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MIMOSA2: a metabolic network-based tool for inferring mechanism-supported relationships in microbiome-metabolome data.
Noecker, Cecilia; Eng, Alexander; Muller, Efrat; Borenstein, Elhanan.
Afiliação
  • Noecker C; Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
  • Eng A; Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
  • Muller E; Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 6997801, Israel.
  • Borenstein E; Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 6997801, Israel.
Bioinformatics ; 38(6): 1615-1623, 2022 03 04.
Article em En | MEDLINE | ID: mdl-34999748
ABSTRACT
MOTIVATION Recent technological developments have facilitated an expansion of microbiome-metabolome studies, in which samples are assayed using both genomic and metabolomic technologies to characterize the abundances of microbial taxa and metabolites. A common goal of these studies is to identify microbial 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 microbe-metabolite links.

RESULTS:

We present MIMOSA2, an R package and web application for model-based integrative analysis of microbiome-metabolome datasets. MIMOSA2 uses genomic and metabolic 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 taxonomic contributors to metabolite variation. MIMOSA2 supports various input data types and customization with user-defined metabolic pathways. We establish MIMOSA2's ability to identify ground truth microbial mechanisms in simulation datasets, compare its results with experimentally inferred mechanisms in honeybee microbiota, and demonstrate its application in two human studies of inflammatory bowel disease. 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 at http//elbo-spice.cs.tau.ac.il/shiny/MIMOSA2shiny/ and as an R package from http//www.borensteinlab.com/software_MIMOSA2.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Metaboloma / Microbiota Tipo de estudo: Prognostic_studies Limite: Animals / Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Metaboloma / Microbiota Tipo de estudo: Prognostic_studies Limite: Animals / Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article