matchPDict-inexact        package: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':

        *  (1) Find all the exact matches of all the elements in the
           Trusted Band.

        *  (2) For each element in the Trusted Band that has at least
           one exact match, compare the head and the tail of this
           element with the flanking sequences of the matches found in
           (1).

     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. Pages

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)
       countIndex(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)
       countIndex(m)  # now pattern 1 has 1 match too
       m[[1]]

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

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

       ## Unlist all matches:
       unlist(m)

       ## ---------------------------------------------------------------------
       ## D. WITH IUPAC AMBIGUITY CODES IN THE SUBJECT
       ## ---------------------------------------------------------------------
       pdict <- PDict(c("ACAC", "TCCG"))
       as.list(matchPDict(pdict, DNAString("ACNCCGT")))
       as.list(matchPDict(pdict, DNAString("ACNCCGT"), fixed="pattern"))
       as.list(matchPDict(pdict, DNAString("ACWCCGT"), fixed="pattern"))
       as.list(matchPDict(pdict, DNAString("ACRCCGT"), fixed="pattern"))
       as.list(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)))

