ChromHMM: automating chromatin-state discovery and characterization
To the Editor: Chromatin-state annotation using combinations of chromatin modification patterns has emerged as a powerful approach for discovering regulatory regions and their cell type–specific activity patterns and for interpreting disease-association studies1, 2, 3, 4, 5. However, the computatio...
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Nature Publishing Group
2014
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| Online Access: | http://hdl.handle.net/1721.1/87104 |
| _version_ | 1826190046528536576 |
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| author | Ernst, Jason Kellis, Manolis |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Ernst, Jason Kellis, Manolis |
| author_sort | Ernst, Jason |
| collection | MIT |
| description | To the Editor:
Chromatin-state annotation using combinations of chromatin modification patterns has emerged as a powerful approach for discovering regulatory regions and their cell type–specific activity patterns and for interpreting disease-association studies1, 2, 3, 4, 5. However, the computational challenge of learning chromatin-state models from large numbers of chromatin modification datasets in multiple cell types still requires extensive bioinformatics expertise. To address this challenge, we developed ChromHMM, an automated computational system for learning chromatin states, characterizing their biological functions and correlations with large-scale functional datasets and visualizing the resulting genome-wide maps of chromatin-state annotations. |
| first_indexed | 2024-09-23T08:34:11Z |
| format | Article |
| id | mit-1721.1/87104 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:34:11Z |
| publishDate | 2014 |
| publisher | Nature Publishing Group |
| record_format | dspace |
| spelling | mit-1721.1/871042021-09-13T13:55:30Z ChromHMM: automating chromatin-state discovery and characterization Ernst, Jason Kellis, Manolis Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Ernst, Jason Kellis, Manolis To the Editor: Chromatin-state annotation using combinations of chromatin modification patterns has emerged as a powerful approach for discovering regulatory regions and their cell type–specific activity patterns and for interpreting disease-association studies1, 2, 3, 4, 5. However, the computational challenge of learning chromatin-state models from large numbers of chromatin modification datasets in multiple cell types still requires extensive bioinformatics expertise. To address this challenge, we developed ChromHMM, an automated computational system for learning chromatin states, characterizing their biological functions and correlations with large-scale functional datasets and visualizing the resulting genome-wide maps of chromatin-state annotations. Massachusetts Institute of Technology. Computational and Systems Biology Initiative National Science Foundation (U.S.) (postdoctoral fellowship 0905968) National Institutes of Health (U.S.) (1-RC1- HG005334) National Institutes of Health (U.S.) (1 U54 HG004570) 2014-05-22T18:17:13Z 2014-05-22T18:17:13Z 2012-02 Article http://purl.org/eprint/type/JournalArticle 1548-7091 1548-7105 http://hdl.handle.net/1721.1/87104 Ernst, Jason, and Manolis Kellis. “ChromHMM: Automating Chromatin-State Discovery and Characterization.” Nature Methods 9, no. 3 (February 28, 2012): 215–216. en_US http://dx.doi.org/10.1038/nmeth.1906 Nature Methods 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 Nature Publishing Group PMC |
| spellingShingle | Ernst, Jason Kellis, Manolis ChromHMM: automating chromatin-state discovery and characterization |
| title | ChromHMM: automating chromatin-state discovery and characterization |
| title_full | ChromHMM: automating chromatin-state discovery and characterization |
| title_fullStr | ChromHMM: automating chromatin-state discovery and characterization |
| title_full_unstemmed | ChromHMM: automating chromatin-state discovery and characterization |
| title_short | ChromHMM: automating chromatin-state discovery and characterization |
| title_sort | chromhmm automating chromatin state discovery and characterization |
| url | http://hdl.handle.net/1721.1/87104 |
| work_keys_str_mv | AT ernstjason chromhmmautomatingchromatinstatediscoveryandcharacterization AT kellismanolis chromhmmautomatingchromatinstatediscoveryandcharacterization |