Predicting adenine base editing efficiencies in different cellular contexts by deep learning

Predicting adenine base editing efficiencies in different cellular contexts by deep learning

Abstract Background Adenine base editors (ABEs) enable the conversion of A•T to G•C base pairs. Since the sequence of the target locus influences base editing efficiency, efforts have been made to develop computational models that can predict base editing outcomes based on the targeted sequence. How...

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Bibliographic Details
Main Authors: Lucas Kissling, Amina Mollaysa, Sharan Janjuha, Nicolas Mathis, Kim F. Marquart, Yanik Weber, Woohyun J. Moon, Paulo J. C. Lin, Steven H. Y. Fan, Hiromi Muramatsu, Máté Vadovics, Ahmed Allam, Norbert Pardi, Ying K. Tam, Michael Krauthammer, Gerald Schwank
Format: Article
Language:English
Published: BMC 2025-05-01
Series:Genome Biology
Subjects:
Genomics
In vivo
Mouse
Machine learning
CRISPR-Cas9 genome editing
Online Access:https://doi.org/10.1186/s13059-025-03586-7
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https://doi.org/10.1186/s13059-025-03586-7

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