Decoding Chromatin States with Epigenome Data Advanced Topics in Computa8onal Genomics
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1 Decoding Chromatin States with Epigenome Data Advanced Topics in Computa8onal Genomics
2 HMMs for Decoding Chromatin States Epigene8c modifica8ons of the genome have been associated with Establishing cell iden88es during development DNA repair, replica8on Human diseases De novo discovery of chroma8n states given epigene8c marks with HMMs Emission probabili8es: which histone marks co- occur? Transi8on probabili8es: how chroma8n states are distributed spa8ally across the genome
3 Dataset Genome- wide occupancy data in human CD4 T- cells from ChIP- seq experiments 38 different histone methyla8on and acetyla8on marks Histone variant H2AZ RNA polymerase II CTCF E.g., H3K9me3 trimethylated lysine 9 of histone 3
4 HMMs for Decoding Chromatin States Hidden states for unknown chroma8n states Models with varying number of states 79 states, pruned to 51 states Histone mark data as observa8ons Data are binarized (a\er thresholding) for each window of size 200bp Binomial distribu8on for each histone mark as emission probability All histone marks are treated as independent
5 Example of Chromatin State Annotation Posterior probability of states at each locus, given data
6 Estimated Chromatin States - Emission Probabilities Emission probabili8es Genomic func8onal enrichment
7 GO Enrichment for Promoter States Although states 3-8 were promoter states, each state is enriched for genes with different GO categories
8 Comparison of Promoter States Different promoter states peak at different sites
9 Comparison of Transcribed States
10 GWAS and Chromatin States GWAS- enriched chroma8n state 33
11 Power for Discovering Chromatin States
12 Feature Selection We may not need all of the histone marks to explain the chroma8n state Feature selec8on as step- wise forward selec8on to select a subset of histone marks that describe the chroma8n state
13 Feature Selection
14 Epigenome and Gene Expression
15 Epigenome and Transcription Histone modifica8on levels can influence gene expressions Nucleosome posi8ons can influence gene expressions DNA sequence specifici8es of nucleosome and transcrip8on factor binding sites Nucleosomes as repressors Methyla8on usually represses transcrip8on
16 Key Questions Is there a quan8ta8ve rela8onship between histone modifica8ons levels and transcrip8on? Is there a subset of histone modifica8ons that predict transcrip8on becer than others? Are there different requirements for epigene8c marks for different promoter types? Do these rela8onships between histone modifica8ons and transcrip8on hold in different 8ssue types?
17 Dataset 38 histone modifica8ons and one histone variant in human CD4+ T- cells ChIP- seq data In a region of 4,001 bp surrounding the transcrip8on start sites of 14,801 RefSeq genes Gene expression levels in the CD4+ T- cells 9 histone modifica8ons in CD36+ and CD133+ cells Gene expression levels in CD36+ and CD133+ cells Histone modifica8on levels are predic8ve for gene expression. (Karlic et al., PNAS, 2010)
18 Linear Models Linear regression method Predictors: histone marks No binariza8on For genes with no histone modifica8ons for par8cular modifica8ons, add a pseudocount Responses: gene expressions Promoter regions of different genes as samples
19 Linear Models Full model including all histone modifica8ons Compute r 2 between observed gene expressions and predicted values to assess the predic8ve power of the model
20 Linear Models Selec8ng the histone modifica8ons with the most predic8ve power
21 Linear Models Selec8ng the histone modifica8ons with the most predic8ve power with BIC scores
22 Prediction Accuracy
23 Searching for Histone Modifications with the Most Predictive Power The most frequently appearing histone modifica8ons in models with 1, 2, 3 histone modifica8ons
24 Model with One Histone Modification Correla8ons between expressions and each histone modifica8on Redundancy in histone modifica8ons
25 Histone Modifications and Promoter Types Different promoter types to be considered LCPs : low CpG content promoters HCPs : high CpG content promoters Nucleosomes in HCPs almost always have H3K4me3 marks, whereas nucleosomes in LCPs carry this modifica8on only when they are expressed. Hypothesis: expression levels of genes with LCPs and HCPs can be predicted by different sets of histone modifica8ons
26 Histone Modifications and Promoter Types Experimental setup 1,779 LCPs and 7,089 HCPs in the dataset Fit different models to each of LCPs and HCPs and compare them with the model es8mated from the full dataset
27 Histone Modifications and Promoter Types
28 Considering Different Tissue Types Used the model trained on CD4+ data to predict gene expressions in CD133+ and CD36+ cells Used only those gene expressions with more than five fold differences between CD4+ and CD133+ (also between CD4+ and CD36+)
29 Nucleosome and Transcription DNA sequence mo8fs with high nucleosome binding affini8es Poten8ally related to bending DNA around the nucleosomes DNA sequence mo8fs with high transcrip8on factor binding affini8es TF concentra8on can also influence gene expression Compe88on between nucleosomes and transcrip8on factors can influence the transcrip8on
30 DNA Sequence, DNA-binding Proteins, and Gene Expression Mixture model for predic8ng gene expressions from nucleosomes and other DNA binding proteins E: gene expression C: protein configura8ons
31 DNA Sequence, DNA-binding Proteins, and Gene Expression Mixture propor8ons Mixture component models
32 Nucleosome and Transcription
33 Nucleosome and Transcription
34 Competition between Nucleosomes and Transcription Factors
35 Competition between Nucleosomes and Transcription Factors
36 Transcriptional Noise
37 Cooperative Binding Reduces Transcriptional Noise
38 Fuzzy Nucleosomes Well- posi8oned vs. fuzzy nucleosomes Can be inferred from DNA sequences In fuzzy nucleosomes, many nucleosome posi8ons are observed Well- posi8oned nucleosomes Fuzzy nucleosomes
39 Summary Histone modifica8ons contain informa8on on chroma8n states. Chroma8n states can be poten8ally decoded from epigene8c data. Epigene8cs and gene expressions histone modifica8ons can influence gene expression nucleosome posi8ons and the compe88on between TFs and nucleosome can influence gene expression
Nucleosome Positioning and Organization Advanced Topics in Computa8onal Genomics
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