def tiers_allvars(in_file, out_stem, gene_file, pop, yaml_cmds):
# populate parameters from YAML module specifications
freq = yaml_cmds[yaml_keys.kModules][yaml_keys.kTiering][yaml_keys.kTRareAlleleFreqCutoff]
gene_name_col_header = yaml_cmds[yaml_keys.kModules][yaml_keys.kTiering][yaml_keys.kTGeneNameCol]
functional_column_headers = yaml_utils.convertColumns(yaml_cmds[yaml_keys.kModules][yaml_keys.kTiering][yaml_keys.kTFunctionalCols], yaml_cmds)
skip_filter_pass_check = yaml_cmds[yaml_keys.kModules][yaml_keys.kTiering][yaml_keys.kTSkipFilterPassCheck]
# check for whether any variant contains "PASS"
cmd = 'grep "PASS" {infile}|grep -v "#"|wc -l'.format(infile=in_file)
process = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout,stderr = process.communicate()
returncode = process.returncode
if(returncode != 0):
raise ValueError('Failed to search annotated VCF for "PASS" prior to tiering.\ncmd: ' + str(cmd) + '\nErr: ' + str(stderr) + '\nReturncode: ' + returncode + '\nOutput: ' + str(stdout))
#else
numHits = int(stdout)
# TODO move this to pre-checks (prior to even annotation)
if(numHits == 0 and not skip_filter_pass_check):
raise ValueError('No variants detected that passed filtering. Re-run STMP with "Skip_Filter_Pass_Check: True" in modules.yml to prioritize all variants anyway.')
elif(numHits == 0):
print 'NOTICE: no variants detected that passed filtering. Skipping filter PASS check and prioritizing all variants anyway.'
else:
print 'Found ' + str(numHits) + ' variants that passed filtering.' + ' Tiering just these variants.'
skip_filter_pass_check = False
#open input and output files and initialize counters and lists for background populations
filein = open(in_file, "r")
output_log = open(out_stem+".metrics", "w")
output_log.write("Metrics for stmp filtering, all variants from reference\n")
header = filein.readline().rstrip("\n")
headlist = header.split("\t")
if(gene_file != None):
g_file = open(gene_file, "r")
fileoutrare = open(out_stem+'.rare.txt', 'w')
fileout0 = open(out_stem+".tier0.txt", 'w')
fileout1 = open(out_stem+".tier1.txt", "w")
fileout2 = open(out_stem+".tier2.txt", "w")
fileout3 = open(out_stem+".tier3.txt", "w")
fileout4 = open(out_stem+".tier4.txt", "w")
fileoutrare.write(header+"\n")
fileout0.write("tier\t"+header + "\n")
fileout1.write("tier\t"+header + "\n")
fileout2.write("tier\t"+header + "\n")
fileout3.write("tier\t"+header + "\n")
fileout4.write("tier\t"+header + "\n")
total = 0
damaging0 = 0
damaging1 = 0
damaging2 = 0
damaging3 = 0
damaging4 = 0
target_genes = 0
rarevars = 0
allele_freq_cols = yaml_utils.convertColumns(yaml_cmds[yaml_keys.kModules][yaml_keys.kTiering][yaml_keys.kTAlleleFreqCols], yaml_cmds) #convertTieringColumns(yaml_cmds)
#debug
print 'allele freq cols: ' + str(allele_freq_cols)
backpoplist = vcfUtils.get_listindex(headlist, allele_freq_cols)
#debug
# print 'backpoplist: ' + str(backpoplist)
#initialize gene list for region prioritization
if(gene_file != None):
genes = {}
for line in g_file:
if line.startswith('#'):
continue
linelist = line.rstrip("\n").split("\t")
gene = linelist[0]
if not genes.has_key(gene):
genes[gene] = 1
else:
# debug: uncomment if not debugging
# print 'warning: duplicate gene ' + gene + ' in gene list ' + gene_file
None
