Web Tools for the Prioritization of Candidate Disease Genes

Methods in Molecular Biology

Volumn 760, Issue , 2011, Pages 189-206

  Oti, Martin ^a   Ballouz, Sara ^a,b   Wouters, Merridee A ^a,b,c  

^a VICTOR CHANG CARDIAC RESEARCH INSTITUTE   (Australia)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

^c DEAKIN UNIVERSITY   (Australia)

Author keywords

bioinformatics; Disease gene prediction; disease gene prioritization; genetic diseases; genome; phenotype

Indexed keywords

ANIMAL; BIOLOGY; COMPUTER PROGRAM; GENETIC ASSOCIATION; GENETIC PREDISPOSITION; GENETICS; HUMAN; INTERNET; METHODOLOGY; REVIEW;

ANIMALS; COMPUTATIONAL BIOLOGY; GENETIC ASSOCIATION STUDIES; GENETIC PREDISPOSITION TO DISEASE; HUMANS; INTERNET; SOFTWARE;

EID: 79960904149     PISSN: 10643745     EISSN: 19406029     Source Type: Book Series    
DOI: 10.1007/978-1-61779-176-5_12     Document Type: Chapter

References (76)

1. Altshuler, D., Daly, M. J., Lander, E. S. (2008) Genetic mapping in human disease. Science 322, 881–888.
2. Kann, M. G. (2010) Advances in translational bioinformatics: computational approaches for the hunting of disease genes. Brief Bioinform 11, 96–110.
3. Oti, M., Brunner, H. G. (2007) The modular nature of genetic diseases. Clin Genet 71, 1–11.
4. Tiffin, N., Andrade-Navarro, M. A., Perez-Iratxeta, C. (2009) Linking genes to diseases: it’s all in the data. Genome Med 1, 77.
5. van Driel, M. A., Brunner, H. G. (2006) Bioinformatics methods for identifying candidate disease genes. Hum Genomics 2, 429–432.
6. Tranchevent, L., Capdevila, F. B., Nitsch, D., De Moor, B., De Causmaecker, P., Moreau, Y. (2010) A guide to web tools to prioritize candidate genes. Brief Bioinform 11, 1–11.
7. Yilmaz, S., Jonveaux, P., Bicep, C., et al. (2009) Gene-disease relationship discovery based on model-driven data integration and database view definition. Bioinformatics 25, 230–236.
8. Gaulton, K. J., Mohlke, K. L., Vision, T. J. (2007) A computational system to select candidate genes for complex human traits. Bioinformatics 23, 1132–1140.
9. Shriner, D., Baye, T. M., Padilla, M. A., et al. (2008) Commonality of functional annotation: a method for prioritization of candidate genes from genome-wide linkage studies. Nucleic Acids Res 36, e26.
10. Li, Y., Patra, J. C. (2010) Integration of multiple data sources to prioritize candidate genes using discounted rating system. BMC Bioinformatics 11(Suppl 1), S20.
11. McEachin, R. C., Keller, B. J. (2009) Identifying hypothetical genetic influences on complex disease phenotypes. BMC Bioinformatics 10(Suppl 2), S13.
12. Turner, F. S., Clutterbuck, D. R., Sem-ple, C. A. (2003) POCUS: mining genomic sequence annotation to predict disease genes. Genome Biol 4, R75.
13. Vanunu, O., Magger, O., Ruppin, E., et al. (2010) Associating genes and protein complexes with disease via network propagation. PLoS Comput Biol 6, e1000641.
14. Ala, U., Piro, R. M., Grassi, E., et al. (2008) Prediction of human disease genes by human-mouse conserved coexpression analysis. PLoS Comput Biol 4, e1000043.
15. Care, M. A., Bradford, J. R., Needham, C. J., et al. (2009) Combining the interactome and deleterious SNP predictions to improve disease gene identification. Hum Mutat 30, 485–492.
16. Freudenberg, J., Propping, P. (2002) A similarity-based method for genome-wide prediction of disease-relevant human genes. Bioinformatics 18(Suppl 2), S110–115.
17. Karni, S., Soreq, H., Sharan, R. (2009) A network-based method for predicting disease-causing genes. J Comput Biol 16, 181–189.
18. Lage, K., Karlberg, E. O., Storling, Z. M., et al. (2007) A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat Biotechnol 25, 309–316.
19. Li, Y., Agarwal, P. (2009) A pathway-based view of human diseases and disease relationships. PLoS One 4, e4346.
20. Linghu, B., Snitkin, E. S., Hu, Z., et al. (2009) Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network. Genome Biol 10, R91.
21. Oti, M., Snel, B., Huynen, M. A., Brunner, H. G. (2006) Predicting disease genes using protein-protein interactions. J Med Genet 43, 691–698.
22. Oti, M., van Reeuwijk, J., Huynen, M. A., Brunner, H. G. (2008) Conserved coexpression for candidate disease gene prioritization. BMC Bioinformatics 9, 208.
