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Bayesian classification of DNA barcodes.

机译:DNA条码的贝叶斯分类。

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摘要

DNA barcodes are short strands of nucleotide bases taken from the cytochrome c oxidase subunit 1 (COI) of the mitochondrial DNA (mtDNA). A single barcode may have the form C C G G C A T A G T A G G C A C T G... and typically ranges in length from 255 to around 700 nucleotide bases. Unlike nuclear DNA (nDNA), mtDNA remains largely unchanged as it is passed from mother to offspring. It has been proposed that these barcodes may be used as a method of differentiating between biological species (Hebert, Ratnasingham, and deWaard 2003). While this proposal is sharply debated among some taxonomists (Will and Rubinoff 2004), it has gained momentum and attention from biologists. One issue at the heart of the controversy is the use of genetic distance measures as a tool for species differentiation. Current methods of species classification utilize these distance measures that are heavily dependent on both evolutionary model assumptions as well as a clearly defined "gap" between intra- and interspecies variation (Meyer and Paulay 2005). We point out the limitations of such distance measures and propose a character-based method of species classification which utilizes an application of Bayes' rule to overcome these deficiencies. The proposed method is shown to provide accurate species-level classification. The proposed methods also provide answers to important questions not addressable with current methods.
机译:DNA条码是从线粒体DNA(mtDNA)的细胞色素C氧化酶亚基1(COI)提取的核苷酸碱基的短链。单个条形码的形式可以为C C G G C A T G G A C G C A C T G ...,并且长度通常在255至约700个核苷酸碱基之间。与核DNA(nDNA)不同,mtDNA从母体传给后代时基本上保持不变。已经提出,这些条形码可以用作区分生物物种的方法(Hebert,Ratnasingham和deWaard 2003)。尽管该建议在一些分类学家中进行了激烈的辩论(Will and Rubinoff 2004),但它却得到了生物学家的推动和关注。争议的核心问题是使用遗传距离测量作为物种分化的工具。当前的物种分类方法利用了这些距离测度,这些测度在很大程度上取决于进化模型的假设以及种间和种间变异之间明确定义的“差距”(Meyer and Paulay 2005)。我们指出了这种距离测量的局限性,并提出了一种基于字符的物种分类方法,该方法利用贝叶斯法则来克服这些缺陷。所提出的方法显示可以提供准确的物种级别分类。提出的方法还提供了当前方法无法解决的重要问题的答案。

著录项

  • 作者

    Anderson, Michael P.;

  • 作者单位

    Kansas State University.;

  • 授予单位 Kansas State University.;
  • 学科 Biology Genetics.;Biology Bioinformatics.;Statistics.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 210 p.
  • 总页数 210
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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