Work

Unsupervised pattern discovery in human chromatin structure through genomic segmentation

Year: 2013

Type: conference-paper

Abstract: Sequence census methods like ChIP-seq now produce an unprecedented amount of genome-anchored data. We have developed an integrative method to identify patterns from multiple experiments simultaneously while taking full advantage of high-resolution data, discovering joint patterns across different assay types. We apply this method to ENCODE chromatin data for the human chronic myeloid leukemia cell line K562, including ChIP-seq data on covalent histone modifications and transcription factor binding, and DNase-seq and FAIRE-seq readouts of open chromatin. In an unsupervised fashion, we identify patterns associated with transcription start sites, gene ends, enhancers, CTCF elements, and repressed regions. The method yields a model which elucidates the relationship between assay observations and functional elements in the genome. This model identifies sequences likely to affect transcription, and we verify these predictions in laboratory experiments. We have made software and an integrative genome browser track freely available (noble.gs.washington.edu/proj/segway/).

Source: ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)

DOI: 10.1145/2506583.2506701

Authors: Michael M. Hoffman, Orion J. Buske, Jie Wang, Zhiping Weng, Jeff A. Bilmes, William Stafford Noble

Institutions: University of Washington, University of Toronto, University of Massachusetts Chan Medical School, University at Buffalo, State University of New York

Language: English

Topic: Genomics and Chromatin Dynamics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funder: National Human Genome Research Institute

Awards: Pattern Discovery for comparative epigenomics, EDAC: ENCODE Data Analysis Center, Identification of Transcriptional Factor Binding Sites in Human Promoters