The metaRbolomics Toolbox in Bioconductor and beyond

Jan Stanstrup; 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; Steffen Neumann

doi:10.3390/metabo9100200

The metaRbolomics Toolbox in Bioconductor and beyond

Jan Stanstrup^*, 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, Steffen Neumann

^*Corresponding author for this work

Nutrition and Health

23 Citations (Scopus)

21 Downloads (Pure)

Abstract

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.

Original language	English
Article number	200
Journal	Metabolites
Volume	9
Issue number	10
Number of pages	55
ISSN	2218-1989
DOIs	https://doi.org/10.3390/metabo9100200
Publication status	Published - Oct 2019

Keywords

Faculty of Science
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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10.3390/metabo9100200Licence: CC BY

Stanstrup et al_Metabolites_2019_Vol 9(10)_e200_(Review)Final published version, 1.64 MBLicence: CC BY

Cite this

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. (2019). The metaRbolomics Toolbox in Bioconductor and beyond. Metabolites, 9(10), [200]. https://doi.org/10.3390/metabo9100200

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 2019, 'The metaRbolomics Toolbox in Bioconductor and beyond', Metabolites, vol. 9, no. 10, 200. https://doi.org/10.3390/metabo9100200

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abstract = "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.",

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The metaRbolomics Toolbox in Bioconductor and beyond

Abstract

Keywords

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