riboStreamR: A web application for quality control, analysis, and visualization of Ribo-seq data

Patrick Perkins, S. Heber
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Abstract

Ribo-seq is a popular technique for studying translation and its regulation. Various software tools for data preprocessing, quality assessment, analysis, and visualization of Ribo-seq data have been developed. However, many of them are inaccessible to users without a thorough practical knowledge of software applications, and often multiple different tools have to be used in combination with each other. Here, 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. The platform's environment is centered on providing a user-friendly experience, and includes numerous options for graphical customization and report generation. In practice, Ribo-seq data analysis can be sensitive to data quality issues such as read length variation, low read periodicities, and contaminations with ribosomal and transfer RNA. What constitutes ‘high quality’ data is often unclear. Our goal is to develop novel functionality to automatically highlight quality issues and anomalies in the data. This NSF-supported project is performed in collaboration with Jose Alonso, Anna Stepanova, Serina Mazzoni-Putman, and Cranos Williams.
riboStreamR:一个用于质量控制、分析和可视化核糖序列数据的web应用程序
核糖核酸序列是研究翻译及其调控的常用技术。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,如果用户对软件应用程序没有全面的实践知识,他们就无法使用其中的许多工具,而且通常必须将多个不同的工具相互组合使用。在这里,我们介绍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。该平台的环境以提供用户友好的体验为中心,并包括许多用于图形定制和报表生成的选项。在实践中,核糖核酸序列数据分析可能对数据质量问题很敏感,如读取长度变化、低读取周期以及核糖体和转移RNA的污染。什么是“高质量”的数据通常是不清楚的。我们的目标是开发新的功能来自动突出数据中的质量问题和异常。这个nsf支持的项目是与Jose Alonso, Anna Stepanova, Serina Mazzoni-Putman和Cranos Williams合作完成的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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