标题： 基于基因组序列使用对数行列式距离识别物种网络拓扑的可辨识性 显示英文标题

标题： Identifiability of species network topologies from genomic sequences using the logDet distance

摘要： 从基因组数据推断物种间类似网络的进化关系必须处理来自基因流和不完全谱系分选的交织信号。 此问题的标准方法所需的大量计算需求严重限制了可分析的数据集大小，无论是物种数量还是遗传位点数量。 在这里，我们通过证明从全基因组序列计算出的logDet距离保留了足够的信息来恢复一级超度量情况下的网络关系，从而为更高效的方法提供了理论指导。 这一结果是在Network多物种共祖先模型与个体基因树上的广义可逆序列演化模型混合的情况下得出的，但并不依赖于按基因划分序列。 因此，在标准随机模型下，从序列中进行网络关系的统计上合理的推断可以实现，而无需考虑单个基因或基因树。 显示英文摘要

摘要： Inference of network-like evolutionary relationships between species from genomic data must address the interwoven signals from both gene flow and incomplete lineage sorting. The heavy computational demands of standard approaches to this problem severely limit the size of datasets that may be analyzed, in both the number of species and the number of genetic loci. Here we provide a theoretical pointer to more efficient methods, by showing that logDet distances computed from genomic-scale sequences retain sufficient information to recover network relationships in the level-1 ultrametric case. This result is obtained under the Network Multispecies Coalescent model combined with a mixture of General Time-Reversible sequence evolution models across individual gene trees, but does not depend on partitioning sequences by genes. Thus under standard stochastic models statistically justifiable inference of network relationships from sequences can be accomplished without consideration of individual genes or gene trees.