Preview

Бюллетень сибирской медицины

Расширенный поиск

Генная онтология для геномики и биологии

https://doi.org/10.20538/1682-0363-2026-1-176-184

Аннотация

Цель исследования – рассмотреть роль генной онтологии (GO) и Консорциума GO в формировании базиса знаний для геномики, протеомики и биологии. Генная онтология позволяет систематизировать и постоянно обновляет данные о молекулярных функциях и биологических процессах, в которых участвуют гены и их продукты.
Рассмотрена структура GO, особенности иерархии терминов GO и отношения между ними, элементы каждого из терминов. Приведены особенности сервисов, обеспечивающих возможности работы исследователей с базой знаний с помощью различных способов доступа к данным GO. Помимо характеристик терминов в GO большое внимание уделяется аннотациям – утверждениям, связывающим продукт гена с конкретным термином онтологии. Процесс аннотации фиксирует действие и локализацию генного продукта с помощью терминов, предоставляя ссылку и вид доказательств.
Рассмотрены направления применения генной онтологии, связанные с анализом данных геномики и протеомики. Основные подходы, используемые исследователями, – это функциональная аннотация генов, анализ обогащения путей. Анализ больших объемов данных (например, при оценке экспрессии генов) позволяет получить знания о вовлеченности тех или иных генов и их продуктов в различные процессы в организме, извлечь биологический смысл и оценить особенности молекулярных механизмов при различных заболеваниях. Показана возрастающая роль GO в формировании новых знаний в соответствующей области.

Об авторе

Н. Ю. Часовских
Сибирский государственный медицинский университет (СибГМУ)
Россия

Часовских Наталия Юрьевна – д-р мед. наук, доцент, зав. кафедрой медицинской и биологической кибернетики

Россия, 634050, г. Томск, Московский тракт, 2



Список литературы

1. Подколодный Н.Л., Подколодная О.А. Онтологии в биоинформатике и системной биологии. Вавиловский журнал генетики и селекции. 2015;19(6):652–660. DOI: 10.18699/VJ15.090.

2. Chandrasekaran B., Josephson J.R., Benjamins V.R. What are ontologies and why do we need them? IEEE Intelligent Systems. 1999;14(1):20–26. DOI: 10.1109/5254.747902.

3. Gruber T.R. Toward principles for the design of ontologies used for knowledge sharing. Int. J. Human-Computer Studies. 1995;43(5–6):907–928. DOI: 10.1006/ijhc.1995.1081.

4. Ashburner M. On the representation of “gene function” in databases [Internet]. EMBL – European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge; 1998 June 19. Availabel: 2025 May 15. DOI: 10.5281/zenodo.5504412.

5. Ashburner M., Ball C.A., Blake J.A., Botstein D., Butler H., Cherry J.M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 2000;25(1):25–29. DOI: 10.1038/75556.

6. Rubin G.M., Yandell M.D., Wortman J.R., Gabor Miklos G.L., Nelson C.R., Hariharan I.K. et al. Comparative genomics of the eukaryotes. Science. 2000;287:2204–2215. DOI: 10.1126/science.287.5461.2204.

7. Tang Z., Kuo T., Shen J., Lin R.J. Biochemical and genetic conservation of fission yeast Dsk1 and human SR protein-specific kinase 1. Mol. Cell. Biol. 2000;20:816–824. DOI: 10.1128/mcb.20.3.816-824.2000.

8. Vajo Z., King L.M., Jonassen T., Wilkin D.J., Ho N., Munnich A. et al. Conservation of the Caenorhabditis elegans timing gene clk-1 from yeast to human: a gene required for ubiquinone biosynthesis with potential implications for aging. Mamm. Genome. 1999;10:1000–1004. DOI: 10.1007/s003359901147.

9. Ohi R., Feoktistova A., McCann S., Valentine V., Look A.T., Lipsick J.S. et al. Myb-related Schizosaccharomyces pombe cdc5p is structurally and functionally conserved in eukaryotes. Mol. Cell. Biol. 1998;18:4097–4108. DOI: 10.1128/mcb.18.7.4097.

10. Bassett D.E. Jr., Boguski M.S., Spencer F., Reeves R., Kim S., Weaver T. et al. Genome cross-referencing and XREFdb: implications for the identification and analysis of genes mutated in human disease. Nat. Genet. 1997;15:339–344. DOI: 10.1038/ng0497-339.

11. Kataoka T., Powers S., Cameron S., Fasano O., Goldfarb M., Broach J. et al. Functional homology of mammalian and yeast RAS genes. Cell. 1985;40:19–26. DOI: 10.1016/0092-8674(85)90304-6.

12. Botstein D., Fink G.R. Yeast: an experimental organism for modern biology. Science. 1988;240:1439–1443. DOI: 10.1126/science.3287619.

13. Bairoch A., Apweiler R. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 2000;28:45–48. DOI: 10.1093/nar/28.1.45.

14. Benson D.A., Karsch-Mizrachi I., Lipman D.J., Ostell J., Rapp B.A., Wheeler D.L. et al. GenBank Nucleic Acids Res. 2000;28:15–18. DOI: 10.1093/nar/28.1.15.

15. Tateno Y., Miyazaki S., Ota M., Sugawara H., Gojobori T. DNA data bank of Japan (DDBJ) in collaboration with mass sequencing teams. Nucleic Acids Res. 2000;28(1):24–26. DOI: 10.1093/nar/28.1.24.

16. Barker W.C., Garavelli J.S., Huang H., McGarvey P.B., Orcutt B.C., Srinivasarao G.Y. et al. The Protein information resource (PIR). Nucleic Acids Res. 2000;28:41–44. DOI: 10.1093/nar/28.1.41.

17. Mewes H.W., Frishman D., Gruber C., Geier B., Haase D., Kaps A. et al. MIPS: a database for genomes and protein sequences. Nucleic Acids Res. 2000;28:37–40. DOI: 10.1093/nar/28.1.37.

18. Costanzo M.C., Hogan J.D., Cusick M.E., Davis B.P., Fancher A.M., Hodges P.E. et al. The Yeast Proteome Database (YPD) and Caenorhabditis elegans Proteome Database (WormPD): comprehensive resources for the organization and comparison of model organism protein information. Nucleic Acids Res. 2000;28:73–76. DOI: 10.1093/nar/28.1.73.

19. Bateman A., Birney E., Durbin R., Eddy S.R., Howe K.L., Sonnhammer E.L. The pfam protein families database. Nucleic Acids Res. 2000;28:263–266. DOI: 10.1093/nar/28.1.263.

20. Lo Conte L., Ailey B., Hubbard T.J., Brenner S.E., Murzin A.G., Chothia C. SCOP: a structural classification of proteins database. Nucleic Acids Res. 2000;28:257–259. DOI: 10.1093/nar/28.1.257.

21. Bairoch A. The ENZYME database in 2000. Nucleic Acids Res. 2000;28:304–305. DOI: 10.1093/nar/28.1.304.

22. The Gene Ontology Resource. Release statistics. Consortium is funded by the National Human Genome Research Institute (US National Institutes of Health). Availabel: 2025 May 15. URL: https://geneontology.org/stats.html

23. Gaudet P., Škunca N., Hu J.C., Dessimoz C. Primer on the gene ontology. In: Dessimoz C., Škunca N. (eds.). The gene ontology handbook. Methods in molecular biology, vol. 1446. Humana Press, 2016: Chapter 3. DOI: 10.1007/978-1-4939-3743-1.

24. Carbon S., Ireland A., Mungall C.J., Shu S., Marshall B., Lewis S. et al. AmiGO: online access to ontology and annotation data. Bioinformatics. 2009;25(2):288–289. DOI: 10.1093/bioinformatics/btn615.

25. Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res. 2015;43(Database issue):D1049–1056. DOI: 10.1093/nar/gku1179.

