Chromatin-state discovery and genome annotation with ChromHMM
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 in...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Springer Science and Business Media LLC
2020
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| Online Access: | https://hdl.handle.net/1721.1/126278 |
| _version_ | 1826212230259015680 |
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| author | Ernst, Jason Kellis, Manolis |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Ernst, Jason Kellis, Manolis |
| author_sort | Ernst, Jason |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T15:18:07Z |
| format | Article |
| id | mit-1721.1/126278 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:18:07Z |
| publishDate | 2020 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1262782022-10-02T02:02:23Z Chromatin-state discovery and genome annotation with ChromHMM Ernst, Jason Kellis, Manolis 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 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. 2020-07-21T15:38:02Z 2020-07-21T15:38:02Z 2017-11 2019-06-07T13:02:30Z Article http://purl.org/eprint/type/JournalArticle 1754-2189 1750-2799 https://hdl.handle.net/1721.1/126278 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 en http://dx.doi.org/10.1038/nprot.2017.124 Nature Protocols 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. application/pdf Springer Science and Business Media LLC PMC |
| spellingShingle | Ernst, Jason Kellis, Manolis Chromatin-state discovery and genome annotation with ChromHMM |
| title | Chromatin-state discovery and genome annotation with ChromHMM |
| title_full | Chromatin-state discovery and genome annotation with ChromHMM |
| title_fullStr | Chromatin-state discovery and genome annotation with ChromHMM |
| title_full_unstemmed | Chromatin-state discovery and genome annotation with ChromHMM |
| title_short | Chromatin-state discovery and genome annotation with ChromHMM |
| title_sort | chromatin state discovery and genome annotation with chromhmm |
| url | https://hdl.handle.net/1721.1/126278 |
| work_keys_str_mv | AT ernstjason chromatinstatediscoveryandgenomeannotationwithchromhmm AT kellismanolis chromatinstatediscoveryandgenomeannotationwithchromhmm |