Bacteria classification using minimal absent words

Abstract

Bacteria classification has been deeply investigated with different tools for many purposes,such as early diagnosis, metagenomics, phylogenetics. Classification methods based on ribosomalDNA sequences are considered a reference in this area. We present a new classificatier for bacteriaspecies based on a dissimilarity measure of purely combinatorial nature. This measure is based onthe notion of Minimal Absent Words, a combinatorial definition that recently found applications inbioinformatics. We can therefore incorporate this measure into a probabilistic neural network in orderto classify bacteria species. Our approach is motivated by the fact that there is a vast literature on thecombinatorics of Minimal Absent Words in relation with the degree of repetitiveness of a sequence.We ran our experiments on a public dataset of Ribosomal RNA Sequences from the complex 16S. Ourapproach showed a very high score in the accuracy of the classification, proving hence that our methodis comparable with the standard tools available for the automatic classification of bacteria species.

Keywords

• bacteria classification
• supervised classification
• probabilistic neural network
• minimal absent word
• combinatorics on words