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‎docs/source/conf.py‎

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"sklearn": ("https://scikit-learn.org/stable", None),
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"numpy": ("https://numpy.org/doc/stable", None),
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"scipy": ("https://docs.scipy.org/doc/scipy", None),
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"sssom": ("https://mapping-commons.github.io/sssom-py/", None),
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"bioregistry": ("https://bioregistry.readthedocs.io/en/stable/", None),
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"biomappings": ("https://biomappings.readthedocs.io/en/stable/", None),
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"curies": ("https://curies.readthedocs.io/en/stable/", None),
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"flask": ("https://flask.palletsprojects.com/", None),
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"pydantic": ("https://docs.pydantic.dev/latest/", None),
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"rdflib": ("https://rdflib.readthedocs.io/en/stable/", None),
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}
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autoclass_content = "both"

‎docs/source/index.rst‎

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want to make their toolchain more generic for loading, assembling, processing, and
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outputting semantic mappings.
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SeMRA is generally applicable in **any domain**, from biomedicine to particle physics to
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the digital humanities.
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Features
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--------
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1. An object model for semantic mappings (based on SSSOM)
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1. An object model for semantic mappings (based on the `Simple Standard for Sharing
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Ontological Mappings (SSSOM) <https://mapping-commons.github.io/sssom/>`_ and
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:mod:`sssom`)
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2. Functionality for assembling and reasoning over semantic mappings at scale
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3. A provenance model for automatically generated mappings
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4. A confidence model granular at the curator-level, mapping set-level, and community

‎src/semra/database.py‎

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.. code-block:: console
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$ uv pip install semra
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$ semra build
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The ``semra build`` command downloads and process all resource and constructs
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a database of unprocessed mappings.
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.. note::
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Downloading raw data resources can take on the order of hours to tens
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of hours depending on your internet connection and the reliability of
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the resources' respective servers.
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Processing and analysis can be run overnight on commodity hardware
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(e.g., a 2023 MacBook Pro with 36GB RAM).
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The SeMRA Raw Mappings Database can be downloaded from Zenodo at |raw|.
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After downloading all files and unzipping then, a web application wrapping
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the SeMRA Raw Mappings Database run locally on Docker with:
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.. code-block:: console
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$ sh run_on_docker.sh
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Navigate to http://localhost:8773 to see the web application.
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.. |raw| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.11082038.svg
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:target: https://doi.org/10.5281/zenodo.11082038
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"""
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import subprocess

‎src/semra/landscape/__init__.py‎

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.. code-block:: console
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$ git clone https://github.com/biopragmatics/semra.git
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$ cd semra
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$ uv pip install .[landscape]
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$ uv pip install semra[landscape]
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$ semra landscape
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The ``semra landscape`` command runs all pre-configured domain-specific mapping
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from .disease import DISEASE_CONFIGURATION
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from .gene import GENE_CONFIGURATION
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from .taxrank import TAXRANK_CONFIGURATION
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from ..pipeline import Configuration
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#: A list of domain-specific configurations
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CONFIGURATIONS: list[Configuration] = [
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ANATOMY_CONFIGURATION,
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CELL_CONFIGURATION,
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COMPLEX_CONFIGURATION,
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DISEASE_CONFIGURATION,
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GENE_CONFIGURATION,
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TAXRANK_CONFIGURATION,
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]
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__all__ = [
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"ANATOMY_CONFIGURATION",
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"CELL_CONFIGURATION",
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"COMPLEX_CONFIGURATION",
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"CONFIGURATIONS",
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"DISEASE_CONFIGURATION",
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"GENE_CONFIGURATION",
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"TAXRANK_CONFIGURATION",

