Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches
Application of CRISPR-Cas13d is limited by the inability to predict on- and off-targets. Here the authors perform CRISPR-Cas13d proliferation screens followed by modeling of Cas13d on- and off-targets; they design a deep learning model, DeepCas13, to predict the on-target activity of a gRNA.
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-02-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-36316-3 |
| _version_ | 1811165889282703360 |
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| author | Xiaolong Cheng Zexu Li Ruocheng Shan Zihan Li Shengnan Wang Wenchang Zhao Han Zhang Lumen Chao Jian Peng Teng Fei Wei Li |
| author_facet | Xiaolong Cheng Zexu Li Ruocheng Shan Zihan Li Shengnan Wang Wenchang Zhao Han Zhang Lumen Chao Jian Peng Teng Fei Wei Li |
| author_sort | Xiaolong Cheng |
| collection | DOAJ |
| description | Application of CRISPR-Cas13d is limited by the inability to predict on- and off-targets. Here the authors perform CRISPR-Cas13d proliferation screens followed by modeling of Cas13d on- and off-targets; they design a deep learning model, DeepCas13, to predict the on-target activity of a gRNA. |
| first_indexed | 2024-04-10T15:43:36Z |
| format | Article |
| id | doaj.art-dbd017ba01ea4d2dbe7fb0ab5721ed76 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-10T15:43:36Z |
| publishDate | 2023-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-dbd017ba01ea4d2dbe7fb0ab5721ed762023-02-12T12:15:46ZengNature PortfolioNature Communications2041-17232023-02-0114111410.1038/s41467-023-36316-3Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approachesXiaolong Cheng0Zexu Li1Ruocheng Shan2Zihan Li3Shengnan Wang4Wenchang Zhao5Han Zhang6Lumen Chao7Jian Peng8Teng Fei9Wei Li10Center for Genetic Medicine Research, Children’s National HospitalNational Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern UniversityCenter for Genetic Medicine Research, Children’s National HospitalNational Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern UniversityNational Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern UniversityNational Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern UniversityNational Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern UniversityCenter for Genetic Medicine Research, Children’s National HospitalDepartment of Computer Science, University of Illinois at Urbana-ChampaignNational Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern UniversityCenter for Genetic Medicine Research, Children’s National HospitalApplication of CRISPR-Cas13d is limited by the inability to predict on- and off-targets. Here the authors perform CRISPR-Cas13d proliferation screens followed by modeling of Cas13d on- and off-targets; they design a deep learning model, DeepCas13, to predict the on-target activity of a gRNA.https://doi.org/10.1038/s41467-023-36316-3 |
| spellingShingle | Xiaolong Cheng Zexu Li Ruocheng Shan Zihan Li Shengnan Wang Wenchang Zhao Han Zhang Lumen Chao Jian Peng Teng Fei Wei Li Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches Nature Communications |
| title | Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches |
| title_full | Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches |
| title_fullStr | Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches |
| title_full_unstemmed | Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches |
| title_short | Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches |
| title_sort | modeling crispr cas13d on target and off target effects using machine learning approaches |
| url | https://doi.org/10.1038/s41467-023-36316-3 |
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