Beiko Robert G, Liu Chaoyue, Cavalcante João Vitor, Fink Ryan C
Faculty of Computer Science and Institute for Comparative Genomics, Dalhousie University, 6050 University Avenue, Halifax, Nova Scotia B3H 4R2, Canada.
Department of Mathematics and Statistics, Dalhousie University, Halifax, Nova Scotia B3H 4R2, Canada.
Genome Biol Evol. 2025 May 30;17(6). doi: 10.1093/gbe/evaf092.
Concerted gains and losses of genomic features such as genes and mobile genetic elements can provide key clues into related functional roles and shared evolutionary trajectories. By capturing phylogenetic signals, a coevolutionary model can outperform comparative methods based on shared presence and absence of features. We previously developed the Community Coevolution Model, which represents the gain/loss probability of each feature as a combination of its own intrinsic rate, combined with the joint probabilities of gain and loss with all other features. Originally implemented as an R library, we have now developed an R wrapper that adds parallelization and several options to pre-filter the features to increase the efficiency of comparisons. Here we describe the functionality of ParallelEvolCCM and apply it to a dataset of 1000 genomes of the genus Bifidobacterium. ParallelEvolCCM is released under the MIT license and available at https://github.com/beiko-lab/arete/blob/master/bin/ParallelEvolCCM.R.
基因和移动遗传元件等基因组特征的协同得失可为相关功能作用和共享进化轨迹提供关键线索。通过捕捉系统发育信号,共进化模型可以优于基于特征的共同存在和缺失的比较方法。我们之前开发了社区共进化模型,该模型将每个特征的得失概率表示为其自身内在速率的组合,再加上与所有其他特征的得失联合概率。最初作为一个R库实现,我们现在开发了一个R包装器,它增加了并行化和几个预过滤特征的选项,以提高比较效率。在这里,我们描述了ParallelEvolCCM的功能,并将其应用于双歧杆菌属的1000个基因组数据集。ParallelEvolCCM根据麻省理工学院许可发布,可在https://github.com/beiko-lab/arete/blob/master/bin/ParallelEvolCCM.R获取。