We focus on the development and application of computational methods to investigate the molecular basis of disease, and we explore new ways to apply the knowledge generated in improving disease prevention, diagnosis and therapy. We collect and combine different types of genetic and biochemical data, in particular data relating genetic variation and human disease, genome-wide functional studies in model organisms, or data identifying relevant biochemical components and their relationships (protein interaction, expression regulation). The combined data is then used to model disease processes and better understand how and why disease occurs.
A common principle in investigating a specific disease is to use prior knowledge of the genetics, molecular components and processes involved in order to identify promising candidates obtained from high-throughput experiments or genome-wide screens. In this respect genes or proteins known to be involved or related to disease are prioritised. Alternatively prior knowledge is also used to identify new processes of interest that were not previously known to play a role that particular specific disease.
Structural analysis of genetic variation
Protein structural models allows us to investigate
the impact of genetic variants in protein structure
and function in order to better understand the
molecular mechanisms involved in disease.
|Common sources of experimental data are genome wide association studies, functional screens in model organisms, orprotein interaction data. We apply and develop approaches for systematic retrieval of data from genome annotation databases, function annotation and protein interaction resources. The different types of data are integrated and processed within the Galaxy platform, which we are currently extending by developing additional functionality. We generate network models to investigate functional relationships between proteins/genes and we routinely investigate and analyse biological process and pathways that are statistically enriched in given experimental datasets. We also investigate the possible effect of the different genetic variants associated with a given trait and their potential impact at the gene level and at the corresponding protein structure and function.|
The work is mostly application oriented, where we aim to use the most appropriate tools for each tasks. Nevertheless we are also committed to method development by improving existing tools or developing new ones. We routinely use Python, Perl and R programming languages for data access, processing and integration, and we rely on Java, C and C++ for method development.