Affordable Access

deepdyve-link
Publisher Website

3D RNA-seq: a powerful and flexible tool for rapid and accurate differential expression and alternative splicing analysis of RNA-seq data for biologists.

Authors
  • Guo, Wenbin1, 2
  • Tzioutziou, Nikoleta A1
  • Stephen, Gordon2
  • Milne, Iain2
  • Calixto, Cristiane Pg1
  • Waugh, Robbie1, 3
  • Brown, John W S1, 3
  • Zhang, Runxuan2
  • 1 Division of Plant Sciences, University of Dundee at the James Hutton Institute, Dundee, UK.
  • 2 Information and Computational Sciences, The James Hutton Institute, Dundee, UK.
  • 3 Cell and Molecular Sciences, The James Hutton Institute, Dundee, UK.
Type
Published Article
Journal
RNA Biology
Publisher
Landes Bioscience
Publication Date
Nov 01, 2021
Volume
18
Issue
11
Pages
1574–1587
Identifiers
DOI: 10.1080/15476286.2020.1858253
PMID: 33345702
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

RNA-sequencing (RNA-seq) analysis of gene expression and alternative splicing should be routine and robust but is often a bottleneck for biologists because of different and complex analysis programs and reliance on specialized bioinformatics skills. We have developed the '3D RNA-seq' App, an R shiny App and web-based pipeline for the comprehensive analysis of RNA-seq data from any organism. It represents an easy-to-use, flexible and powerful tool for analysis of both gene and transcript-level gene expression to identify differential gene/transcript expression, differential alternative splicing and differential transcript usage (3D) as well as isoform switching from RNA-seq data. 3D RNA-seq integrates state-of-the-art differential expression analysis tools and adopts best practice for RNA-seq analysis. The program is designed to be run by biologists with minimal bioinformatics experience (or by bioinformaticians) allowing lab scientists to analyse their RNA-seq data. It achieves this by operating through a user-friendly graphical interface which automates the data flow through the programs in the pipeline. The comprehensive analysis performed by 3D RNA-seq is extremely rapid and accurate, can handle complex experimental designs, allows user setting of statistical parameters, visualizes the results through graphics and tables, and generates publication quality figures such as heat-maps, expression profiles and GO enrichment plots. The utility of 3D RNA-seq is illustrated by analysis of data from a time-series of cold-treated Arabidopsis plants and from dexamethasone-treated male and female mouse cortex and hypothalamus data identifying dexamethasone-induced sex- and brain region-specific differential gene expression and alternative splicing.

Report this publication

Statistics

Seen <100 times