Gen AI for the Genome: LLM Predicts Characteristics of COVID Variants

A widely acclaimed large language model for genomic data has demonstrated its ability to generate gene sequences that closely resemble real-world variants of SARS-CoV-2, the virus behind COVID-19.

Called GenSLMs, the model, which last year won the Gordon Bell special prize for high performance computing-based COVID-19 research, was trained on a dataset of nucleotide sequences — the building blocks of DNA and RNA. It was developed by researchers from Argonne National Laboratory, NVIDIA, the University of Chicago and a score of other academic and commercial collaborators.

When the researchers looked back at the nucleotide sequences generated by GenSLMs, they discovered that specific characteristics of the AI-generated sequences closely matched the real-world Eris and Pirola subvariants that have been prevalent this year — even though the AI was only trained on COVID-19 virus genomes from the first year of the pandemic.

“Our model’s generative process is extremely naive, lacking any specific information or constraints around what a new COVID variant should look like,” said Arvind Ramanathan, lead researcher on the project and a computational biologist at Argonne. “The AI’s ability to predict the kinds of gene mutations present in recent COVID strains — despite having only seen the Alpha and Beta variants during training — is a strong validation of its capabilities.”

In addition to generating its own sequences, GenSLMs can also classify and cluster different COVID genome sequences by distinguishing between variants. In a demo coming soon to NGC, NVIDIA’s hub for accelerated software, users can explore visualizations of GenSLMs’ analysis of the evolutionary patterns of various proteins within the COVID viral genome.


Reading Between the Lines, Uncovering Evolutionary Patterns

A key feature of GenSLMs is its ability to interpret long strings of nucleotides — represented with sequences of the letters A, T, G and C in DNA, or A, U, G and C in RNA — in the same way an LLM trained on English text would interpret a sentence. This capability enables the model to understand the relationship between different areas of the genome, which in coronaviruses consists of around 30,000 nucleotides.

In the demo, users will be able to choose from among eight different COVID variants to understand how the AI model tracks mutations across various proteins of the viral genome. The visualization depicts evolutionary couplings across the viral proteins — highlighting which snippets of the genome are likely to be seen in a given variant.

“Understanding how different parts of the genome are co-evolving gives us clues about how the virus may develop new vulnerabilities or new forms of resistance,” Ramanathan said. “Looking at the model’s understanding of which mutations are particularly strong in a variant may help scientists with downstream tasks like determining how a specific strain can evade the human immune system.”


GenSLMs was trained on more than 110 million prokaryotic genome sequences and fine-tuned with a global dataset of around 1.5 million COVID viral sequences using open-source data from the Bacterial and Viral Bioinformatics Resource Center. In the future, the model could be fine-tuned on the genomes of other viruses or bacteria, enabling new research applications.

To train the model, the researchers used NVIDIA A100 Tensor Core GPU-powered supercomputers, including Argonne’s Polaris system, the U.S. Department of Energy’s Perlmutter and NVIDIA’s Selene.

The GenSLMs research team’s Gordon Bell special prize was awarded at last year’s SC22 supercomputing conference. At this week’s SC23, in Denver, NVIDIA is sharing a new range of groundbreaking work in the field of accelerated computing. View the full schedule.

NVIDIA Research comprises hundreds of scientists and engineers worldwide, with teams focused on topics including AI, computer graphics, computer vision, self-driving cars and robotics. Learn more about NVIDIA Research and subscribe to NVIDIA healthcare news.

Main image courtesy of Argonne National Laboratory’s Bharat Kale. 

This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. DOE Office of Science and the National Nuclear Security Administration. Research was supported by the DOE through the National Virtual Biotechnology Laboratory, a consortium of DOE national laboratories focused on response to COVID-19, with funding from the Coronavirus CARES Act.

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