ChIP- seq Analysis. BaRC Hot Topics - Feb 24 th 2015 BioinformaBcs and Research CompuBng Whitehead InsBtute. hgp://barc.wi.mit.

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1 ChIP- seq Analysis BaRC Hot Topics - Feb 24 th 2015 BioinformaBcs and Research CompuBng Whitehead InsBtute hgp://barc.wi.mit.edu/hot_topics/

2 Before we start: 1. Log into tak (step 0 on the exercises) 2. Go to your lab space and create a folder for the class (see separate hand out) 3. Connect to your lab space through the wi- htdata network and open the folder you created 2

3 Outline ChIP- seq overview Quality control Experimental design Mapping Map reads Remove unmapped reads and convert to bam files Check the profile of the mapped reads Peak calling More detail about MACS opbons Linking peaks to genes Visualizing ChIP- seq data with ngsplot 3

4 ChIP-Seq overview Park, P. J., ChIP- seq: advantages and challenges of a maturing technology, Nat Rev Genet. Oct;10(10): (2009) 4

5 Steps in data analysis 1. Quality control 2. EffecBve mapping Treat IP and control the same way (preprocessing and mapping) 3. Peak calling i) Read extension and signal profile generabon ii) Peak assignment Pepke, S. et al. ComputaBon for ChIP- seq and RNA- seq studies, Nat Methods. Nov (2009) 5

6 Experimental design Include a control sample. If the protein of interest binds repebbve regions, using paired end sequencing may reduce the mapping ambiguity. Otherwise single reads should be fine. Include at least two biological replicates. If you have replicates you may want to use the parameter IDR irreproducible discovery rate. See us for details. If only a small percentage of the reads maps to the genome, you may have to troubleshoot your ChIP protocol. 6

7 Data that we will use on the hands on the exercises ENCODE human ChIP- seq experiment EncodeBroadHistoneHepg2H3k4me3 EncodeBroadHistoneHepg2Control For quality control and mapping we will use a subset of the data (10%) For running MACS we will use the full set but only chr1 reads 7

8 Quality control (before read mapping) See Hot Topics: Quality Control and Mapping Reads Run fastqc Look at output and determine 1. What type of quality encoding (Illumina versions) your reads have Input qualibes - - solexa- quals <= phred64 Illumina versions phred33 >= If you need to remove adapter, trim reads, filter reads based on quality etc. 8

9 Hands- on Step 1: Quality control Run step 1 commands (fastqc) bsub fastqc Hepg2Control_subset.fastq bsub fastqc Hepg2H3k4me3_subset.fastq 9

10 Fastqc Output 10

11 Mapping We have to map reads to the genome. The mapping solware doesn t have to know about splicing. BowBe, bowbe2 (allows gaps) are good choices Bwa (allows gaps) is fine too Treat IP and control samples the same way during preprocessing and mapping. 11

12 Hands- on Step 2: Mapping bsub bowbe2 - - phred33- quals - N 1 - x /nfs/genomes/ human_gp_feb_09_no_random/bowbe/hg19 - U Hepg2Control_subset.fastq - S Hepg2Control_subset_hg19.N1.sam bsub bowbe2 - - phred33- quals - N 1 - x /nfs/genomes/ human_gp_feb_09_no_random/bowbe/hg19 - U Hepg2H3k4me3_subset.fastq - S Hepg2H3k4me3_subset_hg19.N1.sam - - phred33- quals Because we have Illumina N <int> max # mismatches in seed alignment; can be 0 or 1 (default=0) - x path to the bowbe2 index - U input sequence - S output.sam 12

13 Hands- on Steps 3: Remove unmapped reads and convert to BAM. Sort and index the bam file. Remove unmapped reads bsub "samtools view - hs - F 4 Hepg2Control_subset_hg19.N1.sam samtools view - b - S - > Hepg2Control_subset_hg19.N1.mapped_only.bam" bsub "samtools view - hs - F 4 Hepg2H3k4me3_subset_hg19.N1.sam samtools view - b - S - > Hepg2H3k4me3_subset_hg19.N1.mapped_only.bam" Sort reads bsub samtools sort - T Control_subset - o Control_subset_hg19.N1.mapped_only.sorted.bam Hepg2Control_subset_hg19.N1.mapped_only.bam bsub samtools sort - T H3k4me3_subset - o H3k4me3_subset_hg19.N1.mapped_only.sorted.bam Hepg2H3k4me3_subset_hg19.N1.mapped_only.bam - T PREFIX Write temporary files to PREFIX.nnnn.bam - o FILE Write final output to FILE rather than standard output Index reads bsub samtools index Control_subset_hg19.N1.mapped_only.sorted.bam bsub samtools index H3k4me3_subset_hg19.N1.mapped_only.sorted.bam 13

