Recent Publications

The metaRbolomics Toolbox in Bioconductor and beyond


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.


Stanstrup, J. et al. The metaRbolomics Toolbox in Bioconductor and beyond. Metabolites (2019)

jmzTab-M: A Reference Parser, Writer, and Validator for the Proteomics Standards Initiative mzTab 2.0 Metabolomics Standard


mzTab 2.0 for metabolomics (mzTab-M) is the most recent standard format developed in collaboration by the Proteomics and Metabolomics Standards Initiatives including contributions by the recently founded Lipidomics Standards Initiative. mzTab-M is a redesign of the original mzTab format which was geared toward reporting of proteomics results and, as such, provided only limited support for metabolites. As a tab-delimited, spreadsheet-like format, mzTab-M captures experimental metadata, summary information on small molecules across assays, MS features as a basis for quantitation, and evidence to support the reporting of individual or feature group identifications. Here, we present the Java reference implementation for reading, writing, and validating mzTab-M files. Furthermore, we provide a web application for conveniently validating mzTab-M files by a graphical user interface, and a command line validator that accompanies the library. The jmzTab-M library, version 1.0.4 (, is available at and from Maven Central at under the terms of the open source Apache License 2.0. The web application as well as the Python and R implementations are available at The respective Web sites link to additional API documentation, as well as to usage examples.


Hoffmann, N. et al. jmzTab-M: A Reference Parser, Writer, and Validator for the Proteomics Standards Initiative mzTab 2.0 Metabolomics Standard. Analytical Chemistry (2019)

Quantitative Fragmentation Model for Bottom-up Shotgun Lipidomics


Quantitative bottom-up shotgun lipidomics relies on molecular species-specific “signature” fragments consistently detectable in MS/MS spectra of analytes and standards. Molecular species of glycerophospholipids are typically quantified using carboxylate fragments of their fatty acid moieties produced by higher-energy collisional dissociation of their molecular anions. However, employing standards whose fatty acids moieties are similar, yet not identical to the target lipids could severely compromise their quantification. We developed a generic and portable fragmentation model implemented in the open-source LipidXte software that harmonizes the abundances of carboxylate anion fragments originating from fatty acid moieties having different sn-1/2 position at the glycerol backbone, length of the hydrocarbon chain, number and location of double bonds. The post-acquisition adjustment enables unbiased absolute (molar) quantification of glycerophospholipid species independent of instrument settings, collision energy, and employed internal standards.


Schuhmann, K. et al. Quantitative Fragmentation Model for Bottom-up Shotgun Lipidomics. Analytical Chemistry (2019)

Lipidomics needs more standardization


Modern mass spectrometric technologies provide quantitative readouts for a wide variety of lipid specimens. However, many studies do not report absolute lipid concentrations and differ vastly in methodologies, workflows and data presentation. Therefore, we encourage researchers to engage with the Lipidomics Standards Initiative to develop common standards for minimum acceptable data quality and reporting for lipidomics data, to take lipidomics research to the next level.

Lipidomics Standards Initiative Consortium Lipidomics needs more standardization. Nature Metabolism (2019)