Fernando M A Thanuja M, Fu Jinzhong, Adamowicz Sarah J
Department of Integrative Biology University of Guelph Guelph Ontario Canada.
Ecol Evol. 2025 Jan 7;15(1):e70817. doi: 10.1002/ece3.70817. eCollection 2025 Jan.
Advancements in DNA sequencing technology have facilitated the generation of a vast number of DNA sequences, posing opportunities and challenges for constructing large phylogenetic trees. DNA barcode sequences, particularly COI, represent extensive orthologous sequences suitable for phylogenetic analysis. Phylogenetic placement analysis offers a promising method to integrate COI data into tree-building efforts, yet the impacts of backbone tree completeness and species composition remain under-explored. Using a dataset comprising 27 genes and 4520 species of bony fishes, we assessed the accuracy of phylogenetic inference by "placing" COI sequences onto backbone trees. The backbone tree completeness was varied by subsampling 20%, 40%, 60%, 80%, and 99% of the total species separately, followed by placement of those missing species based on their COI sequences using software packages EPA-ng and APPLES. We also compared the effects of biased, random, and stratified sampling strategies; the latter ensured the representation of all major lineages (Family) of bony fish. Our findings indicate that the placement accuracy is consistently high across all levels of backbone tree completeness, where 70%-78% missing species are correctly placed (by EPA-ng) in the same locations as the reference tree derived from the complete data. High completeness produces slightly high placement accuracy, although in many cases the differences are nonsignificant. For example, at the 99% completeness level with stratified sampling, EPA-ng placed 78% missing species correctly, and when only considering placement with high confidence (LWR > 0.9), the percentage is 87%. Additionally, stratified sampling outperforms random sampling in most cases, and biased sampling has the worst performance. The likelihood-based EPA-ng consistently provide higher accurate placements than the distance-based APPLES. In conclusion, COI-based placement analysis represents a potential route of using the available vast barcoding data for building large phylogenetic trees.
DNA测序技术的进步推动了大量DNA序列的产生，这为构建大型系统发育树带来了机遇和挑战。DNA条形码序列，尤其是细胞色素氧化酶亚基I（COI），代表了适合系统发育分析的广泛直系同源序列。系统发育定位分析为将COI数据整合到建树工作中提供了一种很有前景的方法，但主干树完整性和物种组成的影响仍未得到充分探索。我们使用一个包含27个基因和4520种硬骨鱼的数据集，通过将COI序列“定位”到主干树上，评估了系统发育推断的准确性。通过分别对总物种的20%、40%、60%、80%和99%进行二次抽样来改变主干树的完整性，然后使用软件包EPA-ng和APPLES根据缺失物种的COI序列对其进行定位。我们还比较了有偏抽样、随机抽样和分层抽样策略的效果；后者确保了硬骨鱼所有主要谱系（科）的代表性。我们的研究结果表明，在主干树完整性的所有水平上，定位准确性一直很高，其中70%-78%的缺失物种（由EPA-ng）被正确定位在与完整数据得出的参考树相同的位置。高完整性会产生略高的定位准确性，尽管在许多情况下差异并不显著。例如，在分层抽样的99%完整性水平下，EPA-ng正确定位了78%的缺失物种，当仅考虑高置信度（LWR>0.9）的定位时，这一比例为87%。此外，在大多数情况下，分层抽样优于随机抽样，而有偏抽样表现最差。基于似然法的EPA-ng始终比基于距离法的APPLES提供更高的准确定位。总之，基于COI的定位分析是利用现有的大量条形码数据构建大型系统发育树的一条潜在途径。
