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Improving Comprehension of Large Taxonomic Graphs

机译:改进大型分类图的理解

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Taxonomic trees have been used for decades to visualize taxonomies; however, taxonomies involving many taxa may be difficult to interpret due to problems related to over- plotting. This problem can manifest itself in metagenomics studies, where the set of detected taxa can have a cardinality in the hundreds or thousands. We present a method by which a phylogenetic tree’s complexity may be reduced by removing nodes with little support or with trivial out-degree (as defined by the user). A diffusive model is then used to color the nodes based on their taxonomic proximity; the color of ancestor nodes are a mixture of their descendents. This method results in compact taxonomic trees whose color gradually diffuses to white at the root. Our second example application of the technique is a more generalized structure of pedigrees. We show that related taxa can be easily located by reducing the complexity of the graph via pruning and coloring of related vertices.
机译:分类树已经使用了数十年,以可视化分类法。但是,由于涉及过度绘制的问题,涉及许多分类单元的分类法可能难以解释。这个问题可以在宏基因组学研究中体现出来,在该研究中,一组检测到的分类单元可能具有成百上千的基数。我们提出了一种方法,可以通过删除几乎没有支持或程度不高(由用户定义)的节点来减少系统发育树的复杂性。然后,使用扩散模型根据节点的分类接近性为节点着色。祖先节点的颜色是其后代的混合。该方法产生紧凑的分类树,其颜色在根部逐渐扩散为白色。该技术的第二个示例应用是谱系的更一般化的结构。我们表明,通过对相关顶点进行修剪和着色来降低图形的复杂度,可以很容易地找到相关的分类单元。

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