Chromatin-state discovery and genome annotation with ChromHMM
Name
nihms963922.pdf
Description
Accepted version
Size
1021.93 KB
Format
Adobe PDF
Checksum (MD5)
08f3cb9915b31775d2eeed41d3011699
Author(s)
Ernst, Jason
Kellis, Manolis
Date Issued
November 2017
Journal
Nature Protocols
Publisher
Springer Science and Business Media LLC
Citation
Ernst, Jason and Manolis Kellis. "Chromatin-state discovery and genome annotation with ChromHMM." Nature Protocols 12, 12 (November 2017): 2478–2492. © 2017 Macmillan Publishers Limited, part of Springer Nature
Version
Author's final manuscript
Abstract
Non-coding DNA regions have central roles in human biology, evolution, and disease. ChromHMM helps to annotate the noncoding genome using epigenomic information across one or multiple cell types. It combines multiple genome-wide epigenomic maps, and uses combinatorial and spatial mark patterns to infer a complete annotation for each cell type. ChromHMM learns chromatin-state signatures using a multivariate hidden Markov model (HMM) that explicitly models the combinatorial presence or absence of each mark. ChromHMM uses these signatures to generate a genome-wide annotation for each cell type by calculating the most probable state for each genomic segment. ChromHMM provides an automated enrichment analysis of the resulting annotations to facilitate the functional interpretations of each chromatin state. ChromHMM is distinguished by its modeling emphasis on combinations of marks, its tight integration with downstream functional enrichment analyses, its speed, and its ease of use. Chromatin states are learned, annotations are produced, and enrichments are computed within 1 day.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Broad Institute of MIT and Harvard
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/nprot.2017.124
