def main():
logging.basicConfig(format='[%(asctime)s - %(name)s] %(message)s',
datefmt='%H:%M:%S',
level=logging.INFO)
parser = argparse.ArgumentParser(
'Calculate read coverage depth from a bam.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('bam', help='.fasta/fastq file.')
parser.add_argument('-r',
'--regions',
nargs='+',
help='Only process given regions.')
parser.add_argument('-p',
'--prefix',
help='Prefix for output, defaults to basename of bam.')
parser.add_argument('-s',
'--stride',
type=int,
default=1000,
help='Stride in genomic coordinate.')
args = parser.parse_args()
bam = pysam.AlignmentFile(args.bam)
ref_lengths = dict(zip(bam.references, bam.lengths))
if args.regions is not None:
regions = parse_regions(args.regions, ref_lengths=ref_lengths)
else:
regions = [
Region(ref_name=r, start=0, end=ref_lengths[r])
for r in bam.references
]
summary = {}
for region in regions:
# write final depth
prefix = args.prefix
if prefix is None:
prefix = os.path.splitext(os.path.basename(args.bam))[0]
region_str = '{}_{}_{}'.format(region.ref_name, region.start,
region.end)
depth_fp = '{}_{}.depth.txt'.format(prefix, region_str)
df = coverage_of_region(region, args.bam, args.stride)
df.to_csv(depth_fp, sep='\t', index=False)
summary[region_str] = df['depth'].describe()
summary_fp = '{}_depth_summary.txt'.format(prefix)
summary_df = pd.DataFrame(summary).T.reset_index().rename(
columns={'index': 'region'})
summary_df.to_csv(summary_fp, index=False, sep='\t')
def main():
logging.basicConfig(format='[%(asctime)s - %(name)s] %(message)s',
datefmt='%H:%M:%S',
level=logging.INFO)
parser = argparse.ArgumentParser(
'subsample bam to uniform or proportional depth',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('bam', help='input bam file.')
parser.add_argument('depth', nargs='+', type=int, help='Target depth.')
parser.add_argument('-o',
'--output_prefix',
default='sub_sampled',
help='Output prefix')
parser.add_argument('-r',
'--regions',
nargs='+',
help='Only process given regions.')
parser.add_argument(
'-p',
'--profile',
type=int,
default=1000,
help='Stride in genomic coordinates for depth profile.')
parser.add_argument('-O',
'--orientation',
choices=['fwd', 'rev'],
help='Sample only forward or reverse reads.')
parser.add_argument('-t',
'--threads',
type=int,
default=-1,
help='Number of threads to use.')
parser.add_argument('-q',
'--quality',
type=float,
help='Filter reads by mean qscore.')
parser.add_argument('-a',
'--accuracy',
type=float,
help='Filter reads by accuracy.')
parser.add_argument(
'-c',
'--coverage',
type=float,
help='Filter reads by coverage (what fraction of the read aligns).')
eparser = parser.add_mutually_exclusive_group()
eparser.add_argument(
'--any_fail',
action='store_true',
help='Exit with an error if any region has insufficient coverage.')
eparser.add_argument(
'--all_fail',
action='store_true',
help='Exit with an error if all regions have insufficient coverage.')
uparser = parser.add_argument_group('Uniform sampling options')
uparser.add_argument(
'-x',
'--patience',
default=5,
type=int,
help=
'Maximum iterations with no change in median coverage before aborting.'
)
uparser.add_argument(
'-s',
'--stride',
type=int,
default=1000,
help=
'Stride in genomic coordinates when searching for new reads. Smaller can lead to more compact pileup.'
)
pparser = parser.add_argument_group('Proportional sampling options')
pparser.add_argument(
'-P',
'--proportional',
default=False,
action='store_true',
help=
'Activate proportional sampling, thus keeping depth variations of the pileup.'
