Smart Health pp 261-273 | Cite as

State-of-the-Art and Future Challenges in the Integration of Biobank Catalogues

  • Heimo MüllerEmail author
  • Robert Reihs
  • Kurt Zatloukal
  • Fleur Jeanquartier
  • Roxana Merino-Martinez
  • David van Enckevort
  • Morris A. Swertz
  • Andreas Holzinger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8700)


Biobanks are essential for the realization of P4-medicine, hence indispensable for smart health. One of the grand challenges in biobank research is to close the research cycle in such a way that all the data generated by one research study can be consistently associated to the original samples, therefore data and knowledge can be reused in other studies. A catalogue must provide the information hub connecting all relevant information sources. The key knowledge embedded in a biobank catalogue is the availability and quality of proper samples to perform a research project. Depending on the study type, the samples can reflect a healthy reference population, a cross sectional representation of a certain group of people (healthy or with various diseases) or a certain disease type or stage. To overview and compare collections from different catalogues, we introduce visual analytics techniques, especially glyph based visualization techniques, which were successfully applied for knowledge discovery of single biobank catalogues. In this paper, we describe the state-of-the art in the integration of biobank catalogues addressing the challenge of combining heterogeneous data sources in a unified and meaningful way, consequently enabling the discovery and visualization of data from different sources. Finally we present open questions both in data integration and visualization of unified catalogues and propose future research in data integration with a linked data approach and the fusion of multi level glyph and network visualization.


Biobank catalogue Linked data Minimum information about biobank data sharing (MIABIS) Knowledge discovery Visualization Glyph 



The work was performed and supported in the context of the Austrian national node of BBMRI-ERIC. Our thanks are due to all partners for their contributions and various discussions and to Ms Penelope Kungl for proofreading.


