Computational Evaluation of DNA Metabarcoding for Universal Diagnostics of Invasive Insect Pests

Abstract

Appropriate design and selection of PCR primers plays a critical role in determining the sensitivity and specificity of a metabarcoding assay. Despite several studies applying metabarcoding to insect pest surveillance, the diagnostic performance of the short “mini-barcodes” required by high-throughput sequencing platforms has not been established across the broader taxonomic diversity of invasive insects. We address this by computationally evaluating the diagnostic sensitivity and predicted amplification bias for 68 published and novel cytochrome c oxidase subunit 1 (COI) primers on a curated database of 110,676 insect species, including 2,625 registered on global invasive species lists. We find that mini-barcodes between 125-257 bp can provide comparable resolution to the full-length barcode for both invasive insect pests and the broader Insecta, conditional upon the subregion of COI targeted and the genetic similarity threshold used to identify species. Taxa that could not be identified by any barcode lengths were phylogenetically clustered within ‘problem groups’, many arising through taxonomic inconsistencies rather than insufficient diagnostic information within the barcode itself. Substantial variation in predicted PCR bias was seen across published primers, with those including 4-5 degenerate nucleotide bases showing almost no mismatch to major insect orders. While not completely universal, a single COI mini-barcode can successfully differentiate the majority of pest and non-pest insects from their congenerics, even at the small amplicon size imposed by 2 × 150 bp sequencing. We provide a ranked summary of high-performing primers and discuss the bioinformatic steps required to curate reliable reference databases for metabarcoding studies.