def index(self, sequence, seqset, append=False):
if not append:
self._match_position = self._MatchPosition()
self.n_hitseqs = 0
self.n_hitsites = 0
self.n_seqs = 0
rc_sequence = revcomp(sequence)
p = re.compile('({0})|({1})'.format(
sequence.replace('n', '[atcg]'), rc_sequence.replace('n', '[atcg]')),
re.IGNORECASE)
seqid = 0
for gene_name, i in seqset:
seqid += 1
i = i.lower()
self.n_seqs += 1
has_match = False
for j in p.finditer(i):
has_match = True
self.n_hitsites += 1
if self.reverse_complement:
if j.group(1):
self._match_position.add(
gene_name, seqid, sequence, i, j.start(), j.end(), False)
elif j.group(2):
self._match_position.add(
gene_name, seqid, rc_sequence, i, j.start(), j.end(), True)
elif j.group(1):
self._match_position.add(
gene_name, seqid, sequence, i, j.start(), j.end(), False)
if has_match:
self.n_hitseqs += 1
def extract_match_info(self, fasta_handle):
matches = set()
match_info = []
for pattern in self._strands:
pattern.build_matchtable_pset(
parse_fasta(fasta_handle), self.reverse_complement)
for pattern in self._strands:
for seqid, pos in pattern.matchtable_pset.pos_matches.iteritems():
for i, j in enumerate(pos):
gene_name = pattern.matchtable_pset.gene_name.get(seqid)
match_strand, match_sequence = pattern.matchtable_pset.match_sequences.get(seqid)[i]
match_start = pattern.matchtable_pset.pos_matches.get(seqid)[i][0] + 1
match = (seqid, match_strand, tuple(j))
if match in matches:
continue
matches.add(match)
if match_strand == 2:
match_sequence = revcomp(match_sequence)
match_info.append((gene_name, match_strand, match_start, match_sequence))
return match_info
def pfm(pattern_sequence):
"""Claculate position frequency matrix (PFM) of matching sequences"""
if isinstance(pattern_sequence, Pattern):
sequences = []
for match_sequences in pattern_sequence.matchtable_pset.match_sequences.itervalues():
for strand, sequence in match_sequences:
if strand == 2:
sequences.append(revcomp(sequence))
else:
sequences.append(sequence)
else:
sequences = pattern_sequence
ncol = len(sequences[0])
matrix = {
'a': [0] * ncol,
't': [0] * ncol,
'c': [0] * ncol,
'g': [0] * ncol,
}
total = [0] * ncol
for s in sequences:
for i, j in enumerate(s):
matrix.get(j)[i] += 1
total[i] += 1
# Normalization
for i in xrange(ncol):
matrix.get('a')[i] = float(matrix.get('a')[i]) / total[i]
matrix.get('t')[i] = float(matrix.get('t')[i]) / total[i]
matrix.get('c')[i] = float(matrix.get('c')[i]) / total[i]
matrix.get('g')[i] = float(matrix.get('g')[i]) / total[i]
return matrix
def extract_match_info(self, fasta_handle):
pos_matches = {}
seq_matches = {}
match_info = []
for pattern in self._strands:
pattern.build_matchtable_pset(
parse_fasta(fasta_handle), self.reverse_complement)
for pattern in self._strands:
for seqid, pos in pattern.matchtable_pset.pos_matches.iteritems():
for i, j in enumerate(pos):
gene_name = pattern.matchtable_pset.gene_name.get(seqid)
match_strand, match_sequence = pattern.matchtable_pset.match_sequences.get(seqid)[i]
match_start = pattern.matchtable_pset.pos_matches.get(seqid)[i][0] + 1
if match_strand == 2:
match_sequence = revcomp(match_sequence)
tj = tuple(j)
if seqid in pos_matches and tj in pos_matches.get(seqid) and match_sequence in seq_matches.get(seqid).get(tj):
continue
if seqid in pos_matches:
pos_matches.get(seqid).append(tj)
else:
pos_matches.update({seqid: [tj]})
if seqid in seq_matches:
if tj in seq_matches.get(seqid):
seq_matches.get(seqid).get(tj).append(match_sequence)
else:
seq_matches.get(seqid).update({tj: [match_sequence]})
else:
seq_matches.update({seqid: {tj: [match_sequence]}})
match_info.append((gene_name, match_strand, match_start, match_sequence))
return match_info
def merge_sequences(seq_1, seq_2, reverse_complement=False):
arrlen = len(seq_1) + len(seq_2) - 1
max_score = -1
best_seq1 = None
best_seq2 = None
best_strands = None
strands_1 = [1, 1]
for i in xrange(arrlen - len(seq_1) + 1):
for j in xrange(arrlen - len(seq_2) + 1):
if j > i:
left_1 = ''
left_2 = 'n' * (j - i)
elif i > j:
left_1 = 'n' * (i - j)
left_2 = ''
else:
left_1 = ''
left_2 = ''
if len(seq_2) + j - 1 > len(seq_1) + i - 1:
right_1 = 'n' * ((len(seq_2) + j - 1) - (len(seq_1) + i - 1))
right_2 = ''
elif len(seq_1) + i - 1 > len(seq_2) + j - 1:
right_1 = ''
right_2 = 'n' * ((len(seq_1) + i - 1) - (len(seq_2) + j - 1))
else:
right_1 = ''
right_2 = ''
s1 = '%s%s%s' % (left_1, seq_1, right_1)
s2 = '%s%s%s' % (left_2, seq_2, right_2)
score = full_alignment_scoring(s1, s2)
if score > max_score:
max_score = score
best_seq1 = s1
best_seq2 = s2
best_strands = strands_1
if reverse_complement:
strands_2 = [1, 2]
seq_2 = revcomp(seq_2)
for i in xrange(arrlen - len(seq_1) + 1):
for j in xrange(arrlen - len(seq_2) + 1):
if j > i:
left_1 = ''
left_2 = 'n' * (j - i)
elif i > j:
left_1 = 'n' * (i - j)
left_2 = ''
else:
left_1 = ''
left_2 = ''
if len(seq_2) + j - 1 > len(seq_1) + i - 1:
right_1 = 'n' * ((len(seq_2) + j - 1) - (len(seq_1) + i - 1))
right_2 = ''
elif len(seq_1) + i - 1 > len(seq_2) + j - 1:
right_1 = ''
right_2 = 'n' * ((len(seq_1) + i - 1) - (len(seq_2) + j - 1))
else:
right_1 = ''
right_2 = ''
s1 = '%s%s%s' % (left_1, seq_1, right_1)
s2 = '%s%s%s' % (left_2, seq_2, right_2)
score = full_alignment_scoring(s1, s2)
if score > max_score:
max_score = score
best_seq1 = s1
best_seq2 = s2
best_strands = strands_2
return (best_seq1, best_seq2, best_strands)
def add(self, pattern):
assert isinstance(pattern, Pattern)
if self.reverse_complement and revcomp(pattern.sequence) in self._collect:
self._collect.update({revcomp(pattern.sequence): pattern})
else:
self._collect.update({pattern.sequence: pattern})
