Bujaki Thomas, Van Looyen Katharine, Rodrigue Nicolas
Department of Biology, Carleton University, Ontario K1S 5B6, Canada.
Institute of Biochemistry, Carleton University, Ontario K1S 5B6, Canada.
Bioinform Adv. 2023 Jul 14;3(1):vbad091. doi: 10.1093/bioadv/vbad091. eCollection 2023.
Traditional approaches to probabilistic phylogenetic inference have relied on information-theoretic criteria to select among a relatively small set of substitution models. These model selection criteria have recently been called into question when applied to richer models, including models that invoke mixtures of nucleotide frequency profiles. At the nucleotide level, we are therefore left without a clear picture of mixture models' contribution to overall predictive power relative to other modeling approaches. Here, we utilize a Bayesian cross-validation method to directly measure the predictive performance of a wide range of nucleotide substitution models. We compare the relative contributions of free nucleotide exchangeability parameters, gamma-distributed rates across sites, and mixtures of nucleotide frequencies with both finite and infinite mixture frameworks. We find that the most important contributor to a model's predictive power is the use of a sufficiently rich mixture of nucleotide frequencies. These results suggest that mixture models should be given greater consideration in nucleotide-level phylogenetic inference.
传统的概率系统发育推断方法依赖于信息论标准，以便在相对较少的一组替换模型中进行选择。当应用于更丰富的模型时，这些模型选择标准最近受到了质疑，这些模型包括调用核苷酸频率分布混合的模型。因此，在核苷酸水平上，相对于其他建模方法，我们并不清楚混合模型对整体预测能力的贡献。在这里，我们利用贝叶斯交叉验证方法直接测量各种核苷酸替换模型的预测性能。我们比较了自由核苷酸交换性参数、跨位点的伽马分布速率以及有限和无限混合框架下核苷酸频率混合的相对贡献。我们发现，对模型预测能力最重要的贡献因素是使用足够丰富的核苷酸频率混合。这些结果表明，在核苷酸水平的系统发育推断中，应更多地考虑混合模型。
