DeepRVAT

Rare variant association testing using deep learning and data-driven burden scores

Installation

Clone this repository:

git clone git@github.com:PMBio/deeprvat.git

Change directory to the repository: cd deeprvat
Install the conda environment. We recommend using mamba, though you may also replace mamba with conda

note: the current deeprvat env does not support cuda when installed with conda, install using mamba for cuda support.

mamba env create -n deeprvat -f deeprvat_env.yaml

Activate the environment: mamba activate deeprvat
Install the deeprvat package: pip install -e .

If you don't want to install the gpu related requirements use the deeprvat_env_no_gpu.yml environment instead.

mamba env create -n deeprvat -f deeprvat_env_no_gpu.yaml

Basic usage

Customize pipelines

Before running any of the snakefiles, you may want to adjust the number of threads used by different steps in the pipeline. To do this, modify the threads: property of a given rule.

If you are running on an computing cluster, you will need a profile and may need to add resources: directives to the snakefiles.

Run the preprocessing pipeline on VCF files

Instructions here

Annotate variants

Instructions here

Try the full training and association testing pipeline on some example data

mkdir example
cd example
ln -s [path_to_deeprvat]/example/* .
snakemake -j 1 --snakefile [path_to_deeprvat]/pipelines/training_association_testing.snakefile

Replace [path_to_deeprvat] with the path to your clone of the repository.

Note that the example data is randomly generated, and so is only suited for testing whether the deeprvat package has been correctly installed.

Run the association testing pipeline with pretrained models

mkdir example
cd example
ln -s [path_to_deeprvat]/example/* .
ln -s [path_to_deeprvat]/pretrained_models
snakemake -j 1 --snakefile [path_to_deeprvat]/pipelines/association_testing_pretrained.snakefile

Replace [path_to_deeprvat] with the path to your clone of the repository.

Again, note that the example data is randomly generated, and so is only suited for testing whether the deeprvat package has been correctly installed.

Credits

Portions of code for the seed gene discovery methods have been adapted from SEAK

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

Cookiecutter: https://github.com/audreyr/cookiecutter audreyr/cookiecutter-pypackage: https://github.com/audreyr/cookiecutter-pypackage