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Analysis of protein sequence and interaction data for candidate disease gene prediction
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 fo...
Autores principales: | , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2006
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1636487/ https://www.ncbi.nlm.nih.gov/pubmed/17020920 http://dx.doi.org/10.1093/nar/gkl707 |
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author | George, Richard A. Liu, Jason Y. Feng, Lina L. Bryson-Richardson, Robert J. Fatkin, Diane Wouters, Merridee A. |
author_facet | George, Richard A. Liu, Jason Y. Feng, Lina L. Bryson-Richardson, Robert J. Fatkin, Diane Wouters, Merridee A. |
author_sort | George, Richard A. |
collection | PubMed |
description | 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. |
format | Text |
id | pubmed-1636487 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2006 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-16364872006-11-29 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 Wouters, Merridee A. Nucleic Acids Res Methods Online 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. Oxford University Press 2006-11 2006-10-04 /pmc/articles/PMC1636487/ /pubmed/17020920 http://dx.doi.org/10.1093/nar/gkl707 Text en © 2006 The Author(s) |
spellingShingle | Methods Online George, Richard A. Liu, Jason Y. Feng, Lina L. Bryson-Richardson, Robert J. Fatkin, Diane Wouters, Merridee A. Analysis of protein sequence and interaction data for candidate disease gene prediction |
title | Analysis of protein sequence and interaction data for candidate disease gene prediction |
title_full | Analysis of protein sequence and interaction data for candidate disease gene prediction |
title_fullStr | Analysis of protein sequence and interaction data for candidate disease gene prediction |
title_full_unstemmed | Analysis of protein sequence and interaction data for candidate disease gene prediction |
title_short | Analysis of protein sequence and interaction data for candidate disease gene prediction |
title_sort | analysis of protein sequence and interaction data for candidate disease gene prediction |
topic | Methods Online |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1636487/ https://www.ncbi.nlm.nih.gov/pubmed/17020920 http://dx.doi.org/10.1093/nar/gkl707 |
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