This is a GitHub repository for Reg-Seq, an experimental protocol for deciphering the regulatory architecture of bacterial promoters. Specifically, the goal of Reg-Seq is to take previously uncharacterized or partially characterized promoters and to determine the constellation of RNAP and transcription factor binding sites and to determine what transcription factors bind those sites. In addition, the method allows for a determination of energy matrices that characterize, in k_BT units, the binding strength of transcription factors to their target sites.
The website for the original Reg-Seq paper can be found here.
Check out the Wiki tab to see the full experimental protocol for Reg-Seq.
To reproduce this work, you will need to use the RegSeq module -- a homegrown Python software package written explicitly for this work. We recommend having installed Anaconda3. The package relies on the mpathic package, a software package for quantitative modeling of massively parallel experiments and developed by GitHub user jbkinney, which is automatically installend when the instructions below are followed. A link to the GitHub page for the mpathic package is available here (You do not need to download the package from there). mpathic relies on Python 3.6.9. Since this is not the most recent python version, it is very likely that a new Python environment is necessary. To create this environment, simply navigate into the RegSeq/ folder and run the following line in the terminal:
conda env create -f environment.yml
Now there should be new python environment titled mpathic_env. The environment can be activated by:
conda activate mpathic_env
And deactivated by:
conda deactivate
The commands conda activate and conda deactivate only work for conda versions 4.6 and higher. If you run an older conda version, then you need to use source activate and source deactivate for Linux and macOS, and activate and deactivate for windows.
If any commands are run from the command line, it needs to be done inside the created environment. To confirm that the environment is functional, run the following line after activating the new environment:
mpathic learn_model --help
Which verifies that the installation proceeded as expected. Running this command should populate the command terminal with a list of available functions.
Finally, to use this environment in a Jupyter notebook, a kernel needs to be created. Therefore, activate the environment and run the following line:
python -m ipykernel install --user --name mpathic_env --display-name "Python (mpathic)"
When opening a notebook from the RegSeq/code folder, click on 'Kernel', 'Change Kernel', and select the newly-created kernel. You will now be able to import the package and execute the code.
You should be set up to use all code provided in this repository. If you encounter any issues with the installation, please contact us through GitHub.
The repository is split into four main directories, many of which have
subdirectories. This structure has been designed to be easily navigable by
humans and computers alike, allowing for rapid location of specific files and
instructions. Within each directory is a README.md file which summarizes the
purpose of that directory as well as some examples where necessary.
Contains Jupyter notebooks to perform computational steps of the Reg-Seq protocol from start to finish. Where all of the executed code lives. This includes pipelines, scripts, and figure files.
Contains prior data files, such as designed sequences, negative controls, or output analysis files. Example files for each step of the analysis are provided.
Python module which can be easily executed to perform your own analyses.
Contain all figures which are shown in the Wiki.
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