BVAS Documentation

This is the documentation for BVAS, a package for inferring selection effects from genomic surveillance data using Bayesian methods, in particular Bayesian Viral Allele Selection. Please see the pre-print and the GitHub repo for more details.

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 .

Basic usage

The main functionality of BVAS is available through the BVASSelector class. See the Jupyter notebooks in the notebooks directory for detailed example usage.

Contents:

• BVAS Selector
  • BVASSelector
• BVAS Sampler
  • BVASSampler
• Simulation
  • simulate_data()
• MAP Inference
  • map_inference()
• Laplace Inference
  • laplace_inference()

Index

• Index