1 Motivation

1.1 Background

VCF objects of the VariantAnnotation package contain a plethora of information imported from specific fields of source VCF files and stored in dedicated slots (e.g. fixed, info, geno), as well as optional Ensembl VEP predictions (McLaren et al. 2010) stored under a given key of their INFO slot.

This information may be used to identify and filter variants of interest for further analysis. However, the size of genetic data sets and the variety of filter rules—and their combinatorial explosion—create considerable challenges in terms of workspace memory and entropy (i.e. size and number of objects in the workspace, respectively).

The FilterRules class implemented in the S4Vectors package provides a powerful tool to create flexible and lightweight filter rules defined in the form of expression and function objects that can be evaluated within given environments. The TVTB package extends this FilterRules class into novel classes of VCF filter rules, applicable to information stored in the distinct slots of VCF objects (i.e. CollapsedVCF and ExpandedVCF classes), as described below:

Motivation for each of the new classes extending FilterRules, to define VCF filter rules.
Class Motivation
VcfFixedRules Filter rules applied to the fixed slot of a VCF object.
VcfInfoRules Filter rules applied to the info slot of a VCF object.
VcfVepRules Filter rules applied to the Ensembl VEP predictions stored in a given INFO key of a VCF object.
VcfFilterRules Combination of VcfFixedRules, VcfInfoRules, and VcfVepRules applicable to a VCF object.

Table: Motivation for each of the new classes extending FilterRules to define VCF filter rules.

Note that FilterRules objects themselves are applicable to VCF objects, with two important difference from the above specialised classes:

  • Expressions must explicitely refer to the different VCF slots
  • As a consequence, a single expression can refer to fields from different VCF slots, for instance:
fr <- S4Vectors::FilterRules(list(
    mixed = function(x){
        VariantAnnotation::fixed(x)[,"FILTER"] == "PASS" &
            VariantAnnotation::info(x)[,"MAF"] >= 0.05
    }
))
fr
## FilterRules of length 1
## names(1): mixed

1.2 Features

As they inherit from the FilterRules class, these new classes benefit from accessors and methods defined for their parent class, including:

  • VCF filter rules can be toggled individually between an active and an inactive states
  • VCF filter rules can be subsetted, edited, replaced, and deleted

To account for the more complex structure of VCF objects, some of the new VCF filter rules classes implemented in the TVTB package require additional information stored in new dedicated slots, associated with the appropriate accessors and setters. For instance:

  • VcfVepRules require the INFO key where predictions of the Ensembl Variant Effect Predictor are stored in a VCF object. The vep accessor method may be used to access this slot.
  • VcfFilterRules—which may combine any number of filter rules stored in FixedRules, VcfFixedRules, VcfInfoRules, VcfVepRules, and other VcfFilterRules objects— mark each filter rule with their type in the combined object. The information is stored in the type slot, which may be accessed using the read-only accessor method type.

2 Demonstration data

For the purpose of demonstrating the utility and usage of VCF filter rules, a set of variants and associated phenotype information was obtained from the 1000 Genomes Project Phase 3 release. It can be imported as a CollapsedVCF object using the following code:

library(TVTB)
extdata <- system.file("extdata", package = "TVTB")
vcfFile <- file.path(extdata, "chr15.phase3_integrated.vcf.gz")
tabixVcf <- Rsamtools::TabixFile(file = vcfFile)
vcf <- VariantAnnotation::readVcf(file = tabixVcf)

VCF filter rules may be applied to ExpandedVCF objects equally:

evcf <- VariantAnnotation::expand(x = vcf, row.names = TRUE)

2.1 CollapsedVCF and ExpandedVCF

As described in the documentation of the VariantAnnotation package, the key difference between CollapsedVCF and ExpandedVCF objects —both extending the VCF class—is the expansion of multi-allelic records into bi-allelic records, respectively. In other words (quoting the VariantAnnotation documentation):

CollapsedVCF objects contains the ALT data as a DNAStringSetList allowing for multiple alleles per variant. In contrast, the ExpandedVCF stores the ALT data as a DNAStringSet where the ALT column has been expanded to create a flat form of the data with one row per variant-allele combination.”

This difference has implications for filter rules using the "ALT" field of the info slot, as demonstrated in a later section.

