def calculate_distance_distribution(interleave_fhand, index_fpath,
max_clipping, max_distance=None,
tempdir=None, threads=None):
bam_fhand = NamedTemporaryFile(suffix='.bam')
extra_params = ['-a', '-M']
bwa = map_with_bwamem(index_fpath, interleave_fpath=interleave_fhand.name,
extra_params=extra_params)
map_process_to_sortedbam(bwa, bam_fhand.name, key='queryname',
tempdir=tempdir)
bamfile = Samfile(bam_fhand.name)
stats = {'outies': IntCounter(), 'innies': IntCounter(),
'others': IntCounter()}
for grouped_mates in _group_alignments_reads_by_qname(bamfile):
mates = _split_mates(grouped_mates)
for aligned_read1 in _get_totally_mapped_alignments(mates[0],
max_clipping):
for aligned_read2 in _get_totally_mapped_alignments(mates[1],
max_clipping):
if aligned_read1.rname == aligned_read2.rname:
aligned_reads = [aligned_read1, aligned_read2]
distance = _find_distance(aligned_reads)
if _mates_are_outies(aligned_reads):
kind = 'outies'
elif _mates_are_innies(aligned_reads):
kind = 'innies'
else:
kind = 'others'
if max_distance is None or max_distance > distance:
stats[kind][distance] += 1
return stats
def test_classify_paired_reads(self):
index_fpath = os.path.join(TEST_DATA_DIR, 'ref_example.fasta')
# Non chimeric
query1 = '>seq1 1:N:0:GATCAG\nGGGATCGCAGACCCATCTCGTCAGCATGTACCCTTGCTACATTGAACTT\n'
query2 = '>seq1 2:N:0:GATCAG\nAGGAGGGATCGGGCACCCACGGCGCGGTAGACTGAGGCCTTCTCGAACT\n'
# Chimeric
query3 = '>seq2 1:N:0:GATCAG\nAAGTTCAATGTAGCAAGGGTACATGCTGACGAGATGGGTCTGCGATCCC\n'
query4 = '>seq2 2:N:0:GATCAG\nACGTGGATGCGGCGACGGCCCTACGGCACATACTGTTATTAGGGTCACT\n'
# unknown
query5 = '>seq3 1:N:0:GATCAG\nAGTGACCCTAATAACAGTATGTGCCGTAGGGCCGTCGCCGCATCCACGT\n'
query6 = '>seq3 2:N:0:GATCAG\nGTCGTGCGCAGCCATTGAGACCTTCCTAGGGTTTTCCCCATGGAATCGG\n'
query = query1 + query2 + query5 + query6 + query3 + query4
in_fhand = NamedTemporaryFile()
in_fhand.write(query)
in_fhand.flush()
bam_fhand = NamedTemporaryFile(suffix='.bam')
extra_params = ['-a', '-M']
bwa = map_with_bwamem(index_fpath,
interleave_fpath=in_fhand.name,
extra_params=extra_params)
map_process_to_sortedbam(bwa, bam_fhand.name, key='queryname')
result = classify_mapped_reads(bam_fhand, mate_distance=2000)
for pair, kind in result:
if kind == NON_CHIMERIC:
assert 'seq1' in get_name(pair[0])
elif kind == UNKNOWN:
assert 'seq3' in get_name(pair[0])
elif kind == CHIMERA:
assert 'seq2' in get_name(pair[0])
else:
self.fail()
def classify_chimeras(in_fhand,
index_fpath,
mate_distance,
out_fhand,
chimeras_fhand=None,
unknown_fhand=None,
tempdir=None,
threads=None,
settings=get_setting('CHIMERAS_SETTINGS')):
'''It maps sequences from input files, sorts them and writes to output
files according to its classification'''
bam_fhand = NamedTemporaryFile(suffix='.bam')
extra_params = ['-a', '-M']
bwa = map_with_bwamem(index_fpath,
interleave_fpath=in_fhand.name,
extra_params=extra_params)
map_process_to_sortedbam(bwa,
bam_fhand.name,
key='queryname',
tempdir=tempdir)
for pair, kind in classify_mapped_reads(bam_fhand,
settings=settings,
mate_distance=mate_distance):
if kind is NON_CHIMERIC:
write_seqs(pair, out_fhand)
elif kind is CHIMERA and chimeras_fhand is not None:
write_seqs(pair, chimeras_fhand)
elif kind is UNKNOWN and unknown_fhand is not None:
write_seqs(pair, unknown_fhand)
def test_classify_paired_reads(self):
index_fpath = os.path.join(TEST_DATA_DIR, 'ref_example.fasta')
#Non chimeric
query1 = '>seq1 1:N:0:GATCAG\nGGGATCGCAGACCCATCTCGTCAGCATGTACCCTTGCTACATTGAACTT\n'
query2 = '>seq1 2:N:0:GATCAG\nAGGAGGGATCGGGCACCCACGGCGCGGTAGACTGAGGCCTTCTCGAACT\n'
#Chimeric
query3 = '>seq2 1:N:0:GATCAG\nAAGTTCAATGTAGCAAGGGTACATGCTGACGAGATGGGTCTGCGATCCC\n'
query4 = '>seq2 2:N:0:GATCAG\nACGTGGATGCGGCGACGGCCCTACGGCACATACTGTTATTAGGGTCACT\n'
#unknown
query5 = '>seq3 1:N:0:GATCAG\nAGTGACCCTAATAACAGTATGTGCCGTAGGGCCGTCGCCGCATCCACGT\n'
query6 = '>seq3 2:N:0:GATCAG\nGTCGTGCGCAGCCATTGAGACCTTCCTAGGGTTTTCCCCATGGAATCGG\n'
query = query1 + query2 + query5 + query6 + query3 + query4
in_fhand = NamedTemporaryFile()
in_fhand.write(query)
