A resource of quantitative functional annotation for homo sapiens genes

G3: Genes, Genomes, Genetics

Volumn 2, Issue 2, 2012, Pages 223-233

  Taşan, Murat ^a,b   Drabkin, Harold J ^c   Beaver, John E ^b   Chua, Hon Nian ^a,b   Dunham, Julie ^a   Tian, Weidong ^d   Blake, Judith A ^c   Roth, Frederick P ^a,b,e,f  

^a UNIVERSITY OF TORONTO   (Canada)

^b HARVARD MEDICAL SCHOOL   (United States)

^c JACKSON LABORATORY   (United States)

^d FUDAN UNIVERSITY   (China)

^e DANA FARBER CANCER INSTITUTE   (United States)

^f MOUNT SINAI HOSPITAL   (Canada)

Author keywords

Function prediction; Gene function human; Gene Ontology; Machine learning

Indexed keywords

EID: 84882860336     PISSN: None     EISSN: 21601836     Source Type: Journal    
DOI: 10.1534/g3.111.000828     Document Type: Article

References (49)

1. Barrell, D., E. Dimmer, R. P. Huntley, D. Binns, C. O'Donovan et al., 2009 The GOA database in 2009-an integrated Gene Ontology Annotation resource. Nucleic Acids Res. 37: 396-403.
2. Barrett, T., D. B. Troup, S. E. Wilhite, P. Ledoux, D. Rudnev, et al. 2009 NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res. 37: D885-D890.
3. Beaver, J. E., M. Tasan, F. D. Gibbons, W. Tian, T. R. Hughes, et al. 2010 FuncBase: a resource for quantitative gene function annotation. Bioinformatics 26: 1806-1807.
4. Benjamini, Y., and Y. Hochberg, 1995 Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc., B 57: 289-300.
5. Berriz, G. F., and F. P. Roth, 2008 The Synergizer service for translating gene, protein and other biological identifiers. Bioinformatics 24: 2272-2273.
6. Bieri, T., D. Blasiar, P. Ozersky, I. Antoshechkin, C. Bastiani, et al. 2007 WormBase: new content and better access. Nucleic Acids Res. 35 (Database issue): 506-510.
7. Bologna, G., A.-L. Veuthey, M. Pagni, L. Lane, and A. Bairoch 2011 A preliminary study on the prediction of human protein functions, in 'Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I', IWINAC'11, Springer-Verlag, Berlin, Heidelberg, pp. 334-343. Available at: http://dl. acm.org/citation.cfm?id=2009405.2009440
8. Bredel, M., C. Bredel, D. Juric, G. R. Harsh, H. Vogel, et al. 2005 Functional network analysis reveals extended gliomagenesis pathway maps and three novel MYC-interacting genes in human gliomas. Cancer Res. 65: 8679-8689.
9. Breiman, L., 2001 Random forests. Mach. Learn. 45(1): 5-32.
10. Crosby, M. A., J. L. Goodman, V. B. Strelets, P. Zhang, and W. M. Gelbart, 2007 FlyBase: genomes by the dozen. Nucleic Acids Res. 35(Database issue): 486-491.
11. Dai, M., P.Wang, A. D. Boyd, G. Kostov, B. Athey, et al. 2005 Evolving gene/ transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res. 33: e175.
12. Deng, M., T. Chen, and F. Sun, 2004 An integrated probabilistic model for functional prediction of proteins. J. Comput. Biol. 11(2-3): 463-475.
13. Eppig, J., J. A. Blake, C. Bult, J. Kadin, and J. Richardson Mouse Genome Informatics Group, 2007 The mouse genome database (mgd): new features facilitating a model system. Nucleic Acids Res. 35(Database issue): D630-D637.
14. Gunsalus, K. C., W. C. Yueh, P. MacMenamin, and F. Piano, 2004 RNAiDB and PhenoBlast: web tools for genome-wide phenotypic mapping projects. Nucleic Acids Res. 32: D406-D410.
15. Gunsalus, K. C., H. Ge, A. J. Schetter, D. S. Goldberg, J. D. Han, et al. 2005 Predictive models of molecular machines involved in Caenorhabditis elegans early embryogenesis. Nature 436: 861-865.
16. Hamosh, A., A. F. Scott, J. S. Amberger, C. A. Bocchini, and V. A. McKusick, 2005 Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 33(Database issue): 514-517.
17. Huttenhower, C., E. M. Haley, M. A. Hibbs, V. Dumeaux, D. R. Barrett, et al. 2009 Exploring the human genome with functional maps. Genome Res. 19: 1093-1106.
18. Huttenhower, C., and O. Hofmann, 2010 A quick guide to large-scale genomic data mining. PLOS Comput. Biol. 6: e1000779.
19. Joshi, T., Y. Chen, J. M. Becker, N. Alexandrov, and D. Xu, 2004 Genomescale gene function prediction using multiple sources of high-throughput data in yeast Saccharomyces cerevisiae. OMICS 8: 322-333.
20. Karaoz, U., T. M. Murali, S. Letovsky, Y. Zheng, C. Ding, et al. 2004 Wholegenome annotation by using evidence integration in functional-linkage networks. Proc. Natl. Acad. Sci. USA 101: 2888-2893.
21. Keshava Prasad, T. S., R. Goel, K. Kandasamy, S. Keerthikumar, S. Kumar, et al. 2009 Human Protein Reference Database-2009 update. Nucleic Acids Res. 37: D767-D772.
22. King, O. D., J. C. Lee, A. M. Dudley, D. M. Janse, G. M. Church, et al. 2003 Predicting phenotype from patterns of annotation. Bioinformatics 19(Suppl 1): 183-189.
