Advertisement

Construction and Visualization of Dynamic Biological Networks: Benchmarking the Neo4J Graph Database

  • Lena Wiese
  • Chimi Wangmo
  • Lukas Steuernagel
  • Armin O. Schmitt
  • Mehmet Gültas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11371)

Abstract

Genome analysis is a major precondition for future advances in the life sciences. The complex organization of genome data and the interactions between genomic components can often be modeled and visualized in graph structures. In this paper we propose the integration of several data sets into a graph database. We study the aptness of the database system in terms of analysis and visualization of a genome regulatory network (GRN) by running a benchmark on it. Major advantages of using a database system are the modifiability of the data set, the immediate visualization of query results as well as built-in indexing and caching features.

Notes

Acknowledgements

Chimi Wangmo participated in the preparation of this article while visiting the University of Göttingen with a Go International Plus scholarship by the Erasmus+ Key Action of the European Commission.

References

  1. 1.
    Albers, D., Dewey, C., Gleicher, M.: Sequence surveyor: leveraging overview for scalable genomic alignment visualization. IEEE Trans. Vis. Comput. Graph. 17(12), 2392–2401 (2011)CrossRefGoogle Scholar
  2. 2.
    van Arensbergen, J., van Steensel, B., Bussemaker, H.J.: In search of the determinants of enhancer-promoter interaction specificity. Trends Cell Biol. 24(11), 695–702 (2014). http://www.sciencedirect.com/science/article/pii/S0962892414001184CrossRefGoogle Scholar
  3. 3.
    Baker, C.A., Carpendale, M.S.T., Prusinkiewicz, P., Surette, M.G.: GeneVis: visualization tools for genetic regulatory network dynamics. In: Visualization, VIS 2002. IEEE. pp. 243–250 (2002)Google Scholar
  4. 4.
    Bozdag, S., Li, A., Wuchty, S., Fine, H.A.: FastMEDUSA: a parallelized tool to infer gene regulatory networks. Bioinformatics 26(14), 1792–1793 (2010)CrossRefGoogle Scholar
  5. 5.
    Van den Bulcke, T., et al.: SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms. BMC Bioinform. 7(1), 43 (2006).  https://doi.org/10.1186/1471-2105-7-43CrossRefGoogle Scholar
  6. 6.
    Chatraryamontri, A., et al.: The BioGRID interaction database: 2015 update. Nucleic Acids Res. (2014). http://nar.oxfordjournals.org/content/early/2014/11/26/nar.gku1204.abstract
  7. 7.
    Fiannaca, A., La Rosa, M., La Paglia, L., Messina, A., Urso, A.: BioGraphDB: a new graphDB collecting heterogeneous data for bioinformatics analysis. In: Proceedings of BIOTECHNO (2016)Google Scholar
  8. 8.
    Gomez, J., et al.: BioJS: an open source Javascript framework for biological data visualization. Bioinformatics 29(8), 1103–1104 (2013).  https://doi.org/10.1093/bioinformatics/btt100CrossRefGoogle Scholar
  9. 9.
    Have, C.T., Jensen, L.J.: Are graph databases ready for bioinformatics? Bioinformatics 29(24), 3107 (2013)CrossRefGoogle Scholar
  10. 10.
    Jupiter, D., Chen, H., VanBuren, V.: STARNET2: a web-based tool for accelerating discovery of gene regulatory networks using microarray co-expression data. BMC Bioinform. 10(1), 332 (2009)CrossRefGoogle Scholar
  11. 11.
    Karolchik, D., et al.: The UCSC table browser data retrieval tool. Nucleic Acids Res. 32(suppl–1), D493–D496 (2004).  https://doi.org/10.1093/nar/gkh103CrossRefGoogle Scholar
  12. 12.
    Kel-Margoulis, O., Kel, A., Reuter, I., Deineko, I., Wingender, E.: TRANSCompel: a database on composite regulatory elements in eukaryotic genes. Nucleic Acids Res. 30, 332–334 (2002)CrossRefGoogle Scholar
  13. 13.
    Kerren, A., Kucher, K., Li, Y.F., Schreiber, F.: Biovis explorer: a visual guide for biological data visualization techniques. PLOS ONE 12(11), 1–14 (2017).  https://doi.org/10.1371/journal.pone.0187341CrossRefGoogle Scholar
  14. 14.
    Kharumnuid, G., Roy, S.: Tools for in-silico reconstruction and visualization of gene regulatory networks (GRN). In: 2015 Second International Conference on Advances in Computing and Communication Engineering (ICACCE), pp. 421–426. IEEE (2015)Google Scholar
  15. 15.
    Kirlew, P.W.: Life science data repositories in the publications of scientists and librarians. Issues Sci. Technol. Libr. 65 (2011)Google Scholar
  16. 16.
    Krupp, M., Marquardt, J.U., Sahin, U., Galle, P.R., Castle, J., Teufel, A.: RNA-Seq Atlas - a reference database for gene expression profiling in normal tissue by next-generation sequencing. Bioinformatics 28(8), 1184–1185 (2012).  https://doi.org/10.1093/bioinformatics/bts084CrossRefGoogle Scholar
  17. 17.
    Lizio, M., et al.: Gateways to the FANTOM5 promoter level mammalian expression atlas. Genome Biol. 16(1), 22 (2015).  https://doi.org/10.1186/s13059-014-0560-6CrossRefGoogle Scholar
  18. 18.
    Longabaugh, W.J., Davidson, E.H., Bolouri, H.: Visualization, documentation, analysis, and communication of large-scale gene regulatory networks. Biochim. Biophys. Acta (BBA) - Gene Regul. Mech. 1789(4), 363–374 (2009). http://www.sciencedirect.com/science/article/pii/S1874939908001624CrossRefGoogle Scholar
  19. 19.
    Margolin, A.A., et al.: ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinform. 7(1), S7 (2006)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Matharu, N., Ahituv, N.: Minor loops in major folds: enhancer-promoter looping, chromatin restructuring, and their association with transcriptional regulation and disease. PLOS Genet. 11(12), 1–14 (2015).  https://doi.org/10.1371/journal.pgen.1005640CrossRefGoogle Scholar
  21. 21.
    Meckbach, C., Tacke, R., Hua, X., Waack, S., Wingender, E., Gültas, M.: PC-TraFF: identification of potentially collaborating transcription factors using pointwise mutual information. BMC Bioinform. 16(1), 400 (2015).  https://doi.org/10.1186/s12859-015-0827-2CrossRefGoogle Scholar
  22. 22.
    Mora, A., Sandve, G.K., Gabrielsen, O.S., Eskeland, R.: In the loop: promoter-enhancer interactions and bioinformatics. Brief. Bioinform. 17(6), 980–995 (2016).  https://doi.org/10.1093/bib/bbv097CrossRefGoogle Scholar
  23. 23.
    O’Donoghue, S.I., et al.: Visualizing biological data - now and in the future. Nature Methods 7(3), S2 (2010)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Petryszak, R., et al.: Expression Atlas update - an integrated database of gene and protein expression in humans, animals and plants. Nucleic Acids Res. 44(D1), D746–D752 (2015)CrossRefGoogle Scholar
  25. 25.
    Ren, J., Lu, J., Wang, L., Chen, D.: Data visualization in bioinformatics. Adv. Inf. Sci. Serv. Sci. 4(22) (2012)Google Scholar
  26. 26.
    Roy, S., Bhattacharyya, D.K., Kalita, J.K.: Reconstruction of gene co-expression network from microarray data using local expression patterns. BMC Bioinform. 15(7), S10 (2014)CrossRefGoogle Scholar
  27. 27.
    Schaffter, T., Marbach, D., Floreano, D.: Genenetweaver: in silico benchmark generation and performance profiling of network inference methods. Bioinformatics 27(16), 2263–2270 (2011)CrossRefGoogle Scholar
  28. 28.
    Smoot, M.E., Ono, K., Ruscheinski, J., Wang, P.L., Ideker, T.: Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27(3), 431–432 (2010)CrossRefGoogle Scholar
  29. 29.
    Sonawane, A.R., et al.: Understanding tissue-specific gene regulation. Cell Rep. 21(4), 1077–1088 (2017)CrossRefGoogle Scholar
  30. 30.
    Steuernagel, L., Wiese, L., Gültas, M.: Repository visualization of dynamic biological networks. https://github.com/azifiDils/Visualization-of-DynamicBiological-Networks-
  31. 31.
    Tripathi, S., Dehmer, M., Emmert-Streib, F.: NetBioV: an R package for visualizing large network data in biology and medicine. Bioinformatics 30(19), 2834–2836 (2014)CrossRefGoogle Scholar
  32. 32.
    Wang, M., et al.: LegumeGRN: a gene regulatory network prediction server for functional and comparative studies. PloS One 8(7), e67434 (2013)CrossRefGoogle Scholar
  33. 33.
    Whitfield, T.W., et al.: Functional analysis of transcription factor binding sites in human promoters. Genome Biol. 13(9), R50 (2012).  https://doi.org/10.1186/gb-2012-13-9-r50CrossRefGoogle Scholar
  34. 34.
    Wiese, L.: Advanced Data Management for SQL, NoSQL. Cloud and Distributed Databases, DeGruyter/Oldenbourg (2015)Google Scholar
  35. 35.
    Wiese, L., Schmitt, A.O., Gültas, M.: Big data technologies for DNA sequencing. In: Sakr, S., Zomaya, A. (eds.) Encyclopedia of Big Data Technologies. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-63962-8CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Computer ScienceUniversity of GöttingenGöttingenGermany
  2. 2.Gyalpozhing College of Information TechnologyRoyal University of BhutanThimphuBhutan
  3. 3.Breeding Informatics Group, Department of Animal SciencesUniversity of GöttingenGöttingenGermany
  4. 4.Center for Integrated Breeding Research (CiBreed)University of GöttingenGöttingenGermany

Personalised recommendations