• Wyatt Travis ClarkEmail author
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)


Characterizing the functional behavior of individual proteins in a variety of different contexts is an important step in understanding life at the molecular level. Endeavors such as understanding biological pathways, investigating disease, and developing drugs to cure those diseases depend on being able to describe the actions of individual proteins or genes, both in terms of their physiochemical molecular function, involvement in biological processes, and the subcellular location at which these actions are carried out.


Gene Ontology Enzyme Commission Unify Medical Language System Functional Term Annotation Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Addou, S., Rentzsch, R., Lee, D., Orengo, C.A.: Domain-based and family-specific sequence identity thresholds increase the levels of reliable protein function transfer. J. Mol. Biol. 387(2), 416–430 (2009)CrossRefGoogle Scholar
  2. 2.
    Aerts, S., Lambrechts, D., Maity, S., Van Loo, P., Coessens, B., De Smet, F., Tranchevent, L.-C., De Moor, B., Marynen, P., Hassan, B., et al.: Gene prioritization through genomic data fusion. Nat. Biotechnol. 24(5), 537–544 (2006)CrossRefGoogle Scholar
  3. 3.
    Alterovitz, G., Michael, X., Hill, D.P., Jane, L., Jonathan, L., Michael, C., Jonathan, D., Chris, M., Harris, M.A., Dolan, M.E., et al.: Ontology engineering. Nat. Biotechnol. 28(2), 128–130 (2010)Google Scholar
  4. 4.
    Ashburner, M., et al.: Gene ontology: tool for the unification of biology. Nat. Genet. 25(1), 25–29 (2000)CrossRefGoogle Scholar
  5. 5.
    Amos, B., Rolf, A., Wu, C.H., Barker, W.C., Brigitte, B., Serenella, F., Elisabeth, G., Hongzhan, H., Rodrigo, L., Michele, M., et al.: The universal protein resource (UniProt). Nucleic Acids Res. 33(1), D154–D159 (2005)Google Scholar
  6. 6.
    Bandyopadhyay, D., Huan, J., Liu, J., Prins, J., Snoeyink, J., Wang, W., Tropsha, A.: A structure-based function inference using protein family-specific fingerprints. Protein Sci. 15(6), 1537–1543 (2006)CrossRefGoogle Scholar
  7. 7.
    Barutcuoglu, Z., Schapire, R.E., Troyanskaya, O.G.: Hierarchical multi-label prediction of gene function. Bioinformatics 22(7), 830–836 (2006)CrossRefGoogle Scholar
  8. 8.
    Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32(Database issue), D267–D270 (2004)Google Scholar
  9. 9.
    Brown, D., Sjolander, K.: Functional classification using phylogenomic inference. PLoS Comput. Biol. 2(6), e77 (2006)CrossRefGoogle Scholar
  10. 10.
    Clark, W.T., Radivojac, P.: Analysis of protein function and its prediction from amino acid sequence. Proteins Struct. Funct. Bioinf. 79(7), 2086–2096 (2011)CrossRefGoogle Scholar
  11. 11.
    Costello, J.C., Dalkilic, M.M., Beason, S.M., Gehlhausen, J.R., Patwardhan, R., Middha, S., Eads, B.D., Andrews, J.R., et al.: Gene networks in Drosophila melanogaster: integrating experimental data to predict gene function. Genome Biol. 10(9), R97 (2009)Google Scholar
  12. 12.
    Cozzetto, D., Jones, D.T.: The contribution of intrinsic disorder prediction to the elucidation of protein function. Curr. Opin. Struct. Biol. 23, 467–472 (2013)Google Scholar
  13. 13.
    Dahdul, W.M., Balhoff, J.P., Blackburn, D.C., Diehl, A.D., Haendel, M.A., Hall, B.K., Lapp, H., Lundberg, J.G., Mungall, C.J., Ringwald, M., et al.: A unified anatomy ontology of the vertebrate skeletal system. PloS One 7(12), e51070 (2012)Google Scholar
  14. 14.
    Dalkilic, M.M., Costello, J.C., Clark, W.T., Radivojac, P.: From protein-disease associations to disease informatics. Front. Biosci. 13, 3391–3407 (2008)CrossRefGoogle Scholar
  15. 15.
    Deng, M., Zhang, K., Mehta, S., Chen, T., Sun, F.: Prediction of protein function using protein-protein interaction data. J. Comput. Biol. 10(6), 947–960 (2003)CrossRefGoogle Scholar
  16. 16.
    Devos, D., Valencia, A.: Practical limits of function prediction. Proteins 41(1), 98–107 (2000)CrossRefGoogle Scholar
  17. 17.