  1. 1.
    Norlin, L., Fransson, M.N., Eriksson, M., Merino-Martinez, R., Anderberg, M., Kurtovic, S., Litton, J.-E.: A minimum data set for sharing biobank samples, information, and data: MIABIS. Biopreservation Biobanking 10(4), 343–348 (2012). doi: 10.1089/bio.2012.0003 CrossRefGoogle Scholar
  2. 2.
    Müller, H., Reihs, R., Zatloukal, K., Holzinger, A.: Analysis of biomedical data with multilevel glyphs. BMC Bioinform. 15(Suppl 6), S5 (2014). doi: 10.1186/1471-2105-15-S6-S5 CrossRefGoogle Scholar
  3. 3.
    Huppertz, B., Holzinger, A.: Biobanks – A source of large biological data sets: open problems and future challenges. In: Holzinger, A., Jurisica, I. (eds.) Knowledge Discovery and Data Mining. LNCS, vol. 8401, pp. 317–330. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  4. 4.
    Ward, M.O.: Multivariate data glyphs: Principles and practice. In: Handbook of Data Visualization, pp. 179–198. Springer, Berlin (2008). doi: 10.1007/978-3-540-33037-0_8
  5. 5.
    Fortier, I., Doiron, D., Little, J., et al.: Is rigorous retrospective harmonization possible? Application of the DataSHaPER approach across 53 large studies. Int. J. Epidemiol. 40, 1314–1328 (2011). doi: 10.1093/ije/dyr106 CrossRefGoogle Scholar
  6. 6.
    Doiron, D., Burton, P., Marcon, Y., Gaye, A., Wolffenbuttel, B.H.R., Perola, M., Stolk, R.P., Minelli, F.L., Waldenberger, M., Holle, R., Kvaløy, K., Hillege, H.L., Tassé, A.M., Ferretti, V., Fortier, I.: Data harmonization and federated analysis of population-based studies: the BioSHaRE project. Emerg. Themes Epidemiol. 10(1), 12 (2013). doi: 10.1186/1742-7622-10-12 CrossRefGoogle Scholar
  7. 7.
    Wolfson, M., Wallace, S.E., Masca, N., Rowe, G., Sheehan, N.A., Ferretti, V., LaFlamme, P., Tobin, M.D., Macleod, J., Little, J., Fortier, I., Knoppers, B.M., Burton, P.R.: DataSHIELD: resolving a conflict in contemporary bioscience–performing a pooled analysis of individual-level data without sharing the data. Int. J. Epidemiol. 39(5), 1372–1382 (2010). doi: 10.1093/ije/dyq111 CrossRefGoogle Scholar
  8. 8.
    Vasilevsky, N., Johnson, T., Corday, K., Torniai, C., Brush, M., Segerdell, E., Wilson, M., Shaffer, C., Robinson, D., Haendel, M.: Research resources: curating the new eagle-i discovery system. Database (Oxford). 2012 Mar 20;2012:bar067. doi: 10.1093/database/bar067
  9. 9.
    Brochhausen, M., Fransson, M.N., Kanaskar, N.V., Eriksson, M., Merino-Martinez, R., Hall, R.A., Litton, J.-E.: Developing a semantically rich ontology for the biobank-administration domain. J. Biomed. Semant. 4(1), 23 (2013). doi: 10.1186/2041-1480-4-23 CrossRefGoogle Scholar
  10. 10.
    Swertz, M.A., Dijkstra, M., Adamusiak, T., van der Velde, J.K., Kanterakis, A., Roos, E.T., Lops, J., Thorisson, G.A., Arends, D., Byelas, G., Muilu, J., Brookes, A.J., de Brock, E., Jansen, R.C., Parkinson, H.: The MOLGENIS toolkit: rapid prototyping of biosoftware at the push of a button. BMC Bioinform. 11(Suppl 1), S12 (2010). doi: 10.1186/1471-2105-11-S12-S12 CrossRefGoogle Scholar
  11. 11.
    Pang, C., Hendriksen, D., Dijkstra, M., van der Velde, K.J., Kuiper, J., Hillege, H., Swertz, M.: BiobankConnect: software to rapidly connect data elements for pooled analysis across biobanks using ontological and lexical indexing. J. Am. Med. Inform. Assoc. 2014 Oct 31. doi: 10.1136/amiajnl-2013-002577. [Epub ahead of print] PubMed PMID: 25361575
  12. 12.
    O’Donoghue, S.I., Gavin, A.-C., Gehlenborg, N., Goodsell, D.S., Hériché, J.-K., Nielsen, C.B., Olson, A.J., Procter, J.B., Shattuck, D.W., Walter, T., Wong, B.: Visualizing biological data-now and in the future. Nat. Methods 7(3 Suppl), S2–S4 (2010). doi: 10.1038/nmeth.f.301 CrossRefGoogle Scholar
  13. 13.
    Turkay, C., Jeanquartier, F., Holzinger, A., Hauser, H.: On computationally-enhanced visual analysis of heterogeneous data and its application in biomedical informatics. In: Holzinger, A., Jurisica, I. (eds.) Knowledge Discovery and Data Mining. LNCS, vol. 8401, pp. 117–140. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  14. 14.
    Peng, Z., Laramee, R.S.: Higher dimensional vector field visualization: A survey. In: Tang, W., Collomosse, J. (eds.) Theory and Practice of Computer Graphics, pp. 149–163. The Eurographics Association (2009). doi: 10.2312/LocalChapterEvents/TPCG/TPCG09/149-163
  15. 15.
