ArticlePLoS computational biology2020
Characterizing chromatin folding coordinate and landscape with deep learning.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 28 citations in OpenAlex.
- Physical models reveal indirect reader protein interactions that facilitate epigenetic crosstalk.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Toward decoding the mechanisms that shape sub-megabase-scale genome organization.Current opinion in structural biology · 2025Review
- ChromoGen: Diffusion model predicts single-cell chromatin conformations.Science advances · 2025Article
- Interpretable online network dictionary learning for inferring long-range chromatin interactions.PLoS computational biology · 2024Article
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- Large-scale data-driven and physics-based models offer insights into the relationships among the structures, dynamics, and functions of chromosomes.Journal of molecular cell biology · 2023Review
- Efficient Hi-C inversion facilitates chromatin folding mechanism discovery and structure prediction.Biophysical journal · 2023Article
- Efficient Hi-C inversion facilitates chromatin folding mechanism discovery and structure prediction.bioRxiv : the preprint server for biology · 2023Article
- Polymer folding through active processes recreates features of genome organization.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Compartmentalization with nuclear landmarks yields random, yet precise, genome organization.Biophysical journal · 2023Article
- Phase Separation and Correlated Motions in Motorized Genome.The journal of physical chemistry. B · 2022Article
- Characterizing the variation in chromosome structure ensembles in the context of the nuclear microenvironment.PLoS computational biology · 2022Article
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- BRD2 compartmentalizes the accessible genome.Nature genetics · 2022Article
- Deciphering the molecular mechanism of the cancer formation by chromosome structural dynamics.PLoS computational biology · 2021Article
- Multiscale modeling of genome organization with maximum entropy optimization.The Journal of chemical physics · 2021Article
- Data-Driven Polymer Model for Mechanistic Exploration of Diploid Genome Organization.Biophysical journal · 2020Article
- Quantifying the Stability of Coupled Genetic and Epigenetic Switches With Variational Methods.Frontiers in genetics · 2020Article
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Authors and funding
3 authors at 1 institution in 1 country.
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Abstract
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.
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