### Details

Available algorithms are: “naive exact”, “naive inexact”, “Boyer-Moore-like”, “shift-or” and “indels”. Not all of them can be used in all situations: restrictions apply depending on the "search criteria" i.e. on the values of the `pattern`, `subject`, `max.mismatch`, `min.mismatch`, `with.indels` and `fixed` arguments.

It is important to note that the `algorithm` argument is not part of the search criteria. This is because the supported algorithms are interchangeable, that is, if 2 different algorithms are compatible with a given search criteria, then choosing one or the other will not affect the result (but will most likely affect the performance). So there is no "wrong choice" of algorithm (strictly speaking).

Using `algorithm="auto"` (the default) is recommended because then the best suited algorithm will automatically be selected among the set of algorithms that are valid for the given search criteria.

### Value

An XStringViews object for `matchPattern`.

A single integer for `countPattern`.

An MIndex object for `vmatchPattern`.

An integer vector for `vcountPattern`, with each element in the vector corresponding to the number of matches in the corresponding element of `subject`.

### Note

Use `matchPDict` if you need to match a (big) set of patterns against a reference sequence.

Use `pairwiseAlignment` if you need to solve a (Needleman-Wunsch) global alignment, a (Smith-Waterman) local alignment, or an (ends-free) overlap alignment problem.

### See Also

lowlevel-matching, `matchPDict`, `pairwiseAlignment`, `mismatch`, `matchLRPatterns`, `matchProbePair`, `maskMotif`, `alphabetFrequency`, XStringViews-class, MIndex-class

### Examples

    ## ---------------------------------------------------------------------
    ## A. matchPattern()/countPattern()
    ## ---------------------------------------------------------------------

    ## A simple inexact matching example with a short subject:
    x <- DNAString("AAGCGCGATATG")
    m1 <- matchPattern("GCNNNAT", x)
    m1
    m2 <- matchPattern("GCNNNAT", x, fixed=FALSE)
    m2
    as.matrix(m2)

    ## With DNA sequence of yeast chromosome number 1:
    data(yeastSEQCHR1)
    yeast1 <- DNAString(yeastSEQCHR1)
    PpiI <- "GAACNNNNNCTC"  # a restriction enzyme pattern
    match1.PpiI <- matchPattern(PpiI, yeast1, fixed=FALSE)
    match2.PpiI <- matchPattern(PpiI, yeast1, max.mismatch=1, fixed=FALSE)

    ## With a genome containing isolated Ns:
    library(BSgenome.Celegans.UCSC.ce2)
    chrII <- Celegans[["chrII"]]
    alphabetFrequency(chrII)
    matchPattern("N", chrII)
    matchPattern("TGGGTGTCTTT", chrII)  # no match
    matchPattern("TGGGTGTCTTT", chrII, fixed=FALSE)  # 1 match

    ## Using wildcards ("N") in the pattern on a genome containing N-blocks:
    library(BSgenome.Dmelanogaster.UCSC.dm3)
    chrX <- maskMotif(Dmelanogaster$chrX, "N")
    as(chrX, "Views")  # 4 non masked regions
    matchPattern("TTTATGNTTGGTA", chrX, fixed=FALSE)
    ## Can also be achieved with no mask:
    masks(chrX) <- NULL
    matchPattern("TTTATGNTTGGTA", chrX, fixed="subject")

    ## ---------------------------------------------------------------------
    ## B. vmatchPattern()/vcountPattern()
    ## ---------------------------------------------------------------------

    ## Load Fly upstream sequences (i.e. the sequences 2000 bases upstream of
    ## annotated transcription starts):
    dm3_upstream_filepath <- system.file("extdata",
                                         "dm3_upstream2000.fa.gz",
                                         package="Biostrings")
    dm3_upstream <- readDNAStringSet(dm3_upstream_filepath)
    dm3_upstream

    Ebox <- DNAString("CANNTG")
    subject <- dm3_upstream
    mindex <- vmatchPattern(Ebox, subject, fixed="subject")
    nmatch_per_seq <- elementNROWS(mindex)  # Get the number of matches per
                                            # subject element.
    sum(nmatch_per_seq)  # Total number of matches.
    table(nmatch_per_seq)

    ## Let's have a closer look at one of the upstream sequences with most
    ## matches:
    i0 <- which.max(nmatch_per_seq)
    subject0 <- subject[[i0]]
    ir0 <- mindex[[i0]]   # matches in 'subject0' as an IRanges object
    ir0
    Views(subject0, ir0)  # matches in 'subject0' as a Views object

    ## ---------------------------------------------------------------------
    ## C. WITH INDELS
    ## ---------------------------------------------------------------------

    library(BSgenome.Celegans.UCSC.ce2)
    subject <- Celegans$chrI
    pattern1 <- DNAString("ACGGACCTAATGTTATC")
    pattern2 <- DNAString("ACGGACCTVATGTTRTC")

    ## Allowing up to 2 mismatching letters doesn't give any match:
    m1a <- matchPattern(pattern1, subject, max.mismatch=2)

    ## But allowing up to 2 edit operations gives 3 matches:
    system.time(m1b <- matchPattern(pattern1, subject, max.mismatch=2,
                                    with.indels=TRUE))
    m1b

    ## pairwiseAlignment() returns the (first) best match only:
    if (interactive()) {
      mat <- nucleotideSubstitutionMatrix(match=1, mismatch=0, baseOnly=TRUE)
      ## Note that this call to pairwiseAlignment() will need to
      ## allocate 733.5 Mb of memory (i.e. length(pattern) * length(subject)
      ## * 3 bytes).
      system.time(pwa <- pairwiseAlignment(pattern1, subject, type="local",
                                           substitutionMatrix=mat,
                                           gapOpening=0, gapExtension=1))
      pwa
    }

    ## With IUPAC ambiguities in the pattern:
    m2a <- matchPattern(pattern2, subject, max.mismatch=2,
                        fixed="subject")
    m2b <- matchPattern(pattern2, subject, max.mismatch=2,
                        with.indels=TRUE, fixed="subject")

    ## All the matches in 'm1b' and 'm2a' should also appear in 'm2b':
    stopifnot(suppressWarnings(all(ranges(m1b) %in% ranges(m2b))))
    stopifnot(suppressWarnings(all(ranges(m2a) %in% ranges(m2b))))

    ## ---------------------------------------------------------------------
    ## D. WHEN 'with.indels=TRUE', ONLY "BEST LOCAL MATCHES" ARE REPORTED
    ## ---------------------------------------------------------------------

    ## With deletions in the subject:
    subject <- BString("ACDEFxxxCDEFxxxABCE")
    matchPattern("ABCDEF", subject, max.mismatch=2, with.indels=TRUE)
    matchPattern("ABCDEF", subject, max.mismatch=2)

    ## With insertions in the subject:
    subject <- BString("AiBCDiEFxxxABCDiiFxxxAiBCDEFxxxABCiDEF")
    matchPattern("ABCDEF", subject, max.mismatch=2, with.indels=TRUE)
    matchPattern("ABCDEF", subject, max.mismatch=2)

    ## With substitutions (note that the "best local matches" can introduce
    ## indels and therefore be shorter than 6):
    subject <- BString("AsCDEFxxxABDCEFxxxBACDEFxxxABCEDF")
    matchPattern("ABCDEF", subject, max.mismatch=2, with.indels=TRUE)
    matchPattern("ABCDEF", subject, max.mismatch=2)

[Package Biostrings version 2.56.0 Index]