Statistical analysis of ChIP-seq data
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1 Statistical analysis of ChIP-seq data Sündüz Keleş Department of Statistics Department of Biostatistics and Medical Informatics University of Wisconsin, Madison Feb 9, 2012 BMI 776 (Spring 12) 02/09/ / 91
2 Basic principles of gene expression Each cell contains a complete copy of the organism s genome (the same hardware!). Cells are of many different types and states. E.g. skin, blood, and nerve cells, cancerous cells, etc. What makes the cells different? Each cell utilizes only a subset of the whole set of genes. Differential gene expression, i.e., when, where, and how much each gene is expressed. The mechanism that controls gene expression is called the regulation of gene expression. BMI 776 (Spring 12) 02/09/ / 91
3 Stages of regulation of gene expression during chromatin modifications (DNA packaging), during transcription control, splicing, transport and translation control. Transcriptional control: most common way of regulation; occurs during the transcription phase when the DNA is transcribed into RNA. Basic elements of transcriptional control: Transcription factors, DNA binding sites (regulatory motifs), enhancers, Promoters. BMI 776 (Spring 12) 02/09/ / 91
4 Complexity of eukaryotic transcriptional regulation PROMOTER REGION TGA-T- [A/T]CGCG[A/T] TCACGTG CCG----CGG GGC----GCC Harbison et al. Nature (2004) BMI 776 (Spring 12) 02/09/ / 91
5 Data collection for the motif finding problem Through microarray technology: Genomewide gene expression data: Relative abundance of gene expression in different cell types is measured. ChIP-chip data: Genome-wide maps of DNA and protein interactions (Ren, B. et al. (2000) & Iyer, V.R. et al. (2001), Simon, I. (2001), Lieb, J. (2001), Lee et al. (2002).) [a.k.a. ChIP-chip data] Through comparative genomics: Multiple genome sequences from related species. Through high throughput sequencing: Genome-wide maps of DNA and protein interactions by ChIP-Seq experiments. BMI 776 (Spring 12) 02/09/ / 91
6 Data collection for motif finding problem Based on expression or multiple species data, we extract bps upstream of the transcription start sites (TSS). ChIP-chip/seq data generates specific coordinates of binding which may not be restricted to upstream of the TSS. BMI 776 (Spring 12) 02/09/ / 91
7 High throughput ChIP assay (ChIP-seq): Chromatin immunoprecipitation combined with high throughput sequencing ChIP: Chromatin immunoprecipitation (in vivo). seq: High throughput sequencing (mainly Illumina for us). ChIP-seq: ChIP followed by high throughput sequencing. BMI 776 (Spring 12) 02/09/ / 91
8 ChIP assay Target protein = BMI 776 (Spring 12) 02/09/ / 91
9 ChIP assay Target protein = 1. Crosslink DNA and protein in vivo by exposing cells to formaldehyde. BMI 776 (Spring 12) 02/09/ / 91
10 ChIP assay Target protein = 1. Crosslink DNA and protein in vivo by exposing cells to formaldehyde. 2. Extract the chromatin from cells and fragment by sonication (~ bps). BMI 776 (Spring 12) 02/09/ / 91
11 ChIP assay Target protein = 1. Crosslink DNA and protein in vivo by exposing cells to formaldehyde. 2. Extract the chromatin from cells and fragment by sonication (~ bps). 3. Immunoprecipitate using a target protein-specific antibody. Selectively binds to the target protein. BMI 776 (Spring 12) 02/09/ / 91
