See Dadonaite et al, Nature (2024) for the paper describing this study.
For documentation of the analysis, see https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_spike_DMS/
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.
In addition to the main dms-vep-pipeline-3, there are some custom snakemake rules in custom_rules.smk and the associated notebooks in ./notebooks/ that run some rules and analyses specific to this repo.
An analysis by Ben Murrell of SARS-CoV-2 clade growth using multinomial logistic models in the repo at https://github.com/MurrellGroup/MultinomialLogisticGrowth is added as a submodule in this repo, and is used in some of the analyses.
The configuration for the pipeline is in config.yaml and the files in ./data/ referenced therein. To run the pipeline, do:
snakemake -j 8 --use-conda -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 8 run_Hutch_cluster.bash
The results of running the pipeline are placed in ./results/. Only some of these results are tracked to save space (see .gitignore).
The pipeline builds HTML documentation for the pipeline in ./docs/, which is rendered via GitHub Pages at https://dms-vep.github.io/SARS-CoV-2_XBB.1.5_spike_DMS/.
The design of the mutant library is contained in ./library_design/. That design is not part of the pipeline but contains code that must be run separately with its own conda environment.