#iterate over input file and parse into tiers
for line in filein:
total+=1
if ((skip_filter_pass_check or "PASS" in line) and ("#" in line) == 0 and vcfUtils.is_rare(line, freq, backpoplist, yaml_cmds)
and not vcfUtils.contains_text('MT', line, [stmp_consts.vcf_col_header_chrom], headlist, yaml_cmds, case_sensitive=False)
and not vcfUtils.contains_text('ncRNA', line, functional_column_headers, headlist, yaml_cmds, case_sensitive=True)
):
rarevars+=1
fileoutrare.write(line)
linelist = line.rstrip("\n").split("\t")
# for now
tmp = linelist[headlist.index(gene_name_col_header)].split(',')
gene = tmp[0]
if gene_file == None or genes.has_key(gene):
target_genes+=1
# tier 0: clinvar
if(vcfUtils.isClinvarPathogenicOrLikelyPathogenic(line, headlist, yaml_cmds) and not vcfUtils.contains_text('0', line, [yaml_utils.get_datasets(yaml_cmds)['clinvar'][yaml_keys.kDAnnotation]+'_'+vcfHeaders.kClinvarStarHeader], headlist, yaml_cmds, case_sensitive=False)):
fileout0.write("0\t"+line)
damaging0+=1
# tier 1
elif vcfUtils.is_functional(line, "stoploss stopgain splicing frameshift", functional_column_headers, headlist):
fileout1.write("1\t"+line)
damaging1+=1
# tier 2
elif ((vcfUtils.is_functional(line, "nonsynonymous", functional_column_headers, headlist) and vcfUtils.is_conserved(line, headlist, yaml_cmds)) or vcfUtils.is_functional(line, "nonframeshift", functional_column_headers, headlist)):
fileout2.write("2\t"+line)
damaging2+=1
# tier 3
elif vcfUtils.is_functional(line, "nonsynonymous", functional_column_headers, headlist) and vcfUtils.is_pathogenic(line, headlist, yaml_cmds):
fileout3.write("3\t"+line)
damaging3+=1
# tier 4
elif vcfUtils.tolerance_pass(line, headlist, yaml_cmds) and vcfUtils.is_functional(line, "exonic splicing", functional_column_headers, headlist):
fileout4.write("4\t"+line)
damaging4+=1
# else ignore variant
output_log.write("Total variants queried: "+str(total)+"\n")
output_log.write("Rare variants (allele freq < {freq}) queried: ".format(freq=str(freq))+str(rarevars)+"\n")
output_log.write("Rare variants in {num} target genes: ".format(num=str(len(genes)) if gene_file != None else '')+str(target_genes)+"\n")
output_log.write("Candidate variants, tier 0 (rare clinvar pathogenic or likely pathogenic variants with rating > 0 stars): "+str(damaging0)+"\n")
output_log.write("Candidate variants, tier 1 (rare LOF variants -- stoploss, stopgain, splicing, and frameshift): "+str(damaging1)+"\n")
output_log.write("Candidate variants, tier 2 (rare nonframeshift or (nonsynonymous and conserved) variants): "+str(damaging2)+"\n")
output_log.write("Candidate variants, tier 3 (rare nonsynonymous pathogenic variants): "+str(damaging3)+"\n")
output_log.write("Candidate variants, tier 4 (all other rare exonic/splicing variants with ExAC tolerance z-score (syn_z or mis_z or lof_z) > 2): "+str(damaging4)+"\n")
filein.close()
if(gene_file != None):
g_file.close()
fileoutrare.close()
fileout0.close()
fileout1.close()
fileout2.close()
fileout3.close()
fileout4.close()
gto_list = linelist[offspring].split(":")
if (int(gto_list[dp_num]) == 0):
oimputedcounter += 1
gto_list = linelist[mother].split(":")
if (int(gto_list[dp_num]) == 0):
mimputedcounter += 1
gto_list = linelist[father].split(":")
