Difference between revisions of "SK met"
From Crop Genomics Lab.
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== snp in puredmr snpeff == | == snp in puredmr snpeff == | ||
193 에서 puregenicdmr 의 gene 이름 grep | 193 에서 puregenicdmr 의 gene 이름 grep | ||
− | /data/haggui/tr_mapping 에서 python 이용하여 +- 4kb 이내의 snp 잡아냄 (snp in dmr이 input file) | + | /data/haggui/tr_mapping 에서 python 이용하여 +- 4kb 이내의 snp 잡아냄 (snp in dmr이 input file) puredmr.genic.snp |
vcf 모으기 | vcf 모으기 |
Revision as of 08:38, 29 May 2019
Contents |
materials
raw_data : /NGS/NGS/VignaRadiata/DNA/SK/ /mount_test/sdd1/Mungbean_assemble/ver6/SNP/Vradi_ver6.reseqKJ.sorted.bam
SK010 | TN1509D0657--TCCGGAGA-AGGCTATA |
SK068 | TN1509D0710--TAATGCGC-TAAGATTA |
SK186 | TN1510D0932--TCTCGCGC-TCAGAGCC |
SK049 | TN1509D0695--GAATTCGT-ACGTCCTG |
SK094 | TN1509D0736--TCTCGCGC-GTCAGTAC |
SK152 | TN1510D0898--CTGAAGCT-GCCTCTAT |
SK176 | TN1510D0922--TCCGCGAA-GCCTCTAT |
SK156 | TN1510D0902--CTGAAGCT-TAAGATTA |
- 244 > 63 /data/haggui/raw_data/ 에 sample#.fq.gz 로 저장함
ref : 244 /mount_test/sdd1/Mungbean_assemble/ver6/SNP/Vradi_ver6.fa
- 244 > 63 data/haggui/Vradi_ver6.fa
process
- java -jar jar /jungminh/~/Trimmomatic-0.36/trimmomatic-0.36.jar PE -phred33 <input_forward.fq.gz> <input_reverse.fq.gz> <output_forward_paired.fq.gz> <output_forward_unpaired.fq.gz> <output_reverse_paired.fq.gz> <output_reverse_unpaired.fq.gz> ILLUMINACLIP:/jungminh/~/Trimmomatic-0.36/adapters/TruSeq3-PE.fa:2:30:10 SLIDINGWINDOW:4:20 MINLEN:36
- bwa index <ref.fa>
- zcat unpaired_1 unpaired_2 | gzip '-c' > unpaired.fq.gz
- bwa mem Vradi_ver6.fa ./raw_data/{}_1.fq.gz ./raw_data/{}_2.fq.gz | samtools view -bS -q 30 -> {}.bam" (1.5일)
- bwa mem Vradi_ver6.fa unpaired.fq.gz | samtools view -bS -q 30 -> unpaired.bam
- samtools sort
- samtools merge
- samtools depth {sorted.bam} > {.dep}
- wc -l *.dep
- cat {sort.bam.dep} |awk '{sum += $3} END { print sum}'
- samtools rmdup
- /data/haggui$ /data/haggui/samtools-1.3.1/samtools mpileup -f Vradi_ver6.fa -v -t DP,AD,ADF,ADR,SP,INFO/AD,INFO/ADF,INFO/ADR -u -b {bamfile.list}| bcftools call -v -m -O v > output
- vcftools --vcf SK_met_variant.vcf --minQ 30 --minDP 5 --maxDP 100 --recode --out SK_met_variant.vcf.SNP.q30.d5D100 --remove-indels (SK는 d3D20, K는 d10D100) = rmdup.tr.combined.SK.variant.vcf.SNP.q30.d3D20.recode.vcf
- homo and all genotype 있는 것만 골라내기 rmdup.tr.combined.SK.variant.vcf.SNP.q30.d3D20.recode.vcf.homo
- -Xmx4g -jar /data2/chojam96/methylation/snpEff_program/snpEff/snpEff.jar -v Vradi_ensembl KJ.vcf > KJ.snpeff
- mergeskkjvcf.py > filteredSNP.combined
- python temsnpindmr.py /data2/chojam96/methylation/metilene/metilene_v0.2-7/CpG_Vr-Met.min3.qval.0.05_out ./filteredSNP.combined.but.missing4 > snpindmr0325_withmissing
- python metlevel.py > .cov.dmr
- python dmrmetlevel.py > cov.dmr.metlevel
- python combinemetlevel.without#gen.py CpG_Vr-Met-S_bismark.cov.dmr.metlevel CpG_Vr-Met-SK010_bismark.cov.dmr.metlevel CpG_Vr-Met-SK049_bismark.cov.dmr.metlevel CpG_Vr-Met-SK068_bismark.cov.dmr.metlevel CpG_Vr-Met-SK094_bismark.cov.dmr.metlevel CpG_Vr-Met-SK152_bismark.cov.dmr.metlevel CpG_Vr-Met-SK156_bismark.cov.dmr.metlevel CpG_Vr-Met-SK176_bismark.cov.dmr.metlevel CpG_Vr-Met-SK186_bismark.cov.dmr.metlevel CpG_Vr-Met-K_bismark.cov.dmr.metlevel snpindmr0325_withmissing > snpindmr0325_withmissing.dic
- python combinemetlevel.py CpG_Vr-Met-S_bismark.cov.dmr.metlevel CpG_Vr-Met-SK010_bismark.cov.dmr.metlevel CpG_Vr-Met-SK049_bismark.cov.dmr.metlevel CpG_Vr-Met-SK068_bismark.cov.dmr.metlevel CpG_Vr-Met-SK094_bismark.cov.dmr.metlevel CpG_Vr-Met-SK152_bismark.cov.dmr.metlevel CpG_Vr-Met-SK156_bismark.cov.dmr.metlevel CpG_Vr-Met-SK176_bismark.cov.dmr.metlevel CpG_Vr-Met-SK186_bismark.cov.dmr.metlevel CpG_Vr-Met-K_bismark.cov.dmr.metlevel snpindmr0311_2 | sort > dmr.in.snp.over8
vcf를 193 으로 옮긴 후, snpeff 실행
with filler
python temsnpindmr.py /data2/chojam96/methylation/metilene/metilene_v0.2-7/CpG_total.min3.withfiller_qval.0.05.out ../filteredSNP.combined > snpindmr0408.CpG python temsnpindmr.py /data2/chojam96/methylation/metilene/metilene_v0.2-7/CHG_total.min3.withfiller_qval.0.05.out ../filteredSNP.combined > snpindmr0408.CHG cat list | parallel --gnu -j 5 --max-args=2 python metlevel.py /data2/chojam96/methylation/metilene/metilene_v0.2-7/CHG_total.min3.withfiller_qval.0.05.out /data2/chojam96/methylation/metilene/metilene_v0.2-7/{1}/CHG_{1}_bismark.cov.gz ">" CHG.{2} cat list | parallel --gnu -j 5 --max-args=2 python metlevel.py /data2/chojam96/methylation/metilene/metilene_v0.2-7/CpG_total.min3.withfiller_qval.0.05.out /data2/chojam96/methylation/metilene/metilene_v0.2-7/{1}/CpG_{1}_bismark.cov.gz ">" {2} cat puredmrgenelist | parallel --gnu --max-args=3 grep {1} /data2/chojam96/methylation/snpEff/snpEff/*.region "| grep {2} | grep {3}"
etc
python svgmaking.py /data2/chojam96/methylation/metilene/metilene_v0.2-7/CpG_total.min3.withfiller_qval.0.05.out > CpG.filler.svg
cat list | parallel --gnu -j 8 samtools depth /data/haggui/tr_mapping/{} ">" ./{}.dep
wc -l combined.SK*
418018271 combined.SK010.sort.bam.dep 408094197 combined.SK049.sort.bam.dep 418324160 combined.SK068.sort.bam.dep 414726647 combined.SK094.sort.bam.dep 413928679 combined.SK152.sort.bam.dep 416137202 combined.SK156.sort.bam.dep 412230731 combined.SK176.sort.bam.dep 417275769 combined.SK186.sort.bam.dep
python countingsnp.py rmdup.tr.combined.SK.variant.vcf.SNP.q30.d3D20.recode.vcf
snp in puredmr snpeff
193 에서 puregenicdmr 의 gene 이름 grep /data/haggui/tr_mapping 에서 python 이용하여 +- 4kb 이내의 snp 잡아냄 (snp in dmr이 input file) puredmr.genic.snp vcf 모으기