4 år sedan · c4355a44d6
--- a/codescripts/.DS_Store
+++ b/codescripts/.DS_Store
--- a/codescripts/D5_D6.py
+++ b/codescripts/D5_D6.py
 from __future__ import division
 import pandas as pd
 import sys, argparse, os
 # input arguments
 parser = argparse.ArgumentParser(description="this script is to calculate reproducibility between Quartet_D5 and Quartet_D6s")
 parser.add_argument('-sister', '--sister', type=str, help='sister.txt',  required=True)
 parser.add_argument('-project', '--project', type=str, help='project name',  required=True)
 args = parser.parse_args()
 sister_file = args.sister
 project_name = args.project
 # output file
 output_name = project_name + '.sister.reproducibility.txt'
 output_file = open(output_name,'w')
 # input files
 sister_dat = pd.read_table(sister_file)
 sister_same = 0
 sister_diff = 0
 for row in sister_dat.itertuples():
 	# sister
 	if row[5] == row[6]:
 		if row[5] == './.':
 			mendelian = 'noInfo'
 			sister_count = "no"
 		elif row[5] == '0/0':
 			mendelian = 'Ref'
 			sister_count = "no"
 		else:
 			mendelian = '1'
 			sister_count = "yes_same"
 	else:
 		mendelian = '0'
 		if (row[5] == './.' or row[5] == '0/0') and (row[6] == './.' or row[6] == '0/0'):
 			sister_count = "no"
 		else:
 			sister_count = "yes_diff"
 	if sister_count == 'yes_same':
 		sister_same += 1
 	elif sister_count == 'yes_diff':
 		sister_diff += 1
 	else:
 		pass
 sister = sister_same/(sister_same + sister_diff)
 outcolumn =  'Project\tReproducibility_D5_D6\n'
 outResult = project_name + '\t' + str(sister)  + '\n'
 output_file.write(outcolumn)
 output_file.write(outResult)
--- a/codescripts/Indel_bed.py
+++ b/codescripts/Indel_bed.py
 import pandas as pd
 import sys, argparse, os
 mut = pd.read_table('/mnt/pgx_src_data_pool_4/home/renluyao/manuscript/MIE/vcf/mutation_type',header=None)
 outIndel = open(sys.argv[1],'w')
 for row in mut.itertuples():
 	if ',' in row._4:
 		alt_seq = row._4.split(',')
 		alt_len = [len(i) for i in alt_seq]
 		alt = max(alt_len)
 	else:
 		alt = len(row._4)
 	ref = row._3
 	pos = row._2
 	if len(ref) == 1 and alt == 1:
 		pass
 	elif len(ref) > alt:
 		StartPos = int(pos) - 1
 		EndPos = int(pos) + (len(ref) - 1)
 		outline_indel = row._1 + '\t' + str(StartPos) + '\t' + str(EndPos) + '\n'
 		outIndel.write(outline_indel)
 	elif alt > len(ref):
 		StartPos = int(pos) - 1
 		EndPos = int(pos) + (alt - 1)
 		outline_indel = row._1 + '\t' + str(StartPos) + '\t' + str(EndPos) + '\n'
 		outIndel.write(outline_indel)
 	elif len(ref) == alt:
 		StartPos = int(pos) - 1
 		EndPos = int(pos) + (alt - 1)
 		outline_indel = row._1 + '\t' + str(StartPos) + '\t' + str(EndPos) + '\n'
 		outIndel.write(outline_indel)
--- a/codescripts/bed_region.py
+++ b/codescripts/bed_region.py
 import pandas as pd
 import sys, argparse, os
 mut = mut = pd.read_table('/mnt/pgx_src_data_pool_4/home/renluyao/manuscript/benchmark_calls/vcf/mutation_type',header=None)
 vote = pd.read_table('/mnt/pgx_src_data_pool_4/home/renluyao/manuscript/benchmark_calls/all_info/benchmark.vote.mendelian.txt',header=None)
 merged_df = pd.merge(vote, mut,  how='inner', left_on=[0,1], right_on = [0,1])
 outFile = open(sys.argv[1],'w')
 outIndel = open(sys.argv[2],'w')
 for row in merged_df.itertuples():
 #d5
 	if ',' in row._7:
 		d5 = row._7.split(',')
 		d5_len = [len(i) for i in d5]
 		d5_alt = max(d5_len)
 	else:
 		d5_alt = len(row._7)
 #d6
 	if ',' in row._15:
 		d6 = row._15.split(',')
 		d6_len = [len(i) for i in d6]
 		d6_alt = max(d6_len)
 	else:
 		d6_alt = len(row._15)
 #f7
 	if ',' in row._23:
 		f7 = row._23.split(',')
 		f7_len = [len(i) for i in f7]
 		f7_alt = max(f7_len)
 	else:
 		f7_alt = len(row._23)
 #m8
 	if ',' in row._31:
 		m8 = row._31.split(',')
 		m8_len = [len(i) for i in m8]
 		m8_alt = max(m8_len)
 	else:
 		m8_alt = len(row._31)
 	all_length = [d5_alt,d6_alt,f7_alt,m8_alt]
 	alt = max(all_length)
 	ref = row._35
 	pos = int(row._2)
 	if len(ref) == 1 and alt == 1:
 		StartPos = int(pos) -1
 		EndPos = int(pos)
 		cate = 'SNV'
 	elif len(ref) > alt:
 		StartPos = int(pos) - 1
 		EndPos = int(pos) + (len(ref) - 1)
 		cate = 'INDEL'
 		outline_indel = row._1 + '\t' + str(StartPos) + '\t' + str(EndPos) + '\n'
 		outIndel.write(outline_indel)
 	elif alt > len(ref):
 		StartPos = int(pos) - 1
 		EndPos = int(pos) + (alt - 1)
 		cate = 'INDEL'
 		outline_indel = row._1 + '\t' + str(StartPos) + '\t' + str(EndPos) + '\n'
 		outIndel.write(outline_indel)
 	elif len(ref) == alt:
 		StartPos = int(pos) - 1
 		EndPos = int(pos) + (alt - 1)
 		cate = 'INDEL'
 		outline_indel = row._1 + '\t' + str(StartPos) + '\t' + str(EndPos) + '\n'
 		outIndel.write(outline_indel)
 	outline = row._1 + '\t' + str(StartPos) + '\t' + str(EndPos) + '\t' + str(row._2) + '\t' + cate + '\n'
 	outFile.write(outline)
--- a/codescripts/cluster.sh
+++ b/codescripts/cluster.sh
 cat benchmark.men.vote.diffbed.lengthlessthan50.txt | awk '{print $1"\t"$2"\t"".""\t"$35"\t"$7"\t.\t.\t.\tGT\t"$6}' | grep -v '0/0' > LCL5.body
 cat benchmark.men.vote.diffbed.lengthlessthan50.txt | awk '{print $1"\t"$2"\t"".""\t"$35"\t"$15"\t.\t.\t.\tGT\t"$14}' | grep -v '0/0' > LCL6.body
 cat benchmark.men.vote.diffbed.lengthlessthan50.txt | awk '{print $1"\t"$2"\t"".""\t"$35"\t"$23"\t.\t.\t.\tGT\t"$22}' | grep -v '0/0'> LCL7.body
 cat benchmark.men.vote.diffbed.lengthlessthan50.txt | awk '{print $1"\t"$2"\t"".""\t"$35"\t"$31"\t.\t.\t.\tGT\t"$30}'| grep -v '0/0' > LCL8.body
 cat header5 LCL5.body > LCL5.beforediffbed.vcf
 cat header6 LCL6.body > LCL6.beforediffbed.vcf
 cat header7 LCL7.body > LCL7.beforediffbed.vcf
 cat header8 LCL8.body > LCL8.beforediffbed.vcf
 rtg bgzip *beforediffbed.vcf
 rtg index *beforediffbed.vcf.gz
 rtg vcffilter -i LCL5.beforediffbed.vcf.gz --exclude-bed=/mnt/pgx_src_data_pool_4/home/renluyao/manuscript/benchmark_calls/MIE/diff.merged.bed -o LCL5.afterfilterdiffbed.vcf.gz
 rtg vcffilter -i LCL6.beforediffbed.vcf.gz --exclude-bed=/mnt/pgx_src_data_pool_4/home/renluyao/manuscript/benchmark_calls/MIE/diff.merged.bed -o LCL6.afterfilterdiffbed.vcf.gz
 rtg vcffilter -i LCL7.beforediffbed.vcf.gz --exclude-bed=/mnt/pgx_src_data_pool_4/home/renluyao/manuscript/benchmark_calls/MIE/diff.merged.bed -o LCL7.afterfilterdiffbed.vcf.gz
 rtg vcffilter -i LCL8.beforediffbed.vcf.gz --exclude-bed=/mnt/pgx_src_data_pool_4/home/renluyao/manuscript/benchmark_calls/MIE/diff.merged.bed -o LCL8.afterfilterdiffbed.vcf.gz
 /mnt/pgx_src_data_pool_4/home/renluyao/softwares/annovar/table_annovar.pl LCL5.beforediffbed.vcf.gz /mnt/pgx_src_data_pool_4/home/renluyao/softwares/annovar/humandb \
 -buildver hg38 \
 -out LCL5 \
 -remove \
 -protocol 1000g2015aug_all,1000g2015aug_afr,1000g2015aug_amr,1000g2015aug_eas,1000g2015aug_eur,1000g2015aug_sas,clinvar_20190305,gnomad211_genome \
 -operation f,f,f,f,f,f,f,f \
 -nastring . \
 -vcfinput \
 --thread 8
 rtg vcfeval -b /mnt/pgx_src_data_pool_4/home/renluyao/Quartet/GIAB/NA12878_HG001/HG001_GRCh38_GIAB_highconf_CG-IllFB-IllGATKHC-Ion-10X-SOLID_CHROM1-X_v.3.3.2_highconf_PGandRTGphasetransfer.vcf.gz -c LCL5.afterfilterdiffbed.vcf.gz -o LCL5_NIST -t /mnt/pgx_src_data_pool_4/home/renluyao/annotation/hg38/GRCh38.d1.vd1.sdf/
 rtg vcfeval -b /mnt/pgx_src_data_pool_4/home/renluyao/Quartet/GIAB/NA12878_HG001/HG001_GRCh38_GIAB_highconf_CG-IllFB-IllGATKHC-Ion-10X-SOLID_CHROM1-X_v.3.3.2_highconf_PGandRTGphasetransfer.vcf.gz -c LCL6.afterfilterdiffbed.vcf.gz -o LCL6_NIST -t /mnt/pgx_src_data_pool_4/home/renluyao/annotation/hg38/GRCh38.d1.vd1.sdf/
 rtg vcfeval -b /mnt/pgx_src_data_pool_4/home/renluyao/Quartet/GIAB/NA12878_HG001/HG001_GRCh38_GIAB_highconf_CG-IllFB-IllGATKHC-Ion-10X-SOLID_CHROM1-X_v.3.3.2_highconf_PGandRTGphasetransfer.vcf.gz -c LCL7.afterfilterdiffbed.vcf.gz -o LCL7_NIST -t /mnt/pgx_src_data_pool_4/home/renluyao/annotation/hg38/GRCh38.d1.vd1.sdf/
 rtg vcfeval -b /mnt/pgx_src_data_pool_4/home/renluyao/Quartet/GIAB/NA12878_HG001/HG001_GRCh38_GIAB_highconf_CG-IllFB-IllGATKHC-Ion-10X-SOLID_CHROM1-X_v.3.3.2_highconf_PGandRTGphasetransfer.vcf.gz -c LCL8.afterfilterdiffbed.vcf.gz -o LCL8_NIST -t /mnt/pgx_src_data_pool_4/home/renluyao/annotation/hg38/GRCh38.d1.vd1.sdf/
 zcat LCL5.afterfilterdiffbed.vcf.gz | grep -v '#' | awk '{ if ((length($4) == 1) && (length($5) == 1)) { print } }' | wc -l
 zcat LCL6.afterfilterdiffbed.vcf.gz | grep -v '#' | awk '{ if ((length($4) == 1) && (length($5) == 1)) { print } }' | wc -l
 zcat LCL7.afterfilterdiffbed.vcf.gz | grep -v '#' | awk '{ if ((length($4) == 1) && (length($5) == 1)) { print } }' | wc -l
 zcat LCL8.afterfilterdiffbed.vcf.gz | grep -v '#' | awk '{ if ((length($4) == 1) && (length($5) == 1)) { print } }' | wc -l
 zcat LCL5.afterfilterdiffbed.vcf.gz | grep -v '#' | awk '{ if ((length($4) == 1) && (length($5) < 11) && (length($5) > 1)) { print } }' | wc -l
 zcat LCL6.afterfilterdiffbed.vcf.gz | grep -v '#' | awk '{ if ((length($4) == 1) && (length($5) < 11) && (length($5) > 1)) { print } }' | wc -l
 zcat LCL7.afterfilterdiffbed.vcf.gz | grep -v '#' | awk '{ if ((length($4) == 1) && (length($5) < 11) && (length($5) > 1)) { print } }' | wc -l
 zcat LCL8.afterfilterdiffbed.vcf.gz | grep -v '#' | awk '{ if ((length($4) == 1) && (length($5) < 11) && (length($5) > 1)) { print } }' | wc -l
 zcat LCL5.afterfilterdiffbed.vcf.gz | grep -v '#' | awk '{ if ((length($4) == 1) && (length($5) > 10)) { print } }' | wc -l
 zcat LCL6.afterfilterdiffbed.vcf.gz | grep -v '#' | awk '{ if ((length($4) == 1) && (length($5) > 10)) { print } }' | wc -l
 zcat LCL7.afterfilterdiffbed.vcf.gz | grep -v '#' | awk '{ if ((length($4) == 1) && (length($5) > 10)) { print } }' | wc -l
 zcat LCL8.afterfilterdiffbed.vcf.gz | grep -v '#' | awk '{ if ((length($4) == 1) && (length($5) > 10)) { print } }' | wc -l
 bedtools subtract -a LCL5.27.homo_ref.consensus.bed -b /mnt/pgx_src_data_pool_4/home/renluyao/manuscript/benchmark_calls/MIE/diff.merged.bed > LCL5.27.homo_ref.consensus.filtereddiffbed.bed
 bedtools subtract -a LCL6.27.homo_ref.consensus.bed -b /mnt/pgx_src_data_pool_4/home/renluyao/manuscript/benchmark_calls/MIE/diff.merged.bed > LCL6.27.homo_ref.consensus.filtereddiffbed.bed
 bedtools subtract -a LCL7.27.homo_ref.consensus.bed -b /mnt/pgx_src_data_pool_4/home/renluyao/manuscript/benchmark_calls/MIE/diff.merged.bed > LCL7.27.homo_ref.consensus.filtereddiffbed.bed
 bedtools subtract -a LCL8.27.homo_ref.consensus.bed -b /mnt/pgx_src_data_pool_4/home/renluyao/manuscript/benchmark_calls/MIE/diff.merged.bed > LCL8.27.homo_ref.consensus.filtereddiffbed.bed
 python vcf2bed.py LCL5.body LCL5.variants.bed
 python vcf2bed.py LCL6.body LCL6.variants.bed
 python vcf2bed.py LCL7.body LCL7.variants.bed
 python vcf2bed.py LCL8.body LCL8.variants.bed
 cat /mnt/pgx_src_data_pool_4/home/renluyao/manuscript/benchmark_calls/all_info/LCL5.variants.bed | cut -f1,11,12 | cat - LCL5.27.homo_ref.consensus.filtereddiffbed.bed | sort -k1,1 -k2,2n > LCL5.high.confidence.bed
--- a/codescripts/contig.txt
+++ b/codescripts/contig.txt
 ##contig=<ID=chr1,length=248956422>
 ##contig=<ID=chr2,length=242193529>
 ##contig=<ID=chr3,length=198295559>
 ##contig=<ID=chr4,length=190214555>
 ##contig=<ID=chr5,length=181538259>
 ##contig=<ID=chr6,length=170805979>
 ##contig=<ID=chr7,length=159345973>
 ##contig=<ID=chr8,length=145138636>
 ##contig=<ID=chr9,length=138394717>
 ##contig=<ID=chr10,length=133797422>
 ##contig=<ID=chr11,length=135086622>
 ##contig=<ID=chr12,length=133275309>
 ##contig=<ID=chr13,length=114364328>
 ##contig=<ID=chr14,length=107043718>
 ##contig=<ID=chr15,length=101991189>
 ##contig=<ID=chr16,length=90338345>
 ##contig=<ID=chr17,length=83257441>
 ##contig=<ID=chr18,length=80373285>
 ##contig=<ID=chr19,length=58617616>
 ##contig=<ID=chr20,length=64444167>
 ##contig=<ID=chr21,length=46709983>
 ##contig=<ID=chr22,length=50818468>
 ##contig=<ID=chrX,length=156040895>
--- a/codescripts/extract_multiqc.py
+++ b/codescripts/extract_multiqc.py
 import json
 import pandas as pd
 import sys, argparse, os
 parser = argparse.ArgumentParser(description="This script is to get information from multiqc")
 parser.add_argument('-fastqc_qualimap', '--fastqc_qualimap', type=str, help='multiqc_general_stats.txt',  required=True)
 parser.add_argument('-fastqc', '--fastqc', type=str, help='multiqc_fastqc.txt',  required=True)
 parser.add_argument('-fastqscreen', '--fastqscreen', type=str, help='multiqc_fastq_screen.txt',  required=True)
 parser.add_argument('-hap', '--happy', type=str, help='multiqc_happy_data.json',  required=True)
 args = parser.parse_args()
 # Rename input:
 fastqc_qualimap_file = args.fastqc_qualimap
 fastqc_file = args.fastqc
 fastqscreen_file = args.fastqscreen
 hap_file = args.happy
 # fastqc and qualimap
 dat = pd.read_table(fastqc_qualimap_file)
 fastqc = dat.loc[:, dat.columns.str.startswith('FastQC')]
 fastqc.insert(loc=0, column='Sample', value=dat['Sample'])
 fastqc_stat = fastqc.dropna()
 # qulimap
 qualimap = dat.loc[:, dat.columns.str.startswith('QualiMap')]
 qualimap.insert(loc=0, column='Sample', value=dat['Sample'])
 qualimap_stat = qualimap.dropna()
 # fastqc
 dat = pd.read_table(fastqc_file)
 fastqc_module = dat.loc[:, "per_base_sequence_quality":"kmer_content"]
 fastqc_module.insert(loc=0, column='Sample', value=dat['Sample'])
 fastqc_all = pd.merge(fastqc_stat,fastqc_module,  how='outer', left_on=['Sample'], right_on = ['Sample'])
 # fastqscreen
 dat = pd.read_table(fastqscreen_file)
 fastqscreen = dat.loc[:, dat.columns.str.endswith('percentage')]
 dat['Sample'] = [i.replace('_screen','') for i in dat['Sample']]
 fastqscreen.insert(loc=0, column='Sample', value=dat['Sample'])
 # benchmark
 with open(hap_file) as hap_json:
 	happy = json.load(hap_json)
 dat =pd.DataFrame.from_records(happy)
 dat = dat.loc[:, dat.columns.str.endswith('ALL')]
 dat_transposed = dat.T
 benchmark = dat_transposed.loc[:,['sample_id','METRIC.Precision','METRIC.Recall']]
 benchmark.columns = ['Sample','Precision','Recall']
 #output
 fastqc_all.to_csv('fastqc.final.result.txt',sep="\t",index=0)
 fastqscreen.to_csv('fastqscreen.final.result.txt',sep="\t",index=0)
 qualimap_stat.to_csv('qualimap.final.result.txt',sep="\t",index=0)
 benchmark.to_csv('benchmark.final.result.txt',sep="\t",index=0)
--- a/codescripts/extract_vcf_information.py
+++ b/codescripts/extract_vcf_information.py
 import sys,getopt
 import os
 import re
 import fileinput
 import pandas as pd
 def usage():
 	print(
 		"""
 Usage: python extract_vcf_information.py -i input_merged_vcf_file -o parsed_file
 This script will extract SNVs and Indels information from the vcf files and output a tab-delimited files.
 Input:
 -i the selected vcf file
 Output:
 -o tab-delimited parsed file
 		""")
 # select supported small variants
 def process(oneLine):
 	line = oneLine.rstrip()
 	strings = line.strip().split('\t')
 	infoParsed = parse_INFO(strings[7])
 	formatKeys = strings[8].split(':')
 	formatValues = strings[9].split(':')
 	for i in range(0,len(formatKeys) -1) :
 		if formatKeys[i] == 'AD':
 			ra = formatValues[i].split(',')
 			infoParsed['RefDP'] = ra[0]
 			infoParsed['AltDP'] = ra[1]
 			if (int(ra[1]) + int(ra[0])) != 0:
 				infoParsed['af'] = float(int(ra[1])/(int(ra[1]) + int(ra[0])))
 			else:
 				pass
 		else:
 			infoParsed[formatKeys[i]] = formatValues[i]
 	infoParsed['chromo'] = strings[0]
 	infoParsed['pos'] = strings[1]
 	infoParsed['id'] = strings[2]
 	infoParsed['ref'] = strings[3]
 	infoParsed['alt'] = strings[4]
 	infoParsed['qual'] = strings[5]
 	return infoParsed
 def parse_INFO(info):
 	strings = info.strip().split(';')
 	keys = []
 	values = []
 	for i in strings:
 		kv = i.split('=')
 		if kv[0] == 'DB':
 			keys.append('DB')
 			values.append('1')
 		elif kv[0] == 'AF':
 			pass
 		else:
 			keys.append(kv[0])
 			values.append(kv[1])
 	infoDict = dict(zip(keys, values))
 	return infoDict
 opts,args = getopt.getopt(sys.argv[1:],"hi:o:") 
 for op,value in opts:
 	if op == "-i":
 		inputFile=value
 	elif op == "-o":
 		outputFile=value	
 	elif op == "-h":
 		usage()
 		sys.exit()
 if len(sys.argv[1:]) < 3:
 	usage()
 	sys.exit()
 allDict = []
 for line in fileinput.input(inputFile):
 	m = re.match('^\#',line)
 	if m is not None:
 		pass
 	else:
 		oneDict = process(line)
 		allDict.append(oneDict)
 allTable = pd.DataFrame(allDict)
 allTable.to_csv(outputFile,sep='\t',index=False)
--- a/codescripts/filter_indel_over_50_cluster.py
+++ b/codescripts/filter_indel_over_50_cluster.py
 import sys,getopt
 from itertools import islice
 over_50_outfile = open("indel_lenth_over_50.txt",'w')
 less_50_outfile = open("benchmark.men.vote.diffbed.lengthlessthan50.txt","w")
 def process(line):
 	strings = line.strip().split('\t')
 #d5
 	if ',' in strings[6]:
 		d5 = strings[6].split(',')
 		d5_len = [len(i) for i in d5]
 		d5_alt = max(d5_len)
 	else:
 		d5_alt = len(strings[6])
 #d6
 	if ',' in strings[14]:
 		d6 = strings[14].split(',')
 		d6_len = [len(i) for i in d6]
 		d6_alt = max(d6_len)
 	else:
 		d6_alt = len(strings[14])
 #f7
 	if ',' in strings[22]:
 		f7 = strings[22].split(',')
 		f7_len = [len(i) for i in f7]
 		f7_alt = max(f7_len)
 	else:
 		f7_alt = len(strings[22])
 #m8
 	if ',' in strings[30]:
 		m8 = strings[30].split(',')
 		m8_len = [len(i) for i in m8]
 		m8_alt = max(m8_len)
 	else:
 		m8_alt = len(strings[30])
 #ref
 	ref_len = len(strings[34])
 	if (d5_alt > 50) or (d6_alt > 50) or (f7_alt > 50) or (m8_alt > 50) or (ref_len > 50):
 		over_50_outfile.write(line)
 	else:
 		less_50_outfile.write(line)
 input_file = open(sys.argv[1])  
 for line in islice(input_file, 1, None):  
 	process(line)
--- a/codescripts/filter_indel_over_50_mendelian.py
+++ b/codescripts/filter_indel_over_50_mendelian.py
 from itertools import islice
 import fileinput
 import sys, argparse, os
 # input arguments
 parser = argparse.ArgumentParser(description="this script is to exclude indel over 50bp")
 parser.add_argument('-i', '--mergedGVCF', type=str, help='merged gVCF txt with only chr, pos, ref, alt and genotypes',  required=True)
 parser.add_argument('-prefix', '--prefix', type=str, help='prefix of output file',  required=True)
 args = parser.parse_args()
 input_dat = args.mergedGVCF
 prefix = args.prefix
 # output file
 output_name = prefix + '.indel.lessthan50bp.txt'
 outfile = open(output_name,'w')
 def process(line):
 	strings = line.strip().split('\t')
 #d5
 	if ',' in strings[3]:
 		alt = strings[3].split(',')
 		alt_len = [len(i) for i in alt]
 		alt_max = max(alt_len)
 	else:
 		alt_max = len(strings[3])
 #ref
 	ref_len = len(strings[2])
 	if (alt_max > 50) or (ref_len > 50):
 		pass
 	else:
 		outfile.write(line)
 for line in fileinput.input(input_dat):
 	process(line)
--- a/codescripts/get_filtered_benchmark_vcfinfo.py
+++ b/codescripts/get_filtered_benchmark_vcfinfo.py
 from __future__ import division
 import pandas as pd
 import sys, argparse, os
 import fileinput
 # input arguments
 parser = argparse.ArgumentParser(description="this script is to get filtered and benchmark vcf info")
 parser.add_argument('-filtered', '--filtered', type=str, help='filtered position',  required=True)
 parser.add_argument('-benchmark', '--benchmark', type=str, help='benchmark position',  required=True)
 parser.add_argument('-vcf', '--vcf', type=str, help='one specific vcf',  required=True)
 parser.add_argument('-filename', '--filename', type=str, help='output file name',  required=True)
 args = parser.parse_args()
 filtered = args.filtered
 benchmark = args.benchmark
 vcf = args.vcf
 filename = args.filename
 # output file
 filtered_filename = filename + '.filtered.txt'
 benchmark_filename = filename + '.benchmark.txt'
 # input files
 filtered_dat = pd.read_table(filtered,header=None)
 benchmark_dat = pd.read_table(benchmark,header=None)
 vcf_dat = pd.read_table(vcf)
 filtered_merged_df = pd.merge(filtered_dat, vcf_dat,  how='inner',left_on=[0,1], right_on = ['#CHROM','POS'])
 benchmark_merged_df = pd.merge(benchmark_dat,vcf_dat, how='inner',left_on=[0,1], right_on = ['#CHROM','POS'])
 filtered_merged_df.to_csv(filtered_filename,sep='\t',index=False)
 benchmark_merged_df.to_csv(benchmark_filename,sep='\t',index=False)
--- a/codescripts/high_confidence_call_vote.py
+++ b/codescripts/high_confidence_call_vote.py
 from __future__ import division 
 import sys, argparse, os
 import fileinput
 import re
 import pandas as pd
 from operator import itemgetter
 from collections import Counter
 from itertools import islice
 from numpy import *
 import statistics
 # input arguments
 parser = argparse.ArgumentParser(description="this script is to count voting number")
 parser.add_argument('-vcf', '--multi_sample_vcf', type=str, help='The VCF file you want to count the voting number',  required=True)
 parser.add_argument('-dup', '--dup_list', type=str, help='Duplication list',  required=True)
 parser.add_argument('-sample', '--sample_name', type=str, help='which sample of quartet',  required=True)
 parser.add_argument('-prefix', '--prefix', type=str, help='Prefix of output file name',  required=True)
 args = parser.parse_args()
 multi_sample_vcf = args.multi_sample_vcf
 dup_list = args.dup_list
 prefix = args.prefix
 sample_name = args.sample_name
 vcf_header = '''##fileformat=VCFv4.2
 ##fileDate=20200331
 ##source=high_confidence_calls_intergration(choppy app)
 ##reference=GRCh38.d1.vd1
 ##INFO=<ID=location,Number=1,Type=String,Description="Repeat region">
 ##INFO=<ID=DETECTED,Number=1,Type=Integer,Description="Number of detected votes">
 ##INFO=<ID=VOTED,Number=1,Type=Integer,Description="Number of consnesus votes">
 ##INFO=<ID=FAM,Number=1,Type=Integer,Description="Number mendelian consisitent votes">
 ##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
 ##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Sum depth of all samples">
 ##FORMAT=<ID=ALT,Number=1,Type=Integer,Description="Sum alternative depth of all samples">
 ##FORMAT=<ID=AF,Number=1,Type=Float,Description="Allele frequency, sum alternative depth / sum depth">
 ##FORMAT=<ID=GQ,Number=1,Type=Float,Description="Average genotype quality">
 ##FORMAT=<ID=QD,Number=1,Type=Float,Description="Average Variant Confidence/Quality by Depth">
 ##FORMAT=<ID=MQ,Number=1,Type=Float,Description="Average mapping quality">
 ##FORMAT=<ID=FS,Number=1,Type=Float,Description="Average Phred-scaled p-value using Fisher's exact test to detect strand bias">
 ##FORMAT=<ID=QUALI,Number=1,Type=Float,Description="Average variant quality">
 ##contig=<ID=chr1,length=248956422>
 ##contig=<ID=chr2,length=242193529>
 ##contig=<ID=chr3,length=198295559>
 ##contig=<ID=chr4,length=190214555>
 ##contig=<ID=chr5,length=181538259>
 ##contig=<ID=chr6,length=170805979>
 ##contig=<ID=chr7,length=159345973>
 ##contig=<ID=chr8,length=145138636>
 ##contig=<ID=chr9,length=138394717>
 ##contig=<ID=chr10,length=133797422>
 ##contig=<ID=chr11,length=135086622>
 ##contig=<ID=chr12,length=133275309>
 ##contig=<ID=chr13,length=114364328>
 ##contig=<ID=chr14,length=107043718>
 ##contig=<ID=chr15,length=101991189>
 ##contig=<ID=chr16,length=90338345>
 ##contig=<ID=chr17,length=83257441>
 ##contig=<ID=chr18,length=80373285>
 ##contig=<ID=chr19,length=58617616>
 ##contig=<ID=chr20,length=64444167>
 ##contig=<ID=chr21,length=46709983>
 ##contig=<ID=chr22,length=50818468>
 ##contig=<ID=chrX,length=156040895>
 '''
 vcf_header_all_sample = '''##fileformat=VCFv4.2
 ##fileDate=20200331
 ##reference=GRCh38.d1.vd1
 ##INFO=<ID=location,Number=1,Type=String,Description="Repeat region">
 ##INFO=<ID=DUP,Number=1,Type=Flag,Description="Duplicated variant records">
 ##INFO=<ID=DETECTED,Number=1,Type=Integer,Description="Number of detected votes">
 ##INFO=<ID=VOTED,Number=1,Type=Integer,Description="Number of consnesus votes">
 ##INFO=<ID=FAM,Number=1,Type=Integer,Description="Number mendelian consisitent votes">
 ##INFO=<ID=ALL_ALT,Number=1,Type=Float,Description="Sum of alternative reads of all samples">
 ##INFO=<ID=ALL_DP,Number=1,Type=Float,Description="Sum of depth of all samples">
 ##INFO=<ID=ALL_AF,Number=1,Type=Float,Description="Allele frequency of net alternatice reads and net depth">
 ##INFO=<ID=GQ_MEAN,Number=1,Type=Float,Description="Mean of genotype quality of all samples">
 ##INFO=<ID=QD_MEAN,Number=1,Type=Float,Description="Average Variant Confidence/Quality by Depth">
 ##INFO=<ID=MQ_MEAN,Number=1,Type=Float,Description="Mean of mapping quality of all samples">
 ##INFO=<ID=FS_MEAN,Number=1,Type=Float,Description="Average Phred-scaled p-value using Fisher's exact test to detect strand bias">
 ##INFO=<ID=QUAL_MEAN,Number=1,Type=Float,Description="Average variant quality">
 ##INFO=<ID=PCR,Number=1,Type=String,Description="Consensus of PCR votes">
 ##INFO=<ID=PCR_FREE,Number=1,Type=String,Description="Consensus of PCR-free votes">
 ##INFO=<ID=CONSENSUS,Number=1,Type=String,Description="Consensus calls">
 ##INFO=<ID=CONSENSUS_SEQ,Number=1,Type=String,Description="Consensus sequence">
 ##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
 ##FORMAT=<ID=DP,Number=1,Type=String,Description="Depth">
 ##FORMAT=<ID=ALT,Number=1,Type=Integer,Description="Alternative Depth">
 ##FORMAT=<ID=AF,Number=1,Type=String,Description="Allele frequency">
 ##FORMAT=<ID=GQ,Number=1,Type=String,Description="Genotype quality">
 ##FORMAT=<ID=MQ,Number=1,Type=String,Description="Mapping quality">
 ##FORMAT=<ID=TWINS,Number=1,Type=String,Description="1 is twins shared, 0 is twins discordant ">
 ##FORMAT=<ID=TRIO5,Number=1,Type=String,Description="1 is LCL7, LCL8 and LCL5 mendelian consistent, 0 is mendelian vioaltion">
 ##FORMAT=<ID=TRIO6,Number=1,Type=String,Description="1 is LCL7, LCL8 and LCL6 mendelian consistent, 0 is mendelian vioaltion">
 ##contig=<ID=chr1,length=248956422>
 ##contig=<ID=chr2,length=242193529>
 ##contig=<ID=chr3,length=198295559>
 ##contig=<ID=chr4,length=190214555>
 ##contig=<ID=chr5,length=181538259>
 ##contig=<ID=chr6,length=170805979>
 ##contig=<ID=chr7,length=159345973>
 ##contig=<ID=chr8,length=145138636>
 ##contig=<ID=chr9,length=138394717>
 ##contig=<ID=chr10,length=133797422>
 ##contig=<ID=chr11,length=135086622>
 ##contig=<ID=chr12,length=133275309>
 ##contig=<ID=chr13,length=114364328>
 ##contig=<ID=chr14,length=107043718>
 ##contig=<ID=chr15,length=101991189>
 ##contig=<ID=chr16,length=90338345>
 ##contig=<ID=chr17,length=83257441>
 ##contig=<ID=chr18,length=80373285>
 ##contig=<ID=chr19,length=58617616>
 ##contig=<ID=chr20,length=64444167>
 ##contig=<ID=chr21,length=46709983>
 ##contig=<ID=chr22,length=50818468>
 ##contig=<ID=chrX,length=156040895>
 '''
 # read in duplication list
 dup = pd.read_table(dup_list,header=None)
 var_dup = dup[0].tolist()
 # output file
 benchmark_file_name = prefix + '_voted.vcf'
 benchmark_outfile = open(benchmark_file_name,'w')
 all_sample_file_name = prefix + '_all_sample_information.vcf'
 all_sample_outfile = open(all_sample_file_name,'w')
 # write VCF
 outputcolumn = '#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\t' + sample_name + '_benchmark_calls\n'
 benchmark_outfile.write(vcf_header)
 benchmark_outfile.write(outputcolumn)
 outputcolumn_all_sample = '#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\t'+ \
 'Quartet_DNA_BGI_SEQ2000_BGI_1_20180518\tQuartet_DNA_BGI_SEQ2000_BGI_2_20180530\tQuartet_DNA_BGI_SEQ2000_BGI_3_20180530\t' + \
 'Quartet_DNA_BGI_T7_WGE_1_20191105\tQuartet_DNA_BGI_T7_WGE_2_20191105\tQuartet_DNA_BGI_T7_WGE_3_20191105\t' + \
 'Quartet_DNA_ILM_Nova_ARD_1_20181108\tQuartet_DNA_ILM_Nova_ARD_2_20181108\tQuartet_DNA_ILM_Nova_ARD_3_20181108\t' + \
 'Quartet_DNA_ILM_Nova_ARD_4_20190111\tQuartet_DNA_ILM_Nova_ARD_5_20190111\tQuartet_DNA_ILM_Nova_ARD_6_20190111\t' + \
 'Quartet_DNA_ILM_Nova_BRG_1_20180930\tQuartet_DNA_ILM_Nova_BRG_2_20180930\tQuartet_DNA_ILM_Nova_BRG_3_20180930\t' + \
 'Quartet_DNA_ILM_Nova_WUX_1_20190917\tQuartet_DNA_ILM_Nova_WUX_2_20190917\tQuartet_DNA_ILM_Nova_WUX_3_20190917\t' + \
 'Quartet_DNA_ILM_XTen_ARD_1_20170403\tQuartet_DNA_ILM_XTen_ARD_2_20170403\tQuartet_DNA_ILM_XTen_ARD_3_20170403\t' + \
 'Quartet_DNA_ILM_XTen_NVG_1_20170329\tQuartet_DNA_ILM_XTen_NVG_2_20170329\tQuartet_DNA_ILM_XTen_NVG_3_20170329\t' + \
 'Quartet_DNA_ILM_XTen_WUX_1_20170216\tQuartet_DNA_ILM_XTen_WUX_2_20170216\tQuartet_DNA_ILM_XTen_WUX_3_20170216\n'
 all_sample_outfile.write(vcf_header_all_sample)
 all_sample_outfile.write(outputcolumn_all_sample)
 #function
 def replace_nan(strings_list):
 	updated_list = []
 	for i in strings_list:
 		if i == '.':
 			updated_list.append('.:.:.:.:.:.:.:.:.:.:.:.')
 		else:
 			updated_list.append(i)
 	return updated_list
 def remove_dot(strings_list):
 	updated_list = []
 	for i in strings_list:
 		if i == '.':
 			pass
 		else:
 			updated_list.append(i)
 	return updated_list	
 def detected_number(strings):
 	gt = [x.split(':')[0] for x in strings]
 	percentage = 27 - gt.count('.')
 	return(str(percentage))
 def vote_number(strings,consensus_call):
 	gt = [x.split(':')[0] for x in strings]
 	gt = [x.replace('.','0/0') for x in gt]
 	gt = list(map(gt_uniform,[i for i in gt]))
 	vote_num = gt.count(consensus_call)
 	return(str(vote_num))
 def family_vote(strings,consensus_call):
 	gt = [x.split(':')[0] for x in strings]
 	gt = [x.replace('.','0/0') for x in gt]
 	gt = list(map(gt_uniform,[i for i in gt]))
 	mendelian = [':'.join(x.split(':')[1:4]) for x in strings]
 	indices = [i for i, x in enumerate(gt) if x == consensus_call]
 	matched_mendelian = itemgetter(*indices)(mendelian)
 	mendelian_num = matched_mendelian.count('1:1:1')
 	return(str(mendelian_num))
 def gt_uniform(strings):
 	uniformed_gt = ''
 	allele1 = strings.split('/')[0]
 	allele2 = strings.split('/')[1]
 	if int(allele1) > int(allele2):
 		uniformed_gt = allele2 + '/' + allele1
 	else:
 		uniformed_gt = allele1 + '/' + allele2
 	return uniformed_gt
 def decide_by_rep(strings):
 	consensus_rep = ''
 	mendelian = [':'.join(x.split(':')[1:4]) for x in strings]
 	gt = [x.split(':')[0] for x in strings]
 	gt = [x.replace('.','0/0') for x in gt]
 	# modified gt turn 2/1 to 1/2
 	gt = list(map(gt_uniform,[i for i in gt]))
 	# mendelian consistent?
 	mendelian_dict = Counter(mendelian)
 	highest_mendelian = mendelian_dict.most_common(1)
 	candidate_mendelian = highest_mendelian[0][0]
 	freq_mendelian = highest_mendelian[0][1]
 	if (candidate_mendelian == '1:1:1') and (freq_mendelian >= 2):
 		gt_num_dict = Counter(gt)
 		highest_gt = gt_num_dict.most_common(1)
 		candidate_gt = highest_gt[0][0]
 		freq_gt = highest_gt[0][1]
 		if (candidate_gt != '0/0') and (freq_gt >= 2):
 			consensus_rep = candidate_gt
 		elif (candidate_gt == '0/0') and (freq_gt >= 2):
 			consensus_rep = '0/0'
 		else:
 			consensus_rep = 'inconGT'
 	elif (candidate_mendelian == '') and (freq_mendelian >= 2):
 		consensus_rep = 'noInfo'
 	else:
 		consensus_rep = 'inconMen'
 	return consensus_rep
 def main():
 	for line in fileinput.input(multi_sample_vcf):
 		headline = re.match('^\#',line)
 		if headline is not None:
 			pass
 		else:
 			line = line.strip()
 			strings = line.split('\t')
 			variant_id = '_'.join([strings[0],strings[1]])
 			# check if the variants location is duplicated
 			if variant_id in var_dup:
 				strings[7] = strings[7] + ';DUP'
 				outLine = '\t'.join(strings) + '\n'
 				all_sample_outfile.write(outLine)
 			else:
 				# pre-define
 				pcr_consensus = '.'
 				pcr_free_consensus = '.'
 				consensus_call = '.'
 				consensus_alt_seq = '.'
