Evidence map›Paper›PMID 33345702›Full record

ArticleRNA biology2021

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

Wenbin Guo, Nikoleta A Tzioutziou, Gordon Stephen, Iain Milne, Cristiane Pg Calixto, Robbie Waugh, John W S Brown, Runxuan Zhang

Open access · bronzeAbstract read
In one paragraph

Article in RNA biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
79citing papers in PubMed, 1 pooled it
5.5field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

79 citing papers in PubMed, 1 synthesis or guideline pooled it, 143 citations in OpenAlex.

  1. Pooled it
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  19. SON-dependent nuclear speckle rehabilitation alleviates proteinopathies.bioRxiv : the preprint server for biology · 2025
    Article
  20. Article

19 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 1 institution in 1 country.

Wenbin GuoDivision of Plant Sciences, University of Dundee at the James Hutton Institute, Dundee, UK.ORCID 0000-0002-1829-6044
Nikoleta A TzioutziouDivision of Plant Sciences, University of Dundee at the James Hutton Institute, Dundee, UK.
Gordon StephenInformation and Computational Sciences, The James Hutton Institute, Dundee, UK.
Iain MilneInformation and Computational Sciences, The James Hutton Institute, Dundee, UK.ORCID 0000-0002-4126-0859
Cristiane Pg CalixtoDivision of Plant Sciences, University of Dundee at the James Hutton Institute, Dundee, UK.ORCID 0000-0002-9935-9325
Robbie WaughDivision of Plant Sciences, University of Dundee at the James Hutton Institute, Dundee, UK.
John W S BrownDivision of Plant Sciences, University of Dundee at the James Hutton Institute, Dundee, UK.
Runxuan ZhangInformation and Computational Sciences, The James Hutton Institute, Dundee, UK.ORCID 0000-0001-7558-765X
James Hutton Institute · GB

Funding

Biotechnology and Biological Sciences Research Council BB/P009751/1Biotechnology and Biological Sciences Research Council BB/R014582/1Biotechnology and Biological Sciences Research Council BB/S004610/1Biotechnology and Biological Sciences Research Council BB/S020160/1
6 · The paper itself

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.

Indexed as

Alternative SplicingAnimalsArabidopsisCerebellar CortexCold TemperatureComputational BiologyDexamethasoneGene Expression RegulationGlucocorticoidsHypothalamusMiceRNARNA-SeqSoftwareDexamethasoneGlucocorticoidsRNAArabidopsisdexamethasonedifferential alternative splicingdifferential gene/transcript expressiondifferential transcript usageinteractive GUImouseRNA-seqtime-series

Identifiers

PMID33345702
PMCPMC8594885
OpenAlexW3113761085

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.