Schrempf Dominik, Minh Bui Quang, De Maio Nicola, von Haeseler Arndt, Kosiol Carolin
Institut für Populationsgenetik, Vetmeduni Vienna, Wien, Austria; Vienna Graduate School of Population Genetics, Wien, Austria.
Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, Austria.
J Theor Biol. 2016 Oct 21;407:362-370. doi: 10.1016/j.jtbi.2016.07.042. Epub 2016 Jul 29.
We present a reversible Polymorphism-Aware Phylogenetic Model (revPoMo) for species tree estimation from genome-wide data. revPoMo enables the reconstruction of large scale species trees for many within-species samples. It expands the alphabet of DNA substitution models to include polymorphic states, thereby, naturally accounting for incomplete lineage sorting. We implemented revPoMo in the maximum likelihood software IQ-TREE. A simulation study and an application to great apes data show that the runtimes of our approach and standard substitution models are comparable but that revPoMo has much better accuracy in estimating trees, divergence times and mutation rates. The advantage of revPoMo is that an increase of sample size per species improves estimations but does not increase runtime. Therefore, revPoMo is a valuable tool with several applications, from speciation dating to species tree reconstruction.
我们提出了一种用于从全基因组数据估计物种树的可逆多态性感知系统发育模型（revPoMo）。revPoMo能够为许多物种内样本重建大规模物种树。它扩展了DNA替换模型的字母表，以纳入多态状态，从而自然地考虑了不完全谱系分选。我们在最大似然软件IQ-TREE中实现了revPoMo。一项模拟研究以及对大猩猩数据的应用表明，我们的方法和标准替换模型的运行时间相当，但revPoMo在估计树、分歧时间和突变率方面具有更高的准确性。revPoMo的优势在于，每个物种样本量的增加会提高估计效果，但不会增加运行时间。因此，revPoMo是一种有价值的工具，具有多种应用，从物种形成年代测定到物种树重建。
