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Chemical Analysis of Archaea and Bacteria: A Critical Evaluation of its Use in Taxonomy and Identification

  • B. J. Tindall
Part of the Federation of European Microbiological Societies Symposium Series book series (FEMS, volume 75)

Abstract

I wonder if any task in microbiology can be so fraught with problems, prejudices, wrong information, or plain lack of scientific interest as trying to explain the full potential of the chemical analysis of prokaryotic cells? Our modern taxonomy has at its fingertips an array of powerful tools, and at no time in the history of microbial taxonomy have we been able to approach the subject in such a critical fashion. Despite this, confusion and uncertainty are rife. Much of the confusion, however, lies in very unexpected corners. I cannot help wondering how we continue to read of the “inferior” quality and interpretation of a phenetic taxonomy compared with a “phylogenetic” taxonomy. Yet, despite nearly 20 years of RNA analysis, a closer examination of these data reveals their phenetic origin, and only recently has classical cladistic methodology entered the field. Surely the power of the early analyses, based on Sab values, was its ability to illustrate a novel phonetic basis of prokaryotic taxonomy, which was the result of clearer/simpler markers of the present state of the evolution. Perhaps this is due to the general opinion that a phonetic system is confused with a methodology and philosophy pioneered and championed, among others by Sneath and Sokal (1973). It is not a phenetic data set which “fails” to be “phylogenetic” in its approach, but a particular method, given the name “phenetic taxonomy” or “numerical taxonomy” by various scientists, which did not have phylogeny as its primary objective; “a basic attitude of numerical taxonomists is the strict separation of phylogenetic speculation from taxonomic procedure” (Sneath and Sokal, 1973) . Nor can one criticise “numerical taxonomy” for “failing” to encompass methods other than morphology and biochemical tests when its users so blatantly refrained from even attempting to incorporate other data (sequence data, chemical data) so clearly outlined in the scope of the methodology.

Keywords

Chemical Data Nucleic Acid Sequencing Numerical Taxonomy Fatty Acid Pattern Microbial Lipid 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer Science+Business Media New York 1994

Authors and Affiliations

  • B. J. Tindall
    • 1
  1. 1.DSM-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbHBraunschweigFederal Republic of Germany

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