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Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning
The design of CRISPR gRNAs requires accurate on-target efficiency predictions, which demand high-quality gRNA activity data and efficient modeling. To advance, we here report on the generation of on-target gRNA activity data for 10,592 SpCas9 gRNAs. Integrating these with complementary published dat...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8163799/ https://www.ncbi.nlm.nih.gov/pubmed/34050182 http://dx.doi.org/10.1038/s41467-021-23576-0 |
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author | Xiang, Xi Corsi, Giulia I. Anthon, Christian Qu, Kunli Pan, Xiaoguang Liang, Xue Han, Peng Dong, Zhanying Liu, Lijun Zhong, Jiayan Ma, Tao Wang, Jinbao Zhang, Xiuqing Jiang, Hui Xu, Fengping Liu, Xin Xu, Xun Wang, Jian Yang, Huanming Bolund, Lars Church, George M. Lin, Lin Gorodkin, Jan Luo, Yonglun |
author_facet | Xiang, Xi Corsi, Giulia I. Anthon, Christian Qu, Kunli Pan, Xiaoguang Liang, Xue Han, Peng Dong, Zhanying Liu, Lijun Zhong, Jiayan Ma, Tao Wang, Jinbao Zhang, Xiuqing Jiang, Hui Xu, Fengping Liu, Xin Xu, Xun Wang, Jian Yang, Huanming Bolund, Lars Church, George M. Lin, Lin Gorodkin, Jan Luo, Yonglun |
author_sort | Xiang, Xi |
collection | PubMed |
description | The design of CRISPR gRNAs requires accurate on-target efficiency predictions, which demand high-quality gRNA activity data and efficient modeling. To advance, we here report on the generation of on-target gRNA activity data for 10,592 SpCas9 gRNAs. Integrating these with complementary published data, we train a deep learning model, CRISPRon, on 23,902 gRNAs. Compared to existing tools, CRISPRon exhibits significantly higher prediction performances on four test datasets not overlapping with training data used for the development of these tools. Furthermore, we present an interactive gRNA design webserver based on the CRISPRon standalone software, both available via https://rth.dk/resources/crispr/. CRISPRon advances CRISPR applications by providing more accurate gRNA efficiency predictions than the existing tools. |
format | Online Article Text |
id | pubmed-8163799 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-81637992021-06-11 Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning Xiang, Xi Corsi, Giulia I. Anthon, Christian Qu, Kunli Pan, Xiaoguang Liang, Xue Han, Peng Dong, Zhanying Liu, Lijun Zhong, Jiayan Ma, Tao Wang, Jinbao Zhang, Xiuqing Jiang, Hui Xu, Fengping Liu, Xin Xu, Xun Wang, Jian Yang, Huanming Bolund, Lars Church, George M. Lin, Lin Gorodkin, Jan Luo, Yonglun Nat Commun Article The design of CRISPR gRNAs requires accurate on-target efficiency predictions, which demand high-quality gRNA activity data and efficient modeling. To advance, we here report on the generation of on-target gRNA activity data for 10,592 SpCas9 gRNAs. Integrating these with complementary published data, we train a deep learning model, CRISPRon, on 23,902 gRNAs. Compared to existing tools, CRISPRon exhibits significantly higher prediction performances on four test datasets not overlapping with training data used for the development of these tools. Furthermore, we present an interactive gRNA design webserver based on the CRISPRon standalone software, both available via https://rth.dk/resources/crispr/. CRISPRon advances CRISPR applications by providing more accurate gRNA efficiency predictions than the existing tools. Nature Publishing Group UK 2021-05-28 /pmc/articles/PMC8163799/ /pubmed/34050182 http://dx.doi.org/10.1038/s41467-021-23576-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Xiang, Xi Corsi, Giulia I. Anthon, Christian Qu, Kunli Pan, Xiaoguang Liang, Xue Han, Peng Dong, Zhanying Liu, Lijun Zhong, Jiayan Ma, Tao Wang, Jinbao Zhang, Xiuqing Jiang, Hui Xu, Fengping Liu, Xin Xu, Xun Wang, Jian Yang, Huanming Bolund, Lars Church, George M. Lin, Lin Gorodkin, Jan Luo, Yonglun Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8163799/ https://www.ncbi.nlm.nih.gov/pubmed/34050182 http://dx.doi.org/10.1038/s41467-021-23576-0 |
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