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 >>>
Some recent research 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 "
The Spanish Ministry of Science, Innovation and Universities, via the grant BFU2017-89833-P "RegioMut", and via "Juan de la Cierva" and FPU fellowships.
Lab core funding and a student fellowship are funded by the Severo Ochoa excellence award to the IRB Barcelona.
Fran Supek is funded by the ICREA Research Professor program.
Fran Supek is an EMBO Young Investigator.
Croatian Science Foundation, via grant "AIGEN: Augmented intelligence for prediction, discovery and understanding in (pharmaco)genomics"
Marie Sklodowska-Curie Actions for postdoctoral fellowships.
AGAUR Catalan research agency for project funding and PhD fellowships.
"Prediction is very difficult, especially about the future." -- Niels Bohr.