3 Fields available for the definition of filter rules

First, let us examine which fields (i.e. column names) are available in the VCF objects to create VCF filter rules:

fixedVcf <- colnames(fixed(vcf))
fixedVcf
## [1] "REF"    "ALT"    "QUAL"   "FILTER"
infoVcf <- colnames(info(vcf))
infoVcf
##  [1] "CIEND"         "CIPOS"         "CS"            "END"          
##  [5] "IMPRECISE"     "MC"            "MEINFO"        "MEND"         
##  [9] "MLEN"          "MSTART"        "SVLEN"         "SVTYPE"       
## [13] "TSD"           "AC"            "AF"            "NS"           
## [17] "AN"            "EAS_AF"        "EUR_AF"        "AFR_AF"       
## [21] "AMR_AF"        "SAS_AF"        "DP"            "AA"           
## [25] "VT"            "EX_TARGET"     "MULTI_ALLELIC" "CSQ"
csq <- ensemblVEP::parseCSQToGRanges(x = evcf)
vepVcf <- colnames(mcols(csq))
vepVcf
##  [1] "Allele"             "Consequence"        "IMPACT"            
##  [4] "SYMBOL"             "Gene"               "Feature_type"      
##  [7] "Feature"            "BIOTYPE"            "EXON"              
## [10] "INTRON"             "HGVSc"              "HGVSp"             
## [13] "cDNA_position"      "CDS_position"       "Protein_position"  
## [16] "Amino_acids"        "Codons"             "Existing_variation"
## [19] "DISTANCE"           "STRAND"             "FLAGS"             
## [22] "VARIANT_CLASS"      "SYMBOL_SOURCE"      "HGNC_ID"           
## [25] "CANONICAL"          "TSL"                "APPRIS"            
## [28] "CCDS"               "ENSP"               "SWISSPROT"         
## [31] "TREMBL"             "UNIPARC"            "GENE_PHENO"        
## [34] "SIFT"               "PolyPhen"           "DOMAINS"           
## [37] "HGVS_OFFSET"        "GMAF"               "AFR_MAF"           
## [40] "AMR_MAF"            "EAS_MAF"            "EUR_MAF"           
## [43] "SAS_MAF"            "AA_MAF"             "EA_MAF"            
## [46] "ExAC_MAF"           "ExAC_Adj_MAF"       "ExAC_AFR_MAF"      
## [49] "ExAC_AMR_MAF"       "ExAC_EAS_MAF"       "ExAC_FIN_MAF"      
## [52] "ExAC_NFE_MAF"       "ExAC_OTH_MAF"       "ExAC_SAS_MAF"      
## [55] "CLIN_SIG"           "SOMATIC"            "PHENO"             
## [58] "PUBMED"             "MOTIF_NAME"         "MOTIF_POS"         
## [61] "HIGH_INF_POS"       "MOTIF_SCORE_CHANGE" "CADD_PHRED"        
## [64] "CADD_RAW"

4 Usage of VCF filter rules

4.1 Filter rules using a single field

The value of a particular field can be used to define expressions that represent simple filter rules based on that value alone. Multiple rules may be stored in any one FilterRules objects. Ideally, VCF filter rules should be named to facilitate their use, but also as a reminder of the purpose of each particular rule. For instance, in the chunk of code below, two filter rules are defined using fields of the fixed slot:

  • A rule named "pass" identifies variants for which the value in the FILTER field is "PASS"
  • A rule named "qual20" identifies variants where the value in the QUAL field is greater than or equal to 20
fixedRules <- VcfFixedRules(exprs = list(
    pass = expression(FILTER == "PASS"),
    qual20 = expression(QUAL >= 20)
))
active(fixedRules)["qual20"] <- FALSE
summary(evalSeparately(fixedRules, vcf))
##    pass          qual20       
##  Mode:logical   Mode:logical  
##  TRUE:479       TRUE:479

In the example above, all variants pass the active "pass" filter, while the deactivated rules "qual20") automatically returns TRUE for all variants.

4.2 Filter rules using multiple fields

It is also possible for VCF filter rules to use multiple fields (of the same VCF slot) in a single expression. In the chunk of code below, the VCF filter rule identifies variants for which both the "REF" and "ALT" values (in the INFO slot) are one of the four nucleotides (i.e. a simple definition of single nucleotide polymorphisms; SNPs):

nucleotides <- c("A", "T", "G", "C")
SNPrule <- VcfFixedRules(exprs = list(
    SNP = expression(
    as.character(REF) %in% nucleotides &
        as.character(ALT) %in% nucleotides)
))
summary(evalSeparately(SNPrule, evcf, enclos = .GlobalEnv))
##     SNP         
##  Mode :logical  
##  FALSE:14       
##  TRUE :467

Some considerations regarding the above filter rule:

  • considering that the filter rule requires the nucleotides character vector, the global environment must be supplied as the enclosing environment to successfully evaluate the expression
  • "REF" and "ALT" are stored as DNAStringSet in CollapsedVCF objects and must be converted to character in order to successfully apply the method %in%.

