from __future__ import division from glob import glob import sys, argparse, os import fileinput import re import pandas as pd from operator import itemgetter from collections import Counter from itertools import islice from numpy import * import statistics # input arguments parser = argparse.ArgumentParser(description="this script is to merge mendelian and vcfinfo, and extract high_confidence_calls") parser.add_argument('-folder', '--folder', type=str, help='directory that holds all the mendelian info', required=True) parser.add_argument('-vcf', '--vcf', type=str, help='merged multiple sample vcf', required=True) args = parser.parse_args() folder = args.folder vcf = args.vcf # input files folder = folder + '/*.txt' filenames = glob(folder) dataframes = [] for filename in filenames: dataframes.append(pd.read_table(filename,header=None)) dfs = [df.set_index([0, 1]) for df in dataframes] merged_mendelian = pd.concat(dfs, axis=1).reset_index() family_name = [i.split('/')[-1].replace('.txt','') for i in filenames] columns = ['CHROM','POS'] + family_name merged_mendelian.columns = columns vcf_dat = pd.read_table(vcf) merged_df = pd.merge(merged_mendelian, vcf_dat, how='outer', left_on=['CHROM','POS'], right_on = ['#CHROM','POS']) merged_df = merged_df.fillna('nan') vcf_header = '''##fileformat=VCFv4.2 ##fileDate=20200501 ##source=high_confidence_calls_intergration(choppy app) ##reference=GRCh38.d1.vd1 ##INFO= ##FORMAT= ##FORMAT= ##FORMAT= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ##contig= ''' # output files benchmark_LCL5 = open('LCL5_voted.vcf','w') benchmark_LCL6 = open('LCL6_voted.vcf','w') benchmark_LCL7 = open('LCL7_voted.vcf','w') benchmark_LCL8 = open('LCL8_voted.vcf','w') all_sample_outfile = open('all_sample_information.txt','w') # write VCF LCL5_col = '#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tLCL5_benchmark_calls\n' LCL6_col = '#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tLCL6_benchmark_calls\n' LCL7_col = '#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tLCL7_benchmark_calls\n' LCL8_col = '#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tLCL8_benchmark_calls\n' benchmark_LCL5.write(vcf_header) benchmark_LCL5.write(LCL5_col) benchmark_LCL6.write(vcf_header) benchmark_LCL6.write(LCL6_col) benchmark_LCL7.write(vcf_header) benchmark_LCL7.write(LCL7_col) benchmark_LCL8.write(vcf_header) benchmark_LCL8.write(LCL8_col) # all info all_info_col = 'CHROM\tPOS\tLCL5_pcr_consensus\tLCL5_pcr_free_consensus\tLCL5_mendelian_num\tLCL5_consensus_call\tLCL5_consensus_alt_seq\tLCL5_alt\tLCL5_dp\tLCL5_detected_num\tLCL6_pcr_consensus\tLCL6_pcr_free_consensus\tLCL6_mendelian_num\tLCL6_consensus_call\tLCL6_consensus_alt_seq\tLCL6_alt\tLCL6_dp\tLCL6_detected_num\tLCL7_pcr_consensus\tLCL7_pcr_free_consensus\tLCL7_mendelian_num\t LCL7_consensus_call\tLCL7_consensus_alt_seq\tLCL7_alt\tLCL7_dp\tLCL7_detected_num\tLCL8_pcr_consensus\tLCL8_pcr_free_consensus\tLCL8_mendelian_num\tLCL8_consensus_call\tLCL8_consensus_alt_seq\tLCL8_alt\tLCL8_dp\tLCL8_detected_num\n' all_sample_outfile.write(all_info_col) # function def decide_by_rep(vcf_list,mendelian_list): consensus_rep = '' gt = [x.split(':')[0] for x in vcf_list] # mendelian consistent? mendelian_dict = Counter(mendelian_list) highest_mendelian = mendelian_dict.most_common(1) candidate_mendelian = highest_mendelian[0][0] freq_mendelian = highest_mendelian[0][1] if (candidate_mendelian == '1:1:1') and (freq_mendelian >= 2): con_loc = [i for i in range(len(mendelian_list)) if mendelian_list[i] == '1:1:1'] gt_con = itemgetter(*con_loc)(gt) gt_num_dict = Counter(gt_con) highest_gt = gt_num_dict.most_common(1) candidate_gt = highest_gt[0][0] freq_gt = highest_gt[0][1] if (candidate_gt != './.') and (freq_gt >= 2): consensus_rep = candidate_gt elif (candidate_gt == './.') and (freq_gt >= 2): consensus_rep = 'noGTInfo' else: consensus_rep = 'inconGT' elif (candidate_mendelian == 'nan') and (freq_mendelian >= 2): consensus_rep = 'noMenInfo' else: consensus_rep = 'inconMen' return consensus_rep def consensus_call(vcf_info_list,mendelian_list,alt_seq): pcr_consensus = '.' pcr_free_consensus = '.' mendelian_num = '.' consensus_call = '.' consensus_alt_seq = '.' # pcr SEQ2000 = decide_by_rep(vcf_info_list[0:3],mendelian_list[0:3]) XTen_ARD = decide_by_rep(vcf_info_list[18:21],mendelian_list[18:21]) XTen_NVG = decide_by_rep(vcf_info_list[21:24],mendelian_list[21:24]) XTen_WUX = decide_by_rep(vcf_info_list[24:27],mendelian_list[24:27]) pcr_sequence_site = [SEQ2000,XTen_ARD,XTen_NVG,XTen_WUX] pcr_sequence_dict = Counter(pcr_sequence_site) pcr_highest_sequence = pcr_sequence_dict.most_common(1) pcr_candidate_sequence = pcr_highest_sequence[0][0] pcr_freq_sequence = pcr_highest_sequence[0][1] if pcr_freq_sequence > 2: pcr_consensus = pcr_candidate_sequence else: pcr_consensus = 'inconSequenceSite' # pcr-free T7_WGE = decide_by_rep(vcf_info_list[3:6],mendelian_list[3:6]) Nova_ARD_1 = decide_by_rep(vcf_info_list[6:9],mendelian_list[6:9]) Nova_ARD_2 = decide_by_rep(vcf_info_list[9:12],mendelian_list[9:12]) Nova_BRG = decide_by_rep(vcf_info_list[12:15],mendelian_list[12:15]) Nova_WUX = decide_by_rep(vcf_info_list[15:18],mendelian_list[15:18]) sequence_site = [T7_WGE,Nova_ARD_1,Nova_ARD_2,Nova_BRG,Nova_WUX] sequence_dict = Counter(sequence_site) highest_sequence = sequence_dict.most_common(1) candidate_sequence = highest_sequence[0][0] freq_sequence = highest_sequence[0][1] if freq_sequence > 3: pcr_free_consensus = candidate_sequence else: pcr_free_consensus = 'inconSequenceSite' # net alt, dp # alt AD = [x.split(':')[1] for x in vcf_info_list] ALT = [x.split(',')[1] for x in AD] ALT = [int(x) for x in ALT] ALL_ALT = sum(ALT) # dp DP = [x.split(':')[2] for x in vcf_info_list] DP = [int(x) for x in DP] ALL_DP = sum(DP) # detected number gt = [x.split(':')[0] for x in vcf_info_list] gt = [x.replace('0/0','.') for x in gt] gt = [x.replace('./.','.') for x in gt] detected_num = 27 - gt.count('.') # decide consensus calls tag = ['inconGT','noMenInfo','inconMen','inconSequenceSite','noGTInfo'] if (pcr_consensus != '0/0') and (pcr_consensus == pcr_free_consensus) and (pcr_consensus not in tag): consensus_call = pcr_consensus gt = [x.split(':')[0] for x in vcf_info_list] indices = [i for i, x in enumerate(gt) if x == consensus_call] matched_mendelian = itemgetter(*indices)(mendelian_list) mendelian_num = matched_mendelian.count('1:1:1') # Delete multiple alternative genotype to necessary expression alt_gt = alt_seq.split(',') if len(alt_gt) > 1: allele1 = consensus_call.split('/')[0] allele2 = consensus_call.split('/')[1] if allele1 == '0': allele2_seq = alt_gt[int(allele2) - 1] consensus_alt_seq = allele2_seq consensus_call = '0/1' else: allele1_seq = alt_gt[int(allele1) - 1] allele2_seq = alt_gt[int(allele2) - 1] if int(allele1) > int(allele2): consensus_alt_seq = allele2_seq + ',' + allele1_seq consensus_call = '1/2' elif int(allele1) < int(allele2): consensus_alt_seq = allele1_seq + ',' + allele2_seq consensus_call = '1/2' else: consensus_alt_seq = allele1_seq consensus_call = '1/1' else: consensus_alt_seq = alt_seq elif (pcr_consensus in tag) and (pcr_free_consensus in tag): consensus_call = 'filtered' elif ((pcr_consensus == './.') or (pcr_consensus in tag)) and ((pcr_free_consensus not in tag) and (pcr_free_consensus != './.')): consensus_call = 'pcr-free-speicifc' elif ((pcr_consensus != './.') or (pcr_consensus not in tag)) and ((pcr_free_consensus in tag) and (pcr_free_consensus == './.')): consensus_call = 'pcr-speicifc' elif (pcr_consensus == '0/0') and (pcr_free_consensus == '0/0'): consensus_call = '0/0' else: consensus_call = 'filtered' return pcr_consensus, pcr_free_consensus, mendelian_num, consensus_call, consensus_alt_seq, ALL_ALT, ALL_DP, detected_num for row in merged_df.itertuples(): mendelian_list = [row.Quartet_DNA_BGI_SEQ2000_BGI_1_20180518,row.Quartet_DNA_BGI_SEQ2000_BGI_2_20180530,row.Quartet_DNA_BGI_SEQ2000_BGI_3_20180530, \ row.Quartet_DNA_BGI_T7_WGE_1_20191105,row.Quartet_DNA_BGI_T7_WGE_2_20191105,row.Quartet_DNA_BGI_T7_WGE_3_20191105, \ row.Quartet_DNA_ILM_Nova_ARD_1_20181108,row.Quartet_DNA_ILM_Nova_ARD_2_20181108,row.Quartet_DNA_ILM_Nova_ARD_3_20181108, \ row.Quartet_DNA_ILM_Nova_ARD_4_20190111,row.Quartet_DNA_ILM_Nova_ARD_5_20190111,row.Quartet_DNA_ILM_Nova_ARD_6_20190111, \ row.Quartet_DNA_ILM_Nova_BRG_1_20180930,row.Quartet_DNA_ILM_Nova_BRG_2_20180930,row.Quartet_DNA_ILM_Nova_BRG_3_20180930, \ row.Quartet_DNA_ILM_Nova_WUX_1_20190917,row.Quartet_DNA_ILM_Nova_WUX_2_20190917,row.Quartet_DNA_ILM_Nova_WUX_3_20190917, \ row.Quartet_DNA_ILM_XTen_ARD_1_20170403,row.Quartet_DNA_ILM_XTen_ARD_2_20170403,row.Quartet_DNA_ILM_XTen_ARD_3_20170403, \ row.Quartet_DNA_ILM_XTen_NVG_1_20170329,row.Quartet_DNA_ILM_XTen_NVG_2_20170329,row.Quartet_DNA_ILM_XTen_NVG_3_20170329, \ row.Quartet_DNA_ILM_XTen_WUX_1_20170216,row.Quartet_DNA_ILM_XTen_WUX_2_20170216,row.Quartet_DNA_ILM_XTen_WUX_3_20170216] lcl5_list = [row.Quartet_DNA_BGI_SEQ2000_BGI_LCL5_1_20180518,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL5_2_20180530,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL5_3_20180530, \ row.Quartet_DNA_BGI_T7_WGE_LCL5_1_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL5_2_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL5_3_20191105, \ row.Quartet_DNA_ILM_Nova_ARD_LCL5_1_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL5_2_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL5_3_20181108, \ row.Quartet_DNA_ILM_Nova_ARD_LCL5_4_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL5_5_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL5_6_20190111, \ row.Quartet_DNA_ILM_Nova_BRG_LCL5_1_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL5_2_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL5_3_20180930, \ row.Quartet_DNA_ILM_Nova_WUX_LCL5_1_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL5_2_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL5_3_20190917, \ row.Quartet_DNA_ILM_XTen_ARD_LCL5_1_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL5_2_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL5_3_20170403, \ row.Quartet_DNA_ILM_XTen_NVG_LCL5_1_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL5_2_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL5_3_20170329, \ row.Quartet_DNA_ILM_XTen_WUX_LCL5_1_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL5_2_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL5_3_20170216] lcl6_list = [row.Quartet_DNA_BGI_SEQ2000_BGI_LCL6_1_20180518,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL6_2_20180530,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL6_3_20180530, \ row.Quartet_DNA_BGI_T7_WGE_LCL6_1_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL6_2_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL6_3_20191105, \ row.Quartet_DNA_ILM_Nova_ARD_LCL6_1_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL6_2_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL6_3_20181108, \ row.Quartet_DNA_ILM_Nova_ARD_LCL6_4_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL6_5_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL6_6_20190111, \ row.Quartet_DNA_ILM_Nova_BRG_LCL6_1_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL6_2_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL6_3_20180930, \ row.Quartet_DNA_ILM_Nova_WUX_LCL6_1_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL6_2_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL6_3_20190917, \ row.Quartet_DNA_ILM_XTen_ARD_LCL6_1_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL6_2_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL6_3_20170403, \ row.Quartet_DNA_ILM_XTen_NVG_LCL6_1_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL6_2_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL6_3_20170329, \ row.Quartet_DNA_ILM_XTen_WUX_LCL6_1_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL6_2_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL6_3_20170216] lcl7_list = [row.Quartet_DNA_BGI_SEQ2000_BGI_LCL7_1_20180518,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL7_2_20180530,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL7_3_20180530, \ row.Quartet_DNA_BGI_T7_WGE_LCL7_1_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL7_2_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL7_3_20191105, \ row.Quartet_DNA_ILM_Nova_ARD_LCL7_1_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL7_2_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL7_3_20181108, \ row.Quartet_DNA_ILM_Nova_ARD_LCL7_4_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL7_5_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL7_6_20190111, \ row.Quartet_DNA_ILM_Nova_BRG_LCL7_1_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL7_2_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL7_3_20180930, \ row.Quartet_DNA_ILM_Nova_WUX_LCL7_1_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL7_2_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL7_3_20190917, \ row.Quartet_DNA_ILM_XTen_ARD_LCL7_1_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL7_2_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL7_3_20170403, \ row.Quartet_DNA_ILM_XTen_NVG_LCL7_1_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL7_2_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL7_3_20170329, \ row.Quartet_DNA_ILM_XTen_WUX_LCL7_1_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL7_2_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL7_3_20170216] lcl8_list = [row.Quartet_DNA_BGI_SEQ2000_BGI_LCL8_1_20180518,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL8_2_20180530,row.Quartet_DNA_BGI_SEQ2000_BGI_LCL8_3_20180530, \ row.Quartet_DNA_BGI_T7_WGE_LCL8_1_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL8_2_20191105,row.Quartet_DNA_BGI_T7_WGE_LCL8_3_20191105, \ row.Quartet_DNA_ILM_Nova_ARD_LCL8_1_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL8_2_20181108,row.Quartet_DNA_ILM_Nova_ARD_LCL8_3_20181108, \ row.Quartet_DNA_ILM_Nova_ARD_LCL8_4_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL8_5_20190111,row.Quartet_DNA_ILM_Nova_ARD_LCL8_6_20190111, \ row.Quartet_DNA_ILM_Nova_BRG_LCL8_1_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL8_2_20180930,row.Quartet_DNA_ILM_Nova_BRG_LCL8_3_20180930, \ row.Quartet_DNA_ILM_Nova_WUX_LCL8_1_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL8_2_20190917,row.Quartet_DNA_ILM_Nova_WUX_LCL8_3_20190917, \ row.Quartet_DNA_ILM_XTen_ARD_LCL8_1_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL8_2_20170403,row.Quartet_DNA_ILM_XTen_ARD_LCL8_3_20170403, \ row.Quartet_DNA_ILM_XTen_NVG_LCL8_1_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL8_2_20170329,row.Quartet_DNA_ILM_XTen_NVG_LCL8_3_20170329, \ row.Quartet_DNA_ILM_XTen_WUX_LCL8_1_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL8_2_20170216,row.Quartet_DNA_ILM_XTen_WUX_LCL8_3_20170216] # LCL5 LCL5_pcr_consensus, LCL5_pcr_free_consensus, LCL5_mendelian_num, LCL5_consensus_call, LCL5_consensus_alt_seq, LCL5_alt, LCL5_dp, LCL5_detected_num = consensus_call(lcl5_list,mendelian_list,row.ALT) if LCL5_mendelian_num != '.': LCL5_output = row.CHROM + '\t' + str(row.POS) + '\t' + '.' + '\t' + row.REF + '\t' + LCL5_consensus_alt_seq + '\t' + '.' + '\t' + '.' + '\t' +'VOTED=' + str(LCL5_mendelian_num) + '\t' + 'GT:ALT:DP' + '\t' + LCL5_consensus_call + ':' + str(LCL5_alt) + ':' + str(LCL5_dp) + '\n' benchmark_LCL5.write(LCL5_output) # LCL6 LCL6_pcr_consensus, LCL6_pcr_free_consensus, LCL6_mendelian_num, LCL6_consensus_call, LCL6_consensus_alt_seq, LCL6_alt, LCL6_dp, LCL6_detected_num = consensus_call(lcl6_list,mendelian_list,row.ALT) if LCL6_mendelian_num != '.': LCL6_output = row.CHROM + '\t' + str(row.POS) + '\t' + '.' + '\t' + row.REF + '\t' + LCL6_consensus_alt_seq + '\t' + '.' + '\t' + '.' + '\t' +'VOTED=' + str(LCL6_mendelian_num) + '\t' + 'GT:ALT:DP' + '\t' + LCL6_consensus_call + ':' + str(LCL6_alt) + ':' + str(LCL6_dp) + '\n' benchmark_LCL6.write(LCL6_output) # LCL7 LCL7_pcr_consensus, LCL7_pcr_free_consensus, LCL7_mendelian_num, LCL7_consensus_call, LCL7_consensus_alt_seq, LCL7_alt, LCL7_dp, LCL7_detected_num = consensus_call(lcl7_list,mendelian_list,row.ALT) if LCL7_mendelian_num != '.': LCL7_output = row.CHROM + '\t' + str(row.POS) + '\t' + '.' + '\t' + row.REF + '\t' + LCL7_consensus_alt_seq + '\t' + '.' + '\t' + '.' + '\t' +'VOTED=' + str(LCL7_mendelian_num) + '\t' + 'GT:ALT:DP' + '\t' + LCL7_consensus_call + ':' + str(LCL7_alt) + ':' + str(LCL7_dp) + '\n' benchmark_LCL7.write(LCL7_output) # LCL8 LCL8_pcr_consensus, LCL8_pcr_free_consensus, LCL8_mendelian_num, LCL8_consensus_call, LCL8_consensus_alt_seq, LCL8_alt, LCL8_dp, LCL8_detected_num = consensus_call(lcl8_list,mendelian_list,row.ALT) if LCL8_mendelian_num != '.': LCL8_output = row.CHROM + '\t' + str(row.POS) + '\t' + '.' + '\t' + row.REF + '\t' + LCL8_consensus_alt_seq + '\t' + '.' + '\t' + '.' + '\t' +'VOTED=' + str(LCL8_mendelian_num) + '\t' + 'GT:ALT:DP' + '\t' + LCL8_consensus_call + ':' + str(LCL8_alt) + ':' + str(LCL8_dp) + '\n' benchmark_LCL8.write(LCL8_output) # all data all_output = row.CHROM + '\t' + str(row.POS) + '\t' + LCL5_pcr_consensus + '\t' + LCL5_pcr_free_consensus + '\t' + str(LCL5_mendelian_num) + '\t' + LCL5_consensus_call + '\t' + LCL5_consensus_alt_seq + '\t' + str(LCL5_alt) + '\t' + str(LCL5_dp) + '\t' + str(LCL5_detected_num) + '\t' +\ LCL6_pcr_consensus + '\t' + LCL6_pcr_free_consensus + '\t' + str(LCL6_mendelian_num) + '\t' + LCL6_consensus_call + '\t' + LCL6_consensus_alt_seq + '\t' + str(LCL6_alt) + '\t' + str(LCL6_dp) + '\t' + str(LCL6_detected_num) + '\t' +\ LCL7_pcr_consensus + '\t' + LCL7_pcr_free_consensus + '\t' + str(LCL7_mendelian_num) + '\t' + LCL7_consensus_call + '\t' + LCL7_consensus_alt_seq + '\t' + str(LCL7_alt) + '\t' + str(LCL7_dp) + '\t' + str(LCL7_detected_num) + '\t' +\ LCL8_pcr_consensus + '\t' + LCL8_pcr_free_consensus + '\t' + str(LCL8_mendelian_num) + '\t' + LCL8_consensus_call + '\t' + LCL8_consensus_alt_seq + '\t' + str(LCL8_alt) + '\t' + str(LCL8_dp) + '\t' + str(LCL8_detected_num) + '\n' all_sample_outfile.write(all_output)