Chromatin-state discovery and genome annotation with ChromHMM

Abstract

Noncoding DNA regions have central roles in human biology, evolution, and disease. ChromHMM helps to annotate the noncoding genome using epigenomic information across one or multiple cell types. It combines multiple genome-wide epigenomic maps, and uses combinatorial and spatial mark patterns to infer a complete annotation for each cell type. ChromHMM learns chromatin-state signatures using a multivariate hidden Markov model (HMM) that explicitly models the combinatorial presence or absence of each mark. ChromHMM uses these signatures to generate a genome-wide annotation for each cell type by calculating the most probable state for each genomic segment. ChromHMM provides an automated enrichment analysis of the resulting annotations to facilitate the functional interpretations of each chromatin state. ChromHMM is distinguished by its modeling emphasis on combinations of marks, its tight integration with downstream functional enrichment analyses, its speed, and its ease of use. Chromatin states are learned, annotations are produced, and enrichments are computed within 1 d.

Figure 1. Overview of ChromHMM

(a) Tracks of multiple histone modifications are shown from one cell type, IMR90. From such types of tracks, ChromHMM learns a set of chromatin state definitions de novo, and then assigns each location in the genome to an instance of each state. The chromatin states assignments for IMR90 based on the model in (b) are shown below the histone modifications. (b) The panel displays on left a heatmap of emission parameters where each row corresponds to a different state and each column a different mark for the Roadmap Epigenomics 18-state expanded model defined based on the observed data for six histone modifications (H3K4me1, H3K4me3, H3K9ac, H3K27ac, H3K36me3, and H3K9me3) from Ref. . The darker the blue color corresponds to a greater probability of observing the mark in the state. The heatmap to the right of the emission parameters displays the overlap enrichment for various external genomic annotations in IMR90 cells (epigenome E017) similar to what was previously shown for H1-hESC cells in Ref. . A darker blue color corresponds to a greater fold enrichment for a column specific coloring scale. The heatmap to the right of that shows fold enrichment for each state for each 200bp bin position within 2kb around a set of transcription start sites (TSS). A darker blue color corresponds to a greater fold enrichment and there is one color scale for the entire heatmap. Shown to the right of that are candidate state descriptions for each state followed by a state mnemonic. (c) The panel displays the browser view of ChromHMM genome annotation based on the model in (b), which was defined across 98 cell and tissue types. Each row below the genes corresponds to one of the cell or tissue type. (d) The panel highlights application areas of ChromHMM, which include GWAS analysis, gene regulation, and cellular differentiation among others. The GWAS example shows overlap between chromatin state annotations and single-nucleotide polymorphisms (SNPs) associated with Alzheimer’s Disease (AD) similar to Ref. . Roadmap epigenomics chromatin state annotations based on the model in (b) for primary monocytes cells (E029) and below it brain hippocampus (E071).

Figure 2. Overview of Different Options for Handling Multiple Cell Types in ChromHMM

(Left) Multiple cell types are treated independently leading to a different model and annotation for each cell type. (Center) Multiple cell types are effectively concatenated leading to one shared model for all cell types but cell type specific annotations. (Right) Data from multiple cell types are stacked leading to one model based on an expanded set of features and one annotation of the genome.

Figure 3. Example webpage screenshot

The figure displays a screenshot of a portion of the webpage automatically generated by the ChromHMM LearnModel command on the sample data. The webpage contains images and links to the files generated by ChromHMM.