In the GenomeDataLab, we use statistical genome analyses and machine learning methodologies for studies of massive genomic, epigenomic and transcriptomic data sets.
We aim to address outstanding questions in biology and biomedicine by insightful analysis of data originating from human tumors (somatic mutations, transcriptomes), human populations (germline variation) and metagenomes (incl. human microbiomes).
We study mechanisms of maintaining genome integrity in human cells via statistical analyses of mutation patterns in cancer [ 1 ]. Next, we are interested in how mRNA synthesis and turnover pathways shape genomes and transcriptomes in health and disease [ 2 ]. Finally, we combine experimental work and genomics to scan cancer genomes for driver genes and for genetic interactions to predict tumor evolution and identify novel synthetic lethalities [ 3 ]. More generally, we study machine learning methods to infer gene function from genomics [ 4 ].
Some recent publications from the GenomeDataLab:
Meet the GenomeDataLab team:
We gratefully acknowledge our funders:
Some collaborations of the GenomeDataLab:
A Avgustinova*, A Symeonidi, [...],
F Supek*, S Aznar-Benitah* (2018)
Nature Cell Biology.
"Prediction is very difficult, especially about the future." -- Niels Bohr.