12 ChIP assay Target protein = 1. Crosslink DNA and protein in vivo by exposing cells to formaldehyde. 2. Extract the chromatin from cells and fragment by sonication (~ bps). 3. Immunoprecipitate using a target protein-specific antibody. Selectively binds to the target protein. 4. Reverse the cross-links and purify DNA. BMI 776 (Spring 12) 02/09/ / 91
13 ChIP assay Target protein = 1. Crosslink DNA and protein in vivo by exposing cells to formaldehyde. 2. Extract the chromatin from cells and fragment by sonication (~ bps). 3. Immunoprecipitate using a target protein-specific antibody. Selectively binds to the target protein. 4. Reverse the cross-links and purify DNA. 5. Find the identity of the isolated DNA fragments. BMI 776 (Spring 12) 02/09/ / 91
14 Variations: MNase-seq for nucleosome occupancy In high throughput experiments experiments measuring nucleosome occupancy, an enzyme called Micrococcal nuclease is used to digest nucleosome free regions instead of sonication + immunoprecipitation. BMI 776 (Spring 12) 02/09/ / 91
15 Traditional ChIP assay The identity of the DNA fragments isolated in complex with the protein of interest can then be determined by polymerase chain reaction (PCR) using primers specific for the DNA regions that the protein in question is hypothesized to bind. One experiment per hypothesized region. Identify the identity of all the immunoprecipitated regions? Sequencing (ChIP-Seq) (previously with ChIP-chip). BMI 776 (Spring 12) 02/09/ / 91
16 ChIP-Seq data generation Next Gen Seq Tech BMI 776 (Spring 12) 02/09/ / 91
17 ChIP-Seq data generation Next Gen Seq Tech Sequencing Tags Sequencing depth: Total tags/sequenced fragments ATCCGATCT BMI 776 (Spring 12) 02/09/ / 91
18 ChIP-Seq data generation Next Gen Seq Tech Sequencing Tags Sequencing depth: Total tags/sequenced fragments ATCCGATCT Mapping to reference genome ATTCCGTTAACGTAGGTTAACCGGTAGCATGCAATGGCCGTATGACGATGACGATGACAGTTTAAGCGGCAGATTGCCGATTGGACCGATGGCACCG BMI 776 (Spring 12) 02/09/ / 91
19 ChIP-Seq data generation Next Gen Seq Tech Sequencing Tags Sequencing depth: Total tags/sequenced fragments ATCCGATCT Mapping to reference genome ATTCCGTTAACGTAGGTTAACCGGTAGCATGCAATGGCCGTATGACGATGACGATGACAGTTTAAGCGGCAGATTGCCGATTGGACCGATGGCACCG Summarize total tag counts in each bin Partition the genome into small bins (genomic windows). ATTCCGTTAACGTAGGTTAACCGGTAGCATGCAATGGCCGTATGACGATGACATGACAGTTTAAGCGGCAGATTGCCGATTGGACCGATGGCACCG BMI 776 (Spring 12) 02/09/ / 91
20 ChIP-Seq data generation Next Gen Seq Tech Sequencing Uniquely aligned tags Tags Sequencing depth: Total tags/sequenced fragments ATCCGATCT Mapping to reference genome ATTCCGTTAACGTAGGTTAACCGGTAGCATGCAATGGCCGTATGACGATGACGATGACAGTTTAAGCGGCAGATTGCCGATTGGACCGATGGCACCG Summarize total tag counts in each bin Partition the genome into small bins (genomic windows). ATTCCGTTAACGTAGGTTAACCGGTAGCATGCAATGGCCGTATGACGATGACATGACAGTTTAAGCGGCAGATTGCCGATTGGACCGATGGCACCG BMI 776 (Spring 12) 02/09/ / 91
21 FASTQ CAGAAGTGCATCAAACATGATTTAGAGCTTGTTTAT +HWUSI-EAS1789_0000:5:1:1049:9966#GGCTAN/1 ddadc^ayc\b\ybc^d^^cdd\cdddccaccc^d The first line begins with symbol and is followed by the sequence The second line contains the base call (in this case for each of 36 nucleotides). The third line begins with a + symbol and may (or may not) repeat the sequence name. The fourth line contains a symbol that measures the quality score for the corresponding base call as listed on the second line. There should be one symbol for each base call. The symbol on the fourth line uses an ASCII character (American Standard Code for Information Interchange) to encode the quality score. BMI 776 (Spring 12) 02/09/ / 91
