def run(args):
logging.basicConfig(level=int(round(10*args.verbose_level)))
assert args.n_core <= multiprocessing.cpu_count(), 'Requested n_core={} > cpu_count={}'.format(
args.n_core, multiprocessing.cpu_count())
def Start():
LOG.info('Started a worker in {} from parent {}'.format(
os.getpid(), os.getppid()))
exe_pool = Pool(args.n_core, initializer=Start)
if args.trim:
get_consensus = get_consensus_with_trim
else:
get_consensus = get_consensus_without_trim
K = 8
config = args.min_cov, K, \
args.max_n_read, args.min_idt, args.edge_tolerance, \
args.trim_size, args.min_cov_aln, args.max_cov_aln, \
args.allow_external_mapping
# TODO: pass config object, not tuple, so we can add fields
inputs = []
for datum in get_seq_data(config, args.min_n_read, args.min_len_aln):
inputs.append((get_consensus, datum))
try:
LOG.info('running {!r}'.format(get_consensus))
for res in exe_pool.imap(io.run_func, inputs):
process_get_consensus_result(res, args)
LOG.info('finished {!r}'.format(get_consensus))
except:
LOG.exception('failed gen_consensus')
exe_pool.terminate()
raise
def run(args):
logging.basicConfig(level=int(round(10*args.verbose_level)))
assert args.n_core <= multiprocessing.cpu_count(), 'Requested n_core={} > cpu_count={}'.format(
args.n_core, multiprocessing.cpu_count())
def Start():
LOG.info('Started a worker in {} from parent {}'.format(
os.getpid(), os.getppid()))
exe_pool = Pool(args.n_core, initializer=Start)
if args.trim:
get_consensus = get_consensus_with_trim
else:
get_consensus = get_consensus_without_trim
K = 8
config = args.min_cov, K, \
args.max_n_read, args.min_idt, args.edge_tolerance, args.trim_size, args.min_cov_aln, args.max_cov_aln
# TODO: pass config object, not tuple, so we can add fields
inputs = []
for datum in get_seq_data(config, args.min_n_read, args.min_len_aln):
inputs.append((get_consensus, datum))
try:
LOG.info('running {!r}'.format(get_consensus))
for res in exe_pool.imap(io.run_func, inputs):
process_get_consensus_result(res, args)
LOG.info('finished {!r}'.format(get_consensus))
except:
LOG.exception('failed gen_consensus')
exe_pool.terminate()
raise
def run(args):
logging.basicConfig(level=int(round(10 * args.verbose_level)))
good_region = re.compile("[ACGT]+")
assert args.n_core <= multiprocessing.cpu_count(
), 'Requested n_core={} > cpu_count={}'.format(args.n_core,
multiprocessing.cpu_count())
def Start():
LOG.info('Started a worker in {} from parent {}'.format(
os.getpid(), os.getppid()))
exe_pool = Pool(args.n_core, initializer=Start)
if args.trim:
get_consensus = get_consensus_with_trim
else:
get_consensus = get_consensus_without_trim
K = 8
config = args.min_cov, K, \
args.max_n_read, args.min_idt, args.edge_tolerance, args.trim_size, args.min_cov_aln, args.max_cov_aln
# TODO: pass config object, not tuple, so we can add fields
for res in exe_pool.imap(
get_consensus,
get_seq_data(config, args.min_n_read, args.min_len_aln)):
cns, seed_id = res
if len(cns) < 500:
continue
if args.output_full:
print(">" + seed_id + "_f")
print(cns)
else:
cns = good_region.findall(cns)
if len(cns) == 0:
continue
if args.output_multi:
seq_i = 0
for cns_seq in cns:
if len(cns_seq) < 500:
continue
if seq_i >= 10:
break
print(">prolog/%s%01d/%d_%d" %
(seed_id, seq_i, 0, len(cns_seq)))
print(format_seq(cns_seq, 80))
seq_i += 1
else:
cns.sort(key=lambda x: len(x))
print(">" + seed_id)
print(cns[-1])
def fc_ovlp_filter(n_core, fofn, max_diff, max_cov, min_cov, min_len, bestn,
db_fn, debug, silent):
global LOG
if silent:
LOG = write_nothing
exe_pool = Pool(n_core)
file_list = open(fofn).read().split("\n")
inputs = []
for fn in file_list:
if len(fn) != 0:
inputs.append((db_fn, fn, max_diff, max_cov, min_cov, min_len))
ignore_all = []
for res in exe_pool.imap(filter_stage1, inputs):
ignore_all.extend(res[1])
inputs = []
ignore_all = set(ignore_all)
for fn in file_list:
if len(fn) != 0:
inputs.append(
(db_fn, fn, max_diff, max_cov, min_cov, min_len, ignore_all))
contained = set()
for res in exe_pool.imap(filter_stage2, inputs):
contained.update(res[1])
