def __init__(self,alignment,min_intron_size=68):
#self._alns = []
self._min_intron_size=min_intron_size
self._aligned_query = None
self._hpas = []
self._has_quality = False # can be changed when add_alignment uses one that has quality
self._alignment = alignment
self._quality_distro = None # gets set by analyze_quality
self._deletion_type = None
self._query_errors = None
self._target_errors = None
self._context_query_errors = None
self._context_target_errors = None
astrings = self._alignment.get_alignment_strings(min_intron_size=self._min_intron_size)
if self._alignment.get_query_quality(): self._has_quality = True
if len(astrings) == 0: return None
alns = []
for i in range(len(astrings[0])):
if self._alignment.get_strand() == '+':
alns.append({'query':astrings[0][i],'target':astrings[1][i],'query_quality':astrings[2][i]})
else:
alns.insert(0,{'query':rc(astrings[0][i]),'target':rc(astrings[1][i]),'query_quality':astrings[2][i][::-1]})
#if self._alignment.get_strand() == '-':
# alns = alns[::-1]
#get homopolymer alignments
self._hpas = self._misalign_split(alns) # split alignment into homopolymer groups
self._query_hpas = []
self._target_hpas = []
qi = 0
for i in range(len(self._hpas)):
prev = None
if i > 0: prev = self._hpas[i-1]
foll = None
if i + 1 < len(self._hpas): foll = self._hpas[i+1]
qlen = len(self._hpas[i].get_query())
for j in range(0,qlen):
self._query_hpas.append({'hpa':self._hpas[i],'pos':j,'prev-hpa':prev,'next-hpa':foll})
qi+=qlen
ti = 0
for i in range(len(self._hpas)):
prev = None
if i > 0: prev = self._hpas[i-1]
foll = None
if i + 1 < len(self._hpas): foll = self._hpas[i+1]
tlen = len(self._hpas[i].get_target())
for j in range(0,tlen):
self._target_hpas.append({'hpa':self._hpas[i],'pos':j,'prev-hpa':prev,'next-hpa':foll})
ti+=tlen
self._target_errors = self.get_target_errors()
self._query_errors = self.get_query_errors()
self._context_target_errors = self.get_context_target_errors()
def get_SAM(self, min_intron_size=68):
from Bio.Format.Sam import SAM
#ar is target then query
qname = self.get_alignment_ranges()[0][1].chr
flag = 0
if self.get_strand() == '-': flag = 16
rname = self.get_alignment_ranges()[0][0].chr
pos = self.get_alignment_ranges()[0][0].start
mapq = 255
cigar = self.construct_cigar(min_intron_size)
rnext = '*'
pnext = 0
tlen = self.get_target_range().length()
seq = self.get_query_sequence()
if not seq: seq = '*'
qual = self.get_query_quality()
if not qual: qual = '*'
#seq = '*'
#qual = '*'
if self.get_strand() == '-':
seq = rc(seq)
qual = qual[::-1]
ln = qname + "\t" + str(flag) + "\t" + rname + "\t" + \
str(pos) + "\t" + str(mapq) + "\t" + cigar + "\t" + \
rnext + "\t" + str(pnext) + "\t" + str(tlen) + "\t" + \
seq + "\t" + qual
return SAM(ln, reference=self._reference)
def get_SAM(self,min_intron_size=68):
from Bio.Format.Sam import SAM
#ar is target then query
qname = self.get_alignment_ranges()[0][1].chr
flag = 0
if self.get_strand() == '-': flag = 16
rname = self.get_alignment_ranges()[0][0].chr
pos = self.get_alignment_ranges()[0][0].start
mapq = 255
cigar = self.construct_cigar(min_intron_size)
rnext = '*'
pnext = 0
tlen = self.get_target_range().length()
seq = self.get_query_sequence()
if not seq: seq = '*'
qual = self.get_query_quality()
if not qual: qual = '*'
#seq = '*'
#qual = '*'
if self.get_strand() == '-':
seq = rc(seq)
qual = qual[::-1]
ln = qname + "\t" + str(flag) + "\t" + rname + "\t" + \
str(pos) + "\t" + str(mapq) + "\t" + cigar + "\t" + \
rnext + "\t" + str(pnext) + "\t" + str(tlen) + "\t" + \
seq + "\t" + qual
