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| 001 | 1050167 | ||
| 005 | 20260106202636.0 | ||
| 024 | 7 | _ | |a 10.1093/gigascience/giaf100 |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2025-05863 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2025-05863 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Ürel, Harika |0 0009-0004-3732-4858 |b 0 |
| 245 | _ | _ | |a Nanopore- and AI-empowered microbial viability inference |
| 260 | _ | _ | |a Oxford |c 2025 |b Oxford University Press |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a 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 setting of UV-induced Escherichiacell death. The application of explainable artificial intelligence (AI) tools then allows us to pinpoint the signal patterns in the nanopore raw data that allow the model to make viability predictions at high accuracy. Using the model predictions as well as explainable AI, we show that our framework can be leveraged in a real-world application to estimate the viability of obligate intracellular Chlamydia, where traditional culture-based methods suffer from inherently high false-negative rates. This application shows that our viability model captures predictive patterns in the nanopore signal that can be utilized to predict viability across taxonomic boundaries. We finally show the limits of our model’s generalizability through antibiotic exposure of a simple mock microbial community, where a new model specific to the killing method had to be trained to obtain accurate viability predictions. Conclusions While the potential of our computational framework’s generalizability and applicability to metagenomic studies needs to be assessed in more detail, we here demonstrate for the first time the analysis of freely available nanopore signal data to infer the viability of microorganisms, with many potential applications in environmental, veterinary, and clinical settings. |
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| 536 | _ | _ | |a Helmholtz AI Consultant Team FB Information (E54.303.11) |0 G:(DE-Juel-1)E54.303.11 |c E54.303.11 |x 1 |
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| 700 | 1 | _ | |a Benassou, Sabrina |0 P:(DE-Juel1)192312 |b 1 |u fzj |
| 700 | 1 | _ | |a Marti, Hanna |0 0000-0002-8398-4708 |b 2 |
| 700 | 1 | _ | |a Reska, Tim |0 0009-0001-9700-5128 |b 3 |
| 700 | 1 | _ | |a Sauerborn, Ela |0 0000-0003-1347-0346 |b 4 |
| 700 | 1 | _ | |a Pinheiro Alves De Souza, Yuri |0 0000-0001-9854-6383 |b 5 |
| 700 | 1 | _ | |a Perlas, Albert |0 0000-0002-4035-2436 |b 6 |
| 700 | 1 | _ | |a Rayo, Enrique |0 0000-0002-4497-4339 |b 7 |
| 700 | 1 | _ | |a Biggel, Michael |0 0000-0002-1337-2132 |b 8 |
| 700 | 1 | _ | |a Kesselheim, Stefan |0 P:(DE-Juel1)185654 |b 9 |
| 700 | 1 | _ | |a Borel, Nicole |0 0000-0002-1556-9262 |b 10 |
| 700 | 1 | _ | |a Martin, Edward J |0 0009-0000-4180-4900 |b 11 |
| 700 | 1 | _ | |a Venegas, Constanza B |0 0000-0002-8359-8209 |b 12 |
| 700 | 1 | _ | |a Schloter, Michael |0 0000-0003-1671-1125 |b 13 |
| 700 | 1 | _ | |a Schröder, Kathrin |0 P:(DE-HGF)0 |b 14 |
| 700 | 1 | _ | |a Mittelstrass, Jana |0 0000-0001-5207-5520 |b 15 |
| 700 | 1 | _ | |a Prospero, Simone |0 0000-0002-9129-8556 |b 16 |
| 700 | 1 | _ | |a Ferguson, James M |0 0000-0002-6192-6937 |b 17 |
| 700 | 1 | _ | |a Urban, Lara |0 0000-0002-5445-9314 |b 18 |e Corresponding author |
| 773 | _ | _ | |a 10.1093/gigascience/giaf100 |g Vol. 14, p. giaf100 |0 PERI:(DE-600)2708999-X |p giaf100 |t GigaScience |v 14 |y 2025 |x 2047-217X |
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