Functional Genomics Applications in GRID

Functional Genomics Applications in GRID

Luciano Milanesi (Istituto di Tecnologie Biomediche at the Consiglio Nazionale delle Ricerche, Italy), Ivan Merelli (Istituto di Tecnologie Biomediche at the Consiglio Nazionale delle Ricerche, Italy) and Gabriele Trombetti (Istituto di Tecnologie Biomediche – Consiglio Nazionale delle Ricerche, Italy)
DOI: 10.4018/978-1-60566-374-6.ch008
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A common ongoing task for Functional Genomics is to compare full organisms’ genome with those of related species, to search in huge database for functional annotation of novel sequences and to identify specific patterns of them, such as ESTs, genes, and microRNA. The prediction of these patterns has a relevant computational cost, while public genome archives exceed one billion sequence traces from over 1,000 organisms and this number is increasing rapidly as costs decline, but powerful solution must be enabled in order to perform efficient searches. This means that Functional Genomics applications require significant computational infrastructures, where reusable tools and resources can be accessed. In particular, grid computing seems to fulfill both the computational and data management requirements, even if porting applications on this infrastructure can be difficult. The implementation of a suitable environment for the management of distributed computations can provide reliable advantage, reducing the gap between the requirements of the functional genomic domain and the potential of this technology.
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Grid technology is a very important step forward from the Web, which simply allows the sharing of information over the internet. This paradigm of distributed computing aims to promote the development and advancement of technologies that provide seamless and scalable access to wide area distributed resources. Computational grids enable the sharing, selection, and aggregation of a wide variety of geographically distributed computational resources, such as supercomputers, compute clusters, storage systems, data sources, instruments and people. This idea is analogous to the electric power grid network, in which power generators are distributed but the users are able to access electric power without bothering about the source of energy and its location. Indeed, grid infrastructures present themselves as single, unified resources for solving large-scale compute and data intensive computing applications.

Key Terms in this Chapter

Xdelta: Compute the differences between two versions of the same file, providing the possible to downgrade the latest version to a previous one.

Virtual Organization: A consortium that uses informatics tools to enable, maintain and sustain the sharing of resources in distributed work environments.

gLite: A set of components designed to enable communications and resource sharing over geographical dispersed computing facilities, usually referred as middleware.

EGEE: The acronym of Enabling Grids for E-sciencE, which is a European Commission founded project which aims to develop a grid service infrastructure working on the gLite middleware.

Sequence Alignment: A way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships.

Functional Genomics: A field of molecular biology that attempts to make use of the vast wealth of data produced by genomic projects.

Phylogenetics: The study of evolutionary relatedness among various groups of organisms.

microRNA: Are single-stranded RNA molecules encoded by genes, transcribed from DNA but not translated into protein, which regulate gene expression.

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