return SAM(ln,reference=self._reference)
def random_flip(sequence, rnum=None):
randin = rnum
if not randin:
randin = RandomSource()
if randin.random() < 0.5:
return rc(sequence)
return sequence
def get_alignment_strings(self, min_intron_size=68):
qseq = self.get_query_sequence()
if not qseq:
sys.exit(
"ERROR: Query sequence must be accessable to get alignment strings\n"
)
sys.exit()
ref = self.get_reference()
qual = self.get_query_quality()
if not qual:
qual = 'I' * len(qseq) # for a placeholder quality
if self.get_strand() == '-':
qseq = rc(qseq)
qual = qual[::-1]
tarr = []
qarr = []
yarr = []
tdone = ''
qdone = ''
ydone = '' #query quality
for i in range(len(self.get_alignment_ranges())):
[t, q] = self.get_alignment_ranges()[i]
textra = ''
qextra = ''
yextra = ''
if i >= 1:
dift = t.start - self.get_alignment_ranges()[i - 1][0].end - 1
difq = q.start - self.get_alignment_ranges()[i - 1][1].end - 1
if dift < min_intron_size:
if dift > 0:
textra = ref[t.chr][t.start - dift - 1:t.start -
1].upper()
qextra = '-' * dift
yextra = '\0' * dift
elif difq > 0:
textra = '-' * difq
qextra = qseq[q.start - difq - 1:q.start - 1].upper()
yextra = qual[q.start - difq - 1:q.start - 1]
else:
tarr.append(tdone)
qarr.append(qdone)
yarr.append(ydone)
tdone = ''
qdone = ''
ydone = ''
tdone += textra + ref[t.chr][t.start - 1:t.end].upper()
qdone += qextra + qseq[q.start - 1:q.end].upper()
ydone += yextra + qual[q.start - 1:q.end]
if len(tdone) > 0:
tarr.append(tdone)
qarr.append(qdone)
yarr.append(ydone)
if self.get_query_quality() == '*':
yarr = [x.replace('I', ' ') for x in yarr]
#query, target, query_quality
return [qarr, tarr, yarr]
def set_sequence(self,ref_dict):
strand = '+'
if not self._direction:
sys.stderr.write("WARNING: no strand information for the transcript\n")
if self._direction: strand = self._direction
chr = self.get_chrom()
seq = ''
for e in [x.get_range() for x in self.exons]:
seq += ref_dict[chr][e.start-1:e.end]
if strand == '-': seq = rc(seq)
self._sequence = seq.upper()
def get_alignment_strings(self,min_intron_size=68):
qseq = self.get_query_sequence()
if not qseq:
sys.exit("ERROR: Query sequence must be accessable to get alignment strings\n")
sys.exit()
ref = self.get_reference()
qual = self.get_query_quality()
if not qual:
qual = 'I'*len(qseq) # for a placeholder quality
if self.get_strand() == '-':
qseq = rc(qseq)
qual = qual[::-1]
tarr = []
qarr = []
yarr = []
tdone = ''
qdone = ''
ydone = '' #query quality
for i in range(len(self.get_alignment_ranges())):
[t,q] = self.get_alignment_ranges()[i]
textra = ''
qextra = ''
yextra = ''
if i >= 1:
dift = t.start-self.get_alignment_ranges()[i-1][0].end-1
difq = q.start-self.get_alignment_ranges()[i-1][1].end-1
if dift < min_intron_size:
if dift > 0:
textra = ref[t.chr][t.start-dift-1:t.start-1].upper()
qextra = '-'*dift
yextra = '\0'*dift
elif difq > 0:
textra = '-'*difq
qextra = qseq[q.start-difq-1:q.start-1].upper()
yextra = qual[q.start-difq-1:q.start-1]
else:
tarr.append(tdone)
qarr.append(qdone)
yarr.append(ydone)
tdone = ''
qdone = ''
ydone = ''
tdone += textra+ref[t.chr][t.start-1:t.end].upper()
qdone += qextra+qseq[q.start-1:q.end].upper()
ydone += yextra+qual[q.start-1:q.end]
if len(tdone) > 0:
tarr.append(tdone)
qarr.append(qdone)
yarr.append(ydone)
if self.get_query_quality() == '*': yarr = [x.replace('I',' ') for x in yarr]
#query, target, query_quality
return [qarr,tarr,yarr]
def set_sequence(self, ref_dict):
self._initialize()
strand = '+'
if not self._direction:
sys.stderr.write(
"WARNING: no strand information for the transcript\n")
