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About snakemake

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. See the official Snakemake paper for a full overview.

Training material and documentation

Community-developed workflows in snakemake

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.

Running the proof of concept Snakemake pipeline

  1. Install snakemake by following the instructions here
  2. If you haven't done that already, clone this repository to your local system and enter the directory snakemake.
  3. 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.yaml provided in this example to determine which samples to run.
  4. 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

Notes and Contribution

We welcome contributions to the documentation and workflow, please create an issue or submit a pull request!

How to cite Snakemake

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.