Optimization of C-to-G base editors with sequence context preference predictable by machine learning methods
C->G transversions can be highly desirable editing outcomes. Here the authors optimise CGBEs and provide a deep learning model for predicting editing outcomes based on sequence context.
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| Autores principales: | , , , , , , , , , , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/84852509bdb0448faac72ae929a50ecc |
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