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The Scientific World Journal
Volume 2014 (2014), Article ID 350487, 9 pages
Research Article

Large Scale Explorative Oligonucleotide Probe Selection for Thousands of Genetic Groups on a Computing Grid: Application to Phylogenetic Probe Design Using a Curated Small Subunit Ribosomal RNA Gene Database

1UMR CNRS 6158, ISIMA/LIMOS, Clermont Université et Université Blaise Pascal, F63173 Aubière, France
2Clermont Université et Université d’Auvergne, EA 4678 CIDAM, BP 10448, F63001 Clermont-Ferrand Cedex 1, France
3Clermont Université et Université d’Auvergne, UFR Pharmacie, F63001 Clermont-Ferrand Cedex 1, France
4CNRS, UMR 6023, LMGE, F63171 Aubière, France
5Clermont Université, CRRI, F63177 Aubière, France

Received 25 September 2013; Accepted 5 December 2013; Published 6 January 2014

Academic Editors: Y. Lai and S. Ma

Copyright © 2014 Faouzi Jaziri et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Phylogenetic Oligonucleotide Arrays (POAs) were recently adapted for studying the huge microbial communities in a flexible and easy-to-use way. POA coupled with the use of explorative probes to detect the unknown part is now one of the most powerful approaches for a better understanding of microbial community functioning. However, the selection of probes remains a very difficult task. The rapid growth of environmental databases has led to an exponential increase of data to be managed for an efficient design. Consequently, the use of high performance computing facilities is mandatory. In this paper, we present an efficient parallelization method to select known and explorative oligonucleotide probes at large scale using computing grids. We implemented a software that generates and monitors thousands of jobs over the European Computing Grid Infrastructure (EGI). We also developed a new algorithm for the construction of a high-quality curated phylogenetic database to avoid erroneous design due to bad sequence affiliation. We present here the performance and statistics of our method on real biological datasets based on a phylogenetic prokaryotic database at the genus level and a complete design of about 20,000 probes for 2,069 genera of prokaryotes.