Next Article in Journal
Application of 1H-NMR Metabolomics for the Discovery of Blood Plasma Biomarkers of a Mediterranean Diet
Previous Article in Journal
Steroidomics for the Prevention, Assessment, and Management of Cancers: A Systematic Review and Functional Analysis

The metaRbolomics Toolbox in Bioconductor and beyond

Preventive and Clinical Nutrition, University of Copenhagen, Rolighedsvej 30, 1958 Frederiksberg C, Denmark
Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO 80523, USA
Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
Leibniz-Institut für Analytische Wissenschaften—ISAS—e.V., Otto-Hahn-Straße 6b, 44227 Dortmund, Germany
Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany
The Australian Wine Research Institute, Metabolomics Australia, PO Box 197, Adelaide SA 5064, Australia
Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany
Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, 39100 Bolzano, Italy
The International Agency for Research on Cancer, 150 cours Albert Thomas, CEDEX 08, 69372 Lyon, France
Department of Effect-Directed Analysis, Helmholtz Centre for Environmental Research—UFZ, Permoserstraße 15, 04318 Leipzig, Germany
Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
Eawag, Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dubendorf, Switzerland
CEA, LIST, Laboratory for Data Sciences and Decision, MetaboHUB, Gif-Sur-Yvette F-91191, France
Phenome Centre Birmingham and School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands
Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, 85764 Neuherberg, Germany
Chair of Analytical Food Chemistry, Technische Universität München, 85354 Weihenstephan, Germany
German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig Deutscher, Platz 5e, 04103 Leipzig, Germany
Authors to whom correspondence should be addressed.
Metabolites 2019, 9(10), 200;
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
Show Figures

Graphical abstract

MDPI and ACS Style

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.

AMA Style

Stanstrup J, Broeckling CD, Helmus R, Hoffmann N, Mathé E, Naake T, Nicolotti L, Peters K, Rainer J, Salek RM, Schulze T, Schymanski EL, Stravs MA, Thévenot EA, Treutler H, Weber RJM, Willighagen E, Witting M, Neumann S. The metaRbolomics Toolbox in Bioconductor and beyond. Metabolites. 2019; 9(10):200.

Chicago/Turabian Style

Stanstrup, Jan, Corey D. Broeckling, Rick Helmus, Nils Hoffmann, Ewy Mathé, Thomas Naake, Luca Nicolotti, Kristian Peters, Johannes Rainer, Reza M. Salek, Tobias Schulze, Emma L. Schymanski, Michael A. Stravs, Etienne A. Thévenot, Hendrik Treutler, Ralf J.M. Weber, Egon Willighagen, Michael Witting, and Steffen Neumann. 2019. "The metaRbolomics Toolbox in Bioconductor and beyond" Metabolites 9, no. 10: 200.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop