A sequential naïve Bayes classifier for DNA barcodes

Anderson Michael P. () and Dubnicka Suzanne R.
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Anderson Michael P.: Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center Oklahoma City, OK, USA
Dubnicka Suzanne R.: Department of Statistics, Kansas State University, Manhattan, KS, USA

Statistical Applications in Genetics and Molecular Biology, 2014, vol. 13, issue 4, 423-434

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.

Keywords: Naïve Bayes classifier; DNA barcoding; phylogenetic analysis; sequential analysis; species classification; species discovery (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1515/sagmb-2013-0025

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