14 Strand cross- correlabon analysis Bailey, et al. PracBcal Guidelines for the Comprehensive Analysis of ChIP- seq Data, PLOS ComputaPonal Biology Nov 2013 Strand cross- correlabon is computed as the Pearson correlabon between the posibve and the negabve strand profiles at different strand shil distances, k 14

15 Strand cross- correlabon analysis outputs 15

16 Hands- on Steps 4: Quality control aler read mapping Check the strand cross- correlabon peak and predominant fragment length. bsub /nfs/barc_public/phantompeakqualtools/run_spp.r - c=control_chr1.bam - savp - out=control_chr1.run_spp.out bsub /nfs/barc_public/phantompeakqualtools/run_spp.r - c=h3k4me3_chr1.bam - savp - out=h3k4me3_chr1.run_spp.out - savp, save cross- correlabon plot 16

17 Peak calling i) Read extension and signal profile generabon strand cross- correlabon can be used to calculate fragment length ii) Peak evaluabon Look for fold enrichment of the sample over input or expected background EsBmate the significance of the fold enrichment using: Poisson distribubon negabve binomial distribubon background distribubon from input DNA model background data to adjust for local variabon (MACS) Pepke, S. et al. ComputaBon for ChIP- seq and RNA- seq studies, Nat Methods. Nov

18 Peak calling: MACS MACS uses a two- step strategy: 1. Builds a model using high quality peaks and infers shil size from it. 2. Shils the reads and does a second round to call the final peaks. It uses a Poisson distribubon to assign p- values to peaks. But the distribubon has a dynamic parameter, local lambda, to capture the influence of local biases. MACS default is to filter out redundant tags at the same locabon and with the same strand by allowing at most 1 tag. This works well. - g: You need to set up this parameter accordingly: EffecBve genome size. It can be 1.0e+9 or , or shortcuts: 'hs' for human (2.7e9), 'mm' for mouse (1.87e9), 'ce' for C. elegans (9e7) and 'dm' for fruit fly (1.2e8), Default:hs For broad peaks like some histone modificabons it is recommended to use --nomodel and if there is not input sample to use --nolambda. 18

19 Hands- on Steps 5: MACS Input files: Hepg2H3k4me3_chr1.bam Control_chr1.bam MACS command bsub macs2 callpeak - t H3k4me3_chr1.bam - c Control_chr1.bam - - name H3k4me3_chr1 - - format BAM PARAMETERS - t TFILE Treatment file - c CFILE Control file - - name NAME Experiment name, which will be used to generate output file names. DEFAULT: NA - - format FORMAT Format of tag file, BED or SAM or BAM or BOWTIE. DEFAULT: BED - - extsize EXTSIZE The arbitrary extension size in bp. When nomodel is true, MACS will use this value as fragment size to extend each read towards 3' end, then, pile them up. You can use the value from the strand cross- correla3on analysis. 19

20 MACS output Output files: 1. Excel peaks file contains the following columns Chr, start, end, length, abs_summit, pileup, -LOG10(pvalue), -LOG10(qvalue), name 2. Bed peaks file chr, start, end, -LOG10(qvalue) 3. Summits.bed: contains the peak summits locabons for every peaks. The 5th column in this file is - log10qvalue the same as NAME_peaks.bed 4. Peaks.narrowPeak is BED6+4 format file. Contains the peak locabons together with peak summit, fold- change, pvalue and qvalue. 5. Model.r is an R script which you can use to produce a PDF image about the model based on your data. (Rscript NAME_model.r) To look at the peaks on a genome browser you can upload one of the output bed files or you can also make a bedgraph file with columns (step 6 of hands on): chr, start, end, fold_enrichment 20

21 Visualize peaks in IGV Hepg2H3k4me3_chr1.b edgraph Control_chr1.sorted.bam Cov Control_chr1.sorted.bam Hepg2H3k4me3_chr1.sort ed.bam Coverage Hepg2H3k4me3_chr1.sort ed.bam 21

22 Other recommendabons Look at your mapped reads and peaks in a genome browser to verify peak calling thresholds OpBonal: remove reads mapping to the ENCODE and 1000 Genomes blacklisted regions hgps://sites.google.com/site/anshulkundaje/projects/blacklists 22

23 Linking peaks to genes: intersectbed Bed tools slopbed closestbed coveragebed groupby It groups rows based on the value of a given column/s and it summarizes the other columns 23

