The metaRbolomics Toolbox in Bioconductor and beyond
Research output: Contribution to journal › Review › Research › peer-review
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The metaRbolomics Toolbox in Bioconductor and beyond. / 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.
In: Metabolites, Vol. 9, No. 10, 200, 2019.Research output: Contribution to journal › Review › Research › peer-review
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TY - JOUR
T1 - The metaRbolomics Toolbox in Bioconductor and beyond
AU - Stanstrup, Jan
AU - Broeckling, Corey D
AU - Helmus, Rick
AU - Hoffmann, Nils
AU - Mathé, Ewy
AU - Naake, Thomas
AU - Nicolotti, Luca
AU - Peters, Kristian
AU - Rainer, Johannes
AU - Salek, Reza M
AU - Schulze, Tobias
AU - Schymanski, Emma L
AU - Stravs, Michael A
AU - Thévenot, Etienne A
AU - Treutler, Hendrik
AU - Weber, Ralf J M
AU - Willighagen, Egon
AU - Witting, Michael
AU - Neumann, Steffen
N1 - CURIS 2019 NEXS 315
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Faculty of Science
KW - Metabolomics
KW - Lipidomics
KW - Mass spectrometry
KW - NMR spectroscopy
KW - R
KW - CRAN
KW - Bioconductor
KW - Signal processing
KW - Statistical data analysis
KW - Feature selection
KW - Compound identification
KW - Metabolite networks
KW - Data integration
U2 - 10.3390/metabo9100200
DO - 10.3390/metabo9100200
M3 - Review
C2 - 31548506
VL - 9
JO - Metabolites
JF - Metabolites
SN - 2218-1989
IS - 10
M1 - 200
ER -
ID: 228088539