ChromHMM Tutorial. Jason Ernst Assistant Professor University of California, Los Angeles
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1 ChromHMM Tutorial Jason Ernst Assistant Professor University of California, Los Angeles
2 Talk Outline Chromatin states analysis and ChromHMM Accessing chromatin state annotations for ENCODE2 and Roadmap Epigenomics Running the ChromHMM software
3 Talk Outline Chromatin states analysis and ChromHMM Accessing chromatin state annotations for ENCODE2 and Roadmap Epigenomics Running the ChromHMM software
4 Chromatin Marks for Genome Annotation 100+ histone modifications Specificity in: Histone protein Amino acid residue Chemical modification (e.g. methyl, acetylation) Number of occurrence of the modifications Examples H3K4me1 Enhancers H3K4me3 Promoters H3K27me3 Repressive H3K9me3 Repressive H3K36me3 Transcribed Image source: Histone Modifications can be Mapped Genome-wide with ChIP-seq
5 From chromatin marks to chromatin states Ernst and Kellis, Nat Biotech 2010 Promoter states Transcribed states Active Intergenic Learn de novo significant combinational and spatial patterns of chromatin marks Repressed Use for genome annotation
6 Approach: Multivariate Hidden Markov Model (ChromHMM) Enhancer Gene Starts Gene - Transcribed Region DNA Unobserved Binarized chromatin marks. Called based on a poisson distribution H3K4me1 H3K4me3 H3K4me3 H3K4me1 H3K36me3 H3K36me3 H3K36me3 H3K36me3 H3K27ac H3K4me1 Most likely Hidden State base pair interval Emission distribution is a product of independent Bernoulli random variables Parameters are learned from the data 1: 2: 3: 0.8 H3K4me1 0.9 H3K4me3 0.9 H3K4me3 K27ac 0.8 K4me : 5: 6: H3K4me1 0.9 H3K36me3 Emission probabilities - High Probability Chromatin Marks in State Ernst and Kellis, Nat Biotech 2010 ; Ernst and Kellis, Nature Methods Transition probabilities Red high probability Black medium probability 6
7 ENCODE: Study nine marks in nine human cell lines 9 marks 9 human cell types H3K4me1 H3K4me2 H3K4me3 H3K27ac H3K9ac H3K27me3 H4K20me1 H3K36me3 CTCF +WCE +RNA HUVEC Umbilical vein endothelial NHEK Keratinocytes GM12878 Lymphoblastoid x K562 Myelogenous leukemia HepG2 Liver carcinoma NHLF Normal human lung fibroblast HMEC Mammary epithelial cell HSMM Skeletal muscle myoblasts H1 Embryonic Brad Bernstein ENCODE Group HUVEC NHEK GM12878 K562 HepG2 NHLF HMEC HSMM H1 81 Chromatin Mark Tracks Learned jointly across cell types (virtual concatenation) State definitions are common State locations are dynamic Ernst et al, Nature 2011
8 Chromatin states dynamics across nine ENCODE cell types Single annotation track for each cell type Summarize cell-type activity at a glance Can study 9-cell activity pattern across Ernst et al, Nature 2011
9 Chromatin states to interpret disease variants Specific chromatin states enriched in GWAS catalog Enhancers from different cell types enriched in different traits Ernst and Kellis, Nature Biotech 2010 Ernst et al, Nature 2011 Imputation based chromatin state used in dissection FTO loci Claussnitzer et al, NEJM 2015 Many other examples in the literature De Jager et al, Nature Neuroscience 2014 Interpreting epigenetic disease associated variation in Alzheimer s disease
10 Talk Outline Chromatin states analysis and ChromHMM Accessing chromatin state annotations for ENCODE2 and Roadmap Epigenomics Running the ChromHMM software
11 Chromatin States Defined Across 127 Cell/Tissues Types 5 marks observed data Roadmap Epigenomics Consortium et al, Nature cell types from ENCODE2
12 Extended Chromatin State Model with H3K27ac 6 marks observed data Defined on 98 cell/tissue types only 16 cell types from ENCODE2 Roadmap Epigenomics Consortium et al, Nature 2015
13 Chromatin States Defined on Imputed Data Marks 12 marks imputed data Defined on all cell/tissue types ChromImpute method Ernst and Kellis, Nature Biotech 2015
