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README.md

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+#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/extract_vcf_information.py

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+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/high_confidence_call_vote.py

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+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/merge_mendelian_vcfinfo.py

@@ -0,0 +1,129 @@
+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/oneClass.py

@@ -0,0 +1,109 @@
+# 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

@@ -0,0 +1,144 @@
+# 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 .
+	# LCL5 uniq
+	if strings[11] == '.':
+		strings[11] = '0/0'
+		strings[9] = strings[12]
+		strings[10] = strings[13]
+	else:
+		pass
+	# LCL6 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/vcf2bed.py

@@ -0,0 +1,36 @@
+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

family.tsv

@@ -0,0 +1 @@
+#LCL5vcf	LCL6vcf	LCL7vcf	LCL8vcf	LCL5name	LCL6name	LCL7name	LCL8name	familyname

inputs

@@ -0,0 +1,43 @@
+{
+  "{{ project_name }}.LCL7merge.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest",
+  "{{ project_name }}.fasta": "GRCh38.d1.vd1.fa",
+  "{{ project_name }}.LCL6bed_annotation.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest",
+  "{{ project_name }}.LCL7bed_annotation.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest",
+  "{{ project_name }}.LCL8bed_annotation.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest",
+  "{{ project_name }}.LCL7votes.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/high_confidence_call_manuscript:v1.1",
+  "{{ project_name }}.LCL6votes.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/high_confidence_call_manuscript:v1.1",
+  "{{ project_name }}.LCL5VCFrename.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest",
+  "{{ project_name }}.LCL8mergeInfo.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/high_confidence_call_manuscript:v1.1",
+  "{{ project_name }}.LCL6mendelian.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/vbt:v1.1",
+  "{{ project_name }}.LCL5mergeInfo.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/high_confidence_call_manuscript: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 }}.LCL6allInfozipIndex.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest",
+  "{{ project_name }}.LCL5allInfozipIndex.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest",
+  "{{ project_name }}.disk_size": "150",
+  "{{ project_name }}.LCL7mergeInfo.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/high_confidence_call_manuscript:v1.1",
+  "{{ project_name }}.inputSamplesFile": "{{ inputSamplesFile }}",
+  "{{ project_name }}.repeat_bed": "oss://pgx-result/renluyao/manuscript/all.repeat.bed",
+  "{{ project_name }}.LCL6merge.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest",
+  "{{ 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 }}.LCL7allInfozipIndex.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest",
+  "{{ project_name }}.LCL5votes.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/high_confidence_call_manuscript:v1.1",
+  "{{ project_name }}.LCL6mergeInfo.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/high_confidence_call_manuscript:v1.1",
+  "{{ project_name }}.LCL5bed_annotation.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 }}.LCL8votes.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/high_confidence_call_manuscript:v1.1",
+  "{{ project_name }}.reformVCF.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/high_confidence_call_manuscript:v1.1",
+  "{{ project_name }}.LCL5zipIndex.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest",
+  "{{ project_name }}.cluster_config": "OnDemand bcs.b4.xlarge img-ubuntu-vpc",
+  "{{ project_name }}.LCL8allInfozipIndex.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 }}.LCL8merge.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest",
+  "{{ 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 }}.ref_dir": "oss://chinese-quartet/quartet-storage-data/reference_data/"
+}
+
+
+

tasks/VCFinfo.wdl

@@ -0,0 +1,32 @@
+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/VCFrename.wdl

@@ -0,0 +1,31 @@
+task VCFrename {
+	File trio_vcf_gz
+	File trio_vcf_idx
+	String mother_name
+	String father_name
+	String child_name
+	String family_name
+	String child
+	String docker
+	String cluster_config
+	String disk_size
+	
+	command <<<
+		echo "MOTHER ${mother_name}.${child}
+		FATHER ${father_name}.${child}
+		CHILD ${child_name}" > rename.txt
+
+		rtg vcfannotate -i ${trio_vcf_gz} -o ${family_name}.${child}.rename.vcf.gz --relabel=rename.txt
+	>>>
+
+	runtime {
+		docker:docker
+		cluster: cluster_config
+		systemDisk: "cloud_ssd 40"
+		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
+	}
+	output {
+		File rename_trio_vcf_gz = "${family_name}.${child}.rename.vcf.gz"
+		File rename_trio_vcf_idx = "${family_name}.${child}.rename.vcf.gz.tbi"
+	}
+}

tasks/bed_annotation.wdl

@@ -0,0 +1,27 @@
+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

@@ -0,0 +1,26 @@
+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/mendelian.wdl