22 Mapping reads to reference genome: Tools Eland Bowtie MAQ... BMI 776 (Spring 12) 02/09/ / 91
23 Mapping reads to reference genome From an eland extended output: >HWUSI-EAS1789_0000:6:1:14464:6867#TTAGG./1 ACTGGTAGTCTGACTGTACATTGAAACATTCCTTAA 1:0:0 chr8.fa: f36 >HWUSI-EAS1789_0000:6:120:18004:6530#TTAGGC/1 AAGTCTGCTCTGTGTAAAGGATCGTTCGACTCTGTG 0:2:0 chr1.fa: r27a8, r27a8 >HWUSI-EAS1789_0000:6:120:17588:21429#TTAGGC/1 GAATCTGAAAGTGGATATTTGGATAGCTTTGCGGAT 0:2:32 chr2.fa: f31a4,chr9.fa: f31a4 chr9.fa: f31a4 denotes a match with 1 mismatch to position in the forward strand of chr A+4 = 36 bps. As a result of mapping, we obtain the observations/measurements that we can do statistical inference on. BMI 776 (Spring 12) 02/09/ / 91
24 What does the aligned data look like? BMI 776 (Spring 12) 02/09/ / 91
25 Practical challenges of dealing with Next Gen data Useful to know some scripting language, e.g. perl, python. Storage of the data is a big problem. An aligned read file with 95 million reads is around 15-20GB. This is typically one sample for us. Then for each treatment sample we typically have a control sample. If we are going beyond identifying peaks, e.g., differential binding etc, we have at least 2 reps per treatment. A simple study adds up to 8 samples (or to 200 GB). The good news is that, often, we can build statistical inference on data extracted from the aligned files. In our work, we call these bin files. We partition the genome into non-overlapping intervals of 200 bps and count the number of reads falling into each interval reduces size to 200MB. chr chr chr chr chr chr BMI 776 (Spring 12) 02/09/ / 91
26 ChIP-Seq data structure Protein ACGCGTCACGTCAAAACGCGCTAGATAGATAGCCGCTAGCGAGAGAGA BMI 776 (Spring 12) 02/09/ / 91
27 ChIP-Seq data structure Total tag counts in one bin Protein ACGCGTCACGTCAAAACGCGCTAGATAGATAGCCGCTAGCGAGAGAGA BMI 776 (Spring 12) 02/09/ / 91
28 ChIP-Seq data structure Total tag counts in one bin Protein ACGCGTCACGTCAAAACGCGCTAGATAGATAGCCGCTAGCGAGAGAGA BMI 776 (Spring 12) 02/09/ / 91
29 ChIP-Seq data structure ChIP Seq tag count Genomic position BMI 776 (Spring 12) 02/09/ / 91
30 ChIP-Seq data structure Which of these are real peaks? How would the data look like under the null distribution? = Same threshold for all the peaks? Same null distribution along the genome? BMI 776 (Spring 12) 02/09/ / 91
31 A key plot for ChIP-seq data r1 ChIP vs Input Counts r1 ChIP r1 Input BMI 776 (Spring 12) 02/09/ / 91
32 A key plot for ChIP-seq data r2 ChIP vs Input r2 ChIP Counts r2 Input BMI 776 (Spring 12) 02/09/ / 91
33 A key plot for ChIP-seq data r1 ChIP vs r2 ChIP 4.47 r2 ChIP Counts r1 ChIP BMI 776 (Spring 12) 02/09/ / 91
34 A key plot for ChIP-seq data r1 ChIP vs r2 ChIP 4.47 r2 ChIP Counts r1 ChIP BMI 776 (Spring 12) 02/09/ / 91
35 ChIP-Seq vs. Control experiments: Sono-Seq (Input-Seq), Naked-DNA-Seq Auerbach et al. PNAS 2009; 106: BMI 776 (Spring 12) 02/09/ / 91
36 ChIP-Seq, Sono-Seq, Naked-DNA-Seq STAT1 Frequency e+05 STAT1 ChIP Tag count BMI 776 (Spring 12) 02/09/ / 91
37 ChIP-Seq, Sono-Seq, Naked-DNA-Seq STAT1 Frequency e+05 Stim Input DNA STAT1 ChIP Tag count BMI 776 (Spring 12) 02/09/ / 91
38 ChIP-Seq, Sono-Seq, Naked-DNA-Seq STAT1 Frequency e+05 Naked DNA Stim Input DNA STAT1 ChIP Tag count BMI 776 (Spring 12) 02/09/ / 91
