Deep mutational scanning of SARS-CoV-2 Delta variant spike using a barcoded lentiviral platform

Study by Bernadeta Dadonaite, Jesse Bloom, et al. The pre-print for this study is here.

Measures functional effects of mutations and escape from antibody REGN10933 and Delta breakthrough sera (267C and 279C).

For documentation of the analysis, see https://dms-vep.github.io/SARS-CoV-2_Delta_spike_DMS_REGN10933.

Organization of this repo

dms-vep-pipeline submodule

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

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

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

cd dms-vep-pipeline
git checkout <commit>

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

Code and configuration

The snakemake pipeline itself is run by the Snakefile, which includes dms-vep-pipeline 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.

Additional scripts and notebooks that are specific to this analysis and not part of dms-vep-pipeline are in ./scripts/ and ./notebooks/.

Input data

Input data for the pipeline are in ./data/.

Results and documentation

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_Delta_spike_DMS/.

Library design

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.

Running the pipeline

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

conda activate dms-vep-pipeline
snakemake -j 32 --use-conda --rerun-incomplete

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

sbatch -c 32 run_Hutch_cluster.bash