Probabilistic Identification of Bacteria for Windows

Bibliography

Published Bacterial Identification Matrices

The following table lists probabilistic identification matrices that has been complied by various means including bibliographic searching and cross referencing of known papers. The list covers papers published since 1980. This list is not be exhaustive, but it was compiled with the intention of identifying as many probabilistic identification matrices as possible. If you know of any omissions please let me know T.N.Bryant@southampton.ac.uk

Species/Groups covered Taxa Tests Data pub. Matrix eval Clus Anal Method % id ID coef Media References
Gram-negative, aerobic, non-fermenters 66 83 y y n 2 91.5 .999 Conventional Holmes et al., 1986b
Gram-negative, aerobic rod-shaped fermenters 110 66 y y n 2 89.2 .999 Conventional Holmes et al., 1986a
Enterobacteriaceae 41 16 y y n 2 91.8 .95 Conventional Multipoint inoculation Clayton et al., 1986
Enterobacteriaceae 90 50 y y . . . . Conventional Holmes & Costas, 1992
Pseudomonas species 26 70 y y . . . . Conventional Costas et al., 1992
Vibrios 31 50 y y n 2 79.4 .99 ConventionalAPI 20E Dawson & Sneath, 1985
Vibrio and related species 38 81 y y y 1 74.0 .999 Conventional Bryant et al., 1986
Aeromonas species 14 30 y y y 1 71.5 .90 microtitre Kämpfer & Altwegg, 1992
Aeromonas hybridization groups 15 32 y y . . . . Conventional Oakley et al., 1996
Aeromonas species 14 31 y             Carson et al., 2000
Campylobacteria 23 42 n y y 1 . 1 Conventional Holmes et al., 1992
Lactic acid bacteria 37 49 n n n 3 79.0 .90 API 50CH Cox & Thomsen, 1990
Lactic acid bacteria 59 27 y n n 3 98.0 .70 Conventional Döring et al., 1988
Lactic acid bacteria 11 53 y n n 2 . .999 API 50CH Maissin et al., 1987
Streptomyces species 23 41 y y y 1 81.3 .85 Conventional Williams et al., 1983
Streptomyces species (minor clusters) 26
28
50
39
y y y 1 77.0 .85 Conventional Langham et al., 1989
Streptomyces species 52 50 y y y 1 78.1 .90 Miniaturised Kämpfer & Kroppenstedt, 1991
Streptoverticillum species 24 41 y y y 1 . .95 Conventional Williams et al., 1985
Actinomyces species 25 53 y y y 1 71.9. . Conventional Seong et al., 1995
Actinomyces species 20 18 y n n 2 . . Conventional Sarkonen et al., 2001
Actionplanes species 15 53 y y y 1 . . Conventional Long, 1994
Phytopathogenic corynebacteria (subclusters) 35 19
27
y y y 1 . . Conventional Firrao & Locci, 1989
Coryneform bacteria 31 58 y y y 1 82.5 .90 Miniaturised Kampfer et al., 1993
Nocardioform bacteria 25 35 y y y 1 80 .90 Miniaturised Kampfer et al., 1990
Slowly growing mycobacteria 24 33 y n y 1 . . Conventional Wayne et al., 1991
Non-tuberculous mycobacteria 27 23 y n n 3 94.5 . Conventional Tortoli et al., 1992
Bacillus species 44 30 y y y 1 70.0 .95 Conventional Priest & Alexander, 1988
Bacillus sphaericus 14 29 y y y 1 100 .999 Conventional Alexander & Priest, 1990
Bacillus species 36 44 y y y 1 73.4 .99 Miniaturised Kampfer 1991
Aerobic Gram-positive cocci catalase positive 39 60 y y y 1 93.0 .999 API 50CH
API 20E
Feltham & Sneath, 1982
Aerobic Gram-positive cocci catalase negative 33 60 y y y 1 85.0 .999 API 50CH
API 20E
Feltham & Sneath, 1982
Micrococcus 29 35 n y y 1 . . . Alderson et al., 1991
Staphylococcus species 12 15 y y n 2 94.6 . API Staph Geary et al., 1989
Alaskan marine bacteria 86 61 n n y 1 93.0 .999 Conventional Davis et al., 1983
Medical bacteria to genus level 60 20 y y n 3 . .99 various Feltham et al., 1984
Acinetobacter 10 22 y y y 1 98 .90 conventional Kampfer et al., 1993



