论文标题
根据马尔可夫过程产生的数据，区分1级的系统发育网络
Distinguishing level-1 phylogenetic networks on the basis of data generated by Markov processes
论文作者
论文摘要
系统发育网络可以代表系统发育树无法描述的进化事件。这些网络能够结合网状进化事件，例如杂交，渗入和横向基因转移。最近，已经引入了基于网络的Markov模型，以及基于模型的方法来重建系统发育网络。为了使这些方法保持一致，需要从模型下生成的数据中识别网络参数。在这里，我们表明，在Jukes-Cantor，Kimura 2-Parameter或Kimura 3参数约束下，具有任何固定数量的网状顶点的无三角级网络模型的半定向网络参数通常可识别。
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.
