The metaRbolomics Toolbox in Bioconductor and beyond

Area of Science:

• Computational metabolomics
• Bioinformatics
• Data science in biology

Background:

• Metabolomics studies complex biological systems using advanced analytical techniques.
• These studies generate large, high-dimensional datasets requiring sophisticated processing and analysis.
• Open-source tools are crucial for innovative, open, and reproducible scientific practices.

Purpose of the Study:

• To provide a comprehensive overview of R packages for computational metabolomics.
• To cover various stages of the metabolomics workflow, from data processing to pathway analysis.
• To highlight the utility of R for creating integrated and reproducible metabolomics workflows.

Main Methods:

• Systematic review of R packages for metabolomics.
• Categorization of packages based on their function in the metabolomics workflow (data processing, biostatistics, annotation, pathway analysis).
• Inclusion of packages from CRAN, Bioconductor, and GitHub.

Main Results:

• Identification and summary of over 200 R packages relevant to metabolomics.
• Discussion of packages supporting data processing, biostatistics, metabolite identification, and network analysis.
• Exploration of multifunctional workflows, user interfaces, and integration with workflow management systems.

Conclusions:

• The R environment offers a powerful and flexible platform for metabolomics data analysis.
• Numerous open-source R packages facilitate reproducible and integrated metabolomics workflows.
• This review serves as a valuable resource for researchers in computational metabolomics.