#print res[0], len(res[1]), len(contained)
#print "all", len(contained)
inputs = []
ignore_all = set(ignore_all)
for fn in file_list:
if len(fn) != 0:
inputs.append((db_fn, fn, max_diff, max_cov, min_cov, min_len,
ignore_all, contained, bestn))
for res in exe_pool.imap(filter_stage3, inputs):
for l in res[1]:
print " ".join(l)
def run(args):
logging.basicConfig(level=int(round(10*args.verbose_level)))
good_region = re.compile("[ACGT]+")
assert args.n_core <= multiprocessing.cpu_count(), 'Requested n_core={} > cpu_count={}'.format(
args.n_core, multiprocessing.cpu_count())
def Start():
LOG.info('Started a worker in {} from parent {}'.format(
os.getpid(), os.getppid()))
exe_pool = Pool(args.n_core, initializer=Start)
if args.trim:
get_consensus = get_consensus_with_trim
else:
get_consensus = get_consensus_without_trim
K = 8
config = args.min_cov, K, \
args.max_n_read, args.min_idt, args.edge_tolerance, args.trim_size, args.min_cov_aln, args.max_cov_aln
# TODO: pass config object, not tuple, so we can add fields
for res in exe_pool.imap(get_consensus, get_seq_data(config, args.min_n_read, args.min_len_aln)):
cns, seed_id = res
if len(cns) < 500:
continue
if args.output_full:
print(">" + seed_id + "_f")
print(cns)
else:
cns = good_region.findall(cns)
if len(cns) == 0:
continue
if args.output_multi:
seq_i = 0
for cns_seq in cns:
if len(cns_seq) < 500:
continue
if seq_i >= 10:
break
print(">prolog/%s%01d/%d_%d" % (seed_id, seq_i, 0, len(cns_seq)))
print(format_seq(cns_seq, 80))
seq_i += 1
else:
cns.sort(key=lambda x: len(x))
print(">" + seed_id)
print(cns[-1])
def main(*argv):
parser = argparse.ArgumentParser(description='a simple multi-processes LAS ovelap data filter')
parser.add_argument('--n_core', type=int, default=4,
help='number of processes used for generating consensus; '
'0 for main process only (default=%(default)s)')
parser.add_argument('--fofn', type=str, help='file contains the path of all LAS file to be processed in parallel')
parser.add_argument('--min_len', type=int, default=2500, help="min length of the reads")
args = parser.parse_args(argv)
exe_pool = Pool(args.n_core)
file_list = open(args.fofn).read().split("\n")
#print "all", len(contained)
inputs = []
for fn in file_list:
if len(fn) != 0:
inputs.append( (fn, args.min_len ) )
for res in exe_pool.imap(run_filter_stats, inputs):
for l in res[1]:
print " ".join([str(c) for c in l])
def main(*argv):
parser = argparse.ArgumentParser(description='a simple multi-processor consensus sequence generator')
parser.add_argument('--n_core', type=int, default=24,
help='number of processes used for generating consensus; '
'0 for main process only (default=%(default)s)')
parser.add_argument('--local_match_count_window', type=int, default=12,
help='local match window size (obsoleted, no effect)')
parser.add_argument('--local_match_count_threshold', type=int, default=6,
help='local match count threshold (obsoleted, no effect)')
parser.add_argument('--min_cov', type=int, default=6,
help='minimum coverage to break the consensus')
parser.add_argument('--min_cov_aln', type=int, default=10,
help='minimum coverage of alignment data; an alignment with fewer reads will be completely ignored')
parser.add_argument('--min_len_aln', type=int, default=100,
help='minimum length of a sequence in an alignment to be used in consensus; any shorter sequence will be completely ignored')
parser.add_argument('--max_n_read', type=int, default=500,
help='maximum number of reads used in generating the consensus')
parser.add_argument('--trim', action="store_true", default=False,
help='trim the input sequence with k-mer spare dynamic programming to find the mapped range')
parser.add_argument('--output_full', action="store_true", default=False,
help='output uncorrected regions too')
parser.add_argument('--output_multi', action="store_true", default=False,
help='output multi correct regions')
parser.add_argument('--output_dformat', action="store_true", default=False,
help='output daligner compatible header, only work with --output_multi')
