Research Laboratory - Computational Genetics Laboratory
The computational genetics lab focuses on the development of computational tools for the analysis of population genetics data; we are mostly interested in the development of tools that enable and facilitate genetic studies of common complex diseases, such as cancer, cardiovascular diseases, or type 2 Diabetes. These studies shed important light on the biological mechanisms of these diseases, and they will pave the way to improved diagnosis and a personalized treatment based on an individual's genetics.
In addition to the disease studies, the lab is also developing method for the inference of ancestry and family relations based on DNA data. These methods have application in personalized genomics and in population genetics studies that explore human history, including the identification of genomic regions that have been under natural selection, and the inference of DNA of extinct human populations.
Our activities touch upon a wide range of quantitative disciplines, including combinatorial and optimization algorithms, machine learning, statistical genetics, population genetics, and bioinformatics. We are working closely with many groups of geneticists around the world on genetic studies of different diseases, and so far we successfully identified genes associated with Non-Hodgkin's Lymphoma, coronary artery disease, and related conditions.
The lab is supervised by: Dr. Eran Halperin.