title: Multi-scale chromatin state annotation using a hierarchical hidden Markov model.
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.
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02215, USA.
authors.forename: Eugenio
authors.initials: E
authors.lastname: Marco
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology and Broad Institute, Cambridge, Massachusetts 02139, USA.
authors.forename: Wouter
authors.initials: W
authors.lastname: Meuleman
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02215, USA.
authors.forename: Jialiang
authors.initials: J
authors.lastname: Huang
authors.affiliation: Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02215, USA.
authors.forename: Kimberly
authors.initials: K
authors.lastname: Glass
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02215, USA.
authors.forename: Luca
authors.initials: L
authors.lastname: Pinello
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology and Broad Institute, Cambridge, Massachusetts 02139, USA.
authors.forename: Jianrong
authors.initials: J
authors.lastname: Wang
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology and Broad Institute, Cambridge, Massachusetts 02139, USA.
authors.forename: Manolis
authors.initials: M
authors.lastname: Kellis
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02215, USA.
authors.forename: Guo-Cheng
authors.identifier: 0000-0002-2283-4714
authors.initials: GC
authors.lastname: Yuan
mesh_terms: D000465:Algorithms; D002460:Cell Line; D002843:Chromatin / Q000235:genetics* / Q000378:metabolism; D005786:Gene Expression Regulation; D006657:Histones / Q000378:metabolism; D006801:Humans; D020014:K562 Cells; D008390:Markov Chains; D009707:Nucleosomes / Q000235:genetics* / Q000378:metabolism; D063146:Polycomb-Group Proteins / Q000235:genetics / Q000378:metabolism; D011401:Promoter Regions, Genetic / Q000235:genetics
chemical_list: D002843:Chromatin; D006657:Histones; D009707:Nucleosomes; D063146:Polycomb-Group Proteins