Virtual Laboratory and Its Application in Genomics

  • L. Handschuh
  • M. Lawenda
  • N. Meyer
  • P. Stȩpniak
  • M. Figlerowicz
  • M. Stroiński
  • J. Wȩglarz
Conference paper

Abstract

Nowadays, there is no science domain that does not use specialized software, on-line tools, and computational resources. Genomics is a new branch of science that developed rapidly in the last decade. As the genome research is very complex it must be supported by professional informatics. In a microarray field the following steps cannot be performed without computational work: design of probes, quantitative analysis of hybridization results, post processing, and finally data storage and management. Here, the general aspects of Virtual Laboratory systems are presented, together with perspectives of their implementation in genomics in order to automate and facilitate this area of research.

Keywords

Virtual Laboratory Genomics Digital science library Remote instrumentation 

Notes

Acknowledgement

The work was supported by the grant from the Polish State Committee for Scientific Research No. PBZ-MniI-2/1/2005 to M.F.

References

  1. 1.
    Bioconductor project. http://www.bioconductor.org/
  2. 2.
    L.A. Garraway and W.R. Sellers. Array-based approaches to cancer genome analysis. Drug Discovery Today, 2(2):171–177, 2005.Google Scholar
  3. 3.
    D. Gershon. More than gene expression. Nature, 437:1195–1198, 2005.CrossRefGoogle Scholar
  4. 4.
  5. 5.
    T. Haferlach, A. Kohlmann, S. Schnittger, M. Dugas, W. Hiddemann, W. Kern, and C. Schoch. Global approach to the diagnosis of leukemia using gene expression profiling. Blood, 4:1189–1198, 2005.CrossRefGoogle Scholar
  6. 6.
    D.N. Howbrook, A.M. van der Valk, M.C. O’Shaugnessy, D.K. Sarker, S.C. Baker, and A.W. Lloyd. Developments in microarray technologies. Drug Discovery Today, 8(14):642–651, 2003.CrossRefGoogle Scholar
  7. 7.
    O. Margalit, R. Somech, N. Amariglio, and G. Rechavi. Microarray-based gene expression profiling of hematologic malignancies: basic concepts and clinical applications. Blood Reviews, 19:223–234, 2005.CrossRefGoogle Scholar
  8. 8.
    PROGRESS – Polish Research on Grid Environment for SUN Servers. http://progress.psnc.pl/English/index.html
  9. 9.
    J. Quackenbush. Microarray data normalization and transformation. Nature Genetics Supplement, 32:496–501, 2002.CrossRefGoogle Scholar
  10. 10.
    B.I.P. Rubinstein, J. McAuliffe, S. Cawley, M. Palaniswami, K. Ramamohanarao, and T.P. Speed. Machine learning in Low-level microarray analysis. ACM SIGKDD Explorations Newsletters, 5(2, m14), 2003.Google Scholar
  11. 11.
    G.K. Smyth and T.P. Speed. Normalization of cDNA microarray data. Methods, 31:265–273, 2003.CrossRefGoogle Scholar
  12. 12.
    G.K. Smyth, Y.H. Yang, and T.P. Speed. Statistical issues in cDNA microarray data analysis. Methods in Molecular Biology, 224:111–36, 2003.Google Scholar
  13. 13.
    V. Trevino, F. Falciani, and H.A. Barrera-Saldaña. DNA microarrays: a powerful genomic tool for biomedical and clinical research. Molecular Medicine, 13:527–541, 2007.CrossRefGoogle Scholar
  14. 14.
    S. Venkatasubbarao. Microarrays – status and prospects. Trends Biotechnology, 22:630–637, 2004.CrossRefGoogle Scholar
  15. 15.
    Virtual Laboratory PSNC. http://vlab.psnc.pl/
  16. 16.
    Y.H. Yang, S. Dudoit, P. Luu, D.M. Lin, V. Peng, J. Ngai, and T.P. Speed. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variations. Nucleic Acids Research, 4, e15, 2002.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • L. Handschuh
    • 1
    • 2
  • M. Lawenda
    • 3
  • N. Meyer
    • 3
  • P. Stȩpniak
    • 1
  • M. Figlerowicz
    • 1
  • M. Stroiński
    • 3
  • J. Wȩglarz
    • 3
  1. 1.Institute of Bioorganic Chemistry PASPoznańPoland
  2. 2.Departament of HematologyPoznań University of Medical SciencesPoznańPoland
  3. 3.Poznań Supercomputing and Networking CenterPoznańPoland

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