Computational Molecular Biology 2025, Vol.15, No.2, 102-111 http://bioscipublisher.com/index.php/cmb 10 2 Review Article Open Access Computational Prediction of Off-Target Effects in CRISPR Systems Wenzhong Huang Biomass Research Center, Hainan Institute of Tropical Agricultural Resouces, Sanya, 572025, Hainan, China Corresponding author: wenzhong.huang@hitar.org Computational Molecular Biology, 2025, Vol.15, No.2 doi: 10.5376/cmb.2025.15.0010 Received: 13 Feb., 2025 Accepted: 25 Mar., 2025 Published: 16 Apr., 2025 Copyright © 2025 Huang, 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.6 Preferred citation for this article: Huang W.Z., 2025, Computational prediction of off-Target effects in CRISPR systems, Computational Molecular Biology, 15(2): 102-111 (doi: 10.5376/cmb.2025.15.0010) Abstract 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. Keywords CRISPR; Off-target effect; Computational prediction; Machine learning; Functional genomics 1 Introduction Since the CRISPR/Cas9 system was officially applied to gene editing in 2012, this technology has rapidly become one of the core tools in life science research (Guo et al., 2023). Compared with the traditional zinc finger nucleases (ZFNs) and transcription activator effector nucleases (TALENs), the CRISPR/Cas system is not only easy to construct, but also shows good applicability in a variety of biological systems (Naeem et al., 2020). However, with its wide application in basic research, medical treatment and agricultural improvement and other fields, off-target effects have gradually become a safety hazard that urgently needs attention. Off-target effect refers to the cleavage or regulation of Cas nucleases at unexpected targets, thereby causing non-specific alterations in the genome. This phenomenon may lead to incorrect knockout of functional genes, accumulation of potential mutations, and even cause unpredictable biological consequences. Therefore, how to effectively predict and reduce off-target effects is the key to whether CRISPR technology can further move towards precision and application. At present, researchers have developed a variety of off-target detection methods, such as experimental techniques like guiding seq, Digenome-seq, and CIRCLE seq (Martin et al., 2016). However, these methods are usually costly, time-consuming, and difficult to promote in large-scale research. In contrast, computational prediction methods have become an important supplement to the study of off-target effects due to their characteristics of rapidity, low cost and high throughput. By establishing mathematical models and algorithmic tools, researchers can conduct a comprehensive assessment of potential off-target sites before gene editing experiments, thereby improving the rationality of the design and the success rate of the experiments (Zhang et al., 2020). The existing computational prediction strategies can be roughly divided into three categories: methods based on sequence alignment, methods based on rules and machine learning, and prediction frameworks based on deep learning. Sequence alignment methods have become the earliest applied means due to their simplicity.
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