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

• 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

• [1] G.K. Smyth, Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 3, Article 3, 2004.
• [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.
• [3] G.K. Smyth, J. Michaud, H. Scott, The use of withinarray replicate spots for assessing differential expression in microarray experiments. Bioinformatics 21 (9), 2067-2075 (2005).
• [4] G.A. Milliken, D.E. Johnson, Analysis of Messy Data. Volume 1: Designed Experiments. Chapman & Hall, New York, 1992.
• [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).
• [11] D.N. Howbrook, A.M. van der Valk, M.C. O’Shaugnessy, D.K. Sarker, S.C. Baker, A.W. Lloyd, Developments in microarray technologies. Drug Discov. Today 8 (14), 642-651 (2003)
• [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