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Updated: Jul 5, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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Published on: September 25, 2021

PandoGen: Generating complete instances of future SARS-CoV-2 sequences using Deep Learning.

Anand Ramachandran¹, Steven S Lumetta¹, Deming Chen¹

¹University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.

Plos Computational Biology

|January 19, 2024

Summary

This summary is machine-generated.

Forecasting future viral protein sequences is crucial for pandemic preparedness. A new method, PandoGen, accurately predicts novel variants, outperforming larger models in identifying high-spread sequences.

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Area of Science:

Computational Biology
Virology
Machine Learning

Background:

Viral pandemics present challenges due to novel variants with altered characteristics.
Forecasting future viral sequences enables proactive preparation against emerging threats.

Purpose of the Study:

To develop a method for generating future viral protein sequences using protein language models.
To enhance pandemic preparedness by predicting undiscovered viral strains.

Main Methods:

Developed PandoGen, a novel method for training protein language models for pandemic forecasting.
PandoGen integrates synthetic data generation, conditional sequence generation, and reward-based learning.
Applied PandoGen to model SARS-CoV-2 Spike protein sequences.

Main Results:

PandoGen forecasted twice as many novel sequences with five times the case counts compared to a significantly larger model.
The method successfully predicted unseen lineages months in advance.
PandoGen accurately forecasted sequences of Variants of Concern within tight sequence budgets.

Conclusions:

PandoGen offers a powerful approach for forecasting future viral protein sequences.
This method significantly improves pandemic preparedness by enabling early identification of high-risk variants.
PandoGen demonstrates superior performance over larger models in predicting novel, high-spread viral sequences.