Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas, USA; email:
Annu Rev Biomed Data Sci. 2022 Aug 10;5:183-204. doi: 10.1146/annurev-biodatasci-102521-012018. Epub 2022 May 10.
Decoding how genomic sequence and its variations affect 3D genome architecture is indispensable for understanding the genetic architecture of various traits and diseases. The 3D genome organization can be significantly altered by genome variations and in turn impact the function of the genomic sequence. Techniques for measuring the 3D genome architecture across spatial scales have opened up new possibilities for understanding how the 3D genome depends upon the genomic sequence and how it can be altered by sequence variations. Computational methods have become instrumental in analyzing and modeling the sequence effects on 3D genome architecture, and recent development in deep learning sequence models have opened up new opportunities for studying the interplay between sequence variations and the 3D genome. In this review, we focus on computational approaches for both the detection and modeling of sequence variation effects on the 3D genome, and we discuss the opportunities presented by these approaches.
解码基因组序列及其变异如何影响 3D 基因组结构对于理解各种性状和疾病的遗传结构是必不可少的。基因组变异可以显著改变 3D 基因组组织，进而影响基因组序列的功能。测量跨空间尺度的 3D 基因组结构的技术为理解 3D 基因组如何依赖于基因组序列以及序列变异如何改变它提供了新的可能性。计算方法在分析和建模序列对 3D 基因组结构的影响方面变得至关重要，深度学习序列模型的最新发展为研究序列变异和 3D 基因组之间的相互作用开辟了新的机会。在这篇综述中，我们重点介绍了用于检测和建模序列变异对 3D 基因组影响的计算方法，并讨论了这些方法带来的机遇。
