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high_confidence_calls_manuscript
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LUYAO REN 5 years ago
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c0415bc2de
commit
1c7be9f554
11 changed files with 319 additions and 580 deletions
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  1. +0
    -295
      codescripts/FinalResult2VCF.py
  2. +206
    -54
      codescripts/high_confidence_call_vote.py
  3. +15
    -13
      codescripts/merge_mendelian_vcfinfo.py
  4. +0
    -62
      codescripts/select_small_variants_supported_by_all_callsets.py
  5. +0
    -81
      codescripts/variants_quality_location_intergration.py
  6. +0
    -52
      codescripts/vcf_mq_af.py
  7. +11
    -2
      inputs
  8. +4
    -4
      tasks/bed_annotation.wdl
  9. +4
    -3
      tasks/merge.wdl
  10. +4
    -14
      tasks/votes.wdl
  11. +75
    -0
      workflow.wdl

+ 0
- 295
codescripts/FinalResult2VCF.py View File

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('-prefix', '--prefix', type=str, help='The prefix of output filenames', 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
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=DPCT,Number=1,Type=Float,Description="Percentage of detected votes">
##INFO=<ID=VPCT,Number=1,Type=Float,Description="Percentage of consnesus votes">
##INFO=<ID=FPCT,Number=1,Type=Float,Description="Percentage of mendelian consisitent votes">
##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
##FORMAT=<ID=DP,Number=1,Type=Int,Description="Depth">
##FORMAT=<ID=AF,Number=1,Type=Float,Description="Allele frequency">
##FORMAT=<ID=GQ,Number=1,Type=Float,Description="Genotype quality">
##FORMAT=<ID=MQ,Number=1,Type=Float,Description="Mapping 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=DPCT,Number=1,Type=Float,Description="Percentage of detected votes">
##INFO=<ID=VPCT,Number=1,Type=Float,Description="Percentage of consnesus votes">
##INFO=<ID=FPCT,Number=1,Type=Float,Description="Percentage of 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=MQ_MEAN,Number=1,Type=Float,Description="Mean of mapping quality of all samples">
##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=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>
'''

# output file
file_name = prefix + '_benchmarking_calls.vcf'
outfile = open(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 + '_high_confidence_calls\n'
outfile.write(vcf_header)
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)

# 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('.')

# function
def single_sample_format(format_x,strings_x,strings_y):
gt = '.'
dp = '.'
af = '.'
gq = '.'
mq = '.'
twins = '.'
trio5 = '.'
trio6 = '.'
# GT:DP:AF:GQ:MQ:TWINS:TRIO5:TRIO6
# strings_x
format_strings = format_x.split(':')
if (strings_x == '.') and (strings_y != '.'):
element_strings_y = str(strings_y).split(':')
gt = '0/0'
dp = '.'
af = '.'
gq = '.'
mq = '.'
twins = element_strings_y[1]
trio5 = element_strings_y[2]
trio6 = element_strings_y[3]
elif (strings_x != '.') and (strings_y == '.'):
element_strings_x = strings_x.split(':')
formatDict = dict(zip(format_strings, element_strings_x))
gt = formatDict['GT']
dp = formatDict['DP']
af = formatDict['AF']
gq = formatDict['GQ']
mq = formatDict['MQ']
twins = '.'
trio5 = '.'
trio6 = '.'
elif (strings_x != '.') and (strings_y != '.'):
element_strings_y = str(strings_y).split(':')
element_strings_x = strings_x.split(':')
formatDict = dict(zip(format_strings, element_strings_x))
gt = formatDict['GT']
dp = formatDict['DP']
af = formatDict['AF']
gq = formatDict['GQ']
mq = formatDict['MQ']
twins = element_strings_y[1]
trio5 = element_strings_y[2]
trio6 = element_strings_y[3]
else:
pass
merged_format = gt + ':' + dp + ':' + af + ':' + gq + ':' + mq + ':' + twins + ':' + trio5 + ':' + trio6
return(merged_format)

#
for row in merged_df.itertuples():
vcf_count = row[10:37].count('.')
mendelian_count = row[50:77].count('.')
if vcf_count == mendelian_count:
info = 'location=' + str(row.location) + ';' + str(row.INFO_y)
if row.FILTER_y == 'reproducible':
ref = row.DP - row._42
FORMAT = row[79] + ':' + str(int(ref)) + ',' + str(int(row._42)) + ':' + str(int(row.DP)) + ':' + str(round(row.AF,2)) + ':' + str(round(row.GQ,2)) + ':' + str(round(row.MQ,2))
outline1 = str(row._1) + '\t' + str(row.POS) + '\t' + str(row.ID_x) + '\t' + str(row.REF_y) + '\t' + str(row[80]) + '\t' + '.' + '\t' + '.' + '\t' + str(info) + '\t' + 'GT:AD:DP:AF:GQ:MQ' + '\t' + str(FORMAT) + '\n'
outfile.write(outline1)
else:
pass
if row.INFO_x != '.':
if row.AF=='.':
info = 'location=' + str(row.location) + ';' + str(row.INFO_y) + ';' + 'ALL_ALT=' + str(int(row._42)) + ';' + 'ALL_DP=' + str(int(row.DP)) + ';' + 'ALL_AF=' + 'NA' + ';' + 'GQ_MEAN=' + str(row.GQ) + ';' + 'MQ_MEAN=' + str(row.MQ) + ';' + 'PCR=' + str(row[77]) + ';' + 'PCR_FREE=' + str(row[78]) + ';' + 'CONSENSUS=' + str(row[79]) + ';' + 'CONSENSUS_SEQ=' + str(row[80])
else:
info = 'location=' + str(row.location) + ';' + str(row.INFO_y) + ';' + 'ALL_ALT=' + str(int(row._42)) + ';' + 'ALL_DP=' + str(int(row.DP)) + ';' + 'ALL_AF=' + str(round(float(row.AF),2)) + ';' + 'GQ_MEAN=' + str(row.GQ) + ';' + 'MQ_MEAN=' + str(row.MQ) + ';' + 'PCR=' + str(row[77]) + ';' + 'PCR_FREE=' + str(row[78]) + ';' + 'CONSENSUS=' + str(row[79]) + ';' + 'CONSENSUS_SEQ=' + str(row[80])
Quartet_DNA_BGI_SEQ2000_BGI_1_20180518_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_BGI_SEQ2000_BGI_1_20180518_LCL5_x, row.Quartet_DNA_BGI_SEQ2000_BGI_1_20180518_LCL5_y)
Quartet_DNA_BGI_SEQ2000_BGI_2_20180530_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_BGI_SEQ2000_BGI_2_20180530_LCL5_x, row.Quartet_DNA_BGI_SEQ2000_BGI_2_20180530_LCL5_y)
Quartet_DNA_BGI_SEQ2000_BGI_3_20180530_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_BGI_SEQ2000_BGI_3_20180530_LCL5_x, row.Quartet_DNA_BGI_SEQ2000_BGI_3_20180530_LCL5_y)
Quartet_DNA_BGI_T7_WGE_1_20191105_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_BGI_T7_WGE_1_20191105_LCL5_x, row.Quartet_DNA_BGI_T7_WGE_1_20191105_LCL5_y)
Quartet_DNA_BGI_T7_WGE_2_20191105_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_BGI_T7_WGE_2_20191105_LCL5_x, row.Quartet_DNA_BGI_T7_WGE_2_20191105_LCL5_y)
Quartet_DNA_BGI_T7_WGE_3_20191105_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_BGI_T7_WGE_3_20191105_LCL5_x, row.Quartet_DNA_BGI_T7_WGE_3_20191105_LCL5_y)
Quartet_DNA_ILM_Nova_ARD_1_20181108_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_ARD_1_20181108_LCL5_x, row.Quartet_DNA_ILM_Nova_ARD_1_20181108_LCL5_y)
Quartet_DNA_ILM_Nova_ARD_2_20181108_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_ARD_2_20181108_LCL5_x, row.Quartet_DNA_ILM_Nova_ARD_2_20181108_LCL5_y)
Quartet_DNA_ILM_Nova_ARD_3_20181108_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_ARD_3_20181108_LCL5_x, row.Quartet_DNA_ILM_Nova_ARD_3_20181108_LCL5_y)
Quartet_DNA_ILM_Nova_ARD_4_20190111_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_ARD_4_20190111_LCL5_x, row.Quartet_DNA_ILM_Nova_ARD_4_20190111_LCL5_y)
Quartet_DNA_ILM_Nova_ARD_5_20190111_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_ARD_5_20190111_LCL5_x, row.Quartet_DNA_ILM_Nova_ARD_5_20190111_LCL5_y)
Quartet_DNA_ILM_Nova_ARD_6_20190111_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_ARD_6_20190111_LCL5_x, row.Quartet_DNA_ILM_Nova_ARD_6_20190111_LCL5_y)
Quartet_DNA_ILM_Nova_BRG_1_20180930_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_BRG_1_20180930_LCL5_x, row.Quartet_DNA_ILM_Nova_BRG_1_20180930_LCL5_y)
Quartet_DNA_ILM_Nova_BRG_2_20180930_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_BRG_2_20180930_LCL5_x, row.Quartet_DNA_ILM_Nova_BRG_2_20180930_LCL5_y)
Quartet_DNA_ILM_Nova_BRG_3_20180930_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_BRG_3_20180930_LCL5_x, row.Quartet_DNA_ILM_Nova_BRG_3_20180930_LCL5_y)
Quartet_DNA_ILM_Nova_WUX_1_20190917_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_WUX_1_20190917_LCL5_x, row.Quartet_DNA_ILM_Nova_WUX_1_20190917_LCL5_y)
Quartet_DNA_ILM_Nova_WUX_2_20190917_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_WUX_2_20190917_LCL5_x, row.Quartet_DNA_ILM_Nova_WUX_2_20190917_LCL5_y)
Quartet_DNA_ILM_Nova_WUX_3_20190917_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_WUX_3_20190917_LCL5_x, row.Quartet_DNA_ILM_Nova_WUX_3_20190917_LCL5_y)
Quartet_DNA_ILM_XTen_ARD_1_20170403_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_XTen_ARD_1_20170403_LCL5_x, row.Quartet_DNA_ILM_XTen_ARD_1_20170403_LCL5_y)
Quartet_DNA_ILM_XTen_ARD_2_20170403_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_XTen_ARD_2_20170403_LCL5_x, row.Quartet_DNA_ILM_XTen_ARD_2_20170403_LCL5_y)
Quartet_DNA_ILM_XTen_ARD_3_20170403_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_XTen_ARD_3_20170403_LCL5_x, row.Quartet_DNA_ILM_XTen_ARD_3_20170403_LCL5_y)
Quartet_DNA_ILM_XTen_NVG_1_20170329_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_XTen_NVG_1_20170329_LCL5_x, row.Quartet_DNA_ILM_XTen_NVG_1_20170329_LCL5_y)
Quartet_DNA_ILM_XTen_NVG_2_20170329_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_XTen_NVG_2_20170329_LCL5_x, row.Quartet_DNA_ILM_XTen_NVG_2_20170329_LCL5_y)
Quartet_DNA_ILM_XTen_NVG_3_20170329_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_XTen_NVG_3_20170329_LCL5_x, row.Quartet_DNA_ILM_XTen_NVG_3_20170329_LCL5_y)
Quartet_DNA_ILM_XTen_WUX_1_20170216_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_XTen_WUX_1_20170216_LCL5_x, row.Quartet_DNA_ILM_XTen_WUX_1_20170216_LCL5_y)
Quartet_DNA_ILM_XTen_WUX_2_20170216_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_XTen_WUX_2_20170216_LCL5_x, row.Quartet_DNA_ILM_XTen_WUX_2_20170216_LCL5_y)
Quartet_DNA_ILM_XTen_WUX_3_20170216_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_XTen_WUX_3_20170216_LCL5_x, row.Quartet_DNA_ILM_XTen_WUX_3_20170216_LCL5_y)
outline2 = str(row._1) + '\t' + str(row.POS) + '\t' + str(row.ID_x) +'\t' + str(row.REF_x) + '\t' + str(row.ALT_x) + '\t' + '.' + '\t' + '.' + '\t' + str(info) + '\t' + 'GT:DP:AF:GQ:MQ:TWINS:TRIO5:TRIO6' + '\t' \
+ str(Quartet_DNA_BGI_SEQ2000_BGI_1_20180518_LCL5) + '\t' + str(Quartet_DNA_BGI_SEQ2000_BGI_2_20180530_LCL5) + '\t' + str(Quartet_DNA_BGI_SEQ2000_BGI_3_20180530_LCL5) + '\t' \
+ str(Quartet_DNA_BGI_T7_WGE_1_20191105_LCL5) + '\t' + str(Quartet_DNA_BGI_T7_WGE_2_20191105_LCL5) + '\t' + str(Quartet_DNA_BGI_T7_WGE_3_20191105_LCL5) + '\t' \
+ str(Quartet_DNA_ILM_Nova_ARD_1_20181108_LCL5) + '\t' + str(Quartet_DNA_ILM_Nova_ARD_2_20181108_LCL5) + '\t' + str(Quartet_DNA_ILM_Nova_ARD_3_20181108_LCL5) + '\t' \
+ str(Quartet_DNA_ILM_Nova_ARD_4_20190111_LCL5) + '\t' + str(Quartet_DNA_ILM_Nova_ARD_5_20190111_LCL5) + '\t' + str(Quartet_DNA_ILM_Nova_ARD_6_20190111_LCL5) + '\t' \
+ str(Quartet_DNA_ILM_Nova_BRG_1_20180930_LCL5) + '\t' + str(Quartet_DNA_ILM_Nova_BRG_2_20180930_LCL5) + '\t' + str(Quartet_DNA_ILM_Nova_BRG_3_20180930_LCL5) + '\t' \
