Genomic Virtual Laboratory

• Autorzy: Cegielska B. , Kaliszan D. , Handschuh L. , Figlerowicz M. , Meyer N.
• Języki publikacji: EN

Abstrakty

EN

In contemporary science, virtual laboratories give a chance to improve research by facilitating access to high-throughput technologies and bioinformatics methods. The Genomic Virtual Laboratory (GVL) presented here was developed for automate analysis of data retrieved from a microarray experiment. The system was implemented for R Bioconductor-based analysis of results obtained in the study on human acute myeloid leukaemia (AML). The article extends the theoretical aspects of GVL presented earlier [8] and describes how the particular elements were integrated to establish the advanced system of two-colour microarray data analysis.

Słowa kluczowe

EN

genomics; microarray; virtual laboratory; remote instrumentation; measurement scenario; Bioconductor; R

• Wydawca: Institute of Bioorganic Chemistry Scientific Publishers OWN, Polish Academy of Sciences
• Czasopismo: Computational Methods in Science and Technology
• Rocznik: 2010
• Tom: Vol. 16, No. 1
• Strony: 39--49
• Opis fizyczny: Bibliogr. 18 poz., rys.

Twórcy

autor

Cegielska B.

autor

Kaliszan D.

autor

Handschuh L.

autor

Figlerowicz M.

autor

Meyer N.

• Poznań Supercomputing and Networking Center ul. Noskowskiego 10, 61-704 Poznań, Poland,

Bibliografia

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• [2] Y.H. Yang, T.P. Speed, Design and analysis of comparative microarray experiments. In: T.P. Speed, editor, Statistical Analysis of Gene Expression Microarray Data, pages 35-91. Chapman & Hall/CRC Press, 2003.
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• [5] Y.H. Yang, S. Dudoit, P. Luu, T.P. Speed. Normalization for cDNA Microarray Data SPIE BiOS 2001, San Jose, California, January 2001.
• [6] R. Gentleman, V.J. Carey, W. Huber, R.A. Irizarry, S. Dudoit, Bioinformatics and Computational Biology Solutions Using R and Bioconductor, Springer.
• [7] F. Hahne, W. Huber, R. Gentleman, S. Falcon, Bioconductor Case Studies. Springer.
• [8] L. Handschuh, M. Lawenda, M. Stępniak, M. Figlerowicz, M. Stroiński, J. Węglarz, Computational Methods in Science and Technology 15 (1), 31-40 (2009).
• [9] V. Trevino, F. Falciani, H.A. Barrera-Saldaña, DNA microarrays: a powerful genomic tool for biomedical and clinical research, Mol. Med. 13, 527-541 (2007).
• [10] L.A. Garraway, W.R. Sellers, Array-based approaches to cancer genome analysis. Drug Discov. Today 2 (2), 171-177 (2005).
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• [12] S. Venkatasubbarao, Microarrays – status and prospects. Trends Biotechnol. 22, 630-637 (2004).
• [13] T. Haferlach, A. Kohlmann, S. Schnittger, M. Dugas, W. Hiddemann, W. Kern, C. Schoch, Global approach to the diagnosis of leukemia using gene expression profiling. Blood 4, 1189-1198 (2005).
• [14] O. Margalit, R. Somech, N. Amariglio, G. Rechavi, Microarray-based gene expression profiling of hematologic malignancies: basic concepts and clinical applications. Blood Rev. 19, 223-234 (2005).
• [15] X. Chen, E. Jorgenson, S.T. Cheung, New tools for functional genomic analysis. Drug Discov. Today 14 (15-16), 754-760 (2009).
• [16] Data Management System Web page http://dms.progress.psnc.pl/
• [17] http://pl.wikipedia.org/wiki/GUID
• [18] http://vlab.psnc.pl