Global Quantitative Modeling of Chromatin Factor Interactions

By Jian Zhou, Olga G. Troyanskaya. 2014

28 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

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Citations in the corpus

Iterative random forests to discover predictive and stable high-order interactions (2017, methodology): “While several methods have been developed to identify interactions in large genomics datasets, for example (Zhou and Troyanskaya, 2014; Lundberg et al., 2016; Yoshida et al., 2017), these approaches either focus on pairwise relationships or require explicit enumeration of higher-order interactions, ”
Learning the human chromatin network from all ENCODE ChIP-seq data (2015, methodology): “We calculated precision for the inverse covariance of binary data using the same bootstrap procedure as the authors, and compared against Markov random ﬁeld precision numbers from their published precision-recall plot [55].”