if self._direction: strand = self._direction
chr = self.get_chrom()
seq = ''
for e in [x.get_range() for x in self.exons]:
seq += ref_dict[chr][e.start - 1:e.end]
if strand == '-': seq = rc(seq)
self._sequence = seq.upper()
def main(args):
# check outputs
if len(args.output) > 1 and not args.sr:
sys.stderr.write(
"Error: Long reads don't support multiple output files\n")
sys.exit()
elif len(args.output) > 2:
sys.stderr.wrtie(
"Error: Short reads support at most two output files (paired end)\n"
)
sys.exit()
if args.sr_length < args.minimum_read_length:
args.minimum_read_length = args.sr_length
inf = sys.stdin
if args.emitter != '-':
inf = open(args.emitter)
sys.stderr.write("reading in transcriptome emitter\n")
indata = pickle.loads(
zlib.decompress(base64.b64decode(inf.read().rstrip())))
txome = Transcriptome()
txome.load_serialized(indata['txome'])
rnum = RandomSource()
rnum_tx = RandomSource() # for drawing transcripts
if args.seed:
rnum = RandomSource(args.seed)
rnum_tx = RandomSource(args.seed)
# Load in error profile data
ep = None
if args.error_profile:
sys.stderr.write("read in error profile\n")
ep = ErrorProfilePermuter(args.error_profile, rnum,
args.skew_profile_error_rate)
txemitter = TranscriptomeEmitter(txome, rand=rnum_tx)
if indata['weight_type'] == 'expression_table':
sys.stderr.write(
"Using expression table defined transcript expression\n")
txweight = indata['weights']
txemitter.set_weights_by_dict(txweight)
elif indata['weight_type'] == 'exponential_distribution':
sys.stderr.write(
"ERROR not yet implemented exponential distribution\n")
sys.exit()
elif indata['weight_type'] == 'uniform_distribution':
sys.stderr.write(
"Using uniform distribution of transcript expression\n")
cutter = MakeCuts(rand=rnum_tx)
if args.sr:
cutter.set_custom(args.sr_gauss_min, args.sr_gauss_mu,
args.sr_gauss_sigma)
elif args.lr:
cutter.set_custom(args.lr_gauss_min, args.lr_gauss_mu,
args.lr_gauss_sigma)
# Prepare outputs
of1 = sys.stdout
if args.output[0][-3:] == '.gz':
of1 = gzip.open(args.output[0], 'w')
elif args.output[0] != '-':
of1 = open(args.output[0], 'w')
of2 = None
if len(args.output) > 1:
if args.output[1][-3:] == '.gz':
of2 = gzip.open(args.output[1], 'w')
elif args.output[0] != '-':
of2 = open(args.ouptput[1], 'w')
of_origin = None
if args.output_original_source:
if args.output_original_source[-3:] == '.gz':
of_origin = gzip.open(args.output_original_source, 'w')
else:
of_origin = open(args.output_original_source, 'w')
of_sc = None
if args.output_sequence_change:
if args.output_sequence_change[-3:] == '.gz':
of_sc = gzip.open(args.output_sequence_change, 'w')
else:
of_sc = open(args.output_sequence_change, 'w')
absmax = args.count * 100
finished_count = 0
z = 0
while finished_count < args.count:
z += 1
if z > absmax: break
tx = txemitter.emit_transcript()
seq = tx.get_sequence()
stage1seq = seq
if args.trim_5prime or args.trim_3prime:
fivestart = 0
threeend = len(seq)
if args.trim_5prime:
lcut = int(args.trim_5prime[0] * len(seq))
rcut = int(args.trim_5prime[1] * len(seq))
fivestart = rnum_tx.randint(lcut, rcut)
if args.trim_3prime:
lcut = int(args.trim_3prime[0] * len(seq))
rcut = int(args.trim_3prime[1] * len(seq))
threeend = rnum_tx.randint(lcut, rcut)
# set sequence to its new trimmed bounds
seq = seq[fivestart:threeend]
# flip sequence if necessary
if not args.no_flip:
seq = random_flip(seq, rnum_tx)
l_read = create_name(rnum)
r_read = None
if args.sr or args.lr:
cutseq = cutter.get_cut(seq)
else:
cutseq = seq #case for no_fragmentation
