Node normalization takes a CURIE, and returns:
- The preferred CURIE for this entity
- All other known equivalent identifiers for the entity
- Semantic types for the entity as defined by the Biolink Model
The data currently served by Node Normalization is created by the prototype project Babel, which attempts to find identifier equivalences, and makes sure that CURIE prefixes are BioLink Model compliant. The NodeNormalization service, however, is independent of Babel and as improved identifier equivalence tools are developed, their results can be easily incorporated.
To determine whether Node Normalization is likely to be useful, check /get_semantic_types, which lists the BioLink semantic types for which normalization has been attempted, and /get_curie_prefixes, which lists the number of times each prefix is used for a semantic type.
For examples of service usage, see the example notebook.
Most users of NodeNormalization can access it via the public service but instructions follow for standing up a new instance of the service.
Create a virtual environment
> python -m venv nodeNormalization-env
Activate the virtual environment
# on Linux
> source nodeNodemaization-env/bin/activate
# on Windows
> source nodeNormalization-env/Scripts/activate
Install requirements
> pip install -r requirements.txt
The Load script can be used to put data to a running Redis instance. Inline with this we recommend using R3 (Redis-REST with referencing).
Once we have a running
redis-server we can modify our config file located at ./config.json as the following.
{
"compendium_directory": "<path to files>",
"redis_port": <redis-server-port>,
"redis_host": "<redis-host>",
"redis_password": "<redis-password>"
}
compendium_directory Is a path to the files that are going to be loaded to the redis instance. And example of the files' contents
looks like :
{id": {"identifier": "PUBCHEM:50986940"}, "equivalent_identifiers": [{"identifier": "PUBCHEM:50986940"}, {"identifier": "INCHIKEY:CYMOSKLLKPIPCD-UHFFFAOYSA-N"}], "type": ["chemical_substance", "named_thing", "biological_entity", "molecular_entity"]}
{"id": {"identifier": "CHEMBL.COMPOUND:CHEMBL1546789", "label": "CHEMBL1546789"}, "equivalent_identifiers": [{"identifier": "CHEMBL.COMPOUND:CHEMBL1546789", "label": "CHEMBL1546789"}, {"identifier": "PUBCHEM:4879549"}, {"identifier": "INCHIKEY:FUIYIXDZTPMQEH-UHFFFAOYSA-N"}], "type": ["chemical_substance", "named_thing", "biological_entity", "molecular_entity"]}
where each line is a json parsable entry.
After the proper configuration run
> cd src
> python load.py