Rethinking bacterial relationships in light of their molecular abilities

Determining the repertoire of a microbe’s molecular functions is a central question in microbial genomics. Modern techniques achieve this goal by comparing microbial genetic material against reference databases of functionally annotated genes/proteins or known taxonomic markers such as 16S rRNA. Here we describe a novel approach to exploring bacterial functional repertoires without reference databases. Our Fusion scheme establishes functional relationships between bacteria and thus assigns organisms to Fusion taxa that differ from otherwise defined taxonomic clades. Three key findings of our work stand out. First, Fusion profile comparisons outperform existing functional annotation schemes in recovering taxonomic labels. Second, Fusion-derived functional co-occurrence profiles reflect known metabolic pathways, suggesting a route for discovery of new ones. Finally, our alignment-free nucleic acid-based Siamese Neural Network model, trained using Fusion functions, enables finding shared functionality of very distant, possibly structurally different, microbial homologs. Our work can thus help annotate functional repertoires of bacterial organisms and further guide our understanding of microbial communities.