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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| Lenguaje: | EN |
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Nature Portfolio
2021
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| Acceso en línea: | https://doaj.org/article/d2630a00271e4fa5a1f663c1d03d658b |
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oai:doaj.org-article:d2630a00271e4fa5a1f663c1d03d658b2021-12-02T15:00:50ZEnhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning10.1038/s41467-021-23576-02041-1723https://doaj.org/article/d2630a00271e4fa5a1f663c1d03d658b2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-23576-0https://doaj.org/toc/2041-1723High-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.Xi XiangGiulia I. CorsiChristian AnthonKunli QuXiaoguang PanXue LiangPeng HanZhanying DongLijun LiuJiayan ZhongTao MaJinbao WangXiuqing ZhangHui JiangFengping XuXin LiuXun XuJian WangHuanming YangLars BolundGeorge M. ChurchLin LinJan GorodkinYonglun LuoNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-9 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Science Q |
| spellingShingle |
Science Q 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 |
| 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. |
| format |
article |
| 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 |
| 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 Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/d2630a00271e4fa5a1f663c1d03d658b |
| work_keys_str_mv |
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