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）会引发基因系统发育历史（即基因树）与物种系统发育历史（即物种树）之间的不一致性，这会增加系统发育推断的复杂度。本文提出一种全新的基于溯祖理论（coalescent）的最大似然物种树推断算法。本文首先介绍了一种改进的给定物种树时基因树拓扑结构概率计算方法，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作为输入，并以最大似然法推断物种树。该算法从初始物种树出发，通过启发式搜索寻找似然值更高的最优物种树。这款被命名为STELLS的算法已通过程序实现，使用者可从作者个人网页下载该程序。模拟实验结果表明，针对多数数据集，尤其是当基因树存在噪声时，STELLS算法的推断精度优于现有最大似然方法。此外，本文还证实STELLS算法具备高效性，可应用于真实生物数据集的分析。