RBiomirGS

RBiomirGS: Logistic regression-based miRNA gene set enrichment analysis

RBiomirGS performs integrated miRNA gene set analysis by mapping target messenger RNAs (mRNAs), analyzing miRNA expression profiles, and estimating regulatory effects using logistic regression-based gene set enrichment analysis.

Key Features:

• Target mRNA Mapping: Maps miRNA target messenger RNAs (mRNAs) using multiple databases to characterize miRNA–mRNA interactions.
• miRNA Effect Estimation: Estimates miRNA regulatory impact on target mRNAs from expression profiles using logistic regression-based gene set enrichment analysis.
• Human Ortholog Entrez ID Conversion: Converts human ortholog Entrez Gene IDs for target mRNAs to support cross-species analyses.
• Integrated Workflow: Consolidates miRNA gene set analysis steps within a unified computational framework.
• Modular Architecture: Provides discrete analytical components accessible at different stages of the workflow.

Scientific Applications:

• miRNA Functional Analysis: Identifies functional roles of miRNAs in gene regulation, disease mechanisms, and cellular phenotypes through gene set enrichment analysis.
• Regulatory Network Characterization: Elucidates miRNA–mRNA regulatory networks under specific experimental conditions.

Methodology:

RBiomirGS integrates miRNA expression data with curated miRNA–mRNA target databases, maps target mRNAs, converts human ortholog Entrez Gene IDs when required, and applies logistic regression-based gene set enrichment analysis to quantify miRNA effects on predefined gene sets.