snakemake is a workflow management system relying on a Python based DSL. Snakemake tries to make it as easy as possible to obtain workflows with maximum reproducibility, adaptability, and transparency. Importantly, Snakemake workflows are not limited to shell commands but can tightly integrate with custom scripts and Jupyter notebooks, as well as wrappers for common tasks. Check out the pipeline code for examples of the available functionality. See the official Snakemake paper for a full overview.
A comprehensive catalog of Snakemake workflows can be found here [https://snakemake.github.io/snakemake-workflow-catalog]. In particular, check out the "Standardized usage" category, and choose workflows that pass all QC criteria to get an impression of best practices.
- Install
snakemakeby following the instructions here - If you haven't done that already, clone this repository to your local system and enter the directory
snakemake. - Run the a dry-run of the pipeline with the following command:
snakemake -n --use-conda- Snakemake will make use of the configuration file
config/config.yamlprovided in this example to determine which samples to run.
- Run with the following command, specifying the number of cores
snakemake --cores [numcores] --use-conda- For example, to run with 2 cores the command would be:
snakemake --cores 2 --use-conda- All required software will be automatically deployed by Snakemake via the mamba package manager
We welcome contributions to the documentation and workflow, please create an issue or submit a pull request!
Mölder, F., Jablonski, K.P., Letcher, B., Hall, M.B., Tomkins-Tinch, C.H., Sochat, V., Forster, J., Lee, S., Twardziok, S.O., Kanitz, A., Wilm, A., Holtgrewe, M., Rahmann, S., Nahnsen, S., Köster, J., 2021. Sustainable data analysis with Snakemake. F1000Res 10, 33, DOI:10.12688/f1000research.29032.2.