MetaboShiny: interactive analysis and metabolite annotation of mass spectrometry-based metabolomics data
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SHORT COMMUNICATION
MetaboShiny: interactive analysis and metabolite annotation of mass spectrometry‑based metabolomics data Joanna C. Wolthuis1,2 · Stefania Magnusdottir1 · Mia Pras‑Raves3 · Maryam Moshiri1 · Judith J. M. Jans3 · Boudewijn Burgering1,2 · Saskia van Mil1 · Jeroen de Ridder1,2 Received: 4 February 2020 / Accepted: 24 August 2020 © The Author(s) 2020
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. Keywords Metabolomics · R · Direct infusion · Annotation · Mass spectrometry · Statistics · Machine learning
1 Introduction The metabolome is the underlying biochemical layer of the genome, transcriptome and proteome, which reflects all the information expressed and modulated by these omics layers. Because metabolomics provides an almost direct readout of metabolic activity in the organism, metabolomics can be Saskia van Mil and Jeroen de Ridder are joint senior authors Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11306-020-01717-8) contains supplementary material, which is available to authorized users. * Saskia van Mil [email protected] * Jeroen de Ridder J.deRidder‑[email protected] Mia Pras‑Raves [email protected] 1
Center for Molecular Medicine, University Medical Center Utrecht and Utrecht University, STR3.217, PO Box 85060, 3508 AB Utrecht, The Netherlands
2
Oncode Institute, Utrecht, The Netherlands
3
Section Metabolic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
used to diagnose diseases from biofluids, discover new drugs and drug targets, and further precision medicine (Wishart 2016). A common method to acquire metabolomics data is mass spectrometry (MS), which records the input metabolites’ mass to charge ratios (m/z). An example of a MS method is direct infusion mass spectrometry (DI-MS), which detects tens to hundreds of thousands of m/z values representing metabolites at single part per million (ppm) accuracy (de Sain-van der Velden et al. 2017). DI-MS runtimes are in the order of one minute per sample, making it highly suitable for high-throughput applications, such as for instance in diagnostics applications. Problematically, DI-MS routinely produces over a hundred thousand unidentified m/z values, which
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