update

README.md

 >
 > E-mail：18110700050@fudan.edu.cn
 >
-> Git：http://choppy.3steps.cn/renluyao/high_confidence_calls_intergration.git
+> Git：http://choppy.3steps.cn/renluyao/high_confidence_calls_manuscript.git
 >
-> Last Updates: 12/6/2019
+> Last Updates: 18/03/2020
 
 ## 安装指南
 
 # 激活choppy环境
 source activate choppy
 # 安装app
-choppy install renluyao/high_confidence_calls_intergration
+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。
 
-Quartet家系的四个样本的DNA标准物质被送往多加测序公司，用PCRfree或者PCR的建库方法建立3个技术重复，用多个测序平台（Illumina XTen、Illumina Novaseq、BGISEQ-500、BGISEQ-2000和BGISEQ-T7）进行测序。获得的原始数据用多个分析流程进行分析（比对软件：BWA-mem、Novoalign、Bowtie2，突变检出软件：HaplotyperCaller、Strelka2、FreeBayes）。
 
-经过以上各种因素的组合每个样本得到216个VCF文件，本APP的目的是整合这些VCF文件的结果得到一组可信度较高的突变。
 
 ## 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

codescripts/bed_for_bamReadcount.py

-import sys,getopt
-import os
-import re
-import fileinput
-
-def usage():
-    print(
-        """
-Usage: python bed_for_bamReadcount.py -i input_vcf_file -o prefix
-
-This script selects SNPs and Indels supported by all callsets.
-
-Please notice that bam-readcount only takes in 1-based coordinates.
-
-Input:
--i a vcf file
-
-Output:
--o  a indel bed file for bam-readcount
-        """)
-
-# select supported small variants
-def process(oneLine):
-    m = re.match('^\#',oneLine)
-    if m is not None:
-        pass
-    else:
-        line = oneLine.rstrip()
-        strings = line.strip().split('\t')
-        # convert the position to bed file for bam-readcount
-        # deletion
-        if len(strings[3]) > 1 and len(strings[4]) == 1:
-            pos = int(strings[1]) + 1
-            outline = strings[0] + '\t' + str(pos) + '\t' + str(pos) + '\t' + strings[3] + '\t' + strings[4]+'\n'
-            outINDEL.write(outline)
-        # insertion
-        elif len(strings[3]) == 1 and len(strings[4]) > 1 and (',' not in strings[4]):
-            outline = strings[0] + '\t' + strings[1] + '\t' + strings[1] +  '\t' + strings[3] +  '\t' + strings[4] + '\n'
-            outINDEL.write(outline)
-        else:
-            outMNP.write(oneLine)
-
-opts,args = getopt.getopt(sys.argv[1:],"hi:o:")
-for op,value in opts:
-    if op == "-i":
-        inputFile=value
-    elif op == "-o":
-        prefix=value    
-    elif op == "-h":
-        usage()
-        sys.exit()
-
-if len(sys.argv[1:]) < 3:
-    usage()
-    sys.exit()
-
-INDELname = prefix + '.bed'
-MNPname = prefix + '_MNP.txt'
-
-outINDEL = open(INDELname,'w')
-outMNP = open(MNPname,'w')
-
-for line in fileinput.input(inputFile):
-    process(line)
-
-outINDEL.close()
-outMNP.close()
-

codescripts/extract_vcf_information.py

 			values.append('1')
 		elif kv[0] == 'AF':
 			pass
-		elif kv[0] == 'POSITIVE_TRAIN_SITE':
-			pass
-		elif kv[0] == 'NEGATIVE_TRAIN_SITE':
-			pass
 		else:
 			keys.append(kv[0])
 			values.append(kv[1])

codescripts/high_confidence_call_vote.py

-# import modules
+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 itertools import islice
+from numpy import *
+import statistics
 
 # input arguments
 parser = argparse.ArgumentParser(description="this script is to count voting number")
 sample_name = args.sample_name
 
