multi-omics integration
Definition
Multi-omics integration is the systematic combination and analysis of data from multiple omics layers—such as genomics, transcriptomics, proteomics, metabolomics, and epigenomics—to generate comprehensive biological insights. This approach recognizes that biological systems operate through interconnected molecular networks spanning DNA, RNA, proteins, and metabolites. By integrating these complementary data types, researchers can identify regulatory relationships, discover biomarkers, understand disease mechanisms, and reveal how genetic variations translate into phenotypic outcomes. Multi-omics integration addresses the limitation of single-omics studies by capturing the flow of biological information across molecular scales, enabling more accurate disease classification, therapeutic target identification, and personalized medicine approaches.
Visualize multi-omics integration in Nodes Bio
Nodes Bio enables researchers to construct integrated network models that connect nodes from different omics layers—genes, transcripts, proteins, metabolites, and epigenetic marks. Users can visualize cross-omics regulatory cascades, identify hub molecules that coordinate multiple biological processes, and perform pathway enrichment across integrated datasets. The platform's network analysis tools help reveal emergent properties and molecular interactions that are invisible when analyzing single omics layers in isolation.
Visualization Ideas:
- Multi-layer networks connecting genes, proteins, and metabolites across omics types
- Integrated pathway maps showing information flow from DNA to phenotype
- Correlation networks identifying cross-omics regulatory hubs and biomarker signatures
Example Use Case
A cancer research team investigating drug resistance integrates genomic mutation data, RNA-seq expression profiles, phosphoproteomics, and metabolomics from treatment-resistant and sensitive tumor samples. By constructing a multi-layer network, they discover that a specific mutation upregulates a transcription factor, which increases expression of metabolic enzymes, ultimately rewiring cellular metabolism to bypass drug effects. This multi-omics view reveals the complete mechanistic pathway from genetic alteration to phenotypic outcome, identifying multiple potential intervention points for combination therapy strategies that single-omics analysis would have missed.