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|Abstract:||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.|
|Electronic Publication Date:||27-Mar-2014|
|Citation:||Zhou, J, Troyanskaya, OG. (2014). Global Quantitative Modeling of Chromatin Factor Interactions. PLoS Computational Biology, 10 (10.1371/journal.pcbi.1003525|
|Type of Material:||Journal Article|
|Journal/Proceeding Title:||PLoS Computational Biology|
|Version:||Final published version. Article is made available in OAR by the publisher's permission or policy.|
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