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

Kim F. Marquart, Ahmed Allam, Sharan Janjuha, Anna Sintsova, Lukas Villiger, Nina Frey, Michael Krauthammer () and Gerald Schwank ()
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Kim F. Marquart: ETH Zurich
Ahmed Allam: University of Zurich
Sharan Janjuha: University of Zurich
Anna Sintsova: University of Zurich
Lukas Villiger: University of Zurich
Nina Frey: ETH Zurich
Michael Krauthammer: University of Zurich
Gerald Schwank: University of Zurich

Nature Communications, 2021, vol. 12, issue 1, 1-9

Abstract: Abstract Base editors are chimeric ribonucleoprotein complexes consisting of a DNA-targeting CRISPR-Cas module and a single-stranded DNA deaminase. They enable transition of C•G into T•A base pairs and vice versa on genomic DNA. While base editors have great potential as genome editing tools for basic research and gene therapy, their application has been hampered by a broad variation in editing efficiencies on different genomic loci. Here we perform an extensive analysis of adenine- and cytosine base editors on a library of 28,294 lentivirally integrated genetic sequences and establish BE-DICT, an attention-based deep learning algorithm capable of predicting base editing outcomes with high accuracy. BE-DICT is a versatile tool that in principle can be trained on any novel base editor variant, facilitating the application of base editing for research and therapy.

Date: 2021
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DOI: 10.1038/s41467-021-25375-z

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