Parsing, validation, and analysis of BEL graphs
Project description
PyBEL is a Python software package that parses BEL scripts, validates their semantics, applies common graph
algorithms, and facilitates data interchange between common formats and database systems like JSON, CSV, SQL, and Neo4J.
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Biological Expression Language (BEL) is a domain-specific language that enables the expression of complex molecular relationships and their context in a machine-readable form. Its simple grammar and expressive power have led to its successful use in the IMI project, AETIONOMY, to describe complex disease networks with several thousands of relationships.
PyBEL provides a simple API so bioinformaticians and scientists with limited programming knowledge can easily
use it to interface with BEL graphs, but is built on a rich framework that can be extended to develop new algorithms.
>>> import pybel, networkx
>>> g = pybel.from_url('http://resource.belframework.org/belframework/1.0/knowledge/small_corpus.bel')
>>> networkx.draw(g) # NOTE: requires matplotlib as a dependency, which isn't automatically installedPyBEL also installs a command line interface with the command pybel for simple utilities such as data
conversion. Need help? All logs go to ~/.pybel or add -v for verbose output to the standard error
stream. In this example, a BEL file is exported to GraphML for viewing in Cytoscape.
$ pybel convert --path ~/Desktop/example.bel --graphml ~/Desktop/example.graphmlIn Cytoscape, open with Import > Network > From File.
Installation
PyBEL can be installed easily from PyPI with the following code in your favorite terminal:
pip install pybelSee the installation documentation for more advanced
instructions. Also, check the change log at CHANGELOG.rst.
Contributing
Contributions, whether filing an issue, making a pull request, or forking, are appreciated. See
CONTRIBUTING.rst for more information on getting involved.
Acknowledgements
This software is proudly built with Paul McGuire’s PyParsing package.
Scott Colby designed our logo and provided sage advice
Core Team: Charles Tapley Hoyt, Andrej Konotopez, and Christian Ebeling
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