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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Sanguinetti, Guido


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Volume 13, Issue 4

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A sequential naïve Bayes classifier for DNA barcodes

Michael P. Anderson
  • Corresponding author
  • Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center Oklahoma City, OK, USA
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  • Other articles by this author:
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/ Suzanne R. Dubnicka
Published Online: 2014-07-03 | DOI: https://doi.org/10.1515/sagmb-2013-0025

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.

This article offers supplementary material which is provided at the end of the article.

Keywords: Naïve Bayes classifier; DNA barcoding; phylogenetic analysis; sequential analysis; species classification; species discovery

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About the article

Corresponding author: Michael P. Anderson, Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center Oklahoma City, OK, USA, e-mail:


Published Online: 2014-07-03

Published in Print: 2014-08-01


Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 13, Issue 4, Pages 423–434, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.1515/sagmb-2013-0025.

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