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Genome Biol. 2016 Apr 30;17:82. doi: 10.1186/s13059-016-0925-0.

ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data.

Author information

1
Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
2
Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.
3
Princess Margaret Cancer Centre, Toronto, ON, Canada.
4
Department of Computer Science, University of Toronto, Toronto, ON, Canada.
5
Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA. suinlee@uw.edu.
6
Department of Genome Sciences, University of Washington, Seattle, WA, USA. suinlee@uw.edu.

Abstract

A cell's epigenome arises from interactions among regulatory factors-transcription factors and histone modifications-co-localized at particular genomic regions. We developed a novel statistical method, ChromNet, to infer a network of these interactions, the chromatin network, by inferring conditional-dependence relationships among a large number of ChIP-seq data sets. We applied ChromNet to all available 1451 ChIP-seq data sets from the ENCODE Project, and showed that ChromNet revealed previously known physical interactions better than alternative approaches. We experimentally validated one of the previously unreported interactions, MYC-HCFC1. An interactive visualization tool is available at http://chromnet.cs.washington.edu.

PMID:
27139377
PMCID:
PMC4852466
DOI:
10.1186/s13059-016-0925-0
[Indexed for MEDLINE]
Free PMC Article

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