High-throughput deep learning variant effect prediction with Sequence UNET
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Abstract
Understanding coding mutations is important for many applications in biology and medicine but the vast mutation space makes comprehensive experimental characterisation impossible. Current predictors are often computationally intensive and difficult to scale, including recent deep learning models. We introduce Sequence UNET, a highly scalable deep learning architecture that classifies and predicts variant frequency from sequence alone using multi-scale representations from a fully convolutional compression/expansion architecture. It achieves comparable pathogenicity prediction to recent methods. We demonstrate scalability by analysing 8.3B variants in 904,134 proteins detected through large-scale proteomics. Sequence UNET runs on modest hardware with a simple Python package.
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- Authors
- Dunham, A. S., Beltrao, P., & AlQuraishi, M.
- Affiliations
- European Bioinformatics Institute (GB)
- Columbia University (US)
- Corresponding author
- Dunham, Alistair S.
- Venue
- Genome Biology
- Published
- 2023-05-09
- Volume
- 24
- Issue
- 1
- Pages
- 110-110
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC10169183
- OpenAlex
- W4376133226
- Cited by
- 31
- References
- 57
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 11 · 2023: 7 · 2022: 1
- FWCI
- 4.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Genome biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection
- Funders
- Wellcome Trust (220540/Z/20/A)
- Helmut Horten Stiftung
- ETH Zürich Foundation
- EMBL´s European Bioinformatics Institute (EMBL-EBI)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- variant effect prediction, coding mutations, pathogenicity prediction, protein mutations
- MeSH terms
- Deep Learning, Mutation, Proteomics, Data Compression
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