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基于马尔可夫过程生成的数据区分一级系统发育网络。

Distinguishing level-1 phylogenetic networks on the basis of data generated by Markov processes.

机构信息

Department of Mathematics, University of Hawai'i at Mānoa, 2565 McCarthy Mall, Honolulu, HI, 96822, USA.

Delft Institute of Applied Mathematics, Delft University of Technology, Mekelweg 4, 2628 CD, Delft, The Netherlands.

出版信息

J Math Biol. 2021 Sep 4;83(3):32. doi: 10.1007/s00285-021-01653-8.

Abstract

Phylogenetic networks can represent evolutionary events that cannot be described by phylogenetic trees. These networks are able to incorporate reticulate evolutionary events such as hybridization, introgression, and lateral gene transfer. Recently, network-based Markov models of DNA sequence evolution have been introduced along with model-based methods for reconstructing phylogenetic networks. For these methods to be consistent, the network parameter needs to be identifiable from data generated under the model. Here, we show that the semi-directed network parameter of a triangle-free, level-1 network model with any fixed number of reticulation vertices is generically identifiable under the Jukes-Cantor, Kimura 2-parameter, or Kimura 3-parameter constraints.

摘要

系统发育网络可以表示无法用系统发生树描述的进化事件。这些网络能够整合杂交、基因渗入和侧向基因转移等网状进化事件。最近,人们引入了基于网络的 DNA 序列进化马尔可夫模型以及基于模型的重建系统发育网络的方法。为了使这些方法保持一致,需要从模型生成的数据中识别网络参数。在这里,我们证明了在 Jukes-Cantor、Kimura 2-parameter 或 Kimura 3-parameter 约束下,任何固定数量的网状顶点的无三角、1 级网络模型的半定向网络参数在一般情况下是可识别的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa5d/8418599/e1a9bd12aa7b/285_2021_1653_Fig1_HTML.jpg

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