A sequential naïve Bayes classifier for DNA barcodes

• Published in: Statistical applications in genetics and molecular biology Vol. 13; no. 4; pp. 423 - 434
• Main Authors: Anderson, Michael P., Dubnicka, Suzanne R.
• Format: Journal Article
• Language: English
• Published: Germany De Gruyter 01.08.2014
• Subjects: Bayes Theorem; DNA barcoding; DNA Barcoding, Taxonomic - methods; DNA, Mitochondrial - genetics; Models, Genetic; Naïve Bayes classifier; phylogenetic analysis; sequential analysis; species classification; species discovery
• ISSN: 2194-6302; 1544-6115; 1544-6115
• DOI: 10.1515/sagmb-2013-0025

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.