PAReTT:一个用于从时间树资源中自动检索和管理发散时间数据的Python包,用于下游分析。

IF 2.1 3区 生物学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY
Louis-Stéphane Le Clercq, Antoinette Kotzé, J Paul Grobler, Desiré Lee Dalton
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引用次数: 0

摘要

进化过程随着时间的推移而逐渐发生,因此被认为是时间依赖的。此外,一些进化过程要么是适应当地栖息地,要么是改变栖息地,否则就受到限制。由于推动物种形成的进化过程是在环境和时间范围内发生的,一些已发表的研究旨在提供对现存和灭绝物种分化时间的准确、化石校准的估计。正确的校准对于将进化适应和物种形成归因于促成它的时间和古地理至关重要。来自4000多项研究和近15万个物种的数据可从中央时间树资源中获得,并提供了以各种格式检索大多数脊椎动物的分化时间、进化时间线和时间树的机会。这些数据大大提高了研究人员研究进化的能力。然而,在研究需要批量检索的物种列表时,功能有限。为了克服这个问题,创建了一个PYTHON包,称为PYTHON -自动检索时间树数据(PAReTT),以促进生物学家与时间树资源的友好交互。在这里,我们通过三个示例来说明该包的使用,其中包括时间轴数据、时间树数据和发散时间数据的使用。此外,PAReTT先前被用于候选基因的荟萃分析,以说明分化时间与候选迁移基因之间的关系。PAReTT包可以从GitHub下载,也可以作为预编译的Windows可执行文件,在GitHub wiki页面上可以找到关于依赖项、安装和各种功能实现的大量文档。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

PAReTT: A Python Package for the Automated Retrieval and Management of Divergence Time Data from the TimeTree Resource for Downstream Analyses.

PAReTT: A Python Package for the Automated Retrieval and Management of Divergence Time Data from the TimeTree Resource for Downstream Analyses.

PAReTT: A Python Package for the Automated Retrieval and Management of Divergence Time Data from the TimeTree Resource for Downstream Analyses.

PAReTT: A Python Package for the Automated Retrieval and Management of Divergence Time Data from the TimeTree Resource for Downstream Analyses.

Evolutionary processes happen gradually over time and are, thus, considered time dependent. In addition, several evolutionary processes are either adaptations to local habitats or changing habitats, otherwise restricted thereby. Since evolutionary processes driving speciation take place within the landscape of environmental and temporal bounds, several published studies have aimed at providing accurate, fossil-calibrated, estimates of the divergence times of both extant and extinct species. Correct calibration is critical towards attributing evolutionary adaptations and speciation both to the time and paleogeography that contributed to it. Data from more than 4000 studies and nearly 1,50,000 species are available from a central TimeTree resource and provide opportunities of retrieving divergence times, evolutionary timelines, and time trees in various formats for most vertebrates. These data greatly enhance the ability of researchers to investigate evolution. However, there is limited functionality when studying lists of species that require batch retrieval. To overcome this, a PYTHON package termed Python-Automated Retrieval of TimeTree data (PAReTT) was created to facilitate a biologist-friendly interaction with the TimeTree resource. Here, we illustrate the use of the package through three examples that includes the use of timeline data, time tree data, and divergence time data. Furthermore, PAReTT was previously used in a meta-analysis of candidate genes to illustrate the relationship between divergence times and candidate genes of migration. The PAReTT package is available for download from GitHub or as a pre-compiled Windows executable, with extensive documentation on the package available on GitHub wiki pages regarding dependencies, installation, and implementation of the various functions.

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来源期刊
Journal of Molecular Evolution
Journal of Molecular Evolution 生物-进化生物学
CiteScore
5.50
自引率
2.60%
发文量
36
审稿时长
3 months
期刊介绍: Journal of Molecular Evolution covers experimental, computational, and theoretical work aimed at deciphering features of molecular evolution and the processes bearing on these features, from the initial formation of macromolecular systems through their evolution at the molecular level, the co-evolution of their functions in cellular and organismal systems, and their influence on organismal adaptation, speciation, and ecology. Topics addressed include the evolution of informational macromolecules and their relation to more complex levels of biological organization, including populations and taxa, as well as the molecular basis for the evolution of ecological interactions of species and the use of molecular data to infer fundamental processes in evolutionary ecology. This coverage accommodates such subfields as new genome sequences, comparative structural and functional genomics, population genetics, the molecular evolution of development, the evolution of gene regulation and gene interaction networks, and in vitro evolution of DNA and RNA, molecular evolutionary ecology, and the development of methods and theory that enable molecular evolutionary inference, including but not limited to, phylogenetic methods.
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