The resolution of the Tree of Life has accelerated with advances in DNA sequencing technology. To achieve dense taxon sampling, it is often necessary to obtain DNA from historical museum specimens to supplement modern genetic samples. However, DNA from historical material is generally degraded, which presents various challenges. In this study, we evaluated how the coverage at variant sites and missing data among historical and modern samples impacts phylogenomic inference. We explored these patterns in the brush-tongued parrots (lories and lorikeets) of Australasia by sampling ultraconserved elements in 105 taxa. Trees estimated with low coverage characters had several clades where relationships appeared to be influenced by whether the sample came from historical or modern specimens, which were not observed when more stringent filtering was applied. To assess if the topologies were affected by missing data, we performed an outlier analysis of sites and loci, and a data reduction approach where we excluded sites based on data completeness. Depending on the outlier test, 0.15% of total sites or 38% of loci were driving the topological differences among trees, and at these sites, historical samples had 10.9x more missing data than modern ones. In contrast, 70% data completeness was necessary to avoid spurious relationships. Predictive modeling found that outlier analysis scores were correlated with parsimony informative sites in the clades whose topologies changed the most by filtering. After accounting for biased loci and understanding the stability of relationships, we inferred a more robust phylogenetic hypothesis for lories and lorikeets.

中文翻译：

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Lory和Lorikeets中的数据丢失不均，歪斜了植物生理学关系。

随着DNA测序技术的进步，生命之树的分辨率得到了提高。为了获得密集的分类单元采样，通常有必要从历史博物馆标本中获取DNA以补充现代遗传样本。但是，历史材料中的DNA通常会被降解，这带来了各种挑战。在这项研究中，我们评估了变种位点的覆盖范围以及历史和现代样本之间的缺失数据如何影响植物学推断。我们通过对105个分类单元中的超保守元素进行采样，探索了澳大利亚刷毛过的鹦鹉（鹦鹉和鹦鹉）中的这些模式。估计覆盖率较低的树木有多个分支，它们之间的关系似乎受到样本来自历史样本还是现代样本的影响，应用更严格的过滤时未观察到这些。为了评估拓扑是否受丢失数据的影响，我们对站点和基因座进行了异常分析，并采用了数据缩减方法，其中基于数据完整性排除了站点。根据离群值测试，占总位点的0.15％或38％的基因座是导致树木间拓扑差异的原因，在这些位点上，历史样本的缺失数据比现代样本多10.9倍。相反，必须避免70％的数据完整性才能避免虚假关系。预测建模发现，离群分析得分与进化枝中拓扑最易识别的进化枝相关，这些进化枝的拓扑通过过滤而变化最大。在考虑了偏向的基因座并了解了关系的稳定性之后，