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

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


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

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Chromatin / chemistry
  • Chromatin / genetics*
  • Chromatin / metabolism
  • Chromatin Assembly and Disassembly*
  • Chromatin Immunoprecipitation / methods*
  • Gene Regulatory Networks*
  • Histones / metabolism
  • Humans
  • Software*
  • Transcription Factors / metabolism


  • Chromatin
  • Histones
  • Transcription Factors