V-Pipe on Cloud: Front-End for On-Demand Analysis

This project provides a front-end interface for two primary use cases:

• On-demand heatmaps of mutations to identify new variants emerging, based on V-Pipe outputs.
• On-demand variant deconvolution powered by LolliPop.
• On-demand heatmaps of resistance mutation – querying LAPIS of clincial data.
• On-demand heatmaps of resistance mutations - querying LAPS of wastewater data.

Overview

This front-end application is part of the "V-Pipe on Cloud" initiative, which aims to bring the capabilities of V-Pipe to the cloud, making it more accessible and scalable. The application leverages Streamlit to provide an interactive interface for users to generate heatmaps and perform variant deconvolution on-demand.

For more information about V-Pipe, visit the V-Pipe website.

Tech Stack

• Python: The core programming language used for the project.
• Streamlit: Used for creating the front-end interface.
• Docker: Used to containerize the application, ensuring consistency across different environments.
• AWS S3: Used for storing and retrieving data files.

Work in Progress

This project is a work in progress and is being actively developed. Contributions and feedback are welcome.

Hackathon Project

This project was initiated as part of a hackathon project at the BioHackathon Europe 2024.

Related Repositories

This repository relates to the back-end at vpipe-biohack24-backend.

Deployment

The current deployment of this project can be accessed at biohack24.g15n.net.

Getting Started

Prerequisites

• Docker
• AWS credentials with access to the required S3 buckets

Installation

1. Clone the repository:

   git clone https://github.com/cbg-ethz/vpipe-biohack24-frontend.git
   cd vpipe-biohack24-frontend

2. Create a .secrets.toml file in the .streamlit directory with your AWS credentials and S3 bucket information:

   AWS_ACCESS_KEY_ID=your_access_key_id
   AWS_SECRET_ACCESS_KEY=your_secret_access_key
   S3_BUCKET_NAME=your_s3_bucket_name

3. Configure the server IP addresses to LAPIS API for clinical and wastewater data in config.yaml including ports:

   server:
       ip_address: "http://3.71.80.16:8000"
       lapis_address: "http://3.126.120.246:8080"

4. Build the Docker image:

   docker build -t vpipe-frontend .

5. Run the Docker container:

   docker run -p 8000:8000 vpipe-frontend

Usage

1. Open your web browser and navigate to http://localhost:8000 to access the application.

2. Follow the on-screen instructions to upload your data and generate heatmaps or perform variant deconvolution.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your changes. For major changes, please open an issue first to discuss what you would like to change.

License

This project is licensed under the MIT License. See the LICENSE file for details.