MARS Streamlit Application

Overview

This Streamlit application integrates the functionalities of MARS (Microbial Abundances Retrieved from Sequencing data) and ANT (Automated NCBI Taxonomy) to provide a comprehensive tool for microbiome researchers. It automates the processing of metagenomic datasets, taxonomic name resolution, and mapping to resources like AGORA2, thereby facilitating a streamlined workflow for microbiome data analysis.

Online version

A web-based version of MARS can be found here: https://mars-pipeline.streamlit.app

Tutorial

The tutorial on how to use the interface can be found here: https://github.com/ThieleLab/MARS-tutorial

Python-Based function version

A version of MARS as a python function can be found here: https://github.com/ThieleLab/MARS. This version does not have the ANT functionality

Installation

To run the application on your local machine, you will need to have Python (3.9 or above) installed, along with the necessary libraries. Follow these steps to get started:

1. Clone the repository: git clone https://www.github.com/ThieleLAB/mars-pipeline

2. Navigate to the cloned repository's directory: cd mars-pipeline

3. Install the required Python packages: pip install -r requirements.txt

4. In the terminal enter command: streamlit run app.py

Usage

After installation, you can run the application using Streamlit:

The application will open in your default web browser, or you can access it at http://localhost:8501.

Running the Streamlit Application with Docker

Prerequisites

Before proceeding, ensure that you have Docker installed on your system. You can download and install Docker from Docker's official website.

Steps to Run the Application

1. Build the Docker Image First, you need to build a Docker image from your application. Open a terminal or command prompt and navigate to the directory containing your application's Dockerfile.

Run the following command to build the Docker image:

docker build -t my_streamlit_app .

This command builds a new Docker image with the name my_streamlit_app based on the instructions in your Dockerfile. It may take some time as it installs all necessary dependencies.

2. Run the Docker Container After the image is successfully built, run the container using:

docker run -p 8501:8501 my_streamlit_app

This command runs the Docker container and maps port 8501 of the container to port 8501 on your host machine.

3. Accessing the Application Once the container is running, open your web browser and navigate to:

http://localhost:8501

You should now see your Streamlit application running.

Stopping the Container

To stop the running Docker container, you can press CTRL+C in the terminal where the container is running.

Alternatively, you can stop the container using the following steps:

Open a new terminal or command prompt. Run docker ps to list all running containers. Find the container ID of your Streamlit app. Run docker stop <container_id> to stop the container. Troubleshooting

If the application is not loading, check if Docker is running correctly and if the correct port is exposed and mapped. If you encounter errors during the build process, verify that your Dockerfile and requirements.txt are set up correctly.

Input Data

The application accepts input data in specific formats. For MARS, a feature table and taxonomy table, or an already combined table are required. Please refer to the sample input files provided in the tests/test_files directory: feature_table.txt, taxonomy.tsv and merged.csv

Contributing

Contributions to improve the application are welcome.

Support

For support, please open an issue in the GitHub repository or contact ines.thiele@universityofgalway.ie.

Authors

• Tim Hulshof - Initial work on MARS and ANT
• Bram Nap - Contributions
• Filippo Martinelli - Contributions