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Detecting Microsatellites within Genomes: Significant Variation Among Algorithms

Abstract : BACKGROUND: Microsatellites are short, tandemly-repeated DNA sequences which are widely distributed among genomes. Their structure, role and evolution can be analyzed based on exhaustive extraction from sequenced genomes. Several dedicated algorithms have been developed for this purpose. Here, we compared the detection efficiency of five of them (TRF, Mreps, Sputnik, STAR, and RepeatMasker). RESULTS: Our analysis was first conducted on the human X chromosome, and microsatellite distributions were characterized by microsatellite number, length, and divergence from a pure motif. The algorithms work with user-defined parameters, and we demonstrate that the parameter values chosen can strongly influence microsatellite distributions. The five algorithms were then compared by fixing parameters settings, and the analysis was extended to three other genomes (Saccharomyces cerevisiae, Neurospora crassa and Drosophila melanogaster) spanning a wide range of size and structure. Significant differences for all characteristics of microsatellites were observed among algorithms, but not among genomes, for both perfect and imperfect microsatellites. Striking differences were detected for short microsatellites (below 20 bp), regardless of motif. CONCLUSION: Since the algorithm used strongly influences empirical distributions, studies analyzing microsatellite evolution based on a comparison between empirical and theoretical size distributions should therefore be considered with caution. We also discuss why a typological definition of microsatellites limits our capacity to capture their genomic distributions.
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Déposant : Eric Rivals <>
Soumis le : lundi 3 décembre 2007 - 10:29:39
Dernière modification le : vendredi 18 septembre 2020 - 14:34:44
Archivage à long terme le : : jeudi 27 septembre 2012 - 10:35:58


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Sébastien Leclercq, Eric Rivals, Philippe Jarne. Detecting Microsatellites within Genomes: Significant Variation Among Algorithms. BMC Bioinformatics, BioMed Central, 2007, 8 (1), pp.125. ⟨10.1186/1471-2105-8-125⟩. ⟨lirmm-00193269⟩



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