This repository contains instructions and code to run the Variant-to-Phenotype (V2P) variant effect predictor. For many users, accessing V2P via our website, v2p.ai, will be the best option. There, users may process up to 100,000 variants at a time. For advanced users or those who need higher throughput, offline installation may be a better option.
git clone https://github.com/davidfstein/v2p.git
Set the V2P working directory to the location that you cloned the repository.
export V2P_DIR=/path/to/dir
You can add this to your bash config if you would like it to persist.
echo "export V2P_DIR=/path/to/dir" >> ~/.bashrc
V2P's dependencies may be installed with conda.
conda env create --file v2p.yaml
Activate the conda environment.
conda activate v2p
An additional python dependency must be installed after creating the conda environment.
This only needs to be done once.
cd bin/scikit-multilearn
pip install .
V2P also requires an installation of docker or singularity to be available on the system. See here for installation instructions: https://docs.docker.com/engine/install/. If using docker, be sure to add your user to the docker group: https://docs.docker.com/engine/install/linux-postinstall/.
V2P relies on a large set of features to derive its predictions. These features are collected from Ensembl's VEP and other sources. In total, downloading the feature data requires ~564GB of free disk space.
Downloading data with the aws cli may be faster than curl/wget. Both options are shown below, choose only one. See https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html for aws cli configuration.
cd v2p
# These should be placed in the V2P repository directory
aws s3 cp s3://v2p-data/vep.tar.gz - | pv | tar xzf - > ${V2P_DIR}/.vep
aws s3 cp s3://v2p-data/hpo.db.gz - | pv | gunzip -c > ${V2P_DIR}/hpo.db
# This can be placed anywhere on your system. You must provide the path to this data
# when running V2P
mkdir cadd_data
cd cadd_data
aws s3 cp s3://v2p-data/cadd.tar.gz - | pv | tar xzf -
cd v2p
# These should be placed in the V2P repository directory
wget -O - https://v2p-data.s3.us-east-2.amazonaws.com/vep.tar.gz | tar xzf - > ${V2P_DIR}/.vep
wget -O - https://v2p-data.s3.us-east-2.amazonaws.com/hpo.db.gz | gunzip -c > ${V2P_DIR}/hpo.db
# This can be placed anywhere on your system. You must provide the path to this data
# when running V2P
mkdir cadd_data
cd cadd_data
wget -O - https://v2p-data.s3.us-east-2.amazonaws.com/cadd.tar.gz | tar xzf -
Precomputed predictions for all posible single nucleotide variants and gnomAD indels are available for download. Installation will result in greatly increased speed for most variant sets. However, there is an additional disk space requirement (~806GB).
# This can be placed anywhere on your system.
mkdir predictions
cd predictions
aws s3 cp s3://v2p-data/snv_predictions.tar.gz - | pv | tar xzf -
mv */*db .
aws s3 cp s3://v2p-data/indel_predictions.tar.gz - | pv | tar xzf -
mv */*db .
# This can be placed anywhere on your system.
mkdir predictions
cd predictions
wget -O - https://v2p-data.s3.us-east-2.amazonaws.com/snv_predictions.tar.gz | tar xzf -
mv */*db .
wget -O - https://v2p-data.s3.us-east-2.amazonaws.com/indel_predictions.tar.gz | tar xzf -
mv */*db .
The input to V2P is a VCF file containing the variants you wish to score in hg38 coordinates.
The VCF file must contain #CHROM POS ID REF ALT QUAL FILTER INFO in the header.
Other header lines should not be included.
To run V2P provide the path to the VCF, a path where the output will be stored, and the path to the downloaded features.
You may optionally pass the path to the precomputed predictions, a number of CPUs to be used (default=1) and,
if scoring variants from a single gene, you may provide a gene name to be prioritized by VEP.
Run bash get_predictions.sh -h for a description of the available parameters.
Example
bash get_predictions.sh -i /path/to/input -o /path/to/output -a /absolute/path/to/annotations -p /path/to/precomputed_predictions -c 5
with singularity instead of docker
bash get_predictions.sh -i /path/to/input -o /path/to/output -a /absolute/path/to/annotations -p /path/to/precomputed_predictions -c 5 -s