Rapid and accurate taxonomic classification of insect (Class Insecta) cytochrome c oxidase subunit 1 (COI) DNA barcode sequences using a naïve Bayesian classifier

Current methods to identify unknown insect (class Insecta) cytochrome c oxidase (COI barcode) sequences often rely on difficult to define thresholds of distances, sequence similarity cutoffs, or monophyly. Most methods do not provide a measure of confidence for the taxonomic assignments they provide. The aim of this study is to use a naïve Bayesian classifier (Wang et al., 2007) to automate unsupervised taxonomic assignments for large batches of insect COI sequences such as data obtained from environmental barcoding using next generation sequencing platforms. This method provides rank-flexible taxonomic assignments with an associated bootstrap support value and it is faster than the BLAST-based methods commonly used in environmental sequence surveys. We have developed and rigorously tested the performance of three different training sets using leave-one-out cross-validation, two field datasets, and targeted testing of Lepidoptera, Diptera, and Mantodea sequences obtained from the Barcod...