IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v13y2014i4p12n3.html
   My bibliography  Save this article

A sequential naïve Bayes classifier for DNA barcodes

Author

Listed:
  • 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)

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.

Suggested Citation

  • Anderson Michael P. & Dubnicka Suzanne R., 2014. "A sequential naïve Bayes classifier for DNA barcodes," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(4), pages 1-12, August.
    • Handle: RePEc:bpj:sagmbi:v:13:y:2014:i:4:p:12:n:3
    DOI: 10.1515/sagmb-2013-0025
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/sagmb-2013-0025
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/sagmb-2013-0025?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:sagmbi:v:13:y:2014:i:4:p:12:n:3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.