Maximum likelihood (ML) analysis of nucleotide or amino-acid alignments is widely used to infer evolutionary relationships among species. Computing the likelihood of a phylogenetic tree from such alignments is a complicated task because the evolutionary processes typically vary across sites. A number of studies have shown that partitioning alignments into sub-alignments of sites, where each sub-alignment is analyzed using a different model of evolution (e.g., GTR + I + G), is a sensible strategy. Current partitioning methods group sites into subsets based on the inferred rates of evolution at the sites. However, these do not provide sufficient information to adequately reflect the substitution processes of characters at the sites. Moreover, the site rate-based methods group all invariant sites into one subset, potentially resulting in wrong phylogenetic trees. In this study, we propose a partitioning method, called mPartition, that combines not only the evolutionary rates but also substitution models at sites to partition alignments. Analyses of different partitioning methods on both real and simulated datasets showed that mPartition was better than the other partitioning methods tested. Notably, mPartition overcame the pitfall of grouping all invariant sites into one subset. Using mPartition may lead to increased accuracy of ML-based phylogenetic inference, especially for multiple loci or whole genome datasets.

核苷酸或氨基酸比对的最大似然 (ML) 分析被广泛用于推断物种之间的进化关系。从这种比对中计算系统发育树的可能性是一项复杂的任务，因为进化过程通常因站点而异。许多研究表明，将比对划分为位点的子比对，其中每个子比对使用不同的进化模型（例如，GTR + I + G）进行分析，是一种明智的策略。当前的分区方法根据站点的推断进化速率将站点分组为子集。然而，这些并没有提供足够的信息来充分反映站点字符的替换过程。此外，基于站点率的方法将所有不变站点归为一个子集，可能导致错误的系统发育树。在这项研究中，我们提出了一种称为 mPartition 的分区方法，它不仅结合了进化速率，还结合了位点的替代模型来划分对齐。对真实数据集和模拟数据集的不同分区方法的分析表明，mPartition 优于测试的其他分区方法。值得注意的是，mPartition 克服了将所有不变站点分组为一个子集的缺陷。使用 mPartition 可能会提高基于 ML 的系统发育推断的准确性，尤其是对于多个基因座或全基因组数据集。对真实数据集和模拟数据集的不同分区方法的分析表明，mPartition 优于测试的其他分区方法。值得注意的是，mPartition 克服了将所有不变站点分组为一个子集的缺陷。使用 mPartition 可能会提高基于 ML 的系统发育推断的准确性，尤其是对于多个基因座或全基因组数据集。对真实数据集和模拟数据集的不同分区方法的分析表明，mPartition 优于测试的其他分区方法。值得注意的是，mPartition 克服了将所有不变站点分组为一个子集的缺陷。使用 mPartition 可能会提高基于 ML 的系统发育推断的准确性，尤其是对于多个基因座或全基因组数据集。