ChromHMM: automating chromatin-state discovery and characterization
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Kellis_ChromHMM.pdf
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420.16 KB
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Adobe PDF
Checksum (MD5)
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Author(s)
Ernst, Jason
Kellis, Manolis
Date Issued
February 2012
Journal
Nature Methods
Publisher
Nature Publishing Group
Citation
Ernst, Jason, and Manolis Kellis. “ChromHMM: Automating Chromatin-State Discovery and Characterization.” Nature Methods 9, no. 3 (February 28, 2012): 215–216.
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/nmeth.1906
