利用logDet距离从基因组序列识别物种网络拓扑
Identifiability of species network topologies from genomic sequences using the logDet distance
摘要：
从基因组数据推断物种之间的网络状进化关系必须解决基因流和不完全谱系排序交织的信号。这个问题的标准方法的繁重的计算需求严重限制了可以分析的数据集的大小，无论是物种的数量还是遗传位点的数量。在这里，我们提供了一个理论指针，更有效的方法，通过显示logDet距离计算的基因组规模的序列保留足够的信息，以恢复网络关系的水平-1超度量的情况下。这一结果是在网络多物种合并模型下得到的，该模型结合了跨单个基因树的一般时间可逆序列进化模型的混合物，但不依赖于基因对序列的分区。因此，在标准的随机模型下，可以在不考虑单个基因或基因树的情况下完成从序列中对网络关系的统计上合理的推断。
英文摘要：
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.
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