A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles

文献类型： 外文期刊

第一作者： Wang, Yunlong

作者： Wang, Yunlong;Kong, Siyuan;Zhang, Yubo;Zhou, Cong;Fang, Yaping;Li, Guoliang;Zhou, Cong;Fang, Yaping;Li, Guoliang;Zhou, Cong;Fang, Yaping;Li, Guoliang;Wang, Yanfang;Zhang, Yubo

作者机构：

关键词： 3D genomics; epigenetics; deep learning; multi-omics; chromatin interactions

期刊名称：BRIEFINGS IN BIOINFORMATICS （ 影响因子：7.7； 五年影响因子：8.7 ）

ISSN： 1467-5463

年卷期： 2024 年 26 卷 1 期

页码：

收录情况： SCI

摘要： Advances in three-dimensional (3D) genomics have revealed the spatial characteristics of chromatin interactions in gene expression regulation, which is crucial for understanding molecular mechanisms in biological processes. High-throughput technologies like ChIA-PET, Hi-C, and their derivatives methods have greatly enhanced our knowledge of 3D chromatin architecture. However, the chromatin interaction mechanisms remain largely unexplored. Deep learning, with its powerful feature extraction and pattern recognition capabilities, offers a promising approach for integrating multi-omics data, to build accurate predictive models of chromatin interaction matrices. This review systematically summarizes recent advances in chromatin interaction matrix prediction models. By integrating DNA sequences and epigenetic signals, we investigate the latest developments in these methods. This article details various models, focusing on how one-dimensional (1D) information transforms into the 3D structure chromatin interactions, and how the integration of different deep learning modules specifically affects model accuracy. Additionally, we discuss the critical role of DNA sequence information and epigenetic markers in shaping 3D genome interaction patterns. Finally, this review addresses the challenges in predicting chromatin interaction matrices, in order to improve the precise mapping of chromatin interaction matrices and DNA sequence, and supporting the transformation and theoretical development of 3D genomics across biological systems.

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