StonPy package¶

Installation¶

StonPy can be installed using pip:

pip install stonpy

You may also install the Neo4j APOC core Library, which is an optional dependency for StonPy. When installed, the library will make the get_map function of the stonpy.core module significantly faster.

Quickstart¶

The following snippet of code will make you started with StonPy. For a more complete tutorial, a Jupyter Notebook is available here.

from stonpy import STON

ston = STON("URI", "USER", "PASSWORD")
ston.create_map(sbgn_map="my_sbgn_file.sbgn", map_id="my_map_id")
my_query = """
    MATCH (m:Map {id: 'my_map_id'})-[r:HAS_GLYPH]->(p:StoichiometricProcess)
    RETURN p
"""
sbgn_files = ston.query_to_sbgn_file(
    query=my_query,
    sbgn_file="my_query_result.sbgn",
    merge_records=False
)

print(sbgn_files)

Package documentation¶

The complete documentation of the different modules of the package can be found here.

Completion algorithm¶

When the result of a query is a subgraph (including a unique node or relationship), it may be completed to form a “complete subgraph” using the stonpy.completion module. The documentation for the completion algorithm used in this module can be found here.

Command line interface¶

The StonPy package includes a command line interface (CLI) which allows users to perform all the operations supported by the library. StonPy’s CLI is installed with the package, and may be executed with the stonpy command:

stonpy --help

The complete documentation for the CLI can be found here.

Use cases¶

StonPy has been used in several projects for the integration and analysis of SBGN and CellDesigner maps. One may refer to the following papers for more details:

Anna Niarakis et al., A versatile and interoperable computational framework for the analysis and modeling of covid-19 disease mechanisms, bioRxiv preprint, 2022 [link]
Alexander Mazein, Adrien Rougny, Jonathan R Karr, Julio Saez-Rodriguez, Marek Ostaszewski, and Reinhard Schneider, Reusability and composability in process description maps: Ras–raf–mek–erk signalling, Briefings in bioinformatics, 22(5):bbab103, 2021 [link]
Adrien Rougny, Vasundra Touré, John Albanese, Dagmar Waltemath, Denis Shirshov, Anatoly Sorokin, Gary D Bader, Michael L Blinov, and Alexander Mazein, Sbgn bricks ontology as a tool to describe recurring concepts in molecular networks, Briefings in bioinformatics, 22(5):bbab049, 2021 [link]