Sophisticated bioinformatics analysis plays a key role in understanding the molecular mechanisms of disease and in translating complex biological data into clinically relevant information. While bioinformatics in general has long been essential for handling extensive data sets from high-throughput (omics) experiments – such as genome sequencing or mass-spectrometric analysis of proteins and metabolites – the emerging science of translational bioinformatics focuses specifically on turning these data into actionable knowledge for clinical application. Translational bioinformatics thus aims to enhance human health and well-being as well as treatment outcomes through a range of computational methods.
- Local instance of with extensions for clinically actionable variants
- Large collection of established workflows for next-generation sequencing
- Customized data analysis and cross-omics data integration
- Omics data management according to the FAIR Guiding Principles
- VCFPy: a Python library supporting reading and writing variant call format (VCF) files
From bench to bedside
The Bioinformatics Core Unit provides bioinformatics and data analysis expertise for translational research projects. It is part of the Berlin Institute of Health (BIH), which is a joint undertaking of the Max Delbrück Center and Charité – Universitätsmedizin Berlin. To achieve this, we combine a growing portfolio of standardized data processing workflows with project-specific bioinformatics solutions, exploratory statistics, visualization, data-mining methods, machine-learning algorithms, and customized data integration. We perform data analysis in close collaboration with researchers and clinicians. This enables us to refine intermediate results and to optimize data interpretation strategies in an iterative and incremental manner.
Established data analysis workflows cover a range of high-throughput sequencing technologies and allow the identification of relevant genetic variants in rare diseases. Our cancer genomics approaches provide rich annotations of variants that offer clinical intervention possibilities and aid researchers in defining immunological properties of cancer cells (neoepitope prediction, HLA typing). Workflows for RNA sequencing enable thorough statistical analyses and broad functional annotations for bulk as well as single-cell data.
We have built an omics data management system according to the internationally recognized FAIR Guiding Principles, whose aim is to make all research data Findable, Accessible, Interoperable, and Reusable. The system provides transparent long-term storage and enables cross-project data integration as well as efficient access to current analysis results. In addition, the Bioinformatics Core Unit organizes courses and workshops on high-performance computing, workflow management, and data analysis, while also advising on method selection, experimental design, and analysis planning.
Towards personalized cancer diagnostics and treatment
Our group provides a local instance of a web-based cancer genomics platform () that supports the analysis and visualization of large-scale cancer genomic data sets. We are continuously working to develop extensions and add-ons in order to facilitate better identification and interpretation of somatic variants and pathway dysregulation. The system is used not only by researchers to better understand cancer biology, but is also a key tool for the molecular tumor board at the Charité Comprehensive Cancer Center, where oncologists, pathologists, and bioinformaticians work together to make optimal treatment decisions for individual cancer patients based on molecular data.
Our methods have also helped to determine patient-specific neoepitopes for cancer immune therapy, to identify causative variants in genetic disorders, and to reveal the molecular processes underlying cardiovascular and neurodegenerative diseases.