)
pparser.add_argument(
'-S',
'--seed',
default=None,
type=int,
help='Random seed for proportional downsampling of reads.')
args = parser.parse_args()
if args.threads == -1:
args.threads = multiprocessing.cpu_count()
with pysam.AlignmentFile(args.bam) as bam:
ref_lengths = dict(zip(bam.references, bam.lengths))
if args.regions is not None:
regions = parse_regions(args.regions, ref_lengths=ref_lengths)
else:
regions = [
Region(ref_name=r, start=0, end=ref_lengths[r])
for r in bam.references
]
if args.proportional:
worker = functools.partial(subsample_region_proportionally, args=args)
else:
worker = functools.partial(subsample_region_uniformly, args=args)
enough_depth = []
with ProcessPoolExecutor(max_workers=args.threads) as executor:
for res in executor.map(worker, regions):
enough_depth.append(res)
if args.any_fail and not all(enough_depth):
raise RuntimeError(
'Insufficient read coverage for one or more requested regions.')
if args.all_fail and all([not x for x in enough_depth]):
raise RuntimeError(
'Insufficient read coverage for all requested regions.')
def main():
logging.basicConfig(format='[%(asctime)s - %(name)s] %(message)s',
datefmt='%H:%M:%S',
level=logging.INFO)
parser = argparse.ArgumentParser(
'subsample bam and fastx to create fastx with uniform depth')
parser.add_argument('bam', help='input bam file.')
parser.add_argument('fastx', help='input .fasta/fastq file of reads.')
parser.add_argument('depth', type=int, help='Target depth.')
parser.add_argument('-i',
'--ifmt',
default='fasta',
choices=['fasta', 'fastq'],
help='Input format.')
parser.add_argument('-o',
'--output_prefix',
default='sub_sampled',
help='Output prefix')
parser.add_argument(
'-d',
'--damping',
default=0.5,
type=float,
help=
'Fraction of reads required to achieve target depth to add at each iteration. '
+
'Setting 1 will achieve target depth in one iteration, but will likely result in '
+ 'excess depth in places, and thus less uniform depth.')
parser.add_argument('-c',
'--chkpnt',
default=100,
type=int,
help='Frequency at which to write depths and reads.')
parser.add_argument('-r',
'--regions',
nargs='+',
help='Only process given regions.')
parser.add_argument('-s',
'--stride',
type=int,
default=1,
help='Stride in genomic coordinates.')
parser.add_argument('-D',
'--direction',
choices=['fwd', 'rev'],
help='Sample only forward or reverse reads.')
args = parser.parse_args()
bam = pysam.AlignmentFile(args.bam)
ref_lengths = dict(zip(bam.references, bam.lengths))
if args.regions is not None:
regions = parse_regions(args.regions, ref_lengths=ref_lengths)
else:
regions = [
Region(ref_name=r, start=0, end=ref_lengths[r])
for r in bam.references
]
_read_filter_ = {
'fwd': lambda r: not r.is_reverse,
'rev': lambda r: r.is_reverse,
None: True,
}
for region in regions:
reads_kept = set()
bins = np.arange(region.start, region.end, args.stride)
msg = 'Processing region {}:{}-{}'
logging.info(msg.format(region.ref_name, bins[0], bins[-1]))
coverage = np.zeros(len(bins))
count = 0
low_cov_sites = {}
prefix = '{}_{}X'.format(args.output_prefix, args.depth)
if args.direction is not None:
prefix = '{}_{}'.format(prefix, args.direction)
while True:
bin_i = np.argmin(coverage)
if coverage[bin_i] >= args.depth:
break
count += 1
if count % args.chkpnt == 0:
logging.info('Min depth {} at {} (Target depth {})'.format(
coverage[bin_i], bins[bin_i], args.depth))
checkpoint(region.ref_name, bins, coverage, reads_kept,
