eCOMET : an R package for evaluating metabolic diversity and enrichment from LC‐MS / MS data to test ecological hypotheses from individuals to ecosystems

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Summary: Methods in metabolomics have grown exponentially in recent years, providing new insight into the ecological function and evolutionary impact of diverse plant metabolites. Metabolomics requires a command of numerous tools, the outputs of which are typically integrated through in‐house custom code that presents a workflow bottleneck and a barrier to entry for researchers in ecology, evolution, and behavior who may benefit from adding a metabolomics perspective to their research. We introduce eCOMET , an R package for integrating and harmonizing the outputs of common metabolomics bioinformatics tools and conducting statistical analyses and data visualization methods useful for ecological metabolomics. Our package combines metabolome feature metadata with quantification tables (e.g. mzmine ), feature dissimilarity matrices (e.g. modified cosine and DreaMS ), and feature annotations (e.g. SIRIUS ) into a cohesive R data object to facilitate downstream analyses, including the calculation of diversity and disparity metrics and differential accumulation analysis. We provide two tutorials, each explores herbivore‐induced Arabidopsis thaliana metabolome and 10 co‐occurring tropical trees species metabolomes. Our goal is to make metabolomics accessible to a wider range of researchers in ecology, evolution, and behavior to unlock the potential of ecological metabolomics to generate novel insight into these fields.
Funding Information
  • Seoul National University
  • National Research Foundation of Korea (NRF‐2022R1A4A3024451)
  • National Research Foundation of Korea (RS‐2021‐NR061394)
  • National Research Foundation of Korea (RS‐2023‐00301976)
  • National Research Foundation of Korea (RS‐2024‐00434162)
  • National Research Foundation of Korea (RS‐2026‐25475700)
  • Graduate School, University of Texas at Austin (Stengl‐Wyer grant SWG‐22‐01)
  • Division of Environmental Biology (DEB 2240430)

This publication has 53 references indexed in Scilit: