TitleIntegrative annotation of chromatin elements from ENCODE data
Publication TypeJournal Article
Year of Publication2012
AuthorsHoffman MM, Ernst J, Wilder SP, Kundaje A, Harris RS, Libbrecht M, Giardine B, Ellenbogen PM, Bilmes JA, Birney E, Hardison RC, Dunham I, Kellis M, Noble W S
JournalNucleic Acids Research
VolumeIn Press
Keywordschromatin, ENCODE, enhancers, evolutionary constraint, functional genomics, genome-wide association study, human, machine learning, regulatory regions, segmentation, unsupervised learning
AbstractThe ENCODE Project has generated a wealth of experimental information mapping diverse chromatin properties in several human cell lines. While each such data track is independently informative towards 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 data sets of the ENCODE Project, we apply unsupervised learning methodologies, converting dozens of chromatin data sets 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.
Citation Keyhoffman:integrative
Refereed DesignationRefereed