ParallelEvolCCM:量化基因组特征之间的共同进化模式。

IF 3.2 2区 生物学 Q2 EVOLUTIONARY BIOLOGY
Robert G Beiko, Chaoyue Liu, João Vitor Cavalcante, Ryan C Fink
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引用次数: 0

摘要

基因组特征(如基因和可移动遗传元件)的协同得失可以为相关功能角色和共享的进化轨迹提供关键线索。通过捕获系统发育信号,共同进化模型可以胜过基于共享存在和缺失特征的比较方法。我们之前开发了社区共同进化模型,该模型将每个特征的增益/损失概率表示为其自身固有速率的组合,并结合所有其他特征的增益和损失的联合概率。最初是作为R库实现的,现在我们开发了一个R包装器,它增加了并行化和几个选项来预过滤特性,以提高比较的效率。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在MIT许可下发布,可从https://github.com/beiko-lab/arete/blob/master/bin/ParallelEvolCCM.R获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ParallelEvolCCM: Quantifying co-evolutionary patterns among genomic features.

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 co-evolutionary 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.

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来源期刊
Genome Biology and Evolution
Genome Biology and Evolution EVOLUTIONARY BIOLOGY-GENETICS & HEREDITY
CiteScore
5.80
自引率
6.10%
发文量
169
审稿时长
1 months
期刊介绍: About the journal Genome Biology and Evolution (GBE) publishes leading original research at the interface between evolutionary biology and genomics. Papers considered for publication report novel evolutionary findings that concern natural genome diversity, population genomics, the structure, function, organisation and expression of genomes, comparative genomics, proteomics, and environmental genomic interactions. Major evolutionary insights from the fields of computational biology, structural biology, developmental biology, and cell biology are also considered, as are theoretical advances in the field of genome evolution. GBE’s scope embraces genome-wide evolutionary investigations at all taxonomic levels and for all forms of life — within populations or across domains. Its aims are to further the understanding of genomes in their evolutionary context and further the understanding of evolution from a genome-wide perspective.
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