    Doolittle, R.F.: Of URFS and ORFS: a primer on how to analyze derived amino acid sequences. University Science Books, Mill Valley (1986)Google Scholar
  18. 18.
    Eisen, J.A.: Phylogenomics: improving functional predictions for uncharacterized genes by evolutionary analysis. Genome Res. 8, 163–167 (1998)CrossRefGoogle Scholar
  19. 19.
    Enault, F., Suhre, K., Claverie, J.-M.: Phydbac “Gene Function Predictor”: a gene annotation tool based on genomic context analusis. BMC Bioinf. 6(257), 247 (2005)Google Scholar
  20. 20.
    Engelhardt, B.E., Jordan, M.I., Muratore, K.E., Brenner, S.E.: Protein molecular function prediction by Bayesian phylogenomics. PLoS Comput. Biol. 1(5), e45 (2005)CrossRefGoogle Scholar
  21. 21.
    Friedberg, I.: Automated protein function prediction-the genomic challenge. Briefings Bioinf. 7(3), 225–242 (2006)CrossRefGoogle Scholar
  22. 22.
    Gaudet, P., Livstone, M.S., Lewis, S.E., Thomas, P.D.: Phylogenetic-based propagation of functional annotations within the gene ontology consortium. Briefings Bioinf. 12(5), 449–462 (2011)CrossRefGoogle Scholar
  23. 23.
    Guzzi, P.H., et al.: Semantic similarity analysis of protein data: assessment with biological features and issues. Briefings Bioinf. 13(5), 569–585 (2012)CrossRefGoogle Scholar
  24. 24.
    Hamp, T., Kassner, R., Seemayer, S., Vicedo, E., Schaefer, C., Achten, D., Auer, F., Boehm, A., Braun, T., Hecht, M., et al.: Homology-based inference sets the bar high for protein function prediction. BMC Bioinf. 14(Suppl 3), S7 (2013)CrossRefGoogle Scholar
  25. 25.
    Hawkins, T., Luban, S., Kihara, D.: Enhanced automated function prediction using distantly related sequences and contextual association by PFP. Protein Sci. 15(6), 1550–1556 (2006)CrossRefGoogle Scholar
  26. 26.
    Hermann, J.C., Marti-Arbona, R., Fedorov, A.A., Fedorov, E., Almo, S.C., Shoichet, B.K., Raushel, F.M.: Structure-based activity prediction for an enzyme of unknown function. Nature 448(7155), 775–779 (2007)CrossRefGoogle Scholar
  27. 27.
    Huttenhower, C., Hibbs, M., Myers, C., Troyanskaya, O.G.: A scalable method for integration and functional analysis of multiple microarray datasets. Bioinformatics 22(23), 2890–2897 (2006)Google Scholar
  28. 28.
    Jensen, L.J., Gupta, R., Staerfeldt, H.H., Brunak, S.: Prediction of human protein function according to gene ontology categories. Bioinformatics 19(5), 635–642 (2003)CrossRefGoogle Scholar
  29. 29.
    Jensen, L.J., Gupta, R., Blom, N., Devos, D., Tamames, J., Kesmir, C., Nielsen, H., Stærfeldt, H.H., Rapacki, K., Workman, C., et al.: Prediction of human protein function from post-translational modifications and localization features. J. Mol. Biol. 319(5), 1257–1266 (2002)Google Scholar
  30. 30.
    Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. In: International Conference on Research in Computational Linguistics, pp. 19–33 (1997)Google Scholar
  31. 31.
    Jones, C.E., Schwerdt, J., Bretag, T.A., Baumann, U., Brown, A.L.: Gosling: a rule-based protein annotator using blast and go. Bioinformatics 24(22), 2628–2629 (2008)CrossRefGoogle Scholar
  32. 32.
    Kann, M.G.: Protein interactions and disease: computational approaches to uncover the etiology of diseases. Briefings Bioinf. 8(5), 333–346 (2007)Google Scholar
  33. 33.
    Kourmpetis, Y.A.I., van Dijk, A.D.J., Bink, M.C.A.M., van Ham, R.C.H.J., Ter Braak, C.J.F.: Bayesian Markov random field analysis for protein function prediction based on network data. PloS One 5(2), e9293 (2010)Google Scholar
  34. 34.
    Kourmpetis, Y.A.I., van Dijk, A.D.J., ter Braak, C.J.F.: Gene ontology consistent protein function prediction: the falcon algorithm applied to six eukaryotic genomes. Algorithms Mol. Biol. 8(1), 10 (2013)Google Scholar
  35. 35.
