Computational Biology - Analysis of genetic data

The Technology

The research in Eran Halperin’s lab mainly focuses on the development of computational tools for the analysis of genetic data; we are mostly interested in the development of tools that enable and facilitate genetic studies of common complex diseases, such as cancer or cardiovascular diseases. These studies shed important light on the biological mechanisms of these diseases, and they will pave the way to improved diagnosis and a personalize treatment based on an individual's genetics.

We develop methods for the analysis of genetic variants, such as single nucleotide polymorphisms (SNPs) or sequenced DNA and RNA using high-throughput sequencing and genotying technologies. These methods include statistical methods for the analysis of disease association studies, and methods for preprocessing of the data including read mapping, haplotype inference, population structure and inference, and identity by descent.

Our activities touch upon a wide range of disciplines, including combinatorial and optimization algorithms, machine learning, statistical genetics, population genetics, and bioinformatics. We are working closely with many groups of epidemiologists and geneticists around the world on genetic studies of different diseases, among those are Non-Hodgkin's Lymphoma, type 2 diabetes, coronary artery disease, and Amyotrohpic Lateral Sclerosis (ALS).

The Need

Disease association studies shed important light on the biological mechanisms of these diseases, and they will pave the way to improved diagnosis and a personalize treatment based on an individual's genetics. The analysis of these studies requires novel computational and statistical methods that are developed in our lab.

Potential Application

Software packages that can be used in the analysis of disease association studies.

Project manager

Oren Calfon
VP Business Development, ICT

Project researchers

Eran Halperin
T.A.U Tel Aviv University, Life Sciences
Molecular Microbiology-Biotechnology