Part-II: Statistical analysis of ChIP-seq data
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- Giles Johnston
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1 Part-II: Statistical analysis of ChIP-seq data Outline ChIP-seq data, features, detailed modeling aspects (today). Other ChIP-seq related problems - overview (next lecture). IDR (next lecture) Stat 877 (Spring 17) 04/11-04/18 62 / 237
2 Why study protein-dna interactions? A tale of GATA2 (In collaboration with Bresnick Lab, UW Madison. Journal of Clinical Investigation, 2012). Stat 877 (Spring 17) 04/11-04/18 63 / 237
3 Why study protein-dna interactions? A tale of GATA2 (Bresnick Lab, UW Madison). Stat 877 (Spring 17) 04/11-04/18 64 / 237
4 Why study protein-dna interactions? A tale of GATA2 (Bresnick Lab, UW Madison). Stat 877 (Spring 17) 04/11-04/18 65 / 237
5 Why study protein-dna interactions? A tale of GATA2 (Bresnick Lab, UW Madison). Stat 877 (Spring 17) 04/11-04/18 66 / 237
6 Why study protein-dna interactions? A tale of GATA2 (Bresnick Lab, UW Madison). Stat 877 (Spring 17) 04/11-04/18 67 / 237
7 Why study protein-dna interactions? A tale of GATA2 (Bresnick Lab, UW Madison). Stat 877 (Spring 17) 04/11-04/18 68 / 237
8 Why study protein-dna interactions? A tale of GATA2 (Bresnick Lab, UW Madison). Causal deletion: We identified a MonoMAC (a rare genetic disorder) patient with a heterozygous deletion that disrupts the +9.5 composite element. Median age of survival: 36.5 years. Stat 877 (Spring 17) 04/11-04/18 69 / 237
9 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. Stat 877 (Spring 17) 04/11-04/18 70 / 237
10 ChIP assay Target protein = Stat 877 (Spring 17) 04/11-04/18 71 / 237
11 ChIP assay Target protein = 1. Crosslink DNA and protein in vivo by exposing cells to formaldehyde. Stat 877 (Spring 17) 04/11-04/18 72 / 237
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). Stat 877 (Spring 17) 04/11-04/18 73 / 237
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. Stat 877 (Spring 17) 04/11-04/18 74 / 237
14 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. Stat 877 (Spring 17) 04/11-04/18 75 / 237
15 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. Stat 877 (Spring 17) 04/11-04/18 76 / 237
16 Variations: MNase-seq for nucleosome occupancy In high throughput experiments measuring nucleosome occupancy, an enzyme called Micrococcal nuclease is used to digest nucleosome free regions instead of sonication + immunoprecipitation. Stat 877 (Spring 17) 04/11-04/18 77 / 237
17 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). Stat 877 (Spring 17) 04/11-04/18 78 / 237
18 ChIP-seq data generation Stat 877 (Spring 17) 04/11-04/18 79 / 237
19 ChIP-seq data generation Machine EAS00184:1:1:8:1368#0/1 CGTCACATCCCATACACCACAATGCNNACCCAGGGCCTTA +HWI- EAS00184:1:1:8:1368#0/1 EAS00184:1:1:8:1763#0/1 TAAACCATCATTCTTAGCAAACTATNNCGAGTACATAAAA +HWI- EAS00184:1:1:8:1763#0/1 `a`bbbabbbbbbb]aa_^ababahdd[\bnh]bblb_bb Stat 877 (Spring 17) 04/11-04/18 80 / 237
20 ChIP-seq data generation Machine EAS00184:1:1:8:1368#0/1 CGTCACATCCCATACACCACAATGCNNACCCAGGGCCTTA +HWI- EAS00184:1:1:8:1368#0/1 EAS00184:1:1:8:1763#0/1 TAAACCATCATTCTTAGCAAACTATNNCGAGTACATAAAA +HWI- EAS00184:1:1:8:1763#0/1 `a`bbbabbbbbbb]aa_^ababahdd[\bnh]bblb_bb Align to reference EAS00184:1:1:8:1368#0/1 - chr EAS00184:1:1:8:1763#0/1` + chr TAAACCATCATTCTTAGCAAACTATNNCGAGTACATAAAA Stat 877 (Spring 17) 04/11-04/18 81 / 237
