Welcome to Gentrepid 2.0, a new approach to candidate disease gene prediction.

Gentrepid utilizes methodology from the fields of structural bioinformatics and systems biology. Two algorithms are applied: Common Module Profiling (CMP) and Common Pathway Scanning (CPS). 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.

Unsure where to start? Try quicksearch. If you wish to be able to store your data, register or login. If you want to find out more about the tool, check out the tutorial. Any comments or questions feel free to contact us!


Ballouz S, Liu JY, Oti M, Gaeta B, Fatkin D, Bahlo M,and Wouters MA (2011) Analysis of genome-wide association study data using the protein knowledge base. BMC genetics 12(1):98. Pubmed

Teber ET, Liu JY, Ballouz S, Fatkin D & Wouters MA (2009) Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies. BMC Bioinformatics 10Suppl 1,S69. Pubmed

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. 34, e130. Pubmed

You may access Gentrepid benchmark results here

You may access GWAS results here

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