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

Journal: Nature communications
PMID: 34050182

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.

Authors

  • Xi Xiang
    BGI-Shenzhen, Shenzhen, China.
  • Giulia I Corsi
    Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark.
  • Christian Anthon
    Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark.
  • Kunli Qu
    Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, Qingdao, China.
  • Xiaoguang Pan
    Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, Qingdao, China.
  • Xue Liang
    Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, Qingdao, China.
  • Peng Han
    Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, Qingdao, China.
  • Zhanying Dong
    Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, Qingdao, China.
  • Lijun Liu
    Department of Pediatric Surgery, West China Hospital, Sichuan University, Chengdu Sichuan, 610041, P.R.China.
  • Jiayan Zhong
    MGI, BGI-Shenzhen, Shenzhen, China.
  • Tao Ma
    School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 102488, China.
  • Jinbao Wang
    MGI, BGI-Shenzhen, Shenzhen, China.
  • Xiuqing Zhang
    BGI-Shenzhen, Shenzhen, Guangdong, 518083, China.
  • Hui Jiang
    Queensland Alliance for Environmental Health Science (QAEHS), University of Queensland, 20 Cornwall Street, Woolloongabba, QLD 4012, Australia.
  • Fengping Xu
    Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, Qingdao, China.
  • Xin Liu
    Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences, Weifang, Shandong, China.
  • Xun Xu
    BGI-Shenzhen, Shenzhen 518083, China.
  • Jian Wang
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.
  • Huanming Yang
    BGI-Shenzhen, Shenzhen 518083, China.
  • Lars Bolund
    Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, Qingdao, Shandong, 266555, China.
  • George M Church
    Wyss Institute for Biologically Inspired Engineering , Boston, Massachusetts 02115, United States.
  • Lin Lin
    Central Laboratory, The First Affiliated Hospital of Xiamen University, Xiamen, China, zhibinli33@163.com, liusuhuan@xmu.edu.cn.
  • Jan Gorodkin
    Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark. gorodkin@rth.dk.
  • Yonglun Luo
    BGI-Shenzhen, Shenzhen, China.

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