Candidate gene discovery and prioritization in rare diseases

Methods in Molecular Biology

Volumn 1168, Issue , 2014, Pages 295-312

  Jegga, Anil G ^a  

^a NONE

Author keywords

Gene prioritization; Gene ranking; Orphan disease; Rare disease; Test set; Training seed gene set

Indexed keywords

ARTICLE; CONTROLLED STUDY; DNAJC5 GENE; EXOME; FOLLOW UP; GENE; GENE EXPRESSION; GENE IDENTIFICATION; GENE MUTATION; GENE REGULATORY NETWORK; GENETIC ANALYSIS; GENETIC ASSOCIATION; GENETIC VARIABILITY; GENOME; HEREDITARY MOTOR SENSORY NEUROPATHY; HUMAN; INTERNET; LIPJ GENE; NEURONAL CEROID LIPOFUSCINOSIS; PDCD6IP GENE; PRIORITY JOURNAL; PROTEIN DOMAIN; PROTEIN PROTEIN INTERACTION; RARE DISEASE; REGULATORY MECHANISM; BIOLOGY; GENETIC PREDISPOSITION; GENETICS; PROCEDURES;

COMPUTATIONAL BIOLOGY; GENETIC PREDISPOSITION TO DISEASE; HUMANS; RARE DISEASES;

EID: 84922294281     PISSN: 10643745     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-1-4939-0847-9_17     Document Type: Article

References (58)

