Aug 4, 2015

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

BioRxiv : the Preprint Server for Biology
Scott M LundbergSu-In Lee


Introduction: A cell's epigenome arises from interactions among regulatory factors \---| transcription factors, histone modifications, and other DNA-associated proteins \---| co-localized at particular genomic regions. Identifying the network of interactions among regulatory factors, the chromatin network, is of paramount importance in understanding epigenome regulation. Methods: We developed a novel computational approach, ChromNet, to infer the chromatin network from a set of ChIP-seq datasets. ChromNet has four key features that enable its use on large collections of ChIP-seq data. First, rather than using pairwise co-localization of factors along the genome, ChromNet identifies conditional dependence relationships that better discriminate direct and indirect interactions. Second, our novel statistical technique, the group graphical model, improves inference of conditional dependence on highly correlated datasets. Such datasets are common because some transcription factors form a complex and the same transcription factor is often assayed in different laboratories or cell types. Third, ChromNet's computationally efficient method and the group graphical model enable the learning of a joint network across all cell types, which ...Continue Reading

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Mentioned in this Paper

Establishment and Maintenance of Localization
Transcription, Genetic
DNA, Viral
Chromatin Immunoprecipitation
Protein-Protein Interaction
Histone Modification
Cell Type

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