 vcf_header = '''##fileformat=VCFv4.2
-##fileDate=20191224
+##fileDate=20200331
 ##source=high_confidence_calls_intergration(choppy app)
 ##reference=GRCh38.d1.vd1
-##INFO=<ID=PCT,Number=1,Type=Float,Description="Percentage of votes">
+##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=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">
+##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=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
-file_name = prefix + '_annotated.vcf'
-outfile = open(file_name,'w')
+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\tQuartet_DNA_BGI_SEQ2000_BGI_1_20180518_LCL5\tQuartet_DNA_BGI_SEQ2000_BGI_2_20180530_LCL5\tQuartet_DNA_BGI_SEQ2000_BGI_3_20180530_LCL5\tQuartet_DNA_BGI_SEQ2000_WGE_1_20190402_LCL5\tQuartet_DNA_BGI_SEQ2000_WGE_2_20190402_LCL5\tQuartet_DNA_BGI_SEQ500_BGI_1_20180328_LCL5 \tQuartet_DNA_BGI_SEQ500_BGI_2_20180328_LCL5\tQuartet_DNA_BGI_SEQ500_BGI_3_20180328_LCL5\tQuartet_DNA_ILM_Nova_ARD_1_20181108_LCL5\tQuartet_DNA_ILM_Nova_ARD_2_20181108_LCL5\tQuartet_DNA_ILM_Nova_ARD_3_20181108_LCL5\tQuartet_DNA_ILM_Nova_ARD_4_20190111_LCL5\tQuartet_DNA_ILM_Nova_ARD_5_20190111_LCL5\tQuartet_DNA_ILM_Nova_ARD_6_20190111_LCL5\tQuartet_DNA_ILM_Nova_BRG_1_20171024_LCL5\tQuartet_DNA_ILM_Nova_BRG_1_20180930_LCL5\tQuartet_DNA_ILM_Nova_BRG_2_20180930_LCL5\tQuartet_DNA_ILM_Nova_BRG_3_20180930_LCL5\tQuartet_DNA_ILM_Nova_GAC_1_20171025_LCL5\tQuartet_DNA_ILM_Nova_NVG_1_20171024_LCL5\tQuartet_DNA_ILM_Nova_WUX_1_20171024_LCL5\tQuartet_DNA_ILM_XTen_ARD_1_20170403_LCL5\tQuartet_DNA_ILM_XTen_ARD_2_20170403_LCL5\tQuartet_DNA_ILM_XTen_ARD_3_20170403_LCL5\tQuartet_DNA_ILM_XTen_NVG_1_20170329_LCL5\tQuartet_DNA_ILM_XTen_NVG_2_20170329_LCL5\tQuartet_DNA_ILM_XTen_NVG_3_20170329_LCL5\tQuartet_DNA_ILM_XTen_WUX_1_20170216_LCL5\tQuartet_DNA_ILM_XTen_WUX_2_20170216_LCL5\tQuartet_DNA_ILM_XTen_WUX_3_20170216_LCL5\tQuartet_DNA_ILM_XTen_WUX_4_20180703_LCL5\tQuartet_DNA_ILM_XTen_WUX_5_20180703_LCL5\tQuartet_DNA_ILM_XTen_WUX_6_20180703_LCL5' +'\t'+ sample_name+'_pcr'+'\t' + sample_name+'_pcr-free'+ '\t'+ sample_name +'consensus' + '\n'
-outfile.write(vcf_header)
-outfile.write(outputcolumn)
+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 vote_percentage(strings):
-	strings = [x.replace('0/0','.') for x in strings]
+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 = round((33 - gt.count('.'))/33,4)
+	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 = [x[-5:] for x in strings]
-	strings = [x.replace('.','0/0') for x in strings]
+	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)
 			consensus_rep = '0/0'
 		else:
 			consensus_rep = 'inconGT'
-	elif (candidate_mendelian == '.') and (freq_mendelian >= 2):
+	elif (candidate_mendelian == '') and (freq_mendelian >= 2):
 		consensus_rep = 'noInfo'
 	else:
 		consensus_rep = 'inconMen'
 			variant_id = '_'.join([strings[0],strings[1]])
 			# check if the variants location is duplicated
 			if variant_id in var_dup:
-				outLine = '\t'.join(strings) + '\t' + '.' +'\t' + '.' + '\t' + 'dupVar' + '\n'
-				outfile.write(outLine)
+				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 = ''
+				pcr_consensus = '.'
+				pcr_free_consensus = '.'
+				consensus_call = '.'
+				consensus_alt_seq = '.'
 				# pcr 
-				pcr = itemgetter(*[9,10,11,12,14,15,16,23,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41])(strings)
-				SEQ2000 = decide_by_rep(pcr[0:4])
-				SEQ500 = decide_by_rep(pcr[4:7])
-				Nova = decide_by_rep(pcr[7:11])
-				XTen_ARD = decide_by_rep(pcr[11:14])
-				XTen_NVG = decide_by_rep(pcr[14:17])
-				XTen_WUX_1 = decide_by_rep(pcr[17:20])
-				XTen_WUX_2 = decide_by_rep(pcr[20:23])
-				sequence_site = [SEQ2000,SEQ500,Nova,XTen_ARD,XTen_NVG,XTen_WUX_1,XTen_WUX_2]
+				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 > 4:
+				if freq_sequence > 2:
 					pcr_consensus = candidate_sequence
 				else:
 					pcr_consensus = 'inconSequenceSite'
 				# pcr-free
-				pcr_free = itemgetter(*[13,17,18,19,20,21,22,24,25,26])(strings)
-				SEQ2000 = decide_by_rep(pcr_free[0])
-				Nova_ARD_1 = decide_by_rep(pcr_free[1:4])
-				Nova_ARD_2 = decide_by_rep(pcr_free[4:7])
-				Nova_BRG = decide_by_rep(pcr_free[7:10])
-				sequence_site = [SEQ2000,Nova_ARD_1,Nova_ARD_2,Nova_BRG]
+				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 > 2:
+				if freq_sequence > 3:
 					pcr_free_consensus = candidate_sequence
 				else:
 					pcr_free_consensus = 'inconSequenceSite'
 				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
-					strings[6] = 'reproducible'
-				elif (pcr_consensus in tag) or (pcr_free_consensus in tag):
+					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'
-					strings[6] = '.'
-				elif (pcr_consensus == '0/0') and (pcr_free_consensus not in tag) and (pcr_free_consensus != '0/0'):
+					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'
-					strings[6] = '.'
-				elif (pcr_consensus != '0/0') and (pcr_consensus not in tag) and (pcr_free_consensus == '0/0'):
+					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'
-					strings[6] = '.'
+					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'
-					strings[6] = '.'					
+					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'
-					strings[6] = '.'
-				# percentage
-				percentage = vote_percentage(strings[9:])
-				strings[7] = 'PCT=' + percentage
-				# output 
-				outLine = '\t'.join(strings) + '\t' + pcr_consensus +'\t' + pcr_free_consensus + '\t' + consensus_call + '\n'
-				outfile.write(outLine)
-
+					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/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/reformVCF.py

