MINI REVIEW article
Front. Genet.
Sec. Statistical Genetics and Methodology
Volume 16 - 2025 | doi: 10.3389/fgene.2025.1602234
This article is part of the Research TopicExpanding Insights Into Structure, Function, and Disorder of Genome by the Power of Artificial Intelligence in BioinformaticsView all 4 articles
Topologically Associating Domains of chromatin on single-cell Hi-C data: A survey of bioinformatic tools and applications in the light of artificial intelligence
Provisionally accepted
- Xi'an Jiaotong University, Xi'an, China
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Topologically associating domains (TADs) uncovered on bulk Hi-C data are regarded as fundamental building blocks of three-dimensional genome, and are believed to effectively participate in the regulatory programs of gene expression. While entering the era of single-cell transcriptomics, the computational analysis of TADs on single-cell Hi-C (scHi-C) data becomes a fascinating point, since it may tell us more beyond that on bulk Hi-C data. Unfortunately, the contact matrix for a single cell is ultra-sparse due to low sequencing depth. Coupled with noises, artifacts, and dropout events from experiments, as well as cell heterogeneity caused by cell cycle and transcription status, the computational analysis of TAD structures at single-cell level has encountered some challenges that have never been met at bulk level. Herein, we try to conduct a survey of bioinformatic tools and applications for TAD structures at single-cell level in the light of artificial intelligence, including imputation of scHi-C data, identification of TAD boundaries and hierarchy, and differential analysis of TAD structures. The categories, characteristics, and evolutions of the latest available methods are summarized, especially the artificial intelligence strategies involved in these issues. Then comes a discussion on why deep neural networks are attractive when discovering complex patterns from scHi-C data with an enormous number of cells, and how it is promoting the computational analysis of TADs at single-cell level. Furthermore, the challenges that may be encountered in the analysis are outlined, and an outlook on the merging trends in the near future are presented cautiously.
Keywords: Topologically associating domains, single-cell Hi-C, bioinformatic tools and applications, artificial intelligence, challenges and emerging trends
Received: 29 Mar 2025; Accepted: 04 Jun 2025.
Copyright: © 2025 Lyu, Li, Chen, Liu and Xiaoliang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Hongqiang Lyu, Xi'an Jiaotong University, Xi'an, China
Cheng Xiaoliang, Xi'an Jiaotong University, Xi'an, China
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