pymodulon 0.2.1

Python package for analyzing and visualizing iModulons.

What is an iModulon?

To learn about iModulons, how they are computed, and what they can tell you, see our about page.

Installation

With Docker

The easiest way to get started with PyModulon is using the Docker container.

1. Install Docker

2. Open terminal and navigate to your work folder

3. Run the following commands to start a Jupyter Notebook server:

   docker run -p 8888:8888 -v "${PWD}":/home/jovyan/work sbrg/pymodulon

4. Copy the URL from terminal to connect to the Jupyter notebook

5. Navigate to the work folder, which has your current directory mounted.

6. To close the notebook, press Ctrl+C in terminal. All changes made to files in your current directory are saved to your local machine.

With Pip

You can install PyModulon from PyPI using pip as follows:

python -m pip install pymodulon

With Conda

Alternatively, you can install using Conda:

conda install -c conda-forge pymodulon

We recommend installing through a conda environment:

conda create -n pymodulon -c conda-forge pymodulon
conda activate pymodulon

Optional Dependencies

Some features of PyModulon require additional dependencies. Follow the links below for installation instructions.

1. GraphViz

2. NCBI BLAST

3. MEME Suite

This step is not necessary if you use the Docker container.

Documentation

The documentation for PyModulon can be found here.

Development

If you would like to assist in development, please install pre-commit to ensure code style and consistency.

We recommend using an editable pip installation for development:

git clone https://github.com/SBRG/pymodulon.git
cd pymodulon
python -m pip install -e .

This method of installation will automatically update your package each time you pull from this repository.

To update your code, run the following from your local PyModulon folder:

git pull

Cite

Please cite the original E. coli iModulon paper: Sastry et al. Nature Communications. 2019.

If you use organism-specific datasets in your work, please cite these datasets:

• Escherichia coli: Sastry et al. Nature Communications. 2019.

• Bacillus subtilis: Rychel et al. Nature Communications. 2020.

• Staphylococcus aureus: Poudel et al. PNAS. 2020.