Ins?tute for Computa?onal Biomedicine. ChIP- seq. Olivier Elemento, PhD TA: Jenny Giannopoulou, PhD

Transcription

1 Ins?tute for Computa?onal Biomedicine ChIP- seq Olivier Elemento, PhD TA: Jenny Giannopoulou, PhD

2 Plan 1. ChIP- seq 2. Quality Control of ChIP- seq data 3. ChIP- seq Peak detec?on 4. Peak Analysis and Interpreta?on 5. A few interes?ng ChIP- seq papers

3 1. ChIP- seq

4 ChIP-seq Transcrip?on factor of interest (or histone modifica?on) An?body Illumina

5 Control: input DNA Can use IgG as addi?onal control Illumina

6 ACCAATAACCGAGGCTCATGCTAAGGCGTTAGCCACAGATGGAAGTCCGACGGCTTGATCCAGAATGGTGTGTGGATTGCCTTGGAACTGATTAGTGAATTC! TGGTTATTGGCTCCGAGTACGATTCCGCAATCGGTGTCTACCTTCAGGCTGCCGAACTAGGTCTTACCACACACCTAACGGAACCTTGACTAATCACTTAAG! Average length ~ 170bp

7 40-100bp ACCAATAACCGAGGCTCATGCTAAGGCGTTAGCCACAGATGGAAGTCCGACGGCTTGATCCAGAATGGTGTGTGGATTGCCTTGGAACTGATTAGTGAATTC! TGGTTATTGGCTCCGAGTACGATTCCGCAATCGGTGTCTACCTTCAGGCTGCCGAACTAGGTCTTACCACACACCTAACGGAACCTTGACTAATCACTTAAG! Average length ~ 170bp

8 40-100bp ACCAATAACCGAGGCTCATGCTAAGGCGTTAGCCACAGATGGAAGTCCGACGGCTTGATCCAGAATGGTGTGTGGATTGCCTTGGAACTGATTAGTGAATTC! TGGTTATTGGCTCCGAGTACGATTCCGCAATCGGTGTCTACCTTCAGGCTGCCGAACTAGGTCTTACCACACACCTAACGGAACCTTGACTAATCACTTAAG! Average length ~ 170bp

9 BWA tutorial (for aligning single end reads to genome) Get genome, e.g., from UCSC hxp://hgdownload.cse.ucsc.edu/goldenpath/hg19/bigzips/chromfa.tar.gz Combine into 1 file tar zvfx chromfa.tar.gz cat *.fa > wg.fa Indexing the genome bwa index - p hg19bwaidx - a bwtsw wg.fa Align ChIP reads to reference genome bwa aln - t 4 hg19bwaidx s_3_sequence.txt.gz > s_3_sequence.txt.bwa Convert to SAM format bwa samse hg19bwaidx s_3_sequence.txt.bwa s_3_sequence.txt.gz > s_3_sequence.txt.sam Align input reads to same reference genome bwa aln - t 4 hg19bwaidx s_4_sequence.txt.gz > s_4_sequence.txt.bwa Convert to SAM format bwa samse hg19bwaidx s_4_sequence.txt.bwa s_4_sequence.txt.gz > s_4_sequence.txt.sam

