Supek Group

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 (DNA repair) and the transcriptome (NMD), as well as gene functional networks (epistasis).














We perform large-scale bioinformatic studies of multi-omic data from human tumors (somatic mutations, epigenomes 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.  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 genetic interactions to predict tumor evolution and identify novel synthetic lethalities.  More generally, we study use machine learning approaches to infer gene function from massive genomic data.


































  • [1] Genomics of DNA repair. We aim to understand mechanisms of maintaining genome integrity in human cells via statistical analyses of mutation patterns in cancer.

  • [2] mRNA quality control. We are interested in how the pathways dealing with synthesis and turnover of messenger RNAs shape genomes and transcriptomes, in health and disease.

  • [3] predicting cancer evolution. Combining experiment and genomics, we scan cancer genomes for causal genes and for genetic interactions to better understand tumor evolution.

  • [4] bioinformatics of gene function. From coli to human genome, the function of a substantial fraction of the genes is unknown; we think that machine learning approaches can help solve this.




















Selected publications