Comparison between two PCR-based bacterial identification methods through artificial neural network data analysis

Abstract

The 16S ribosomal ribonucleic acid (rRNA) and 16S-23S rRNA spacer region genes are commonly used as taxonomic and phylogenetic tools. In this study, two pairs of fluorescent-labeled primers for 16S rRNA genes and one pair of primers for 16S-23S rRNA spacer region genes were selected to amplify target sequences of 317 isolates from positive blood cultures. The polymerase chain reaction (PCR) products of both were then subjected to restriction fragment length polymorphism (RFLP) analysis by capillary electrophoresis after incomplete digestion by Hae III. For products of 16S rRNA genes, single-strand conformation polymorphism (SSCP) analysis was also performed directly. When the data were processed by artificial neural network (ANN), the accuracy of prediction based on 16S-23S rRNA spacer region gene RFLP data was much higher than that of prediction based on 16S rRNA gene SSCP analysis data (98.0% vs. 79.6%). This study proved that the utilization of ANN as a pattern recognition method was a valuable strategy to simplify bacterial identification when relatively complex data were encountered.