parser.add_argument('--min_idt', type=float, default=0.70,
help='minimum identity of the alignments used for correction')
parser.add_argument('--edge_tolerance', type=int, default=1000,
help='for trimming, the there is unaligned edge leng > edge_tolerance, ignore the read')
parser.add_argument('--trim_size', type=int, default=50,
help='the size for triming both ends from initial sparse aligned region')
good_region = re.compile("[ACGT]+")
args = parser.parse_args(argv[1:])
exe_pool = Pool(args.n_core)
if args.trim:
get_consensus = get_consensus_with_trim
else:
get_consensus = get_consensus_without_trim
K = 8
config = args.min_cov, K, args.local_match_count_window, args.local_match_count_threshold,\
args.max_n_read, args.min_idt, args.edge_tolerance, args.trim_size
# TODO: pass config object, not tuple, so we can add fields
for res in exe_pool.imap(get_consensus, get_seq_data(config, args.min_cov_aln, args.min_len_aln)):
cns, seed_id = res
if len(cns) < 500:
continue
if args.output_full == True:
print ">"+seed_id+"_f"
print cns
else:
cns = good_region.findall(cns)
if len(cns) == 0:
continue
if args.output_multi == True:
seq_i = 0
for cns_seq in cns:
if len(cns_seq) < 500:
continue
if args.output_dformat:
if seq_i >= 10:
break
print ">prolog/%s%01d/%d_%d" % (seed_id, seq_i, 0, len(cns_seq))
print format_seq(cns_seq, 80)
else:
print ">"+seed_id+"_%d" % seq_i
print cns_seq
seq_i += 1
else:
cns.sort(key = lambda x: len(x))
print ">"+seed_id
print cns[-1]
def main(argv=sys.argv):
parser = argparse.ArgumentParser(description='a simple multi-processor consensus sequence generator')
parser.add_argument('--n_core', type=int, default=24,
help='number of processes used for generating consensus; '
'0 for main process only (default=%(default)s)')
parser.add_argument('--local_match_count_window', type=int, default=12,
help='local match window size (obsoleted, no effect)')
parser.add_argument('--local_match_count_threshold', type=int, default=6,
help='local match count threshold (obsoleted, no effect)')
parser.add_argument('--min_cov', type=int, default=6,
help='minimum coverage to break the consensus')
parser.add_argument('--min_cov_aln', type=int, default=10,
help='minimum coverage of alignment data; a seed read with less than MIN_COV_ALN average depth' + \
' of coverage will be completely ignored')
parser.add_argument('--max_cov_aln', type=int, default=0, # 0 to emulate previous behavior
help='maximum coverage of alignment data; a seed read with more than MAX_COV_ALN average depth' + \
' of coverage of the longest alignments will be capped, excess shorter alignments will be ignored')
parser.add_argument('--min_len_aln', type=int, default=0, # 0 to emulate previous behavior
help='minimum length of a sequence in an alignment to be used in consensus; any shorter sequence will be completely ignored')
parser.add_argument('--min_n_read', type=int, default=10,
help='minimum number of reads used in generating the consensus; a seed read with fewer alignments will '+ \
'be completely ignored (obsoleted, formerly called `--min_cov_aln\')')
parser.add_argument('--max_n_read', type=int, default=500,
help='maximum number of reads used in generating the consensus')
parser.add_argument('--trim', action="store_true", default=False,
help='trim the input sequence with k-mer spare dynamic programming to find the mapped range')
parser.add_argument('--output_full', action="store_true", default=False,
help='output uncorrected regions too')
parser.add_argument('--output_multi', action="store_true", default=False,
help='output multi correct regions; implies --output_dformat, unless --output-simple-fasta-header')
parser.add_argument('--output_dformat', action="store_true", default=True,
help='output daligner compatible header, only work with --output_multi; DEPRECATED and ignored, as this is the default now')
parser.add_argument('--output_simple_fasta_header', action='store_true', default=False,
help='Turn off --output_dformat. This was for older (pre spring 2015) DALIGNER. Never needed now.')
