def process_read(mpa, args):
# Filter entries by a minimum alignment coverage
newentries = []
for i in [
i for i in range(0, len(mpa.entries))
if mpa.entries[i].get_coverage() > args.minimum_alignment_coverage
]:
newentries.append(mpa.entries[i])
mpa.entries = newentries
# Find best singles
bestsingle = None
bestsinglescore = -1
for i in range(0, len(mpa.entries)):
totalcov = mpa.entries[i].get_coverage()
weightedcov = float(mpa.entries[i].get_coverage()) * float(
mpa.entries[i].get_quality())
if weightedcov > bestsinglescore:
bestsinglescore = weightedcov
bestsingle = i
if bestsinglescore == -1:
sys.stderr.write("failed to find a single path\n")
return None
my_max_intron = args.maximum_intron
if args.fusion: my_max_intron = -1 # we can look any distance for a group
mpa.compatible_graph(
max_intron=my_max_intron,
max_query_overlap=args.maximum_query_overlap,
max_gap=args.maximum_query_gap,
max_target_overlap=args.maximum_target_overlap,
max_query_fraction_overlap=args.maximum_query_fraction_overlap)
ps = mpa.get_root_paths()
bestpath = [bestsingle]
bestscore = 0
besttotalcov = 0
allscores = []
allcov = []
best_path_index = -1
zz = 0
for path in ps:
totalcov = sum([mpa.entries[i].get_coverage() for i in path])
weightedcov = sum([
float(mpa.entries[i].get_coverage()) *
float(mpa.entries[i].get_quality()) for i in path
])
allscores.append(weightedcov)
allcov.append(totalcov)
if weightedcov > bestscore:
bestscore = weightedcov
bestpath = path
besttotalcov = totalcov
best_path_index = zz
zz += 1
#if not bestpath: return None
otherpaths = []
for i in range(0, len(ps)):
if i != best_path_index:
otherpaths.append(ps[i])
query_target_coverages = []
for other_path in otherpaths:
qcov = 0
tcov = 0
for other_entry in [mpa.entries[i] for i in other_path]:
for entry in [mpa.entries[j] for j in bestpath]:
qcov += other_entry.query_overlap_size(entry)
tcov += other_entry.target_overlap_size(entry)
query_target_coverages.append(str(qcov) + '/' + str(tcov))
gapsizes = []
if len(bestpath) > 1:
gapsizes = [
mpa.entries[bestpath[j + 1]].get_query_bed().start -
mpa.entries[bestpath[j]].get_query_bed().end - 1
for j in range(0,
len(bestpath) - 1)
]
#print mpa.g.get_status_string()
#print [mpa.entries[i].get_target_bed().get_range_string() for i in bestpath]
#print [mpa.entries[i].get_query_bed().get_range_string() for i in bestpath]
#print [mpa.entries[i].get_quality() for i in bestpath]
#print [mpa.entries[i].get_coverage() for i in bestpath]
#print gapsizes
#print bestscore
#print bestsinglescore
#See if we should use the single path score instead
if len(path) > 1 and bestsinglescore * (
1 + args.multipath_score_improvement) > bestscore:
bestpath = [bestsingle]
besttotalcov = mpa.entries[bestsingle].get_coverage()
bestscore = bestsinglescore
query_span = mpa.entries[bestpath[0]].get_query_bed()
loci = Loci()
loci.set_use_direction(True)
loci.set_minimum_distance(args.maximum_intron)
for i in bestpath:
r = mpa.entries[i].get_target_bed()
locus = Locus()
locus.set_use_direction(True)
locus.add_member(r)
loci.add_locus(locus)
loci.update_loci()
if len(bestpath) > 1:
for i in bestpath[1:]:
query_span = mpa.entries[i].get_query_bed().merge(query_span)
report = ''
report += mpa.entries[bestpath[0]].value('qName') + "\t"
report += str(len(bestpath)) + "\t"
report += str(len(loci.loci)) + "\t"
report += query_span.get_range_string() + "\t"
report += ','.join([mpa.entries[i].value('strand')
for i in bestpath]) + "\t"
report += ','.join(
[mpa.entries[i].get_query_bed().get_range_string()
for i in bestpath]) + "\t"
report += ','.join(
[mpa.entries[i].get_target_bed().get_range_string()
for i in bestpath]) + "\t"
report += ','.join([str(mpa.entries[i].get_quality())
for i in bestpath]) + "\t"
report += ','.join([str(mpa.entries[i].get_coverage())
for i in bestpath]) + "\t"
report += ','.join([str(x) for x in gapsizes]) + "\t"
report += str(besttotalcov) + "\t"
report += str(bestscore) + "\t"
report += str(bestsinglescore) + "\t"
report += str(','.join(query_target_coverages) + "\t")
#if args.best_report:
# best_report_fh.write(report+"\n")
#for i in bestpath:
# args.output.write(mpa.entries[i].get_line()+"\n")
return [report, [mpa.entries[i].get_line() for i in bestpath]]