4.3 Calculations in filter rules

Expressions that define filter rules may also include calculations. In the chunk of code below, two simple VCF filter rules are defined using fields of the info slot:

  • A rule named "samples" identifies variants where at least 90% of samples have data (i.e. the NS value is greater than or equal to 0.9 times the total number of samples)
  • A rule named "avgSuperPopAF" calculates the average of the allele frequencies calculated in each the five super-populations (available in several INFO fields), and subsequently identifies variants with an average value greater than 0.05.
infoRules <- VcfInfoRules(exprs = list(
    samples = expression(NS > (0.9 * ncol(evcf))),
    avgSuperPopAF = expression(
        (EAS_AF + EUR_AF + AFR_AF + AMR_AF + SAS_AF) / 5 > 0.05
    )
))
summary(evalSeparately(infoRules, evcf, enclos = .GlobalEnv))
##  samples        avgSuperPopAF  
##  Mode:logical   Mode :logical  
##  TRUE:481       FALSE:452      
##                 TRUE :29

4.4 Functions in filter rules

It may be more convenient to define filters as function objects. For instance, the chunk of code below:

  • first, defines a function that:
    • expects the info slot of a VCF object as input
    • identifies variants where at least two thirds of the super-populations show an allele frequency greater than 5%
  • next, defines a VCF filter rule using the above function
AFcutoff <- 0.05
popCutoff <- 2/3
filterFUN <- function(envir){
    # info(vcf) returns a DataFrame; rowSums below requires a data.frame
    df <- as.data.frame(envir)
    # Identify fields storing allele frequency in super-populations
    popFreqCols <- grep("[[:alpha:]]{3}_AF", colnames(df))
    # Count how many super-population have an allele freq above the cutoff
    popCount <- rowSums(df[,popFreqCols] > AFcutoff)
    # Convert the cutoff ratio to a integer count
    popCutOff <- popCutoff * length(popFreqCols)
    # Identifies variants where enough super-population pass the cutoff
    testRes <- (popCount > popCutOff)
    # Return a boolean vector, required by the eval method
    return(testRes)
}
funFilter <- VcfInfoRules(exprs = list(
    commonSuperPops = filterFUN
))
summary(evalSeparately(funFilter, evcf))
##  commonSuperPops
##  Mode :logical  
##  FALSE:464      
##  TRUE :17

Notably, the filterFUN function may also be applied separately to the info slot of VCF objects:

summary(filterFUN(info(evcf)))
##    Mode   FALSE    TRUE 
## logical     464      17

4.5 Pattern matching in filter rules

The grepl function is particularly suited for the purpose of FilterRules as they return a logical vector:

missenseFilter <- VcfVepRules(
    exprs = list(
        exact = expression(Consequence == "missense_variant"),
        grepl = expression(grepl("missense", Consequence))
        ),
    vep = "CSQ")
summary(evalSeparately(missenseFilter, evcf))
##    exact           grepl        
##  Mode :logical   Mode :logical  
##  FALSE:454       FALSE:452      
##  TRUE :27        TRUE :29

In the above chunk of code:

  • the filter rule named "exact" matches only the given value, associated with 27 variants,
  • the filter rule named "grepl" also matches an extra two variants associated with the value "missense_variant&splice_region_variant" matched by the given pattern. By deduction, the two rules indicate together that those two variants were not assigned the "missense_variant" prediction.

5 Using ALT data in the fixed slot of VCF objects

As detailed in an earlier section introducing the demonstration data, and more thoroughly in the documentation of the VariantAnnotation package, CollapsedVCF and ExpandedVCF classes differ in the class of data stored in the "ALT" field of their respective fixed slot. As as result, VCF filter rules using data from this field must take into account the VCF class in order to handle the data appropriately:

5.1 ExpandedVCF objects

A key aspect of ExpandedVCF objects is that the "ALT" field of their fixed slot may store only a single allele per record as a DNAStringSet object.

For instance, in an earlier section that demonstrated Filter rules using multiple raw fields, ALT data of the fixed slot in an ExpandedVCF object had to be re-typed from DNAStringSet to character before the %in% function could be applied.

