A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R
2022
Johannes Rainer | Andrea Vicini | Liesa Salzer | Jan Stanstrup | Josep M. Badia | Steffen Neumann | Michael A. Stravs | Vinicius Verri Hernandes | Laurent Gatto | Sebastian Gibb | Michael Witting
Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics experiments have become increasingly popular because of the wide range of metabolites that can be analyzed and the possibility to measure novel compounds. LC-MS instrumentation and analysis conditions can differ substantially among laboratories and experiments, thus resulting in non-standardized datasets demanding customized annotation workflows. We present an ecosystem of R packages, centered around the <i>MetaboCoreUtils</i>, <i>MetaboAnnotation</i> and <i>CompoundDb</i> packages that together provide a modular infrastructure for the annotation of untargeted metabolomics data. Initial annotation can be performed based on MS<sup>1</sup> properties such as <i>m/z</i> and retention times, followed by an MS<sup>2</sup>-based annotation in which experimental fragment spectra are compared against a reference library. Such reference databases can be created and managed with the <i>CompoundDb</i> package. The ecosystem supports data from a variety of formats, including, but not limited to, MSP, MGF, mzML, mzXML, netCDF as well as MassBank text files and SQL databases. Through its highly customizable functionality, the presented infrastructure allows to build reproducible annotation workflows tailored for and adapted to most untargeted LC-MS-based datasets. All core functionality, which supports base R data types, is exported, also facilitating its re-use in other R packages. Finally, all packages are thoroughly unit-tested and documented and are available on GitHub and through Bioconductor.
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