Gene Regula*on, ChIP- X and DNA Mo*fs. Statistics in Genomics Hongkai Ji

Transcription

1 Gene Regula*on, ChIP- X and DNA Mo*fs Statistics in Genomics Hongkai Ji (hji@jhsph.edu)

2 Genetic information is stored in DNA TCAGTTGGAGCTGCTCCCCCACGGCCTCTCCTCACATTCCACGTCCTGTAGCTCTATGACCTCCACCTTTGAGTCCCTCCTC TCACACCTGACATGAAAAGGCACATGAGGATCCTCAAATACCCCGTGATCAGTCTCAGGGTAGCTCTCATAGCCTGGACAGG GCCCCCCTCGGGGGTTGCGCCCAGGTCCAGGCGGGGGATGCACAGCAACAGTCACCGAAGCAGAAGCCGTCACAGTGGTGAT GGGCTGGCAGTAGCTGGGCACAGAGCTGCCCATGGCGGTGGACGTTGGGTTCCGAGGGTTGTGAGAACGGGCCCCACGGGGC CCTGAGCGGTCCCTATTGCTAGGGCCAGAATGCCCTTCAGTAGAAATTTCAAAAGCGTCTCTGCGCGGTCTGTAGGGGGGTG GCCGCAAGCCTTCTCTAGGGGGATCCCTTCGAGGCTGCTGGCCTTGCCGTCCAGGGGACAAGGAGCCAGAGTCCAGGTGGGG CTGTTGCCGAGGGGTCAAGGGAGGCTGATGTCTGGAGTCCGGATGGACCACCTGCAGAGGAGAGACATAGGTCAACACAGGG AGGTAGGATGGTGGTGATGTTCCACCCACAAAAGAAAACCTATTCCTTTAGAAACCTCCAGGATGTGAATCCTGCCTGCACC TGCACAGCTGGCTGGAGGCATATAGCCACTGCCCATAGATCTCAACTTACCCTCACAACCAACTGCCCCCAGGCCTAAGTTC TCTGCCTCAAAACTGCCAAGGCCTGGATAGCCAAGAGCCTGGGTGTCTTGGAAATATGCAACCATAAATAGTAGCTTTTAGA AGTATAAGGCTCCTGTTTCTGGGTCATATTAGTGTTGTTTTCACCTGTCCCCAGCCCTAAGCCAGGTGTGGCCAGAAGCAAA TGTACTGTAAGAGCAGAGCAAAAACTTCCACACAGATAGTTCTGTTAGGCAATACATCTCTGCCTGACTATTAGGAATCTGG TTTCTGGGTCCTCTGTACAAAGCTCGGAGCAACACAGTGGCCACATCAATCAAAAGGACCGTGACCAACTTCAAAGTCGGTG AGCTTGTACCTATTTTTAGGCTCCTGCTGAACAGAACCAGATTCACACTACAGCTCAGCAGGGCATCGTCACGGGTGTGTGT GTGTGTGTGTGTGTGTGTGTGTGTGTGTGTTGGGGGGGGGGGGTGGACAGAGGACGGGGACACAATTCACTGGCCAGCCCTT CTCTCCTTCAAGGAAGGCTGCTCTAGCCTGGGACTGGAATACACATTTCCTGTAAACATGGTGGGGGCCTCAGGCAAGCCAG AGTTTTGGAGCCTTCCTTAACTCTTCAAGGTGAGCATCTTGACTTGGAGGGTGGGGGTGCGGGTAAGGAAGGAACCTGTGGA CTCCTCCCTACAAGACAGAAAAGGAATAAGCCACGAAGACAATAACGATTTTTGTATCAAGCGTCCTCTCCCATTTCAGCTT ACCTGACAATGAAATCAAATTCGGACCCTGCAAGCATCAGTACACCCAGCAGAGTGGACACAGCACCGTCCAGAACGGGAGC AAACATGTGCTCCAGAGCGAGCATAGCCCTGTGGTTCTTGTCCCCAATGGCTGTCAGAAAGGCCTGAACAAAGGAGAAAATT GACACGGTCACATTCTGGGTGTGGTAAAGTGCTCAGCTGTGTCTATACTTGGGTTTTGTAT Basic units of DNA: A, C, G, T Total amount of DNA in human genome: 3 * 10 9 base pairs (bp)

