GEMMA - A Grid environment for microarray management and analysis in bone marrow stem cells experiments

Authors:
Francesco Beltrame;Adam Papadimitropoulos;Ivan Porro;Silvia Scaglione;Andrea Schenone;Livia Torterolo;Federica Viti
Affiliations:
Department of Communication Computer and System Sciences, University of Genoa, Italy;Department of Communication Computer and System Sciences, University of Genoa, Italy;Department of Communication Computer and System Sciences, University of Genoa, Italy;Department of Communication Computer and System Sciences, University of Genoa, Italy;Department of Communication Computer and System Sciences, University of Genoa, Italy;Department of Communication Computer and System Sciences, University of Genoa, Italy;Department of Communication Computer and System Sciences, University of Genoa, Italy
Venue:
Future Generation Computer Systems
Year:
2007

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Abstract

Microarray techniques are successfully used to investigate thousands gene expression profiling in a variety of genomic analyses such as gene identification, drug discovery and clinical diagnosis, providing a large amount of genomic data for the overall research community. A Grid based Environment for distributed Microarray data Management and Analysis (GEMMA) is being built. This platform is planned to provide shared, standardized and reliable tools for managing and analyzing biological data related to bone marrow stem cell cultures, in order to maximize the results of distributed experiments. Different microarray analysis algorithms may be offered to the end-user, through a web interface. A set of modular and independent applications may be published on the portal, and either single algorithms or a combination of them might be invoked by the user, through a workflow strategy. Services may be implemented within an existing Grid computing infrastructure to solve problems concerning both large datasets storage (data intensive problem) and large computational times (computing intensive problem). Moreover, experimental data annotation may be collected according to the same rules and stored through the Grid portal, by using a metadata schema, which allows a comprehensive and replicable sharing of microarray experiments among different researchers. The environment has been tested, so far, as regards performance results concerning Grid parallelization of a microarray based gene expression analysis. First results show a very promising speedup ratio.