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[CHIP-seq] 哈佛大学刘小乐教授实验室是如何做chip-seq数据分析的

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发表于 2017-3-11 18:03:21 | 显示全部楼层 |阅读模式
都写在网上了:http://cistrome.org/db/#/about

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Tools and parameters used for data analysis

Cistrome DB uses the standard analysis pipeline ChiLin (1) to process all chromatin profiling reads. Here is a brief introduction to the tools and parameters used for data analysis.

read mapping: Mapping is done using bwa (2).

bwa aln -q 5 -l 32 -k 2 -t 8 index FASTQ > sai bwa samse index sai FASTQ > sam

read filtering: Processing of mapped reads is carried out using samtools (3).

samtools view -bS -t chromInfo_file.txt -q 1 sam > bam

read sorting: samtools sort -m 4000000000 bam > bam_sort

read mapping statistics: samtools flagstat bam > bam.stat

peak calling: Peaks are identified using MACS2 (4).

macs2 callpeak --SPMR -B -q 0.01 --keep-dup 1 --extsize=146 --nomodel -g hs -t bam -n test

bigwiggle generation: Signal tracks for browser viewer are generated using BEDTools (5) and UCSC tools (6).

bedtools intersect -a bedGraph -b chrom.bed -wa -f 1.00 > bedGraph.tmp bedGraphToBigWig bedGraph.tmp chromInfo_file.txt bigwiggle

motif scanning: Motif significance is based on an analysis of the position of motifs relative to peak summits (7). In samples with more than 5,000 peaks this analysis if based on the most significant 5,000 peak.

MDSeqPos.py -d -w 600 -p 0.001 -m cistrome.xml -O output bed hg38

target gene analysis: Target genes are identified using BETA (8). This function is also available at [url]https://github.com/cfce/chilin/tree/master/chilin2/modules/regulatory.[/url] Peaks with less than 5 fold signal to background ratio are filtered out, and the most significant 10,000 peaks amongst these are selected. A regulatory potential for is calculated for each RefSeq gene: RegPotential.py -t top -g refGene -n test -d 100000

 




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