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Updated: Jun 17, 2026

Vinyl Chloride and High-Fat Diet as a Model of Environment and Obesity Interaction

Vinyl Chloride and High-Fat Diet as a Model of Environment and Obesity Interaction

Published on: January 12, 2020

Managing experimental data using FuGE.

Andrew R Jones¹, Allyson L Lister

¹Department of Pre-clinical Veterinary Science, Faculty of Veterinary Science, University of Liverpool, Liverpool, UK. andrew.jones@liv.ac.uk

Methods in Molecular Biology (Clifton, N.J.)

|December 17, 2009

Summary

This summary is machine-generated.

Managing omics data is complex. The Functional Genomics Experiment (FuGE) Model offers a standardized approach for experimental data and workflow descriptions, aiding data integration for systems biology.

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Area of Science:

Omics science
Bioinformatics
Data management

Background:

Omics data management is challenging due to evolving techniques, diverse instruments, and large data volumes.
Existing methods struggle to capture comprehensive experimental details across different omics disciplines.

Purpose of the Study:

To introduce the Functional Genomics Experiment (FuGE) Model for standardized omics data description.
To provide a framework for capturing experimental workflows and multidimensional data.
To facilitate data integration across diverse experimental types for systems biology.

Main Methods:

The FuGE Model is an object model converted to an XML implementation for data exchange.
Software toolkits facilitate data handling and integration with relational databases.
The model serves as a framework for developing new technology-specific data standards.

Main Results:

FuGE provides a unified model for describing sample processing and experimental protocols.
It enables data integration across various omics experiments, supporting systems biology.
FuGE has been adopted by the Proteomics Standards Initiative (PSI) for new data formats.

Conclusions:

The FuGE Model offers a practical solution for managing complex omics data.
It promotes data standardization and interoperability, crucial for advancing systems biology.
The model empowers laboratories and developers to enhance data management and create new standards.