if (int(gto_list[dp_num]) == 0):
fimputedcounter += 1
if ((("3" in alleles2) == 0) and (("4" in alleles2) == 0)):
tempcode = int(inmap[alleles2])
n_confident += 1
if tempcode in [1, 2, 5, 6, 8, 11, 15, 18, 20, 21, 24, 25]:
MIEcounter += 1
if ("#" in line) == 0 and vcfUtils.is_rare(
line, freq, backpopindex) and mq >= mapQual and mq0 <= mapQual0:
if ((("3" in alleles2) == 0) and (("4" in alleles2) == 0)):
#simple de novo case
if alleles2 == "0,0,1":
deNovo.write(line)
counterDN += 1
#rare homozygous case
elif alleles2 == "1,1,2":
rareHomozygous.write(line)
counterRH += 1
#hemizygosity or gene conversion case
elif alleles2 == "0,0,2" or alleles2 == "1,0,2" or alleles2 == "0,1,2" or alleles2 == "1,2,0" or alleles2 == "2,1,0":
def tiers_allvars(in_file, out_stem, gene_file, pop, yaml_cmds):
# populate parameters from YAML module specifications
freq = yaml_cmds[yaml_keys.kModules][yaml_keys.kTiering][yaml_keys.kTRareAlleleFreqCutoff]
gene_name_col_header = yaml_cmds[yaml_keys.kModules][yaml_keys.kTiering][yaml_keys.kTGeneNameCol]
functional_column_headers = yaml_utils.convertColumns(yaml_cmds[yaml_keys.kModules][yaml_keys.kTiering][yaml_keys.kTFunctionalCols], yaml_cmds)
#open input and output files and initialize counters and lists for background populations
filein = open(in_file, "r")
output_log = open(out_stem+".metrics", "w")
output_log.write("Metrics for stmp filtering, all variants from reference\n")
header = filein.readline().rstrip("\n")
headlist = header.split("\t")
if(gene_file != None):
g_file = open(gene_file, "r")
fileoutrare = open(out_stem+'.rare.txt', 'w')
fileout0 = open(out_stem+".tier0.txt", 'w')
fileout1 = open(out_stem+".tier1.txt", "w")
fileout2 = open(out_stem+".tier2.txt", "w")
fileout3 = open(out_stem+".tier3.txt", "w")
fileout4 = open(out_stem+".tier4.txt", "w")
fileoutrare.write(header+"\n")
fileout0.write("tier\t"+header + "\n")
fileout1.write("tier\t"+header + "\n")
fileout2.write("tier\t"+header + "\n")
fileout3.write("tier\t"+header + "\n")
fileout4.write("tier\t"+header + "\n")
total = 0
damaging0 = 0
damaging1 = 0
damaging2 = 0
damaging3 = 0
damaging4 = 0
target_genes = 0
rarevars = 0
allele_freq_cols = yaml_utils.convertColumns(yaml_cmds[yaml_keys.kModules][yaml_keys.kTiering][yaml_keys.kTAlleleFreqCols], yaml_cmds) #convertTieringColumns(yaml_cmds)
backpoplist = vcfUtils.get_listindex(headlist, allele_freq_cols)
#initialize gene list for region prioritization
if(gene_file != None):
genes = {}
for line in g_file:
if line.startswith('#'):
continue
linelist = line.rstrip("\n").split("\t")
gene = linelist[0]
if not genes.has_key(gene):
genes[gene] = 1
else:
# debug: uncomment if not debugging
# print 'warning: duplicate gene ' + gene + ' in gene list ' + gene_file
None
#iterate over input file and parse into tiers
for line in filein:
total+=1
if (("PASS" in line) and ("#" in line) == 0 and vcfUtils.is_rare(line, freq, backpoplist)
and not vcfUtils.contains_text('MT', line, [stmp_consts.vcf_col_header_chrom], headlist, yaml_cmds, case_sensitive=False)
and not vcfUtils.contains_text('ncRNA', line, functional_column_headers, headlist, yaml_cmds, case_sensitive=True)
):
rarevars+=1
fileoutrare.write(line)
linelist = line.rstrip("\n").split("\t")
