Integrated Multi-Omic Analysis:
Enhancing Mechanistic Insights and
Biomarker Discovery

Panome Bio offers integrated multi-omic analysis through a proprietary bioinformatics pipeline that interweaves ‘omics profiles into a singular dataset for higher-level analysis.

Integration of multi-omic data sets starts with signal integration. We combine multiple ‘omics datasets from the same sample into a single feature matrix, detailing the relative abundance of each analyte (transcript, protein, metabolite) across all samples. This integrated data enhances multi-variate statistical analyses, improving the separation of sample groups. By analyzing multiple molecular layers together, our approach significantly enhances group discrimination, especially in large-cohort studies.

We then leverage our proprietary database of known connections to perform network integration.  This involves mapping multiple ‘omics datasets from the same or different samples onto shared biochemical networks, connecting genes, transcripts, proteins, and metabolites based on known interactions. Seeing how pathways are dysregulated across ‘omics layers, and were there are concordant changes,  provides insights into complex biological processes and allows for more meaningful biological interpretations. Our approach to network integration leverages our proprietary database—built from scientific literature, public datasets, and our own data

Advantages of our Integrated Multi-Omic Analysis:

• Integrated multi-omics provides enhanced mechanistic insight through broader and deeper biochemical pathway coverage.
• Magnify biomarker discovery through the evaluation of more molecules in one sample, improving group separation and imparting confidence when observations from multiple analyte types co-cluster in one pathway or network.
• The potential within multi-omic approaches is not realized by simply producing multiple ‘omic datasets, but by integrating the information provided with data-driven and literature-guided approaches. Integrated multi-omics data enables more meaningful pathway analysis, greater hit confidence, and reaction-level insights.
• Disease states originate within different molecular layers (gene-level, transcript-level, protein-level, metabolite-level). By measuring multiple analyte types in a pathway, biological dysregulation can be pinpointed to single reactions, enabling elucidation of actionable targets.
• Our approach to network integration can uncover previously unknown protein-metabolite interactions that could prove transformative to your work.