39 ChIP-Seq, Sono-Seq, Naked-DNA-Seq STAT1 Frequency e+05 Sequence bias Chromatin Strong peak Tag count BMI 776 (Spring 12) 02/09/ / 91
40 Mappability and GC biases in ChIP-Seq data Mappability bias: due to retaining only uniquely aligning tags. Rozowsky et al. (2009). 79.6% of the human genome is uniquely mappable using 30bp tags. (91.1% for 75bp tags). GC bias: Dohm et al. (2008), Vega et al. (2009). > Background for ChIP-Seq data is not uniform = need for region/location specific cut-offs for calling peaks. > Naked DNA sequencing data provides an excellent platform to investigate and model these effects. BMI 776 (Spring 12) 02/09/ / 91
41 Sequence bias in ChIP-Seq data: Mean tag count vs Mappability ( bin-level ) and GC content: HeLa S3 Naked-DNA-Seq (GSE14022) Mappability GC NakedDNA chr6 NakedDNA chr6 2.94% > % < 0.3 Mean tag count Mean tag count M GC M-GC Same relationships hold for data from mouse. Mouse BMI 776 (Spring 12) 02/09/ / 91
42 Background/Null model for one-sample ChIP-Seq data Existing methods: Poisson distribution. Negative binomial distribution (CisGenome, Ji et al. (2008)). Excess zeroes and over-dispersion. Mappability and GC bias = bin specific distributions. Variance Bin level mean/variance relationship Mean BMI 776 (Spring 12) 02/09/ / 91
43 Poisson vs. Neative Binomial Distributions a: shape, b:scale. Y Poisson(λ), = E[Y ] = λ, var[y ] = λ. Y = NegBin(a, b) = E[Y ] = a/b, var[y ] = a(1 + b)/b 2. Alternative parametrization (in R) Y = NegBin(ρ, µ) = E[Y ] = µ, var[y ] = µ + µ 2 /ρ, where ρ = a, µ = a/b. 1/ρ is referred to as the dispersion parameter. BMI 776 (Spring 12) 02/09/ / 91
44 Poisson, mean = 10 NegBin, mean = 10, var = 30 Density Density Poisson, mean = 110 NegBin, mean = 10, var = 110 Density Density BMI 776 (Spring 12) 02/09/ / 91
45 Background/Null model for one sample ChIP-Seq data Y j : observed tag counts for bin j. N j : background tag counts for the bin. M j : average mappability score. Candidate models for µ j GC j : average GC content. 1 µ j = exp(β 0 ) Non-homogeneous background Y j N j N j µ j g(µ j ) Candidate models for g(µ j ) 1 g(µ j ) Po(µ j ) (Poi Reg) 2 g(µ j ) NegBin(a, a/µ j ) (NegBin Reg) 2 µ j = exp(β 0 + β M log 2 (M j + 1)) 3 µ j = exp(β 0 + β GC GC j ) 4 µ j = exp(β 0 +β M log 2 (M j +1)+β GC GC j ) 5 µ j = exp(β 0 + β GC Sp(GC j )) 6 µ j = exp(β 0 + β M log 2 (M j + 1) + β GC Sp(GC j )) CisGenome (Ji et al. (2009)) Y j NegBin(a, b) BMI 776 (Spring 12) 02/09/ / 91
46 Goodness of Fit HeLa S3 NakedDNA Frequency e+05 1e+06 Actual data Tag count Black line: Actual data BMI 776 (Spring 12) 02/09/ / 91
47 Goodness of Fit: CisGenome NegBin HeLa S3 NakedDNA Frequency e+05 1e+06 Actual data Sim: CisGenome NegBin Tag count Black line: Actual data Red line: Simulated data from the fitted CisGenome NegBin BMI 776 (Spring 12) 02/09/ / 91
48 Goodness of Fit: CisGenome NegBin HeLa S3 NakedDNA Frequency e+05 1e+06 Actual data Sim: CisGenome NegBin Tag count Over-estimated background! BMI 776 (Spring 12) 02/09/ / 91
49 Goodness of Fit: Poisson Reg (M, GC) HeLa S3 NakedDNA Frequency e+05 1e+06 Actual data Sim: Poisson(M, GC) Tag count Black line: Actual data Red line: Simulated data from the fitted Poisson Reg(M, GC) BMI 776 (Spring 12) 02/09/ / 91
50 Goodness of Fit: NegBin Reg (GC) HeLa S3 NakedDNA Frequency e+05 1e+06 Actual data Sim: NegBin(GC) Tag count Black line: Actual data Red line: Simulated data from the fitted NegBin Reg (GC) BMI 776 (Spring 12) 02/09/ / 91
