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. Next, we are interested in how mRNA synthesis and turnover pathways shape genomes and transcriptomes in health and disease. 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.

read more about our research interests >>> [ 1 ] [ 2 ] [ 3 ] [ 4 ]

Some recent publications from the GenomeDataLab:

Meet the GenomeDataLab team:

We gratefully acknowledge our funders:

European Research Council ERC Starting Grant #757700 HYPER-INSIGHT "Insight into genome maintenance and cancer vulnerabilities provided by an extreme burden of somatic mutations "

Fran Supek is an EMBO Young Investigator.

Lab funding and fellowships are funded by the Severo Ochoa excellence award to the IRB Barcelona.

PI is tenured by the ICREA Research Professor program.

We are further funded by the European Union Horizon2020 program (DECIDER), the Spanish government (REPAIRSCAPE), the Catalan science agency, and the Croatian Science foundation. More information on projects >>>

We are a part of the Institute for Research in Biomedicine (IRB Barcelona), a member of the Barcelona Institute of Science and Technology:

"Prediction is very difficult, especially about the future." -- Niels Bohr.