parser.add_argument('--min_idt', type=float, default=0.70,
help='minimum identity of the alignments used for correction')
parser.add_argument('--edge_tolerance', type=int, default=1000,
help='for trimming, the there is unaligned edge leng > edge_tolerance, ignore the read')
parser.add_argument('--trim_size', type=int, default=50,
help='the size for triming both ends from initial sparse aligned region')
good_region = re.compile("[ACGT]+")
args = parser.parse_args(argv[1:])
def Start():
print>>sys.stderr, 'Started a worker in %d from parent %d' %(os.getpid(), os.getppid())
exe_pool = Pool(args.n_core, initializer=Start)
if args.trim:
get_consensus = get_consensus_with_trim
else:
get_consensus = get_consensus_without_trim
K = 8
config = args.min_cov, K, args.local_match_count_window, args.local_match_count_threshold,\
args.max_n_read, args.min_idt, args.edge_tolerance, args.trim_size, args.min_cov_aln, args.max_cov_aln
# TODO: pass config object, not tuple, so we can add fields
for res in exe_pool.imap(get_consensus, get_seq_data(config, args.min_n_read, args.min_len_aln)):
cns, seed_id = res
if len(cns) < 500:
continue
if args.output_full:
print ">"+seed_id+"_f"
print cns
else:
cns = good_region.findall(cns)
if len(cns) == 0:
continue
if args.output_multi:
seq_i = 0
for cns_seq in cns:
if len(cns_seq) < 500:
continue
if not args.output_simple_fasta_header:
if seq_i >= 10:
break
print ">prolog/%s%01d/%d_%d" % (seed_id, seq_i, 0, len(cns_seq))
print format_seq(cns_seq, 80)
else:
print ">"+seed_id+"_%d" % seq_i
print cns_seq
seq_i += 1
else:
cns.sort(key = lambda x: len(x))
print ">"+seed_id
print cns[-1]
def main(argv=sys.argv):
parser = argparse.ArgumentParser(description='a simple multi-processor consensus sequence generator',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--n_core', type=int, default=24,
help='number of processes used for generating consensus; '
'0 for main process only')
parser.add_argument('--min_cov', type=int, default=6,
help='minimum coverage to break the consensus')
parser.add_argument('--min_cov_aln', type=int, default=10,
help='minimum coverage of alignment data; a seed read with less than MIN_COV_ALN average depth' + \
' of coverage will be completely ignored')
parser.add_argument('--max_cov_aln', type=int, default=0, # 0 to emulate previous behavior
help='maximum coverage of alignment data; a seed read with more than MAX_COV_ALN average depth' + \
' of coverage of the longest alignments will be capped, excess shorter alignments will be ignored')
parser.add_argument('--min_len_aln', type=int, default=0, # 0 to emulate previous behavior
help='minimum length of a sequence in an alignment to be used in consensus; any shorter sequence will be completely ignored')
parser.add_argument('--min_n_read', type=int, default=10,
help='1 + minimum number of reads used in generating the consensus; a seed read with fewer alignments will '+ \
'be completely ignored')
parser.add_argument('--max_n_read', type=int, default=500,
help='1 + maximum number of reads used in generating the consensus')
parser.add_argument('--trim', action="store_true", default=False,
help='trim the input sequence with k-mer spare dynamic programming to find the mapped range')
parser.add_argument('--output_full', action="store_true", default=False,
help='output uncorrected regions too')
parser.add_argument('--output_multi', action="store_true", default=False,
help='output multi correct regions')
parser.add_argument('--min_idt', type=float, default=0.70,
help='minimum identity of the alignments used for correction')
parser.add_argument('--edge_tolerance', type=int, default=1000,
help='for trimming, the there is unaligned edge leng > edge_tolerance, ignore the read')
parser.add_argument('--trim_size', type=int, default=50,
help='the size for triming both ends from initial sparse aligned region')
good_region = re.compile("[ACGT]+")
args = parser.parse_args(argv[1:])
def Start():
print>>sys.stderr, 'Started a worker in %d from parent %d' %(os.getpid(), os.getppid())
exe_pool = Pool(args.n_core, initializer=Start)
if args.trim:
get_consensus = get_consensus_with_trim
else:
get_consensus = get_consensus_without_trim
K = 8
config = args.min_cov, K, \
args.max_n_read, args.min_idt, args.edge_tolerance, args.trim_size, args.min_cov_aln, args.max_cov_aln
# TODO: pass config object, not tuple, so we can add fields
for res in exe_pool.imap(get_consensus, get_seq_data(config, args.min_n_read, args.min_len_aln)):
cns, seed_id = res
if len(cns) < 500:
continue
if args.output_full:
print ">"+seed_id+"_f"
print cns
else:
cns = good_region.findall(cns)
if len(cns) == 0:
continue
if args.output_multi:
seq_i = 0
for cns_seq in cns:
if len(cns_seq) < 500:
continue
if seq_i >= 10:
break
print ">prolog/%s%01d/%d_%d" % (seed_id, seq_i, 0, len(cns_seq))
print format_seq(cns_seq, 80)
seq_i += 1
else:
cns.sort(key = lambda x: len(x))
print ">"+seed_id
print cns[-1]
def main(argv=sys.argv):
parser = argparse.ArgumentParser(
description='a simple multi-processor consensus sequence generator')
parser.add_argument(
'--n_core',
type=int,
default=24,
help='number of processes used for generating consensus; '
'0 for main process only (default=%(default)s)')
parser.add_argument('--local_match_count_window',
type=int,
default=12,
help='local match window size (obsoleted, no effect)')
parser.add_argument(
'--local_match_count_threshold',
type=int,
default=6,
help='local match count threshold (obsoleted, no effect)')
parser.add_argument('--min_cov',
type=int,
default=6,
help='minimum coverage to break the consensus')
parser.add_argument('--min_cov_aln', type=int, default=10,
help='minimum coverage of alignment data; a seed read with less than MIN_COV_ALN average depth' + \
' of coverage will be completely ignored')
parser.add_argument('--max_cov_aln', type=int, default=0, # 0 to emulate previous behavior
help='maximum coverage of alignment data; a seed read with more than MAX_COV_ALN average depth' + \
' of coverage of the longest alignments will be capped, excess shorter alignments will be ignored')
parser.add_argument(
'--min_len_aln',
type=int,
default=0, # 0 to emulate previous behavior
help=
'minimum length of a sequence in an alignment to be used in consensus; any shorter sequence will be completely ignored'
)
parser.add_argument('--min_n_read', type=int, default=10,
help='minimum number of reads used in generating the consensus; a seed read with fewer alignments will '+ \
'be completely ignored (obsoleted, formerly called `--min_cov_aln\')')
parser.add_argument(
'--max_n_read',
type=int,
default=500,
help='maximum number of reads used in generating the consensus')
parser.add_argument(
'--trim',
action="store_true",
default=False,
help=
'trim the input sequence with k-mer spare dynamic programming to find the mapped range'
)
parser.add_argument('--output_full',
action="store_true",
default=False,
help='output uncorrected regions too')
parser.add_argument(
'--output_multi',
action="store_true",
default=False,
help=
'output multi correct regions; implies --output_dformat, unless --output-simple-fasta-header'
)
parser.add_argument(
'--output_dformat',
action="store_true",
default=True,
help=
'output daligner compatible header, only work with --output_multi; DEPRECATED and ignored, as this is the default now'
)
parser.add_argument(
'--output_simple_fasta_header',
action='store_true',
default=False,
help=
'Turn off --output_dformat. This was for older (pre spring 2015) DALIGNER. Never needed now.'
)
parser.add_argument(
'--min_idt',
type=float,
default=0.70,
help='minimum identity of the alignments used for correction')
parser.add_argument(
'--edge_tolerance',
type=int,
default=1000,
help=
'for trimming, the there is unaligned edge leng > edge_tolerance, ignore the read'
)
parser.add_argument(
'--trim_size',
type=int,
default=50,
help='the size for triming both ends from initial sparse aligned region'
)
good_region = re.compile("[ACGT]+")
args = parser.parse_args(argv[1:])
def Start():
print >> sys.stderr, 'Started a worker in %d from parent %d' % (
os.getpid(), os.getppid())
exe_pool = Pool(args.n_core, initializer=Start)
if args.trim:
get_consensus = get_consensus_with_trim
else:
get_consensus = get_consensus_without_trim
K = 8
config = args.min_cov, K, args.local_match_count_window, args.local_match_count_threshold,\
args.max_n_read, args.min_idt, args.edge_tolerance, args.trim_size, args.min_cov_aln, args.max_cov_aln
# TODO: pass config object, not tuple, so we can add fields
for res in exe_pool.imap(
get_consensus,
get_seq_data(config, args.min_n_read, args.min_len_aln)):
cns, seed_id = res
if len(cns) < 500:
continue
if args.output_full:
print ">" + seed_id + "_f"
print cns
else:
cns = good_region.findall(cns)
if len(cns) == 0:
continue
if args.output_multi:
seq_i = 0
for cns_seq in cns:
if len(cns_seq) < 500:
continue
if not args.output_simple_fasta_header:
if seq_i >= 10:
break
print ">prolog/%s%01d/%d_%d" % (seed_id, seq_i, 0,
len(cns_seq))
print format_seq(cns_seq, 80)
else:
print ">" + seed_id + "_%d" % seq_i
print cns_seq
seq_i += 1
else:
cns.sort(key=lambda x: len(x))
print ">" + seed_id
print cns[-1]
def main(argv=sys.argv):
parser = argparse.ArgumentParser(
description='a simple multi-processor consensus sequence generator',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
'--n_core',
type=int,
default=24,
help='number of processes used for generating consensus; '
'0 for main process only')
parser.add_argument('--min_cov',
type=int,
default=6,
help='minimum coverage to break the consensus')
parser.add_argument(
'--min_cov_aln',
type=int,
default=10,
help=
'minimum coverage of alignment data; a seed read with less than MIN_COV_ALN average depth'
+ ' of coverage will be completely ignored')
parser.add_argument('--max_cov_aln', type=int, default=0, # 0 to emulate previous behavior
help='maximum coverage of alignment data; a seed read with more than MAX_COV_ALN average depth' + \
' of coverage of the longest alignments will be capped, excess shorter alignments will be ignored')
parser.add_argument(
'--min_len_aln',
type=int,
default=0, # 0 to emulate previous behavior
help=
'minimum length of a sequence in an alignment to be used in consensus; any shorter sequence will be completely ignored'
)
parser.add_argument(
'--min_n_read',
type=int,
default=10,
help=
'1 + minimum number of reads used in generating the consensus; a seed read with fewer alignments will '
+ 'be completely ignored')
parser.add_argument(
'--max_n_read',
type=int,
default=500,
help='1 + maximum number of reads used in generating the consensus')
parser.add_argument(
'--trim',
action="store_true",
default=False,
help=
'trim the input sequence with k-mer spare dynamic programming to find the mapped range'
)
parser.add_argument('--output_full',
action="store_true",
default=False,
help='output uncorrected regions too')
parser.add_argument('--output_multi',
action="store_true",
default=False,
help='output multi correct regions')
parser.add_argument(
'--min_idt',
type=float,
default=0.70,
help='minimum identity of the alignments used for correction')
parser.add_argument(
'--edge_tolerance',
type=int,
default=1000,
help=
'for trimming, the there is unaligned edge leng > edge_tolerance, ignore the read'
)
parser.add_argument(
'--trim_size',
type=int,
default=50,
help='the size for triming both ends from initial sparse aligned region'
)
good_region = re.compile("[ACGT]+")
args = parser.parse_args(argv[1:])
def Start():
print >> sys.stderr, 'Started a worker in %d from parent %d' % (
os.getpid(), os.getppid())
exe_pool = Pool(args.n_core, initializer=Start)
if args.trim:
get_consensus = get_consensus_with_trim
else:
get_consensus = get_consensus_without_trim
K = 8
config = args.min_cov, K, \
args.max_n_read, args.min_idt, args.edge_tolerance, args.trim_size, args.min_cov_aln, args.max_cov_aln
# TODO: pass config object, not tuple, so we can add fields
for res in exe_pool.imap(
get_consensus,
get_seq_data(config, args.min_n_read, args.min_len_aln)):
cns, seed_id = res
if len(cns) < 500:
continue
if args.output_full:
print ">" + seed_id + "_f"
print cns
else:
cns = good_region.findall(cns)
if len(cns) == 0:
continue
if args.output_multi:
seq_i = 0
for cns_seq in cns:
if len(cns_seq) < 500:
continue
if seq_i >= 10:
break
print ">prolog/%s%01d/%d_%d" % (seed_id, seq_i, 0,
len(cns_seq))
print format_seq(cns_seq, 80)
seq_i += 1
else:
cns.sort(key=lambda x: len(x))
print ">" + seed_id
print cns[-1]
