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

Xi Xiang, Giulia I. Corsi, Christian Anthon, Kunli Qu, Xiaoguang Pan, Xue Liang, Peng Han, Zhanying Dong, Lijun Liu, Jiayan Zhong, Tao Ma, Jinbao Wang, Xiuqing Zhang, Hui Jiang, Fengping Xu, Xin Liu, Xun Xu, Jian Wang, Huanming Yang, Lars Bolund, George M. Church, Lin Lin, Jan Gorodkin () and Yonglun Luo ()
Additional contact information
Xi Xiang: Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao
Giulia I. Corsi: University of Copenhagen
Christian Anthon: University of Copenhagen
Kunli Qu: Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao
Xiaoguang Pan: Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao
Xue Liang: Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao
Peng Han: Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao
Zhanying Dong: Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao
Lijun Liu: Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao
Jiayan Zhong: MGI, BGI-Shenzhen
Tao Ma: MGI, BGI-Shenzhen
Jinbao Wang: MGI, BGI-Shenzhen
Xiuqing Zhang: BGI-Shenzhen
Hui Jiang: MGI, BGI-Shenzhen
Fengping Xu: Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao
Xin Liu: BGI-Shenzhen
Xun Xu: BGI-Shenzhen
Jian Wang: BGI-Shenzhen
Huanming Yang: BGI-Shenzhen
Lars Bolund: Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao
George M. Church: Blavatnik Institute, Harvard Medical School
Lin Lin: Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao
Jan Gorodkin: University of Copenhagen
Yonglun Luo: Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao

Nature Communications, 2021, vol. 12, issue 1, 1-9

Abstract: Abstract 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.

Date: 2021
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DOI: 10.1038/s41467-021-23576-0

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