first commit

README.md

+#高置信突变位点的整合
+
+> Author： Run Luyao
+>
+> E-mail：18110700050@fudan.edu.cn
+>
+> Git：http://choppy.3steps.cn/renluyao/high_confidence_calls_manuscript.git
+>
+> Last Updates: 18/03/2020
+
+## 安装指南
+
+```bash
+# 激活choppy环境
+source activate choppy
+# 安装app
+choppy install renluyao/high_confidence_calls_manuscript
+```
+
+## App概述
+
+中华家系1号全基因组高置信small variants（SNVs和Indels）的整合流程。
+
+
+
+## 流程与参数
+
+####1. variantsNorm
+
+保留chr1-22，X上的突变
+
+用bcftools norm进行突变格式的统一
+
+####2. mendelian
+
+LCL5、LCL7和LCL8为三口之家，进行trio-analysis，分析符合孟德尔和不符合孟德尔遗传规律的突变位点
+
+LCL6、LCL7和LCL8为三口之家，进行trio-analysis，分析符合孟德尔和不符合孟德尔遗传规律的突变位点
+
+得到LCL5和LCL6两个家系合并的vcf文件
+
+####3. zipIndex
+
+对LCL5和LCL6两个家系合并的文件压缩和检索引
+
+####4. VCFrename
+
+将VBT的输出结果，VCF文件中的MOTHER FATHER CHILD改成对应的样本名
+
+####5. mergeSister
+
+将LCL5和LCL6修改过名字后的家系VCF文件合并
+
+####6. reformVCF
+
+根据两个三口之家和姐妹的孟德尔遗传的信息，将之前和合并的VCF分解成4个人的vcf，并且包含了家系遗传的信息
+
+####7. familyzipIndex
+
+将上一步输出的4个文件进行压缩和检索引
+
+####8. merge
+
+将所有注释后的LCL5 vcf文件合并
+
+将所有注释后的LCL6 vcf文件合并
+
+将所有注释后的LCL7 vcf文件合并
+
+将所有注释后的LCL8 vcf文件合并
+
+####9. vote
+
+投票选择高置信的突变位点
+
+t
+
+## App输入变量与输入文件
+
+inputSamplesFile的格式如下
+
+```bash
+#LCL5_VCF	#LCL6_VCF	#LCL7_VCF	#LCL8_VCF	#LCL5_sampleName	#LCL6_sampleName	#LCL7_sampleName	#LCL8_sampleName	#familyName
+```
+
+最终版的整合文件包括：
+
+
+
+## App输出文件
+
+
+
+## 结果展示与解读
+
+
+
+## CHANGELOG
+
+
+
+## FAQ
+

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)
+
+
+
+
+
+
+

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)
+
+
+
+
+
+
+
+

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
+
+
+
+

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)
+

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)

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)
+
+

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()
+
+
+
+
+
+
+
+
+
+
+
+

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)
+
+

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
+

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)

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)

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')
+
+# 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[:,[0,1,22,23,24,25,26,27,7,17]]
+merged_genotype_df_sub.columns = ['CHROM', 'POS', 'REF', 'ALT','LCL5','LCL6','LCL7','LCL8', 'TRIO5', 'TRIO6']
+
+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"
+	# 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)

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)
+
+

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)
+		
+

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_calls\tLCL6_consensus_calls\tLCL7_consensus_calls\tLCL8_consensus_calls\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
+	else:
+		consensus_call = 'notConsensus'
+	return consensus_call, detected_num, same_diff
+
+
+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]
+	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)
+
+
+
+
+
+
+
+

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

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)
+

family.tsv

+#LCL5vcf	LCL6vcf	LCL7vcf	LCL8vcf	LCL5name	LCL6name	LCL7name	LCL8name	familyname

inputs

+{
+  "{{ project_name }}.fasta": "GRCh38.d1.vd1.fa",
+  "{{ project_name }}.replicate_consensus.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/high_confidence_call_manuscript:v1.4",
+  "{{ project_name }}.chromo_file": "{{ chromo_file }}",
+  "{{ project_name }}.mendelian.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/vbt:v1.1",
+  "{{ project_name }}.disk_size": "150",
+  "{{ project_name }}.chromo": "{{ chromo }}",
+  "{{ project_name }}.cluster_config": "OnDemand bcs.b4.xlarge img-ubuntu-vpc",
+  "{{ project_name }}.ref_dir": "oss://chinese-quartet/quartet-storage-data/reference_data/"
+}

