• JARVIS ("Junk" Annotation genome-wide Residual Variation Intolerance Score): a comprehensive deep learning framework to prioritise non-coding variants in whole genomes, using human-lineage purifying selection features and primary sequence context.

• gwRVIS (genome-wide Residual Variation Intolerance Score): genome-wide intolerance to variation score

• Python dependencies

conda create -n jarvis python=3.7 r r-devtools r-tidyverse 
conda config --add channels bioconda
conda config --add channels conda-forge
conda activate jarvis  


conda install --file requirements.txt  

• R dependencies

install.packages(c("glm2", "glmnet", "lmridge", "plotmo", "pRoc", "ggplot2", "ggridges", "RColorBrewer")) devtools::install_github("thomasp85/patchwork")

• Instructions to generate the JARVIS and gwRVIS scores are available in the README.md file within modules.
• Subsequent sub-folders may also contain their own README files with instructions to run them independently or for ad-hoc analyses.
• Other folders and their sub-folders (such as ensembl/, gnomad/ and other_datasets/) are accompanied with README files and scripts to download and pre-process any other required datafiles that are not available in the JARVIS GitHub repositoy.

JARVIS and gwRVIS scores, across the whole genome, are publicly available at the following location: http://jarvis.public.cgr.astrazeneca.com

All scores have been generated based on the hg19 human assembly version.