def test_len(self):
# with fasta
seq = SeqItem(name='s1', lines=['>s1\n', 'ACTG\n', 'GTAC\n'])
seq = SeqWrapper(SEQITEM, seq, 'fasta')
assert get_length(seq) == 8
# with fastq
seq = SeqItem(name='seq', lines=['@seq\n', 'aaaa\n', '+\n', '????\n'])
seq = SeqWrapper(SEQITEM, seq, 'fastq')
assert get_length(seq) == 4
def test_len(self):
# with fasta
seq = SeqItem(name='s1', lines=['>s1\n', 'ACTG\n', 'GTAC\n'])
seq = SeqWrapper(SEQITEM, seq, 'fasta')
assert get_length(seq) == 8
# with fastq
seq = SeqItem(name='seq',
lines=['@seq\n', 'aaaa\n', '+\n', '????\n'])
seq = SeqWrapper(SEQITEM, seq, 'fastq')
assert get_length(seq) == 4
def _do_trim(self, aligned_reads):
max_clipping = self.max_clipping
primary_alignment = _get_primary_alignment(aligned_reads)
_5end = _get_longest_5end_alinged_read(aligned_reads, max_clipping)
seq = alignedread_to_seqitem(primary_alignment)
segments = None
if _5end is not None:
if not _read_is_totally_mapped([_5end], max_clipping):
if not _5end.is_reverse:
qend = _get_qend(_5end)
else:
qend = get_length(seq) - _get_qstart(_5end)
segments = [(qend, get_length(seq) - 1)]
if segments is not None:
_add_trim_segments(segments, seq, kind=OTHER)
return seq
def _do_trim(self, seq):
'It trims the edges of the given seqs.'
annots = get_annotations(seq)
if not TRIMMING_RECOMMENDATIONS in annots:
return seq
trim_rec = annots[TRIMMING_RECOMMENDATIONS]
# fixing the trimming recommendations
if TRIMMING_RECOMMENDATIONS in annots:
del annots[TRIMMING_RECOMMENDATIONS]
trim_segments = []
for trim_kind in TRIMMING_KINDS:
trim_segments.extend(trim_rec.get(trim_kind, []))
# masking
if self.mask:
seq = _mask_sequence(seq, trim_segments)
else:
# trimming
if trim_segments:
trim_limits = get_longest_complementary_segment(
trim_segments, get_length(seq))
if trim_limits is None:
# there's no sequence left
return None
else:
trim_limits = []
if trim_limits:
seq = slice_seq(seq, trim_limits[0], trim_limits[1] + 1)
return seq
def _do_trim(self, seq):
'It trims the edges of the given seqs.'
annots = get_annotations(seq)
if not TRIMMING_RECOMMENDATIONS in annots:
return seq
trim_rec = annots[TRIMMING_RECOMMENDATIONS]
# fixing the trimming recommendations
if TRIMMING_RECOMMENDATIONS in annots:
del annots[TRIMMING_RECOMMENDATIONS]
trim_segments = []
for trim_kind in TRIMMING_KINDS:
trim_segments.extend(trim_rec.get(trim_kind, []))
# masking
if self.mask:
seq = _mask_sequence(seq, trim_segments)
else:
# trimming
if trim_segments:
trim_limits = get_longest_complementary_segment(
trim_segments, get_length(seq))
if trim_limits is None:
# there's no sequence left
return None
else:
trim_limits = []
if trim_limits:
seq = slice_seq(seq, trim_limits[0], trim_limits[1] + 1)
return seq
def count_seqs(seqs):
'It counts the number of sequences and the total length.'
num_seqs = 0
total_len = 0
for seq in seqs:
total_len += get_length(seq)
num_seqs += 1
return {'num_seqs': num_seqs, 'total_length': total_len}
def _do_trim(self, seq):
'It trims the edges of the given seqs.'
left = self.left
right = self.right
segments = [(0, left - 1)] if left else []
if right:
seq_len = get_length(seq)
segments.append((seq_len - right, seq_len - 1))
_add_trim_segments(segments, seq, kind=OTHER)
return seq
def _do_trim(self, seq):
'It trims the edges of the given seqs.'
left = self.left
right = self.right
segments = [(0, left - 1)] if left else []
if right:
seq_len = get_length(seq)
segments.append((seq_len - right, seq_len - 1))
_add_trim_segments(segments, seq, kind=OTHER)
return seq
def count_seqs(seqs):
'It counts the number of sequences and the total length.'
num_seqs = 0
total_len = 0
for seq in seqs:
total_len += get_length(seq)
num_seqs += 1
return {'num_seqs': num_seqs,
'total_length': total_len}
def _do_check(self, seq):
min_ = self.min
max_ = self.max
length = uppercase_length(get_str_seq(seq)) if self.ignore_masked else get_length(seq)
passed = True
if min_ is not None and length < min_:
passed = False
if max_ is not None and length > max_:
passed = False
return passed
def _do_check(self, seq):
min_ = self.min
max_ = self.max
length = uppercase_length(get_str_seq(seq)) if self.ignore_masked else get_length(seq)
passed = True
if min_ is not None and length < min_:
passed = False
if max_ is not None and length > max_:
passed = False
return passed
def _mask_sequence(seq, segments):
'It masks the given segments of the sequence'
if not segments:
return seq
segments = merge_overlaping_segments(segments)
segments = get_all_segments(segments, get_length(seq))
str_seq = get_str_seq(seq)
new_seq = ''
for segment in segments:
start = segment[0][0]
end = segment[0][1] + 1
str_seq_ = str_seq[start:end]
if segment[1]:
str_seq_ = str_seq_.lower()
new_seq += str_seq_
if seq.kind == SEQRECORD:
new_seq = Seq(new_seq, alphabet=seq.object.seq.alphabet)
return copy_seq(seq, seq=new_seq)
def _mask_sequence(seq, segments):
'It masks the given segments of the sequence'
if not segments:
return seq
segments = merge_overlaping_segments(segments)
segments = get_all_segments(segments, get_length(seq))
str_seq = get_str_seq(seq)
new_seq = ''
for segment in segments:
start = segment[0][0]
end = segment[0][1] + 1
str_seq_ = str_seq[start:end]
if segment[1]:
str_seq_ = str_seq_.lower()
new_seq += str_seq_
if seq.kind == SEQRECORD:
new_seq = Seq(new_seq, alphabet=seq.object.seq.alphabet)
return copy_seq(seq, seq=new_seq)
def _get_chrom_lengths(self):
chrom_lens = OrderedDict()
if self._ref_fhand is None:
vcf_fhand = gzip.open(self._reader.fhand.name)
for line in vcf_fhand:
line = line.strip()
if line.startswith('#'):
continue
items = line.split()
chrom = items[0]
loc = int(items[1])
if chrom not in chrom_lens:
chrom_lens[chrom] = loc
else:
if loc > chrom_lens[chrom]:
chrom_lens[chrom] = loc
else:
for read in read_seqs([self._ref_fhand]):
chrom_lens[get_name(read)] = get_length(read)
return chrom_lens
def __call__(self, seqs):
'It trims the edges of the given seqs.'
mask = self.mask
processed_seqs = []
for seq in seqs:
annots = get_annotations(seq)
if not TRIMMING_RECOMMENDATIONS in annots:
processed_seqs.append(copy_seq(seq))
continue
trim_rec = annots[TRIMMING_RECOMMENDATIONS]
# fixing the trimming recommendations
if TRIMMING_RECOMMENDATIONS in annots:
del annots[TRIMMING_RECOMMENDATIONS]
trim_segments = []
for trim_kind in TRIMMING_KINDS:
trim_segments.extend(trim_rec.get(trim_kind, []))
# masking
if mask:
seq = _mask_sequence(seq, trim_segments)
else:
# trimming
if trim_segments:
trim_limits = get_longest_complementary_segment(
trim_segments, get_length(seq))
if trim_limits is None:
# there's no sequence left
continue
else:
trim_limits = []
if trim_limits:
seq = slice_seq(seq, trim_limits[0], trim_limits[1] + 1)
processed_seqs.append(seq)
return processed_seqs
class MatePairSplitter(object):
'It splits the input sequences with the provided linkers.'
def __init__(self, linkers=None):
'The initiator'
if linkers is None:
linkers = get_setting('LINKERS')
linkers = [SeqItem(str(i), '>%d\n%s\n' % (i, l)) for i, l in enumerate(linkers)]
linkers = assing_kind_to_seqs(SEQITEM, linkers, 'fasta')
self.linkers = list(linkers)
def __call__(self, seqs):
'It splits a list of sequences with the provided linkers'
seq_fhand = write_seqs(seqs, file_format='fasta')
seq_fhand.flush()
min_identity = 87.0
min_len = 13
filters = [{'kind': 'min_length', 'min_num_residues': min_len,
'length_in_query': False, 'filter_match_parts': True},
{'kind': 'score_threshold', 'score_key': 'identity',
'min_score': min_identity}]
matcher = BlasterForFewSubjects(seq_fhand.name, self.linkers,
program='blastn', filters=filters,
params={'task': 'blastn-short'},
elongate_for_global=True,
seqs_type=NUCL)
new_seqs = []
for seq in seqs:
segments = matcher.get_matched_segments_for_read(get_name(seq))
if segments is not None:
split_seqs = self._split_by_mate_linker(seq, segments)
else:
split_seqs = [seq]
for seq in split_seqs:
new_seqs.append(seq)
return new_seqs
def _split_by_mate_linker(self, seq, (segments, is_partial)):
'It splits the seqs using segments'
if not segments:
return [copy_seq(seq)]
elongated_match = is_partial
if len(segments) == 1:
segment_start = segments[0][0]
segment_end = segments[0][1]
seq_end = get_length(seq) - 1
if segment_start == 0:
new_seq = slice_seq(seq, segment_end + 1, None)
return [new_seq]
elif segment_end == seq_end:
new_seq = slice_seq(seq, None, segment_start)
return [new_seq]
elif segment_end > seq_end:
msg = 'The segment ends after the sequence has ended'
raise RuntimeError(msg)
else:
new_seq1 = slice_seq(seq, None, segment_start)
new_seq2 = slice_seq(seq, segment_end + 1, None)
if elongated_match:
name = get_name(seq) + '_pl'
else:
name = get_name(seq)
new_seq1 = copy_seq(new_seq1, name=name + r'\1')
new_seq2 = copy_seq(new_seq2, name=name + r'\2')
return [new_seq1, new_seq2]
else:
seqs = []
counter = 1
seq_start = 0
for segment_start, segment_end in segments:
if segment_start == 0:
continue
new_seq = slice_seq(seq, seq_start, segment_start)
seq_name = get_name(seq) + '_mlc.part{0:d}'.format(counter)
new_seq = copy_seq(new_seq, name=seq_name)
seqs.append(new_seq)
counter += 1
seq_start = segment_end + 1
else:
if segment_end != get_length(seq) + 1:
new_seq = slice_seq(seq, segment_end + 1, None)
name = get_name(seq) + '_mlc.part{0:d}'.format(counter)
new_seq = copy_seq(new_seq, name=name)
seqs.append(new_seq)
return seqs
def _get_seq_lengths(fhand):
return {get_name(seq): get_length(seq) for seq in read_seqs([fhand])}
def calculate_sequence_stats(seqs, kmer_size=None, do_dust_stats=False,
nxs=None):
'It calculates some stats for the given seqs.'
