Data from: Parsimonious inference of hybridization in the presence of incomplete lineage sorting

Hybridization plays an important evolutionary role in several groups of organisms. A phylogenetic approach to detect hybridization entails sequencing multiple loci across the genomes of a group of species of interest, reconstructing their gene trees, and taking their differences as indicators of hybridization. However, methods that follow this approach mostly ignore population effects, such as incomplete lineage sorting (ILS). Given that hybridization occurs between closely related organisms, ILS may very well be at play and, hence, must be accounted for in the analysis framework. To address this issue, we present a parsimony criterion for reconciling gene trees within the branches of a phylogenetic network, and a local search heuristic for inferring phylogenetic networks from collections of gene-tree topologies under this criterion. This framework enables phylogenetic analyses while accounting for both hybridization and ILS. Further, we propose two techniques for incorporating information about uncertainty in gene-tree estimates. Our simulation studies demonstrate the good performance of our framework in terms of identifying the location of hybridization events, as well as estimating the proportions of genes that underwent hybridization. Also, our framework shows good performance in terms of efficiency on handling large data sets in our experiments. Further, in analysing a yeast data set, we demonstrate issues that arise when analysing real data sets. Although a probabilistic approach was recently introduced for this problem, and although parsimonious reconciliations have accuracy issues under certain settings, our parsimony framework provides a much more computationally efficient technique for this type of analysis. Our framework now allows for genome-wide scans for hybridization, while also accounting for ILS.杂交在诸多生物类群的演化进程中发挥着关键作用。用于检测杂交事件的系统发育研究方法，通常需要对目标物种类群的全基因组多个基因座进行测序，重构其基因树，并以基因树间的拓扑差异作为杂交事件的指示依据。然而，此类现有方法大多忽略了种群遗传学效应，例如不完全谱系分选（incomplete lineage sorting, ILS）。由于杂交多发生在亲缘关系较近的类群之间，不完全谱系分选很可能会干扰分析结果，因此必须将其纳入分析框架之中。为解决这一问题，我们提出了一种可在系统发育网络的分支内协调基因树的简约准则，并基于该准则构建了一种可从多组基因树拓扑结构中推断系统发育网络的局部搜索启发式算法。该分析框架可在同时考量杂交事件与不完全谱系分选的前提下开展系统发育研究。此外，我们还提出了两种可整合基因树估计结果不确定性信息的技术方案。模拟实验结果表明，我们的框架在识别杂交事件发生位置以及估算发生杂交的基因比例两方面均表现出色。同时，实验结果显示，该框架在处理大规模数据集时具备优异的计算效率。此外，通过对酵母数据集的分析，我们展示了真实数据集分析过程中可能出现的各类问题。尽管近期已有针对该问题的概率方法被提出，且在特定场景下简约式协调存在精度缺陷，但我们提出的简约框架为该类分析提供了计算效率大幅提升的技术手段。当前，该框架已可用于开展全基因组范围的杂交事件扫描，同时兼顾不完全谱系分选的影响。