 				# pcr 
 				strings[9:] = replace_nan(strings[9:])
 				pcr = itemgetter(*[9,10,11,27,28,29,30,31,32,33,34,35])(strings)
 				SEQ2000 = decide_by_rep(pcr[0:3])
 				XTen_ARD = decide_by_rep(pcr[3:6])
 				XTen_NVG = decide_by_rep(pcr[6:9])
 				XTen_WUX = decide_by_rep(pcr[9:12])
 				sequence_site = [SEQ2000,XTen_ARD,XTen_NVG,XTen_WUX]
 				sequence_dict = Counter(sequence_site)
 				highest_sequence = sequence_dict.most_common(1)
 				candidate_sequence = highest_sequence[0][0]
 				freq_sequence = highest_sequence[0][1]
 				if freq_sequence > 2:
 					pcr_consensus = candidate_sequence
 				else:
 					pcr_consensus = 'inconSequenceSite'
 				# pcr-free
 				pcr_free = itemgetter(*[12,13,14,15,16,17,18,19,20,21,22,23,24,25,26])(strings)
 				T7_WGE = decide_by_rep(pcr_free[0:3])
 				Nova_ARD_1 = decide_by_rep(pcr_free[3:6])
 				Nova_ARD_2 = decide_by_rep(pcr_free[6:9])
 				Nova_BRG = decide_by_rep(pcr_free[9:12])
 				Nova_WUX = decide_by_rep(pcr_free[12:15])
 				sequence_site = [T7_WGE,Nova_ARD_1,Nova_ARD_2,Nova_BRG,Nova_WUX]
 				highest_sequence = sequence_dict.most_common(1)
 				candidate_sequence = highest_sequence[0][0]
 				freq_sequence = highest_sequence[0][1]
 				if freq_sequence > 3:
 					pcr_free_consensus = candidate_sequence
 				else:
 					pcr_free_consensus = 'inconSequenceSite'
 				# pcr and pcr-free
 				tag = ['inconGT','noInfo','inconMen','inconSequenceSite']
 				if (pcr_consensus == pcr_free_consensus) and (pcr_consensus not in tag) and (pcr_consensus != '0/0'):
 					consensus_call = pcr_consensus
 					VOTED = vote_number(strings[9:],consensus_call)
 					strings[7] = strings[7] + ';VOTED=' + VOTED
 					DETECTED = detected_number(strings[9:])
 					strings[7] = strings[7] + ';DETECTED=' + DETECTED
 					FAM = family_vote(strings[9:],consensus_call)
 					strings[7] = strings[7] + ';FAM=' + FAM
 					# Delete multiple alternative genotype to necessary expression
 					alt = strings[4]
 					alt_gt = alt.split(',')
 					if len(alt_gt) > 1:
 						allele1 = consensus_call.split('/')[0]
 						allele2 = consensus_call.split('/')[1]
 						if allele1 == '0':
 							allele2_seq = alt_gt[int(allele2) - 1]
 							consensus_alt_seq = allele2_seq
 							consensus_call = '0/1'
 						else:
 							allele1_seq = alt_gt[int(allele1) - 1]
 							allele2_seq = alt_gt[int(allele2) - 1]
 							if int(allele1) > int(allele2):
 								consensus_alt_seq = allele2_seq + ',' + allele1_seq
 								consensus_call = '1/2'
 							elif int(allele1) < int(allele2):
 								consensus_alt_seq = allele1_seq + ',' + allele2_seq
 								consensus_call = '1/2'
 							else:
 								consensus_alt_seq = allele1_seq 
 								consensus_call = '1/1'
 					else:
 						consensus_alt_seq = alt
 					# GT:DP:ALT:AF:GQ:QD:MQ:FS:QUAL
 					# GT:TWINS:TRIO5:TRIO6:DP:ALT:AF:GQ:QD:MQ:FS:QUAL:rawGT
 					# DP
 					DP = [x.split(':')[4] for x in strings[9:]]
 					DP = remove_dot(DP)
 					DP = [int(x) for x in DP]
 					ALL_DP = sum(DP)
 					# AF
 					ALT = [x.split(':')[5] for x in strings[9:]]
 					ALT = remove_dot(ALT)
 					ALT = [int(x) for x in ALT]
 					ALL_ALT = sum(ALT)
 					ALL_AF = round(ALL_ALT/ALL_DP,2)
 					# GQ
 					GQ = [x.split(':')[7] for x in strings[9:]]
 					GQ = remove_dot(GQ)
 					GQ = [int(x) for x in GQ]
 					GQ_MEAN = round(mean(GQ),2)
 					# QD
 					QD = [x.split(':')[8] for x in strings[9:]]
 					QD = remove_dot(QD)
 					QD = [float(x) for x in QD]
 					QD_MEAN = round(mean(QD),2)
 					# MQ
 					MQ = [x.split(':')[9] for x in strings[9:]]
 					MQ = remove_dot(MQ)
 					MQ = [float(x) for x in MQ]
 					MQ_MEAN = round(mean(MQ),2)
 					# FS
 					FS = [x.split(':')[10] for x in strings[9:]]
 					FS = remove_dot(FS)
 					FS = [float(x) for x in FS]
 					FS_MEAN = round(mean(FS),2)
 					# QUAL
 					QUAL = [x.split(':')[11] for x in strings[9:]]
 					QUAL = remove_dot(QUAL)
 					QUAL = [float(x) for x in QUAL]
 					QUAL_MEAN = round(mean(QUAL),2)
 					# benchmark output 
 					output_format = consensus_call + ':' + str(ALL_DP) + ':' + str(ALL_ALT) + ':' + str(ALL_AF) + ':' + str(GQ_MEAN) + ':' + str(QD_MEAN) + ':' + str(MQ_MEAN) + ':' + str(FS_MEAN) + ':' + str(QUAL_MEAN)
 					outLine = strings[0] + '\t' + strings[1] + '\t' + strings[2] + '\t' + strings[3] + '\t' + consensus_alt_seq + '\t' + '.' + '\t' + '.' + '\t' + strings[7] + '\t' + 'GT:DP:ALT:AF:GQ:QD:MQ:FS:QUAL' + '\t' + output_format + '\n'
 					benchmark_outfile.write(outLine)
 					# all sample output
 					strings[7] = strings[7] + ';ALL_ALT=' + str(ALL_ALT) + ';ALL_DP=' + str(ALL_DP) + ';ALL_AF=' + str(ALL_AF) \
 					+ ';GQ_MEAN=' + str(GQ_MEAN) + ';QD_MEAN=' + str(QD_MEAN) + ';MQ_MEAN=' + str(MQ_MEAN) + ';FS_MEAN=' + str(FS_MEAN) \
 					+ ';QUAL_MEAN=' + str(QUAL_MEAN) + ';PCR=' + consensus_call + ';PCR_FREE=' + consensus_call + ';CONSENSUS=' + consensus_call \
 					+ ';CONSENSUS_SEQ=' + consensus_alt_seq
 					all_sample_outLine = '\t'.join(strings) + '\n'
 					all_sample_outfile.write(all_sample_outLine)
 				elif (pcr_consensus in tag) and (pcr_free_consensus in tag):
 					consensus_call = 'filtered'
 					DETECTED = detected_number(strings[9:])
 					strings[7] = strings[7] + ';DETECTED=' + DETECTED
 					strings[7] = strings[7] + ';CONSENSUS=' + consensus_call
 					all_sample_outLine = '\t'.join(strings) + '\n'
 					all_sample_outfile.write(all_sample_outLine)					
 				elif ((pcr_consensus == '0/0') or (pcr_consensus in tag)) and ((pcr_free_consensus not in tag) and (pcr_free_consensus != '0/0')):
 					consensus_call = 'pcr-free-speicifc'
 					DETECTED = detected_number(strings[9:])
 					strings[7] = strings[7] + ';DETECTED=' + DETECTED
 					strings[7] = strings[7] + ';CONSENSUS=' + consensus_call
 					all_sample_outLine = '\t'.join(strings) + '\n'
 					all_sample_outfile.write(all_sample_outLine)					
 				elif ((pcr_consensus != '0/0') or (pcr_consensus not in tag)) and ((pcr_free_consensus in tag) and (pcr_free_consensus == '0/0')):
 					consensus_call = 'pcr-speicifc'
 					DETECTED = detected_number(strings[9:])
 					strings[7] = strings[7] + ';DETECTED=' + DETECTED
 					strings[7] = strings[7] + ';CONSENSUS=' + consensus_call + ';PCR=' + pcr_consensus + ';PCR_FREE=' + pcr_free_consensus
 					all_sample_outLine = '\t'.join(strings) + '\n'
 					all_sample_outfile.write(all_sample_outLine)					
 				elif (pcr_consensus == '0/0') and (pcr_free_consensus == '0/0'):
 					consensus_call = 'confirm for parents'				
 					DETECTED = detected_number(strings[9:])
 					strings[7] = strings[7] + ';DETECTED=' + DETECTED
 					strings[7] = strings[7] + ';CONSENSUS=' + consensus_call
 					all_sample_outLine = '\t'.join(strings) + '\n'
 					all_sample_outfile.write(all_sample_outLine)					
 				else:
 					consensus_call = 'filtered'
 					DETECTED = detected_number(strings[9:])
 					strings[7] = strings[7] + ';DETECTED=' + DETECTED
 					strings[7] = strings[7] + ';CONSENSUS=' + consensus_call
 					all_sample_outLine = '\t'.join(strings) + '\n'
 					all_sample_outfile.write(all_sample_outLine)					
 if __name__ == '__main__':
 	main()
--- a/codescripts/high_voted_mendelian_bed.py
+++ b/codescripts/high_voted_mendelian_bed.py
 import pandas as pd
 import sys, argparse, os
 mut = pd.read_table(sys.argv[1])
 outFile = open(sys.argv[2],'w')
 for row in mut.itertuples():
 #d5
 	if ',' in row.V4:
 		alt = row.V4.split(',')
 		alt_len = [len(i) for i in alt]
 		alt_max = max(alt_len)
 	else:
 		alt_max = len(row.V4)
 #d6
 	alt = alt_max
 	ref = row.V3
 	pos = int(row.V2)
 	if len(ref) == 1 and alt == 1:
 		StartPos = int(pos) -1
 		EndPos = int(pos)
 		cate = 'SNV'
 	elif len(ref) > alt:
 		StartPos = int(pos) - 1
 		EndPos = int(pos) + (len(ref) - 1)
 		cate = 'INDEL'
 	elif alt > len(ref):
 		StartPos = int(pos) - 1
 		EndPos = int(pos) + (alt - 1)
 		cate = 'INDEL'
 	elif len(ref) == alt:
 		StartPos = int(pos) - 1
 		EndPos = int(pos) + (alt - 1)
 		cate = 'INDEL'
 	outline = row.V1 + '\t' + str(StartPos) + '\t' + str(EndPos) + '\t' + str(row.V2) + '\t' + cate + '\n'
 	outFile.write(outline)
--- a/codescripts/how_many_samples.py
+++ b/codescripts/how_many_samples.py
 import pandas as pd
 import sys, argparse, os
 from operator import itemgetter
 parser = argparse.ArgumentParser(description="This script is to get how many samples")
 parser.add_argument('-sample', '--sample', type=str, help='quartet_sample',  required=True)
 parser.add_argument('-rep', '--rep', type=str, help='quartet_rep',  required=True)
 args = parser.parse_args()
 # Rename input:
 sample = args.sample
 rep = args.rep
 quartet_sample = pd.read_table(sample,header=None)
 quartet_sample = list(quartet_sample[0])
 quartet_rep = pd.read_table(rep,header=None)
 quartet_rep = quartet_rep[0]
 #tags
 sister_tag = 'false'
 quartet_tag = 'false'
 quartet_rep_unique = list(set(quartet_rep))
 single_rep = [i for i in range(len(quartet_rep)) if quartet_rep[i] == quartet_rep_unique[0]]
 single_batch_sample = itemgetter(*single_rep)(quartet_sample)
 num = len(single_batch_sample)
 if num == 1:
 	sister_tag = 'false'
 	quartet_tag = 'false'
 elif num == 2:
 	if set(single_batch_sample) == set(['LCL5','LCL6']):
 		sister_tag = 'true'
 		quartet_tag = 'false'
 elif num == 3:
 	if ('LCL5' in single_batch_sample) and ('LCL6' in single_batch_sample):
 		sister_tag = 'true'
 		quartet_tag = 'false'
 elif num == 4:
 	if set(single_batch_sample) == set(['LCL5','LCL6','LCL7','LCL8']):
 		sister_tag = 'false'
 		quartet_tag = 'true'
 sister_outfile = open('sister_tag','w')
 quartet_outfile = open('quartet_tag','w')
 sister_outfile.write(sister_tag)
 quartet_outfile.write(quartet_tag)
--- a/codescripts/lcl5_all_called_variants.py
+++ b/codescripts/lcl5_all_called_variants.py
 from __future__ import division
 import sys, argparse, os
 import pandas as pd
 from collections import Counter
 # input arguments
 parser = argparse.ArgumentParser(description="this script is to merge mendelian and vcfinfo, and extract high_confidence_calls")
 parser.add_argument('-vcf', '--vcf', type=str, help='merged multiple sample vcf',  required=True)
 args = parser.parse_args()
 vcf = args.vcf
 lcl5_outfile = open('LCL5_all_variants.txt','w')
 filtered_outfile = open('LCL5_filtered_variants.txt','w')
 vcf_dat = pd.read_table(vcf)
 for row in vcf_dat.itertuples():
 	lcl5_list = [row.Quartet_DNA_BGI_SEQ2000_BGI_LCL5_1_20180518,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL5_2_20180530,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL5_3_20180530, \
 					row.Quartet_DNA_BGI_T7_WGE_LCL5_1_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL5_2_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL5_3_20191105, \
 					row.Quartet_DNA_ILM_Nova_ARD_LCL5_1_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL5_2_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL5_3_20181108, \
 					row.Quartet_DNA_ILM_Nova_ARD_LCL5_4_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL5_5_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL5_6_20190111, \
 					row.Quartet_DNA_ILM_Nova_BRG_LCL5_1_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL5_2_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL5_3_20180930, \
 					row.Quartet_DNA_ILM_Nova_WUX_LCL5_1_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL5_2_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL5_3_20190917, \
 					row.Quartet_DNA_ILM_XTen_ARD_LCL5_1_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL5_2_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL5_3_20170403, \
 					row.Quartet_DNA_ILM_XTen_NVG_LCL5_1_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL5_2_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL5_3_20170329, \
 					row.Quartet_DNA_ILM_XTen_WUX_LCL5_1_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL5_2_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL5_3_20170216]
 	lcl5_vcf_gt = [x.split(':')[0] for x in lcl5_list]
 	lcl5_gt=[item.replace('./.', '0/0') for item in lcl5_vcf_gt]
 	gt_dict = Counter(lcl5_gt)
 	highest_gt = gt_dict.most_common(1)
 	candidate_gt = highest_gt[0][0]
 	freq_gt = highest_gt[0][1]
 	output = row._1 + '\t' + str(row.POS) + '\t' + '\t'.join(lcl5_gt) + '\n'
 	if (candidate_gt == '0/0') and (freq_gt == 27):
 		filtered_outfile.write(output)
 	else:
 		lcl5_outfile.write(output)
--- a/codescripts/linux_command.sh
+++ b/codescripts/linux_command.sh
 cat benchmark.men.vote.diffbed.filtered | awk '{print $1"\t"$2"\t"".""\t"$35"\t"$7"\t.\t.\t.\tGT\t"$6}' | grep -v '2_y' > LCL5.body
 cat benchmark.men.vote.diffbed.filtered | awk '{print $1"\t"$2"\t"".""\t"$35"\t"$15"\t.\t.\t.\tGT\t"$14}' | grep -v '2_y' > LCL6.body
 cat benchmark.men.vote.diffbed.filtered | awk '{print $1"\t"$2"\t"".""\t"$35"\t"$23"\t.\t.\t.\tGT\t"$22}' | grep -v '2_y' > LCL7.body
 cat benchmark.men.vote.diffbed.filtered | awk '{print $1"\t"$2"\t"".""\t"$35"\t"$31"\t.\t.\t.\tGT\t"$30}' | grep -v '2_y' > LCL8.body
 for i in *txt; do cat $i | awk '{ if ((length($3) == 1) && (length($4) == 1)) { print } }' | grep -v '#' | cut -f3,4 | sort |uniq -c | sed 's/\s\+/\t/g' | cut -f2 > $i.mut; done
--- a/codescripts/merge_mendelian_vcfinfo.py
+++ b/codescripts/merge_mendelian_vcfinfo.py
 from __future__ import division
 import pandas as pd
 import sys, argparse, os
 import fileinput
 import re
 # input arguments
 parser = argparse.ArgumentParser(description="this script is to get final high confidence calls and information of all replicates")
 parser.add_argument('-vcfInfo', '--vcfInfo', type=str, help='The txt file of variants information, this file is named as prefix__variant_quality_location.txt',  required=True)
 parser.add_argument('-mendelianInfo', '--mendelianInfo', type=str, help='The merged mendelian information of all samples',  required=True)
 parser.add_argument('-sample', '--sample_name', type=str, help='which sample of quartet',  required=True)
 args = parser.parse_args()
 vcfInfo = args.vcfInfo
 mendelianInfo = args.mendelianInfo
 sample_name = args.sample_name
 #GT:TWINS:TRIO5:TRIO6:DP:AF:GQ:QD:MQ:FS:QUAL
 vcf_header = '''##fileformat=VCFv4.2
 ##fileDate=20200331
 ##source=high_confidence_calls_intergration(choppy app)
 ##reference=GRCh38.d1.vd1
 ##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
 ##FORMAT=<ID=TWINS,Number=1,Type=Flag,Description="1 for sister consistent, 0 for sister different">
 ##FORMAT=<ID=TRIO5,Number=1,Type=Flag,Description="1 for LCL7, LCL8 and LCL5 mendelian consistent, 0 for family violation">
 ##FORMAT=<ID=TRIO6,Number=1,Type=Flag,Description="1 for LCL7, LCL8 and LCL6 mendelian consistent, 0 for family violation">
 ##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Depth">
 ##FORMAT=<ID=ALT,Number=1,Type=Integer,Description="Alternative Depth">
 ##FORMAT=<ID=AF,Number=1,Type=Float,Description="Allele frequency">
 ##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype quality">
 ##FORMAT=<ID=QD,Number=1,Type=Float,Description="Variant Confidence/Quality by Depth">
 ##FORMAT=<ID=MQ,Number=1,Type=Float,Description="Mapping quality">
 ##FORMAT=<ID=FS,Number=1,Type=Float,Description="Phred-scaled p-value using Fisher's exact test to detect strand bias">
 ##FORMAT=<ID=QUAL,Number=1,Type=Float,Description="variant quality">
 ##contig=<ID=chr1,length=248956422>
 ##contig=<ID=chr2,length=242193529>
 ##contig=<ID=chr3,length=198295559>
 ##contig=<ID=chr4,length=190214555>
 ##contig=<ID=chr5,length=181538259>
 ##contig=<ID=chr6,length=170805979>
 ##contig=<ID=chr7,length=159345973>
 ##contig=<ID=chr8,length=145138636>
 ##contig=<ID=chr9,length=138394717>
 ##contig=<ID=chr10,length=133797422>
 ##contig=<ID=chr11,length=135086622>
 ##contig=<ID=chr12,length=133275309>
 ##contig=<ID=chr13,length=114364328>
 ##contig=<ID=chr14,length=107043718>
 ##contig=<ID=chr15,length=101991189>
 ##contig=<ID=chr16,length=90338345>
 ##contig=<ID=chr17,length=83257441>
 ##contig=<ID=chr18,length=80373285>
 ##contig=<ID=chr19,length=58617616>
 ##contig=<ID=chr20,length=64444167>
 ##contig=<ID=chr21,length=46709983>
 ##contig=<ID=chr22,length=50818468>
 ##contig=<ID=chrX,length=156040895>
 '''
 # output file
 file_name = sample_name + '_mendelian_vcfInfo.vcf'
 outfile = open(file_name,'w')
 outputcolumn = '#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\t' +  sample_name + '\n'
 outfile.write(vcf_header)
 outfile.write(outputcolumn)
 # input files
 vcf_info = pd.read_table(vcfInfo)
 mendelian_info = pd.read_table(mendelianInfo)
 merged_df = pd.merge(vcf_info, mendelian_info,  how='outer', left_on=['#CHROM','POS'], right_on = ['#CHROM','POS'])
 merged_df = merged_df.fillna('.')
 #
 def parse_INFO(info):
 	strings = info.strip().split(';')
 	keys = []
 	values = []
 	for i in strings:
 		kv = i.split('=')
 		if kv[0] == 'DB':
 			keys.append('DB')
 			values.append('1')
 		else:
 			keys.append(kv[0])
 			values.append(kv[1])
 	infoDict = dict(zip(keys, values))
 	return infoDict
 #
 for row in merged_df.itertuples():
 	if row[18] != '.':
 		# format
 		# GT:TWINS:TRIO5:TRIO6:DP:AF:GQ:QD:MQ:FS:QUAL
 		FORMAT_x = row[10].split(':')
 		ALT = int(FORMAT_x[1].split(',')[1])
 		if int(FORMAT_x[2]) != 0:
 			AF = round(ALT/int(FORMAT_x[2]),2)
 		else:
 			AF = '.'
 		INFO_x = parse_INFO(row.INFO_x)
 		if FORMAT_x[2] == '0':
 			INFO_x['QD'] = '.'
 		else:
 			pass
 		FORMAT = row[18] + ':' + FORMAT_x[2] + ':' + str(ALT) + ':' + str(AF) + ':' + FORMAT_x[3] + ':' + INFO_x['QD'] + ':' + INFO_x['MQ'] + ':' + INFO_x['FS'] + ':' + str(row.QUAL_x)
 		# outline
 		outline = row._1 + '\t' + str(row.POS) + '\t' + row.ID_x + '\t' + row.REF_y + '\t' + row.ALT_y + '\t' + '.' + '\t' + '.' + '\t' + '.' + '\t' + 'GT:TWINS:TRIO5:TRIO6:DP:ALT:AF:GQ:QD:MQ:FS:QUAL' + '\t' + FORMAT + '\n'
 	else:
 		rawGT = row[10].split(':')
 		FORMAT_x = row[10].split(':')
 		ALT = int(FORMAT_x[1].split(',')[1])
 		if int(FORMAT_x[2]) != 0:
 			AF = round(ALT/int(FORMAT_x[2]),2)
 		else:
 			AF = '.'
 		INFO_x = parse_INFO(row.INFO_x)
 		if FORMAT_x[2] == '0':
 			INFO_x['QD'] = '.'
 		else:
 			pass
 		FORMAT = '.:.:.:.' + ':' + FORMAT_x[2] + ':' + str(ALT) + ':' + str(AF) + ':' + FORMAT_x[3] + ':' + INFO_x['QD'] + ':' + INFO_x['MQ'] + ':' + INFO_x['FS'] + ':' + str(row.QUAL_x) + ':' + rawGT[0]
 		# outline
 		outline = row._1 + '\t' + str(row.POS) + '\t' + row.ID_x + '\t' + row.REF_x + '\t' + row.ALT_x + '\t' + '.' + '\t' + '.' + '\t' + '.' + '\t' + 'GT:TWINS:TRIO5:TRIO6:DP:ALT:AF:GQ:QD:MQ:FS:QUAL:rawGT' + '\t' + FORMAT + '\n'
 	outfile.write(outline)
--- a/codescripts/merge_two_family.py
+++ b/codescripts/merge_two_family.py
 from __future__ import division
 import pandas as pd
 import sys, argparse, os
 import fileinput
 import re
 # input arguments
 parser = argparse.ArgumentParser(description="this script is to extract mendelian concordance information")
 parser.add_argument('-LCL5', '--LCL5', type=str, help='LCL5 family info',  required=True)
 parser.add_argument('-LCL6', '--LCL6', type=str, help='LCL6 family info',  required=True)
 parser.add_argument('-family', '--family', type=str, help='family name',  required=True)
 args = parser.parse_args()
 lcl5 = args.LCL5
 lcl6 = args.LCL6
 family = args.family
 # output file
 family_name = family + '.txt'
 family_file = open(family_name,'w')
 # input files
 lcl5_dat = pd.read_table(lcl5)
 lcl6_dat = pd.read_table(lcl6)
 merged_df = pd.merge(lcl5_dat, lcl6_dat,  how='outer', left_on=['#CHROM','POS'], right_on = ['#CHROM','POS'])
 def alt_seq(alt, genotype):
 	if genotype == './.':
 		seq = './.'
 	elif genotype == '0/0':
 		seq = '0/0'
 	else:
 		alt = alt.split(',')
 		genotype = genotype.split('/')
 		if genotype[0] == '0':
 			allele2 = alt[int(genotype[1]) - 1]
 			seq = '0/' + allele2
 		else:
 			allele1 = alt[int(genotype[0]) - 1]
 			allele2 = alt[int(genotype[1]) - 1]
 			seq = allele1 + '/' +  allele2
 	return seq
 for row in merged_df.itertuples():
 	# correction of multiallele
 	if pd.isnull(row.INFO_x) == True or pd.isnull(row.INFO_y) == True:
 		mendelian = '.'
 	else:
 		lcl5_seq = alt_seq(row.ALT_x, row.CHILD_x)
 		lcl6_seq = alt_seq(row.ALT_y, row.CHILD_y)
 		if lcl5_seq == lcl6_seq:
 			mendelian = '1'
 		else:
 			mendelian = '0'
 	if pd.isnull(row.INFO_x) == True:
 		mendelian = mendelian + ':.'
 	else:
 		mendelian = mendelian + ':' + row.INFO_x.split('=')[1]
 	if pd.isnull(row.INFO_y) == True:
 		mendelian = mendelian + ':.'
 	else:
 		mendelian = mendelian + ':' + row.INFO_y.split('=')[1]
 	outline = row._1 + '\t' + str(row.POS) + '\t' + mendelian + '\n'
 	family_file.write(outline)
--- a/codescripts/merge_two_family_with_genotype.py
+++ b/codescripts/merge_two_family_with_genotype.py
 from __future__ import division
 import pandas as pd
 import sys, argparse, os
 import fileinput
 import re
 # input arguments
 parser = argparse.ArgumentParser(description="this script is to extract mendelian concordance information")
 parser.add_argument('-LCL5', '--LCL5', type=str, help='LCL5 family info',  required=True)
 parser.add_argument('-LCL6', '--LCL6', type=str, help='LCL6 family info',  required=True)
 parser.add_argument('-genotype', '--genotype', type=str, help='Genotype information of a set of four family members',  required=True)
 parser.add_argument('-family', '--family', type=str, help='family name',  required=True)
 args = parser.parse_args()
 lcl5 = args.LCL5
 lcl6 = args.LCL6
 genotype = args.genotype
 family = args.family
 # output file
 family_name = family + '.txt'
 family_file = open(family_name,'w')
 summary_name = family + '.summary.txt'
 summary_file = open(summary_name,'w')
 # input files
 lcl5_dat = pd.read_table(lcl5)
 lcl6_dat = pd.read_table(lcl6)
 genotype_dat = pd.read_table(genotype)
 merged_df = pd.merge(lcl5_dat, lcl6_dat,  how='outer', left_on=['#CHROM','POS'], right_on = ['#CHROM','POS'])
 merged_genotype_df = pd.merge(merged_df, genotype_dat,  how='outer', left_on=['#CHROM','POS'], right_on = ['#CHROM','POS'])
 merged_genotype_df_sub = merged_genotype_df.iloc[:,[0,1,23,24,29,30,31,32,7,17]]
 merged_genotype_df_sub.columns = ['CHROM', 'POS', 'REF', 'ALT','LCL5','LCL6','LCL7','LCL8', 'TRIO5', 'TRIO6']
 sister_same = 0
 sister_diff = 0
 family_all = 0
 family_mendelian = 0
 for row in merged_genotype_df_sub.itertuples():
 	# sister
 	if row.LCL5 == row.LCL6:
 		if row.LCL5 == './.':
 			mendelian = 'noInfo'
 			sister_count = "no"
 		elif row.LCL5 == '0/0':
 			mendelian = 'Ref'
 			sister_count = "no"
 		else:
 			mendelian = '1'
 			sister_count = "yes_same"
 	else:
 		mendelian = '0'
 		if (row.LCL5 == './.' or row.LCL5 == '0/0') and (row.LCL6 == './.' or row.LCL6 == '0/0'):
 			sister_count = "no"
 		else:
 			sister_count = "yes_diff"
 	if sister_count == 'yes_same':
 		sister_same += 1
 	elif sister_count == 'yes_diff':
 		sister_diff += 1
 	else:
 		pass
 	# family trio5
 	if row.LCL5 == row. LCL7 == row.LCL8 == './.':
 		mendelian = mendelian + ':noInfo'
 	elif row.LCL5 == row. LCL7 == row.LCL8 == '0/0':
 		mendelian = mendelian + ':Ref'
 	elif pd.isnull(row.TRIO5) == True:
 		mendelian = mendelian + ':unVBT'
 	else:
 		mendelian = mendelian + ':' + row.TRIO5.split('=')[1]
 	# family trio6
 	if row.LCL6 == row.LCL7 == row.LCL8 == './.':
 		mendelian = mendelian + ':noInfo'
 	elif row.LCL6 == row. LCL7 == row.LCL8 == '0/0':
 		mendelian = mendelian + ':Ref'
 	elif pd.isnull(row.TRIO6) == True:
 		mendelian = mendelian + ':unVBT'
 	else:
 		mendelian =  mendelian + ':' + row.TRIO6.split('=')[1]
 	# not count into family
 	if (row.LCL5 == './.' or row.LCL5 == '0/0') and (row.LCL6 == './.' or row.LCL6 == '0/0') and (row.LCL7 == './.' or row.LCL7 == '0/0') and (row.LCL8 == './.' or row.LCL8 == '0/0'):
 		mendelian_count = "no"
 	else:
 		mendelian_count = "yes"
 	outline = row.CHROM + '\t' + str(row.POS) + '\t' + row.REF + '\t' + row.ALT + '\t' + row.LCL5 + '\t' + row.LCL6 + '\t' + row.LCL7 + '\t' + row.LCL8 + '\t' + str(row.TRIO5) + '\t' + str(row.TRIO6) + '\t' + str(mendelian) + '\t' + str(mendelian_count) + '\t' + str(sister_count) + '\n'
 	family_file.write(outline)
 	if mendelian_count == 'yes':
 		family_all += 1
 	else:
 		pass
 	if mendelian == '1:1:1':
 		family_mendelian += 1
 	elif mendelian == 'Ref:1:1':
 		family_mendelian += 1
 	else:
 		pass
 sister = sister_same/(sister_same + sister_diff)
 quartet = family_mendelian/family_all
 outcolumn =  'Family\tReproducibility_D5_D6\tMendelian_Concordance_Quartet\n'
 outResult = family + '\t' + str(sister) + '\t' + str(quartet) + '\n'
 summary_file.write(outcolumn)
 summary_file.write(outResult)
--- a/codescripts/oneClass.py
+++ b/codescripts/oneClass.py
 # import modules
 import numpy as np
 import pandas as pd
 from sklearn import svm
 from sklearn import preprocessing
 import sys, argparse, os
 from vcf2bed import position_to_bed,padding_region
 parser = argparse.ArgumentParser(description="this script is to preform one calss svm on each chromosome")
 parser.add_argument('-train', '--trainDataset', type=str, help='training dataset generated from extracting vcf information part, with mutaitons supported by callsets',  required=True)
 parser.add_argument('-test', '--testDataset', type=str, help='testing dataset generated from extracting vcf information part, with mutaitons not called by all callsets',  required=True)
 parser.add_argument('-name', '--sampleName', type=str, help='sample name for output file name',  required=True)
 args = parser.parse_args()
 # Rename input:
 train_input = args.trainDataset
 test_input = args.testDataset
 sample_name = args.sampleName
 # default columns, which will be included in the included in the calssifier
 chromosome = ['chr1','chr2','chr3','chr4','chr5','chr6','chr7','chr8','chr9','chr10','chr11','chr12','chr13','chr14','chr15' ,'chr16','chr17','chr18','chr19','chr20','chr21','chr22','chrX','chrY']
 feature_heter_cols = ['AltDP','BaseQRankSum','DB','DP','FS','GQ','MQ','MQRankSum','QD','ReadPosRankSum','RefDP','SOR','af']
 feature_homo_cols = ['AltDP','DB','DP','FS','GQ','MQ','QD','RefDP','SOR','af']
