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pipeline/wdl/call_mutation.wdl

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调整call_mutation
2023-09-27 10:47:03 +08:00
task mutation_calling_umi {
String name
String output_dir
String rmdup_bam
String ref
String bed
command <<<
if [ ! -d ${output_dir}/mutation ];then
mkdir ${output_dir}/mutation
fi
#1条call
# 这个情况是reads数目只有1，但是如果去掉了这个reads数导致数据量减少很多
# -r 3 是指有3条这样样的reads支撑
# -f 是指频率 以2条方式的call出来的变异频率可以比1条的方式更可信
添加药物
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# hotspot 修改了 -r 3 为 -r 2 -f 0.001 为 -f 0.0005
调整call_mutation
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java -jar /dataseq/jmdna/software/VarDict-1.8.3/lib/VarDict-1.8.3.jar \
-G ${ref} \
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-f 0.0005 \
调整call_mutation
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-N ${name} \
-b ${rmdup_bam} \
添加药物
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-UN -Q 20 -m 3 -r 2 -th 10 -z 1 -c 1 -S 2 -E 3 -g 4 ${bed} \
|/dataseq/jmdna/software/VarDict-1.8.3/bin/teststrandbias.R \
| /dataseq/jmdna/software/VarDict-1.8.3/bin/var2vcf_valid.pl \
-N ${name} -E -f 0.0005 > ${output_dir}/mutation/${name}.1r.vcf
#提取>=2条矫正的序列
bam_fetch.py ${output_dir}/alignment/${name}.rmdup.bam ${output_dir}/alignment/${name}.2r.rmdup.bam
samtools index ${output_dir}/alignment/${name}.2r.rmdup.bam
# 保证 1r call mut umi family 里面有2条reads
#2条矫正的call
java -jar /dataseq/jmdna/software/VarDict-1.8.3/lib/VarDict-1.8.3.jar \
-G ${ref} \
-f 0.0001 \
-N ${name}_2r \
-b ${output_dir}/alignment/${name}.2r.rmdup.bam \
-UN -Q 20 -m 3 -r 1 -th 10 -z 1 -c 1 -S 2 -E 3 -g 4 ${bed} \
|/dataseq/jmdna/software/VarDict-1.8.3/bin/teststrandbias.R \
|/dataseq/jmdna/software/VarDict-1.8.3/bin/var2vcf_valid.pl -N ${name} -E -f 0.0001 >${output_dir}/mutation/${name}.2r.vcf
# merge突变，以1条方式call的>0.01的突变+两条方式的对一条方式的低频区域（AF<0.01）进行矫正。
correct_umi_1r_plus_2r.pl \
${output_dir}/mutation/${name}.1r.vcf \
${output_dir}/mutation/${name}.2r.vcf \
${output_dir}/mutation/${name}.raw.vcf
vcf_filter.py \
-i ${output_dir}/mutation/${name}.raw.vcf \
-e 'INFO/AF[0] >= 0.001 && INFO/VD >= 3' \
-o ${output_dir}/mutation/${name}.raw.snp_indel.vcf
# add msi and strandbias flag
稳定
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vcf_add_tag_msi.pl ${output_dir}/mutation/${name}.raw.snp_indel.vcf ${output_dir}/mutation/${name}.raw.addtagmsi.snp_indel.vcf 160 c
添加药物
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# add malt flag
grep -v ^# ${output_dir}/mutation/${name}.raw.snp_indel.vcf | awk '{OFS="\t"}{print $1,$2-1,$2}' - > \
${output_dir}/mutation/${name}.raw.snp_indel.vcf.bed
samtools mpileup -aBq 20 -Q 20 -f ${ref} -l ${output_dir}/mutation/${name}.raw.snp_indel.vcf.bed \
${output_dir}/alignment/${name}.2r.rmdup.bam -o ${output_dir}/mutation/${name}.raw.snp_indel.vcf.pileup
vcf_add_tag_mutalt.py ${output_dir}/mutation/${name}.raw.snp_indel.vcf.pileup \
${output_dir}/mutation/${name}.raw.addtagmsi.snp_indel.vcf \
${output_dir}/mutation/${name}.raw.addtagmsi.addmutalt.snp_indel.vcf
cp ${output_dir}/mutation/${name}.raw.addtagmsi.addmutalt.snp_indel.vcf ${output_dir}/mutation/${name}.snp_indel.somatic.vcf
vcf_filter.py \
-i ${output_dir}/mutation/${name}.raw.snp_indel.vcf \
-e 'INFO/AF[0] >= 0.1' \
-o ${output_dir}/mutation/${name}.snp_indel.germline.vcf
>>>
output {
String somatic_vcf = "${output_dir}/mutation/${name}.snp_indel.somatic.vcf"
String germline_vcf = "${output_dir}/mutation/${name}.snp_indel.germline.vcf"
String raw_vcf = "${output_dir}/mutation/${name}.1r.vcf"
}
}
task mutation_calling_umi_control {
String name
String bed
String ref
String output_dir
