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Open Access Methodology article

Genome-wide computational prediction of tandem gene arrays: application in yeasts

Laurence Despons1*, Philippe V Baret2, Lionel Frangeul3, Véronique Leh Louis1, Pascal Durrens4 and Jean-Luc Souciet1

Author affiliations

1 Université de Strasbourg, CNRS UMR7156, F-67083 Strasbourg, France

2 Université Catholique de Louvain, B-1348, Louvain-la-Neuve, Belgium

3 Institut Pasteur, Plate-forme Intégration et analyse génomique, F-75015 Paris, France

4 Université Bordeaux 1, CNRS UMR5800, LaBRI INRIA Bordeaux Sud-Ouest (MAGNOME), F-33405 Talence, France

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Citation and License

BMC Genomics 2010, 11:56  doi:10.1186/1471-2164-11-56

Published: 21 January 2010

Abstract

Background

This paper describes an efficient in silico method for detecting tandem gene arrays (TGAs) in fully sequenced and compact genomes such as those of prokaryotes or unicellular eukaryotes. The originality of this method lies in the search of protein sequence similarities in the vicinity of each coding sequence, which allows the prediction of tandem duplicated gene copies independently of their functionality.

Results

Applied to nine hemiascomycete yeast genomes, this method predicts that 2% of the genes are involved in TGAs and gene relics are present in 11% of TGAs. The frequency of TGAs with degenerated gene copies means that a significant fraction of tandem duplicated genes follows the birth-and-death model of evolution. A comparison of sequence identity distributions between sets of homologous gene pairs shows that the different copies of tandem arrayed paralogs are less divergent than copies of dispersed paralogs in yeast genomes. It suggests that paralogs included in tandem structures are more recent or more subject to the gene conversion mechanism than other paralogs.

Conclusion

The method reported here is a useful computational tool to provide a database of TGAs composed of functional or nonfunctional gene copies. Such a database has obvious applications in the fields of structural and comparative genomics. Notably, a detailed study of the TGA catalog will make it possible to tackle the fundamental questions of the origin and evolution of tandem gene clusters.