26. Binns D., Dimmer E., Huntley R., Barrell D., O’Donovan C., Apweiler R. QuickGO: a web-based tool for Gen Ontology searching. Bioinformatics. 2009;25(22):3045–3046. DOI: 10.1093/bioinformatics/btp536.

27. Huang D.W., Sherman B.T., Lempicki R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 2018;4(1):44–57. DOI: 10.1038/nprot.2008.211.

28. Sherman B.T., Hao M., Qiu J., Jiao X., Baseler M.W., Lane H.C. et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 2022;50(W1):W216–W221. DOI: 10.1093/nar/gkac194.

29. Conesa A., Götz S. Blast2GO: A comprehensive suite for functional analysis in plant genomics. Int. J. Plant. Genomics. 2008;2008:619832. DOI: 10.1155/2008/619832.

30. Thomas P.D., Kejariwal A., Campbell M.J., Mi H., Diemer K., Guo N. et al. “PANTHER: a browsable database of gene products organized by biological function, using curated protein family and subfamily classification”. Nucleic Acids Res. 2003;31(1):334–341. DOI: 10.1093/nar/gkg115.

31. Mi H., Muruganujan A., Casagrande J.T., Thomas P.D. Largescale gene function analysis with the PANTHER classifi cation system. Nat. Protoc. 2013;8(8):1551–1566. DOI: 10.1038/nprot.2013.092.

32. Hastings J., de Matos P., Dekker A., Ennis M., Harsha B., Kale N. et al. The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic Аcids Res. 2013;41(Database issue):D456–463. DOI: 10.1093/nar/gks1146.

33. Mungall C., Torniai C., Gkoutos G., Lewis S., Haendel M. Uberon, an integrative multi species anatomy ontology. Gen. Biol. 2012;13(1):R5. DOI: 10.1186/gb-2012-13-1-r5.

34. Cooper L., Walls R.L., Elser J., Gandolfo M.A., Stevenson D.W., Smith B. et al. The Plant Ontology as a tool for comparative plant anatomy and genomic analyses. Plant Cell Physiol. 2013;54(2):e1. DOI: 10.1093/pcp/pcs163.

35. Rivals I., Personnaz L., Taing L., Potier M.C. Enrichment or depletion of a GO category within a class of genes: which test? Bioinformatics. 2007;23(4):401–407. DOI: 10.1093/bioinformatics/btl633.

36. Smoot M.E., Ono K., Ruscheinski J., Wang P.L., Ideker T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011;27(3):431–432. DOI: 10.1093/bioinformatics/btq675.

37. Bastian M., Heymann S., Jacomy M. Gephi: an open source software for exploring and manipulating networks. Proceedings of the International AAAI Conference on Web and Social Media. 2009;3(1):361–362. DOI: 10.1609/icwsm.v3i1.13937.

38. Batagelj V. Exploratory social network analysis with Pajek (Structural analysis in the social sciences). Cambridge, Cambridge University Press, 2011:442. DOI: 10.1017/9781108565691.

39. Merico D., Isserlin R., Stueker O., Emili A., Bader G.D. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One. 2010;5(11):e13984. DOI: 10.1371/journal.pone.0013984.

40. Maere S., Heymans K., Kuiper M. BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 2005;21(16):3448–3449. DOI: 10.1093/bioinformatics/bti551.

41. Bindea G., Mlecnik B., Hackl H., Charoentong P., Tosolini M., Kirilovsky A. et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 2009;25(8):1091–1093. DOI: 10.1093/bioinformatics/btp101.

42. Supek F., Bošnjak M., Škunca N., Šmuc T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One. 2011;6(7):e21800. DOI: 10.1371/journal.pone.0021800.

43. Kolde R., Vilo J. GOsummaries: an R Package for Visual Functional Annotation of Experimental Data. F1000Res. 2015;4:574. DOI: 10.12688/f1000research.6925.1.

44. Bright L.A., Mujahid N., Nanduri B., McCarthy F.M., Costa L.R., Burgess S.C. et al. Functional modelling of an equine bronchoalveolar lavage fluid proteome provides experimental confirmation and functional annotation of equine genome sequences. Anim. Genet. 2011;42(4):395–405. DOI: 10.1111/j.1365-2052.2010.02158.x.

45. Chloe Li K.Y., Cook A.C., Lovering R.C. GOing forward with the cardiac conduction system using Gene Ontology. Front Genet. 2022;13:802393. DOI: 10.3389/fgene.2022.802393.

46. Vinterhalter G., Kovačević J.J., Uversky V.N., Pavlović-Lažetić G.M. Bioinformatics analysis of correlation between protein function and intrinsic disorder. Int. J. Biol. Macromol. 2021;167:446–456. DOI: 10.1016/j.ijbiomac.2020.11.211.

47. Luo Z., Li W., Li J., Zhang Y. A new Tec family-based clinical model predicts survival in differentiated thyroid cancer patients via machine learning. Thyroid Res. 2025;18(1):18. DOI: 10.1186/s13044-025-00234-x.

48. Wang W., Wang H.T., Liu X., Zhu L.M., Lin T.T. Proteomic analysis of meibomian gland carcinoma cells after overexpression of thrombospondin 1. Zhonghua Yan Ke Za Zhi. 2025;61(5):376–383. DOI: 10.3760/cma.j.cn112142-20240709-00294.

49. He S., Nie H., Yin X., Zhong Z. Identification of key extracellular proteins as the potential biomarkers in thyroid eye disease. PLoS One. 2025;20(4):e0322415. DOI: 10.1371/journal.pone.0322415.

50. Buza T.J., McCarthy F.M., Burgess S.C. Experimental-confirmation and functional-annotation of predicted proteins in the chicken genome. BMC Genomics. 2007;8:425. DOI: 10.1186/1471-2164-8-425.

51. Piovesan D., Profiti G., Martelli P.L., Fariselli P., Fontanesi L., Casadio R. SUS-BAR: a database of pig proteins with statistically validated structural and functional annotation. Database (Oxford). 2013;2013:bat065. DOI: 10.1093/database/bat065.

52. Zhu Z., McClintock T.S., Bieberich E. Transcriptomics analysis reveals potential regulatory role of nSMase2 (Smpd3) in nervous system development and function of middle-aged mouse brains. Genes Brain Behav. 2024;23(4):e12911. DOI: 10.1111/gbb.12911.

53. Lohse M., Nagel A., Herter T., May P., Schroda M., Zrenner R. et al. Mercator: a fast and simple web server for genome scale functional annotation of plant sequence data. Plant Cell Environ. 2014;37(5):1250–1258. DOI: 10.1111/pce.12231.

54. Wimalanathan K., Lawrence-Dill C.J. Gene Ontology Meta Annotator for Plants (GOMAP). Plant Methods. 2021;17(1):54. DOI: 10.1186/s13007-021-00754-1.

55. Foulger R.E., Denny P., Hardy J., Martin M.J., Sawford T., Lovering R.C. Using the gene ontology to annotate key players in parkinson’s disease. Neuroinformatics. 2016;14(3):297–304. DOI: 10.1007/s12021-015-9293-2.

56. Sessa E.B., Masalia R.R., Arrigo N., Barker M.S., Pelosi J.A. GOgetter: A pipeline for summarizing and visualizing GO slim annotations for plant genetic data. Appl. Plant Sci. 2023;11(4):e11536. DOI: 10.1002/aps3.11536.

57. Yue Q., Huang C., Song P., Wang S., Chen H., Wang D. et al. Transcriptomic analysis reveals the molecular mechanisms underlying osteoclast differentiation in the estrogen-deficient pullets. Poult. Sci. 2023;102(3):102453. DOI: 10.1016/j.psj.2022.102453.

58. Zhai J., Lyu T., Guo Y., An Y., Xiang Y., Xie L. et al. OTX2 expression contributes progression of gastric cancer in young adults. Sci. Rep. 2025;15(1):16146. DOI: 10.1038/s41598-025-99632-2.

