Ensembl’s Variant Effect Predictor is described in McLaren et al. (2016).
Prior to Bioconductor 3.19, the ensemblVEP package provided access to Ensembl’s predictions through an interface between Perl and MySQL.
In 3.19 VariantAnnotation supports the use of the VEP component of the REST API at https://rest.ensembl.org.
The function vep_by_region will accept a VCF object as
defined in VariantAnnotation.
library(VariantAnnotation)
fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation")
r22 = readVcf(fl)
r22## class: CollapsedVCF
## dim: 10376 5
## rowRanges(vcf):
## GRanges with 5 metadata columns: paramRangeID, REF, ALT, QUAL, FILTER
## info(vcf):
## DataFrame with 22 columns: LDAF, AVGPOST, RSQ, ERATE, THETA, CIEND, CIPOS,...
## info(header(vcf)):
## Number Type Description
## LDAF 1 Float MLE Allele Frequency Accounting for LD
## AVGPOST 1 Float Average posterior probability from MaCH/Thunder
## RSQ 1 Float Genotype imputation quality from MaCH/Thunder
## ERATE 1 Float Per-marker Mutation rate from MaCH/Thunder
## THETA 1 Float Per-marker Transition rate from MaCH/Thunder
## CIEND 2 Integer Confidence interval around END for imprecise var...
## CIPOS 2 Integer Confidence interval around POS for imprecise var...
## END 1 Integer End position of the variant described in this re...
## HOMLEN . Integer Length of base pair identical micro-homology at ...
## HOMSEQ . String Sequence of base pair identical micro-homology a...
## SVLEN 1 Integer Difference in length between REF and ALT alleles
## SVTYPE 1 String Type of structural variant
## AC . Integer Alternate Allele Count
## AN 1 Integer Total Allele Count
## AA 1 String Ancestral Allele, ftp://ftp.1000genomes.ebi.ac.u...
## AF 1 Float Global Allele Frequency based on AC/AN
## AMR_AF 1 Float Allele Frequency for samples from AMR based on A...
## ASN_AF 1 Float Allele Frequency for samples from ASN based on A...
## AFR_AF 1 Float Allele Frequency for samples from AFR based on A...
## EUR_AF 1 Float Allele Frequency for samples from EUR based on A...
## VT 1 String indicates what type of variant the line represents
## SNPSOURCE . String indicates if a snp was called when analysing the...
## geno(vcf):
## List of length 3: GT, DS, GL
## geno(header(vcf)):
## Number Type Description
## GT 1 String Genotype
## DS 1 Float Genotype dosage from MaCH/Thunder
## GL G Float Genotype LikelihoodsIn this example we confine attention to single nucleotide variants.
There is a limit of 200 locations in a request, and 55000 requests per hour. We’ll base our query on 100 positions in the chr22 VCF.
dr = which(width(rowRanges(r22))!=1)
r22s = r22[-dr]
res = vep_by_region(r22[1:100], snv_only=FALSE, chk_max=FALSE)
jans = toJSON(content(res))There are various ways to work with the result of this query to the API. We’ll use the rjsoncons JSON processing infrastructure to dig in and understand aspects of the API behavior.
First, the top-level concepts produced for each variant can be retrieved using
## [1] "start" "seq_region_name"
## [3] "input" "allele_string"
## [5] "assembly_name" "most_severe_consequence"
## [7] "id" "end"
## [9] "transcript_consequences" "strand"
## [11] "colocated_variants" "regulatory_feature_consequences"Annotation of the most severe consequence known will typically be of interest:
##
## 5_prime_UTR_variant intron_variant splice_region_variant
## 1 98 1There is variability in the structure of data returned for each query.
## [[1]]
## consequence_terms impact regulatory_feature_id biotype variant_allele
## 1 regulato.... MODIFIER ENSR22_5.... CTCF_bin.... TFurthermore, the content of the motif feature consequences field seems very peculiar.
## < table of extent 0 >We’ll consider the following approach to converting the API response to a GenomicRanges GRanges instance. Eventually this may become part of the package.
library(GenomicRanges)
.make_GRanges = function( vep_response ) {
stopifnot(inherits(vep_response, "response")) # httr
nested = fromJSON(toJSON(content(vep_response)))
ini = GRanges(seqnames = unlist(nested$seq_region_name),
IRanges(start=unlist(nested$start), end=unlist(nested$end)))
dr = match(c("seq_region_name", "start", "end"), names(nested))
mcols(ini) = DataFrame(nested[,-dr])
ini
}
tstg = .make_GRanges( res )
tstg[,1] # full print is unwieldy## GRanges object with 100 ranges and 1 metadata column:
## seqnames ranges strand | input
## <Rle> <IRanges> <Rle> | <list>
## [1] 22 50300078 * | 22 50300078 rs741029..
## [2] 22 50300086 * | 22 50300086 rs147922..
## [3] 22 50300101 * | 22 50300101 rs114143..
## [4] 22 50300113 * | 22 50300113 rs141778..
## [5] 22 50300166 * | 22 50300166 rs182170..
## ... ... ... ... . ...
## [96] 22 50304748 * | 22 50304748 rs141641..
## [97] 22 50304805 * | 22 50304805 rs761151..
## [98] 22 50304935 * | 22 50304935 rs121677..
## [99] 22 50304943 * | 22 50304943 rs186556..
## [100] 22 50305084 * | 22 50305084 rs116244..
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths## [1] "input" "allele_string"
## [3] "assembly_name" "most_severe_consequence"
## [5] "id" "transcript_consequences"
## [7] "strand" "colocated_variants"
## [9] "regulatory_feature_consequences"Now information about variants can be retrieved with range operations. Deep annotation requires nested structure of the metadata columns.
## [[1]]
## consequence_terms impact hgnc_id variant_allele gene_symbol_source
## 1 intron_v.... MODIFIER HGNC:9104 G HGNC
## 2 intron_v.... MODIFIER HGNC:9104 G HGNC
## 3 intron_v.... MODIFIER HGNC:9104 G HGNC
## 4 intron_v.... MODIFIER HGNC:9104 G HGNC
## gene_symbol transcript_id gene_id biotype strand flags
## 1 PLXNB2 ENST0000.... ENSG0000.... protein_.... -1
## 2 PLXNB2 ENST0000.... ENSG0000.... protein_.... -1 cds_end_NF
## 3 PLXNB2 ENST0000.... ENSG0000.... protein_.... -1 cds_end_NF
## 4 PLXNB2 ENST0000.... ENSG0000.... protein_.... -1An important element of prior work in ensemblVEP supports feeding annotation back into the VCF used to generate the effect prediction query. This seems feasible but concrete use cases are of interest.