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A Journal on Biotechnology and Molecular Biology
Indexed/Abstracted in: EMBASE, Science Citation Index Expanded (SciSearch), Scopus
Impact Factor 0,246
ORIGINAL ARTICLES MICROARRAY MEETING 2003: III CONVEGNO ITALIANO SULLA TECNOLOGIA
Segrate (MI), 9-10 Giugno 2003
Minerva Biotecnologica 2003 December;15(4):207-16
Gene Array Analyzer Software: a multi-user platform for management, analysis and visualization of gene expression data from replicate experiments
Masseroli M. 1, Cerveri P. 1,2, Pelicci P. G. 3, Pinciroli F. 1, Alcalay M. 3,4
1 Bioengineering Department, Politecnico di Milano, Milan, Italy;
2 Bioengineering Center, Fondazione Don Gnocchi IRCCS ONLUS, Milan, Italy;
3 IEO - European Institute of Oncology, Milan, Italy;
4 IFOM - FIRC Institute of Molecular Oncology, Milan, Italy
Background. Availability of gene sequences identified by many genome projects and improvements of nanotechnology have made high-throughput experiments a powerful tool to study the differential expression of thousands of genes at once. Nevertheless, these experiments produce a huge amount of data presenting variability of gene expression levels and noise. An adequate software framework is therefore required to manage and analyze these data in order to mine new biological information. Design and implementation of the gene array analyzer software (GAAS), a new application providing efficient management and appropriate analysis of a great quantity of gene expression data from replicate experiments, is described.
Methods. In a multi-user environment a database based management system provides flexibility in handling data from distinct high-throughput technologies and custom data output formats. Analysis algorithms allow background and spot quality evaluation, data normalization, and assessment of statistical significance of gene differential expressions also from replicate experiments.
Results. The developed GAAS application, composed of management, analysis, and visualization frameworks, enables each user to perform parametric gene differential expression analyses and to store in output databases analysis parameter used and obtained results. An intuitive user interface enables interactive browsing of expression profiles and analysis results, providing visualization of identified candidate regulated gene data both in tabular and graphical form.
Conclusion. GAAS is a powerful software framework for flexible management and fast automatic suitable analyses of gene expression data across multiple replica experiments. It is freely available for downloading for academic and non-profit use at http://www.medinfopoli.polimi.it/GAAS/.