Data from: Specimens at the center: an informatics workflow and toolkit for specimen-level analysis of public DNA database data
Group, The Global Carex et al. (2017), Data from: Specimens at the center: an informatics workflow and toolkit for specimen-level analysis of public DNA database data, Dryad, Dataset, https://doi.org/10.5061/dryad.6tn70
Major public DNA databases — NCBI GenBank, the DNA DataBank of Japan (DDBJ), and the European Molecular Biology Laboratory (EMBL) — are invaluable biodiversity libraries. Systematists and other biodiversity scientists commonly mine these databases for sequence data to use in phylogenetic studies, but such studies generally use only the taxonomic identity of the sequenced tissue, not the specimen identity. Thus studies that use DNA supermatrices to construct phylogenetic trees with species at the tips typically do not take advantage of the fact that for many individuals in the public DNA databases, several DNA regions have been sampled; and for many species, two or more individuals have been sampled. Thus these studies typically do not make full use of the multigene datasets in public DNA databases to test species coherence and select optimal sequences to represent a species. In this study, we introduce a set of tools developed in the R programming language to construct individual-based trees from NCBI GenBank data and present a set of trees for the genus Carex (Cyperaceae) constructed using these methods. For the more than 770 species for which we found sequence data, our approach recovered an average of 1.85 gene regions per specimen, up to seven for some specimens, and more than 450 species represented by two or more specimens. Depending on the subset of genes analyzed, we found up to 42% of species monophyletic. We introduce a simple tree statistic—the Taxonomic Disparity Index (TDI)—to assist in curating specimen-level datasets and provide code for selecting maximally informative (or, conversely, minimally misleading) sequences as species exemplars. While tailored to the Carex dataset, the approach and code presented in this paper can readily be generalized to constructing individual-level trees from large amounts of data for any species group.