Predicting three-dimensional genome organization with chromatin states

We introduce a computational model to simulate chromatin structure and dynamics. Starting from one-dimensional genomics and epigenomics data that are available for hundreds of cell types, this model enables de novo prediction of chromatin structures at five-kilo-base resolution. Simulated chromatin...

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Bibliographic Details
Main Authors: Qi, Yifeng (Author), Zhang, Bin (Author)
Other Authors: Massachusetts Institute of Technology. Department of Chemistry (Contributor)
Format: Article
Language:English
Published: Public Library of Science (PLoS), 2020-05-22T13:49:31Z.
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Online Access:Get fulltext
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100 1 0 |a Qi, Yifeng  |e author 
100 1 0 |a Massachusetts Institute of Technology. Department of Chemistry  |e contributor 
700 1 0 |a Zhang, Bin  |e author 
245 0 0 |a Predicting three-dimensional genome organization with chromatin states 
260 |b Public Library of Science (PLoS),   |c 2020-05-22T13:49:31Z. 
856 |z Get fulltext  |u https://hdl.handle.net/1721.1/125406 
520 |a We introduce a computational model to simulate chromatin structure and dynamics. Starting from one-dimensional genomics and epigenomics data that are available for hundreds of cell types, this model enables de novo prediction of chromatin structures at five-kilo-base resolution. Simulated chromatin structures recapitulate known features of genome organization, including the formation of chromatin loops, topologically associating domains (TADs) and compartments, and are in quantitative agreement with chromosome conformation capture experiments and super-resolution microscopy measurements. Detailed characterization of the predicted structural ensemble reveals the dynamical flexibility of chromatin loops and the presence of cross-talk among neighboring TADs. Analysis of the model's energy function uncovers distinct mechanisms for chromatin folding at various length scales and suggests a need to go beyond simple A/B compartment types to predict specific contacts between regulatory elements using polymer simulations. 
520 |a National Science Foundation (U.S.) (Grant MCB-1715859) 
546 |a en 
655 7 |a Article 
773 |t 10.1371/journal.pcbi.1007024 
773 |t PLOS computational biology