23. Tiffin, N., Kelso, J. F., Powell, A. R., et al. (2005) Integration of text-and data-mining using ontologies successfully selects disease gene candidates. Nucleic Acids Res 33, 1544–1552.
24. Lopez-Bigas, N., Ouzounis, C. A. (2004) Genome-wide identification of genes likely to be involved in human genetic disease. Nucleic Acids Res 32, 3108–3114.
25. Franke, L., van Bakel, H., Fokkens, L., et al. (2006) Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Am J Hum Genet 78, 1011–1025.
26. Sadasivam, R. S., Sundar, G., Vaughan, L. K., et al. (2009) Genetic region characterization (Gene RECQuest) – software to assist in identification and selection of candidate genes from genomic regions. BMC Res Notes 2, 201.
27. Aerts, S., Lambrechts, D., Maity, S., et al. (2006) Gene prioritization through genomic data fusion. Nat Biotechnol 24, 537–544.
28. Hutz, J. E., Kraja, A. T., McLeod, H. L., Province, M. A. (2008) CANDID: a flexible method for prioritizing candidate genes for complex human traits. Genet Epidemiol 32, 779–790.
29. Elbers, C. C., van Eijk, K. R., Franke, L., et al. (2009) Using genome-wide pathway analysis to unravel the etiology of complex diseases. Genet Epidemiol 33, 419–431.
30. Pan, W. (2008) Network-based model weighting to detect multiple loci influencing complex diseases. Hum Genet 124, 225–234.
31. Perry, J. R., McCarthy, M. I., Hattersley, A. T., et al. (2009) Interrogating type 2 diabetes genome-wide association data using a biological pathway-based approach. Diabetes 58, 1463–1467.
32. Torkamani, A., Schork, N. J. (2009) Pathway and network analysis with high-density allelic association data. Methods Mol Biol 563, 289–301.
33. Torkamani, A., Topol, E. J., Schork, N. J. (2008) Pathway analysis of seven common diseases assessed by genome-wide association. Genomics 92, 265–272.
34. Wang, K., Li, M., Bucan, M. (2007) Pathway-Based Approaches for Analysis of Genomewide Association Studies. Am J Hum Genet 81, 1278–1283.
35. Ashburner, M., Ball, C. A., Blake, J. A., et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25, 25–29.
36. Oti, M., Huynen, M. A., Brunner, H. G. (2008) Phenome connections. Trends Genet 24, 103–106.
37. Oti, M., Huynen, M. A., Brunner, H. G. (2009) The biological coherence of human phenome databases. Am J Hum Genet 85, 801–808.
38. Kobayashi, N., Toyoda, T. (2008) Statistical search on the Semantic Web. Bioinformatics 24, 1002–1010.
39. Elbers, C. C., Onland-Moret, N. C., Franke, L., et al. (2007) A strategy to search for common obesity and type 2 diabetes genes. Trends Endocrinol Metab 18, 19–26.
40. Teber, E. T., Liu, J. Y., Ballouz, S., et al. (2009) Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies. BMC Bioinfo 10(Suppl 1), S69.
41. Thornblad, T. A., Elliott, K. S., Jowett, J., Visscher, P. M. (2007) Prioritization of positional candidate genes using multiple web-based software tools. Twin Res Hum Genet 10, 861–870.
42. Tiffin, N., Adie, E., Turner, F., et al. (2006) Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes. Nucleic Acids Res 34, 3067–3081.
43. Tiffin, N., Okpechi, I., Perez-Iratxeta, C., et al. (2008) Prioritization of candidate disease genes for metabolic syndrome by computational analysis of its defining phenotypes. Physiol Genomics 35, 55–64.
44. Thiel, C. T., Horn, D., Zabel, B., et al. (2005) Severely incapacitating mutations in patients with extreme short stature identify RNA-processing endoribonuclease RMRP as an essential cell growth regulator. Am J Hum Genet 77, 795–806.
45. Sparrow, D. B., Guillen-Navarro, E., Fatkin, D., Dunwoodie, S. L. (2008) Mutation of Hairy-and-Enhancer-of-Split-7 in humans causes spondylocostal dysostosis. Hum Mol Genet 17, 3761–3766.
46. Tremblay, K., Lemire, M., Potvin, C., et al. (2008) Genes to diseases (G2D) computational method to identify asthma candidate genes, PLoS One 3, e2907.
47. Aerts, S., Vilain, S., Hu, S., et al. (2009) Integrating computational biology and forward genetics in Drosophila. PLoS Genet 5, e1000351.
48. Tranchevent, L. C., Barriot, R., Yu, S., et al. (2008) ENDEAVOUR update: a web resource for gene prioritization in multiple species. Nucleic Acids Res 36, W377–384.
49. Smedley, D., Haider, S., Ballester, B., et al. (2009) BioMart – biological queries made easy. BMC Genomics 10, 22.
