GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics
Open accessPreprint — published as 10.1177/10943420231201154 · Paper page
Abstract
We seek to transform how new and emergent variants of pandemiccausing 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 pretraining on over 110 million prokaryotic gene sequences and finetuning 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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Artificial intelligence-driven plant bio-genomics research: a new era
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Protein Generation via Genome-scale Language Models with Bio-physical Scoring
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A Feature Engineering Method for Whole-Genome DNA Sequence with Nucleotide Resolution
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Applications of Machine Learning in SARS-COV-2 Genomics for Varient Detection and Disease Prediction
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FGeneBERT: function-driven pre-trained gene language model for metagenomics
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Generative artificial intelligence for enzyme design and biocatalysis
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TrainBF: High-Performance DNN Training Engine Using BFloat16 on AI Accelerators
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Self-citationMutation prediction in the SARS-CoV-2 genome using attention-based neural machine translation
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Large-scale multi-omic biosequence transformers for modeling protein–nucleic acid interactions
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KVDirect: Distributed Disaggregated LLM Inference
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“Beyond chatbots, LLMs have become integral in diverse fields, fueling innovations from genomic research [48,54] to molecular design [4,7,25].”
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DTIO: Data Stack for AI-driven Workflows
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Cross-domain transfer learning from peptides to metabolites using a multi-property fine-tuned LLM
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Evolutionary Reasoning Does Not Arise in Standard Usage of Protein Language Models
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Variation and evolution analysis of SARS-CoV-2 using self-game sequence optimization
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Solving Mystery Planning Problems Using Category Theory, Functors, and Large Language Models
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DASH-CAM: Dynamic Approximate SearcH Content Addressable Memory for genome classificationPreprint
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Harnessing A Unified Multi-modal Sequence Modeling to unveil Protein-DNA InterdependencyPreprint
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Predicting emergent phenotypes from single cell populations using CELLECTIONPreprint
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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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- 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., Gregory Pauloski, J., 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)
- Nvidia (United States) (US)
- 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.
- 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
- openRxiv
- Published
- 2022-10-11
- Volume
- 37
- Issue
- 6
- Pages
- 683-705
- ISSN
- 0890-2720 (ISSN-L)
- 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
- cc-by-nc-nd
- 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
- National Science Foundation (DEAC02-05CH11231, DE-AC02–06CH11357, 2117896, 17-SC-20-SC)
- U.S. Department of Energy (DE‐ AC02‐06CH11357, COVID-19, 17‐SC‐20‐SC, -AC02-05CH11231, AC02‐06CH11357, 05CH11231, DE AC02 05CH11231, 06CH11357, DE-AC02-)
- National Energy Research Scientific Computing Center (DE–AC02–05CH11231, 05CH11231, DE-AC02-06CH11357, AC02-05CH11231)
- National Institutes of Health (DE-AC02–05CH11231, COVID-19, DE-AC02–06CH11357, 17-SC-20-SC)
- Office of Science (AC02‐06CH11357, 17-SC20-SC, DEAC02–05CH11231, AC02-05CH11231, DE-AC02, 05CH11231, 06CH11357, DE-AC02-06CH11357)
- National Nuclear Security Administration (AC02-05CH11231, 17- SC-20-SC, DE-AC02-05CH11231, DE-AC02-06CH11357)
- National Institute of Allergy and Infectious Diseases (COVID-19, P01AI165077, DE-AC02-06CH11357)
- Argonne National Laboratory (DE- AC02-06-CH11357, DE-AC02, AC02–06CH11357, DE-AC02-05CH11231, 06CH11357)
- Lawrence Berkeley National Laboratory (AC02‐05CH11231, DE-AC02-05CH11231, DE-AC02-06CH11357, 05CH11231)
- 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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