matchPattern {Biostrings} | R Documentation |
A set of functions for finding all the occurences (aka "matches" or "hits") of a given pattern (typically short) in a (typically long) reference sequence or set of sequences (aka the subject)
matchPattern(pattern, subject, algorithm="auto", max.mismatch=0, with.indels=FALSE, fixed=TRUE) countPattern(pattern, subject, algorithm="auto", max.mismatch=0, with.indels=FALSE, fixed=TRUE) vmatchPattern(pattern, subject, algorithm="auto", max.mismatch=0, with.indels=FALSE, fixed=TRUE) vcountPattern(pattern, subject, algorithm="auto", max.mismatch=0, with.indels=FALSE, fixed=TRUE)
pattern |
The pattern string. |
subject |
An XString, XStringViews or MaskedXString
object for matchPattern and countPattern .
An XStringSet or XStringViews object for vmatchPattern and vcountPattern .
|
algorithm |
One of the following: "auto" , "naive-exact" ,
"naive-inexact" , "boyer-moore" , "shift-or"
or "indels" .
|
max.mismatch |
The maximum number of mismatching letters allowed (see
isMatchingAt for the details).
If non-zero, an inexact matching algorithm is used.
|
with.indels |
If TRUE then indels are allowed. In that case 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 be aligned with letters in P (i.e. they match letters in P). |
fixed |
If FALSE then IUPAC extended letters are interpreted as ambiguities
(see isMatchingAt for the 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
depend on the length of the pattern, the class of the subject,
and the values of max.mismatch
, with.indels
and fixed
. All those parameters form the search criteria.
Note that the choice of an algorithm is not part of the search criteria. This is because algorithms are interchangeable, that is, if 2 different algorithms are compatible with a given search criteria, then choosing one over 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"
is recommended because then the fastest
algorithm will automatically be picked up among the set of compatible
algorithms (if there is more than one).
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
.
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.
matchPDict
,
pairwiseAlignment
,
isMatchingAt
,
mismatch
,
matchLRPatterns
,
matchProbePair
,
maskMotif
,
alphabetFrequency
,
XStringViews-class,
MIndex-class
## --------------------------------------------------------------------- ## 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$chrX, "N") as(chrX, "XStringViews") # 4 non masked regions matchPattern("TTTATGNTTGGTA", chrX, fixed=FALSE) ## Can also be achieved with no mask: masks(chrX) <- NULL matchPattern("TTTATGNTTGGTA", chrX, fixed="subject") ## --------------------------------------------------------------------- ## B. vmatchPattern()/vcountPattern() ## --------------------------------------------------------------------- Ebox <- DNAString("CANNTG") subject <- Celegans$upstream5000 mindex <- vmatchPattern(Ebox, subject, fixed=FALSE) count_index <- countIndex(mindex) # Get the number of matches per # subject element. sum(count_index) # Total number of matches. table(count_index) i0 <- which(count_index == max(count_index)) subject[i0] # The subject element with most matches. ## The matches in 'subject[i0]' as an IRanges object: mindex[[i0]] ## The matches in 'subject[i0]' as an XStringViews object: Views(subject[[i0]], start=start(mindex[[i0]]), end=end(mindex[[i0]])) ## --------------------------------------------------------------------- ## C. With indels ## --------------------------------------------------------------------- library(BSgenome.Celegans.UCSC.ce2) pattern <- DNAString("ACGGACCTAATGTTATC") subject <- Celegans$chrI ## Allowing up to 2 mismatching letters doesn't give any match: matchPattern(pattern, subject, max.mismatch=2) ## But allowing up to 2 edit operations gives 3 matches: system.time(m <- matchPattern(pattern, subject, max.mismatch=2, with.indels=TRUE)) m ## pairwiseAlignment() returns the (first) best match only: mat <- nucleotideSubstitutionMatrix(match=1, mismatch=0, baseOnly=TRUE) system.time(pwa <- pairwiseAlignment(pattern, subject, type="local", substitutionMatrix=mat, gapOpening=0, gapExtension=1)) pwa ## 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)