MetaboShiny: interactive analysis and metabolite annotation of mass spectrometry-based metabolomics data

Joanna C. Wolthuis, Stefania Magnusdottir, Mia Pras-Raves, Maryam Moshiri, Judith J. M. Jans, Boudewijn Burgering, Saskia van Mil, Jeroen de Ridder

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5 Citations (Scopus)

Abstract

Direct infusion untargeted mass spectrometry-based metabolomics allows for rapid insight into a sample’s metabolic activity. However, analysis is often complicated by the large array of detected m/z values and the difficulty to prioritize important m/z and simultaneously annotate their putative identities. To address this challenge, we developed MetaboShiny, a novel R/RShiny-based metabolomics package featuring data analysis, database- and formula-prediction-based annotation and visualization. To demonstrate this, we reproduce and further explore a MetaboLights metabolomics bioinformatics study on lung cancer patient urine samples. MetaboShiny enables rapid and rigorous analysis and interpretation of direct infusion untargeted mass spectrometry-based metabolomics data.
Original languageEnglish
Article number99
JournalMetabolomics
Volume16
Issue number9
DOIs
Publication statusPublished - 1 Sep 2020

Keywords

  • Annotation
  • Direct infusion
  • Machine learning
  • Mass spectrometry
  • Metabolomics
  • R
  • Statistics

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