def find_homologous_regions(seq, genomesource, segments, window_size=500, offset=500):
homologous_regions = []
for i in range(0, max(1, len(seq)-window_size), offset):
# print("---", i, "---")
curseq = seq[i:i+window_size]
cur_alns = genomesource.bwa.align(curseq, secondary_hit_cutoff=0.5)
if len(cur_alns) > 0: print("BEST:", cur_alns[0])
for i, cur_aln in enumerate(cur_alns):
chrom = genomesource.bwa.ChrIDToName(cur_aln.reference_id)
locus = intervals.Locus(chrom, cur_aln.reference_start, cur_aln.reference_end, "+")
if not intervals.overlaps(locus, segments):
homologous_regions.append(locus)
# print(cur_aln)
# print()
clustered = cluster_loci(homologous_regions)
# for l in clustered:
# print(l)
return clustered
def blacklist(self, blacklist_loci):
self._blacklist = []
for locus in blacklist_loci:
cur_chrom = misc.match_chrom_format(locus.chrom, list(self.keys()))
self._blacklist.append(
intervals.Locus(cur_chrom, locus.start, locus.end,
locus.strand))
def _get_pair_locus(aln1, aln2):
assert aln1.reference_name == aln2.reference_name
chrom = aln1.reference_name
start = min(aln1.reference_start, aln2.reference_start)
end = max(aln1.reference_end, aln2.reference_end)
locus = intervals.Locus(chrom, start, end, "+")
return locus
def loci(self):
if self.aln1.reference_id == self.aln2.reference_id:
chrom = self.aln1.chrom
start = min(self.aln1.reference_start, self.aln2.reference_start)
end = max(self.aln1.reference_end, self.aln2.reference_end)
locus = intervals.Locus(chrom, start, end, "+")
return [locus]
else:
return [self.aln1.locus, self.aln2.locus]
def get_internal_segments(sv, extend=20):
chrom_parts = sv.chrom_parts("ref")
internal_segments = []
for part in chrom_parts:
for i, segment in enumerate(part.segments):
if i == len(part.segments) - 1:
internal_segment = intervals.Locus(segment.chrom,
segment.start - extend,
segment.start + extend, "+")
internal_segments.append(internal_segment)
elif i == 0:
internal_segment = intervals.Locus(segment.chrom,
segment.end - extend,
segment.end + extend, "+")
internal_segments.append(internal_segment)
else:
internal_segment = intervals.Locus(segment.chrom,
segment.start - extend,
segment.end + extend, "+")
internal_segments.append(internal_segment)
return internal_segments
def do_homology_search(datahub):
logger.info(
"Finding homologous genomic regions (segmental duplications)...")
variant = datahub.variant
segment_loci = []
for part in variant.chrom_parts("ref"):
for segment in part.segments:
cur_chrom = misc.match_chrom_format(segment.chrom,
datahub.genome.keys())
segment = intervals.Locus(cur_chrom, max(0, segment.start - 100),
segment.end + 100, "+")
segment_loci.append(segment)
for part in variant.chrom_parts("ref"):
breakpoints = numpy.cumsum([len(segment)
for segment in part.segments])[:-1]
seq = part.get_seq()
homologous_regions = find_homologous_regions(seq, datahub.genome,
segment_loci)
plot_homologous_regions(seq, homologous_regions, datahub.genome,
part.id, breakpoints)
# also look for any reasonably-sized segments that are unique to the alt allele
ref_ids = set(segment.id for part in variant.chrom_parts("ref")
for segment in part.segments)
alt_ids = set(segment.id for part in variant.chrom_parts("alt")
for segment in part.segments)
alt_only_ids = alt_ids - ref_ids
alt_only_segments = [
segment for part in variant.chrom_parts("alt")
for segment in part.segments if segment.id in alt_only_ids
]
for segment in alt_only_segments:
if len(segment) >= 50:
seq = datahub.variant.sources[segment.chrom].get_seq(
segment.chrom, segment.start, segment.end, segment.strand)
homologous_regions = find_homologous_regions(
seq, datahub.genome, [])
plot_homologous_regions(seq,
homologous_regions,
datahub.genome,
label="segment_{}".format(segment))
def locus(self):
chrom = self.chrom
if chrom is None:
chrom = self._read.reference_name
start = self._read.reference_start
end = self._read.reference_end
# if self.cigartuples[0][0] == 4:
# start += self.cigartuples[0][1]
# if self.cigartuples[-1][0] == 4:
# end -= self.cigartuples[-1][1]
locus = intervals.Locus(chrom, start, end,
"-" if self.is_reverse else "+")
return locus
def set_read_supports_allele(aln_set, aln, allele, score, read_stats,
breakpoint_collection, min_overlap):
if not aln.concordant(read_stats):
return 0
assert len(aln.loci) == 1
aln_locus = aln.loci[0]
try:
chrom = aln.aln1.chrom
start = max(aln.aln1.reference_start, aln.aln2.reference_start)
end = min(aln.aln1.reference_end, aln.aln2.reference_end)
unsequenced_insert_locus = intervals.Locus(chrom, start, end, "+")
except AttributeError:
unsequenced_insert_locus = None
try:
if aln.insert_size > read_stats.max_reasonable_insert_size():
return 0
if aln.insert_size < read_stats.min_reasonable_insert_size():
return 0
except (IndexError, AttributeError):
pass
overlaps = get_overlaps(aln_locus, unsequenced_insert_locus,
breakpoint_collection)
if len(overlaps) == 0:
return 0
best_overlap = max(list(zip(*overlaps.values()))[1])
aln_set.supports_allele = allele
aln_set.support_prob = score / 40.0 #(1 - mapq.phred_to_prob(score, 10.0))
aln_set.supporting_aln = aln
aln.set_tag("OV", best_overlap)
aln.set_tag("Ov", json.dumps(overlaps))
aln.overlap = best_overlap
return aln_set.support_prob
