SCV - Sequence Coverage Visualizer

A web application for protein sequence coverage 3D visualization.

Publication

• "Sequence Coverage Visualizer: A Web Application for Protein Sequence Coverage 3D Visualization" by Xinhao Shao, Christopher Grams, and Yu Gao. Published in Journal of Proteomics, 2023. Link to publication

Table of Contents

• Description
• Dependencies
• Installation
• Configuration

Dependencies

Make sure you have the following dependencies installed before running the project:

• Docker - Containerization platform used to build and run the application.

If you wish to run the project locally without Docker or Nginx, you will also need to install the following dependencies:

• Python >= 3.8 - Programming language used to build the backend API.
• pip - Package installer for Python.
• PyMOL - Molecular visualization system used to generate the 3D protein structure images.
  • Debian/Ubuntu: sudo apt-get install pymol
  • Arch/Manjaro: sudo pacman -S pymol
  • macOS: brew install pymol
  • Windows: See Open-Source PyMOL - PyMOL Wiki

Installation

To run this project locally using Docker, follow the steps below:

1. Clone the repository:

   git clone https://github.com/chrisagrams/SCV.git
   cd SCV

2. Build and run docker-compose:

   docker compose up --build

3. Once finished, navigate to http://localhost:8080. The API endpoints can be tested under http://localhost:8080/docs

Configuration

The application uses the following files for configuration: ".env", "rates.json", and "logging.ini".