3 A gene needs to be expressed in order to execute its function Human: ~30,000 genes Gene Gene Gene Gene Gene DNA A A G G T C T T C C A G G C T A Transcription Gene Expression mrna A A G G U C G U A G C G U Translation Protein I K V R Q Function Cell skeleton Enzymes Signal molecules

4 Gene expression is *ghtly regulated Expression No Expression Spatially X Y Z Temporally X A B A B A B Y Z C C C X Y Z X Y Z

5 Transcrip*onal regula*on is an important way to control gene expression Transcription factors (TF): TF1 TF2 Transcription factor binding sites (TFBS): CCACCCAC, TAATAAAAT TF1 TF2 TF1 TTATGTAACCTGCACTTACTACCACCCACAACATAATAAAATCTAAACCACTGAATGAAATACAAAATCTATGTATGA... TF2 TTATGTAACCTGCACTTACTACCACCCACAACATAATAAAATCTAAACCACTGAATGAAATACAAAATCTATGTATGA...

6 Transcrip*on factors recognize specific mo*fs TF GTATGTACTTACTATGGGTGGTCAACAAATCTATGTATGA TF TAACATGTGACTCCTATAACCTCTTTGGGTGGTACATGAA TF CTGGGAGGTCCTCGGTTCAGAGTCACAGAGCAGATAATCA TF TTAGAGGCACAATTGCTTGGGTGGTGCACAAAAAAACAAG TF AACAGCCTTGGATTAGCTGCTGGGGGGGTGAGTGGTCCAC TF ATCAGAATGGGTGGTCCATATATCCCAAAGAAGAGGGTAG Transcription Factor Binding Sites (TFBS) TGGGTGGTC TGGGTGGTA TGGGAGGTC TGGGTGGTG TGAGTGGTC TGGGTGGTC A C G T Motif A C G T