 # import datasets sepearate the records with or without BaseQRankSum annotation, etc.
 def load_dat(dat_file_name):
    dat = pd.read_table(dat_file_name)
    dat['DB'] = dat['DB'].fillna(0)
    dat = dat[dat['DP'] != 0]
    dat['af'] = dat['AltDP']/(dat['AltDP'] + dat['RefDP'])
    homo_rows = dat[dat['BaseQRankSum'].isnull()]
    heter_rows = dat[dat['BaseQRankSum'].notnull()]
    return homo_rows,heter_rows
 train_homo,train_heter = load_dat(train_input)
 test_homo,test_heter = load_dat(test_input)
 clf = svm.OneClassSVM(nu=0.05,kernel='rbf', gamma='auto_deprecated',cache_size=500)
 def prepare_dat(train_dat,test_dat,feature_cols,chromo):
 	chr_train = train_dat[train_dat['chromo'] == chromo]
 	chr_test = test_dat[test_dat['chromo'] == chromo]
 	train_dat = chr_train.loc[:,feature_cols]
 	test_dat = chr_test.loc[:,feature_cols]
 	train_dat_scaled = preprocessing.scale(train_dat)
 	test_dat_scaled = preprocessing.scale(test_dat)
 	return chr_test,train_dat_scaled,test_dat_scaled
 def oneclass(X_train,X_test,chr_test):
 	clf.fit(X_train)
 	y_pred_test = clf.predict(X_test)
 	test_true_dat = chr_test[y_pred_test == 1]
 	test_false_dat = chr_test[y_pred_test == -1]
 	return test_true_dat,test_false_dat
 predicted_true = pd.DataFrame(columns=train_homo.columns)
 predicted_false = pd.DataFrame(columns=train_homo.columns)
 for chromo in chromosome:
 	# homo datasets
 	chr_test_homo,X_train_homo,X_test_homo = prepare_dat(train_homo,test_homo,feature_homo_cols,chromo)
 	test_true_homo,test_false_homo = oneclass(X_train_homo,X_test_homo,chr_test_homo)
 	predicted_true = predicted_true.append(test_true_homo)
 	predicted_false = predicted_false.append(test_false_homo)
 	# heter datasets
 	chr_test_heter,X_train_heter,X_test_heter = prepare_dat(train_heter,test_heter,feature_heter_cols,chromo)
 	test_true_heter,test_false_heter = oneclass(X_train_heter,X_test_heter,chr_test_heter)
 	predicted_true = predicted_true.append(test_true_heter)
 	predicted_false = predicted_false.append(test_false_heter)
 predicted_true_filename = sample_name + '_predicted_true.txt'
 predicted_false_filename = sample_name + '_predicted_false.txt'
 predicted_true.to_csv(predicted_true_filename,sep='\t',index=False)
 predicted_false.to_csv(predicted_false_filename,sep='\t',index=False)
 # output the bed file and padding bed region 50bp
 predicted_true_bed_filename = sample_name + '_predicted_true.bed'
 predicted_false_bed_filename = sample_name + '_predicted_false.bed'
 padding_filename = sample_name + '_padding.bed'
 predicted_true_bed = open(predicted_true_bed_filename,'w')
 predicted_false_bed = open(predicted_false_bed_filename,'w')
 padding = open(padding_filename,'w')
 #
 for index,row in predicted_false.iterrows():
 	chromo,pos1,pos2 = position_to_bed(row['chromo'],row['pos'],row['ref'],row['alt'])
 	outline_pos = chromo + '\t' + str(pos1) + '\t' + str(pos2) + '\n'
 	predicted_false_bed.write(outline_pos)
 	chromo,pad_pos1,pad_pos2,pad_pos3,pad_pos4 = padding_region(chromo,pos1,pos2,50)
 	outline_pad_1 = chromo + '\t' + str(pad_pos1) + '\t' + str(pad_pos2) + '\n'
 	outline_pad_2 = chromo + '\t' + str(pad_pos3) + '\t' + str(pad_pos4) + '\n'
 	padding.write(outline_pad_1)
 	padding.write(outline_pad_2)
 for index,row in predicted_true.iterrows():
 	chromo,pos1,pos2 = position_to_bed(row['chromo'],row['pos'],row['ref'],row['alt'])
 	outline_pos = chromo + '\t' + str(pos1) + '\t' + str(pos2) + '\n'
 	predicted_true_bed.write(outline_pos)
--- a/codescripts/reformVCF.py
+++ b/codescripts/reformVCF.py
 # import modules
 import sys, argparse, os
 import fileinput
 import re
 parser = argparse.ArgumentParser(description="This script is to split samples in VCF files and rewrite to the right style")
 parser.add_argument('-vcf', '--familyVCF', type=str, help='VCF with sister and mendelian infomation',  required=True)
 parser.add_argument('-name', '--familyName', type=str, help='Family name of the VCF file',  required=True)
 args = parser.parse_args()
 # Rename input:
 inputFile = args.familyVCF
 family_name = args.familyName
 # output filename
 LCL5_name = family_name + '.LCL5.vcf'
 LCL5file = open(LCL5_name,'w')
 LCL6_name = family_name + '.LCL6.vcf'
 LCL6file = open(LCL6_name,'w')
 LCL7_name = family_name + '.LCL7.vcf'
 LCL7file = open(LCL7_name,'w')
 LCL8_name = family_name + '.LCL8.vcf'
 LCL8file = open(LCL8_name,'w')
 family_filename = family_name + '.vcf'
 familyfile = open(family_filename,'w')
 # default columns, which will be included in the included in the calssifier
 vcfheader = '''##fileformat=VCFv4.2
 ##FILTER=<ID=PASS,Description="the same genotype between twin sister and mendelian consistent in 578 and 678">
 ##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
 ##FORMAT=<ID=TWINS,Number=0,Type=Flag,Description="0 for sister consistent, 1 for sister inconsistent">
 ##FORMAT=<ID=TRIO5,Number=0,Type=Flag,Description="0 for trio consistent, 1 for trio inconsistent">
 ##FORMAT=<ID=TRIO6,Number=0,Type=Flag,Description="0 for trio consistent, 1 for trio inconsistent">
 ##contig=<ID=chr1,length=248956422>
 ##contig=<ID=chr2,length=242193529>
 ##contig=<ID=chr3,length=198295559>
 ##contig=<ID=chr4,length=190214555>
 ##contig=<ID=chr5,length=181538259>
 ##contig=<ID=chr6,length=170805979>
 ##contig=<ID=chr7,length=159345973>
 ##contig=<ID=chr8,length=145138636>
 ##contig=<ID=chr9,length=138394717>
 ##contig=<ID=chr10,length=133797422>
 ##contig=<ID=chr11,length=135086622>
 ##contig=<ID=chr12,length=133275309>
 ##contig=<ID=chr13,length=114364328>
 ##contig=<ID=chr14,length=107043718>
 ##contig=<ID=chr15,length=101991189>
 ##contig=<ID=chr16,length=90338345>
 ##contig=<ID=chr17,length=83257441>
 ##contig=<ID=chr18,length=80373285>
 ##contig=<ID=chr19,length=58617616>
 ##contig=<ID=chr20,length=64444167>
 ##contig=<ID=chr21,length=46709983>
 ##contig=<ID=chr22,length=50818468>
 ##contig=<ID=chrX,length=156040895>
 '''
 # write VCF
 LCL5colname = '#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\t'+family_name+'_LCL5'+'\n'
 LCL5file.write(vcfheader)
 LCL5file.write(LCL5colname)
 LCL6colname = '#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\t'+family_name+'_LCL6'+'\n'
 LCL6file.write(vcfheader)
 LCL6file.write(LCL6colname)
 LCL7colname = '#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\t'+family_name+'_LCL7'+'\n'
 LCL7file.write(vcfheader)
 LCL7file.write(LCL7colname)
 LCL8colname = '#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\t'+family_name+'_LCL8'+'\n'
 LCL8file.write(vcfheader)
 LCL8file.write(LCL8colname)
 familycolname = '#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\t'+'LCL5\t'+'LCL6\t'+'LCL7\t'+'LCL8'+'\n'
 familyfile.write(vcfheader)
 familyfile.write(familycolname)
 # reform VCF
 def process(oneLine):
 	line = oneLine.rstrip()
 	strings = line.strip().split('\t')
 	# replace .
 	# LCL6 uniq
 	if strings[11] == '.':
 		strings[11] = '0/0'
 		strings[9] = strings[12]
 		strings[10] = strings[13]
 	else:
 		pass
 	# LCL5 uniq
 	if strings[14] == '.':
 		strings[14] = '0/0'
 		strings[12] = strings[9]
 		strings[13] = strings[10]
 	else:
 		pass
 	# sister
 	if strings[11] == strings[14]:
 		add_format = ":1"
 	else:
 		add_format = ":0"
 	# trioLCL5
 	if strings[15] == 'MD=1':
 		add_format = add_format + ":1"
 	else:
 		add_format = add_format + ":0"
 	# trioLCL6
 	if strings[7] == 'MD=1':
 		add_format = add_format + ":1"
 	else:
 		add_format = add_format + ":0"
 	# filter
 	if (strings[11] == strings[14]) and (strings[15] == 'MD=1') and (strings[7] == 'MD=1'):
 		strings[6] = 'PASS'
 	else:
 		strings[6] = '.'
 	# output LCL5
 	LCL5outLine = strings[0]+'\t'+strings[1]+'\t'+strings[2]+'\t'+strings[3]+'\t'+strings[4]+'\t'+'.'+'\t'+strings[6]+'\t'+ '.' +'\t'+ 'GT:TWINS:TRIO5:TRIO6' + '\t' + strings[14] + add_format + '\n'
 	LCL5file.write(LCL5outLine)
 	# output LCL6
 	LCL6outLine = strings[0]+'\t'+strings[1]+'\t'+strings[2]+'\t'+strings[3]+'\t'+strings[4]+'\t'+'.'+'\t'+strings[6]+'\t'+ '.' +'\t'+ 'GT:TWINS:TRIO5:TRIO6' + '\t' + strings[11] + add_format + '\n'
 	LCL6file.write(LCL6outLine)
 	# output LCL7
 	LCL7outLine = strings[0]+'\t'+strings[1]+'\t'+strings[2]+'\t'+strings[3]+'\t'+strings[4]+'\t'+'.'+'\t'+strings[6]+'\t'+ '.' +'\t'+ 'GT:TWINS:TRIO5:TRIO6' + '\t' + strings[10] + add_format + '\n'
 	LCL7file.write(LCL7outLine)
 	# output LCL8
 	LCL8outLine = strings[0]+'\t'+strings[1]+'\t'+strings[2]+'\t'+strings[3]+'\t'+strings[4]+'\t'+'.'+'\t'+strings[6]+'\t'+ '.' +'\t'+ 'GT:TWINS:TRIO5:TRIO6' + '\t' + strings[9] + add_format + '\n'
 	LCL8file.write(LCL8outLine)
 	# output family
 	familyoutLine = strings[0]+'\t'+strings[1]+'\t'+strings[2]+'\t'+strings[3]+'\t'+strings[4]+'\t'+ '.'+'\t'+strings[6]+'\t'+ '.' +'\t'+ 'GT:TWINS:TRIO5:TRIO6' + '\t' + strings[14] + add_format +'\t' + strings[11] + add_format + '\t' + strings[10] + add_format +'\t' + strings[9] + add_format + '\n'
 	familyfile.write(familyoutLine)
 for line in fileinput.input(inputFile):
 	m = re.match('^\#',line)
 	if m is not None:
 		pass
 	else:
 		process(line)
--- a/codescripts/replicates_consensus.py
+++ b/codescripts/replicates_consensus.py
 from __future__ import division
 from glob import glob
 import sys, argparse, os
 import fileinput
 import re
 import pandas as pd
 from operator import itemgetter
 from collections import Counter
 from itertools import islice
 from numpy import *
 import statistics
 # input arguments
 parser = argparse.ArgumentParser(description="this script is to merge mendelian and vcfinfo, and extract high_confidence_calls")
 parser.add_argument('-prefix', '--prefix', type=str, help='prefix of output file',  required=True)
 parser.add_argument('-vcf', '--vcf', type=str, help='merged multiple sample vcf',  required=True)
 args = parser.parse_args()
 prefix = args.prefix
 vcf = args.vcf
 # input files
 vcf_dat = pd.read_table(vcf)
 # all info
 all_file_name = prefix + "_all_summary.txt"
 all_sample_outfile = open(all_file_name,'w')
 all_info_col = 'CHROM\tPOS\tREF\tALT\tLCL5_consensus_calls\tLCL5_detect_number\tLCL5_same_diff\tLCL6_consensus_calls\tLCL6_detect_number\tLCl6_same_diff\tLCL7_consensus_calls\tLCL7_detect_number\tLCL7_same_diff\tLCL8_consensus_calls\tLCL8_detect_number\tLCL8_same_diff\n'
 all_sample_outfile.write(all_info_col)
 # filtered info
 vcf_header = '''##fileformat=VCFv4.2
 ##fileDate=20200501
 ##source=high_confidence_calls_intergration(choppy app)
 ##reference=GRCh38.d1.vd1
 ##INFO=<ID=VOTED,Number=1,Type=Integer,Description="Number mendelian consisitent votes">
 ##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
 ##contig=<ID=chr1,length=248956422>
 ##contig=<ID=chr2,length=242193529>
 ##contig=<ID=chr3,length=198295559>
 ##contig=<ID=chr4,length=190214555>
 ##contig=<ID=chr5,length=181538259>
 ##contig=<ID=chr6,length=170805979>
 ##contig=<ID=chr7,length=159345973>
 ##contig=<ID=chr8,length=145138636>
 ##contig=<ID=chr9,length=138394717>
 ##contig=<ID=chr10,length=133797422>
 ##contig=<ID=chr11,length=135086622>
 ##contig=<ID=chr12,length=133275309>
 ##contig=<ID=chr13,length=114364328>
 ##contig=<ID=chr14,length=107043718>
 ##contig=<ID=chr15,length=101991189>
 ##contig=<ID=chr16,length=90338345>
 ##contig=<ID=chr17,length=83257441>
 ##contig=<ID=chr18,length=80373285>
 ##contig=<ID=chr19,length=58617616>
 ##contig=<ID=chr20,length=64444167>
 ##contig=<ID=chr21,length=46709983>
 ##contig=<ID=chr22,length=50818468>
 ##contig=<ID=chrX,length=156040895>
 '''
 consensus_file_name = prefix + "_consensus.vcf"
 consensus_outfile = open(consensus_file_name,'w')
 consensus_col = '#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tLCL5_consensus_call\tLCL6_consensus_call\tLCL7_consensus_call\tLCL8_consensus_call\n'
 consensus_outfile.write(vcf_header)
 consensus_outfile.write(consensus_col)
 # function
 def decide_by_rep(vcf_list):
 	consensus_rep = ''
 	gt = [x.split(':')[0] for x in vcf_list]
 	gt_num_dict = Counter(gt)
 	highest_gt = gt_num_dict.most_common(1)
 	candidate_gt = highest_gt[0][0]
 	freq_gt = highest_gt[0][1]
 	if freq_gt >= 2:
 		consensus_rep = candidate_gt
 	else:
 		consensus_rep = 'inconGT'
 	return consensus_rep
 def consensus_call(vcf_info_list):
 	consensus_call = '.'
 	detect_number = '.'
 	same_diff = '.'
 	# pcr
 	SEQ2000 = decide_by_rep(vcf_info_list[0:3])
 	XTen_ARD = decide_by_rep(vcf_info_list[18:21])
 	XTen_NVG = decide_by_rep(vcf_info_list[21:24])
 	XTen_WUX = decide_by_rep(vcf_info_list[24:27])
 	Nova_WUX = decide_by_rep(vcf_info_list[15:18])
 	pcr_sequence_site = [SEQ2000,XTen_ARD,XTen_NVG,XTen_WUX,Nova_WUX]
 	pcr_sequence_dict = Counter(pcr_sequence_site)
 	pcr_highest_sequence = pcr_sequence_dict.most_common(1)
 	pcr_candidate_sequence = pcr_highest_sequence[0][0]
 	pcr_freq_sequence = pcr_highest_sequence[0][1]
 	if pcr_freq_sequence > 3:
 		pcr_consensus = pcr_candidate_sequence
 	else:
 		pcr_consensus = 'inconSequenceSite'
 	# pcr-free
 	T7_WGE = decide_by_rep(vcf_info_list[3:6])
 	Nova_ARD_1 = decide_by_rep(vcf_info_list[6:9])
 	Nova_ARD_2 = decide_by_rep(vcf_info_list[9:12])
 	Nova_BRG = decide_by_rep(vcf_info_list[12:15])
 	sequence_site = [T7_WGE,Nova_ARD_1,Nova_ARD_2,Nova_BRG]
 	sequence_dict = Counter(sequence_site)
 	highest_sequence = sequence_dict.most_common(1)
 	candidate_sequence = highest_sequence[0][0]
 	freq_sequence = highest_sequence[0][1]
 	if freq_sequence > 2:
 		pcr_free_consensus = candidate_sequence
 	else:
 		pcr_free_consensus = 'inconSequenceSite'
 	gt = [x.split(':')[0] for x in vcf_info_list]
 	gt = [x.replace('./.','.') for x in gt]
 	detected_num = 27 - gt.count('.')
