SCALEUS: Semantic Web Services Integration for Biomedical Applications

  • Pedro Sernadela
  • Lorena González-Castro
  • José Luís Oliveira
Systems-Level Quality Improvement
Part of the following topical collections:
  1. Systems-Level Quality Improvement

Abstract

In recent years, we have witnessed an explosion of biological data resulting largely from the demands of life science research. The vast majority of these data are freely available via diverse bioinformatics platforms, including relational databases and conventional keyword search applications. This type of approach has achieved great results in the last few years, but proved to be unfeasible when information needs to be combined or shared among different and scattered sources. During recent years, many of these data distribution challenges have been solved with the adoption of semantic web. Despite the evident benefits of this technology, its adoption introduced new challenges related with the migration process, from existent systems to the semantic level. To facilitate this transition, we have developed Scaleus, a semantic web migration tool that can be deployed on top of traditional systems in order to bring knowledge, inference rules, and query federation to the existent data. Targeted at the biomedical domain, this web-based platform offers, in a single package, straightforward data integration and semantic web services that help developers and researchers in the creation process of new semantically enhanced information systems. SCALEUS is available as open source at http://bioinformatics-ua.github.io/scaleus/.

Keywords

Triplestore Database Data integration Semantic web Web services Data management 

References

  1. 1.
    van Dijk, E.L., Auger, H., Jaszczyszyn, Y., and Thermes, C., Ten years of next-generation sequencing technology. Trends Genet. 30(9):418–426, 2014.CrossRefPubMedGoogle Scholar
  2. 2.
    Gomez-Cabrero, D., Abugessaisa, I., Maier, D., Teschendorff, A., Merkenschlager, M., Gisel, A., Ballestar, E., Bongcam-Rudloff, E., Conesa, A., and Tegnér, J., Data integration in the era of omics: current and future challenges. BMC Syst. Biol. 8(Suppl 2):I1, 2014.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Berners-Lee, T., Hendler, J., and Lassila, O., The semantic web. Sci. Am. 284(5):28–37, 2001.CrossRefGoogle Scholar
  4. 4.
    Passin, T., Explorer’s guide to the semantic web, 2004.Google Scholar
  5. 5.
    Machado, C.M., Rebholz-Schuhmann, D., Freitas, A.T., and Couto, F.M., The semantic web in translational medicine: current applications and future directions. Brief. Bioinform. 16(1):89–103, 2015.CrossRefPubMedGoogle Scholar
  6. 6.
    Belleau, F., Nolin, M.-A., Tourigny, N., Rigault, P., and Morissette, J., Bio2RDF: towards a mashup to build bioinformatics knowledge systems. J. Biomed. Inform. 41(5):706–716, 2008.CrossRefPubMedGoogle Scholar
  7. 7.
    Jupp, S., Malone, J., Bolleman, J., Brandizi, M., Davies, M., Garcia, L., Gaulton, A., Gehant, S., Laibe, C., and Redaschi, N., The EBI RDF platform: linked open data for the life sciences. Bioinformatics. 30(9):1338–1339, 2014.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Sernadela, P., Lopes, P., and Oliveira, J.L., A knowledge federation architecture for rare disease patient registries and biobanks. J. Inf. Syst. Eng. Manag. 1(1):83–90, 2016.Google Scholar
  9. 9.
    Freitas, A., Curry, E., Oliveira, J.G., and O’Riain, S., Querying heterogeneous datasets on the linked data web: challenges, approaches, and trends. IEEE Internet Comput. 16(1):24–33, 2012.CrossRefGoogle Scholar
  10. 10.
    J. Pathak, R. Kiefer, and C. Chute, Using semantic web technologies for cohort identification from electronic health records for clinical research. AMIA Summits Transl. Sci. Proc. 2012, 2012.Google Scholar
  11. 11.
    C. David, C. Olivier, and B. Guillaume, A survey of RDF storage approaches. ARIMA J., 2012.Google Scholar
  12. 12.
    O. Erling, Virtuoso, a hybrid RDBMS/graph column store. IEEE Data Eng. Bull., 2012.Google Scholar
  13. 13.
    Lopes, P., and Oliveira, J.L., COEUS: ‘semantic web in a box’ for biomedical applications. J. Biomed. Semantics. 3(1):11, 2012.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Broekstra, J., Kampman, A., and Van Harmelen, F., Sesame: A generic architecture for storing and querying rdf and rdf schema. Semant. Web — ISWC 2002. 2342:54–68, 2002.CrossRefGoogle Scholar
  15. 15.
    Aasman, J., Allegro graph: RDF triple database. Franz Inc., Cid. Oakl, 2006.Google Scholar
  16. 16.
    G. E. Modoni, M. Sacco, and W. Terkaj, A survey of RDF store solutions. In 2014 International conference on engineering, Technology and Innovation: Engineering Responsible Innovation in Products and Services, ICE 2014, 2014.Google Scholar
  17. 17.