    Laskowski, R.A., Thornton, J.M.: Understanding the molecular machinery of genetics through 3D structures. Nat. Rev. Genet. 9(2), 141–151 (2008)CrossRefGoogle Scholar
  36. 36.
    Lee, D., Redfern, O., Orengo, C.: Predicting protein function from sequence and structure. Nat. Rev. Mol. Cell Biol. 8(12), 995–1005 (2007)CrossRefGoogle Scholar
  37. 37.
    Lee, I., Date, S.V., Adai, A.T., Marcotte, E.M.: A probabilistic functional network of yeast genes. Science 306(5701), 1555–1558 (2004)CrossRefGoogle Scholar
  38. 38.
    Letovsky, S., Kasif, S.: Predicting protein function from protein/protein interaction data: a probabilistic approach. Bioinformatics 19(Suppl 1), i197–204 (2003)CrossRefGoogle Scholar
  39. 39.
    Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the 15th International Conference on Machine Learning, pp. 296–304. Morgan Kaufmann, San Francisco (1998)Google Scholar
  40. 40.
    Liolios, K., Mavromatis, K., Tavernarakis, N., Kyrpides, N.C.: The genomes on line database (gold) in 2007: status of genomic and metagenomic projects and their associated metadata. Nucleic Acids Res. 36(Database issue), D475–D479 (2008)Google Scholar
  41. 41.
    Lord, P.W., et al.: Investigating semantic similarity measures across the gene ontology: the relationship between sequence and annotation. Bioinformatics 19(10), 1275–1283 (2003)CrossRefMathSciNetGoogle Scholar
  42. 42.
    Marcotte, E.M., Pellegrini, M., Ng, H.-L., Rice, D.W., Yeates, T.O., Eisenberg, D.: Detecting protein function and protein-protein interactions from genome sequences. Science 285(5428), 751–753 (1999)Google Scholar
  43. 43.
    Martin, D.M., et al.: GOtcha: a new method for prediction of protein function assessed by the annotation of seven genomes. BMC Bioinf. 5, 178 (2004)CrossRefGoogle Scholar
  44. 44.
    Minneci, F., Piovesan, D., Cozzetto, D., Jones, D.T.: FFPred 2.0: improved homology-independent prediction of gene ontology terms for eukaryotic protein sequences. PLoS One 8(5), e63754 (2013)Google Scholar
  45. 45.
    Nabieva, E., Jim, K., Agarwal, A., Chazelle, B., Singh, M.: Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics 21(Suppl 1), i302–310 (2005)CrossRefGoogle Scholar
  46. 46.
    (NC-IUBMB) NCotIUoBaMB. Enzyme nomenclature. Academic Press, New York (1992)Google Scholar
  47. 47.
    Pal, D., Eisenberg, D.: Inference of protein function from protein structure. Structure 13(1), 121–130 (2005)CrossRefGoogle Scholar
  48. 48.
    Pazos, F., Sternberg, M.J.: Automated prediction of protein function and detection of functional sites from structure. Proc. Nat. Acad. Sci. U.S.A. 101(41), 14754–14759 (2004)CrossRefGoogle Scholar
  49. 49.
    Pellegrini, M., Marcotte, E.M., Thompson, M.J., Eisenberg, D., Yeates, T.O.: Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc. Nat. Acad. Sci. U.S.A. 96(8), 4285–4288 (1999)CrossRefGoogle Scholar
  50. 50.
    Pesquita, C., et al.: Semantic similarity in biomedical ontologies. PLoS Comput. Biol. 5(7), e1000443 (2009)CrossRefMathSciNetGoogle Scholar
  51. 51.
    Punta, M., Ofran, Y.: The rough guide to in silico function prediction, or how to use sequence and structure information to predict protein function. PLoS Comput. Biol. 4(10), e1000160 (2008)CrossRefGoogle Scholar
  52. 52.
    Rada, R., et al.: Development and application of a metric on semantic nets. IEEE Trans. Syst. Man Cybern. 19(1), 17–30 (1989)CrossRefGoogle Scholar
  53. 53.
    Radivojac, P., Clark, W.T., et al.: A large-scale evaluation of computational protein function prediction. Nat. Methods 10(3), 221–227 (2013)Google Scholar
  54. 54.
    Remm, M., Storm, C.E., Sonnhammer, E.L.: Automatic clustering of orthologs and in-paralogs from pairwise species comparisons. J. Mol. Biol. 314(5), 1041–1052 (2001)CrossRefGoogle Scholar
  55. 55.