23. Ko, S., and H. Lee, 2009 Integrative approaches to the prediction of protein functions based on the feature selection. BMC Bioinformatics 10: 455.
24. Lanckriet, G. R. G., T. De Bie, N. Cristianini, M. I. Jordan, and W. S. Noble, 2004 A statistical framework for genomic data fusion. Bioinformatics 20: 2626-2635.
25. Lee, I., S. V. Date, A. T. Adai, and E. M. Marcotte, 2004 A probabilistic functional network of yeast genes. Science 306: 1555-1558.
26. Lee, I., B. Lehner, T. Vavouri, J. Shin, A. G. Fraser, and E. M. Marcotte, 2010 Predicting genetic modifier loci using functional gene networks. Genome Res. 20: 1143-1153
27. Lee, I., U. M. Blom, P. I. Wang, J. E. Shim, and E. M. Marcotte, 2011 Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res. 21: 1109-1121.
28. Letovsky, S., and S. Kasif, 2003 Predicting protein function from protein/ protein interaction data: a probabilistic approach. Bioinformatics 19 (Suppl 1): 197-204.
29. Li, Y., and G. Bu, 2005 LRP5/6 in Wnt signaling and tumorigenesis. Future Oncol. 1: 673-681.
30. Linghu, B., E. S. Snitkin, Z. Hu, Y. Xia, and C. DeLisi, 2009 Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network. Genome Biol. 10: R91.
31. Luu, H. H., R. Zhang, R. C. Haydon, E. Rayburn, Q. Kang, et al. 2004 Wnt/ beta-catenin signaling pathway as a novel cancer drug target. Curr. Cancer Drug Targets 4: 653-671.
32. Mulder, N. J., R. Apweiler, T. K. Attwood, A. Bairoch, A. Bateman, et al. 2005 InterPro, progress and status in 2005. Nucleic Acids Res. 33(Database issue): 201-205.
33. Murali, T. M., C. J. Wu, and S. Kasif, 2006 The art of gene function prediction. Nat. Biotechnol. 24: 1474-1475.
34. Nash, R., S. Weng, B. Hitz, R. Balakrishnan, K. R. Christie, et al. 2007 Expanded protein information at SGD: new pages and proteome browser. Nucleic Acids Res. 35(Database issue): 468-471.
35. O'Brien, K. P., M. Remm, and E. L. L. Sonnhammer, 2005 Inparanoid: a comprehensive database of eukaryotic orthologs. Nucleic Acids Res. 33 (Database issue): 476-480.
36. Pena-Castillo, L., M. Tasan, C. L. Myers, H. Lee, T. Joshi, et al. 2008 A critical assessment of Mus musculus gene function prediction using integrated genomic evidence. Genome Biol. 9(Suppl 1): S2.
37. Piekutowska-Abramczuk, D., E. Ciara, E. Popowska, W. Grajkowska, B. Dembowska-Bagiska, et al. 2009 The frequency of NBN molecular variants in pediatric astrocytic tumors. J. Neuro-oncol. 96: 161-168.
38. Pujana, M. A., J. D. Han, L. M. Starita, K. N. Stevens, M. Tewari, et al. 2007 Network modeling links breast cancer susceptibility and centrosome dysfunction. Nat. Genet. 39: 1338-1349.
39. Schwikowski, B., P. Uetz, and S. Fields, 2000 A network of protein-protein interactions in yeast. Nat. Biotechnol. 18: 1257-1261.
40. Shete, S., F. J. Hosking, L. B. Robertson, S. E. Dobbins, M. Sanson, et al. 2009 Genome-wide association study identifies five susceptibility loci for glioma. Nat. Genet. 41: 899-904.
41. Smith, C. L., C.-A. W. Goldsmith, and J. T. Eppig, 2005 The Mammalian Phenotype Ontology as a tool for annotating, analyzing and comparing phenotypic information. Genome Biol. 6(1): R7.
42. Sokolov, A., and A. Ben-Hur, 2010 Hierarchical classification of gene ontology terms using the GOstruct method. J. Bioinform. Comput. Biol. 8: 357-376.
43. Szklarczyk, D., A. Franceschini, M. Kuhn, M. Simonovic, A. Roth, et al. 2011 The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 39: D561-D568.
44. Tasan, M., W. Tian, D. P. Hill, F. D. Gibbons, J. A. Blake, et al. 2008 An en masse phenotype and function prediction system for Mus musculus. Genome Biol. 9(Suppl 1): S8.
45. Tian, W., L. V. Zhang, M. Taan, F. D. Gibbons, O. D. King, et al. 2008 Combining guilt-by-association and guilt-by-profiling to predict Saccharomyces cerevisiae gene function. Genome Biol. 9(Suppl 1): S7.
46. Troyanskaya, O. G., K. Dolinski, A. B. Owen, R. B. Altman, and D. Botstein, 2003 A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae). Proc. Natl. Acad. Sci. USA 100: 8348-8353.
47. Wang, P. I., and E. M. Marcotte, 2010 'It's the machine that matters: predicting gene function and phenotype from protein networks'. J. Proteomics 73: 2277-2289.
48. Wong, S. L., L. V. Zhang, A. H. Y. Tong, Z. Li, D. S. Goldberg, et al. 2004 Combining biological networks to predict genetic interactions. Proc. Natl. Acad. Sci. USA 101: 15682-15687.
49. Wu, Z., R. A. Irizarry, R. Gentleman, F. Martinez-Murillo, and F. Spencer, 2004 A model-based background adjustment for oligonucleotide expression arrays. J. Am. Stat. Assoc. 99: 909-917.