Artificial Neural Networks in Bacteria Taxonomic Classification

Abstract

In 1980s, the face of the microbiology dramatically changed with the rRNA-based phylogenetic classifications, by Carl Woese. He delineated the three main branches of life. He used the technique not only to explore microbial diversity but also as a method for bacterial annotation. Today, rRNA-based analysis remains a central method in microbiology. Many researchers followed this track, using several new generations of Artificial Neural Networks they obtained high accuracies using available datasets of their time. Recently the number of known bacteria increased enormously. In this article we used ANN's to annotate bacterial 16S rRNA gene sequences from five selected phylums in Greengenes database taxonomy: Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria, and Chloroflexi. 93% average accuracy is obtained in classif-ications. When we used the bundle testing technique, the average accuracy easily raised to 100%.