GENAVi

GENAVi performs normalization, differential expression analysis, visualization, and gene set enrichment on next-generation sequencing (NGS) RNA-Seq feature-count data from human and mouse to support interpretation of gene expression.

Key Features:

• Integration of Bioconductor and R packages: Integrates R and Bioconductor packages commonly used for normalization, clustering, visualization, and differential expression analysis of RNA-Seq data.
• Normalization methods: Supports a range of normalization methods tailored to human or mouse feature count–level RNA-Seq data.
• Differential Expression Analysis (DEA): Provides functionality to identify significant changes in gene expression between experimental conditions.
• Clustering and correlation: Enables sample clustering based on gene expression or sample–sample correlation.
• Principal Components Analysis (PCA): Calculates and plots PCA results for dimensionality reduction and variance inspection.
• Gene Set Enrichment Analysis (GSEA): Performs gene set enrichment analysis to identify biological pathways associated with differentially expressed genes.
• Expression visualization: Visualizes gene expression across samples and clusters to aid interpretation.
• Reproducibility and session reporting: Produces clear and complete session reports for reproducible analyses.
• Pre-loaded cancer cell line panel: Includes a panel of 20 cell lines commonly used in breast and ovarian cancer research as pre-loaded reference data.

Scientific Applications:

• Gene expression profiling in human and mouse: Analysis of gene expression patterns and differential expression in human and mouse RNA-Seq datasets.
• Oncology research: Comparison of experimental data to a pre-loaded panel of 20 breast and ovarian cancer cell lines and investigation of cancer-related expression changes.
• Pathway and functional interpretation: Identification of biological pathways and functional enrichment associated with differentially expressed genes via GSEA.

Methodology:

Integration of R/Bioconductor packages; normalization of feature count–level RNA-Seq data for human and mouse; differential expression analysis; sample clustering and correlation analyses; calculation and plotting of principal components analysis; gene set enrichment analysis; generation of session reports; inclusion of a pre-loaded panel of 20 breast and ovarian cancer cell lines.