A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R.

Johannes Rainer,Michael Witting,Steffen Neumann,Laurent Gatto,Sebastian Gibb,Vinicius Verri Hernandes,Michael A Stravs,Josep M Badia,Andrea Vicini,Liesa Salzer,Jan Stanstrup

doi:10.3390/metabo12020173

Abstract

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 MetaboCoreUtils, MetaboAnnotation and CompoundDb packages that together provide a modular infrastructure for the annotation of untargeted metabolomics data. Initial annotation can be performed based on MS1 properties such as m/z and retention times, followed by an MS2-based annotation in which experimental fragment spectra are compared against a reference library. Such reference databases can be created and managed with the CompoundDb 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.

Highlights

Metabolomics studies small molecules that are substrates, products, or intermediates of metabolic reactions in a biological organism or system
We implemented a set of R packages that together provides a comprehensive and modular infrastructure for untargeted metabolomics data annotation
Here, we present the R packages MetaboCoreUtils and MetaboAnnotation that are well integrated with the R packages Spectra, MsCoreUtils, MsBackendMgf, MsBackendMsp and MsBackendMassbank, and together provide a modular and expandable ecosystem for the annotation of features from untargeted Liquid chromatography-mass spectrometry (LC-MS) and LC-MS/MS experiments using standard and established algorithms

Summary

Introduction

Metabolomics studies small molecules that are substrates, products, or intermediates of metabolic reactions in a biological organism or system. Similar analyses are carried out in environmental research or exposomics. This type of analysis yields features, defined as mass-to-charge-ratios (m/z)retention time (RT) pairs, their corresponding intensity or peak area in the respective samples and potentially one or more associated fragmentation spectra. These features need to be assigned to metabolite entities in order to make sense of metabolomics data, a process called metabolite annotation

Methods

Results

Discussion

Conclusion