+ str(Quartet_DNA_ILM_Nova_WUX_1_20190917_LCL5) + '\t' + str(Quartet_DNA_ILM_Nova_WUX_2_20190917_LCL5) + '\t' + str(Quartet_DNA_ILM_Nova_WUX_3_20190917_LCL5) + '\t' \
+ str(Quartet_DNA_ILM_XTen_ARD_1_20170403_LCL5) + '\t' + str(Quartet_DNA_ILM_XTen_ARD_2_20170403_LCL5) + '\t' + str(Quartet_DNA_ILM_XTen_ARD_3_20170403_LCL5) + '\t' \
+ str(Quartet_DNA_ILM_XTen_NVG_1_20170329_LCL5) + '\t' + str(Quartet_DNA_ILM_XTen_NVG_2_20170329_LCL5) + '\t' + str(Quartet_DNA_ILM_XTen_NVG_3_20170329_LCL5) + '\t' \
+ str(Quartet_DNA_ILM_XTen_WUX_1_20170216_LCL5) + '\t' + str(Quartet_DNA_ILM_XTen_WUX_2_20170216_LCL5) + '\t' + str(Quartet_DNA_ILM_XTen_WUX_3_20170216_LCL5) + '\n'
all_sample_outfile.write(outline2)
else:
info = '.'
Quartet_DNA_BGI_SEQ2000_BGI_1_20180518_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_BGI_SEQ2000_BGI_1_20180518_LCL5_x, row.Quartet_DNA_BGI_SEQ2000_BGI_1_20180518_LCL5_y)
Quartet_DNA_BGI_SEQ2000_BGI_2_20180530_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_BGI_SEQ2000_BGI_2_20180530_LCL5_x, row.Quartet_DNA_BGI_SEQ2000_BGI_2_20180530_LCL5_y)
Quartet_DNA_BGI_SEQ2000_BGI_3_20180530_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_BGI_SEQ2000_BGI_3_20180530_LCL5_x, row.Quartet_DNA_BGI_SEQ2000_BGI_3_20180530_LCL5_y)
Quartet_DNA_BGI_T7_WGE_1_20191105_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_BGI_T7_WGE_1_20191105_LCL5_x, row.Quartet_DNA_BGI_T7_WGE_1_20191105_LCL5_y)
Quartet_DNA_BGI_T7_WGE_2_20191105_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_BGI_T7_WGE_2_20191105_LCL5_x, row.Quartet_DNA_BGI_T7_WGE_2_20191105_LCL5_y)
Quartet_DNA_BGI_T7_WGE_3_20191105_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_BGI_T7_WGE_3_20191105_LCL5_x, row.Quartet_DNA_BGI_T7_WGE_3_20191105_LCL5_y)
Quartet_DNA_ILM_Nova_ARD_1_20181108_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_ARD_1_20181108_LCL5_x, row.Quartet_DNA_ILM_Nova_ARD_1_20181108_LCL5_y)
Quartet_DNA_ILM_Nova_ARD_2_20181108_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_ARD_2_20181108_LCL5_x, row.Quartet_DNA_ILM_Nova_ARD_2_20181108_LCL5_y)
Quartet_DNA_ILM_Nova_ARD_3_20181108_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_ARD_3_20181108_LCL5_x, row.Quartet_DNA_ILM_Nova_ARD_3_20181108_LCL5_y)
Quartet_DNA_ILM_Nova_ARD_4_20190111_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_ARD_4_20190111_LCL5_x, row.Quartet_DNA_ILM_Nova_ARD_4_20190111_LCL5_y)
Quartet_DNA_ILM_Nova_ARD_5_20190111_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_ARD_5_20190111_LCL5_x, row.Quartet_DNA_ILM_Nova_ARD_5_20190111_LCL5_y)
Quartet_DNA_ILM_Nova_ARD_6_20190111_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_ARD_6_20190111_LCL5_x, row.Quartet_DNA_ILM_Nova_ARD_6_20190111_LCL5_y)
Quartet_DNA_ILM_Nova_BRG_1_20180930_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_BRG_1_20180930_LCL5_x, row.Quartet_DNA_ILM_Nova_BRG_1_20180930_LCL5_y)
Quartet_DNA_ILM_Nova_BRG_2_20180930_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_BRG_2_20180930_LCL5_x, row.Quartet_DNA_ILM_Nova_BRG_2_20180930_LCL5_y)
Quartet_DNA_ILM_Nova_BRG_3_20180930_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_BRG_3_20180930_LCL5_x, row.Quartet_DNA_ILM_Nova_BRG_3_20180930_LCL5_y)
Quartet_DNA_ILM_Nova_WUX_1_20190917_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_WUX_1_20190917_LCL5_x, row.Quartet_DNA_ILM_Nova_WUX_1_20190917_LCL5_y)
Quartet_DNA_ILM_Nova_WUX_2_20190917_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_WUX_2_20190917_LCL5_x, row.Quartet_DNA_ILM_Nova_WUX_2_20190917_LCL5_y)
Quartet_DNA_ILM_Nova_WUX_3_20190917_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_Nova_WUX_3_20190917_LCL5_x, row.Quartet_DNA_ILM_Nova_WUX_3_20190917_LCL5_y)
Quartet_DNA_ILM_XTen_ARD_1_20170403_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_XTen_ARD_1_20170403_LCL5_x, row.Quartet_DNA_ILM_XTen_ARD_1_20170403_LCL5_y)
Quartet_DNA_ILM_XTen_ARD_2_20170403_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_XTen_ARD_2_20170403_LCL5_x, row.Quartet_DNA_ILM_XTen_ARD_2_20170403_LCL5_y)
Quartet_DNA_ILM_XTen_ARD_3_20170403_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_XTen_ARD_3_20170403_LCL5_x, row.Quartet_DNA_ILM_XTen_ARD_3_20170403_LCL5_y)
Quartet_DNA_ILM_XTen_NVG_1_20170329_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_XTen_NVG_1_20170329_LCL5_x, row.Quartet_DNA_ILM_XTen_NVG_1_20170329_LCL5_y)
Quartet_DNA_ILM_XTen_NVG_2_20170329_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_XTen_NVG_2_20170329_LCL5_x, row.Quartet_DNA_ILM_XTen_NVG_2_20170329_LCL5_y)
Quartet_DNA_ILM_XTen_NVG_3_20170329_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_XTen_NVG_3_20170329_LCL5_x, row.Quartet_DNA_ILM_XTen_NVG_3_20170329_LCL5_y)
Quartet_DNA_ILM_XTen_WUX_1_20170216_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_XTen_WUX_1_20170216_LCL5_x, row.Quartet_DNA_ILM_XTen_WUX_1_20170216_LCL5_y)
Quartet_DNA_ILM_XTen_WUX_2_20170216_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_XTen_WUX_2_20170216_LCL5_x, row.Quartet_DNA_ILM_XTen_WUX_2_20170216_LCL5_y)
Quartet_DNA_ILM_XTen_WUX_3_20170216_LCL5 = single_sample_format(row.FORMAT_x, row.Quartet_DNA_ILM_XTen_WUX_3_20170216_LCL5_x, row.Quartet_DNA_ILM_XTen_WUX_3_20170216_LCL5_y)
outline2 = str(row._1) + '\t' + str(row.POS) + '\t' + str(row.ID_x) +'\t' + str(row.REF_x) + '\t' + str(row.ALT_x) + '\t' + '.' + '\t' + '.' + '\t' + str(info) + '\t' + 'GT:DP:AF:GQ:MQ:TWINS:TRIO5:TRIO6' + '\t' \
+ str(Quartet_DNA_BGI_SEQ2000_BGI_1_20180518_LCL5) + '\t' + str(Quartet_DNA_BGI_SEQ2000_BGI_2_20180530_LCL5) + '\t' + str(Quartet_DNA_BGI_SEQ2000_BGI_3_20180530_LCL5) + '\t' \
+ str(Quartet_DNA_BGI_T7_WGE_1_20191105_LCL5) + '\t' + str(Quartet_DNA_BGI_T7_WGE_2_20191105_LCL5) + '\t' + str(Quartet_DNA_BGI_T7_WGE_3_20191105_LCL5) + '\t' \
+ str(Quartet_DNA_ILM_Nova_ARD_1_20181108_LCL5) + '\t' + str(Quartet_DNA_ILM_Nova_ARD_2_20181108_LCL5) + '\t' + str(Quartet_DNA_ILM_Nova_ARD_3_20181108_LCL5) + '\t' \
+ str(Quartet_DNA_ILM_Nova_ARD_4_20190111_LCL5) + '\t' + str(Quartet_DNA_ILM_Nova_ARD_5_20190111_LCL5) + '\t' + str(Quartet_DNA_ILM_Nova_ARD_6_20190111_LCL5) + '\t' \
+ str(Quartet_DNA_ILM_Nova_BRG_1_20180930_LCL5) + '\t' + str(Quartet_DNA_ILM_Nova_BRG_2_20180930_LCL5) + '\t' + str(Quartet_DNA_ILM_Nova_BRG_3_20180930_LCL5) + '\t' \
+ str(Quartet_DNA_ILM_Nova_WUX_1_20190917_LCL5) + '\t' + str(Quartet_DNA_ILM_Nova_WUX_2_20190917_LCL5) + '\t' + str(Quartet_DNA_ILM_Nova_WUX_3_20190917_LCL5) + '\t' \
+ str(Quartet_DNA_ILM_XTen_ARD_1_20170403_LCL5) + '\t' + str(Quartet_DNA_ILM_XTen_ARD_2_20170403_LCL5) + '\t' + str(Quartet_DNA_ILM_XTen_ARD_3_20170403_LCL5) + '\t' \
+ str(Quartet_DNA_ILM_XTen_NVG_1_20170329_LCL5) + '\t' + str(Quartet_DNA_ILM_XTen_NVG_2_20170329_LCL5) + '\t' + str(Quartet_DNA_ILM_XTen_NVG_3_20170329_LCL5) + '\t' \
+ str(Quartet_DNA_ILM_XTen_WUX_1_20170216_LCL5) + '\t' + str(Quartet_DNA_ILM_XTen_WUX_2_20170216_LCL5) + '\t' + str(Quartet_DNA_ILM_XTen_WUX_3_20170216_LCL5) + '\n'
all_sample_outfile.write(outline2)
else:








