School of Fisheries, Aquaculture, and Aquatic Sciences, Auburn University, Auburn, AL, United States of America.
Agricultural Research Service, Aquatic Animal Health Research Unit, United States Department of Agriculture, Auburn, AL, United States of America.
PLoS One. 2020 Apr 24;15(4):e0232110. doi: 10.1371/journal.pone.0232110. eCollection 2020.
The software programs STRUCTURE and NEWHYBRIDS are widely used population genetic programs useful in addressing questions related to genetic structure, admixture, and hybridization. These programs usually require a large number of independent runs with many iterations to provide robust data for downstream analyses, thus significantly increasing computation time. Programs such as Structure_threader and parallelnewhybrid were previously developed to address this problem by processing tasks in parallel on a multi-threaded processor; however some programming knowledge (e.g., R, Bash) is required to run these programs. We developed EasyParallel as a community resource to facilitate practical and routine population structure and hybridization analyses. The multi-threaded parallelization of EasyParallel allows processing of large genetic datasets in a very efficient way, with its point-and-click GUI providing ready access to users who have little experience in script programming. Performance evaluation of EasyParallel using simulated datasets showed similar speed-up and parallel execution time when compared to Structure_threader and Parallelnewhybrid. EasyParallel is written in Python 3 and freely available on the GitHub site https://github.com/hzz0024/EasyParallel.
软件程序 STRUCTURE 和 NEWHYBRIDS 是广泛使用的群体遗传程序,可用于解决与遗传结构、混合和杂交相关的问题。这些程序通常需要进行大量独立的多次迭代运行,以便为下游分析提供稳健的数据,从而显著增加计算时间。以前,为了解决这个问题,开发了 Structure_threader 和 parallelnewhybrid 等程序,这些程序通过在多线程处理器上并行处理任务来实现;然而,运行这些程序需要一些编程知识(例如,R、Bash)。我们开发了 EasyParallel 作为一个社区资源,以促进实用和常规的群体结构和杂交分析。EasyParallel 的多线程并行化允许以非常有效的方式处理大型遗传数据集,其点击式 GUI 为那些在脚本编程方面经验较少的用户提供了便捷的访问方式。使用模拟数据集对 EasyParallel 的性能评估表明,与 Structure_threader 和 Parallelnewhybrid 相比,它具有相似的加速和并行执行时间。EasyParallel 是用 Python 3 编写的,并可在 GitHub 网站 https://github.com/hzz0024/EasyParallel 上免费获取。