Center for Biological Data Science, Virginia Commonwealth University, Richmond, VA, USA.
Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.
Methods Mol Biol. 2025;2856:357-400. doi: 10.1007/978-1-0716-4136-1_22.
Three-dimensional (3D) chromatin interactions, such as enhancer-promoter interactions (EPIs), loops, topologically associating domains (TADs), and A/B compartments, play critical roles in a wide range of cellular processes by regulating gene expression. Recent development of chromatin conformation capture technologies has enabled genome-wide profiling of various 3D structures, even with single cells. However, current catalogs of 3D structures remain incomplete and unreliable due to differences in technology, tools, and low data resolution. Machine learning methods have emerged as an alternative to obtain missing 3D interactions and/or improve resolution. Such methods frequently use genome annotation data (ChIP-seq, DNAse-seq, etc.), DNA sequencing information (k-mers and transcription factor binding site (TFBS) motifs), and other genomic properties to learn the associations between genomic features and chromatin interactions. In this review, we discuss computational tools for predicting three types of 3D interactions (EPIs, chromatin interactions, and TAD boundaries) and analyze their pros and cons. We also point out obstacles to the computational prediction of 3D interactions and suggest future research directions.
三维（3D）染色质相互作用，如增强子-启动子相互作用（EPIs）、环、拓扑关联域（TADs）和 A/B 区室，通过调节基因表达在广泛的细胞过程中发挥关键作用。最近，染色质构象捕获技术的发展使得全基因组范围内的各种 3D 结构的分析成为可能，甚至可以对单细胞进行分析。然而，由于技术、工具和低数据分辨率的差异，目前的 3D 结构目录仍然不完整和不可靠。机器学习方法已成为获取缺失的 3D 相互作用和/或提高分辨率的替代方法。这些方法经常使用基因组注释数据（ChIP-seq、DNAse-seq 等）、DNA 测序信息（k-mers 和转录因子结合位点（TFBS）基序）和其他基因组特性来学习基因组特征与染色质相互作用之间的关联。在这篇综述中，我们讨论了预测三种类型的 3D 相互作用（EPIs、染色质相互作用和 TAD 边界）的计算工具，并分析了它们的优缺点。我们还指出了 3D 相互作用的计算预测中的障碍，并提出了未来的研究方向。
