Multi-scale chromatin state annotation using a hierarchical hidden Markov model

• Published in: Nature communications Vol. 8; no. 1; p. 15011
• Main Authors: Marco, Eugenio, Meuleman, Wouter, Huang, Jialiang, Glass, Kimberly, Pinello, Luca, Wang, Jianrong, Kellis, Manolis, Yuan, Guo-Cheng
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 07.04.2017; Nature Portfolio
• Subjects: Annotations; Genes; Genomes; Methods; Stem cells; United States > US; Massachusetts
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/ncomms15011

Chromatin-state analysis is widely applied in the studies of development and diseases. However, existing methods operate at a single length scale, and therefore cannot distinguish large domains from isolated elements of the same type. To overcome this limitation, we present a hierarchical hidden Markov model, diHMM, to systematically annotate chromatin states at multiple length scales. We apply diHMM to analyse a public ChIP-seq data set. diHMM not only accurately captures nucleosome-level information, but identifies domain-level states that vary in nucleosome-level state composition, spatial distribution and functionality. The domain-level states recapitulate known patterns such as super-enhancers, bivalent promoters and Polycomb repressed regions, and identify additional patterns whose biological functions are not yet characterized. By integrating chromatin-state information with gene expression and Hi-C data, we identify context-dependent functions of nucleosome-level states. Thus, diHMM provides a powerful tool for investigating the role of higher-order chromatin structure in gene regulation.