pairwiseAlignment {Biostrings} | R Documentation

## Optimal Pairwise Alignment

### Description

Solves (Needleman-Wunsch) global alignment, (Smith-Waterman) local alignment, and (ends-free) overlap alignment problems.

### Usage

    pairwiseAlignment(pattern, subject, ...)

    ## S4 method for signature 'ANY,ANY'
    pairwiseAlignment(pattern, subject,
                      patternQuality=PhredQuality(22L),
                      subjectQuality=PhredQuality(22L),
                      type="global",
                      substitutionMatrix=NULL, fuzzyMatrix=NULL,
                      gapOpening=10, gapExtension=4,
                      scoreOnly=FALSE)

    ## S4 method for signature 'QualityScaledXStringSet,QualityScaledXStringSet'
    pairwiseAlignment(pattern, subject,
                      type="global",
                      substitutionMatrix=NULL, fuzzyMatrix=NULL,
                      gapOpening=10, gapExtension=4,
                      scoreOnly=FALSE)

### Arguments

`pattern` | a character vector of any length, an `XString`, or an `XStringSet` object.
`subject` | a character vector of length 1, an `XString`, or an `XStringSet` object of length 1.
`patternQuality, subjectQuality` | objects of class `XStringQuality` representing the respective quality scores for `pattern` and `subject` that are used in a quality-based method for generating a substitution matrix. These two arguments are ignored if `!is.null(substitutionMatrix)` or if its respective string set (`pattern`, `subject`) is of class `QualityScaledXStringSet`.
`type` | type of alignment. One of `"global"`, `"local"`, `"overlap"`, `"global-local"`, and `"local-global"` where `"global"` = align whole strings with end gap penalties, `"local"` = align string fragments, `"overlap"` = align whole strings without end gap penalties, `"global-local"` = align whole strings in `pattern` with consecutive subsequence of `subject`, `"local-global"` = align consecutive subsequence of `pattern` with whole strings in `subject`.
`substitutionMatrix` | substitution matrix representing the fixed substitution scores for an alignment. It cannot be used in conjunction with `patternQuality` and `subjectQuality` arguments.
`fuzzyMatrix` | fuzzy match matrix for quality-based alignments. It takes values between 0 and 1; where 0 is an unambiguous mismatch, 1 is an unambiguous match, and values in between represent a fraction of "matchiness". (See details section below.)
`gapOpening` | the cost for opening a gap in the alignment.
`gapExtension` | the incremental cost incurred along the length of the gap in the alignment.
`scoreOnly` | logical to denote whether or not to return just the scores of the optimal pairwise alignment.
`...` | optional arguments to generic function to support additional methods.

### Details

Quality-based alignments are based on the paper the Bioinformatics article by Ketil Malde listed in the Reference section below. Let ε_i be the probability of an error in the base read. For `"Phred"` quality measures Q in [0, 99], these error probabilities are given by ε_i = 10^{-Q/10}. For `"Solexa"` quality measures Q in [-5, 99], they are given by ε_i = 1 - 1/(1 + 10^{-Q/10}). Assuming independence within and between base reads, the combined error probability of a mismatch when the underlying bases do match is ε_c = ε_1 + ε_2 - (n/(n-1)) * ε_1 * ε_2, where n is the number of letters in the underlying alphabet (i.e. n = 4 for DNA input, n = 20 for amino acid input, otherwise n is the number of distinct letters in the input). Using ε_c, the substitution score is given by b * \log_2(γ_{x,y} * (1 - ε_c) * n + (1 - γ_{x,y}) * ε_c * (n/(n-1))), where b is the bit-scaling for the scoring and γ_{x,y} is the probability that characters x and y represents the same underlying information (e.g. using IUPAC, γ_{A,A} = 1 and γ_{A,N} = 1/4. In the arguments listed above `fuzzyMatch` represents γ_{x,y} and `patternQuality` and `subjectQuality` represents ε_1 and ε_2 respectively.

