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https://github.com/pybel/pybel
🌶️ An ecosystem in Python for working with the Biological Expression Language (BEL)
https://github.com/pybel/pybel
bioinformatics biological-expression-language bioregistry domain-specific-language networks networks-biology pybel systems-biology
Last synced: about 2 months ago
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🌶️ An ecosystem in Python for working with the Biological Expression Language (BEL)
- Host: GitHub
- URL: https://github.com/pybel/pybel
- Owner: pybel
- License: mit
- Created: 2016-09-16T12:09:49.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2022-07-19T15:58:44.000Z (about 2 years ago)
- Last Synced: 2024-05-02T02:19:41.764Z (5 months ago)
- Topics: bioinformatics, biological-expression-language, bioregistry, domain-specific-language, networks, networks-biology, pybel, systems-biology
- Language: Python
- Homepage: http://pybel.readthedocs.io
- Size: 12.3 MB
- Stars: 130
- Watchers: 9
- Forks: 30
- Open Issues: 34
-
Metadata Files:
- Readme: README.rst
- Changelog: CHANGELOG.rst
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
PyBEL |zenodo| |build| |coverage| |documentation| |bioregistry| |black|
=======================================================================
`PyBEL `_ is a pure Python package for parsing and handling biological networks encoded in
the `Biological Expression Language `_
(BEL).
It facilitates data interchange between data formats like `NetworkX `_,
Node-Link JSON, `JGIF `_, CSV, SIF,
`Cytoscape `_, `CX `_,
`INDRA `_, and `GraphDati `_; database systems
like SQL and `Neo4J `_; and web services like `NDEx `_,
`BioDati Studio `_, and `BEL Commons `_. It also
provides exports for analytical tools like `HiPathia `_,
`Drug2ways `_ and `SPIA `_;
machine learning tools like `PyKEEN `_ and
`OpenBioLink `_; and others.
Its companion package, `PyBEL Tools `_, contains a
suite of functions and pipelines for analyzing the resulting biological networks.
We realize that we have a name conflict with the python wrapper for the cheminformatics package, OpenBabel. If you're
looking for their python wrapper, see `here `_.
Citation
--------
If you find PyBEL useful for your work, please consider citing:
.. [1] Hoyt, C. T., *et al.* (2017). `PyBEL: a Computational Framework for Biological Expression Language
`_. *Bioinformatics*, 34(December), 1–2.
Installation |pypi_version| |python_versions| |pypi_license|
------------------------------------------------------------
PyBEL can be installed easily from `PyPI `_ with the following code in
your favorite shell:
.. code-block:: sh
$ pip install pybel
or from the latest code on `GitHub `_ with:
.. code-block:: sh
$ pip install git+https://github.com/pybel/pybel.git
See the `installation documentation `_ for more advanced
instructions. Also, check the change log at `CHANGELOG.rst `_.
Getting Started
---------------
More examples can be found in the `documentation `_ and in the
`PyBEL Notebooks `_ repository.
Compiling and Saving a BEL Graph
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This example illustrates how the a BEL document from the `Human Brain Pharmacome
`_ project can be loaded and compiled directly from GitHub.
.. code-block:: python
>>> import pybel
>>> url = 'https://raw.githubusercontent.com/pharmacome/conib/master/hbp_knowledge/proteostasis/kim2013.bel'
>>> graph = pybel.from_bel_script_url(url)
Other functions for loading BEL content from many formats can be found in the
`I/O documentation `_.
Note that PyBEL can handle `BEL 1.0 `_
and `BEL 2.0+ `_
simultaneously.
After you have a BEL graph, there are numerous ways to save it. The ``pybel.dump`` function knows
how to output it in many formats based on the file extension you give. For all of the possibilities,
check the `I/O documentation `_.
.. code-block:: python
>>> import pybel
>>> graph = ...
>>> # write as BEL
>>> pybel.dump(graph, 'my_graph.bel')
>>> # write as Node-Link JSON for network viewers like D3
>>> pybel.dump(graph, 'my_graph.bel.nodelink.json')
>>> # write as GraphDati JSON for BioDati
>>> pybel.dump(graph, 'my_graph.bel.graphdati.json')
>>> # write as CX JSON for NDEx
>>> pybel.dump(graph, 'my_graph.bel.cx.json')
>>> # write as INDRA JSON for INDRA
>>> pybel.dump(graph, 'my_graph.indra.json')
Summarizing the Contents of the Graph
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The ``BELGraph`` object has several "dispatches" which are properties that organize its various functionalities.
One is the ``BELGraph.summarize`` dispatch, which allows for printing summaries to the console.
