MPRAsnakeflow pipeline processes sequencing data from Massively Parallel Reporter Assays (MPRAs) to create count tables for candidate sequences tested in the experiment.
MPRAsnakeflow is built on top of Snakemake. Insert your code into the respective folders, i.e.
envs. Define the entry point of the workflow in the
Snakefile and the main configuration in a
If you use this workflow in a paper, don’t forget to give credits to the authors by citing the URL of the (original) repository and, if available, it’s DOI. (see above)
- Installation & Getting Started
Instructions for the Installation of the program and some examples to get you started.
- MPRAsnakeflow Workflows
An overview of how MPRAsnakeflow works and documentation for the MPRAsnakeflow sub workflows.
- MPRAsnakeflow Examples
Muliple examples from the literature are listed for every sub workflow in MPRAsnakeflow.
- Project Information
More information on the project, including the changelog, list of contributing authors, and contribution instructions.
To run MPRAsnakeflow, first activate the snakemake environment with the following command:
conda activate snakemake
And then run the main workflow with:
snakemake --use-conda --cores $N --configfile config/config.yaml
This utility uses mamba to efficiently query repositories and query package dependencies.
This utility sets the number of cores ($N) to be used by MPRAsnakeflow.
This file (e.g.,
config/config.yaml) contains the project, its objects and properties, and sub-properties and its objects that must be set before running MPRAsnakeflow.
After successful execution, you can create a self-contained interactive HTML report with all results via:
snakemake --report report.html --configfile conf/config.yaml
This report can be forwarded to your collaborators. An example of a generated report (using some trivial test data) can be seen here.
Feel free to leave feedback(s), ask question(s), or report bug(s) at our issues page: MPRAsnakeflow Issues.