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Nowadays, gene discovery has been made very efficient with the combination of deep sequencing and the exploitation of natural variation. Just in Arabidopsis, hundreds of genetic loci have been identified as influencing a wide variety of processes, and we aim to go from gene-of-interest to characterized protein product using approaches to “take a picture” of the comprehensive metabolome of the plant.

The IPB is currently operating a wide range of NMR and mass spectrometry instruments for metabolomics across all four departments, which are integrated into our Metabolomics Platform.

The experimental work is complemented by extensive Cheminformatics and Bioinformatics research to process and interpret the huge amounts of data. The IPB is operating the first European MassBank server, and hosts several online tools for metabolite identification.

Contact partner for all interests concerning the metabolomics platform is Dr. Steffen Neumann.

Publications by Tag: Metabolomics

Displaying results 1 to 1 of 1.


González-Beltrán, A., Neumann, S., Maguire, E., Sansone, S.-A. & Rocca-Serra, P. The Risa R/Bioconductor package: integrative data analysis from experimental metadata and back again. BMC Bioinformatics 15 (Suppl 1), S:11, (2014) DOI: 10.1186/1471-2105-15-S1-S11


matic and accessible format that abstracts away common constructs for describing Investigations, Studies and Assays, ISA is increasingly popular. To attract further interest towards the format and extend support to ensure reproducible research and reusable data, we present the Risa package, which delivers a central component to support the ISA format by enabling effortless integration with R, the popular, open source data crunching environment.


The Risa package bridges the gap between the metadata collection and curation in an ISA-compliant way and the data analysis using the widely used statistical computing environment R. The package offers functionality for: i) parsing ISA-Tab datasets into R objects, ii) augmenting annotation with extra metadata not explicitly stated in the ISA syntax; iii) interfacing with domain specific R packages iv) suggesting potentially useful R packages available in Bioconductor for subsequent processing of the experimental data described in the ISA format; and finally v) saving back to ISA-Tab files augmented with analysis specific metadata from R. We demonstrate these features by presenting use cases for mass spectrometry data and DNA microarray data.


The Risa package is open source (with LGPL license) and freely available through Bioconductor. By making Risa available, we aim to facilitate the task of processing experimental data, encouraging a uniform representation of experimental information and results while delivering tools for ensuring traceability and provenance tracking.

Software availability

The Risa package is available since Bioconductor 2.11 (version 1.0.0) and version 1.2.1 appeared in Bioconductor 2.12, both along with documentation and examples. The latest version of the code is at the development branch in Bioconductor and can also be accessed from GitHub https://github.com/ISA-tools/Risa webcite, where the issue tracker allows users to report bugs or feature requests.

This page was last modified on 10.03.2014.

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