def _align_pairwise(s1,
s2,
mprobs,
psub,
TM,
local,
return_alignment=True,
return_score=False,
**kw):
"""Generic alignment with any substitution model and indel model"""
[p1, p2] = [makeLikelihoodTreeLeaf(seq) for seq in [s1, s2]]
[p1, p2] = [pairwise.AlignableSeq(leaf) for leaf in [p1, p2]]
pair = pairwise.Pair(p1, p2)
EP = pair.makeSimpleEmissionProbs(mprobs, [psub])
hmm = EP.makePairHMM(TM)
vpath = hmm.getViterbiPath(local=local, **kw)
score = vpath.getScore()
if return_alignment:
alignment = vpath.getAlignment()
if return_score:
return (alignment, score)
else:
return alignment
else:
return score
def countMotifs(self, alignment, include_ambiguity=False, recode_gaps=True):
result = None
for seq_name in alignment.getSeqNames():
sequence = alignment.getGappedSeq(seq_name, recode_gaps)
leaf = makeLikelihoodTreeLeaf(sequence, self.getCountedAlphabet(),
seq_name)
count = leaf.getMotifCounts(include_ambiguity=include_ambiguity)
if result is None:
result = count.copy()
else:
result += count
return result
def countMotifs(self, alignment, include_ambiguity=False, recode_gaps=True):
result = None
for seq_name in alignment.getSeqNames():
sequence = alignment.getGappedSeq(seq_name, recode_gaps)
leaf = makeLikelihoodTreeLeaf(sequence, self.getCountedAlphabet(),
seq_name)
count = leaf.getMotifCounts(include_ambiguity=include_ambiguity)
if result is None:
result = count.copy()
else:
result += count
return result
def _align_pairwise(s1, s2, mprobs, psub, TM, local, return_score=False, **kw):
"""Generic alignment with any substitution model and indel model"""
[p1, p2] = [makeLikelihoodTreeLeaf(seq) for seq in [s1, s2]]
[p1, p2] = [pairwise.AlignableSeq(leaf) for leaf in [p1, p2]]
pair = pairwise.Pair(p1, p2)
EP = pair.makeSimpleEmissionProbs(mprobs, [psub])
hmm = EP.makePairHMM(TM)
if local:
(score, alignment) = hmm.getLocalViterbiScoreAndAlignment(**kw)
else:
(score, alignment) = hmm.getViterbiScoreAndAlignment(**kw)
if return_score:
return alignment, score
else:
return alignment
def _align_pairwise(s1, s2, mprobs, psub, TM, local, return_score=False, **kw):
"""Generic alignment with any substitution model and indel model"""
[p1, p2] = [makeLikelihoodTreeLeaf(seq) for seq in [s1, s2]]
[p1, p2] = [pairwise.AlignableSeq(leaf) for leaf in [p1, p2]]
pair = pairwise.Pair(p1, p2)
EP = pair.makeSimpleEmissionProbs(mprobs, [psub])
hmm = EP.makePairHMM(TM)
if local:
(score, alignment) = hmm.getLocalViterbiScoreAndAlignment(**kw)
else:
(score, alignment) = hmm.getViterbiScoreAndAlignment(**kw)
if return_score:
return alignment, score
else:
return alignment
def _align_pairwise(s1, s2, mprobs, psub, TM, local, return_alignment=True, return_score=False, **kw):
"""Generic alignment with any substitution model and indel model"""
[p1, p2] = [makeLikelihoodTreeLeaf(seq) for seq in [s1, s2]]
[p1, p2] = [pairwise.AlignableSeq(leaf) for leaf in [p1, p2]]
pair = pairwise.Pair(p1, p2)
EP = pair.makeSimpleEmissionProbs(mprobs, [psub])
hmm = EP.makePairHMM(TM)
vpath = hmm.getViterbiPath(local=local, **kw)
score = vpath.getScore()
if return_alignment:
alignment = vpath.getAlignment()
if return_score:
return (alignment, score)
else:
return alignment
else:
return score
def convertSequence(self, sequence, name):
# makeLikelihoodTreeLeaf, sort of an indexed profile where duplicate
# columns stored once, so likelihoods only calc'd once
return makeLikelihoodTreeLeaf(sequence, self.getAlphabet(), name)
def convertSequence(self, sequence, name):
# makeLikelihoodTreeLeaf, sort of an indexed profile where duplicate
# columns stored once, so likelihoods only calc'd once
return makeLikelihoodTreeLeaf(sequence, self.getAlphabet(), name)