Improving specimen identification: Informative DNA using a statistical Bayesian method

Abstract

<p>This work investigates the assignment of unknown sequences to their species of origin. In particular, I examine four questions: Is existing (GenBank) data reliable for accurate species identification? Does a segregating sites algorithm make accurate species identifications and how does it compare to another Bayesian method? Does broad sampling of reference species improve the information content of reference data? And does an extended model (of the theory of segregating sites) describe the genetic variation in a set of sequences (of a species or population) better? Though we did not find unusually similar between-species sequences in GenBank, there was evidence of unusually divergent within-species sequences, suggesting that caution and a firm understanding of GenBank species should be exercised before utilizing GenBank data. To address challenging identifications resulting from an overlap between within- and between species variation, we introduced a Bayesian treeless statistical assignment method that makes use of segregating sites. Assignments with simulated and <em>Drosophila</em> (fruit fly) sequences show that this method can provide fast, high probability assignments for recently diverged species. To address reference sequences with low information content, the addition of even one broadly sampled reference sequence can increase the number of correct assignments. Finally, an extended theory of segregating sites generates more realistic probability estimates of the genetic variability of a set of sequences. Species are dynamic entities and this work will highlight ideas and methods to address dynamic genetic patterns in species.</p>