# for now
tmp = linelist[headlist.index(gene_name_col_header)].split(',')
gene = tmp[0]
if gene_file == None or genes.has_key(gene):
target_genes+=1
# tier 0: clinvar
if(vcfUtils.isClinvarPathogenicOrLikelyPathogenic(line, headlist, yaml_cmds) and not vcfUtils.contains_text('0', line, [yaml_cmds['clinvar'][yaml_keys.kDAnnotation]+'_'+vcfHeaders.kClinvarStarHeader], headlist, yaml_cmds, case_sensitive=False)):
fileout0.write("0\t"+line)
damaging0+=1
elif vcfUtils.is_functional(line, "stoploss stopgain splicing frameshift", functional_column_headers, headlist):
fileout1.write("1\t"+line)
damaging1+=1
elif ((vcfUtils.is_functional(line, "nonsynonymous", functional_column_headers, headlist) and vcfUtils.is_conserved(line, headlist, yaml_cmds)) or vcfUtils.is_functional(line, "nonframeshift", functional_column_headers, headlist)):
fileout2.write("2\t"+line)
damaging2+=1
elif vcfUtils.is_functional(line, "nonsynonymous", functional_column_headers, headlist) and vcfUtils.is_pathogenic(line, headlist, yaml_cmds):
fileout3.write("3\t"+line)
damaging3+=1
elif vcfUtils.tolerance_pass(line, headlist, yaml_cmds):
fileout4.write("4\t"+line)
damaging4+=1
# else ignore variant
output_log.write("Total variants queried: "+str(total)+"\n")
output_log.write("Rare variants (allele freq < {freq}) queried: ".format(freq=str(freq))+str(rarevars)+"\n")
output_log.write("Rare variants in {num} target genes: ".format(num=str(len(genes)) if gene_file != None else '')+str(target_genes)+"\n")
output_log.write("Candidate variants, tier 0 (rare clinvar pathogenic or likely pathogenic variants): "+str(damaging0)+"\n")
output_log.write("Candidate variants, tier 1 (rare LOF variants -- stoploss, stopgain, splicing, and frameshift): "+str(damaging1)+"\n")
output_log.write("Candidate variants, tier 2 (rare nonframeshift or (nonsynonymous and conserved) variants): "+str(damaging2)+"\n")
output_log.write("Candidate variants, tier 3 (rare nonsynonymous pathogenic variants): "+str(damaging3)+"\n")
output_log.write("Candidate variants, tier 4 (all other rare variants with ExAC tolerance z-score (syn_z or mis_z or lof_z) > 2): "+str(damaging4)+"\n")
filein.close()
if(gene_file != None):
g_file.close()
fileoutrare.close()
fileout0.close()
fileout1.close()
fileout2.close()
fileout3.close()
fileout4.close()
def tiers_allvars(in_file, out_stem, gene_file, pop, yaml_cmds, freq=0.01, geneNameCol=82):
#open input and output files and initialize counters and lists for background populations
filein = open(in_file, "r")
output_log = open(out_stem+".metrics", "w")
output_log.write("Metrics for stmp filtering, all variants from reference\n")
header = filein.readline().rstrip("\n")
headlist = header.split("\t")
g_file = open(gene_file, "r")
fileout0 = open(out_stem+".tier0.txt", 'w')
fileout1 = open(out_stem+".tier1.txt", "w")
fileout2 = open(out_stem+".tier2.txt", "w")
fileout3 = open(out_stem+".tier3.txt", "w")
fileout4 = open(out_stem+".tier4.txt", "w")
fileout0.write(header + "\n")
fileout1.write(header + "\n")
fileout2.write(header + "\n")
fileout3.write(header + "\n")
fileout4.write(header + "\n")
total = 0
damaging0 = 0
damaging1 = 0
damaging2 = 0
damaging3 = 0
damaging4 = 0
target_genes = 0
rarevars = 0
if (pop == "CEU") or (pop == "c"):
backpoplist = vcfUtils.get_listindex(headlist, [vcfHeaders.kHapMap2And3_CEU, vcfHeaders.k1000g_all, vcfHeaders.k1000g_eur, vcfHeaders.kCg69, vcfHeaders.kEsp6500si_ALL, vcfHeaders.kEsp6500si_EA])
elif (pop == "ASN") or (pop == "a"):
backpoplist = vcfUtils.get_listindex(headlist, [vcfHeaders.k_hapmap2and3_CHB, vcfHeaders.k1000g_all, vcfHeaders.kCg69, vcfHeaders.kEsp6500si_ALL])
elif (pop == "AFR") or (pop == "f"):
backpoplist = vcfUtils.get_listindex(headlist, [vcfHeaders.k_hapmap2and3_YRI, vcfHeaders.k1000g_all, vcfHeaders.k1000g_afr, vcfHeaders.kCg69, vcfHeaders.kEsp6500si_ALL, vcfHeaders.k_esp6500si_AA])
else:
print >> sys.stderr, "Error in diseaseUtils.tiers_allvars - Population specified is not supported"
exit(1)
#initialize gene list for region prioritization
genes = {}
for line in g_file:
linelist = line.split("\t")
gene = linelist[1]
if genes.has_key(gene) == 0:
genes[gene] = linelist[2]+":"+linelist[3]
else:
genes[gene] = genes[gene]+";"+linelist[2]+":"+linelist[3]
#iterate over input file and parse into tiers
for line in filein:
total+=1
if ("PASS" in line) and ("#" in line) == 0 and vcfUtils.is_rare(line, freq, backpoplist):
rarevars+=1
linelist = line.split("\t")
# for now
tmp = linelist[geneNameCol].split(',')
gene = tmp[0]
if genes.has_key(gene):
target_genes+=1
# tier 0: clinvar
if(vcfUtils.isClinvarPathogenicOrLikelyPathogenic(line, headlist, yaml_cmds)):
fileout0.write(line)
damaging0+=1
elif vcfUtils.is_functional(line, "stoploss stopgain splicing frameshift"):
fileout1.write(line)
damaging1+=1
elif (vcfUtils.is_functional(line, "nonsynonymous") and vcfUtils.is_conserved(line, headlist, 2)) or ("nonframeshift" in line):
fileout2.write(line)
damaging2+=1
elif vcfUtils.is_functional(line, "nonsynonymous") and vcfUtils.is_pathogenic(line, headlist, 2):
fileout3.write(line)
damaging3+=1
else:
fileout4.write(line)
damaging4+=1
output_log.write("Total variants queried: "+str(total)+"\n")
output_log.write("Rare variants queried: "+str(rarevars)+"\n")
output_log.write("Rare variants in target genes: "+str(target_genes)+"\n")
output_log.write("Candidate variants, tier 0: "+str(damaging0)+"\n")
output_log.write("Candidate variants, tier 1: "+str(damaging1)+"\n")
output_log.write("Candidate variants, tier 2: "+str(damaging2)+"\n")
output_log.write("Candidate variants, tier 3: "+str(damaging3)+"\n")
output_log.write("Candidate variants, tier 4: "+str(damaging4)+"\n")
filein.close()
g_file.close()
fileout0.close()
fileout1.close()
fileout2.close()
fileout3.close()
fileout4.close()
def tiers_target(in_file, out_stem, gene_file, pop, yaml_cmds, freq=0.01):
#open input and output files and initialize counters and lists for background populations
filein = open(in_file, "r")
g_file = open(gene_file, "r")
header = filein.readline().rstrip("\n")
headlist = header.split("\t")
output_log = open(out_stem+".metrics", "w")
output_log.write("Metrics for stmp filtering, clinvar variants\n")
fileout0 = open(out_stem+".tier0.txt", 'w')
fileout1 = open(out_stem+".tier1.txt", "w")
fileout2 = open(out_stem+".tier2.txt", "w")
fileout3 = open(out_stem+".tier3.txt", "w")
fileout4 = open(out_stem+".tier4.txt", "w")
fileout1.write(header + "\n")
fileout2.write(header + "\n")
fileout3.write(header + "\n")
fileout4.write(header + "\n")
