Identifiability of tree-child phylogenetic networks under a probabilistic recombination-mutation model of evolution
Identifiability of tree-child phylogenetic networks under a probabilistic recombination-mutation model of evolution
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DOI：
10.1016/j.jtbi.2018.03.011
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发表时间：
2018-06-07
影响因子：
2
通讯作者：
Moulton, Vincent
中科院分区：
文献类型：
--
作者：
Francis, Andrew;Moulton, Vincent
系统发育网络是系统发育树的延伸，用于表示发生网状事件（例如重组和杂交）的进化历史。这种网络的一个核心问题是可识别性，它本质上是问在什么情况下我们可以可靠地识别产生观察数据的系统发育网络？最近，出现了与序列进化模型相关的网络的可识别性结果，该模型概括了用于系统发育树的标准马尔可夫模型。然而，就所考虑的网络的复杂性而言，这些结果非常有限。在本文中，通过引入一种基于网络进化的替代概率模型，该模型基于 Thatte 对谱系的一些开创性工作，我们能够获得更大类别的系统发育网络（本质上是所谓的树子网络类别）的可识别性结果。为了证明我们的主要定理，我们得出了一些用于组合识别树子网络的新结果，然后采用 Thatte 为谱系开发的一些技术来表明我们的组合结果意味着概率设置中的可识别性。我们希望引入新的网络模型能够带来可靠构建系统发育网络的新方法。 (C) 2018 Elsevier Ltd. 保留所有权利。
Phylogenetic networks are an extension of phylogenetic trees which are used to represent evolutionary histories in which reticulation events (such as recombination and hybridization) have occurred. A central question for such networks is that of identifiability, which essentially asks under what circumstances can we reliably identify the phylogenetic network that gave rise to the observed data? Recently, identifiability results have appeared for networks relative to a model of sequence evolution that generalizes the standard Markov models used for phylogenetic trees. However, these results are quite limited in terms of the complexity of the networks that are considered. In this paper, by introducing an alternative probabilistic model for evolution along a network that is based on some ground-breaking work by Thatte for pedigrees, we are able to obtain an identifiability result for a much larger class of phylogenetic networks (essentially the class of so-called tree-child networks). To prove our main theorem, we derive some new results for identifying tree-child networks combinatorially, and then adapt some techniques developed by Thatte for pedigrees to show that our combinatorial results imply identifiability in the probabilistic setting. We hope that the introduction of our new model for networks could lead to new approaches to reliably construct phylogenetic networks. (C) 2018 Elsevier Ltd. All rights reserved.