############# if we pass this we will really start with this one
if len(cutseq) < args.minimum_read_length: continue
# can now log our read name
if of_origin:
of_origin.write(l_read + "\t" + tx.get_gene_name() + "\t" +
tx.get_transcript_name() + "\n")
stage2seq = cutseq
r = None
if args.sr:
r_read = l_read
l = cutseq[0:args.sr_length]
r = rc(cutseq[-1 * args.sr_length:])
elif args.lr:
l = cutseq
else:
l = cutseq
stage3left = l
stage3right = r
if not stage3right: stage3right = ''
#################
# l (or l and r) contains the sequence prior to errors being added
l_qual = 'I' * len(l)
r_qual = None
if r: r_qual = 'I' * len(r)
if args.fixed_quality:
#sys.stderr.write("Use fixed quality\n")
if len(args.fixed_quality) != 1:
sys.stderr.write(
"ERROR fixed quaility should be 1 character\n")
sys.exit()
l_qual = args.fixed_quality * len(l)
if r: r_qual = args.fixed_quality * len(r)
elif args.quality_from_error_rate:
#sys.stderr.write("Set quality from error rate\n")
qchar = chr(
int(-10 * math.log10(args.quality_from_error_rate)) + 33)
l_qual = qchar * len(l)
if r: r_qual = qchar * len(r)
else: #default is generate quality from profile
if not ep:
sys.stderr.write(
"ERROR: cannot generate quality from a profile. Set error profile or chooce quaility from error rate or fixed quality\n"
)
sys.exit()
l_qual = ep.emit_qual(len(l))
if r: r_qual = ep.emit_qual(len(r))
# Now prior to errors l_qual and r_qual contain our qualities
l_fastq = Fastq([l_read, l, '+', l_qual])
r_fastq = None
if r:
r_fastq = Fastq([r_read, r, '+', r_qual])
# Permute sequences by a specific error rate
if args.specific_errors:
rate = args.specific_errors
me = MakeErrors(rand=rnum)
if args.specific_before_context:
me.set_before_context(args.specific_before_context)
if args.specific_after_context:
me.set_after_context(args.specific_after_context)
if args.specific_reference_base:
if args.specific_reference_base != '-':
me.set_observed_base(args.specific_reference_base)
if args.specific_modified_base:
if args.specific_modified_base != '-':
me.set_modified_base(args.specific_modified_base)
if args.specific_reference_base == '-': #doing insertions
l_fastq = me.random_insertion(l_fastq, rate)
if r_fastq: r_fastq = me.random_insertion(r_fastq, rate)
elif args.specific_modified_base == '-': #doing deletions
l_fastq = me.random_deletion(l_fastq, rate)
if r_fastq: r_fastq = me.random_insertion(r_fastq, rate)
else:
l_fastq = me.random_substitution(l_fastq, rate)
if r_fastq: r_fastq = me.random_insertion(r_fastq, rate)
elif args.uniform_any_error:
l_fastq = do_uniform_any(l_fastq, rnum, args.uniform_any_error)
if r_fastq:
r_fastq = do_uniform_any(r_fastq, rnum, args.uniform_any_error)
elif args.uniform_mismatch_error:
l_fastq = do_uniform_mismatch(l_fastq, rnum,
args.uniform_mismatch_error)
if r_fastq:
r_fastq = do_uniform_mismatch(r_fastq, rnum,
args.uniform_mismatch_error)
elif args.any_error_by_quality:
l_fastq = do_quality_any(l_fastq, rnum)
if r_fastq: r_fastq = do_quality_any(r_fastq, rnum)
elif args.mismatch_error_by_quality:
l_fastq = do_quality_mismatch(l_fastq, rnum)
if r_fastq: r_fastq = do_quality_mismatch(r_fastq, rnum)
elif args.profile_context_error:
l_fastq = ep.permute_context(l_fastq)
if r_fastq: r_fastq = ep.permute_context(r_fastq)
elif args.profile_general_error:
l_fastq = ep.permute_general(l_fastq)
if r_fastq: r_fastq = ep.permute_general(r_fastq)
# if SR grown/shrink to appropriate length
if args.sr and len(l_fastq) != args.sr_length:
l_fastq = fit_length(l_fastq, args.sr_length, rnum)
if r:
if args.sr and len(r_fastq) != args.sr_length:
r_fastq = fit_length(r_fastq, args.sr_length, rnum)
of1.write(l_fastq.fastq())
if of2:
of2.write(r_fastq.fastq())
stage4left = l_fastq.seq
stage4right = ''
if of_sc:
of_sc.write(l_fastq.name+"\t"+tx.get_gene_name()+"\t"+tx.get_transcript_name()+"\t" \
+ stage1seq+"\t"+stage2seq+"\t"+stage3left+"\t"+stage3right+"\t"+stage4left+"\t"+stage4right+"\n")
if r_fastq: stage4right = r_fastq.seq
finished_count += 1
if finished_count % 1000 == 0:
sys.stderr.write(
str(finished_count) + '/' + str(args.count) + " \r")
sys.stderr.write("\n")
of1.close()
if of2:
of2.close()
if of_origin:
of_origin.close()
if of_sc:
of_sc.close()
# Temporary working directory step 3 of 3 - Cleanup
if not args.specific_tempdir:
rmtree(args.tempdir)
def get_query_sequence(self):
if self.value('seq') == '*': return None
if self.check_flag(0x10): return rc(self.value('seq'))
return self.value('seq')
def get_query_sequence(self):
if self.value('seq') == '*': return None
if self.check_flag(0x10): return rc(self.value('seq'))
return self.value('seq')
def main(args):
# check outputs
if len(args.output) > 1 and not args.sr:
sys.stderr.write("Error: Long reads don't support multiple output files\n")
sys.exit()
elif len(args.output) > 2:
sys.stderr.wrtie("Error: Short reads support at most two output files (paired end)\n")
sys.exit()
if args.sr_length < args.minimum_read_length:
args.minimum_read_length = args.sr_length
inf = sys.stdin
if args.emitter != '-':
inf = open(args.emitter)
sys.stderr.write("reading in transcriptome emitter\n")
indata = pickle.loads(zlib.decompress(base64.b64decode(inf.read().rstrip())))
txome = Transcriptome()
txome.load_serialized(indata['txome'])
rnum = RandomSource()
rnum_tx = RandomSource() # for drawing transcripts
if args.seed:
rnum = RandomSource(args.seed)
rnum_tx = RandomSource(args.seed)
# Load in error profile data
ep = None
if args.error_profile:
sys.stderr.write("read in error profile\n")
ep = ErrorProfilePermuter(args.error_profile,rnum,args.skew_profile_error_rate)
txemitter = TranscriptomeEmitter(txome,rand=rnum_tx)
if indata['weight_type'] == 'expression_table':
sys.stderr.write("Using expression table defined transcript expression\n")
txweight = indata['weights']
txemitter.set_weights_by_dict(txweight)
elif indata['weight_type'] == 'exponential_distribution':
sys.stderr.write("ERROR not yet implemented exponential distribution\n")
sys.exit()
elif indata['weight_type'] == 'uniform_distribution':
sys.stderr.write("Using uniform distribution of transcript expression\n")
cutter = MakeCuts(rand=rnum_tx)
if args.sr:
cutter.set_custom(args.sr_gauss_min,args.sr_gauss_mu,args.sr_gauss_sigma)
elif args.lr:
cutter.set_custom(args.lr_gauss_min,args.lr_gauss_mu,args.lr_gauss_sigma)
# Prepare outputs
of1 = sys.stdout
if args.output[0][-3:] == '.gz':
of1 = gzip.open(args.output[0],'w')
elif args.output[0] != '-':
of1 = open(args.output[0],'w')
of2 = None
if len(args.output) > 1:
if args.output[1][-3:] == '.gz':
of2 = gzip.open(args.output[1],'w')
elif args.output[0] != '-':
of2 = open(args.ouptput[1],'w')
of_origin = None
if args.output_original_source:
if args.output_original_source[-3:]=='.gz':
of_origin = gzip.open(args.output_original_source,'w')
else:
of_origin = open(args.output_original_source,'w')
of_sc = None
if args.output_sequence_change:
if args.output_sequence_change[-3:]=='.gz':
of_sc = gzip.open(args.output_sequence_change,'w')
else:
of_sc = open(args.output_sequence_change,'w')
absmax = args.count*100
finished_count = 0
z = 0
while finished_count < args.count:
z += 1
if z > absmax: break
tx = txemitter.emit_transcript()
seq = tx.get_sequence()
stage1seq = seq
if args.trim_5prime or args.trim_3prime:
fivestart = 0
threeend = len(seq)
if args.trim_5prime:
lcut = int(args.trim_5prime[0]*len(seq))
rcut = int(args.trim_5prime[1]*len(seq))
fivestart = rnum_tx.randint(lcut,rcut)
if args.trim_3prime:
lcut = int(args.trim_3prime[0]*len(seq))
rcut = int(args.trim_3prime[1]*len(seq))
threeend = rnum_tx.randint(lcut,rcut)
# set sequence to its new trimmed bounds
seq = seq[fivestart:threeend]
# flip sequence if necessary
if not args.no_flip:
seq = random_flip(seq,rnum_tx)
l_read = create_name(rnum)
r_read = None
if args.sr or args.lr:
cutseq = cutter.get_cut(seq)
else: cutseq = seq #case for no_fragmentation
############# if we pass this we will really start with this one
if len(cutseq) < args.minimum_read_length: continue
# can now log our read name
if of_origin:
of_origin.write(l_read+"\t"+tx.get_gene_name()+"\t"+tx.get_transcript_name()+"\n")
stage2seq = cutseq
r = None
if args.sr:
r_read = l_read
l = cutseq[0:args.sr_length]
r = rc(cutseq[-1*args.sr_length:])
elif args.lr:
l = cutseq
else: l = cutseq
stage3left = l
stage3right = r
if not stage3right: stage3right = ''
#################
# l (or l and r) contains the sequence prior to errors being added
l_qual = 'I'*len(l)
r_qual = None
if r: r_qual = 'I'*len(r)
if args.fixed_quality:
#sys.stderr.write("Use fixed quality\n")
if len(args.fixed_quality) != 1:
sys.stderr.write("ERROR fixed quaility should be 1 character\n")
sys.exit()
l_qual = args.fixed_quality*len(l)
if r: r_qual = args.fixed_quality*len(r)
elif args.quality_from_error_rate:
#sys.stderr.write("Set quality from error rate\n")
qchar = chr(int(-10*math.log10(args.quality_from_error_rate))+33)
l_qual = qchar*len(l)
if r: r_qual = qchar*len(r)
else: #default is generate quality from profile
if not ep:
sys.stderr.write("ERROR: cannot generate quality from a profile. Set error profile or chooce quaility from error rate or fixed quality\n")
sys.exit()
l_qual = ep.emit_qual(len(l))
if r: r_qual = ep.emit_qual(len(r))
# Now prior to errors l_qual and r_qual contain our qualities
l_fastq = Fastq([l_read,l,'+',l_qual])
r_fastq = None
if r:
r_fastq = Fastq([r_read,r,'+',r_qual])
# Permute sequences by a specific error rate
if args.specific_errors:
rate = args.specific_errors
me = MakeErrors(rand=rnum)
if args.specific_before_context: me.set_before_context(args.specific_before_context)
if args.specific_after_context: me.set_after_context(args.specific_after_context)
if args.specific_reference_base:
if args.specific_reference_base != '-':
me.set_observed_base(args.specific_reference_base)
if args.specific_modified_base:
if args.specific_modified_base != '-':
me.set_modified_base(args.specific_modified_base)
if args.specific_reference_base == '-': #doing insertions
l_fastq = me.random_insertion(l_fastq,rate)
if r_fastq: r_fastq = me.random_insertion(r_fastq,rate)
elif args.specific_modified_base == '-': #doing deletions
l_fastq = me.random_deletion(l_fastq,rate)
if r_fastq: r_fastq = me.random_insertion(r_fastq,rate)
else:
l_fastq = me.random_substitution(l_fastq,rate)
if r_fastq: r_fastq = me.random_insertion(r_fastq,rate)
elif args.uniform_any_error:
l_fastq = do_uniform_any(l_fastq,rnum,args.uniform_any_error)
if r_fastq: r_fastq = do_uniform_any(r_fastq,rnum,args.uniform_any_error)
elif args.uniform_mismatch_error:
l_fastq = do_uniform_mismatch(l_fastq,rnum,args.uniform_mismatch_error)
