Currently molecular biology is generating information on the molecular properties of cells and organisms at an incredible pace. For example we know the complete genome sequence of a large number of species and the number of these genome sequences is increasing. Not only do these so called high-throughput experiments generate complete pictures of the genetic information of cells, but other techniques measure the level of expression of all genes at the same time, or measure all the interactions between all the proteins present in a cell. Bioinformatics is obviously needed for the storage and primary analysis of these huge volumes of biomolecular data. More interestingly, these data uniquely allow bioinformatics to make biological discoveries that hitherto were not possible.
We here use these genome scale data to perform research in evolutionary genomics: i.e. what rules govern the presence and absence of genes across genomes, how do protein complexes evolve, how does the protein interaction network evolve, how do signal transduction pathways evolve, what is the role of gene duplication in the evolution of protein complexes and the interaction network. These research questions are carried out using bionformatics to compare genome sequences and integrate highthrougput data sets such as yeast-2-hybrid data or micro-array expression data. All research efforts rely heavily on advanced sequence analysis and orthology detection. At the same time our insights from genome evolution and the analysis of high-throughput data allow the development of tools to predict the cellular function of experimentally poorly characterized genes.