Advances and Frontier Prospects of Computational Detection Methods for Chromatin Loops

Abstract

The eukaryotic genome exists in a highly folded three-dimensional structure within the nucleus. As the core functional unit of the 3D genome, chromatin loops are formed via the loop extrusion mechanism mediated by CTCF proteins and cohesin complexes. They directly facilitate the spatial interaction between distal enhancers and gene promoters, and precisely regulate essential biological processes including gene transcription, DNA replication and damage repair. Structural abnormalities of chromatin loops are closely associated with the occurrence and progression of numerous diseases, such as cancers, developmental defects and neurodegenerative disorders. Chromosome conformation capture technologies represented by Hi-C, ChIA-PET and HiChIP have laid a critical foundation for the experimental identification of chromatin loops, yet they suffer from inherent limitations including high sequencing costs, limited data resolution, prominent noise interference and obstacles in single-cell detection. Computational detection methods for chromatin loops eliminate the reliance on large-scale experimental expenditures. These approaches can automatically mine characteristic patterns from DNA sequences, epigenomic profiles and 3D genomic contact matrices, enabling efficient, accurate and low-cost chromatin loop identification, and have emerged as a vital solution to compensate for the shortcomings of experimental techniques. This review systematically summarizes the molecular formation mechanisms, biological functions of chromatin loops, and technical characteristics of mainstream experimental detection methods. Following the trajectory of technological evolution, existing computational detection approaches are classified into three categories: statistical methods, conventional machine learning methods and deep learning methods. The algorithmic principles, input data types, core advantages and application limitations of each category are elaborated respectively. Furthermore, this paper integrates emerging chromatin loop detection technologies developed since 2023 based on diffusion models, multimodal data fusion, nanopore sequencing, self-supervised learning and single-cell 3D genomics, and details their technological innovations and performance breakthroughs. It also concludes major challenges in this field, including data imbalance, low-resolution adaptation, cross-cell line generalization, the absence of universal gold standards, and the dynamic analysis of single-cell chromatin loops. In addition, future research directions are prospected from the perspectives of multimodal fusion, weakly supervised learning, single-cell dynamic modeling, disease correlation analysis, and lightweight interpretable models. This review aims to provide a comprehensive reference for researchers in the field of 3D genomics regarding computational detection methods of chromatin loops, and promote the in-depth integration of algorithm development and biomedical applications.