Integrated Multi-Omic Analysis

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.

Enhance Mechanistic Insights

Integrated multi-omics provides enhanced mechanistic insight through broader and deeper biochemical pathway coverage.

Magnify Biomarker Discovery

An integrated multi-omic approach enhances 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.

Maximize Potential

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.

Improve Understanding of Disease

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.

The Power of Multi-Omic Data

An integrated multi-omic approach enhances biomarker discovery through the evaluation of more molecules in one sample, improving group separation (Figure 1) and imparting confidence when observations from multiple analyte types co-cluster in one pathway or network.

Integrated multi-omics also provides enhanced mechanistic insight through broader and deeper biochemical pathway coverage (Figure 2).

The Multi-Omic Integration Approach

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With signal integration, multiple ‘omics datasets acquired on the same sample are “concatenated” or “integrated” into a single feature matrix that provides the relative abundance of each analyte (transcript, protein, metabolite) across all samples. The concatenated data can then be used to improve multi-variate statistical analyses where sample groups (e.g., responders vs non-responders, diseased vs healthy, treated vs untreated) are separated based on a combination of multiple analyte levels.

As many diseases and perturbations occur at multiple molecular layers (transcriptome, proteome, metabolome), utilizing multiple analyte types in a single analysis greatly improves group discrimination. This is particularly helpful for large-cohort studies where machine learning approaches can be harnessed to build predictive models of disease course, drug efficacy, and more.

With network integration, multiple ‘omics datasets acquired on the same or different samples are mapped onto shared biochemical networks to improve mechanistic understanding.

As part of this network integration, analytes (genes, transcripts, proteins, metabolites) are connected based on known interactions (e.g., a transcription factor mapped to the transcript it regulates or metabolic enzymes mapped to their associated metabolite substrates and products).

Panome Bio has assembled a propriety database of these interactions to leverage which was built using scientific literature, publicly-available datasets, and Panome Bio’s own datasets.To uncover novel interactions measured in a multi-omic experiment, we also perform data-driven network analyses to determine analytes with concordant changes within a dataset.

Integrated Multi-omics Analysis Reveals Systemic and Localized Metabolic Disruptions in Colorectal Cancer

Multi-omic analysis is a powerful tool that allows for a more comprehensive view of the biochemical processes occurring within an organism. Review our data from a multi-omic study combining proteomics, metabolomics, and transcriptomics to analyze metabolic shifts during colorectal cancer (CRC) progression.

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Multi-Omic Analysis Requirements & Study Design

Panome Bio offers integrated multi-omics analysis of data generated through our Next-Generation Metabolomics™ or proteomics workflows or existing customer-provided data.

Learn about data requirements & study design

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