This repository provides guidelines to publish Nanopublications as a user with the Nanobench, and query the published Nanopublications network as a researcher searching for answers 💬

Services has been been deployed publicly to query the Nanopublications network using Translator standards to retrieve the Knowledge Collaboratory graph, a collection of drug indications annotated using preferred identifiers (usually from MONDO, CHEBI, DrugBank, etc).

Check the Translator Reasoner API to query the Nanopublications network SPARQL endpoint at

🔗 https://api.collaboratory.semanticscience.org

• Supports Translator Reasoner API 1.1.0
• Include operation to retrieve the Knowledge Collaboratory nanopublications in the kgx format

The Knowledge Collaboratory concept

Publish Nanopublications

Publish with the Nanobench UI

Requirements: Java 8+

Use the Nanobench 🛋️ to publish and explore nanopublications using a convenient web UI.

Download the latest release of the nanobench.zip file from GitHub: https://github.com/peta-pico/nanobench/releases/tag/nanobench-1.19

Unzip the file, and put the jar file in a convenient folder (at ~/.nanopub/nanobench.jar for example, to be able to call it from anywhere easily)

Visit the complete nanobench installation instructions for more details.

Run the Nanobench on http://localhost:37373. It will use the ids_rsa key in the .nanopub folder to authenticate, or guide you to generate one:

java -jar ~/.nanopub/nanobench.jar -httpPort 37373 -resetExtract

See also:

• Check the vemonet/nanobench wiki to get a full tutorial to publish associations!
• Templates for the Translator (e.g. "Defining a biomedical association with its context") can be seen and improved in the MaastrichtU-IDS/nanobench-templates GitHub repository.

Publish a spreadsheet as nanopublications

Check the BioLink JSON-LD Context file to find ontologies supported by the Translator (for better integrations with other Translator tools)

Checkout the src/nanopubs/drug_indications_from_gdocs.py file to see an example for publishing drug indications from a gdocs spreadsheet as Nanopublications using the BioLink model

1. Clone this repository:

git clone https://github.com/MaastrichtU-IDS/knowledge-collaboratory-api
cd knowledge-collaboratory-api

2. Install the required dependencies:

pip3 install -r requirements.txt

3. Test the script to generate nanopubs from the gdoc spreadsheet, and inspect the RDF generated:

python3 src/nanopubs/drug_indications_from_gdocs.py

When you are ready, you can run and publish each row of the spreadsheet as a nanopublication:

python3 src/nanopubs/drug_indications_from_gdocs.py --publish --count 10

Query the Collaboratory API

Overview of the different operations available in the Knowledge Collaboratory Translator Reasoner API (supporting kgx)

Query operation

The user sends a ReasonerAPI query to the Knowledge Collaboratory Nanopublications in the BioLink format (e.g. drug indications). The query is a graph with nodes and edges defined in JSON, and uses classes from the BioLink model.

Predicates operation

The /predicates operation will return the entities and relations provided by this API in a JSON object (following the ReasonerAPI specifications).

Kgx operation

Experimental, the /kgx operation will return the complete Knowledge Collaboratory drug indications in KGX TSV format (in a .zip file)

Current resources in the Collaboratory

• Drugs indications from the PREDICT publication

• Drugs indications from the NeuroDKG, and off-label drug indications

Deploy the Knowledge Collaboratory API 🚀

Starts the Translator Reasoner API to query the Nanopublications SPARQL endpoint

• Query the Knowledge Collaboratory Nanopublications (drug indications in the BioLink format) using the ReasonerAPI standards and KGX
  • Supports Translator Reasoner API 1.1.0
  • Include operation to retrieve the Knowledge Collaboratory nanopublications in the kgx format
  • The TRAPI-SPARQL interface and kgx transformer are implemented in Python in the src/ folder
• OpenAPI 3 with Swagger UI, built in Python using zalando/connexion

Available at https://api.collaboratory.semanticscience.org 🔗

Starts the Translator Reasoner API to query the Nanopublications SPARQL endpoint, supporting kgx and TRAPI 1.1.0 (defined in this repo in src/)

Requires Python 3.8+ and pip

Clone the repository first

git clone https://github.com/MaastrichtU-IDS/knowledge-collaboratory-api.git
cd knowledge-collaboratory-api

Start the API locally 🛩️

Install dependencies

pip3 install -e .

If you are facing conflict with already installed packages, then you might want to use a Virtual Environment to isolate the installation in the current folder before installing knowledge-collaboratory-api:

# Create the virtual environment folder in your workspace
python3.8 -m venv .venv
# Activate it using a script in the created folder
source .venv/bin/activate

Start the API in production mode on http://localhost:8808:

uvicorn src.main:app --port 8808

Or start the API in debug mode (the API will be reloaded automatically at each change to the code):

uvicorn src.main:app --port 8808 --reload

Check CONTRIBUTING.md for more details on how to run the API locally and contribute.

Start with Docker 🐳

Requirements: Docker.

Build and start the container with docker-compose 🐳

docker-compose up -d --build

Access the Swagger UI at http://localhost:8808

We use nginx-proxy and docker-letsencrypt-nginx-proxy-companion as reverse proxy for HTTP and HTTPS in production. You can change the proxy URL and port via environment variables VIRTUAL_HOST, VIRTUAL_PORT and LETSENCRYPT_HOST in the docker-compose.yml file.

Check the logs:

docker-compose logs

Stop the container:

docker-compose down

Test the Collaboratory API

See the TESTING.md file for more details on testing the API.

Acknowledgments

Service funded by the NIH NCATS Translator project.