{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Urel H"],"funding":["Munich School for Data Science","Helmholtz Association Initiative and Networking Fund","Helmholtz Principal Investigator Grant","Vontobel-Stiftung","Biotechnology and Biological Sciences Research Council","University of Zurich","STFC Food Network+ Scoping Grant"],"pagination":["giaf100"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12405693"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["14"],"pubmed_abstract":["<h4>Background</h4>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.<h4>Results</h4>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"],"journal":["GigaScience"],"pubmed_title":["Nanopore- and AI-empowered microbial viability inference."],"pmcid":["PMC12405693"],"funding_grant_id":["BB/M010996/1","HIDSS-006","1336/2004"],"pubmed_authors":["Borel N","Biggel M","Kesselheim S","Schroder K","Mittelstrass J","Sauerborn E","Ferguson JM","Urel H","Schloter M","Martin EJ","Pinheiro Alves De Souza Y","Urban L","Prospero S","Reska T","Rayo E","Marti H","Perlas A","Benassou S","Venegas CB"],"additional_accession":[]},"is_claimable":false,"name":"Nanopore- and AI-empowered microbial viability inference.","description":"<h4>Background</h4>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.<h4>Results</h4>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","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jan","modification":"2026-05-29T21:43:17.073Z","creation":"2026-04-08T06:06:08.82Z"},"accession":"S-EPMC12405693","cross_references":{"pubmed":["40899150"],"doi":["10.1093/gigascience/giaf100"]}}