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DeepCRISPR: optimized CRISPR guide RNA design by deep learning
A major challenge for effective application of CRISPR systems is to accurately predict the single guide RNA (sgRNA) on-target knockout efficacy and off-target profile, which would facilitate the optimized design of sgRNAs with high sensitivity and specificity. Here we present DeepCRISPR, a comprehen...
| Autores principales: | , , , , , , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2018
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6020378/ https://www.ncbi.nlm.nih.gov/pubmed/29945655 http://dx.doi.org/10.1186/s13059-018-1459-4 |
| _version_ | 1783335284551712768 |
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| author | Chuai, Guohui Ma, Hanhui Yan, Jifang Chen, Ming Hong, Nanfang Xue, Dongyu Zhou, Chi Zhu, Chenyu Chen, Ke Duan, Bin Gu, Feng Qu, Sheng Huang, Deshuang Wei, Jia Liu, Qi |
| author_facet | Chuai, Guohui Ma, Hanhui Yan, Jifang Chen, Ming Hong, Nanfang Xue, Dongyu Zhou, Chi Zhu, Chenyu Chen, Ke Duan, Bin Gu, Feng Qu, Sheng Huang, Deshuang Wei, Jia Liu, Qi |
| author_sort | Chuai, Guohui |
| collection | PubMed |
| description | A major challenge for effective application of CRISPR systems is to accurately predict the single guide RNA (sgRNA) on-target knockout efficacy and off-target profile, which would facilitate the optimized design of sgRNAs with high sensitivity and specificity. Here we present DeepCRISPR, a comprehensive computational platform to unify sgRNA on-target and off-target site prediction into one framework with deep learning, surpassing available state-of-the-art in silico tools. In addition, DeepCRISPR fully automates the identification of sequence and epigenetic features that may affect sgRNA knockout efficacy in a data-driven manner. DeepCRISPR is available at http://www.deepcrispr.net/. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-018-1459-4) contains supplementary material, which is available to authorized users. |
| format | Online Article Text |
| id | pubmed-6020378 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2018 |
| publisher | BioMed Central |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-60203782018-07-06 DeepCRISPR: optimized CRISPR guide RNA design by deep learning Chuai, Guohui Ma, Hanhui Yan, Jifang Chen, Ming Hong, Nanfang Xue, Dongyu Zhou, Chi Zhu, Chenyu Chen, Ke Duan, Bin Gu, Feng Qu, Sheng Huang, Deshuang Wei, Jia Liu, Qi Genome Biol Method A major challenge for effective application of CRISPR systems is to accurately predict the single guide RNA (sgRNA) on-target knockout efficacy and off-target profile, which would facilitate the optimized design of sgRNAs with high sensitivity and specificity. Here we present DeepCRISPR, a comprehensive computational platform to unify sgRNA on-target and off-target site prediction into one framework with deep learning, surpassing available state-of-the-art in silico tools. In addition, DeepCRISPR fully automates the identification of sequence and epigenetic features that may affect sgRNA knockout efficacy in a data-driven manner. DeepCRISPR is available at http://www.deepcrispr.net/. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-018-1459-4) contains supplementary material, which is available to authorized users. BioMed Central 2018-06-26 /pmc/articles/PMC6020378/ /pubmed/29945655 http://dx.doi.org/10.1186/s13059-018-1459-4 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
| spellingShingle | Method Chuai, Guohui Ma, Hanhui Yan, Jifang Chen, Ming Hong, Nanfang Xue, Dongyu Zhou, Chi Zhu, Chenyu Chen, Ke Duan, Bin Gu, Feng Qu, Sheng Huang, Deshuang Wei, Jia Liu, Qi DeepCRISPR: optimized CRISPR guide RNA design by deep learning |
| title | DeepCRISPR: optimized CRISPR guide RNA design by deep learning |
| title_full | DeepCRISPR: optimized CRISPR guide RNA design by deep learning |
| title_fullStr | DeepCRISPR: optimized CRISPR guide RNA design by deep learning |
| title_full_unstemmed | DeepCRISPR: optimized CRISPR guide RNA design by deep learning |
| title_short | DeepCRISPR: optimized CRISPR guide RNA design by deep learning |
| title_sort | deepcrispr: optimized crispr guide rna design by deep learning |
| topic | Method |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6020378/ https://www.ncbi.nlm.nih.gov/pubmed/29945655 http://dx.doi.org/10.1186/s13059-018-1459-4 |
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