@@ -0,0 +1,34 @@
+task mendelian {
+	File child_vcf
+	File LCL7_vcf
+	File LCL8_vcf
+	String LCL7_name
+	String LCL8_name
+	String child_name
+	File ref_dir
+	String fasta
+	String docker
+	String cluster_config
+	String disk_size
+	
+	command <<<
+		export LD_LIBRARY_PATH=/opt/htslib-1.9
+		nt=$(nproc)
+		mkdir VBT
+
+		/opt/VBT-TrioAnalysis/vbt mendelian -ref ${ref_dir}/${fasta} -mother ${LCL8_vcf} -father ${LCL7_vcf} -child ${child_vcf} -outDir VBT -out-prefix ${child_name}.family --output-violation-regions -thread-count $nt
+
+		cat VBT/${child_name}.family_trio.vcf > ${child_name}.family.vcf
+	>>>
+
+	runtime {
+		docker:docker
+		cluster: cluster_config
+		systemDisk: "cloud_ssd 40"
+		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
+	}
+	output {
+		Array[File] vbt_mendelian = glob("VBT/*")
+		File trio_vcf = "${child_name}.family.vcf"
+	}
+}

tasks/merge.wdl

@@ -0,0 +1,28 @@
+task merge {
+	Array[File] family_vcf_gz
+	Array[File] family_vcf_idx
+	String sample
+	String docker
+	String cluster_config
+	String disk_size
+	
+	command <<<
+
+		rtg vcfmerge --force-merge-all -o ${sample}.merged.vcf.gz ${sep=" " family_vcf_gz}
+
+		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 > ${sample}.vcf_dup.txt
+
+	>>>
+
+	runtime {
+		docker:docker
+		cluster: cluster_config
+		systemDisk: "cloud_ssd 40"
+		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
+	}
+	output {
+		File merged_vcf_gz = "${sample}.merged.vcf.gz"
+		File merged_vcf_idx = "${sample}.merged.vcf.gz.tbi"
+		File vcf_dup = "${sample}.vcf_dup.txt"
+	}
+}

tasks/mergeSister.wdl

@@ -0,0 +1,34 @@
+task mergeSister {
+	File LCL5_trio_vcf_gz
+	File LCL5_trio_vcf_idx
+	File LCL6_trio_vcf_gz
+	File LCL6_trio_vcf_idx
+	String family_name
+	String docker
+	String cluster_config
+	String disk_size
+	
+	command <<<
+		rtg vcfmerge -o LCL5.LCL6.merged.vcf.gz ${LCL5_trio_vcf_gz} ${LCL6_trio_vcf_gz}
+
+		rtg vcfmerge -o LCL6.LCL5.merged.vcf.gz ${LCL6_trio_vcf_gz} ${LCL5_trio_vcf_gz}
+
+		zcat LCL5.LCL6.merged.vcf.gz | grep '##' > header
+		zcat LCL5.LCL6.merged.vcf.gz | grep -v '##' | cut -f8 > LCL5.mendelian
+		zcat LCL6.LCL5.merged.vcf.gz | grep -v '##' | paste - LCL5.mendelian > body
+
+		cat header body > ${family_name}.trio.info.vcf
+	>>>
+
+	runtime {
+		docker:docker
+		cluster: cluster_config
+		systemDisk: "cloud_ssd 40"
+		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
+	}
+
+	output {
+		File family_mendelian_info = "${family_name}.trio.info.vcf"
+	}
+
+}

tasks/mergeVCFInfo.wdl

@@ -0,0 +1,25 @@
+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

@@ -0,0 +1,24 @@
+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/oneClass.wdl