 	line = oneLine.rstrip()
 	strings = line.strip().split('\t')
 	# replace .
-	# LCL5 uniq
+	# LCL6 uniq
 	if strings[11] == '.':
 		strings[11] = '0/0'
 		strings[9] = strings[12]
 		strings[10] = strings[13]
 	else:
 		pass
-	# LCL6 uniq
+	# LCL5 uniq
 	if strings[14] == '.':
 		strings[14] = '0/0'
 		strings[12] = strings[9]

codescripts/select_small_variants_supported_by_all_callsets.py

-import sys,getopt
-import os
-import re
-import fileinput
-
-def usage():
-	print(
-		"""
-Usage: python select_small_variants_supported_by_all_callsets.py -i input_merged_vcf_file -o prefix
-
-This script selects SNPs and Indels supported by all callsets.
-
-Input:
--i a merged vcf file
-
-Output:
--o  a vcf file containd the selected SNPs and Indels
-		""")
-
-# select supported small variants
-def process(oneLine):
-	m = re.match('^\#',oneLine)
-	if m is not None:
-		outVCF.write(oneLine)
-		OUTname.write(oneLine)
-	else:
-		line = oneLine.rstrip()
-		strings = line.strip().split('\t')
-		gt = [i.split(':', 1)[0] for i in strings[9:len(strings)]]
-		if all(e == gt[0] for e in gt) and (gt[0] != '.'):
-			# output the record to vcf
-			outVCF.write(oneLine)
-		else:
-			OUTname.write(oneLine)
-
-
-opts,args = getopt.getopt(sys.argv[1:],"hi:o:")
-for op,value in opts:
-	if op == "-i":
-		inputFile=value
-	elif op == "-o":
-		prefix=value	
-	elif op == "-h":
-		usage()
-		sys.exit()
-
-if len(sys.argv[1:]) < 3:
-	usage()
-	sys.exit()
-
-VCFname = prefix + '.vcf'
-OUTname = prefix + '_outlier.vcf'
-
-outVCF = open(VCFname,'w')
-OUTname = open(OUTname,'w')
-
-for line in fileinput.input(inputFile):
-	process(line)
-
-outVCF.close()
-OUTname.close()
-

