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Network-Thinking: Graphs to Analyze Microbial Complexity and Evolution.

Eduardo Corel1, Philippe Lopez1, Raphaël Méheust1

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Trends in Microbiology
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Summary
This summary is machine-generated.

Evolutionary biology is shifting from tree models to network-based approaches. New network methods, like sequence-similarity networks, enhance the analysis of molecular data for complex evolutionary studies.

Keywords:
bipartite graphevolutiongene transfergraph theoryintrogressionsymbiosis

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Area of Science:

  • Evolutionary Biology
  • Bioinformatics
  • Genomics

Background:

  • Tree-based models have been foundational in evolutionary studies.
  • Reticulate evolutionary processes are increasingly recognized for their importance across biological organization.
  • A paradigm shift towards network-thinking is emerging in evolutionary biology.

Purpose of the Study:

  • To introduce and highlight the utility of network-based models and methods in evolutionary biology.
  • To demonstrate how recent network approaches enhance the analysis of molecular datasets.
  • To showcase the application of these methods in understanding complex evolutionary phenomena.

Main Methods:

  • Utilizing sequence-similarity networks.
  • Employing genome networks.
  • Analyzing gene families-genomes bipartite graphs.

Main Results:

  • Network-based methods significantly enhance the usage of molecular datasets in comparative studies.
  • These analyses provide powerful tools for investigating intricate evolutionary processes.
  • Demonstrated applicability to diverse phenomena such as gene transfer and genome evolution.

Conclusions:

  • Network-based approaches represent a crucial complement to traditional tree-based models in evolutionary biology.
  • The adoption of network-thinking facilitates a deeper understanding of reticulate evolution.
  • Emerging network methods offer novel insights into gene, genome, and holobiont evolution.