Study by Sheri Harari in the Bloom lab. See Harari et al for the paper describing this study.

Most of the analysis is done by the dms-vep-pipeline-3, which was added as a git submodule to this pipeline via:

git submodule add https://github.com/dms-vep/dms-vep-pipeline-3

This added the file .gitmodules and the submodule dms-vep-pipeline-3, which was then committed to the repo. Note that if you want a specific commit or tag of dms-vep-pipeline-3 or to update to a new commit, follow the steps here, basically:

cd dms-vep-pipeline-3
git checkout <commit>

and then cd ../ back to the top-level directory, and add and commit the updated dms-vep-pipeline-3 submodule. You can also make changes to the dms-vep-pipeline-3 that you commit back to that repo.

The snakemake pipeline itself is run by dms-vep-pipeline-3/Snakefile which reads its configuration from config.yaml. The conda environment used by the pipeline is that specified in the environment.yml file in dms-vep-pipeline-3.

Input data utilized by the pipeline are located in ./data/.

The results of running the pipeline are placed in ./results/. Due to space, only some results are tracked. For those that are not, see the .gitignore document. The pipeline builds HTML documentation for the pipeline in ./results/docs and ./results/publish_docs. To visualize these docs via GitHub Pages, run:

./dms-vep-pipeline-3/publish_docs_gh-pages.sh

This pushes the docs to the gh-pages branch, where they can be viewed on GitHub Pages at https://dms-vep.org/229E_spike_1984_DMS/.

To do a test run of the pipeline you can execute the following command snakemake -n -s dms-vep-pipeline-3/Snakefile --rerun-incomplete

To run the pipeline, build the conda environment dms-vep-pipeline-3 in the environment.yml file of dms-vep-pipeline-3, activate it, and run snakemake, such as:

conda activate dms-vep-pipeline-3
snakemake -j 16 -s dms-vep-pipeline-3/Snakefile

To run on the Hutch cluster via slurm, you can run the file run_Hutch_cluster.bash:

sbatch -c 16 run_analysis.bash