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

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  • Xi Xiang

    (Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao
    BGI Education Center, University of Chinese Academy of Sciences
    BGI-Shenzhen
    Aarhus University)

  • 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
    University of Copenhagen)

  • 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
    University of Copenhagen)

  • Peng Han

    (Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao
    University of Copenhagen)

  • 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
    BGI-Shenzhen)

  • Xin Liu

    (BGI-Shenzhen)

  • Xun Xu

    (BGI-Shenzhen
    Guangdong Provincial Key Laboratory of Genome Read and Write, BGI-Shenzhen)

  • Jian Wang

    (BGI-Shenzhen)

  • Huanming Yang

    (BGI-Shenzhen
    Guangdong Provincial Academician Workstation of BGI Synthetic Genomics, BGI-Shenzhen)

  • Lars Bolund

    (Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao
    BGI-Shenzhen
    Aarhus University)

  • 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
    Aarhus University
    Steno Diabetes Center Aarhus, Aarhus University)

  • Jan Gorodkin

    (University of Copenhagen)

  • Yonglun Luo

    (Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao
    BGI-Shenzhen
    Aarhus University
    Steno Diabetes Center Aarhus, Aarhus University)

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.

Suggested Citation

  • 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, 2021. "Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23576-0
    DOI: 10.1038/s41467-021-23576-0
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    Citations

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    Cited by:

    1. Dipankar Baisya & Adithya Ramesh & Cory Schwartz & Stefano Lonardi & Ian Wheeldon, 2022. "Genome-wide functional screens enable the prediction of high activity CRISPR-Cas9 and -Cas12a guides in Yarrowia lipolytica," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    2. Peter C. DeWeirdt & Abby V. McGee & Fengyi Zheng & Ifunanya Nwolah & Mudra Hegde & John G. Doench, 2022. "Accounting for small variations in the tracrRNA sequence improves sgRNA activity predictions for CRISPR screening," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    3. Jianli Tao & Daniel E. Bauer & Roberto Chiarle, 2023. "Assessing and advancing the safety of CRISPR-Cas tools: from DNA to RNA editing," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    4. Giulia I. Corsi & Kunli Qu & Ferhat Alkan & Xiaoguang Pan & Yonglun Luo & Jan Gorodkin, 2022. "CRISPR/Cas9 gRNA activity depends on free energy changes and on the target PAM context," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

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