Data from: SuperFine: fast and accurate supertree estimation

Many research groups are estimating trees containing anywhere from a few thousand to hundreds of thousands of species, towards the eventual goal of the estimation of a Tree of Life, containing perhaps as many as several million leaves. These phylogenetic estimations present enormous computational challenges, and current computational methods are likely to fail to run even on datasets in the low end of this range. One approach to estimate a large species tree is to use phylogenetic estimation methods (such as maximum likelihood) on a supermatrix produced by concatenating multiple sequence alignments for a collection of markers; however, the most accurate of these phylogenetic estimation methods are extremely computationally intensive for datasets with more than a few thousand sequences. Supertree methods, which assemble phylogenetic trees from a collection of trees on subsets of the taxa, are important tools for phylogeny estimation where phylogenetic analyses based upon maximum likelihood are infeasible. In this paper, we introduce SuperFine, a meta-method that utilizes a novel two-step procedure in order to improve the accuracy and scalability of supertree methods. Our study, using both simulated and empirical data, shows that SuperFine-boosted supertree methods produce more accurate trees than standard supertree methods, and run quickly on very datasets with thousands of sequences. Furthermore, SuperFine-boosted MRP (Matrix Representation with Parsimony, the most well known supertree method) approaches the accuracy of maximum likelihood methods on supermatrix datasets under realistic conditions.诸多研究团队正构建涵盖数千至数十万物种的系统发育树，以期最终完成生命之树（Tree of Life）的推断——该树或包含数百万个物种节点。此类系统发育推断面临着极高的计算挑战，现有计算方法即便面对该范围下限的数据集也难以顺利运行。构建大型物种树的经典路径之一，是将多个标记物的多序列比对结果进行串联以生成超矩阵，随后运用系统发育推断方法（如最大似然法）；但此类方法中精度最优的那些，在处理数千条以上序列的数据集时，计算负荷极为繁重。超树方法通过对分类群子集上构建的若干系统发育树进行组装，是当基于最大似然法的系统发育分析难以实施时，用于系统发育推断的关键工具。本文提出了SuperFine这一元方法，其采用全新的两步流程，旨在提升超树方法的精度与可扩展性。本研究通过模拟数据与实测数据开展验证，结果显示经SuperFine优化后的超树方法，相较标准超树方法可生成精度更高的系统发育树，且能在包含数千条序列的大型数据集上快速运行。此外，在现实场景下，经SuperFine优化后的MRP（Matrix Representation with Parsimony，即最常用的超树方法），在超矩阵数据集上的推断精度可接近最大似然法的水平。