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inDAGO: a user-friendly interface for seamless dual and bulk RNA-Seq analysis.

Gaetano Aufiero1, Carmine Fruggiero1, Nunzio D'Agostino1

  • 1Department of Agricultural Sciences, University of Naples Federico II, Portici, Italy.

Frontiers in Bioinformatics
|December 8, 2025
PubMed
Summary

inDAGO is a new graphical tool that simplifies dual RNA sequencing analysis for biologists. This open-source software enables complex transcriptomic analyses without coding, making it accessible for various biological interactions.

Keywords:
R-shiny frameworkcross-species RNA-seqdifferentially expressed genesgene expression profilinggraphical user interface (GUI)transcriptomic dynamics

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Area of Science:

  • Transcriptomics
  • Bioinformatics
  • Computational Biology

Background:

  • Dual RNA sequencing (RNA-seq) allows simultaneous transcriptomic profiling of interacting organisms.
  • Analyzing dual RNA-seq data is complex and typically requires programming expertise.
  • This limits accessibility for many biologists studying host-parasite or cross-kingdom interactions.

Purpose of the Study:

  • To develop an accessible, user-friendly graphical interface for dual RNA-seq analysis.
  • To provide a tool that supports both bulk and dual RNA sequencing workflows.
  • To enable biologists without coding skills to perform robust transcriptomic analyses.

Main Methods:

  • Developed inDAGO, a free, open-source, cross-platform graphical user interface (GUI).
  • inDAGO guides users through quality control, alignment, summarization, and differential gene expression analysis.
  • The tool supports both sequential and combined dual RNA-seq approaches and runs on standard hardware.

Main Results:

  • inDAGO successfully performs complete dual RNA-seq analyses on a standard laptop.
  • The GUI simplifies complex bioinformatics workflows, generating publication-ready plots and intermediate outputs.
  • Validation with diverse real datasets confirmed the reliability and usability of inDAGO.

Conclusions:

  • inDAGO significantly lowers the technical barrier to dual RNA sequencing.
  • It empowers biologists without coding experience to conduct reproducible and robust transcriptomic analyses.
  • The tool enhances the study of complex biological interactions through accessible data analysis.