1887

Abstract

The character state data obtained for clusters defined in a previous phenetic classification were used to construct two probabilistic matrices for species. These superseded an original published identification matrix by exclusion of other genera and the inclusion of more species. Separate matrices were constructed for major and minor clusters. The minimum number of diagnostic characters for each matrix was selected by computer programs for determination of character separation indices () and a selection of group diagnostic properties (). The resulting matrices consisted of 26 phena × 50 characters (major clusters) and 28 phena × 39 characters (minor clusters). Cluster overlap ( program) was small in both matrices. Identification scores were used to evaluate both matrices. The theoretically best scores for the most typical example of each cluster ( program) were all satisfactory. Input of test data for randomly selected cluster representatives resulted in correct identification with high scores. The major cluster matrix was shown to be practically sound by its application to 35 unknown soil isolates, 77% of which were clearly identified. The minor cluster matrix provides tentative probabilistic identifications as the small number of strains in each cluster reduces its ability to withstand test variation. A diagnostic table for single-membered clusters, constructed using the and programs, was also produced.

Loading

Article metrics loading...

/content/journal/micro/10.1099/00221287-135-1-121
1989-01-01
2024-03-29
Loading full text...

Full text loading...

/deliver/fulltext/micro/135/1/mic-135-1-121.html?itemId=/content/journal/micro/10.1099/00221287-135-1-121&mimeType=html&fmt=ahah

References

  1. Davis A.W., Atlas K.M., Krichevsky M.I. 1983; Development of probability matrices for identification of Alaskan marine bacteria. International Journal of Systematic Bacteriology 33:803–810
    [Google Scholar]
  2. Dawson C.W., Sneath P.H.A. 1985; A probability matrix for the identification of vibrios. Journal of Applied Bacteriology 58:407–423
    [Google Scholar]
  3. Goodfellow M., Williams S.T., Alderson G. 1986a; Transfer of Kitasatoa purpurea Matsumae and Hara to the genus Streptomyces as Streptomyces purpureus comb. nov. Systematic and Applied Microbiology 8:65–66
    [Google Scholar]
  4. Goodfellow M., Williams S.T., Alderson G. 1986b; Transfer of Chainia species to the genus Streptomyces with emended description of species. Systematic and Applied Microbiology 8:55–60
    [Google Scholar]
  5. Goodfellow M., Lonsdale C., James A.L., Mcnamara O.C. 1987; Rapid biochemical tests for the characterisation of streptomycetes. FEMS Microbiology Letters 43:39–44
    [Google Scholar]
  6. Hill L.R. 1974; Theoretical aspects of numerical identification. International Journal of Systematic Bacteriology 24:494–499
    [Google Scholar]
  7. Hill L.R., Lapage S.P., Bowie I.S. 1978; Computer-assisted identification of coryneform bacteria. In Coryneform Bacteria pp. 181–215 Bousfield I.J., Callely A.G. Edited by London: Academic Press;
    [Google Scholar]
  8. Holmes B., Dawson C.A., Pinning C.A. 1986; A revised probability matrix for the identification of Gram-negative, aerobic, rod-shaped, fermentative bacteria. Journal of General Microbiology 132:3113–3135
    [Google Scholar]
  9. Lapage S.P., Bascomb S., Willcox W.R., Curtis M.A. 1983; Identification of bacteria by computer: general aspects and perspectives. Journal of General Microbiology 77:273–290
    [Google Scholar]
  10. Locci R., Rogers J., Sardi P., Schofield G.M. 1981; A preliminary numerical study on named species of the genus Streptoverticillium. Annali di microbiologia 31:115–121
    [Google Scholar]
  11. Mordarski M., Goodfellow M., Williams S.T. 1986; Evaluation of species groups in the genus Streptomyces. In Biological, Biochemical and Biomedical Aspects of Actinomycetes pp. 517–525 Szabo G., Biro S., Goodfellow M. Edited by Budapest: Akademiai Kiado;
    [Google Scholar]
  12. Saddler G.S., O’donnell A.G., Goodfellow M., Minnikin D.E. 1987; SIMCA pattern recognition in the analysis of streptomycete fatty acids. Journal of General Microbiology 133:1137–1147
    [Google Scholar]
  13. Sneath P.H.A. 1974; Test reproducibility in relation to identification. International Journal of Systematic Bacteriology 24:508–523
    [Google Scholar]
  14. Sneath P.H.A. 1978; Identification of microorganisms. In Essays in Microbiology pp. 10/1–10/32 Norris J.R., Richmond M.H. Edited by Chichester: Wiley;
    [Google Scholar]
  15. Sneath P.H.A. 1979a; BASIC program for identification of an unknown with presence-absence data against an identification matrix of percent positive characters. Computers & Geosciences 5:195–213
    [Google Scholar]
  16. Sneath P.H.A. 1979b; BASIC program for character separation indices from an identification matrix of percent positive characters. Computers & Geosciences 5:349–357
    [Google Scholar]
  17. Sneath P.H.A. 1980a; BASIC program for the most diagnostic properties of groups from an identification matrix of percent positive characters. Computers & Geosciences 6:21–26
    [Google Scholar]
  18. Sneath P.H.A. 1980b; BASIC program for determining the best identification scores possible from the most typical examples when compared with an identification matrix of percent positive characters. Computers & Geosciences 6:27–34
    [Google Scholar]
  19. Sneath P.H.A. 1980c; BASIC program for determining overlap between groups in an identification matrix of percent positive characters. Computers & Geosciences 6:267–278
    [Google Scholar]
  20. Sneath P.H.A., Johnson R. 1972; The influence on numerical taxonomic similarities of errors in microbiological tests. Journal of General Microbiology 72:377–392
    [Google Scholar]
  21. Sneath P.H.A., Sokal R.R. 1973 Numerical Taxonomy: the Principles and Practice of Numerical Classification. San Francisco: W.H. Freeman;
    [Google Scholar]
  22. Vickers J.C., Williams S.T., Ross G.W. 1984; A taxonomic approach to selective isolation of streptomycetes from soil. In Biological, Biochemical and Biomedical Aspects of Actinomycetes pp. 553–561 Ortiz-Ortiz L., Bojalil L.F., Yakoleff V. Edited by Orlando: Academic Press;
    [Google Scholar]
  23. Wayne L.G., Krichevsky E.J., Love L.L., Johnson R., Krichevsky M.I. 1980; Taxonomic probability matrix for use with slowly- growing mycobacteria. International Journal of Systematic Bacteriology 30:528–538
    [Google Scholar]
  24. Willcox W.R., Lapage S.P., Bascomb S., Curtis M.A. 1973; Identification of bacteria by computer: theory and programming. Journal of General Microbiology 77:317–330
    [Google Scholar]
  25. Williams S.T., Goodfellow M., Alderson G., Wellington E.M.H., Sneath P.H.A., Sackin M.J. 1983a; Numerical classification of Strepto- myces and related genera. Journal of General Microbiology 129:1743–1813
    [Google Scholar]
  26. Williams S.T., Goodfellow M., Wellington E.M.H., Vickers J.C., Alderson G., Sneath P.H.A., Sackin M.J., Mortimer A.M. 1983b; A probability matrix for identification of some streptomycetes. Journal of General Microbiology 129:1815–1830
    [Google Scholar]
  27. Williams S.T., Locci R., Vickers J.C., Schofield G.M., Sneath P.H.A., Mortimer A.M. 1985; Probabilistic identification of Streptoverti- cillium species. Journal of General Microbiology 131:1681–1689
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/micro/10.1099/00221287-135-1-121
Loading
/content/journal/micro/10.1099/00221287-135-1-121
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error