@@ -0,0 +1,39 @@
+task oneClass {
+	File snv_train_vcf
+	File snv_test_vcf
+	File indel_train_vcf
+	File indel_test_vcf
+	String sampleName = basename(snv_train_vcf,".normed.snv.train.txt")
+	String docker
+	String cluster_config
+	String disk_size
+	
+	command <<<
+
+	python /opt/oneClass.py -train ${snv_train_vcf} -test ${snv_test_vcf} -name ${sampleName}_snv
+
+	python /opt/oneClass.py -train ${indel_train_vcf} -test ${indel_test_vcf} -name ${sampleName}_indel	
+
+	>>>
+
+	runtime {
+		docker:docker
+		cluster: cluster_config
+		systemDisk: "cloud_ssd 40"
+		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
+	}
+
+	output {
+		File snv_true_txt = "${sampleName}_snv_predicted_true.txt"
+		File snv_false_txt = "${sampleName}_snv_predicted_false.txt"
+		File snv_true_bed = "${sampleName}_snv_predicted_true.bed"
+		File snv_false_bed = "${sampleName}_snv_predicted_false.bed"
+		File snv_padding = "${sampleName}_snv_padding.bed"
+		File indel_true_txt = "${sampleName}_indel_predicted_true.txt"
+		File indel_false_txt = "${sampleName}_indel_predicted_false.txt"
+		File indel_true_bed = "${sampleName}_indel_predicted_true.bed"
+		File indel_false_bed = "${sampleName}_indel_predicted_false.bed"
+		File indel_padding = "${sampleName}_indel_padding.bed"
+	}
+}
+

tasks/reformVCF.wdl

@@ -0,0 +1,38 @@
+task reformVCF {
+	File family_mendelian_info
+	File family_name
+	String docker
+	String cluster_config
+	String disk_size
+	
+	command <<<
+
+	python /opt/reformVCF.py -vcf ${family_mendelian_info} -name ${family_name}
+
+	cat ${family_name}.LCL5.vcf | grep -v '##' | grep -v '0/0' > ${family_name}.LCL5.txt
+	cat ${family_name}.LCL6.vcf | grep -v '##' | grep -v '0/0' > ${family_name}.LCL6.txt
+	cat ${family_name}.LCL7.vcf | grep -v '##' | grep -v '0/0' > ${family_name}.LCL7.txt
+	cat ${family_name}.LCL8.vcf | grep -v '##' | grep -v '0/0' > ${family_name}.LCL8.txt
+
+	>>>
+
+	runtime {
+		docker:docker
+		cluster: cluster_config
+		systemDisk: "cloud_ssd 40"
+		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
+	}
+
+	output {
+		File LCL5_family_info = "${family_name}.LCL5.vcf"
+		File LCL6_family_info = "${family_name}.LCL6.vcf"
+		File LCL7_family_info = "${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/sister.wdl

@@ -0,0 +1,35 @@
+task sister {
+	File LCL5_vcf
+	File LCL6_vcf
+	File ref_dir
+	String fasta
+	String family_name
+	String docker
+	String cluster_config
+	String disk_size
+	
+	command <<<
+		export LD_LIBRARY_PATH=/opt/htslib-1.9
+
+		mkdir sister
+		/opt/VBT-TrioAnalysis/vbt varcomp -called ${LCL5_vcf} -base ${LCL6_vcf} -ref ${ref_dir}/${fasta} -outDir sister -filter none
+
+		mv sister/TPBase.vcf ${family_name}.sister.consistent.vcf
+		mv sister/FP.vcf ${family_name}.LCL5.uniq.vcf
+		mv sister/FN.vcf ${family_name}.LCL6.uniq.vcf
+		mv sister/log.txt ${family_name}.sister.vbt.log.txt
+	>>>
+
+	runtime {
+		docker:docker
+		cluster: cluster_config
+		systemDisk: "cloud_ssd 40"
+		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
+	}
+	output {
+		File sister_consistent_vcf = "${family_name}.sister.consistent.vcf"
+		File LCL5_uniq = "${family_name}.LCL5.uniq.vcf"
+		File LCL6_uniq = "${family_name}.LCL6.uniq.vcf"
+		File log = "${family_name}.sister.vbt.log.txt"
+	}
+}

