matchPattern: String searching functions
In Bioconductor/Biostrings: Efficient manipulation of biological strings

matchPattern

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 from the pwalign package if you need to solve a (Needleman-Wunsch) global alignment, a (Smith-Waterman) local alignment, or an (ends-free) overlap alignment problem.

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

## pwalign::pairwiseAlignment() returns the (first) best match only:
if (interactive()) {
  library(pwalign)
  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)

Bioconductor/Biostrings documentation built on Dec. 16, 2024, 8:46 a.m.