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Online resources on gene networks containing human and animal data

https://doi.org/10.18699/VJ17.310

Abstract

Gene networks are molecular genetic systems that ensure the formation of phenotypic characteristics of organisms (molecular, biochemical, structural, morphological, behavioral, etc.) based on  information encoded in their genomes. Reconstruction of gene networks provides a methodological basis for modern systems biology. In this regard, the information on the structural and functional organization of gene networks accumulated in modern databases is extremely valuable. This review characterizes a number of Internetaccessible information resources oriented to humans and animals and containing data on gene networks and their functional modules. Without pretending to fully cover all information resources containing data related to humans and animals on the subject, the current review was created to report the current status of the problem and to present the criteria according to which we propose to evaluate the utility of webresources for specific research tasks. On this basis, we compiled and characterized a collection of databases containing information on metabolic and signaling pathways, as well as pathways of regulation of biological processes at the cellular and organismal levels. In addition, we observed the characteristics of several well­known databases containing data on interactions between biomolecules of various types. The following characteristics of databases were considered: (1) the types of information accumulated in the databases; (2) methods of data presentation; (3) methods of data collection; (4) data sources; (5) special search tools and options for data analysis. A comparison of the above characteristics showed that the databases are very heterogeneous according to their scopes, sources and types of data, interfaces, as well as according to their search options and data analysis tools. It was concluded that at the first step of the gene network reconstruction it is important to form a full set of information resources from which the data can be obtained. The web portals accumulating information about the databases that may be useful for the reconstruction and analysis of gene networks are specified.

About the Authors

E. V. Ignatieva
Institute of Cytology and Genetics SB RAS; Novosibirsk State University.
Russian Federation
Novosibirsk.


D. A. Afonnikov
Institute of Cytology and Genetics SB RAS; Novosibirsk State University.
Russian Federation
Novosibirsk.


N. A.  Kolchanov
Institute of Cytology and Genetics SB RAS; Novosibirsk State University.
Russian Federation
Novosibirsk.


References

1. Galperin M.Y., Fernández-Suárez X.M., Rigden D.J. The 24th annual Nucleic Acids Research database issue: a look back and upcoming changes. Nucleic Acids Res. 2017;45(D1):D1-D11. DOI 10.1093/nar/gkw1188.

2. Glotov A.S., Tiys E.S., Vashukova E.S., Pakin V.S., Demenkov P.S., Saik O.V., Ivanisenko T.V., Arzhanova O.N., Mozgovaya E.V., Zainulina M.S., Kolchanov N.A., Baranov V.S., Ivanisenko V.A. Molecular association of pathogenetic contributors to pre-eclampsia (pre-eclampsia associome). BMC Syst. Biol. 2015;9(Suppl.2):S4. DOI 10.1186/1752-0509-9-S2-S4.

3. Ignatieva E.V., Afonnikov D.A., Saik O.V., Rogaev E.I., Kolchanov N.A. A compendium of human genes regulating feeding behavior and body weight, its functional characterization and identification of GWAS genes involved in brain-specific PPI network. BMC Genet. 2016;17(Suppl.3):158. DOI 10.1186/s12863-016-0466-2.

4. Ivanisenko V.A., Saik O.V., Ivanisenko N.V., Tiys E.S., Ivanisenko T.V., Demenkov P.S., Kolchanov N.A. ANDSystem: an Associative Network Discovery System for automated literature mining in the field of biology. BMC Syst. Biol. 2015;9(Suppl.2):S2. DOI 10.1186/1752-0509-9-S2-S2.

5. Kauffman S. Homeostasis and differentiation in random genetic control networks. Nature. 1969;224(5215):177-178.

6. Khatri P., Sirota M., Butte A.J. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput. Biol. 2012;8(2):e1002375. DOI 10.1371/journal.pcbi.1002375.

7. Kolchanov N.A., Anan’ko E.A., Kolpakov F.A., Podkolodnaya O.A., Ignatieva E.V., Goryachkovskaya T.N., Stepanenko E.L. Gene networks. Molekulyarnaya biologiya = Molecular Biology (Moscow). 2000;34(4):533-544. (in Russian)

8. Kolchanov N.A., Ignatieva E.V., Podkolodnaya O.A., Likhoshvai V.А., Matushkin Yu.G. Gene networks. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2013;17(4/2): 833-850. (in Russian)

9. Kolchanov N.A., Merkulova T.I., Ignatieva E.V., Ananko E.A., Oshchep kov D.Y., Levitsky V.G., Vasiliev G.V., Klimova N.V., Merkulov V.M., Hodgman T.C. Combined experimental and computational approaches to study the regulatory elements in eukaryotic genes. Brief. Bioinform. 2007;8(4):266-274.

10. Kutmon M., Riutta A., Nunes N., Hanspers K., Willighagen E.L., Bohler A., Mélius J., Waagmeester A., Sinha S.R., Miller R., Coort S.L., Cirillo E., Smeets B., Evelo C.T., Pico A.R. WikiPathways: capturing the full diversity of pathway knowledge. Nucleic Acids Res. 2016;44(D1):D488-494. DOI 10.1093/nar/gkv1024.

