Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning
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- Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning
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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.
|Status||Udgivet - 2021|
This project was partially supported by the Sanming Project of Medicine in Shenzhen (SZSM201612074, to L.B. and Y.L.), Qingdao-Europe Advanced Institute for Life Sciences Grant (Y.L.), Guangdong Provincial Key Laboratory of Genome Read and Write (No. 2017B030301011 to X.X.), Guangdong Provincial Academician Workstation of BGI Synthetic Genomics (No. 2017B090904014 to X.X.), the Innovation Fund Denmark (4108-00008B, 4096-00001B to J.G.) and the Danish Research Council (9041-00317B to J.G.), Danish Research Council (9041-00317B to Y.L.), European Union’s Horizon 2020 research and innovation program under grant agreement No 899417 (Y.L.), the Lund-beck Foundation (R219–2016-1375 to L.L.), the DFF Sapere Aude Starting grant (8048-00072 A to L.L.), and the National Human Genome Research Institute of the National Institutes of Health (RM1HG008525 to G.C.). We thank the China National GeneBank for the support of executing the project under the framework of Genome Read and Write.
© 2021, The Author(s).
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