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)
+

inputs

 {
   "{{ project_name }}.fasta": "GRCh38.d1.vd1.fa",
-  "{{ project_name }}.LCL6familyzipIndex.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest",
-  "{{ project_name }}.LCL5VCFrename.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest",
   "{{ project_name }}.LCL6mendelian.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/vbt:v1.1",
-  "{{ project_name }}.mergeSister.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest",
   "{{ project_name }}.LCL5mendelian.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/vbt:v1.1",
-  "{{ project_name }}.test_name": "{{ test_name }}",
   "{{ project_name }}.disk_size": "150",
+  "{{ project_name }}.chromo": "{{ chromo }}",
   "{{ project_name }}.inputSamplesFile": "{{ inputSamplesFile }}",
-  "{{ project_name }}.LCL6variantsNorm.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/bcftools:v1.9",
-  "{{ project_name }}.LCL6zipIndex.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest",
-  "{{ project_name }}.LCL7familyzipIndex.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest",
-  "{{ project_name }}.LCL5familyzipIndex.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest",
-  "{{ project_name }}.LCL6VCFrename.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest",
-  "{{ project_name }}.LCL5merge.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest",
-  "{{ project_name }}.reformVCF.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/high_confidence_call:v1.1",
-  "{{ project_name }}.LCL5zipIndex.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest",
-  "{{ project_name }}.cluster_config": "OnDemand bcs.a2.xlarge img-ubuntu-vpc",
-  "{{ project_name }}.LCL8familyzipIndex.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest",
-  "{{ project_name }}.LCL7variantsNorm.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/bcftools:v1.9",
-  "{{ project_name }}.LCL5variantsNorm.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/bcftools:v1.9",
-  "{{ project_name }}.LCL8variantsNorm.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/bcftools:v1.9",
+  "{{ project_name }}.cluster_config": "OnDemand bcs.b4.xlarge img-ubuntu-vpc",
+  "{{ project_name }}.vcf": "{{ vcf }}",
   "{{ project_name }}.ref_dir": "oss://chinese-quartet/quartet-storage-data/reference_data/"
 }
-

tasks/VCFinfo.wdl

+task VCFinfo {
+	File repeat_annotated_vcf
+	String sample
+	String docker
+	String cluster_config
+	String disk_size
+	
+	command <<<
+
+		python /opt/variants_quality_location_intergration.py -vcf ${repeat_annotated_vcf} -prefix ${sample}
+
+		cat ${sample}_variant_quality_location.txt | grep '#CHROM' > header
+
+		for i in chr1 chr2 chr3 chr4 chr5 chr6 chr7 chr8 chr9 chr10 chr11 chr12 chr13 chr14 chr15 chr16 chr17 chr18 chr19 chr20 chr21 chr22 chrX
+		do
+			cat ${sample}_variant_quality_location.txt | grep -w $i | cat header - > ${sample}.$i.vcfInfo.txt
+		done
+
+
+	>>>
+
+	runtime {
+		docker:docker
+		cluster: cluster_config
+		systemDisk: "cloud_ssd 40"
+		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
+	}
+	output {
+		File extracted_info = "${sample}_variant_quality_location.txt"
+		Array[File] chromo_vcfInfo = glob("*.vcfInfo.txt")
+	}
+}

tasks/bed_annotation.wdl

+task bed_annotation {
+	File merged_vcf_gz
+	File merged_vcf_idx
+	File repeat_bed
+	String sample
+	String docker
+	String cluster_config
+	String disk_size
+	
+	command <<<
+
+		rtg vcfannotate --bed-info=${repeat_bed} -i ${merged_vcf_gz} -o ${sample}.mendelian.merged.repeatAnno.vcf.gz
+
+		gunzip ${sample}.mendelian.merged.repeatAnno.vcf.gz
+
+	>>>
+
+	runtime {
+		docker:docker
+		cluster: cluster_config
+		systemDisk: "cloud_ssd 40"
+		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
+	}
+	output {
+		File repeat_annotated_vcf = "${sample}.mendelian.merged.repeatAnno.vcf"
+	}
+}

tasks/final_result.wdl

+task FinalResult {
+	File extracted_info
+	File annotated_txt
+	String prefix = basename(annotated_txt,".mendelian.txt")
+	String sample
+	String docker
+	String cluster_config
+	String disk_size
+	
+	command <<<
+
+		python /opt/FinalResult2VCF.py -vcfInfo ${extracted_info} -mendelianInfo ${annotated_txt} -prefix ${prefix} -sample ${sample}
+
+	>>>
+
+	runtime {
+		docker:docker
+		cluster: cluster_config
+		systemDisk: "cloud_ssd 40"
+		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
+	}
+	output {
+		File benchmarking_calls = "${prefix}_benchmarking_calls.vcf"
+		File all_info = "${prefix}_all_sample_information.vcf"
+	}
+}