1. Rados C (2003) Orphan products: hope for people with rare diseases. FDA Consum 37: 10-15.
2. Wastfelt M, Fadeel B, Henter JI (2006) A journey of hope: lessons learned from studies on rare diseases and orphan drugs. J Intern Med 260: 1-10.
3. Freudenberg J, Propping P (2002) A similaritybased method for genome-wide prediction of disease-relevant human genes. Bioinformatics 18 Suppl 2S: 110-115.
4. Turner FS, Clutterbuck DR, Semple CA (2003) POCUS: mining genomic sequence annotation to predict disease genes. Genome Biol 4: R75.
5. Tiffin N, Kelso JF, Powell AR et al (2005) Integration of text-and data-mining using ontologies successfully selects disease gene candidates. Nucleic Acids Res 33: 1544-1552.
6. Adie EA, Adams RR, Evans KL et al (2005) Speeding disease gene discovery by sequence based candidate prioritization. BMC Bioinformatics 6: 55.
7. Aerts S, Lambrechts D, Maity S et al (2006) Gene prioritization through genomic data fusion. Nat Biotechnol 24: 537-544.
8. Thornblad TA, Elliott KS, Jowett J et al (2007) Prioritization of positional candidate genes using multiple web-based software tools. Twin Res Hum Genet 10: 861-870.
9. Zhu M, Zhao S (2007) Candidate gene identification approach: progress and challenges. Int J Biol Sci 3: 420-427.
10. 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.
11. Adie EA, Adams RR, Evans KL et al (2006) SUSPECTS: enabling fast and effective prioritization of positional candidates. Bioinformatics 22: 773-774.
12. Chen J, Xu H, Aronow BJ et al (2007) Improved human disease candidate gene prioritization using mouse phenotype. BMC Bioinformatics 8: 392.
13. Chen J, Bardes EE, Aronow BJ et al (2009) ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 37(Web Server issue): W305-W311.
14. Piro RM, Di Cunto F (2012) Computational approaches to disease-gene prediction: rationale, classification and successes. FEBS J 279: 678-696.
15. Goh KI, Cusick ME, Valle D et al (2007) The human disease network. Proc Natl Acad Sci U S A 104: 8685-8690.
16. Jimenez-Sanchez G, Childs B, Valle D (2001) Human disease genes. Nature 409: 853-855.
17. Smith NG, Eyre-Walker A (2003) Human disease genes: patterns and predictions. Gene 318: 169-175.
18. Tranchevent LC, Barriot R, Yu S (2008) ENDEAVOUR update: a web resource for gene prioritization in multiple species. Nucleic Acids Res 36(Web Server issue): W377-W384.
19. Junker BH, Koschutzki D, Schreiber F (2006) Exploration of biological network centralities with CentiBiN. BMC Bioinformatics 7: 219.
20. George RA, Liu JY, Feng LL et al (2006) Analysis of protein sequence and interaction data for candidate disease gene prediction. Nucleic Acids Res 34: e130.
21. Kann MG (2007) Protein interactions and disease: computational approaches to uncover the etiology of diseases. Brief Bioinform 8: 333-346.
22. Kohler S, Bauer S, Horn D et al (2008) Walking the interactome for prioritization of candidate disease genes. Am J Hum Genet 82: 949-958.
23. Wu X, Jiang R, Zhang MQ et al (2008) Network-based global inference of human disease genes. Mol Syst Biol 4: 189.
24. Xu J, Li Y (2006) Discovering disease-genes by topological features in human protein-protein interaction network. Bioinformatics 22: 2800-2805.
25. Chen JY, Shen C, Sivachenko AY (2006) Mining Alzheimer disease relevant proteins from integrated protein interactome data. Pac Symp Biocomput 367-378.
26. Ortutay C, Vihinen M (2009) Identification of candidate disease genes by integrating Gene Ontologies and protein-interaction networks: case study of primary immunodeficiencies. Nucleic Acids Res 37: 622-628.
27. Chen J, Aronow BJ, Jegga AG (2009) Disease candidate gene identification and prioritization using protein interaction networks. BMC Bioinformatics 10: 73.
28. Gilissen C, Hoischen A, Brunner HG et al (2012) Disease gene identification strategies for exome sequencing. Eur J Hum Genet 20: 490-497.
29. Zhang M, Zhu C, Jacomy A et al (2011) The orphan disease networks. Am J Hum Genet 88: 755-766.
30. Botstein D, Risch N (2003) Discovering genotypes underlying human phenotypes: past successes for Mendelian disease, future approaches for complex disease. Nat Genet 33(Suppl): 228-237.
31. Bainbridge MN, Wiszniewski W, Murdock DR et al (2011) Whole-genome sequencing for optimized patient management. Sci Transl Med 3: 87re3.
32. Kingsmore SF, Saunders CJ (2011) Deep sequencing of patient genomes for disease diagnosis: when will it become routine? Sci Transl Med 3: 87ps23.
33. Benitez BA, Alvarado D, Cai Y et al (2011) Exome-sequencing confirms DNAJC5 mutations as cause of adult neuronal ceroidlipofuscinosis. PLoS One 6: e26741.
34. Erlich Y, Edvardson S, Hodges E et al (2011) Exome sequencing and disease-network analysis of a single family implicate a mutation in KIF1A in hereditary spastic paraparesis. Genome Res 21: 658-664.
35. Popescu M, Keller JM, Mitchell JA (2006) Fuzzy measures on the Gene Ontology for gene product similarity. IEEE/ACM Trans Comput Biol Bioinform 3: 263-274.
36. Hamosh A, Scott A, Amberger J et al (2005) Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res 33: D514-D517.
37. Becker KG, Barnes KC, Bright TJ et al (2004) The genetic association database. Nat Genet 36: 431-432.
38. Hindorff LA, Sethupathy P, Junkins HA et al (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A 106: 9362-9367.
39. Davis AP, Murphy CG, Saraceni-Richards CA et al (2009) Comparative Toxicogenomics Database: a knowledgebase and discovery tool for chemical-gene-disease networks. Nucleic Acids Res 37(Database issue): D786-D792.
40. Linghu B, Snitkin ES, 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.
41. Szklarczyk D, Franceschini A, Kuhn M et al (2011) The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 39: D561-D568.
42. Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13: 2498-2504.
43. King MC, Wilson AC (1975) Evolution at two levels in humans and chimpanzees. Science 188: 107-116.
44. Korstanje R, Paigen B (2002) From QTL to gene: the harvest begins. Nat Genet 31: 235-236.
45. Mackay TF (2001) Quantitative trait loci in Drosophila. Nat Rev Genet 2: 11-20.
46. Bromberg Y (2013) Chapter 15: disease gene prioritization. PLoS Comput Biol 9: e1002902.
47. Navlakha S, Kingsford C (2010) The power of protein interaction networks for associating genes with diseases. Bioinformatics 26: 1057-1063.
48. Perez-Iratxeta C, Bork P, Andrade MA (2002) Association of genes to genetically inherited diseases using data mining. Nat Genet 31: 316-319.
49. Perez-Iratxeta C, Wjst M, Bork P et al (2005) G2D: a tool for mining genes associated with disease. BMC Genet 6: 45.
50. Hristovski D, Peterlin B, Mitchell JA et al (2005) Using literature-based discovery to identify disease candidate genes. Int J Med Inform 74: 289-298.
51. van Driel MA, Cuelenaere K, Kemmeren PP 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.
52. van Driel MA, Cuelenaere K, Kemmeren PP et al (2005) GeneSeeker: extraction and integration of human disease-related information from web-based genetic databases. Nucleic Acids Res 33(Web Server issue): W758-W761.
53. Masseroli M, Galati O, Pinciroli F (2005) GFINDer: genetic disease and phenotype location statistical analysis and mining of dynamically annotated gene lists. Nucleic Acids Res 33(Web Server issue): W717-W723.
54. Masseroli M, Martucci D, Pinciroli F (2004) GFINDer: Genome Function INtegrated Discoverer through dynamic annotation, statistical analysis, and mining. Nucleic Acids Res 32(Web Server issue): W293-W300.
55. Baker EJ, Jay JJ, Bubier JA et al (2012) GeneWeaver: a web-based system for integrative functional genomics. Nucleic Acids Res 40(Database issue): D1067-D1076.
56. Chen YA, Tripathi LP, Mizuguchi K (2011) TargetMine, an integrated data warehouse for candidate gene prioritisation and target discovery. PLoS One 6: e17844.
57. Liekens AM, De Knijf J, Daelemans W et al (2011) BioGraph: unsupervised biomedical knowledge discovery via automated hypothesis generation. Genome Biol 12: R57.
58. Makita Y, Kobayashi N, Yoshida Y et al (2013) PosMed: ranking genes and bioresources based on Semantic Web Association Study. Nucleic Acids Res 41(Web Server issue): W109-W114.