In the @GenomeDataLab, we strive to understand the links between mutagenesis, natural selection, gene function and phenotype by means of statistical genome analyses.

In particular, we use cutting-edge computational techniques and machine learning methodologies for analyses of massive genomic, epigenomic and transcriptomic data sets.

We aim to answer outstanding questions in biomedicine by insightful analysis of data originating from human tumors (somatic mutations, chromosomal alterations, transcriptomes), human populations (germline variants), metagenomes (including human microbiomes), microbial genomes and phenomic data.

We are particularly interested in how mutational processes and selection acting upon somatic or germline mutations vary between individuals, and how this results in a diversity of disease phenotypes, including cancer. >>> research lines of the lab >>>