String tumor_rmdup_bam
String normal_rmdup_bam
command <<<
if [ ! -d ${output_dir}/mutation ];then
mkdir ${output_dir}/mutation
fi
# 对照样本
java -jar /dataseq/jmdna/software/VarDict-1.8.3/lib/VarDict-1.8.3.jar \
-G ${ref} \
-f 0.01 \
-N ${name} \
-b ${normal_rmdup_bam} \
-UN \
-Q 20 \
-m 3 \
-r 3 \
-th 10 \
-c 1 -S 2 -E 3 -g 4 ${bed} |/dataseq/jmdna/software/VarDict-1.8.3/bin/teststrandbias.R \
|/dataseq/jmdna/software/VarDict-1.8.3/bin/var2vcf_valid.pl -N ${name} -E -f 0.01 >${output_dir}/mutation/${name}.normal.vcf
# 实验样本
java -jar /dataseq/jmdna/software/VarDict-1.8.3/lib/VarDict-1.8.3.jar \
-G ${ref} \
-f 0.0005 \
-N ${name} \
-b ${tumor_rmdup_bam} \
-UN -Q 20 -m 3 -r 2 -th 10 -c 1 -S 2 -E 3 -g 4 ${bed} \
调整call_mutation
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| /dataseq/jmdna/software/VarDict-1.8.3/bin/teststrandbias.R \
| /dataseq/jmdna/software/VarDict-1.8.3/bin/var2vcf_valid.pl \
添加药物
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-N ${name} -E -f 0.0005 > ${output_dir}/mutation/${name}.1r.vcf
调整call_mutation
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#提取>=2条矫正的序列
msi完成
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bam_fetch.py ${output_dir}/alignment/${name}.rmdup.bam ${output_dir}/alignment/${name}.2r.rmdup.bam
调整call_mutation
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samtools index ${output_dir}/alignment/${name}.2r.rmdup.bam
# 保证 1r call mut umi family 里面有2条reads
#2条矫正的call
java -jar /dataseq/jmdna/software/VarDict-1.8.3/lib/VarDict-1.8.3.jar -G ${ref} \
-f 0.0001 -N ${name}_2r -b ${output_dir}/alignment/${name}.2r.rmdup.bam \
-UN -Q 20 -m 3 -r 1 -th 10 -c 1 -S 2 -E 3 -g 4 ${bed} | /dataseq/jmdna/software/VarDict-1.8.3/bin/teststrandbias.R \
添加药物
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| /dataseq/jmdna/software/VarDict-1.8.3/bin/var2vcf_valid.pl -N ${name} -E -f 0.0001 >${output_dir}/mutation/${name}.2r.vcf
调整call_mutation
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添加药物
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# merge突变，以1条方式call的>0.01的突变+两条方式的对一条方式的低频区域（AF<0.01）进行矫正。
msi完成
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correct_umi_1r_plus_2r.pl \
稳定
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${output_dir}/mutation/${name}.1r.vcf \
${output_dir}/mutation/${name}.2r.vcf \
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${output_dir}/mutation/${name}.pre_raw.vcf
msi完成
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添加药物
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# 去除normal 中的突变位点
correct_umi_subnormal.pl \
${output_dir}/mutation/${name}.pre_raw.vcf \
${output_dir}/mutation/${name}.normal.vcf \
${output_dir}/mutation/${name}.raw.vcf
msi完成
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添加药物
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vcf_filter.py \
-i ${output_dir}/mutation/${name}.raw.vcf \
-e 'INFO/AF[0] >= 0.001 && INFO/VD >= 3' \
-o ${output_dir}/mutation/${name}.raw.snp_indel.vcf
# add msi and strandbias flag
稳定
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vcf_add_tag_msi.pl ${output_dir}/mutation/${name}.raw.snp_indel.vcf ${output_dir}/mutation/${name}.raw.addtagmsi.snp_indel.vcf 160 c
添加药物
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# add malt flag
grep -v ^# ${output_dir}/mutation/${name}.raw.snp_indel.vcf | awk '{OFS="\t"}{print $1,$2-1,$2}' - > \
${output_dir}/mutation/${name}.raw.snp_indel.vcf.bed
samtools mpileup -aBq 20 -Q 20 -f ${ref} -l ${output_dir}/mutation/${name}.raw.snp_indel.vcf.bed \
${output_dir}/alignment/${name}.2r.rmdup.bam -o ${output_dir}/mutation/${name}.raw.snp_indel.vcf.pileup
