Alignment. Peak Detection

Transcription

1 ChIP seq

2 ChIP Seq Hongkai Ji et al. Nature Biotechnology 26:

3 ChIP Seq Analysis Alignment Peak Detection Annotation Visualization Sequence Analysis Motif Analysis

4 Alignment ELAND Bowtie SOAP SeqMap

5 Peak detection FindPeaks CHiPSeq BS Seq SISSRs QuEST MACS CisGenome

6 Two common designs One sample experiment contains only a ChIP d sample Two sample experiment contains a ChIP d sample and a negative control sample

7 One sample analysis A simple way is the sliding window method Poisson background model is commonly used to estimate error rate k i ~ Poisson(λ 0 ) k i Or people use Monte Carlo simulations Both are based on the assumption that read sampling rate is a constant across the genome. Ji et al. Nat Biotechnol, 26:

8 The constant rate assumptiondoes not hold! Negative binomial model fits the data better! k i λ i ~ Poisson(λ i ) λ i ~ Gamma(α, β) Marginally, k i ~ NegBinom(α, β) Hongkai Ji et al. Nature Biotechnology 26:

9 FDR estimation based on Poisson and negative binomial model Hongkai Ji et al. Nature Biotechnology 26:

10 Read direction provides extra information

11 CisGenome procedure Alignment Exploration FDR computation Negative binomial model Peak Detection Post Processing Use read direction to refine peak boundary and filter low quality peaks

12 Two sample analysis Reason: read sample rates at the same genomic locus are correlated across different samples. Hongkai Ji et al. Nature Biotechnology 26:

13 CisGenome two sample analysis Alignment k 1i Exploration n i =k 1i + k 2i k 1i n i ~ Binom(n i, p 0 ) k 2i FDR computation Peak Detection Post Processing

14 A comparative study of ChIP chip and ChIP seq NRSF ChIP chip 2 ChIP + 2 Mock IP in Jurkat cells, profiled using Affymetrix Human Tiling 2.0R arrays. NRSF ChIP seq ChIP + Negative Control in Jurkat cells sequenced with the ChIP + Negative Control in Jurkat cells, sequenced with the next generation sequencer made by Illumina/Solexa.

15 Intersection Before post processing After post processing Hongkai Ji et al. Nature Biotechnology 26:

16 Signal correlation Hongkai Ji et al. Nature Biotechnology 26:

17 Visual comparison Hongkai Ji et al. Nature Biotechnology 26:

18 Comparison of peak detection results Hongkai Ji et al. Nature Biotechnology 26:

19 Are array specific peaks noise or signal? Hongkai Ji et al. Nature Biotechnology 26:

20 Effects of read number in ChIP seq Hongkai Ji et al. Nature Biotechnology 26:

21 Motif Analysis

22 Sequence motif a pattern of nucleotide or amino acid sequences DNA motif: TF GTATGTACTTACTATGGGTGGTCAACAAATCTATGTATGA TF TAACATGTGACTCCTATAACCTCTTTGGGTGGTACATGAA TF CTGGGAGGTCCTCGGTTCAGAGTCACAGAGCAGATAATCA TF TTAGAGGCACAATTGCTTGGGTGGTGCACAAAAAAACAAG TF AACAGCCTTGGATTAGCTGCTGGGGGGGTGAGTGGTCCAC TF ATCAGAATGGGTGGTCCATATATCCCAAAGAAGAGGGTAG Transcription Factor Binding Sites (TFBS) Protein motif: TGGGTGGTC TGGGTGGTA TGGGAGGTC TGGGTGGTG TGAGTGGTC TGGGTGGTC

23 Motif representation

24 Consensus sequence Example: CACSTG

25 Sequence Logo Schneider & Stephens, Nucleic Acids Res. 18: (1990) Entropy (Shannon) a measurement of uncertainty The amount of uncertainty reduced by observing sequences is the amount of information (or information content) we obtained: This is the height of each position in the logo plot. Height of each nucleotide is proportional to its frequency

26 Two questions in motif analysis Known motif mapping Finding occurrences of a motif in nucleotide or amino acid sequences De novo motif discovery Finding motifs that are previously unknown

27 Known motif mapping Consensus mapping STEP 1: provide a motif (e.g. CACSTG = CAC[C,G]TG) STEP 2: specify number of mismatches allowed (e.g. <=1) STEP 3: scan the sequence CGCCGGGACCAGATCAACGCCGAGATCCGGCACATGAAGGAGCT CC G C G CCGGC C G GG m=3, no m=1, yes A useful tool: CisGenome (

28 Known motif mapping Motif matrix mapping (CisGenome) STEP 1: provide a motif and background model STEP 2: specify a likelihood ratio cutoff (e.g. LR>=500) STEP 3: scan the sequence Background: θ 0 Motif: Θ A C G T A C G T A C G T GTATGTACTTACTATGGGTGGTCAACAAATCTATGTATGACTGGGAGGTCCTCGGTTCAGAGTCACAGAGCA LR>500, yes LR<500, no Another tool for matrix mapping MAST ( intro.html)

29 Denovo motif discovery Two major class of methods: 1. Word enumeration 2. Matrix updating

30 Word enumeration STEP 1: enumerate possible words; STEP 2: count word occurrences; STEP 3: compare observed word count with random expectation. Example: Sinha & Tompa, Nucleic Acids Res. 30: (2002)

31 Matrix updating CONSENSUS (Stormo & Hartzell, PNAS, 86: , 1990) STEP 1: use all k mers in the first sequence as seeds; STEP 2: find matches (often use best matches) of each seed in the second sequence; STEP 3: update seed matrices, exclude matrices with low information content; STEP 4: repeat step 2 and 3 for all sequences.

32 Motif discovery a mixture model method A C G T A C G T A C G T Background: θ 0 Motif: Θ, W q = [q 0,q 1 ] q 0 q 1 S: GTATGTACTTACTATGGGTGGTCAACAAATCTATGTATGACTGGGAGGTCCTCGGTTCAGAGTCACAGAGCA A: f ( A, Θ, W, q S, θ 0 ) f ( S, A Θ, W, q, θ 0 ) 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.

33 Cis-regulatory lt module dl discovery (Zhou and Wong, PNAS 2004) Module structure: consider co-localization of motif sites. θ 0 Θ L L Θ K Motif 1 Motif 2 Motif 3 q 0 q 1 q K Hierarchical Mixture modeling K: # of motifs B 1 r r S M

34 Phylogenetic Footprinting For example, exons are conserved due to the selection pressure. Introns and intergenic regions are less likely to be conserved.

35 Phylogenetic footprinting & motif discovery Evolutionary model based approach EMnEM (Moses et al. 2004) PhyME (Sinha et al. 2004) PhyloGibbs (Siddharthan et al. 2005) Tree Sampler (Li and Wong, 2005)