This vignette outlines a workflow of detecting retrotransposed
transcripts (RTs) from Variant Call Format (VCF) using the
svaRetro
package.
The svaRetro
package can be installed from
Bioconductor as follows:
This package uses a breakend-centric event notation adopted from the
StructuralVariantAnnotation
package. In short, breakends
are stored in a GRanges object with strand used to indicate breakpoint
orientation, where breakpoints are represented using a
partner
field containing the name of the breakend at the
other side of the breakend. This notation was chosen as it simplifies
the annotations of RTs which are detected at breakend-level.
VCF data is parsed into a VCF
object using the
readVCF
function from the Bioconductor package
VariantAnnotation
. Simple filters could be applied to a
VCF
object to remove unwanted calls. The VCF
object is then converted to a GRanges
object with
breakend-centric notations using
StructuralVariantAnnotation
. More information about
VCF
objects and breakend-centric GRanges object can be
found by consulting the vignettes in the corresponding packages with
browseVignettes("VariantAnnotation")
and
browseVignettes("StructuralVariantAnnotation")
.
library(StructuralVariantAnnotation)
library(VariantAnnotation)
library(svaRetro)
RT_vcf <- readVcf(system.file("extdata", "diploidSV.vcf", package = "svaRetro"))
Note that StructuralVariantAnnotation
requires the
GRanges
object to be composed entirely of valid
breakpoints. Please consult the vignette of the
StructuralVariantAnnotation
package for ensuring breakpoint
consistency.
The package provides rtDetect
to identify RTs using the
provided SV calls. This is achieved by detecting intronic deletions,
which are breakpoints at exon-intron (and intron-exon) boundaries of a
transcript. Fusions consisting of an exon boundary and a second genomic
location are reported as potential insertion sites. Due to the
complexity of RT events, insertion sites can be discovered on both left
and right sides, only one side, or none at all.
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(dplyr)
hg19.genes <- TxDb.Hsapiens.UCSC.hg19.knownGene
RT <- rtDetect(RT_gr, hg19.genes, maxgap=10, minscore=0.8)
The output is a list of GRanges
object consisting of two
sets of GRanges
calls, insSite
and
junctions
, containing candidate insertion sites and
exon-exon junctions respectively. Candidate insertion sites are
annotated by the source transcripts and whether exon-exon junctions are
detected for the source transcripts. RT junction breakends are annotated
by the UCSC exon IDs, corresponding transcripts, and NCBI gene
symbols.
RT$SKA3
#> $junctions
#> GRanges object with 14 ranges and 17 metadata columns:
#> seqnames ranges strand | paramRangeID REF ALT QUAL FILTER sourceId partner svtype svLen insSeq insLen event HOMLEN exon txs exons gene_symbol
#> <Rle> <IRanges> <Rle> | <factor> <character> <character> <numeric> <character> <character> <character> <character> <numeric> <character> <numeric> <character> <numeric> <integer> <list> <list> <list>
#> MantaDEL:245251:6:6:0:0:0_bp2 13 21729832 - | NA TCTGCAACAGATACAAATAA.. T 999 PASS MantaDEL:245251:6:6:.. MantaDEL:245251:6:6:.. DEL -542 0 <NA> 1 176912 uc001unt.3,uc001unv.3 176912 SKA3
#> MantaDEL:245251:5:8:0:0:0_bp2 13 21732061 - | NA G <DEL> 999 PASS MantaDEL:245251:5:8:.. MantaDEL:245251:5:8:.. DEL -2110 <NA> 0 <NA> 2 176913 uc001unt.3,uc001unv.3 176913 SKA3
#> MantaDEL:245251:5:9:0:0:0_bp2 13 21734038 - | NA A <DEL> 525 PASS MantaDEL:245251:5:9:.. MantaDEL:245251:5:9:.. DEL -1776 <NA> 0 <NA> 4 176914 uc001unt.3,uc001unv.3 176914 SKA3
#> MantaDEL:245251:7:10:0:0:0_bp2 13 21735929 - | NA T <DEL> 539 PASS MantaDEL:245251:7:10.. MantaDEL:245251:7:10.. DEL -1802 <NA> 0 <NA> 1 176915 uc001unt.3,uc001unv.3 176915 SKA3
