Abstract
Motivation Recent technological developments have facilitated an expansion of microbiome-metabolome studies, in which a set of microbiome samples are assayed using both genomic and metabolomic technologies to characterize the composition of microbial taxa and the concentrations of various metabolites. A common goal of many of these studies is to identify microbial features (species or genes) that contribute to differences in metabolite levels across samples. Previous work indicated that integrating these datasets with reference knowledge on microbial metabolic capacities may enable more precise and confident inference of such microbe-metabolite links.
Results We present MIMOSA2, an R package and web application for model-based integrative analysis of microbiome-metabolome datasets. MIMOSA2 uses reference databases to construct a community metabolic model based on microbiome data and uses this model to predict differences in metabolite levels across samples. These predictions are compared with metabolomics data to identify putative microbiome-governed metabolites and specific taxonomic contributors to metabolite variation. MIMOSA2 supports various input data types and can be customized to incorporate user-defined metabolic pathways. We demonstrate MIMOSA2’s ability to identify ground truth microbial mechanisms in simulation datasets, and compare its results with experimentally inferred mechanisms in a dataset describing honeybee gut microbiota. Overall, MIMOSA2 combines reference databases, a validated statistical framework, and a user-friendly interface to facilitate modeling and evaluating relationships between members of the microbiota and their metabolic products.
Availability and Implementation MIMOSA2 is implemented in R under the GNU General Public License v3.0 and is freely available as a web server and R package from www.borensteinlab.com/software_MIMOSA2.html.
Competing Interest Statement
The authors have declared no competing interest.
