Measuring semantic similarities by combining gene ontology annotations and gene co-function networks

BMC Bioinformatics

Volumn 16, Issue 1, 2015, Pages

  Peng, Jiajie ^a,b   Uygun, Sahra ^b   Kim, Taehyong ^c   Wang, Yadong ^a   Rhee, Seung Y ^c   Chen, Jin ^b,d  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b MICHIGAN STATE UNIVERSITY   (United States)

^c GEOPHYSICAL LABORATORY   (United States)

^d Michigan State University   (United States)

Author keywords

Co Function network; Gene function annotation; Gene ontology; Semantic similarity

Indexed keywords

LEARNING ALGORITHMS; SEMANTICS;

CO-FUNCTIONS; CYTOCHROME P450 MONOOXYGENASES; FUNCTIONAL CHARACTERIZATION; GENE FUNCTION; GENE ONTOLOGY; GENE ONTOLOGY ANNOTATIONS; SEMANTIC SIMILARITY; SEMANTIC SIMILARITY MEASURES;

GENES;

ARABIDOPSIS;

ARABIDOPSIS PROTEIN; CYTOCHROME P450; SACCHAROMYCES CEREVISIAE PROTEIN;

ALGORITHM; BIOLOGY; COMPUTER PROGRAM; CONTROLLED VOCABULARY; GENE ONTOLOGY; GENE REGULATORY NETWORK; GENETICS; HUMAN; METABOLISM; MOLECULAR GENETICS; MULTIGENE FAMILY; PROCEDURES; SEMANTICS;

ALGORITHMS; ARABIDOPSIS PROTEINS; COMPUTATIONAL BIOLOGY; CYTOCHROME P-450 ENZYME SYSTEM; GENE ONTOLOGY; GENE REGULATORY NETWORKS; HUMANS; METABOLIC NETWORKS AND PATHWAYS; MOLECULAR SEQUENCE ANNOTATION; MULTIGENE FAMILY; SACCHAROMYCES CEREVISIAE PROTEINS; SEMANTICS; SOFTWARE; VOCABULARY, CONTROLLED;

EID: 84924071510     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-015-0474-7     Document Type: Article

References (47)

1. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology, The Gene Ontology Consortium. Nat Genet. 2000;25(1):25-9.
2. Falcon S, Gentleman R. Using GOstats to test gene lists for GO term association. Bioinformatics. 2007;23(2):257-8.
3. Chen JL, Liu Y, Sam LT, Li J, Lussier YA. Evaluation of high-throughput functional categorization of human disease genes. BMC Bioinformatics. 2007;8 Suppl 3:S7.
4. Kemmeren P, Kockelkorn TTJP, Bijma T, Donders R, Holstege FCP. Predicting gene function through systematic analysis and quality assessment of high-throughput data. Bioinformatics. 2005;21(8):1644-52.
5. Zhu MZ, Gao L, Guo Z, Li YH, Wang D, Wang J, et al. Globally predicting protein functions based on co-expressed protein-protein interaction networks and ontology taxonomy similarities. Gene. 2007;391(1-2):113-9.
6. Verspoor K, Cohn J, Mniszewski S, Joslyn C. A categorization approach to automated ontological function annotation. Protein Sci. 2006;15(6):1544-9.
7. Resnik P. Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. J Art Intel Res. 1999;11:95-130.
8. Schlicker A, Domingues FS, Rahnenfuhrer J, Lengauer T. A new measure for functional similarity of gene products based on Gene Ontology. BMC Bioinformatics. 2006;7:302.
9. Wang JZ, Du ZD, Payattakool R, Yu PS, Chen CF. A new method to measure the semantic similarity of GO terms. Bioinformatics. 2007;23(10):1274-81.
10. Teng Z, Guo M, Liu X, Dai Q, Wang C, Xuan P. Measuring gene functional similarity based on group-wise comparison of GO terms. Bioinformatics. 2013;29(11):1424-32.
11. Wu X, Pang E, Lin K, Pei ZM. Improving the measurement of semantic similarity between gene ontology terms and gene products: insights from an edge- and IC-based hybrid method. PLoS One. 2013;8(5):e66745.
12. Pesquita C, Faria D, Falcao AO, Lord P, Couto FM. Semantic similarity in biomedical ontologies. PLoS Comput Biol. 2009;5(7):e1000443.
13. Joslyn CA, Mniszewski SM, Fulmer A, Heaton G. The gene ontology categorizer. Bioinformatics. 2004;20 Suppl 1:i169-77.
14. Hawkins T, Luban S, Kihara D. Enhanced automated function prediction using distantly related sequences and contextual association by PFP. Protein Sci. 2006;15(6):1550-6.
15. Lee I, Li ZH, Marcotte EM. An improved, bias-reduced probabilistic functional gene network of baker's yeast. Saccharomyces cerevisiae. PLoS One. 2007;2(10):e988.
16. Lee I, Ambaru B, Thakkar P, Marcotte EM, Rhee SY. Rational association of genes with traits using a genome-scale gene network for Arabidopsis thaliana. Nat Biotechnol. 2010;28(2):149-56.
17. Lee I, Blom UM, Wang PI, Shim JE, Marcotte EM. Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res. 2011;21(7):1109-21.
18. Rhee SY, Wood V, Dolinski K, Draghici S. Use and misuse of the gene ontology annotations. Nat Rev Genet. 2008;9(7):509-15.
19. Lamesch P, Berardini TZ, Li DH, Swarbreck D, Wilks C, Sasidharan R, et al. The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools. Nucleic Acids Res. 2012;40(D1):D1202-10.
20. Brun C, Chevenet F, Martin D, Wojcik J, Guenoche A, Jacq B. Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network. Genome Biol. 2003;5(1):R6.
21. O'Madadhain J, Fisher D, Smyth P, White S, Boey YB. Analysis and visualization of network data using JUNG. J Stat Software. 2005;10(2):1-25.
22. Cherry JM, Hong EL, Amundsen C, Balakrishnan R, Binkley G, Chan ET, et al. Saccharomyces Genome Database: the genomics resource of budding yeast. Nucleic Acids Res. 2012;40(Database issue):D700-5.
23. Zhang P, Foerster H, Tissier CP, Mueller L, Paley S, Karp PD, et al. MetaCyc and AraCyc. Metabolic pathway databases for plant research. Plant Physiol. 2005;138(1):27-37.
24. Romero P, Wagg J, Green ML, Kaiser D, Krummenacker M, Karp PD. Computational prediction of human metabolic pathways from the complete human genome. Genome Biol. 2005;6(1):R2.
25. Karp PD, Paley S, Romero P. The Pathway Tools software. Bioinformatics. 2002;18 Suppl 1:S225-32.
26. Caspi R, Altman T, Billington R, Dreher K, Foerster H, Fulcher CA, et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res. 2014;42(Database issue):D459-71.
27. Szappanos B, Kovacs K, Szamecz B, Honti F, Costanzo M, Baryshnikova A, et al. An integrated approach to characterize genetic interaction networks in yeast metabolism. Nat Genet. 2011;43(7):656-62.
28. Mizutani M, Ohta D. Diversification of P450 genes during land plant evolution. Annu Rev Plant Biol. 2010;61:291-315.
29. Chae L, Sudat S, Dudoit S, Zhu T, Luan S. Diverse transcriptional programs associated with environmental stress and hormones in the Arabidopsis receptor-like kinase gene family. Mol Plant. 2009;2(1):84-107.
30. Jin J, Zhang H, Kong L, Gao G, Luo J. PlantTFDB 3.0: a portal for the functional and evolutionary study of plant transcription factors. Nucleic Acids Res. 2014;42(Database issue):D1182-7.
31. Pesquita C, Pessoa D, Faria D, Couto FM. CESSM: Collaborative Evaluation of Semantic Similarity Measures. In: JB2009: Challenges in Bioinformatics. 2009.
32. Allison DB, Cui X, Page GP, Sabripour M. Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet. 2006;7(1):55-65.
33. Yu HY, Jansen R, Stolovitzky G, Gerstein M. Total ancestry measure: quantifying the similarity in tree-like classification, with genomic applications. Bioinformatics. 2007;23(16):2163-73.
34. Mani R, Onge RPS, Hartman JL, Giaever G, Roth FP. Defining genetic interaction. Proc Natl Acad Sci U S A. 2008;105(9):3461-6.
35. Wang J, Tsang WW, Marsaglia G. Evaluating Kolmogorov's distribution. J Stat Softw. 2003;8:i18.
36. Rhee SY, Mutwil M. Towards revealing the functions of all genes in plants. Trends Plant Sci. 2014;19(4):212-21.
37. Pagel P, Kovac S, Oesterheld M, Brauner B, Dunger-Kaltenbach I, Frishman G, et al. The MIPS mammalian protein-protein interaction database. Bioinformatics. 2005;21(6):832-4.
38. Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 2013;41(Database issue):D808-15.
39. Riechmann JL, Heard J, Martin G, Reuber L, Jiang C, Keddie J, et al. Arabidopsis transcription factors: genome-wide comparative analysis among eukaryotes. Science. 2000;290(5499):2105-10.
40. Gordon AD. Classification. 2nd ed. Chapman & Hall/CRC: Boca Raton; 1999.
41. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215(3):403-10.
42. Guengerich FP. Cytochrome p450 and chemical toxicology. Chem Res Toxicol. 2008;21(1):70-83.
43. Zhang PF, Dreher K, Karthikeyan A, Chi A, Pujar A, Caspi R, et al. Creation of a genome-wide metabolic pathway database for populus trichocarpa using a new approach for reconstruction and curation of metabolic pathways for plants. Plant Physiol. 2010;153(4):1479-91.
44. Murtagh F. Multidimensional clustering algorithms. Compstat Lectures, Vienna: Physika Verlag. 1985;1:21-27.
45. Hubert L, Arabie P. Comparing partitions. J Classif. 1985;2(2-3):193-218.
46. Romano S, James B, Vinh N, Karin V. Standardized mutual information for clustering comparisons: one step further in adjustment for chance. In: Proceedings of the 31st International Conference on Machine Learning. 2014. p. 1143-51.
47. Binns D, Dimmer E, Huntley R, Barrell D, O'Donovan C, Apweiler R. QuickGO: a web-based tool for Gene Ontology searching. Bioinformatics. 2009;25(22):3045-6.