Analysis of deep mutational scanning of barcoded codon variants of SARS-CoV-2 RBD
Study and analysis by Tyler Starr, Allie Greaney, Jesse Bloom, and co-authors.
For the full paper, see Deep mutational scanning of SARS-CoV-2 receptor binding domain reveals constraints on folding and ACE2 binding.
NOTE added 3 March, 2022. We have an improved deep mutational scanning dataset that we recommend using in place of this 2020 dataset. Please find the updated data here, the preprint describing the new datasets here, and the new GitHub repository here.
For a summary of the workflow and links to key results files, click here. Reading this summary is the best way to understand the analysis.
The analysis consists of three components, all of which are contained in this repository:
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Instructions to build the computing environment.
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The computer code itself.
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The required input data.
First, set up the computing environment, which is partially done via conda.
Ensure you have conda installed; if not install it via Miniconda as described here.
The environment is specified in environment.yml.
If you have not previously built the conda environment, then build the environment specified in environment.yml to ./env with:
conda env create -f environment.yml -p ./env
Then activate it with:
conda activate ./env
If you've previously built the environment into ./env, just do the activation step.
Setting up the conda environment above installs everything to run all parts of the analysis except the R markdown notebooks.
For those, the pipeline currently uses the Fred Hutch computing cluster module R/3.6.1-foss-2018b as specified in Snakefile.
That module is not packaged with this repo, so if you aren't on the Fred Hutch cluster you'll have to create a similar R environment yourself (all the R packages are listed at the beginning of their output in the summary results.
Now you can run the entire analysis.
The analysis consists primarily of a series of Jupyter notebooks and R markdown in to the top-level directory along with some additional code in Snakefile.
You can run the analysis by using Snakemake to run Snakefile, specifying the conda environment in ./env, as in:
snakemake --use-conda --conda-prefix ./env
However, you probably want to use the server to help with computationally intensive parts of the analysis. To run using the cluster configuration for the Fred Hutch server, simply run the bash script run_Hutch_cluster.bash, which executes Snakefile in a way that takes advantage of the Hutch server resources. This bash script also automates the environment building steps above, so really all you have to do is run this script. You likely want to submit run_Hutch_cluster.bash itself to the cluster (since it takes a while to run) with:
sbatch -t 7-0 run_Hutch_cluster.bash
The configuration for the analysis is specifed in config.yaml.
This file defines key variables for the analysis, and should be relatively self-explanatory.
You should modify the analysis by changing this configuration file; do not hard-code crucial experiment-specific variables within the Jupyter notebooks or Snakefile.
The input files pointed to by config.yaml are in the ./data/ subdirectory. See the ./data/README.md file for details.
Note that the raw sequencing data are on the SRA in BioProject PRJNA639956 as well as on the Hutch cluster.
There is a cluster configuration file cluster.yaml that configures Snakefile for the Fred Hutch cluster, as recommended by the Snakemake documentation. The run_Hutch_cluster.bash script uses this configuration to run Snakefile. If you are using a different cluster than the Fred Hutch one, you may need to modify the cluster configuration file.
The Jupyter notebooks and R markdown dscripts that perform most of the analysis are in this top-level directory with the extension *.ipynb or *.Rmd.
These notebooks read the key configuration values from config.yaml.
There is also a ./scripts/ subdirectory with related scripts.
The notebooks need to be run in the order described in the workflow and results summary. This will occur automatically if you run them via Snakefile as described above.
Results are placed in the ./results/ subdirectory.
Many of the files created in this subdirectory are not tracked in the git repo as they are very large.
However, key results files are tracked as well as a summary that shows the code and results.
Click here to see that summary.
The large results files are tracked via git-lfs.
This requires git-lfs to be installed, which it is in the conda environment specified by environment.yml.
The following commands were then run:
git lfs install
You may need to run this if you are tracking these files and haven't installed git-lfs in your user account.
Then the large results files were added for tracking with:
git lfs track results/variants/codon_variant_table.csv
git lfs track results/counts/variant_counts.csv
git lfs track results/expression_meanFs/expression_meanFs.csv
git lfs track results/expression_meanFs/expression_meanFs_homologs.csv
git lfs track results/binding_Kds/binding_Kds.csv
git lfs track results/binding_Kds/binding_Kds_homologs.csv
git lfs track results/single_mut_effects/single_mut_effects.csv
git lfs track results/single_mut_effects/homolog_effects.csv
git lfs track results/structure_function/6m0j_b-factor-mean-bind.pdb
git lfs track results/structure_function/6m0j_b-factor-mean-expr.pdb
The one exception is the interactive_heatmap.html which is placed in ./docs/_includes/ so that it can be rendered using GitHub Pages.
The environment.yml file contains a fully pinned conda environment. An environment without all of the versions pinned is in environment_unpinned.yml. If you need to update the environment, the suggested way to do it is add the new requirement to environment_unpinned.yml, then build that environment and finally export the pinned version:
conda env create -f environment_unpinned.yml --prefix ./env
Then activate the environment with:
conda activate ./env
Finally, export the pinned version with:
conda env export --prefix ./env > environment.yml