Below we compare the results of annotating variants via VariantAnnotation specifically repeating a subset of the steps in vignette Introduction to VariantAnnotation. We compare using data from 1,000 Genomes Phase 1 Variants:
First we read in the data from the VCF file:
library(VariantAnnotation)fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation")
vcf <- readVcf(fl, "hg19")
vcf <- renameSeqlevels(vcf, c("22"="chr22"))
vcf## 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, C...
## info(header(vcf)):
## Number Type Description
## LDAF 1 Float MLE Allele Frequency Accounting for LD
## AVGPOST 1 Float Average posterior probability from MaCH/Thu...
## 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 imprecis...
## CIPOS 2 Integer Confidence interval around POS for imprecis...
## END 1 Integer End position of the variant described in th...
## HOMLEN . Integer Length of base pair identical micro-homolog...
## HOMSEQ . String Sequence of base pair identical micro-homol...
## SVLEN 1 Integer Difference in length between REF and ALT al...
## 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...
## AF 1 Float Global Allele Frequency based on AC/AN
## AMR_AF 1 Float Allele Frequency for samples from AMR based...
## ASN_AF 1 Float Allele Frequency for samples from ASN based...
## AFR_AF 1 Float Allele Frequency for samples from AFR based...
## EUR_AF 1 Float Allele Frequency for samples from EUR based...
## VT 1 String indicates what type of variant the line rep...
## SNPSOURCE . String indicates if a snp was called when analysin...
## geno(vcf):
## SimpleList 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 . Float Genotype LikelihoodsThe file chr22.vcf.gz within package VariantAnnotation holds data for 5 of the 1,092 individuals in 1,000 Genomes, starting at position 50300078 and ending at position 50999964.
HG00096 HG00097 HG00099 HG00100 HG00101
Important data differences to note:
chr22.vcf.gz. They are the only two variants within the genomic range with ALT == <DEL>.library(GoogleGenomics)
# This vignette is authenticated on package load from the env variable GOOGLE_API_KEY.
# When running interactively, call the authenticate method.
# ?authenticate# We're just getting the first few variants so that this runs quickly.
# If we wanted to get them all, we sould set end=50999964.
granges <- getVariants(datasetId="10473108253681171589",
chromosome="22",
start=50300077,
end=50303000,
converter=variantsToGRanges)## Fetching variants page.## Continuing variant query with the nextPageToken: CIWK_hcQ_Nr-8ZGlk78i## Fetching variants page.## Continuing variant query with the nextPageToken: CIaM_hcQvM_un46SmcJ0## Fetching variants page.## Continuing variant query with the nextPageToken: CJGN_hcQiYna3cSwse_RAQ## Fetching variants page.## Continuing variant query with the nextPageToken: CMKQ_hcQr9OUpKu4n_xz## Fetching variants page.## Continuing variant query with the nextPageToken: CMKS_hcQn-nIneaph7WJAQ## Fetching variants page.## Continuing variant query with the nextPageToken: CKOU_hcQ2eCCwsmBysdd## Fetching variants page.## Continuing variant query with the nextPageToken: CLWV_hcQmo-n-tX08uPyAQ## Fetching variants page.## Continuing variant query with the nextPageToken: CMqX_hcQ__3H4KKV9tUR## Fetching variants page.## Continuing variant query with the nextPageToken: CKuZ_hcQ9pHXwa742bKgAQ## Fetching variants page.## Continuing variant query with the nextPageToken: CLqa_hcQ-6zjo_Dc86gy## Fetching variants page.## Continuing variant query with the nextPageToken: CLSb_hcQ3vrDo5XJwsB_## Fetching variants page.## Continuing variant query with the nextPageToken: CKOd_hcQ6IPgk-PVjskK## Fetching variants page.## Continuing variant query with the nextPageToken: CPGe_hcQ-8Saw6Ojg8yXAQ## Fetching variants page.## Continuing variant query with the nextPageToken: CPGf_hcQuKDdodiCvLUm## Fetching variants page.## Variants are now available.Ensure that the data retrieved by each matches:
vcf <- vcf[1:length(granges)] # Truncate the VCF data so that it is the same
# set as what was retrieved from the API.library(testthat)expect_equal(start(granges), start(vcf))
expect_equal(end(granges), end(vcf))
expect_equal(as.character(granges$REF), as.character(ref(vcf)))
expect_equal(as.character(unlist(granges$ALT)), as.character(unlist(alt(vcf))))
expect_equal(granges$QUAL, qual(vcf))
expect_equal(granges$FILTER, filt(vcf))Now locate the protein coding variants in each:
library(TxDb.Hsapiens.UCSC.hg19.knownGene)txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
rd <- rowRanges(vcf)
vcf_locations <- locateVariants(rd, txdb, CodingVariants())## 'select()' returned many:1 mapping between keys and columnsvcf_locations## GRanges object with 7 ranges and 9 metadata columns:
## seqnames ranges strand | LOCATION LOCSTART LOCEND
## <Rle> <IRanges> <Rle> | <factor> <integer> <integer>
## 1 chr22 [50301422, 50301422] - | coding 939 939
## 2 chr22 [50301476, 50301476] - | coding 885 885
## 3 chr22 [50301488, 50301488] - | coding 873 873
## 4 chr22 [50301494, 50301494] - | coding 867 867
## 5 chr22 [50301584, 50301584] - | coding 777 777
## 6 chr22 [50302962, 50302962] - | coding 698 698
## 7 chr22 [50302995, 50302995] - | coding 665 665
## QUERYID TXID CDSID GENEID PRECEDEID
## <integer> <character> <IntegerList> <character> <CharacterList>
## 1 24 75253 218562 79087
## 2 25 75253 218562 79087
## 3 26 75253 218562 79087
## 4 27 75253 218562 79087
## 5 28 75253 218562 79087
## 6 57 75253 218563 79087
## 7 58 75253 218563 79087
## FOLLOWID
## <CharacterList>
## 1
## 2
## 3
## 4
## 5
## 6
## 7
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsgranges_locations <- locateVariants(granges, txdb, CodingVariants())## 'select()' returned many:1 mapping between keys and columnsgranges_locations## GRanges object with 7 ranges and 9 metadata columns:
## seqnames ranges strand | LOCATION LOCSTART LOCEND
## <Rle> <IRanges> <Rle> | <factor> <integer> <integer>
## 1 chr22 [50301422, 50301422] - | coding 939 939
## 2 chr22 [50301476, 50301476] - | coding 885 885
## 3 chr22 [50301488, 50301488] - | coding 873 873
## 4 chr22 [50301494, 50301494] - | coding 867 867
## 5 chr22 [50301584, 50301584] - | coding 777 777
## 6 chr22 [50302962, 50302962] - | coding 698 698
## 7 chr22 [50302995, 50302995] - | coding 665 665
## QUERYID TXID CDSID GENEID PRECEDEID
## <integer> <character> <IntegerList> <character> <CharacterList>
## 1 24 75253 218562 79087
## 2 25 75253 218562 79087
## 3 26 75253 218562 79087
## 4 27 75253 218562 79087
## 5 28 75253 218562 79087
## 6 57 75253 218563 79087
## 7 58 75253 218563 79087
## FOLLOWID
## <CharacterList>
## 1
## 2
## 3
## 4
## 5
## 6
## 7
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsexpect_equal(granges_locations, vcf_locations)And predict the effect of the protein coding variants:
library(BSgenome.Hsapiens.UCSC.hg19)vcf_coding <- predictCoding(vcf, txdb, seqSource=Hsapiens)
vcf_coding## GRanges object with 7 ranges and 17 metadata columns:
## seqnames ranges strand | paramRangeID
## <Rle> <IRanges> <Rle> | <factor>
## rs114335781 chr22 [50301422, 50301422] - | <NA>
## rs8135963 chr22 [50301476, 50301476] - | <NA>
## 22:50301488_C/T chr22 [50301488, 50301488] - | <NA>
## 22:50301494_G/A chr22 [50301494, 50301494] - | <NA>
## 22:50301584_C/T chr22 [50301584, 50301584] - | <NA>
## rs114264124 chr22 [50302962, 50302962] - | <NA>
## rs149209714 chr22 [50302995, 50302995] - | <NA>
## REF ALT QUAL FILTER
## <DNAStringSet> <DNAStringSetList> <numeric> <character>
## rs114335781 G A 100 PASS
## rs8135963 T C 100 PASS
## 22:50301488_C/T C T 100 PASS
## 22:50301494_G/A G A 100 PASS
## 22:50301584_C/T C T 100 PASS
## rs114264124 C T 100 PASS
## rs149209714 C G 100 PASS
## varAllele CDSLOC PROTEINLOC QUERYID
## <DNAStringSet> <IRanges> <IntegerList> <integer>
## rs114335781 T [939, 939] 313 24
## rs8135963 G [885, 885] 295 25
## 22:50301488_C/T A [873, 873] 291 26
## 22:50301494_G/A T [867, 867] 289 27
## 22:50301584_C/T A [777, 777] 259 28
## rs114264124 A [698, 698] 233 57
## rs149209714 C [665, 665] 222 58
## TXID CDSID GENEID CONSEQUENCE
## <character> <IntegerList> <character> <factor>
## rs114335781 75253 218562 79087 synonymous
## rs8135963 75253 218562 79087 synonymous
## 22:50301488_C/T 75253 218562 79087 synonymous
## 22:50301494_G/A 75253 218562 79087 synonymous
## 22:50301584_C/T 75253 218562 79087 synonymous
## rs114264124 75253 218563 79087 nonsynonymous
## rs149209714 75253 218563 79087 nonsynonymous
## REFCODON VARCODON REFAA
## <DNAStringSet> <DNAStringSet> <AAStringSet>
## rs114335781 ATC ATT I
## rs8135963 GCA GCG A
## 22:50301488_C/T CCG CCA P
## 22:50301494_G/A CAC CAT H
## 22:50301584_C/T CCG CCA P
## rs114264124 CGG CAG R
## rs149209714 GGA GCA G
## VARAA
## <AAStringSet>
## rs114335781 I
## rs8135963 A
## 22:50301488_C/T P
## 22:50301494_G/A H
## 22:50301584_C/T P
## rs114264124 Q
## rs149209714 A
## -------
## seqinfo: 1 sequence from hg19 genome; no seqlengthsgranges_coding <- predictCoding(rep(granges, elementNROWS(granges$ALT)),
txdb,
seqSource=Hsapiens,
varAllele=unlist(granges$ALT, use.names=FALSE))
granges_coding## GRanges object with 7 ranges and 16 metadata columns:
## seqnames ranges strand | REF
## <Rle> <IRanges> <Rle> | <DNAStringSet>
## rs114335781 chr22 [50301422, 50301422] - | G
## rs8135963 chr22 [50301476, 50301476] - | T
## rs200080075 chr22 [50301488, 50301488] - | C
## rs147801200 chr22 [50301494, 50301494] - | G
## rs138060012 chr22 [50301584, 50301584] - | C
## rs114264124 chr22 [50302962, 50302962] - | C
## rs149209714 chr22 [50302995, 50302995] - | C
## ALT QUAL FILTER varAllele
## <DNAStringSetList> <numeric> <character> <DNAStringSet>
## rs114335781 A 100 PASS T
## rs8135963 C 100 PASS G
## rs200080075 T 100 PASS A
## rs147801200 A 100 PASS T
## rs138060012 T 100 PASS A
## rs114264124 T 100 PASS A
## rs149209714 G 100 PASS C
## CDSLOC PROTEINLOC QUERYID TXID CDSID
## <IRanges> <IntegerList> <integer> <character> <IntegerList>
## rs114335781 [939, 939] 313 24 75253 218562
## rs8135963 [885, 885] 295 25 75253 218562
## rs200080075 [873, 873] 291 26 75253 218562
## rs147801200 [867, 867] 289 27 75253 218562
## rs138060012 [777, 777] 259 28 75253 218562
## rs114264124 [698, 698] 233 57 75253 218563
## rs149209714 [665, 665] 222 58 75253 218563