51 Goodness of Fit: NegBin Reg (M) HeLa S3 NakedDNA Frequency e+05 1e+06 Actual data Sim: NegBin(M) Tag count Black line: Actual data Red line: Simulated data from the fitted NegBin Reg (M) BMI 776 (Spring 12) 02/09/ / 91
52 Goodness of Fit: NegBin Reg (M, GC) HeLa S3 NakedDNA Frequency e+05 1e+06 Actual data Sim: NegBin(M, GC) Tag count Black line: Actual data Red line: Simulated data from the fitted NegBin Reg (M, GC) BMI 776 (Spring 12) 02/09/ / 91
53 Goodness of Fit HeLa S3 NakedDNA Frequency e+05 1e+06 Actual data Sim: CisGenome NegBin Sim: Poisson(M, GC) Sim: NegBin(M, GC) Tag count Bayesian Information Criterion (BIC) for model selection (smaller better): None> GC > M > M + GC > M + Sp(GC). BMI 776 (Spring 12) 02/09/ / 91
54 Mixture model for one-sample ChIP-Seq data Z j : unknown/latent state, Z j = 1(0) if bound (unbound). Y j Z j = 0 N j NegBin Reg(M j, GC j ) Y j Z j = 1 N j + S j S j : protein-binding signal 1 S j NegBin(b 1, c 1 ) (1-component) 2 S j p 1 NegBin(b 1, c 1 ) + (1 p 1 )NegBin(b 2, c 2 ) (2-component) Estimate unknown parameters with maximum likelihood method using the EM algorithm. BMI 776 (Spring 12) 02/09/ / 91
55 Case Study 1: STAT1 ChIP-Seq data STAT1 in IFN-γ-stimulated HeLa S3 ChIP-Seq data (GSE12782). 6 lanes of Illumina sequencing data, 23 million mapped reads. Mappability: HeLa S3 STAT1 GC: HeLa S3 STAT1 Mean tag count M Mean tag count GC Case Study 2: GATA1 Sono-Seq BMI 776 (Spring 12) 02/09/ / 91
56 Case Study 1: STAT1 ChIP-Seq data STAT1 ChIP Frequency e+05 1e+06 Actual Data Tag count BMI 776 (Spring 12) 02/09/ / 91
57 Case Study 1: STAT1 ChIP-Seq data STAT1 ChIP Frequency e+05 1e+06 Background: NegBin Reg(M, GC) Actual Data Tag count BMI 776 (Spring 12) 02/09/ / 91
58 Case Study 1: STAT1 ChIP-Seq data STAT1 ChIP Frequency e+05 1e+06 Background: NegBin Reg(M, GC) Actual Data Background: CisGenome NegBin Tag count BMI 776 (Spring 12) 02/09/ / 91
59 Case Study 1: STAT1 ChIP-Seq data STAT1 ChIP Frequency e+05 1e+06 Background: NegBin Reg(M, GC) Actual Data NegBin Reg(M, GC) Background + 1- comp signal Tag count BMI 776 (Spring 12) 02/09/ / 91
60 Case Study 1: STAT1 ChIP-Seq data STAT1 ChIP Frequency e+05 1e+06 Background: NegBin Reg(M, GC) Actual Data NegBin Reg(M, GC) Background + 2- comp signal Tag count BMI 776 (Spring 12) 02/09/ / 91
61 MOSAiCS with Input-Seq: Two-sample analysis Y j : tag count from ChIP-Seq; X j : tag count from Input-Seq. Mean ChIP tag count Input tag count BMI 776 (Spring 12) 02/09/ / 91
62 ChIP-Seq vs Input-Seq: Does Input-Seq account for all the M and GC bias? Mean ChIP tag count Input Input Input Input Input Input Input Input Mappability Input Input Input Input Input Input BMI 776 (Spring 12) 02/09/ / 91 Input Input
63 ChIP-Seq vs Input-Seq: Does Input-Seq account for all the M and GC bias? More Mean ChIP tag count Input Input Input Input Input Input Input Input GC Input Input Input Input Input Input Input BMI 776 (Spring 12) 02/09/ / 91 Input
64 MOSAiCS two-sample background model Y j Z j = 0, X j, M j, GC j NegBin(a, a/µ j ) µ j = exp(β 0 + f (M j, GC j, X j )) where f (M, GC, X ) = I (X c) [β M log 2 (M + 1) + β C Sp(GC) + βx 1 X d] +I (X > c)βx 2 X d BMI 776 (Spring 12) 02/09/ / 91
65 MOSAiCS two-sample background model Y j Z j = 0, X j, M j, GC j NegBin(a, a/µ j ) µ j = exp(β 0 + f (M j, GC j, X j )) where f (M, GC, X ) = I (X c) [β M log 2 (M + 1) + β C Sp(GC) + βx 1 X d] +I (X > c)βx 2 X d BMI 776 (Spring 12) 02/09/ / 91
