def write_data(transcript_d, sample='', prefix=''):
fo = open(prefix + 'SNPTABLE.csv', 'w')
candidates = []
sys.stderr.write('%s: Writing SNP/transcript table...\n' % (get_timestamp()))
for k in sorted(transcript_d):
transcript_shown = False
# show_snps = reduce(lambda x,y:x or y, [snp.is_covered for snp in transcript_d[k].snps])
if not transcript_d[k].has_covered_snps():
continue
# transcript_d[k].calculate_binom_score(sample=sample)
# transcript_d[k].check_for_mobility(sample=sample)
if transcript_d[k].is_mobile and transcript_d[k].binom_score > 10.0 ** -5:
candidates.append(k)
for snp in transcript_d[k].snps:
if not snp.is_covered:
continue
if not transcript_shown:
fo.write(transcript_d[k].get_full_string() + '\n')
transcript_shown = True
fo.write('\t' + str(snp) + '\n')
fo.close()
fo = open(prefix + 'RANKED.csv', 'w')
sys.stderr.write('%s: Ranking candidate mobile transcripts (%i candidates)...\n' % (get_timestamp(), len(candidates)))
ranked = sorted([transcript_d[k] for k in candidates if transcript_d[k].is_mobile],
key=lambda x:x.binom_score, reverse=True)
sys.stderr.write('%s: Writing ranked transcripts...\n' % (get_timestamp()))
for transcript in ranked:
fo.write(transcript.get_full_string() + '\n')
fo.close()
sys.stderr.write('%s: Generating candidate shortlist...\n' % get_timestamp())
snp_ratio_cutoff = 0.5
if sample.lower() == 'col':
shortlist = [transcript for transcript in ranked
if float(transcript.support_ped)/transcript.support_total >= snp_ratio_cutoff]
elif sample.lower() == 'ped':
shortlist = [transcript for transcript in ranked
if float(transcript.support_col)/transcript.support_total >= snp_ratio_cutoff]
n_snp_cutoff = 3
shortlist = [transcript for transcript in shortlist
if transcript.support_total >= n_snp_cutoff]
fo = open(prefix + 'SHORTLIST.csv', 'w')
sys.stderr.write('%s: Writing candidate shortlist...\n' % get_timestamp())
for transcript in shortlist:
fo.write(transcript.get_full_string() + '\n')
fo.close()
pass
def main(argv):
sys.stderr.write('%s: Reading transcript/SNP data\n' % get_timestamp())
transcript_d, snp_d = read_transcript_data(open(argv[0]))
for bam_fn in argv[1:]:
sys.stderr.write('%s: Processing file %s...\n' % (get_timestamp(), bam_fn))
infile = pysam.Samfile(bam_fn, 'rb')
mapping_reads = get_mapping_reads(infile, snp_d)
bam_fn_out = bam_fn.replace('.bam', '.prefiltered.bam')
outfile = pysam.Samfile(bam_fn_out, 'wb', template=infile)
for read in infile.fetch():
if read.qname in mapping_reads:
outfile.write(read)
outfile.close()
infile.close()
pass
def get_mapping_reads(bamfile, snp_d):
N = len(snp_d)
i = 1.0
valid_reads = set()
for k in snp_d:
if (i*100/N) % 5 == 0:
sys.stderr.write('%s: %i SNPs processed (%i%%)\n' % (get_timestamp(), i, i/N * 100))
i += 1.0
snp = snp_d[k][0]
for read in bamfile.fetch(region=snp.get_region(k[0])):
if read.flag & 0x4 == 0:
valid_reads.add(read.qname)
return valid_reads
def process_pileups(bamfile, snp_d, read_checklist):
N = len(snp_d)
i = 1.0
for k in snp_d:
if (i*100/N) % 5 == 0:
sys.stderr.write('%s: %i SNPs processed (%i%%)\n' % (get_timestamp(), i, i/N * 100))
i += 1.0
snp = snp_d[k][0]
snp.test += 1
base_count = {'A': 0, 'C': 0, 'G': 0, 'T': 0, 'U': 0, 'N': 0,
'low_qual': 0, 'del': 0, 'mates_disagree': 0, 'unique_reads': 0}
reads_at_pos = dict(zip(base_count.keys(), [[] for key in base_count.keys()]))
valid_reads = []
# print snp.get_region(k[0])
for col in bamfile.pileup(region=snp.get_region(k[0])):
if snp.pos_0 == col.pos:
base_count, weighted, valid_reads, reads_at_pos = count_bases(col)
break
if base_count is None or reads_at_pos is None:
print 'Skipping', snp.get_region(k[0])
continue
snp.add_pileup(base_count, reads_at_pos)
# print 'V', valid_reads
valid_reads = set([(read, base==snp.mutation)
for read, base, seq, qpos in valid_reads
if base in (snp.refbase, snp.mutation)])
read_checklist.update(valid_reads)
# This is a simulation to see how ambiguous SNPs can be removed from the read count
# print snp_d.keys()
# snp_d[('Chr4', 10952350)][0].r_support_mutation.append('HWI-ST377:127:D0PHGACXX:7:1306:11252:6941')
# read_checklist.add(('HWI-ST377:127:D0PHGACXX:7:1306:11252:6941', True))
# print 'x', sorted(snp_d[('Chr4', 10952350)][0].r_support_refbase[:10])
# print 'x', sorted(snp_d[('Chr4', 10951240)][0].r_support_refbase[:10])
filtered_reads = set(filter_ambiguous_reads(read_checklist))
# print len(read_checklist), len(filtered_reads)
for k in snp_d:
snp = snp_d[k][0]
if snp.is_covered:
snp.cleanse_counts(filtered_reads)
pass
def main(argv):
"""
Input EITHER (to generate per-transcript-results from pileup counts)
0. transcript/snp data
1. sample name {Col,Ped}
2. output prefix (output is <prefix>_<DATATYPE>.pickled)
3-n. number of bamfiles
OR (to summarise per-transcript-results)
0. name of a <arbitrary>_TRANSCRIPTDATA.pickled file
1. sample name {Col,Ped}
"""
if len(argv) > 3:
sys.stderr.write('%s: MODE1\n' % get_timestamp())
sys.stderr.write('')
transcript_d, snp_d = read_transcript_data(open(argv[0]))
# show_data(transcript_d, snp_d)
sample = argv[1]
prefix = argv[2]
read_checklist = set([])
for bam_fn in argv[3:]:
sys.stderr.write('%s: Processing file %s...\n' % (get_timestamp(), bam_fn))
process_pileups(pysam.Samfile(bam_fn, 'rb'), snp_d, read_checklist)
# show_data(transcript_d, snp_d)
# break
# print list(read_checklist), len(read_checklist)
ts = get_timestamp()
pickle.dump(snp_d, open(prefix + '_SNPDATA.pickled', 'wb'))
pickle.dump(transcript_d, open(prefix + '_TRANSCRIPTDATA.pickled', 'wb'))
pickle.dump(read_checklist, open(prefix + '_READCHECKLIST.pickled', 'wb'))
# sys.exit(0)
else:
sys.stderr.write('%s: MODE2\n' % get_timestamp())
sys.stderr.write('%s: Loading data from %s.\n' % (get_timestamp(), argv[0]))
# transcript_d = pickle.load(open(argv[0], 'rb'))
transcript_d = load_transcript_data(open(argv[0], 'rb'), sample=argv[1])
sys.stderr.write('%s: Finished loading %s.\n' % (get_timestamp(), argv[0]))
prefix = argv[0].rstrip('TRANSCRIPTDATA.pickled')
# print '======'
#show_data(transcript_d, None, sample=argv[1])
write_data(transcript_d, sample=argv[1], prefix=prefix)
pass