total = 0
damaging0 = 0
damaging1 = 0
damaging2 = 0
damaging3 = 0
damaging4 = 0
target_genes = 0
# TODO: generate these headers as dynamically as possible using info in the YAML.
# TODO test to make sure each of these headers actually exists in the annotated file and warn if any are missing
if (pop == "CEU") or (pop == "c"):
backpoplist = vcfUtils.get_listindex(headlist, "hg19_hapmap2and3_CEU_info hg19_popfreq_all_20150413_1000g_all hg19_popfreq_all_20150413_1000g_eur hg19_cg69_info hg19_esp6500si_all_info hg19_popfreq_all_20150413_esp6500siv2_all hg19_esp6500si_ea_info hg19_popfreq_all_20150413_esp6500siv2_ea")
# backpoplist = vcfUtils.get_listindex(headlist, "hapmap2and3_CEU 1000g2010nov_ALL 1000g2011may_ALL 1000g2012apr_ALL 1000g2012apr_EUR cg69 esp6500si_ALL esp6500si_EA")
elif (pop == "ASN") or (pop == "a"):
backpoplist = vcfUtils.get_listindex(headlist, "hg19_hapmap2and3_CHB_info hg19_popfreq_all_20150413_1000g_all 1000g2012apr_ASN hg19_cg69_info hg19_esp6500si_all_info hg19_popfreq_all_20150413_esp6500siv2_all") # WARNING missing 1000g_ASN in curent datasets
# backpoplist = vcfUtils.get_listindex(headlist, "hapmap2and3_CHB 1000g2010nov_ALL 1000g2011may_ALL 1000g2012apr_ALL 1000g2012apr_ASN cg69 esp6500si_ALL")
elif (pop == "AFR") or (pop == "f"):
backpoplist = vcfUtils.get_listindex(headlist, "hg19_hapmap2and3_YRI_info hg19_popfreq_all_20150413_1000g_all hg19_popfreq_all_20150413_1000g_afr hg19_cg69_info hg19_esp6500si_all_info hg19_popfreq_all_20150413_esp6500siv2_all hg19_esp6500si_aa_info hg19_popfreq_all_20150413_esp6500siv2_aa")
else:
print >> sys.stderr, "Error in diseaseUtils.tiers_allvars - Population specified is not supported"
exit(1)
#initialize gene list for region prioritization
genes = {}
for line in g_file:
linelist = line.split("\t")
gene = linelist[1]
if genes.has_key(gene) == 0:
genes[gene] = linelist[2]+":"+linelist[3]
else:
genes[gene] = genes[gene]+";"+linelist[2]+":"+linelist[3]
#iterate over input file and parse into tiers
for line in filein:
total+=1
if ("PASS" in line) and (("#" in line) == 0) and (("0/1" in line) or ("1/1" in line)):
linelist = line.split("\t")
gene = linelist[1]
if genes.has_key(gene):
target_genes+=1
# tier 0: clinvar
if(vcfUtils.isClinvarPathogenicOrLikelyPathogenic(line, headlist, yaml_cmds)):
fileout0.write(line)
damaging0+=1
elif vcfUtils.is_functional(line, "stoploss stopgain splicing frameshift"):
fileout1.write(line)
damaging1+=1
elif vcfUtils.is_rare(line, freq, backpoplist):
fileout2.write(line)
damaging2+=1
elif vcfUtils.is_functional(line, "nonframeshift nonsynonymous"):
fileout3.write(line)
damaging3+=1
else:
fileout4.write(line)
damaging4+=1
output_log.write("Total variants queried: "+str(total)+"\n")
output_log.write("Total variants in gene list: "+str(target_genes)+"\n")
output_log.write("Candidate variants, tier 0: "+str(damaging0)+"\n")
output_log.write("Candidate variants, tier 1: "+str(damaging1)+"\n")
output_log.write("Candidate variants, tier 2: "+str(damaging2)+"\n")
output_log.write("Candidate variants, tier 3: "+str(damaging3)+"\n")
output_log.write("Candidate variants, tier 4: "+str(damaging4)+"\n")
filein.close()
g_file.close()
fileout0.close()
fileout1.close()
fileout2.close()
fileout3.close()
fileout4.close()
def tiers_allvars(in_file, out_stem, gene_file, pop, yaml_cmds):
# populate parameters from YAML module specifications
freq = yaml_cmds[yaml_keys.kModules][yaml_keys.kTiering][
yaml_keys.kTRareAlleleFreqCutoff]
gene_name_col_header = yaml_cmds[yaml_keys.kModules][yaml_keys.kTiering][
yaml_keys.kTGeneNameCol]
functional_column_headers = yaml_utils.convertColumns(
yaml_cmds[yaml_keys.kModules][yaml_keys.kTiering][
yaml_keys.kTFunctionalCols], yaml_cmds)
skip_filter_pass_check = yaml_cmds[yaml_keys.kModules][yaml_keys.kTiering][
yaml_keys.kTSkipFilterPassCheck]
# check for whether any variant contains "PASS"
cmd = 'grep "PASS" {infile}|grep -v "#"|wc -l'.format(infile=in_file)
process = subprocess.Popen(cmd,
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
stdout, stderr = process.communicate()
returncode = process.returncode
if (returncode != 0):
raise ValueError(
'Failed to search annotated VCF for "PASS" prior to tiering.\ncmd: '
+ str(cmd) + '\nErr: ' + str(stderr) + '\nReturncode: ' +
returncode + '\nOutput: ' + str(stdout))
#else
numHits = int(stdout)
# TODO move this to pre-checks (prior to even annotation)
if (numHits == 0 and not skip_filter_pass_check):
raise ValueError(
'No variants detected that passed filtering. Re-run STMP with "Skip_Filter_Pass_Check: True" in modules.yml to prioritize all variants anyway.'