Nevertheless, VCF filter rules may also make use of methods associated with the DNAStringSet class. For instance, genetic insertions may be identified using the fields "REF" and "ALT" fields of the fixed slot:

fixedInsertionFilter <- VcfFixedRules(exprs = list(
    isInsertion = expression(
        Biostrings::width(ALT) > Biostrings::width(REF)
    )
))
evcf_fixedIns <- subsetByFilter(evcf, fixedInsertionFilter)
as.data.frame(fixed(evcf_fixedIns)[,c("REF", "ALT")])
##   REF ALT
## 1   A  AC
## 2   A  AT
## 3   C  CA
## 4   T  TA

Here, the above VcfFixedRules is synonym to a distinct VcfVepRules using the Ensembl VEP prediction "VARIANT_CLASS":

vepInsertionFilter <- VcfVepRules(exprs = list(
    isInsertion = expression(VARIANT_CLASS == "insertion")
))
evcf_vepIns <- subsetByFilter(evcf, vepInsertionFilter)
as.data.frame(fixed(evcf_vepIns)[,c("REF", "ALT")])
##   REF ALT
## 1   A  AC
## 2   A  AT
## 3   C  CA
## 4   T  TA

5.2 CollapsedVCF objects

In contrast to ExpandedVCF, CollapsedVCF may contain more than one allele per record in their "ALT" field (fixed slot), represented by a DNAStringSetList object.

As a result, VCF filter rules using the "ALT" field of the info slot in CollapsedVCF objects may use methods dedicated to DNAStringSetList to handle the data. For instance, multi-allelic variants may be identified by the following VcfFixedRules:

multiallelicFilter <- VcfFixedRules(exprs = list(
    multiallelic = expression(lengths(ALT) > 1)
))
summary(eval(multiallelicFilter, vcf))
##    Mode   FALSE    TRUE 
## logical     477       2

6 Combination of multiple types of VCF filter rules

Any number of VcfFixedRules, VcfInfoRules, and VcfVepRules—or even VcfFilterRules themselves—may be combined into a larger object of class VcfFilterRules. Notably, the active state of each filter rule is transferred to the combined object. Even though the VcfFilterRules class acts as a container for multiple types of VCF filter rules, the resulting VcfFilterRules object also extends the FilterRules class, and as a result can be evaluated and used to subset VCF objects identically to any of the specialised more specialised classes.

During the creation of VcfFixedRules objects, each VCF filter rule being combined is marked with a type value, indicating the VCF slot in which the filter rule must be evaluated. This information is stored in the new type slot of VcfFixedRules objects. For instance, it is possible to combine two VcfFixedRules (containing two and one filter rules, respectively), one VcfInfoRules, and one VcfVepRules defined earlier in this vignette:

vignetteRules <- VcfFilterRules(
    fixedRules,
    SNPrule,
    infoRules,
    vepInsertionFilter
)
vignetteRules
## VcfFilterRules of length 6
## names(6): pass qual20 SNP samples avgSuperPopAF isInsertion
active(vignetteRules)
##          pass        qual20           SNP       samples avgSuperPopAF 
##          TRUE         FALSE          TRUE          TRUE          TRUE 
##   isInsertion 
##          TRUE
type(vignetteRules)
##          pass        qual20           SNP       samples avgSuperPopAF 
##       "fixed"       "fixed"       "fixed"        "info"        "info" 
##   isInsertion 
##         "vep"
summary(evalSeparately(vignetteRules, evcf, enclos = .GlobalEnv))
##    pass          qual20           SNP          samples        avgSuperPopAF  
##  Mode:logical   Mode:logical   Mode :logical   Mode:logical   Mode :logical  
##  TRUE:481       TRUE:481       FALSE:14        TRUE:481       FALSE:452      
##                                TRUE :467                      TRUE :29       
##  isInsertion    
##  Mode :logical  
##  FALSE:477      
##  TRUE :4

Clearly1 This statement below would be more evident if the summary method was displaying the result of evalSeparately in this vignette as it does it in an R session., the VCF filter rules SNP and isInsertion are mutually exclusive, which explains the final 0 variants left after filtering. Conveniently, either of these rules may be deactivated before evaluating the remaining active filter rules:

active(vignetteRules)["SNP"] <- FALSE
summary(evalSeparately(vignetteRules, evcf, enclos = .GlobalEnv))
##    pass          qual20          SNP          samples        avgSuperPopAF  
##  Mode:logical   Mode:logical   Mode:logical   Mode:logical   Mode :logical  
##  TRUE:481       TRUE:481       TRUE:481       TRUE:481       FALSE:452      
##                                                              TRUE :29       
##  isInsertion    
##  Mode :logical  
##  FALSE:477      
##  TRUE :4

