LUYAO REN
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README.md
Näytä tiedosto
| #高置信突变位点的整合 | |||||
| > Author： Run Luyao | |||||
| > | |||||
| > E-mail：18110700050@fudan.edu.cn | |||||
| > | |||||
| > 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 | |||||
| ## App输出文件 | |||||
| ## 结果展示与解读 | |||||
| ## CHANGELOG | |||||
| ## FAQ | |||||
BIN
codescripts/.DS_Store
Näytä tiedosto
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codescripts/bed_for_bamReadcount.py
Näytä tiedosto
| 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() | |||||
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codescripts/extract_vcf_information.py
Näytä tiedosto
| 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 | |||||
| elif kv[0] == 'POSITIVE_TRAIN_SITE': | |||||
| pass | |||||
| elif kv[0] == 'NEGATIVE_TRAIN_SITE': | |||||
| 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) | |||||
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codescripts/high_confidence_call_vote.py
Näytä tiedosto
| # import modules | |||||
| import sys, argparse, os | |||||
| import fileinput | |||||
| import re | |||||
| import pandas as pd | |||||
| from operator import itemgetter | |||||
| from collections import Counter | |||||
| from itertools import islice | |||||
| # 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=20191224 | |||||
| ##source=high_confidence_calls_intergration(choppy app) | |||||
| ##reference=GRCh38.d1.vd1 | |||||
| ##INFO=<ID=DPCT,Number=1,Type=Float,Description="Percentage of detected votes"> | |||||
| ##INFO=<ID=VPCT,Number=1,Type=Float,Description="Percentage of consnesus votes"> | |||||
| ##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype"> | |||||
| ##contig=<ID=chr1,length=248956422> | |||||
| ##contig=<ID=chr2,length=242193529> | |||||
| ##contig=<ID=chr3,length=198295559> | |||||
| ##contig=<ID=chr4,length=190214555> | |||||
| ##contig=<ID=chr5,length=181538259> | |||||
| ##contig=<ID=chr6,length=170805979> | |||||
| ##contig=<ID=chr7,length=159345973> | |||||
| ##contig=<ID=chr8,length=145138636> | |||||
| ##contig=<ID=chr9,length=138394717> | |||||
| ##contig=<ID=chr10,length=133797422> | |||||
| ##contig=<ID=chr11,length=135086622> | |||||
| ##contig=<ID=chr12,length=133275309> | |||||
| ##contig=<ID=chr13,length=114364328> | |||||
| ##contig=<ID=chr14,length=107043718> | |||||
| ##contig=<ID=chr15,length=101991189> | |||||
| ##contig=<ID=chr16,length=90338345> | |||||
| ##contig=<ID=chr17,length=83257441> | |||||
| ##contig=<ID=chr18,length=80373285> | |||||
| ##contig=<ID=chr19,length=58617616> | |||||
| ##contig=<ID=chr20,length=64444167> | |||||
| ##contig=<ID=chr21,length=46709983> | |||||
| ##contig=<ID=chr22,length=50818468> | |||||
| ##contig=<ID=chrX,length=156040895> | |||||
| ''' | |||||
| # 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') | |||||
| # 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' + '\t' + sample_name + '_consensus_alt_seq' +'\n' | |||||
| outfile.write(vcf_header) | |||||
| outfile.write(outputcolumn) | |||||
| #function | |||||
| def detected_percentage(strings): | |||||
| strings = [x.replace('0/0','.') for x in strings] | |||||
| gt = [x.split(':')[0] for x in strings] | |||||
| percentage = round((33 - gt.count('.'))/33,4) | |||||
| return(str(percentage)) | |||||
| def vote_percentage(strings,consensus_call): | |||||
| strings = [x.replace('.','0/0') for x in strings] | |||||
| gt = [x.split(':')[0] for x in strings] | |||||
| gt = list(map(gt_uniform,[i for i in gt])) | |||||
| percentage = round(gt.count(consensus_call)/33,4) | |||||
| return(str(percentage)) | |||||
| def family_vote(strings,consensus_call): | |||||
| pass | |||||
| 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] | |||||
| gt = [x.split(':')[0] for x in strings] | |||||
| # 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[6] = 'dupVar' | |||||
| outLine = '\t'.join(strings) + '\t' + '.' +'\t' + '.' + '\t' + 'dupVar' + '\t' + '.' +'\n' | |||||
| outfile.write(outLine) | |||||
| else: | |||||
| # pre-define | |||||
| 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[0:3]) | |||||
| 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] | |||||
| 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: | |||||
| 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] | |||||
| highest_sequence = sequence_dict.most_common(1) | |||||
| candidate_sequence = highest_sequence[0][0] | |||||
| freq_sequence = highest_sequence[0][1] | |||||
| if freq_sequence > 2: | |||||
