def load_from_inputs(args):
#Read in the VCF file
sys.stderr.write("Reading in the VCF file\n")
alleles = {}
#with open(args.phased_VCF) as inf:
with open(args.inputs[1]) as inf:
for line in inf:
vcf = VCF(line)
if not vcf.is_snp(): continue
g = vcf.get_phased_genotype()
if not g: continue
if vcf.value('chrom') not in alleles:
alleles[vcf.value('chrom')] = {}
if vcf.value('pos') in alleles[vcf.value('chrom')]:
sys.stderr.write("WARNING: seeing the same position twice.\n" +
line.rstrip() + "\n")
alleles[vcf.value('chrom')][vcf.value(
'pos')] = g # set our left and right
sys.stderr.write("Reading in the reference genome\n")
#ref = read_fasta_into_hash(args.reference_genome)
ref = read_fasta_into_hash(args.inputs[0])
res1 = []
res2 = []
p = None
sys.stderr.write("Introducing VCF changes to reference sequences\n")
# Pretty memory intesnive to so don't go with all possible threads
if args.threads > 1: p = Pool(processes=max(1, int(args.threads / 4)))
for chrom in ref:
# handle the case where there is no allele information
if chrom not in alleles:
r1q = Queue()
r1q.put([0, chrom, ref[chrom]])
res1.append(r1q)
r2q = Queue()
r2q.put([0, chrom, ref[chrom]])
res2.append(r2q)
elif args.threads > 1:
res1.append(
p.apply_async(adjust_reference_genome,
args=(alleles[chrom], ref[chrom], 0, chrom)))
res2.append(
p.apply_async(adjust_reference_genome,
args=(alleles[chrom], ref[chrom], 1, chrom)))
else:
r1q = Queue()
r1q.put(
adjust_reference_genome(alleles[chrom], ref[chrom], 0, chrom))
res1.append(r1q)
r2q = Queue()
r2q.put(
adjust_reference_genome(alleles[chrom], ref[chrom], 1, chrom))
res2.append(r2q)
if args.threads > 1:
p.close()
p.join()
# now we can fill reference 1 with all our new sequences
ref1 = {}
c1 = 0
for i in range(0, len(res1)):
res = res1[i].get()
c1 += res[0]
ref1[res[1]] = res[2]
# now we can fill reference 2 with all our new sequences
ref2 = {}
c2 = 0
for i in range(0, len(res2)):
res = res2[i].get()
c2 += res[0]
ref2[res[1]] = res[2]
sys.stderr.write("Made " + str(c1) + "|" + str(c2) +
" changes to the reference\n")
# Now ref1 and ref2 have are the diploid sources of the transcriptome
gpdnames = {}
txn1 = Transcriptome()
txn2 = Transcriptome()
txn1.set_reference_genome_dictionary(ref1)
txn2.set_reference_genome_dictionary(ref2)
#with open(args.transcripts_genepred) as inf:
with open(args.inputs[2]) as inf:
for line in inf:
if line[0] == '#': continue
txn1.add_genepred_line(line.rstrip())
txn2.add_genepred_line(line.rstrip())
gpd = GenePredEntry(line.rstrip())
gpdnames[gpd.value('name')] = gpd.value('gene_name')
# The transcriptomes are set but we dont' really need the references anymore
# Empty our big memory things
txn1.ref_hash = None
txn2.ref_hash = None
for chrom in ref1.keys():
del ref1[chrom]
for chrom in ref2.keys():
del ref2[chrom]
for chrom in ref.keys():
del ref[chrom]
if not args.locus_by_gene_name:
#[locus2name,name2locus] = get_loci(args.transcripts_genepred)
[locus2name, name2locus] = get_loci(args.inputs[2])
else: # set locus by gene name
sys.stderr.write("Organizing loci by gene name\n")
locus2name = {}
name2locus = {}
numname = {}
m = 0
for name in sorted(gpdnames):
gene = gpdnames[name]
if gene not in numname:
m += 1
numname[gene] = m
num = numname[gene]
if num not in locus2name:
locus2name[num] = set()
locus2name[num].add(name)
name2locus[name] = num