+ 206
- 54
codescripts/high_confidence_call_vote.py View File

from operator import itemgetter from operator import itemgetter
from collections import Counter from collections import Counter
from itertools import islice from itertools import islice
from numpy import *
import statistics


# input arguments # input arguments
parser = argparse.ArgumentParser(description="this script is to count voting number") parser = argparse.ArgumentParser(description="this script is to count voting number")
sample_name = args.sample_name sample_name = args.sample_name


vcf_header = '''##fileformat=VCFv4.2 vcf_header = '''##fileformat=VCFv4.2
##fileDate=20191224
##fileDate=20200331
##source=high_confidence_calls_intergration(choppy app) ##source=high_confidence_calls_intergration(choppy app)
##reference=GRCh38.d1.vd1 ##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">
##INFO=<ID=FPCT,Number=1,Type=Float,Description="Percentage of mendelian consisitent votes">
##INFO=<ID=location,Number=1,Type=String,Description="Repeat region">
##INFO=<ID=DETECTED,Number=1,Type=Integer,Description="Number of detected votes">
##INFO=<ID=VOTED,Number=1,Type=Integer,Description="Number of consnesus votes">
##INFO=<ID=FAM,Number=1,Type=Integer,Description="Number mendelian consisitent votes">
##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype"> ##FORMAT=<ID=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=chr1,length=248956422>
##contig=<ID=chr2,length=242193529> ##contig=<ID=chr2,length=242193529>
##contig=<ID=chr3,length=198295559> ##contig=<ID=chr3,length=198295559>
##contig=<ID=chrX,length=156040895> ##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 # read in duplication list
dup = pd.read_table(dup_list,header=None) dup = pd.read_table(dup_list,header=None)
var_dup = dup[0].tolist() var_dup = dup[0].tolist()


