Unveiling chromatin dynamics with virtual epigenome

Ming-Yu Lin, Yu-Cheng Lo and Jui-Hung Hung ()
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Ming-Yu Lin: National Yang Ming Chiao Tung University
Yu-Cheng Lo: National Yang Ming Chiao Tung University
Jui-Hung Hung: National Yang Ming Chiao Tung University

Nature Communications, 2025, vol. 16, issue 1, 1-16

Abstract: Abstract The three-dimensional organization of chromatin is essential for gene regulation and cellular function, with epigenome playing a key role. Hi-C methods have expanded our understanding of chromatin interactions, but their high cost and complexity limit their use. Existing models for predicting chromatin interactions rely on limited ChIP-seq inputs, reducing their accuracy and generalizability. In this work, we present a computational approach, EpiVerse, which leverages imputed epigenetic signals and advanced deep learning techniques. EpiVerse significantly improves the accuracy of cross-cell-type Hi-C prediction, while also enhancing model interpretability by incorporating chromatin state prediction within a multitask learning framework. Moreover, EpiVerse predicts Hi-C contact maps across an array of 39 human tissues, which provides a comprehensive view of the complex relationship between chromatin structure and gene regulation. Furthermore, EpiVerse facilitates unprecedented in silico perturbation experiments at the “epigenome-level” to unveil the chromatin architecture under specific conditions. EpiVerse is available on GitHub: https://github.com/jhhung/EpiVerse .

Date: 2025
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DOI: 10.1038/s41467-025-58481-3

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