The metaRbolomics Toolbox in Bioconductor and beyond.

Clicks: 437
ID: 52996
2019
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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.
Reference Key
stanstrup2019themetabolites Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Stanstrup, Jan;Broeckling, Corey D;Helmus, Rick;Hoffmann, Nils;Mathé, Ewy;Naake, Thomas;Nicolotti, Luca;Peters, Kristian;Rainer, Johannes;Salek, Reza M;Schulze, Tobias;Schymanski, Emma L;Stravs, Michael A;Thévenot, Etienne A;Treutler, Hendrik;Weber, Ralf J M;Willighagen, Egon;Witting, Michael;Neumann, Steffen;
Journal Metabolites
Year 2019
DOI
E200
URL
Keywords
Mass spectrometry data integration r metabolomics nmr spectroscopy feature selection signal processing lipidomics cran bioconductor compound identification metabolite networks statistical data analysis

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