The germline variant annotator (gvanno) is a software package intended for analysis and interpretation of human DNA variants of germline origin. Variants and genes are annotated with disease-related and functional associations from a wide range of sources (see below). Technically, the workflow is built with the Docker technology, and it can also be installed through the Singularity framework.
gvanno accepts query files encoded in the VCF format, and can analyze both SNVs and short InDels. The workflow relies heavily upon Ensembl’s Variant Effect Predictor (VEP), and vcfanno. It produces an annotated VCF file and a file of tab-separated values (.tsv), the latter listing all annotations pr. variant record. Note that if your input VCF contains data (genotypes) from multiple samples (i.e. a multisample VCF), the output TSV file will contain one line/record per sample variant.
- April 22nd 2021 - dev update
- Data updates (ClinVar, UniProt, GWAS Catalog, dbNSFP, Pfam, Open Targets Platform)
- Software update (VEP 103)
- Two new options added:
--vep_regulatory- annotates variants for overlap with regulatory regions--docker-uid- set Docker user id
- December 7th 2020 - 1.4.1 release
- Data updates (ClinVar, UniProt, GWAS Catalog, Open Targets Platform)
- Software update (VEP 102)
- Skipped DisGenet annotations (Open Targets serve similar purpose)
- VEP - Variant Effect Predictor v103 (GENCODE v37/v19 as the gene reference dataset)
- dBNSFP - Database of non-synonymous functional predictions (v4.2, March 2021)
- gnomAD - Germline variant frequencies exome-wide (release 2.1, October 2018) - from VEP
- dbSNP - Database of short genetic variants (build 153) - from VEP
- 1000 Genomes Project - phase3 - Germline variant frequencies genome-wide (May 2013) - from VEP
- ClinVar - Database of variants related to human health/disease phenotypes (April 2021)
- CancerMine - literature-mined database of drivers, oncogenes and tumor suppressors in cancer (version 34)
- Open Targets Platform - Target-disease and target-drug associations (2021_02, February 2021)
- UniProt/SwissProt KnowledgeBase - Resource on protein sequence and functional information (2021_02, April 2021)
- Pfam - Database of protein families and domains (v34.0, March 2021)
- NHGRI-EBI GWAS Catalog - Catalog of published genome-wide association studies (April 12th 2021)
An installation of Python (version >=3.6) is required to run gvanno. Check that Python is installed by typing python --version in your terminal window.
- Install the Docker engine on your preferred platform
- installing Docker on Linux
- installing Docker on Mac OS
- NOTE: We have not yet been able to perform enough testing on the Windows platform, and we have received feedback that particular versions of Docker/Windows do not work with PCGR (an example being mounting of data volumes)
- Test that Docker is running, e.g. by typing
docker psordocker imagesin the terminal window - Adjust the computing resources dedicated to the Docker, i.e.:
- Memory: minimum 5GB
- CPUs: minimum 4
- How to - Mac OS X
-
Note: this works for Singularity version 3.0 and higher.
-
Test that singularity works by running
singularity --version -
If you are in the gvanno directory, build the singularity image like so:
cd srcsudo ./buildSingularity.sh
-
Clone the latest version in development
-
Download and unpack the latest assembly-specific data bundle in the gvanno directory
- grch37 data bundle (approx 18Gb)
- grch38 data bundle (approx 20Gb)
- Unpacking:
gzip -dc gvanno.databundle.grch37.YYYYMMDD.tgz | tar xvf -
A data/ folder within the gvanno-X.X software folder should now have been produced
-
Pull the gvanno Docker image (dev) from DockerHub (approx 2.4Gb):
docker pull sigven/gvanno:dev(gvanno annotation engine)
The gvanno workflow accepts a single input file:
- An unannotated, single-sample VCF file (>= v4.2) with germline variants (SNVs/InDels)
We strongly recommend that the input VCF is compressed and indexed using bgzip and tabix. NOTE: If the input VCF contains multi-allelic sites, these will be subject to decomposition.
Run the workflow with gvanno.py, which takes the following arguments and options:
usage:
gvanno.py -h [options]
--query_vcf <QUERY_VCF>
--gvanno_dir <GVANNO_DIR>
--output_dir <OUTPUT_DIR>
--genome_assembly <grch37|grch38>
--sample_id <SAMPLE_ID>
--container <docker|singularity>
gvanno - workflow for functional and clinical annotation of germline nucleotide variants
Required arguments:
--query_vcf QUERY_VCF
VCF input file with germline query variants (SNVs/InDels).
--gvanno_dir GVANNO_DIR
Directory that contains the gvanno data bundle, e.g. ~/gvanno-dev
--output_dir OUTPUT_DIR
Output directory
--genome_assembly {grch37,grch38}
Genome assembly build: grch37 or grch38
--container {docker,singularity}
Run gvanno with docker or singularity
--sample_id SAMPLE_ID
Sample identifier - prefix for output files
VEP optional arguments:
--vep_regulatory Enable Variant Effect Predictor (VEP) to look for overlap with regulatory regions (option --regulatory in VEP).
--vep_lof_prediction Predict loss-of-function variants with Loftee plugin in Variant Effect Predictor (VEP), default: False
--vep_n_forks VEP_N_FORKS
Number of forks for Variant Effect Predictor (VEP) processing, default: 4
--vep_buffer_size VEP_BUFFER_SIZE
Variant buffer size (variants read into memory simultaneously) for Variant Effect Predictor (VEP) processing
- set lower to reduce memory usage, default: 5000
--vep_pick_order VEP_PICK_ORDER
Comma-separated string of ordered transcript properties for primary variant pick in
Variant Effect Predictor (VEP) processing, default: canonical,appris,biotype,ccds,rank,tsl,length,mane
--vep_skip_intergenic
Skip intergenic variants in Variant Effect Predictor (VEP) processing, default: False
Other optional arguments:
--force_overwrite By default, the script will fail with an error if any output file already exists.
You can force the overwrite of existing result files by using this flag, default: False
--version show program's version number and exit
--no_vcf_validate Skip validation of input VCF with Ensembl's vcf-validator, default: False
--docker_uid DOCKER_USER_ID
Docker user ID. default is the host system user ID. If you are experiencing permission errors, try setting this up to root (`--docker-uid root`)
--vcfanno_n_processes VCFANNO_N_PROCESSES
Number of processes for vcfanno processing (see https://github.com/brentp/vcfanno#-p), default: 4
The examples folder contains an example VCF file. Analysis of the example VCF can be performed by the following command:
python ~/gvanno-dev/gvanno.py
--query_vcf ~/gvanno-dev/examples/example.grch37.vcf.gz
--gvanno_dir ~/gvanno-dev
--output_dir ~/gvanno-dev
--sample_id example
--genome_assembly grch37
--container docker
--force_overwrite
This command will run the Docker-based gvanno workflow and produce the following output files in the examples folder:
- example_gvanno_pass_grch37.vcf.gz (.tbi) - Bgzipped VCF file with rich set of functional/clinical annotations
- example_gvanno_pass_grch37.tsv.gz - Compressed TSV file with rich set of functional/clinical annotations
Similar files are produced for all variants, not only variants with a PASS designation in the VCF FILTER column.
Documentation of the various variant and gene annotations should be interrogated from the header of the annotated VCF file. The column names of the tab-separated values (TSV) file will be identical to the INFO tags that are documented in the VCF file.
sigven AT ifi.uio.no