Python frontend to ontologies.
A Python frontend to ontologies.
🚩 Table of Contents
Pronto is a Python library to parse, browse, create, and export
ontologies, supporting several ontology languages and formats. It
implement the specifications of the
Open Biomedical Ontologies 1.4
in the form of an safe high-level interface. If you're only interested in
parsing OBO or OBO Graphs document, you may wish to consider
🏳️ Supported Languages
- Open Biomedical Ontologies 1.4. Because this format is fairly new, not all OBO ontologies can be parsed at the moment. See the OBO Foundry roadmap listing the compliant ontologies, and don't hesitate to contact their developers to push adoption forward.
- OBO Graphs in JSON format. The format is not yet stabilized to the results may change from file to file.
- Ontology Web Language 2 in RDF/XML format. OWL2 ontologies are reverse translated to OBO using the mapping defined in the OBO 1.4 Semantics.
pip is the easiest:
# pip install pronto # if you have the admin rights $ pip install pronto --user # install it in a user-site directory
There is also a
conda recipe in the
$ conda install -c bioconda pronto
Finally, a development version can be installed from GitHub
setuptools, provided you have the right dependencies
$ git clone https://github.com/althonos/pronto $ cd pronto # python setup.py install
If you're only reading ontologies, you'll only use the
class, which is the main entry point.
>>> from pronto import Ontology
It can be instantiated from a path to an ontology in one of the supported formats, even if the file is compressed:
>>> go = Ontology("tests/data/go.obo.gz")
Loading a file from a persistent URL is also supported, although you may also
want to use the
Ontology.from_obo_library method if you're using persistent
URLs a lot:
>>> cl = Ontology("http://purl.obolibrary.org/obo/cl.obo") >>> stato = Ontology.from_obo_library("stato.owl")
🏷️ Get a term by accession
Ontology objects can be used as mappings to access any entity
they contain from their identifier in compact form:
>>> cl['CL:0002116'] Term('CL:0002116', name='B220-low CD38-positive unswitched memory B cell')
Note that when loading an OWL ontology, URIs will be compacted to CURIEs whenever possible:
>>> aeo = Ontology.from_obo_library("aeo.owl") >>> aeo["AEO:0000078"] Term('AEO:0000078', name='lumen of tube')
🖊️ Create a new term from scratch
We can load an ontology, and edit it locally. Here, we add a new protein class to the Protein Ontology.
>>> pr = Ontology.from_obo_library("pr.obo") >>> brh = ms.create_term("PR:XXXXXXXX") >>> brh.name = "Bacteriorhodopsin" >>> brh.superclasses().add(pr["PR:000001094"]) # is a rhodopsin-like G-protein >>> brh.disjoint_from.add(pr["PR:000036194"]) # disjoint from eukaryotic proteins
✏️ Convert an OWL ontology to OBO format
Ontology.dump method can be used to serialize an ontology to any of the
supported formats (currently OBO and OBO JSON):
>>> edam = Ontology("http://edamontology.org/EDAM.owl") >>> with open("edam.obo", "wb") as f: ... edam.dump(f, format="obo")
🌿 Find ontology terms without subclasses
terms method of
Ontology instances can be used to
iterate over all the terms in the ontology (including the
ones that are imported). We can then use the
Term objects to check is the term is a leaf in the
class inclusion graph.
>>> ms = Ontology("ms.obo") >>> for term in ms.terms(): ... if term.is_leaf(): ... print(term.id) MS:0000000 MS:1000001 ...
🤫 Silence warnings
pronto is explicit about the parts of the code that are doing
non-standard assumptions, or missing capabilities to handle certain
constructs. It does so by raising warnings with the
which can get quite verbose.
If you are fine with the inconsistencies, you can manually disable
warning reports in your consumer code with the
import warnings import pronto warnings.filterwarnings("ignore", category=pronto.warnings.ProntoWarning)
📖 API Reference
A complete API reference can be found in the
online documentation, or
directly from the command line using
$ pydoc pronto.Ontology
This library is provided under the open-source MIT license. Please cite this library if you are using it in a scientific context using the following DOI: 10.5281/zenodo.595572
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