def sam_string_to_aligned_segment(sam_string, header=None):
"""Convert a correctly formatted sam string into a pysam AlignedSegment object
:param sam_string: correctly formatted SAM string
:param header: AlignmentHeader object
:return AlignedSegment
"""
if not header:
header = AlignmentHeader.from_references([sam_string.split("\t")[2]],
[100000000])
new_segment = AlignedSegment.fromstring(sam_string, header)
return new_segment
def run(self):
with CSVLogger(summary_file(), sep='\t') as summary:
for read, res in self.iterator:
seq = res['sequence']
qstring = res.get('qstring', '*')
mean_qscore = res.get('mean_qscore', mean_qscore_from_qstring(qstring))
mapping = res.get('mapping', False)
mods_tags = res.get('mods', [])
if self.duplex:
samples = len(read[0].signal) + len(read[1].signal)
read_id = '%s;%s' % (read[0].read_id, read[1].read_id)
else:
samples = len(read.signal)
read_id = read.read_id
tags = [
f'RG:Z:{read.run_id}_{self.group_key}',
f'qs:i:{round(mean_qscore)}',
*read.tagdata(),
*mods_tags,
]
if len(seq):
if self.mode == 'wfq':
write_fastq(read_id, seq, qstring, fd=self.fd, tags=tags)
else:
self.output.write(
AlignedSegment.fromstring(
sam_record(read_id, seq, qstring, mapping, tags=tags),
self.output.header
)
)
if self.duplex:
summary.append(duplex_summary_row(read[0], read[1], len(seq), mean_qscore, alignment=mapping))
else:
summary.append(summary_row(read, len(seq), mean_qscore, alignment=mapping))
self.log.append((read_id, samples))
else:
logger.warn("> skipping empty sequence %s", read_id)
def run(self):
chunks = []
targets = []
lengths = []
with CSVLogger(summary_file(), sep='\t') as summary:
for read, ctc_data in self.iterator:
seq = ctc_data['sequence']
qstring = ctc_data['qstring']
mean_qscore = ctc_data.get('mean_qscore', mean_qscore_from_qstring(qstring))
mapping = ctc_data.get('mapping', False)
self.log.append((read.read_id, len(read.signal)))
if len(seq) == 0 or mapping is None:
continue
cov = (mapping.q_en - mapping.q_st) / len(seq)
acc = mapping.mlen / mapping.blen
refseq = self.aligner.seq(mapping.ctg, mapping.r_st, mapping.r_en)
if acc < self.min_accuracy or cov < self.min_coverage or 'N' in refseq:
continue
self.output.write(
AlignedSegment.fromstring(
sam_record(read.read_id, seq, qstring, mapping),
self.output.header
)
)
summary.append(summary_row(read, len(seq), mean_qscore, alignment=mapping))
if mapping.strand == -1:
refseq = mappy.revcomp(refseq)
target = [int(x) for x in refseq.translate({65: '1', 67: '2', 71: '3', 84: '4'})]
targets.append(target)
chunks.append(read.signal)
lengths.append(len(target))
if len(chunks) == 0:
sys.stderr.write("> no suitable ctc data to write\n")
return
chunks = np.array(chunks, dtype=np.float16)
targets_ = np.zeros((chunks.shape[0], max(lengths)), dtype=np.uint8)
for idx, target in enumerate(targets): targets_[idx, :len(target)] = target
lengths = np.array(lengths, dtype=np.uint16)
indices = np.random.permutation(typical_indices(lengths))
chunks = chunks[indices]
targets_ = targets_[indices]
lengths = lengths[indices]
summary = pd.read_csv(summary_file(), sep='\t')
summary.iloc[indices].to_csv(summary_file(), sep='\t', index=False)
output_directory = '.' if sys.stdout.isatty() else dirname(realpath('/dev/fd/1'))
np.save(os.path.join(output_directory, "chunks.npy"), chunks)
np.save(os.path.join(output_directory, "references.npy"), targets_)
np.save(os.path.join(output_directory, "reference_lengths.npy"), lengths)
sys.stderr.write("> written ctc training data\n")
sys.stderr.write(" - chunks.npy with shape (%s)\n" % ','.join(map(str, chunks.shape)))
sys.stderr.write(" - references.npy with shape (%s)\n" % ','.join(map(str, targets_.shape)))
sys.stderr.write(" - reference_lengths.npy shape (%s)\n" % ','.join(map(str, lengths.shape)))
def write(self, fields):
line = '\t'.join(map(str, fields))
segment = AlignedSegment.fromstring(line, self.writer.header)
self.writer.write(segment)
def create_bam_record(sam_line):
sam_header = AlignmentHeader.from_text("@SQ SN:chr1_subsampled LN:2755\n"
"@PG ID:dummy PN:dummy VN:dummy CL:dummy")
bam_record = BamRecord(AlignedSegment.fromstring(sam_line, sam_header))
return bam_record
