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  The metaRbolomics Toolbox in Bioconductor and beyond

Stanstrup, J., Broeckling, C. D., Helmus, R., Hoffmann, N., Mathé, E., Naake, T., et al. (2019). The metaRbolomics Toolbox in Bioconductor and beyond. Metabolites, 9(10): 200. doi:10.3390/metabo9100200.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-C1A8-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-C1A9-3
Genre: Journal Article

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 Creators:
Stanstrup, Jan1, Author
Broeckling, Corey D.1, Author
Helmus, Rick1, Author
Hoffmann, Nils1, Author
Mathé, Ewy1, Author
Naake, T.2, Author              
Nicolotti, Luca1, Author
Peters, Kristian1, Author
Rainer, Johannes1, Author
Salek, Reza M.1, Author
Schulze, Tobias1, Author
Schymanski, Emma L.1, Author
Stravs, Michael A.1, Author
Thévenot, Etienne A.1, Author
Treutler, Hendrik1, Author
Weber, Ralf J. M.1, Author
Willighagen, Egon1, Author
Witting, Michael1, Author
Neumann, Steffen1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Central Metabolism, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753339              

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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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Language(s): eng - English
 Dates: 2019
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.3390/metabo9100200
BibTex Citekey: metabo9100200
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Title: Metabolites
Source Genre: Journal
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Publ. Info: MDPI AG
Pages: - Volume / Issue: 9 (10) Sequence Number: 200 Start / End Page: - Identifier: Other: 2218-1989
CoNE: https://pure.mpg.de/cone/journals/resource/2218-1989