TitleGlobal quantitative modeling of chromatin factor interactions.
Publication TypeJournal Article
Year of Publication2014
AuthorsZhou, J, Troyanskaya, OG
JournalPLoS Comput Biol
Date Published2014 Mar
KeywordsAlgorithms, Animals, Area Under Curve, Chromatin, Computational Biology, Drosophila melanogaster, Entropy, Gene Expression Regulation, Histones, Models, Statistical, Normal Distribution, Probability, Reproducibility of Results, Systems Biology, Transcription Factors, Transcription, Genetic

Chromatin is the driver of gene regulation, yet understanding the molecular interactions underlying chromatin factor combinatorial patterns (or the "chromatin codes") remains a fundamental challenge in chromatin biology. Here we developed a global modeling framework that leverages chromatin profiling data to produce a systems-level view of the macromolecular complex of chromatin. Our model ultilizes maximum entropy modeling with regularization-based structure learning to statistically dissect dependencies between chromatin factors and produce an accurate probability distribution of chromatin code. Our unsupervised quantitative model, trained on genome-wide chromatin profiles of 73 histone marks and chromatin proteins from modENCODE, enabled making various data-driven inferences about chromatin profiles and interactions. We provided a highly accurate predictor of chromatin factor pairwise interactions validated by known experimental evidence, and for the first time enabled higher-order interaction prediction. Our predictions can thus help guide future experimental studies. The model can also serve as an inference engine for predicting unknown chromatin profiles--we demonstrated that with this approach we can leverage data from well-characterized cell types to help understand less-studied cell type or conditions.

Alternate JournalPLoS Comput. Biol.