A variant annotation tool that parses vcf files and fetches variant information from the Ensembl Variant Effect Predictor (VEP) REST API.
vcfannotatoR parses vcf files and annotates each variant in the vcf with the following information:
- Type of variation (substitution, insertion, CNV, etc.).
- Functional consequence (missense, silent, intergenic, etc.). If there are multiple effects, the variant will be annotated with the most deleterious consequence.
- Sequence reading depth at each variant site.
- Number of reads supporting the alternative allele.
- Percentage of reads supporting the alternative allele versus those supporting the reference allele.
- Allele frequency of variant (1000 genomes project) from Ensembl Variant Effect Predictor (VEP) REST API (API documentation is available here: http://grch37.rest.ensembl.org/documentation/info/vep_hgvs_post).
- Additional annotations: rsid, minor_allele_freq, minor_allele, clinvar_significance, pubmed, transcript_id, gene_id, impact (a subjective classification of the severity of the variant consequence, based on agreement with SNPEff), gene_symbol (for each transcript), biotype, polyphen_prediction, and sift_prediction
vcfannotatoR is open-source and available on GitHub
vcfannotatoR needs the following:
- R (tested on R version 4.1.1)
- An internet connection
- The following R libraries: (The number is the version tested during development)
data.table (1.14.2) tidyverse (1.3.1) optparse (1.7.1)
foreach (1.5.1) doParallel (1.0.16) jsonlite (1.7.2)
httr (1.4.2) xml2 (1.3.3) vcfannotatoR will check if the required R libraries are installed and automatically install those not found. You can also use the following command in R to download the libraries before running vcfannotatoR:
# create a function to check dependencies and install packages if they are not already installed <!-- omit in toc -->
check_package <- function(package) {
if (!require(package, character.only = TRUE, quietly = TRUE, warn.conflicts = FALSE)) {
install.packages(package, dependencies = TRUE, repos='http://cran.us.r-project.org')
library(package, character.only = TRUE, quietly = TRUE, warn.conflicts = FALSE)
}
}
# check dependencies and install packages if they are not already installed <!-- omit in toc -->
packages <- c("optparse", "data.table", "tidyverse", "httr", "jsonlite", "xml2", "foreach", "doParallel")
load_packages <- lapply(packages, check_package)note: "data.table" and "tidyverse" are used for data manipulation, "optparse" is used to catch the arguments from the command line, "foreach" and "doParallel" are used to perform for loops in parallel, and "jsonlite", "httr", and "xml2" are used to fetch variant consequences from the Ensembl VEP REST API.
You can clone the GitHub repository:
git clone https://github.com/XUKEREN/vcfannotatoR.git
cd vcfannotatoRvcfannotatoR.R is ready for use in your command line.
To run vcfannotatoR execute the vcfannotatoR.R script. This script catches the arguments from the command line and produces a tsv with variant annotations.
You can type the following in the command line to check available options:
Rscript vcfannotatoR.R -h | Argument | Description | Required |
|---|---|---|
| --input_dir | directory with the input vcf file | Yes |
| -I, --input | vcf file to be processed | Yes |
| -M, --getmeta | TRUE or FALSE, indicates whether to generate meta data tsv | No (default = FALSE) |
| -F, --getinfo | TRUE or FALSE, indicates whether to generate info fields tsv | No (default = FALSE) |
| -M, --getformat | TRUE or FALSE, indicates whether to generate format fields tsv | No (default = FALSE) |
| --totalread | format field total read depth | No (default = DP) |
| --refread | format field reference allele read depth | No (default = RO) |
| --altread | format field alternative allele read depth | No (default = AO) |
| --varianttype | format field variant type | No (default = TYPE) |
Use case 1: You can type the following in the command line, which will return a tsv file Challenge_data.annotated.tsv in the input directory:
Rscript vcfannotatoR.R --input_dir ./data -I Challenge_data.vcfAn example output can be found here: Challenge_data.annotated.tsv
Use case 2: You can type the following in the command line, which will return a complete set of output files in the input directory:
Rscript vcfannotatoR.R --input_dir ./data -I Challenge_data.vcf --getmeta TRUE --getinfo TRUE --getformat TRUE note: User can define the format fields for total read depth, ref allele read depth, alt allele read depth, and variant type. The default fields are DP, RO, AO, and TYPE. For VCF that does not have a variant type field, user should assign any available field to --varianttype.
