Computational Biology Department, School of Computer Science, Carnegie Mellon University, United States. Electronic address: https://twitter.com/muyu_wendy_yang.
Computational Biology Department, School of Computer Science, Carnegie Mellon University, United States.
J Mol Biol. 2022 Aug 15;434(15):167666. doi: 10.1016/j.jmb.2022.167666. Epub 2022 Jun 2.
In higher eukaryotic cells, chromosomes are folded inside the nucleus. Recent advances in whole-genome mapping technologies have revealed the multiscale features of 3D genome organization that are intertwined with fundamental genome functions. However, DNA sequence determinants that modulate the formation of 3D genome organization remain poorly characterized. In the past few years, predicting 3D genome organization based on DNA sequence features has become an active area of research. Here, we review the recent progress in computational approaches to unraveling important sequence elements for 3D genome organization. In particular, we discuss the rapid development of machine learning-based methods that facilitate the connections between DNA sequence features and 3D genome architectures at different scales. While much progress has been made in developing predictive models for revealing important sequence features for 3D genome organization, new research is urgently needed to incorporate multi-omic data and enhance model interpretability, further advancing our understanding of gene regulation mechanisms through the lens of 3D genome organization.
在高等真核细胞中，染色体在细胞核内折叠。全基因组作图技术的最新进展揭示了与基本基因组功能交织在一起的 3D 基因组组织的多尺度特征。然而，调节 3D 基因组组织形成的 DNA 序列决定因素仍未得到很好的描述。在过去的几年中，基于 DNA 序列特征预测 3D 基因组组织已成为一个活跃的研究领域。在这里，我们回顾了近年来在揭示 3D 基因组组织重要序列元件的计算方法方面的进展。特别是，我们讨论了基于机器学习的方法的快速发展，这些方法促进了不同尺度下 DNA 序列特征与 3D 基因组结构之间的联系。虽然在开发用于揭示 3D 基因组组织重要序列特征的预测模型方面已经取得了很大进展，但迫切需要开展新的研究，以整合多组学数据并增强模型可解释性，通过 3D 基因组组织的视角进一步推进我们对基因调控机制的理解。