11. Lei X., Wu S., Ge L., Zhang A. Clustering and overlapping modules detection in PPI network based on IBFO. Proteomics. 2013;13(2):278290. DOI 10.1002/pmic.201200309.

12. Levitsky V.G., Ignatieva E.V., Ananko E.A., Turnaev I.I., Merkulova T.I., Kolchanov N.A., Hodgman T.C. Effective transcription factor binding site prediction using a combination of optimization, a genetic algorithm and discriminant analysis to capture distant interactions. BMC Bioinformatics. 2007;8:481. DOI 10.1186/14712105-8-481.

13. Li J.R., Suzuki T., Nishimura H., Kishima M., Maeda S., Suzuki H. Asymmetric regulation of peripheral genes by two transcriptional regulatory networks. PLoS ONE. 2016;11(8):e0160459. DOI 10.1371/journal.pone.0160459.

14. McMullen P.D., Bhattacharya S., Woods C.G., Sun B., Yarborough K., Ross S.M., Miller M.E., McBride M.T., LeCluyse E.L., Clewell R.A., Andersen M.E. A map of the PPARα transcription regulatory network for primary human hepatocytes. Chem. Biol. Interact. 2014;209:14-24. DOI 10.1016/j.cbi.2013.11.006.

15. Mustafin Z.S., Lashin S.A., Matushkin Y.G., Gunbin K.V., Afonnikov D.A. Orthoscape: a cytoscape application for grouping and visualization KEGG based gene networks by taxonomy and homology principles. BMC Bioinformatics. 2017;18(Suppl.1):1427. DOI 10.1186/s12859-016-1427-5.

16. Neph S., Stergachis A.B., Reynolds A., Sandstrom R., Borenstein E., Stamatoyannopoulos J.A. Circuitry and dynamics of human transcription factor regulatory networks. Cell. 2012;150(6):1274-1286. DOI 10.1016/j.cell.2012.04.040.

17. Obermayer B., Levine E. Exploring the miRNA regulatory network using evolutionary correlations. PLoS Comput. Biol. 2014;10(10): e1003860. DOI 10.1371/journal.pcbi.1003860.

18. Plaisier C.L., Pan M., Baliga N.S. A miRNA-regulatory network explains how dysregulated miRNAs perturb oncogenic processes across diverse cancers. Genome Res. 2012;22(11):2302-2314. DOI 10.1101/gr.133991.111.

19. Podkolodnaya O.A., Tverdokhleb N.N., Podkolodnyy N.L. Computational modeling of the cell-autonomous mammalian circadian oscillator. BMC Syst. Biol. 2017;11(Suppl.1):379. DOI 10.1186/s12918016-0379-8.

20. Podkolodnyy N.L., Tverdokhleb N.N., Podkolodnaya O.A. Computational model for mammalian circadian oscillator: interacting with NAD+/SIRT1 pathway and age-related changes in gene expression of circadian oscillator. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2016;20(6):848856. DOI 10.18699/VJ16.201. (in Russian)

21. Ratner V.A. Geneticheskie upravlyayushchie sistemy [Genetic Control Systems]. Novosibirsk: Nauka Publ., 1966. (in Russian)

22. Reece-Hoyes J.S., Deplancke B., Shingles J., Grove C.A., Hope I.A., Walhout A.J. A compendium of Caenorhabditis elegans regulatory transcription factors: a resource for mapping transcription regulatory networks. Genome Biol. 2005;6(13):R110.

23. Saik O.V., Ivanisenko T.V., Demenkov P.S., Ivanisenko V.A. Interactome of the hepatitis C virus: Literature mining with ANDSystem. Virus Res. 2016;218:40-48. DOI 10.1016/j.virusres.2015.12.003.

24. Stergachis A.B., Neph S., Sandstrom R., Haugen E., Reynolds A.P., Zhang M., Byron R., Canfield T., Stelhing-Sun S., Lee K., Thurman R.E., Vong S., Bates D., Neri F., Diegel M., Giste E., Dunn D., Vierstra J., Hansen R.S., Johnson A.K., Sabo P.J., Wilken M.S., Reh T.A., Treuting P.M., Kaul R., Groudine M., Bender M.A., Borenstein E., Stamatoyannopoulos J.A. Conservation of trans-acting circuitry during mammalian regulatory evolution. Nature. 2014; 515(7527):365-370. DOI 10.1038/nature13972.

25. Tomaru Y., Hasegawa R., Suzuki T., Sato T., Kubosaki A., Suzuki M., Kawaji H., Forrest A.R., Hayashizaki Y., FANTOM Consortium, Shin J.W., Suzuki H. A transient disruption of fibroblastic transcriptional regulatory network facilitates trans-differentiation. Nucleic Acids Res. 2014;42(14):8905-8913. DOI 10.1093/nar/gku567.


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