The genetic study of inherited disease, using linkage or SNP analysis, can identify a list of candidate genes that might be responsible for the disease. There are often hundreds of candidates, making the experimental methods employed to pinpoint the disease gene arduous and expensive. At the Victor Chang Cardiac Research Institute we have developed a computational system, Gentrepid, to catch the disease gene from the hundreds of of possible candidates.
Recent advances in high-throughput experimentation have shifted the emphasis of biological research from single molecules to whole systems. The partial map of the human-interactome, a map of all protein-protein interactions, has permitted a genome-scale perspective of disease. Proteins perform their functions by interacting with other proteins and molecules in the body. Diseases that have common symptoms are likely to be associated with disruption in proteins that participate in the same complex or pathway. Using advanced bioinformatics techniques we can, for the first time, identify these disease networks.
Gentrepid utilizes information from protein sequence and interaction databases, enabling accurate disease gene identification. Gentrepid accelerates the disease gene discovery process, significantly reducing the cost of expensive experimental studies. Successful identification of the disease gene enables targeted research on how mutations in the gene contribute to disease and provides specific leads towards cures.
George RA, Liu JY, Feng LL, Bryson-Richardson RJ, Fatkin D and Wouters MA
(2006) Analysis of protein sequence and interaction data for candidate disease gene prediction. Nucleic Acids Res. Vol. 34, No. 19, e130.
To access Gentrepid click on the following link http://www.gentrepid.org
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