A package for generating curation sheets for rationally enriching a BEL graph using INDRA and PyBEL.
A package for generating curation sheets for rationally enriching a BEL graph.
If you find bel_enrichment useful in your work, please consider citing :
|||Hoyt, C. T., et al (2019). Re-curation and Rational Enrichment of Knowledge Graphs in Biological Expression Language. Database, Volume 2019, 2019, baz068.|
bel_enrichment can be installed from PyPI with the following command:
$ pip install bel_enrichment
The latest version can be installed from GitHub with:
$ pip install git+https://github.com/bel-enrichment/bel-enrichment.git
You’ll need to set the INDRA_DB_REST_URL and INDRA_DB_REST_API_KEY in the ~/.config/indra/config.ini file. Please contact the INDRA team for credentials.
Generate a folder full of curation sheets based on the given BEL graph that has been pre-compiled by PyBEL. Use --info-cutoff to specify the minimum information density cutoff. 1.0 means that the node has no edges, .5 means one edge, and so on. Use --belief-cutoff to specify the minimum belief score from INDRA for adding the statement to the sheet. Higher belief means the more chance a statement is already right.
$ bel-enrichment from-graph zhang2011.bel --directory ~/Desktop/zhang-enrichment
Generate a ranking for genes based on the information content in a given BEL graph that has been pre-compiled by PyBEL.
$ bel-enrichment ranks zhang2011.bel
If you want to make a curation sheet based on a PubMed identifier (or list of them) do this:
$ bel-enrichment from-pmids 20585587 20585588 > ~/Desktop/document_based.tsv
If you want to make a curation sheet based on an entity, do this:
$ bel-enrichment from-agents MAPT GSK3B > ~/Desktop/topic_based.tsv
|||Gyori, B. M., et al. (2017). From word models to executable models of signaling networks using automated assembly. Molecular Systems Biology, 13(11), 954.|
|||Hoyt, C. T., Konotopez, A., Ebeling, C., (2017). PyBEL: a computational framework for Biological Expression Language. Bioinformatics (Oxford, England), 34(4), 703–704.|
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