def process_read(mpa,args):
# Filter entries by a minimum alignment coverage
newentries = []
for i in [i for i in range(0,len(mpa.entries)) if mpa.entries[i].get_coverage() > args.minimum_alignment_coverage]:
newentries.append(mpa.entries[i])
mpa.entries = newentries
# Find best singles
bestsingle = None
bestsinglescore = -1
for i in range(0,len(mpa.entries)):
totalcov = mpa.entries[i].get_coverage()
weightedcov = float(mpa.entries[i].get_coverage())*float(mpa.entries[i].get_quality())
if weightedcov > bestsinglescore:
bestsinglescore = weightedcov
bestsingle = i
if bestsinglescore == -1:
sys.stderr.write("failed to find a single path\n")
return None
my_max_intron = args.maximum_intron
if args.fusion: my_max_intron = -1 # we can look any distance for a group
mpa.compatible_graph(max_intron=my_max_intron,max_query_overlap=args.maximum_query_overlap,max_gap=args.maximum_query_gap,max_target_overlap=args.maximum_target_overlap,max_query_fraction_overlap=args.maximum_query_fraction_overlap)
ps = mpa.get_root_paths()
bestpath = [bestsingle]
bestscore = 0
besttotalcov = 0
allscores = []
allcov = []
best_path_index = -1
zz = 0
for path in ps:
totalcov = sum([mpa.entries[i].get_coverage() for i in path])
weightedcov = sum([float(mpa.entries[i].get_coverage())*float(mpa.entries[i].get_quality()) for i in path])
allscores.append(weightedcov)
allcov.append(totalcov)
if weightedcov > bestscore:
bestscore = weightedcov
bestpath = path
besttotalcov = totalcov
best_path_index = zz
zz+=1
#if not bestpath: return None
otherpaths = []
for i in range(0,len(ps)):
if i != best_path_index:
otherpaths.append(ps[i])
query_target_coverages = []
for other_path in otherpaths:
qcov = 0
tcov = 0
for other_entry in [mpa.entries[i] for i in other_path]:
for entry in [mpa.entries[j] for j in bestpath]:
qcov += other_entry.query_overlap_size(entry)
tcov += other_entry.target_overlap_size(entry)
query_target_coverages.append(str(qcov)+'/'+str(tcov))
gapsizes = []
if len(bestpath) > 1:
gapsizes = [mpa.entries[bestpath[j+1]].get_query_bed().start - mpa.entries[bestpath[j]].get_query_bed().end -1 for j in range(0,len(bestpath)-1)]
#print mpa.g.get_status_string()
#print [mpa.entries[i].get_target_bed().get_range_string() for i in bestpath]
#print [mpa.entries[i].get_query_bed().get_range_string() for i in bestpath]
#print [mpa.entries[i].get_quality() for i in bestpath]
#print [mpa.entries[i].get_coverage() for i in bestpath]
#print gapsizes
#print bestscore
#print bestsinglescore
#See if we should use the single path score instead
if len(path) > 1 and bestsinglescore*(1+args.multipath_score_improvement) > bestscore:
bestpath = [bestsingle]
besttotalcov = mpa.entries[bestsingle].get_coverage()
bestscore = bestsinglescore
query_span = mpa.entries[bestpath[0]].get_query_bed()
loci = Loci()
loci.set_use_direction(True)
loci.set_minimum_distance(args.maximum_intron)
for i in bestpath:
r = mpa.entries[i].get_target_bed()
locus = Locus()
locus.set_use_direction(True)
locus.add_member(r)
loci.add_locus(locus)
loci.update_loci()
if len(bestpath) > 1:
for i in bestpath[1:]:
query_span = mpa.entries[i].get_query_bed().merge(query_span)
report = ''
report += mpa.entries[bestpath[0]].value('qName')+"\t"
report += str(len(bestpath))+"\t"
report += str(len(loci.loci))+"\t"
report += query_span.get_range_string()+"\t"
report += ','.join([mpa.entries[i].value('strand') for i in bestpath])+"\t"
report += ','.join([mpa.entries[i].get_query_bed().get_range_string() for i in bestpath])+"\t"
report += ','.join([mpa.entries[i].get_target_bed().get_range_string() for i in bestpath])+"\t"
report += ','.join([str(mpa.entries[i].get_quality()) for i in bestpath])+"\t"
report += ','.join([str(mpa.entries[i].get_coverage()) for i in bestpath])+"\t"
report += ','.join([str(x) for x in gapsizes])+"\t"
report += str(besttotalcov)+"\t"
report += str(bestscore)+"\t"
report += str(bestsinglescore)+"\t"
report += str(','.join(query_target_coverages)+"\t")
#if args.best_report:
# best_report_fh.write(report+"\n")
#for i in bestpath:
# args.output.write(mpa.entries[i].get_line()+"\n")
return [report, [mpa.entries[i].get_line() for i in bestpath]]