21 ChIP-seq data generation Machine EAS00184:1:1:8:1368#0/1 CGTCACATCCCATACACCACAATGCNNACCCAGGGCCTTA +HWI- EAS00184:1:1:8:1368#0/1 EAS00184:1:1:8:1763#0/1 TAAACCATCATTCTTAGCAAACTATNNCGAGTACATAAAA +HWI- EAS00184:1:1:8:1763#0/1 `a`bbbabbbbbbb]aa_^ababahdd[\bnh]bblb_bb Align to reference EAS00184:1:1:8:1368#0/1 - chr EAS00184:1:1:8:1763#0/1` + chr TAAACCATCATTCTTAGCAAACTATNNCGAGTACATAAAA chr chr chr chr chr Create count data over bps non- overlapping genomic intervals Stat 877 (Spring 17) 04/11-04/18 82 / 237
![TAAACCATCATTCTTAGCAAACTATNNCGAGTACATAAAA +HWI- EAS00184:1:1:8:1763#0/1 `a`bbbabbbbbbb]aa_^ababahdd[\bnh]bblb_bb Align to](/docs-images/86/93791075/images/22-2.jpg "reference genome Only keep uniquely aligning reads @HWI- EAS00184:1:1:8:1368#0/1 - chr12 29078411")
22 ChIP-seq data generation Machine EAS00184:1:1:8:1368#0/1 CGTCACATCCCATACACCACAATGCNNACCCAGGGCCTTA +HWI- EAS00184:1:1:8:1368#0/1 EAS00184:1:1:8:1763#0/1 TAAACCATCATTCTTAGCAAACTATNNCGAGTACATAAAA +HWI- EAS00184:1:1:8:1763#0/1 `a`bbbabbbbbbb]aa_^ababahdd[\bnh]bblb_bb Align to reference genome Only keep uniquely aligning EAS00184:1:1:8:1368#0/1 - chr EAS00184:1:1:8:1763#0/1` + chr TAAACCATCATTCTTAGCAAACTATNNCGAGTACATAAAA chr chr chr chr chr Create count data over bps non- overlapping genomic intervals Stat 877 (Spring 17) 04/11-04/18 83 / 237
23 ChIP-seq data structure Protein ACGCGTCACGTCAAAACGCGCTAGATAGATAGCCGCTAGCGAGAGAGA Stat 877 (Spring 17) 04/11-04/18 84 / 237
24 ChIP-seq data structure Total tag counts in one bin Protein ACGCGTCACGTCAAAACGCGCTAGATAGATAGCCGCTAGCGAGAGAGA Stat 877 (Spring 17) 04/11-04/18 85 / 237
25 ChIP-seq data structure Total tag counts in one bin Protein ACGCGTCACGTCAAAACGCGCTAGATAGATAGCCGCTAGCGAGAGAGA Stat 877 (Spring 17) 04/11-04/18 86 / 237
26 ChIP-seq data structure Stat 877 (Spring 17) 04/11-04/18 87 / 237
27 ChIP-seq data structure Basic inference problem in ChIP-seq data: Identifying peaks ChIP experiments are typically accompanied by a control experiment that measures background. Stat 877 (Spring 17) 04/11-04/18 88 / 237
28 Outline Q1: Basic inference for ChIP-seq data: Finding peaks. Q2: Finding peaks in repetitive regions: Utilizing multi-reads in ChIP-seq data analysis. Q3: Power (sequencing depth) calculations. Q4: Differential occupancy: Comparing peaks across conditions. Q5: Detecting binding events within close proximity of each other: Deconvolving peaks. Q6: Joint analysis of multiple ChIP-seq datasets. Stat 877 (Spring 17) 04/11-04/18 89 / 237
29 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. Stat 877 (Spring 17) 04/11-04/18 90 / 237
30 Mapping reads to reference genome: Tools Eland Bowtie (*) MAQ Bwa (*) Stat 877 (Spring 17) 04/11-04/18 91 / 237
31 Mapping reads to reference genome Sam file format: AMELIA:402:C53G2ACXX:1:1101:1778: chr M * 0 0 GGF@GC9D?)6)9BDFGC<B8=)8FF78.=C@GEE>CE;?EDBDECDC:@6>;6@C##### XA:i:1 MD:Z:47T53 NM:i:1 The read aligns to chr 6 position with 1 mismatch [MD:Z:47T53]. 101M: CIGAR string - used to indicate which bases align (either a match/mismatch) with the reference, are deleted from the reference, and are insertions that are not in the reference. NM:i:1: Refers to edit distance to the genome. XA:i:1: Alternative hits. As a result of mapping, we obtain the observations/measurements that we can do statistical inference on. Stat 877 (Spring 17) 04/11-04/18 92 / 237
32 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 Stat 877 (Spring 17) 04/11-04/18 93 / 237
33 ChIP-Seq vs. Control experiments: Sono-Seq (Input-Seq), Naked-DNA-Seq Auerbach et al. PNAS 2009; 106: Stat 877 (Spring 17) 04/11-04/18 94 / 237
