Snakemake workflow: NodeRAD

NodeRAD is a Snakemake workflow for analysis of single-end reads from RAD sequencing without the presence of a reference genome. It detects loci and genomic variants using sequencing error and heterozygosity rates. For more information please have a look at the associated bachelor thesis.

Note: Currently the workflow is limited to diploid species.

Authors

• Antonie Vietor (@AntonieV)

This workflow is part of a bachelor thesis The topic and the underlying model were developed by:

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

Usage

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 in the config/ folder. Adjust config.yaml to configure the workflow execution, and samples.tsv to specify your sample setup.

Step 3: Install Snakemake

1. Install conda.
2. Install mamba in conda base and create a snakemake conda environment:

    conda install -n base -c conda-forge mamba
    conda create -c bioconda -c conda-forge -n snakemake snakemake

For installation details, see the instructions in the Snakemake documentation.

Step 4: Execute workflow

You can run the workflow with some examples through the script start.sh. To use your own data, change the paths in config/config.yaml for samples, fastq-data and eval-data (if there is data from a ddRAGE simulation).

Activate the conda environment:

conda activate snakemake

Test your configuration by performing a dry-run via

snakemake --use-conda --conda-frontend mamba -n

Execute the workflow locally via

snakemake --use-conda --conda-frontend mamba --cores $N

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

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

or

snakemake --use-conda --conda-frontend mamba --drmaa --jobs 100

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

snakemake --use-conda --conda-frontend mamba --use-singularity

in combination with any of the modes above. See the Snakemake documentation for further details.

Step 5: 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 6: 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 7: 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 [email protected]:TharjaX/NodeRAD.git or git remote add -f upstream https://github.com/TharjaX/NodeRAD.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 workflow > 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. If so, do it manually, and only where necessary, since you would otherwise likely overwrite your settings and samples.

Step 8: 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

More test cases are in the subfolder .test. To run the workflow with one of the test data sets adjust the paths for samples, fastq-data and eval-data in .test/config/config.yaml for the desired test dataset and run the script start_test.sh. Also note that for the different data sets you may have to adjust the threshold values in the configuration file. Test cases are also automatically executed via continuous integration with Github Actions.