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

Abstract
Base editors are chimeric ribonucleoprotein complexes consisting of a DNA-targeting CRISPR-Cas module and a single-stranded DNA deaminase. They enable conversion of C•G into T•A base pairs and vice versa on genomic DNA. While base editors have vast 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...
Paper Details
Title
Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens
DOI
Published Date
Jul 5, 2020
Journal
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