#!/bin/sh
#PBS -N noUMI_varscan_pair
#PBS -j oe
#PBS -l ncpus=12
#PBS -l nodes=1
#PBS -l mem=20G

#inputpath=$1
#sample=$2
source  /home/liuxiangqiong/miniconda3/bin/activate base
hg19=/cgdata/Database/GATK/b37/human_g1k_v37_decoy.fasta
target=/cgdata/liuxiangqiong/work62pancancer/Client/v0/script/refdata/NanOnco_Plus_Panel_v2.0_Covered_b37_cg.parY2X.sort.bed
#min_af=0.005 for blood,0.01 for tissue
outputdir=${inputpath}/1SV_varscan_pair
#######################1.pileup file########################################################################
samtools mpileup -B -q 10 -d 1000000 -f ${hg19} -l $target ${bam_dir}/${tumor}_clean.bam > ${outputdir}/${tumor}.pileup
samtools mpileup -B -q 10 -d 1000000 -f ${hg19} -l $target ${bam_dir}/${normal}_clean.bam > ${outputdir}/${normal}.pileup

########################2.pair########################################################################
varscan somatic  ${outputdir}/${normal}.pileup  ${outputdir}/${tumor}.pileup --output-snp  ${outputdir}/${sample}.snp --output-indel  ${outputdir}/${sample}.indel --tumor-purity 0.01 --output-vcf

########################3.Run the processSomatic subcommand to divide the output into separate files based on somatic status and confidence########
#3.1获得高置信的突变
varscan processSomatic ${outputdir}/${sample}.snp.vcf --min-tumor-freq 0.001 --max-normal-freq 0.05 --p-value 0.5
varscan processSomatic ${outputdir}/${sample}.indel.vcf --min-tumor-freq 0.001 --max-normal-freq 0.05 --p-value 0.5
#3.2merge the hc
less ${outputdir}/${sample}.snp.Somatic.hc.vcf | grep -vE "^#"> ${outputdir}/${sample}.merge.Somatic.hc.nohead.vcf
less ${outputdir}/${sample}.indel.Somatic.hc.vcf | grep -vE "^#" >> ${outputdir}/${sample}.merge.Somatic.hc.nohead.vcf
#3.3 将邻近的位点进行合并#3.3 将邻近的位点进行合并
#python3 /cgdata/liuxiangqiong/work62pancancer/Client/v0/script/complex_variant_new.py -i  ${outputdir}/${sample}.merge.Somatic.hc.nohead.vcf -b ${bam_dir}/${tumor}_clean.bam -f ${hg19} -mode 2 -n 10 -p 0.5 -o ${outputdir}/${sample}.merge.Somatic.hc.nohead.combind.vcf
#grep "^#" ${outputdir}/${sample}.snp.Somatic.hc.vcf >${outputdir}/${sample}.merge.Somatic.hc.combind.vcf
#less ${outputdir}/${sample}.merge.Somatic.hc.nohead.combind.vcf >>${outputdir}/${sample}.merge.Somatic.hc.combind.vcf
#rm -rf  ${outputdir}/${sample}.merge.Somatic.hc.nohead.combind.vcf

##将VCF按照染色体进行拆分
python3 /cgdata/liuxiangqiong/work62pancancer/Client/v0/script/20220705/vcfsplit_v0_20220929_finish.py -i ${outputdir}/${sample}.merge.Somatic.hc.nohead.vcf
fun() {
        hg19=/cgdata/Database/GATK/b37/human_g1k_v37_decoy.fasta
        python3 /cgdata/liuxiangqiong/work62pancancer/Client/v0/script/complex_variant_new.py -i  ${file} -b  ${bam_dir}/${tumor}_clean.bam -f ${hg19} -mode 2 -n 10 -p 0.5 -o ${file}_chr.vcf &
        wait
        echo "fun is end."
}
for file in ${outputdir}/${sample}.merge.Somatic.hc.nohead_*.vcf;
do
    fun  ${tumor} ${file} ${bam_dir} &
done
wait
echo "the vcfmergeing is finished"
grep "^#" ${outputdir}/${sample}.snp.Somatic.hc.vcf >${outputdir}/${sample}.merge.Somatic.hc.combind.vcf
cat ${outputdir}/${sample}.merge.Somatic.hc.nohead_*.vcf_chr.vcf>>${outputdir}/${sample}.merge.Somatic.hc.combind.vcf
rm -rf ${outputdir}/${sample}.merge.Somatic.hc.nohead_*.vcf
rm -rf ${outputdir}/${sample}.merge.Somatic.hc.nohead_*.vcf_chr.vcf