#> MantaDEL:245251:4:11:0:0:0_bp2 13 21742127 - | NA A <DEL> 999 PASS MantaDEL:245251:4:11.. MantaDEL:245251:4:11.. DEL -6112 <NA> 0 <NA> 2 176916 uc001unt.3,uc001unv.3 176916 SKA3
#> ... ... ... ... . ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
#> MantaDEL:245251:5:9:0:0:0_bp1 13 21732261 + | NA A <DEL> 525 PASS MantaDEL:245251:5:9:.. MantaDEL:245251:5:9:.. DEL -1776 <NA> 0 <NA> 4 176913 uc001unt.3,uc001unv.3 176913 SKA3
#> MantaDEL:245251:7:10:0:0:0_bp1 13 21734126 + | NA T <DEL> 539 PASS MantaDEL:245251:7:10.. MantaDEL:245251:7:10.. DEL -1802 <NA> 0 <NA> 1 176914 uc001unt.3,uc001unv.3 176914 SKA3
#> MantaDEL:245251:4:11:0:0:0_bp1 13 21736014 + | NA A <DEL> 999 PASS MantaDEL:245251:4:11.. MantaDEL:245251:4:11.. DEL -6112 <NA> 0 <NA> 2 176915 uc001unt.3,uc001unv.3 176915 SKA3
#> MantaDEL:245251:3:4:0:0:0_bp1 13 21742538 + | NA A <DEL> 999 PASS MantaDEL:245251:3:4:.. MantaDEL:245251:3:4:.. DEL -3939 <NA> 0 <NA> 2 176916 uc001unt.3,uc001unv.3 176916 SKA3
#> MantaDEL:245251:2:3:0:0:0_bp1 13 21746642 + | NA T <DEL> 999 PASS MantaDEL:245251:2:3:.. MantaDEL:245251:2:3:.. DEL -3870 <NA> 0 <NA> 2 176917 uc001unt.3,uc001unv.3 176917 SKA3
#> -------
#> seqinfo: 25 sequences from an unspecified genome
#>
#> $insSite
#> GRanges object with 2 ranges and 18 metadata columns:
#> seqnames ranges strand | paramRangeID REF ALT QUAL FILTER sourceId partner svtype svLen insSeq insLen event HOMLEN exons txs rtFound rtFoundSum gene_symbol
#> <Rle> <IRanges> <Rle> | <factor> <character> <character> <numeric> <character> <character> <character> <character> <numeric> <character> <numeric> <character> <numeric> <list> <list> <list> <logical> <list>
#> MantaBND:245251:0:3:0:0:0:0 13 21746762 + | NA T T[11:108585702[ 49 PASS MantaBND:245251:0:3:.. MantaBND:245251:0:3:.. BND NA 0 <NA> 0 176918 uc001unt.3,uc001unu.3 TRUE,FALSE TRUE SKA3
#> MantaBND:245251:0:3:0:0:0:1 11 108585702 - | NA T ]13:21746762]T 49 PASS MantaBND:245251:0:3:.. MantaBND:245251:0:3:.. BND NA 0 <NA> 0 <NA> <NA> <NA> <NA> <NA>
#> -------
#> seqinfo: 25 sequences from an unspecified genome
One way of visualising RT is by circos plots. Here we use the package
circlize
to demonstrate the visualisation of insertion site and exon-exon
junctions.
To generate a simple circos plot of RT event with SKA3 transcript:
library(circlize)
rt_chr_prefix <- c(RT$SKA3$junctions, RT$SKA3$insSite)
seqlevelsStyle(rt_chr_prefix) <- "UCSC"
pairs <- breakpointgr2pairs(rt_chr_prefix)
pairs
To see supporting breakpoints clearly, we generate the circos plot according to the loci of event.
sessionInfo()
#> R version 4.4.2 (2024-10-31)
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#> [1] circlize_0.4.16 dplyr_1.1.4 TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 GenomicFeatures_1.59.1 AnnotationDbi_1.69.0 svaRetro_1.13.0 StructuralVariantAnnotation_1.23.0 VariantAnnotation_1.53.0 Rsamtools_2.23.1 Biostrings_2.75.3 XVector_0.47.0 SummarizedExperiment_1.37.0 Biobase_2.67.0 MatrixGenerics_1.19.0 matrixStats_1.4.1 rtracklayer_1.67.0 GenomicRanges_1.59.1 GenomeInfoDb_1.43.2 IRanges_2.41.2 S4Vectors_0.45.2 BiocGenerics_0.53.3 generics_0.1.3 BiocStyle_2.35.0
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#> [40] BiocManager_1.30.25 restfulr_0.0.15 vctrs_0.6.5 Matrix_1.7-1 jsonlite_1.8.9 bit64_4.5.2 maketools_1.3.1 jquerylib_0.1.4 glue_1.8.0 codetools_0.2-20 shape_1.4.6.1 stringi_1.8.4 BiocIO_1.17.1 UCSC.utils_1.3.0 tibble_3.2.1 pillar_1.10.0 htmltools_0.5.8.1 GenomeInfoDbData_1.2.13 BSgenome_1.75.0 R6_2.5.1 evaluate_1.0.1 lattice_0.22-6 png_0.1-8 memoise_2.0.1 bslib_0.8.0 SparseArray_1.7.2 xfun_0.49 GlobalOptions_0.1.2 buildtools_1.0.0 pkgconfig_2.0.3