RAMONA

RAMONA: Bayesian Multi-Omics Enrichment Analysis

RAMONA integrates multi-omics datasets and applies a Bayesian enrichment method to infer overrepresented biological processes within specified gene sets, quantifying overrepresentation as interpretable term probabilities.

Key Features:

• Integrated Multi-Omics Analysis: Combines datasets across multiple molecular levels to identify gene activity alterations.
• Bayesian Enrichment Method: Infers overrepresented biological processes using a Bayesian framework that computes interpretable term probabilities.
• Redundancy and Multiple Testing Control: Manages gene set overlap and corrects for multiple testing to improve statistical reliability.
• High-Throughput Ontology Processing: Processes ontologies containing thousands of terms with high computational efficiency.

Scientific Applications:

• Multi-Omics Functional Analysis: Identifies coordinated biological process alterations across genomics, transcriptomics, proteomics, and metabolomics datasets.
• Gene Set Interpretation: Characterizes functional implications of gene sets derived from high-throughput experiments.

Methodology:

RAMONA applies a Bayesian enrichment model to gene sets derived from multi-omics data, estimating posterior probabilities for biological process terms while accounting for gene set overlap and multiple hypothesis testing to ensure statistically robust inference.