    Rentzsch, R., Orengo, C.A.: Protein function prediction-the power of multiplicity. Trends Biotechnol. 27(4), 210–219 (2009)CrossRefGoogle Scholar
  56. 56.
    Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp. 448–453 (1995)Google Scholar
  57. 57.
    Resnik, P.: Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J. Artif. Intell. Res. 11, 95–130 (1999)zbMATHGoogle Scholar
  58. 58.
    Robinson, P.N., Bauer, S.: Introduction to bio-ontologies. CRC Press, Boca Raton (2011)Google Scholar
  59. 59.
    Robinson, P.N., Mundlos, S.: The human phenotype ontology. Clin. Genetics 77(6), 525–534 (2010)CrossRefGoogle Scholar
  60. 60.
    Rost, B., Liu, J., Nair, R., Wrzeszczynski, K.O., Ofran, Y.: Automatic prediction of protein function. Cell. Mol. Life Sci. 60(12), 2637–2650 (2003)CrossRefGoogle Scholar
  61. 61.
    Ruepp, A., Zollner, A., Maier, D., Albermann, K., Hani, J., Mokrejs, M., Tetko, I., Guldener, U., Mannhaupt, G., Munsterkotter, M., Mewes, H.W.: The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes. Nucleic Acids Res. 32(18), 5539–5545 (2004)CrossRefGoogle Scholar
  62. 62.
    Schlicker, A., et al.: A new measure for functional similarity of gene products based on gene ontology. BMC Bioinf. 7, 302 (2006)CrossRefGoogle Scholar
  63. 63.
    Schriml, L.M., Arze, C., Nadendla, S., Chang, Y.-W.W., Mazaitis, M., Felix, V., Feng, G., Kibbe, W.A.: Disease ontology: a backbone for disease semantic integration. Nucleic Acids Res. 40(D1), D940–D946 (2012)Google Scholar
  64. 64.
    Sharan, R., et al.: Network-based prediction of protein function. Mol. Syst. Biol. 3, 88 (2007)CrossRefGoogle Scholar
  65. 65.
    Škunca, N., Bošnjak, M., Kriško, A., Panov, P., Džeroski, S., Šmuc, T., Supek, F.: Phyletic profiling with cliques of orthologs is enhanced by signatures of paralogy relationships. PLoS Comput. Biol. 1553, 734X (2013)Google Scholar
  66. 66.
    Sokolov, A., Ben-Hur, A.: Hierarchical classification of gene ontology terms using the Gostruct method. J. Bioinf. Comput. Biol. 8(2), 357–376 (2010)CrossRefGoogle Scholar
  67. 67.
    Tian, W., Skolnick, J.: How well is enzyme function conserved as a function of pairwise sequence identity? J. Mol. Biol. 333(4), 863–882 (2003)CrossRefGoogle Scholar
  68. 68.
    Todd, A.E., Orengo, C.A., Thornton, J.M.: Evolution of function in protein superfamilies, from a structural perspective. J. Mol. Biol. 307(4), 1113–1143 (2001)CrossRefGoogle Scholar
  69. 69.
    Troyanskaya, O.G., Dolinski, K., Owen, A.B., Altman, R.B., Botstein, D.: A bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae). Proc. Nat. Acad. Sci. U.S.A. 100(14), 8348–8353 (2003)CrossRefGoogle Scholar
  70. 70.
    Vazquez, A., Flammini, A., Maritan, A., Vespignani, A.: Global protein function prediction from protein-protein interaction networks. Nat. Biotechnol. 21(6), 697–700 (2003)CrossRefGoogle Scholar
  71. 71.
    Warde-Farley, D., Donaldson, S.L., Comes, O., Zuberi, K., Badrawi, R., Chao, P., Franz, M., Grouios, C., Kazi, F., Lopes, CT., et al.: The genemania prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 38 (suppl 2), W214–W220 (2010) Google Scholar
  72. 72.
    Wass, M.N., Sternberg, M.J.: ConFunc-functional annotation in the twilight zone. Bioinformatics 24(6), 798–806 (2008)CrossRefGoogle Scholar
  73. 73.
    Wilson, C.A., Kreychman, J., Gerstein, M.: Assessing annotation transfer for genomics: quantifying the relations between protein sequence, structure and function through traditional and probabilistic scores. J. Mol. Biol. 297(1), 233–249 (2000)CrossRefGoogle Scholar

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© The Author(s) 2014

Authors and Affiliations

  1. 1.Department of Molecular Biophysics and BiochemistryYale UniversityNew HavenUSA

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