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Curation of Neurodegeneration in Biological Expression Language (CONIB)

Home Page: https://pharmacome.github.io/conib

License: Creative Commons Zero v1.0 Universal

Python 0.07% Jupyter Notebook 99.93%
biological-expression-language biocuration networks-biology

conib's Introduction

Curation of Neurodegeneration in BEL (CONIB)
Travis CI PyPI DOI

This knowledge repository, developed during the Human Brain Pharmacome project, contains documents in Biological Expression Language (BEL) describing neurodegenerative disease phenomena such as tauopathies.

InstallationUsageContributingLicense

Content

  • conib: This folder contains curated BEL documents. Most have been organized by category (protein of interest, biological process of interest, pathway, etc.). Almost BEL documents correspond to a single publication, but a select few contain content from several due to topic-based curation.
  • docs: This folder contains automatically generated HTML summaries of the content that can best be accesed through https://pharmacome.github.io/conib.
  • notebooks: This folder contains various Jupyter notebooks associated with the repository. Since many of them use PyBEL-Jupyter, they may be best viewed using NBViewer.

Curation Planning

Here are a few links for navigating the GitHub issues and pull requests:

Installation

conib can be installed from PyPI with the following command:

$ pip install conib

or the latest version can be installed from GitHub with:

$ pip install git+https://github.com/pharmacome/conib.git

Usage

The graph can be loaded with:

import conib

# Get a dictionary of names to graphs
graphs = conib.get_graphs()

# Get all BEL documents as a single graph
graph = conib.get_graph()

# Get all knowledge as INDRA statements
stmts = conib.get_indra_statements()

The latest compiled version of the graph can be retrieved remotely with:

import requests
import pybel

URL = 'https://github.com/pharmacome/conib/raw/master/conib/_cache.bel.nodelink.json'
res_json = requests.get(URL).json()

graph = pybel.from_nodelink(res_json)

# Get INDRA statements
from indra.sources.bel import process_pybel_graph
pbp = process_pybel_graph(graph)
pbp.get_statements()
stmts = pbp.statements

# Quick version
stmts = pybel.to_indra_statements(graph)

Contributing

Contributions are welcome! Please submit all pull requests to https://github.com/pharmacome/conib.

License

  • BEL scripts in this repository are licensed under the CC0 1.0 Universal.
  • Python source code in this repository is licensed under the MIT license.

More BEL Content

See A Listing of Publicly Available Content in the Biological Expression Language (BEL) on Charles Tapley Hoyt's blog for more BEL content.

conib's People

Contributors

aldisirana avatar cthoyt avatar ddomingof avatar ewollert avatar lingling93 avatar sgebel avatar sspalek avatar trushaadeshara8 avatar yojanagadiya avatar

Stargazers

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Watchers

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conib's Issues

Investigate overlapping pathways between diseases

  1. Find all genes related to each disease via annotations or association relationships
  2. Run pathway enrichment analysis to identify pathways of relevance
  3. Investigate if these annotations can be made at a document level?

Rebranding

This has sort of gone beyond the moniker of TauBase. Should this be NeuroMMSig?

  • Keep calling it TauBase, even though it's much more than just Tau/tauopathies
  • NeuroMMSig (but we need to maintain the difference between this work and AETIONOMY. NeuroMMSig can be pragmatic and pull in lots of sources)
  • Curation of Neurodegeneration in Biological Expression Language (CONIBEL) yeahh look at that acronym, just like CONSO

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