标题： 分割得分：量化基因组规模数据中系统发育信号的工具 显示英文标题

标题： Split scores: a tool to quantify phylogenetic signal in genome-scale data

摘要： 检测染色体上进化过程的变化在全基因组数据越来越广泛可用的情况下变得越来越重要。 例如，不完全的谱系排序、水平基因转移和染色体重排等因素预计会导致染色体上基础基因树的变化，而不同基因组区域的选择压力和突变率的变化可能导致基础突变过程的变化。 我们提出了一种称为分割得分的方法，用于量化基因组数据集中特定系统发育关系的支持程度。 由于分割得分基于位点模式频率矩阵的代数特性，即使对于包含大量分类单元和/或对齐长度较大的数据集，也可以快速计算，这比其他方法（如最大似然法）具有优势，这些方法通常用于评估此类支持。 通过模拟，我们探讨了分割得分的特性，包括其对序列长度、分支长度、分割大小的依赖性以及在基础树中检测真实分割的能力。 使用滑动窗口分析，我们展示了分割得分可以以计算高效的方式检测灵长类动物、蚊子和病毒基因组数据中的基础进化过程的变化。 分割得分的计算已在软件包SplitSup中实现。 显示英文摘要

摘要： Detecting variation in the evolutionary process along chromosomes is increasingly important as whole-genome data becomes more widely available. For example, factors such as incomplete lineage sorting, horizontal gene transfer, and chromosomal inversion are expected to result in changes in the underlying gene trees along a chromosome, while changes in selective pressure and mutational rates for different genomic regions may lead to shifts in the underlying mutational process. We propose the split score as a general method for quantifying support for a particular phylogenetic relationship within a genomic data set. Because the split score is based on algebraic properties of a matrix of site pattern frequencies, it can be rapidly computed, even for data sets that are large in the number of taxa and/or in the length of the alignment, providing an advantage over other methods (e.g., maximum likelihood) that are often used to assess such support. Using simulation we explore the properties of the split score, including its dependence on sequence length, branch length, size of a split and its ability to detect true splits in the underlying tree. Using a sliding window analysis, we show that split scores can be used to detect changes in the underlying evolutionary process for genome-scale data from primates, mosquitoes, and viruses in a computationally efficient manner. Computation of the split score has been implemented in the software package SplitSup.