A transcriptome-based analytical workflow for identifying loci for species diagnosis: a case study with Bactrocera fruit flies (Diptera: Tephritidae)

Abstract

Abstract Development of novel molecular methods for accurate and economical identification of species has become critical both for pure biological research and for a wide range of applied areas. The most widely used current molecular diagnostic tool, the mitochondrial cytochrome c oxidase subunit 1 gene (COI), the so-called DNA barcode, has been highly criticised and is known to be ineffective at distinguishing species in many groups. Alternative markers are needed to circumvent these issues and provide diagnosticians with a greater range of tools for making accurate identifications. To address this, we describe here a novel analytical workflow for diagnostic marker development that utilises near-genomic-scale data to search for potential informative loci. The workflow takes advantage of orthologous gene databases, in combination with tests of phylogenetic resolution, and benchmarking of nucleotide variation against COI, to determine putative loci that might outperform COI. We use transcriptomes of 14 tephritid fruit flies and especially the taxonomically complex genus Bactrocera, as a case study. Of 1646 orthologues searched, our workflow retained a total of five loci following our conservative filtering strategy. One locus, POP4, had strong potential as a novel diagnostic marker for Bactrocera fruit flies. POP4 discriminates most species in the training set of taxa, but like COI fails to separate the sibling species Bactrocera tryoni and Bactrocera neohumeralis. Further validation of this potential new marker against a broader taxonomic sample is ongoing. We advocate that this simple and efficient workflow is, with minor modification, customisable for diagnostic development in almost any taxonomic group.