Genomic Virtual Laboratory
Cegielska Barbara 2, Kaliszan Damian 1, Handschuh Luiza 2,3, Figlerowicz Marek 2, Meyer Norbert 1
1Poznań Supercomputing and Networking Center
ul. Noskowskiego 10, 61-704 Poznań, Poland
2Institute of Bioorganic Chemistry, Polish Academy of Sciences
ul. Noskowskiego 12/14, 61-704 Poznań, Poland
3Poznań University of Medical Sciences, Departament of Hematology
ul. Szamarzewskiego 84, 60-569 Poznań, Poland
e-mail: vlab@man.poznan.pl
Received:
Received: 2 February 2010; revised: 2 March 2010; accepted 10 March 2010; published online: 20 April 2010
DOI: 10.12921/cmst.2010.16.01.39-49
OAI: oai:lib.psnc.pl:713
Abstract:
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.
Key words:
Bioconductor, genomics, measurement scenario, microarray, R, remote instrumentation, virtual laboratory
References:
[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
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.
Key words:
Bioconductor, genomics, measurement scenario, microarray, R, remote instrumentation, virtual laboratory
References:
[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
