Library for working with OBO Library Ontologies and associations
Project description
ontobio - a python API for working with ontology graphs
=======================================================
This module provides objects and utility methods for working with
ontologies and associations of entities (genes, variants, etc) to
ontology classes.
The ontologies and associations can either be local files or provided by
remote services (currently the OntoBee SPARQL service for ontologies and
a Monarch or GO Golr service for associations).
Ontologies
==========
There are two ways of initiating an ontology object:
- via a local obo-json file
- via remote connections to OBO PURLs
- via remote query to a SPARQL service (currently ontobee, but soon
others)
Persistent caching is used (currently cachier) to avoid repeated
expensive I/O connections
This is handled via the `ontol\_manager.py <ontobio/ontol_manager.py>`__
module. This creates an ontology object (see
`ontol.py <ontobio/ontol.py>`__ ).
Note that object modeling is lightweight - we use the python networkx
package for representing the basic graph portion of an ontology. See
also the `obographs <https://github.com/geneontology/obographs>`__ spec.
Associations
============
See the ontobio.golr backage
Command Line Usage
==================
Initial Setup
-------------
::
export PATH $HOME/repos/bioink-api/ontobio/bin
ogr -h
Note you need to be connected to a network
Note: command line interface may change
Connecting to ontologies
------------------------
Specify an ontology with the ``-r`` option. this will always be the OBO
name, for example ``go``, ``cl``, ``mp``, etc
- ``-r go`` connect to GO via default method (currently SPARQL)
- ``-r obo:go`` connect to GO via download and cache of ontology from
PURL
- ``-r /users/my/my-ontologies/go.json`` use local download of ontology
In the following we assume default method, but can be substituted.
Ancestors queries
-----------------
List all ancestors:
::
ogr -r cl neuron
Show ancestors as tree, following only subclass:
::
ogr -r cl -p subClassOf -t tree neuron
generates:
::
% GO:0005623 ! cell
% CL:0000003 ! native cell
% CL:0000255 ! eukaryotic cell
% CL:0000548 ! animal cell
% CL:0002319 ! neural cell
% CL:0000540 ! neuron *
% CL:0002371 ! somatic cell
% CL:0002319 ! neural cell
% CL:0000540 ! neuron *
Descendants of neuron, parts and subtypes
::
ogr -r cl -p subClassOf -p BFO:0000050 -t tree -d d neuron
Descendants and ancestors of neuron, parts and subtypes
::
ogr -r cl -p subClassOf -p BFO:0000050 -t tree -d du neuron
All ancestors of all classes 2 levels down from subclass-roots within
CL:
::
ogr -r cl -P CL -p subClassOf -t tree -d u -L 2
Visualization using obographviz
-------------------------------
Requires: https://www.npmjs.com/package/obographviz
Add og2dot.js to path
::
ogr -p subClassOf BFO:0000050 -r go -t png a nucleus
This proceeds by:
1. Using the python ontobio library to extract a networkx subgraph
around the specified node
2. Write as obographs-json
3. Calls og2dot.js
Output:
.. figure:: https://github.com/biolink/biolink-api/raw/master/ontobio/docs/nucleus.png
:alt: img
img
Search
------
List exact matches to neuron
::
ogr -r cl neuron
Terms starting with neuron, SQL style
::
ogr -r cl neuron%
Terms starting with neuron, regex (equivalent to above)
::
ogr -r cl -s r ^neuron
Terms ending with neuron
::
ogr -r cl -s r neuron$
Terms containing the string neuron
::
ogr -r cl -s r neuron
Note: any of the above can be fed into other renderers, e.g. trees,
graphs
E.g. terms containing neuron, to obo
::
ogr -r cl %neuron% -t obo
E.g. terms ending neuron, to tree
::
ogr -r cl %neuron -t tree
=======================================================
This module provides objects and utility methods for working with
ontologies and associations of entities (genes, variants, etc) to
ontology classes.
The ontologies and associations can either be local files or provided by
remote services (currently the OntoBee SPARQL service for ontologies and
a Monarch or GO Golr service for associations).
Ontologies
==========
There are two ways of initiating an ontology object:
- via a local obo-json file
- via remote connections to OBO PURLs
- via remote query to a SPARQL service (currently ontobee, but soon
others)
Persistent caching is used (currently cachier) to avoid repeated
expensive I/O connections
This is handled via the `ontol\_manager.py <ontobio/ontol_manager.py>`__
module. This creates an ontology object (see
`ontol.py <ontobio/ontol.py>`__ ).
Note that object modeling is lightweight - we use the python networkx
package for representing the basic graph portion of an ontology. See
also the `obographs <https://github.com/geneontology/obographs>`__ spec.
Associations
============
See the ontobio.golr backage
Command Line Usage
==================
Initial Setup
-------------
::
export PATH $HOME/repos/bioink-api/ontobio/bin
ogr -h
Note you need to be connected to a network
Note: command line interface may change
Connecting to ontologies
------------------------
Specify an ontology with the ``-r`` option. this will always be the OBO
name, for example ``go``, ``cl``, ``mp``, etc
- ``-r go`` connect to GO via default method (currently SPARQL)
- ``-r obo:go`` connect to GO via download and cache of ontology from
PURL
- ``-r /users/my/my-ontologies/go.json`` use local download of ontology
In the following we assume default method, but can be substituted.
Ancestors queries
-----------------
List all ancestors:
::
ogr -r cl neuron
Show ancestors as tree, following only subclass:
::
ogr -r cl -p subClassOf -t tree neuron
generates:
::
% GO:0005623 ! cell
% CL:0000003 ! native cell
% CL:0000255 ! eukaryotic cell
% CL:0000548 ! animal cell
% CL:0002319 ! neural cell
% CL:0000540 ! neuron *
% CL:0002371 ! somatic cell
% CL:0002319 ! neural cell
% CL:0000540 ! neuron *
Descendants of neuron, parts and subtypes
::
ogr -r cl -p subClassOf -p BFO:0000050 -t tree -d d neuron
Descendants and ancestors of neuron, parts and subtypes
::
ogr -r cl -p subClassOf -p BFO:0000050 -t tree -d du neuron
All ancestors of all classes 2 levels down from subclass-roots within
CL:
::
ogr -r cl -P CL -p subClassOf -t tree -d u -L 2
Visualization using obographviz
-------------------------------
Requires: https://www.npmjs.com/package/obographviz
Add og2dot.js to path
::
ogr -p subClassOf BFO:0000050 -r go -t png a nucleus
This proceeds by:
1. Using the python ontobio library to extract a networkx subgraph
around the specified node
2. Write as obographs-json
3. Calls og2dot.js
Output:
.. figure:: https://github.com/biolink/biolink-api/raw/master/ontobio/docs/nucleus.png
:alt: img
img
Search
------
List exact matches to neuron
::
ogr -r cl neuron
Terms starting with neuron, SQL style
::
ogr -r cl neuron%
Terms starting with neuron, regex (equivalent to above)
::
ogr -r cl -s r ^neuron
Terms ending with neuron
::
ogr -r cl -s r neuron$
Terms containing the string neuron
::
ogr -r cl -s r neuron
Note: any of the above can be fed into other renderers, e.g. trees,
graphs
E.g. terms containing neuron, to obo
::
ogr -r cl %neuron% -t obo
E.g. terms ending neuron, to tree
::
ogr -r cl %neuron -t tree
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