Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens
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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.
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Editing efficiency of both ABEs and CBEs relies on factors such as the editing position within the protospacer and the tri-nucleotide sequence around the editing site.
“It has been previously shown that the editing efficiency of both ABEs and CBEs relies on factors such as the editing position within the protospacer and the tri-nucleotide sequence around the editing site49.”
From One-step generation of tumor models by base editor multiplexing in adult stem cell-derived organoids (2023) · cites this paper as [49]
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Massively parallel assessment of human variants with base editor screens
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- Authors
- Marquart, K. F., Allam, A., Janjuha, S., Sintsova, A., Villiger, L., Frey, N., Krauthammer, M., & Schwank, G.
- Affiliations
- University of Zurich (CH)
- McGovern Institute for Brain Research (US)
- Corresponding author
- Krauthammer, Michael
- Schwank, Gerald
- ORCID
- Venue
- Nature Communications
- Published
- 2021-08-25
- Volume
- 12
- Issue
- 1
- Pages
- 5114-5114
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC8387386
- OpenAlex
- W3194369599
- Cited by
- 102
- References
- 11
- Citations by year
- 2026: 20 · 2025: 26 · 2024: 22 · 2023: 13 · 2022: 14 · 2021: 7
- FWCI
- 4.37
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Repository for Publications and Research Data (ETH Zurich) (publishedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (185293, 310030)
- Helmut Horten Stiftung
- Functional Genomics Center Zurich
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA regulation and disease
- Health Sciences › Medicine › Oncology › CAR-T cell therapy research
- Keywords
- base editing, attention-based deep learning, adenine base editors, cytosine base editors, editing efficiency, genome editing
- MeSH terms
- Gene Editing, Deep Learning, Algorithms, Humans, Gene Library, Genome, Base Pairing, High-Throughput Nucleotide Sequencing
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