A graph neural network-based interpretable framework reveals a novel DNA fragility–associated chromatin structural unit

Author:

Sun Yu,Xu Xiang,Lin Lin,Xu Kang,Zheng Yang,Ren Chao,Tao Huan,Wang Xu,Zhao Huan,Tu Weiwei,Bai Xuemei,Wang Junting,Huang Qiya,Li Yaru,Chen Hebing,Li HaoORCID,Bo Xiaochen

Abstract

Abstract Background DNA double-strand breaks (DSBs) are among the most deleterious DNA lesions, and they can cause cancer if improperly repaired. Recent chromosome conformation capture techniques, such as Hi-C, have enabled the identification of relationships between the 3D chromatin structure and DSBs, but little is known about how to explain these relationships, especially from global contact maps, or their contributions to DSB formation. Results Here, we propose a framework that integrates graph neural network (GNN) to unravel the relationship between 3D chromatin structure and DSBs using an advanced interpretable technique GNNExplainer. We identify a new chromatin structural unit named the DNA fragility–associated chromatin interaction network (FaCIN). FaCIN is a bottleneck-like structure, and it helps to reveal a universal form of how the fragility of a piece of DNA might be affected by the whole genome through chromatin interactions. Moreover, we demonstrate that neck interactions in FaCIN can serve as chromatin structural determinants of DSB formation. Conclusions Our study provides a more systematic and refined view enabling a better understanding of the mechanisms of DSB formation under the context of the 3D genome.

Funder

Natural Science Foundation of Beijing Municipality

National Natural Science Foundation of China

Beijing Nova Program

Publisher

Springer Science and Business Media LLC

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Computational methods for analysing multiscale 3D genome organization;Nature Reviews Genetics;2023-09-06

2. Automated 3D Pre-Training for Molecular Property Prediction;Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2023-08-04

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