Organism-Specific Genome-Scale Metabolic Model Continuous Validation with Memote

This is a Memote repository for a genome-scale metabolic model of Curtobacterium. Current and past reports may be viewed at https://pnnl-predictive-phenomics.github.io/csc009-gem

Overview

This repository contains the continuous validation environment for an organism-specific genome-scale metabolic model (GEM) using Memote. Memote is a software tool that provides a suite of tests to ensure the quality and consistency of metabolic models. By integrating Memote into a continuous integration (CI) workflow, we can automatically validate updates to the GEM, ensuring that model modifications improve or maintain the model's integrity.

Features

• Automated Testing: Utilize Memote for automated testing of metabolic model properties such as stoichiometric consistency, gene-protein-reaction (GPR) associations, and reaction SBO terms.
• Continuous Integration: Integrate with CI platforms like GitHub Actions or Travis CI to run Memote tests on every commit, ensuring model quality over time.
• Version Control: Track changes to the GEM using Git, allowing for revertible updates and collaborative development.
• Memote Report: Generate and store Memote history reports to visualize the model's quality over time and identify areas for improvement.
• Documentation: Detailed instructions for setting up the environment, running tests, and interpreting Memote reports.

Quick Start

1. Clone the Repository

   git clone https://github.com/pnnl-predictive-phenomics/csc009-gem.git
   cd csc009-gem
   

2. Install Dependencies

   pip install -r requirements.txt
   

3. Run Memote Tests Locally

   memote run --filename "model.xml" --pytest-args "-v"
   

4. Set Up Continuous Integration

   • Configure your CI tool of choice by following the provided CI configuration guide.

5. Interpret Results

   • Review the output of Memote tests and reports for insights into model quality and validation status.

Contributing

We welcome contributions to improve the model's quality and extend its capabilities. Please read CONTRIBUTING.md for guidelines on how to contribute.

License

This project is licensed under the Apache 2.0 license. See the LICENSE file for details.

Contact

For support or to report issues, please file an issue on the GitHub issue tracker or contact the repository maintainers at [[email protected]].

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