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PheGee@Home: A Grid-Based Tool for Comparative Genomics

PheGee@Home: A Grid-Based Tool for Comparative Genomics

Bertil Schmidt, Chen Chen, Weiguo Liu, Wayne P. Mitchell
Copyright: © 2012 |Pages: 19
ISBN13: 9781466608795|ISBN10: 146660879X|EISBN13: 9781466608801
DOI: 10.4018/978-1-4666-0879-5.ch809
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MLA

Schmidt, Bertil, et al. "PheGee@Home: A Grid-Based Tool for Comparative Genomics." Grid and Cloud Computing: Concepts, Methodologies, Tools and Applications, edited by Information Resources Management Association, IGI Global, 2012, pp. 1885-1903. https://doi.org/10.4018/978-1-4666-0879-5.ch809

APA

Schmidt, B., Chen, C., Liu, W., & Mitchell, W. P. (2012). PheGee@Home: A Grid-Based Tool for Comparative Genomics. In I. Management Association (Ed.), Grid and Cloud Computing: Concepts, Methodologies, Tools and Applications (pp. 1885-1903). IGI Global. https://doi.org/10.4018/978-1-4666-0879-5.ch809

Chicago

Schmidt, Bertil, et al. "PheGee@Home: A Grid-Based Tool for Comparative Genomics." In Grid and Cloud Computing: Concepts, Methodologies, Tools and Applications, edited by Information Resources Management Association, 1885-1903. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-0879-5.ch809

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Abstract

In this chapter we present PheGee@Home, a grid-based comparative genomics tool that nominates candidate genes responsible for a given phenotype. A phenotype is the physical manifestation of the interplay of genetic, epigenetic and environmental factors. Our tool is designed to facilitate the discovery and prioritization of candidate genes controlling or contributing to the genetically determined portion of a specified phenotype. However, in order to make reliable nominations of candidate genes from sequence data, several genome-size sequence datasets are required. This makes the approach impractical on traditional computer architectures leading to prohibitively long runtimes. Therefore, we use a computational architecture based on a desktop grid environment and commodity graphics hardware to significantly accelerate PheGee. We validate this approach by showing the deployment and evaluation on a grid testbed for the comparison of microbial genomes.

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