10 Reads can map to multiple locations/chromosomes Read 1 Read 2 Reference Human Genome (hg18)

11 Reads map to one strand or the other Read 1 Read 2 hg18

12 SAM format DH1608P1_0130:6:1103:10579:166379#TTAGGC 16 chr M * 0 0 GGGCGTGACTCTGATCTCAGGCATCGTCTCCGCCGCGCTCCCGGACCCGCG eb`xxybzdadee^cev]x][cctcc^ebeece eeewbeeeeeeeceeaee XX:Z:NM_017871,32 NM:i:0 MD:Z:51 DH1608P1_0130:6:1102:3415:150915#TTAGGC 16 chr M * 0 0 GGGCGGGACTCTGATCTCAGGCATCGTCTCCGCCGCGCTCCCGGACCCGCG BBBBBBBBBBBac]bbbceedaeddeZceeea_ba_\_eee eeeedaeeee XX:Z:NM_017871,32 NM:i:1 MD:Z:5T45 DH1608P1_0130:6:1102:13118:62644#TTAGGC 16 chr M * 0 0 GGGCGTGCCTCGGATCTCAGGCATCGTCTCCGCCGCGCTCCCGGACCCGCG BBBBBBBBBBBBBBBBBBBBB`XTbSa`cffegdggeccbe effdeggggg XX:Z:NM_017871,32 NM:i:2 MD:Z:7A3T39 DH1608P1_0130:6:1203:3012:157120#TTAGGC 16 chr M * 0 0 AAGGCCGTGACTCTGATCTCAGCCCTCGTCTCCGCCGCGCTCCCGGACCCG BBBBBBBB^`QWZZ]UXYSZSTFRU]Z SO[adcc[acdV \`Y]YWY][_ XX:Z:NM_017871,34 NM:i:3 MD:Z:4G17G1A26 DH1608P1_0130:6:2206:4445:12756#TTAGGC 16 chr M3487N50M * 0 0 CCAAAGGGTGTGACTCTGATCTCGGGCATCGTCTCCGCCGCGCTCCCGGAC BBBBBBBBBBBBBBBBBBBBBBBB`YdddYdc\ cacanddddcdddaeeee XX:Z:NM_017871,37 NM:i:3 MD:Z:2C5C14A27 DH1608P1_0130:6:2203:7903:43788#TTAGGC 16 chr M3487N50M * 0 0 CCCAAGGGCGTGACTCTGATCTCAGGCATCGTCTCCGCCGCGCTCCCGGAC adbe[rcbccb_cb^cb^^c^edgegggggdf ggefffgrfggggegeg XX:Z:NM_017871,37 NM:i:0 MD:Z:51 CIGAR string, eg 5M3487N46M = 5bp- long block, 3487 insert, 46bp- long block MD tag, e.g, MD:Z:4T46 = 5 matches, 1 mismatch (T in read), 46 matches XT tag, e.g. XT:A:U = unique mapper; XT:A:R = more than 1 high- scoring matches

13 Quality Control

14 Clonal reads hxp://biowhat.ucsd.edu/homer/chipseq/qc.html

15 Fragment size analysis

16 Fragment size analysis

17 Fragment size analysis using opposite strand autocorrela?on hxp://biowhat.ucsd.edu/homer/chipseq/qc.html

18 Fragment size analysis hxp://biowhat.ucsd.edu/homer/chipseq/qc.html

19 GC- content analysis hxp://biowhat.ucsd.edu/homer/chipseq/qc.html

20 GC- content analysis hxp://biowhat.ucsd.edu/homer/chipseq/qc.html

21 Other QC measures Number of peaks: 0 or very few peaks, even at permissive peak calling thresholds = bad experiment Mo?f enrichment is expected mo?f enriched in peaks?

22 ChIP- seq peak calling

23 MACS

24 Es?mate d based on high quality peaks MACS 2d The Poisson distribu?on x 1 0 P(X x) =1 λ x e λ x! λ=expected # of reads within an interval of 2d bp # in R P(X>=5 λ=0.001) is 1- sum(dpois(0:4, 0.001))

25 BayesPeak

26 BayesPeak (Bayesian Hidden Markov Models) Observed variable Hidden states Parameters es?mated using Bayesian treatment

27 BayesPeak

28 Peak detection using ChIPseeqer hxp://icb.med.cornell.edu/wiki/index.php/elementolab/chipseeqer_tutorial (Elemento and Giannopoulou, 2011)

29

30 A nice peak

31 Not all peaks are that nice

32 Peak detection Calculate read count at each position (bp) in genome (we don t use a sliding window) Determine if read count is greater than expected (at each position - bp)

33 Peak detection We need to correct for input DNA reads (control) - non-uniformaly distributed (form peaks too) - vastly different numbers of reads between ChIP and input

34 Use Bioanalyzer (remove adapter lengths) genome Read count Expected read count T A T T A A T T A T C C C C A T A T A T G A T A T genome Expected read count = total number of reads * extended fragment length / chr length

35 Is the observed read count at a given genomic position greater than expected? Read counts follow a Poisson distribu?on Frequency P(X x) =1 x 1 0 λ x e λ x = observed read count λ = expected read count x! Read count The Poisson distribu?on

36 Is the observed read count at a given genomic position greater than expected? P(X x) =1 x 1 0 x = 10 reads (observed) λ = 0.5 reads (expected) λ x e λ x! genome The Poisson distribu?on P(X>=10) = 1.7 x log10 P(X>=10) = log10 P(X>=10) = 9.77 # in R P(X>=10 λ=0.5) is 1- sum(dpois(0:9, 0.5))

37 Read count Expected read count P c (X x) =1 x 1 0 λ c x e λ c x! - Log(p) Expected read count = total number of reads * extended frag len / chr len

38 Read count Expected read count - Log(p) Expected read count = total number of reads * extended frag len / chr len Input reads

39 ChIP INPUT Read count Expected read count Read count Expected read count Genome posi?ons (bp) Genome posi?ons (bp) P c (X x) =1 x 1 0 λ c x e λ c x! P i (X x) =1 x 1 0 λ i x e λ i x! - Log(P c ) - Log(P i ) [- Log(P c )] - [- Log(P i )] Threshold

40 Normalized Peak score (at each bp) P(X ChIP ) R = - log10 P(Xinput ) Will detect peaks with high read counts in ChIP, low in Input Works when no input DNA! (x=0) P i (X x) =1 x 1 0 λ i x e λ i x!

