BVAS: Bayesian Viral Allele Selection
Welcome to the GitHub repository for Inferring selection effects in SARS-CoV-2 with Bayesian Viral Allele Selection. The results from our main analysis are available in paper. More recent inference results are also available: 08.10.22, 09.22.22, 05.18.23, 06.15.23, 06.30.23, 07.11.23, 09.13.23, and 09.20.23. Thanks to Benjamin Kotzen (@bkotzen) for help generating these more recent results!
Requirements
BVAS requires Python 3.8 or later and the following Python packages: PyTorch, pandas, and pyro.
Note that if you wish to run BVAS on a GPU you need to install PyTorch with CUDA support. In particular if you run the following command from your terminal it should report True:
python -c 'import torch; print(torch.cuda.is_available())'
Installation instructions
Install directly from GitHub:
pip install git+https://github.com/broadinstitute/bvas.git
Install from source:
git clone git@github.com:broadinstitute/bvas.git
cd bvas
pip install .
Documentation
The documentation is available here.
Repo organization
This repo is organized as follows:
- bvas: all the core code: inference algorithms and simulations
- paper: some of the figures and inference results contained in the paper
- notebooks: Jupyter notebooks demonstrating BVAS usage
- basic_demo.ipynb: demo using simulated data
- S_gene_demo.ipynb: demo using GISAID data restricted only to the SARS-CoV-2 S gene
- data: pre-processing scripts and (some of the) data used in the analysis
- docs: source code for the documentation
- example_scripts: example scripts that demo BVAS usage
- tests: unit tests for verifying the correctness of inference algorithms and other code