7 Transcrip*on factor binding sites are regulatory codes in the genome TCAGTTGGAGCTGCTCCCCCACGGCCTCTCCTCACATTCCACGTCCTGTAGCTCTATGACCTCCACCTTTGAGTCCCTCCTC TCACACCACCCATGTTTTGTTTATGAGGATCCTCAAATACCCCGTGATCAGTCTCAGGGTAGCTCTCATAGCCTGGACAGGG CCCCCCTCGGGGGTTGCGCCCAGGTCCAGGCGGGGGATGCACAGCAACAGTCACCGAAGCAGAAGCCGTCACAGTGGTGATG GGCTGGCAGTAGCTGGGCACAGAGCTGCCCATGGCGGTGGACGTTGGGTTCCGAGGGTTGTGAGAACGGGCCCCACGGGGCC CTGAGCGGTCCCTATTGCTAGGGCCAGAATGCCCTTCAGTAGAAATTTCAAAAGCGTCTCTGCGCGGTCTGTAGGGGGGTGG CCGCAAGCCTTCTCTAGGGGGATCCCTTCGTTGCTGCTGGCCTTGCCGTCCAGGGGACAAGGAGCCAGAGTCCAGGTGGGGC TGTTGCCGAGGGGTCAAGGGAGGCTGATGTCTGGAGTCCGGATGGACCACCTGCAGAGGAGAGACATAGGTCAACACAGGGA GGTAGGATGGTGGTGATGTTCCACCCACAAAAGAAAACCTATTCCTTTAGAAACCTCCAGGATGTGAATCCTGCCTGCACCT GCACAGCTGGCTGGAGGCATATAGCCACTGCCCATAGATCTCAACTTACCCTCACAACCAACTGCCCCCAGGCCTAAGTTCT CTGCCTCAAAACTGCCAAGGCCTGGATAGCCAAGAGCCTGGGTGTCTTGGAAATATGCAACCATAAATAGTAGCTTTTAGAA GTATAAGGCTCCTGTTTCTGGGTCATATTAGTTTTGTTTTCACCTGTCCCCACCCATAAGCCAGGTGTGGCCAGAAGCAAAT GTACTGTAAGAGCAGAGCAAAAACTTCCACACAGATAGTTCTGTTAGGCAATACATCTCTGCCTGACTATTAGGAATCTGGT TTCTGGGTCCTCTGTACAAAGCTCGGAGCAACACAGTGGCCACATCAATCAAAAGGACCGTGACCAACTTCAAAGTCGGTGA GCTTGTACCTATTTTTAGGCTCCTGCTGAACAGAACCAGATTCACACTACAGCTCAGCAGGGCATCGTCACGGGTGTGTGTG TGTGTGTGTGTGTGTGTGTGTGTGTGTGTTGGGGGGGGGGGGTGGACAGAGGACGGGGACACAATTCACTGGCCAGCCCTTC TCTCCTTCAAGGAAGGCTGCTCTAGCCTGGGACTGGAATACACATTTCCTGTAAACATGGTGGGGGCCTCAGGCAAGCCAGA GTTTTGGAGCCTTCCTTAACTCTTCAAGGTGAGCATCTTGACTTGGAGGGTGGGGGTGCGGGTAAGGAAGGAACCTGTGGAC TCCACCCAACAAGACAGAAAAGGAATAAGCCACGAAGACAATAACGATTTTTGTATCAAGCGTCCTCTCCCATTTCAGCTTA CCTGACAATGAAATCAAATTCGGACCCTGCAAGCATCAGTACACCCAGCAGAGTGGACACAGCACCGTCCAGAACGGGAGCA AACATGTGCTCCAGAGCGAGCATAGCCCTGTGGTTCTTGTCCCCAATGGCTGTCAGAAAGGCCTGAACAAAGGAGAAAATTG ACACGGTCACATTCTGGGTGTGGTAAAGTGCTCAGCTGTGTCTATACTTGGGTTTTGTAT Transcription Factor Binding Sites (TFBS) Gene

8 Transcrip*on factor binding mo*fs/sites are gene*c basis for understanding gene regulatory network TF1 TF2 TF1 TF2 TACTACCACCCACAACATAATAAAATCTAA TF2 TF1 TTAATAAAATACCACCCACAACCTAAGGAT Gene1 Gene2 Transcription factors Other genes Activation Repression Other Interactions TF3 TF3 Gene3 Diseases Misregulation

9 Mo*f discovery and decoding regulatory programs in the genome Genomic Language GGCCCTGAGCGGTCCCTATTGCTGGGTGGTCAATGCCCTTCATCTGAAATTTCA AAAGCGTCTCTGCGCGGTCTGTAGGGGGGTGGCCGCAAGCCTTCTCTAGGGGGG CCCTGAGCGGTCCCTATTGCTAGGGCCAGAATGCCCTTCAGTAGAAATTTC step1 Dictionary GGCCCTGAGCGGTCCCTATTGCTGGGTGGTCAATGCCCTTCATCTGGAATTTCAstep2 AAAGCGTCTCTGCGCGGTCTGTAGGGGGGTGGCCGCAAGCCTTCTCTAGGGGGG CCCTGAGCGGTCCCTATTGCTAGGGCCAGAATGCCCTTCAGTAGAAATTTC Human Language guesswhatthestoryisaslongasyouknowthela nguageitshouldbeprettyeasy Guess what the story is. As long as you know the language, it should be pretty easy. step1 step2 Dictionary Know Guess Be

