Fran Supek

Fran Supek is a Professor at the University of Copenhagen's Faculty of Health and Medical Sciences, where he leads the Genome Data Science laboratory at the Biotech Research & Innovation Centre (BRIC). He also maintains a research group at the IRB Barcelona. Supek is the Principal Investigator of ERC Starting and ERC Consolidator projects, an EMBO Young Investigator, and holds an ICREA tenured professor position (currently on leave).

Dr. Supek obtained his PhD in Molecular Biology in 2010 from the University of Zagreb, followed by postdoctoral training at the Centre for Genomic Regulation (CRG) as a Marie Curie fellow. He established his independent laboratory at IRB Barcelona in 2017 as a "Ramón y Cajal" tenure-track fellow, and expanded to BRIC in 2024.

His research focuses on statistical genomics and machine learning approaches to study genome stability mechanisms, particularly through analysis of mutation patterns in cancer. The Genome Data Science lab performs large-scale bioinformatic studies of multi-omic data from human tumors, populations, and metagenomes. Key research areas include DNA repair mechanisms, mRNA synthesis and turnover pathways, genetic interactions in cancer genomes, and machine learning approaches for inferring gene function from genomic data.

Analysis of Supek's recent publications reveals a strong emphasis on cancer genomics, with particular focus on mutational signatures, DNA repair deficiencies, and the application of artificial intelligence to genomic data analysis. His work spans from fundamental mechanisms of genome instability to translational applications in cancer diagnosis and treatment.

• ERC Starting Grant recipient
• ERC Consolidator Grant recipient
• EMBO Young Investigator
• ICREA tenured professor (on leave)

As Principal Investigator of multiple ERC projects, Supek leads a research program with significant funding for innovative genomic research. His laboratory at BRIC and IRB Barcelona conducts interdisciplinary research combining computational approaches with experimental validation to address fundamental questions in genome biology and cancer evolution.

The Genome Data Science lab maintains active collaborations across multiple institutions, with research spanning statistical genomics, machine learning applications in bioinformatics, and experimental validation of computational predictions in cancer biology.