Structural analysis of biodiversity.

Structural analysis of biodiversity.
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DOI:
10.1371/journal.pone.0009266
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发表时间:
2010-02-24
期刊:
影响因子:
3.7
通讯作者:
Zhang Y
Zhang Y
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Sirovich L;Stoeckle MY;Zhang Y

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大型的,最近可用的基因组数据库涵盖了广泛的生命形式,这表明有机会深入了解生物多样性的遗传结构。在这项研究中,我们完善了我们最近描述的技术,使用指示载体来分析和可视化核苷酸序列。指示向量方法生成相关矩阵,称为Klee图,它代表了一种组装和查看大型基因组数据集的新方法。为了探索其潜在的实用性,在这里,我们应用改进的算法,以收集近17000个DNA条形码序列,涵盖12个广泛分离的动物类群,表明分类的指示向量在所有11000个测试用例中给出了正确的分配。指标向量分析显示,相应的物种和更高层次的分类部门的不连续性,提出了一个有效的方法来分类的生物从研究不足的群体。与标准距离度量相比,指示向量保留了诊断特征概率,使测试序列的自动分类成为可能,并生成高信息密度的单页显示。这些结果支持应用指示向量对大的核苷酸数据集进行比较分析,并提高了深入了解生物多样性遗传结构中大尺度模式的前景。
Large, recently-available genomic databases cover a wide range of life forms, suggesting opportunity for insights into genetic structure of biodiversity. In this study we refine our recently-described technique using indicator vectors to analyze and visualize nucleotide sequences. The indicator vector approach generates correlation matrices, dubbed Klee diagrams, which represent a novel way of assembling and viewing large genomic datasets. To explore its potential utility, here we apply the improved algorithm to a collection of almost 17000 DNA barcode sequences covering 12 widely-separated animal taxa, demonstrating that indicator vectors for classification gave correct assignment in all 11000 test cases. Indicator vector analysis revealed discontinuities corresponding to species- and higher-level taxonomic divisions, suggesting an efficient approach to classification of organisms from poorly-studied groups. As compared to standard distance metrics, indicator vectors preserve diagnostic character probabilities, enable automated classification of test sequences, and generate high-information density single-page displays. These results support application of indicator vectors for comparative analysis of large nucleotide data sets and raise prospect of gaining insight into broad-scale patterns in the genetic structure of biodiversity.
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