tasks/indelNorm.wdl

-task indelNorm {
-	File vcf
-	File ref_dir
-	String fasta
-	String sampleName
-	String docker
-	String cluster_config
-	String disk_size
-	
-	command <<<
-
-		cat ${vcf} | grep '#' > header
-		cat ${vcf} | grep -v '#' > body
-		cat body | grep -w '^chr1\|^chr2\|^chr3\|^chr4\|^chr5\|^chr6\|^chr7\|^chr8\|^chr9\|^chr10\|^chr11\|^chr12\|^chr13\|^chr14\|^chr15\|^chr16\|^chr17\|^chr18\|^chr19\|^chr20\|^chr21\|^chr22\|^chrX\' > body.filtered
-		cat header body.filtered > ${sampleName}.filtered.vcf
-
-		/opt/hall-lab/bcftools-1.9/bin/bcftools norm -f ${ref_dir}/${fasta} ${sampleName}.filtered.vcf > ${sampleName}.normed.vcf
-
-	>>>
-
-	runtime {
-		docker:docker
-		cluster: cluster_config
-		systemDisk: "cloud_ssd 40"
-		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
-	}
-	output {
-		File normed_vcf = "${sampleName}.normed.vcf"
-	}
-}

tasks/merge.wdl

 task merge {
 	Array[File] family_vcf_gz
 	Array[File] family_vcf_idx
-	String test_name
 	String sample
 	String docker
 	String cluster_config
 	command <<<
 
 		rtg vcfmerge --force-merge-all -o ${sample}.merged.vcf.gz ${sep=" " family_vcf_gz}
-		rtg vcffilter -i ${sample}.merged.vcf.gz -o ${sample}.snv.merged.vcf.gz --snps-only --all-samples
-		rtg vcffilter -i ${sample}.merged.vcf.gz -o ${sample}.indel.merged.vcf.gz --non-snps-only --all-samples
-
-		zcat ${sample}.indel.merged.vcf.gz | grep '#CHROM' | cut -f10-12 > name
-
-		for i in {10..12}; do zcat ${sample}.snv.merged.vcf.gz | grep -v '#' | cut -f$i | cut -d ':' -f2-4 | grep -v '\.'| sort | uniq -c | awk '{print $1, substr($1,0,7)}' | sed 's/\s\+/\t/g' | cut -f1 > $i.snv.txt; done
-
-		paste *.snv.txt | cat name - > snv.txt
-
-		for i in {10..12}; do zcat ${sample}.indel.merged.vcf.gz | grep -v '#' | cut -f$i | cut -d ':' -f2-4 | grep -v '\.'| sort | uniq -c | awk '{print $1, substr($1,0,7)}' | sed 's/\s\+/\t/g' | cut -f1 > $i.indel.txt; done
-
-		paste *.indel.txt | cat name - > index.txt
-
-		echo 'type' > column
-		echo '0,0,0' >> column
-		echo '0,0,1' >> column
-		echo '0,1,0' >> column
-		echo '0,1,1' >> column
-		echo '1,0,0' >> column
-		echo '1,0,1' >> column
-		echo '1,1,0' >> column
-		echo '1,1,1' >> column
-
-		paste column snv.txt > ${test_name}.snv.txt
-
-		paste column indel.txt > ${test_name}.indel.txt
 
+		zcat ${sample}.merged.vcf.gz | grep -v '#' | cut -f1-2 | sed s'/\t/_/g' | sort | uniq -c | sed 's/\s\+/\t/g' | awk '{ if ($1 != 1) { print } }' | cut -f3 > ${sample}.vcf_dup.txt
 
 	>>>
 
 		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
-		File merged_vcf = "${sample}.merged.vcf.gz"
+		File merged_vcf_gz = "${sample}.merged.vcf.gz"
 		File merged_vcf_idx = "${sample}.merged.vcf.gz.tbi"
-		File merged_snv = "${sample}.snv.merged.vcf.gz"
-		File merged_snv_idx = "${sample}.snv.merged.vcf.gz.tbi"
-		File merged_indel = "${sample}.indel.merged.vcf.gz"
-		File merged_indel_idx = "${sample}.indel.merged.vcf.gz.tbi"
-		File snv = "${test_name}.snv.txt"
-		File indel = "${test_name}.indel.txt"
+		File vcf_dup = "${sample}.vcf_dup.txt"
 	}
 }

tasks/mergeVCFInfo.wdl

+task mergeVCFInfo {
+	Array[File] vcf_gz
+	Array[File] vcf_idx
+	String sample
+	String docker
+	String cluster_config
+	String disk_size
+	
+	command <<<
+
+		rtg vcfmerge --force-merge-all -o ${sample}.merged.info.vcf.gz ${sep=" " vcf_gz}
+ 
+	>>>
+
+	runtime {
+		docker:docker
+		cluster: cluster_config
+		systemDisk: "cloud_ssd 40"
+		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
+	}
+	output {
+		File merged_vcf = "${sample}.merged.info.vcf.gz"
+		File merged_vcf_idx = "${sample}.merged.info.vcf.gz.tbi"
+	}
+}

