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.

Bibliographic Details
Main Authors: Kim F. Marquart, Ahmed Allam, Sharan Janjuha, Anna Sintsova, Lukas Villiger, Nina Frey, Michael Krauthammer, Gerald Schwank
Format: Article
Language:English
Published: Nature Publishing Group 2021-08-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-021-25375-z

Internet

https://doi.org/10.1038/s41467-021-25375-z

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