Optimization of C-to-G base editors with sequence context preference predictable by machine learning methods

Efficient and precise base editors (BEs) for C-to-G transversion are highly desirable. However, the sequence context affecting editing outcome largely remains unclear. Here we report engineered C-to-G BEs of high efficiency and fidelity, with the sequence context predictable via machine-learning met...

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Detalles Bibliográficos
Autores principales: Yuan, Tanglong, Yan, Nana, Fei, Tianyi, Zheng, Jitan, Meng, Juan, Li, Nana, Liu, Jing, Zhang, Haihang, Xie, Long, Ying, Wenqin, Li, Di, Shi, Lei, Sun, Yongsen, Li, Yongyao, Li, Yixue, Sun, Yidi, Zuo, Erwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8361092/
https://www.ncbi.nlm.nih.gov/pubmed/34385461
http://dx.doi.org/10.1038/s41467-021-25217-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8361092/
https://www.ncbi.nlm.nih.gov/pubmed/34385461
http://dx.doi.org/10.1038/s41467-021-25217-y

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