14 ChromHMM Models across Many Roadmap/ENCODE2 Cell and Tissue Types 127 Cell/Tissue Types H3K4me1 H3K4me3 H3K27me3 H3K9me3 H3K36me3 98 Cell/Tissue Types H3K4me1 H3K4me3 H3K27me3 H3K9me3 H3K36me3 H3K27ac Images from UCSC genome browser Note: additional ChromHMM annotations based on older ENCODE models available from UCSC genome browser 127 Cell/Tissue Types H3K4me1 H3K4me3 H3K27me3 H3K9me3 H3K36me3 H3K27ac H3K9ac H4K20me1 H3K79me2 H3K4me2 H2A.Z DNase
15 Accessing Roadmap/ENCODE2 ChromHMM through the UCSC Genome Browser Track Hubs
16 Accessing Roadmap/ENCODE2 ChromHMM through the UCSC Genome Browser Track Hubs
17 Accessing Roadmap/ENCODE2 ChromHMM through the UCSC Genome Browser Track Hubs Note: Different than track hub Roadmap Epigenomics Data Complete Collection at Wash U VizHub
18 Accessing Roadmap/ENCODE2 ChromHMM through the UCSC Genome Browser Track Hubs
19 Accessing Roadmap/ENCODE2 ChromHMM through the UCSC Genome Browser Track Hubs
20 Accessing Roadmap/ENCODE2 ChromHMM through the UCSC Genome Browser Track Hubs
21 Accessing Roadmap/ENCODE2 ChromHMM through the UCSC Genome Browser Track Hubs
22 Accessing Roadmap/ENCODE2 ChromHMM through the UCSC Genome Browser Track Hubs
23 Human Epigenome Browser at Washington University
24 Roadmap Epigenomics Integrative Analysis Portal
25 Roadmap Epigenomics Integrative Analysis Portal
26 Talk Outline Chromatin states analysis and ChromHMM Accessing chromatin state annotations for ENCODE2 and Roadmap Epigenomics Running the ChromHMM software
27 ChromHMM Website Software download
28 ChromHMM Website Software manual
29 Try to Run ChromHMM on Sample Data on Your Computer (Java should already be installed on your computer) 1. Download 2. Unzip ChromHMM.zip 3. Open a command line 4. Change into the ChromHMM directory 5. Enter the command: java -mx1600m -jar ChromHMM.jar LearnModel -p 0 SAMPLEDATA_HG18 OUTPUTSAMPLE 10 hg18
30 Input to ChromHMM ChromHMM models are learned from binarized data using its LearnModel command Binarized data is typically obtained starting from aligned reads. Apply BinarizeBed if reads are in BED format Apply BinarizeBam if reads are in BAM format
31 BinarizeBed Java command -mx1600m specifies memory to Java
32 BinarizeBed ChromHMM command
33 BinarizeBed File with the chromosome lengths for the assembly
34 BinarizeBed DIRECTORY of BED files
35 BinarizeBed cell mark cell-mark Cell-mark-file table Control data is optional and can also be treated as a mark
36 BinarizeBed Output directory
37 LearnModel -p 0 Use as many processors as available -p N Use up to N processors (default N=1)
38 LearnModel Directory with the Binarized Input
39 LearnModel Directory where the output goes
40 LearnModel Number of states
41 LearnModel Genome assembly
42 ChromHMM Report
43 ChromHMM Report
44 Emission Parameters
45 Transition Parameters
46 Model Parameter File
47 Segmentation File
48 Browser Files Can load into browser UCSC Genome, IGV
49 Enrichments
50 Positional Plots
51 Enrichments for Additional Cell Types
52 Additional Commands CompareModels the command allows the comparison of the emission parameters of a selected model to a set of models in terms of correlation.
53 Additional Commands MakeBrowserFiles (re)generates browser files from segmentation files and allows specifying the coloring
54 Additional Commands OverlapEnrichment (re)computes enrichments of a segmentation for a set of annotations
55 Additional Commands NeighborhoodEnrichment (re)computes enrichments of a segmentation around a set of anchor positions
56 Additional Commands Reorder reorders the states of the model
57 Summary Pre-computed ChromHMM chromatin state annotations available across over 100 cell/tissue types ChromHMM software available to run on your own data
58 Collaborators and Acknowledgements Manolis Kellis ENCODE consortium Brad Bernstein production group Roadmap Epigenomics consortium Funding NHGRI, NIH, NSF, HHMI, Sloan Foundation
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