Multimodal Phylogeny for Taxonomy: integrating information from nucleotide and aminoacid sequences.

The crucial role played by the analysis of microbial diversity in biotechnology-based innovations has increased the interest in the microbial taxonomy research area. Phylogenetic sequence analyses have contributed significantly to the advances in this field, also in the view of the large amount of sequence data collected in recent years. Phylogenetic analyses could be realized on the basis of protein-encoding nucleotide sequences or encoded amino acid molecules: these two mechanisms present different peculiarities, still starting from two alternative representations of the same information. This complementarity could be exploited to achieve a multimodal phylogenetic scheme that is able to integrate gene and protein information in order to realize a single final tree. This aspect has been poorly addressed in the literature. In this paper, we propose to integrate the two phylogenetic analyses using basic schemes derived from the multimodality fusion theory (or multiclassifier systems theory), a well-founded and rigorous branch for which its powerfulness has already been demonstrated in other pattern recognition contexts. The proposed approach could be applied to distance matrix-based phylogenetic techniques (like neighbor joining), resulting in a smart and fast method. The proposed methodology has been tested in a real case involving sequences of some species of lactic acid bacteria. With this dataset, both nucleotide sequence- and amino acid sequence-based phylogenetic analyses present some drawbacks, which are overcome with the multimodal analysis.