 	gt_remain = [e for e in gt if e not in {'.'}]
 	gt_set = set(gt_remain)
 	if len(gt_set) == 1:
 		same_diff = 'same'
 	else:
 		same_diff = 'diff'
 	tag = ['inconGT','inconSequenceSite']
 	if (pcr_consensus == pcr_free_consensus) and (pcr_consensus not in tag):
 		consensus_call = pcr_consensus
 	elif (pcr_consensus in tag) and (pcr_free_consensus in tag):
 		consensus_call = 'notAgree'				
 	else:
 		consensus_call = 'notConsensus'
 	return consensus_call, detected_num, same_diff
 	elif (pcr_consensus in tag) and (pcr_free_consensus in tag):
 		consensus_call = 'filtered'				
 	elif ((pcr_consensus == './.') or (pcr_consensus in tag)) and ((pcr_free_consensus not in tag) and (pcr_free_consensus != './.')):
 		consensus_call = 'pcr-free-speicifc'				
 	elif ((pcr_consensus != './.') or (pcr_consensus not in tag)) and ((pcr_free_consensus in tag) and (pcr_free_consensus == './.')):
 		consensus_call = 'pcr-speicifc'			
 	elif (pcr_consensus == '0/0') and (pcr_free_consensus == '0/0'):
 		consensus_call = '0/0'								
 	else:
 		consensus_call = 'filtered'
 for row in vcf_dat.itertuples():
 # length
 #alt
 	if ',' in row.ALT:
 		alt = row.ALT.split(',')
 		alt_len = [len(i) for i in alt]
 		alt_max = max(alt_len)
 	else:
 		alt_max = len(row.ALT)
 #ref
 	ref_len = len(row.REF)
 	if (alt_max > 50) or (ref_len > 50):
 		pass
 	else:
 # consensus
 		lcl5_list = [row.Quartet_DNA_BGI_SEQ2000_BGI_LCL5_1_20180518,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL5_2_20180530,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL5_3_20180530, \
 						row.Quartet_DNA_BGI_T7_WGE_LCL5_1_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL5_2_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL5_3_20191105, \
 						row.Quartet_DNA_ILM_Nova_ARD_LCL5_1_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL5_2_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL5_3_20181108, \
 						row.Quartet_DNA_ILM_Nova_ARD_LCL5_4_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL5_5_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL5_6_20190111, \
 						row.Quartet_DNA_ILM_Nova_BRG_LCL5_1_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL5_2_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL5_3_20180930, \
 						row.Quartet_DNA_ILM_Nova_WUX_LCL5_1_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL5_2_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL5_3_20190917, \
 						row.Quartet_DNA_ILM_XTen_ARD_LCL5_1_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL5_2_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL5_3_20170403, \
 						row.Quartet_DNA_ILM_XTen_NVG_LCL5_1_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL5_2_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL5_3_20170329, \
 						row.Quartet_DNA_ILM_XTen_WUX_LCL5_1_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL5_2_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL5_3_20170216]
 		lcl6_list = [row.Quartet_DNA_BGI_SEQ2000_BGI_LCL6_1_20180518,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL6_2_20180530,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL6_3_20180530, \
 						row.Quartet_DNA_BGI_T7_WGE_LCL6_1_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL6_2_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL6_3_20191105, \
 						row.Quartet_DNA_ILM_Nova_ARD_LCL6_1_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL6_2_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL6_3_20181108, \
 						row.Quartet_DNA_ILM_Nova_ARD_LCL6_4_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL6_5_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL6_6_20190111, \
 						row.Quartet_DNA_ILM_Nova_BRG_LCL6_1_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL6_2_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL6_3_20180930, \
 						row.Quartet_DNA_ILM_Nova_WUX_LCL6_1_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL6_2_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL6_3_20190917, \
 						row.Quartet_DNA_ILM_XTen_ARD_LCL6_1_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL6_2_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL6_3_20170403, \
 						row.Quartet_DNA_ILM_XTen_NVG_LCL6_1_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL6_2_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL6_3_20170329, \
 						row.Quartet_DNA_ILM_XTen_WUX_LCL6_1_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL6_2_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL6_3_20170216]
 		lcl7_list = [row.Quartet_DNA_BGI_SEQ2000_BGI_LCL7_1_20180518,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL7_2_20180530,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL7_3_20180530, \
 						row.Quartet_DNA_BGI_T7_WGE_LCL7_1_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL7_2_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL7_3_20191105, \
 						row.Quartet_DNA_ILM_Nova_ARD_LCL7_1_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL7_2_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL7_3_20181108, \
 						row.Quartet_DNA_ILM_Nova_ARD_LCL7_4_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL7_5_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL7_6_20190111, \
 						row.Quartet_DNA_ILM_Nova_BRG_LCL7_1_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL7_2_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL7_3_20180930, \
 						row.Quartet_DNA_ILM_Nova_WUX_LCL7_1_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL7_2_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL7_3_20190917, \
 						row.Quartet_DNA_ILM_XTen_ARD_LCL7_1_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL7_2_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL7_3_20170403, \
 						row.Quartet_DNA_ILM_XTen_NVG_LCL7_1_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL7_2_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL7_3_20170329, \
 						row.Quartet_DNA_ILM_XTen_WUX_LCL7_1_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL7_2_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL7_3_20170216]
 		lcl8_list = [row.Quartet_DNA_BGI_SEQ2000_BGI_LCL8_1_20180518,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL8_2_20180530,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL8_3_20180530, \
 						row.Quartet_DNA_BGI_T7_WGE_LCL8_1_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL8_2_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL8_3_20191105, \
 						row.Quartet_DNA_ILM_Nova_ARD_LCL8_1_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL8_2_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL8_3_20181108, \
 						row.Quartet_DNA_ILM_Nova_ARD_LCL8_4_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL8_5_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL8_6_20190111, \
 						row.Quartet_DNA_ILM_Nova_BRG_LCL8_1_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL8_2_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL8_3_20180930, \
 						row.Quartet_DNA_ILM_Nova_WUX_LCL8_1_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL8_2_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL8_3_20190917, \
 						row.Quartet_DNA_ILM_XTen_ARD_LCL8_1_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL8_2_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL8_3_20170403, \
 						row.Quartet_DNA_ILM_XTen_NVG_LCL8_1_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL8_2_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL8_3_20170329, \
 						row.Quartet_DNA_ILM_XTen_WUX_LCL8_1_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL8_2_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL8_3_20170216]
 		# LCL5
 		LCL5_consensus_call, LCL5_detected_num, LCL5_same_diff = consensus_call(lcl5_list)
 		# LCL6
 		LCL6_consensus_call, LCL6_detected_num, LCL6_same_diff = consensus_call(lcl6_list)
 		# LCL7
 		LCL7_consensus_call, LCL7_detected_num, LCL7_same_diff = consensus_call(lcl7_list)
 		# LCL8
 		LCL8_consensus_call, LCL8_detected_num, LCL8_same_diff = consensus_call(lcl8_list)
 		# all data
 		all_output = row._1 + '\t' + str(row.POS) + '\t' + row.REF + '\t' + row.ALT + '\t' +  LCL5_consensus_call + '\t' + str(LCL5_detected_num) + '\t' + LCL5_same_diff + '\t' +\
 						 LCL6_consensus_call + '\t' + str(LCL6_detected_num) + '\t' + LCL6_same_diff + '\t' +\
 						 LCL7_consensus_call + '\t' + str(LCL7_detected_num) + '\t' + LCL7_same_diff + '\t' +\
 						 LCL8_consensus_call + '\t' + str(LCL8_detected_num) + '\t' + LCL8_same_diff + '\n'
 		all_sample_outfile.write(all_output)
 		#consensus vcf
 		one_position = [LCL5_consensus_call,LCL6_consensus_call,LCL7_consensus_call,LCL8_consensus_call]
 		if ('notConsensus' in one_position) or (((len(set(one_position)) == 1) and ('./.' in set(one_position))) or ((len(set(one_position)) == 1) and ('0/0' in set(one_position))) or ((len(set(one_position)) == 2) and ('0/0' in set(one_position) and ('./.' in set(one_position))))):
 			pass
 		else:
 			consensus_output = row._1 + '\t' + str(row.POS) + '\t' + '.' + '\t' + row.REF + '\t' + row.ALT + '\t' + '.' + '\t' + '.' + '\t' +'.' + '\t' + 'GT' + '\t' + LCL5_consensus_call + '\t' + LCL6_consensus_call + '\t' + LCL7_consensus_call + '\t' + LCL8_consensus_call +'\n'
 			consensus_outfile.write(consensus_output)
--- a/codescripts/vcf2bed.py
+++ b/codescripts/vcf2bed.py
 import re
 def position_to_bed(chromo,pos,ref,alt):
    # snv
    # Start cooridinate BED = start coordinate VCF - 1
    # End cooridinate BED = start coordinate VCF 
    if len(ref) == 1 and len(alt) == 1:
        StartPos = int(pos) -1
        EndPos = int(pos)
    # deletions
    # Start cooridinate BED = start coordinate VCF - 1
    # End cooridinate BED = start coordinate VCF + (reference length - alternate length)
    elif len(ref) > 1 and len(alt) == 1:
        StartPos = int(pos) - 1
        EndPos = int(pos) + (len(ref) - 1)
    #insertions
    # For insertions:
    # Start cooridinate BED = start coordinate VCF - 1
    # End cooridinate BED = start coordinate VCF + (alternate length - reference length)
    else:
        StartPos = int(pos) - 1
        EndPos = int(pos) + (len(alt) - 1)
    return chromo,StartPos,EndPos
 def padding_region(chromo,pos1,pos2,padding):
    StartPos1 = pos1 - padding
    EndPos1 = pos1
    StartPos2 = pos2
    EndPos2 = pos2 + padding
    return chromo,StartPos1,EndPos1,StartPos2,EndPos2
--- a/codescripts/voted_by_vcfinfo_mendelianinfo.py
+++ b/codescripts/voted_by_vcfinfo_mendelianinfo.py
 from __future__ import division
 from glob import glob
 import sys, argparse, os
 import fileinput
 import re
 import pandas as pd
 from operator import itemgetter
 from collections import Counter
 from itertools import islice
 from numpy import *
 import statistics
 # input arguments
 parser = argparse.ArgumentParser(description="this script is to merge mendelian and vcfinfo, and extract high_confidence_calls")
 parser.add_argument('-folder', '--folder', type=str, help='directory that holds all the mendelian info',  required=True)
 parser.add_argument('-vcf', '--vcf', type=str, help='merged multiple sample vcf',  required=True)
 args = parser.parse_args()
 folder = args.folder
 vcf = args.vcf
 # input files
 folder = folder + '/*.txt'
 filenames = glob(folder)
 dataframes = []
 for filename in filenames:
 	dataframes.append(pd.read_table(filename,header=None))
 dfs = [df.set_index([0, 1]) for df in dataframes]
 merged_mendelian = pd.concat(dfs, axis=1).reset_index()
 family_name = [i.split('/')[-1].replace('.txt','') for i in filenames]
 columns = ['CHROM','POS'] + family_name
 merged_mendelian.columns = columns
 vcf_dat = pd.read_table(vcf)
 merged_df = pd.merge(merged_mendelian, vcf_dat,  how='outer', left_on=['CHROM','POS'], right_on = ['#CHROM','POS'])
 merged_df = merged_df.fillna('nan')
 vcf_header = '''##fileformat=VCFv4.2
 ##fileDate=20200501
 ##source=high_confidence_calls_intergration(choppy app)
 ##reference=GRCh38.d1.vd1
 ##INFO=<ID=VOTED,Number=1,Type=Integer,Description="Number mendelian consisitent votes">
 ##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
 ##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Sum depth of all samples">
 ##FORMAT=<ID=ALT,Number=1,Type=Integer,Description="Sum alternative depth of all samples">
 ##contig=<ID=chr1,length=248956422>
 ##contig=<ID=chr2,length=242193529>
 ##contig=<ID=chr3,length=198295559>
 ##contig=<ID=chr4,length=190214555>
 ##contig=<ID=chr5,length=181538259>
 ##contig=<ID=chr6,length=170805979>
 ##contig=<ID=chr7,length=159345973>
 ##contig=<ID=chr8,length=145138636>
 ##contig=<ID=chr9,length=138394717>
 ##contig=<ID=chr10,length=133797422>
 ##contig=<ID=chr11,length=135086622>
 ##contig=<ID=chr12,length=133275309>
 ##contig=<ID=chr13,length=114364328>
 ##contig=<ID=chr14,length=107043718>
 ##contig=<ID=chr15,length=101991189>
 ##contig=<ID=chr16,length=90338345>
 ##contig=<ID=chr17,length=83257441>
 ##contig=<ID=chr18,length=80373285>
 ##contig=<ID=chr19,length=58617616>
 ##contig=<ID=chr20,length=64444167>
 ##contig=<ID=chr21,length=46709983>
 ##contig=<ID=chr22,length=50818468>
 ##contig=<ID=chrX,length=156040895>
 '''
 # output files
 benchmark_LCL5 = open('LCL5_voted.vcf','w')
 benchmark_LCL6 = open('LCL6_voted.vcf','w')
 benchmark_LCL7 = open('LCL7_voted.vcf','w')
 benchmark_LCL8 = open('LCL8_voted.vcf','w')
 all_sample_outfile = open('all_sample_information.txt','w')
 # write VCF
 LCL5_col = '#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tLCL5_benchmark_calls\n'
 LCL6_col = '#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tLCL6_benchmark_calls\n'
 LCL7_col = '#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tLCL7_benchmark_calls\n'
 LCL8_col = '#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tLCL8_benchmark_calls\n'
 benchmark_LCL5.write(vcf_header)
 benchmark_LCL5.write(LCL5_col)
 benchmark_LCL6.write(vcf_header)
 benchmark_LCL6.write(LCL6_col)
 benchmark_LCL7.write(vcf_header)
 benchmark_LCL7.write(LCL7_col)
 benchmark_LCL8.write(vcf_header)
 benchmark_LCL8.write(LCL8_col)
 # all info
 all_info_col = 'CHROM\tPOS\tLCL5_pcr_consensus\tLCL5_pcr_free_consensus\tLCL5_mendelian_num\tLCL5_consensus_call\tLCL5_consensus_alt_seq\tLCL5_alt\tLCL5_dp\tLCL5_detected_num\tLCL6_pcr_consensus\tLCL6_pcr_free_consensus\tLCL6_mendelian_num\tLCL6_consensus_call\tLCL6_consensus_alt_seq\tLCL6_alt\tLCL6_dp\tLCL6_detected_num\tLCL7_pcr_consensus\tLCL7_pcr_free_consensus\tLCL7_mendelian_num\t LCL7_consensus_call\tLCL7_consensus_alt_seq\tLCL7_alt\tLCL7_dp\tLCL7_detected_num\tLCL8_pcr_consensus\tLCL8_pcr_free_consensus\tLCL8_mendelian_num\tLCL8_consensus_call\tLCL8_consensus_alt_seq\tLCL8_alt\tLCL8_dp\tLCL8_detected_num\n'
 all_sample_outfile.write(all_info_col)
 # function
 def decide_by_rep(vcf_list,mendelian_list):
 	consensus_rep = ''
 	gt = [x.split(':')[0] for x in vcf_list]
 	# mendelian consistent?
 	mendelian_dict = Counter(mendelian_list)
 	highest_mendelian = mendelian_dict.most_common(1)
 	candidate_mendelian = highest_mendelian[0][0]
 	freq_mendelian = highest_mendelian[0][1]
 	if (candidate_mendelian == '1:1:1') and (freq_mendelian >= 2):
 		con_loc = [i for i in range(len(mendelian_list)) if mendelian_list[i] == '1:1:1']
 		gt_con = itemgetter(*con_loc)(gt)
 		gt_num_dict = Counter(gt_con)
 		highest_gt = gt_num_dict.most_common(1)
 		candidate_gt = highest_gt[0][0]
 		freq_gt = highest_gt[0][1]
 		if (candidate_gt != './.') and (freq_gt >= 2):
 			consensus_rep = candidate_gt
 		elif (candidate_gt == './.') and (freq_gt >= 2):
 			consensus_rep = 'noGTInfo'
 		else:
 			consensus_rep = 'inconGT'
 	elif (candidate_mendelian == 'nan') and (freq_mendelian >= 2):
 		consensus_rep = 'noMenInfo'
 	else:
 		consensus_rep = 'inconMen'
 	return consensus_rep
 def consensus_call(vcf_info_list,mendelian_list,alt_seq):
 	pcr_consensus = '.'
 	pcr_free_consensus = '.'
 	mendelian_num = '.'
 	consensus_call = '.'
 	consensus_alt_seq = '.'
 	# pcr
 	SEQ2000 = decide_by_rep(vcf_info_list[0:3],mendelian_list[0:3])
 	XTen_ARD = decide_by_rep(vcf_info_list[18:21],mendelian_list[18:21])
 	XTen_NVG = decide_by_rep(vcf_info_list[21:24],mendelian_list[21:24])
 	XTen_WUX = decide_by_rep(vcf_info_list[24:27],mendelian_list[24:27])
 	pcr_sequence_site = [SEQ2000,XTen_ARD,XTen_NVG,XTen_WUX]
 	pcr_sequence_dict = Counter(pcr_sequence_site)
 	pcr_highest_sequence = pcr_sequence_dict.most_common(1)
 	pcr_candidate_sequence = pcr_highest_sequence[0][0]
 	pcr_freq_sequence = pcr_highest_sequence[0][1]
 	if pcr_freq_sequence > 2:
 		pcr_consensus = pcr_candidate_sequence
 	else:
 		pcr_consensus = 'inconSequenceSite'
 	# pcr-free
 	T7_WGE = decide_by_rep(vcf_info_list[3:6],mendelian_list[3:6])
 	Nova_ARD_1 = decide_by_rep(vcf_info_list[6:9],mendelian_list[6:9])
 	Nova_ARD_2 = decide_by_rep(vcf_info_list[9:12],mendelian_list[9:12])
 	Nova_BRG = decide_by_rep(vcf_info_list[12:15],mendelian_list[12:15])
 	Nova_WUX = decide_by_rep(vcf_info_list[15:18],mendelian_list[15:18])
 	sequence_site = [T7_WGE,Nova_ARD_1,Nova_ARD_2,Nova_BRG,Nova_WUX]
 	sequence_dict = Counter(sequence_site)
 	highest_sequence = sequence_dict.most_common(1)
 	candidate_sequence = highest_sequence[0][0]
 	freq_sequence = highest_sequence[0][1]
 	if freq_sequence > 3:
 		pcr_free_consensus = candidate_sequence
 	else:
 		pcr_free_consensus = 'inconSequenceSite'
 	# net alt, dp
 	# alt
 	AD = [x.split(':')[1] for x in vcf_info_list]
 	ALT = [x.split(',')[1] for x in AD]
 	ALT = [int(x) for x in ALT]
 	ALL_ALT = sum(ALT)
 	# dp
 	DP = [x.split(':')[2] for x in vcf_info_list]
 	DP = [int(x) for x in DP]
 	ALL_DP = sum(DP)
 	# detected number
 	gt = [x.split(':')[0] for x in vcf_info_list]
 	gt = [x.replace('0/0','.') for x in gt]
 	gt = [x.replace('./.','.') for x in gt]
 	detected_num = 27 - gt.count('.')
 	# decide consensus calls
 	tag = ['inconGT','noMenInfo','inconMen','inconSequenceSite','noGTInfo']
 	if (pcr_consensus != '0/0') and (pcr_consensus == pcr_free_consensus) and (pcr_consensus not in tag):
 		consensus_call = pcr_consensus
 		gt = [x.split(':')[0] for x in vcf_info_list]
 		indices = [i for i, x in enumerate(gt) if x == consensus_call]
 		matched_mendelian = itemgetter(*indices)(mendelian_list)
 		mendelian_num = matched_mendelian.count('1:1:1')
 		# Delete multiple alternative genotype to necessary expression
 		alt_gt = alt_seq.split(',')
 		if len(alt_gt) > 1:
 			allele1 = consensus_call.split('/')[0]
 			allele2 = consensus_call.split('/')[1]
 			if allele1 == '0':
 				allele2_seq = alt_gt[int(allele2) - 1]
 				consensus_alt_seq = allele2_seq
 				consensus_call = '0/1'
 			else:
 				allele1_seq = alt_gt[int(allele1) - 1]
 				allele2_seq = alt_gt[int(allele2) - 1]
 				if int(allele1) > int(allele2):
 					consensus_alt_seq = allele2_seq + ',' + allele1_seq
 					consensus_call = '1/2'
 				elif int(allele1) < int(allele2):
 					consensus_alt_seq = allele1_seq + ',' + allele2_seq
 					consensus_call = '1/2'
 				else:
 					consensus_alt_seq = allele1_seq 
 					consensus_call = '1/1'
 		else:
 			consensus_alt_seq = alt_seq
 	elif (pcr_consensus in tag) and (pcr_free_consensus in tag):
 		consensus_call = 'filtered'				
 	elif ((pcr_consensus == './.') or (pcr_consensus in tag)) and ((pcr_free_consensus not in tag) and (pcr_free_consensus != './.')):
 		consensus_call = 'pcr-free-speicifc'				
 	elif ((pcr_consensus != './.') or (pcr_consensus not in tag)) and ((pcr_free_consensus in tag) and (pcr_free_consensus == './.')):
 		consensus_call = 'pcr-speicifc'			
 	elif (pcr_consensus == '0/0') and (pcr_free_consensus == '0/0'):
 		consensus_call = '0/0'								
 	else:
 		consensus_call = 'filtered'
 	return pcr_consensus, pcr_free_consensus, mendelian_num, consensus_call, consensus_alt_seq, ALL_ALT, ALL_DP, detected_num
 for row in merged_df.itertuples():
 	mendelian_list = [row.Quartet_DNA_BGI_SEQ2000_BGI_1_20180518,row.Quartet_DNA_BGI_SEQ2000_BGI_2_20180530,row.Quartet_DNA_BGI_SEQ2000_BGI_3_20180530, \
 					row.Quartet_DNA_BGI_T7_WGE_1_20191105,row.Quartet_DNA_BGI_T7_WGE_2_20191105,row.Quartet_DNA_BGI_T7_WGE_3_20191105, \
 					row.Quartet_DNA_ILM_Nova_ARD_1_20181108,row.Quartet_DNA_ILM_Nova_ARD_2_20181108,row.Quartet_DNA_ILM_Nova_ARD_3_20181108, \
 					row.Quartet_DNA_ILM_Nova_ARD_4_20190111,row.Quartet_DNA_ILM_Nova_ARD_5_20190111,row.Quartet_DNA_ILM_Nova_ARD_6_20190111, \
 					row.Quartet_DNA_ILM_Nova_BRG_1_20180930,row.Quartet_DNA_ILM_Nova_BRG_2_20180930,row.Quartet_DNA_ILM_Nova_BRG_3_20180930, \
 					row.Quartet_DNA_ILM_Nova_WUX_1_20190917,row.Quartet_DNA_ILM_Nova_WUX_2_20190917,row.Quartet_DNA_ILM_Nova_WUX_3_20190917, \
 					row.Quartet_DNA_ILM_XTen_ARD_1_20170403,row.Quartet_DNA_ILM_XTen_ARD_2_20170403,row.Quartet_DNA_ILM_XTen_ARD_3_20170403, \
 					row.Quartet_DNA_ILM_XTen_NVG_1_20170329,row.Quartet_DNA_ILM_XTen_NVG_2_20170329,row.Quartet_DNA_ILM_XTen_NVG_3_20170329, \
 					row.Quartet_DNA_ILM_XTen_WUX_1_20170216,row.Quartet_DNA_ILM_XTen_WUX_2_20170216,row.Quartet_DNA_ILM_XTen_WUX_3_20170216]
 	lcl5_list = [row.Quartet_DNA_BGI_SEQ2000_BGI_LCL5_1_20180518,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL5_2_20180530,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL5_3_20180530, \
 					row.Quartet_DNA_BGI_T7_WGE_LCL5_1_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL5_2_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL5_3_20191105, \
 					row.Quartet_DNA_ILM_Nova_ARD_LCL5_1_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL5_2_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL5_3_20181108, \
