Ropiquet Anne, Li Blaise, Hassanin Alexandre
UMR 7205 - Origine, structure et évolution de la biodiversité, Muséum national d'histoire naturelle, case postale 51, 55, rue Buffon, 75005 Paris, France.
C R Biol. 2009 Sep;332(9):832-47. doi: 10.1016/j.crvi.2009.05.001. Epub 2009 Jun 18.
Supermatrix and supertree are two methods for constructing a phylogenetic tree by using multiple data sets. However, these methods are not a panacea, as conflicting signals between data sets can lead to misinterpret the evolutionary history of taxa. In particular, the supermatrix approach is expected to be misleading if the species-tree signal is not dominant after the combination of the data sets. Moreover, most current supertree methods suffer from two limitations: (i) they ignore or misinterpret secondary (non-dominant) phylogenetic signals of the different data sets; and (ii) the logical basis of node robustness measures is unclear. To overcome these limitations, we propose a new approach, called SuperTRI, which is based on the branch support analyses of the independent data sets, and where the reliability of the nodes is assessed using three measures: the supertree Bootstrap percentage and two other values calculated from the separate analyses: the mean branch support (mean Bootstrap percentage or mean posterior probability) and the reproducibility index. The SuperTRI approach is tested on a data matrix including seven genes for 82 taxa of the family Bovidae (Mammalia, Ruminantia), and the results are compared to those found with the supermatrix approach. The phylogenetic analyses of the supermatrix and independent data sets were done using four methods of tree reconstruction: Bayesian inference, maximum likelihood, and unweighted and weighted maximum parsimony. The results indicate, firstly, that the SuperTRI approach shows less sensitivity to the four phylogenetic methods, secondly, that it is more accurate to interpret the relationships among taxa, and thirdly, that interesting conclusions on introgression and radiation can be drawn from the comparisons between SuperTRI and supermatrix analyses.
超级矩阵和超级树是利用多个数据集构建系统发育树的两种方法。然而，这些方法并非万灵药，因为数据集之间相互冲突的信号可能导致对分类单元进化历史的错误解读。特别是，如果在数据集合并后物种树信号不占主导地位，那么超级矩阵方法预计会产生误导。此外，当前大多数超级树方法存在两个局限性：（i）它们忽略或错误解读了不同数据集的次要（非主导）系统发育信号；（ii）节点稳健性度量的逻辑基础不明确。为了克服这些局限性，我们提出了一种新方法，称为SuperTRI，它基于对独立数据集的分支支持分析，并且使用三种度量来评估节点的可靠性：超级树自展百分比以及从单独分析中计算出的另外两个值：平均分支支持（平均自展百分比或平均后验概率）和再现性指数。SuperTRI方法在一个包含牛科（哺乳纲，反刍亚目）82个分类单元的七个基因的数据矩阵上进行了测试，并将结果与超级矩阵方法的结果进行了比较。使用四种树重建方法对超级矩阵和独立数据集进行了系统发育分析：贝叶斯推断、最大似然法以及不加权和加权最大简约法。结果表明，首先，SuperTRI方法对这四种系统发育方法的敏感性较低；其次，它对分类单元之间关系的解读更准确；第三，通过SuperTRI与超级矩阵分析之间的比较，可以得出关于基因渗入和辐射的有趣结论。
