Metabolon, Inc., the global leader in providing metabolomics solutions advancing a wide variety of life science research, diagnostic, therapeutic development, and precision medicine applications, announces new multiomics biomarker discovery capabilities within Metabolon's recently launched Integrated Bioinformatics Platform. This new functionality includes multiomics predictive modeling, latent factor analysis, multiomics pathway analysis using public tools like Reactome, and sophisticated multiomic data visualization resources.

The global multiomics market was valued at $2.4 billion in 2023 and is projected to reach $6.4 billion by 2030.  Since 2012, scientific publications featuring multiomic data have increased 63% annually.  Despite the growing importance of multiomics research, successfully combining genomic, transcriptomic, proteomic, and metabolomic data can be time-consuming and expensive.  Metabolon's new multiomics biomarker discovery functionality provides powerful and easily accessible online bioinformatics tools that enable multiomics research and provide the most complete out-of-the-box representation of the phenotype available.

"Metabolon's new multiomic biomarker discovery capabilities make integrating disparate omics data sets easier and help our customers build powerful studies that incorporate metabolite and lipid data seamlessly with other omics information," said Dr. Karl Bradshaw, Chief Business Officer at Metabolon.

"Reactome's comprehensive and expertly curated pathway data provides a critical foundation for multiomic analysis," said Dr. Lincoln Stein, Lead Principal Investigator at Reactome.  "We're thrilled to see Metabolon harnessing our resource to enable researchers to unravel the complexities of biological systems."

Metabolon's new bioinformatics functionality is available via a comprehensive suite of web-based tools:

Multiomics Predictive Modeling

• Upload and integrate diverse omics data using algorithms like logistic regression and random forest to build multiomics models.
• Explore model performance and evaluate multiomic feature contributions to identify potential biomarkers for further analysis.

Latent Factor Analysis of Multiomics Data

• Identify latent factors representing underlying biological variation.
• Explore relationships that span multiomic layers to uncover biologically relevant biomarkers and guide further research.

Multiomics Pathway Enrichment Analysis Using Reactome (https://reactome.org/)

• Map metabolites to other omics entities (e.g., genes and proteins) using the most comprehensive and curated reference of biological pathways.
• Statistically rank pathways from the Reactome database that are driving variation/signal found in your specific dataset.

Leverage Metabolon's integrated bioinformatics platform to gain improved insights into your most critical research questions – explore the interplay of your biomarkers and their effects on wider-reaching biological mechanisms.