Optimal Pairwise Alignment
Solves (Needleman-Wunsch) global alignment, (Smith-Waterman) local alignment, and (ends-free) overlap alignment problems.
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)pattern |
a character vector of any length, an |
subject |
a character vector of length 1, an |
patternQuality, subjectQuality |
objects of class
|
type |
type of alignment. One of |
substitutionMatrix |
substitution matrix representing the fixed
substitution scores for an alignment. It cannot be used in conjunction with
|
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. |
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.
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.
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.
P. Aboyoun and H. Pagès
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.
## 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.