RiboStreamR: a web application for quality control, analysis, and visualization of Ribo-seq data
RiboStreamR: a web application for quality control, analysis, and visualization of Ribo-seq data
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DOI：
10.1186/s12864-019-5700-7
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发表时间：
2019-06-06
期刊：
影响因子：
4.4
通讯作者：
Heber, Steffen
中科院分区：
文献类型：
--
作者：
Perkins, Patrick;Mazzoni-Putman, Serina;Heber, Steffen
背景：核糖核酸序列（Ribo-seq）是研究翻译及其调控的常用技术。核糖测序实验产生了细胞转录组内活跃翻译核糖体的位置和丰度的快照。在实践中，核糖核酸序列数据分析可以敏感的质量问题，如读取长度变化，低读取周期，污染与核糖体和转移RNA。各种软件工具的数据预处理，质量评估，分析和可视化的核糖核酸序列数据已经开发。然而，这些工具中的许多都需要相当多的软件应用程序的实践知识，并且通常必须将多个不同的工具相互组合使用。结果：我们提出了riboStreamR，一个全面的核糖序列质量控制（QC）平台，以R Shiny web应用程序的形式。RiboStreamR为各种Ribo-seq QC指标提供可视化和分析工具，包括读取长度分布，读取周期性和翻译效率。我们的平台专注于提供用户友好的体验，包括各种图形定制选项，报告生成和Ribo-seq数据集中的异常检测。结论：RiboStreamR利用R和Bioconductor环境提供的大量资源，并利用Shiny R包确保高水平的可用性。我们的目标是开发一种工具，通过提供参考数据集和自动突出数据集中的质量问题和异常，促进Ribo-seq数据的深度质量评估。
Background: Ribo-seq is a popular technique for studying translation and its regulation. A Ribo-seq experiment produces a snap-shot of the location and abundance of actively translating ribosomes within a cell's transcriptome. In practice, Ribo-seq data analysis can be sensitive to quality issues such as read length variation, low read periodicities, and contaminations with ribosomal and transfer RNA. Various software tools for data preprocessing, quality assessment, analysis, and visualization of Ribo-seq data have been developed. However, many of these tools require considerable practical knowledge of software applications, and often multiple different tools have to be used in combination with each other.Results: We present riboStreamR, a comprehensive Ribo-seq quality control (QC) platform in the form of an R Shiny web application. RiboStreamR provides visualization and analysis tools for various Ribo-seq QC metrics, including read length distribution, read periodicity, and translational efficiency. Our platform is focused on providing a user-friendly experience, and includes various options for graphical customization, report generation, and anomaly detection within Ribo-seq datasets.Conclusions: RiboStreamR takes advantage of the vast resources provided by the R and Bioconductor environments, and utilizes the Shiny R package to ensure a high level of usability. Our goal is to develop a tool which facilitates indepth quality assessment of Ribo-seq data by providing reference datasets and automatically highlighting quality issues and anomalies within datasets.