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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, 09.13.23, 09.20.23, 11.03.2023, 11.17.23, 11.29.2023, and 12.14.2023. 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
  • 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