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

Eugenio Marco, Wouter Meuleman, Jialiang Huang, Kimberly Glass, Luca Pinello, Jianrong Wang, Manolis Kellis () and Guo-Cheng Yuan ()
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Eugenio Marco: Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health
Wouter Meuleman: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology and Broad Institute
Jialiang Huang: Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health
Kimberly Glass: Brigham and Women’s Hospital and Harvard Medical School
Luca Pinello: Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health
Jianrong Wang: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology and Broad Institute
Manolis Kellis: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology and Broad Institute
Guo-Cheng Yuan: Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health

Nature Communications, 2017, vol. 8, issue 1, 1-9

Abstract: Abstract 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.

Date: 2017
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DOI: 10.1038/ncomms15011

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