‎src/semra/landscape/anatomy.py‎

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"""A configuration for assembling mappings for anatomical terms.
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"""
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The SeMRA Anatomy Mappings Database assembles semantic mappings to the following
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resources:
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========================================= =========================================================
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Prefix Name
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========================================= =========================================================
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`uberon <https://bioregistry.io/uberon>`_ Uber Anatomy Ontology
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`mesh <https://bioregistry.io/mesh>`_ Medical Subject Headings
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`bto <https://bioregistry.io/bto>`_ BRENDA Tissue Ontology
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`caro <https://bioregistry.io/caro>`_ Common Anatomy Reference Ontology
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`ncit <https://bioregistry.io/ncit>`_ NCI Thesaurus
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`umls <https://bioregistry.io/umls>`_ Unified Medical Language System Concept Unique Identifier
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========================================= =========================================================
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Reproduction
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************
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The SeMRA Anatomy Mappings Database can be rebuilt with the following commands:
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$ uv pip install .[landscape]
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$ python -m semra.landscape.anatomy
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The artifacts can be downloaded from `Zenodo
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<https://doi.org/10.5281/zenodo.11091802>`_. After running Docker locally, downloading
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all files, and unzipping then, the SeMRA web application can be run with:
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Web Application
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***************
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The pre-built artifacts for this mapping database can be downloaded from Zenodo
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at |anatomy| and unzipped. The web application can be run
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locally on Docker from inside the folder where the data was unzipped with:
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.. code-block:: console
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$ sh run_on_docker.sh
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Navigate to http://localhost:8773 to see the web application.
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"""
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If you reproduced the database yourself, you can ``cd``
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to the right folder and run with:
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.. code-block:: console
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$ cd ~/.data/semra/case-studies/anatomy
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$ sh run_on_docker.sh
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Finally, navigate in your web browser to http://localhost:8773 to see the web
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application.
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.. |anatomy| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.11091803.svg
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:target: https://doi.org/10.5281/zenodo.11091803
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""" # noqa:D205,D400
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import pystow
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__all__ = [
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"ANATOMY_CONFIGURATION",
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"MODULE",
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]
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MODULE = pystow.module("semra", "case-studies", "anatomy")

‎src/semra/landscape/cell.py‎

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"""A configuration for assembling mappings for cell and cell line terms.
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This configuration can be used to reproduce the results from the Biomappings paper by
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doing the following:
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1. Load positive mappings - PyOBO: EFO, DepMap, CCLE - Custom: Cellosaurus - Biomappings
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2. Upgrade mappings from dbxrefs to skos:exactMatch
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3. Use transitive closure to infer new mappings
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4. Load negative mappings from Biomappings
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5. Filter out negative mappings
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6. Subset a CCLE->EFO consolidation set
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7. Output SSSOM
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"""
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The SeMRA Cell and Cell Line Mappings Database assembles semantic mappings to the following
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resources:
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=================================================== =========================================================
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Prefix Name
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=================================================== =========================================================
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`mesh <https://bioregistry.io/mesh>`_ Medical Subject Headings
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`efo <https://bioregistry.io/efo>`_ Experimental Factor Ontology
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`cellosaurus <https://bioregistry.io/cellosaurus>`_ Cellosaurus
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`ccle <https://bioregistry.io/ccle>`_ Cancer Cell Line Encyclopedia Cells
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`depmap <https://bioregistry.io/depmap>`_ DepMap Cell Lines
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`bto <https://bioregistry.io/bto>`_ BRENDA Tissue Ontology
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`cl <https://bioregistry.io/cl>`_ Cell Ontology
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`clo <https://bioregistry.io/clo>`_ Cell Line Ontology
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`ncit <https://bioregistry.io/ncit>`_ NCI Thesaurus
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`umls <https://bioregistry.io/umls>`_ Unified Medical Language System Concept Unique Identifier
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=================================================== =========================================================
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Reproduction
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************
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The SeMRA Cell and Cell Line Mappings Database can be rebuilt with the following commands:
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.. code-block:: console
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$ git clone https://github.com/biopragmatics/semra.git
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$ cd semra
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$ uv pip install .[landscape]
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$ python -m semra.landscape.cell
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Web Application
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***************
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The pre-built artifacts for this mapping database can be downloaded from Zenodo
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at |cell| and unzipped. The web application can be run
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locally on Docker from inside the folder where the data was unzipped with:
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.. code-block:: console
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$ sh run_on_docker.sh
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If you reproduced the database yourself, you can ``cd``
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to the right folder and run with:
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.. code-block:: console
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$ cd ~/.data/semra/case-studies/cell
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$ sh run_on_docker.sh
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Finally, navigate in your web browser to http://localhost:8773 to see the web
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application.
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.. |cell| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.11091581.svg
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:target: https://doi.org/10.5281/zenodo.11091581
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""" # noqa:D205,D400
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import click
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import pystow
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__all__ = [
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"CELL_CONFIGURATION",
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"MODULE",
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]
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MODULE = pystow.module("semra", "case-studies", "cells")

