2019 article

RiboStreamR: a web application for quality control, analysis, and visualization of Ribo-seq data

Perkins, P., Mazzoni-Putman, S., Stepanova, A., Alonso, J., & Heber, S. (2019, June 6). BMC GENOMICS, Vol. 20.

By: P. Perkins n, S. Mazzoni-Putman, A. Stepanova n , J. Alonso  n & S. Heber n 

author keywords: Ribo-seq; Next-generation sequencing; Data analysis; Web application
MeSH headings : Computer Graphics; Genomics / methods; High-Throughput Nucleotide Sequencing / methods; Humans; Protein Biosynthesis; Quality Control; RNA, Messenger / genetics; RNA, Messenger / metabolism; Ribosomes / metabolism; Sequence Analysis, RNA; Software; Transcriptome; Web Browser
TL;DR: RiboStreamR is presented, a comprehensive Ribo-seq quality control (QC) platform in the form of an R Shiny web application that provides visualization and analysis tools for various Ribe-seq QC metrics, including read length distribution, read periodicity, and translational efficiency. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: July 1, 2019

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.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.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 in-depth quality assessment of Ribo-seq data by providing reference datasets and automatically highlighting quality issues and anomalies within datasets.