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Network analysis based on unique spectral features enables an efficient selection of genomically diverse operational isolation units

Charles Dumolin (UGent) , Charlotte Peeters (UGent) , Evelien De Canck (UGent) , Nico Boon (UGent) and Peter Vandamme (UGent)
(2021) MICROORGANISMS. 9(2).
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Abstract
Culturomics-based bacterial diversity studies benefit from the implementation of MALDI-TOF MS to remove genomically redundant isolates from isolate collections. We previously introduced SPeDE, a novel tool designed to dereplicate spectral datasets at an infraspecific level into operational isolation units (OIUs) based on unique spectral features. However, biological and technical variation may result in methodology-induced differences in MALDI-TOF mass spectra and hence provoke the detection of genomically redundant OIUs. In the present study, we used three datasets to analyze to which extent hierarchical clustering and network analysis allowed to eliminate redundant OIUs obtained through biological and technical sample variation and to describe the diversity within a set of spectra obtained from 134 unknown soil isolates. Overall, network analysis based on unique spectral features in MALDI-TOF mass spectra enabled a superior selection of genomically diverse OIUs compared to hierarchical clustering analysis and provided a better understanding of the inter-OIU relationships.
Keywords
MALDI-TOF MS, network cluster analysis, bacterial diversity, species subgrouping

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MLA
Dumolin, Charles, et al. “Network Analysis Based on Unique Spectral Features Enables an Efficient Selection of Genomically Diverse Operational Isolation Units.” MICROORGANISMS, vol. 9, no. 2, 2021, doi:10.3390/microorganisms9020416.
APA
Dumolin, C., Peeters, C., De Canck, E., Boon, N., & Vandamme, P. (2021). Network analysis based on unique spectral features enables an efficient selection of genomically diverse operational isolation units. MICROORGANISMS, 9(2). https://doi.org/10.3390/microorganisms9020416
Chicago author-date
Dumolin, Charles, Charlotte Peeters, Evelien De Canck, Nico Boon, and Peter Vandamme. 2021. “Network Analysis Based on Unique Spectral Features Enables an Efficient Selection of Genomically Diverse Operational Isolation Units.” MICROORGANISMS 9 (2). https://doi.org/10.3390/microorganisms9020416.
Chicago author-date (all authors)
Dumolin, Charles, Charlotte Peeters, Evelien De Canck, Nico Boon, and Peter Vandamme. 2021. “Network Analysis Based on Unique Spectral Features Enables an Efficient Selection of Genomically Diverse Operational Isolation Units.” MICROORGANISMS 9 (2). doi:10.3390/microorganisms9020416.
Vancouver
1.
Dumolin C, Peeters C, De Canck E, Boon N, Vandamme P. Network analysis based on unique spectral features enables an efficient selection of genomically diverse operational isolation units. MICROORGANISMS. 2021;9(2).
IEEE
[1]
C. Dumolin, C. Peeters, E. De Canck, N. Boon, and P. Vandamme, “Network analysis based on unique spectral features enables an efficient selection of genomically diverse operational isolation units,” MICROORGANISMS, vol. 9, no. 2, 2021.
@article{8698542,
  abstract     = {{Culturomics-based bacterial diversity studies benefit from the implementation of MALDI-TOF MS to remove genomically redundant isolates from isolate collections. We previously introduced SPeDE, a novel tool designed to dereplicate spectral datasets at an infraspecific level into operational isolation units (OIUs) based on unique spectral features. However, biological and technical variation may result in methodology-induced differences in MALDI-TOF mass spectra and hence provoke the detection of genomically redundant OIUs. In the present study, we used three datasets to analyze to which extent hierarchical clustering and network analysis allowed to eliminate redundant OIUs obtained through biological and technical sample variation and to describe the diversity within a set of spectra obtained from 134 unknown soil isolates. Overall, network analysis based on unique spectral features in MALDI-TOF mass spectra enabled a superior selection of genomically diverse OIUs compared to hierarchical clustering analysis and provided a better understanding of the inter-OIU relationships.}},
  articleno    = {{416}},
  author       = {{Dumolin, Charles and Peeters, Charlotte and De Canck, Evelien and Boon, Nico and Vandamme, Peter}},
  issn         = {{2076-2607}},
  journal      = {{MICROORGANISMS}},
  keywords     = {{MALDI-TOF MS,network cluster analysis,bacterial diversity,species subgrouping}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{11}},
  title        = {{Network analysis based on unique spectral features enables an efficient selection of genomically diverse operational isolation units}},
  url          = {{http://dx.doi.org/10.3390/microorganisms9020416}},
  volume       = {{9}},
  year         = {{2021}},
}

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