# output file # output file
file_name = prefix + '_annotated.vcf'
outfile = open(file_name,'w')
benchmark_file_name = prefix + '_voted.vcf'
benchmark_outfile = open(benchmark_file_name,'w')

all_sample_file_name = prefix + '_all_sample_information.vcf'
all_sample_outfile = open(all_sample_file_name,'w')


# write VCF # 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_T7_WGE_1_20191105_LCL5\tQuartet_DNA_BGI_T7_WGE_2_20191105_LCL5\tQuartet_DNA_BGI_T7_WGE_3_20191105_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_20180930_LCL5\tQuartet_DNA_ILM_Nova_BRG_2_20180930_LCL5\tQuartet_DNA_ILM_Nova_BRG_3_20180930_LCL5\tQuartet_DNA_ILM_Nova_WUX_1_20190917_LCL5\tQuartet_DNA_ILM_Nova_WUX_2_20190917_LCL5\tQuartet_DNA_ILM_Nova_WUX_3_20190917_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' +'\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)
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 #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 detected_percentage(strings):
strings = [x.replace('0/0','.') for x in strings]
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] gt = [x.split(':')[0] for x in strings]
percentage = round((27 - gt.count('.'))/27,4)
percentage = 27 - gt.count('.')
return(str(percentage)) return(str(percentage))


def vote_percentage(strings,consensus_call):
strings = [x.replace('.','0/0') for x in strings]
def vote_number(strings,consensus_call):
gt = [x.split(':')[0] for x in strings] 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])) gt = list(map(gt_uniform,[i for i in gt]))
percentage = round(gt.count(consensus_call)/27,4)
return(str(percentage))
vote_num = gt.count(consensus_call)
return(str(vote_num))


def family_vote(strings,consensus_call): def family_vote(strings,consensus_call):
strings = [x.replace('.','0/0') for x in strings]
gt = [x.split(':')[0] for x in strings] 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])) gt = list(map(gt_uniform,[i for i in gt]))
mendelian = [x[-5:] for x in strings]
mendelian = [':'.join(x.split(':')[1:4]) for x in strings]
indices = [i for i, x in enumerate(gt) if x == consensus_call] indices = [i for i, x in enumerate(gt) if x == consensus_call]
matched_mendelian = itemgetter(*indices)(mendelian) matched_mendelian = itemgetter(*indices)(mendelian)
percentage = round(matched_mendelian.count('1:1:1')/27,4)
return(str(percentage))
mendelian_num = matched_mendelian.count('1:1:1')
return(str(mendelian_num))


def gt_uniform(strings): def gt_uniform(strings):
uniformed_gt = '' uniformed_gt = ''


def decide_by_rep(strings): def decide_by_rep(strings):
consensus_rep = '' consensus_rep = ''
mendelian = [x[-5:] for x in strings]
strings = [x.replace('.','0/0') for x in strings]
mendelian = [':'.join(x.split(':')[1:4]) for x in strings]
gt = [x.split(':')[0] for x in strings] gt = [x.split(':')[0] for x in strings]
gt = [x.replace('.','0/0') for x in gt]
# modified gt turn 2/1 to 1/2 # modified gt turn 2/1 to 1/2
gt = list(map(gt_uniform,[i for i in gt])) gt = list(map(gt_uniform,[i for i in gt]))
# mendelian consistent? # mendelian consistent?
consensus_rep = '0/0' consensus_rep = '0/0'
else: else:
consensus_rep = 'inconGT' consensus_rep = 'inconGT'
elif (candidate_mendelian == '.') and (freq_mendelian >= 2):
elif (candidate_mendelian == '') and (freq_mendelian >= 2):
consensus_rep = 'noInfo' consensus_rep = 'noInfo'
else: else:
consensus_rep = 'inconMen' consensus_rep = 'inconMen'
variant_id = '_'.join([strings[0],strings[1]]) variant_id = '_'.join([strings[0],strings[1]])
# check if the variants location is duplicated # check if the variants location is duplicated
if variant_id in var_dup: if variant_id in var_dup:
strings[6] = 'dupVar'
outLine = '\t'.join(strings) + '\t' + '.' +'\t' + '.' + '\t' + 'dupVar' + '\t' + '.' +'\n'
outfile.write(outLine)
strings[7] = strings[7] + ';DUP'
outLine = '\t'.join(strings) + '\n'
all_sample_outfile.write(outLine)
else: else:
# pre-define # pre-define
pcr_consensus = '.' pcr_consensus = '.'
consensus_call = '.' consensus_call = '.'
consensus_alt_seq = '.' consensus_alt_seq = '.'
# pcr # pcr
strings[9:] = replace_nan(strings[9:])
pcr = itemgetter(*[9,10,11,27,28,29,30,31,32,33,34,35])(strings) pcr = itemgetter(*[9,10,11,27,28,29,30,31,32,33,34,35])(strings)
SEQ2000 = decide_by_rep(pcr[0:3]) SEQ2000 = decide_by_rep(pcr[0:3])
XTen_ARD = decide_by_rep(pcr[3:6]) XTen_ARD = decide_by_rep(pcr[3:6])
pcr_consensus = 'inconSequenceSite' pcr_consensus = 'inconSequenceSite'
# pcr-free # pcr-free
pcr_free = itemgetter(*[12,13,14,15,16,17,18,19,20,21,22,23,24,25,26])(strings) pcr_free = itemgetter(*[12,13,14,15,16,17,18,19,20,21,22,23,24,25,26])(strings)
#SEQ2000 = decide_by_rep(pcr_free[0])
T7_WGE = decide_by_rep(pcr_free[0:3]) T7_WGE = decide_by_rep(pcr_free[0:3])
Nova_ARD_1 = decide_by_rep(pcr_free[3:6]) Nova_ARD_1 = decide_by_rep(pcr_free[3:6])
Nova_ARD_2 = decide_by_rep(pcr_free[6:9]) Nova_ARD_2 = decide_by_rep(pcr_free[6:9])
tag = ['inconGT','noInfo','inconMen','inconSequenceSite'] tag = ['inconGT','noInfo','inconMen','inconSequenceSite']
if (pcr_consensus == pcr_free_consensus) and (pcr_consensus not in tag) and (pcr_consensus != '0/0'): if (pcr_consensus == pcr_free_consensus) and (pcr_consensus not in tag) and (pcr_consensus != '0/0'):
consensus_call = pcr_consensus 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
FPCT = family_vote(strings[9:],consensus_call)
strings[7] = strings[7] + ';FPCT=' + FPCT
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 # Delete multiple alternative genotype to necessary expression
strings[6] = 'reproducible'
alt = strings[4] alt = strings[4]
alt_gt = alt.split(',') alt_gt = alt.split(',')
if len(alt_gt) > 1: if len(alt_gt) > 1:
consensus_call = '1/1' consensus_call = '1/1'
else: else:
consensus_alt_seq = alt 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): elif (pcr_consensus in tag) and (pcr_free_consensus in tag):
consensus_call = 'filtered' consensus_call = 'filtered'
strings[6] = 'filtered'
DPCT = detected_percentage(strings[9:])
strings[7] = 'DPCT=' + DPCT
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')): 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' consensus_call = 'pcr-free-speicifc'
strings[6] = 'pcr-free-speicifc'
DPCT = detected_percentage(strings[9:])
strings[7] = 'DPCT=' + DPCT
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')): 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' consensus_call = 'pcr-speicifc'
strings[6] = 'pcr-speicifc'
DPCT = detected_percentage(strings[9:])
strings[7] = 'DPCT=' + DPCT
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'): 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
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: else:
consensus_call = 'filtered' 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)

