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Showing papers by "Jose Castresana published in 2014"


Journal ArticleDOI
07 May 2014-PLOS ONE
TL;DR: A bioinformatic pipeline is used to extract intron sequences from rodent genomes available in databases and a series of filters are applied that allowed the identification of 208 introns that adequately fulfilled several criteria for these studies.
Abstract: One of the major challenges in the analysis of closely related species, speciation and phylogeography is the identification of variable sequence markers that allow the determination of genealogical relationships in multiple genomic regions using coalescent and species tree approaches. Rodent species represent nearly half of the mammalian diversity, but so far no systematic study has been carried out to detect suitable informative markers for this group. Here, we used a bioinformatic pipeline to extract intron sequences from rodent genomes available in databases and applied a series of filters that allowed the identification of 208 introns that adequately fulfilled several criteria for these studies. The main required characteristics of the introns were that they had the maximum possible mutation rates, that they were part of single-copy genes, that they had an appropriate sequence length for amplification, and that they were flanked by exons with suitable regions for primer design. In addition, in order to determine the validity of this approach, we chose ten of these introns for primer design and tested them in a panel of eleven rodent species belonging to different representative families. We show that all these introns can be amplified in the majority of species and that, overall, 79% of the amplifications worked with minimum optimization of the annealing temperature. In addition, we confirmed for a pair of sister species the relatively high level of sequence divergence of these introns. Therefore, we provide here a set of adequate intron markers that can be applied to different species of Rodentia for their use in studies that require significant sequence variability.

9 citations