As a result, the deactivated filter rule ("SNP") now returns TRUE for all variants, leaving a final 2 variants2 Again, this statement would benefit from the result of evalSeparately being displayed identically to an R session. pass the remaining active filter rules:

  • INFO/FILTER equal to "PASS"
  • INFO/NS greater than 90% of the number of samples in the data set
  • Average of super-population allele frequencies greater than 0.05
  • Ensembl VEP prediction VARIANT_CLASS equal to "insertion"

Finally, the following chunk of code demonstrates how VcfFilterRules may also be created from the combination of VcfFilterRules, either with themselves or with any of the classes that define more specific VCF filter rules. Notably, when VcfFilterRules objects are combined, the type and active value of each filter rule is transferred to the combined object:

Combine VcfFilterRules with VcfVepRules

combinedFilters <- VcfFilterRules(
    vignetteRules, # VcfFilterRules
    missenseFilter # VcfVepRules
)
type(vignetteRules)
##          pass        qual20           SNP       samples avgSuperPopAF 
##       "fixed"       "fixed"       "fixed"        "info"        "info" 
##   isInsertion 
##         "vep"
type(combinedFilters)
##          pass        qual20           SNP       samples avgSuperPopAF 
##       "fixed"       "fixed"       "fixed"        "info"        "info" 
##   isInsertion         exact         grepl 
##         "vep"         "vep"         "vep"
active(vignetteRules)
##          pass        qual20           SNP       samples avgSuperPopAF 
##          TRUE         FALSE         FALSE          TRUE          TRUE 
##   isInsertion 
##          TRUE
active(missenseFilter)
## exact grepl 
##  TRUE  TRUE
active(combinedFilters)
##          pass        qual20           SNP       samples avgSuperPopAF 
##          TRUE         FALSE         FALSE          TRUE          TRUE 
##   isInsertion         exact         grepl 
##          TRUE          TRUE          TRUE

Combine multiple VcfFilterRules with VcfFilterRules (and more)

To demonstrate this action, another VcfFilterRules must first be created. This can be achieve by simply re-typing a VcfVepRules defined earlier:

secondVcfFilter <- VcfFilterRules(missenseFilter)
secondVcfFilter
## VcfFilterRules of length 2
## names(2): exact grepl

It is now possible to combine the two VcfFilterRules. Let us even combine another VcfInfoRules object in the same step:

manyRules <- VcfFilterRules(
    vignetteRules, # VcfFilterRules
    secondVcfFilter, # VcfFilterRules
    funFilter # VcfInfoRules
)
manyRules
## VcfFilterRules of length 9
## names(9): pass qual20 SNP samples avgSuperPopAF isInsertion exact grepl commonSuperPops
active(manyRules)
##            pass          qual20             SNP         samples   avgSuperPopAF 
##            TRUE           FALSE           FALSE            TRUE            TRUE 
##     isInsertion           exact           grepl commonSuperPops 
##            TRUE            TRUE            TRUE            TRUE
type(manyRules)
##            pass          qual20             SNP         samples   avgSuperPopAF 
##         "fixed"         "fixed"         "fixed"          "info"          "info" 
##     isInsertion           exact           grepl commonSuperPops 
##           "vep"           "vep"           "vep"          "info"
summary(evalSeparately(manyRules, evcf, enclos = .GlobalEnv))
##    pass          qual20          SNP          samples        avgSuperPopAF  
##  Mode:logical   Mode:logical   Mode:logical   Mode:logical   Mode :logical  
##  TRUE:481       TRUE:481       TRUE:481       TRUE:481       FALSE:452      
##                                                              TRUE :29       
##  isInsertion       exact           grepl         commonSuperPops
##  Mode :logical   Mode :logical   Mode :logical   Mode :logical  
##  FALSE:477       FALSE:454       FALSE:452       FALSE:464      
##  TRUE :4         TRUE :27        TRUE :29        TRUE :17

Critically, users must be careful to combine rules all compatible with the class of VCF object in which it will be evaluated (i.e. CollapsedVCF or ExpandedVCF).

