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 ].

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