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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 9:200 (2019)
Publ. Version/Full Text DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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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Publication type Article: Journal article
Document type Review
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
Language
Publication Year 2019
HGF-reported in Year 2019
ISSN (print) / ISBN 2218-1989
e-ISSN 2218-1989
Journal Metabolites
Quellenangaben Volume: 9, Issue: 10, Pages: , Article Number: 200 Supplement: ,
Publisher MDPI
Publishing Place St Alban-anlage 66, Ch-4052 Basel, Switzerland
Reviewing status Peer reviewed
Institute(s) Research Unit BioGeoChemistry and Analytics (BGC)
POF-Topic(s) 30202 - Environmental Health
Research field(s) Environmental Sciences
PSP Element(s) G-504800-001
DOI 10.3390/metabo9100200
WOS ID WOS:000494273300008
Scopus ID 85073434016
PubMed ID 31548506
Erfassungsdatum 2019-09-26
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