Nanopore- and AI-empowered microbial viability inference
A new AI-driven approach uses raw Oxford Nanopore ‘squiggle’ data to distinguish viable from dead microorganisms — overcoming a key limitation of traditional metagenomic sequencing methods. Knowing whether microbes are alive matters because DNA from dead cells can persist and skew analyses, leading to false conclusions about antibiotic response, infection risk, microbial activity, and ecosystem function. By accurately classifying UV-killed and viable E. Coli DNA, this computational framework could pave the way for viability-aware metagenomics in environmental, veterinary, and clinical applications.
'Any future nanopore-based metagenomic study could make viability predictions for free without additional costs and laboratory work, and any existing archived nanopore data could be assessed in terms of its microorganisms’ viability'
Ürel et al. 2025
Sample type: bacterial cultures
Kit: Rapid Barcoding Kit