Key to Abbreviations

Data Pub = Identification data given in publication

Matrix eval = Identification matrix evaluated for overlap or uniqueness of taxa

Clus Anal = Data analysed using Cluster analysis prior to creation of starting matrix

Method = The method used to collect the data for the identification matrix

  1. Cluster Analysis of strain data
  2. Use of data from identified isolates
  3. Data collected from the literature

%id = Percentage of strains identified when matrix evaluated

ID coef = Threshold (Willcox score) used for identification

References

Alderson, G., Amadi, E.N., Pulverer, G. and Zai, S. (1991) Recent advances in the classification and identification of the genus Micrococcus. In: Jeljaszewicz/Ciborowski, (Ed.) The Staphylococci, Zentralblatt für Bakteriologie Supplement 21, pp. 103-109. Stuggart: Gustav Fisher Verlag.

Alexander, B. and Priest, F.G. (1990) Numerical classification and identification of Bacillus sphaericus including some strains pathogenic for mosquito larvaeJournal of General Microbiology 136, 367-376.

Bryant, T.N., Lee, J.V., West, P.A. and Colwell, R.R. (1986) A probability matrix for the identification of species of Vibrio and related generaJournal of Applied Bacteriology 61, 469-480.

Carson, J., Wagner,T., Wilson,T. and  Donachie, L. (2001) Miniaturized tests for computer-assisted identification of motile Aeromonas species with an improved probability matrix.  Journal of  Applied  Microbiology 90, 190-200.

Clayton, P., Feltham, R.K.A., Mitchell, C.J. and Sneath, P.H.A. (1986) Constructing a database for low cost identification Gram negative rods in clinical laboratoriesJournal of Clinical Pathology 39, 798-802.

Costas, M., Holmes, B., On, S.L.W. and Stead, D.E. (1992) Identification of medically important Pseudomonas species using computerized methods. In: Board, R.G., Jones, D. and Skinner, F.A. (Eds.) Identification methods in applied and environmental microbiology, pp. 1-27. Oxford: Blackwell Scientific Publications]

Cox, R.P. and Thomsen, J.K. (1990) Computer-aided identification of lactic acid bacteria using the API 50 CHL system.Letters in Applied Microbiology 10, 257-259.

Davis, A.W., Atlas, R.M. and Krichevsky, M.I. (1983) Development of probability matrices for identification of marine bacteria. International Journal of Systematic Bacteriology 33, 803-810.

Dawson, C.A. and Sneath, P.H.A. (1985) A probability matrix for the identification of vibriosJournal of Applied Bacteriology 58, 407-423.

Döring, B., Ehrhardt, S., Lücke, F. and Schillinger, U. (1988) Computer-assisted identification of lactic acid bacteria from meats. Systematic and Applied Microbiology 11, 67-74.

Feltham, R.K.A. and Sneath, P.H.A. (1982) Construction of matrices for computer-assisted identification of aerobic Gram-positive cocci. Journal of General Microbiology 128, 713-120.

Feltham, R.K.A., Wood, P.A. and Sneath, P.H.A. (1984) A general-purpose system for characterizing medically important bacteria to genus levelJournal of Applied Bacteriology 57, 279-290.

Firrao, G. and Locci, R. (1989) Identification by probabilistic methods of plant pathogenic corynebacteria. Annuals of Microbiology 39, 81-92.

Geary, C., Stevens, M., Sneath, P.H.A. and Mitchell, C.J. (1989) Construction of a database to identify Staphylococcus species. Journal of Clinical Pathology 42, 289-294.

Holmes, B. and Costas, M. (1992) Identification of Enterobacteriaceae by computerized methods. In: Board, R.G., Jones, D. and Skinner, F.A. (Eds.) Identification methods in applied and environmental microbiology, pp. 127-149. Oxford: Blackwell Scientific Publications]

Holmes, B., Dawson, C.A. and 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.

Holmes, B., On, S.L.W., Ganner, M. and Costas, M. (1992) Some new applications of probabilistic identification. In: Schindler, J. (Ed.) Proceedings of the conference on taxonomy and automated identification of bacteria, pp. 6-9. Prague:

Holmes, B., Pinning, C.A. and Dawson, C.A. (1986) A probability matrix for the identification of Gram-negative, aerobic, non-fermentative bacteria that grow on nutrient agarJournal of General Microbiology 132, 1827-1842.

Jilly, B.J. (1988) Microcomputer application of bayesean probability testing for the identification of bacteria.International Journal of Biomedical Computing 22, 107-119.

Kämpfer, P. (1991) Application of Miniaturised physiological tests in numerical classification and identification of some Bacilli . Journal of General and Applied Microbiology 37, 225-247.

Kämpfer, P. and Altwegg, M. (1992) Numerical classification and identification of Aeromonas genospeciesJournal of Applied Bacteriology 72, 341-351.

Kämpfer, P. Dott, W. and Kroppenstedt, R.M. (1990) Numerical classification and identification of some nocardioform bacteria. Journal of General and Applied Microbiology 36, 309-301.

Kämpfer, P. and Kroppenstedt, R.M. (1991) Probabilistic identification of streptomyces using miniaturized physiological tests. Journal of General Microbiology 137, 1893-1902.

Kämpfer, and Seiler, H. (1993) Probabilistic identification of coryneform bacteria. Journal of General and Applied Microbiology 39, 215-236.

Kämpfer, P. Tjernberg, I. and Ursing, J. (1993) Numerical classification and identification of Acinetobacter genomic species.  Journal of Applied Bacteriology 75, 259-268.

Langham, C.D., Williams, S.T., Sneath, P.H.A. and Mortimer, A.M. (1989) New probability matrices for identification of StreptomycesJournal of General Microbiology 135, 121-133.

Long, P.F. (1994) Identification of some industrially important Actioplanes species. Journal Industrial Microbiology 13, 300-310.

Maissin, R., Bernard, A., Duquenne, V., Baeten, S., Gerard, G. and Decallonne, J. (1987) Characteristics of a computer-aided procedure for the identification of lactobacilli. Belgian Journal of Food Chemistry and Biotechnology 42, 176-183.

Oakley, H.J., Ellis, J.E. and Gibson, L.F. (1996) A biochemical protocol for the differentiation of current genomospecies of Aeromonas. Zentralblatt fur bakteriologie-International Journal of Medical Microbiology Virology Parasitology and Infectious diseases 284, 32-46.

On, S.L.W., Holmes, B. and Sackin, M.J. (1996) A probability matrix for the identification of Campylobacters Helicobacters and allied taxaJournal of Applied Bacteriology 81,425-432.

Priest, F.G. and Alexander, B. (1988) A frequency matrix for the probabilistic identification of some bacilliJournal of General Microbiology 134, 3011-3018.

Seong, C.N., Park, S.K., Goodfellow, M., Kim, S.B., and Hah Y.C. (1995) Construction of probability matrix and selective medium for acidophilic actinomycetes using numerical classification data. Journal of Microbiology 33, 95-102.

Sarkonen, N., Könnönen, E., Summanen, P., Könnönen, M., and Jousimies-Somer, H., (2001) Phenotypic identification of Actinomyces and related species isolated from Human sources. Journal of Clinical Microbiology 39, 3955-3961.

Tortoli, E., Boddi, V. and Penati, V. (1992) Development and evaluation of a program and probability matrix for the computer-aided identification of non-tuberculous mycobacteria. Binary - Computing in Microbiology 4, 200-203.

Wayne, L.G., Good, R.C., Krichevsky, M.I., Blacklock, Z., David, H.L., Dawson, D., Gross, W., Hawkins, J., Vincent Levy-Frebault, V., McManus, C., Portaels, F., Rusch-Gerdes, S., Schroder, K.H., Silcox, V.A., Tsukamura, M., Van Den Breen, L. and Yarkrus, M.A. (1991) Fourth report of the cooperative, open-ended study of slowly growing mycobacteria by the international working group on mycobacterial taxonomy. International Journal of Systematic Bacteriology 41, 463-472.

Williams, S.T., Goodfellow, M., Wellington, E.M.H., Vickers, J.C., Alderson, G., Sneath, P.H.A., Sackin, M.J. and Mortimer, A.M. (1983) A probability matrix for identification of some streptomycetesJournal of General Microbiology129, 1815-1830.

Williams, S.T., Locci, R., Vickers, J.C., Schofield, G.M., Sneath, P.H.A. and Mortimer, A.M. (1985) Probabilistic identification of Streptoverticillium species. Journal of General Microbiology 131, 1681-1689.