Nature Communications (2021-05-01)

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,
  • Yonglun Luo

DOI
https://doi.org/10.1038/s41467-021-23576-0
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 9

Abstract

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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.