34 ChIP-seq QC Landt et al., (2012), ChIP-seq guidelines and practices of the ENCODE and modencode consortia Genome Res Sep; 22(9): Stat 877 (Spring 17) 04/11-04/18 95 / 237
35 A key plot for ChIP-seq data r1 ChIP vs Input Counts r1 ChIP r1 Input Stat 877 (Spring 17) 04/11-04/18 96 / 237
36 A key plot for ChIP-seq data r2 ChIP vs Input r2 ChIP Counts r2 Input Stat 877 (Spring 17) 04/11-04/18 97 / 237
37 A key plot for ChIP-seq data r1 ChIP vs r2 ChIP 4.47 r2 ChIP Counts r1 ChIP Stat 877 (Spring 17) 04/11-04/18 98 / 237
38 A key plot for ChIP-seq data r1 ChIP vs r2 ChIP 4.47 r2 ChIP Counts r1 ChIP Stat 877 (Spring 17) 04/11-04/18 99 / 237
39 A key plot for ChIP-seq data SA TTX ChIP vs Input 1.5 SA TTX ChIP Counts SA TTX Input Stat 877 (Spring 17) 04/11-04/ / 237
40 ChIP-seq data structure Basic inference problem in ChIP-seq data: Identifying peaks ChIP experiments are typically accompanied by a control experiment that measures background. How would the data look like under the null distribution? = Same threshold for all the peaks? Same null distribution along the genome? Stat 877 (Spring 17) 04/11-04/ / 237
41 ChIP-Seq vs. Control experiments: Sono-Seq (Input-Seq), Naked-DNA-Seq Auerbach et al. PNAS 2009; 106: Stat 877 (Spring 17) 04/11-04/ / 237
42 ChIP-Seq, Sono-Seq, Naked-DNA-Seq STAT1 Frequency e+05 STAT1 ChIP Tag count Stat 877 (Spring 17) 04/11-04/ / 237
43 ChIP-Seq, Sono-Seq, Naked-DNA-Seq STAT1 Frequency e+05 Stim Input DNA STAT1 ChIP Tag count Stat 877 (Spring 17) 04/11-04/ / 237
44 ChIP-Seq, Sono-Seq, Naked-DNA-Seq STAT1 Frequency e+05 Naked DNA Stim Input DNA STAT1 ChIP Tag count Stat 877 (Spring 17) 04/11-04/ / 237
45 ChIP-Seq, Sono-Seq, Naked-DNA-Seq STAT1 Frequency e+05 Sequence bias Chromatin Strong peak Tag count Stat 877 (Spring 17) 04/11-04/ / 237
46 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. Stat 877 (Spring 17) 04/11-04/ / 237
47 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 Stat 877 (Spring 17) 04/11-04/ / 237
48 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 Stat 877 (Spring 17) 04/11-04/ / 237
49 Poisson vs. Negative 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. Stat 877 (Spring 17) 04/11-04/ / 237
50 Poisson, mean = 10 NegBin, mean = 10, var = 30 Density Density Poisson, mean = 110 NegBin, mean = 10, var = 110 Density Density Stat 877 (Spring 17) 04/11-04/ / 237
51 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) Stat 877 (Spring 17) 04/11-04/ / 237
52 Goodness of Fit HeLa S3 NakedDNA Frequency e+05 1e+06 Actual data Tag count Black line: Actual data Stat 877 (Spring 17) 04/11-04/ / 237
53 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 Stat 877 (Spring 17) 04/11-04/ / 237
54 Goodness of Fit: CisGenome NegBin HeLa S3 NakedDNA Frequency e+05 1e+06 Actual data Sim: CisGenome NegBin Tag count Over-estimated background! Stat 877 (Spring 17) 04/11-04/ / 237
55 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) Stat 877 (Spring 17) 04/11-04/ / 237
56 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) Stat 877 (Spring 17) 04/11-04/ / 237
57 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) Stat 877 (Spring 17) 04/11-04/ / 237
58 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) Stat 877 (Spring 17) 04/11-04/ / 237
59 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). Stat 877 (Spring 17) 04/11-04/ / 237
60 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. Stat 877 (Spring 17) 04/11-04/ / 237
61 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 Stat 877 (Spring 17) 04/11-04/ / 237
62 Case Study 1: STAT1 ChIP-Seq data STAT1 ChIP Frequency e+05 1e+06 Actual Data Tag count Stat 877 (Spring 17) 04/11-04/ / 237
