Université de Montréal, Département de Sciences Biologiques, C.P. 6128, Succ. Centre-ville, Montréal, Québec, H3C 3J7, Canada.
Evol Bioinform Online. 2010 May 12;6:57-71. doi: 10.4137/ebo.s4527.
combined or separate analysis. In the first approach, different datasets are combined in a concatenated supermatrix. In the second, datasets are analyzed separately and the phylogenetic trees are then combined in a supertree. The supertree method is an interesting alternative to avoid missing data, since datasets that are analyzed separately do not need to represent identical taxa. However, the supertree approach and the corresponding consensus methods have been highly criticized for not providing valid phylogenetic hypotheses. In this study, congruence of trees estimated by consensus and supertree approaches were compared to model trees obtained from a combined analysis of complete mitochondrial sequences of 102 species representing 93 mammal families. The consensus methods produced poorly resolved consensus trees and did not perform well, except for the majority rule consensus with compatible groupings. The weighted supertree and matrix representation with parsimony methods performed equally well and were highly congruent with the model trees. The most similar supertree method was the least congruent with the model trees. We conclude that some of the methods tested are worth considering in a phylogenomic context.
联合或单独分析。在第一种方法中，不同的数据集在串联超矩阵中组合。在第二种方法中，数据集分别进行分析，然后在超级树上合并系统发育树。超级树方法是避免缺失数据的一种有趣选择，因为分别进行分析的数据集不需要代表相同的分类单元。然而，超级树方法和相应的共识方法因不能提供有效系统发育假说而受到高度批评。在这项研究中，通过共识和超级树方法估计的树的一致性与从 102 个代表 93 个哺乳动物科的物种的完整线粒体序列的联合分析获得的模型树进行了比较。共识方法产生了分辨率较低的共识树，除了具有兼容分组的多数规则共识外，表现不佳。加权超级树和基于简约的矩阵表示方法的性能相同，与模型树高度一致。最相似的超级树方法与模型树的一致性最低。我们得出结论，在系统基因组学背景下，一些测试的方法值得考虑。
