New publication "Predicting adenine base editing efficiencies in different cellular contexts by deep learning"
Kissling, L., Mollaysa, A., Janjuha, S., Mathis, N., Marquart, K. F., Weber, Y., Moon, W. J., Lin, P. J. C., Fan, S. H. Y., Muramatsu, H., Vadovics, M., Allam, A., Pardi, N., Tam, Y. K., Krauthammer, M., & Schwank, G. (2025).
Genome Biology 26, Article number: 115 (2025)
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. However, these models were trained on base editing datasets generated in cell lines and their predictive power for base editing in primary cells in vivo remains uncertain. Continue reading