The efficiency of algorithms or of data representation is often the difference between feasible and infeasible data analysis. By improving our algorithms, we enable research that would not otherwise be possible. This project is concerned with the development of efficient algorithms and efficient implementation of software for bioinformatics problems.
Evolutionary genomics is an interdisciplinary research field that draws inspiration from diverse research areas such as population genetics, molecular evolution and comparative genomics. Much have been learned in the last decade with the sequencing of numerous new genomes, including everything from virus and bacteria to model organisms such as fruit flies and nematodes as well as several vertebrates, none more important that the genome of our own species. With the recent sequencing of some of our closest living relatives we now have an unique opportunity to gain insight into some of the most fundamental questions in evolutionary biology: How, why and when do genomes evolve and which forces are responsible for governing the rate and direction of the evolution of genomes.
At BiRC we are currently involved in several different projects that all in some way or another deal with how, why and when genomes change over time. In particular we work on understanding the variation in evolutionary rates between and within species as well as how it changes over time. To do this we are developing novel models and methods to incorporate divergence and diversity data into large scale comparative analyzes. These types of analyzes have among other things provided valuable new insights into the mammalian evolutionary history and have allowed us to give qualitative and quantitative statements about the evolutionary processed and recent population size dynamics that have shaped the genomes of humans and other primates.
An important challenge in medicine and human genetics is to locate disease affecting genes and gene variants, to be able to study diseases, screen for high-risk individuals, and ultimately to help prevent or cure diseases. In spite of intense research there are still many unsolved problems such as understanding the underlying biology of common diseases, how best to address important disease questions with statistics and how to provide efficient computational methods to analysis large-scale data sets.
At BiRC we are involved in different projects, collaborating with local groups at the university and with groups outside the university. For example, we are involved in the development of computational and statistical methods and computer tools for locating disease genes, and for inferring genomic and epigenomic changes in cancer. The amount of data and the complexity of the problems make computer and statistical tools essential for successful studies. With the recent improvements in technologies that now allow simultaneous determination of many thousands (even hundreds of thousands) markers, the analysis of data is becoming the bottleneck of studies, and hence it is increasingly important to develop better and faster analysis methods.
Understanding the structure, function and dynamics of proteins are important aspects of studying molecular systems and designing novel drugs. Structural bioinformatics tries to understand these aspects using computational and mathematical models while relying on chemical and physical properties of molecules and their surroundings.
At BiRC we are particularly focused on the development of models and tools for RNA folding as part of the COFOLD project.