Integrative annotation of chromatin elements from ENCODE data
Name
Hoffman-2012-Integrative annotation of chromatin elements from ENCODE data.pdf
Size
3.08 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Ernst, Jason
Kellis, Manolis
Hoffman, Michael M.
Wilder, Steven P.
Kundaje, Anshul
Harris, Robert S.
Libbrecht, Max
Giardine, Belinda
Ellenbogen, Paul M.
Bilmes, Jeffrey A.
Date Issued
December 2012
Journal
Nucleic Acids Research
Publisher
Oxford University Press
Citation
Hoffman, M. M. et al. “Integrative Annotation of Chromatin Elements from ENCODE Data.” Nucleic Acids Research 41.2 (2012): 827–841.
Version
Final published version
Abstract
The ENCODE Project has generated a wealth of experimental information mapping diverse chromatin properties in several human cell lines. Although each such data track is independently informative toward the annotation of regulatory elements, their interrelations contain much richer information for the systematic annotation of regulatory elements. To uncover these interrelations and to generate an interpretable summary of the massive datasets of the ENCODE Project, we apply unsupervised learning methodologies, converting dozens of chromatin datasets into discrete annotation maps of regulatory regions and other chromatin elements across the human genome. These methods rediscover and summarize diverse aspects of chromatin architecture, elucidate the interplay between chromatin activity and RNA transcription, and reveal that a large proportion of the genome lies in a quiescent state, even across multiple cell types. The resulting annotation of non-coding regulatory elements correlate strongly with mammalian evolutionary constraint, and provide an unbiased approach for evaluating metrics of evolutionary constraint in human. Lastly, we use the regulatory annotations to revisit previously uncharacterized disease-associated loci, resulting in focused, testable hypotheses through the lens of the chromatin landscape.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1093/nar/gks1284