vcf_add_tag_mutalt.py ${output_dir}/mutation/${name}.raw.snp_indel.vcf.pileup \
${output_dir}/mutation/${name}.raw.addtagmsi.snp_indel.vcf \
${output_dir}/mutation/${name}.raw.addtagmsi.addmutalt.snp_indel.vcf
cp ${output_dir}/mutation/${name}.raw.addtagmsi.addmutalt.snp_indel.vcf ${output_dir}/mutation/${name}.snp_indel.somatic.vcf
cp ${output_dir}/mutation/${name}.normal.vcf ${output_dir}/mutation/${name}.snp_indel.germline.vcf
调整call_mutation
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>>>
output {
添加药物
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String somatic_vcf = "${output_dir}/mutation/${name}.snp_indel.somatic.vcf"
String germline_vcf = "${output_dir}/mutation/${name}.snp_indel.germline.vcf"
String raw_vcf = "${output_dir}/mutation/${name}.1r.vcf"
调整call_mutation
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}
}
task mutation_calling_tissue {
String name
String bed
String ref
String output_dir
String rmdup_bam
command <<<
if [ ! -d ${output_dir}/mutation ];then
mkdir ${output_dir}/mutation
fi
# vardict
java -jar /dataseq/jmdna/software/VarDict-1.8.3/lib/VarDict-1.8.3.jar \
-G ${ref} \
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-f 0.005 \
调整call_mutation
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-N ${name} \
-b ${rmdup_bam} \
-UN \
-Q 20 \
-m 3 \
添加药物
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-r 2 \
msi完成
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-z 1 \
调整call_mutation
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-th 10 \
msi完成
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-c 1 -S 2 -E 3 -g 4 ${bed} \
|/dataseq/jmdna/software/VarDict-1.8.3/bin/teststrandbias.R \
添加药物
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|/dataseq/jmdna/software/VarDict-1.8.3/bin/var2vcf_valid.pl -N ${name} -E -f 0.005 \
>${output_dir}/mutation/${name}.raw.vcf
msi完成
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添加药物
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vcf_filter.py \
-i ${output_dir}/mutation/${name}.raw.vcf \
-e 'INFO/AF[0] >= 0.01 && INFO/VD >= 3' \
-o ${output_dir}/mutation/${name}.raw.snp_indel.vcf
msi完成
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添加药物
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# add msi and strandbias flag
稳定
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vcf_add_tag_msi.pl ${output_dir}/mutation/${name}.raw.snp_indel.vcf ${output_dir}/mutation/${name}.raw.addtagmsi.snp_indel.vcf 160 t
添加药物
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cp ${output_dir}/mutation/${name}.raw.addtagmsi.snp_indel.vcf ${output_dir}/mutation/${name}.snp_indel.somatic.vcf
vcf_filter.py \
-i ${output_dir}/mutation/${name}.raw.snp_indel.vcf \
msi完成
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-e 'INFO/AF[0] > 0.1' \
添加药物
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-o ${output_dir}/mutation/${name}.snp_indel.germline.vcf
调整call_mutation
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>>>
output {
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String somatic_vcf = "${output_dir}/mutation/${name}.snp_indel.somatic.vcf"
String germline_vcf = "${output_dir}/mutation/${name}.snp_indel.germline.vcf"
String raw_vcf = "${output_dir}/mutation/${name}.raw.vcf"
调整call_mutation
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}
}
task mutation_calling_tissue_control {
String name
String bed
String ref
String output_dir
String tumor_rmdup_bam
String normal_rmdup_bam
command <<<
if [ ! -d ${output_dir}/mutation ];then
mkdir ${output_dir}/mutation
fi
java -jar /dataseq/jmdna/software/VarDict-1.8.3/lib/VarDict-1.8.3.jar \
-G ${ref} \
添加药物
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-f 0.005 \