#########################4.filter the false positive########################################################################
grep -v "#" ${outputdir}/${sample}.merge.Somatic.hc.combind.vcf | awk '{a=$2+10;print $1"\t"$2"\t"a}' > ${outputdir}/${sample}.merge.Somatic.hc.combind.bed
#将bed分成多个小文件
split -l 3000 ${outputdir}/${sample}.merge.Somatic.hc.combind.bed -d -a 2 ${outputdir}/${sample}.merge.Somatic.hc.combind.bed_
conda deactivate
source  /home/liuxiangqiong/miniconda3/bin/activate  python2
#bam-readcount -q 20 -b 20 -w 100 -f ${hg19} -l ${outputdir}/${sample}.merge.Somatic.hc.combind.bed ${bam_dir}/${tumor}_clean.bam >${outputdir}/${sample}.merge.Somatic.hc.combind.readcount
fun() {
        hg19=/cgdata/Database/GATK/b37/human_g1k_v37_decoy.fasta
        bam-readcount -q 20 -b 20 -w 100 -f ${hg19} -l ${file} ${bam_dir}/${tumor}_clean.bam >${file}.readcount &
        wait
        echo "fun is end."
}
for file in ${outputdir}/${sample}.merge.Somatic.hc.combind.bed_*;
do
    fun ${inputpath} ${tumor} ${file} ${bam_dir} &
done
wait
echo "the bam-readcount is finished"
cat ${outputdir}/${sample}.merge.Somatic.hc.combind.bed_*readcount>${outputdir}/${sample}.merge.Somatic.hc.combind.readcount
rm -rf ${outputdir}/${sample}.merge.Somatic.hc.combind.bed_*readcount
rm -rf ${outputdir}/${sample}.merge.Somatic.hc.combind.bed_*
conda deactivate
source  /home/liuxiangqiong/miniconda3/bin/activate base
varscan fpfilter \
${outputdir}/${sample}.merge.Somatic.hc.combind.vcf \
${outputdir}/${sample}.merge.Somatic.hc.combind.readcount \
--min-var-count 5 \
--min-var-basequal 20 \
--min-ref-basequal 20 \
--min-var-freq ${min_af} \
--output-file ${outputdir}/${sample}.merge.Somatic.hc.combind.pass.vcf

##########################5.注释########################################################################
#5.1对没有过滤的进行注释对高置信的过滤
#perl /cgdata/CGTools/soft/src/annovar_20200608/table_annovar.pl ${outputdir}/${sample}.merge.Somatic.hc.head.combind.vcf /cgdata/soft/src/annovar.archive/DB/ -buildver hg19 -out ${outputdir}/${sample}.merge.Somatic.hc.head.combind.anno -remove -protocol refGene,popfreq_max_20150413,gnomad_genome,avsnp150,snp138NonFlagged,dbnsfp33a,cosmic94,clinvar_20210501,intervar_20170202,icgc28 -operation g,f,f,f,f,f,f,f,f,f -nastring . -vcfinput --thread 40
#perl /cgdata/CGTools/soft/src/annovar_20200608/table_annovar.pl ${outputdir}/${sample}.snp.Somatic.hc.vcf /cgdata/soft/src/annovar.archive/DB/ -buildver hg19 -out ${outputdir}/${sample}.merge.Somatic.hc.anno -remove -protocol refGene,popfreq_max_20150413,gnomad_genome,avsnp150,snp138NonFlagged,dbnsfp33a,cosmic94,clinvar_20210501,intervar_20170202,icgc28 -operation g,f,f,f,f,f,f,f,f,f -nastring . -vcfinput --thread 40


