Reconstructing Phylogenies Using Branch-Variable Substitution Models and Unaligned Biomolecular Sequences: A Performance Study and New Resampling Method
Reconstructing Phylogenies Using Branch-Variable Substitution Models and Unaligned Biomolecular Sequences: A Performance Study and New Resampling Method
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使用分支变量替换模型和未对齐的生物分子序列重建系统发育：性能研究和新的重采样方法
DOI：
10.1145/3584371.3613011
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Liu, Kevin
中科院分区：
文献类型：
--
作者：
Doko, Rei;Liu, Kevin
在生命树的许多分支中，核苷酸替换率和碱基频率被假设为随着基因组进化随着时间的推移而改变。对这一假说的严格检验有赖于在合适的生物分子序列进化模型下进行准确的系统发育重建。到目前为止，最常见的系统发育重建方法是“两阶段”分析，即首先对未比对的生物分子序列数据进行比对，然后将得到的多序列比对(MSA)用作下游系统发育重建的输入。对于一个固定在物种系统发展史上的传统的“同源”替代模型，长期以来人们已经确定，准确的系统发育推断和学习需要准确的上游多序列比对。但对于不同进化分支的异质替代过程模型，同样的问题还没有得到认真的研究。因此，我们进行了一项全面的性能研究，以量化上游MSA估计误差对下游核苷酸替代分支变量模型下的系统发育推断和学习的影响。在具有10或20个分类群的模型条件下，并跨越一系列进化分歧，我们发现上游和下游估计误差之间存在一致和显著的正相关。这种关系对MSA估计方法的选择以及替代模型的错误指定都是稳健的。我们进一步量化了与其他实验因素相比，上游MSA估计误差对下游系统发育重建质量的相对较大贡献。我们还对开花的单子叶植物进行了实证研究。对支系中同源基因的系统发育分析证实了模拟研究的结果，使用分支变量替代模型的物种树估计揭示了对序列进化异质性的新见解。我们的发现强调了最新技术中的几个关键差距，包括需要在不同的序列进化模型下进行MSA感知的系统发育推断和学习方法。为此，我们引入了一种新的计算方法NoHTS(非齐次树支持)，以直接评估由于MSA估计误差和其他因素造成的系统发育估计的不确定性。新方法使用顺序感知的统计重采样，在分支变量替代模型下估计的系统发育上设置可信区间。我们证明了它相对于系统发育和系统发育研究中的事实上的标准--系统发育自举方法--的第一类和第二类错误。
In many clades in the Tree of Life, nucleotide substitution rates and base frequencies are hypothesized to have changed as genome evolution unfolded over time. Rigorous testing of this hypothesis relies on accurate phylogenetic reconstruction under suitable models of biomolecular sequence evolution. By far the most common approach for phylogenetic reconstruction is a "two-phase" analysis, where unaligned biomolecular sequence data are first aligned, and the resulting multiple sequence alignment (MSA) is used as input to downstream phylogenetic reconstruction. For a traditional "homogeneous" substitution model that is fixed across a species phylogeny, it has long been established that accurate phylogenetic inference and learning requires accurate upstream multiple sequence alignments. But the same question has not been carefully studied for "heterogeneous" models of substitution processes that can vary across the branches of a phylogeny.We therefore conducted a comprehensive performance study to quantify the impact of upstream MSA estimation error on downstream phylogenetic inference and learning under branch-variable models of nucleotide substitution. Across model conditions with either 10 or 20 taxa and spanning a range of evolutionary divergence, we find a consistent and significantly positive association between upstream and downstream estimation error. The relationship is robust to the choice of MSA estimation method as well as substitution model mis-specification. We further quantify the relatively large contribution of upstream MSA estimation error to downstream phylogenetic reconstruction quality, compared to other experimental factors. We also conducted an empirical study of flowering monocots. Phylogenetic analyses of orthologous genes in the clade confirm the simulation study findings, and species tree estimation using branch-variable substitution models reveals new insights into sequence evolution heterogeneity. Our findings underscore several key gaps in the state of the art, including the need for MSA-aware phylogenetic inference and learning methods under heterogeneous models of sequence evolution.To this end, we introduce a new computational method, NoHTS ("Non-Homogeneous Tree Support"), to directly assess phylogenetic estimation uncertainty due to MSA estimation error and other factors. The new method uses sequence-aware statistical resampling to place confidence intervals on a phylogeny estimated under a branch-variable substitution model. We demonstrate its superior type I and type II error versus a de facto standard in phylogenetic and phylogenomic studies - the phylogenetic bootstrap method.
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