def add_tags(alignedSegment: pysam.AlignedSegment) -> pysam.AlignedSegment:
""" Takes an AlignedSegment and add percent identity and alignment length as tags
alignment length = MID
mismatches = NM
percent identity = (MID - NM) / MID
The percent identity is a value between 0.0 and 1.0
If the segment is unmapped then it is returned as with a percent identity of 0
and an alignment length of 0.
:param alignedSegment: The pysam AlignedSegment object
:return: alignedSegment: The updated pysam AlignedSegment object
"""
# Assuming that if the id tag is present that the other tags are also there.
if alignedSegment.has_tag('id'):
return alignedSegment
if alignedSegment.is_unmapped:
alignedSegment.set_tag('id', 0.0, 'f')
alignedSegment.set_tag('al', 0, 'i')
alignedSegment.set_tag('qc', 0.0, 'f')
return alignedSegment
alnlength = sum(alignedSegment.get_cigar_stats()[0][0:3])
query_covered_bases = sum(alignedSegment.get_cigar_stats()[0][0:2])
query_length = alignedSegment.infer_read_length()
mismatches = alignedSegment.get_tag('NM')
percid = (alnlength - mismatches) / float(alnlength)
qcov = query_covered_bases / float(query_length)
alignedSegment.set_tag('id', percid, 'f')
alignedSegment.set_tag('qc', qcov, 'f')
alignedSegment.set_tag('al', alnlength, 'i')
return alignedSegment
def from_aligned_segment(cls,
align: pysam.AlignedSegment) -> "AlignmentRecord":
"""Extract information from a pysam Aligned segment"""
read_name, read_idx, align_idx = align.query_name.split(":")
read_idx, align_idx = int(read_idx), int(align_idx)
if align.is_unmapped:
align_cat = "unmapped"
chrom, start, end, align_score = "NULL", 0, 0, 0
read_length = align.query_length
quals = align.query_qualities
# TODO: handle this more gracefully
if quals is None:
align_base_qscore = 0
else:
align_base_qscore = mean_qscore(np.array(
align.query_qualities))
else:
chrom, start, end = (align.reference_name, align.reference_start,
align.reference_end)
read_length = align.infer_read_length()
align_score = align.get_tag("AS")
align_base_qscore = mean_qscore(
np.array(align.query_alignment_qualities))
if align.is_secondary:
align_cat = "secondary"
elif align.is_supplementary:
align_cat = "supplementary"
else:
align_cat = "primary"
optional = {}
for key, tag in [("haplotype", "HP"), ("phase_set", "PS"),
("phase_qual", "PC")]:
if align.has_tag(tag):
optional[key] = int(align.get_tag(tag))
return cls(
read_idx=read_idx,
align_idx=align_idx,
align_type=align_cat,
chrom=chrom,
start=start,
end=end,
strand=not align.is_reverse,
read_name=read_name,
read_length=read_length,
read_start=align.query_alignment_start,
read_end=align.query_alignment_end,
mapping_quality=align.mapq,
align_score=align_score,
align_base_qscore=np.rint(align_base_qscore),
**optional,
)