#5.2先拆分再标准化对齐
#vt decompose ${outputdir}/${sample}.merge.Somatic.hc.combind.pass.vcf |vt normalize - -r ${hg19} -o ${outputdir}/${sample}.merge.Somatic.hc.combind.pass.norm.vcf
conda deactivate
#5.3对经过标准化的进行vep注释
source  /home/liuxiangqiong/miniconda3/bin/activate  ensemble-vep
PERL5LIB=/home/liuxiangqiong/miniconda3/envs/ensemble-vep/share/ensembl-vep-105.0-1/Bio/EnsEMBL/Variation/Utils/DB/HTS/Faidx
export PERL5LIB=$PERL5LIB
inputdir0=${outputdir}/${sample}.merge.Somatic.hc.combind.pass.vcf
outdir1_0=${outputdir}/${sample}.merge.Somatic.hc.combind.pass.norm.vepanno.vcf
vep --cache --fork 16 \
-i ${inputdir0} \
-o ${outdir1_0} \
--assembly GRCh37 \
--cache_version 104 \
--dir_cache /home/liuxiangqiong/miniconda3/share/ensembl-vep-88.9-0/hg19 \
--refseq \
--canonical \
--offline \
--hgvs \
--fasta /cgdata/Database/GATK/b37/human_g1k_v37_decoy.fasta \
--hgvsg \
--max_af \
--check_existing \
--numbers \
--symbol \
--canonical \
--offline \
-vcf \
--use_given_ref \
--af_gnomad \
--check_existing

#5.4对vep的再进行annovar注释
outdir2_0=${outputdir}/${sample}.merge.Somatic.hc.combind.pass.norm.vepanno.annovar
perl /cgdata/CGTools/soft/src/annovar_20200608/table_annovar.pl ${outdir1_0} /cgdata/soft/src/annovar.archive/DB/ -buildver hg19 -out ${outdir2_0} -remove -protocol refGene,popfreq_max_20150413,gnomad_genome,avsnp150,snp138NonFlagged,dbnsfp33a,cosmic94,clinvar_20220829,intervar_20170202,icgc28 -operation g,f,f,f,f,f,f,f,f,f -nastring . -vcfinput --thread 40

###############################6.删除中间文件
#rm -rf ${outputdir}/${normal}.pileup 
#rm -rf ${outputdir}/${tumor}.pileup
rm -rf ${outputdir}/${sample}.merge.Somatic.hc.readcount

conda deactivate
source  /home/liuxiangqiong/miniconda3/bin/activate base
#对varscan的结果进行过滤
python3 /cgdata/liuxiangqiong/work62pancancer/Client/v0/script/20220705/datafile_somatic_filter_varscan_v0_20220929_finish.py -i ${inputpath} -s ${sample}
##对varscan和vardict的方法结合获得最后的突变
#python3 /cgdata/liuxiangqiong/work62pancancer/Client/v0/script/20220705/datafile_somatic_filter_mergeresult_v0_20220929_finish.py -i ${inputpath} -s ${sample}

#########################################################################原始的结果进行合并并注释#####################
##对原始的结果进行合并并注释
less ${outputdir}/${sample}.snp.vcf | grep -vE "^#"  > ${outputdir}/${sample}.merge.Somatic.vcf
less ${outputdir}/${sample}.indel.vcf | grep -vE "^#" >> ${outputdir}/${sample}.merge.Somatic.vcf
#3.3 将邻近的位点进行合并#3.3 将邻近的位点进行合并
#python3 /cgdata/liuxiangqiong/work62pancancer/Client/v0/script/complex_variant_new.py -i  ${outputdir}/${sample}.merge.Somatic.vcf -b  ${bam_dir}/${tumor}_clean.bam -f ${hg19} -mode 2 -n 10 -p 0.5 -o ${outputdir}/${sample}.merge.Somatic.combind1.vcf
#grep "^#" ${outputdir}/${sample}.snp.vcf >${outputdir}/${sample}.merge.Somatic.combind.vcf
#less  ${outputdir}/${sample}.merge.Somatic.combind1.vcf >>${outputdir}/${sample}.merge.Somatic.combind.vcf
#rm -rf  ${outputdir}/${sample}.merge.Somatic.combind1.vcf