调整call_mutation
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-N ${name} \
-b "${tumor_rmdup_bam}|${normal_rmdup_bam}" \
融合
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-UN \
-Q 20 \
-m 3 \
添加药物
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-r 2 \
msi完成
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-th 20 \
-z 1 -c 1 -S 2 -E 3 -g 4 ${bed} | /dataseq/jmdna/software/VarDict-1.8.3/bin/testsomatic.R \
添加药物
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| /dataseq/jmdna/software/VarDict-1.8.3/bin/var2vcf_paired.pl -N ${name} -f 0.005 \
>${output_dir}/mutation/${name}.raw.vcf
调整call_mutation
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添加药物
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vcf_filter.py \
-i ${output_dir}/mutation/${name}.raw.vcf \
-e 'INFO/AF[0] >= 0.01 && INFO/VD >= 3' \
-o ${output_dir}/mutation/${name}.raw.snp_indel.vcf
融合
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添加药物
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# add msi and strandbias flag
稳定
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vcf_add_tag_msi.pl ${output_dir}/mutation/${name}.raw.snp_indel.vcf ${output_dir}/mutation/${name}.raw.addtagmsi.snp_indel.vcf 160 t
融合
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添加药物
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vcf_filter.py -i ${output_dir}/mutation/${name}.raw.addtagmsi.snp_indel.vcf \
-o ${output_dir}/mutation/${name}.snp_indel.somatic.vcf \
融合
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-e 'INFO/STATUS="StrongSomatic" | ( INFO/STATUS="LikelySomatic" && FORMAT/AF[0] > 3*FORMAT/AF[1] )'
添加药物
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vcf_filter.py -i ${output_dir}/mutation/${name}.raw.snp_indel.vcf \
-o ${output_dir}/mutation/${name}.snp_indel.germline.vcf \
-e 'INFO/STATUS="Germline"'
处理过滤
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>>>
output {
添加药物
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String somatic_vcf = "${output_dir}/mutation/${name}.snp_indel.somatic.vcf"
String germline_vcf = "${output_dir}/mutation/${name}.snp_indel.germline.vcf"
String raw_vcf = "${output_dir}/mutation/${name}.raw.vcf"
处理过滤
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}
}
添加药物
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task hotspot {
处理过滤
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String name
String output_dir
添加药物
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String raw_vcf
Float threshold
处理过滤
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command <<<
if [ ! -d ${output_dir}/mutation ];then
mkdir ${output_dir}/mutation
fi
添加药物
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# hotspot
vcf_filter.py \
-i ${raw_vcf} \
-e 'INFO/AF[0] < ${threshold}' \
-o ${output_dir}/mutation/${name}.raw.hotspot.vcf
处理过滤
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添加药物
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vcf_operations.py ${output_dir}/mutation/${name}.raw.hotspot.vcf \
-b $PUBLIC/hotspot/hotspot_delins.bed \
-o ${output_dir}/mutation/${name}.hotspot.delins.vcf
处理过滤
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添加药物
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vcf_operations.py ${output_dir}/mutation/${name}.raw.hotspot.vcf \
-v $PUBLIC/hotspot/hotspot_snv.vcf \
-o ${output_dir}/mutation/${name}.hotspot.snv.vcf
msi完成
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添加药物
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vcf_operations.py ${output_dir}/mutation/${name}.hotspot.snv.vcf \
-v ${output_dir}/mutation/${name}.hotspot.delins.vcf \