| pcr_free_consensus = candidate_sequence | |||||
| else: | |||||
| pcr_free_consensus = 'inconSequenceSite' | |||||
| # 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 | |||||
| VPCT = vote_percentage(strings[9:],consensus_call) | |||||
| strings[7] = 'VPCT=' + VPCT | |||||
| DPCT = detected_percentage(strings[9:]) | |||||
| strings[7] = strings[7] + ';DPCT=' + DPCT | |||||
| # Delete multiple alternative genotype to necessary expression | |||||
| strings[6] = 'reproducible' | |||||
| 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 | |||||
| elif (pcr_consensus in tag) and (pcr_free_consensus in tag): | |||||
| consensus_call = 'filtered' | |||||
| strings[6] = 'filtered' | |||||
| DPCT = detected_percentage(strings[9:]) | |||||
| strings[7] = 'DPCT=' + DPCT | |||||
| 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] = 'pcr-free-speicifc' | |||||
| DPCT = detected_percentage(strings[9:]) | |||||
| strings[7] = 'DPCT=' + DPCT | |||||
| 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] = 'pcr-speicifc' | |||||
| DPCT = detected_percentage(strings[9:]) | |||||
| strings[7] = 'DPCT=' + DPCT | |||||
| elif (pcr_consensus == '0/0') and (pcr_free_consensus == '0/0'): | |||||
| consensus_call = 'confirm for parents' | |||||
| strings[6] = 'confirm for parents' | |||||
| DPCT = detected_percentage(strings[9:]) | |||||
| strings[7] = 'DPCT=' + DPCT | |||||
| else: | |||||
| consensus_call = 'filtered' | |||||
| strings[6] = 'filtered' | |||||
| DPCT = detected_percentage(strings[9:]) | |||||
| strings[7] = 'DPCT=' + DPCT | |||||
| # output | |||||
| outLine = '\t'.join(strings) + '\t' + pcr_consensus +'\t' + pcr_free_consensus + '\t' + consensus_call + '\t' + consensus_alt_seq + '\n' | |||||
| outfile.write(outLine) | |||||
| if __name__ == '__main__': | |||||
| main() | |||||
+ 109
- 0
codescripts/oneClass.py
Näytä tiedosto
| # 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) | |||||
+ 144
- 0
codescripts/reformVCF.py
Näytä tiedosto
| # 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) | |||||
+ 62
- 0
codescripts/select_small_variants_supported_by_all_callsets.py
Näytä tiedosto
| 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() | |||||
+ 36
- 0
codescripts/vcf2bed.py
Näytä tiedosto
| 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 |
+ 1
- 0
family.tsv
Näytä tiedosto
| #LCL5vcf LCL6vcf LCL7vcf LCL8vcf LCL5name LCL6name LCL7name LCL8name familyname |
+ 29
- 0
inputs
Näytä tiedosto
| { | |||||
| "{{ 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 }}.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 }}.disk_size": "150", | |||||
| "{{ project_name }}.inputSamplesFile": "{{ inputSamplesFile }}", | |||||
| "{{ 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 }}.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 }}.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/" | |||||
| } | |||||
BIN
pictures/.DS_Store
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BIN
pictures/workflow.png
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| Before | After |
|---|---|
|
|
| Width: 2178 | Height: 1290 | Size: 180KB |
BIN
tasks/.DS_Store
Näytä tiedosto
+ 23
- 0
tasks/VCFinfo.wdl
Näytä tiedosto
| task VCFinfo { | |||||
| File merged_info | |||||
| String sample | |||||
| String docker | |||||
| String cluster_config | |||||
| String disk_size | |||||
| command <<< | |||||
| >>> | |||||
| runtime { | |||||
| docker:docker | |||||
| cluster: cluster_config | |||||
| systemDisk: "cloud_ssd 40" | |||||
| dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/" | |||||
| } | |||||
| output { | |||||
| File extracted_info = "" | |||||
| } | |||||
| } |
+ 31
- 0
tasks/VCFrename.wdl
Näytä tiedosto
| 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" | |||||
| } | |||||
| } |
+ 30
- 0
tasks/indelNorm.wdl
Näytä tiedosto
| 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" | |||||
| } | |||||
| } |
+ 34
- 0
tasks/mendelian.wdl
Näytä tiedosto
| 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" | |||||
| } | |||||
| } |
+ 27
- 0
tasks/merge.wdl
Näytä tiedosto
| 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 --no-gzip -o ${sample}.merged.vcf ${sep=" " family_vcf_gz} | |||||
| cat ${sample}.merged.vcf | grep -v '#' | cut -f1-2 | sed s'/\t/_/g' | sort | uniq -c | sed 's/\s\+/\t/g' | awk '{ if ($1 != 1) { print } }' | cut -f3 > ${sample}.vcf_dup.txt | |||||
| >>> | |||||
| runtime { | |||||
| docker:docker | |||||
| cluster: cluster_config | |||||
| systemDisk: "cloud_ssd 40" | |||||
| dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/" | |||||
| } | |||||
| output { | |||||
| File merged_vcf = "${sample}.merged.vcf" | |||||
| File vcf_dup = "${sample}.vcf_dup.txt" | |||||
| } | |||||
| } |
+ 34
- 0
tasks/mergeSister.wdl
Näytä tiedosto
| 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" | |||||
| } | |||||
| } |
+ 24
- 0
tasks/mergeVCFInfo.wdl
Näytä tiedosto
| 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 --no-gzip -o ${sample}.merged.info.vcf ${sep=" " vcf_gz} | |||||
| >>> | |||||
| runtime { | |||||
| docker:docker | |||||
| cluster: cluster_config | |||||
| systemDisk: "cloud_ssd 40" | |||||
| dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/" | |||||
| } | |||||
| output { | |||||
| File merged_info = "${sample}.merged.info.vcf" | |||||
| } | |||||
| } |
+ 39
- 0
tasks/oneClass.wdl
Näytä tiedosto
| 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" | |||||
| } | |||||
| } | |||||
+ 30
- 0
tasks/reformVCF.wdl
Näytä tiedosto
| 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 ${family_name} | |||||
| >>> | |||||
| runtime { | |||||
| docker:docker | |||||
| cluster: cluster_config | |||||
| systemDisk: "cloud_ssd 40" | |||||
| dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/" | |||||
| } | |||||
| output { | |||||
| File LCL5_family_info = "${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" | |||||
| } | |||||
| } | |||||
+ 35
- 0
tasks/sister.wdl
Näytä tiedosto
| 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" | |||||
| } | |||||
| } |
+ 30
- 0
tasks/variantsNorm.wdl
Näytä tiedosto
| 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 > ${sampleName}.normed.vcf | |||||
| >>> | |||||
| runtime { | |||||
| docker:docker | |||||
| cluster: cluster_config | |||||
| systemDisk: "cloud_ssd 40" | |||||
| dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/" | |||||
| } | |||||
| output { | |||||
| File normed_vcf = "${sampleName}.normed.vcf" | |||||
| } | |||||
| } |
+ 25
- 0
tasks/votes.wdl
Näytä tiedosto
| task votes { | |||||
| File merged_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 ${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' > ${prefix}_bechmarking_calls.vcf | |||||
| >>> | |||||
| runtime { | |||||
| docker:docker | |||||
| cluster: cluster_config | |||||
| systemDisk: "cloud_ssd 40" | |||||
| dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/" | |||||
| } | |||||
| output { | |||||
| File annotated_vcf = "${prefix}_annotated.vcf" | |||||
| File benchmark_call = "${prefix}_bechmarking_calls.vcf" | |||||
| } | |||||
| } |
+ 25
- 0
tasks/zipIndex.wdl
Näytä tiedosto
| task zipIndex { | |||||
| File vcf | |||||
| String sample | |||||
| String family_name | |||||
| 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 | |||||
| >>> | |||||
| runtime { | |||||
| docker:docker | |||||
| cluster: cluster_config | |||||
| systemDisk: "cloud_ssd 40" | |||||
| 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" | |||||
| } | |||||
| } |
+ 207
- 0
workflow.wdl
Näytä tiedosto
| 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 | |||||
| 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 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, | |||||
| 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=LCL6variantsNorm.normed_vcf, | |||||
| LCL7_vcf=LCL7variantsNorm.normed_vcf, | |||||
| LCL8_vcf=LCL8variantsNorm.normed_vcf, | |||||
| 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, | |||||
| 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", | |||||
| cluster_config=cluster_config, | |||||
| disk_size=disk_size | |||||
| } | |||||
| call merge.merge as LCL6merge { | |||||
| input: | |||||
| family_vcf_gz=LCL6familyzipIndex.vcf_gz, | |||||
| family_vcf_idx=LCL6familyzipIndex.vcf_idx, | |||||
| sample="LCL6", | |||||
| cluster_config=cluster_config, | |||||
| disk_size=disk_size | |||||
| } | |||||
| call merge.merge as LCL7merge { | |||||
| input: | |||||
| family_vcf_gz=LCL7familyzipIndex.vcf_gz, | |||||
| family_vcf_idx=LCL7familyzipIndex.vcf_idx, | |||||
| sample="LCL7", | |||||
| cluster_config=cluster_config, | |||||
| disk_size=disk_size | |||||
| } | |||||
| call merge.merge as LCL8merge { | |||||
| input: | |||||
| family_vcf_gz=LCL8familyzipIndex.vcf_gz, | |||||
| family_vcf_idx=LCL8familyzipIndex.vcf_idx, | |||||
| sample="LCL8", | |||||
| cluster_config=cluster_config, | |||||
| disk_size=disk_size | |||||
| } | |||||
| } | |||||
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