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CRISPR genome editing using computational approaches: A survey
Clustered regularly interspaced short palindromic repeats (CRISPR)-based gene editing has been widely used in various cell types and organisms. To make genome editing with Clustered regularly interspaced short palindromic repeats far more precise and practical, we must concentrate on the design of o...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9875887/ https://www.ncbi.nlm.nih.gov/pubmed/36710911 http://dx.doi.org/10.3389/fbinf.2022.1001131 |
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author | Alipanahi, Roghayyeh Safari, Leila Khanteymoori, Alireza |
author_facet | Alipanahi, Roghayyeh Safari, Leila Khanteymoori, Alireza |
author_sort | Alipanahi, Roghayyeh |
collection | PubMed |
description | Clustered regularly interspaced short palindromic repeats (CRISPR)-based gene editing has been widely used in various cell types and organisms. To make genome editing with Clustered regularly interspaced short palindromic repeats far more precise and practical, we must concentrate on the design of optimal gRNA and the selection of appropriate Cas enzymes. Numerous computational tools have been created in recent years to help researchers design the best gRNA for Clustered regularly interspaced short palindromic repeats researches. There are two approaches for designing an appropriate gRNA sequence (which targets our desired sites with high precision): experimental and predicting-based approaches. It is essential to reduce off-target sites when designing an optimal gRNA. Here we review both traditional and machine learning-based approaches for designing an appropriate gRNA sequence and predicting off-target sites. In this review, we summarize the key characteristics of all available tools (as far as possible) and compare them together. Machine learning-based tools and web servers are believed to become the most effective and reliable methods for predicting on-target and off-target activities of Clustered regularly interspaced short palindromic repeats in the future. However, these predictions are not so precise now and the performance of these algorithms -especially deep learning one’s-depends on the amount of data used during training phase. So, as more features are discovered and incorporated into these models, predictions become more in line with experimental observations. We must concentrate on the creation of ideal gRNA and the choice of suitable Cas enzymes in order to make genome editing with Clustered regularly interspaced short palindromic repeats far more accurate and feasible. |
format | Online Article Text |
id | pubmed-9875887 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98758872023-01-26 CRISPR genome editing using computational approaches: A survey Alipanahi, Roghayyeh Safari, Leila Khanteymoori, Alireza Front Bioinform Bioinformatics Clustered regularly interspaced short palindromic repeats (CRISPR)-based gene editing has been widely used in various cell types and organisms. To make genome editing with Clustered regularly interspaced short palindromic repeats far more precise and practical, we must concentrate on the design of optimal gRNA and the selection of appropriate Cas enzymes. Numerous computational tools have been created in recent years to help researchers design the best gRNA for Clustered regularly interspaced short palindromic repeats researches. There are two approaches for designing an appropriate gRNA sequence (which targets our desired sites with high precision): experimental and predicting-based approaches. It is essential to reduce off-target sites when designing an optimal gRNA. Here we review both traditional and machine learning-based approaches for designing an appropriate gRNA sequence and predicting off-target sites. In this review, we summarize the key characteristics of all available tools (as far as possible) and compare them together. Machine learning-based tools and web servers are believed to become the most effective and reliable methods for predicting on-target and off-target activities of Clustered regularly interspaced short palindromic repeats in the future. However, these predictions are not so precise now and the performance of these algorithms -especially deep learning one’s-depends on the amount of data used during training phase. So, as more features are discovered and incorporated into these models, predictions become more in line with experimental observations. We must concentrate on the creation of ideal gRNA and the choice of suitable Cas enzymes in order to make genome editing with Clustered regularly interspaced short palindromic repeats far more accurate and feasible. Frontiers Media S.A. 2023-01-11 /pmc/articles/PMC9875887/ /pubmed/36710911 http://dx.doi.org/10.3389/fbinf.2022.1001131 Text en Copyright © 2023 Alipanahi, Safari and Khanteymoori. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioinformatics Alipanahi, Roghayyeh Safari, Leila Khanteymoori, Alireza CRISPR genome editing using computational approaches: A survey |
title | CRISPR genome editing using computational approaches: A survey |
title_full | CRISPR genome editing using computational approaches: A survey |
title_fullStr | CRISPR genome editing using computational approaches: A survey |
title_full_unstemmed | CRISPR genome editing using computational approaches: A survey |
title_short | CRISPR genome editing using computational approaches: A survey |
title_sort | crispr genome editing using computational approaches: a survey |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9875887/ https://www.ncbi.nlm.nih.gov/pubmed/36710911 http://dx.doi.org/10.3389/fbinf.2022.1001131 |
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