-o ${output_dir}/mutation/${name}.hotspot.vcf \
-m
msi完成
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>>>
output {
添加药物
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String hotspot_vcf = "${output_dir}/mutation/${name}.hotspot.vcf"
msi完成
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}
}
task annovar {
String prefix
String output_dir
String ref
String vcf
command <<<
if [ ! -d ${output_dir}/mutation ];then
mkdir ${output_dir}/mutation
fi
添加药物
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# 有突变记录的进行注释
if grep -E -q "^[^#]" ${vcf}; then
msi完成
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table_annovar.pl \
${vcf} \
/dataseq/jmdna/software/annovar/humandb/ \
-buildver hg19 -nastring . -vcfinput -remove -otherinfo \
-protocol refGene,avsnp150,cosmic91,clinvar_20220320,1000g2015aug_all,1000g2015aug_eas,esp6500siv2_all,exac03nontcga,gnomad_genome,dbnsfp35c,cytoBand \
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-argument '-splicing_threshold 2 -hgvs ',,,,,,,,,, \
msi完成
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-intronhgvs 50 \
-operation g,f,f,f,f,f,f,f,f,f,r \
-outfile ${output_dir}/mutation/${prefix} \
-dot2underline
添加药物
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else
# 无突变记录创建文件
touch ${output_dir}/mutation/${prefix}.hg19_multianno.txt ${output_dir}/mutation/${prefix}.hg19_multianno.vcf
fi
msi完成
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>>>
output {
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String anno_txt = "${output_dir}/mutation/${prefix}.hg19_multianno.txt"
String anno_vcf = "${output_dir}/mutation/${prefix}.hg19_multianno.txt"
msi完成
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}
}
融合
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msi完成
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task filter_umi {
String name
添加药物
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String file
msi完成
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String project
添加药物
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String sample_type
String pipeline
msi完成
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String output_dir
String tumor_rmdup_bam
command <<<
if [ ! -d ${output_dir}/mutation ];then
mkdir ${output_dir}/mutation
fi
添加药物
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filter_snpindel.pl \
${file} \
${output_dir}/mutation/${name}.snp_indel.${pipeline}.hg19_multianno.filter_pre.txt \
${output_dir}/mutation/${name}.snp_indel.${pipeline}.hg19_multianno.tag.txt \
msi完成
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${project} \
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${sample_type} \
${pipeline}
msi完成
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稳定
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if [ "${pipeline}" = 'hotspot' ] ;then
cp ${output_dir}/mutation/${name}.snp_indel.${pipeline}.hg19_multianno.filter_pre.txt \
${output_dir}/mutation/${name}.snp_indel.${pipeline}.hg19_multianno.filter.txt
else
correct_umi_overlap_reads.py \
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${output_dir}/mutation/${name}.snp_indel.${pipeline}.hg19_multianno.filter_pre.txt \
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${tumor_rmdup_bam} \
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${output_dir}/mutation/${name}.snp_indel.${pipeline}.hg19_multianno.filter.txt
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fi
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>>>
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output {
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String snvindel_filtered= "${output_dir}/mutation/${name}.snp_indel.${pipeline}.hg19_multianno.filter.txt"
