Description Usage Arguments Details Value Note Author(s) References See Also Examples
Solves (NeedlemanWunsch) global alignment, (SmithWaterman) local alignment, and (endsfree) overlap alignment problems.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  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 qualitybased 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. 
Qualitybased 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/(n1)) * 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/(n1))),
where b is the bitscaling 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] AGTA; subject: [1] AACTA
is chosen over
pattern: [1] AGTA; subject: [1] AACTA
or
pattern: [1] AGTA; 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):897900.
writePairwiseAlignments
,
stringDist
,
PairwiseAlignmentsclass,
XStringQualityclass,
substitution.matrices,
matchPattern
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43  ## 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 qualitybased 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)

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