The pipeline includes the following software and steps:

• preprocessing
• shiver (https://github.com/ChrisHIV/shiver)
• iqtree (http:https://www.iqtree.org/)
• pangolin (https://github.com/cov-lineages/pangolin)
• qc

The snakemake workflow will fail upon error. Once an issue is resolved, it can be restarted and will do so at the point of failure.

• Slurm Workload Manager
• Miniconda 3
• Snakemake

The pipeline has been tested on a Linux environment using a Slurm Workload Manager.

The project relies on a Kraken 2 database, which is not yet part of this repo so this solution is not currently (standalone) portable.

Ensure the conda environment exists:

conda activate shivercovid
conda deactivate

If it doesn't exist, create it. E.g:

conda env create -f ./snakemake/environment.yml

Ensure the conda environment exists:

conda activate pangolin
conda deactivate

If it doesn't exist, create it using the instructions here:

https://github.com/cov-lineages/pangolin#install-pangolin

1. Create a directory for data processing of the form YYYY-MM-DD_<Batch Name(s)> and navigate to that directory.

2. Create a samples.txt file containing a list of samples for processing, with one sample per row.

3. Clone this repo:

   git clone [email protected]:BDI-pathogens/ShiverCovid.git
   

4. Change directory:

   cd ShiverCovid
   

5. Replace all <to_be_completed> references in ./snakemake/_config.yaml with information specific to your environment.

6. Run the preprocessing script:

   ./scripts/preprocessing/setup.sh
   

   You will be prompted for input parameter(s).

7. Activate the conda environment:

   conda activate shivercovid
   

8. Execute the Snakemake workflow.

   A script has been provided to submit the job to a Slurm Workload Manager: ./snakemake/submit.sh

   Further information about executing Snakemake can be found in the documentation (https://snakemake.readthedocs.io/en/stable/).

The location of logs associated with each script will be detailed in the Snakemake output.