Major technological advances have made it possible to make many types of detections and measurements at unprecedented speed and affordability. This has resulted in huge amounts of biological and medical data being generated. The sequencing of the genome of man and thousands of other organisms has paved the way for the identification of genetic variation at the level of individuals or even of single cells.
Sequencing the DNA or RNA of tumour cells from a cancer patient may help diagnose the disease precisely and find the best therapy for this particular patient. This is now on its way into clinical practice, but it requires efficient and robust methodology.
At the same time, hardware developments have provided us with smaller, faster, cheaper and more energy-efficient computers. The ever-increasing number of processing cores available in the computers enables massive parallelisation but also requires sophisticated programming to exploit well.
We design and implement methods and tools for processing, analysing and visualizing molecular data. To this end we use advanced algorithms and data structures as well as mathematical models and statistical analysis. In our aim for reproducible science we prefer to work with open source software and strive for the best software development practices.