def filter_mapped_reads(sample,
fragment,
PCR=1,
maxreads=-1,
VERBOSE=0,
n_cycles=600,
max_mismatches=100,
match_len_min=30,
trim_bad_cigars=3,
summary=True):
'''Filter the reads to good chunks'''
pname = sample.patient
samplename_pat = sample.name
samplenames_seq = sample.samples_seq.index.tolist()
if VERBOSE >= 1:
print 'Filtering reads:', pname, samplename_pat, fragment, PCR
reffilename = get_initial_reference_filename(pname, fragment)
refseq = SeqIO.read(reffilename, 'fasta')
ref = np.array(refseq)
outfilename = get_mapped_filtered_filename(pname,
samplename_pat,
fragment,
type='bam',
PCR=PCR,
decontaminated=False)
trashfilename = outfilename[:-4] + '_trashed.bam'
infilenames = [
get_mapped_to_initial_filename(pname,
samplename_pat,
samplename,
fragment,
type='bam',
PCR=PCR)
for samplename in samplenames_seq
]
infilenames = filter(os.path.isfile, infilenames)
if not len(infilenames):
print('WARNING: No mapped files found: ' +
', '.join([pname, samplename_pat, fragment,
str(PCR)]))
return
# Take reads evenly distributed across sequencing repetitions
maxreads /= len(infilenames)
if VERBOSE >= 2:
print 'Input mapped filenames:',
if len(infilenames) >= 2:
print ''
print '\n'.join(infilenames)
# Use first file as template for the new bamfile
infilename = infilenames[0]
if not os.path.isfile(infilename):
convert_sam_to_bam(infilename)
with pysam.Samfile(infilename, 'rb') as bamfile:
with pysam.Samfile(outfilename, 'wb', template=bamfile) as outfile,\
pysam.Samfile(trashfilename, 'wb', template=bamfile) as trashfile:
n_good = 0
n_wrongname = 0
n_unmapped = 0
n_unpaired = 0
n_mutator = 0
n_badcigar = 0
n_tiny = 0
binsize = 200
hist_distance_from_consensus = np.zeros(n_cycles + 1, int)
hist_dist_along = np.zeros((len(ref) // binsize + 1, n_cycles + 1),
int)
# Iterate over input files, the first is already open
for infilename in infilenames:
if infilename != infilename[0]:
file_open = lambda: pysam.Samfile(infilename, 'rb')
file_close = lambda f: f.close()
if not os.path.isfile(infilename):
convert_sam_to_bam(infilename)
else:
file_open = lambda: bamfile
file_close = lambda f: None
try:
bamfile = file_open()
for irp, reads in enumerate(pair_generator(bamfile)):
if irp == maxreads:
break
pair_type = filter_read_pair(
reads,
ref,
hist_distance_from_consensus,
hist_dist_along,
binsize,
max_mismatches=max_mismatches,
match_len_min=match_len_min,
trim_bad_cigars=trim_bad_cigars,
VERBOSE=VERBOSE)
if pair_type == 'unmapped':
n_unmapped += 1
map(trashfile.write, reads)
elif pair_type == 'unpaired':
n_unpaired += 1
map(trashfile.write, reads)
elif pair_type == 'mutator':
n_mutator += 1
map(trashfile.write, reads)
elif pair_type == 'bad_cigar':
n_badcigar += 1
map(trashfile.write, reads)
elif pair_type == 'tiny':
n_tiny += 1
map(trashfile.write, reads)
else:
n_good += 1
map(outfile.write, reads)
finally:
file_close(bamfile)
if VERBOSE >= 1:
print 'Read pairs: '
print 'Good:', n_good
print 'Unmapped:', n_unmapped
print 'Unpaired:', n_unpaired
print 'Many-mutations:', n_mutator
print 'Bad CIGARs:', n_badcigar
print 'Tiny:', n_tiny
print
if summary:
sfn = get_filter_mapped_init_summary_filename(pname,
samplename_pat,
fragment,
PCR=PCR)
with open(sfn, 'a') as f:
f.write('Filter results: pname ' + pname + ', ' + samplename_pat +
', ' + fragment + '\n')
f.write('Total:\t\t\t' + str(irp + 1) + '\n')
f.write('Good:\t\t\t' + str(n_good) + '\n')
f.write('Unmapped:\t\t' + str(n_unmapped) + '\n')
f.write('Unpaired:\t\t' + str(n_unpaired) + '\n')
f.write('Many-mutations:\t\t' + str(n_mutator) + '\n')
f.write('Bad CIGARs:\t\t' + str(n_badcigar) + '\n')
f.write('Tiny:\t\t\t' + str(n_tiny) + '\n')
def filter_mapped_reads(sample, fragment,
PCR=1,
maxreads=-1,
VERBOSE=0,
n_cycles=600,
max_mismatches=100,
match_len_min=30,
trim_bad_cigars=3,
summary=True):
'''Filter the reads to good chunks'''
pname = sample.patient
samplename_pat = sample.name
samplenames_seq = sample.samples_seq.index.tolist()
if VERBOSE >= 1:
print 'Filtering reads:', pname, samplename_pat, fragment, PCR
reffilename = get_initial_reference_filename(pname, fragment)
refseq = SeqIO.read(reffilename, 'fasta')
ref = np.array(refseq)
outfilename = get_mapped_filtered_filename(pname, samplename_pat, fragment,
type='bam', PCR=PCR,
decontaminated=False)
trashfilename = outfilename[:-4]+'_trashed.bam'
infilenames = [get_mapped_to_initial_filename(pname, samplename_pat,
samplename, fragment,
type='bam', PCR=PCR)
for samplename in samplenames_seq]
infilenames = filter(os.path.isfile, infilenames)
if not len(infilenames):
print ('WARNING: No mapped files found: '+', '.join([pname, samplename_pat,
fragment, str(PCR)]))
return
# Take reads evenly distributed across sequencing repetitions
maxreads /= len(infilenames)
if VERBOSE >= 2:
print 'Input mapped filenames:',
if len(infilenames) >= 2:
print ''
print '\n'.join(infilenames)
# Use first file as template for the new bamfile
infilename = infilenames[0]
if not os.path.isfile(infilename):
convert_sam_to_bam(infilename)
with pysam.Samfile(infilename, 'rb') as bamfile:
with pysam.Samfile(outfilename, 'wb', template=bamfile) as outfile,\
pysam.Samfile(trashfilename, 'wb', template=bamfile) as trashfile:
n_good = 0
n_wrongname = 0
n_unmapped = 0
n_unpaired = 0
n_mutator = 0
n_badcigar = 0
n_tiny = 0
binsize = 200
hist_distance_from_consensus = np.zeros(n_cycles + 1, int)
hist_dist_along = np.zeros((len(ref) // binsize + 1, n_cycles + 1), int)
# Iterate over input files, the first is already open
for infilename in infilenames:
if infilename != infilename[0]:
file_open = lambda: pysam.Samfile(infilename, 'rb')
file_close = lambda f: f.close()
if not os.path.isfile(infilename):
convert_sam_to_bam(infilename)
else:
file_open = lambda: bamfile
file_close = lambda f: None
try:
bamfile = file_open()
for irp, reads in enumerate(pair_generator(bamfile)):
if irp == maxreads:
break
pair_type = filter_read_pair(reads, ref,
hist_distance_from_consensus,
hist_dist_along,
binsize,
max_mismatches=max_mismatches,
match_len_min=match_len_min,
trim_bad_cigars=trim_bad_cigars,
VERBOSE=VERBOSE)
if pair_type == 'unmapped':
n_unmapped += 1
map(trashfile.write, reads)
elif pair_type == 'unpaired':
n_unpaired += 1
map(trashfile.write, reads)
elif pair_type == 'mutator':
n_mutator += 1
map(trashfile.write, reads)
elif pair_type == 'bad_cigar':
n_badcigar += 1
map(trashfile.write, reads)
elif pair_type == 'tiny':
n_tiny += 1
map(trashfile.write, reads)
else:
n_good += 1
map(outfile.write, reads)
finally:
file_close(bamfile)
if VERBOSE >= 1:
print 'Read pairs: '
print 'Good:', n_good
print 'Unmapped:', n_unmapped
print 'Unpaired:', n_unpaired
print 'Many-mutations:', n_mutator
print 'Bad CIGARs:', n_badcigar
print 'Tiny:', n_tiny
print
if summary:
sfn = get_filter_mapped_init_summary_filename(pname, samplename_pat, fragment, PCR=PCR)
with open(sfn, 'a') as f:
f.write('Filter results: pname '+pname+', '+samplename_pat+', '+fragment+'\n')
f.write('Total:\t\t\t'+str(irp + 1)+'\n')
f.write('Good:\t\t\t'+str(n_good)+'\n')
f.write('Unmapped:\t\t'+str(n_unmapped)+'\n')
f.write('Unpaired:\t\t'+str(n_unpaired)+'\n')
f.write('Many-mutations:\t\t'+str(n_mutator)+'\n')
f.write('Bad CIGARs:\t\t'+str(n_badcigar)+'\n')
f.write('Tiny:\t\t\t'+str(n_tiny)+'\n')
pname = sample_pat.patient
PCR = int(PCR)
for fragment in fragments:
if submit:
fork_self(samplename_pat,
fragment,
VERBOSE=VERBOSE,
n_pairs=n_pairs,
PCR=PCR,
summary=summary)
continue
if summary:
sfn = get_filter_mapped_init_summary_filename(pname,
samplename_pat,
fragment,
PCR=PCR)
with open(sfn, 'w') as f:
f.write('Call: python filter_mapped_reads.py'+\
' --samples '+samplename_pat+\
' --fragments '+fragment+\
' --verbose '+str(VERBOSE))
if n_pairs != -1:
f.write(' --maxreads ' + str(n_pairs))
f.write('\n')
filter_mapped_reads(sample_pat,
fragment,
PCR=PCR,
VERBOSE=VERBOSE,
maxreads=n_pairs,
samples_seq_group = samples_seq.loc[samples_seq.index.isin(samplenames_seq)]
sample_pat.samples_seq = samples_seq_group
pname = sample_pat.patient
PCR = int(PCR)
for fragment in fragments:
if submit:
fork_self(samplename_pat, fragment,
VERBOSE=VERBOSE,
n_pairs=n_pairs,
PCR=PCR,
summary=summary)
continue
if summary:
sfn = get_filter_mapped_init_summary_filename(pname, samplename_pat, fragment, PCR=PCR)
with open(sfn, 'w') as f:
f.write('Call: python filter_mapped_reads.py'+\
' --samples '+samplename_pat+\
' --fragments '+fragment+\
' --verbose '+str(VERBOSE))
if n_pairs != -1:
f.write(' --maxreads '+str(n_pairs))
f.write('\n')
filter_mapped_reads(sample_pat, fragment,
PCR=PCR,
VERBOSE=VERBOSE, maxreads=n_pairs,
summary=summary)