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__': if __name__ == '__main__':
main() main()

+ 15
- 13
codescripts/merge_mendelian_vcfinfo.py View File

##fileDate=20200331 ##fileDate=20200331
##source=high_confidence_calls_intergration(choppy app) ##source=high_confidence_calls_intergration(choppy app)
##reference=GRCh38.d1.vd1 ##reference=GRCh38.d1.vd1
#FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
#FORMAT=<ID=TWINS,Number=1,Type=String,Description="1 for sister consistent, 0 for sister different">
#FORMAT=<ID=TRIO5,Number=1,Type=String,Description="1 for LCL7, LCL8 and LCL5 mendelian consistent, 0 for family violation">
#FORMAT=<ID=TRIO6,Number=1,Type=String,Description="1 for LCL7, LCL8 and LCL6 mendelian consistent, 0 for family violation">
##FORMAT=<ID=DP,Number=1,Type=Int,Description="Depth">
##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=AF,Number=1,Type=Float,Description="Allele frequency">
##FORMAT=<ID=GQ,Number=1,Type=Float,Description="Genotype quality">
##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=QD,Number=1,Type=Float,Description="Variant Confidence/Quality by Depth">
##FORMAT=<ID=MQ,Number=1,Type=Float,Description="Mapping quality"> ##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 bia"> ##FORMAT=<ID=FS,Number=1,Type=Float,Description="Phred-scaled p-value using Fisher's exact test to detect strand bia">
return infoDict return infoDict
# #
for row in merged_df.itertuples(): for row in merged_df.itertuples():
if row.Quartet_DNA_BGI_SEQ2000_BGI_1_20180518_LCL5 != '.':
if row[18] != '.':
# format # format
# GT:TWINS:TRIO5:TRIO6:DP:AF:GQ:QD:MQ:FS:QUAL # GT:TWINS:TRIO5:TRIO6:DP:AF:GQ:QD:MQ:FS:QUAL
FORMAT_x = row.Quartet_DNA_BGI_SEQ2000_BGI_LCL5_1_20180518.split(':')
FORMAT_x = row[10].split(':')
ALT = int(FORMAT_x[1].split(',')[1]) ALT = int(FORMAT_x[1].split(',')[1])
if int(FORMAT_x[2]) != 0: if int(FORMAT_x[2]) != 0:
AF = round(ALT/int(FORMAT_x[2]),2) AF = round(ALT/int(FORMAT_x[2]),2)
INFO_x['QD'] = '.' INFO_x['QD'] = '.'
else: else:
pass pass
FORMAT = row.Quartet_DNA_BGI_SEQ2000_BGI_1_20180518_LCL5 + ':' + FORMAT_x[2] + ':' + str(AF) + ':' + FORMAT_x[3] + ':' + INFO_x['QD'] + ':' + INFO_x['MQ'] + ':' + INFO_x['FS'] + ':' + str(row.QUAL_x)
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
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:AF:GQ:QD:MQ:FS:QUAL' + '\t' + FORMAT + '\n'
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: else:
FORMAT_x = row.Quartet_DNA_BGI_SEQ2000_BGI_LCL5_1_20180518.split(':')
rawGT = row[10].split(':')
FORMAT_x = row[10].split(':')
ALT = int(FORMAT_x[1].split(',')[1]) ALT = int(FORMAT_x[1].split(',')[1])
if int(FORMAT_x[2]) != 0: if int(FORMAT_x[2]) != 0:
AF = round(ALT/int(FORMAT_x[2]),2) AF = round(ALT/int(FORMAT_x[2]),2)
INFO_x['QD'] = '.' INFO_x['QD'] = '.'
else: else:
pass pass
FORMAT = '.:.:.:.' + ':' + FORMAT_x[2] + ':' + str(AF) + ':' + FORMAT_x[3] + ':' + INFO_x['QD'] + ':' + INFO_x['MQ'] + ':' + INFO_x['FS'] + ':' + str(row.QUAL_x)
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
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:AF:GQ:QD:MQ:FS:QUAL' + '\t' + FORMAT + '\n'
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) outfile.write(outline)

+ 0
- 62
codescripts/select_small_variants_supported_by_all_callsets.py View File

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()


+ 0
- 81
codescripts/variants_quality_location_intergration.py View File

from __future__ import division
import sys, argparse, os
import fileinput
import re
import statistics

# input arguments
parser = argparse.ArgumentParser(description="this script is to intergeate vcf information, variants quality and location")

