Modularization in Snakemake comes at different levels.

  1. The most fine-grained level are wrappers. They are available and can be published at the Snakemake Wrapper Repository. These wrappers can then be composed and customized according to your needs, by copying skeleton rules into your workflow. In combination with conda integration, wrappers also automatically deploy the needed software dependencies into isolated environments.
  2. For larger, reusable parts that shall be integrated into a common workflow, it is recommended to write small Snakefiles and include them into a master Snakefile via the include statement. In such a setup, all rules share a common config file.
  3. The third level of separation are subworkflows. Importantly, these are rather meant as links between otherwise separate data analyses.


The wrapper directive allows to have re-usable wrapper scripts around e.g. command line tools. In contrast to modularization strategies like include or subworkflows, the wrapper directive allows to re-wire the DAG of jobs. For example

rule samtools_sort:
        "-m 4G"
    threads: 8

Refers to the wrapper "0.0.8/bio/samtools_sort" to create the output from the input. Snakemake will automatically download the wrapper from the Snakemake Wrapper Repository. Thereby, 0.0.8 can be replaced with the git version tag you want to use, or a commit id (see here). This ensures reproducibility since changes in the wrapper implementation won’t be propagated automatically to your workflow. Alternatively, e.g., for development, the wrapper directive can also point to full URLs, including URLs to local files with absolute paths file:// or relative paths file:. Examples for each wrapper can be found in the READMEs located in the wrapper subdirectories at the Snakemake Wrapper Repository.

The Snakemake Wrapper Repository is meant as a collaborative project and pull requests are very welcome.

Common-Workflow-Language (CWL) support

With Snakemake 4.8.0, it is possible to refer to CWL tool definitions in rules instead of specifying a wrapper or a plain shell command. A CWL tool definition can be used as follows.

rule samtools_sort:
        threads=lambda wildcards, threads: threads,
    threads: 8

It is advisable to use a github URL that includes the commit as above instead of a branch name, in order to ensure reproducible results. Snakemake will execute the rule by invoking cwltool, which has to be available via your $PATH variable, and can be, e.g., installed via conda or pip. When using in combination with –use-singularity, Snakemake will instruct cwltool to execute the command via Singularity in user space. Otherwise, cwltool will in most cases use a Docker container, which requires Docker to be set up properly.

The advantage is that predefined tools available via the official repository can be used in any supporting workflow management system. In contrast to a Snakemake wrapper, CWL tool definitions are in general not suited to alter the behavior of a tool, e.g., by normalizing output names or special input handling. As you can see in comparison to the analog wrapper declaration above, the rule becomes slightly more verbose, because input, output, and params have to be dispatched to the specific expectations of the CWL tool definition.


Another Snakefile with all its rules can be included into the current:

include: "path/to/other/snakefile"

The default target rule (often called the all-rule), won’t be affected by the include. I.e. it will always be the first rule in your Snakefile, no matter how many includes you have above your first rule. Includes are relative to the directory of the Snakefile in which they occur. For example, if above Snakefile resides in the directory my/dir, then Snakemake will search for the include at my/dir/path/to/other/snakefile, regardless of the working directory.


In addition to including rules of another workflow, Snakemake allows to depend on the output of other workflows as sub-workflows. A sub-workflow is executed independently before the current workflow is executed. Thereby, Snakemake ensures that all files the current workflow depends on are created or updated if necessary. This allows to create links between otherwise separate data analyses.

subworkflow otherworkflow:
    workdir: "../path/to/otherworkflow"
    snakefile: "../path/to/otherworkflow/Snakefile"

rule a:
    input:  otherworkflow("test.txt")
    output: ...
    shell:  ...

Here, the subworkflow is named “otherworkflow” and it is located in the working directory ../path/to/otherworkflow. The snakefile is in the same directory and called Snakefile. If snakefile is not defined for the subworkflow, it is assumed be located in the workdir location and called Snakefile, hence, above we could have left the snakefile keyword out as well. If workdir is not specified, it is assumed to be the same as the current one. Files that are output from the subworkflow that we depend on are marked with the otherworkflow function (see the input of rule a). This function automatically determines the absolute path to the file (here ../path/to/otherworkflow/test.txt).

When executing, snakemake first tries to create (or update, if necessary) test.txt (and all other possibly mentioned dependencies) by executing the subworkflow. Then the current workflow is executed. This can also happen recursively, since the subworkflow may have its own subworkflows as well.