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] = [make_likelihood_tree_leaf(seq) for seq in [s1, s2]]
[p1, p2] = [pairwise.AlignableSeq(leaf) for leaf in [p1, p2]]
pair = pairwise.Pair(p1, p2)
EP = pair.make_simple_emission_probs(mprobs, [psub])
hmm = EP.make_pair_HMM(TM)
vpath = hmm.get_viterbi_path(local=local, **kw)
score = vpath.get_score()
if return_alignment:
alignment = vpath.get_alignment()
if return_score:
return (alignment, score)
else:
return alignment
else:
return score
def count_motifs(self, alignment, include_ambiguity=False, recode_gaps=True):
result = None
try:
mtype = self.alphabet.moltype
except AttributeError:
mtype = self.monomer_alphabet.moltype
for seq_name in alignment.names:
sequence = alignment.get_gapped_seq(seq_name, recode_gaps, moltype=mtype)
leaf = make_likelihood_tree_leaf(
sequence, self.get_counted_alphabet(), seq_name
)
count = leaf.get_motif_counts(include_ambiguity=include_ambiguity)
if result is None:
result = count.copy()
else:
result += count
return result
def convert_sequence(self, sequence, name):
# make_likelihood_tree_leaf, sort of an indexed profile where duplicate
# columns stored once, so likelihoods only calc'd once
return make_likelihood_tree_leaf(sequence, self.get_alphabet(), name)