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Open AccessReview

The metaRbolomics Toolbox in Bioconductor and beyond

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Preventive and Clinical Nutrition, University of Copenhagen, Rolighedsvej 30, 1958 Frederiksberg C, Denmark
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Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO 80523, USA
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Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
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Leibniz-Institut für Analytische Wissenschaften—ISAS—e.V., Otto-Hahn-Straße 6b, 44227 Dortmund, Germany
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Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
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Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany
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The Australian Wine Research Institute, Metabolomics Australia, PO Box 197, Adelaide SA 5064, Australia
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Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany
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Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, 39100 Bolzano, Italy
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The International Agency for Research on Cancer, 150 cours Albert Thomas, CEDEX 08, 69372 Lyon, France
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Department of Effect-Directed Analysis, Helmholtz Centre for Environmental Research—UFZ, Permoserstraße 15, 04318 Leipzig, Germany
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Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
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Eawag, Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dubendorf, Switzerland
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CEA, LIST, Laboratory for Data Sciences and Decision, MetaboHUB, Gif-Sur-Yvette F-91191, France
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Phenome Centre Birmingham and School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
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Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands
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Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, 85764 Neuherberg, Germany
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Chair of Analytical Food Chemistry, Technische Universität München, 85354 Weihenstephan, Germany
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German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig Deutscher, Platz 5e, 04103 Leipzig, Germany
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Authors to whom correspondence should be addressed.
Metabolites 2019, 9(10), 200; https://doi.org/10.3390/metabo9100200
Received: 11 August 2019 / Revised: 16 September 2019 / Accepted: 17 September 2019 / Published: 23 September 2019
(This article belongs to the Special Issue Computational Metabolomics)
Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub. View Full-Text
Keywords: metabolomics; lipidomics; mass Spectrometry; NMR spectroscopy; R; CRAN; bioconductor; signal processing; statistical data analysis; feature selection; compound identification; metabolite networks; data integration metabolomics; lipidomics; mass Spectrometry; NMR spectroscopy; R; CRAN; bioconductor; signal processing; statistical data analysis; feature selection; compound identification; metabolite networks; data integration
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Stanstrup, J.; Broeckling, C.D.; Helmus, R.; Hoffmann, N.; Mathé, E.; Naake, T.; Nicolotti, L.; Peters, K.; Rainer, J.; Salek, R.M.; Schulze, T.; Schymanski, E.L.; Stravs, M.A.; Thévenot, E.A.; Treutler, H.; Weber, R.J.M.; Willighagen, E.; Witting, M.; Neumann, S. The metaRbolomics Toolbox in Bioconductor and beyond. Metabolites 2019, 9, 200.

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