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normalize-by-median-pct.py
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normalize-by-median-pct.py
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#! /usr/bin/env python
# This file is part of khmer, https://github.com/dib-lab/khmer/, and is
# Copyright (C) 2011-2015, Michigan State University.
# Copyright (C) 2015, The Regents of the University of California.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
#
# * Neither the name of the Michigan State University nor the names
# of its contributors may be used to endorse or promote products
# derived from this software without specific prior written
# permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# Contact: khmer-project@idyll.org
"""
Eliminate reads with median k-mer abundance higher than
DESIRED_COVERAGE. Output sequences will be placed in 'infile.keep'.
% python sandbox/normalize-by-median-pct.py [ -C <cutoff> ] <data1> <data2> ...
Use '-h' for parameter help.
"""
import sys
import screed
import os
import khmer
from khmer.khmer_args import build_counting_args, DEFAULT_MAX_TABLESIZE
import argparse
DEFAULT_DESIRED_COVERAGE = 5
# Iterate a collection in arbitrary batches
# from: http://stackoverflow.com/questions/4628290/pairs-from-single-list
def batchwise(t, size):
it = iter(t)
return zip(*[it] * size)
# Returns true if the pair of records are properly pairs
def validpair(r0, r1):
return r0.name[-1] == "1" and \
r1.name[-1] == "2" and \
r0.name[0:-1] == r1.name[0:-1]
def main():
parser = build_counting_args()
parser.add_argument('-C', '--cutoff', type=int, dest='cutoff',
default=DEFAULT_DESIRED_COVERAGE)
parser.add_argument('-p', '--paired', action='store_true')
parser.add_argument('-s', '--savehash', dest='savehash', default='')
parser.add_argument('-l', '--loadhash', dest='loadhash',
default='')
parser.add_argument('-R', '--report-to-file', dest='report_file',
type=argparse.FileType('w'))
parser.add_argument('input_filenames', nargs='+')
args = parser.parse_args()
if not args.quiet:
if args.min_hashsize == DEFAULT_MAX_HASHSIZE and not args.loadhash:
print("** WARNING: hashsize is default! You absodefly want to increase this!\n** Please read the docs!", file=sys.stderr)
print('\nPARAMETERS:', file=sys.stderr)
print(' - kmer size = %d \t\t(-k)' % args.ksize, file=sys.stderr)
print(' - n hashes = %d \t\t(-N)' % args.n_hashes, file=sys.stderr)
print(' - min hashsize = %-5.2g \t(-x)' % args.min_hashsize, file=sys.stderr)
print(' - paired = %s \t\t(-p)' % args.paired, file=sys.stderr)
print('', file=sys.stderr)
print('Estimated memory usage is %.2g bytes (n_hashes x min_hashsize)' % (
args.n_hashes * args.min_hashsize), file=sys.stderr)
print('-' * 8, file=sys.stderr)
K = args.ksize
HT_SIZE = args.min_hashsize
N_HT = args.n_hashes
DESIRED_COVERAGE = args.cutoff
report_fp = args.report_file
filenames = args.input_filenames
# In paired mode we read two records at a time
batch_size = 1
if args.paired:
batch_size = 2
if args.loadhash:
print('loading hashtable from', args.loadhash)
ht = khmer.load_countgraph(args.loadhash)
else:
print('making hashtable')
ht = khmer.Countgraph(K, HT_SIZE, N_HT)
total = 0
discarded = 0
for input_filename in filenames:
output_name = os.path.basename(input_filename) + '.keepmedpct'
outfp = open(output_name, 'w')
n = -1
for n, batch in enumerate(batchwise(screed.open(input_filename), batch_size)):
if n > 0 and n % 100000 == 0:
print('... kept', total - discarded, 'of', total, ', or', \
int(100. - discarded / float(total) * 100.), '%')
print('... in file', input_filename)
if report_fp:
print(total, total - discarded, \
1. - (discarded / float(total)), file=report_fp)
report_fp.flush()
total += batch_size
# If in paired mode, check that the reads are properly interleaved
if args.paired:
if not validpair(batch[0], batch[1]):
print('Error: Improperly interleaved pairs %s %s' % (
batch[0].name, batch[1].name), file=sys.stderr)
sys.exit(-1)
# Emit the batch of reads if any read passes the filter
# and all reads are longer than K
passed_filter = False
passed_length = True
for record in batch:
if len(record.sequence) < K:
passed_length = False
continue
seq = record.sequence.replace('N', 'A')
med, avg, dev = ht.get_median_count(seq)
pct = 0.
if avg:
pct = dev / avg * 100
if med < DESIRED_COVERAGE and pct < 100:
ht.consume(seq)
passed_filter = True
# Emit records if any passed
if passed_length and passed_filter:
for record in batch:
if hasattr(record, 'quality'):
outfp.write('@%s\n%s\n+\n%s\n' % (record.name,
record.sequence,
record.quality))
else:
outfp.write('>%s\n%s\n' %
(record.name, record.sequence))
else:
discarded += batch_size
if -1 < n:
print('DONE with', input_filename, '; kept', total - discarded, 'of',\
total, 'or', int(100. - discarded / float(total) * 100.), '%')
print('output in', output_name)
else:
print('SKIPPED empty file', input_filename)
if args.savehash:
print('Saving hashfile through', input_filename)
print('...saving to', args.savehash)
ht.save(args.savehash)
# Change 0.2 only if you really grok it. HINT: You don't.
fp_rate = khmer.calc_expected_collisions(ht)
print('fp rate estimated to be %1.3f' % fp_rate)
if fp_rate > 0.20:
print("**", file=sys.stderr)
print("** ERROR: the counting hash is too small for", file=sys.stderr)
print("** this data set. Increase hashsize/num ht.", file=sys.stderr)
print("**", file=sys.stderr)
print("** Do not use these results!!", file=sys.stderr)
sys.exit(-1)
if __name__ == '__main__':
main()