Accessing Bio-databases with OGSA-DAI - A Performance Analysis

  • Samatha Kottha
  • Kumar Abhinav
  • Ralph Müller-Pfefferkorn
  • Hartmut Mix
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4360)


Open Grid Service Architecture - Data Access and Integration (OGSA-DAI) is a middleware which aims to provide a unique interface to heterogeneous database management systems and to special type of files like SwissProt files. It could become a vital tool for data integration in life sciences since the data is produced by different sources and residing in different data management systems. With it, users will have more flexibility in accessing the data than using static interfaces of Web Services.

OGSA-DAI was tested to determine in which way it could be used efficiently in a Grid application called RNAi screening. It was evaluated in accessing data from bio-databases using the queries that a potential user of RNAi screening would execute. The observations show that OGSA-DAI has some considerable overhead compared to a JDBC connection but provides additional features like security which in turn are very important for distributed processing in life sciences.


OGSA-DAI Grid computing performance analysis bio databases 


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  1. 1.
    Antonioletti, M., Atkinson, M.P., Baxter, R., Borley, A., Hong, N.P.C., Collins, B., Hardman, N., Hume, A., Knox, A., Jackson, M., Krause, A., Laws, S., Magowan, J., Paton, N.W., Pearson, D., Sugden, T., Watson, P., Westhead, M.: The design and implementation of grid database services in ogsa-dai. Concurrency and Computation: Practice and Experience 17(24), 357–376 (2005)CrossRefGoogle Scholar
  2. 2.
    Winter, C., Henschel, A., Kim, W.K., Schroeder, M.: Scoppi: A structural classification of protein-protein interfaces. Nucleic Acids Research (Accepted 2005)Google Scholar
  3. 3.
    May, P., Steinke, T.: Theseus - protein structure prediction at zib. In preparation (2006)Google Scholar
  4. 4.
    May, P., Steinke, T.: THESEUS - protein structure prediction at ZIB. ZIB Report 06-24 (2006)Google Scholar
  5. 5.
    May, P., Ehrlich, H.C., Steinke, T.: ZIB Structure Prediction Pipeline: Composing a Complex Biological Workflow through Web Services. In: Nagel, W.E., Walter, W.V., Lehner, W. (eds.) Euro-Par 2006. LNCS, vol. 4128, pp. 1148–1158. Springer, Berlin Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Murzin, A.G., Brenner, S.E., Hubbard, T., Chothia, C.: Scop: A structural classification of proteins database for the investigation of sequences and structures. Journal of Molecular Biology 247(4), 536–554 (1995)CrossRefGoogle Scholar
  7. 7.
    Doms, A., Schroeder, M.: Gopubmed: Exploring pubmed with the geneontology. Nucleic Acids Research 33, 783–786 (2005)CrossRefGoogle Scholar
  8. 8.
    Huang, B., Schroeder, M.: Using residue propensities and tightness of fit to improve rigid-body protein-protein docking. In: Proceedings of German Bioinformatics Conference, GI LNI71 (2005)Google Scholar
  9. 9.
    Rajasekar, A., Wan, M., Moore, R., Schroeder, W., Kremenek, G., Jagatheesan, A., Cowart, C., Chen, S.Y., Olaschanowsky, R.: Storage resource broker - managing distributed data in a grid. J. Comput. Soc. 33(4), 41–53 (2003)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Samatha Kottha
    • 1
  • Kumar Abhinav
    • 1
    • 2
  • Ralph Müller-Pfefferkorn
    • 1
  • Hartmut Mix
    • 1
  1. 1.Center for Information Services and High Performance Computing, TU DresdenGermany
  2. 2.Vellore Institute of TechnologyIndia

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