likely_stutter += nreads[1]
return array([ sum(nreads), likely_stutter, 1, 1 if likely_stutter else 0 ])
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument('--error-distn-file', metavar='file', type=str,
required=True,
help="File to which STR polymorphism error rates will be written.")
parser.add_argument('--filter-metrics-file', metavar='file', type=str,
help='File to store metrics related to locus and read filtering.')
parser.add_argument('--single-cell', action='store_true', default=False,
help="Library was generated from a single cell. Disables the " \
"binomial model for >1 primary alleles.")
STRLocusIterator.add_parser_args(parser)
args = parser.parse_args()
# Many of the command line args are STRLocusWalker parameters
lw_params = dict(vars(args))
del(lw_params['error_distn_file'])
del(lw_params['single_cell'])
del(lw_params['filter_metrics_file'])
# Step 1. Generate an STR length polymorphism error profile and save it.
errors = profile_error_distn(lw_params, is_single_cell=args.single_cell)
save_error_distn(args.error_distn_file, errors)
# Step 2. Now that we have an empirical distribution of STR polymorphism
# error rates, genotype the loci. The results are printed to stdout.
genotype(lw_params, args.filter_metrics_file, errors)
should be haploid and thus deviations from expectation should reflect
experimental errors.
* maximum mapQ option: --mapq60, only use mapq60 (very high confidence)
alignments
* minimum repeat unit option: --min-unit (recommended=3, default=1?). Only
consider loci where the repeat unit (end-start+1) / unit size is greater
than the specified value. This could be useful to remove questionable
loci, like 2~3 repeat units of tri or tetranucleotide repeats.
"""
import sys
from argparse import ArgumentParser
from strlocusiterator import STRLocusIterator
parser = ArgumentParser()
STRLocusIterator.add_parser_args(parser)
args = parser.parse_args()
locus_f = STRLocusIterator(**vars(args))
for (chrom, start, end, unit, region, reads) in locus_f:
# Don't do anything, just accumulate metrics
continue
for (description, value) in locus_f.filter_metrics():
print("%s\t%d" % (description, value))
for (description, hist) in locus_f.hist_metrics():
print(description)
for k in sorted(hist.keys()):
print("%s\t%d" % (k, hist[k]))
