Department of Agriculture and Natural Resources, Delaware State University, Dover, DE 19901, USA.
Syst Biol. 2011 Oct;60(5):661-7. doi: 10.1093/sysbio/syr027. Epub 2011 Mar 28.
In this study, we develop a distance method for inferring unrooted species trees from a collection of unrooted gene trees. The species tree is estimated by the neighbor joining (NJ) tree built from a distance matrix in which the distance between two species is defined as the average number of internodes between two species across gene trees, that is, average gene-tree internode distance. The distance method is named NJ(st) to distinguish it from the original NJ method. Under the coalescent model, we show that if gene trees are known or estimated correctly, the NJ(st) method is statistically consistent in estimating unrooted species trees. The simulation results suggest that NJ(st) and STAR (another coalescence-based method for inferring species trees) perform almost equally well in estimating topologies of species trees, whereas the Bayesian coalescence-based method, BEST, outperforms both NJ(st) and STAR. Unlike BEST and STAR, the NJ(st) method can take unrooted gene trees to infer species trees without using an outgroup. In addition, the NJ(st) method can handle missing data and is thus useful in phylogenomic studies in which data sets often contain missing loci for some individuals.
在这项研究中，我们开发了一种从一组无根基因树推断无根种系树的距离方法。种系树是通过从距离矩阵构建的邻接连接（NJ）树来估计的，其中两个物种之间的距离定义为两个物种跨越基因树的平均节间数，即平均基因树节间距离。该距离方法被命名为 NJ(st)，以将其与原始的 NJ 方法区分开来。在合并模型下，我们表明，如果基因树被正确地识别或估计，那么 NJ(st)方法在估计无根种系树方面具有统计学一致性。模拟结果表明，在估计种系树的拓扑结构方面，NJ(st)和 STAR（另一种基于合并的推断种系树的方法）的表现几乎相同，而基于贝叶斯合并的 BEST 方法则优于 NJ(st)和 STAR。与 BEST 和 STAR 不同，NJ(st)方法可以在不使用外群的情况下，使用无根基因树推断种系树。此外，NJ(st)方法可以处理缺失数据，因此在基因组学研究中很有用，其中数据集通常包含某些个体的缺失基因座。
