CMB_2025v15n2

Computational Molecular Biology 2025, Vol.15, No.2, 102-111 http://bioscipublisher.com/index.php/cmb 11 0 Chuai G., Ma H., Yan J., Chen M., Hong N., Xue D., Zhou C., Zhu C., Chen K., Duan B., Gu F., Qu S., Huang D., Wei J., and Liu Q., 2018, DeepCRISPR: optimized CRISPR guide RNA design by deep learning, Genome Biology, 19(1): 80. https://doi.org/10.1186/s13059-018-1459-4 Fan Y., and Xu H., 2021, Prediction of off-target effects in CRISPR/Cas9 system by ensemble learning, Current Bioinformatics, 16(9): 1169-1178. https://doi.org/10.2174/1574893616666210811100938 Guo C., and Zhen D., 2020, Prediction of off-target effects of the CRISPR/Cas9 system for design of sgRNA, In: E3S web of conferences, EDP Sciences, 185: 04018. https://doi.org/10.1051/e3sconf/202018504018 Guo X., Ma Y., Gao F., and Guo, Y., 2023, Off-target effects in CRISPR/Cas9 gene editing and their potential solutions, Frontiers in Bioengineering and Biotechnology, 11: 1143157. https://doi.org/10.3389/fbioe.2023.1143157 Kimata K., and Satou K., 2025, Improved CRISPR/Cas9 off-target prediction with DNABERT and epigenetic features, bioRxiv, 2025: 649101. https://doi.org/10.1101/2025.04.16.649101 Lin J., and Wong K., 2018, Off-target predictions in CRISPR-Cas9 gene editing using deep learning, Bioinformatics, 34(17): i656-i663. https://doi.org/10.1093/bioinformatics/bty554 Lin J., Zhang Z., Zhang S., Chen J., and Wong K.C., 2020, CRISPR-Net: a recurrent convolutional network quantifies CRISPR off-target activities with mismatches and indels, Advanced Science, 7(13): 1903562. https://doi.org/10.1002/advs.201903562 Martin F., Sánchez-Hernández S., Gutiérrez-Guerrero A., Pinedo-Gomez J., and Benabdellah K., 2016, Biased and unbiased methods for the detection of off-target cleavage by CRISPR/Cas9: an overview, International Journal of Molecular Sciences, 17(9): 1507. https://doi.org/10.1038/mtna.2016.90 Matsumoto D., Matsugi E., Kishi K., Inoue Y., Nigorikawa K., and Nomura W., 2024, SpCas9-HF1 enhances accuracy of cell cycle-dependent genome editing, Molecular Therapy-Nucleic Acids, 35(1): 102124. https://doi.org/10.1016/j.omtn.2024.102124 Naeem M., Majeed S., Hoque M.Z., and Ahmad I., 2020, Latest developed strategies to minimize the off-target effects in CRISPR-Cas-mediated genome editing, Cells, 9(7): 1608. https://doi.org/10.3390/cells9071608 Niu B., Peng J., Zhang Z., and Shang X., 2021, R-CRISPR: a deep learning network to predict off-target activities with mismatches insertion and deletion in CRISPR-Cas9 system, Genes, 12(12): 1878. https://doi.org/10.3390/genes12121878 Sherkatghanad Z., Abdar M., Charlier J., and Makarenkov V., 2023, Using traditional machine learning and deep learning methods for on-and off-target prediction in CRISPR/Cas9: a review, Briefings in Bioinformatics, 24(3): bbad131. https://doi.org/10.1093/bib/bbad131 Störtz F., Mak J., and Minary P., 2023, piCRISPR: physically informed deep learning models for CRISPR/Cas9 off-target cleavage prediction, Artificial Intelligence in the Life Sciences, 3: 100075. https://doi.org/10.1016/j.ailsci.2023.100075 Sun H., 2023, The challenge facing CRISPR/Cas9 system: off-target effects and their optimization, Highlights in Science, Engineering and Technology, 74: 782-787. https://doi.org/10.54097/psd28z73 Tian R., Cao C., He D., Dong D., Sun L., Liu J., Chen Y., Wang Y., Huang Z., Li L., Jin Z., Huang Z., Xie H., Zhao T., Zhong C., Hong Y., and Hu Z., 2023, Massively parallel CRISPR off-target detection enables rapid off-target prediction model building, Med, 4(7): 478-492. e6. https://doi.org/10.1016/j.medj.2023.05.005 Toufikuzzaman M., Abul M., Samee H., and Rahman M.S., 2024, CRISPR-DIPOFF: an interpretable deep learning approach for CRISPR Cas-9 off-target prediction, Briefings in Bioinformatics, 25(2): bbad530. https://doi.org/10.1093/bib/bbad530 Tsai S.Q., Nguyen N.T., Malagón-López J., Topkar V.V., Aryee M.J., and Joung J.K., 2017, CIRCLE-seq: a highly sensitive in vitro screen for genome-wide CRISPR-Cas9 nuclease off-targets, Nature Methods, 14(6): 607-614. https://doi.org/10.1038/nmeth.4278 Wang G., Wang C., Chu T., Wu X., Anderson C.M., Huang D., and Li J., 2023, Deleting specific residues from the HNH linkers creates a CRISPR-SpCas9 variant with high fidelity and efficiency, Journal of Biotechnology, 368: 42-52. https://doi.org/10.1016/j.jbiotec.2023.04.008 Yuan S., 2024, Mitigating the off-target effects in CRISPR/Cas9-mediated genetic editing with bioinformatic technologies, Transactions on Materials, Biotechnology and Life Sciences, 3: 318-326. https://doi.org/10.62051/dpgwbz03 Zhang G., Luo Y., Dai X., and Dai Z., 2023, Benchmarking deep learning methods for predicting CRISPR/Cas9 sgRNA on- and off-target activities, Briefings in Bioinformatics, 24(6): bbad333. https://doi.org/10.1093/bib/bbad333

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