tasks/merge_info.wdl

+task merge_info {
+	File vcfInfo
+	File mendelianInfo
+	String sample
+	String docker
+	String cluster_config
+	String disk_size
+	
+	command <<<
+
+		python /opt/merge_mendelian_vcfinfo.py -vcfInfo ${vcfInfo} -mendelianInfo ${mendelianInfo} -sample ${sample}
+
+	>>>
+
+	runtime {
+		docker:docker
+		cluster: cluster_config
+		systemDisk: "cloud_ssd 40"
+		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
+	}
+	output {
+		File all_info = "${sample}_mendelian_vcfInfo.vcf"
+	}
+}

tasks/reformVCF.wdl

 	command <<<
 
 	python /opt/reformVCF.py -vcf ${family_mendelian_info} -name ${family_name}
-	
+
+	cat ${family_name}.LCL5.vcf | grep -v '##' | grep -v '0/0' | grep -v '\./\.' > ${family_name}.LCL5.txt
+	cat ${family_name}.LCL6.vcf | grep -v '##' | grep -v '0/0' | grep -v '\./\.' > ${family_name}.LCL6.txt
+	cat ${family_name}.LCL7.vcf | grep -v '##' | grep -v '0/0' | grep -v '\./\.' > ${family_name}.LCL7.txt
+	cat ${family_name}.LCL8.vcf | grep -v '##' | grep -v '0/0' | grep -v '\./\.' > ${family_name}.LCL8.txt
 
 	>>>
 
 		File LCL7_family_info = "${family_name}.LCL7.vcf"
 		File LCL8_family_info = "${family_name}.LCL8.vcf"
 		File family_info = "${family_name}.vcf"
+		File LCL5_family_info_txt = "${family_name}.LCL5.txt"
+		File LCL6_family_info_txt = "${family_name}.LCL6.txt"
+		File LCL7_family_info_txt = "${family_name}.LCL7.txt"
+		File LCL8_family_info_txt = "${family_name}.LCL8.txt"
 	}
 }
 

tasks/two_family_merge.wdl

+task two_family_merge {
+	File LCL5_trio_vcf
+	File LCL6_trio_vcf
+	String family_name
+	String docker
+	String cluster_config
+	String disk_size
+	
+	command <<<
+		cat ${LCL5_trio_vcf} | grep -v '##' > ${family_name}.LCL5.txt
+		cat ${LCL6_trio_vcf} | grep -v '##' > ${family_name}.LCL6.txt
+		python /opt/merge_two_family.py -LCL5 ${family_name}.LCL5.txt -LCL6 ${family_name}.LCL6.txt -family ${family_name}
+	>>>
+
+	runtime {
+		docker:docker
+		cluster: cluster_config
+		systemDisk: "cloud_ssd 40"
+		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
+	}
+
+	output {
+		File family_mendelian_info = "${family_name}.txt"
+	}
+
+}

tasks/variantsNorm.wdl

 
 		/opt/hall-lab/bcftools-1.9/bin/bcftools norm -f ${ref_dir}/${fasta} ${sampleName}.filtered.vcf > ${sampleName}.normed.vcf
 
+		cat ${sampleName}.normed.vcf | grep -v '##' > ${sampleName}.normed.txt
+
 	>>>
 
 	runtime {
 	}
 	output {
 		File normed_vcf = "${sampleName}.normed.vcf"
+		File normed_txt = "${sampleName}.normed.txt"
 	}
 }

tasks/votes.wdl

 task votes {
-	File merged_vcf
-	File vcf_dup
-	String sample
-	String prefix
+	Array[File] family_mendelian_info
+	File vcf 
+	String chromo
 	String docker
 	String cluster_config
 	String disk_size
 	
