Mergeomics

Mergeomics integrates genetic, transcriptomic, and epigenomic association data with functional genomics and molecular interaction networks to identify biological pathways, gene networks, and key regulators underlying complex human disease mechanisms.

Key Features:

• Multidimensional Data Integration: Integrates genetic, transcriptomic, and epigenomic disease-association data with functional genomic annotations and molecular interaction networks to retrieve pathways, gene networks, and central regulators.
• Curated Genomic Resources: Utilizes curated resources including tissue-specific expression quantitative trait loci (eQTLs), ENCODE functional annotations, biological pathway databases, and molecular networks.
• Computational Methods: Implements Marker Dependency Filtering (MDF), Marker Set Enrichment Analysis (MSEA), Meta-MSEA, and weighted Key Driver Analysis (wKDA) for marker filtering, enrichment analysis, cross-dataset integration, and identification of network key drivers.
• Summary-level Dataset Analysis: Analyzes user-provided summary-level genomic association datasets to derive biologically interpretable results.

Scientific Applications:

• Mechanistic Hypothesis Generation: Generates mechanistic hypotheses about disease development by integrating multi-omics and network data.
• Identification of Tissue-specific Regulators and Pathways: Identifies tissue-specific key regulators, biological pathways, and gene networks implicated in disease processes.
• Cross-study Integration and Meta-analysis: Combines multiple datasets via Meta-MSEA to uncover broader patterns and associations across studies.

Methodology:

Integrates genetic, transcriptomic, and epigenomic association data with functional genomics (tissue-specific eQTLs, ENCODE annotations), biological pathways, and molecular networks, and applies Marker Dependency Filtering (MDF), Marker Set Enrichment Analysis (MSEA), Meta-MSEA, and weighted Key Driver Analysis (wKDA).