Snakemake workflow: dna-seq-gatk-variant-calling

This Snakemake pipeline implements the GATK best-practices workflow for calling small germline variants.

Authors

• Johannes Köster (https://koesterlab.github.io)

Usage

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 (original) repository and, if available, its DOI (see above).

Step 1: Obtain a copy of this workflow

1. Create a new github repository using this workflow as a template.
2. Clone the newly created repository to your local system, into the place where you want to perform the data analysis.

Step 2: Configure workflow

Configure the workflow according to your needs via editing the files config.yaml, samples.tsv and units.tsv.

Step 3: Execute workflow

This workflow will automatically download reference genomes and annotation. In order to save time and space, consider to use between workflow caching by adding the flag --cache to any of the commands below. The workflow already defines which rules are eligible for caching, so no further arguments are required. When caching is enabled, Snakemake will automatically share those steps between different instances of this workflow.

Test your configuration by performing a dry-run via

snakemake --use-conda -n

Execute the workflow locally via

snakemake --use-conda --cores $N

using $N cores or run it in a cluster environment via

snakemake --use-conda --cluster qsub --jobs 100

or

snakemake --use-conda --drmaa --jobs 100

If you not only want to fix the software stack but also the underlying OS, use

snakemake --use-conda --use-singularity

in combination with any of the modes above.

See the Snakemake documentation for further details (e.g. cloud execution).

Step 4: Investigate results

After successful execution, you can create a self-contained interactive HTML report with all results via:

snakemake --report report.html

This report can, e.g., be forwarded to your collaborators. An example (using some trivial test data) can be seen here.

Step 5: Commit changes

Whenever you change something, don't forget to commit the changes back to your github copy of the repository:

git commit -a
git push

Step 6: Obtain updates from upstream

Whenever you want to synchronize your workflow copy with new developments from upstream, do the following.

1. Once, register the upstream repository in your local copy: git remote add -f upstream git@github.com:snakemake-workflows/dna-seq-gatk-variant-calling.git or git remote add -f upstream https://github.com/snakemake-workflows/dna-seq-gatk-variant-calling.git if you do not have setup ssh keys.
2. Update the upstream version: git fetch upstream.
3. Create a diff with the current version: git diff HEAD upstream/master rules scripts envs schemas report > upstream-changes.diff.
4. Investigate the changes: vim upstream-changes.diff.
5. Apply the modified diff via: git apply upstream-changes.diff.
6. Carefully check whether you need to update the config files: git diff HEAD upstream/master config.yaml samples.tsv units.tsv. If so, do it manually, and only where necessary, since you would otherwise likely overwrite your settings and samples.

Step 7: Contribute back

In case you have also changed or added steps, please consider contributing them back to the original repository:

1. Fork the original repo to a personal or lab account.
2. Clone the fork to your local system, to a different place than where you ran your analysis.
3. Copy the modified files from your analysis to the clone of your fork, e.g., cp -r workflow path/to/fork. Make sure to not accidentally copy config file contents or sample sheets. Instead, manually update the example config files if necessary.
4. Commit and push your changes to your fork.
5. Create a pull request against the original repository.

Testing

Test cases are in the subfolder .test. They are automtically executed via continuous integration with Github actions.