标题： 收敛-发散模型：描述随着时间推移的基因流的系统发育树广义模型 显示英文标题

标题： Convergence-divergence models: Generalizations of phylogenetic trees modeling gene flow over time

摘要： 系统发育树是进化过程的简单模型。 它们描述了从共同祖先出发的条件独立的分支进化。 系统发育树通常缺乏足够的灵活性来充分建模所有的进化过程。 例如，基因渐渗，其中基因可以从一个分类群流向另一个分类群。 系统发育网络则用于建模系统发育树无法完全描述的进化。 然而，许多系统发育网络模型假设祖先分类群会瞬间合并形成“杂交”后代分类群。 相比之下，我们的趋同-发散模型保留了一个单一的潜在的“主要”树，但允许基因在整个任意时间框架内流动。 或者，趋同-发散模型可以描述导致分类群在一段时间内变得更相似的其他生物过程，例如复制进化。 在这里，我们提出了基于最大似然的新算法，以推断大部分涉及$N$个分类群的趋同-发散模型方面，许多情况下是一致的，使用四元组方法。 这些算法可以应用于限制于基因或基因组窗口的多重序列比对，或者应用于基因存在/缺失数据集。 显示英文摘要

摘要： Phylogenetic trees are simple models of evolutionary processes. They describe conditionally independent divergent evolution of taxa from common ancestors. Phylogenetic trees commonly do not have enough flexibility to adequately model all evolutionary processes. For example, introgressive hybridization, where genes can flow from one taxon to another. Phylogenetic networks model evolution not fully described by a phylogenetic tree. However, many phylogenetic network models assume ancestral taxa merge instantaneously to form ``hybrid'' descendant taxa. In contrast, our convergence-divergence models retain a single underlying ``principal'' tree, but permit gene flow over arbitrary time frames. Alternatively, convergence-divergence models can describe other biological processes leading to taxa becoming more similar over a time frame, such as replicated evolution. Here we present novel maximum likelihood-based algorithms to infer most aspects of $N$-taxon convergence-divergence models, many consistently, using a quartet-based approach. The algorithms can be applied to multiple sequence alignments restricted to genes or genomic windows or to gene presence/absence datasets.