ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Lundberg, Scott; Tu, William; Raught, Brian; Penn, Linda; Hoffman, Michael; Lee, Su-In
• Is Part Of: Genome biology. Volume 17:Issue 1(2016)
• Page Start: 1
• Page End: 19
• Publication Date: 2016-12
• Subjects: Genomes -- Periodicals; Biology -- Periodicals; Molecular biology -- Periodicals; 572.8633
• DOI: 10.1186/s13059-016-0925-0
• Languages: English
• ISSNs: 1474-760X

Abstract

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, thechromatin 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 athttp://chromnet.cs.washington.edu .