Snakemake workflow: Book Popularity Predictions
This is the template for a new Snakemake workflow. Replace this text with a comprehensive description covering the purpose and domain.
Insert your code into the respective folders, i.e. scripts, rules, and envs. Define the entry point of the workflow in the Snakefile and the main configuration in the config.yaml file.
Authors
- Thomas Battenfeld (@thomasbtf)
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
- Create a new github repository using this workflow as a template.
- 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
Install Snakemake using conda:
conda create -c bioconda -c conda-forge -n snakemake snakemake
For installation details, see the instructions in the Snakemake documentation.
Step 4: Execute workflow
Activate the conda environment:
conda activate snakemake
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.
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.
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.