Xu Shijie, Onoda Akira
Graduate School of Environmental Science, Hokkaido University, Kita 10 Nishi 5, Kita-ku, Sapporo, 060-0810, Hokkaido, Japan.
Faculty of Environmental Earth Science, Hokkaido University, Kita 10 Nishi 5, Kita-ku, Sapporo, 060-0810, Hokkaido, Japan.
J Mol Evol. 2024 Dec;92(6):874-890. doi: 10.1007/s00239-024-10215-7. Epub 2024 Dec 5.
Phylogenetics has been widely used in molecular biology to infer the evolutionary relationships among species. With the rapid development of sequencing technology, genomic data with thousands of sites become increasingly common in phylogenetic analysis, while heterogeneity among sites arises as one of the major challenges. A single homogeneous model is not sufficient to describe the evolution of all sites and partitioned models are often employed to model the evolution of heterogeneous sites by partitioning them into distinct groups and utilizing distinct evolutionary models for each group. It is crucial to determine the best partitioning, which greatly affects the reconstruction correctness of phylogeny. However, the best partitioning is usually intractable to obtain in practice. Traditional partitioning methods rely on heuristic algorithms or greedy search to determine the best ones in their solution space, are usually time consuming, and with no guarantee of optimality. In this study, we propose a novel partitioning approach, termed PsiPartition, based on the parameterized sorting indices of sites and Bayesian optimization. We apply our method to empirical datasets, and it performs significantly better compared to existing methods, in terms of Bayesian information criterion (BIC) and the corrected Akaike information criterion (AICc). We test PsiPartition on the simulated datasets with different site heterogeneity, alignment lengths, and number of loci. It is demonstrated that PsiPartition evidently and stably outperforms other methods in terms of the Robinson-Foulds (RF) distance between the true simulated trees and the reconstructed trees, especially on the data with more site heterogeneity. More importantly, our proposed Bayesian optimization-based method, for the first time, provides a new general framework to efficiently determine the optimal number of partitions. The corresponding reproducible source code and data are available at http://github.com/xu-shi-jie/PsiPartition .
系统发育学已在分子生物学中广泛用于推断物种间的进化关系。随着测序技术的快速发展，具有数千个位点的基因组数据在系统发育分析中变得越来越普遍，而位点间的异质性成为主要挑战之一。单一的均匀模型不足以描述所有位点的进化，因此常常采用分区模型，即将异质位点划分为不同的组，并对每个组使用不同的进化模型来模拟其进化。确定最佳分区至关重要，因为这会极大地影响系统发育的重建正确性。然而，在实践中通常很难获得最佳分区。传统的分区方法依靠启发式算法或贪婪搜索在其解空间中确定最佳分区，通常耗时且无法保证最优性。在本研究中，我们基于位点的参数化排序指标和贝叶斯优化提出了一种新颖的分区方法，称为PsiPartition。我们将我们的方法应用于实证数据集，在贝叶斯信息准则（BIC）和校正的赤池信息准则（AICc）方面，它的表现明显优于现有方法。我们在具有不同位点异质性、比对长度和基因座数量的模拟数据集上测试了PsiPartition。结果表明，在真实模拟树与重建树之间的罗宾逊-福尔兹（RF）距离方面，PsiPartition明显且稳定地优于其他方法，尤其是在具有更多位点异质性的数据上。更重要的是，我们提出的基于贝叶斯优化的方法首次提供了一个新的通用框架，以有效地确定最佳分区数量。相应的可重现源代码和数据可在http://github.com/xu-shi-jie/PsiPartition获取。
