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chipseq分析流程.txt
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#!/bin/bash
#SBATCH -c 6
#SBATCH -n 2
#SBATCH -N 2
#SBATCH -J Quant.Quant.SingleQuant
#SBATCH -p SANGERDEV
#SBATCH --mem=50G
#SBATCH -o bowtie_%j.out
#SBATCH -e bowtie_%j.err
cd /mnt/ilustre/users/sanger-dev/sg-users/deqing/chipseq/Analysis/bowtie_align
/mnt/ilustre/users/sanger-dev/app/bioinfo/align/bowtie2-2.2.9/bowtie2-build --large-index \
--threads 4 \
/mnt/ilustre/users/sanger-dev/sg-users/deqing/chipseq/Analysis/mmu_annotation/Mus_musculus.GRCm38.dna.toplevel.fa \
/mnt/ilustre/users/sanger-dev/sg-users/deqing/chipseq/Analysis/bowtie_index/ref_index \
&& \
/mnt/ilustre/users/sanger-dev/app/bioinfo/align/bowtie2-2.2.9/bowtie2 \
-x /mnt/ilustre/users/sanger-dev/sg-users/deqing/chipseq/Analysis/bowtie_index/ref_index \
-1 /mnt/ilustre/users/sanger-dev/sg-users/deqing/chipseq/Cleandata/Input/Input_R1.fq \
-2 /mnt/ilustre/users/sanger-dev/sg-users/deqing/chipseq/Cleandata/Input/Input_R2.fq \
-S Input/Input.sam \
--un-conc-gz Input/Input.unmapped.gz \
-N 1 \
-p 6 \
&& \
/mnt/ilustre/users/sanger-dev/app/bioinfo/align/bowtie2-2.2.9/bowtie2 \
-x /mnt/ilustre/users/sanger-dev/sg-users/deqing/chipseq/Analysis/bowtie_index/ref_index \
-1 /mnt/ilustre/users/sanger-dev/sg-users/deqing/chipseq/Cleandata/SMC3/SMC3_R1.fq \
-2 /mnt/ilustre/users/sanger-dev/sg-users/deqing/chipseq/Cleandata/SMC3/SMC3_R2.fq \
-S SMC3/SMC3.sam \
--un-conc-gz SMC3/SMC3.unmapped.gz \
-N 1 \
-p 6 \
&& \
/mnt/ilustre/users/sanger-dev/app/program/Python/bin/samtools view -h -F 12 -q 30 Input/Input.sam > Input/Input_mapped.sam \
&& \
/mnt/ilustre/users/sanger-dev/app/program/Python/bin/samtools view -h -F 12 -q 30 SMC3/SMC3.sam > SMC3/SMC3_mapped.sam \
&& \
python /mnt/ilustre/users/sanger-dev/sg-users/deqing/chipseq/MACS-1.4.2/bin/macs14 -g mm -t SMC3/SMC3_mapped.sam -c Input/Input_mapped.sam --format SAM --name "SMC3" --keep-dup 1 --wig --single-profile --space=50 --diag
# 关于mapping quality的选择 http://pubmedcentralcanada.ca/pmcc/articles/PMC5443578/?lang=en
# http://homer.ucsd.edu/homer/ngs/peakMotifs.html
# ---------------------------------------------------------------
# 获得gene注释信息
sed 's/gene_id\s"\([A-Za-z0-9]\+\)";/\1\t/g' gene.bed | less
# peak 注释
# /mnt/ilustre/users/sanger-dev/app/bioinfo/rna/bedtools2-master/bin/bedtools
sed -n '24,$p' SMC3_peaks.xls | awk -F '\t' -OFS='\t' '{if (NR==1){print $0"\tpeak_id"} else {print $0"\tMACS_peak_"NR-1} }' > SMC3_peaks.bed
less Mus_musculus.GRCm38.89.gtf | awk -F '\t' '$3=="gene"' | cut -f1,4- | cut -f1,2,3,5,7 > gene.bed
/mnt/ilustre/users/sanger-dev/app/bioinfo/rna/bedtools2-master/bin/bedtools intersect -b ../mmu_annotation/gtf/gene.bed -a SMC3_peaks.bed -wo > bedtools.intersect.result
# motif 分析， 使用MEME-chip， http://meme-suite.org/doc/download.html?man_type=web
awk -F '\t' '{print $1,$2-300,$3+300,$4,$5}' OFS='\t' SMC3_summits.bed > centred600peak.bed
在线分析
# 用homer 注释peak. http://homer.ucsd.edu/homer/ngs/annotation.html
perl /mnt/ilustre/users/sanger-dev/sg-users/deqing/chipseq/HOMER/bin/annotatePeaks.pl SMC3_peaks.bed ../mmu_annotation/Mus_musculus.GRCm38.dna.toplevel.fa -gtf ../mmu_annotation/gtf/Mus_musculus.GRCm38.89.gtf -gid -go go_result > SMC3_peaks.annot
# 脚本拼接结果
# python add_table.py -i peak.annot.tmp -col 12 -table gene.annot -ind 5 -o peak.annot.xls
# 画饼图
a.plot.pie(subplots=True, autopct='%1.2f%%', pctdistance = 0.6, legend=False, cmap='rainbow')
plt.axis('equal')