def extract_mismatched_reads( min_distance, max_distance, mappable, bam, in_fh, out_fh, bam_to_sam ):
bias.log_stderr( 'Filtering based on min distance %i, max_distance %i, mappable %s' % ( min_distance, max_distance, mappable ) )
allowed_tags = set()
candidates = 0
for line in in_fh:
fields = line.strip().split(',')
if len(fields) == 5 and fields[0].isdigit():
candidates += 1
distance = abs(int(fields[3]))
line_mappable = int(fields[4]) == 0
if distance >= min_distance and distance <= max_distance:
if line_mappable and mappable or not line_mappable and not mappable:
allowed_tags.add( fields[2] )
bias.log_stderr( '%i allowed tags from %i possibles' % ( len(allowed_tags), candidates ) )
bias.SamFilter( sam_fh=bias.BamReaderExternal( bam_to_sam, bam ), target_fh=out_fh, allowed_tags=allowed_tags, log=bias.log_stderr )
def compare_bams( bams, mapq, compare_position, subset_detail, mismatch_detail, xmfa, origin, target, out_fh, bam_to_sam ):
diff = bias.SamDiff( [ bias.BamReaderExternal( bam_to_sam, sam_file ) for sam_file in bams ], mapq_min=mapq, compare_position=compare_position, subset_detail=subset_detail, mismatch_detail=None if mismatch_detail == -1 else mismatch_detail )
out_fh.write( "mapq stats\n==========\n" )
out_fh.write( "i:\tn\tmax\tmin\tmean\tsd\tfilename\n" )
for idx, stats in enumerate( diff.mapq_stats ):
out_fh.write( '%i:\t%i\t%.2f\t%.2f\t%.2f\t%.2f\t%s\n' % ( idx, stats['mapped'], stats['max'], stats['min'], stats['mean'], stats['sd'], bams[idx] ) )
out_fh.write( "\nmapped vs unmapped commonality\n===================\n" )
for key in sorted( diff.totals.keys() ):
out_fh.write( ( "{0:0%ib}: {1}\n" % ( len(bams) ) ).format( key, diff.totals[key] ) )
if compare_position:
#out_fh.write( "\nmapped vs unmapped commonality including position differences\n===================\n" )
#for key in sorted( diff.position_totals.keys() ):
# out_fh.write( ( "{0:0%ib}: {1}\n" % ( len(bams) ) ).format( key, diff.position_totals[key] ) )
pass
if subset_detail:
out_fh.write( "\nmapq vs position differences\n===================\n" )
out_fh.write( "i:\tmax\tmin\tmean\tsd\thist\n" )
for key, value in diff.mapq_subset_stats.items():
bin_key = ( "{0:0%ib}" % ( len(bams) ) ).format( key )
out_fh.write( '%s:\t%.2f\t%.2f\t%.2f\t%.2f\t%s\n' % ( bin_key, value['max'], value['min'], value['mean'], value['sd'], value['hist'] ) )
if mismatch_detail > -1:
out_fh.write( "\nmismatch details\n===================\n" )
if xmfa is None:
out_fh.write( "pos,alt,read_id,wrongness\n" )
else:
mauve_map = bias.MauveMap( open(xmfa, 'r'), src_strand=origin, target_strand=target )
out_fh.write( "pos,alt,read_id,wrongness,nearestmap\n" )
mismatch_count = 0
unpaired_count = 0
for read, value in diff.mismatch_stats.items():
if 'p' in value and 'a' in value:
if xmfa is None:
out_fh.write( '%i,%i,%s,%i\n' % ( value['p'], value['a'], read, value['p'] - value['a'] ) )
else:
nearest = mauve_map.find_nearest_target( int(value['a']) )
out_fh.write( '%i,%i,%s,%i,%i\n' % ( value['p'], value['a'], read, value['p'] - value['a'], nearest ) )
mismatch_count += 1
else:
unpaired_count += 1
#print "missing values", read, value
bias.log_stderr( "%i mismatches with incorrect alternatives; %i unpaired reads" % ( mismatch_count, unpaired_count ) )
def run(cmd):
"""
run a system command
"""
bias.log_stderr(cmd)
os.system(cmd)
for line in open(stats_tmp, "r"):
fields = line.strip().split()
if len(fields) > 3 and fields[3] == "mapped":
return int(fields[0])
finally:
run("rm {0} {1}".format(sam_tmp, stats_tmp))
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Choose reference")
parser.add_argument("--fastq", help="set of reads")
parser.add_argument("--sample", type=int, default=10000, help="number of reads to sample")
parser.add_argument("--skipindex", action="store_true", default=False, help="skip fasta indexing step")
parser.add_argument("references", nargs="+", help="reference fasta file(s)")
args = parser.parse_args()
# sample fastq
fastq_tmp = "tmp{0}.fq".format(random.randint(1, 1e6))
sample_fastq(args.fastq, fastq_tmp, args.sample)
# assess with unmapped
results = {}
bias.log_stderr("{0} reference sequences".format(len(args.references)))
for reference in args.references:
results[reference] = unmapped_count(reference, fastq_tmp, skipindex=args.skipindex)
for k in sorted(results, key=results.get):
sys.stdout.write("{0}\t{1}\n".format(k, results[k]))
run("rm {0}".format(fastq_tmp))
