def main(args):
sys.stderr.write("Reading reference fasta\n")
ref_genome = FastaData(open(args.reference_fasta,'rb').read())
sys.stderr.write("Reading in transcriptome\n")
output = {}
txome = Transcriptome()
z = 0
with open(args.reference_gpd) as inf:
for line in inf:
z+=1
if z%1000==0: sys.stderr.write(str(z)+" \r")
gpd = GPD(line)
gpd.set_sequence(ref_genome)
txome.add_transcript(gpd)
sys.stderr.write("\n")
sys.stderr.write("Serializing transcriptome\n")
output['txome'] = txome.dump_serialized()
txweights = {}
weight_type = 'uniform_distribution' #default
if args.expression_table:
weight_type = 'expression_table'
inf = None
if args.expression_table[-3:]=='.gz':
inf = gzip.open(args.expression_table)
else: inf = open(args.expression_table)
for line in inf:
f = line.rstrip().split("\t")
txweights[f[0]] = float(f[1])
elif args.exponential_distribution: weight_type = 'exponential_distribution'
output['weight_type'] = weight_type
output['weights'] = txweights #only matters for expression based
of = sys.stdout
if args.output: of = open(args.output,'w')
of.write(base64.b64encode(zlib.compress(pickle.dumps(output)))+"\n")
of.close()
# Temporary working directory step 3 of 3 - Cleanup
if not args.specific_tempdir:
rmtree(args.tempdir)
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 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)