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}
}
task filter_tissue {
String name
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String file
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String project
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String sample_type
String pipeline
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String output_dir
command <<<
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if [ ! -d ${output_dir}/mutation ];then
mkdir ${output_dir}/mutation
fi
filter_snpindel.pl \
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${file} \
${output_dir}/mutation/${name}.snp_indel.${pipeline}.hg19_multianno.filter.txt \
${output_dir}/mutation/${name}.snp_indel.${pipeline}.hg19_multianno.tag.txt \
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${project} \
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${sample_type} \
${pipeline}
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>>>
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output {
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String snvindel_filtered= "${output_dir}/mutation/${name}.snp_indel.${pipeline}.hg19_multianno.filter.txt"
}
}
task mutation_sum {
String name
String somatic_txt
String germline_txt
String hotspot_txt
String output_dir
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String cancer
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command <<<
if [ ! -d ${output_dir}/mutation ];then
mkdir ${output_dir}/mutation
fi
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{ head -n 1 ${somatic_txt} ; tail -n +2 ${somatic_txt} ; tail -n +2 ${hotspot_txt} ;} |\
{ head -n 1 ; awk -F '\t' '{$1 = 1; OFS="\t" ; key=""; for (i=2; i<=NF-1; i++) { key = key $i "," } \
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if (key in data) { data[key] = data[key] ";" $NF; } \
else { data[key] = $0; }} END { for (i in data) { print data[i]; } }' ; } > \
${output_dir}/mutation/${name}.snp_indel.somatic.hg19_multianno.filter.sum.txt
target_therapy_snpindel.pl ${output_dir}/mutation/${name}.snp_indel.somatic.hg19_multianno.filter.sum.txt \
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${output_dir}/mutation/${name}.snp_indel.somatic.hg19_multianno.filter.sum ${cancer}
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>>>
output {
String snvindel_filtered_sum = "${output_dir}/mutation/${name}.snp_indel.somatic.hg19_multianno.filter.sum.txt"
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}
}
workflow call_mutation {
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Boolean run=true
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String tumor
String tumor_rmdup_bam
String? normal
String? normal_rmdup_bam
Boolean umi
String output_dir
String ref
String bed
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String project
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String cancer
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# pipe 执行 mutation_calling => annovar => filter
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if (run) {
# 单样本
if (!defined(normal)) {
if (umi) {
call mutation_calling_umi {
input:
name=tumor,
output_dir=output_dir,
ref=ref,
bed=bed,
rmdup_bam=tumor_rmdup_bam
}
call hotspot as hotspot_calling_umi {
input:
name=tumor,
output_dir=output_dir,
raw_vcf=mutation_calling_umi.raw_vcf,
threshold=0.1
}
call annovar as anno_somatic_umi {
input:
prefix="${tumor}.snp_indel.somatic",
output_dir=output_dir,
ref=ref,
vcf=mutation_calling_umi.somatic_vcf
}
call annovar as anno_germline_umi {
input:
prefix="${tumor}.snp_indel.germline",
output_dir=output_dir,