63 Case Study 1: STAT1 ChIP-Seq data STAT1 ChIP Frequency e+05 1e+06 Background: NegBin Reg(M, GC) Actual Data Tag count Stat 877 (Spring 17) 04/11-04/ / 237
64 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 Stat 877 (Spring 17) 04/11-04/ / 237
65 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 Stat 877 (Spring 17) 04/11-04/ / 237
66 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 Stat 877 (Spring 17) 04/11-04/ / 237
67 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 Stat 877 (Spring 17) 04/11-04/ / 237
68 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 Stat 877 (Spring 17) 04/11-04/ / 237 Input Input
69 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 Stat 877 (Spring 17) 04/11-04/ / 237 Input
70 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 Stat 877 (Spring 17) 04/11-04/ / 237
71 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 Stat 877 (Spring 17) 04/11-04/ / 237
72 MOSAiCS two-sample model GOF for STAT1 ChIP-Seq STAT1 ChIP Frequency e+05 Actual Data Tag count Stat 877 (Spring 17) 04/11-04/ / 237
73 MOSAiCS two-sample model GOF for STAT1 ChIP-Seq STAT1 ChIP Frequency e+05 Background: NegBin Reg(Input) Actual Data Tag count Stat 877 (Spring 17) 04/11-04/ / 237
74 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 Stat 877 (Spring 17) 04/11-04/ / 237
75 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 Stat 877 (Spring 17) 04/11-04/ / 237
76 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 Stat 877 (Spring 17) 04/11-04/ / 237
77 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 Stat 877 (Spring 17) 04/11-04/ / 237
78 CTCF motif scans ENCODE pipeline evalua/on ENCODE Peak Caller Evalua/on Subgroup 2013 Courtesy of Gifford Lab. Stat 877 (Spring 17) 04/11-04/ / 237
79 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 Stat 877 (Spring 17) 04/11-04/ / 237
80 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 Stat 877 (Spring 17) 04/11-04/ / 237
81 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 Stat 877 (Spring 17) 04/11-04/ / 237
82 Software implementation: R package mosaics Available through Bioconductor bioc/html/mosaics.html This recent version has more post-processing options. MOSAiCS-HMM: Extension to histone ChIP-seq. Stat 877 (Spring 17) 04/11-04/ / 237
83 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", Stat 877 (Spring 17) 04/11-04/ / 237
84 R package: mosaics d=0.25, signalmodel="bic", maxgap=200, minsize=50, thres=40, ncore=20) Stat 877 (Spring 17) 04/11-04/ / 237
85 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! Stat 877 (Spring 17) 04/11-04/ / 237
86 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 Stat 877 (Spring 17) 04/11-04/ / 237
87 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. Stat 877 (Spring 17) 04/11-04/ / 237
88 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. Stat 877 (Spring 17) 04/11-04/ / 237
89 Summary: MOSAiCS implements a model-based approach for ChIP-seq data analysis; available as a R package through Bioconductor and as a Galaxy tool from the Galaxy tool shed; provides basic pre-processing functions for ChIP-seq data; provides functions for generating wig formatted data to upload to UCSC Genome Browser; provides plotting functions; an HMM extension to accommodate histone modifications; a fully ENCODE compatible MOSAiCS pipeline (finds summits, utilizes IDR (irreproducible discovery rate) for deciding on the number of peaks). Stat 877 (Spring 17) 04/11-04/ / 237
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