low_cov_sites, prefix)
pos = bins[bin_i]
reads = [
r for r in bam.fetch(
contig=region.ref_name, start=pos, end=pos + 1)
if _read_filter_[args.direction](r)
]
reads_set = set((r.query_name for r in reads))
reads_in_common = reads_kept.intersection(reads_set)
reads_not_used = reads_set.difference(reads_in_common)
if len(reads_not_used) == 0:
low_cov_sites[bins[bin_i]] = coverage[bin_i]
coverage[bin_i] = np.inf
logging.info('Insufficient depth ({}X) at {}:{}'.format(
low_cov_sites[bins[bin_i]], region.ref_name, pos))
continue
n_reads_to_add = max(
int(round(args.damping * (args.depth - coverage[bin_i]), 0)),
1)
logging.info('Adding {} reads at {}:{} (depth {})'.format(
n_reads_to_add, region.ref_name, pos, coverage[bin_i]))
n_reads_to_add = min(n_reads_to_add, len(reads_not_used))
reads_to_add = np.random.choice(list(reads_not_used),
n_reads_to_add,
replace=False)
reads_kept.update(reads_to_add)
# update coverage
r_objs = [r for r in reads if r.query_name in reads_to_add]
for r_obj in r_objs:
start_i = max((r_obj.reference_start - bins[0]) // args.stride,
0)
end_i = min((r_obj.reference_end - bins[0]) // args.stride,
len(bins))
coverage[start_i:end_i] += 1
# write final depth and reads
checkpoint(region.ref_name, bins, coverage, reads_kept, low_cov_sites,
prefix)
fastx_ndx = SeqIO.index(args.fastx, args.ifmt)
output = '{}_{}.{}'.format(prefix, region.ref_name, args.ifmt)
logging.info('Writing {} sequences to {}'.format(
len(reads_kept), output))
with open(output, 'w') as fh:
seqs = (fastx_ndx[k] for k in reads_kept)
SeqIO.write(seqs, fh, args.ifmt)
def main():
logging.basicConfig(format='[%(asctime)s - %(name)s] %(message)s',
datefmt='%H:%M:%S',
level=logging.INFO)
parser = argparse.ArgumentParser(
'Calculate read coverage depth from a bam.')
parser.add_argument('bam', help='.fasta/fastq file.')
parser.add_argument('-r',
'--regions',
nargs='+',
help='Only process given regions.')
parser.add_argument('-p',
'--prefix',
help='Prefix for output, defaults to basename of bam.')
parser.add_argument('-s',
'--stride',
type=int,
default=1,
help='Stride in genomic coordinate.')
args = parser.parse_args()
bam = pysam.AlignmentFile(args.bam)
ref_lengths = dict(zip(bam.references, bam.lengths))
if args.regions is not None:
regions = parse_regions(args.regions, ref_lengths=ref_lengths)
else:
regions = [
Region(ref_name=r, start=0, end=ref_lengths[r])
for r in bam.references
]
for region in regions:
ref_len = ref_lengths[region.ref_name]
bins = np.arange(region.start, region.end, args.stride)
msg = 'Processing reference {}:{}-{}'
logging.info(msg.format(region.ref_name, bins[0], bins[-1]))
coverage_by_is_rev = {
True: np.zeros(len(bins)),
False: np.zeros(len(bins))
}
for r_obj in bam.fetch(contig=region.ref_name,
start=region.start,
end=region.end):
start_i = max((r_obj.reference_start - bins[0]) // args.stride, 0)
end_i = min((r_obj.reference_end - bins[0]) // args.stride,
len(bins))
coverage_by_is_rev[r_obj.is_reverse][start_i:end_i] += 1
# write final depth
prefix = args.prefix
if prefix is None:
prefix = os.path.splitext(os.path.basename(args.bam))[0]
depth_fp = '{}_{}_{}_{}.depth.txt'.format(prefix, region.ref_name,
region.start, region.end)
pd.DataFrame({
'pos':
bins,
'depth':
coverage_by_is_rev[True] + coverage_by_is_rev[False],
'depth_fwd':
coverage_by_is_rev[False],
'depth_rev':
coverage_by_is_rev[True],
}).to_csv(depth_fp, sep='\t', index=False)