tasks/mendelian.wdl

+task mendelian {
+	File consensus_vcf
+	File ref_dir
+	String fasta
+	String chromo
+	String docker
+	String cluster_config
+	String disk_size
+	
+	command <<<
+		export LD_LIBRARY_PATH=/opt/htslib-1.9
+		nt=$(nproc)
+	# prepare ped file, D5
+		echo "${chromo}	LCL8_consensus_calls	0	0	2	-9
+		${chromo}	LCL7_consensus_calls	0	0	1	-9
+		${chromo}	LCL5_consensus_calls	LCL7_consensus_calls	LCL8_consensus_calls	2	-9" > ${chromo}.D5.ped
+
+		mkdir VBT_D5
+		/opt/VBT-TrioAnalysis/vbt mendelian -ref ${ref_dir}/${fasta} -mother ${consensus_vcf} -father ${consensus_vcf} -child ${consensus_vcf} -pedigree ${chromo}.D5.ped -outDir VBT_D5 -out-prefix ${chromo}.D5 --output-violation-regions -thread-count $nt
+
+		cat VBT_D5/${chromo}.D5_trio.vcf > ${chromo}.D5.vcf
+	# prepare ped file, D6
+		echo "${chromo}	LCL8_consensus_calls	0	0	2	-9
+		${chromo}	LCL7_consensus_calls	0	0	1	-9
+		${chromo}	LCL6_consensus_calls	LCL7_consensus_calls	LCL8_consensus_calls	2	-9" > ${chromo}.D6.ped
+
+		mkdir VBT_D6
+		/opt/VBT-TrioAnalysis/vbt mendelian -ref ${ref_dir}/${fasta} -mother ${consensus_vcf} -father ${consensus_vcf} -child ${consensus_vcf} -pedigree ${chromo}.D5.ped -outDir VBT_D6 -out-prefix ${chromo}.D6 --output-violation-regions -thread-count $nt
+
+		cat VBT_D6/${chromo}.D6_trio.vcf > ${chromo}.D6.vcf
+	>>>
+
+	runtime {
+		docker:docker
+		cluster: cluster_config
+		systemDisk: "cloud_ssd 40"
+		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
+	}
+	output {
+		Array[File] D5_mendelian = glob("VBT_D5/*")
+		Array[File] D6_mendelian = glob("VBT_D6/*")
+		File D5_trio_vcf = "${chromo}.D5.vcf"
+		File D6_trio_vcf = "${chromo}.D6.vcf"
+		File family_vcf = "${chromo}.vcf"
+	}
+}

tasks/replicate_consensus.wdl

+task replicate_consensus {
+	File chromo_file
+	String chromo
+	String docker
+	String cluster_config
+	String disk_size
+	
+	command <<<
+
+		python /opt/replicates_consensus.py -vcf ${chromo_file} -prefix ${chromo}
+
+
+	>>>
+
+	runtime {
+		docker:docker
+		cluster: cluster_config
+		systemDisk: "cloud_ssd 40"
+		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
+	}
+	output {
+		File chromo_consensus = "${chromo}_all_summary.txt"
+		File consensus_vcf = "${chromo}_consensus.vcf"
+	}
+}

tasks/two_family_merge.wdl

+task two_family_merge {
+	File LCL5_trio_vcf
+	File LCL6_trio_vcf
+	File genotype_file
+	String family_chromo_name
+	String docker
+	String cluster_config
+	String disk_size
+	
+	command <<<
+		cat ${LCL5_trio_vcf} | grep -v '##' > ${family_chromo_name}.LCL5.txt
+		cat ${LCL6_trio_vcf} | grep -v '##' > ${family_chromo_name}.LCL6.txt
+		cat ${genotype_file} | grep -v '##' | awk '
+			BEGIN { OFS = "\t" }
+			NF > 2 && FNR > 1 { 
+				for ( i=9; i<=NF; i++ ) { 
+					split($i,a,":") ;$i = a[1];
+				} 
+			} 
+			{ print }
+		' | cut -f1,2,4,5,10- > ${family_chromo_name}.genotype.txt
+		python /opt/merge_two_family_with_genotype.py -LCL5 ${family_chromo_name}.LCL5.txt -LCL6 ${family_chromo_name}.LCL6.txt -genotype ${family_chromo_name}.genotype.txt -family ${family_chromo_name}
+	>>>
+
+	runtime {
+		docker:docker
+		cluster: cluster_config
+		systemDisk: "cloud_ssd 40"
+		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
+	}
+
+	output {
+		File family_mendelian_info = "${family_chromo_name}.txt"
+	}
+
+}

workflow.wdl

+import "./tasks/replicate_consensus.wdl" as replicate_consensus
+import "./tasks/mendelian.wdl" as mendelian
+
+workflow {{ project_name }} {
+	File ref_dir
+	String fasta
+	File chromo_file
+	String chromo
+	String cluster_config
+	String disk_size
+
+
+	call replicate_consensus.replicate_consensus as replicate_consensus {
+		input:
+		chromo_file=chromo_file,
+		chromo=chromo,
+		cluster_config=cluster_config,
+		disk_size=disk_size
+	}
+	call mendelian.mendelian as mendelian {
+		input:
+		consensus_vcf=replicate_consensus.consensus_vcf,
+		ref_dir=ref_dir,
+		fasta=fasta,
+		chromo=chromo,
+		cluster_config=cluster_config,
+		disk_size=disk_size
+	}
+}