Data from: Developing nuclear DNA phylogenetic markers in the angiosperm genus Leucadendron (Proteaceae): a next-generation sequencing transcriptomic approach
Tonnabel, Jeanne et al. (2013), Data from: Developing nuclear DNA phylogenetic markers in the angiosperm genus Leucadendron (Proteaceae): a next-generation sequencing transcriptomic approach, Dryad, Dataset, https://doi.org/10.5061/dryad.tp5g2
Despite the recent advances in generating molecular data, reconstructing species-level phylogenies for non-models groups remains a challenge. The use of a number of independent genes is required to resolve phylogenetic relationships, especially for groups displaying low polymorphism. In such cases, low-copy nuclear exons and non-coding regions, such as 3′ untranslated regions (3′-UTRs) or introns, constitute a potentially interesting source of nuclear DNA variation. Here, we present a methodology meant to identify new nuclear orthologous markers using both public-nucleotide databases and transcriptomic data generated for the group of interest by using next generation sequencing technology. To identify PCR primers for a non-model group, the genus Leucadendron (Proteaceae), we adopted a framework aimed at minimizing the probability of paralogy and maximizing polymorphism. We anchored when possible the right-hand primer into the 3′-UTR and the left-hand primer into the coding region. Seven new nuclear markers emerged from this search strategy, three of those included 3′-UTRs. We further compared the phylogenetic potential between our new markers and the ribosomal internal transcribed spacer region (ITS). The sequenced 3′-UTRs yielded higher polymorphism rates than the ITS region did. We did not find strong incongruences with the phylogenetic signal contained in the ITS region and the seven new designed markers but they strongly improved the phylogeny of the genus Leucadendron. Overall, this methodology is efficient in isolating orthologous loci and is valid for any non-model group given the availability of transcriptomic data.