The metaRbolomics Toolbox in Bioconductor and beyond

Research output: Contribution to journalReviewResearchpeer-review

Standard

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 journalReviewResearchpeer-review

Harvard

Stanstrup, J, Broeckling, CD, Helmus, R, Hoffmann, N, Mathé, E, Naake, T, Nicolotti, L, Peters, K, Rainer, J, Salek, RM, Schulze, T, Schymanski, EL, Stravs, MA, Thévenot, EA, Treutler, H, Weber, RJM, Willighagen, E, Witting, M & Neumann, S 2019, 'The metaRbolomics Toolbox in Bioconductor and beyond', Metabolites, vol. 9, no. 10, 200. https://doi.org/10.3390/metabo9100200

APA

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

Vancouver

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

Author

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. / The metaRbolomics Toolbox in Bioconductor and beyond. In: Metabolites. 2019 ; Vol. 9, No. 10.

Bibtex

@article{390bc8982c1f4ac1ad171a1c4bd4c92f,
title = "The metaRbolomics Toolbox in Bioconductor and beyond",
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.",
keywords = "Faculty of Science, Metabolomics, Lipidomics, Mass spectrometry, NMR spectroscopy, R, CRAN, Bioconductor, Signal processing, Statistical data analysis, Feature selection, Compound identification, Metabolite networks, Data integration",
author = "Jan Stanstrup and Broeckling, {Corey D} and Rick Helmus and Nils Hoffmann and Ewy Math{\'e} and Thomas Naake and Luca Nicolotti and Kristian Peters and Johannes Rainer and Salek, {Reza M} and Tobias Schulze and Schymanski, {Emma L} and Stravs, {Michael A} and Th{\'e}venot, {Etienne A} and Hendrik Treutler and Weber, {Ralf J M} and Egon Willighagen and Michael Witting and Steffen Neumann",
note = "CURIS 2019 NEXS 315",
year = "2019",
doi = "10.3390/metabo9100200",
language = "English",
volume = "9",
journal = "Metabolites",
issn = "2218-1989",
publisher = "M D P I AG",
number = "10",

}

RIS

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

VL - 9

JO - Metabolites

JF - Metabolites

SN - 2218-1989

IS - 10

M1 - 200

ER -

ID: 228088539