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选择要保存或测序的分类群:理想标准与贪心算法解决方案。

Selecting taxa to save or sequence: desirable criteria and a greedy solution.

作者信息

Bordewich Magnus, Rodrigo Allen G, Semple Charles

机构信息

Department of Computer Science, Durham University, Durham, United Kingdom.

出版信息

Syst Biol. 2008 Dec;57(6):825-34. doi: 10.1080/10635150802552831.

Abstract

Three desirable properties for any method of selecting a subset of evolutionary units (EUs) for conservation or for genomic sequencing are discussed. These properties are spread, stability, and applicability. We are motivated by a practical case in which the maximization of phylogenetic diversity (PD), which has been suggested as a suitable method, appears to lead to counterintuitive collections of EUs and does not meet these three criteria. We define a simple greedy algorithm (GREEDYMMD) as a close approximation to choosing the subset that maximizes the minimum pairwise distance (MMD) between EUs. GREEDYMMD satisfies our three criteria and may be a useful alternative to PD in real-world situations. In particular, we show that this method of selection is suitable under a model of biodiversity in which features arise and/or disappear during evolution. We also show that if distances between EUs satisfy the ultrametric condition, then GREEDYMMD delivers an optimal subset of EUs that maximizes both the minimum pairwise distance and the PD. Finally, because GREEDYMMD works with distances and does not require a tree, it is readily applicable to many data sets.

摘要

本文讨论了为保护或基因组测序选择进化单元(EU)子集的任何方法所需具备的三个理想属性。这些属性是分布、稳定性和适用性。我们的动机来自一个实际案例,在该案例中,被认为是一种合适方法的系统发育多样性(PD)最大化,似乎会导致不符合直觉的进化单元集合,并且不满足这三个标准。我们定义了一种简单的贪心算法(GREEDYMMD),它近似于选择使进化单元之间的最小成对距离(MMD)最大化的子集。GREEDYMMD满足我们的三个标准,并且在实际情况中可能是PD的一个有用替代方法。特别是,我们表明这种选择方法在生物多样性模型下是合适的,在该模型中特征在进化过程中出现和/或消失。我们还表明,如果进化单元之间的距离满足超度量条件,那么GREEDYMMD会给出一个最优的进化单元子集,该子集能同时使最小成对距离和系统发育多样性最大化。最后,由于GREEDYMMD处理的是距离,并且不需要树,所以它很容易应用于许多数据集。

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