Analysis of deep mutational scanning of barcoded codon variants of SARS-CoV-2 RBD in variant backgrounds. This study is described in this manuscript.
The 4 backgrounds examined here are:
- Wuhan-Hu-1 background ("wildtype")
- E484K
- N501Y (found in the B.1.1.7 lineage)
- K417N-E484K-N501Y (found in the B.1.351 lineage)
In addition, we incorporate the RBD DMS data measured via the same experimental platform in the Delta background (L452R+T478K), downloaded from this repository.
Study and analysis by Tyler Starr, Allie Greaney, Jesse Bloom, and co-authors.
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.
We have also made two tools to help visualize the results of our analysis:
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You can use a set of interactive heatmaps to explore the change in ACE2-binding affinity or the change in RBD expression caused by mutations in each RBD variant. To use this tool, click here.
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You can use an interactive widget to visualize epistatic shifts in mutational effects on ACE2-binding affinity between variant RBDs. To use this tool, click here.
The analysis consists of three components, all of which are contained in this repository:
-
Instructions to build the computing environment.
-
The computer code itself.
-
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:
mamba 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/3.6.2-foss-2019b
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
To simplify git tracking of Jupyter notebooks, we have added the filter described here to strip notebook metadata to .gitattributes and .gitconfig. The first time you check out this repo, run the following command to use this configuration (see here):
git config --local include.path ../.gitconfig
Then don't worry about it anymore.
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.
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