New approach to Genomics Experiments Taking Advantage of Virtual Laboratory System
Handschuh Luiza 2,3, Lawenda Marcin 1, Stępniak Piotr 2, Figlerowicz Marek 2, Stroiński Maciej 1, Węglarz Jan 1
1 Poznań Supercomputing and Networking Center
Noskowskiego 10, 61-704 Poznań, Poland
2 Institute of Bioorganic Chemistry PAS
Noskowskiego 12/14, 61-704 Poznań, Poland
3 Poznań University of Medical Sciences, Departament of Hematology
Szamarzewskiego 84, 60-569 Poznań, Poland
e-mail: vlab@man.poznan.pl
Received:
Received: 4 December 2008; published online: 25 March 2009
DOI: 10.12921/cmst.2009.15.01.31-40
OAI: oai:lib.psnc.pl:661
Abstract:
Specialized software, on-line tools and computational resources are very common in contemporary science. One of the exemplary domain is genomics – 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.
Key words:
References:
[1] PROGRESS – Polish Research on Grid Environment for SUN Servers. http://progress.psnc.pl/English/index.html.
[2] Virtual Laboratory PSNC. http://vlab.psnc.pl/ .
[3] B. R. Seavey, E. A. Farr, W. M. Westler and J. L. Markley, A Relational Database for Sequence-Specific Protein NMR Data. J. Biomolecular NMR 1, 217-236 (1991).
[4] NMR data-sets Bank: A repository for raw NMR data-sets. http://nmrb.cbs.cnrs.fr/index.html.
[5] Ch. Steinbeck, S. Krause and S. Kuhn, NMRShiftDB – Constructing a Free Chemical Information System with Open-Source Components. J. Chem. Inf. Comput. Sci. 43, 1733-1739 (2003).
[6] O. Yamamoto, K. Someno, N. Wasada, J. Hiraishi, K. Hayamizu, K. Tanabe, T. Tamura and M. Yanagisawa, An Integrated Spectral Data Base System Including IR, MS, 1H-NMR, 13C-NMR, ESR and Raman Spectra. Anal. Sci. 4, 233-239 (1988).
[7] The National Science Digital Library, http://nsdl.org/ .
[8] Digital Library for Earth System Education, http://www.dlese.org/ .
[9] Poznań Supercomputing and Networking Center, http://www.man.poznan.pl.
[10] Globus Toolkit. http://www.globus.org/toolkit/ .
[11] GRMS, http://www.gridlab.org/WorkPackages/wp-9/ .
[12] Bioconductor project. http://www.bioconductor.org/.
[13] TIGR TM4 Suite (including Spotfinder, MIDAS, MeV). http://www.tm4.org/.
[14] D. Gershon, More than gene expression. Nature 437, 1195-1198 (2005).
[15] 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 Discov. Today 8 (14), 642-651 (2003).
[16] S. Venkatasubbarao, Microarrays – status and prospects. Trends Biotechnol. 22, 630-637 (2004).
[17] V. Trevino, F. Falciani and H. A. Barrera-Saldaña, DNA microarrays: a powerful genomic tool for biomedical and clinical research. Mol. Med. 13, 527-541 (2007).
[18] O. Margalit, R. Somech, N. Amariglio and G. Rechavi, Microarray-based gene expression profiling of hematologic malignancies: basic concepts and clinical applications. Blood Rev. 19, 223-234 (2005).
[19] 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).
[20] 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. Nucl. Acids Res. 4, e15 (2002).
[21] G. K. Smyth and T. P. Speed, Normalization of cDNA microarray data. Methods 31, 265-273 (2003).
[22] J. Quackenbush, Microarray data normalization and transformation. Nature Genet. Suppl. 32, 496-501 (2002).
[23] G. K. Smyth, Y. H Yang and T. Speed, Statistical issues in cDNA microarray data analysis. Methods Mol Biol. 224, 111-36 (2003).
