Computational Molecular Biology 2025, Vol.15, No.2, 102-111 http://bioscipublisher.com/index.php/cmb 10 9 Sequence alignment method, with its simplicity and efficiency, is suitable for preliminary screening, but it lacks consideration of complex biological backgrounds. Rule-based and machine learning-based methods strike a balance between accuracy and efficiency, but are limited by the scale of training data. Deep learning methods, with their powerful feature extraction capabilities, have significantly enhanced prediction accuracy and cross-species generalization ability. However, its reliance on large-scale high-quality data and the "black box effect" issue remain the bottlenecks restricting its wide application. The future development trends are mainly reflected in three aspects: First, the integration of multimodal data will make the prediction model closer to the real environment within cells, thereby enhancing reliability; Secondly, when combined with new Cas variants and optimized sgRNA strategies, it will promote the minimization of off-target risks. Thirdly, in clinical and agricultural applications, establishing unified safety assessment standards and norms will provide a guarantee for the healthy development of CRISPR technology. In conclusion, the value of computational prediction in CRISPR off-target research is not only reflected at the theoretical level but also provides a solid support for the safe application of gene editing. With the continuous iteration of algorithms, the continuous accumulation of data, and the increasingly improved experimental verification methods, in the future, we have every reason to believe that off-target prediction will gradually transform from an "auxiliary tool" to a "core guarantee", opening up broader prospects for precise gene editing and molecular breeding. Acknowledgments I would like to thank Dr. Xie continuous support throughout the development of this study. Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References AlJanahi A., Lazzarotto C., Chen S., Shin T.H., Cordes S., Jabara I., Zhou Y., Young D., Lee B., Yu K., Li Y., Toms B., Tunc I., Hong S.G., Truitt L.L., Klermund J., Kim M.Y., Cathomen T., Gill S., Tsai S.Q., and Dunbar C., 2020, Validation and long-term follow up of CD33 off-targets predicted in vitro and in silico using error-corrected sequencing in rhesus macaques, bioRxiv, 2020: 186858. https://doi.org/10.1101/2020.07.05.186858 Anuradha B., Pradeep T., and Vikrant N., 2024, Machine learning-driven prediction of CRISPR-Cas9 off-target effects and mechanistic insights, The EuroBiotech Journal, 8(4): 213-229. https://doi.org/10.2478/ebtj-2024-0020 Cancellieri S., Zeng J., Lin L.Y., Tognon M., Nguyen M.A., Lin J., Bombieri N., Maitland S.A., Ciuculescu M.F., Katta V., Tsai S.Q., Armant M., Wolfe S.A., Giugno R., Bauer D.E., and Pinello L., 2022, Human genetic diversity alters off-target outcomes of therapeutic gene editing, Nature Genetics, 55(1): 34-43. https://doi.org/10.1038/s41588-022-01257-y Chao R., and Fei J., 2023, Off-target effects of CRISPR/Cas9 and their solutions, Highlights in Science, Engineering and Technology, 45: 296-301. https://doi.org/10.54097/hset.v45i.7444 Charlier J., Nadon R., and Makarenkov V., 2021, Accurate deep learning off-target prediction with novel sgRNA-DNA sequence encoding in CRISPR-Cas9 gene editing, Bioinformatics, 37(16): 2299-2307. https://doi.org/10.1093/bioinformatics/btab112 Chaudhari H., Penterman J., Whitton H.J., Spencer S.J., Flanagan N., Zhang L., Huang E., Khedkar A.S., Toomey J., Shearer C.A., Needham A.W., Ho T.W., Kulman J.D., Cradick T.J., and Kernytsky A., 2020, Evaluation of homology-independent CRISPR-Cas9 off-target assessment methods, The CRISPR Journal, 3(6): 440-453. https://doi.org/10.1089/crispr.2020.0053 Chen J.S., Dagdas Y., Kleinstiver B.P., Welch M.M., Sousa A.A., Harrington L.B., Sternberg S.H., Joung J.K., Yildiz A., and Doudna J.A., 2017, Enhanced proofreading governs CRISPR-Cas9 targeting accuracy, Nature, 550(7676): 407-410. https://doi.org/10.1038/nature24268 Choubisa V., 2024, CRISPR-Cas9 off-target predictions using CNN and double CNN: comparative analysis, International Journal of Science and Technology Research Archive, 12(1): 1074-1080. https://doi.org/10.30574/ijsra.2024.12.1.0927
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