Genome Data Science
laboratory
// 🇩🇰 / University of Copenhagen / Faculty of Health and Medical Sciences / Biotech Research & Innovation Centre / Supek group
// 🇪🇸 / BIST / Institute for Research in Biomedicine (IRB Barcelona) / Cancer Science programme / @GenomeDataLab
In the GenomeDataLab, we use statistical genomics and machine learning to study quality control (QC) mechanisms that protect the integrity of information stored in the cell: its genome and the transcriptome, as well as gene functional links.
We perform large-scale bioinformatic studies of multi-omic data from human tumors (somatic mutations, and transcriptomes), human populations (germline variation) and metagenomes (incl. human microbiomes).
We study mechanisms of maintaining genome stability in human cells via statistical analyses of mutation patterns in cancer, which often result from deficient DNA repair [ 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 genetic interactions to predict tumor evolution and identify novel synthetic lethalities [ 3 ]. More generally, we study novel approaches using machine learning to infer gene function from massive genomic data [ 4 ].
Some recent research from the GenomeDataLab:
read more about our work on: [1] genomic signatures of DNA repair failures; [2] transcriptome quality control via NMD; [3] predicting driver genes and cancer evolution; and [4] automated gene function prediction methods.
Meet the GenomeDataLab team:
: - )
Marcell V
: - )
Sanket
: - )
Davor
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Alejandro
more info on
team members
follow us on Twitter!
@genomeDataLab
We gratefully acknowledge our funders:
European Research Council ERC Consolidator Grant #101088342 STRUCTOMATIC "Mutational processes and impact of structural variants in somatic cells"
CaixaResearch foundation "POTENT-IMMUNO" -Boosting immunotherapy by genomic prediction and NMD inhibitors.
EU H-2020 "DECIDER" Clinical Decision via Integrating Multiple Data Levels to Overcome Chemotherapy Resistance in High-Grade Serous Ovarian Cancer
Novo Nordisk Foundation Starting Package Grant
We are further funded by the "Severo Ochoa" centres of excellence program (to IRB), the Spanish Ministry of Science, Innovation and Universities ("REPAIRSCAPE"), the Catalan Science Agency (AGAUR) SGR support and FI fellowships. More information on projects >>>
Some collaborations of the GenomeDataLab:
J Biayna, I Garcia-Cao, [...]
F Supek*, T Stracker* (2021)
PLOS Biology.
(w/ Travis Stracker lab, currently at NIH)
A Avgustinova*, A Symeonidi, [...],
F Supek*, S Aznar-Benitah* (2018)
Nature Cell Biology.
(w/ Salvador Aznar lab at IRB Barcelona)
Genome-scale quantification and prediction of pathogenic stop codon readthrough by small molecules.
I Toledano, F Supek*, B Lehner* (2023) biorxiv. Under review in Nature Genetics.
(w/ Ben Lehner lab, Sanger Institute/CRG)
Proton and alpha radiation-induced mutational profiles in human cells
TM Delhomme*, M Munteanu*, M Buonanno, V Grilj, J Biayna, F Supek (2023) Scientific Reports
(w/ RARAF facility at Columbia university)
We are a part of the Biotech Research & Innovation Centre (BRIC) at University of Copenhagen; secondary affiliation of the lab is the Institute for Research in Biomedicine (IRB Barcelona):
Other affiliations:
PI is tenured (on leave) via the ICREA Research Professor program of the Catalan government.
Fran Supek is a member of the EMBO Young Investigator programme.
"Prediction is very difficult, especially about the future." -- Niels Bohr.