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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. High-quality gRNA activity data is needed for accurate on-target efficiency predictions. Here the authors generate activity data for over 10,000 gRNA and build a deep learning model CRISPRon for improved performance predictions.
The potential of CRISPR technology is great and ranges from curing genetically engineered diseases to applications in agricultural and industrial biotechnology. However, one of the major challenges is selecting the right gRNA to guide the Cas 9 protein to the right place in the DNA. Researchers at the University of Aarhus and the University of Copenhagen have therefore developed a new method that makes CRISPR gene editing more precise than conventional methods. The new method selects, based on the researchers' new data and implementation of an algorithm, the most suitable gRNAs to help the CRISPR-Cas9 protein with high-precision editing in the right place in our DNA. In their study, they quantified the efficiency of gRNA molecules for more than 10,000 different sites. The work was done using a massive high-throughput library-based method.