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

Nature Communications
Eugenio MarcoGuo-Cheng Yuan

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.

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Citations

Jan 9, 2019·ELife·Liangyu TaoVikas Bhandawat
Nov 10, 2017·Nature Protocols·Jason Ernst, Manolis Kellis
Dec 11, 2019·Life Science Alliance·Matthias BlumMarco Antonio Mendoza-Parra
Mar 27, 2018·Briefings in Functional Genomics·Shan Jiang, Ali Mortazavi
Mar 25, 2020·Science Advances·Thomas P Wytock, Adilson E Motter

Related Concepts

Chromatin
Histone H7
Markov Chains
Polynucleosomes
Early Promoters, Genetic
K562 Cells
Polycomb-Group Protein Complexes
Chromatin
Gene Expression
Spatial Distribution

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