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Mapping the bacterial metabolic niche space

Author

Listed:
  • Ashkaan K. Fahimipour

    (Department of Computer Science
    Southwest Fisheries Science Center)

  • Thilo Gross

    (Department of Computer Science
    Alfred-Wegener-Institut Helmholtz-Centre for Marine and Polar Research
    Helmholtz Institute for Functional Marine Biodiversity (HIFMB)
    University of Oldenburg, Institute for Chemistry and Biology of the Marine Environment)

Abstract

The rise in the availability of bacterial genomes defines a need for synthesis: abstracting from individual taxa, to see larger patterns of bacterial lifestyles across systems. A key concept for such synthesis in ecology is the niche, the set of capabilities that enables a population’s persistence and defines its impact on the environment. The set of possible niches forms the niche space, a conceptual space delineating ways in which persistence in a system is possible. Here we use manifold learning to map the space of metabolic networks representing thousands of bacterial genera. The results suggest a metabolic niche space comprising a collection of discrete clusters and branching manifolds, which constitute strategies spanning life in different habitats and hosts. We further demonstrate that communities from similar ecosystem types map to characteristic regions of this functional coordinate system, permitting coarse-graining of microbiomes in terms of ecological niches that may be filled.

Suggested Citation

  • Ashkaan K. Fahimipour & Thilo Gross, 2020. "Mapping the bacterial metabolic niche space," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18695-z
    DOI: 10.1038/s41467-020-18695-z
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    Cited by:

    1. Zeng, Fanqi & Bode, Nikolai & Gross, Thilo & Homer, Martin, 2024. "Unsupervised pattern and outlier detection for pedestrian trajectories using diffusion maps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 634(C).

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