24 Hands- on 8: Link peaks to nearby genes Take all genes and add 3Kb up and down with slopbed slopbed - b i GRCh37.p13.HumanENSEMBLgenes.bed - g /nfs/ genomes/human_gp_feb_09_no_random/anno/chrominfo.txt > HumanGenesPlusMinus3kb.bed Intersect the slopped genes with peaks and get the list of unique genes overlapping intersectbed - wa - a HumanGenesPlusMinus3kb.bed - b peaks.bed awk '{print $4}' sort - u > Genesat3KborlessfromPeaks.txt intersectbed - wa - a HumanGenesPlusMinus3kb.bed - b peaks.bed head - 3 chr ENSG _CCDC163P chr ENSG _CCDC163P chr ENSG _MIR

25 Hands- on 9: Link peaks to closest genes For each region find the closest gene and filter based on the distance to the gene closestbed - d - a peaks.bed - b GRCh37.p13.HumanENSEMBLgenes.bed head chr H3k4me3_chr1_peak_ chr ENSG _WASH7P 0 chr H3k4me3_chr1_peak_ chr ENSG _MIR chr H3k4me3_chr1_peak_ chr ENSG _WASH7P 0 #the next two steps can also be done on excel closestbed - d - a peaks.bed - b GRCh37.p13.HumanENSEMBLgenes.bed groupby - g 9,10 - c 6,7,8, - o disbnct,disbnct,disbnct head - 3 ENSG _WASH7P 0 chr ENSG _MIR chr ENSG _WASH7P 0 chr closestbed - d - a peaks.bed - b GRCh37.p13.HumanENSEMBLgenes.bed groupby - g 9,10 - c 6,7,8, - o disbnct,disbnct,disbnct awk 'BEGIN {OFS="\t"}{ if ($2<3000) {print $3,$4,$5,$1,$2} } ' head - 5 chr ENSG _WASH7P 0 chr ENSG _MIR chr ENSG _WASH7P 0 chr ENSG _AL chr ENSG _RP11-34P

26 Hands- on 9: Link peaks to closest genes (1 command) For each region find the closest gene and filter based on the distance to the gene closestbed - d - a peaks.bed - b GRCh37.p13.HumanENSEMBLgenes.bed groupby - g 9,10 - c 6,7,8, - o disbnct,disbnct,disbnct awk 'BEGIN {OFS="\t"}{ if ($2<3000) {print $3,$4,$5,$1,$2} }' > closestgeneat3kborless.bed closestbed - d print the distance to the feature in - b groupby - g columns to group on - c columns to summarize - o operabon to use to summarize 26

27 Visualizing ChIP- seq reads with ngsplot See Hot Topics: ngsplot Hands- on 10: bsub ngs.plot.r - G hg19 - R tss - C H3k4me3_chr1.bam - O H3k4me3_chr1.tss - T H3K4me3 - L FL

28 References Previous Hot Topics Quality Control and Mapping Reads hgp://jura.wi.mit.edu/bio/educabon/hot_topics/ngs_qc_mapping_feb2015/ngs_qc_mapping2015_1perpage.pdf SOPs hgp://barcwiki.wi.mit.edu/wiki/sops/chip_seq_peaks Reviews and benchmark papers: ChIP- seq: advantages and challenges of a maturing technology (Oct 09) (hgp:// nrg2641.html) ComputaBon for ChIP- seq and RNA- seq studies (Nov 09) (hgp:// html) PracBcal Guidelines for the Comprehensive Analysis of ChIP- seq Data. PLoS Comput. Biol A computabonal pipeline for comparabve ChIP- seq analyses. Nat. Protoc ChIP- seq guidelines and pracbces of the ENCODE and modencode consorba. Genome Res Quality control and strand cross- correlabon hgp://code.google.com/p/phantompeakqualtools/ MACS: Model- based Analysis of ChIP- Seq (MACS). Genome Biol 2008 hgp://liulab.dfci.harvard.edu/macs/index.html Using MACS to idenbfy peaks from ChIP- Seq data. Curr Protoc BioinformaPcs hgp://onlinelibrary.wiley.com/doi/ / bi0214s34/pdf Bedtools: hgps://code.google.com/p/bedtools/ hgp://bioinformabcs.oxfordjournals.org/content/26/6/841.abstract ngsplot hgps://code.google.com/p/ngsplot/ Shen, L.*, Shao, N., Liu, X. and Nestler, E. (2014) ngs.plot: Quick mining and visualizabon of next- generabon sequencing data by integrabng genomic databases, BMC Genomics, 15,