MoNET

MoNET integrates multi-omics data into network models that connect SNPs, genes/proteins, and metabolites to analyze transomic interactions and molecular mechanisms.

Key Features:

• Integration of Multi-omic Data: Integrates diverse omic data types by combining multiple biological network databases to connect SNPs, genes/proteins, and metabolites within a unified network.
• Functional Interaction Analysis: Analyzes functional interactions among multi-omic biomarkers to reveal molecular relationships and interaction patterns.
• Transomic Interactions: Tracks interaction pathways from SNPs and genes to metabolome levels and traces back potential risk variants or regulators based on altered gene or metabolite profiles.
• Hypothesis Generation: Uncovers transomic interaction patterns to generate hypotheses for experimental validation of disease mechanisms and regulatory relationships.

Scientific Applications:

• Disease Biomarker Discovery: Identifies biomarkers across genomic, proteomic, and metabolomic scales from integrated multi-omic networks to support disease diagnosis and characterization.
• Molecular Mechanism Insights: Elucidates molecular mechanisms by analyzing interactions between omic layers, including metabolite–protein interactions, protein–protein interactions, and SNP–transcription factor binding site relationships.

Methodology:

Constructs an integrated network framework by combining data from multiple biological network databases, representing metabolite–protein interactions, protein–protein interactions, and SNP–transcription factor binding site relationships, and traces interactions across omic layers (SNPs, genes/proteins, metabolites) to identify potential risk factors and regulatory mechanisms associated with altered gene or metabolite expression.