Predicting Mutations Generated by Cas9, Base Editing, and Prime Editing in Mammalian Cells
The first fruits of the CRISPR-Cas revolution are starting to enter the clinic, with gene editing therapies offering solutions to previously incurable genetic diseases. The success of such applications hinges on control over the mutations that are generated, which are known to vary depending on the targeted locus. In this review, we present the current state of understanding and predicting CRISPR-Cas cutting, base editing, and prime editing outcomes in mammalian cells. We first provide an introduction to the basics of DNA repair and machine learning that the models rely on. We then overview the datasets and methods created for characterizing edits at scale, as well as the insights that have been derived from them. The predictions generated from these models serve as a foundation for designing efficient experiments across the broad contexts where these tools are applied.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:6 |
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| Enthalten in: |
The CRISPR journal - 6(2023), 4 vom: 01. Aug., Seite 325-338 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Weller, Juliane [VerfasserIn] |
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| Links: |
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| Themen: |
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| Anmerkungen: |
Date Completed 21.08.2023 Date Revised 22.08.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1089/crispr.2023.0016 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM358409667 |
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| 520 | |a The first fruits of the CRISPR-Cas revolution are starting to enter the clinic, with gene editing therapies offering solutions to previously incurable genetic diseases. The success of such applications hinges on control over the mutations that are generated, which are known to vary depending on the targeted locus. In this review, we present the current state of understanding and predicting CRISPR-Cas cutting, base editing, and prime editing outcomes in mammalian cells. We first provide an introduction to the basics of DNA repair and machine learning that the models rely on. We then overview the datasets and methods created for characterizing edits at scale, as well as the insights that have been derived from them. The predictions generated from these models serve as a foundation for designing efficient experiments across the broad contexts where these tools are applied | ||
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