Use of predictive modeling for Propionibacterium strain classification

Abstract

Computed data analysis of biochemical or molecular profiles is currently used in studies of microbial taxonomy, epidemiology, and microbial diversity. We assessed the use of Partial Least Squares (PLS) regression for multivariate data analysis in bacteriology. We identified clear relationships between RAPD profiles of propionibacteria strains and their species classification, autolytic capacities, and their origins. The PLS regression also predicted species identity of some strains with RAPD profiles partially related to those of reference strains. The PLS analysis also allowed us to identify key characteristics to use to classify strains. PLS regression is particularly well adapted to i) describing a collection of bacterial isolates, ii) justifying bacterial groupings using several sets of data, and iii) predicting phenotypic characters of strains that have been classified by routine typing methods.