matchPattern {Biostrings} | R Documentation |
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)
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", ...)
pattern |
The pattern string. |
subject |
An XString, XStringViews or MaskedXString
object for matchPattern and countPattern .
An XStringSet or XStringViews object for
|
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. |
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.
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.
lowlevel-matching,
matchPDict
,
pairwiseAlignment
,
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]], 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: 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(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)