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This study introduces an R package ecosystem for annotating untargeted metabolomics data from liquid chromatography-mass spectrometry (LC-MS). It enables reproducible data analysis across diverse experimental conditions and formats.

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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.