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Related Experiment Videos

Detecting aberrant strains in bacterial groups as an aid to constructing databases for computer identification.

C D Langham1, P H Sneath, S T Williams

  • 1Department of Microbiology, University of Leicester, UK.

The Journal of Applied Bacteriology
|April 1, 1989
PubMed
Summary

A computer program, OUTLIER, identifies outlying bacterial strains using chi-square analysis. This helps improve database quality by excluding atypical strains, with -log10 Willcox likelihood recommended for routine use.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • High-quality databases are crucial for computer-assisted identification systems.
  • Inclusion of non-target strains compromises database integrity, especially when ancillary identification criteria are unavailable.
  • Identifying and excluding atypical strains is a strategy to enhance database quality.

Purpose of the Study:

  • To evaluate the OUTLIER computer program for detecting outlying bacterial strains.
  • To assess the effectiveness of different identification coefficients within the OUTLIER program.
  • To provide recommendations for objective criteria to improve bacterial database construction.

Main Methods:

  • The OUTLIER program was evaluated for its ability to detect aberrant strains in bacterial clusters.

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  • The program employs chi-square goodness-of-fit as an objective criterion for outlier detection.
  • Four identification coefficients were examined using eight bacterial datasets to determine their relative merits.

    • Main Results:

    • The OUTLIER program effectively identifies atypical strains for exclusion from databases.
    • The -log10 Willcox likelihood and Taxonomic distance squared coefficients showed comparable results, with -log10 Willcox likelihood being more useful.
    • Pattern distance squared indicated metabolically atypical strains, while Variance-weighted Taxonomic distance squared yielded anomalous results.

    Conclusions:

    • The OUTLIER program provides an objective method for identifying and excluding aberrant strains, thereby enhancing bacterial database quality.
    • The -log10 Willcox likelihood coefficient is recommended for routine use in identifying outlying strains.
    • Careful selection of coefficients is essential for accurate outlier detection and robust database construction.