 					row.Quartet_DNA_ILM_Nova_ARD_LCL5_4_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL5_5_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL5_6_20190111, \
 					row.Quartet_DNA_ILM_Nova_BRG_LCL5_1_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL5_2_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL5_3_20180930, \
 					row.Quartet_DNA_ILM_Nova_WUX_LCL5_1_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL5_2_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL5_3_20190917, \
 					row.Quartet_DNA_ILM_XTen_ARD_LCL5_1_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL5_2_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL5_3_20170403, \
 					row.Quartet_DNA_ILM_XTen_NVG_LCL5_1_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL5_2_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL5_3_20170329, \
 					row.Quartet_DNA_ILM_XTen_WUX_LCL5_1_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL5_2_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL5_3_20170216]
 	lcl6_list = [row.Quartet_DNA_BGI_SEQ2000_BGI_LCL6_1_20180518,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL6_2_20180530,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL6_3_20180530, \
 					row.Quartet_DNA_BGI_T7_WGE_LCL6_1_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL6_2_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL6_3_20191105, \
 					row.Quartet_DNA_ILM_Nova_ARD_LCL6_1_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL6_2_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL6_3_20181108, \
 					row.Quartet_DNA_ILM_Nova_ARD_LCL6_4_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL6_5_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL6_6_20190111, \
 					row.Quartet_DNA_ILM_Nova_BRG_LCL6_1_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL6_2_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL6_3_20180930, \
 					row.Quartet_DNA_ILM_Nova_WUX_LCL6_1_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL6_2_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL6_3_20190917, \
 					row.Quartet_DNA_ILM_XTen_ARD_LCL6_1_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL6_2_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL6_3_20170403, \
 					row.Quartet_DNA_ILM_XTen_NVG_LCL6_1_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL6_2_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL6_3_20170329, \
 					row.Quartet_DNA_ILM_XTen_WUX_LCL6_1_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL6_2_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL6_3_20170216]
 	lcl7_list = [row.Quartet_DNA_BGI_SEQ2000_BGI_LCL7_1_20180518,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL7_2_20180530,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL7_3_20180530, \
 					row.Quartet_DNA_BGI_T7_WGE_LCL7_1_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL7_2_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL7_3_20191105, \
 					row.Quartet_DNA_ILM_Nova_ARD_LCL7_1_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL7_2_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL7_3_20181108, \
 					row.Quartet_DNA_ILM_Nova_ARD_LCL7_4_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL7_5_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL7_6_20190111, \
 					row.Quartet_DNA_ILM_Nova_BRG_LCL7_1_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL7_2_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL7_3_20180930, \
 					row.Quartet_DNA_ILM_Nova_WUX_LCL7_1_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL7_2_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL7_3_20190917, \
 					row.Quartet_DNA_ILM_XTen_ARD_LCL7_1_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL7_2_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL7_3_20170403, \
 					row.Quartet_DNA_ILM_XTen_NVG_LCL7_1_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL7_2_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL7_3_20170329, \
 					row.Quartet_DNA_ILM_XTen_WUX_LCL7_1_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL7_2_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL7_3_20170216]
 	lcl8_list = [row.Quartet_DNA_BGI_SEQ2000_BGI_LCL8_1_20180518,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL8_2_20180530,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL8_3_20180530, \
 					row.Quartet_DNA_BGI_T7_WGE_LCL8_1_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL8_2_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL8_3_20191105, \
 					row.Quartet_DNA_ILM_Nova_ARD_LCL8_1_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL8_2_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL8_3_20181108, \
 					row.Quartet_DNA_ILM_Nova_ARD_LCL8_4_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL8_5_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL8_6_20190111, \
 					row.Quartet_DNA_ILM_Nova_BRG_LCL8_1_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL8_2_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL8_3_20180930, \
 					row.Quartet_DNA_ILM_Nova_WUX_LCL8_1_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL8_2_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL8_3_20190917, \
 					row.Quartet_DNA_ILM_XTen_ARD_LCL8_1_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL8_2_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL8_3_20170403, \
 					row.Quartet_DNA_ILM_XTen_NVG_LCL8_1_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL8_2_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL8_3_20170329, \
 					row.Quartet_DNA_ILM_XTen_WUX_LCL8_1_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL8_2_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL8_3_20170216]
 	# LCL5
 	LCL5_pcr_consensus, LCL5_pcr_free_consensus, LCL5_mendelian_num, LCL5_consensus_call, LCL5_consensus_alt_seq, LCL5_alt, LCL5_dp, LCL5_detected_num = consensus_call(lcl5_list,mendelian_list,row.ALT)
 	if LCL5_mendelian_num != '.':
 		LCL5_output = row.CHROM + '\t' + str(row.POS) + '\t' + '.' + '\t' + row.REF + '\t' + LCL5_consensus_alt_seq + '\t' + '.' + '\t' + '.' + '\t' +'VOTED=' + str(LCL5_mendelian_num) + '\t' + 'GT:ALT:DP' + '\t' + LCL5_consensus_call + ':' + str(LCL5_alt) + ':' + str(LCL5_dp) +  '\n'
 		benchmark_LCL5.write(LCL5_output)
 	# LCL6
 	LCL6_pcr_consensus, LCL6_pcr_free_consensus, LCL6_mendelian_num, LCL6_consensus_call, LCL6_consensus_alt_seq, LCL6_alt, LCL6_dp, LCL6_detected_num = consensus_call(lcl6_list,mendelian_list,row.ALT)
 	if LCL6_mendelian_num != '.':
 		LCL6_output = row.CHROM + '\t' + str(row.POS) + '\t' + '.' + '\t' + row.REF + '\t' + LCL6_consensus_alt_seq + '\t' + '.' + '\t' + '.' + '\t' +'VOTED=' + str(LCL6_mendelian_num) + '\t' + 'GT:ALT:DP' + '\t' + LCL6_consensus_call + ':' + str(LCL6_alt) + ':' + str(LCL6_dp) +  '\n'
 		benchmark_LCL6.write(LCL6_output)
 	# LCL7
 	LCL7_pcr_consensus, LCL7_pcr_free_consensus, LCL7_mendelian_num, LCL7_consensus_call, LCL7_consensus_alt_seq, LCL7_alt, LCL7_dp, LCL7_detected_num = consensus_call(lcl7_list,mendelian_list,row.ALT)
 	if LCL7_mendelian_num != '.':
 		LCL7_output = row.CHROM + '\t' + str(row.POS) + '\t' + '.' + '\t' + row.REF + '\t' + LCL7_consensus_alt_seq + '\t' + '.' + '\t' + '.' + '\t' +'VOTED=' + str(LCL7_mendelian_num) + '\t' + 'GT:ALT:DP' + '\t' + LCL7_consensus_call + ':' + str(LCL7_alt) + ':' + str(LCL7_dp) +  '\n'
 		benchmark_LCL7.write(LCL7_output)
 	# LCL8
 	LCL8_pcr_consensus, LCL8_pcr_free_consensus, LCL8_mendelian_num, LCL8_consensus_call, LCL8_consensus_alt_seq, LCL8_alt, LCL8_dp, LCL8_detected_num = consensus_call(lcl8_list,mendelian_list,row.ALT)
 	if LCL8_mendelian_num != '.':
 		LCL8_output = row.CHROM + '\t' + str(row.POS) + '\t' + '.' + '\t' + row.REF + '\t' + LCL8_consensus_alt_seq + '\t' + '.' + '\t' + '.' + '\t' +'VOTED=' + str(LCL8_mendelian_num) + '\t' + 'GT:ALT:DP' + '\t' + LCL8_consensus_call + ':' + str(LCL8_alt) + ':' + str(LCL8_dp) +  '\n'
 		benchmark_LCL8.write(LCL8_output)
 	# all data
 	all_output = row.CHROM + '\t' + str(row.POS) + '\t' + LCL5_pcr_consensus + '\t' + LCL5_pcr_free_consensus + '\t' + str(LCL5_mendelian_num) + '\t' + LCL5_consensus_call + '\t' + LCL5_consensus_alt_seq + '\t' + str(LCL5_alt) + '\t' + str(LCL5_dp)  + '\t' + str(LCL5_detected_num) + '\t' +\
 					LCL6_pcr_consensus + '\t' + LCL6_pcr_free_consensus + '\t' + str(LCL6_mendelian_num) + '\t' + LCL6_consensus_call + '\t' + LCL6_consensus_alt_seq + '\t' + str(LCL6_alt) + '\t' + str(LCL6_dp)  + '\t' + str(LCL6_detected_num) + '\t' +\
 					LCL7_pcr_consensus + '\t' + LCL7_pcr_free_consensus + '\t' + str(LCL7_mendelian_num) + '\t' + LCL7_consensus_call + '\t' + LCL7_consensus_alt_seq + '\t' + str(LCL7_alt) + '\t' + str(LCL7_dp) + '\t' + str(LCL7_detected_num) + '\t' +\
 					LCL8_pcr_consensus + '\t' + LCL8_pcr_free_consensus + '\t' + str(LCL8_mendelian_num) + '\t' + LCL8_consensus_call + '\t' + LCL8_consensus_alt_seq + '\t' + str(LCL8_alt) + '\t' + str(LCL8_dp) + '\t' + str(LCL8_detected_num) + '\n'
 	all_sample_outfile.write(all_output)
--- a/inputs
+++ b/inputs
 {
  "{{ project_name }}.benchmarking_dir": "oss://pgx-result/renluyao/manuscript_v3.0/reference_datasets_v202103/",
  "{{ project_name }}.fasta": "GRCh38.d1.vd1.fa",
  "{{ project_name }}.BENCHMARKdocker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-hap:latest",
  "{{ project_name }}.LCL8": "{{ LCL8 }}",
  "{{ project_name }}.disk_size": "500",
  "{{ project_name }}.LCL5": "{{ LCL5 }}",
  "{{ project_name }}.project": "{{ project }}",
  "{{ project_name }}.SMALLcluster_config": "OnDemand bcs.ps.g.xlarge img-ubuntu-vpc",
  "{{ project_name }}.bed": "oss://pgx-result/renluyao/manuscript_v3.0/reference_datasets_v202105/benchmark_regions.filtered.bed",
  "{{ project_name }}.BIGcluster_config": "OnDemand bcs.ps.g.2xlarge img-ubuntu-vpc",
  "{{ project_name }}.LCL6": "{{ LCL6 }}",
  "{{ project_name }}.sdf": "oss://pgx-reference-data/GRCh38.d1.vd1/GRCh38.d1.vd1.sdf/",
  "{{ project_name }}.contig": "oss://pgx-result/renluyao/NCTR/contig.txt",
  "{{ project_name }}.MENDELIANdocker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/vbt:v1.1",
  "{{ project_name }}.LCL7": "{{ LCL7 }}",
  "{{ project_name }}.DIYdocker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/high_confidence_call_manuscript:v1.4",
  "{{ project_name }}.ref_dir": "oss://pgx-reference-data/GRCh38.d1.vd1/"
 }
--- a/rename.txt
+++ b/rename.txt
 LCL5_10X_1	LCL5
 LCL6_10X	LCL6
 LCL7_10X	LCL7
 LCL8_10X	LCL8
--- a/samples
+++ b/samples
 gvcf_list,project,sample_id,gvcf_idx_list
 oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-0/Quartet_DNA_BGI_SEQ2000_BGI_LCL5_1_20180518_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-1/Quartet_DNA_BGI_SEQ2000_BGI_LCL5_2_20180530_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-10/Quartet_DNA_BGI_SEQ2000_BGI_LCL8_2_20180530_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-11/Quartet_DNA_BGI_SEQ2000_BGI_LCL8_3_20180530_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-2/Quartet_DNA_BGI_SEQ2000_BGI_LCL5_3_20180530_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-3/Quartet_DNA_BGI_SEQ2000_BGI_LCL6_1_20180518_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-4/Quartet_DNA_BGI_SEQ2000_BGI_LCL6_2_20180530_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-5/Quartet_DNA_BGI_SEQ2000_BGI_LCL6_3_20180530_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-6/Quartet_DNA_BGI_SEQ2000_BGI_LCL7_1_20180518_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-7/Quartet_DNA_BGI_SEQ2000_BGI_LCL7_2_20180530_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-8/Quartet_DNA_BGI_SEQ2000_BGI_LCL7_3_20180530_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-9/Quartet_DNA_BGI_SEQ2000_BGI_LCL8_1_20180518_hc.g.vcf,Quartet_DNA_BGI_SEQ2000_BGI_20180530,1,oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-0/Quartet_DNA_BGI_SEQ2000_BGI_LCL5_1_20180518_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-1/Quartet_DNA_BGI_SEQ2000_BGI_LCL5_2_20180530_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-10/Quartet_DNA_BGI_SEQ2000_BGI_LCL8_2_20180530_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-11/Quartet_DNA_BGI_SEQ2000_BGI_LCL8_3_20180530_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-2/Quartet_DNA_BGI_SEQ2000_BGI_LCL5_3_20180530_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-3/Quartet_DNA_BGI_SEQ2000_BGI_LCL6_1_20180518_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-4/Quartet_DNA_BGI_SEQ2000_BGI_LCL6_2_20180530_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-5/Quartet_DNA_BGI_SEQ2000_BGI_LCL6_3_20180530_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-6/Quartet_DNA_BGI_SEQ2000_BGI_LCL7_1_20180518_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-7/Quartet_DNA_BGI_SEQ2000_BGI_LCL7_2_20180530_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-8/Quartet_DNA_BGI_SEQ2000_BGI_LCL7_3_20180530_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-9/Quartet_DNA_BGI_SEQ2000_BGI_LCL8_1_20180518_hc.g.vcf.idx
 oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-72/Quartet_DNA_BGI_T7_WGE_LCL5_1_20191105_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-73/Quartet_DNA_BGI_T7_WGE_LCL5_2_20191105_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-74/Quartet_DNA_BGI_T7_WGE_LCL5_3_20191105_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-75/Quartet_DNA_BGI_T7_WGE_LCL6_1_20191105_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-76/Quartet_DNA_BGI_T7_WGE_LCL6_2_20191105_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-77/Quartet_DNA_BGI_T7_WGE_LCL6_3_20191105_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-78/Quartet_DNA_BGI_T7_WGE_LCL7_1_20191105_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-79/Quartet_DNA_BGI_T7_WGE_LCL7_2_20191105_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-80/Quartet_DNA_BGI_T7_WGE_LCL7_3_20191105_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-81/Quartet_DNA_BGI_T7_WGE_LCL8_1_20191105_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-82/Quartet_DNA_BGI_T7_WGE_LCL8_2_20191105_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-83/Quartet_DNA_BGI_T7_WGE_LCL8_3_20191105_hc.g.vcf,Quartet_DNA_BGI_T7_WGE_20191105,2,oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-72/Quartet_DNA_BGI_T7_WGE_LCL5_1_20191105_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-73/Quartet_DNA_BGI_T7_WGE_LCL5_2_20191105_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-74/Quartet_DNA_BGI_T7_WGE_LCL5_3_20191105_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-75/Quartet_DNA_BGI_T7_WGE_LCL6_1_20191105_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-76/Quartet_DNA_BGI_T7_WGE_LCL6_2_20191105_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-77/Quartet_DNA_BGI_T7_WGE_LCL6_3_20191105_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-78/Quartet_DNA_BGI_T7_WGE_LCL7_1_20191105_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-79/Quartet_DNA_BGI_T7_WGE_LCL7_2_20191105_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-80/Quartet_DNA_BGI_T7_WGE_LCL7_3_20191105_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-81/Quartet_DNA_BGI_T7_WGE_LCL8_1_20191105_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-82/Quartet_DNA_BGI_T7_WGE_LCL8_2_20191105_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-83/Quartet_DNA_BGI_T7_WGE_LCL8_3_20191105_hc.g.vcf.idx
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 oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-12/Quartet_DNA_ILM_XTen_ARD_LCL5_1_20170403_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-13/Quartet_DNA_ILM_XTen_ARD_LCL5_2_20170403_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-14/Quartet_DNA_ILM_XTen_ARD_LCL5_3_20170403_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-15/Quartet_DNA_ILM_XTen_ARD_LCL6_1_20170403_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-16/Quartet_DNA_ILM_XTen_ARD_LCL6_2_20170403_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-17/Quartet_DNA_ILM_XTen_ARD_LCL6_3_20170403_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-18/Quartet_DNA_ILM_XTen_ARD_LCL7_1_20170403_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-19/Quartet_DNA_ILM_XTen_ARD_LCL7_2_20170403_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-20/Quartet_DNA_ILM_XTen_ARD_LCL7_3_20170403_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-21/Quartet_DNA_ILM_XTen_ARD_LCL8_1_20170403_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-22/Quartet_DNA_ILM_XTen_ARD_LCL8_2_20170403_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-23/Quartet_DNA_ILM_XTen_ARD_LCL8_3_20170403_hc.g.vcf,Quartet_DNA_ILM_XTen_ARD_20170403,7,oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-12/Quartet_DNA_ILM_XTen_ARD_LCL5_1_20170403_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-13/Quartet_DNA_ILM_XTen_ARD_LCL5_2_20170403_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-14/Quartet_DNA_ILM_XTen_ARD_LCL5_3_20170403_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-15/Quartet_DNA_ILM_XTen_ARD_LCL6_1_20170403_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-16/Quartet_DNA_ILM_XTen_ARD_LCL6_2_20170403_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-17/Quartet_DNA_ILM_XTen_ARD_LCL6_3_20170403_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-18/Quartet_DNA_ILM_XTen_ARD_LCL7_1_20170403_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-19/Quartet_DNA_ILM_XTen_ARD_LCL7_2_20170403_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-20/Quartet_DNA_ILM_XTen_ARD_LCL7_3_20170403_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-21/Quartet_DNA_ILM_XTen_ARD_LCL8_1_20170403_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-22/Quartet_DNA_ILM_XTen_ARD_LCL8_2_20170403_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-23/Quartet_DNA_ILM_XTen_ARD_LCL8_3_20170403_hc.g.vcf.idx
 oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-24/Quartet_DNA_ILM_XTen_NVG_LCL5_1_20170329_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-25/Quartet_DNA_ILM_XTen_NVG_LCL5_2_20170329_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-26/Quartet_DNA_ILM_XTen_NVG_LCL5_3_20170329_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-27/Quartet_DNA_ILM_XTen_NVG_LCL6_1_20170329_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-28/Quartet_DNA_ILM_XTen_NVG_LCL6_2_20170329_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-29/Quartet_DNA_ILM_XTen_NVG_LCL6_3_20170329_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-30/Quartet_DNA_ILM_XTen_NVG_LCL7_1_20170329_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-31/Quartet_DNA_ILM_XTen_NVG_LCL7_2_20170329_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-32/Quartet_DNA_ILM_XTen_NVG_LCL7_3_20170329_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-33/Quartet_DNA_ILM_XTen_NVG_LCL8_1_20170329_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-34/Quartet_DNA_ILM_XTen_NVG_LCL8_2_20170329_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-35/Quartet_DNA_ILM_XTen_NVG_LCL8_3_20170329_hc.g.vcf,Quartet_DNA_ILM_XTen_NVG_20170329,8,oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-24/Quartet_DNA_ILM_XTen_NVG_LCL5_1_20170329_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-25/Quartet_DNA_ILM_XTen_NVG_LCL5_2_20170329_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-26/Quartet_DNA_ILM_XTen_NVG_LCL5_3_20170329_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-27/Quartet_DNA_ILM_XTen_NVG_LCL6_1_20170329_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-28/Quartet_DNA_ILM_XTen_NVG_LCL6_2_20170329_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-29/Quartet_DNA_ILM_XTen_NVG_LCL6_3_20170329_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-30/Quartet_DNA_ILM_XTen_NVG_LCL7_1_20170329_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-31/Quartet_DNA_ILM_XTen_NVG_LCL7_2_20170329_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-32/Quartet_DNA_ILM_XTen_NVG_LCL7_3_20170329_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-33/Quartet_DNA_ILM_XTen_NVG_LCL8_1_20170329_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-34/Quartet_DNA_ILM_XTen_NVG_LCL8_2_20170329_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-35/Quartet_DNA_ILM_XTen_NVG_LCL8_3_20170329_hc.g.vcf.idx
 oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-36/Quartet_DNA_ILM_XTen_WUX_LCL5_1_20170216_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-37/Quartet_DNA_ILM_XTen_WUX_LCL5_2_20170216_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-38/Quartet_DNA_ILM_XTen_WUX_LCL5_3_20170216_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-39/Quartet_DNA_ILM_XTen_WUX_LCL6_1_20170216_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-40/Quartet_DNA_ILM_XTen_WUX_LCL6_2_20170216_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-41/Quartet_DNA_ILM_XTen_WUX_LCL6_3_20170216_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-42/Quartet_DNA_ILM_XTen_WUX_LCL7_1_20170216_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-43/Quartet_DNA_ILM_XTen_WUX_LCL7_2_20170216_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-44/Quartet_DNA_ILM_XTen_WUX_LCL7_3_20170216_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-45/Quartet_DNA_ILM_XTen_WUX_LCL8_1_20170216_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-46/Quartet_DNA_ILM_XTen_WUX_LCL8_2_20170216_hc.g.vcf;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-47/Quartet_DNA_ILM_XTen_WUX_LCL8_3_20170216_hc.g.vcf,Quartet_DNA_ILM_XTen_WUX_20170216,9,oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-36/Quartet_DNA_ILM_XTen_WUX_LCL5_1_20170216_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-37/Quartet_DNA_ILM_XTen_WUX_LCL5_2_20170216_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-38/Quartet_DNA_ILM_XTen_WUX_LCL5_3_20170216_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-39/Quartet_DNA_ILM_XTen_WUX_LCL6_1_20170216_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-40/Quartet_DNA_ILM_XTen_WUX_LCL6_2_20170216_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-41/Quartet_DNA_ILM_XTen_WUX_LCL6_3_20170216_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-42/Quartet_DNA_ILM_XTen_WUX_LCL7_1_20170216_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-43/Quartet_DNA_ILM_XTen_WUX_LCL7_2_20170216_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-44/Quartet_DNA_ILM_XTen_WUX_LCL7_3_20170216_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-45/Quartet_DNA_ILM_XTen_WUX_LCL8_1_20170216_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-46/Quartet_DNA_ILM_XTen_WUX_LCL8_2_20170216_hc.g.vcf.idx;oss://pgx-result/renluyao/manuscript/20200417_gVCF/call-Haplotyper/shard-47/Quartet_DNA_ILM_XTen_WUX_LCL8_3_20170216_hc.g.vcf.idx