66 MOSAiCS two-sample model GOF for STAT1 ChIP-Seq STAT1 ChIP Frequency e+05 Actual Data Tag count BMI 776 (Spring 12) 02/09/ / 91
67 MOSAiCS two-sample model GOF for STAT1 ChIP-Seq STAT1 ChIP Frequency e+05 Background: NegBin Reg(Input) Actual Data Tag count BMI 776 (Spring 12) 02/09/ / 91
68 MOSAiCS two-sample model GOF for STAT1 ChIP-Seq STAT1 ChIP Frequency e+05 Background: NegBin Reg(Input) Actual Data Background: NegBin Reg(M, GC, Input) Tag count BMI 776 (Spring 12) 02/09/ / 91
69 MOSAiCS two-sample model GOF for STAT1 ChIP-Seq STAT1 ChIP Frequency e+05 Background: NegBin Reg(Input) Actual Data NegBin Reg(M, GC, Input) Background + Signal Background: NegBin Reg(M, GC, Input) Tag count MOSAiCS two-sample for GATA1 BMI 776 (Spring 12) 02/09/ / 91
70 Position STAT1 motif scans 1 Information content Proportion of peaks with motif MOSAiCS OS PeakSeq OS CisGenome OS MACS OS MOSAiCS TS (Input+M+GC) MOSAiCS TS (Input only) PeakSeq TS CisGenome TS MACS TS Rank of peaks BMI 776 (Spring 12) 02/09/ / 91
71 Position STAT1 motif scans 1 Information content Proportion of peaks with motif MOSAiCS OS PeakSeq OS CisGenome OS MACS OS MOSAiCS TS (Input+M+GC) MOSAiCS TS (Input only) PeakSeq TS CisGenome TS MACS TS Pairwise comparisons Rank of peaks BMI 776 (Spring 12) 02/09/ / 91
72 MOSAiCS vs. PeakSeq: Mappability vs GC of Common Peaks Mappability GC BMI 776 (Spring 12) 02/09/ / 91
73 MOSAiCS vs. PeakSeq: Mappability vs GC of PeakSeq only Peaks Mappability GC BMI 776 (Spring 12) 02/09/ / 91
74 MOSAiCS vs. PeakSeq: Mappability vs GC of MOSAiCS only Peaks GC Mappability log 2 (ChIP/Input) vs M log 2 (ChIP/Input) vs GC BMI 776 (Spring 12) 02/09/ / 91
75 GATA1 motif scans: WGATAA Proportion of peaks with motif among top 1500 peaks Median peak length BMI 776 (Spring 12) 02/09/ / 91
76 GATA1 motif scans: WGATAA Proportion of peaks with motif among top 1500 peaks MOSAiCS OS MACS OS PeakSeq OS MACS OS (Summit) CisGenome OS Median peak length BMI 776 (Spring 12) 02/09/ / 91
77 GATA1 motif scans: WGATAA Proportion of peaks with motif among top 1500 peaks MOSAiCS TS (Input+M+GC) MACS TS MOSAiCS OS MACS OS PeakSeq TS MOSAiCS TS (Input only) PeakSeq OS MACS 2S (Summit) MACS OS (Summit) CisGenome TS CisGenome OS Median peak length BMI 776 (Spring 12) 02/09/ / 91
78 If the sample is deeply sequenced E.g., E. coli ChIP-seq - one lane from the Illumina GA-II. Higher eukaryote ChIP-seq sample on Illumina Hi-seq. Frequency Actual data (ChIP) Actual data (Control) Tag count BMI 776 (Spring 12) 02/09/ / 91
79 MOSAiCS on the FNR ChIP-seq data from the Kiley UW Madison GOF on log scale Frequency Actual data (ChIP) Actual data (Control) Sim:N Sim:N+S1 Sim:N+S1+S Tag count BMI 776 (Spring 12) 02/09/ / 91
80 MOSAiCS on the FNR ChIP-seq data from the Kiley UW Madison GOF on log scale If the sample is deeply sequenced, Input-only model fits well. Frequency Actual data (ChIP) Actual data (Control) Sim:N Sim:N+S1 Sim:N+S1+S Tag count BMI 776 (Spring 12) 02/09/ / 91
81 Software implementation: R package mosaics Available through Bioconductor bioc/html/mosaics.html Galaxy > library(mosaics) > library(help = mosaics) Information on package mosaics Description: Package: mosaics Type: Package Title: MOSAiCS (MOdel-based one and two Sample Analysis and Inference for ChIP-Seq) Version: Depends: R (>= ), methods, graphics, Rcpp Imports: MASS, splines, lattice, IRanges Suggests: mosaicsexample, multicore LinkingTo: Rcpp SystemRequirements: Perl Date: Author: Dongjun Chung, Pei Fen Kuan, Sunduz Keles Maintainer: Dongjun Chung <chungdon@stat.wisc.edu> Description: This package provides functions for fitting BMI 776 (Spring 12) MOSAiCS, a statistical framework to analyze 02/09/ / 91
