IRIS-EDA: An integrated RNA-Seq interpretation system for gene expression data analysis.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Next-Generation Sequencing generates large-scale Omics data, offering insights into biological systems.
• RNA-Sequencing (RNA-Seq) is crucial for understanding gene regulatory responses under various conditions and cell types.
• Analyzing RNA-Seq data through methods like differential gene expression (DGE) presents interpretation challenges.

Purpose of the Study:

• To introduce IRIS-EDA, a Shiny web server designed for comprehensive RNA-Seq and scRNA-Seq data analysis.
• To provide a user-friendly platform integrating multiple computational tools for gene expression exploration and DGE analysis.
• To enable streamlined, FAIR data submission of expression data to the NCBI Gene Expression Omnibus.

Main Methods:

• Implementation of three R packages (edgeR, DESeq2, limma) for differential gene expression analysis with flexible experimental design options.
• Integration of seven discovery-driven methods including correlation analysis, PCA, MDS, t-SNE, and clustering for gene expression exploration.
• Incorporation of seven interactive visualization tools to enhance the interpretation of analysis results.

Main Results:

• IRIS-EDA offers a unified platform for diverse RNA-Seq and scRNA-Seq data analyses.
• The tool supports multiple DGE analysis approaches and a wide array of exploratory data analysis methods.
• It facilitates hypothesis generation and identification of key factors for comprehensive gene expression studies.
• IRIS-EDA enables expedited, FAIR-compliant data submission to the Gene Expression Omnibus.

Conclusions:

• IRIS-EDA simplifies and enhances the analysis and interpretation of RNA-Seq and scRNA-Seq data.
• The platform's integration of analysis tools and visualization capabilities aids researchers in biological discovery.
• IRIS-EDA promotes data sharing and reproducibility by adhering to FAIR data principles.