Wenzhong Huang

Biomass Research Center, Hainan Institute of Tropical Agricultural Resouces, Sanya, 572025, Hainan, China
Author    Correspondence author
Computational Molecular Biology, 2025, Vol. 15, No. 2   
Received: 13 Feb., 2025    Accepted: 25 Mar., 2025    Published: 16 Apr., 2025

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

CRISPR/Cas gene editing technology, with its advantages of simple operation, strong specificity and high efficiency, has become an important tool in life science research and molecular breeding. However, the off-target effect has always been a key issue restricting the further application of this technology, especially in clinical and agricultural genetic improvement, and its potential risks need to be addressed urgently. In recent years, methods based on computational prediction have gradually developed into important means for identifying and reducing off-target effects, providing theoretical support and practical guidance for CRISPR experimental design and safety assessment. This article systematically reviews the CRISPR system and the molecular mechanisms underlying its off-target effects, with a focus on three mainstream computational prediction strategies: sequence aligning methods, rule and machine learning-based methods, and deep learning frameworks. The article further explores the commonly used model evaluation indicators and experimental verification methods, and demonstrates the application process of off-target prediction through a case study of the human EMX1 gene. Finally, the contributions of computational prediction methods in enhancing editing specificity were summarized, the current limitations were analyzed, and the future directions for promoting the development of this field through multimodal data integration, algorithm optimization, and preclinical safety assessment were prospected. This article aims to provide a systematic reference for subsequent research on CRISPR-based security applications.

CRISPR; Off-target effect; Computational prediction; Machine learning; Functional genomics