Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning
Open accessAbstract
The design of CRISPR gRNAs requires accurate on-target efficiency predictions, which demand high-quality gRNA activity data and efficient modeling. To advance, we here report on the generation of on-target gRNA activity data for 10,592 SpCas9 gRNAs. Integrating these with complementary published data, we train a deep learning model, CRISPRon, on 23,902 gRNAs. Compared to existing tools, CRISPRon exhibits significantly higher prediction performances on four test datasets not overlapping with training data used for the development of these tools. Furthermore, we present an interactive gRNA design webserver based on the CRISPRon standalone software, both available via https://rth.dk/resources/crispr/ . CRISPRon advances CRISPR applications by providing more accurate gRNA efficiency predictions than the existing tools.
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Integrated use of AI technology assisted with phenomics and genomics tools combined with genome editing tools offers projective modeling in precision editing, reduced off-target activity, minimizing unintended genetic alterations, and increased editing efficiency.
“Thus, integrated use of Artificial intelligence (AI) technology assisted with phenomics and genomics tools combined with genome editing tools offers projective modeling in precision editing, reduced off-target activity, minimizing unintended genetic alterations, and increased editing efficiency with promising outcomes (Xiang et al., 2021).”
From Emerging applications of gene editing technologies for the development of climate-resilient crops (2025) · cites this paper as Xiang et al., 2021
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References 42 · Cited by 209
- Authors
- Xiang, X., Corsi, G. I., Anthon, C., Qu, K., Pan, X., Liang, X., Han, P., Dong, Z., Liu, L., Zhong, J., Ma, T., Wang, J., Zhang, X., Jiang, H., Xu, F., Liu, X., Xu, X., Wang, J., Yang, H., Bolund, L., Church, G. M., Lin, L., Gorodkin, J., & Luo, Y.
- Affiliations
- BGI Group (China) (CN)
- University of Copenhagen (DK)
- MGI (CN)
- Harvard University (US)
- Aarhus University (DK)
- Corresponding author
- Gorodkin, Jan
- Luo, Yonglun
- ORCID
- Venue
- Nature Communications
- Published
- 2021-05-28
- Volume
- 12
- Issue
- 1
- Pages
- 3238-3238
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC8163799
- OpenAlex
- W3194399644
- Cited by
- 209
- References
- 42
- Citations by year
- 2026: 49 · 2025: 48 · 2024: 37 · 2023: 40 · 2022: 34 · 2021: 1
- FWCI
- 8.87
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Scopus (Elsevier) (submittedVersion, cc-by)
- Funders
- EC | Horizon 2020 Framework Programme (899417)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA regulation and disease
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Interference and Gene Delivery
- Keywords
- CRISPR/Cas9, sgRNA design, SpCas9, deep learning, data integration, off-target prediction
- MeSH terms
- Gene Editing, Deep Learning, RNA, Guide, CRISPR-Cas Systems, Genetic Vectors, Humans, Plasmids, Software, Lentivirus, RNA, Guide, Kinetoplastida, Computational Biology, HEK293 Cells, CRISPR-Cas Systems
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