MEGA12: Molecular Evolutionary Genetic Analysis Version 12 for Adaptive and Green Computing.

IF 5.3 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Molecular biology and evolution Pub Date : 2024-12-06 DOI:10.1093/molbev/msae263
Sudhir Kumar, Glen Stecher, Michael Suleski, Maxwell Sanderford, Sudip Sharma, Koichiro Tamura
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Abstract

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.

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MEGA12:用于自适应和绿色计算的分子进化遗传分析版本12。
我们介绍了分子进化遗传学分析(MEGA)软件的第12版。这个最新版本通过减少选择最佳替代模型所需的计算时间和使用最大似然(ML)方法对系统发育进行引导测试,带来了许多重大改进。这些改进是通过实现启发式来实现的,启发式可以最大限度地减少可能不必要的计算。对经验和模拟数据集的分析表明,通过使用这些启发式方法可以节省大量时间,而不会影响结果的准确性。MEGA12还实现了一种进化稀疏学习方法,以识别通过系统基因组分析推断的进化树中的脆弱枝和相关序列。此外,该版本还包括用于ML分析的细粒度并行化、对高分辨率监视器的支持以及增强的Tree Explorer。MEGA12可以从https://www.megasoftware.net下载。
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来源期刊
Molecular biology and evolution
Molecular biology and evolution 生物-进化生物学
CiteScore
19.70
自引率
3.70%
发文量
257
审稿时长
1 months
期刊介绍: Molecular Biology and Evolution Journal Overview: Publishes research at the interface of molecular (including genomics) and evolutionary biology Considers manuscripts containing patterns, processes, and predictions at all levels of organization: population, taxonomic, functional, and phenotypic Interested in fundamental discoveries, new and improved methods, resources, technologies, and theories advancing evolutionary research Publishes balanced reviews of recent developments in genome evolution and forward-looking perspectives suggesting future directions in molecular evolution applications.
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