59. Huang Z., Liu D., Zhang Y., Lu W., Hu L., Zhang J. et al. PITX1 as a grading, prognostic and tumor-infiltrating immune cells marker for chondrosarcoma: a public database-based immunoassay and tissue sample analysis. Front. Oncol. 2025;15:1477649. DOI: 10.3389/fonc.2025.1477649.

60. Sun X., Cheng Y.M., Sun M.W., Zhang X.D., Yu X.Y. et al. High expression of SOX10 is correlated with poor prognosis and immune infiltrates in skin cutaneous melanoma. Front. Oncol. 2025;15:1444670. DOI: 10.3389/fonc.2025.1444670.

61. Li B.Y., Li H.L., Zeng F.E., Luan X.Y., Liu B.Q, Wang Z.Z. et al. Identification of PD-L1-related biomarkers for selecting gastric adenocarcinoma patients for PD-1/PD-L1 inhibitor therapy. Discov. Oncol. 2025;16(1):689. DOI: 10.1007/s12672-025-02515-1.

62. Shakeri Abroudi A., Azizi H., Djamali M., Qorbanee A., Skutella T. Integration of Microarray and Single-Cell RNASeq Data and Machine Learning Allows the Identification of Key Histone Modification Gene Changes in spermatogonial stem cells. Biology (Basel). 2025;14(4):387. DOI: 10.3390/biology14040387.

63. Zheng Y., Yu S.Y., Yan X., Li J.P., Zhang Q., Yuan X. [Gene expression profiling analysis of stress-sensitive genes and their potential functions in myoblasts]. Shanghai Kou Qiang Yi Xue. 2025;34(1):7–13. ( In Chin.).

64. Wang Y., Li Q. Integrated multiomics analysis identifies potential biomarkers and therapeutic targets for autophagy associated AKI to CKD transition. Sci. Rep. 2025;15(1):13687. DOI: 10.1038/s41598-025-97269-9.

65. Qian X., Jia W., Li Y., Chen J., Zhang J., Sun Y. COL4A1 promotes gastric cancer progression by regulating tumor invasion, tumor microenvironment and drug sensitivity. Curr. Med. Chem. 2025. DOI: 10.2174/0109298673351943250314074632.

66. Peng S., Zhang Q., Yang Y., Li Y., Feng W., Zhao D. et al. Genome-wide identification and expression profiling of MYB transcription factors in Artemisia argyi. BMC Genomics. 2025;26(1):384. DOI: 10.1186/s12864-025-11441-z.

67. Tang H., Finn R.D., Thomas P.D. TreeGrafter: phylogenetic tree-based annotation of proteins with Gene Ontology terms and other annotations. Bioinformatics. 2019;35(3):518–520. DOI: 10.1093/bioinformatics/bty625.

68. Gaudet P., Livstone M.S., Lewis S.E., Thomas P.D. Phylogenetic-based propagation of functional annotations within the Gene Ontology consortium. Brief Bioinform. 2011;12(5):449–462. DOI: 10.1093/bib/bbr042.

69. Gaudet P., Dessimoz C. Gene оntology: рitfalls, biases, and remedies. Methods Mol. Biol. 2017;1446:189–205.


Рецензия

Для цитирования:


Часовских Н.Ю. Генная онтология для геномики и биологии. Бюллетень сибирской медицины. 2026;25(1):176-184. https://doi.org/10.20538/1682-0363-2026-1-176-184

For citation:


Chasovskikh N.Yu. Gene ontology for genomics and biology. Bulletin of Siberian Medicine. 2026;25(1):176-184. https://doi.org/10.20538/1682-0363-2026-1-176-184

Просмотров: 47

JATS XML


Creative Commons License
Контент доступен под лицензией Creative Commons Attribution 4.0 License.


ISSN 1682-0363 (Print)
ISSN 1819-3684 (Online)