[24] B. I. P. Rubinstein, McAuliffe, S. Cawley, M. Palaniswami, K. Ramamohanarao and T. P. Speed, Machine Learning in Low-level Microarray Analysis. ACM SIGKDD Explor.
Newsletters 5 (2), m14 (2003).
[25] L. A. Garraway and W. R. Sellers, Array-based approaches to cancer genome analysis. Drug Discov. Today 2 (2), 171-177 (2005).
Specialized software, on-line tools and computational resources are very common in contemporary science. One of the exemplary domain is genomics – 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.
Key words:
References:
[1] PROGRESS – Polish Research on Grid Environment for SUN Servers. http://progress.psnc.pl/English/index.html.
[2] Virtual Laboratory PSNC. http://vlab.psnc.pl/ .
[3] B. R. Seavey, E. A. Farr, W. M. Westler and J. L. Markley, A Relational Database for Sequence-Specific Protein NMR Data. J. Biomolecular NMR 1, 217-236 (1991).
[4] NMR data-sets Bank: A repository for raw NMR data-sets. http://nmrb.cbs.cnrs.fr/index.html.
[5] Ch. Steinbeck, S. Krause and S. Kuhn, NMRShiftDB – Constructing a Free Chemical Information System with Open-Source Components. J. Chem. Inf. Comput. Sci. 43, 1733-1739 (2003).
[6] O. Yamamoto, K. Someno, N. Wasada, J. Hiraishi, K. Hayamizu, K. Tanabe, T. Tamura and M. Yanagisawa, An Integrated Spectral Data Base System Including IR, MS, 1H-NMR, 13C-NMR, ESR and Raman Spectra. Anal. Sci. 4, 233-239 (1988).
[7] The National Science Digital Library, http://nsdl.org/ .
[8] Digital Library for Earth System Education, http://www.dlese.org/ .
[9] Poznań Supercomputing and Networking Center, http://www.man.poznan.pl.
[10] Globus Toolkit. http://www.globus.org/toolkit/ .
[11] GRMS, http://www.gridlab.org/WorkPackages/wp-9/ .
[12] Bioconductor project. http://www.bioconductor.org/.
[13] TIGR TM4 Suite (including Spotfinder, MIDAS, MeV). http://www.tm4.org/.
[14] D. Gershon, More than gene expression. Nature 437, 1195-1198 (2005).
[15] 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 Discov. Today 8 (14), 642-651 (2003).
[16] S. Venkatasubbarao, Microarrays – status and prospects. Trends Biotechnol. 22, 630-637 (2004).
[17] V. Trevino, F. Falciani and H. A. Barrera-Saldaña, DNA microarrays: a powerful genomic tool for biomedical and clinical research. Mol. Med. 13, 527-541 (2007).
[18] O. Margalit, R. Somech, N. Amariglio and G. Rechavi, Microarray-based gene expression profiling of hematologic malignancies: basic concepts and clinical applications. Blood Rev. 19, 223-234 (2005).
[19] 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).
[20] 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. Nucl. Acids Res. 4, e15 (2002).
[21] G. K. Smyth and T. P. Speed, Normalization of cDNA microarray data. Methods 31, 265-273 (2003).
[22] J. Quackenbush, Microarray data normalization and transformation. Nature Genet. Suppl. 32, 496-501 (2002).
[23] G. K. Smyth, Y. H Yang and T. Speed, Statistical issues in cDNA microarray data analysis. Methods Mol Biol. 224, 111-36 (2003).
[24] B. I. P. Rubinstein, McAuliffe, S. Cawley, M. Palaniswami, K. Ramamohanarao and T. P. Speed, Machine Learning in Low-level Microarray Analysis. ACM SIGKDD Explor.
Newsletters 5 (2), m14 (2003).
[25] L. A. Garraway and W. R. Sellers, Array-based approaches to cancer genome analysis. Drug Discov. Today 2 (2), 171-177 (2005).
