The Current State and Future of CRISPR-Cas9 gRNA Design Tools
Recent years have seen the development of computational tools to assist researchers in performing CRISPR-Cas9 experiment optimally. More specifically, these tools aim to maximize on-target activity (guide efficiency) while also minimizing potential off-target effects (guide specificity) by analyzing...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-07-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fphar.2018.00749/full |
| _version_ | 1818241954685648896 |
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| author | Laurence O. W. Wilson Aidan R. O’Brien Aidan R. O’Brien Denis C. Bauer |
| author_facet | Laurence O. W. Wilson Aidan R. O’Brien Aidan R. O’Brien Denis C. Bauer |
| author_sort | Laurence O. W. Wilson |
| collection | DOAJ |
| description | Recent years have seen the development of computational tools to assist researchers in performing CRISPR-Cas9 experiment optimally. More specifically, these tools aim to maximize on-target activity (guide efficiency) while also minimizing potential off-target effects (guide specificity) by analyzing the features of the target site. Nonetheless, currently available tools cannot robustly predict experimental success as prediction accuracy depends on the approximations of the underlying model and how closely the experimental setup matches the data the model was trained on. Here, we present an overview of the available computational tools, their current limitations and future considerations. We discuss new trends around personalized health by taking genomic variants into account when predicting target sites as well as discussing other governing factors that can improve prediction accuracy. |
| first_indexed | 2024-12-12T13:37:33Z |
| format | Article |
| id | doaj.art-154b9d1123a648c1b842dbf5c7df7561 |
| institution | Directory Open Access Journal |
| issn | 1663-9812 |
| language | English |
| last_indexed | 2024-12-12T13:37:33Z |
| publishDate | 2018-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj.art-154b9d1123a648c1b842dbf5c7df75612022-12-22T00:22:54ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122018-07-01910.3389/fphar.2018.00749353115The Current State and Future of CRISPR-Cas9 gRNA Design ToolsLaurence O. W. Wilson0Aidan R. O’Brien1Aidan R. O’Brien2Denis C. Bauer3Commonwealth Scientific and Industrial Research Organisation, Sydney, NSW, AustraliaCommonwealth Scientific and Industrial Research Organisation, Sydney, NSW, AustraliaDepartment of Immunology and Infectious Disease, John Curtin School of Medical Research, Acton, ACT, AustraliaCommonwealth Scientific and Industrial Research Organisation, Sydney, NSW, AustraliaRecent years have seen the development of computational tools to assist researchers in performing CRISPR-Cas9 experiment optimally. More specifically, these tools aim to maximize on-target activity (guide efficiency) while also minimizing potential off-target effects (guide specificity) by analyzing the features of the target site. Nonetheless, currently available tools cannot robustly predict experimental success as prediction accuracy depends on the approximations of the underlying model and how closely the experimental setup matches the data the model was trained on. Here, we present an overview of the available computational tools, their current limitations and future considerations. We discuss new trends around personalized health by taking genomic variants into account when predicting target sites as well as discussing other governing factors that can improve prediction accuracy.https://www.frontiersin.org/article/10.3389/fphar.2018.00749/fullCRISPR-Cas9bioinformaticsoff-target finderactivity predictionchromatinmachine learning |
| spellingShingle | Laurence O. W. Wilson Aidan R. O’Brien Aidan R. O’Brien Denis C. Bauer The Current State and Future of CRISPR-Cas9 gRNA Design Tools Frontiers in Pharmacology CRISPR-Cas9 bioinformatics off-target finder activity prediction chromatin machine learning |
| title | The Current State and Future of CRISPR-Cas9 gRNA Design Tools |
| title_full | The Current State and Future of CRISPR-Cas9 gRNA Design Tools |
| title_fullStr | The Current State and Future of CRISPR-Cas9 gRNA Design Tools |
| title_full_unstemmed | The Current State and Future of CRISPR-Cas9 gRNA Design Tools |
| title_short | The Current State and Future of CRISPR-Cas9 gRNA Design Tools |
| title_sort | current state and future of crispr cas9 grna design tools |
| topic | CRISPR-Cas9 bioinformatics off-target finder activity prediction chromatin machine learning |
| url | https://www.frontiersin.org/article/10.3389/fphar.2018.00749/full |
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