Marco, EugenioMeuleman, WouterHuang, JialiangGlass, KimberlyPinello, LucaWang, JianrongKellis, ManolisYuan, Guo-Cheng2017-05-012017Marco, Eugenio, Wouter Meuleman, Jialiang Huang, Kimberly Glass, Luca Pinello, Jianrong Wang, Manolis Kellis, and Guo-Cheng Yuan. 2017. “Multi-scale chromatin state annotation using a hierarchical hidden Markov model.” Nature Communications 8 (1): 15011. doi:10.1038/ncomms15011. http://dx.doi.org/10.1038/ncomms15011.http://nrs.harvard.edu/urn-3:HUL.InstRepos:32630712Chromatin-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.en-USJournal Article2017-05-0110.1038/ncomms15011