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Benchmarking and integrating genome-wide CRISPR off-target detection and prediction
Systematic evaluation of genome-wide Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) off-target profiles is a fundamental step for the successful application of the CRISPR system to clinical therapies. Many experimental techniques and in silico tools have been proposed for detecti...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7672467/ https://www.ncbi.nlm.nih.gov/pubmed/33137817 http://dx.doi.org/10.1093/nar/gkaa930 |
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author | Yan, Jifang Xue, Dongyu Chuai, Guohui Gao, Yuli Zhang, Gongchen Liu, Qi |
author_facet | Yan, Jifang Xue, Dongyu Chuai, Guohui Gao, Yuli Zhang, Gongchen Liu, Qi |
author_sort | Yan, Jifang |
collection | PubMed |
description | Systematic evaluation of genome-wide Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) off-target profiles is a fundamental step for the successful application of the CRISPR system to clinical therapies. Many experimental techniques and in silico tools have been proposed for detecting and predicting genome-wide CRISPR off-target profiles. These techniques and tools, however, have not been systematically benchmarked. A comprehensive benchmark study and an integrated strategy that takes advantage of the currently available tools to improve predictions of genome-wide CRISPR off-target profiles are needed. We focused on the specificity of the traditional CRISPR SpCas9 system for gene knockout. First, we benchmarked 10 available genome-wide off-target cleavage site (OTS) detection techniques with the published OTS detection datasets. Second, taking the datasets generated from OTS detection techniques as the benchmark datasets, we benchmarked 17 available in silico genome-wide OTS prediction tools to evaluate their genome-wide CRISPR off-target prediction performances. Finally, we present the first one-stop integrated Genome-Wide Off-target cleavage Search platform (iGWOS) that was specifically designed for the optimal genome-wide OTS prediction by integrating the available OTS prediction algorithms with an AdaBoost ensemble framework. |
format | Online Article Text |
id | pubmed-7672467 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-76724672020-11-24 Benchmarking and integrating genome-wide CRISPR off-target detection and prediction Yan, Jifang Xue, Dongyu Chuai, Guohui Gao, Yuli Zhang, Gongchen Liu, Qi Nucleic Acids Res Computational Biology Systematic evaluation of genome-wide Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) off-target profiles is a fundamental step for the successful application of the CRISPR system to clinical therapies. Many experimental techniques and in silico tools have been proposed for detecting and predicting genome-wide CRISPR off-target profiles. These techniques and tools, however, have not been systematically benchmarked. A comprehensive benchmark study and an integrated strategy that takes advantage of the currently available tools to improve predictions of genome-wide CRISPR off-target profiles are needed. We focused on the specificity of the traditional CRISPR SpCas9 system for gene knockout. First, we benchmarked 10 available genome-wide off-target cleavage site (OTS) detection techniques with the published OTS detection datasets. Second, taking the datasets generated from OTS detection techniques as the benchmark datasets, we benchmarked 17 available in silico genome-wide OTS prediction tools to evaluate their genome-wide CRISPR off-target prediction performances. Finally, we present the first one-stop integrated Genome-Wide Off-target cleavage Search platform (iGWOS) that was specifically designed for the optimal genome-wide OTS prediction by integrating the available OTS prediction algorithms with an AdaBoost ensemble framework. Oxford University Press 2020-11-02 /pmc/articles/PMC7672467/ /pubmed/33137817 http://dx.doi.org/10.1093/nar/gkaa930 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Computational Biology Yan, Jifang Xue, Dongyu Chuai, Guohui Gao, Yuli Zhang, Gongchen Liu, Qi Benchmarking and integrating genome-wide CRISPR off-target detection and prediction |
title | Benchmarking and integrating genome-wide CRISPR off-target detection and prediction |
title_full | Benchmarking and integrating genome-wide CRISPR off-target detection and prediction |
title_fullStr | Benchmarking and integrating genome-wide CRISPR off-target detection and prediction |
title_full_unstemmed | Benchmarking and integrating genome-wide CRISPR off-target detection and prediction |
title_short | Benchmarking and integrating genome-wide CRISPR off-target detection and prediction |
title_sort | benchmarking and integrating genome-wide crispr off-target detection and prediction |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7672467/ https://www.ncbi.nlm.nih.gov/pubmed/33137817 http://dx.doi.org/10.1093/nar/gkaa930 |
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