DNA barcoding using chitons (genus Mopalia)

Authors

  • RYAN P. KELLY,

    1. Columbia University, Department of Ecology, Evolution, and Environmental Biology, 10th Floor Schermerhorn Ext., 1200 Amsterdam Avenue, New York, NY 10027, USA,
    2. Division of Invertebrates, American Museum of Natural History, 79th Street at Central Park West, New York New York 10024, USA,
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  • INDRA NEIL SARKAR,

    1. Division of Invertebrates, American Museum of Natural History, 79th Street at Central Park West, New York New York 10024, USA,
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  • DOUGLAS J. EERNISSE,

    1. Department of Biological Science (MH-282), California State University, Fullerton, 800 North State College Blvd., Fullerton, CA 92831-3599, USA
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  • ROB DESALLE

    1. Columbia University, Department of Ecology, Evolution, and Environmental Biology, 10th Floor Schermerhorn Ext., 1200 Amsterdam Avenue, New York, NY 10027, USA,
    2. Division of Invertebrates, American Museum of Natural History, 79th Street at Central Park West, New York New York 10024, USA,
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  • Competing Interests. The authors have declared that no competing interests exist.

    Author Contributions. R.P.K. collected and analysed the molecular data, collected a number of the samples from the field, and wrote the paper. I.N.S. conceived of and wrote the algorithm and the version implemented here. D.J.E. collected the majority of the specimens, preliminarily identified them, and helped to place the data set in a larger biological and intellectual context. R.D. conceived of using the data set for the purpose of testing the method presented herein and sponsored the work, which was carried out in his laboratory.

R. DeSalle, Fax: 212-769-5277; E-mail: desalle@amnh.org

Abstract

Incorporating substantial intraspecific genetic variation for 19 species from 131 individual chitons, genus Mopalia (Mollusca: Polyplacophora), we present rigorous DNA barcodes for this genus as per the currently accepted approaches to DNA barcoding. We also have performed a second kind of analysis that does not rely on blast or the distance-based neighbour-joining approach as currently resides on the Barcode of Life Data Systems website. Our character-based approach, called characteristic attribute organization system, returns fast, accurate, character-based diagnostics and can unambiguously distinguish between even closely related species based on these diagnostics. Using statistical subsampling approaches with our original data matrix, we show that the method outperforms blast and is equally effective as the neighbour-joining approach. Our approach differs from the neighbour-joining approach in that the end-product is a list of diagnostic nucleotide positions that can be used in descriptions of species. In addition, the diagnostics obtained from this character-based approach can be used to design oligonucleotides for detection arrays, polymerase chain reaction drop off diagnostics, TaqMan assays, and design of primers for generating short fragments that encompass regions containing diagnostics in the cytochrome oxidase I gene.

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