if r_fastq: r_fastq = do_uniform_mismatch(r_fastq,rnum,args.uniform_mismatch_error)
elif args.any_error_by_quality:
l_fastq = do_quality_any(l_fastq,rnum)
if r_fastq: r_fastq = do_quality_any(r_fastq,rnum)
elif args.mismatch_error_by_quality:
l_fastq = do_quality_mismatch(l_fastq,rnum)
if r_fastq: r_fastq = do_quality_mismatch(r_fastq,rnum)
elif args.profile_context_error:
l_fastq = ep.permute_context(l_fastq)
if r_fastq: r_fastq = ep.permute_context(r_fastq)
elif args.profile_general_error:
l_fastq = ep.permute_general(l_fastq)
if r_fastq: r_fastq = ep.permute_general(r_fastq)
# if SR grown/shrink to appropriate length
if args.sr and len(l_fastq) != args.sr_length:
l_fastq = fit_length(l_fastq,args.sr_length,rnum)
if r:
if args.sr and len(r_fastq) != args.sr_length:
r_fastq = fit_length(r_fastq,args.sr_length,rnum)
of1.write(l_fastq.fastq())
if of2:
of2.write(r_fastq.fastq())
stage4left = l_fastq.seq
stage4right = ''
if of_sc:
of_sc.write(l_fastq.name+"\t"+tx.get_gene_name()+"\t"+tx.get_transcript_name()+"\t" \
+ stage1seq+"\t"+stage2seq+"\t"+stage3left+"\t"+stage3right+"\t"+stage4left+"\t"+stage4right+"\n")
if r_fastq: stage4right = r_fastq.seq
finished_count += 1
if finished_count %1000==0: sys.stderr.write(str(finished_count)+'/'+str(args.count)+" \r")
sys.stderr.write("\n")
of1.close()
if of2:
of2.close()
if of_origin:
of_origin.close()
if of_sc:
of_sc.close()
# Temporary working directory step 3 of 3 - Cleanup
if not args.specific_tempdir:
rmtree(args.tempdir)
def random_flip(self, sequence):
if self.random.random() < 0.5:
return rc(sequence)
return sequence
def __init__(self, alignment, min_intron_size=68):
#self._alns = []
self._min_intron_size = min_intron_size
self._aligned_query = None
self._hpas = []
self._has_quality = False # can be changed when add_alignment uses one that has quality
self._alignment = alignment
self._quality_distro = None # gets set by analyze_quality
self._deletion_type = None
self._query_errors = None
self._target_errors = None
self._context_query_errors = None
self._context_target_errors = None
astrings = self._alignment.get_alignment_strings(
min_intron_size=self._min_intron_size)
if self._alignment.get_query_quality(): self._has_quality = True
if len(astrings) == 0: return None
alns = []
for i in range(len(astrings[0])):
if self._alignment.get_strand() == '+':
alns.append({
'query': astrings[0][i],
'target': astrings[1][i],
'query_quality': astrings[2][i]
})
else:
alns.insert(
0, {
'query': rc(astrings[0][i]),
'target': rc(astrings[1][i]),
'query_quality': astrings[2][i][::-1]
})
#if self._alignment.get_strand() == '-':
# alns = alns[::-1]
#get homopolymer alignments
self._hpas = self._misalign_split(
alns) # split alignment into homopolymer groups
self._query_hpas = []
self._target_hpas = []
qi = 0
for i in range(len(self._hpas)):
prev = None
if i > 0: prev = self._hpas[i - 1]
foll = None
if i + 1 < len(self._hpas): foll = self._hpas[i + 1]
qlen = len(self._hpas[i].get_query())
for j in range(0, qlen):
self._query_hpas.append({
'hpa': self._hpas[i],
'pos': j,
'prev-hpa': prev,
'next-hpa': foll
})
qi += qlen
ti = 0
for i in range(len(self._hpas)):
prev = None
if i > 0: prev = self._hpas[i - 1]
foll = None
if i + 1 < len(self._hpas): foll = self._hpas[i + 1]
tlen = len(self._hpas[i].get_target())
for j in range(0, tlen):
self._target_hpas.append({
'hpa': self._hpas[i],
'pos': j,
'prev-hpa': prev,
'next-hpa': foll
})
ti += tlen
self._target_errors = self.get_target_errors()
self._query_errors = self.get_query_errors()
self._context_target_errors = self.get_context_target_errors()