41 Mappability

42 Non-mappable fraction of the genome We enumerated all 30-mers, counted # occurrences, calculated non-unique fraction of genome chr / (=12%) chr / chr / chr / chr / chr / chr / chr / chr / chr / chr / chr / chr / chrx / chr / chr / chr / chr / chrm 4628/ chr / chr / chr / chr / chr / chry / (=74%) Unique/mappable frac?on = 1 non- unique frac?on

43 Read count Expected read count P c (X x) =1 x 1 0 λ c x e λ c x! - Log(p) Expected read count = total number of reads * extended frag len / ( chr len * mappable frac?on)

44 Peak detection Determine all genomic regions with R>=15 Merge peaks separated by less than 100bp Output all peaks with length >= 100bp Process 23M reads in <5mins

45 BCL6 ChIP-seq Lymphoma cell line (OCI-Ly1) Illumina 6 GA2x lanes for ChIP, 1 for input DNA, 1 for QC 36nt long sequences 32 Million reads Aligned/mapped to hg18 with BWA With Melnick lab at WCMC

46 BCL6: 18,814 peaks ChIP reads Input reads Detected Peaks 80% are within <20kb of a known gene

47

48 Loading peaks into GRange system( split_samfile s_1_sequence.txt.sam outdir CHIP/ )! system( split_samfile s_2_sequence.txt.sam outdir INPUT/ )! system( ChIPseeqer.bin chipdir CHIP inputdir INPUT t 15 fold 2 outfile peaks.txt )! tpeaks = read.table(paste(datafolder, peaks.txt, sep = ""), header = F)! peaks = RangedData(ranges = IRanges(start = tpeaks[, 2], end = tpeaks[, 3]), space = tpeaks[, 1], summit = tpeaks[, 6], score = tpeaks[, 5])!...!

49 Other peak finders

50 Promoter- based analysis (not peak- based) 2kb h1 h1 h2 h2 All TSS h3 h3 h4 h5 h5 Maximum peak height in 2kb promoter

51 4. Peak analysis and interpreta?on

52 Gene- based peak annota?on

53 Integra?on of mul?ple peak lists RangeData in R

54 Conserva?on analysis fixedstep chrom=chr1 start= step=1! 0.005! 0.009! 0.023! 0.036! 0.048! 0.059! 0.068! 0.077! 0.084! 0.091! 0.097! 0.102! 0.107! 0.110! 0.113! 0.115! 0.116! 0.116! fixedstep chrom=chr1 start= step=1! 0.114! 0.112! 0.109! 0.105! 0.101!...! hxp://hgdownload.cse.ucsc.edu/goldenpath/hg18/phastcons17way/

55 What is the cis-regulatory code of each factor? Does they require any cofactors? DNA Ac:va:on Repression

56 hxp://meme.nbcr.net/meme4_6_1/intro.html

57

58 Discovering regulatory sequences associated with peak regions True TF binding peak? Target regions Yes Yes Yes Yes correla:on is quan:fied using the mutual informa,on True TF peak Random regions Yes Yes No No No No No No Mo?f Absent Present No Yes I(motif ; groups) = P(i, j)log 2 2 i=1 j=1 P(i, j) P(i)P( j)

59 Motif Search Algorithm Highly informa?ve Not informa?ve k-mer MI CTCATCG TCATCGC AAAATTT GATGAGC AAAAATT ATGAGCT TTGCCAC TGCCACC ATCTCAT ACGCGCG CGACGCG TACGCTA ACCCCCT CCACGGC TTCAAAA AGACGCG CGAGAGC CTTATTA MI=0.081 MI=0.045 MI=0.040

60 Discovered Mo?fs Mo?f co- occurrence anallysis Deple?on Enrichment FIRE automa?cally compares discovered mo?fs to known mo?fs in TRANSFAC and JASPAR

61 5. A few interes?ng papers

62 First ChIP- seq paper

63 Epigene?c modifica?ons at enhancer regions

64 Chroma?n states

65 Nucleosome localiza?on

66 Whole-genome nucleosome location mapping in B cells Principal Component Analysis of Nucleosome profiles Yanwen Jiang, PhD