These examples will use the `RAS Model `_ from EMMAA,
so you'll have to be sure to ``pip install indra`` first. The graph can be acquired and summarized with
``BELGraph.summarize.statistics()`` as in:
.. code-block:: python
>>> import pybel
>>> graph = pybel.from_emmaa('rasmodel', date='2020-05-29-17-31-58') # Needs
>>> graph.summarize.statistics()
--------------------- -------------------
Name rasmodel
Version 2020-05-29-17-31-58
Number of Nodes 126
Number of Namespaces 5
Number of Edges 206
Number of Annotations 4
Number of Citations 1
Number of Authors 0
Network Density 1.31E-02
Number of Components 1
Number of Warnings 0
--------------------- -------------------
The number of nodes of each type can be summarized with ``BELGraph.summarize.nodes()`` as in:
.. code-block:: python
>>> graph.summarize.nodes(examples=False)
Type (3) Count
------------ -------
Protein 97
Complex 27
Abundance 2
The number of nodes with each namespace can be summarized with ``BELGraph.summarize.namespaces()`` as in:
.. code-block:: python
>>> graph.summarize.namespaces(examples=False)
Namespace (4) Count
--------------- -------
HGNC 94
FPLX 3
CHEBI 1
TEXT 1
The edges can be summarized with ``BELGraph.summarize.edges()`` as in:
.. code-block:: python
>>> graph.summarize.edges(examples=False)
Edge Type (12) Count
--------------------------------- -------
Protein increases Protein 64
Protein hasVariant Protein 48
Protein partOf Complex 47
Complex increases Protein 20
Protein decreases Protein 9
Complex directlyIncreases Protein 8
Protein increases Complex 3
Abundance partOf Complex 3
Protein increases Abundance 1
Complex partOf Complex 1
Protein decreases Abundance 1
Abundance decreases Protein 1
Grounding the Graph
~~~~~~~~~~~~~~~~~~~
Not all BEL graphs contain both the name and identifier for each entity. Some even use non-standard prefixes
(also called **namespaces** in BEL). Usually, BEL graphs are validated against controlled vocabularies,
so the following demo shows how to add the corresponding identifiers to all nodes.
.. code-block:: python
from urllib.request import urlretrieve
url = 'https://github.com/cthoyt/selventa-knowledge/blob/master/selventa_knowledge/large_corpus.bel.nodelink.json.gz'
urlretrieve(url, 'large_corpus.bel.nodelink.json.gz')
import pybel
graph = pybel.load('large_corpus.bel.nodelink.json.gz')
import pybel.grounding
grounded_graph = pybel.grounding.ground(graph)
Note: you have to install ``pyobo`` for this to work and be running Python 3.7+.
Displaying a BEL Graph in Jupyter
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
After installing ``jinja2`` and ``ipython``, BEL graphs can be displayed in Jupyter notebooks.
.. code-block:: python
>>> from pybel.examples import sialic_acid_graph
>>> from pybel.io.jupyter import to_jupyter
>>> to_jupyter(sialic_acid_graph)
Using the Parser
~~~~~~~~~~~~~~~~
If you don't want to use the ``pybel.BELGraph`` data structure and just want to turn BEL statements into JSON
for your own purposes, you can directly use the ``pybel.parse()`` function.
.. code-block:: python
>>> import pybel
>>> pybel.parse('p(hgnc:4617 ! GSK3B) regulates p(hgnc:6893 ! MAPT)')
{'source': {'function': 'Protein', 'concept': {'namespace': 'hgnc', 'identifier': '4617', 'name': 'GSK3B'}}, 'relation': 'regulates', 'target': {'function': 'Protein', 'concept': {'namespace': 'hgnc', 'identifier': '6893', 'name': 'MAPT'}}}
This functionality can also be exposed through a Flask-based web application with ``python -m pybel.apps.parser`` after
installing ``flask`` with ``pip install flask``. Note that the first run requires about a ~2 second delay to generate
the parser, after which each parse is very fast.
Using the CLI
~~~~~~~~~~~~~
PyBEL also installs a command line interface with the command :code:`pybel` for simple utilities such as data
conversion. In this example, a BEL document is compiled then exported to `GraphML `_
for viewing in Cytoscape.
.. code-block:: sh
$ pybel compile ~/Desktop/example.bel
$ pybel serialize ~/Desktop/example.bel --graphml ~/Desktop/example.graphml
In Cytoscape, open with :code:`Import > Network > From File`.
Contributing
------------
Contributions, whether filing an issue, making a pull request, or forking, are appreciated. See
`CONTRIBUTING.rst `_ for more information on getting
involved.
Acknowledgements
----------------
Support
~~~~~~~
The development of PyBEL has been supported by several projects/organizations (in alphabetical order):
- `The Cytoscape Consortium `_
- `Enveda Biosciences `_
- `Fraunhofer Center for Machine Learning `_
- `Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) `_
- `Harvard Program in Therapeutic Science - Laboratory of Systems Pharmacology `_
- `University of Bonn `_
Funding
~~~~~~~
- DARPA Young Faculty Award W911NF2010255 (PI: Benjamin M. Gyori).
- The `European Union `_, `European Federation of Pharmaceutical Industries and Associations
(EFPIA) `_, and `Innovative Medicines Initiative `_ Joint
Undertaking under `AETIONOMY `_ [grant number 115568], resources of which
are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and
EFPIA companies in kind contribution.
Logo
~~~~
The PyBEL `logo `_ was designed by `Scott Colby `_.
.. |build| image:: https://github.com/pybel/pybel/workflows/Tests/badge.svg
:target: https://github.com/pybel/pybel/actions
:alt: Build Status
.. |coverage| image:: https://codecov.io/gh/pybel/pybel/coverage.svg?branch=develop
:target: https://codecov.io/gh/pybel/pybel/branch/develop
:alt: Development Coverage Status
.. |documentation| image:: https://readthedocs.org/projects/pybel/badge/?version=latest
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:alt: Code Climate
.. |python_versions| image:: https://img.shields.io/pypi/pyversions/PyBEL.svg
:target: https://pypi.python.org/pypi/pybel
:alt: Stable Supported Python Versions
.. |pypi_version| image:: https://img.shields.io/pypi/v/PyBEL.svg
:target: https://pypi.python.org/pypi/pybel
:alt: Current version on PyPI
.. |pypi_license| image:: https://img.shields.io/pypi/l/PyBEL.svg
:target: https://github.com/pybel/pybel/blob/master/LICENSE
:alt: MIT License
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.. |bioregistry| image:: https://img.shields.io/static/v1?label=Powered%20by&message=Bioregistry&color=BA274A&style=flat&logo=image/png;base64,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