82 R package: mosaics ChIP file has 95 million reads. Input file has 14 million reads. mosaics runs in about 2 hrs using a single CPU. library(mosaics) mosaicsrunall( chipdir="/scratch/chipseqdesign/hardisondata/tal1_ter119_r2/", chipfilename="gsm746584_tal1_ter119_r2_mapped.txt", chipfileformat="bowtie", controldir="/scratch/chipseqdesign/hardisondata/tal1_ter119_r2/", controlfilename="gsm746580_input_ter119_mapped.txt", controlfileformat="bowtie", binfiledir="/scratch/chipseqdesign/hardisondata/tal1_ter119_r2/results/bin/", peakdir="/scratch/chipseqdesign/hardisondata/tal1_ter119_r2/results/peak/", peakfilename="tal1_ter119_peak_list_r2.txt", peakfileformat="txt", reportsummary=true, summarydir="/scratch/chipseqdesign/hardisondata/tal1_ter119_r2/results/reports/", summaryfilename="mosaics_summary_tal1_ter119_r2.txt", reportexploratory=false, exploratorydir="/scratch/chipseqdesign/hardisondata/tal1_ter119_r2/results/reports/", exploratoryfilename="mosaics_exploratory_tal1_ter119_r2.pdf", reportgof=true, gofdir="/scratch/chipseqdesign/hardisondata/tal1_ter119_r2/results/reports/", goffilename="mosaics_gof_tal1_ter119_r2.pdf", bychr=false, FDR=0.05, fraglen=200, binsize=200, capping=0, analysistype="io", BMI 776 (Spring 12) 02/09/ / 91
83 R package: mosaics d=0.25, signalmodel="bic", maxgap=200, minsize=50, thres=40, ncore=20) BMI 776 (Spring 12) 02/09/ / 91
84 R package: mosaics Info: constructing bin-level files Info: setting summary Directory of aligned read file: /scratch/chipseqdesign/hardisondata/tal1_ter119_r2/ Name of aligned read file: GSM746584_tal1_ter119_r2_mapped.txt Aligned read file format: Bowtie default Directory of processed bin-level files: /scratch/chipseqdesign/hardisondata/tal1_ter119_r2/results/bin/ Construct bin-level files by chromosome? N Fragment length: 200 Bin size: Info: setting summary Directory of aligned read file: /scratch/chipseqdesign/hardisondata/tal1_ter119_r2/ Name of aligned read file: GSM746580_input_ter119_mapped.txt Aligned read file format: Bowtie default Directory of processed bin-level files: /scratch/chipseqdesign/hardisondata/tal1_ter119_r2/results/bin/ Construct bin-level files by chromosome? N Fragment length: 200 Bin size: Info: reading the aligned read file and processing it into bin-level files... Info: reading the aligned read file and processing it into bin-level files... Info: done! BMI 776 (Spring 12) 02/09/ / 91
85 R package: mosaics Info: processing summary Directory of processed bin-level files: /scratch/chipseqdesign/hardisondata/tal1_ter119_r2/results/bin/ Processed bin-level file: GSM746580_input_ter119_mapped.txt_fragL200_bin200.txt Info: done! Info: processing summary Directory of processed bin-level files: /scratch/chipseqdesign/hardisondata/tal1_ter119_r2/results/bin/ Processed bin-level file: GSM746584_tal1_ter119_r2_mapped.txt_fragL200_bin200.txt BMI 776 (Spring 12) 02/09/ / 91