50. Woollard, P. M. (2010) Asking complex questions of the genome without programming. Methods Mol Biol 628, 39–52.
51. Huang da, W., Sherman, B. T., Lempicki, R. A. (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4, 44–57.
52. Jensen, L. J., Kuhn, M., Stark, M., et al. (2009) STRING 8 – a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res 37, D412–416.
53. Hoffmann, R., Valencia, A. (2004) A gene network for navigating the literature. Nat Genet 36, 664.
54. Halling-Brown, M., Shepherd, A. J. (2008) Constructing computational pipelines. Methods Mol Biol 453, 451–470.
55. Fisher, P., Noyes, H., Kemp, S., et al. (2009) A systematic strategy for the discovery of candidate genes responsible for phenotypic variation. Methods Mol Biol 573, 329–345.
56. Van Vooren, S., Thienpont, B., Menten, B., et al. (2007) Mapping biomedical concepts onto the human genome by mining literature on chromosomal aberrations. Nucleic Acids Res 35, 2533–2543.
57. Wu, X., Jiang, R., Zhang, M. Q., Li, S. (2008) Network-based global inference of human disease genes. Mol Syst Biol 4, 189.
58. Wu, X., Liu, Q., Jiang, R. (2009) Align human interactome with phenome to identify causative genes and networks underlying disease families. Bioinformatics 25, 98–104.
59. Hristovski, D., Peterlin, B., Mitchell, J. A., Humphrey, S. M. (2005) Using literature-based discovery to identify disease candidate genes. Int J Med Inform 74, 289–298.
60. Seelow, D., Schwarz, J. M., Schuelke, M. (2008) GeneDistiller--distilling candidate genes from linkage intervals. PLoS One 3, e3874.
61. Yu, W., Wulf, A., Liu, T., et al. (2008) Gene Prospector: an evidence gateway for evaluating potential susceptibility genes and interacting risk factors for human diseases. BMC Bioinformatics 9, 528.
62. Perez-Iratxeta, C., Bork, P., Andrade, M. A. (2002) Association of genes to genetically inherited diseases using data mining. Nat Genet 31, 316–319.
63. van Driel, M. A., Cuelenaere, K., Kemmeren, P. P., et al. (2003) A new web-based data mining tool for the identification of candidate genes for human genetic disorders. Eur J Hum Genet 11, 57–63.
64. Kohler, S., Bauer, S., Horn, D., Robinson, P. N. (2008) Walking the interactome for prioritization of candidate disease genes. Am J Hum Genet 82, 949–958.
65. George, R. A., Liu, J. Y., Feng, L. L., et al. (2006) Analysis of protein sequence and interaction data for candidate disease gene prediction. Nucleic Acids Res 34, e130.
66. Masseroli, M., Martucci, D., Pinciroli, F. (2004) GFINDer: Genome Function INtegrated Discoverer through dynamic annotation, statistical analysis, and mining. Nucleic Acids Res 32, W293–300.
67. van Driel, M. A., Bruggeman, J., Vriend, G., et al. (2006) A text-mining analysis of the human phenome. Eur J Hum Genet 14, 535–542.
68. Xiong, Q., Qiu, Y., Gu, W. (2008) PGMap-per: a web-based tool linking phenotype to genes. Bioinformatics 24, 1011–1013.
69. Radivojac, P., Peng, K., Clark, W. T., et al. (2008) An integrated approach to inferring gene-disease associations in humans. Proteins 72, 1030–1037.
70. Cheng, D., Knox, C., Young, N., et al. (2008) PolySearch: a web-based text mining system for extracting relationships between human diseases, genes, mutations, drugs and metabolites. Nucleic Acids Res 36, W399–405.
71. Yoshida, Y., Makita, Y., Heida, N., et al. (2009) PosMed (Positional Medline): prioritizing genes with an artificial neural network comprising medical documents to accelerate positional cloning. Nucleic Acids Res 37, W147–W152.
72. Yue, P., Melamud, E., Moult, J. (2006) SNPs3D: candidate gene and SNP selection for association studies. BMC Bioinformatics 7, 166.
73. Adie, E. A., Adams, R. R., Evans, K. L., et al. (2006) SUSPECTS: enabling fast and effective prioritization of positional candidates. Bioinformatics 22, 773–774.
74. Gefen, A., Cohen, R., Birk, O. S. (2010) Syndrome to gene (S2G): in-silico identification of candidate genes for human diseases. Hum Mutat 31, 229–236.
75. Rossi, S., Masotti, D., Nardini, C., et al. (2006) TOM: a web-based integrated approach for identification of candidate disease genes. Nucleic Acids Res 34, W285–292.
76. Chen, J., Bardes, E. E., Aronow, B. J., Jegga, A. G. (2009) ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 37, W305–W311.