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Research
The research areas of the Kohlbacher Lab are rather diverse and span from classical bioinformatics topics including sequence, structure, and systems bioinformatics to translational bioinformatics and personalized medicine. The following list gives an overview of our current research areas and provides links to more detailed information pages on our current projects.
Computational Immunomics
Computational immunomics applies bioinformatics methods to gain a deeper understanding of the immune system. Furthermore it assists medical research by providing computational models which help to solve immunology-related problems. Our group develops various computational immunomics methods, primarily for mass spectrometry (MHC ligandomics) and NGS (HLA typing, neoepitope discovery) based analyses.
More on our current projects can be found here.
Computational Mass Spectrometry
We are one of the leading labs in computational mass spectrometry, developing innovative algorithms and robust software tools to analyze complex proteomic and metabolomic datasets. Our flagship project, OpenMS, is a widely adopted open-source software platform that provides comprehensive workflows and versatile modules for mass spectrometry data analysis. Bridging bioinformatics and mass spectrometry, our research enables high-throughput identification and quantification of proteins, peptides, and metabolites, as well as elucidation of their functional roles in biological systems.
Personalized Medicine
In the domain of Personalized Medicine, we are primarily involved in data integration projects to support personalized clinical decision making and research. This includes the development of the Data Integration Platform (DIP) for the German Network of Personalized Medicine (DNPM), which aims at integrating the Molecular Tumor Boards (MTBs) of 26 university hospitals in Germany. The DIP further acts as one of the clinical data nodes for the oncology and rare diseases use cases of the Modellvorhaben Genomsequenzierung.
In addition, we focus on the development of analysis pipelines for molecular tumor table backends, mainly centered around high-throughput data extraction and integration, automated processing of incoming data, annotation of therapeutic options and visualisation of network-derived contexts and analysis results.
Structural Bioinformatics
Structural Bioinformatics is one of the long established research fields in our group and has strived various subfields comprising theoretical and applied computer-aided drug design (CADD), cheminformatics, molecular mechanics-based modelling, or the prediction of protein-protein complexes. Additionally, we spent a significant amount of our time in the development of high-quality software tools providing solutions to some of these challenges that we make publicly available.
Translational Bioinformatics
Translational Bioinformatics bridges bioinformatics and medical informatics to enhance patient care through data-driven discoveries. By integrating molecular data (bioinformatics) with clinical healthcare information (medical informatics), this interdisciplinary field enables the identification of novel disease mechanisms, supports personalized therapeutic decisions, and facilitates advanced machine learning applications in medicine.
Our ongoing projects focus on developing robust infrastructures and innovative analytical methods. Notable examples include: