GMet’s technology suite for metabolomics utilizes high throughput methods and high-resolution mass spectrometry to maximize analytical throughput, allowing metabolite discrimination in highly complex mixtures. By combining rapid acquisition methods with efficient data pipelines, we are able to exponentially increase sample throughput relative to other metabolomics approaches, and enable researchers to apply the right tool to the problem at hand, supporting questions that range from broad discovery to focused inquiry – using flow-injection and LCMS/MS approaches.  These tools accelerate therapeutics development, pathway engineering, biomarker discovery, and mechanistic cellular research.

A typical workflow to generate, process, and analyze metabolomic data is fragmented and inefficient and leads to high cost, long timelines, and challenges in data analysis and interpretation. In contrast, GMet software serves as a surrogate informatics team for processing, analyzing and interpreting metabolomics data. By automating data processing and enabling dynamic exploration of biology, GMet technology alleviates the computational intensity of metabolomic data and drives researchers rapidly to biologic interpretation.

Our interactive Data Visualization Dashboard makes datasets available for exploration and analysis independently by researchers.  Informed by decades of metabolomics experience and built using state-of-the art web technologies, this flexible tool empowers metabolism researchers to interact with their data in its metabolic context without the need for additional data sciences resources. It facilitates time to biological insight at any scale.

Interactive capabilities include:

• Dynamically select features of interest to explore using intuitive controls and responsive data panels

• Explore results in a dynamic metabolic network map representing thousands of compounds and the biochemical reactions that connect them

• Calculate pairwise differences and pathway enrichment statistics on hundreds of annotated features between arbitrarily complex sample groupings from your experiment in real-time

• View detailed compound annotation tables that report metabolite abundance and differential cohort statistics, with links to public metabolite databases (ChEBI)
• Capture your findings by exporting publication-grade plots and tables

GMet’s platform enables seamless integration of genomics and metabolomics data sets. Our published and peer-reviewed network-based integration methods find relationships between genes and metabolites to uncover novel biological mechanisms. By utilizing the metabolic pathway network itself to facilitate omics integration, the process inherently places multi-omic data in an intuitive, interpretable environment that brings genomic context to metabolomics and metabolites.