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

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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.

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Lundberg, S. M., Tu, W. B., Raught, B., Penn, L. Z., Hoffman, M. M., & Lee, S. I. (2016). ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data. Genome Biology, 17(1). https://doi.org/10.1186/s13059-016-0925-0

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