    Bürger, R., Hauser, H.: Visualization of multi variate scientific data. In: Proceedings of EuroGraphics, pp. 117–134 (2007)Google Scholar
  16. 16.
    Fuchs, R., Hauser, H.: Visualization of multi-variate scientific data. Comput. Graph. Forum 28(6), 1670–1690 (2009). doi: 10.1111/j.1467-8659.2009.01429.x CrossRefGoogle Scholar
  17. 17.
    Keim, D.A.: Visual exploration of large data sets. Commun. ACM 44(8), 38–44 (2001). doi: 10.1145/381641.381656 CrossRefGoogle Scholar
  18. 18.
    Hege, H.-C., Hutanu, A., Kähler, R., Merzky, A., Radke, T., Seidel, E., Ullmer, B.: Progressive retrieval and hierarchical visualization of large remote data. Scalable Comput. Pract. Exp. 6(3), 60–72 (2001)Google Scholar
  19. 19.
    Fayyad, U., Grinstein, G.G., Wierse, A.: Information Visualization in Data Mining and Knowledge Discovery. Morgan Kaufmann, San Francisco (2002)Google Scholar
  20. 20.
    Fekete, J.-D., Plaisant, C.: Interactive information visualization of a million items. In: IEEE Symposium on Information Visualization, INFOVIS 2002, pp. 117–124. IEEE Computer Society (2002). doi: 10.1109/INFVIS.2002.117315
  21. 21.
    Dos Santos, S., Brodlie, K.: Gaining understanding of multivariate and multidimensional data through visualization. Comput. Graph. 28(3), 311–325 (2004). doi: 10.1016/j.cag.2004.03.013 CrossRefGoogle Scholar
  22. 22.
    Borgo, R., Kehrer, J., Chung, D.H.S., Laramee, R.S., Hauser, H., Ward, M., Chen, M.: Glyph-based visualization: Foundations, design guidelines, techniques and applications. In: Eurographics 2013-State of the Art Report, pp. 39–63. The Eurographics Association (2012)Google Scholar
  23. 23.
    Krzywinski, M., Birol, I., Jones, S.J.M., Marra, M.A.: Hive plots–rational approach to visualizing networks. Briefings Bioinform. 13(5), 627–644 (2012). doi: 10.1093/bib/bbr069 CrossRefGoogle Scholar
  24. 24.
    Santamaría, R., Therón, R., Quintales, L.: A visual analytics approach for understanding biclustering results from microarray data. BMC Bioinform. 9, 247 (2008). doi: 10.1186/1471-2105-9-247 CrossRefGoogle Scholar
  25. 25.
    Gehlenborg, N., Brazma, A.: Visualization of large microarray experiments with space maps. BMC Bioinformatics 10(Suppl 13), O7 (2009). doi: 10.1186/1471-2105-10-S13-O7 CrossRefGoogle Scholar
  26. 26.
    Helt, G.A., Nicol, J.W., Erwin, E., Blossom, E., Blanchard, S.G., Chervitz, S.A., Harmon, C., Loraine, A.E.: Genoviz Software Development Kit: Java tool kit for building genomics visualization applications. BMC Bioinform. 10, 266 (2009). doi: 10.1186/1471-2105-10-266 CrossRefGoogle Scholar
  27. 27.
    Konwar, K.M., Hanson, N.W., Pagé, A.P., Hallam, S.J.: MetaPathways: A modular pipeline for constructing pathway/genome databases from environmental sequence information. BMC Bioinform. 14, 202 (2013). doi: 10.1186/1471-2105-14-202 CrossRefGoogle Scholar
  28. 28.
    Legg, P.A., Chung, D.H.S., Parry, M.L., Jones, M.W., Long, R., Griffiths, I.W., Chen, M.: MatchPad: Interactive glyph-based visualization for real-time sports performance analysis. Comput. Graph. Forum 31(3pt4), 1255–1264 (2012). doi: 10.1111/j.1467-8659.2012.03118.x CrossRefGoogle Scholar
  29. 29.
    Maguire, E., Rocca-Serra, P., Sansone, S.A., Davies, J., Chen, M.: Taxonomy-based glyph design – with a case study on visualizing workflows of biological experiments. IEEE Trans. Vis. Comput. Graph. 18(12), 2603–2612 (2012)CrossRefGoogle Scholar
  30. 30.
    Maguire, E., Rocca-Serra, P., Sansone, S.A., Davies, J., Chen, M.: Visual compression of workflow visualizations with automated detection of macro motifs. IEEE Trans. Vis. Comput. Graph. 19(12), 2576–2585 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Heimo Müller
    • 1
    Email author
  • Robert Reihs
    • 1
  • Kurt Zatloukal
    • 1
  • Fleur Jeanquartier
    • 2
  • Roxana Merino-Martinez
    • 3
  • David van Enckevort
    • 4
  • Morris A. Swertz
    • 4
  • Andreas Holzinger
    • 2
  1. 1.Institute of Pathology, BBMRI.atMedical University GrazGrazAustria
  2. 2.Institute for Medical Informatics, Statistics and Documentation Research Unit HCI-KDDMedical University GrazGrazAustria
  3. 3.Department of Medical Epidemiology and Biostatistics (MEB)Karolinska InstitutetStockholmSweden
  4. 4.Genomics Coordination CenterUniversity Medical Center GroningenGroningenThe Netherlands

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