Welcome to DeepRVAT’s documentation!
Rare variant association testing using deep learning and data-driven burden scores.
How to use this documentation
A good place to start is in Basic usage, to install the package and make sure it runs correctly.
To run DeepRVAT on your data, first consult Modes of usage here, then proceed based on which mode is right for your use case.
For all modes, you’ll want to consult Input data: Common requirements for all pipelines and Configuration file: Common parameters here.
For all modes of usage other than association testing with precomputed burdens, you’ll need to preprocess your genotype data, followed by annotating your variants.
To train custom DeepRVAT models, rather than using precomputed burdens or our provided pretrained models, you’ll need to additionally run seed gene discovery.
Finally, consult the relevant section for your use case here.
If running DeepRVAT on a cluster (recommended), some helpful tips are here.
Citation
If you use this package, please cite:
Clarke, Holtkamp et al., “Integration of Variant Annotations Using Deep Set Networks Boosts Rare Variant Association Genetics.” bioRxiv. https://dx.doi.org/10.1101/2023.07.12.548506
Contact
To report a bug or make a feature request, please create an issue on GitHub.