Rhea: a transparent and modular R pipeline for microbial profiling based on 16S rRNA gene amplicons
Rhea: a transparent and modular R pipeline for microbial profiling based on 16S rRNA gene amplicons
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DOI：
10.7717/peerj.2836
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发表时间：
2017-01-11
期刊：
影响因子：
2.7
通讯作者：
Clavel, Thomas
中科院分区：
文献类型：
--
作者：
Lagkouvardos, Ilias;Fischer, Sandra;Clavel, Thomas
16S rRNA基因扩增子图谱对于了解微生物在各种环境中的影响的重要性，加上测序成本的急剧降低，导致微生物测序项目激增。越来越多的科学家和临床医生希望利用测序数据集，他们可以在一系列多用途软件平台中进行选择，这些平台的使用对于非专家用户来说可能是令人生畏的。在高通量16S rRNA基因分析的可用管道选项中，用于统计计算的R编程语言和软件环境因其强大的功能和更高的灵活性而脱颖而出，并且可以遵循最新的最佳实践并根据个别项目需求进行调整。在这里，我们介绍了Rhea管道，这是一组R脚本，编码了一系列有据可查的选择，用于操作分类单位（OTU）表的下游分析，包括归一化步骤，α和β多样性分析，分类组成，统计比较和相关性计算。Rhea主要是初学者的一个简单的起点，但也可以是高级用户的一个框架，他们可以修改和扩展工具。随着社区标准的发展，Rhea将适应以R语言所允许的清晰和全面的方式始终代表微生物谱分析的当前最先进水平。Rhea脚本和文档可以在https://lagkouvardos.github.io/Rhea上免费获得。
The importance of 16S rRNA gene amplicon profiles for understanding the influence of microbes in a variety of environments coupled with the steep reduction in sequencing costs led to a surge of microbial sequencing projects. The expanding crowd of scientists and clinicians wanting to make use of sequencing datasets can choose among a range of multipurpose software platforms, the use of which can be intimidating for non-expert users. Among available pipeline options for high-throughput 16S rRNA gene analysis, the R programming language and software environment for statistical computing stands out for its power and increased flexibility, and the possibility to adhere to most recent best practices and to adjust to individual project needs. Here we present the Rhea pipeline, a set of R scripts that encode a series of well-documented choices for the downstream analysis of Operational Taxonomic Units (OTUs) tables, including normalization steps, alpha- and beta-diversity analysis, taxonomic composition, statistical comparisons, and calculation of correlations. Rhea is primarily a straightforward starting point for beginners, but can also be a framework for advanced users who can modify and expand the tool. As the community standards evolve, Rhea will adapt to always represent the current state-of-the-art in microbial profiles analysis in the clear and comprehensive way allowed by the R language. Rhea scripts and documentation are freely available at https://lagkouvardos.github.io/Rhea.