the genome-scale metabolic model test suite
Our goal in promoting this tool is to achieve two major shifts in the metabolic model building community:
- Models should be version-controlled such that changes can be tracked and if necessary reverted. Ideally, they should be available through a public repository such as GitHub that will allow other researchers to inspect, share, and contribute to the model.
- Models should, for the benefit of the community and for research gain, live up to certain standards and minimal functionality.
The memote tool therefore performs four subfunctions:
- Create a skeleton git repository for the model.
- Run the current model through a test suite that represents the community standard.
- Generate an informative report which details the results of the test suite in a visually appealing manner.
- (Re-)compute test statistics for an existing version controlled history of a metabolic model.
And in order to make this process as easy as possible the generated repository can easily be integrated with continuous integration testing providers such as Travis CI, which means that anytime you push a model change to GitHub, the test suite will be run automatically and a report will be available for you to look at via GitHub pages for your repository.
- Documentation: https://memote.readthedocs.io/.
We highly recommend creating a Python virtualenv for your model testing purposes.
To install memote, run this command in your terminal:
$ pip install memote
This is the preferred method to install memote, as it will always install the most recent stable release.
For comments and questions get in touch via
Are you excited about this project? Consider contributing by adding novel tests, reporting or fixing bugs, and generally help us make this a better software for everyone.
- Copyright (c) 2017, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark.
- Free software: Apache Software License 2.0
Memote relies on click for the command line interface, pytest for unit and model tests, gitpython for interacting with git repositories, pandas for tabular datastructures and data input, jinja2 for interacting with HTML templates, cobrapy for analysing genome-scale metabolic models, python_libsbml for reading and writing Systems Biology Markup Language (SBML), ruamel for handling YAML generation, travispy and travis-encrypt for interacting with Travis CI, pygithub for access to the Github API, sympy for matrix calculations, sqlalchemy for managing history results, numpydoc for beautifully formatted doc strings using sphinx, pylru for caching, goodtables for validation of tabular data, depinfo for pretty printing our dependencies, six and future for backward and forward compatibility.
The initial development of memote has received funding from:
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