 	command <<<
-		python /opt/high_confidence_call_vote.py -vcf ${merged_vcf} -dup ${vcf_dup} -sample ${sample} -prefix ${prefix}
-		cat ${prefix}_annotated.vcf | cut -f1-9,45 | grep -v 'filtered' | grep -v 'confirm for parents' | grep -v 'pcr-free-speicifc' | grep -v 'pcr-speicifc' | grep -v 'dupVar' > ${prefix}_bechmarking_calls.vcf
+		mkdir temp
+		for i in ${sep=" " family_mendelian_info}
+		do
+			cp $i temp
+		done
+
+		cat ${vcf} | grep -v '##' > vcf_info.txt
+
+		python /opt/voted_by_vcfinfo_mendelianinfo.py -folder ./temp -vcf vcf_info.txt
+
+		cp LCL5_voted.vcf LCL5.${chromo}.voted.vcf
+		cp LCL6_voted.vcf LCL6.${chromo}.voted.vcf
+		cp LCL7_voted.vcf LCL7.${chromo}.voted.vcf
+		cp LCL8_voted.vcf LCL8.${chromo}.voted.vcf
+
 	>>>
 
 	runtime {
 		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
-		File annotated_vcf = "${prefix}_annotated.vcf"
-		File benchmark_call = "${prefix}_bechmarking_calls.vcf"
+		File LCL5_voted_vcf = "LCL5.${chromo}.voted.vcf"
+		File LCL6_voted_vcf = "LCL6.${chromo}.voted.vcf"
+		File LCL7_voted_vcf = "LCL7.${chromo}.voted.vcf"
+		File LCL8_voted_vcf = "LCL8.${chromo}.voted.vcf"
+		File all_sample_info = "all_sample_information.txt"
 	}
 }
+

tasks/zipIndex.wdl

 task zipIndex {
 	File vcf
-	String sample
-	String family_name
+	String vcf_name = basename(vcf,".vcf")
 	String docker
 	String cluster_config
 	String disk_size
 	
 	command <<<
-		rtg bgzip ${vcf} -c > ${family_name}.${sample}.vcf.gz
-		rtg index -f vcf ${family_name}.${sample}.vcf.gz
+		rtg bgzip ${vcf} -c > ${vcf_name}.vcf.gz
+		rtg index -f vcf ${vcf_name}.vcf.gz
 
 	>>>
 
 		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
 	}
 	output {
-		File vcf_gz = "${family_name}.${sample}.vcf.gz"
-		File vcf_idx = "${family_name}.${sample}.vcf.gz.tbi"
+		File vcf_gz = "${vcf_name}.vcf.gz"
+		File vcf_idx = "${vcf_name}.vcf.gz.tbi"
 	}
 }

workflow.wdl

-import "./tasks/variantsNorm.wdl" as variantsNorm
 import "./tasks/mendelian.wdl" as mendelian
-import "./tasks/zipIndex.wdl" as zipIndex
-import "./tasks/VCFrename.wdl" as VCFrename
-import "./tasks/mergeSister.wdl" as mergeSister
-import "./tasks/reformVCF.wdl" as reformVCF
-import "./tasks/merge.wdl" as merge
-import "./tasks/votes.wdl" as votes
+
 
 workflow {{ project_name }} {
 	File inputSamplesFile
 	Array[Array[File]] inputSamples = read_tsv(inputSamplesFile)
 	File ref_dir
+	File vcf
+	String chromo
 	String fasta
-	String test_name
 	String cluster_config
 	String disk_size
 
