Pathogen genomic surveillance and the AI revolution.

Journal: Journal of virology
PMID: 39878472

Abstract

The unprecedented sequencing efforts during the COVID-19 pandemic paved the way for genomic surveillance to become a powerful tool for monitoring the evolution of circulating viruses. Herein, we discuss how a state-of-the-art artificial intelligence approach called protein language models (pLMs) can be used for effectively analyzing pathogen genomic data. We highlight examples of pLMs applied to predicting viral properties and evolution and lay out a framework for integrating pLMs into genomic surveillance pipelines.

Authors

  • Spyros Lytras
    Division of Systems Virology, Department of Microbiology and Immunology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
  • Kieran D Lamb
    MRC-University of Glasgow Centre for Virus Research, Glasgow, Scotland, United Kingdom.
  • Jumpei Ito
    Division of Systems Virology, Department of Microbiology and Immunology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
  • Joe Grove
    MRC-University of Glasgow Centre for Virus Research, Glasgow, Scotland, United Kingdom.
  • Ke Yuan
    Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong.
  • Kei Sato
  • Joseph Hughes
    MRC-University of Glasgow Centre for Virus Research, Sir Michael Stoker Building, Garscube Campus, Campus, 464 Bearsden Road, Glasgow, G61 1QH, Scotland, UK.
  • David L Robertson
    MRC-University of Glasgow Centre For Virus Research, Glasgow, United Kingdom.

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