Kumar Sudhir, Stecher Glen, Suleski Michael, Sanderford Maxwell, Sharma Sudip, Tamura Koichiro
Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA.
Department of Biology, Temple University, Philadelphia, PA 19122, USA.
Mol Biol Evol. 2024 Dec 6;41(12). doi: 10.1093/molbev/msae263.
We introduce the 12th version of the Molecular Evolutionary Genetics Analysis (MEGA12) software. This latest version brings many significant improvements by reducing the computational time needed for selecting optimal substitution models and conducting bootstrap tests on phylogenies using maximum likelihood (ML) methods. These improvements are achieved by implementing heuristics that minimize likely unnecessary computations. Analyses of empirical and simulated datasets show substantial time savings by using these heuristics without compromising the accuracy of results. MEGA12 also links-in an evolutionary sparse learning approach to identify fragile clades and associated sequences in evolutionary trees inferred through phylogenomic analyses. In addition, this version includes fine-grained parallelization for ML analyses, support for high-resolution monitors, and an enhanced Tree Explorer. MEGA12 can be downloaded from https://www.megasoftware.net.
我们推出了分子进化遗传学分析软件(MEGA)第12版。此最新版本带来了许多重大改进,通过减少使用最大似然(ML)方法选择最优替换模型以及对系统发育进行自展检验所需的计算时间。这些改进是通过实施启发式算法实现的,这些算法将可能不必要的计算降至最低。对经验数据集和模拟数据集的分析表明,使用这些启发式算法可大幅节省时间,同时不影响结果的准确性。MEGA12还引入了一种进化稀疏学习方法,以识别通过系统发育基因组分析推断出的进化树中的脆弱分支和相关序列。此外,此版本包括用于ML分析的细粒度并行化、对高分辨率显示器的支持以及增强的树浏览器。可从https://www.megasoftware.net下载MEGA12。