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Introduction to ontquery

ontquery
Installation
SciCrunch api key
SciGraphRemote Usage
Building for release
Related issues
InterlexRemote Notes
InterLexRemote Usage

ontquery

a framework querying ontology terms

Installation

Ontquery supports two different use cases each with their own installation instructions.

By default ontquery installs only the stripped down core libraries so that it can be embedded an reused in other applications that need to reduce their dependnecies. For this use case packages can include ontquery as a dependency in their package requirements without any special changes e.g. ontquery>=0.0.6.

The second use case enables remote services via a plugin infrastructure. To install this version you should install or require using the pip extras syntax e.g. pip install "ontquery[services]".

SciCrunch api key

If you don't have your own SciGraph instance you will need a SciCunch API key in order to run the demos (e.g. python __init__.py).

To do this go to SciCrunch and register for an account and then get an api key.

You can then set the SCICRUNCH_API_KEY environment variable. For example in bash export SCICRUNCH_API_KEY=my-api-key.

See https://github.com/tgbugs/ontquery/blob/db8cad7463704bce9010651c3744452aa5370114/ontquery/__init__.py#L557-L558 for how to pass the key in.

SciGraphRemote Usage

from ontquery import OntQuery, OntTerm, OntCuries
from ontquery.plugins.namespaces.nifstd import CURIE_MAP
from ontquery.plugins.services.scigraph import SciGraphRemote

curies = OntCuries(CURIE_MAP)
query = OntQuery(SciGraphRemote(), instrumented=OntTerm)
OntTerm.query = query

list(query('mouse'))

3 potential matches are shown:

Query {'term': 'mouse', 'limit': 10} returned more than one result. Please review.

OntTerm('NCBITaxon:10090', label='Mus musculus', synonyms=['mouse', 'house mouse', 'mice C57BL/6xCBA/CaJ hybrid', 'Mus muscaris'])

OntTerm('NCBITaxon:10088', label='Mus <mouse, genus>', synonyms=['mouse', 'Mus', 'mice'])

OntTerm('BIRNLEX:167', label='Mouse', synonyms=['mouse', 'Mus musculus', 'house mouse'])

The one we are looking for is Mus musculus, and we can select that with OntTerm('NCBITaxon:10090', label'Mus musculus')= or with OntTerm(curie'NCBITaxon:10090')=.

This workflow works for a variety of categories:

species (e.g. 'mouse', 'rat', 'rhesus macaque')
brain area (e.g. 'hippocampus', 'CA1', 'S1')
cell type (e.g. 'mossy cell', 'pyramidal cell')
institution (e.g. 'UC San Francisco', 'Brown University')
disease (e.g. "Parkinson's Disease", 'ALS')

Building for release

python setup.py sdist --release && python setup.py bdist_wheel --universal --release Building a release requires a working install of pyontutils in order to build the scigraph client library. The --release tells setup to build the scigraph client.

InterlexRemote Notes

ilx_id and any key that takes a uri value can also be given a curie of that uri or a fragment and it will still work.

InterLexRemote Usage

To access InterLex programatically you can set SCICRUNCH_API_KEY or you can set INTERLEX_API_KEY either will work, but INTERLEX_API_KEY has priority if both are set.

Importing:

from ontquery.interlex import interlex_client

Setup for TEST:
This Should be used to test if your code works first
```
ilx_cli = interlex_client('test3.scicrunch.org')
```

Setup for PRODUCTION:

ilx_cli = interlex_client('scicrunch.org')

Adding Entity Needed:

added_entity_data = ilx_cli.add_entity(
    label = '',
    type = '', # term, fde, cde, pde, relationship, annotation
)

Adding Entity Example

added_entity_data = ilx_cli.add_entity(
    label = 'Label of entity you wish to create',
    type = 'A type that should be one of the following: term, relationship, annotation, cde, fde, pde',
    # subThingOf can take either iri or curie form of ID
    subThingOf = 'http://uri.interlex.org/base/ilx_0108124', # superclass or subClassOf ILX ID
    definition = 'Entities definition',
    comment = 'A comment to help understand entity',
    synonyms = ['synonym1', {'literal': 'synonym2', 'type': 'hasExactSynonym'}, 'etc'],
    # exisiting IDs are List[dict] with keys iri & curie
    existing_ids = [{'iri':'https://example.org/example_1', 'curie':'EXAMPLE:1'}],
    cid = 504,  # community ID
    predicates = {
        # annotation_entity_ilx_id : 'annotation_value',
        'http://uri.interlex.org/base/tmp_0381624': 'PMID:12345', # annotation
        # relationship_entity_ilx_id : 'entity2_ilx_id',
        'http://uri.interlex.org/base/ilx_0112772': 'http://uri.interlex.org/base/ilx_0100001', # relationship
    }
)

Updating Entity Example

updated_entity = update_entity( 
    ilx_id='ilx_1234567', 
    label='Brain', 
    type='term',  # options: term, pde, fde, cde, annotation, or relationship 
    definition='Official definition for entity.', 
    comment='Additional casual notes for the next person.', 
    superclass='ilx_1234567', 
    add_synonyms=[{ 
        'literal': 'Better Brains',  # label of synonym 
        'type': 'obo:hasExactSynonym',  # Often predicate defined in ref ontology. 
    }], 
    delete_synonyms=[{ 
        'literal': 'Brains',  # label of synonym 
        'type': 'obo:hasExactSynonym',  # Often predicate defined in ref ontology. 
    }], 
    add_existing_ids=[{ 
        'iri': 'http://purl.obolibrary.org/obo/UBERON_0000956', 
        'curie': 'UBERON:0000956',  # Obeys prefix:id structure. 
        'preferred': '1',  # Can be 0 or 1 with a type of either str or int. 
    }], 
    delet_existing_ids=[{ 
        'iri': 'http://purl.obolibrary.org/obo/UBERON_0000955', 
        'curie': 'UBERON:0000955',  # Obeys prefix:id structure. 
    }], 
    cid='504',  # SPARC Community, 
    status='0',  # remove delete 
)

Introduction to ontquery