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('-prefix', '--prefix', type=str, help='Prefix of output file name', required=True)

args = parser.parse_args()
multi_sample_vcf = args.multi_sample_vcf
prefix = args.prefix


def get_location(info):
repeat = ''
if 'ANN' in info:
strings = info.strip().split(';')
for element in strings:
m = re.match('ANN',element)
if m is not None:
repeat = element.split('=')[1]
else:
repeat = '.'
return repeat


def extract_info_normal(FORMAT,strings):
GQ = []
MQ = []
DP = []
ALT = []
format_strings = FORMAT.split(':')
for element in strings:
if element == '.':
pass
else:
element_strings = element.split(':')
formatDict = dict(zip(format_strings, element_strings))
alt = int(formatDict['ALT'])
dp = int(formatDict['DP'])
gq = int(formatDict['GQ'])
mq = float(formatDict['MQ'])
GQ.append(gq)
MQ.append(mq)
DP.append(dp)
ALT.append(alt)
DP_a = sum(DP)
ALT_a = sum(ALT)
if DP_a == 0:
AF_m = 'NA'
else:
AF_m = float(ALT_a/DP_a)
GQ_m = statistics.mean(GQ)
MQ_m = statistics.mean(MQ)
return AF_m,GQ_m,MQ_m,DP_a,ALT_a


file_name = prefix + '_variant_quality_location.txt'
outfile = open(file_name,'w')
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_T7_WGE_1_20191105_LCL5\tQuartet_DNA_BGI_T7_WGE_2_20191105_LCL5\tQuartet_DNA_BGI_T7_WGE_3_20191105_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_20180930_LCL5\tQuartet_DNA_ILM_Nova_BRG_2_20180930_LCL5\tQuartet_DNA_ILM_Nova_BRG_3_20180930_LCL5\tQuartet_DNA_ILM_Nova_WUX_1_20190917_LCL5\tQuartet_DNA_ILM_Nova_WUX_2_20190917_LCL5\tQuartet_DNA_ILM_Nova_WUX_3_20190917_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' +'\t'+ 'location' + '\t' + 'AF' + '\t' + 'GQ' + '\t' + 'MQ' + '\t' + 'DP' + '\t' + 'ALT' +'\n'
outfile.write(outputcolumn)

for line in fileinput.input(multi_sample_vcf):
m = re.match('^\#',line)
if m is not None:
pass
else:
line = line.strip()
strings = line.split('\t')
repeat = get_location(strings[7])
AF,GQ,MQ,DP,ALT = extract_info_normal(strings[8],strings[9:])
outLine = '\t'.join(strings) + '\t' + repeat +'\t' + str(AF) + '\t' + str(GQ) + '\t' + str(MQ) + '\t' + str(DP) + '\t' + str(ALT) + '\n'
outfile.write(outLine)



+ 0
- 52
codescripts/vcf_mq_af.py View File

from __future__ import division
import sys, argparse, os
import fileinput
import re
import statistics

# input arguments
parser = argparse.ArgumentParser(description="this script is to get mapping quality, allele frequency and alternative depth")

parser.add_argument('-vcf', '--normed_vcf', type=str, help='The VCF file you want to used', required=True)
parser.add_argument('-prefix', '--prefix', type=str, help='Prefix of output file name', required=True)

args = parser.parse_args()
normed_vcf = args.normed_vcf
prefix = args.prefix


file_name = prefix + '_variant_quality_location.vcf'
outfile = open(file_name,'w')

for line in fileinput.input(normed_vcf):
m = re.match('^\#',line)
if m is not None:
outfile.write(line)
else:
line = line.strip()
strings = line.split('\t')
strings[8] = strings[8] + ':MQ:ALT:AF'
infos = strings[7].strip().split(';')
## MQ
for element in infos:
m = re.match('MQ=',element)
if m is not None:
MQ = element.split('=')[1]
## ALT
ad = strings[9].split(':')[1]
ad_single = ad.split(',')
ad_single = [int(i) for i in ad_single]
DP = sum(ad_single)
if DP != 0:
ad_single.pop(0)
ALT = sum(ad_single)
AF = ALT/DP
else:
ALT = 0
AF = 'NA'
outLine = '\t'.join(strings) + ':' + MQ + ':' + str(ALT) + ':' + str(AF) + '\n'
outfile.write(outLine)



+ 11
- 2
inputs View File

{ {
"{{ project_name }}.LCL7merge.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest", "{{ 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 }}.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 }}.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 }}.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 }}.LCL6mendelian.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/vbt:v1.1",
"{{ project_name }}.disk_size": "150", "{{ 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 }}.LCL7mergeInfo.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/high_confidence_call_manuscript:v1.1",
"{{ project_name }}.inputSamplesFile": "{{ inputSamplesFile }}", "{{ 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 }}.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 }}.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 }}.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 }}.LCL7allInfozipIndex.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest",
"{{ project_name }}.LCL6mergeInfo.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/high_confidence_call_manuscript:v1.1",
"{{ 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 }}.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 }}.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 }}.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 }}.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 }}.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 }}.LCL8allInfozipIndex.docker": "registry-vpc.cn-shanghai.aliyuncs.com/pgx-docker-registry/rtg-tools:latest",

+ 4
- 4
tasks/bed_annotation.wdl View File

task bed_annotation { task bed_annotation {
File merged_vcf
File merged_vcf_gz
File merged_vcf_idx File merged_vcf_idx
File repeat_bed File repeat_bed
String sample String sample
command <<< command <<<


rtg vcfannotate --bed-info=${repeat_bed} -i ${merged_vcf} -o ${sample}.normed.repeatAnno.vcf.gz
rtg vcfannotate --bed-info=${repeat_bed} -i ${merged_vcf_gz} -o ${sample}.mendelian.merged.repeatAnno.vcf.gz


gunzip ${sample}.normed.repeatAnno.vcf.gz
gunzip ${sample}.mendelian.merged.repeatAnno.vcf.gz


>>> >>>


dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/" dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
} }
output { output {
File repeat_annotated_vcf = "${sample}.normed.repeatAnno.vcf"
File repeat_annotated_vcf = "${sample}.mendelian.merged.repeatAnno.vcf"
} }
} }

+ 4
- 3
tasks/merge.wdl View File

command <<< command <<<


rtg vcfmerge --force-merge-all --no-gzip -o ${sample}.merged.vcf ${sep=" " family_vcf_gz}
rtg vcfmerge --force-merge-all -o ${sample}.merged.vcf.gz ${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
zcat ${sample}.merged.vcf.gz | grep -v '#' | cut -f1-2 | sed s'/\t/_/g' | sort | uniq -c | sed 's/\s\+/\t/g' | awk '{ if ($1 != 1) { print } }' | cut -f3 > ${sample}.vcf_dup.txt


