🤖🧬Genome Data Science laboratory

In the GenomeDataLab, we use statistical genomics and machine learning to study quality control that protects the integrity of information stored in the DNA and mRNA of the cell. We study how DNA repair and chromatin organization protect the human genome  from mutations, and how QC mechanisms operate on the transcriptome to prevent expression of mutated mRNAs. 

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 for 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 drivers and genetic interactions to predict tumor evolution and identify novel synthetic lethalities [ 3 ].  More generally, we study novel approaches using artificial intelligence (AI) to infer DNA functionality from massive genomic data [ 4 ].

👉read more about our research interests >>>