Fran Supek, researcher in the field of bioinformatics and genomics, is starting his Genome Data Science (aGENDAS) laboratory at one of the centres of the Barcelona Institute of Science and Technology, IRB Barcelona, within the Structural and Computational Biology Research Programme.
Dr. Supek’s group aims to tackle important biological questions by insightful statistical analysis of massive data sets originating from human cancer, human populations, environmental DNA sequencing (metagenomics), and also fully sequenced microbial genomes. The recruitment of Supek also fits into the big data research area that BIST has identified as strategic.
The aGENDAS research group will investigate biological mechanisms behind various mutational processes that shape genomes of humans and other organisms during evolution. In particular, one important disease that is caused by mutations in DNA is cancer. Such mutations may also highlight potential therapeutic opportunities. Supek’s group, which will initially include four members, will use cutting-edge artificial intelligence methods to understand what causes mutations in human DNA, which parts of the genome are affected, and will also try to detect tumour vulnerabilities that may open new avenues for personalized treatment.
“While one can easily recognize the mutational signatures produced by, for example, cigarette smoke in lung cancer and sunlight in melanoma, recent analyses of cancer genomes suggest there are many other factors causing mutations in our cells. In most cases, we don’t yet know exactly what causes a certain kind of mutation in DNA. Some mutations result from failures in the processes that the tumour cells use to repair their DNA, thereby causing the cancer to mutate faster and to become more aggressive and resistant to drugs. However, the failed DNA repair is also a weakness of the tumour, which may be exploited to destroy it, while sparing healthy cells,” highlights Fran Supek.
This group’s expertise in machine learning algorithms will allow aGENDAS researchers to reveal patterns in very large, complex, noisy and often incomplete databases derived from human genomes and epigenomes. Machine learning can provide unique approaches for discovering the intricate mechanisms of how the integrity of DNA is maintained in human cells. This includes the resilience to mutations in somatic cells, which make up various human tissues, but also in germline cells, where mutations are passed down to offspring, thus being a potential cause of familial diseases and other heritable phenotypes.
More information on the IRB Barcelona website.