in_fhand.flush()
bam_fhand = NamedTemporaryFile(suffix='.bam')
extra_params = ['-a', '-M']
bwa = map_with_bwamem(index_fpath, interleave_fpath=in_fhand.name,
extra_params=extra_params)
map_process_to_sortedbam(bwa, bam_fhand.name, key='queryname')
result = classify_mapped_reads(bam_fhand, mate_distance=2000)
for pair, kind in result:
if kind == NON_CHIMERIC:
assert 'seq1' in get_name(pair[0])
elif kind == UNKNOWN:
assert 'seq3' in get_name(pair[0])
elif kind == CHIMERA:
assert 'seq2' in get_name(pair[0])
else:
self.fail()
def _pre_trim(self, trim_packet):
seqs = [s for seqs in trim_packet[SEQS_PASSED]for s in seqs]
reads_fhand = NamedTemporaryFile(dir=self._tempdir, suffix='.trimming')
write_seqs(seqs, reads_fhand)
reads_fhand.flush()
bwa = map_with_bwamem(self._index_fpath,
interleave_fpath=reads_fhand.name)
bam_fhand = NamedTemporaryFile(dir=self._tempdir)
map_process_to_sortedbam(bwa, bam_fhand.name, key='queryname',
tempdir=self._tempdir)
self._bam_fhand = bam_fhand
reads_fhand.close()
def _pre_trim(self, trim_packet):
seqs = [s for seqs in trim_packet[SEQS_PASSED]for s in seqs]
reads_fhand = NamedTemporaryFile(dir=self._tempdir, suffix='.trimming')
write_seqs(seqs, reads_fhand)
reads_fhand.flush()
bwa = map_with_bwamem(self._index_fpath,
interleave_fpath=reads_fhand.name)
bam_fhand = NamedTemporaryFile(dir=self._tempdir)
map_process_to_sortedbam(bwa, bam_fhand.name, key='queryname',
tempdir=self._tempdir)
self._bam_fhand = bam_fhand
reads_fhand.close()
def calculate_distance_distribution(interleave_fhand,
index_fpath,
max_clipping,
max_distance=None,
tempdir=None,
threads=None):
bam_fhand = NamedTemporaryFile(suffix='.bam')
extra_params = ['-a', '-M']
bwa = map_with_bwamem(index_fpath,
interleave_fpath=interleave_fhand.name,
extra_params=extra_params,
threads=threads)
map_process_to_sortedbam(bwa,
bam_fhand.name,
key='queryname',
tempdir=tempdir)
return calculate_distance_distribution_in_bam(bam_fhand,
max_clipping=max_clipping,
max_distance=max_distance)
def classify_chimeras(in_fhand, index_fpath, mate_distance, out_fhand,
chimeras_fhand=None, unknown_fhand=None, tempdir=None,
threads=None, settings=get_setting('CHIMERAS_SETTINGS')):
'''It maps sequences from input files, sorts them and writes to output
files according to its classification'''
bam_fhand = NamedTemporaryFile(suffix='.bam')
extra_params = ['-a', '-M']
bwa = map_with_bwamem(index_fpath, interleave_fpath=in_fhand.name,
extra_params=extra_params)
map_process_to_sortedbam(bwa, bam_fhand.name, key='queryname',
tempdir=tempdir)
for pair, kind in classify_mapped_reads(bam_fhand, settings=settings,
mate_distance=mate_distance):
if kind is NON_CHIMERIC:
write_seqs(pair, out_fhand)
elif kind is CHIMERA and chimeras_fhand is not None:
write_seqs(pair, chimeras_fhand)
elif kind is UNKNOWN and unknown_fhand is not None:
write_seqs(pair, unknown_fhand)
def calculate_distance_distribution(interleave_fhand,
index_fpath,
max_clipping,
max_distance=None,
tempdir=None,
threads=None):
bam_fhand = NamedTemporaryFile(suffix='.bam')
extra_params = ['-a', '-M']
bwa = map_with_bwamem(index_fpath,
interleave_fpath=interleave_fhand.name,
extra_params=extra_params,
threads=threads)
map_process_to_sortedbam(bwa,
bam_fhand.name,
key='queryname',
tempdir=tempdir)
bamfile = AlignmentFile(bam_fhand.name)
stats = {
'outies': IntCounter(),
'innies': IntCounter(),
'others': IntCounter()
}
for grouped_mates in _group_alignments_reads_by_qname(bamfile):
mates = _split_mates(grouped_mates)
for aligned_read1 in _get_totally_mapped_alignments(
mates[0], max_clipping):
for aligned_read2 in _get_totally_mapped_alignments(
mates[1], max_clipping):
if aligned_read1.rname == aligned_read2.rname:
aligned_reads = [aligned_read1, aligned_read2]
distance = _find_distance(aligned_reads)
if _mates_are_outies(aligned_reads):
kind = 'outies'
elif _mates_are_innies(aligned_reads):
kind = 'innies'
else:
kind = 'others'
if max_distance is None or max_distance > distance:
stats[kind][distance] += 1
return stats
