@article{7b2365a97e304c7a8f34f001aad7b4ec,
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 = "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",
author = "Jan Stanstrup and Broeckling, {Corey D.} and Rick Helmus and Nils Hoffmann and Ewy Mathe 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 Thevenot, {Etienne A.} and Hendrik Treutler and Weber, {Ralf J. M.} and Egon Willighagen and Michael Witting and Steffen Neumann",
note = "Funding Information: We thank all package developers for their contributions to a vibrant metaRbolomics community, and all participants of several workshops (International Conference of the Metabolomics Society 2016, Dublin, Ireland and Dagstuhl 2017, Germany, and metaRbolomics 2019, Wittenberg, Germany), where R packages were discussed. For comments and advice in several sections we acknowledge Ren{\'e} Meier from IPB Halle and Natoiya Lloyd from Metabolomics Australia (South Australian node) which is funded through Bioplatforms Australia Pty Ltd. (BPA), a National Collaborative Research Infrastructure Strategy (NCRIS), and investment from the South Australian State Government and the AustralianWine Research Institute (AWRI). CB acknowledged funding by US NIH (1U01CA235507-01) and the CSU Office of the Vice President for Research. NH acknowledges funding by the Ministerium f{\"u}r Kultur und Wissenschaft des Landes Nordrhein-Westfalen, the Regierende B{\"u}rgermeister von Berlin-inkl. Wissenschaft und Forschung and from BMBF funding under grant number 031L0108A. EM acknowledges funding by US NIH (1R03CA222428-01). TN acknowledges the support by the IMPRS-PMPG program at the MPI-MP. LNacknowledges “Metabolomics South Australia which is funded through Bioplatforms Australia Pty Ltd. (BPA), a National Collaborative Research Infrastructure Strategy (NCRIS), and investment from the South Australian State Government and The Australian Wine Research Institute”. ELS is supported by the Luxembourg National Research Fund (FNR) for project 12341006. MS acknowledges funding from Eawag. ET acknowledges funding from the ANR (MetaboHUB national infrastructure for metabolomics and fluxomics, ANR-11-INBS-0010). KP, HT and SN acknowledge BMBF funding under grant number 031L0107. ET, SN acknowledge funding from the European Commission PhenoMeNal Grant EC654241. Publisher Copyright: {\textcopyright} 2019 by the authors. Licensee MDPI, Basel, Switzerland.",
year = "2019",
month = oct,
doi = "10.3390/metabo9100200",
language = "English",
volume = "9",
journal = "Metabolites",
issn = "2218-1989",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "10",
}