Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning
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.
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Nature Publishing Group
2021-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-021-23576-0 |
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doaj-d2630a00271e4fa5a1f663c1d03d658b2021-05-30T11:14:29ZengNature Publishing GroupNature Communications2041-17232021-05-011211910.1038/s41467-021-23576-0Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learningXi Xiang0Giulia I. Corsi1Christian Anthon2Kunli Qu3Xiaoguang Pan4Xue Liang5Peng Han6Zhanying Dong7Lijun Liu8Jiayan Zhong9Tao Ma10Jinbao Wang11Xiuqing Zhang12Hui Jiang13Fengping Xu14Xin Liu15Xun Xu16Jian Wang17Huanming Yang18Lars Bolund19George M. Church20Lin Lin21Jan Gorodkin22Yonglun Luo23Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-QingdaoCenter for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of CopenhagenCenter for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of CopenhagenLars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-QingdaoLars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-QingdaoLars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-QingdaoLars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-QingdaoLars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-QingdaoLars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-QingdaoMGI, BGI-ShenzhenMGI, BGI-ShenzhenMGI, BGI-ShenzhenBGI-ShenzhenMGI, BGI-ShenzhenLars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-QingdaoBGI-ShenzhenBGI-ShenzhenBGI-ShenzhenBGI-ShenzhenLars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-QingdaoDepartment of Genetics, Blavatnik Institute, Harvard Medical SchoolLars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-QingdaoCenter for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of CopenhagenLars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-QingdaoHigh-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.https://doi.org/10.1038/s41467-021-23576-0 |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
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 |
| spellingShingle |
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 Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning Nature Communications |
| author_facet |
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 |
| author_sort |
Xi Xiang |
| title |
Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning |
| title_short |
Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning |
| title_full |
Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning |
| title_fullStr |
Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning |
| title_full_unstemmed |
Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning |
| title_sort |
enhancing crispr-cas9 grna efficiency prediction by data integration and deep learning |
| publisher |
Nature Publishing Group |
| series |
Nature Communications |
| issn |
2041-1723 |
| publishDate |
2021-05-01 |
| description |
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. |
| url |
https://doi.org/10.1038/s41467-021-23576-0 |
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