matchPDict-inexact {Biostrings}R Documentation

Inexact matching with matchPDict()/countPDict()/whichPDict()

Description

The matchPDict, countPDict and whichPDict functions efficiently find the occurrences in a text (the subject) of all patterns stored in a preprocessed dictionary.

This man page shows how to use these functions for inexact (or fuzzy) matching or when the original dictionary has a variable width.

See ?matchPDict for how to use these functions for exact matching of a constant width dictionary i.e. a dictionary where all the patterns have the same length (same number of nucleotides).

Details

In this man page, we assume that you know how to preprocess a dictionary of DNA patterns that can then be used with matchPDict, countPDict or whichPDict. Please see ?PDict if you don't.

matchPDict and family support different kinds of inexact matching but with some restrictions. Inexact matching is controlled via the definition of a Trusted Band during the preprocessing step and/or via the max.mismatch, min.mismatch and fixed arguments. Defining a Trusted Band is also required when the original dictionary is not rectangular (variable width), even for exact matching. See ?PDict for how to define a Trusted Band.

Here is how matchPDict and family handle the Trusted Band defined on pdict:

Note that the number of exact matches found in (1) will decrease exponentially with the width of the Trusted Band. Here is a simple guideline in order to get reasonably good performance: if TBW is the width of the Trusted Band (TBW <- tb.width(pdict)) and L the number of letters in the subject (L <- nchar(subject)), then L / (4^TBW) should be kept as small as possible, typically < 10 or 20.

In addition, when a Trusted Band has been defined during preprocessing, then matchPDict and family can be called with fixed=FALSE. In this case, IUPAC ambiguity codes in the head or the tail of the PDict object are treated as ambiguities.

Finally, fixed="pattern" can be used to indicate that IUPAC ambiguity codes in the subject should be treated as ambiguities. It only works if the density of codes is not too high. It works whether or not a Trusted Band has been defined on pdict.

Author(s)

H. Pagès

References

Aho, Alfred V.; Margaret J. Corasick (June 1975). "Efficient string matching: An aid to bibliographic search". Communications of the ACM 18 (6): 333-340.

See Also

PDict-class, MIndex-class, matchPDict

Examples

## ---------------------------------------------------------------------
## A. USING AN EXPLICIT TRUSTED BAND
## ---------------------------------------------------------------------

library(drosophila2probe)
dict0 <- DNAStringSet(drosophila2probe)
dict0  # the original dictionary

## Preprocess the original dictionary by defining a Trusted Band that
## spans nucleotides 1 to 9 of each pattern.
pdict9 <- PDict(dict0, tb.end=9)
pdict9
tail(pdict9)
sum(duplicated(pdict9))
table(patternFrequency(pdict9))

library(BSgenome.Dmelanogaster.UCSC.dm3)
chr3R <- Dmelanogaster$chr3R
chr3R
table(countPDict(pdict9, chr3R, max.mismatch=1))
table(countPDict(pdict9, chr3R, max.mismatch=3))
table(countPDict(pdict9, chr3R, max.mismatch=5))

## ---------------------------------------------------------------------
## B. COMPARISON WITH EXACT MATCHING
## ---------------------------------------------------------------------

## When the original dictionary is of constant width, exact matching
## (i.e. 'max.mismatch=0' and 'fixed=TRUE) will be more efficient with
## a full-width Trusted Band (i.e. a Trusted Band that covers the entire
## dictionary) than with a Trusted Band of width < width(dict0).
pdict0 <- PDict(dict0)
count0 <- countPDict(pdict0, chr3R)
count0b <- countPDict(pdict9, chr3R, max.mismatch=0)
identical(count0b, count0)  # TRUE
  
## ---------------------------------------------------------------------
## C. USING AN EXPLICIT TRUSTED BAND ON A VARIABLE WIDTH DICTIONARY
## ---------------------------------------------------------------------

## Here is a small variable width dictionary that contains IUPAC
## ambiguities (pattern 1 and 3 contain an N):
dict0 <- DNAStringSet(c("TACCNG", "TAGT", "CGGNT", "AGTAG", "TAGT"))
## (Note that pattern 2 and 5 are identical.)

