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


Marquart, Kim F; Allam, Ahmed; Janjuha, Sharan; Sintsova, Anna; Villiger, Lukas; Frey, Nina; Krauthammer, Michael; Schwank, Gerald (2020). Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens. bioRxiv 186544, University of Zurich.

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 extensive analysis of adenine- and cytosine base editors on thousands of 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.

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 extensive analysis of adenine- and cytosine base editors on thousands of 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.

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Additional indexing

Item Type:Working Paper
Communities & Collections:04 Faculty of Medicine > Institute of Pharmacology and Toxicology
07 Faculty of Science > Institute of Pharmacology and Toxicology

07 Faculty of Science > Department of Quantitative Biomedicine
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Language:English
Date:2020
Deposited On:19 Jan 2021 16:35
Last Modified:27 Jan 2021 07:07
Series Name:bioRxiv
ISSN:2164-7844
OA Status:Green
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1101/2020.07.05.186544

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