## GENEID CONSEQUENCE REFCODON VARCODON
## <character> <factor> <DNAStringSet> <DNAStringSet>
## rs114335781 79087 synonymous ATC ATT
## rs8135963 79087 synonymous GCA GCG
## rs200080075 79087 synonymous CCG CCA
## rs147801200 79087 synonymous CAC CAT
## rs138060012 79087 synonymous CCG CCA
## rs114264124 79087 nonsynonymous CGG CAG
## rs149209714 79087 nonsynonymous GGA GCA
## REFAA VARAA
## <AAStringSet> <AAStringSet>
## rs114335781 I I
## rs8135963 A A
## rs200080075 P P
## rs147801200 H H
## rs138060012 P P
## rs114264124 R Q
## rs149209714 G A
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsexpect_equal(as.matrix(granges_coding$REFCODON), as.matrix(vcf_coding$REFCODON))
expect_equal(as.matrix(granges_coding$VARCODON), as.matrix(vcf_coding$VARCODON))
expect_equal(granges_coding$GENEID, vcf_coding$GENEID)
expect_equal(granges_coding$CONSEQUENCE, vcf_coding$CONSEQUENCE)sessionInfo()## R version 3.4.1 (2017-06-30)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.5-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.5-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] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] testthat_1.0.2
## [2] ggbio_1.24.1
## [3] ggplot2_2.2.1
## [4] org.Hs.eg.db_3.4.1
## [5] BSgenome.Hsapiens.UCSC.hg19_1.4.0
## [6] BSgenome_1.44.1
## [7] rtracklayer_1.36.4
## [8] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [9] GenomicFeatures_1.28.5
## [10] AnnotationDbi_1.38.2
## [11] GoogleGenomics_1.8.1
## [12] VariantAnnotation_1.22.3
## [13] GenomicAlignments_1.12.2
## [14] Rsamtools_1.28.0
## [15] Biostrings_2.44.2
## [16] XVector_0.16.0
## [17] SummarizedExperiment_1.6.4
## [18] DelayedArray_0.2.7
## [19] matrixStats_0.52.2
## [20] Biobase_2.36.2
## [21] GenomicRanges_1.28.5
## [22] GenomeInfoDb_1.12.2
## [23] IRanges_2.10.3
## [24] S4Vectors_0.14.4
## [25] BiocGenerics_0.22.0
## [26] knitr_1.17
## [27] BiocStyle_2.4.1
##
## loaded via a namespace (and not attached):
## [1] ProtGenerics_1.8.0 bitops_1.0-6
## [3] bit64_0.9-7 RColorBrewer_1.1-2
## [5] httr_1.3.1 rprojroot_1.2
## [7] tools_3.4.1 backports_1.1.0
## [9] R6_2.2.2 rpart_4.1-11
## [11] Hmisc_4.0-3 DBI_0.7
## [13] lazyeval_0.2.0 colorspace_1.3-2
## [15] nnet_7.3-12 gridExtra_2.3
## [17] GGally_1.3.2 bit_1.1-12
## [19] curl_2.8.1 compiler_3.4.1
## [21] graph_1.54.0 htmlTable_1.9
## [23] labeling_0.3 scales_0.5.0
## [25] checkmate_1.8.3 RBGL_1.52.0
## [27] stringr_1.2.0 digest_0.6.12
## [29] foreign_0.8-69 rmarkdown_1.6
## [31] base64enc_0.1-3 dichromat_2.0-0
## [33] pkgconfig_2.0.1 htmltools_0.3.6
## [35] ensembldb_2.0.4 htmlwidgets_0.9
## [37] rlang_0.1.2 RSQLite_2.0
## [39] BiocInstaller_1.26.1 shiny_1.0.5
## [41] jsonlite_1.5 BiocParallel_1.10.1
## [43] acepack_1.4.1 RCurl_1.95-4.8
## [45] magrittr_1.5 GenomeInfoDbData_0.99.0
## [47] Formula_1.2-2 Matrix_1.2-11
## [49] Rcpp_0.12.12 munsell_0.4.3
## [51] stringi_1.1.5 yaml_2.1.14
## [53] zlibbioc_1.22.0 plyr_1.8.4
## [55] AnnotationHub_2.8.2 grid_3.4.1
## [57] blob_1.1.0 crayon_1.3.4
## [59] lattice_0.20-35 splines_3.4.1
## [61] rjson_0.2.15 reshape2_1.4.2
## [63] biomaRt_2.32.1 XML_3.98-1.9
## [65] evaluate_0.10.1 biovizBase_1.24.0
## [67] latticeExtra_0.6-28 data.table_1.10.4
## [69] httpuv_1.3.5 gtable_0.2.0
## [71] reshape_0.8.7 mime_0.5
## [73] xtable_1.8-2 AnnotationFilter_1.0.0
## [75] survival_2.41-3 OrganismDbi_1.18.0
## [77] tibble_1.3.4 memoise_1.1.0
## [79] cluster_2.0.6 interactiveDisplayBase_1.14.0