)
elif (numHits == 0):
print 'NOTICE: no variants detected that passed filtering. Skipping filter PASS check and prioritizing all variants anyway.'
else:
print 'Found ' + str(
numHits
) + ' variants that passed filtering.' + ' Tiering just these variants.'
skip_filter_pass_check = False
#open input and output files and initialize counters and lists for background populations
filein = open(in_file, "r")
output_log = open(out_stem + ".metrics", "w")
output_log.write(
"Metrics for stmp filtering, all variants from reference\n")
header = filein.readline().rstrip("\n")
headlist = header.split("\t")
if (gene_file != None):
g_file = open(gene_file, "r")
fileoutrare = open(out_stem + '.rare.txt', 'w')
fileout0 = open(out_stem + ".tier0.txt", 'w')
fileout1 = open(out_stem + ".tier1.txt", "w")
fileout2 = open(out_stem + ".tier2.txt", "w")
fileout3 = open(out_stem + ".tier3.txt", "w")
fileout4 = open(out_stem + ".tier4.txt", "w")
fileoutrare.write(header + "\n")
fileout0.write("tier\t" + header + "\n")
fileout1.write("tier\t" + header + "\n")
fileout2.write("tier\t" + header + "\n")
fileout3.write("tier\t" + header + "\n")
fileout4.write("tier\t" + header + "\n")
total = 0
damaging0 = 0
damaging1 = 0
damaging2 = 0
damaging3 = 0
damaging4 = 0
target_genes = 0
rarevars = 0
allele_freq_cols = yaml_utils.convertColumns(
yaml_cmds[yaml_keys.kModules][yaml_keys.kTiering][
yaml_keys.kTAlleleFreqCols],
yaml_cmds) #convertTieringColumns(yaml_cmds)
#debug
print 'allele freq cols: ' + str(allele_freq_cols)
backpoplist = vcfUtils.get_listindex(headlist, allele_freq_cols)
#debug
# print 'backpoplist: ' + str(backpoplist)
#initialize gene list for region prioritization
if (gene_file != None):
genes = {}
for line in g_file:
if line.startswith('#'):
continue
linelist = line.rstrip("\n").split("\t")
gene = linelist[0]
if not genes.has_key(gene):
genes[gene] = 1
else:
# debug: uncomment if not debugging
# print 'warning: duplicate gene ' + gene + ' in gene list ' + gene_file
None
#iterate over input file and parse into tiers
for line in filein:
total += 1
if ((skip_filter_pass_check or "PASS" in line) and ("#" in line) == 0
and vcfUtils.is_rare(line, freq, backpoplist, yaml_cmds)
and not vcfUtils.contains_text(
'MT',
line, [stmp_consts.vcf_col_header_chrom],
headlist,
yaml_cmds,
case_sensitive=False)
and not vcfUtils.contains_text('ncRNA',
line,
functional_column_headers,
headlist,
yaml_cmds,
case_sensitive=True)):
rarevars += 1
fileoutrare.write(line)
linelist = line.rstrip("\n").split("\t")
# for now
tmp = linelist[headlist.index(gene_name_col_header)].split(',')
gene = tmp[0]
if gene_file == None or genes.has_key(gene):
target_genes += 1
# tier 0: clinvar
if (vcfUtils.isClinvarPathogenicOrLikelyPathogenic(
line, headlist, yaml_cmds)
and not vcfUtils.contains_text(
'0',
line, [
yaml_utils.get_datasets(yaml_cmds)['clinvar'][
yaml_keys.kDAnnotation] + '_' +
vcfHeaders.kClinvarStarHeader
],
headlist,
yaml_cmds,
case_sensitive=False)):
fileout0.write("0\t" + line)
damaging0 += 1
# tier 1
elif vcfUtils.is_functional(
line, "stoploss stopgain splicing frameshift",
functional_column_headers, headlist):
fileout1.write("1\t" + line)
damaging1 += 1
# tier 2
elif ((vcfUtils.is_functional(line, "nonsynonymous",
functional_column_headers,
headlist)
and vcfUtils.is_conserved(line, headlist, yaml_cmds))
or vcfUtils.is_functional(line, "nonframeshift",
functional_column_headers,
headlist)):
fileout2.write("2\t" + line)
damaging2 += 1
# tier 3
elif vcfUtils.is_functional(
line, "nonsynonymous", functional_column_headers,
headlist) and vcfUtils.is_pathogenic(
line, headlist, yaml_cmds):
fileout3.write("3\t" + line)
damaging3 += 1
# tier 4
elif vcfUtils.tolerance_pass(
line, headlist, yaml_cmds) and vcfUtils.is_functional(
line, "exonic splicing", functional_column_headers,
headlist):
fileout4.write("4\t" + line)
damaging4 += 1
# else ignore variant
output_log.write("Total variants queried: " + str(total) + "\n")
output_log.write("Rare variants (allele freq < {freq}) queried: ".format(
freq=str(freq)) + str(rarevars) + "\n")
output_log.write("Rare variants in {num} target genes: ".format(
num=str(len(genes)) if gene_file != None else '') + str(target_genes) +
"\n")
output_log.write(
"Candidate variants, tier 0 (rare clinvar pathogenic or likely pathogenic variants with rating > 0 stars): "
+ str(damaging0) + "\n")
output_log.write(
"Candidate variants, tier 1 (rare LOF variants -- stoploss, stopgain, splicing, and frameshift): "
+ str(damaging1) + "\n")
output_log.write(
"Candidate variants, tier 2 (rare nonframeshift or (nonsynonymous and conserved) variants): "
+ str(damaging2) + "\n")
output_log.write(
"Candidate variants, tier 3 (rare nonsynonymous pathogenic variants): "
+ str(damaging3) + "\n")
output_log.write(
"Candidate variants, tier 4 (all other rare exonic/splicing variants with ExAC tolerance z-score (syn_z or mis_z or lof_z) > 2): "
+ str(damaging4) + "\n")
filein.close()
if (gene_file != None):
g_file.close()
fileoutrare.close()
fileout0.close()
fileout1.close()
fileout2.close()
fileout3.close()
fileout4.close()
gto_list = linelist[offspring].split(":")
if (int(gto_list[dp_num]) == 0):
oimputedcounter+=1
gto_list = linelist[mother].split(":")
if (int(gto_list[dp_num]) == 0):
mimputedcounter+=1
gto_list = linelist[father].split(":")
if (int(gto_list[dp_num]) == 0):
fimputedcounter+=1
if ((("3" in alleles2) == 0) and (("4" in alleles2) == 0)):
tempcode = int(inmap[alleles2])
n_confident+=1
if tempcode in [1, 2, 5, 6, 8, 11, 15, 18, 20, 21, 24, 25]:
MIEcounter+=1
if ("#" in line) == 0 and vcfUtils.is_rare(line, freq, backpopindex) and mq >= mapQual and mq0 <= mapQual0:
if ((("3" in alleles2) == 0) and (("4" in alleles2) == 0)):
#simple de novo case
if alleles2 == "0,0,1":
deNovo.write(line)
counterDN+=1
#rare homozygous case
elif alleles2 == "1,1,2":
rareHomozygous.write(line)
counterRH+=1
#hemizygosity or gene conversion case
elif alleles2 == "0,0,2" or alleles2 == "1,0,2" or alleles2 == "0,1,2" or alleles2 == "1,2,0" or alleles2 == "2,1,0":