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Updated: Jun 29, 2025

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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Curare and GenExVis: a versatile toolkit for analyzing and visualizing RNA-Seq data.

Patrick Blumenkamp1, Max Pfister2, Sonja Diedrich2

  • 1Bioinformatics and Systems Biology, Justus Liebig University Giessen, 35392, Giessen, Germany. patrick.blumenkamp@computational.bio.uni-giessen.de.

BMC Bioinformatics
|March 30, 2024
PubMed
Summary
This summary is machine-generated.

Curare and GenExVis streamline RNA-Seq data analysis for differential gene expression (DGE) studies. These tools automate complex workflows, enhancing reproducibility and simplifying result interpretation for researchers.

Keywords:
Data visualizationDifferential gene expressionFAIRRNA-SeqReproducibility

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput RNA-Seq is common, but its computational analysis is manual, error-prone, and lacks reproducibility.
  • Analyzing RNA-Seq data for differential gene expression (DGE) requires specialized bioinformatics skills and tools.

Purpose of the Study:

  • Introduce Curare, a versatile workflow builder for RNA-Seq data analysis.
  • Present GenExVis for straightforward visualization of DGE results.
  • Provide a comprehensive software environment for the entire RNA-Seq data analysis pipeline.

Main Methods:

  • Curare offers a customizable workflow builder with stages for preprocessing, quality control, mapping, and downstream analysis.
  • GenExVis visualizes DGE results using charts and tables from standard data formats.
  • Both tools are designed for ease of use and reproducibility.

Main Results:

  • Curare ensures reproducible analysis of RNA-Seq data through a structured workflow.
  • GenExVis facilitates rapid exploration and visualization of DGE results without data uploads or installations.
  • The combined Curare-GenExVis system simplifies the entire RNA-Seq data processing and interpretation workflow.

Conclusions:

  • Curare and GenExVis significantly ease the computational burden of RNA-Seq data analysis.
  • These tools enhance reproducibility and accessibility of DGE studies.
  • The integrated environment supports researchers from raw data to final interpretation.