--- a/tasks/.DS_Store
+++ b/tasks/.DS_Store
--- a/tasks/BQSR.wdl
+++ b/tasks/BQSR.wdl
 task BQSR {
    File ref_dir
    File dbsnp_dir
    File dbmills_dir
 	String sample
 	String SENTIEON_INSTALL_DIR
 	String fasta
 	String dbsnp
 	String db_mills
 	File realigned_bam
 	File realigned_bam_index
 	String docker
 	String cluster_config
 	String disk_size
 	command <<<
 		set -o pipefail
 		set -e
 		export SENTIEON_LICENSE=192.168.0.55:8990
 		nt=$(nproc)
 		${SENTIEON_INSTALL_DIR}/bin/sentieon driver -r ${ref_dir}/${fasta} -t $nt -i ${realigned_bam} --algo QualCal -k ${dbsnp_dir}/${dbsnp} -k ${dbmills_dir}/${db_mills} ${sample}_recal_data.table
 		${SENTIEON_INSTALL_DIR}/bin/sentieon driver -r ${ref_dir}/${fasta} -t $nt -i ${realigned_bam} -q ${sample}_recal_data.table --algo QualCal -k ${dbsnp_dir}/${dbsnp} -k ${dbmills_dir}/${db_mills} ${sample}_recal_data.table.post --algo ReadWriter ${sample}.sorted.deduped.realigned.recaled.bam
 		${SENTIEON_INSTALL_DIR}/bin/sentieon driver -t $nt --algo QualCal --plot --before ${sample}_recal_data.table --after ${sample}_recal_data.table.post ${sample}_recal_data.csv
 		${SENTIEON_INSTALL_DIR}/bin/sentieon plot QualCal -o ${sample}_bqsrreport.pdf ${sample}_recal_data.csv
 	>>>
 	runtime {
 		docker:docker
 		cluster: cluster_config
 		systemDisk: "cloud_ssd 40"
 		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"	
 	}
 	output {
 		File recal_table = "${sample}_recal_data.table"
 		File recal_post = "${sample}_recal_data.table.post"
 		File recaled_bam = "${sample}.sorted.deduped.realigned.recaled.bam"
 		File recaled_bam_index = "${sample}.sorted.deduped.realigned.recaled.bam.bai"
 		File recal_csv = "${sample}_recal_data.csv"
 		File bqsrreport_pdf = "${sample}_bqsrreport.pdf"
 	}
 }
--- a/tasks/D5_D6.wdl
+++ b/tasks/D5_D6.wdl
 task D5_D6 {
 	Array[File] splited_vcf
 	String project
 	String docker
 	String cluster_config
 	String disk_size
 	command <<<
 		mkdir -p /cromwell_root/tmp/vcf
 		cp ${sep=" " splited_vcf} /cromwell_root/tmp/vcf
 		for i in /cromwell_root/tmp/vcf/*vcf
 		do
 			for j in /cromwell_root/tmp/vcf/*vcf
 			do
 				sample_i=$(echo $i | cut -f7 -d_)
 				sample_j=$(echo $j | cut -f7 -d_)
 				rep_i=$(echo $i | cut -f8 -d_)
 				rep_j=$(echo $j | cut -f8 -d_)
 				if [[ $sample_i == "LCL5" ] && [ $sample_j == "LCL6" ] && [ $rep_i == $rep_j ]];then
 					cat $i | grep -v '##' | cut -f1,2,4,5,10 | cut -d ':' -f1 > LCL5.txt
 					cat $j | grep -v '##' | cut -f10 | cut -d ':' -f1 > LCL6.txt
 					paste LCL5.txt LCL6.txt > sister.txt
 					python /opt/D5_D6.py -sister sister.txt -project ${project}.$rep_i
 				fi
 			done
 		done
 		for i in *.reproducibility.txt
 		do
 		  cat $i |  sed -n '2,2p' >> sister.summary
 		done
 		sed '1i\Family\tReproducibility_D5_D6' sister.summary > ${project}.sister.txt
 	>>>
 	runtime {
 		docker:docker
 		cluster: cluster_config
 		systemDisk: "cloud_ssd 40"
 		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
 		Array[File] sister_file = glob("*.reproducibility.txt")
 		File sister_summary = "${project}.sister.txt"
 	}
 }
--- a/tasks/Dedup.wdl
+++ b/tasks/Dedup.wdl
 task Dedup {
 	String SENTIEON_INSTALL_DIR
 	String sample
 	File sorted_bam
 	File sorted_bam_index
 	String docker
 	String cluster_config
 	String disk_size
 	command <<<
 		set -o pipefail
 		set -e
 		export SENTIEON_LICENSE=192.168.0.55:8990
 		nt=$(nproc)
 		${SENTIEON_INSTALL_DIR}/bin/sentieon driver -t $nt -i ${sorted_bam} --algo LocusCollector --fun score_info ${sample}_score.txt
 		${SENTIEON_INSTALL_DIR}/bin/sentieon driver -t $nt -i ${sorted_bam} --algo Dedup --rmdup --score_info ${sample}_score.txt --metrics ${sample}_dedup_metrics.txt ${sample}.sorted.deduped.bam	
 	>>>
 	runtime {
 		docker:docker
    	cluster: cluster_config
    	systemDisk: "cloud_ssd 40"
    	dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
 		File score = "${sample}_score.txt"
 		File dedup_metrics = "${sample}_dedup_metrics.txt"
 		File Dedup_bam = "${sample}.sorted.deduped.bam"
 		File Dedup_bam_index = "${sample}.sorted.deduped.bam.bai"
 	}
 }
--- a/tasks/GVCFtyper.wdl
+++ b/tasks/GVCFtyper.wdl
 task GVCFtyper {
    File ref_dir
 	String SENTIEON_INSTALL_DIR
 	String fasta
 	Array[File] vcf
 	Array[File] vcf_idx
 	String project
 	String docker
 	String cluster_config
 	String disk_size
 command <<<
 		set -o pipefail
 		set -e
 		export SENTIEON_LICENSE=192.168.0.55:8990
 		nt=$(nproc)	
 		${SENTIEON_INSTALL_DIR}/bin/sentieon driver -r ${ref_dir}/${fasta} --algo GVCFtyper ${project}.joint.g.vcf ${sep=" " vcf}
 	>>>
 	runtime {
 		docker:docker
    	cluster: cluster_config
    	systemDisk: "cloud_ssd 40"
    	dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
 		File merged_gvcf = "${project}.joint.g.vcf"
 		File merged_gvcf_idx = "${project}.joint.g.vcf.idx"
 	}
 }
--- a/tasks/Haplotyper_gVCF.wdl
+++ b/tasks/Haplotyper_gVCF.wdl
 task Haplotyper_gVCF {
    File ref_dir
 	String SENTIEON_INSTALL_DIR
 	String fasta
 	File recaled_bam
 	File recaled_bam_index
 	String sample
 	String docker
 	String cluster_config
 	String disk_size
 command <<<
 		set -o pipefail
 		set -e
 		export SENTIEON_LICENSE=192.168.0.55:8990
 		nt=$(nproc)	
 		${SENTIEON_INSTALL_DIR}/bin/sentieon driver -r ${ref_dir}/${fasta} -t $nt -i ${recaled_bam} --algo Haplotyper --emit_mode gvcf ${sample}_hc.g.vcf
 	>>>
 	runtime {
 		docker:docker
    	cluster: cluster_config
    	systemDisk: "cloud_ssd 40"
    	dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
 		File vcf = "${sample}_hc.g.vcf"
 		File vcf_idx = "${sample}_hc.g.vcf.idx"
 	}
 }
--- a/tasks/Metrics.wdl
+++ b/tasks/Metrics.wdl
 task Metrics {
    File ref_dir
 	String SENTIEON_INSTALL_DIR
 	String sample
 	String docker
 	String cluster_config
 	String fasta
 	File sorted_bam
 	File sorted_bam_index
 	String disk_size
 	command <<<
 		set -o pipefail
 		set -e
 		export SENTIEON_LICENSE=192.168.0.55:8990
 		nt=$(nproc)
 		${SENTIEON_INSTALL_DIR}/bin/sentieon driver -r ${ref_dir}/${fasta} -t $nt -i ${sorted_bam} --algo MeanQualityByCycle ${sample}_mq_metrics.txt --algo QualDistribution ${sample}_qd_metrics.txt --algo GCBias --summary ${sample}_gc_summary.txt ${sample}_gc_metrics.txt --algo AlignmentStat ${sample}_aln_metrics.txt --algo InsertSizeMetricAlgo ${sample}_is_metrics.txt --algo CoverageMetrics --omit_base_output ${sample}_coverage_metrics
 		${SENTIEON_INSTALL_DIR}/bin/sentieon plot metrics -o ${sample}_metrics_report.pdf gc=${sample}_gc_metrics.txt qd=${sample}_qd_metrics.txt mq=${sample}_mq_metrics.txt isize=${sample}_is_metrics.txt
 	>>>
 	runtime {
 		docker:docker
    	cluster: cluster_config
    	systemDisk: "cloud_ssd 40"
    	dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
 		File qd_metrics = "${sample}_qd_metrics.txt"
 		File qd_metrics_pdf = "${sample}_qd_metrics.pdf"
 		File mq_metrics = "${sample}_mq_metrics.txt"
 		File mq_metrics_pdf = "${sample}_mq_metrics.pdf"
 		File is_metrics = "${sample}_is_metrics.txt"
 		File is_metrics_pdf = "${sample}_is_metrics.pdf"
 		File gc_summary = "${sample}_gc_summary.txt"
 		File gc_metrics = "${sample}_gc_metrics.txt"
 		File gc_metrics_pdf = "${sample}_gc_metrics.pdf"
 		File aln_metrics = "${sample}_aln_metrics.txt"
 		File coverage_metrics_sample_summary = "${sample}_coverage_metrics.sample_summary"
 		File coverage_metrics_sample_statistics = "${sample}_coverage_metrics.sample_statistics"
 		File coverage_metrics_sample_interval_statistics = "${sample}_coverage_metrics.sample_interval_statistics"
 		File coverage_metrics_sample_cumulative_coverage_proportions = "${sample}_coverage_metrics.sample_cumulative_coverage_proportions"
 		File coverage_metrics_sample_cumulative_coverage_counts = "${sample}_coverage_metrics.sample_cumulative_coverage_counts"
 	}
 }
--- a/tasks/Realigner.wdl
+++ b/tasks/Realigner.wdl
 task Realigner {
    File ref_dir
    File dbmills_dir
 	String SENTIEON_INSTALL_DIR
 	String sample
 	String fasta
 	File Dedup_bam
 	File Dedup_bam_index
 	String db_mills
 	String docker	
 	String cluster_config
 	String disk_size
 	command <<<
 	set -o pipefail
 	set -e
 	export SENTIEON_LICENSE=192.168.0.55:8990
 	nt=$(nproc)
 	${SENTIEON_INSTALL_DIR}/bin/sentieon driver -r ${ref_dir}/${fasta} -t $nt -i ${Dedup_bam} --algo Realigner -k ${dbmills_dir}/${db_mills} ${sample}.sorted.deduped.realigned.bam
 	>>>
 	runtime {
 		docker:docker
 		cluster: cluster_config
    	systemDisk: "cloud_ssd 40"
    	dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
 		File realigner_bam = "${sample}.sorted.deduped.realigned.bam"
 		File realigner_bam_index = "${sample}.sorted.deduped.realigned.bam.bai"
 	}
 }
--- a/tasks/benchmark.wdl
+++ b/tasks/benchmark.wdl
 task benchmark {
 	File vcf
 	File benchmarking_dir
 	File contig
 	File sdf
 	String project
 	String sample = basename(vcf,".raw.vcf.gz")
 	String docker
 	String cluster_config
 	String disk_size
 	command <<<
 		set -o pipefail
 		set -e
 		nt=$(nproc)
 		mkdir -p /cromwell_root/tmp
 		cp -r ${benchmarking_dir} /cromwell_root/tmp/
 		if [[ ${sample} =~ "gz" ]];then
 			gunzip ${vcf} -c > unzip.vcf
 		else
 			cp ${vcf} unzip.vcf
 		fi
 		cat unzip.vcf | grep '#CHROM' > name
 		cat unzip.vcf | grep -v '#' > body
 		cat body | grep -w '^chr1\|^chr2\|^chr3\|^chr4\|^chr5\|^chr6\|^chr7\|^chr8\|^chr9\|^chr10\|^chr11\|^chr12\|^chr13\|^chr14\|^chr15\|^chr16\|^chr17\|^chr18\|^chr19\|^chr20\|^chr21\|^chr22\|^chrX' > body.filtered
 		if grep -q PASS "body.filtered"; then
 			cat body.filtered | grep -v '0/0' | grep 'PASS' > body.filtered.gt
 			cat unzip.vcf | grep '##' | grep -v 'contig' | cat - ${contig} name body.filtered.gt > filtered.vcf
 		else
 			cat body.filtered | grep -v '0/0' > body.filtered.gt
 			cat unzip.vcf | grep '##' | grep -v 'contig' | cat - ${contig} name body.filtered.gt > filtered.vcf
 		fi			
 		echo -e "#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tLCL5" > LCL5_name
 		echo -e "#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tLCL6" > LCL6_name
 		echo -e "#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tLCL7" > LCL7_name
 		echo -e "#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tLCL8" > LCL8_name
 		/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg bgzip filtered.vcf -c > ${project}.${sample}.rtg.vcf.gz
 		/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg index -f vcf ${project}.${sample}.rtg.vcf.gz
 		/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg vcffilter -i ${project}.${sample}.rtg.vcf.gz -o ${sample}.rtg.SNV.vcf.gz --snps-only
 		/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg vcffilter -i ${project}.${sample}.rtg.vcf.gz -o ${sample}.rtg.INDEL.vcf.gz --non-snps-only
 		if [[ ${sample} =~ "LCL5" ]];then
 			/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg vcfeval -b /cromwell_root/tmp/reference_datasets_v202103/LCL5.high.confidence.calls.SNV.vcf.gz -c ${sample}.rtg.SNV.vcf.gz -o ${sample}_SNV -t ${sdf} --evaluation-regions=/cromwell_root/tmp/reference_datasets_v202103/Quartet.high.confidence.region.v202103.bed
 			/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg vcfeval -b /cromwell_root/tmp/reference_datasets_v202103/LCL5.high.confidence.calls.INDEL.vcf.gz -c ${sample}.rtg.INDEL.vcf.gz -o ${sample}_INDEL -t ${sdf} --evaluation-regions=/cromwell_root/tmp/reference_datasets_v202103/Quartet.high.confidence.region.v202103.bed
 			cat filtered.vcf | grep '##' > header
 			cat filtered.vcf | grep -v '#' > body
 			cat header LCL5_name body > LCL5.vcf
 			/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg bgzip LCL5.vcf -c > ${project}.${sample}.reformed.vcf.gz
 			/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg index -f vcf ${project}.${sample}.reformed.vcf.gz
 	    elif [[ ${sample} =~ "LCL6" ]]; then
 			/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg vcfeval -b /cromwell_root/tmp/reference_datasets_v202103/LCL6.high.confidence.calls.SNV.vcf.gz -c ${sample}.rtg.SNV.vcf.gz -o ${sample}_SNV -t ${sdf} --evaluation-regions=/cromwell_root/tmp/reference_datasets_v202103/Quartet.high.confidence.region.v202103.bed
 			/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg vcfeval -b /cromwell_root/tmp/reference_datasets_v202103/LCL6.high.confidence.calls.INDEL.vcf.gz -c ${sample}.rtg.INDEL.vcf.gz -o ${sample}_INDEL -t ${sdf} --evaluation-regions=/cromwell_root/tmp/reference_datasets_v202103/Quartet.high.confidence.region.v202103.bed
 			cat filtered.vcf | grep '##' > header
 			cat filtered.vcf | grep -v '#' > body
 			cat header LCL6_name body > LCL6.vcf
 			/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg bgzip LCL6.vcf -c > ${project}.${sample}.reformed.vcf.gz
 			/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg index -f vcf ${project}.${sample}.reformed.vcf.gz
        elif [[ ${sample} =~ "LCL7" ]]; then
 			/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg vcfeval -b /cromwell_root/tmp/reference_datasets_v202103/LCL7.high.confidence.calls.SNV.vcf.gz -c ${sample}.rtg.SNV.vcf.gz -o ${sample}_SNV -t ${sdf} --evaluation-regions=/cromwell_root/tmp/reference_datasets_v202103/Quartet.high.confidence.region.v202103.bed
 			/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg vcfeval -b /cromwell_root/tmp/reference_datasets_v202103/LCL7.high.confidence.calls.INDEL.vcf.gz -c ${sample}.rtg.INDEL.vcf.gz -o ${sample}_INDEL -t ${sdf} --evaluation-regions=/cromwell_root/tmp/reference_datasets_v202103/Quartet.high.confidence.region.v202103.bed
 			cat filtered.vcf | grep '##' > header
 			cat filtered.vcf | grep -v '#' > body
 			cat header LCL7_name body > LCL7.vcf
 			/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg bgzip LCL7.vcf -c > ${project}.${sample}.reformed.vcf.gz
 			/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg index -f vcf ${project}.${sample}.reformed.vcf.gz
 	    elif [[ ${sample} =~ "LCL8" ]]; then
 			/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg vcfeval -b /cromwell_root/tmp/reference_datasets_v202103/LCL8.high.confidence.calls.SNV.vcf.gz -c ${sample}.rtg.SNV.vcf.gz -o ${sample}_SNV -t ${sdf} --evaluation-regions=/cromwell_root/tmp/reference_datasets_v202103/Quartet.high.confidence.region.v202103.bed
 			/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg vcfeval -b /cromwell_root/tmp/reference_datasets_v202103/LCL8.high.confidence.calls.INDEL.vcf.gz -c ${sample}.rtg.INDEL.vcf.gz -o ${sample}_INDEL -t ${sdf} --evaluation-regions=/cromwell_root/tmp/reference_datasets_v202103/Quartet.high.confidence.region.v202103.bed
 			cat filtered.vcf | grep '##' > header
 			cat filtered.vcf | grep -v '#' > body
 			cat header LCL8_name body > LCL8.vcf
 			/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg bgzip LCL8.vcf -c > ${project}.${sample}.reformed.vcf.gz
 			/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg index -f vcf ${project}.${sample}.reformed.vcf.gz
        else
        	echo "only for quartet samples"
        fi		
        cat ${sample}_SNV/summary.txt > ${project}.${sample}_SNV_precision_recall.txt
        cat ${sample}_INDEL/summary.txt > ${project}.${sample}_INDEL_precision_recall.txt
 	>>>
 	runtime {
 		docker:docker
 		cluster:cluster_config
 		systemDisk:"cloud_ssd 40"
 		dataDisk:"cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
 		File rtg_vcf = "${project}.${sample}.reformed.vcf.gz"
 		File rtg_vcf_index = "${project}.${sample}.reformed.vcf.gz.tbi"
 		File SNV_result = "${project}.${sample}_SNV_precision_recall.txt"
 		File Indel_result = "${project}.${sample}_INDEL_precision_recall.txt"
 	}
 }
--- a/tasks/corealigner.wdl
+++ b/tasks/corealigner.wdl
 task corealigner {
    File ref_dir
    File dbsnp_dir
    File dbmills_dir
 	String sample
 	String SENTIEON_INSTALL_DIR
 	String docker
 	String cluster_config
 	String fasta
 	String dbsnp
 	String db_mills
 	File tumor_recaled_bam
 	File tumor_recaled_bam_index
 	File normal_recaled_bam
 	File normal_recaled_bam_index
 	String disk_size
 	command <<<
 		set -o pipefail
 		set -e
 		export SENTIEON_LICENSE=192.168.0.55:8990
 		nt=$(nproc)
 		${SENTIEON_INSTALL_DIR}/bin/sentieon driver -r ${ref_dir}/${fasta} -t $nt -i ${tumor_recaled_bam} -i ${normal_recaled_bam} --algo Realigner -k ${db_mills} -k ${dbsnp} ${sample}_corealigned.bam				
 	>>>
 	runtime {
 		docker:docker
    	cluster: cluster_config
    	systemDisk: "cloud_ssd 40"
    	dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
 		File corealigner_bam = "${sample}_corealigned.bam"
 		File corealigner_bam_index = "${sample}_corealigned.bam.bai"
 	}	
 }
--- a/tasks/deduped_Metrics.wdl
+++ b/tasks/deduped_Metrics.wdl
 task deduped_Metrics {
    File ref_dir
 	String SENTIEON_INSTALL_DIR
 	String sample
 	String fasta
 	File Dedup_bam
 	File Dedup_bam_index
 	String docker
 	String cluster_config
 	String disk_size
 	command <<<
 		set -o pipefail
 		set -e
 		export SENTIEON_LICENSE=192.168.0.55:8990
 		nt=$(nproc)
 		${SENTIEON_INSTALL_DIR}/bin/sentieon driver -r ${ref_dir}/${fasta} -t $nt -i ${Dedup_bam} --algo CoverageMetrics --omit_base_output ${sample}_deduped_coverage_metrics --algo MeanQualityByCycle ${sample}_deduped_mq_metrics.txt --algo QualDistribution ${sample}_deduped_qd_metrics.txt --algo GCBias --summary ${sample}_deduped_gc_summary.txt ${sample}_deduped_gc_metrics.txt --algo AlignmentStat ${sample}_deduped_aln_metrics.txt --algo InsertSizeMetricAlgo ${sample}_deduped_is_metrics.txt
 	>>>
 	runtime {
 		docker:docker
    	cluster: cluster_config
    	systemDisk: "cloud_ssd 40"
    	dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/" 
 	}
 	output {
 		File deduped_coverage_metrics_sample_summary = "${sample}_deduped_coverage_metrics.sample_summary"
 		File deduped_coverage_metrics_sample_statistics = "${sample}_deduped_coverage_metrics.sample_statistics"
 		File deduped_coverage_metrics_sample_interval_statistics = "${sample}_deduped_coverage_metrics.sample_interval_statistics"
 		File deduped_coverage_metrics_sample_cumulative_coverage_proportions = "${sample}_deduped_coverage_metrics.sample_cumulative_coverage_proportions"
 		File deduped_coverage_metrics_sample_cumulative_coverage_counts = "${sample}_deduped_coverage_metrics.sample_cumulative_coverage_counts"
 	}
 }
--- a/tasks/extract_multiqc.wdl
+++ b/tasks/extract_multiqc.wdl
 task extract_multiqc {
 	File fastqc_qualimap
 	File fastqc
 	File fastqscreen
 	File hap
 	String docker
 	String cluster_config
 	String disk_size
 	command <<<
 		python /opt/extract_multiqc.py -fastqc_qualimap ${fastqc_qualimap} -fastqc ${fastqc} -fastqscreen ${fastqscreen} -hap ${hap}
 	>>>
 	runtime {
 		docker:docker
 		cluster:cluster_config
 		systemDisk:"cloud_ssd 40"
 		dataDisk:"cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
 		File fastqc_result = "fastqc.final.result.txt"
 		File fastqscreen_result = "fastqscreen.final.result.txt"
 		File qualimap_result = "qualimap.final.result.txt"
 		File hap_result = "benchmark.final.result.txt"
 	}
 }
--- a/tasks/fastqc.wdl
+++ b/tasks/fastqc.wdl
 task fastqc {
 	File read1
 	File read2
 	String docker
 	String cluster_config
 	String disk_size
 	command <<<
 		set -o pipefail
 		set -e
 		nt=$(nproc)
 		fastqc -t $nt -o ./ ${read1}
 		fastqc -t $nt -o ./ ${read2}
 	>>>
 	runtime {
 		docker:docker
    	cluster: cluster_config
    	systemDisk: "cloud_ssd 40"
    	dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
 		File read1_html = sub(basename(read1), "\\.(fastq|fq)\\.gz$", "_fastqc.html")
 		File read1_zip = sub(basename(read1), "\\.(fastq|fq)\\.gz$", "_fastqc.zip")
 		File read2_html = sub(basename(read2), "\\.(fastq|fq)\\.gz$", "_fastqc.html")
 		File read2_zip = sub(basename(read2), "\\.(fastq|fq)\\.gz$", "_fastqc.zip")
 	}
 }
--- a/tasks/fastqscreen.wdl
+++ b/tasks/fastqscreen.wdl
 task fastq_screen {
 	File read1
 	File read2
 	File screen_ref_dir
 	File fastq_screen_conf
 	String read1name = basename(read1,".fastq.gz")
 	String read2name = basename(read2,".fastq.gz")
 	String docker
 	String cluster_config
 	String disk_size
 	command <<<
 		set -o pipefail
 		set -e
 		nt=$(nproc)
 		mkdir -p /cromwell_root/tmp
 		cp -r ${screen_ref_dir} /cromwell_root/tmp/
 		fastq_screen --aligner bowtie2 --conf ${fastq_screen_conf} --top 100000 --threads $nt ${read1}
 		fastq_screen --aligner bowtie2 --conf ${fastq_screen_conf} --top 100000 --threads $nt ${read2}