##将VCF按照染色体进行拆分
python3 /cgdata/liuxiangqiong/work62pancancer/Client/v0/script/20220705/vcfsplit_v0_20220929_finish.py -i ${outputdir}/${sample}.merge.Somatic.vcf
fun() {
        hg19=/cgdata/Database/GATK/b37/human_g1k_v37_decoy.fasta
        python3 /cgdata/liuxiangqiong/work62pancancer/Client/v0/script/complex_variant_new.py -i  ${file} -b  ${bam_dir}/${tumor}_clean.bam -f ${hg19} -mode 2 -n 10 -p 0.5 -o ${file}_chr.vcf &
        wait
        echo "fun is end."
}
for file in ${outputdir}/${sample}.merge.Somatic_*.vcf;
do
    fun  ${tumor} ${file} ${bam_dir} &
done
wait
echo "the vcfmergeing is finished"
grep "^#" ${outputdir}/${sample}.snp.vcf >${outputdir}/${sample}.merge.Somatic.combind.vcf
cat ${outputdir}/${sample}.merge.Somatic_*.vcf_chr.vcf>>${outputdir}/${sample}.merge.Somatic.combind.vcf
rm -rf ${outputdir}/${sample}.merge.Somatic_*.vcf
rm -rf ${outputdir}/${sample}.merge.Somatic_*.vcf_chr.vcf



#perl /cgdata/CGTools/soft/src/annovar_20200608/table_annovar.pl ${outputdir}/${sample}.merge.Somatic.combind.vcf /cgdata/soft/src/annovar.archive/DB/ -buildver hg19 -out ${outputdir}/${sample}.merge.Somatic.combind.anno -remove -protocol refGene,popfreq_max_20150413,gnomad_genome,avsnp150,snp138NonFlagged,dbnsfp33a,cosmic94,clinvar_20210501,intervar_20170202,icgc28 -operation g,f,f,f,f,f,f,f,f,f -nastring . -vcfinput --thread 40
#5.2先拆分再标准化对齐
vt decompose ${outputdir}/${sample}.merge.Somatic.combind.vcf |vt normalize - -r ${hg19} -o ${outputdir}/${sample}.merge.Somatic.combind.norm.vcf
conda deactivate
#5.3对经过标准化的进行vep注释
source  /home/liuxiangqiong/miniconda3/bin/activate  ensemble-vep
PERL5LIB=/home/liuxiangqiong/miniconda3/envs/ensemble-vep/share/ensembl-vep-105.0-1/Bio/EnsEMBL/Variation/Utils/DB/HTS/Faidx
export PERL5LIB=$PERL5LIB
inputdir=${outputdir}/${sample}.merge.Somatic.combind.norm.vcf
outdir1=${outputdir}/${sample}.merge.Somatic.combind.norm.vepanno.vcf
vep --cache --fork 16 \
-i ${inputdir} \
-o ${outdir1} \
--assembly GRCh37 \
--cache_version 104 \
--dir_cache /home/liuxiangqiong/miniconda3/share/ensembl-vep-88.9-0/hg19 \
--refseq \
--canonical \
--offline \
--hgvs \
--fasta /cgdata/Database/GATK/b37/human_g1k_v37_decoy.fasta \
--hgvsg \
--max_af \
--check_existing \
--numbers \
--symbol \
--canonical \
--offline \
-vcf \
--use_given_ref \
--af_gnomad \
--check_existing

#5.4对vep的再进行annovar注释
outdir2=${outputdir}/${sample}.merge.Somatic.combind.norm.vepanno.annovar
perl /cgdata/CGTools/soft/src/annovar_20200608/table_annovar.pl ${outdir1} /cgdata/soft/src/annovar.archive/DB/ -buildver hg19 -out ${outdir2} -remove -protocol refGene,popfreq_max_20150413,gnomad_genome,avsnp150,snp138NonFlagged,dbnsfp33a,cosmic94,clinvar_20220829,intervar_20170202,icgc28 -operation g,f,f,f,f,f,f,f,f,f -nastring . -vcfinput --thread 40
conda deactivate