Adam Siepel

Modern genomic technologies make it relatively easy to generate rich data sets describing genome sequences, RNA expression, chromatin states, and many other aspects of the storage, transmission, and expression of genetic information. For many problems in genetics today, the limiting step is no longer in data generation, but in integrating, interpreting, and understanding the available data. Addressing these challenges requires expertise both in the practical arts of data analysis and in the theoretical underpinnings of statistics, computer science, and genetics.

My group focuses on a diverse collection of research questions in this interdisciplinary area, spanning applications in cancer biology, basic molecular biology, evolutionary genetics, infectious diseases, and agriculture. Over the years, our research has touched on topics including the identification of recombinant strains of HIV, the discovery of new human genes, the characterization of conserved regulatory elements in mammalian genomes, the identification of noncoding mutations important in cancer, and the discovery of ancient gene flow from humans to Neandertals. A general theme in our work is the development of precise mathematical models for the complex processes by which genomes evolve over time, and the use of these models, together with techniques from computer science and statistics, both to peer into the past, and to address questions of practical importance for human health. We collaborate closely with experimentalists in cancer biology, transcriptional regulation, plant breeding and many other areas.