10 Mo*fs can be iden*fied as enriched sequence pakerns in co- regulated genes (Roth et al., 1998; Hughes et al., 2000; etc.) GTATGTACTTACTATGGGTGGTCAACAAATCTATGTATGA Gene1 CTGGGAGGTCCTCGGTTCAGAGTCACAGAGCAGATAATCA Gene2 TAACATGTGACTCCTATAACCTCTTTGGGTGGTACATGAA Gene3 GTATGTACTTACTATGGGTGGTCAACAAATCTATGTATGA CTGGGAGGTCCTCGGTTCAGAGTCACAGAGCAGATAATCA TAACATGTGACTCCTATAACCTCTTTGGGTGGTACATGAA Condition1 Condition2 Gene 1 Gene 2 Gene 3 Gene N

11 De novo mo*f discovery A C G T! A ! C ! G ! T ! ! A ! C ! G ! T ! Background : θ 0! Motif: Θ, W! q 0! q = q 1! [q 0,q 1 ]! S: GTATGTACTTACTATGGGTGGTCAACAAATCTATGTATGACTGGGAGGTCCTCGGTTCAGAGTCACAGAGCA A: f ( A, Θ, W, q S, θ0) f ( S, A Θ, W, q, θ0) π( Θ, W, q) Inference by iterative estimation/sampling Θ,W,q! A! EM: Lawrence and Reilly (1990) Bailey and Elkan (1994), etc. Gibbs Sampler: Lawrence et al. (1993) Liu (1994), Liu et al. (1995), etc.

12 Mo*f discovery is difficult in mammalian genomes due to a low signal- to- noise ra*o yeast 100~1000 bp 100~1000 bp 100~1000 bp Gene1 Gene2 Gene3 human 10k~1000k bp 10k~1000k bp 10k~1000k bp Gene1 Gene2 Gene3

13 Genome- wide chroma*n immunoprecipita*on analysis: ChIP- chip and ChIP- seq

14 Data and nota*on Data: j=ip: Red j=ct: Green rep. k X ijk (i=1,2,...,i; j=1,2,...,j; k=1,2,...,k) I = 40,000,000 J = 2 or more K = 2 or 3 Each Column > $2100 probe i Goal: Identify rows where IP>Control (i.e. Peaks) DNA fragment (500~2000 bp long) Probes 35~300 bp spacing Previous work to detect binding regions: Kampa et al. (2004), Keles et al. (2004)

15 TileMap: a two step approach for peak detec*on (Ji and Wong, 2005, Bioinforma3cs, 21: ) STEP 1: Compute a test statistic for each probe to summarize probe level information STEP 2: Combine probe level test statistics of neighboring probes to help infer binding regions

16 Probe Level Summary n A simple way: Probe k=1 k=2 k=3 sample variance t-statistic i= i= i= i=i noise n Problem: unstable variance estimates due to small number of replicates

17 Variance Shrinkage Es*mator Probe I Sample Variance (df) 2 s 1 2 s 2 2 s 3 2 s I Mean Sum of Squares 2 s S 2 = [ s ( 2 i i s )] 2 Shrinkage Factor Variance Estimates Probe level test statistics B = 2 df + 2 I ( s I df + 2 ) I 1 S ˆ σˆ1 2 σˆ2 2 σˆ3 2 σˆ I ~ ~ t 1 t ~ 2 t ~ t 3 I Variance Shrinkage Estimator ˆ σ 2 2 (1 Bˆ) s Bˆ i = i + A modified t-statistic ~ t = i x 1 K i1 1 x i2 1 + σˆ K 2 i s 2

18 Shrinking variance increases sta*s*cal power Moving Average t-statistic, variance shrinking t-statistic, canonical Mean(X 1 )-Mean(X 2 )

19 Peak 2 (180bp) transgenics Neural tube expression Transgenics

20 Combining neighboring probes TileMap (HMM) 1. Compute the probe level test statistic t for each probe; 2. Estimate the distribution of t under H 0 and H 1 ; 3. Model t by a Hidden Markov Model, and decode the HMM.

