Update of the G2D tool for prioritization of gene candidates to inherited diseases

被引:51
作者
Perez-Iratxeta, Carolina [1 ]
Bork, Peer [2 ]
Andrade-Navarro, Miguel A. [1 ,3 ]
机构
[1] Ottawa Hlth Res Inst, Ontario Genom Innovat Ctr, Ottawa, ON K1H 8L6, Canada
[2] European Mol Biol Lab, D-69117 Heidelberg, Germany
[3] Univ Ottawa, Fac Med, Dept Cellular & Mol Med, Ottawa, ON K1N 6N5, Canada
基金
加拿大健康研究院; 加拿大创新基金会;
关键词
D O I
10.1093/nar/gkm223
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
G2D (genes to diseases) is a web resource for prioritizing genes as candidates for inherited diseases. It uses three algorithms based on different prioritization strategies. The input to the server is the genomic region where the user is looking for the disease-causing mutation, plus an additional piece of information depending on the algorithm used. This information can either be the disease phenotype (described as an online Mendelian inheritance in man (OMIM) identifier), one or several genes known or suspected to be associated with the disease (defined by their Entrez Gene identifiers), or a second genomic region that has been linked as well to the disease. In the latter case, the tool uses known or predicted interactions between genes in the two regions extracted from the STRING database. The output in every case is an ordered list of candidate genes in the region of interest. For the first two of the three methods, the candidate genes are first retrieved through sequence homology search, then scored accordingly to the corresponding method. This means that some of them will correspond to well-known characterized genes, and others will overlap with predicted genes, thus providing a wider analysis. G2D is publicly available at http://www.ogic.ca/projects/g2d_2/.
引用
收藏
页码:W212 / W216
页数:5
相关论文
共 18 条
[1]   SUSPECTS: enabling fast and effective prioritization of positional candidates [J].
Adie, EA ;
Adams, RR ;
Evans, KL ;
Porteous, DJ ;
Pickard, BS .
BIOINFORMATICS, 2006, 22 (06) :773-774
[2]   Gene prioritization through genomic data fusion [J].
Aerts, S ;
Lambrechts, D ;
Maity, S ;
Van Loo, P ;
Coessens, B ;
De Smet, F ;
Tranchevent, LC ;
De Moor, B ;
Marynen, P ;
Hassan, B ;
Carmeliet, P ;
Moreau, Y .
NATURE BIOTECHNOLOGY, 2006, 24 (05) :537-544
[3]   A similarity-based method for genome-wide prediction of disease-relevant human genes [J].
Freudenberg, J ;
Propping, P .
BIOINFORMATICS, 2002, 18 :S110-S115
[4]   Analysis of protein sequence and interaction data for candidate disease gene prediction [J].
George, Richard A. ;
Liu, Jason Y. ;
Feng, Lina L. ;
Bryson-Richardson, Robert J. ;
Fatkin, Diane ;
Wouters, Merridee A. .
NUCLEIC ACIDS RESEARCH, 2006, 34 (19)
[5]   Genome-wide identification of genes likely to be involved in human genetic disease [J].
López-Bigas, N ;
Ouzounis, CA .
NUCLEIC ACIDS RESEARCH, 2004, 32 (10) :3108-3114
[6]   CGI: a new approach for prioritizing genes by combining gene expression and protein-protein interaction data [J].
Ma, Xiaotu ;
Lee, Hyunju ;
Wang, Li ;
Sun, Fengzhu .
BIOINFORMATICS, 2007, 23 (02) :215-221
[7]   Predicting disease genes using protein-protein interactions [J].
Oti, M. ;
Snel, B. ;
Huynen, M. A. ;
Brunner, H. G. .
JOURNAL OF MEDICAL GENETICS, 2006, 43 (08) :691-698
[8]   G2D: a tool for mining genes associated with disease [J].
Perez-Iratxeta, C ;
Wjst, M ;
Bork, P ;
Andrade, MA .
BMC GENETICS, 2005, 6 (1)
[9]   Association of genes to genetically inherited diseases using data mining [J].
Perez-Iratxeta, C ;
Bork, P ;
Andrade, MA .
NATURE GENETICS, 2002, 31 (03) :316-319
[10]   TOM: a web-based integrated approach for identification of candidate disease genes [J].
Rossi, Simona ;
Masotti, Daniele ;
Nardini, Christine ;
Bonora, Elena ;
Romeo, Giovanni ;
Macii, Enrico ;
Benini, Luca ;
Volinia, Stefano .
NUCLEIC ACIDS RESEARCH, 2006, 34 :W285-W292