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

Biology

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

T. Yuan, N. Yan, et al.

Discover groundbreaking advancements in base editing technology! This study reveals efficient and precise C-to-G base editors engineered for high fidelity and predictable outcomes, making a significant leap in genetic editing. Conducted by an expert team including authors from Shenzhen and Shanghai institutes, these findings pave the way for enhanced genetic modifications in various applications.

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~3 min • Beginner • English
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