OntoGraph Package

Description

A lightweight Python package for loading, representing, and efficiently querying biological ontologies as graph structures.

Installation

This package is under development, by this time you can clone the repository and install the package in "editable" (-e or --editable) mode

git clone https://github.com/saezlab/ontograph.git
uv pip install -e .

Usage

Interacting locally with the OBO Foundry catalog

from ontograph.client import ClientCatalog

# Instantiate a client for your catalog
client_catalog = ClientCatalog(cache_dir="./data/out")

# Load the catalog, in case this one doesn't exist it will be downloaded automatically in the cache folder you specify.
client_catalog.load_catalog()

Retrieve the list of ontologies (name_id and description) in OBO Foundries

ontologies_list = client_catalog.list_available_ontologies()

Print the list in a suitable format

client_catalog.print_available_ontologies()

Obtain metadata about an specific ontology

metadata_go = client_catalog.get_ontology_metadata(ontology_id="go", show_metadata=True) # Print in terminal

Interacting with an ontology

Create a client for your ontology

from ontograph.client import ClientOntology

# Instantiate a client for your ontology
client_dummy_ontology = ClientOntology(cache_dir="./data/out")

# Load a dummy ontology, we prepare a simple one to try out this package.
client_dummy_ontology.load(file_path_ontology="./tests/resources/dummy_ontology.obo")

Queries for your ontology

We included a demo file containing the following graph representing an ontology. The Node "Z" represents the Root node of this ontology.

graph TB
    
    Z((Z)) --> A((A))
    Z((Z)) --> B((B))
    Z((Z)) --> C((C))

    A((A)) --> D((D))
    B((B)) --> H((H))
    B((B)) --> I((I))
    C((C)) --> J((J))

    D((D)) --> E((E))
    D((D)) --> F((F))
    D((D)) --> G((G))
    H((H)) --> K((K))
    I((I)) --> L((L))
    J((J)) --> M((M))

    E((E)) --> N((N))
    F((F)) --> O((O))
    F((F)) --> Y((Y))
    G((G)) --> K1((K1))
    G((G)) --> K2((K2))
    K((K)) --> Q((Q))
    K((K)) --> G1((G))
    M((M)) --> S((S))

    G1((G)) --> K11((K1))
    G1((G)) --> K21((K2))
    S((S)) --> T((T))

    T((T)) --> U((U))
    
    U((T)) --> V((V))
    U((U)) --> W((W))
    
    W((W)) --> Y1((Y))

Loading

Navigation

• client_dummy_ontology.get_ancestors(term_id="S")
• list(client_dummy_ontology.get_ancestors_with_distance(term_id="S"))
• client_dummy_ontology.get_children(term_id="A")
• client_dummy_ontology.get_descendants(term_id="U")
• list(client_dummy_ontology.get_descendants_with_distance(term_id="U"))
• client_dummy_ontology.get_parents(term_id="U")
• client_dummy_ontology.get_root()
• client_dummy_ontology.get_siblings(term_id="K1")
• client_dummy_ontology.get_term(term_id="E")

Relations

• client_dummy_ontology.get_common_ancestors(node_ids=["K", "L"])
• client_dummy_ontology.get_lowest_common_ancestors(node_ids=["K", "L"])
• client_dummy_ontology.is_ancestor(ancestor_node="A", descendant_node="N")
• client_dummy_ontology.is_descendant(descendant_node="A", ancestor_node="N")
• client_dummy_ontology.is_sibling(nodeA="F", nodeB="G")

Introspection

• client_dummy_ontology.get_distance_from_root(term_id="V")
• client_dummy_ontology.get_path_between(nodeA="Q", nodeB="B")
• trajectories = client_dummy_ontology.get_trajectories_from_root(term_id="Y")
• client_dummy_ontology.print_term_trajectories_tree(trajectories)

Further steps

If you are interested in loading an ontology from the catalog, just use the name_id and format of that specific ontology. For instance, name_id="go" and format="obo".

client_go = ClientOntology()
client_go.load(name_id="go", format="obo")

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

• TODO: add contribution guidelines. All of them can be modified in the mkdocs documentation (./docs/community)

License

MIT