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

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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Auteurs principaux: Tanglong Yuan, Nana Yan, Tianyi Fei, Jitan Zheng, Juan Meng, Nana Li, Jing Liu, Haihang Zhang, Long Xie, Wenqin Ying, Di Li, Lei Shi, Yongsen Sun, Yongyao Li, Yixue Li, Yidi Sun, Erwei Zuo
Format: article
Langue:EN
Publié: Nature Portfolio 2021
Sujets:
Science
Q
Accès en ligne:https://doaj.org/article/84852509bdb0448faac72ae929a50ecc
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https://doaj.org/article/84852509bdb0448faac72ae929a50ecc

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