Currently, VCF objects of the VariantAnnotation package may be subsetted by indices or names of variant records. The TVTB package extends FilterRules of the S4Vectors package to provide filter rules readily applicable to individual slots of VCF objects. These new classes of filter rules provide containers for powerful expressions and functions to facilitate filtering of variants using combinations of filters applicable to FIXED fields, INFO fields, and Ensembl VEP predictions stored in a given INFO field of VCF files.
TVTB 1.30.1
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:
| 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:
VCF slotsVCF 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): mixedAs they inherit from the FilterRules class,
these new classes benefit from accessors and methods defined for their
parent class, including:
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.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)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):
“
CollapsedVCFobjects contains the ALT data as aDNAStringSetListallowing for multiple alleles per variant. In contrast, theExpandedVCFstores the ALT data as aDNAStringSetwhere 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.
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 <- 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"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:
"pass" identifies variants for which the value in the
FILTER field is "PASS""qual20" identifies variants where the value in the QUAL
field is greater than or equal to 20fixedRules <- 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:479In the example above, all variants pass the active "pass" filter,
while the deactivated rules "qual20") automatically returns TRUE
for all variants.
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 :467Some considerations regarding the above filter rule:
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%.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:
"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)"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 :29It may be more convenient to define filters as function objects.
For instance, the chunk of code below:
info slot of a VCF object as inputAFcutoff <- 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 :17Notably, the filterFUN function may also be applied separately to the
info slot of VCF objects:
summary(filterFUN(info(evcf)))## Mode FALSE TRUE
## logical 464 17The 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 :29In the above chunk of code:
"exact" matches only the given value, associated
with 27 variants,"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.fixed slot of VCF objectsAs 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:
ExpandedVCF objectsA 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 TAHere, 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 TACollapsedVCF objectsIn 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 2Any 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 isInsertionactive(vignetteRules)## pass qual20 SNP samples avgSuperPopAF
## TRUE FALSE TRUE TRUE TRUE
## isInsertion
## TRUEtype(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 :4Clearly1 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 :4As 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:
"PASS"0.05VARIANT_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
## TRUEactive(missenseFilter)## exact grepl
## TRUE TRUEactive(combinedFilters)## pass qual20 SNP samples avgSuperPopAF
## TRUE FALSE FALSE TRUE TRUE
## isInsertion exact grepl
## TRUE TRUE TRUECombine 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 greplIt 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 commonSuperPopsactive(manyRules)## pass qual20 SNP samples avgSuperPopAF
## TRUE FALSE FALSE TRUE TRUE
## isInsertion exact grepl commonSuperPops
## TRUE TRUE TRUE TRUEtype(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 :17Critically, 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).
Here is the output of sessionInfo() on the system on which this
document was compiled:
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB 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
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] TVTB_1.30.1 knitr_1.48 BiocStyle_2.32.1
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 rstudioapi_0.16.0
## [3] jsonlite_1.8.8 magrittr_2.0.3
## [5] magick_2.8.3 GenomicFeatures_1.56.0
## [7] farver_2.1.2 rmarkdown_2.27
## [9] BiocIO_1.14.0 zlibbioc_1.50.0
## [11] vctrs_0.6.5 memoise_2.0.1
## [13] Rsamtools_2.20.0 RCurl_1.98-1.14
## [15] base64enc_0.1-3 tinytex_0.51
## [17] htmltools_0.5.8.1 S4Arrays_1.4.1
## [19] progress_1.2.3 curl_5.2.1
## [21] SparseArray_1.4.8 Formula_1.2-5
## [23] sass_0.4.9 bslib_0.7.0
## [25] htmlwidgets_1.6.4 plyr_1.8.9
## [27] Gviz_1.48.0 httr2_1.0.1
## [29] cachem_1.1.0 GenomicAlignments_1.40.0
## [31] lifecycle_1.0.4 pkgconfig_2.0.3
## [33] Matrix_1.7-0 R6_2.5.1
## [35] fastmap_1.2.0 GenomeInfoDbData_1.2.12
## [37] MatrixGenerics_1.16.0 digest_0.6.36
## [39] colorspace_2.1-0 GGally_2.2.1
## [41] AnnotationDbi_1.66.0 S4Vectors_0.42.1
## [43] Hmisc_5.1-3 GenomicRanges_1.56.1
## [45] RSQLite_2.3.7 labeling_0.4.3
## [47] filelock_1.0.3 fansi_1.0.6
## [49] httr_1.4.7 abind_1.4-5
## [51] compiler_4.4.1 withr_3.0.0
## [53] bit64_4.0.5 pander_0.6.5
## [55] htmlTable_2.4.2 backports_1.5.0
## [57] BiocParallel_1.38.0 DBI_1.2.3
## [59] ggstats_0.6.0 highr_0.11
## [61] biomaRt_2.60.1 rappdirs_0.3.3
## [63] DelayedArray_0.30.1 rjson_0.2.21
## [65] tools_4.4.1 foreign_0.8-87
## [67] nnet_7.3-19 glue_1.7.0
## [69] restfulr_0.0.15 grid_4.4.1
## [71] checkmate_2.3.1 reshape2_1.4.4
## [73] cluster_2.1.6 generics_0.1.3
## [75] gtable_0.3.5 BSgenome_1.72.0
## [77] ensembldb_2.28.0 tidyr_1.3.1
## [79] data.table_1.15.4 hms_1.1.3
## [81] xml2_1.3.6 utf8_1.2.4
## [83] XVector_0.44.0 BiocGenerics_0.50.0
## [85] pillar_1.9.0 stringr_1.5.1
## [87] limma_3.60.3 dplyr_1.1.4
## [89] BiocFileCache_2.12.0 lattice_0.22-6
## [91] deldir_2.0-4 rtracklayer_1.64.0
## [93] bit_4.0.5 EnsDb.Hsapiens.v75_2.99.0
## [95] biovizBase_1.52.0 tidyselect_1.2.1
## [97] Biostrings_2.72.1 gridExtra_2.3
## [99] bookdown_0.40 ProtGenerics_1.36.0
## [101] IRanges_2.38.1 SummarizedExperiment_1.34.0
## [103] stats4_4.4.1 xfun_0.45
## [105] Biobase_2.64.0 statmod_1.5.0
## [107] matrixStats_1.3.0 stringi_1.8.4
## [109] UCSC.utils_1.0.0 lazyeval_0.2.2
## [111] yaml_2.3.9 evaluate_0.24.0
## [113] codetools_0.2-20 interp_1.1-6
## [115] tibble_3.2.1 BiocManager_1.30.23
## [117] cli_3.6.3 rpart_4.1.23
## [119] munsell_0.5.1 jquerylib_0.1.4
## [121] dichromat_2.0-0.1 Rcpp_1.0.12
## [123] GenomeInfoDb_1.40.1 dbplyr_2.5.0
## [125] png_0.1-8 XML_3.99-0.17
## [127] parallel_4.4.1 ggplot2_3.5.1
## [129] blob_1.2.4 prettyunits_1.2.0
## [131] jpeg_0.1-10 latticeExtra_0.6-30
## [133] AnnotationFilter_1.28.0 bitops_1.0-7
## [135] VariantAnnotation_1.50.0 scales_1.3.0
## [137] purrr_1.0.2 crayon_1.5.3
## [139] rlang_1.1.4 KEGGREST_1.44.1McLaren, 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.