tasks/variantsNorm.wdl

@@ -0,0 +1,33 @@
+task variantsNorm {
+	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
+
+		cat ${sampleName}.normed.vcf | grep -v '##' > ${sampleName}.normed.txt
+
+	>>>
+
+	runtime {
+		docker:docker
+		cluster: cluster_config
+		systemDisk: "cloud_ssd 40"
+		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
+	}
+	output {
+		File normed_vcf = "${sampleName}.normed.vcf"
+		File normed_txt = "${sampleName}.normed.txt"
+	}
+}

tasks/votes.wdl

@@ -0,0 +1,26 @@
+task votes {
+	File repeat_annotated_vcf
+	File vcf_dup
+	String sample
+	String prefix
+	String docker
+	String cluster_config
+	String disk_size
+	
+	command <<<
+		python /opt/high_confidence_call_vote.py -vcf ${repeat_annotated_vcf} -dup ${vcf_dup} -sample ${sample} -prefix ${prefix}
+
+	>>>
+
+	runtime {
+		docker:docker
+		cluster: cluster_config
+		systemDisk: "cloud_ssd 40"
+		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
+	}
+	output {
+		File voted_vcf = "${prefix}_voted.vcf"
+		File all_sample_vcf = "${prefix}_all_sample_information.vcf"
+	}
+}
+

tasks/zipIndex.wdl

@@ -0,0 +1,24 @@
+task zipIndex {
+	File vcf
+	String vcf_name = basename(vcf,".vcf")
+	String docker
+	String cluster_config
+	String disk_size
+	
+	command <<<
+		rtg bgzip ${vcf} -c > ${vcf_name}.vcf.gz
+		rtg index -f vcf ${vcf_name}.vcf.gz
+
+	>>>
+
+	runtime {
+		docker:docker
+		cluster: cluster_config
+		systemDisk: "cloud_ssd 40"
+		dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
+	}
+	output {
+		File vcf_gz = "${vcf_name}.vcf.gz"
+		File vcf_idx = "${vcf_name}.vcf.gz.tbi"
+	}
+}

workflow.wdl

@@ -0,0 +1,92 @@
+import "./tasks/mendelian.wdl" as mendelian
+
+workflow {{ project_name }} {
+	File inputSamplesFile
+	Array[Array[File]] inputSamples = read_tsv(inputSamplesFile)
+	File ref_dir
+	String fasta
+	String cluster_config
+	String disk_size
+
+	scatter (quartet in inputSamples){
+		call mendelian.mendelian as LCL5mendelian {
+			input:
+			child_vcf=quartet[0],
+			LCL7_vcf=quartet[2],
+			LCL8_vcf=quartet[3],
+			LCL7_name=quartet[6],
+			LCL8_name=quartet[7],
+			child_name=quartet[4],
+			ref_dir=ref_dir,
+			fasta=fasta,
+			cluster_config=cluster_config,
+			disk_size=disk_size
+		}
+		call mendelian.mendelian as LCL6mendelian {
+			input:
+			child_vcf=quartet[1],
+			LCL7_vcf=quartet[2],
+			LCL8_vcf=quartet[3],
+			LCL7_name=quartet[6],
+			LCL8_name=quartet[7],
+			child_name=quartet[5],
+			ref_dir=ref_dir,
+			fasta=fasta,
+			cluster_config=cluster_config,
+			disk_size=disk_size
+		}
+		call zipIndex.zipIndex as LCL5zipIndex {
+			input:
+			vcf=LCL5mendelian.trio_vcf,
+			cluster_config=cluster_config,
+			disk_size=disk_size
+		}
+		call zipIndex.zipIndex as LCL6zipIndex {
+			input:
+			vcf=LCL6mendelian.trio_vcf,
+			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			
+		}
+	}
+}