A typical input vcf file is provided under ./data Challenge_data.vcf
A complete set of output files are provided under ./data
-
Challenge_data.annotated.tsv has annotations for each variant
CHR POS REF ALT total_read_depth TYPE ref_read_depth alt_read_depth AD_alt_vs_ref most_severe_consequence variant_allele_freq.1kg rsid minor_allele_freq minor_allele clinvar_significance pubmed transcript_id gene_id impact gene_symbol biotype polyphen_prediction sift_prediction 1 931393 G T 4124 snp 4029 95 0.0235790518739141 1 935222 C A 1134 snp 480 652 1.35833333333333 missense_variant 0.4938 rs2298214 0.4938 A NA 32203549 ENST00000428771 ENSG00000188290 MODERATE HES4 protein_coding benign tolerated_low_confidence 1 1277533 T C 786 snp 0 786 Inf synonymous_variant 0.998 rs307362 0.002 T NA NA ENST00000378888,ENST00000378891 ENSG00000107404,ENSG00000107404 LOW,LOW DVL1,DVL1 protein_coding,protein_coding NA,NA NA,NA -
Challenge_data.info_meta.tsv has meta data for the info fields
INFO description NS Number of samples with data DP Total read depth at the locus DPB Total read depth per bp at the locus; bases in reads overlapping / bases in haplotype -
Challenge_data.format_meta.tsv has meta data for the format fields
FORMAT description GT Genotype GQ Genotype Quality, the Phred-scaled marginal (or unconditional) probability of the called genotype GL Genotype Likelihood, log10-scaled likelihoods of the data given the called genotype for each possible genotype generated from the reference and alternate alleles given the sample ploidy -
Challenge_data.info.tsv has all the info fields
CHROM POS ID REF ALT QUAL FILTER AB ABP AC AF AN AO CIGAR DP DPB DPRA EPP EPPR GTI LEN MEANALT MQM MQMR NS NUMALT ODDS PAIRED PAIREDR PAO PQA PQR PRO QA QR RO RPL RPP RPPR RPR RUN SAF SAP SAR SRF SRP SRR TYPE 1 931393 . G T 2.17938e-13 . 0 0 0 0 6 95 1X 4124 4124 0.999031 9.61615 316.776 0 1 1 59.7579 65.2274 2 1 591.29 0.989474 0.966741 0 0 0 0 3774 160284 4029 51 4.13032 101.278 44 1 40 8.15326 55 1663 269.369 2366 snp 1 935222 . C A 16866.7 . 0.574956 58.3503 4 0.666667 6 652 1X 1134 1134 0 44.7878 169.779 0 1 2 70 70 2 1 53.367 0.990798 0.975 0 0 0 0 24492 19222 480 398 72.0711 214.077 254 1 28 1186.05 624 16 910.975 464 snp 1 1277533 . T C 28168.6 . 0 0 6 1 6 786 1X 786 786 0 94.5216 0 0 1 1 70 0 2 1 307.075 0.977099 0 0 0 0 0 31532 0 0 474 75.5143 0 312 1 376 6.20397 410 0 0 0 snp -
Challenge_data.format.tsv has all the format fields with different samples in separate rows
CHROM POS ID REF ALT Indiv GT GQ DP DPR RO QR AO QA 1 931393 . G T normal 0/0/0 132.995 2063 2063,0 2063 82063 0 0 1 931393 . G T vaf5 0/0/0 132.995 2061 2061,95 1966 78221 95 3774 1 935222 . C A normal 0/1/1 160.002 567 567,326 240 9611 326 12246
Keren Xu (kerenxu@usc.edu)