Computational Mass Spectrometry
High-throughput analysis of proteins and proteomes plays an important role in biomedical research and development. Our group develops novel algorithms for the analysis of high-throughput proteomics and metabolomics data. We work in close collaboration with several leading groups world-wide to apply these methods to problems in cancer research, immunology, and stem cell research.
Our current efforts in proteomics are focused on efficient algorithms for labeled and label-free quantification, novel identification methods (consensus identification, de novo methods) and data integration and processing. We contribute to the PSI community standards (mzML, mzIdentML, traML) in order to facilitate data exchange in mass spectrometry. The algorithms and tools developed in this context are all available as open source in the OpenMS project. We also employ these methods in numerous collaborations with experimental partners world-wide to study a wide range of biomedical problems.
In metabolomics, we develop novel methods for metabolite identification and quantification. Such methods play an important role in the discovery of novel biomarkers for different diseases, e.g. type 2 diabetes mellitus. The identification of unknown metabolites poses a challenge, because metabolite databases cover only a small portion of the human metabolome. For this reason, further efforts are put into the annotation of unknown metabolites from different human body tissues and fluids, extending the knowledge of metabolite databases.
Software for Computational Mass Spectrometry
OpenMS is an open source C++ library for LC/MS data management, reduction, evaluation, visualisation, storage and sophisticated statistical analyses. It is intended as a tool for rapid prototyping of novel algorithms in computational mass spectrometry. TOPP - The OpenMS Proteomics Pipeline is a set of applications based on OpenMS that demonstrate the power and flexibility of the framework. More details on OpenMS and TOPP can be found on the OpenMS homepage. The OpenMS proteomics pipeline is currently used in a number of projects, among them the systems biology project SARA. In order to process and analyze proteomics data in a high-throughput setting, we are also involved in parallel computing and Grid Computing.
People working in this area
- Bertsch, A, Leinenbach, A,
Pervukhin, A, Lubeck, M, Baessmann, C, Elnakady, YA, Müller, R, Böcker,
S, Huber, CG, and Kohlbacher, O (2009).
De novo peptide sequencing by tandem mass spectrometry using complementary collision-induced dissociation and electron transfer dissociation
Electrophoresis 30 (21): 3736-3747.
- Pfeifer, N, Leinenbach, A, Huber, CG, and Kohlbacher, O (2009).
Improving Peptide Identification in Proteome Analysis by a Two-Dimensional Retention Time Filtering Approach
J. Proteome Res., 8(8):4109-15.
- Sturm, M and Kohlbacher, O (2009).
TOPPView: An Open-Source Viewer for Mass Spectrometry Data
J. Proteome Res., 8(7):3760-3.
- Schulz-Trieglaff, O, Pfeifer, N, Gröpl, C, Kohlbacher, O, and Reinert, K (2008).
LC-MSsim - a simulation software for Liquid ChromatographyMass Spectrometry data
BMC Bioinformatics, 9:423.
- Kohlbacher, O, Reinert, K, Gröpl, C, Lange, E, Pfeifer, N, Schulz-Trieglaff, O, and Sturm, M (2007).
TOPP - The OpenMS Proteomics Pipeline
- Sturm, M, Quinten, S, Huber, CG, and Kohlbacher, O (2007).
A statistical learning approach to the modeling of chromatographic retention of oligonucleotides incorporating sequence and secondary structure data
Nucl. Acids Res., 35(12):4195-4202.
- Pfeifer, N, Leinenbach, A, Huber, CG, and Kohlbacher, O (2007).
Statistical learning of peptide retention behavior in chromatographic separations: A new kernel-based approach for computational proteomics
BMC Bioinformatics, 8:468.
Kohlbacher, O, Quinten, S, Sturm, M, Mayr, B, and Huber, C (2006).
Structure-Activity Relationships in Chromatography: Retention Prediction of Oligonucleotides with Support Vector Regression
Angew. Chemie Int. Ed., 45(42):7009-7012.
- Mayr, B, Kohlbacher, O, Reinert, K, Sturm, M, Gröpl, C, Lange, E, Klein, C, and Huber, CG (2006).
Absolute Myoglobin Quantitation in Serum by Combining Two-Dimensional Liquid Chromatography-Electrospray Ionization Mass Spectrometry and Novel Data Analysis Algorithms
J. Proteome Res., 5:414–421.