# get data
lengths = IntCounter()
quals_per_pos = IntBoxplot()
nucl_freq = NuclFreqsPlot()
kmer_counter = KmerCounter(kmer_size) if kmer_size else None
dustscores = IntCounter()
for seq in seqs:
lengths[get_length(seq)] += 1
try:
quals = get_qualities(seq)
except AttributeError:
quals = []
for index, qual in enumerate(quals):
quals_per_pos.append(index + 1, qual)
str_seq = get_str_seq(seq)
for index, nucl in enumerate(str_seq):
nucl_freq.append(index, nucl)
if kmer_counter is not None:
kmer_counter.count_seq(str_seq)
if do_dust_stats:
dustscore = calculate_dust_score(seq)
if dustscore is not None:
dustscores[int(dustscore)] += 1
lengths.update_labels({'sum': 'tot. residues', 'items': 'num. seqs.'})
# length distribution
lengths_srt = 'Length stats and distribution.\n'
lengths_srt += '------------------------------\n'
nxs = sorted(nxs) if nxs else []
for nx in sorted(nxs):
lengths_srt += 'N{:d}: {:d}\n'.format(nx, calculate_nx(lengths, nx))
lengths_srt += str(lengths)
lengths_srt += '\n'
# agregate quals
if quals_per_pos:
quals = quals_per_pos.aggregated_array
quals.update_labels({'sum': None, 'items': 'tot. base pairs'})
q30 = quals.count_relative_to_value(30, operator.ge) / quals.count
q30 *= 100
q20 = quals.count_relative_to_value(20, operator.ge) / quals.count
q20 *= 100
# qual distribution
qual_str = 'Quality stats and distribution.\n'
qual_str += '-------------------------------\n'
qual_str += 'Q20: {:.2f}\n'.format(q20)
qual_str += 'Q30: {:.2f}\n'.format(q30)
qual_str += str(quals)
qual_str += '\n'
# qual per position boxplot
qual_boxplot = 'Boxplot for quality per position.\n'
qual_boxplot += '---------------------------------\n'
qual_boxplot += quals_per_pos.ascii_plot
qual_boxplot += '\n'
else:
qual_str = ''
qual_boxplot = ''
# nucl freqs
freq_str = 'Nucleotide frequency per position.\n'
freq_str += '----------------------------------\n'
freq_str += nucl_freq.ascii_plot
freq_str += '\n'
# kmer_distriubution
kmer_str = ''
if kmer_counter is not None:
kmers = IntCounter(kmer_counter.values)
if kmers:
kmers.update_labels({'sum': None, 'items': 'num. kmers'})
kmer_str = 'Kmer distribution\n'
kmer_str += '-----------------\n'
kmer_str += str(kmers)
kmer_str += '\n'
kmer_str += 'Most common kmers:\n'
for kmer, number in kmer_counter.most_common(20):
kmer_str += '\t{}: {}\n'.format(kmer, number)
dust_str = ''
if dustscores:
dustscores.update_labels({'sum': None, 'items': 'num. seqs.'})
dust_str = 'Dustscores stats and distribution.\n'
dust_str += '----------------------------------\n'
dust7 = (dustscores.count_relative_to_value(7, operator.gt) /
dustscores.count)
dust_str += '% above 7 (low complexity): {:.2f}\n'.format(dust7)
dust_str += str(dustscores)
dust_str += '\n'
return {'length': lengths_srt,
'quality': qual_str,
'nucl_freq': freq_str,
'qual_boxplot': qual_boxplot,
'kmer': kmer_str,
'dustscore': dust_str}
