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Practical Applications of the Gene Ontology Resource

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Abstract

The Gene Ontology (GO) is a controlled vocabulary that represents knowledge about the functional attributes of gene products in a structured manner and can be used in both computational and human analyses. This vocabulary has been used by diverse curation groups to associate functional information to individual gene products in the form of annotations. GO has proven an invaluable resource for evaluating and interpreting the biological significance of large data sets, enabling researchers to create hypotheses to direct their future research. This chapter provides an overview of the Gene Ontology, how it can be used, and tips on getting the most out of GO analyses.

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References 15

  1. Al-Shahrour, F., Díaz-Uriarte, R., Dopazo, J.: FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics 20(4), 578–580 (2004)

    Article  Google Scholar 

  2. Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., Harris, M.A., Hill, D.P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J.C., Richardson, J.E., Ringwald, M., Rubin, G.M., Sherlock, G.: Gene Ontology: tool for the unification of biology. Nature Genetics 25(1), 25–29 (2000)

    Article  Google Scholar 

  3. Asikainen, S., Heikkinen, L., Wong, G., Storvik, M.: Functional characterization of endogenous siRNA targets in Caenorhabditis elegans. BMC Genomics 9(270), 1–10 (2008)

    Google Scholar 

  4. Bendtsen, J.D., Neilsen, H., von Heijne, G., Brunak, S.: Improved prediction of signal peptides: SignalP 3.0. Journal of Molecular Biology 340(4), 783–795 (2004)

    Article  Google Scholar 

  5. Brown, K.R., Jurisica, I.: Unequal evolutionary conservation of human protein interactions in interologous networks. Genome Biology 8, R95 (2007)

    Article  Google Scholar 

  6. Camon, E., Magrane, M., Barrell, D., Binns, D., Fleischmann, W., Kersey, P., Mulder, N., Oinn, T., Maslen, J., Cox, A., Apweiler, R.: The Gene Ontology Annotation (GOA) project: Implementation of GO in SWISS-PROT, TrEMBL, and InterPro. Genome Research 13(4), 662–672 (2003)

    Article  Google Scholar 

  7. Camon, E.B., Barrell, D.G., Dimmer, E.C., Lee, V., Magrane, M., Maslen, J., Binns, D., Apweiler, R.: An evaluation of GO annotation retrieval for BioCreAtIvE and GOA. BMC Bioinformatics 6, S17 (2005)

    Article  Google Scholar 

  8. Cao, R., Li, X., Liu, Z., Peng, X., Hu, W., Wang, X., Chen, P., Xie, J., Liang, S.: Integration of a two-phase partition method into proteomics research on rat liver plasma membrane proteins. Journal of Proteome Research 5(3), 634–642 (2006)

    Article  Google Scholar 

  9. Chalmel, F., Lardenois, A., Thompson, J.D., Muller, J., Sahel, J.A., Léveillard, J.A., Poch, O.: GOAnno: GO annotation based on multiple alignment. Bioinformatics 21(9), 2095–2096 (2005)

    Article  Google Scholar 

  10. Conesa, A., Götz, S., García-Gómez, J.M., Terol, J., Talón, M., Robles, M.: Blast2GO: A universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21(18), 3674–3676 (2005)

    Article  Google Scholar 

  11. Dimmer, E.C., Huntley, R.P., Barrell, D.G., Binns, D., Draghici, S., Camon, E.B., Hubank, M., Talmud, P.J., Apweiler, R., Lovering, R.: The Gene Ontology — Providing a functional role in proteomic studies. Proteomics (2008)

    Google Scholar 

  12. Draghici, S., Sellamuthu, S., Khatri, P.: Babel’s tower revisited: A universal resource for crossreferencing across annotation databases. Bioinformatics 22(23), 2934–2939 (2006)

    Article  Google Scholar 

  13. Emanuelsson, O., Neilsen, H., Brunak, S., von Heijne, G.: Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. Journal of Molecular Biology 300(4), 1005–1016 (2000)

    Article  Google Scholar 

  14. Engelhardt, B.E., Jordan, M.I., Muratore, K.E., Brenner, S.E.: Protein molecular function prediction by Bayesian phylogenomics. PLoS Computational Biology 1(5), 432–445 (2005)

    Article  Google Scholar 

  15. Gorter, J.A., van Vliet, E.A., Aronica, E., Breit, T., Rauwerda, H., da Silva, F.A.L., Wadman, W.J.: Potential new antiepileptogenic targets indicated by microarray analysis in a rat model for temporal lobe epilepsy. The Journal of Neuroscience 26(43), 11,083–11,110 (2006)

    Article  Google Scholar 

  16. Khatri, P., Bhavsar, P., Bawa, G., Draghici, S.: Onto-Tools: An ensemble of web-accessible, ontology-based tools for the functional design and interpretation of high-throughput gene expression experiments. Nucleic Acids Research 32, W449–456 (2004)

    Article  Google Scholar 

  17. Khatri, P., Draghici, S.: Ontological analysis of gene expression data: Current tools, limitations, and open problems. Bioinformatics 21(18), 3587–3595 (2005)

    Article  Google Scholar 

  18. Lin, H., Ouyang, S., Egan, A., Nobuta, K., Haas, B.J., Zhu, W., Gu, X., Silva, J.C., Meyers, B.C., Buell, C.R.: Characterization of paralogous protein families in rice. BMC Plant Biology 8(18) (2008)

    Google Scholar 

  19. Quevillon, E., Silventoinen, V., Pillai, S., Harte, N., Mulder, N., Apweiler, R., Lopez, R.: InterProScan: Protein domains identifier. Nucleic Acids Research 33, W116–120 (2005)

    Article  Google Scholar 

  20. Rhee, S.Y., Wood, V., Dolinski, K., Draghici, S.: Use and misuse of the Gene Ontology annotations. Nature Review Genetics 9(7), 509–515 (2008)

    Article  Google Scholar 

  21. Robinson, P.N., Wollstein, A., Böhme, U., Beattie, B.: Ontologizing gene-expression microarray data: Characterizing clusters with Gene Ontology. Bioinformatics 20(6), 979–981 (2004)

    Article  Google Scholar 

  22. Wertheim, B., Kraaijeveld, A.R., Schuster, E., Blanc, E., Hopkins, M., Pletcher, S.D., Strand, M.R., Partridge, L., Godfray, H.C.J.: Genome-wide gene expression in response to parasitoid attack in Drosophila. Genome Biology 6(11), R94 (2005)

    Article  Google Scholar 

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Correspondence to Rachael P. Huntley .

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Huntley, R.P., Dimmer, E.C., Apweiler, R. (2010). Practical Applications of the Gene Ontology Resource. In: Heath, L., Ramakrishnan, N. (eds) Problem Solving Handbook in Computational Biology and Bioinformatics. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09760-2_15

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  • DOI: https://doi.org/10.1007/978-0-387-09760-2_15

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