 	scatter (quartet in inputSamples){
-		call variantsNorm.variantsNorm as LCL5variantsNorm{
-			input:
-			vcf=quartet[0],
-			ref_dir=ref_dir,
-			fasta=fasta,
-			sampleName=quartet[4],
-			cluster_config=cluster_config,
-			disk_size=disk_size
-		}
-		call variantsNorm.variantsNorm as LCL6variantsNorm{
-			input:
-			vcf=quartet[1],
-			ref_dir=ref_dir,
-			fasta=fasta,
-			sampleName=quartet[5],
-			cluster_config=cluster_config,
-			disk_size=disk_size
-		}
-		call variantsNorm.variantsNorm as LCL7variantsNorm{
-			input:
-			vcf=quartet[2],
-			ref_dir=ref_dir,
-			fasta=fasta,
-			sampleName=quartet[6],
-			cluster_config=cluster_config,
-			disk_size=disk_size
-		}
-		call variantsNorm.variantsNorm as LCL8variantsNorm{
-			input:
-			vcf=quartet[3],
-			ref_dir=ref_dir,
-			fasta=fasta,
-			sampleName=quartet[7],
-			cluster_config=cluster_config,
-			disk_size=disk_size
-		}
 		call mendelian.mendelian as LCL5mendelian {
 			input:
-			child_vcf=LCL5variantsNorm.normed_vcf,
-			LCL7_vcf=LCL7variantsNorm.normed_vcf,
-			LCL8_vcf=LCL8variantsNorm.normed_vcf,
+			child_vcf=quartet[0],
+			LCL7_vcf=quartet[2],
+			LCL8_vcf=quartet[3],
 			LCL7_name=quartet[6],
 			LCL8_name=quartet[7],
 			child_name=quartet[4],
 		}
 		call mendelian.mendelian as LCL6mendelian {
 			input:
-			child_vcf=LCL6variantsNorm.normed_vcf,
-			LCL7_vcf=LCL7variantsNorm.normed_vcf,
-			LCL8_vcf=LCL8variantsNorm.normed_vcf,
+			child_vcf=quartet[1],
+			LCL7_vcf=quartet[2],
+			LCL8_vcf=quartet[3],
 			LCL7_name=quartet[6],
 			LCL8_name=quartet[7],
 			child_name=quartet[5],
 			cluster_config=cluster_config,
 			disk_size=disk_size
 		}
-		call zipIndex.zipIndex as LCL5zipIndex {
-			input:
-			vcf=LCL5mendelian.trio_vcf,
-			sample="LCL5",
-			family_name=quartet[8],
-			cluster_config=cluster_config,
-			disk_size=disk_size
-		}
-		call zipIndex.zipIndex as LCL6zipIndex {
-			input:
-			vcf=LCL6mendelian.trio_vcf,
-			sample="LCL6",
-			family_name=quartet[8],
-			cluster_config=cluster_config,
-			disk_size=disk_size
-		}		
-		call VCFrename.VCFrename as LCL5VCFrename {
-			input:
-			trio_vcf_gz=LCL5zipIndex.vcf_gz,
-			trio_vcf_idx=LCL5zipIndex.vcf_idx,
-			mother_name=quartet[7],
-			father_name=quartet[6],
-			child_name=quartet[4],
-			family_name=quartet[8],
-			child="LCL5",
-			cluster_config=cluster_config,
-			disk_size=disk_size
-		}
-		call VCFrename.VCFrename as LCL6VCFrename {
-			input:
-			trio_vcf_gz=LCL6zipIndex.vcf_gz,
-			trio_vcf_idx=LCL6zipIndex.vcf_idx,
-			mother_name=quartet[7],
-			father_name=quartet[6],
-			child_name=quartet[5],
-			family_name=quartet[8],
-			child="LCL6",
-			cluster_config=cluster_config,
-			disk_size=disk_size
-		}
-		call mergeSister.mergeSister as mergeSister {
-			input:
-			LCL5_trio_vcf_gz=LCL5VCFrename.rename_trio_vcf_gz,
-			LCL5_trio_vcf_idx=LCL5VCFrename.rename_trio_vcf_idx,
-			LCL6_trio_vcf_gz=LCL6VCFrename.rename_trio_vcf_gz,
-			LCL6_trio_vcf_idx=LCL6VCFrename.rename_trio_vcf_idx,
-			family_name=quartet[8],
-			cluster_config=cluster_config,
-			disk_size=disk_size
-		}
-		call reformVCF.reformVCF as reformVCF {
-			input:
-			family_mendelian_info=mergeSister.family_mendelian_info,
-			family_name=quartet[8],
-			cluster_config=cluster_config,
-			disk_size=disk_size			
-		}
-		call zipIndex.zipIndex as LCL5familyzipIndex {
-			input:
-			vcf=reformVCF.LCL5_family_info,
-			sample='LCL5',
-			family_name=quartet[8],
-			cluster_config=cluster_config,
-			disk_size=disk_size
-		}
-		call zipIndex.zipIndex as LCL6familyzipIndex {
-			input:
-			vcf=reformVCF.LCL6_family_info,
-			sample='LCL6',
-			family_name=quartet[8],
-			cluster_config=cluster_config,
-			disk_size=disk_size
-		}
-		call zipIndex.zipIndex as LCL7familyzipIndex {
-			input:
-			vcf=reformVCF.LCL7_family_info,
-			sample='LCL7',
-			family_name=quartet[8],
-			cluster_config=cluster_config,
-			disk_size=disk_size
-		}
-		call zipIndex.zipIndex as LCL8familyzipIndex {
-			input:
-			vcf=reformVCF.LCL8_family_info,
-			sample='LCL8',
-			family_name=quartet[8],
-			cluster_config=cluster_config,
-			disk_size=disk_size
-		}		
 	}
-	call merge.merge as LCL5merge {
-		input:
-		family_vcf_gz=LCL5familyzipIndex.vcf_gz,
-		family_vcf_idx=LCL5familyzipIndex.vcf_idx,
-		sample="LCL5",
-		test_name=test_name,
-		cluster_config=cluster_config,
-		disk_size=disk_size
-	}
-
 }