sys.stderr.write("Ended with " + str(len(locus2name.keys())) +
" loci\n")
if args.isoform_expression:
sys.stderr.write("Reading expression from a TSV\n")
with open(args.isoform_expression) as inf:
line1 = inf.readline()
for line in inf:
f = line.rstrip().split("\t")
txn1.add_expression(f[0], float(f[1]))
txn2.add_expression(f[0], float(f[1]))
elif args.cufflinks_isoform_expression:
sys.stderr.write("Using cufflinks expression\n")
cuffz = 0
with open(args.cufflinks_isoform_expression) as inf:
line1 = inf.readline()
for line in inf:
cuffz += 1
sys.stderr.write(str(cuffz) + " cufflinks entries processed\r")
f = line.rstrip().split("\t")
txn1.add_expression_no_update(f[0], float(f[9]))
txn2.add_expression_no_update(f[0], float(f[9]))
txn1.update_expression()
txn2.update_expression()
sys.stderr.write("\n")
elif args.uniform_expression:
sys.stderr.write("Using uniform expression model\n")
else:
sys.stderr.write(
"Warning isoform expression not sepcified, using uniform expression model.\n"
)
# Now we have the transcriptomes set
rhos = {} # The ASE of allele 1 (the left side)
randos = {}
if args.seed:
random.seed(args.seed)
for z in locus2name:
randos[z] = random.random()
sys.stderr.write("Setting rho for each transcript\n")
# Lets set rho for ASE for each transcript
for tname in sorted(txn1.transcripts):
if args.ASE_identical or args.ASE_identical == 0:
rhos[tname] = float(args.ASE_identical)
elif args.ASE_isoform_random:
rhos[tname] = random.random()
else: # we must be on locus random
rhos[tname] = randos[name2locus[tname]]
#Now our dataset is set up
rbe = SimulationBasics.RandomBiallelicTranscriptomeEmitter(txn1, txn2)
rbe.gene_names = gpdnames
rbe.name2locus = name2locus
rbe.set_transcriptome1_rho(rhos)
return rbe
def load_from_inputs(args):
#Read in the VCF file
sys.stderr.write("Reading in the VCF file\n")
alleles = {}
#with open(args.phased_VCF) as inf:
with open(args.inputs[1]) as inf:
for line in inf:
vcf = VCF(line)
if not vcf.is_snp(): continue
g = vcf.get_phased_genotype()
if not g: continue
if vcf.value('chrom') not in alleles:
alleles[vcf.value('chrom')] = {}
if vcf.value('pos') in alleles[vcf.value('chrom')]:
sys.stderr.write("WARNING: seeing the same position twice.\n"+line.rstrip()+"\n")
alleles[vcf.value('chrom')][vcf.value('pos')] = g # set our left and right
sys.stderr.write("Reading in the reference genome\n")
#ref = read_fasta_into_hash(args.reference_genome)
ref = read_fasta_into_hash(args.inputs[0])
res1 = []
res2 = []
p = None
sys.stderr.write("Introducing VCF changes to reference sequences\n")
# Pretty memory intesnive to so don't go with all possible threads
if args.threads > 1: p = Pool(processes=max(1,int(args.threads/4)))
for chrom in ref:
# handle the case where there is no allele information
if chrom not in alleles:
r1q = Queue()
r1q.put([0,chrom,ref[chrom]])
res1.append(r1q)
r2q = Queue()
r2q.put([0,chrom,ref[chrom]])
res2.append(r2q)
elif args.threads > 1:
res1.append(p.apply_async(adjust_reference_genome,args=(alleles[chrom],ref[chrom],0,chrom)))
res2.append(p.apply_async(adjust_reference_genome,args=(alleles[chrom],ref[chrom],1,chrom)))
else:
r1q = Queue()
r1q.put(adjust_reference_genome(alleles[chrom],ref[chrom],0,chrom))
res1.append(r1q)
r2q = Queue()
r2q.put(adjust_reference_genome(alleles[chrom],ref[chrom],1,chrom))
res2.append(r2q)
if args.threads > 1:
p.close()
p.join()
