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Related Experiment Video

Updated: Jun 8, 2026

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

DEVEA: an interactive shiny application for Differential Expression analysis, data Visualization and Enrichment

Miriam Riquelme-Perez^1,2, Fernando Perez-Sanz³, Jean-François Deleuze²

¹Université Paris-Saclay, CEA, CNRS, MIRCen, Laboratoire des Maladies Neurodégénératives, Fontenay-aux-Roses, 92265, France.

|April 3, 2023

Summary

DEVEA is a new R shiny application simplifying transcriptomic data analysis for scientists. It enables differential expression, visualization, and pathway analysis without requiring coding expertise.

Keywords:

Bioinformatics R RNA sequencing Shiny differential expression analysis enrichment analysis interactive reports.transcriptomics visualization

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Last Updated: Jun 8, 2026

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

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Published on: March 5, 2022

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

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Published on: September 18, 2021

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Transcriptomics studies and in silico analysis are rapidly expanding.
RNA sequencing (RNA-Seq) is a primary method for transcriptome analysis, but data processing demands significant statistical and coding skills.
Existing bioinformatics tools often lack user-friendliness for scientists without specialized expertise.

Purpose of the Study:

To introduce DEVEA, an R shiny application designed for accessible transcriptomic data analysis.
To provide an intuitive platform for differential expression, data visualization, and pathway analysis.
To empower scientists with varying bioinformatics backgrounds to explore gene expression data.

Main Methods:

Development of an R shiny application named DEVEA.
Integration of differential expression analysis, data visualization, and enrichment pathway analysis functionalities.
Support for various data inputs, including transcriptomic data and gene lists with or without statistical values.
Creation of an interactive and user-friendly interface with dynamic graphs and tables.

Main Results:

DEVEA offers an intuitive interface for exploring gene expression and statistical results.
The application facilitates statistical comparisons of expression profiles between groups.
Comprehensive pathway analysis is integrated to enhance biological insights.
Customizable HTML reports are generated for extended result exploration.

Conclusions:

DEVEA democratizes transcriptomic data analysis by removing the need for extensive bioinformatics expertise.
The tool streamlines complex analyses, enabling broader scientific participation in transcriptomics research.
DEVEA provides a comprehensive and accessible solution for exploring and interpreting gene expression data.