Nanopore- and AI-empowered microbial viability inference.

ABSTRACT:

Background

The ability to differentiate between viable and dead microorganisms in metagenomic data is crucial for various microbial inferences, ranging from assessing ecosystem functions of environmental microbiomes to inferring the virulence of potential pathogens from metagenomic analysis. Established viability-resolved genomic approaches are labor-intensive as well as biased and lacking in sensitivity.

Results

We here introduce a new fully computational framework that leverages nanopore sequencing technology to assess microbial viability directly from freely available nanopore signal data. Our approach utilizes deep neural networks to learn features from such raw nanopore signal data that can distinguish DNA from viable and dead microorganisms in a controlled experimental

SUBMITTER: Urel H 

PROVIDER: S-EPMC12405693 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature