% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{rel:1050167,
      author       = {Ürel, Harika and Benassou, Sabrina and Marti, Hanna and
                      Reska, Tim and Sauerborn, Ela and Pinheiro Alves De Souza,
                      Yuri and Perlas, Albert and Rayo, Enrique and Biggel,
                      Michael and Kesselheim, Stefan and Borel, Nicole and Martin,
                      Edward J and Venegas, Constanza B and Schloter, Michael and
                      Schröder, Kathrin and Mittelstrass, Jana and Prospero,
                      Simone and Ferguson, James M and Urban, Lara},
      title        = {{N}anopore- and {AI}-empowered microbial viability
                      inference},
      journal      = {GigaScience},
      volume       = {14},
      issn         = {2047-217X},
      address      = {Oxford},
      publisher    = {Oxford University Press},
      reportid     = {FZJ-2025-05863},
      pages        = {giaf100},
      year         = {2025},
      abstract     = {<b>Background</b><br>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.<br><br><b>Results</b><br>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 <i>Escherichia</i>cell 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 <i>Chlamydia</i>, 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.<br><br><b>Conclusions</b><br>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.},
      cin          = {JSC},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / Helmholtz AI Consultant
                      Team FB Information (E54.303.11)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(DE-Juel-1)E54.303.11},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1093/gigascience/giaf100},
      url          = {https://juser.fz-juelich.de/record/1050167},
}