Exemple #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)
    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()
Exemple #2
0
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()
Exemple #3
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()
Exemple #4
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)
    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()