The metaRbolomics Toolbox in Bioconductor and beyond

Jan Stanstrup*, Corey D. Broeckling, Rick Helmus, Nils Hoffmann, Ewy Mathe, Thomas Naake, Luca Nicolotti, Kristian Peters, Johannes Rainer, Reza M. Salek, Tobias Schulze, Emma L. Schymanski, Michael A. Stravs, Etienne A. Thevenot, Hendrik Treutler, Ralf J. M. Weber, Egon Willighagen, Michael Witting, Steffen Neumann*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

41 Citations (Web of Science)

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 languageEnglish
Article number200
Number of pages55
JournalMetabolites
Volume9
Issue number10
DOIs
Publication statusPublished - Oct 2019

Keywords

  • metabolomics
  • lipidomics
  • mass Spectrometry
  • NMR spectroscopy
  • R
  • CRAN
  • bioconductor
  • signal processing
  • statistical data analysis
  • feature selection
  • compound identification
  • metabolite networks
  • data integration
  • MASS-SPECTROMETRY DATA
  • DIFFERENTIAL NETWORK ANALYSIS
  • HUMAN METABOLOME DATABASE
  • MISSING VALUE IMPUTATION
  • OPEN SOURCE SOFTWARE
  • AN R PACKAGE
  • FEATURE-SELECTION
  • HIGH-THROUGHPUT
  • FLOW-INJECTION
  • PEAK DETECTION

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