Gentrepid utilizes methodology from the fields of structural
bioinformatics and systems biology. Two algorithms are applied: Common
Module Profiling and Common Pathway Scanning. CMP is completely novel
and is based on the hypothesis that disruption of genes of similar
function will lead to the same phenotype. CPS assumes that common
phenotypes are associated with dysfunction in proteins that participate
in the same complex or pathway.
We have shown that the use of independent biological data to make
complementary predictions ameliorates the problem of incomplete data
coverage. Gentrepid is a powerful tool in candidate disease gene
prediction and will significantly reduce the time and cost of
CMP uses a domain-based approach to identify genes with a potential functional similarity to known disease genes and is based on the hypothesis that genes of similar function will lead to the same phenotype (1). Gentrepid contains precalculated Pfam-domain (2) annotation for all genes. CMP compares the domain content of each protein within a disease interval to identify putative disease genes. Each protein observed to have disease-like domains is assigned a score based on the sequence similarity between the domains.
CPS is based on the assumption that common phenotypes are associated with proteins that participate in the same complex or pathway (3). CPS applies protein-protein interaction data from the I2D database (4) and pathway data from KEGG (5) and BioCarta (6) to identify relationships between known disease genes and genes in the disease interval.
You can either login with your username and password on the sidebar
or perform a quicksearch:
- Jimenez-Sanchez G, Childs B, and Valle D (2001) Human disease genes. Nature, 409, 853-855
- Bateman A et al. (2004) The Pfam protein families database.Nucleic Acids Res, 32, D138-D141
- Badano JL and Katsanis N (2002) Beyond Mendel: an evolving view of human genetic disease transmission. Nature Rev Genet, 3, 779-789
- Brown KR and Jurisica I (2007) Unequal evolutionary conservation of human protein interactions in interologous networks. Genome Biology, 8, R95
- Kanehisa M, Goto S, Kawashima S, Okuno Y and Hattori M (2004) The KEGG resource for deciphering the genome. Nucleic Acids Res, 32, D277-D280