- Robin Meyers (@robinmeyers)
If you simply want to use this workflow, download and extract the latest release. If you intend to modify and further extend this workflow or want to work under version control, fork this repository as outlined in Advanced. The latter way is recommended.
In any case, if you use this workflow in a paper, don't forget to give credits to the authors by citing the URL of this repository and, if available, its DOI (see above).
Install and activate the conda environment
conda env create -n mpra-gwas-builder -f envs/conda.yaml
conda activate mpra-gwas-builder
Configure the workflow according to your needs via editing the file config.yaml.
Request an API token for LDlink and paste it into a file named .Renviron in this directory
LDLINK_TOKEN=YourTokenHere123
Test your configuration by performing a dry-run via
snakemake -n
Execute the workflow locally via
snakemake --cores $N
See the Snakemake documentation for further details.
The following recipe provides established best practices for running and extending this workflow in a reproducible way.
- Fork the repo to a personal or lab account.
- Clone the fork to the desired working directory for the concrete project/run on your machine.
- Create a new branch (the project-branch) within the clone and switch to it. The branch will contain any project-specific modifications (e.g. to configuration, but also to code).
- Modify the config, and any necessary sheets (and probably the workflow) as needed.
- Commit any changes and push the project-branch to your fork on github.
- Run the analysis.
- Optional: Merge back any valuable and generalizable changes to the upstream repo via a pull request. This would be greatly appreciated.
- Optional: Push results (plots/tables) to the remote branch on your fork.
- Optional: Create a self-contained workflow archive for publication along with the paper (snakemake --archive).
- Optional: Delete the local clone/workdir to free space.