We are a bioinformatics lab that creates innovative data science methods to advance our understanding of disease biology. Our research efforts are always inspired by a biological / clinical question and typically deal with big data, such as large-scale genomics and epigenomics datasets. As a result, much of the research floats on machine learning and data integration algorithms. We also heavily rely on high-performance computing and statistics.
We have ample experience in the development of new applications for the third generation nanopore sequencing platform. For instance, we have recently developed Multi-Contact 4C (with the de Laat lab), a chromatin conformation capture strategy capable of identifying multi-way chromatin interactions based on long-read sequencing. Moreover, we have developed Cyclomics (with the Kloosterman lab), a new way of profiling cell-free tumor DNA using nanopore sequencing.
In addition, we are investigating data integration strategies to, for instance, exploit genome conformation measurements (4C and Hi-C) for the annotation and prioritization of non-coding variations (SVs and SNVs). Finally, we have experience with and interest in constructing classification models for patient cohort data to enable personalisation of treatment strategies. Most recently, we have developed a gene-expression classifier which is able to predict treatment benefit in multiple myeloma.