Participant 14: FU Berlin
Freie Universität (Free University Berlin), Algorithmic Bioinformatics Group, Berlin, Berlin, Germany
Participant leader: Prof. Dr. Knut Reinert
Relevant experience and laboratory resources:
The group of Prof. Dr. Knut Reinert has several years of industry experience in the field of the analysis of complex data sets. Dr. Reinert worked in the Informatics Research division of Celera Genomics and developed algorithms for differential proteome analysis and visualisation of proteomic data on an industrial scale. In particular he was a leading investigator for the analysis of unlabeled samples. This approach does not need expensive labelling techniques, however, it requires a high reproducibility during the separation phase which is achievable given the recent developments in the HPLC-MS area. Since his return to Berlin (Free University) he works together with Prof. Kohlbacher (Tübingen) on an integrated software framework OpenMS, which is a formidable basis for the intended development of algorithms for relative quantification, since this library has already many infrastructural features (data formats are HUPO conforming, a viewer is implemented, basic data types are implemented, etc. In addition, the group collaborates with Prof. Kohlbacher and Prof. Huber (Saarbrücken) together on several EU projects for the absolute quantification of myoglobin in EU reference materials (myoglobin is a diagnostic marker for myocardial necrosis) and on the absolute quantitation and characterization of gliadins in food. Together with Prof. Schlüter, Prof. Reinert is part of the BMBF project Clinical Degradomics.
Principal staff involved:
Prof. Dr. Knut Reinert
Dr. Clemens Gröpl, Computer scientist, coordinates the OpenMS project in Berlin. He is a postdoc who worked the last 4 years continuously in the field of computational biology with a special interest in the analysis of proteomic/metabolomic data.
Role of the participant in the project and main tasks:
The participant will contribute in WP4.1, in particular by developing new methods and models for the targeted and untargeted differential display of the metabolome measured with HPLC/MS.