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Analysis of protein sequence and interaction data for candidate disease gene prediction

George, Richard A., Liu, Jason Y., Feng, Lina L., Bryson-Richardson, Robert J., Fatkin, Diane and Wouters, Merridee A. 2006, Analysis of protein sequence and interaction data for candidate disease gene prediction, Nucleic acids research, vol. 34, no. 19, pp. e130 - 1-e130 - 10, doi: 10.1093/nar/gkl707.

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Title Analysis of protein sequence and interaction data for candidate disease gene prediction
Author(s) George, Richard A.
Liu, Jason Y.
Feng, Lina L.
Bryson-Richardson, Robert J.
Fatkin, Diane
Wouters, Merridee A.
Journal name Nucleic acids research
Volume number 34
Issue number 19
Start page e130 - 1
End page e130 - 10
Total pages 10
Publisher Oxford University Press
Place of publication Oxford, England
Publication date 2006
ISSN 0305-1048
1362-4962
Keyword(s) actin
connectin
myosin binding protein C
myosin heavy chain
myosin light chain
troponin
troponin I
protein
Summary Linkage analysis is a successful procedure to associate diseases with specific genomic regions. These regions are often large, containing hundreds of genes, which make experimental methods employed to identify the disease gene arduous and expensive. We present two methods to prioritize candidates for further experimental study: Common Pathway Scanning (CPS) and Common Module Profiling (CMP). CPS is based on the assumption that common phenotypes are associated with dysfunction in proteins that participate in the same complex or pathway. CPS applies network data derived from protein–protein interaction (PPI) and pathway databases to identify relationships between genes. CMP identifies likely candidates using a domain-dependent sequence similarity approach, based on the hypothesis that disruption of genes of similar function will lead to the same phenotype. Both algorithms use two forms of input data: known disease genes or multiple disease loci. When using known disease genes as input, our combined methods have a sensitivity of 0.52 and a specificity of 0.97 and reduce the candidate list by 13-fold. Using multiple loci, our methods successfully identify disease genes for all benchmark diseases with a sensitivity of 0.84 and a specificity of 0.63. Our combined approach prioritizes good candidates and will accelerate the disease gene discovery process.
Notes Reproduced with the kind permission of the copyright owner.
Language eng
DOI 10.1093/nar/gkl707
Field of Research 060199 Biochemistry and Cell Biology not elsewhere classified
Socio Economic Objective 970106 Expanding Knowledge in the Biological Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2006, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution non-commercial licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30038974

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.