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The Snakemake workflow management system is a tool to create transparent reproducible and scalable data analyses.

  • Workflows are described via a human readable, Python based language.
  • They can be seamlessly scaled to server, cluster, grid and cloud environments, without the need to modify the workflow definition.
  • Snakemake workflows can entail a description of required software (via Mamba/Conda or container images), which will be automatically deployed to any execution environment.
  • Snakemake can automatically create portable, server-less interactive HTML reports that contain all requested results and connect them to data provenance information like code and parameters.
  • On our cluster, Snakemake is configured to automatically avoid malicious IO patterns. No need to manually copy to the local workdir for avoiding NFS stress, Snakemake takes care of these things automatically.
  • The Snakemake homepage gives a high-level overview on the most important features:
  • In case of any issues or questions, reach out for Prof. Johannes Köster (IKIM 4th floor).


  • 2024/03/12: We upgraded the cluster-wide Snakemake profile ikim to requre Snakemake 8.6.0. Please update your Snakemake installation as shown below.


Currently, our cluster setup requires Snakemake >= 8.6.0. Snakemake can be easily installed using the mamba package manager that is preinstalled on the cluster. First, ssh into a shellhost machine

ssh shellhost

Then create a snakemake environment via the preinstalled mamba package manager:

mamba create -c conda-forge -c bioconda --name snakemake snakemake snakemake-storage-plugin-fs snakemake-executor-plugin-slurm

It is recommended to execute your analyses via Slurm, for maintenance and performance reasons (while Snakemake would also work without it). Via a dedicated Snakekemake profile we have ensured that Snakemake transparently uses Slurm when you execute it from a shellhost node, and runs locally if on a non-shellhost node.


It is a good idea to keep the snakemake environment up to date in order to receive bug fixes and feature updates. For updating to the latest versions, run

mamba update --name snakemake snakemake snakemake-storage-plugin-fs snakemake-executor-plugin-slurm



  • You are on one of the shellhost machines (ssh shellhost).
  • You are inside of a working directory that contains a Snakemake workflow (either a file Snakefile or workflow/Snakefile in the same dir)
  • You have completed above installation steps.

Since your Snakemake workflow might run for a longer time, you usually want it to be independent of the current ssh session (otherwise, the Snakemake process would be killed when the session is closed or disconnected). Therefore, we recommend to first generate a so-called tmux session via

tmux new -s SESSIONNAME

with SESSIONNAME being a reasonable name under which you can remember your intended Snakemake run. For a full summary of all tmux functionality, see here. In addition, note for youself the name of the host (hostname -a), so that you can later come back to the same in case you close the ssh session youself out or are disconnected. This is necessary because shellhost is a DNS name that is associated with multiple physical machines and you can never know to which machine you care connected when doing ssh shellhost (this happens because we want to distribute the load of many active users across several machines). In such a case, you can come back to the same host (say, HOSTNAME) and open the tmux session via

tmux attach -t SESSIONNAME

Inside of the tmux session, you can use Snakemake on the slurm cluster by running

nice snakemake --jobs N

with N being the number of jobs you want to run in parallel at most, and it will automatically submit the jobs to the slurm cluster. The nice command before the snakemake invocation ensures that your Snakemake process on the shellhost has less priority than any user interaction. This is important to ensure that the shellhost remains responsive. In order to perform a dry-run (i.e. just see the plan), which is highly recommended before actually executing, run

snakemake -n

For a full tutorial on Snakemake, please check out the official Snakemake tutorial.

Further hints

  • Make sure to follow the best-practices.
  • If you and your colleagues have common tasks that often reoccur in workflows, you can save a lot of time by activating Snakemake's between workflow caching.