Phylogenetic Signal and Noise: Predicting the Power of a Data Set to Resolve Phylogeny
Phylogenetic Signal and Noise: Predicting the Power of a Data Set to Resolve Phylogeny
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DOI：
10.1093/sysbio/sys036
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发表时间：
2012-10-01
影响因子：
6.5
通讯作者：
Tekle, Yonas I.
中科院分区：
文献类型：
--
作者：
Townsend, Jeffrey P.;Su, Zhuo;Tekle, Yonas I.
系统发育实验设计的一个主要目标是预测数据集解析系统发育树中节点的能力。然而，与候选数据集中的信号相比，主动评估系统发育噪声的可能性一直是一个艰巨的挑战。了解收集额外的序列数据以解决不同历史时期顽固的节间的影响，将有助于日益准确和具有成本效益的系统发育研究。在这里，我们基于系统发育树的基本单位四重奏来推导理论，该理论应用对数据集中字符的状态空间和进化率的估计来预测系统发育信号和系统发育噪声，从而预测节点间的解析能力。我们开发并实现了一种蒙特卡罗方法来估计分解的能力，并推导出几乎等同的更快的确定性计算。这些方法被应用于描述两个示例数据集的潜在信号、多段或噪声的分布，一个是最近的(来自双小蜂亚科寄生蜂的细胞色素c氧化酶I和28S核糖体rRNA序列)，另一个是深的(八个核基因和一个系统基因组序列，用于不同的微生物真核生物，包括稻草属、肺泡和根霉)。所分析的基因座的预测分辨能力与基因在系统发育中的历史使用是一致的。
A principal objective for phylogenetic experimental design is to predict the power of a data set to resolve nodes in a phylogenetic tree. However, proactively assessing the potential for phylogenetic noise compared with signal in a candidate data set has been a formidable challenge. Understanding the impact of collection of additional sequence data to resolve recalcitrant internodes at diverse historical times will facilitate increasingly accurate and cost-effective phylogenetic research. Here, we derive theory based on the fundamental unit of the phylogenetic tree, the quartet, that applies estimates of the state space and the rates of evolution of characters in a data set to predict phylogenetic signal and phylogenetic noise and therefore to predict the power to resolve internodes. We develop and implement a Monte Carlo approach to estimating power to resolve as well as deriving a nearly equivalent faster deterministic calculation. These approaches are applied to describe the distribution of potential signal, polytomy, or noise for two example data sets, one recent (cytochrome c oxidase I and 28S ribosomal rRNA sequences from Diplazontinae parasitoid wasps) and one deep (eight nuclear genes and a phylogenomic sequence for diverse microbial eukaryotes including Stramenopiles, Alveolata, and Rhizaria). The predicted power of resolution for the loci analyzed is consistent with the historic use of the genes in phylogenetics.