Characterizing chromatin folding coordinate and landscape with deep learning
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file.pdf
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Published version
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2.07 MB
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Adobe PDF
Checksum (MD5)
bbe2d38fe41cfb191c2600c196738599
Author(s)
Xie, Wen Jun
Qi, Yifeng
Zhang, Bin
Date Issued
2020
Journal
PLoS Computational Biology
Publisher
Public Library of Science (PLoS)
Citation
Xie, Wen Jun, Qi, Yifeng and Zhang, Bin. 2020. "Characterizing chromatin folding coordinate and landscape with deep learning." PLoS Computational Biology, 16 (9).
Version
Final published version
Abstract
© 2020 Xie et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Genome organization is critical for setting up the spatial environment of gene transcription, and substantial progress has been made towards its high-resolution characterization. The underlying molecular mechanism for its establishment is much less understood. We applied a deep-learning approach, variational autoencoder (VAE), to analyze the fluctuation and heterogeneity of chromatin structures revealed by single-cell imaging and to identify a reaction coordinate for chromatin folding. This coordinate connects the seemingly random structures observed in individual cohesin-depleted cells as intermediate states along a folding pathway that leads to the formation of topologically associating domains (TAD). We showed that folding into wild-type-like structures remain energetically favorable in cohesin-depleted cells, potentially as a result of the phase separation between the two chromatin segments with active and repressive histone marks. The energetic stabilization, however, is not strong enough to overcome the entropic penalty, leading to the formation of only partially folded structures and the disappearance of TADs from contact maps upon averaging. Our study suggests that machine learning techniques, when combined with rigorous statistical mechanical analysis, are powerful tools for analyzing structural ensembles of chromatin.
MIT Department
Massachusetts Institute of Technology. Department of Chemistry
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1371/JOURNAL.PCBI.1008262
