ChromHMM: automating chromatin-state discovery and characterization.
Nature methods. 2012 Mar;9(3):215-6.
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- ChromHMM is a multivariate hidden Markov model trained on histone modifications to identify chromatin states, including enhancers. ChromHMM integrates multiple chromatin datasets such as ChIP-Seq data of various histone modifications to discover the number of recurring patterns of marks in the genome. Enrichments of different biological states are calculated over each genomic segment to functionally annotate the putative function of each segment. ChromHMM has been applied on 111 Roadmap primary cell lines and 16 ENCODE cell lines with ~6 histone marks. Joint training across human and mouse is a future direction.
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- Focusing on cell-type-specific patterns of promoters and enhancers, they defined multicell activity profiles for chromatin state, gene expression, regulatory motif enrichment and regulator expression. They use correlations between these profiles to link enhancers to putative target genes, and predict the cell-type-specific activators and repressors that modulate them.
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- HMM regions
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- BED
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- ENCODE2 combined segmentation in 6 cell types
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- HMM regions
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- BED
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- Roadmap+ENCODE chromatin states across 111 Roadmap cell types and 16 ENCODE cell lines
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- HMM regions
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- WUSTL Roadmap Epigenome Browser