Data from: Coalescent-based species tree inference from gene tree topologies under incomplete lineage sorting by maximum likelihood

Incomplete lineage sorting can cause incongruence between the phylogenetic history of genes (the gene tree) and that of the species (the species tree), which can complicate the inference of phylogenies. In this paper, I present a new coalescent-based algorithm for species tree inference with maximum likelihood. I first describe an improved method for computing the probability of a gene tree topology given a species tree, which is much faster than an existing algorithm by Degnan and Salter (2005). Based on this method, I develop a practical algorithm that takes a set of gene tree topologies and infers species trees with maximum likelihood. This algorithm searches for the best species tree by starting from initial species trees and performing heuristic search to obtain better trees with higher likelihood. This algorithm, called STELLS, has been implemented in a program that is downloadable from the author’s web page. The simulation results show that the STELLS algorithm is more accurate than an existing maximum likelihood method for many datasets, especially when there is noise in gene trees. I also show that the STELLS algorithm is efficient and can be applied to real biological datasets.

不完全谱系分选（incomplete lineage sorting）会导致基因的系统发育历史（基因树，gene tree）与物种的系统发育历史（物种树，species tree）之间产生不一致，这会为系统发育推断工作增添复杂度。本文提出一种全新的基于溯祖的最大似然物种树推断算法。本文首先阐述了一种改进方法，用于计算给定物种树时基因树拓扑结构的概率，其运算速度远快于Degnan与Salter（2005）提出的现有算法。基于该方法，本文开发了一款实用算法：该算法可接收一组基因树拓扑结构，并以最大似然法推断物种树。该算法通过从初始物种树出发，执行启发式搜索以获取似然值更高的更优物种树，从而寻找到最优物种树。这款命名为STELLS的算法已被封装为可从作者个人网页下载的程序。仿真实验结果表明，在诸多数据集上，STELLS算法的准确性优于现有最大似然方法，尤其当基因树存在噪声时效果更为突出。本文同时证实，STELLS算法具备高效性，可应用于真实生物数据集的分析。