Research Group Computational Biology

Research Topics: What we work on

Resistance analysis for viral diseases

Viral resistance to drug therapy is one of today’s major medical problems. The problem is particularly pronounced with HIV/AIDS. HIV display high evolutionary dynamics and can quickly evade administered drug therapy. To counter this risk, an arsenal of well over two dozen drugs is available, selections of which are administered in combination. This amounts to hundreds of therapy options. When a therapy fails, a new therapy has to be selected. This is done based on an analysis of the viral genome (genotypic resistance test). The resistance phenotype is estimated from the viral genome sequence with computer-based methods. Statistical learning is applied to large sets of clinical data on viral resistance to effect the prediction.

We are hosting the server geno2pheno for free on the Internet. This server offers predictions for a growing number of resistance phenotypes. The server also includes offers for the analysis of drug resistance of the Hepatitis B and Hepatitis C viruses. The server is used both in research and in clinical routine.

We have also have contributed a prediction method to the prediction engine of HIV resistance provided by the Euresist consortium.

We follow a diversity of research topics on this theme, including the clinical and molecular basis of viral resistance, viral epidemiology and vaccine research. Our research is embedded in the interdisciplinary consortia Arevir/Resina and Euresist. In this framework, we also support data collection and validation of our tools.

Our work is externally funded by DFG (German Excellence Initiative, Excellence Cluster MMCI), EU (CHAIN Project), the German Federal Ministry of Health and Industry (EUCOHIV Project).

Part of this research takes places in the context of the research groups Statistical Learning in Computational Biology and Structural Bioinformatics of Protein Interactions.



Computational epigenetics

If the nineties of the last century and the first years of the new millennium were the age of the genome, the present time can be considered the age of the epigenome: In the next few years, we expect over 1000 reference epigenomes to be sequenced by the International Human Epigenome Consortium (IHEC) alone. We are participating in this quest by providing computational methods and software for analyzing and interpreting the data. For this purpose, we offer a suite of programs including

BiQAnalyzer for low-level analysis of methylation data

RnBeads for the differential analysis of DNA methylation  data

EpiExplorer for fast and effective navigation through epigenomic datasets

Epigraph for the development of statistical models interpreting epigenomic data

Methmarker for a design and optimization of complex epigenetic biomarkers

We are a partner of the EU project BLUEPRINT on epigenomics of the haematopoietic cell line, and we coordinate the data analysis effort of the German Epigenome Program (DEEP) funded by the German Science Ministry.

Research on epigenetics takes place in the frame of the Research Group on Computational Epigenetics, with the Research group Statistical Learning in Computational Biology contributing to the theme.



Protein Structure, Function and Interactions

We have an extensive history in computational structural biology, including development of the early protein threaders 123D and ARBY (phased out) and the Flex* suite of docking programs, which is distributed by BioSolveIT GmbH.

Presently, we are investigating protein interactions, in general, and with a focus on host-virus interactions. This research takes place in the frame of the Research Group Structural Bioinformatics of Protein Interactions.



Statistical Applications in Computational Biology

While much of our work is directed at concrete biomedical application scenarios, we also engage in research on methods. Both lines of research, methodical and applied, are combined in our activities, with varying  emphasis on either side. The research group on Statistical Learning in Computational Biology places a focus on methodical research in statistical learning directed at concrete applications in biology and medicine.


Our research profile evolves over time. For software, which is the result of past research and which the department continues to offer, please see our software page.