Nature Communications (May 2021)
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
Affiliations
- Xi Xiang
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao
- 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
- 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
- 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
- Department of Genetics, Blavatnik Institute, Harvard Medical School
- Lin Lin
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao
- 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
- Yonglun Luo
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao
- DOI
- https://doi.org/10.1038/s41467-021-23576-0
- Journal volume & issue
-
Vol. 12,
no. 1
pp. 1 – 9
Abstract
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.