 	>>>
 	runtime {
 		docker:docker
    	cluster: cluster_config
    	systemDisk: "cloud_ssd 40"
    	dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
 		File png1 = "${read1name}_screen.png"
 		File txt1 = "${read1name}_screen.txt"
 		File html1 = "${read1name}_screen.html"
 		File png2 = "${read2name}_screen.png"
 		File txt2 = "${read2name}_screen.txt"
 		File html2 = "${read2name}_screen.html"
 	}
 }
--- a/tasks/mapping.wdl
+++ b/tasks/mapping.wdl
 task mapping {
    File ref_dir
    String fasta
 	File fastq_1
 	File fastq_2
 	String SENTIEON_INSTALL_DIR
 	String group
 	String sample
 	String pl
 	String docker
 	String cluster_config
 	String disk_size
 	command <<<
 		set -o pipefail
 		set -e	
 		export SENTIEON_LICENSE=192.168.0.55:8990
 		nt=$(nproc)
 		${SENTIEON_INSTALL_DIR}/bin/bwa mem -M -R "@RG\tID:${group}\tSM:${sample}\tPL:${pl}" -t $nt -K 10000000 ${ref_dir}/${fasta} ${fastq_1} ${fastq_2} | ${SENTIEON_INSTALL_DIR}/bin/sentieon util sort -o ${sample}.sorted.bam -t $nt --sam2bam -i -
 	>>>
 	runtime {
 		docker:docker
    	cluster: cluster_config
    	systemDisk: "cloud_ssd 40"
    	dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
 		File sorted_bam = "${sample}.sorted.bam"
 		File sorted_bam_index = "${sample}.sorted.bam.bai"
 	}
 }
--- a/tasks/mendelian.wdl
+++ b/tasks/mendelian.wdl
 task mendelian {
 	File family_vcf
 	File family_vcf_inside
 	File family_vcf_outside
 	File ref_dir
 	String family_name = basename(family_vcf,".family.vcf")
 	String fasta
 	String docker
 	String cluster_config
 	String disk_size
 	command <<<
 		export LD_LIBRARY_PATH=/opt/htslib-1.9
 		nt=$(nproc)
 		echo -e "${family_name}\tLCL8\t0\t0\t2\t-9\n${family_name}\tLCL7\t0\t0\t1\t-9\n${family_name}\tLCL5\tLCL7\tLCL8\t2\t-9" > ${family_name}.D5.ped
 		mkdir VBT_D5
 		/opt/VBT-TrioAnalysis/vbt mendelian -ref ${ref_dir}/${fasta} -mother ${family_vcf} -father ${family_vcf} -child ${family_vcf} -pedigree ${family_name}.D5.ped -outDir VBT_D5 -out-prefix ${family_name}.D5 --output-violation-regions -thread-count $nt
 		cat VBT_D5/${family_name}.D5_trio.vcf > ${family_name}.D5.vcf
 		mkdir VBT_D5_inside
 		/opt/VBT-TrioAnalysis/vbt mendelian -ref ${ref_dir}/${fasta} -mother ${family_vcf_inside} -father ${family_vcf_inside} -child ${family_vcf_inside} -pedigree ${family_name}.D5.ped -outDir VBT_D5_inside -out-prefix ${family_name}.D5.inside --output-violation-regions -thread-count $nt
 		cat VBT_D5_inside/${family_name}.D5.inside_trio.vcf > ${family_name}.D5.inside.vcf
 		mkdir VBT_D5_outside
 		/opt/VBT-TrioAnalysis/vbt mendelian -ref ${ref_dir}/${fasta} -mother ${family_vcf_outside} -father ${family_vcf_outside} -child ${family_vcf_outside} -pedigree ${family_name}.D5.ped -outDir VBT_D5_outside -out-prefix ${family_name}.D5.outside --output-violation-regions -thread-count $nt
 		cat VBT_D5_inside/${family_name}.D5.outside_trio.vcf > ${family_name}.D5.outside.vcf
 		echo -e "${family_name}\tLCL8\t0\t0\t2\t-9\n${family_name}\tLCL7\t0\t0\t1\t-9\n${family_name}\tLCL6\tLCL7\tLCL8\t2\t-9" > ${family_name}.D6.ped
 		mkdir VBT_D6
 		/opt/VBT-TrioAnalysis/vbt mendelian -ref ${ref_dir}/${fasta} -mother ${family_vcf} -father ${family_vcf} -child ${family_vcf} -pedigree ${family_name}.D6.ped -outDir VBT_D6 -out-prefix ${family_name}.D6 --output-violation-regions -thread-count $nt
 		cat VBT_D6/${family_name}.D6_trio.vcf > ${family_name}.D6.vcf
 		mkdir VBT_D6_inside
 		/opt/VBT-TrioAnalysis/vbt mendelian -ref ${ref_dir}/${fasta} -mother ${family_vcf_inside} -father ${family_vcf_inside} -child ${family_vcf_inside} -pedigree ${family_name}.D6.ped -outDir VBT_D6_inside -out-prefix ${family_name}.D6.inside --output-violation-regions -thread-count $nt
 		cat VBT_D6_inside/${family_name}.D6.inside_trio.vcf > ${family_name}.D6.inside.vcf
 		mkdir VBT_D6_outside
 		/opt/VBT-TrioAnalysis/vbt mendelian -ref ${ref_dir}/${fasta} -mother ${family_vcf_outside} -father ${family_vcf_outside} -child ${family_vcf_outside} -pedigree ${family_name}.D6.ped -outDir VBT_D6_outside -out-prefix ${family_name}.D6.outside --output-violation-regions -thread-count $nt
 		cat VBT_D6_inside/${family_name}.D6.outside_trio.vcf > ${family_name}.D6.outside.vcf
 	>>>
 	runtime {
 		docker:docker
 		cluster: cluster_config
 		systemDisk: "cloud_ssd 40"
 		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
 		File D5_ped = "${family_name}.D5.ped"
 		File D6_ped = "${family_name}.D6.ped"
 		Array[File] D5_mendelian = glob("VBT_D5/*")
 		Array[File] D6_mendelian = glob("VBT_D6/*")
 		File D5_trio_vcf = "${family_name}.D5.vcf"
 		File D6_trio_vcf = "${family_name}.D6.vcf"
 		Array[File] D5_mendelian_inside = glob("VBT_D5_inside/*")
 		Array[File] D6_mendelian_inside = glob("VBT_D6_inside/*")
 		File D5_trio_vcf_inside = "${family_name}.D5.inside.vcf"
 		File D6_trio_vcf_inside = "${family_name}.D6.inside.vcf"
 		Array[File] D5_mendelian_outside = glob("VBT_D5_outside/*")
 		Array[File] D6_mendelian_outside = glob("VBT_D6_outside/*")
 		File D5_trio_vcf_outside = "${family_name}.D5.outside.vcf"
 		File D6_trio_vcf_outside = "${family_name}.D6.outside.vcf"
 	}
 }
--- a/tasks/mergeNum.wdl
+++ b/tasks/mergeNum.wdl
 task mergeNum {
 	Array[File] vcfnumber
 	String docker
 	String cluster_config
 	String disk_size
 	command <<<
 		set -o pipefail
 		set -e
 		for i in ${sep=" " vcfnumber}
 		do
 		  cat $i | cut -d':' -f2 | tr '\n' '\t' | sed s'/\t$/\n/g' >> vcfstats
 		done
 		sed '1i\File\tFailed Filters\tPassed Filters\tSNPs\tMNPs\tInsertions\tDeletions\tIndels\tSame as reference\tSNP Transitions/Transversions\tTotal Het/Hom ratio\tSNP Het/Hom ratio\tMNP Het/Hom ratio\tInsertion Het/Hom ratio\tDeletion Het/Hom ratio\tIndel Het/Hom ratio\tInsertion/Deletion ratio\tIndel/SNP+MNP ratio' vcfstats > vcfstats.txt
 	>>>
 	runtime {
 		docker:docker
    	cluster:cluster_config
    	systemDisk:"cloud_ssd 40"
    	dataDisk:"cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
 		File vcfstat="vcfstats.txt"
 	}
 }
--- a/tasks/mergeSentieon.wdl
+++ b/tasks/mergeSentieon.wdl
 task mergeSentieon {
 	Array[File] aln_metrics_header
 	Array[File] aln_metrics_data
 	Array[File] dedup_metrics_header
 	Array[File] dedup_metrics_data
 	Array[File] is_metrics_header
 	Array[File] is_metrics_data
 	Array[File] deduped_coverage_header
 	Array[File] deduped_coverage_data
 	String docker
 	String cluster_config
 	String disk_size
 	command <<<
 		set -o pipefail
 		set -e
 		cat ${sep=" " aln_metrics_header} | sed -n '1,1p' | cat - ${sep=" " aln_metrics_data} > aln_metrics.txt
 		cat ${sep=" " dedup_metrics_header} | sed -n '1,1p' | cat - ${sep=" " dedup_metrics_data} > dedup_metrics.txt
 		cat ${sep=" " is_metrics_header} | sed -n '1,1p' | cat - ${sep=" " is_metrics_data} > is_metrics.txt
 		cat ${sep=" " deduped_coverage_header} | sed -n '1,1p' | cat - ${sep=" " deduped_coverage_data} > deduped_coverage.txt
 	>>>
 	runtime {
 		docker:docker
 		cluster:cluster_config
 		systemDisk:"cloud_ssd 40"
 		dataDisk:"cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
 		File aln_metrics_merge = "aln_metrics.txt"
 		File dedup_metrics_merge = "dedup_metrics.txt"
 		File is_metrics_merge = "is_metrics.txt"
 		File deduped_coverage_merge = "deduped_coverage.txt"
 	}
 }
--- a/tasks/merge_family.wdl
+++ b/tasks/merge_family.wdl
 task merge_family {
 	File LCL5_vcf_gz
 	File LCL5_vcf_idx
 	File LCL6_vcf_gz
 	File LCL6_vcf_idx
 	File LCL7_vcf_gz
 	File LCL7_vcf_idx
 	File LCL8_vcf_gz
 	File LCL8_vcf_idx
 	File bed
 	String project
 	String docker
 	String cluster_config
 	String disk_size
 	command <<<
 		/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg vcfmerge --force-merge-all -o ${project}.family.vcf.gz ${LCL5_vcf_gz} ${LCL6_vcf_gz} ${LCL7_vcf_gz} ${LCL8_vcf_gz}
 		gunzip ${project}.family.vcf.gz
 		/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg vcffilter -i ${LCL5_vcf_gz} --include-bed=${bed} -o LCL5.inside.vcf.gz
 		/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg vcffilter -i ${LCL6_vcf_gz} --include-bed=${bed} -o LCL6.inside.vcf.gz
 		/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg vcffilter -i ${LCL7_vcf_gz} --include-bed=${bed} -o LCL7.inside.vcf.gz
 		/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg vcffilter -i ${LCL8_vcf_gz} --include-bed=${bed} -o LCL8.inside.vcf.gz
 		/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg vcfmerge --force-merge-all -o ${project}.family.inside.vcf.gz LCL5.inside.vcf.gz LCL6.inside.vcf.gz LCL7.inside.vcf.gz LCL8.inside.vcf.gz
 		gunzip ${project}.family.inside.vcf.gz
 		/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg vcffilter -i ${LCL5_vcf_gz} --exclude-bed=${bed} -o LCL5.outside.vcf.gz
 		/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg vcffilter -i ${LCL6_vcf_gz} --exclude-bed=${bed} -o LCL6.outside.vcf.gz
 		/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg vcffilter -i ${LCL7_vcf_gz} --exclude-bed=${bed} -o LCL7.outside.vcf.gz
 		/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg vcffilter -i ${LCL8_vcf_gz} --exclude-bed=${bed} -o LCL8.outside.vcf.gz
 		/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg vcfmerge --force-merge-all -o ${project}.family.outside.vcf.gz LCL5.outside.vcf.gz LCL6.outside.vcf.gz LCL7.outside.vcf.gz LCL8.outside.vcf.gz
 		gunzip ${project}.family.outside.vcf.gz
 	>>>
 	runtime {
 		docker:docker
 		cluster: cluster_config
 		systemDisk: "cloud_ssd 40"
 		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
 		File merged_vcf = "${project}.family.vcf"
 		File outside_vcf = "${project}.family.outside.vcf"
 		File inside_vcf = "${project}.family.inside.vcf"
 	}
 }
--- a/tasks/merge_mendelian.wdl
+++ b/tasks/merge_mendelian.wdl
 task merge_mendelian {
 	File D5_trio_vcf
 	File D6_trio_vcf
 	File family_vcf
 	File D5_trio_vcf_inside
 	File D6_trio_vcf_inside
 	File family_vcf_inside
 	File D5_trio_vcf_outside
 	File D6_trio_vcf_outside
 	File family_vcf_outside
 	String family_name = basename(family_vcf,".family.vcf")
 	String docker
 	String cluster_config
 	String disk_size
 	command <<<
 		cat ${D5_trio_vcf} | grep -v '##' > ${family_name}.D5.txt
 		cat ${D6_trio_vcf} | grep -v '##' > ${family_name}.D6.txt
 		cat ${family_vcf} | grep -v '##' | awk '
 		    BEGIN { OFS = "\t" }
 		    NF > 2 && FNR > 1 { 
 		        for ( i=9; i<=NF; i++ ) { 
 		            split($i,a,":") ;$i = a[1];
 		        } 
 		    } 
 		    { print }
 		' > ${family_name}.consensus.txt
 		python /opt/merge_two_family_with_genotype.py -LCL5 ${family_name}.D5.txt -LCL6 ${family_name}.D6.txt -genotype ${family_name}.consensus.txt -family ${family_name}
 		cat ${D5_trio_vcf_inside} | grep -v '##' > ${family_name}.D5.inside.txt
 		cat ${D6_trio_vcf_inside} | grep -v '##' > ${family_name}.D6.inside.txt
 		cat ${family_vcf_inside} | grep -v '##' | awk '
 		    BEGIN { OFS = "\t" }
 		    NF > 2 && FNR > 1 { 
 		        for ( i=9; i<=NF; i++ ) { 
 		            split($i,a,":") ;$i = a[1];
 		        } 
 		    } 
 		    { print }
 		' > ${family_name}.consensus.inside.txt
 		python /opt/merge_two_family_with_genotype.py -LCL5 ${family_name}.D5.inside.txt -LCL6 ${family_name}.D6.inside.txt -genotype ${family_name}.consensus.inside.txt -family ${family_name}.inside
 		cat ${D5_trio_vcf_outside} | grep -v '##' > ${family_name}.D5.outside.txt
 		cat ${D6_trio_vcf_outside} | grep -v '##' > ${family_name}.D6.outside.txt
 		cat ${family_vcf_outside} | grep -v '##' | awk '
 		    BEGIN { OFS = "\t" }
 		    NF > 2 && FNR > 1 { 
 		        for ( i=9; i<=NF; i++ ) { 
 		            split($i,a,":") ;$i = a[1];
 		        } 
 		    } 
 		    { print }
 		' > ${family_name}.consensus.outside.txt
 		python /opt/merge_two_family_with_genotype.py -LCL5 ${family_name}.D5.outside.txt -LCL6 ${family_name}.D6.outside.txt -genotype ${family_name}.consensus.outside.txt -family ${family_name}.outside
 	>>>
 	runtime {
 		docker:docker
 		cluster: cluster_config
 		systemDisk: "cloud_ssd 40"
 		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
 		File project_mendelian = "${family_name}.txt"
 		File project_mendelian_summary = "${family_name}.summary.txt"
 		File project_mendelian_inside = "${family_name}.inside.txt"
 		File project_mendelian_summary_inside = "${family_name}.inside.summary.txt"
 		File project_mendelian_outside = "${family_name}.outside.txt"
 		File project_mendelian_summary_outside = "${family_name}.outside.summary.txt"
 	}
 }
--- a/tasks/multiqc.wdl
+++ b/tasks/multiqc.wdl
 task multiqc {
 	Array[File] read1_zip
 	Array[File] read2_zip
 	Array[File] txt1
 	Array[File] txt2
 	Array[File] zip
 	Array[File] summary
 	String docker
 	String cluster_config
 	String disk_size
 	command <<<
 		set -o pipefail
 		set -e
 		mkdir -p /cromwell_root/tmp/fastqc
 		mkdir -p /cromwell_root/tmp/fastqscreen
 		mkdir -p /cromwell_root/tmp/bamqc
 		mkdir -p /cromwell_root/tmp/benchmark
 		cp ${sep=" " summary} /cromwell_root/tmp/benchmark
 		cp ${sep=" " read1_zip} ${sep=" " read2_zip} /cromwell_root/tmp/fastqc
 		cp ${sep=" " txt1} ${sep=" " txt2} /cromwell_root/tmp/fastqscreen
 		for i in ${sep=" " zip}
 		do
 		  tar -zxvf $i -C /cromwell_root/tmp/bamqc
 		done
 		multiqc /cromwell_root/tmp/
 		cat multiqc_data/multiqc_general_stats.txt > multiqc_general_stats.txt
 		cat multiqc_data/multiqc_fastqc.txt > multiqc_fastqc.txt
 		cat multiqc_data/multiqc_fastq_screen.txt > multiqc_fastq_screen.txt
 		cat multiqc_data/multiqc_happy_data.json > multiqc_happy_data.json
 	>>>
 	runtime {
 		docker:docker
 		cluster:cluster_config
 		systemDisk:"cloud_ssd 40"
 		dataDisk:"cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
 		File multiqc_html = "multiqc_report.html"
 		Array[File] multiqc_txt = glob("multiqc_data/*")
 		File fastqc_qualimap = "multiqc_general_stats.txt"
 		File fastqc = "multiqc_fastqc.txt"
 		File fastqscreen = "multiqc_fastq_screen.txt"
 		File hap = "multiqc_happy_data.json"
 	}
 }
--- a/tasks/qualimap.wdl
+++ b/tasks/qualimap.wdl
 task qualimap {
 	File bam
 	File bai
 	String bamname = basename(bam,".bam")
 	String docker
 	String cluster_config
 	String disk_size
 	command <<<
 		set -o pipefail
 		set -e
 		nt=$(nproc)
 		/opt/qualimap/qualimap bamqc -bam ${bam} -outformat PDF:HTML -nt $nt -outdir ${bamname} --java-mem-size=60G 
 		tar -zcvf ${bamname}_qualimap.zip ${bamname}
 	>>>
 	runtime {
 		docker:docker
 		cluster:cluster_config
 		systemDisk:"cloud_ssd 40"
 		dataDisk:"cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
 		File zip = "${bamname}_qualimap.zip"
 	}
 }
--- a/tasks/quartet_mendelian.wdl
+++ b/tasks/quartet_mendelian.wdl
 task quartet_mendelian {
 	Array[File] project_mendelian_summary
 	String project
 	String docker
 	String cluster_config
 	String disk_size
 	command <<<
 		for i in ${sep=" " project_mendelian_summary}
 		do
 		  cat $i |  sed -n '2,3p' >> mendelian.summary
 		done
 		sed '1i\Family\tReproducibility_D5_D6\tMendelian_Concordance_Quartet' mendelian.summary > ${project}.mendelian.txt
 	>>>
 	runtime {
 		docker:docker
    	cluster:cluster_config
    	systemDisk:"cloud_ssd 40"
    	dataDisk:"cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
 		File mendelian_summary="${project}.mendelian.txt"
 	}
 }
--- a/tasks/sentieon.wdl
+++ b/tasks/sentieon.wdl
 task sentieon {
 	File aln_metrics
 	File dedup_metrics
 	File is_metrics
 	File deduped_coverage
 	String sample_name
 	String docker
 	String cluster_config
 	String disk_size
 	command <<<
 		set -o pipefail
 		set -e
 		cat ${aln_metrics} | sed -n '2,2p'  > aln_metrics.header
 		cat ${aln_metrics} | sed -n '5,5p'  > ${sample_name}.aln_metrics
 		cat ${dedup_metrics} | sed -n '2,2p' > dedup_metrics.header
 		cat ${dedup_metrics} | sed -n '3,3p' > ${sample_name}.dedup_metrics
 		cat ${is_metrics} | sed -n '2,2p' > is_metrics.header
 		cat ${is_metrics} | sed -n '3,3p' > ${sample_name}.is_metrics
 		cat ${deduped_coverage} | sed -n '1,1p' > deduped_coverage.header
 		cat ${deduped_coverage} | sed -n '2,2p' > ${sample_name}.deduped_coverage
 	>>>
 	runtime {
 		docker:docker
 		cluster:cluster_config
 		systemDisk:"cloud_ssd 40"
 		dataDisk:"cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
 		File aln_metrics_header = "aln_metrics.header"
 		File aln_metrics_data = "${sample_name}.aln_metrics"
 		File dedup_metrics_header = "dedup_metrics.header"
 		File dedup_metrics_data = "${sample_name}.dedup_metrics"
 		File is_metrics_header = "is_metrics.header"
 		File is_metrics_data = "${sample_name}.is_metrics"
 		File deduped_coverage_header = "deduped_coverage.header"
 		File deduped_coverage_data = "${sample_name}.deduped_coverage"
 	}
 }
--- a/tasks/split_gvcf_files.wdl
+++ b/tasks/split_gvcf_files.wdl
 task split_gvcf_files {
 	File merged_gvcf
 	String project
 	String docker
 	String cluster_config
 	String disk_size
 	command <<<
 		cat ${merged_gvcf} | grep '#CHROM' | sed s'/\t/\n/g' > name
 		ncol=`cat name | wc -l`
 		sed -i '1,9d' name
 		cat ${merged_gvcf} | grep '#' > header
 		cat ${merged_gvcf} | grep -v '#' > body
 		cat body | grep -w '^chr1\|^chr2\|^chr3\|^chr4\|^chr5\|^chr6\|^chr7\|^chr8\|^chr9\|^chr10\|^chr11\|^chr12\|^chr13\|^chr14\|^chr15\|^chr16\|^chr17\|^chr18\|^chr19\|^chr20\|^chr21\|^chr22\|^chrX' > body.filtered
 		cat header body.filtered > ${project}.filtered.g.vcf
 		for i in $(seq 10 $ncol);  do  cat ${project}.filtered.g.vcf | cut -f1-9,$i > $i.splited.vcf; done
 		ls *splited.vcf | sort -n | paste - name > rename
 		cat rename | while read a b
 		do
 			mv $a $b.splited.vcf
 			sample=$(echo $b | cut -f6 -d_)
 			rep=$(echo $b | cut -f7 -d_)
 			echo $sample >> quartet_sample
 			echo $rep >> quartet_rep
 		done
 		python /opt/how_many_samples.py -sample quartet_sample -rep quartet_rep
 	>>>
 	runtime {
 		docker:docker
 		cluster: cluster_config
 		systemDisk: "cloud_ssd 40"
 		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
 		Array[File] splited_vcf = glob("*.splited.vcf")
 		File sister_tag = "sister_tag"
 		File quartet_tag = "quartet_tag"
 	}
 }
--- a/tasks/vcfstat.wdl
+++ b/tasks/vcfstat.wdl
 task vcfstat {
 	File rtg_vcf
 	File rtg_vcf_index
 	String sample_name
 	String docker
 	String cluster_config
 	String disk_size
 	command <<<
 		set -o pipefail
 		set -e
 		/opt/rtg-tools/dist/rtg-tools-3.10.1-4d58ead/rtg vcfstats ${rtg_vcf} > ${sample_name}.stats.txt
 	>>>
 	runtime {
 		docker:docker
    	cluster:cluster_config
    	systemDisk:"cloud_ssd 40"
    	dataDisk:"cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
 		File vcfnumber="${sample_name}.stats.txt"
 	}
 }
--- a/workflow.wdl
+++ b/workflow.wdl
 import "./tasks/benchmark.wdl" as benchmark
 import "./tasks/mendelian.wdl" as mendelian
 import "./tasks/merge_mendelian.wdl" as merge_mendelian
 import "./tasks/merge_family.wdl" as merge_family
 workflow {{ project_name }} {
 	File LCL5
 	File LCL6
 	File LCL7
 	File LCL8
 	String BENCHMARKdocker
 	String MENDELIANdocker
 	String DIYdocker
 	String fasta
 	File ref_dir
 	File sdf
 	File contig
 	File bed
 	File benchmarking_dir
 	String project
 	String disk_size
 	String BIGcluster_config
 	String SMALLcluster_config
 	call benchmark.benchmark as LCL5benchmark {
 		input:
 		vcf=LCL5,
 		benchmarking_dir=benchmarking_dir,
 		sdf=sdf,
 		contig=contig,
 		project=project,
 		docker=BENCHMARKdocker,
 		cluster_config=BIGcluster_config,
 		disk_size=disk_size,
 	}
 	call benchmark.benchmark as LCL6benchmark {
 		input:
 		vcf=LCL6,
 		benchmarking_dir=benchmarking_dir,
 		sdf=sdf,
 		contig=contig,
 		project=project,
 		docker=BENCHMARKdocker,
 		cluster_config=BIGcluster_config,
 		disk_size=disk_size,
 	}
 	call benchmark.benchmark as LCL7benchmark {
 		input:
 		vcf=LCL7,
 		benchmarking_dir=benchmarking_dir,
 		sdf=sdf,
 		contig=contig,
 		project=project,
 		docker=BENCHMARKdocker,
 		cluster_config=BIGcluster_config,
 		disk_size=disk_size,
 	}	
 	call benchmark.benchmark as LCL8benchmark {
 		input:
 		vcf=LCL8,
 		benchmarking_dir=benchmarking_dir,
 		sdf=sdf,
 		contig=contig,
 		project=project,
 		docker=BENCHMARKdocker,
 		cluster_config=BIGcluster_config,
 		disk_size=disk_size,
 	}
 	call merge_family.merge_family as merge_family {
 		input:
 		LCL5_vcf_gz=LCL5benchmark.rtg_vcf,
 		LCL5_vcf_idx=LCL5benchmark.rtg_vcf_index,
 		LCL6_vcf_gz=LCL6benchmark.rtg_vcf,
 		LCL6_vcf_idx=LCL6benchmark.rtg_vcf_index,
 		LCL7_vcf_gz=LCL7benchmark.rtg_vcf,
 		LCL7_vcf_idx=LCL7benchmark.rtg_vcf_index,
 		LCL8_vcf_gz=LCL8benchmark.rtg_vcf,
 		LCL8_vcf_idx=LCL8benchmark.rtg_vcf,
 		bed=bed,
 		project=project,
 		docker=BENCHMARKdocker,
 		cluster_config=BIGcluster_config,
 		disk_size=disk_size,
 	}
 	call mendelian.mendelian as mendelian {
 		input:
 		family_vcf=merge_family.merged_vcf,
 		family_vcf_inside=merge_family.inside_vcf,
 		family_vcf_outside=merge_family.outside_vcf,	
 		ref_dir=ref_dir,
 		fasta=fasta,
 		docker=MENDELIANdocker,
 		cluster_config=BIGcluster_config,
 		disk_size=disk_size		
 	}
 	call merge_mendelian.merge_mendelian as merge_mendelian {
 		input:
 		D5_trio_vcf=mendelian.D5_trio_vcf,
 		D6_trio_vcf=mendelian.D6_trio_vcf,
 		family_vcf=merge_family.merged_vcf,
 		D5_trio_vcf_inside=mendelian.D5_trio_vcf_inside,
 		D6_trio_vcf_inside=mendelian.D6_trio_vcf_inside,
 		family_vcf_inside=merge_family.inside_vcf,
 		D5_trio_vcf_outside=mendelian.D5_trio_vcf_outside,
 		D6_trio_vcf_outside=mendelian.D6_trio_vcf_outside,
 		family_vcf_outside=merge_family.outside_vcf,
 		docker=DIYdocker,
 		cluster_config=SMALLcluster_config,
 		disk_size=disk_size
 	}
 }