7 Session info

Here is the output of sessionInfo() on the system on which this document was compiled:

## R Under development (unstable) (2020-03-12 r77934)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.11-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.11-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] TVTB_1.13.1      knitr_1.28       BiocStyle_2.15.6
## 
## loaded via a namespace (and not attached):
##   [1] ProtGenerics_1.19.3         bitops_1.0-6               
##   [3] matrixStats_0.56.0          EnsDb.Hsapiens.v75_2.99.0  
##   [5] bit64_0.9-7                 RColorBrewer_1.1-2         
##   [7] progress_1.2.2              httr_1.4.1                 
##   [9] GenomeInfoDb_1.23.14        backports_1.1.5            
##  [11] tools_4.0.0                 R6_2.4.1                   
##  [13] rpart_4.1-15                Hmisc_4.4-0                
##  [15] DBI_1.1.0                   lazyeval_0.2.2             
##  [17] BiocGenerics_0.33.3         Gviz_1.31.12               
##  [19] colorspace_1.4-1            nnet_7.3-13                
##  [21] gridExtra_2.3               tidyselect_1.0.0           
##  [23] prettyunits_1.1.1           GGally_1.4.0               
##  [25] bit_1.1-15.2                curl_4.3                   
##  [27] compiler_4.0.0              Biobase_2.47.3             
##  [29] htmlTable_1.13.3            DelayedArray_0.13.7        
##  [31] labeling_0.3                rtracklayer_1.47.0         
##  [33] bookdown_0.18               checkmate_2.0.0            
##  [35] scales_1.1.0                askpass_1.1                
##  [37] rappdirs_0.3.1              stringr_1.4.0              
##  [39] digest_0.6.25               Rsamtools_2.3.7            
##  [41] foreign_0.8-76              rmarkdown_2.1              
##  [43] XVector_0.27.2              dichromat_2.0-0            
##  [45] base64enc_0.1-3             jpeg_0.1-8.1               
##  [47] pkgconfig_2.0.3             htmltools_0.4.0            
##  [49] ensembldb_2.11.3            dbplyr_1.4.2               
##  [51] limma_3.43.5                BSgenome_1.55.4            
##  [53] htmlwidgets_1.5.1           rlang_0.4.5                
##  [55] rstudioapi_0.11             RSQLite_2.2.0              
##  [57] farver_2.0.3                BiocParallel_1.21.2        
##  [59] acepack_1.4.1               dplyr_0.8.5                
##  [61] VariantAnnotation_1.33.2    RCurl_1.98-1.1             
##  [63] magrittr_1.5                Formula_1.2-3              
##  [65] GenomeInfoDbData_1.2.2      Matrix_1.2-18              
##  [67] Rcpp_1.0.4                  munsell_0.5.0              
##  [69] S4Vectors_0.25.14           lifecycle_0.2.0            
##  [71] ensemblVEP_1.29.0           stringi_1.4.6              
##  [73] yaml_2.2.1                  SummarizedExperiment_1.17.4
##  [75] zlibbioc_1.33.1             plyr_1.8.6                 
##  [77] BiocFileCache_1.11.4        grid_4.0.0                 
##  [79] blob_1.2.1                  parallel_4.0.0             
##  [81] crayon_1.3.4                lattice_0.20-40            
##  [83] splines_4.0.0               Biostrings_2.55.7          
##  [85] GenomicFeatures_1.39.7      pander_0.6.3               
##  [87] hms_0.5.3                   magick_2.3                 
##  [89] pillar_1.4.3                GenomicRanges_1.39.3       
##  [91] reshape2_1.4.3              biomaRt_2.43.3             
##  [93] stats4_4.0.0                XML_3.99-0.3               
##  [95] glue_1.3.2                  evaluate_0.14              
##  [97] biovizBase_1.35.1           latticeExtra_0.6-29        
##  [99] data.table_1.12.8           BiocManager_1.30.10        
## [101] vctrs_0.2.4                 png_0.1-7                  
## [103] gtable_0.3.0                openssl_1.4.1              
## [105] purrr_0.3.3                 reshape_0.8.8              
## [107] assertthat_0.2.1            ggplot2_3.3.0              
## [109] xfun_0.12                   AnnotationFilter_1.11.0    
## [111] survival_3.1-11             tibble_2.1.3               
## [113] GenomicAlignments_1.23.2    AnnotationDbi_1.49.1       
## [115] memoise_1.1.0               IRanges_2.21.7             
## [117] cluster_2.1.0

References

McLaren, W., B. Pritchard, D. Rios, Y. Chen, P. Flicek, and F. Cunningham. 2010. “Deriving the Consequences of Genomic Variants with the Ensembl API and SNP Effect Predictor.” Journal Article. Bioinformatics 26 (16):2069–70. https://doi.org/10.1093/bioinformatics/btq330.