If `scoreOnly == FALSE`, a pairwise alignment with the maximum alignment score is returned. If more than one pairwise alignment produces the maximum alignment score, then the alignment with the smallest initial deletion whose mismatches occur before its insertions and deletions is chosen. For example, if `pattern = "AGTA"` and `subject = "AACTAACTA"`, then the alignment `pattern: [1] AG-TA; subject: [1] AACTA` is chosen over `pattern: [1] A-GTA; subject: [1] AACTA` or `pattern: [1] AG-TA; subject: [5] AACTA` if they all achieve the maximum alignment score.

### Value

If `scoreOnly == FALSE`, an instance of class `PairwiseAlignments` or `PairwiseAlignmentsSingleSubject` is returned. If `scoreOnly == TRUE`, a numeric vector containing the scores for the optimal pairwise alignments is returned.

### Note

Use `matchPattern` or `vmatchPattern` if you need to find all the occurrences (eventually with indels) of a given pattern in a reference sequence or set of sequences.

Use `matchPDict` if you need to match a (big) set of patterns against a reference sequence.

### Author(s)

P. Aboyoun and H. Pagès

### References

R. Durbin, S. Eddy, A. Krogh, G. Mitchison, Biological Sequence Analysis, Cambridge UP 1998, sec 2.3.

B. Haubold, T. Wiehe, Introduction to Computational Biology, Birkhauser Verlag 2006, Chapter 2.

K. Malde, The effect of sequence quality on sequence alignment, Bioinformatics 2008 24(7):897-900.

### See Also

`writePairwiseAlignments`, `stringDist`, PairwiseAlignments-class, XStringQuality-class, substitution.matrices, `matchPattern`

### Examples

      ## Nucleotide global, local, and overlap alignments
      s1 <-
        DNAString("ACTTCACCAGCTCCCTGGCGGTAAGTTGATCAAAGGAAACGCAAAGTTTTCAAG")
      s2 <-
        DNAString("GTTTCACTACTTCCTTTCGGGTAAGTAAATATATAAATATATAAAAATATAATTTTCATC")

      # First use a fixed substitution matrix
      mat <- nucleotideSubstitutionMatrix(match = 1, mismatch = -3, baseOnly = TRUE)
      globalAlign <-
        pairwiseAlignment(s1, s2, substitutionMatrix = mat,
                          gapOpening = 5, gapExtension = 2)
      localAlign <-
        pairwiseAlignment(s1, s2, type = "local", substitutionMatrix = mat,
                          gapOpening = 5, gapExtension = 2)
      overlapAlign <-
        pairwiseAlignment(s1, s2, type = "overlap", substitutionMatrix = mat,
                          gapOpening = 5, gapExtension = 2)

      # Then use quality-based method for generating a substitution matrix
      pairwiseAlignment(s1, s2,
                        patternQuality = SolexaQuality(rep(c(22L, 12L), times = c(36, 18))),
                        subjectQuality = SolexaQuality(rep(c(22L, 12L), times = c(40, 20))),
                        scoreOnly = TRUE)

      # Now assume can't distinguish between C/T and G/A
      pairwiseAlignment(s1, s2,
                        patternQuality = SolexaQuality(rep(c(22L, 12L), times = c(36, 18))),
                        subjectQuality = SolexaQuality(rep(c(22L, 12L), times = c(40, 20))),
                        type = "local")
      mapping <- diag(4)
      dimnames(mapping) <- list(DNA_BASES, DNA_BASES)
      mapping["C", "T"] <- mapping["T", "C"] <- 1
      mapping["G", "A"] <- mapping["A", "G"] <- 1
      pairwiseAlignment(s1, s2,
                        patternQuality = SolexaQuality(rep(c(22L, 12L), times = c(36, 18))),
                        subjectQuality = SolexaQuality(rep(c(22L, 12L), times = c(40, 20))),
                        fuzzyMatrix = mapping,
                        type = "local")

      ## Amino acid global alignment
      pairwiseAlignment(AAString("PAWHEAE"), AAString("HEAGAWGHEE"),
                        substitutionMatrix = "BLOSUM50",
                        gapOpening = 0, gapExtension = 8)

[Package Biostrings version 2.52.0 Index]