# now we can fill reference 1 with all our new sequences
ref1 = {}
c1 = 0
for i in range(0,len(res1)):
res = res1[i].get()
c1 += res[0]
ref1[res[1]]=res[2]
# now we can fill reference 2 with all our new sequences
ref2 = {}
c2 = 0
for i in range(0,len(res2)):
res = res2[i].get()
c2 += res[0]
ref2[res[1]]=res[2]
sys.stderr.write("Made "+str(c1)+"|"+str(c2)+" changes to the reference\n")
# Now ref1 and ref2 have are the diploid sources of the transcriptome
gpdnames = {}
txn1 = Transcriptome()
txn2 = Transcriptome()
txn1.set_reference_genome_dictionary(ref1)
txn2.set_reference_genome_dictionary(ref2)
#with open(args.transcripts_genepred) as inf:
with open(args.inputs[2]) as inf:
for line in inf:
if line[0]=='#': continue
txn1.add_genepred_line(line.rstrip())
txn2.add_genepred_line(line.rstrip())
gpd = GenePredEntry(line.rstrip())
gpdnames[gpd.value('name')] = gpd.value('gene_name')
# The transcriptomes are set but we dont' really need the references anymore
# Empty our big memory things
txn1.ref_hash = None
txn2.ref_hash = None
for chrom in ref1.keys(): del ref1[chrom]
for chrom in ref2.keys(): del ref2[chrom]
for chrom in ref.keys(): del ref[chrom]
if not args.locus_by_gene_name:
#[locus2name,name2locus] = get_loci(args.transcripts_genepred)
[locus2name,name2locus] = get_loci(args.inputs[2])
else: # set locus by gene name
sys.stderr.write("Organizing loci by gene name\n")
locus2name = {}
name2locus = {}
numname = {}
m = 0
for name in sorted(gpdnames):
gene = gpdnames[name]
if gene not in numname:
m+=1
numname[gene] = m
num = numname[gene]
if num not in locus2name:
locus2name[num] = set()
locus2name[num].add(name)
name2locus[name] = num
sys.stderr.write("Ended with "+str(len(locus2name.keys()))+" loci\n")
if args.isoform_expression:
sys.stderr.write("Reading expression from a TSV\n")
with open(args.isoform_expression) as inf:
line1 = inf.readline()
for line in inf:
f = line.rstrip().split("\t")
txn1.add_expression(f[0],float(f[1]))
txn2.add_expression(f[0],float(f[1]))
elif args.cufflinks_isoform_expression:
sys.stderr.write("Using cufflinks expression\n")
cuffz = 0
with open(args.cufflinks_isoform_expression) as inf:
line1 = inf.readline()
for line in inf:
cuffz +=1
sys.stderr.write(str(cuffz)+" cufflinks entries processed\r")
f = line.rstrip().split("\t")
txn1.add_expression_no_update(f[0],float(f[9]))
txn2.add_expression_no_update(f[0],float(f[9]))
txn1.update_expression()
txn2.update_expression()
sys.stderr.write("\n")
elif args.uniform_expression:
sys.stderr.write("Using uniform expression model\n")
else:
sys.stderr.write("Warning isoform expression not sepcified, using uniform expression model.\n")
# Now we have the transcriptomes set
rhos = {} # The ASE of allele 1 (the left side)
randos = {}
if args.seed:
random.seed(args.seed)
for z in locus2name: randos[z] = random.random()
sys.stderr.write("Setting rho for each transcript\n")
# Lets set rho for ASE for each transcript
for tname in sorted(txn1.transcripts):
if args.ASE_identical or args.ASE_identical == 0:
rhos[tname] = float(args.ASE_identical)
elif args.ASE_isoform_random:
rhos[tname] = random.random()
else: # we must be on locus random
rhos[tname] = randos[name2locus[tname]]
#Now our dataset is set up
rbe = SimulationBasics.RandomBiallelicTranscriptomeEmitter(txn1,txn2)
rbe.gene_names = gpdnames
rbe.name2locus = name2locus
rbe.set_transcriptome1_rho(rhos)
return rbe
