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Towards a genome-based taxonomy for prokaryotes.

Konstantinos T Konstantinidis1, James M Tiedje

  • 1Center for Microbial Ecology, Michigan State University, East Lansing, Michigan 48824-1325, USA.

Journal of Bacteriology
|September 15, 2005
PubMed
Summary

Prokaryotic taxonomy ranks show significant overlap in genetic relatedness, limiting predictive power. Average amino acid identity (AAI) analysis offers a standardized approach for genome-based microbial classification and evaluating phylogenetic markers.

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Area of Science:

  • Microbiology
  • Genomics
  • Bioinformatics

Background:

  • Prokaryotic taxonomy relies on 16S rRNA gene phylogeny, but lacks defined standards for inter-rank genetic relatedness.
  • The comparability of taxonomic ranks across different prokaryotic organisms remains unclear due to the absence of absolute relatedness measures.

Purpose of the Study:

  • To investigate the relationship between shared gene content and genetic relatedness in prokaryotes.
  • To evaluate the predictive power of current taxonomic ranks and propose a standardized approach for microbial classification.

Main Methods:

  • Analyzed 175 fully sequenced prokaryotic strains.
  • Utilized Average Amino Acid Identity (AAI) of shared genes as a measure of genetic relatedness.
  • Assessed the phylogenetic signal of various genetic markers, including 16S rRNA, 23S rRNA, and protein-coding genes.

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Main Results:

  • Adjacent taxonomic ranks (e.g., phylum, class) exhibit substantial overlap in genetic and gene content relatedness, indicating limited predictive power.
  • Non-adjacent rank overlaps are minimal and linked to taxonomic inconsistencies.
  • The 23S rRNA gene performs comparably to the 16S rRNA gene as a phylogenetic marker; protein-coding genes also show strong phylogenetic signals.

Conclusions:

  • The current prokaryotic taxonomy system has limited predictive power regarding genetic relatedness.
  • The AAI-based approach provides a robust method for standardizing taxonomy and evaluating phylogenetic markers.
  • This genome-based approach can significantly advance microbial taxonomy.