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Evaluation and prediction of guide RNA activities in genome-editing tools

Nature Reviews Bioengineering volume 4, pages 82–97 (2026)Cite this article

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Abstract

CRISPR genome-editing tools, including Cas9 and Cas12a nucleases, base editors and prime editors, have revolutionized genome manipulation across various species and cell types. These tools have undergone continuous improvement as new variants or types of editors have been generated to improve their efficiency, specificity and applicability. However, given the vast array of genome editors and the multitude of designable guide RNAs, selecting the optimal combinations for efficient and precise genome editing has become increasingly challenging, especially under variable experimental conditions. To address this issue, several methods for evaluating genome-editing tools in a high-throughput manner have been developed. The resulting large datasets of editing efficiencies or specificities have been used to develop machine learning models that predict efficiency and specificity, greatly facilitating the optimal selection of genome editors and guide RNAs. Here, we review recent developments in high-throughput evaluations and machine learning-based predictions of genome-editing efficiencies and/or off-target effects, together with recent advances in diverse genome-editing tools. We also cover artificial intelligence-based development and evolution of genome-editing tools.

Key points

  • Gene-editing tools such as CRISPR nucleases, base editors and prime editors can manipulate genomes across a wide range of species and cell types.

  • With the expansion of the genome-editing toolbox, the selection of the most suitable tool and the design of appropriate guide RNAs for precise and efficient editing remain challenging.

  • High-throughput methods enable the measurement of protospacer adjacent motif sequence preference, on-target and off-target editing efficiencies, and outcomes at diverse target sequences, facilitating the determination of optimal genome-editing tools and guide RNA combinations from the available options.

  • The large amount of data on genome-editing tools evaluated at thousands of target DNA sequences has facilitated the development of machine learning models that quickly predict genome-editing activity for a given pair of guide RNA and target sequence.

  • Large language models trained on DNA, RNA and protein data enable the artificial intelligence-based design and evolution of genome editors. Combined with high-throughput datasets, they can contribute to the development of next-generation genome-editing tools.

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Fig. 1: Genetic organization of the two Cas system classes and the mechanisms of genome-editing tools.
Fig. 2: High-throughput evaluation methods for genome-editing tools.
Fig. 3: Applications of pairwise libraries of guide RNA and target sequences.
Fig. 4: Schematics of unbiased, genome-wide off-target detection methods.

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Acknowledgements

This work was supported, in part, by the National Research Foundation of Korea grant funded by the Korean government (Ministry of Science and ICT) (RS-2024-00348839 (H.K.K.), RS-2022-NR070713 (H.H.K.), and RS-2025-02214844 (H.H.K.)), The Bio and Medical Technology Development Program of the National Research Foundation funded by the Korean government (Ministry of Science and ICT) (RS-2023-00262533 (H.K.K.), RS-2022-NR067326 (H.H.K.), RS-2022-NR067345 (H.H.K.) and RS-2023-00260968 (H.H.K.)), the Korea Drug Development Fund funded by the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy (RS-2022-DD128884 (H.K.K.)), the Korea–US Collaborative Research Fund (KUCRF) funded by the Ministry of Science and ICT and Ministry of Health & Welfare, Republic of Korea (grant number: RS-2024-00467177 (H.H.K.)), the Yonsei Signature Research Cluster Program of 2024-22-0165 (H.H.K.), the Brain Korea 21 FOUR Project for Medical Science (Yonsei University College of Medicine), the SNUH Kun-hee Lee Child Cancer and Rare Disease Project, Republic of Korea (22B-000-0101 (H.H.K.)), and the Yonsei Fellow Program, funded by Lee Youn Jae.

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Authors and Affiliations

  1. Department of Integrative Biotechnology, Sungkyunkwan University, Suwon, Republic of Korea

    Hui Kwon Kim

  2. Department of MetaBioHealth, Sungkyunkwan University, Suwon, Republic of Korea

    Hui Kwon Kim

  3. Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hyongbum Henry Kim

  4. Graduate School of Medical Science, Brain Korea 21 Plus Project for Medical Sciences, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hyongbum Henry Kim

  5. Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hyongbum Henry Kim

  6. Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Republic of Korea

    Hyongbum Henry Kim

  7. Yonsei-IBS Institute, Yonsei University, Seoul, Republic of Korea

    Hyongbum Henry Kim

  8. Woo Choo Lee Institute for Precision Drug Development, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hyongbum Henry Kim

  9. Institute for Immunology and Immunological Diseases, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hyongbum Henry Kim

  10. Won-Sang Lee Institute for Hearing Loss, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hyongbum Henry Kim

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H.H.K. conceptualized the manuscript. H.K.K. and H.H.K. wrote the manuscript.

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Correspondence to Hyongbum Henry Kim.

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Kim, H.K., Kim, H.H. Evaluation and prediction of guide RNA activities in genome-editing tools. Nat Rev Bioeng 4, 82–97 (2026). https://doi.org/10.1038/s44222-025-00352-z

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