ref=ref,
vcf=mutation_calling_umi.germline_vcf
}
call annovar as anno_hotspot_umi {
input:
prefix="${tumor}.hotspot",
output_dir=output_dir,
ref=ref,
vcf=hotspot_calling_umi.hotspot_vcf
}
call filter_umi as filter_somatic_umi {
input:
name=tumor,
file=anno_somatic_umi.anno_txt,
project=project,
sample_type='c',
pipeline='somatic',
output_dir=output_dir,
tumor_rmdup_bam = tumor_rmdup_bam
}
call filter_umi as filter_hotspot_umi {
input:
name=tumor,
file=anno_hotspot_umi.anno_txt,
project=project,
sample_type='c',
pipeline='hotspot',
output_dir=output_dir,
tumor_rmdup_bam = tumor_rmdup_bam
}
call filter_umi as filter_germline_umi {
input:
name=tumor,
file=anno_germline_umi.anno_txt,
project=project,
sample_type='c',
pipeline='germline',
output_dir=output_dir,
tumor_rmdup_bam = tumor_rmdup_bam
}
call mutation_sum as mutation_sum_umi {
input:
name=tumor,
output_dir=output_dir,
somatic_txt=filter_somatic_umi.snvindel_filtered,
germline_txt=filter_germline_umi.snvindel_filtered,
hotspot_txt=filter_hotspot_umi.snvindel_filtered,
cancer=cancer
}
}
if (!umi) {
call mutation_calling_tissue {
input:
name=tumor,
output_dir=output_dir,
ref=ref,
bed=bed,
rmdup_bam=tumor_rmdup_bam
}
call hotspot as hotspot_calling_tissue {
input:
name=tumor,
output_dir=output_dir,
raw_vcf=mutation_calling_tissue.raw_vcf,
threshold=0.1
}
call annovar as anno_somatic_tissue {
input:
prefix="${tumor}.snp_indel.somatic",
output_dir=output_dir,
ref=ref,
vcf=mutation_calling_tissue.somatic_vcf
}
call annovar as anno_germline_tissue {
input:
prefix="${tumor}.snp_indel.germline",
output_dir=output_dir,
ref=ref,
vcf=mutation_calling_tissue.germline_vcf
}
call annovar as anno_hotspot_tissue {
input:
prefix="${tumor}.hotspot",
output_dir=output_dir,
ref=ref,
vcf=hotspot_calling_tissue.hotspot_vcf
}
call filter_tissue as filter_somatic_tissue {
input:
name=tumor,
file=anno_somatic_tissue.anno_txt,
project=project,
sample_type='t',
pipeline='somatic',
output_dir=output_dir,
}
call filter_tissue as filter_hotspot_tissue {
input:
name=tumor,
file=anno_hotspot_tissue.anno_txt,
project=project,
sample_type='t',
pipeline='hotspot',
output_dir=output_dir,
}
call filter_tissue as filter_germline_tissue {
input:
name=tumor,
file=anno_germline_tissue.anno_txt,
project=project,
sample_type='t',
pipeline='germline',
output_dir=output_dir,
}
call mutation_sum as mutation_sum_tissue {
input:
name=tumor,
output_dir=output_dir,
somatic_txt=filter_somatic_tissue.snvindel_filtered,
germline_txt=filter_germline_tissue.snvindel_filtered,
hotspot_txt=filter_hotspot_tissue.snvindel_filtered,
cancer=cancer
}
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}
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}
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# 双样本
if (defined(normal)) {
if (umi) {
call mutation_calling_umi_control {
input:
name=tumor,
output_dir=output_dir,
ref=ref,
bed=bed,
tumor_rmdup_bam=tumor_rmdup_bam,
normal_rmdup_bam=normal_rmdup_bam
}
call hotspot as hotspot_calling_umi_control {
input:
name=tumor,
output_dir=output_dir,
raw_vcf=mutation_calling_umi_control.raw_vcf,
threshold=0.1
}
call annovar as anno_somatic_umi_control {
input:
prefix="${tumor}.snp_indel.somatic",
output_dir=output_dir,
ref=ref,
vcf=mutation_calling_umi_control.somatic_vcf
}
call annovar as anno_germline_umi_control {
input:
prefix="${tumor}.snp_indel.germline",
output_dir=output_dir,
ref=ref,
vcf=mutation_calling_umi_control.germline_vcf
}
call annovar as anno_hotspot_umi_control {
input:
prefix="${tumor}.hotspot",
output_dir=output_dir,
ref=ref,
vcf=hotspot_calling_umi_control.hotspot_vcf
}
call filter_umi as filter_somatic_umi_control {
input:
name=tumor,
file=anno_somatic_umi_control.anno_txt,
project=project,
sample_type='c',
pipeline='somatic',
output_dir=output_dir,
tumor_rmdup_bam = tumor_rmdup_bam
}
call filter_umi as filter_hotspot_umi_control {
input:
name=tumor,
file=anno_hotspot_umi_control.anno_txt,
project=project,
sample_type='c',
pipeline='hotspot',
output_dir=output_dir,
tumor_rmdup_bam = tumor_rmdup_bam
}
call filter_umi as filter_germline_umi_control {
input:
name=tumor,
file=anno_germline_umi_control.anno_txt,
project=project,
sample_type='c',
pipeline='germline',
output_dir=output_dir,
tumor_rmdup_bam = tumor_rmdup_bam
}
call mutation_sum as mutation_sum_umi_control {
input:
name=tumor,
output_dir=output_dir,
somatic_txt=filter_somatic_umi_control.snvindel_filtered,
germline_txt=filter_germline_umi_control.snvindel_filtered,
hotspot_txt=filter_hotspot_umi_control.snvindel_filtered,
cancer=cancer
}
}
if (!umi) {
call mutation_calling_tissue_control {
input:
name=tumor,
output_dir=output_dir,
ref=ref,
bed=bed,
tumor_rmdup_bam=tumor_rmdup_bam,
normal_rmdup_bam=normal_rmdup_bam
}
call hotspot as hotspot_calling_tissue_control {
input:
name=tumor,
output_dir=output_dir,
raw_vcf=mutation_calling_tissue_control.raw_vcf,
threshold=0.1
}
call annovar as anno_somatic_tissue_control {
input:
prefix="${tumor}.snp_indel.somatic",
output_dir=output_dir,
ref=ref,
vcf=mutation_calling_tissue_control.somatic_vcf
}
call annovar as anno_germline_tissue_control {
input:
prefix="${tumor}.snp_indel.germline",
output_dir=output_dir,
ref=ref,
vcf=mutation_calling_tissue_control.germline_vcf
}
call annovar as anno_hotspot_tissue_control {
input:
prefix="${tumor}.hotspot",
output_dir=output_dir,
ref=ref,
vcf=hotspot_calling_tissue_control.hotspot_vcf
}
call filter_tissue as filter_somatic_tissue_control {
input:
name=tumor,
file=anno_somatic_tissue_control.anno_txt,
project=project,
sample_type='t',
pipeline='somatic',
output_dir=output_dir,
}
call filter_tissue as filter_hotspot_tissue_control {
input:
name=tumor,
file=anno_hotspot_tissue_control.anno_txt,
project=project,
sample_type='t',
pipeline='hotspot',
output_dir=output_dir,
}
call filter_tissue as filter_germline_tissue_control {
input:
name=tumor,
file=anno_germline_tissue_control.anno_txt,
project=project,
sample_type='t',
pipeline='germline',
output_dir=output_dir,
}
call mutation_sum as mutation_sum_tissue_control {
input:
name=tumor,
output_dir=output_dir,
somatic_txt=filter_somatic_tissue_control.snvindel_filtered,
germline_txt=filter_germline_tissue_control.snvindel_filtered,
hotspot_txt=filter_hotspot_tissue_control.snvindel_filtered,
cancer=cancer
}
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}
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}
}
output {
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String raw_vcf = "${output_dir}/mutation/${tumor}.raw.snp_indel.vcf"
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String somatic_vcf = "${output_dir}/mutation/${tumor}.snp_indel.somatic.vcf"
String germline_vcf = "${output_dir}/mutation/${tumor}.snp_indel.germline.vcf"
String hotspot_vcf = "${output_dir}/mutation/${tumor}.hotspot.vcf"
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String somatic_anno_txt = "${output_dir}/mutation/${tumor}.snp_indel.somatic.hg19_multianno.txt"
String somatic_filter = "${output_dir}/mutation/${tumor}.snp_indel.somatic.hg19_multianno.filter.txt"
String germline_filter = "${output_dir}/mutation/${tumor}.snp_indel.germline.hg19_multianno.filter.txt"
String hotspot_filter = "${output_dir}/mutation/${tumor}.snp_indel.hotspot.hg19_multianno.filter.txt"
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}
}