‎src/semra/landscape/complex.py‎

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"""A configuration for assembling mappings for protein complex terms."""
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"""
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The SeMRA Protein Complex Mappings Database assembles semantic mappings to the following
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resources:
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======================================================= ===================================
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Prefix Name
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======================================================= ===================================
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`complexportal <https://bioregistry.io/complexportal>`_ Complex Portal
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`fplx <https://bioregistry.io/fplx>`_ FamPlex
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`go <https://bioregistry.io/go>`_ Gene Ontology
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`chembl.target <https://bioregistry.io/chembl.target>`_ ChEMBL target
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`wikidata <https://bioregistry.io/wikidata>`_ Wikidata
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`scomp <https://bioregistry.io/scomp>`_ Selventa Complexes
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`signor <https://bioregistry.io/signor>`_ Signaling Network Open Resource
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`intact <https://bioregistry.io/intact>`_ IntAct protein interaction database
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======================================================= ===================================
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Reproduction
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************
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The SeMRA Protein Complex Mappings Database can be rebuilt with the following commands:
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.. code-block:: console
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$ git clone https://github.com/biopragmatics/semra.git
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$ cd semra
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$ uv pip install .[landscape]
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$ python -m semra.landscape.complex
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Web Application
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***************
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The pre-built artifacts for this mapping database can be downloaded from Zenodo
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at |complex| and unzipped. The web application can be run
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locally on Docker from inside the folder where the data was unzipped with:
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.. code-block:: console
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$ sh run_on_docker.sh
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If you reproduced the database yourself, you can ``cd``
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to the right folder and run with:
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.. code-block:: console
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$ cd ~/.data/semra/case-studies/complex
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$ sh run_on_docker.sh
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Finally, navigate in your web browser to http://localhost:8773 to see the web
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application.
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.. |complex| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.11091422.svg
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:target: https://doi.org/10.5281/zenodo.11091422
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""" # noqa:D205,D400
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import pystow
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__all__ = [
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"COMPLEX_CONFIGURATION",
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"MODULE",
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]
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MODULE = pystow.module("semra", "case-studies", "complex")

‎src/semra/landscape/disease.py‎

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"""A configuration for assembling mappings for disease terms."""
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"""
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The SeMRA Disease Mappings Database assembles semantic mappings to the following
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resources:
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======================================================= ================================================================================
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Prefix Name
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======================================================= ================================================================================
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`doid <https://bioregistry.io/doid>`_ Human Disease Ontology
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`mondo <https://bioregistry.io/mondo>`_ Mondo Disease Ontology
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`efo <https://bioregistry.io/efo>`_ Experimental Factor Ontology
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`mesh <https://bioregistry.io/mesh>`_ Medical Subject Headings
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`ncit <https://bioregistry.io/ncit>`_ NCI Thesaurus
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`orphanet <https://bioregistry.io/orphanet>`_ Orphanet
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`orphanet.ordo <https://bioregistry.io/orphanet.ordo>`_ Orphanet Rare Disease Ontology
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`umls <https://bioregistry.io/umls>`_ Unified Medical Language System Concept Unique Identifier
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`omim <https://bioregistry.io/omim>`_ Online Mendelian Inheritance in Man
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`omim.ps <https://bioregistry.io/omim.ps>`_ OMIM Phenotypic Series
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`gard <https://bioregistry.io/gard>`_ Genetic and Rare Diseases Information Center
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`icd10 <https://bioregistry.io/icd10>`_ International Classification of Diseases, 10th Revision
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`icd10cm <https://bioregistry.io/icd10cm>`_ International Classification of Diseases, 10th Revision, Clinical Modification
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`icd10pcs <https://bioregistry.io/icd10pcs>`_ International Classification of Diseases, 10th Revision, Procedure Coding System
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`icd11 <https://bioregistry.io/icd11>`_ International Classification of Diseases, 11th Revision (Foundation Component)
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`icd11.code <https://bioregistry.io/icd11.code>`_ ICD 11 Codes
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`icd9 <https://bioregistry.io/icd9>`_ International Classification of Diseases, 9th Revision
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`icd9cm <https://bioregistry.io/icd9cm>`_ International Classification of Diseases, 9th Revision, Clinical Modification
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`icdo <https://bioregistry.io/icdo>`_ International Classification of Diseases for Oncology
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======================================================= ================================================================================
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Reproduction
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************
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The SeMRA Disease Mappings Database can be rebuilt with the following commands:
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.. code-block:: console
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$ git clone https://github.com/biopragmatics/semra.git
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$ cd semra
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$ uv pip install .[landscape]
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$ python -m semra.landscape.disease
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Web Application
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***************
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The pre-built artifacts for this mapping database can be downloaded from Zenodo
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at |disease| and unzipped. The web application can be run
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locally on Docker from inside the folder where the data was unzipped with:
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.. code-block:: console
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$ sh run_on_docker.sh
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If you reproduced the database yourself, you can ``cd``
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to the right folder and run with:
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.. code-block:: console
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$ cd ~/.data/semra/case-studies/disease
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$ sh run_on_docker.sh
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Finally, navigate in your web browser to http://localhost:8773 to see the web
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application.
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.. |disease| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.11091886.svg
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:target: https://doi.org/10.5281/zenodo.11091886
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""" # noqa:D205,D400
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import bioregistry
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import pystow
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__all__ = [
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"DISEASE_CONFIGURATION",
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"MODULE",
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]
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ICD_PREFIXES = bioregistry.get_collection("0000004").resources # type:ignore

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