PrioVar is a cutting-edge bioinformatics application tailored for genetic research centers. It boasts a suite of advanced features including variant prioritization, leveraging a novel machine learning scoring system, and comprehensive variant annotation. PrioVar integrates unique LLM features; one utilizes RAG for searching the PubMed database, while another facilitates knowledge graph retrieval from the Neo4j database. This robust platform is engineered to streamline data analysis processes, utilizing state-of-the-art container technology and a potent mix of backend services, making it a powerhouse for genetic data management and research.

To set up PRIOVAR using Docker, follow these steps to build and run the services:

In the project's root directory, execute the following commands to build and start the services:

docker-compose build
docker-compose up

Once the services are up, you can access the PRIOVAR platform through your browser:

• Frontend: https://localhost:3000
• Flask API: https://localhost:5000
• Spring Boot: https://localhost:8080
• Neo4j Database: Bolt port at 7687

To stop and remove all running services, use the following command:

docker-compose down

For manual installation, follow the step-by-step guide below to set up each component of the PRIOVAR system.

1. Navigate to frontend_cs491_org:

   • Ensure all necessary modules are installed:

     yarn install

   • This might take a few minutes. Once the installation is complete, start the frontend server:

     yarn start

   • The frontend will now be running at https://localhost:3000.

2. Prepare the Neo4j Graph Database:
   • Ensure Neo4j is Running: Before proceeding with the following steps, verify that the Neo4j Graph Database is up and operational.
   • Update Configuration Settings:
     • In the Flask application, update the username and password information in config.py.
     • In the Spring Boot application, modify the username and password details in application.properties.

3. Start the Spring Boot Server:
   • Navigate to the backend directory and follow the instructions specific to Spring Boot to get the server running.
4. Set Up the Flask Server + Python Environment:

   • Navigate to the flask directory.

   • Create a Python virtual environment and activate it:

     python -m venv venv
     source venv/bin/activate

   • Install the necessary libraries:

     pip install -r requirements.txt

   • Run app.py to start the Flask application:

     python app.py

   • After executing this step, you can log in to the system using any actor available on the Neo4j Graph Database.