Pathogen genomic surveillance and the AI revolution
Spyros Lytras, Kieran D. Lamb, Jumpei Ito, Joe Grove, Ke Yuan, Kei Sato, Joseph Hughes, David L. Robertson
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.
Topics & Concepts
BiologyComputational biologyPandemicCoronavirus disease 2019 (COVID-19)Genomic sequencingGenomics2019-20 coronavirus outbreakData scienceSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PathogenGenomeGeneticsVirologyComputer scienceGeneInfectious disease (medical specialty)DiseaseOutbreakMedicinePathologyGenomics and Phylogenetic StudiesRNA and protein synthesis mechanismsMachine Learning in Bioinformatics