A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R

Area of Science:

• Metabolomics
• Bioinformatics
• Analytical Chemistry

Background:

• Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) is widely adopted for metabolite discovery.
• Variability in LC-MS instrumentation and analysis leads to non-standardized datasets.
• Customized annotation workflows are essential for reliable interpretation of metabolomics data.

Purpose of the Study:

• To present a modular R package ecosystem for the annotation of untargeted metabolomics data.
• To provide a flexible infrastructure for creating and managing reference spectral databases.
• To support diverse data formats and enable reproducible annotation workflows.

Main Methods:

• Development of an R package ecosystem including MetaboCoreUtils, MetaboAnnotation, and CompoundDb.
• Implementation of MS1 (m/z, retention time) and MS2 (fragment spectra comparison) based annotation.
• Support for multiple data formats (MSP, MGF, mzML, mzXML, netCDF, MassBank, SQL).

Main Results:

• A modular and customizable infrastructure for untargeted metabolomics data annotation.
• Creation and management of reference compound databases using the CompoundDb package.
• Compatibility with a wide range of LC-MS data formats and R environments.

Conclusions:

• The R package ecosystem facilitates reproducible and tailored annotation workflows for untargeted LC-MS data.
• The modular design allows for integration and re-use of core functionalities in other R packages.
• The developed tools are unit-tested, documented, and available via GitHub and Bioconductor.