    Gurupur, V.P., and Tanik, M.M., A system for building clinical research applications using semantic web-based approach. J. Med. Syst. 36(1):53–59, 2012.CrossRefPubMedGoogle Scholar
  18. 18.
    E. Mezghani, E. Exposito, K. Drira, M. Da Silveira, and C. Pruski, A Semantic Big Data Platform for Integrating Heterogeneous Wearable Data in Healthcare. J. Med. Syst., vol. 39, no. 12, p. 185, 2015.Google Scholar
  19. 19.
    C. Bizer, T. Heath, and T. Berners-Lee, Linked data-the story so far. Int. J. Semant. Web Inf. Syst., 2009.Google Scholar
  20. 20.
    S. Schenk, P. Gearon, and A. Passant, SPARQL 1.1 Update. World Wide Web Consort., 2010.Google Scholar
  21. 21.
    S. Harris, A. Seaborne, and E. Prud’hommeaux, SPARQL 1.1 query language. W3C Recomm. 21, 2013.Google Scholar
  22. 22.
    C. Forgy, Rete: a fast algorithm for the many pattern/many object pattern match problem. Artif. Intell. 1982.Google Scholar
  23. 23.
    Weibel, S., The Dublin Core: a simple content description model for electronic resources. Bull. Am. Soc. Inf. Sci. Technol. 24(1):9–11, 1997.CrossRefGoogle Scholar
  24. 24.
    R. Thompson, L. Johnston, D. Taruscio, L. Monaco, C. Béroud, I. G. Gut, M. G. Hansson, P.-B. A. ‘t Hoen, G. P. Patrinos, H. Dawkins, M. Ensini, K. Zatloukal, D. Koubi, E. Heslop, J. E. Paschall, M. Posada, P. N. Robinson, K. Bushby, and H. Lochmüller, “RD-Connect: An Integrated Platform Connecting Databases, Registries, Biobanks and Clinical Bioinformatics for Rare Disease Research. J. Gen. Intern. Med. 2014.Google Scholar
  25. 25.
    Zollino, M., Ponzi, E., Gobbi, G., and Neri, G., The ring 14 syndrome. Eur. J. Med. Genet. 55(5):374–380, 2012.CrossRefPubMedGoogle Scholar
  26. 26.
    Wilkinson, M.D., Dumontier, M., Aalbersberg, I.J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L.B., Bourne, P.E., Bouwman, J., Brookes, A.J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C.T., Finkers, R., Gonzalez-Beltran, A., Gray, A.J.G., Groth, P., Goble, C., Grethe, J.S., Heringa, J., ‘t Hoen, P.A.T., Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S.J., Martone, M.E., Mons, A., Packer, A.L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R., Sansone, S.-A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M.A., Thompson, M., van der Lei, J., van Mulligen, E., Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., and Mons, B., The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data. 3:160018, 2016.Google Scholar
  27. 27.
    Rath, A., Olry, A., Dhombres, F., Brandt, M.M., Urbero, B., and Ayme, S., Representation of rare diseases in health information systems: the orphanet approach to serve a wide range of end users. Hum. Mutat. 33(5):803–808, 2012.CrossRefPubMedGoogle Scholar
  28. 28.
    Barrell, D., Dimmer, E., Huntley, R.P., Binns, D., O’Donovan, C., and Apweiler, R., The GOA database in 2009--an integrated Gene Ontology Annotation resource. Nucleic Acids Res. 37(Database):D396–D403, 2009.CrossRefPubMedGoogle Scholar
  29. 29.
    Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., Harris, M.A., Hill, D.P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J.C., Richardson, J.E., Ringwald, M., Rubin, G.M., and Sherlock, G., Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25(1):25–29, May 2000.PubMedGoogle Scholar
  30. 30.
    Bairoch, A., Apweiler, R., Wu, C.H., Barker, W.C., Boeckmann, B., Ferro, S., Gasteiger, E., Huang, H., Lopez, R., Magrane, M., Martin, M.J., Natale, D.A., O’Donovan, C., Redaschi, N., and Yeh, L.-S.L., The Universal Protein Resource (UniProt). Nucleic Acids Res. 33(suppl_1):D154–D159, 2005.PubMedGoogle Scholar
  31. 31.
    Kersey, P.J., Duarte, J., Williams, A., Karavidopoulou, Y., Birney, E., and Apweiler, R., The international protein index: an integrated database for proteomics experiments. Proteomics. 4(7):1985–1988, 2004.CrossRefPubMedGoogle Scholar
  32. 32.
    C. Bizer and R. Cyganiak, D2r server-publishing relational databases on the semantic web. 5th Int. Semant. Web Conf., 2006.Google Scholar
  33. 33.
    Pang, C., Sollie, A., Sijtsma, A., Hendriksen, D., Charbon, B., de Haan, M., de Boer, T., Kelpin, F., Jetten, J., van der Velde, J.K., Smidt, N., Sijmons, R., Hillege, H., and Swertz, M.A., SORTA: a system for ontology-based re-coding and technical annotation of biomedical phenotype data. Database. 2015:bav089, 2015.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Campos, D., Lourenco, J., Matos, S., and Oliveira, J.L., Egas: a collaborative and interactive document curation platform. Database. 2014, 2014.Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.DETI/IEETAUniversity of AveiroAveiroPortugal
  2. 2.Galician Research and Development Center in Advanced Telecommunications (GRADIANT)VigoSpain

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