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CRISPRedict: a CRISPR-Cas9 web tool for interpretable efficiency predictions
The development of the CRISPR-Cas9 technology has provided a simple yet powerful system for genome editing. Current gRNA design tools serve as an important platform for the efficient application of the CRISPR systems. However, most of the existing tools are black-box models that suffer from limitati...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9252759/ https://www.ncbi.nlm.nih.gov/pubmed/35670672 http://dx.doi.org/10.1093/nar/gkac466 |
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author | Konstantakos, Vasileios Nentidis, Anastasios Krithara, Anastasia Paliouras, Georgios |
author_facet | Konstantakos, Vasileios Nentidis, Anastasios Krithara, Anastasia Paliouras, Georgios |
author_sort | Konstantakos, Vasileios |
collection | PubMed |
description | The development of the CRISPR-Cas9 technology has provided a simple yet powerful system for genome editing. Current gRNA design tools serve as an important platform for the efficient application of the CRISPR systems. However, most of the existing tools are black-box models that suffer from limitations, such as variable performance and unclear mechanism of decision making. Here, we introduce CRISPRedict, an interpretable gRNA efficiency prediction model for CRISPR-Cas9 gene editing. Its strength lies in the fact that it can accurately predict efficient guide RNAs—with equivalent performance to state-of-the-art tools—while being a simple linear model. Implemented as a user-friendly web server, CRISPRedict offers (i) quick and accurate predictions across various experimental conditions (e.g. U6/T7 transcription); (ii) regression and classification models for scoring gRNAs and (iii) multiple visualizations to explain the obtained results. Given its performance, interpretability, and versatility, we expect that it will assist researchers in the gRNA design process and facilitate genome editing research. CRISPRedict is available for use at http://www.crispredict.org/. |
format | Online Article Text |
id | pubmed-9252759 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-92527592022-07-05 CRISPRedict: a CRISPR-Cas9 web tool for interpretable efficiency predictions Konstantakos, Vasileios Nentidis, Anastasios Krithara, Anastasia Paliouras, Georgios Nucleic Acids Res Web Server Issue The development of the CRISPR-Cas9 technology has provided a simple yet powerful system for genome editing. Current gRNA design tools serve as an important platform for the efficient application of the CRISPR systems. However, most of the existing tools are black-box models that suffer from limitations, such as variable performance and unclear mechanism of decision making. Here, we introduce CRISPRedict, an interpretable gRNA efficiency prediction model for CRISPR-Cas9 gene editing. Its strength lies in the fact that it can accurately predict efficient guide RNAs—with equivalent performance to state-of-the-art tools—while being a simple linear model. Implemented as a user-friendly web server, CRISPRedict offers (i) quick and accurate predictions across various experimental conditions (e.g. U6/T7 transcription); (ii) regression and classification models for scoring gRNAs and (iii) multiple visualizations to explain the obtained results. Given its performance, interpretability, and versatility, we expect that it will assist researchers in the gRNA design process and facilitate genome editing research. CRISPRedict is available for use at http://www.crispredict.org/. Oxford University Press 2022-06-07 /pmc/articles/PMC9252759/ /pubmed/35670672 http://dx.doi.org/10.1093/nar/gkac466 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Web Server Issue Konstantakos, Vasileios Nentidis, Anastasios Krithara, Anastasia Paliouras, Georgios CRISPRedict: a CRISPR-Cas9 web tool for interpretable efficiency predictions |
title | CRISPRedict: a CRISPR-Cas9 web tool for interpretable efficiency predictions |
title_full | CRISPRedict: a CRISPR-Cas9 web tool for interpretable efficiency predictions |
title_fullStr | CRISPRedict: a CRISPR-Cas9 web tool for interpretable efficiency predictions |
title_full_unstemmed | CRISPRedict: a CRISPR-Cas9 web tool for interpretable efficiency predictions |
title_short | CRISPRedict: a CRISPR-Cas9 web tool for interpretable efficiency predictions |
title_sort | crispredict: a crispr-cas9 web tool for interpretable efficiency predictions |
topic | Web Server Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9252759/ https://www.ncbi.nlm.nih.gov/pubmed/35670672 http://dx.doi.org/10.1093/nar/gkac466 |
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