DNase I Seq data Analysis Strategy. Dragon Star 2013 QianQin 同济大学

Transcription

1 DNase I Seq data Analysis Strategy Dragon Star 2013 QianQin 同济大学

2 Workflow Mapping(BWA/Bow8e) QC Reads filtering and format(samtools /Picard) qrqc, FastQC 1. Sampling down by mappable reads 2. Scale mappable reads Peaks Calling (MACS/hotspot) Filtering BedGraph, BED(BEDTOOLS, bedclip) Pileup(Convert to bigwiggle) Peaks BED 1 Peaks BED 2 Correla8on 1. Data comparison(bedops, BEDTOOLS) 2. Union BED 3. Mo8f discovery

3 Warm up

4 Examples on DHS He, H. H., Meyer, C. A., Chen, M. W., Jordan, V. C., Brown, M., & Liu, X. S. (2012). Genome research, 22(6), doi: /gr Neph, S., Vierstra, J., Stergachis, A. B., Reynolds, A. P., Haugen, E., Vernot, B., Thurman, R. E., et al. (2012). Nature, 489(7414), doi: /nature11212

5 Uncompress BAM to Fastq Single End data bamtofastq i path_to_bam fq output.fastq - i input bam files - fq output fastq files - fq2 pair end

6 FASTQ: Format instruction hdp://en.wikipedia.org/wiki/fastq_format SAM/BAM BED, BedGraph, BigBed Wiggle, BigWiggle narrowpeak, broadpeak bed.starch hdps://genome.ucsc.edu/faq/faqformat.html hdp://code.google.com/p/bedops/wiki/starchandunstarch

7 SAM/BAM file instruction BAM is compressed SAM FLAGS for SE: 0 for posi8ve strand, 16 for nega8ve strand, 4 for unmapped FLAGS for PE: R mate reverse strand, r read reverse strand 147 pair2 strand, 99 pair 1 + strand 83 pair1 strand, 163 pair2 + strand Common FLAG: NM for mismatch level XT for custom tags hdp://genome.sph.umich.edu/wiki/sam

8 Tips on shell du h file du sh. grep A input.fastq grep 0 input.fastq cut - f 5 input.sam cut - f 3,4 input.sam uniq wc l cut f 3,4 input.sam grep chr21 wc - l

9 Task 1: get reads mapping location

10 Bowtie/Bowtie2 Index genome Single End bow8e- build chr21.fa chr21 bow8e2- build chr21.fa chr21 bow4e2 chr21 input.fastq - S output.sam bow4e chr21 input.fastq - S output.sam

11 BWA Index genome Mapping bwa index - a bwtsw chr21.fa bwa aln - t 4 chr21.fa input.fastq - f output.fai bwa samse - f output.sam chr21.fa output.fai input.fastq

12 Task 2: Alignment conversion and mapping statistics

13 Samtools / Picard for Conversion Convert SAM to BAM samtools view - bs input.sam - o output.bam samtools sort input.bam output_sorted samtools merge merge.bam input1.bam input2.bam Convert BAM to SAM samtools view - h input.bam - o output.sam samtools view - X input.bam - o output.sam samtools view - x input.bam - o output.sam

14 Samtools / Picard for reads filter and statistics Get reliable aligned reads samtools view - bq 1 input.bam > output.bam Mapping sta8s8cs samtools flagstat input.bam

15 BEDTOOLS/BEDOPS for reads format conversion Convert BAM to BED bamtobed - i input.bam > input.bed Merge BED files bedops - u input1.bed input2.bed > output.bed Equals cat input1.bed input2.bed sort- bed - > output.bed

16 Task 3: Predict open chromatin regions

17 Peaks calling tools MACS14/2 hdps://github.com/taoliu/macs/ Built- in Cistrome, user- friendly Support Pair end mode Hotspot Need shell and Linux opera8on experience Largely dependency hdp://

18 MACS14 macs14 - t test.bam - n test Rscript test_model.r ## model image Keep duplicates or not macs keep- dup all - t test.bam - n test Model failed macs keep- dup all - t test.bam - n test - - nomodel - - shifsize 73

19 MACS2 Peaks calling macs2 callpeak - t test.sam - n test macs2 callpeak - - nomodel - - shimsize 73 - t test.sam - n test Down sampling macs2 randsample - t test.sam - n seed 25 - o test.bed Filter duplicates macs2 filterdup - i test.bam - o test.bed Pileup macs2 pileup - i test.bam extsize 3 - o test.bed sort - k1,1 - k2,2 test.bed > sort.bed

20 Task 4: Replicates consistency

21 bedtools/bedops for comparison Get intersec8on regions bedops i input1.bed input2.bed > output.bed bedtools intersect a input1.bed - b input2.bed > output.be Get input1.bed overlapped regions only bedops e input1.bed input2.bed intersectbed a - u input1.bed input2.bed Get input1.bed complementary regions bedops d input1.bed input2.bed intersectbed v a input1.bed b input2.bed

22 Task 5: data visualization, annotation and Motif discovery

23 MDSeqpos Get most accessible chroma8n regions sort - r - g - k 5 peaks.bed > input.bed Mo8f analysis DSeqPos.py input.bed - d - m cistrome.xml - p 0.05 hg19 - s hs - p p value - s species - d denovo or not - m mo8f databases, transfac.xml, cistrome.xml

24 Data visualization and Cistrome application annotation Get open chroma8n regions nearby genes RegPoten8al.py - t test_peaks.bed - g /mnt/storage/data/ sync_cistrome_lib/ceaslib/genetable/hg19 - n test - d IGV Set data ranges Auto scale Find most enrichment regions Load wiggle and peaks BED

25 Task summary Get Fastq Mapping Get proper format Peaks calling Comparison of replicates peaks Data visualiza8on and mo8f analysis

26 师资队伍 全职教授 江赐忠 曹志伟 张勇 973首席科学家 上海市浦江人才 上海市东方学者计划 上海市曙光计划 教育部新世纪优秀人才 上海市科委科技启明星计划 教育部新世纪优秀人才 海外 讲座教授 Shirley Liu (Harvard) Zhiping Weng (UMass) 千人计划 Wei Li (Baylor) 千人计划 兼职教授 协助引进 李亦学 刘雷 张帆

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