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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Podrobná bibliografie

Hlavní autoři:	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
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	BMC 2025-05-01
Edice:	Genome Biology
Témata:	Genomics In vivo Mouse Machine learning CRISPR-Cas9 genome editing
On-line přístup:	https://doi.org/10.1186/s13059-025-03586-7
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