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DGEAR: a web-based application for differential gene expression analysis and downstream functional insights.

Koushik Bardhan1, Chiranjib Sarkar2

  • 1Computational Systems Biology Lab, Department of Bioinformatics, University of North Bengal, Darjeeling, West Bengal, 734013, India. koushikbardhan2000@gmail.com.

Functional & Integrative Genomics
|December 5, 2025
PubMed
Summary
This summary is machine-generated.

We developed DGEAR, a web tool for Differential Gene Expression (DGE) analysis. It offers a robust and user-friendly platform for analyzing transcriptomic data from microarrays and RNA-seq.

Keywords:
Bioinformatics downstream analysisBioinformatics web toolDifferential gene expression analysisMicroarray dataRNA-Seq dataThree-Tier architecture

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • The increasing volume of transcriptomic data requires advanced analytical tools for Differential Gene Expression (DGE) analysis.
  • Existing DGE analysis methods may lack scalability, efficiency, or user-friendliness for complex datasets.
  • Integrating multiple statistical approaches can improve the accuracy and robustness of DGE analysis.

Purpose of the Study:

  • To present DGEAR, a novel web-based tool for efficient and scalable Differential Gene Expression (DGE) analysis.
  • To provide researchers with an intuitive platform for analyzing microarray and RNA-seq data.
  • To enhance downstream bioinformatics investigations through integrated gene set enrichment and network analysis.

Main Methods:

  • Development of a three-tier architecture: frontend UI, middleware API, and backend data layer.
  • Implementation of the DGEAR algorithm, integrating multiple statistical methods and an ensemble model with majority voting.
  • Incorporation of gene set enrichment analysis and Protein-Protein Interaction (PPI) network construction.
  • Secure data handling with end-to-end encryption and custom parameter selection.

Main Results:

  • DGEAR provides a robust, flexible, and accurate method for identifying differentially expressed genes.
  • The web tool streamlines transcriptomic analysis, offering seamless data submission, visualization, and download capabilities.
  • Integration of downstream analysis tools (gene set enrichment, PPI networks) accelerates biological interpretation.

Conclusions:

  • DGEAR offers a significant advancement in transcriptomic data analysis, providing a high-performance and accessible platform.
  • The tool enhances usability and modularity for researchers and clinicians in bioinformatics investigations.
  • DGEAR is publicly available, promoting wider adoption and facilitating scientific discovery.