Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens

Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens

Base editors enable precise genetic alterations but vary in efficiency at different loci. Here the authors analyse ABEs and CBEs at over 28,000 integrated sequences to train BE-DICT, a machine learning model capable of predicting base editing outcomes.

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Detalles Bibliográficos
Autores principales: Kim F. Marquart, Ahmed Allam, Sharan Janjuha, Anna Sintsova, Lukas Villiger, Nina Frey, Michael Krauthammer, Gerald Schwank
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Science
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Acceso en línea:https://doaj.org/article/c9632ae8e3554eb0a6f6c3e3694e3ef8
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https://doaj.org/article/c9632ae8e3554eb0a6f6c3e3694e3ef8

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