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A New Linear-Time Heuristic Algorithm for Computing the Parsimony Score of Phylogenetic Networks: Theoretical Bounds and Empirical Performance

机译:计算系统进化网络简约分数的新线性时间启发式算法:理论界和经验性能

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Phylogenies play a major role in representing the interrelationships among biological entities. Many methods for reconstructing and studying such phylogenies have been proposed, almost all of which assume that the underlying history of a given set of species can be represented by a binary tree. Although many biological processes can be effectively modeled and summarized in this fashion, others cannot: recombination, hybrid speciation, and horizontal gene transfer result in networks, rather than trees, of relationships. In a series of papers, we have extended the maximum parsimony (MP) criterion to phylogenetic networks, demonstrated its appropriateness, and established the intractability of the problem of scoring the parsimony of a phylogenetic network. In this work we show the hardness of approximation for the general case of the problem, devise a very fast (linear-time) heuristic algorithm for it, and implement it on simulated as well as biological data.
机译:系统发育在代表生物实体之间的相互关系中起着重要作用。已经提出了许多重建和研究这种系统发育的方法,几乎​​所有方法都假定给定物种集的基础历史可以用二叉树表示。尽管可以通过这种方式有效地对许多生物学过程进行建模和总结,但其他过程则不能:重组,杂种形成和水平基因转移产生关系的网络而不是树木的关系。在一系列论文中,我们将最大简约(MP)准则扩展到了系统进化网络,证明了它的适当性,并建立了系统进化网络简约评分问题的难解性。在这项工作中,我们展示了问题一般情况下的近似硬度,为此设计了一种非常快的(线性时间)启发式算法,并在模拟数据和生物学数据上实现了该算法。

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