GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics
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Abstract
We seek to transform how new and emergent variants of pandemic-causing viruses, specifically SARS-CoV-2, are identified and classified. By adapting large language models (LLMs) for genomic data, we build genome-scale language models (GenSLMs) which can learn the evolutionary landscape of SARS-CoV-2 genomes. By pre-training on over 110 million prokaryotic gene sequences and fine-tuning a SARS-CoV-2-specific model on 1.5 million genomes, we show that GenSLMs can accurately and rapidly identify variants of concern. Thus, to our knowledge, GenSLMs represents one of the first whole-genome scale foundation models which can generalize to other prediction tasks. We demonstrate scaling of GenSLMs on GPU-based supercomputers and AI-hardware accelerators utilizing 1.63 Zettaflops in training runs with a sustained performance of 121 PFLOPS in mixed precision and peak of 850 PFLOPS. We present initial scientific insights from examining GenSLMs in tracking evolutionary dynamics of SARS-CoV-2, paving the path to realizing this on large biological data.
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Existing DNA language models are limited by dense Transformer architectures, incur high computational cost that scales quadratically with sequence length, and generally underperform at single-nucleotide or byte-level resolution compared with models trained at coarser resolutions.
“existing attempts to model DNA as a language (15–17) are limited by the prevailing dense Transformer architecture, which incurs high computational cost as input sequence lengths grow relative to model width (scaling quadratically) and generally underperforms at single-nucleotide or byte-level resolution compared with models trained at coarser resolutions (18).”
From Sequence modeling and design from molecular to genome scale with Evo (2024) · cites this paper as [15]
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Experimental — accuracy not yet measuredTransformer-based DNA models are constrained to short context lengths and use token aggregation schemes, sacrificing single-nucleotide resolution.
“As a result, Transformer-based DNA models are constrained to short context lengths and use schemes that aggregate nucleotides into the basic units of language models, called tokens, thereby sacrificing single-nucleotide resolution (15, 16, 21–23).”
From Sequence modeling and design from molecular to genome scale with Evo (2024) · cites this paper as [15]
Section 4.2 contrasts standard 1,024- or 2,048-token contexts with the longer sequences needed for genomes, and Section 5.2.1 says GenSLM groups three nucleotides into each codon-level input token. But the paper does not say this sacrifices the ability to model individual nucleotide changes; Section 5.2.1 says GenSLM models individual mutations at nucleotide scale. The claim therefore overstates the resolution consequence and treats a common context-length challenge as a constraint on Transformer-based DNA models generally. (LLM, conf=0.83)
Experimental — accuracy not yet measuredOn prokaryotic protein DMS datasets, Evo's zero-shot performance exceeded all other tested nucleotide models, including GenSLM.
“On DMS datasets of prokaryotic proteins, Evo’s zero-shot performance exceeded all other nucleotide models tested (Fig. 2B and table S4), including GenSLM (15)”
From Sequence modeling and design from molecular to genome scale with Evo (2024) · cites this paper as [15]
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SARS-Cov2 seasonality and adaptation are driven by solar activity.Preprint
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SARS-Cov2 seasonality and adaptation are driven by solar activity.Preprint
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Exploring a Large Language Model Approach to Visual Appearance Prediction in Forensic Investigations
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Sequence-Based Generative AI-Guided Design of Versatile Tryptophan SynthasesPreprint
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- Authors
- Zvyagin, M., Brace, A., Hippe, K., Deng, Y., Zhang, B., Bohorquez, C. O., Clyde, A., Kale, B., Perez-Rivera, D., Ma, H., Mann, C. M., Irvin, M., Ozgulbas, D. G., Vassilieva, N., Pauloski, J. G., Ward, L., Hayot-Sasson, V., Emani, M., Foreman, S., Xie, Z., Lin, D., Shukla, M., Nie, W., Romero, J., Dallago, C., Vahdat, A., Xiao, C., Gibbs, T., Foster, I., Davis, J. J., Papka, M. E., Brettin, T., Stevens, R., Anandkumar, A., Vishwanath, V., & Ramanathan, A.
- Affiliations
- Argonne National Laboratory (US)
- Harvard University (US)
- Cerebras Systems (United States) (US)
- Northern Illinois University (US)
- Department of Biochemistry, University of Illinois-Urbana Champaign, Champaign, IL, USA
- University of Chicago (US)
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA
- Nvidia (United States) (US)
- NVIDIA Inc., Santa Clara, CA, USA
- University of Illinois Urbana-Champaign (US)
- Corresponding author
- Vishwanath, Venkatram
- Ramanathan, Arvind
- ORCID
- Brace, Alexander
- Hippe, Kyle
- Deng, Yuntian
- Zhang, Bin
- Clyde, Austin
- Kale, Bharat
- Ma, Heng
- Mann, Carla M.
- Ozgulbas, Defne G.
- Vassilieva, Natalia
- Pauloski, James Gregory
- Ward, Logan
- Emani, Murali
- Foreman, Sam
- Lin, Diangen
- Shukla, Maulik
- Nie, Weili
- Dallago, Christian
- Vahdat, Arash
- Xiao, Chaowei
- Gibbs, Thomas
- Foster, Ian
- Davis, James J.
- Papka, Michael E.
- Brettin, Thomas
- Stevens, Rick
- Vishwanath, Venkatram
- Ramanathan, Arvind
- Venue
- The International Journal of High Performance Computing Applications
- Publisher
- SAGE Publications
- Published
- 2023-10-27
- Volume
- 37
- Issue
- 6
- Pages
- 683-705
- ISSN
- 0890-2720 (ISSN-L)
- 1094-3420
- 1741-2846
- PMC
- PMC9709791
- OpenAlex
- W4387994442
- Cited by
- 120
- References
- 57
- Citations by year
- 2026: 30 · 2025: 40 · 2024: 35 · 2023: 14 · 2022: 1
- FWCI
- 24.63
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- The International Journal of High Performance Computing Applications (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- PubMed Central (submittedVersion, cc-by-nc-nd)
- Espace ÉTS (ETS) (submittedVersion)
- Funders
- Exascale Computing Project
- Topics
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- SARS-CoV-2, COVID-19 variants of concern, variant identification, viral evolution, foundation models, mixed-precision training
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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