Metabology: Analysis of metabolomics data using community ecology tools

Several areas such as microbiology, botany, and medicine use genetic information and computational tools to organize, classify and analyze data. However, only recently has it been possible to obtain the chemical ontology of metabolites computationally. The systematic classification of metabolites into classes opens the way for adapting methods that previously used genetic taxonomy to now accept chemical ontology. Community ecology tools are ideal for this adaptation as they have mature methods and enable exploratory data analysis with established statistical tools. This study introduces the Metabology approach, which transforms metabolites into an ecosystem where the metabolites (species) are related by chemical ontology. In the present work, we demonstrate the applicability of this new approach using publicly available data from a metabolomics study of human plasma that searched for prognostic markers of COVID-19, and in an untargeted metabolomics study carried out by our laboratory using Lasiodiplodia theobromae fungal pathogen supernatants. [Display omitted] •Metabology converts metabolomics data into an ecosystem of entities related by chemical ontology.•This ecosystem can be analyzed as a biome of metabolites through community ecology tools.•The approach allows the analysis of the correlation between chemical families and metadata.•Metabology showed a significant change in alpha diversity of metabolites from L. theobromae.•Metabology revealed chemical families related to COVID-19 disease severity.