Department of Microbiology, New York University School of Medicine, New York, 10016, USA.
Department of Medicine, Department of Translational Medicine, New York University School of Medicine, New York, 10016, USA.
BMC Genomics. 2018 Nov 8;19(1):809. doi: 10.1186/s12864-018-5166-z.
Translatomics data, particularly genome-wide ribosome profiling and polysome profiling, provide multiple levels of gene regulatory information that can be used to assess general transcription and translation, as well translational efficiency. The increasing popularity of these techniques has resulted in multiple algorithms to detect translational regulation, typically distributed in the form of command line tools that require a basic level of programming ability. Additionally, due to the static nature of current software, dynamic transcriptional and translational comparative analysis cannot be adequately achieved. In order to streamline hypothesis generation, investigators must have the ability to manipulate and interact with their data in real-time.
To address the lack of integration in current software, we introduce RIVET, Ribosomal Investigation and Visualization to Evaluate Translation, an R shiny based graphical user interface for translatomics data exploration and differential analysis. RIVET can analyze either microarray or RNA sequencing data from polysome profiling and ribosome profiling experiments. RIVET provides multiple choices for statistical analysis as well as integration of transcription, translation, and translational efficiency data analytics and the ability to visualize all results dynamically.
RIVET is a user-friendly tool designed for bench scientists with little to no programming background. RIVET facilitates the data analysis of translatomics data allowing for dynamic generation of results based on user-defined inputs and publication ready visualization. We expect RIVET will allow for scientists to efficiently make more comprehensive data observations that will lead to more robust hypothesis regarding translational regulation.
翻译组学数据，特别是全基因组核糖体分析和多核糖体分析，提供了多层次的基因调控信息，可用于评估一般转录和翻译以及翻译效率。这些技术的普及导致了多种检测翻译调控的算法，通常以命令行工具的形式分发，这些工具需要一定的编程能力。此外，由于当前软件的静态性质，无法充分实现动态转录和翻译比较分析。为了简化假设的生成，研究人员必须能够实时操纵和交互处理他们的数据。
为了解决当前软件中缺乏整合的问题，我们引入了 RIVET，即核糖体研究和可视化评估翻译，这是一个基于 R shiny 的图形用户界面，用于翻译组学数据的探索和差异分析。RIVET 可以分析多核糖体分析和核糖体分析实验的微阵列或 RNA 测序数据。RIVET 为统计分析提供了多种选择，以及转录、翻译和翻译效率数据分析的集成，以及动态可视化所有结果的能力。
RIVET 是一个为具有少量甚至没有编程背景的实验科学家设计的用户友好工具。RIVET 有助于翻译组学数据的分析，允许根据用户定义的输入动态生成结果，并进行发布准备好的可视化。我们预计 RIVET 将使科学家能够更有效地进行更全面的数据观察，从而对翻译调控产生更稳健的假设。