21 Probe Effects

22 MAT Model (Johnson et al., 2006, PNAS, 103: ) Baseline on number of Ts A,C,G,T Count Square A,C,G at each position of the 25mer 25mer Copy Number along the Genome

23 MAT Example

24 TileProbe (Judy and Ji, 2009, Bioinforma3cs, 25: )

25 TileProbe vs. MAT

26 TileProbe vs. MAT

27 ChIP- seq: common designs n One sample analysis A ChIP sample only n Two sample analysis A ChIP sample + a negative control sample

28 CisGenome: two- sample analysis (Ji et al. 2008, Nature Biotechnology, 26: ) Alignment IP Control Exploration k 1i k 2i FDR computation n i =k 1i + k 2i k 1i n i ~ Binom(n i, p 0 ) Peak Detection Post Processing

29 CisGenome: one- sample analysis Alignment IP Exploration FDR computation Poisson Model k i ~ Poisson(λ 0 ) k i Negative Binomial Model k i λ i ~ Poisson(λ i ) λ i ~ Gamma(α, β) Marginally, k i ~ NegBinom(α, β) Peak Detection Post Processing

30 Background Reads Follow Nega*ve Binomial Distribu*on

31 FDR Es*ma*on: Nega*ve Binomial vs. Poisson

32 Boundary Refinement & Single Strand Filtering

33 Peak Length & Mo*f Coverage

34 ChIP- chip and ChIP- seq signals are correlated

35 ChIP and control read sampling rates vary across the genome

36 MACS (Zhang et al., 2008, Genome Biology, 9:R137) Locus-dependent background model: λ local = max(λ BG, [λ 1k,] λ 5k, λ 10k ) Read shift:

37 Others n GC content MOSAiCS (Kuan et al., 2012, JASA) n Peak shape PICS (Zhang et al., 2011, Biometrics, 67:151-63)

38 Mo*f analysis axer ChIP- X Reference: Ji HK, Vokes SA and Wong WH (2006) A compara3ve analysis of genome- wide chroma3n immunoprecipita3on data for mammalian transcrip3on factors. Nucleic Acids Research, 34:e146. doi: /nar/gkl803.

39 Mo*fs can be successfully recovered without prior informa*on

40 Matched controls are crucial for iden*fying the key transcrip*on factor binding mo*fs MDSCAN score: S = log( n) W T i= 1 j= A (Liu, Brutlag and Liu 2002, MDSCAN) p W ij log( p ij q j ) Motifs discovered from Sox2 ChIP-chip

41 Ascertaining the key mo*f by comparing to nega*ve controls regions ChIP-chip Regions Negative Control Regions Relative Enrichment (2.0 sites/1000 bp) / (2.0 sites/1000 bp) = 1 (r 1 ) (1.5 sites/1000 bp) / (0.3 sites/1000 bp) = 5

42 Random genomic control does not solve the problem

43 Matched genomic controls TF TF TF d2 d1 d3 Gene 1 Gene 2 Gene 3 d1 d2 Gene Y Gene X d3 Gene Z

44 Matched controls can solve the problem

45 Some insight: binding regions are GC- rich

46 Beyond ChIP- X n Limitations of ChIP-X (1) One TF at a time (2) Need good antibody n A new approach: predict ChIP-X using chromatin surrogates H3K4me1, H3K4me2, H3K4me3, H3K27ac, Dnase, FAIRE

47 Predict ChIP- X using chroma*n surrogates

48 Dnase predicts ChIP- seq (Pique-Regi R et al., 2011, Genome Res. 21, )

49 Dnase predicts ChIP- seq

50 Summary n Motifs ( ) Low specificity, no context information n ChIP-chip (2000, 2004) Increased specificity, genome-wide but limited to array design, contextaware, low resolution, require large number of cells n ChIP-seq (2007) High resolution, whole-genome, require less material n Dnase or other surrogates etc. (2011) Multiple TFs