86 R package: mosaics Info: analyzing bin-level files... Info: fitting MOSAiCS model & call peaks... Info: reading and preprocessing bin-level data... Info: data contains more than one chromosome. Info: done! Info: background estimation method is determined based on data. Info: background estimation based on robust method of moment Info: two-sample analysis (Input only). Info: use adaptive griding. Info: fitting background model... Info: done! Info: fitting one-signal-component model... Info: fitting two-signal-component model... Info: calculating BIC of fitted models... Info: done! Info: use two-signal-component model. Info: calculating posterior probabilities... Info: calling peaks... Info: done! Info: writing the peak list... Info: peak file was exported in TXT format: Info: file name = tal1_ter119_peak_list_r2.txt Info: directory = /scratch/chipseqdesign/hardisondata/tal1_ter119_r2/results/peak/ Info: generating reports... > proc.time() user system elapsed Run time 2 hours. BMI 776 (Spring 12) 02/09/ / 91
87 R package: mosaics - Main output A peak list with columns: chrid peakstart peakstop peaksize avep minp avechipcount maxchipcount aveinputcount aveinputcountscaled avelog2ratio. Exploratory plots. Mean read count vs. Input, vs. Mappability, vs. GC content. Goodness-of-fit plots. BMI 776 (Spring 12) 02/09/ / 91
88 Some other statistical problems regarding ChIP-seq data Utilizing multi-reads, i.e., read mapping to multiple locations on the reference genome. Differential binding. BMI 776 (Spring 12) 02/09/ / 91
89 Summary MOSAiCS: implements a model-based approach for ChIP-seq data analysis; available as a R package through Bioconductor and a Galaxy tool from the Galaxy tool shed toolshed.g2.bx.psu.edu/; provides basic pre-processing functions for ChIP-seq data; provides plotting functions. BMI 776 (Spring 12) 02/09/ / 91
90 Acknowledgements Dongjun Chung (Department of Statistics, UW Madison). Pei Fen Kuan (Department of Biostatistics, UNC Chapel Hill). Kun Liang (Department of Statistics, UW Madison). Xin Zeng (Department of Statistics, UW Madison). Chen Zuo (Department of Statistics, UW Madison). Colin Dewey (Department of Biostatistics and Medical Informatics, UW Madison); Bo Li (Department of Computer Science, UW Madison). Emery Bresnick; Sanal Kumar ( Department of Cell and Regenerative Biology, UW Madison). Peggy Farnham ( Departments of Biochemistry & Molecular Biology, Keck School of Medicine, USC). Qiang Chang (Cellular and Molecular Neurosciences Core, Waisman Center, UW Madison). NIH, NSF. BMI 776 (Spring 12) 02/09/ / 91
91 Sequence bias in ChIP-Seq data Mappability bias. Original definition by Rozowsky et al. (2009). BMI 776 (Spring 12) 02/09/ / 91
92 Sequence bias in ChIP-Seq data Mappability bias. Original definition by (Rozowsky et al. (2009)). BMI 776 (Spring 12) 02/09/ / 91
93 Tag/Read extension Extending each tag by expected fragment length and strand Partition the genome into small bins (genomic windows). Summarize total tag counts in each bin ATTCCGTTAACGTAGGTTAACCGGTAGCATGCAATGGCCGTATGACGATGACATGACAGTTTAAGCGGCAGATTGCCGATTGGACCGATGGCACCG BMI 776 (Spring 12) 02/09/ / 91
94 Sequence bias in ChIP-Seq data Mappability. Consider tags originating from nearby nucleotides. k: read length mi =[1/(2L)] ( δ j, j = {i-l+1, i} + δ j, j = {i-k+1,,i+l-k} ) GGTATTAGCGCAGAGAGACTCGCTAGTC L BMI 776 (Spring 12) 02/09/ / 91
95 Sequence bias in ChIP-Seq data Mappability. Bin level. Mappability for j th bin Mj = mean(m i )i th bp in the bin. GGTATTAGCGCAGAGAGACTCGCTAGTC BMI 776 (Spring 12) 02/09/ / 91
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