>>> >>>


dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/" dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
} }
output { output {
File merged_vcf = "${sample}.merged.vcf"
File merged_vcf_gz = "${sample}.merged.vcf.gz"
File merged_vcf_idx = "${sample}.merged.vcf.gz.tbi"
File vcf_dup = "${sample}.vcf_dup.txt" File vcf_dup = "${sample}.vcf_dup.txt"
} }
} }

+ 4
- 14
tasks/votes.wdl View File

task votes { task votes {
File merged_vcf
File repeat_annotated_vcf
File vcf_dup File vcf_dup
String sample String sample
String prefix String prefix
String disk_size String disk_size
command <<< command <<<
python /opt/high_confidence_call_vote.py -vcf ${merged_vcf} -dup ${vcf_dup} -sample ${sample} -prefix ${prefix}

cat ${prefix}_annotated.vcf | grep -v '##' > ${prefix}.txt

cat ${prefix}.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 ${prefix}.txt | grep -w $i | cat header - > ${sample}.$i.mendelian.txt
done
python /opt/high_confidence_call_vote.py -vcf ${repeat_annotated_vcf} -dup ${vcf_dup} -sample ${sample} -prefix ${prefix}


>>> >>>


dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/" dataDisk: "cloud_ssd " + disk_size + " /cromwell_root/"
} }
output { output {
File annotated_vcf = "${prefix}_annotated.vcf"
File annotated_txt = "${prefix}.txt"
Array[File] chromo_votes = glob("*.mendelian.txt")
File voted_vcf = "${prefix}_voted.vcf"
File all_sample_vcf = "${prefix}_all_sample_information.vcf"
} }
} }



+ 75
- 0
workflow.wdl View File

import "./tasks/mergeSister.wdl" as mergeSister import "./tasks/mergeSister.wdl" as mergeSister
import "./tasks/reformVCF.wdl" as reformVCF import "./tasks/reformVCF.wdl" as reformVCF
import "./tasks/merge.wdl" as merge import "./tasks/merge.wdl" as merge
import "./tasks/bed_annotation.wdl" as bed_annotation
import "./tasks/votes.wdl" as votes


workflow {{ project_name }} { workflow {{ project_name }} {
File inputSamplesFile File inputSamplesFile
Array[Array[File]] inputSamples = read_tsv(inputSamplesFile) Array[Array[File]] inputSamples = read_tsv(inputSamplesFile)
File ref_dir File ref_dir
File repeat_bed
String fasta String fasta
String cluster_config String cluster_config
String disk_size String disk_size
cluster_config=cluster_config, cluster_config=cluster_config,
disk_size=disk_size disk_size=disk_size
} }
call bed_annotation.bed_annotation as LCL5bed_annotation {
input:
merged_vcf_gz=LCL5merge.merged_vcf_gz,
merged_vcf_idx=LCL5merge.merged_vcf_idx,
repeat_bed=repeat_bed,
sample='LCL5',
cluster_config=cluster_config,
disk_size=disk_size
}
call votes.votes as LCL5votes {
input:
repeat_annotated_vcf=LCL5bed_annotation.repeat_annotated_vcf,
vcf_dup=LCL5merge.vcf_dup,
sample='LCL5',
prefix='LCL5',
cluster_config=cluster_config,
disk_size=disk_size
}
call merge.merge as LCL6merge { call merge.merge as LCL6merge {
input: input:
family_vcf_gz=LCL6allInfozipIndex.vcf_gz, family_vcf_gz=LCL6allInfozipIndex.vcf_gz,
cluster_config=cluster_config, cluster_config=cluster_config,
disk_size=disk_size disk_size=disk_size
} }
call bed_annotation.bed_annotation as LCL6bed_annotation {
input:
merged_vcf_gz=LCL6merge.merged_vcf_gz,
merged_vcf_idx=LCL6merge.merged_vcf_idx,
repeat_bed=repeat_bed,
sample='LCL6',
cluster_config=cluster_config,
disk_size=disk_size
}
call votes.votes as LCL6votes {
input:
repeat_annotated_vcf=LCL6bed_annotation.repeat_annotated_vcf,
vcf_dup=LCL6merge.vcf_dup,
sample='LCL6',
prefix='LCL6',
cluster_config=cluster_config,
disk_size=disk_size
}
call merge.merge as LCL7merge { call merge.merge as LCL7merge {
input: input:
family_vcf_gz=LCL7allInfozipIndex.vcf_gz, family_vcf_gz=LCL7allInfozipIndex.vcf_gz,
cluster_config=cluster_config, cluster_config=cluster_config,
disk_size=disk_size disk_size=disk_size
} }
call bed_annotation.bed_annotation as LCL7bed_annotation {
input:
merged_vcf_gz=LCL7merge.merged_vcf_gz,
merged_vcf_idx=LCL7merge.merged_vcf_idx,
repeat_bed=repeat_bed,
sample='LCL7',
cluster_config=cluster_config,
disk_size=disk_size
}
call votes.votes as LCL7votes {
input:
repeat_annotated_vcf=LCL7bed_annotation.repeat_annotated_vcf,
vcf_dup=LCL7merge.vcf_dup,
sample='LCL7',
prefix='LCL7',
cluster_config=cluster_config,
disk_size=disk_size
}
call merge.merge as LCL8merge { call merge.merge as LCL8merge {
input: input:
family_vcf_gz=LCL8allInfozipIndex.vcf_gz, family_vcf_gz=LCL8allInfozipIndex.vcf_gz,
cluster_config=cluster_config, cluster_config=cluster_config,
disk_size=disk_size disk_size=disk_size
} }
call bed_annotation.bed_annotation as LCL8bed_annotation {
input:
merged_vcf_gz=LCL8merge.merged_vcf_gz,
merged_vcf_idx=LCL8merge.merged_vcf_idx,
repeat_bed=repeat_bed,
sample='LCL8',
cluster_config=cluster_config,
disk_size=disk_size
}
call votes.votes as LCL8votes {
input:
repeat_annotated_vcf=LCL8bed_annotation.repeat_annotated_vcf,
vcf_dup=LCL8merge.vcf_dup,
sample='LCL8',
prefix='LCL8',
cluster_config=cluster_config,
disk_size=disk_size
}
} }

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