title: A sequential naïve Bayes classifier for DNA barcodes.
abstract: DNA barcodes are short strands of 255-700 nucleotide bases taken from the cytochrome c oxidase subunit 1 (COI) region of the mitochondrial DNA. It has been proposed that these barcodes may be used as a method of differentiating between biological species. Current methods of species classification utilize distance measures that are heavily dependent on both evolutionary model assumptions as well as a clearly defined "gap" between intra- and interspecies variation. Such distance measures fail to measure classification uncertainty or to indicate how much of the barcode is necessary for classification. We propose a sequential naïve Bayes classifier for species classification to address these limitations. The proposed method is shown to provide accurate species-level classification on real and simulated data. The method proposed here quantifies the uncertainty of each classification and addresses how much of the barcode is necessary.
authors.forename: Michael P
authors.initials: MP
authors.lastname: Anderson
authors.forename: Suzanne R
authors.initials: SR
authors.lastname: Dubnicka
mesh_terms: D001499:Bayes Theorem; D058893:DNA Barcoding, Taxonomic / Q000379:methods*; D004272:DNA, Mitochondrial / Q000235:genetics*; D008957:Models, Genetic
chemical_list: D004272:DNA, Mitochondrial