## If we only want to do exact matching, then it is recommended to use
## the widest possible Trusted Band i.e. to set its width to
## 'min(width(dict0))' because this is what will give the best
## performance. However, when 'dict0' contains IUPAC ambiguities (like
## in our case), it could be that one of them is falling into the
## Trusted Band so we get an error (only base letters can go in the
## Trusted Band for now):
## Not run: 
  PDict(dict0, tb.end=min(width(dict0)))  # Error!

## End(Not run)

## In our case, the Trusted Band cannot be wider than 3:
pdict <- PDict(dict0, tb.end=3)
tail(pdict)

subject <- DNAString("TAGTACCAGTTTCGGG")

m <- matchPDict(pdict, subject)
elementNROWS(m)  # pattern 2 and 5 have 1 exact match
m[[2]]

## We can take advantage of the fact that our Trusted Band doesn't cover
## the entire dictionary to allow inexact matching on the uncovered parts
## (the tail in our case):

m <- matchPDict(pdict, subject, fixed=FALSE)
elementNROWS(m)  # now pattern 1 has 1 match too
m[[1]]

m <- matchPDict(pdict, subject, max.mismatch=1)
elementNROWS(m)  # now pattern 4 has 1 match too
m[[4]]

m <- matchPDict(pdict, subject, max.mismatch=1, fixed=FALSE)
elementNROWS(m)  # now pattern 3 has 1 match too
m[[3]]  # note that this match is "out of limit"
Views(subject, m[[3]])

m <- matchPDict(pdict, subject, max.mismatch=2)
elementNROWS(m)  # pattern 4 gets 1 additional match
m[[4]]

## Unlist all matches:
unlist(m)

## ---------------------------------------------------------------------
## D. WITH IUPAC AMBIGUITY CODES IN THE PATTERNS
## ---------------------------------------------------------------------
## The Trusted Band cannot contain IUPAC ambiguity codes so patterns
## with ambiguity codes can only be preprocessed if we can define a
## Trusted Band with no ambiguity codes in it.

dict <- DNAStringSet(c("AAACAAKS", "GGGAAA", "TNCCGGG"))
pdict <- PDict(dict, tb.start=3, tb.width=4)
subject <- DNAString("AAACAATCCCGGGAAACAAGG")

matchPDict(pdict, subject)
matchPDict(pdict, subject, fixed="subject")

## Sanity checks:
res1 <- as.list(matchPDict(pdict, subject))
res2 <- as.list(matchPDict(dict, subject))
res3 <- lapply(dict,
  function(pattern)
    as(matchPattern(pattern, subject), "IRanges"))
stopifnot(identical(res1, res2))
stopifnot(identical(res1, res3))

res1 <- as.list(matchPDict(pdict, subject, fixed="subject"))
res2 <- as.list(matchPDict(dict, subject, fixed="subject"))
res3 <- lapply(dict,
  function(pattern)
    as(matchPattern(pattern, subject, fixed="subject"), "IRanges"))
stopifnot(identical(res1, res2))
stopifnot(identical(res1, res3))

## ---------------------------------------------------------------------
## E. WITH IUPAC AMBIGUITY CODES IN THE SUBJECT
## ---------------------------------------------------------------------
## 'fixed="pattern"' (or 'fixed=FALSE') can be used to indicate that
## IUPAC ambiguity codes in the subject should be treated as ambiguities.

pdict <- PDict(c("ACAC", "TCCG"))
matchPDict(pdict, DNAString("ACNCCGT"))
matchPDict(pdict, DNAString("ACNCCGT"), fixed="pattern")
matchPDict(pdict, DNAString("ACWCCGT"), fixed="pattern")
matchPDict(pdict, DNAString("ACRCCGT"), fixed="pattern")
matchPDict(pdict, DNAString("ACKCCGT"), fixed="pattern")

dict <- DNAStringSet(c("TTC", "CTT"))
pdict <- PDict(dict)
subject <- DNAString("CYTCACTTC")
mi1 <- matchPDict(pdict, subject, fixed="pattern")
mi2 <- matchPDict(dict, subject, fixed="pattern")
stopifnot(identical(as.list(mi1), as.list(mi2)))
[Package Biostrings version 2.46.0 Index]