PhenomeNet Variant Predictor (PVP) - User Guide

A phenotype-based tool to annotate and prioritize disease variants in WES and WGS data

This user guide have been tested on Ubuntu version 16.04.

For details regarding model training and evaluation, please refer to dev/ directory above.

Hardware requirements

• At least 32 GB RAM.
• At least 1TB free disk space to process and accommodate the necessary databases for annotation

Software requirements (for native installation)

• Any Unix-based operating system
• Java 8
• Python 2.7 (as a system default version) and install the dependencies (for Python 2.7) with:

	pip install -r requirements.txt

• Run python 2 for the script test.py (available above) to test the installation of the python dependencies. If the script fails, please try again to install the required dependencies ( using "pip2" instead of "pip", checking for permissions, or try the docker image instead).

Native Installation

1. Download the distribution file phenomenet-vp-2.1.zip
2. Download the data files phenomenet-vp-2.1-data.zip
3. Extract the distribution files phenomenet-vp-2.1.zip
4. Extract the data files data.tar.gz inside the directory phenomenet-vp-2.1
5. cd phenomenet-vp-2.1
6. Run the command: bin/phenomenet-vp to display help and parameters.

Database requirements

1. Download CADD database file.
2. Download and run the script generate.sh (Requires TABIX).
3. Copy the generated files cadd.txt.gz and cadd.txt.gz.tbi to directory phenomenet-vp-2.1/data/db.
4. Download DANN database file and its indexed file to directory phenomenet-vp-2.1/data/db.
5. Rename the DANN files as dann.txt.gz and dann.txt.gz.tbi respectively.

Docker Container

1. Install Docker
2. Download the data files phenomenet-vp-2.1-data.zip and database requirements
3. Build phenomenet-vp docker image:

   docker build -t phenomenet-vp .

4. Run phenomenet

   docker run -v $(pwd)/data:/data phenomenet-vp -f data/Miller.vcf -o OMIM:263750 

Parameters

--file, -f
   Path to VCF file
--outfile, -of
   Path to results file
--inh, -i
   Mode of inheritance
   Default: unknown
--json, -j
   Path to PhenoTips JSON file containing phenotypes
--omim, -o
   OMIM ID
--phenotypes, -p
   List of phenotype ids separated by commas
--human, -h
   Propagate human disease phenotypes to genes only
   Default: false
--sp, -s
   Propagate mouse and fish disease phenotypes to genes only
   Default: false
--digenic, -d
   Rank digenic combinations
   Default: false
--trigenic, -t
   Rank trigenic combinations
   Default: false
--combination, -c
   Maximum Number of variant combinations to prioritize (for digenic and
   trigenic cases only)
   Default: 1000
 --ngenes, -n
   Number of genes in oligogenic combinations (more than three)
   Default: 4
--oligogenic, -og
   Rank oligogenic combinations
   Default: false
--python, -y
    Path to Python executable (ex. /usr/bin/python)
    Default: python

Usage:

To run the tool, the user needs to provide a VCF file along with either an OMIM ID of the disease or a list of phenotypes (HPO or MPO terms).

a) Prioritize disease-causing variants using an OMIM ID:

bin/phenomenet-vp -f data/Miller.vcf -o OMIM:263750

b) Prioritize digenic disease-causing variants using an OMIM ID, and gene-to-phenotype datta from human studies only:

bin/phenomenet-vp -f data/Miller.vcf -o OMIM:263750 --human --digenic

c) Prioritize disease-causing variants using a set of phenotypes, and recessive inheritance mode

bin/phenomenet-vp -f data/Miller.vcf -p HP:0000007,HP:0000028,HP:0000054,HP:0000077,HP:0000175 -i recessive 

The result file will be at the directory containg the input file. The output file has the same name as input file with .res extension. For digenic, trigenic or oligogenic prioritization, the result file will have .digenic, .trigenic, or .oligogenic extension repectivly.

Analysis of Rare Variants:

In order to effectively analysis rare variants, it is strongly recommended to filter the input VCF files by MAF prior to running phenomenet-vp on it. To do so, follow the instructions below:

a) Install VCFtools.

b) Run the following command using VCFtools on your input VCF file to filter out variants with MAF > 1%:

vcftools --vcf input_file.vcf --recode --max-maf 0.01 --out filtered

c) Run PVP on the output file filtered.recode.vcf generated from the command above.

PVP 1.0

The original random-forest-based PVP tool is available to download here along with its required data files here. The prepared set of exomes and genomes used for the analysis and results are provided here.

DeepPVP

The updated neural-network model, DeepPVP is available to download here along with its required data files here. The prepared set of exomes used for the analysis and comparative results are provided here. The comparison with PVP is based on PVP-1.1 available here along with its required data files here.

OligoPVP

OligoPVP is provided as part of DeepPVP tool using the parameters --digenic, --trigenicm and --oligogenic for ranking candidate disease-causing variant pairs and triples. Our prepared set of synthetic genomes digenic combinations are available here using data from the DIgenic diseases DAtabase (DIDA). The comparison results with other methods are also provided. Results were obtained using DeepPVP v2.0.

People

PVP is jointly developed by researchers at the University of Birmingham (Prof George Gkoutos and his team), University of Cambridge (Dr Paul Schofield and his team), and King Abdullah University of Science and Technology (Prof Vladimir Bajic, Robert Hoehndorf, and teams).

Publications

[1] Boudellioua I, Mahamad Razali RB, Kulmanov M, Hashish Y, Bajic VB, Goncalves-Serra E, Schoenmakers N, Gkoutos GV., Schofield PN., and Hoehndorf R. (2017) Semantic prioritization of novel causative genomic variants. PLOS Computational Biology. https://doi.org/10.1371/journal.pcbi.1005500

[2] Boudellioua I, Kulmanov M, Schofield PN., Gkoutos GV., and Hoehndorf R . (2018) OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants. Scientific Reports. https://doi.org/10.1038/s41598-018-32876-3

[3] Boudellioua I, Kulmanov M, Schofield PN., Gkoutos GV., and Hoehndorf R . (2019) DeepPVP: phenotype-based prioritization of causative variants using deep learning. BMC Bioinformatics. https://doi.org/10.1186/s12859-019-2633-8

License

Copyright (c) 2016-2018, King Abdullah University of Science and Technology
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