Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning

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 dat...

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Detalles Bibliográficos
Autores principales: Xiang, Xi, Corsi, Giulia I., Anthon, Christian, Qu, Kunli, Pan, Xiaoguang, Liang, Xue, Han, Peng, Dong, Zhanying, Liu, Lijun, Zhong, Jiayan, Ma, Tao, Wang, Jinbao, Zhang, Xiuqing, Jiang, Hui, Xu, Fengping, Liu, Xin, Xu, Xun, Wang, Jian, Yang, Huanming, Bolund, Lars, Church, George M., Lin, Lin, Gorodkin, Jan, Luo, Yonglun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8163799/
https://www.ncbi.nlm.nih.gov/pubmed/34050182
http://dx.doi.org/10.1038/s41467-021-23576-0
Descripción
Sumario: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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