README.md
In PapenfussLab/RTDetect: Retrotransposed transcript detection from structural variants
svaRetro: R package for retrotransposed transcript detection from structural variants
svaRetro contains functions for detecting retrotransposed transcripts
(RTs) from structural variant calls. It takes structural variant calls
in GRanges of breakend notation and identifies RTs by exon-exon
junctions and insertion sites. The candidate RTs are reported by events
and annotated with information of the inserted transcripts.
This package uses a breakend-centric event notation adopted from the
StructuralVariantAnnotation
package. 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").
Installation
svaNUMT is currently
available for download in Bioconductor (since BioC 3.14 & R 4.1):
# install.packages("BiocManager")
BiocManager::install("svaRetro")
The development version can be installed from GitHub:
BiocManager::install("PapenfussLab/svaRetro")
How to cite
If you use svaRetro, please cite svaRetro
here.
@article {Dong2021.08.18.456578,
author = {Dong, Ruining and Cameron, Daniel and Bedo, Justin and Papenfuss, Anthony T},
title = {svaRetro and svaNUMT: Modular packages for annotation of retrotransposed transcripts and nuclear integration of mitochondrial DNA in genome sequencing data},
elocation-id = {2021.08.18.456578},
year = {2021},
doi = {10.1101/2021.08.18.456578},
publisher = {Cold Spring Harbor Laboratory},
abstract = {Background The biological significance of structural variation is now more widely recognized. However, due to the lack of available tools for downstream analysis, including processing and annotating, interpretation of structural variant calls remains a challenge.Findings Here we present svaRetro and svaNUMT, R packages that provide functions for annotating novel genomic events such as non-reference retro-copied transcripts and nuclear integration of mitochondrial DNA. We evaluate the performance of these packages to detect events using simulations and public benchmarking datasets, and annotate processed transcripts in a public structural variant database.Conclusions svaRetro and svaNUMT provide efficient, modular tools for downstream identification and annotation of structural variant calls.Competing Interest StatementThe authors have declared no competing interest.SVstructural variantNUMTnuclear mitochondrial integrationRTretroposed transcriptTSDtarget site duplicationmtDNAmitochondrial DNA},
URL = {https://www.biorxiv.org/content/early/2021/08/19/2021.08.18.456578},
eprint = {https://www.biorxiv.org/content/early/2021/08/19/2021.08.18.456578.full.pdf},
journal = {bioRxiv}
}
Workflow
Below is a workflow example for detecting RTs from a human SV callset.
This example is taken from the vignette of svaRetro.
library(StructuralVariantAnnotation)
library(VariantAnnotation)
library(svaRetro)
RT_vcf <- readVcf(system.file("extdata", "diploidSV.vcf", package = "svaRetro"))
RT_gr <- StructuralVariantAnnotation::breakpointRanges(RT_vcf, nominalPosition=TRUE)
head(RT_gr)
#> GRanges object with 6 ranges and 13 metadata columns:
#> seqnames ranges strand | paramRangeID REF ALT QUAL FILTER sourceId partner svtype svLen insSeq insLen event HOMLEN
#> <Rle> <IRanges> <Rle> | <factor> <character> <character> <numeric> <character> <character> <character> <character> <numeric> <character> <numeric> <character> <numeric>
#> MantaINS:0:775:775:0:1:0_bp1 1 66365 + | NA AATATAATATATAA ATATATATATTATTATATAA.. 999 MaxDepth MantaINS:0:775:775:0.. MantaINS:0:775:775:0.. INS 129 TATATATATTATTATATAAT.. 142 <NA> 0
#> MantaINS:35:0:0:0:0:0_bp1 1 1004204 + | NA G GGCCACGCGGGCTGTGCAGA.. 999 PASS MantaINS:35:0:0:0:0:0 MantaINS:35:0:0:0:0:.. INS 78 GCCACGCGGGCTGTGCAGAT.. 78 <NA> 10
#> MantaDEL:92:0:0:0:0:0_bp1 1 1161716 + | NA CCTGTACGGTCAGGAGGAAA.. CT 999 PASS MantaDEL:92:0:0:0:0:0 MantaDEL:92:0:0:0:0:.. DEL -63 T 1 <NA> 0
#> MantaDEL:127:0:0:0:0:0_bp1 1 1162672 + | NA GGCGGGAAGGCGAGCTCGTG.. G 440 PASS MantaDEL:127:0:0:0:0:0 MantaDEL:127:0:0:0:0.. DEL -174 0 <NA> 9
#> MantaDEL:130:0:0:0:0:0_bp1 1 1183434 + | NA CAGGCTGGATCTCCAACTCT.. C 643 PASS MantaDEL:130:0:0:0:0:0 MantaDEL:130:0:0:0:0.. DEL -263 0 <NA> 7
#> MantaDEL:107:0:0:0:1:0_bp1 1 1302326 + | NA GAATGAGTGGATTGGTGAGT.. GCAGTGTGAA 999 PASS MantaDEL:107:0:0:0:1:0 MantaDEL:107:0:0:0:1.. DEL -197 CAGTGTGAA 9 <NA> 0
#> -------
#> seqinfo: 25 sequences from an unspecified genome
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.
Identifying Retrotransposed Transcripts
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)
#> Loading required package: GenomicFeatures
#> Loading required package: AnnotationDbi
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following object is masked from 'package:AnnotationDbi':
#>
#> select
#> The following object is masked from 'package:VariantAnnotation':
#>
#> select
#> The following objects are masked from 'package:Biostrings':
#>
#> collapse, intersect, setdiff, setequal, union
#> The following object is masked from 'package:XVector':
#>
#> slice
#> The following object is masked from 'package:Biobase':
#>
#> combine
#> The following object is masked from 'package:matrixStats':
#>
#> count
#> The following objects are masked from 'package:GenomicRanges':
#>
#> intersect, setdiff, union
#> The following object is masked from 'package:GenomeInfoDb':
#>
#> intersect
#> The following objects are masked from 'package:IRanges':
#>
#> collapse, desc, intersect, setdiff, slice, union
#> The following objects are masked from 'package:S4Vectors':
#>
#> first, intersect, rename, setdiff, setequal, union
#> The following objects are masked from 'package:BiocGenerics':
#>
#> combine, intersect, setdiff, union
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
hg19.genes <- TxDb.Hsapiens.UCSC.hg19.knownGene
RT_vcf <- readVcf(system.file("extdata", "diploidSV.vcf", package = "svaRetro"))
RT_gr <- StructuralVariantAnnotation::breakpointRanges(RT_vcf, nominalPosition=TRUE)
RT <- rtDetect(RT_gr, hg19.genes, maxgap=50, minscore=0.3)
#> Warning in (function (seqlevels, genome, new_style) : cannot switch some of hg19's seqlevels from UCSC to NCBI style
#> Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
#> - in 'x': MT
#> - in 'y': chrM, HSCHR1_RANDOM_CTG5, HSCHR1_RANDOM_CTG12, HSCHR4_1_CTG9, HSCHR4_RANDOM_CTG2, HSCHR4_RANDOM_CTG3, HSCHR6_MHC_APD_CTG1, HSCHR6_MHC_COX_CTG1, HSCHR6_MHC_DBB_CTG1, HSCHR6_MHC_MANN_CTG1, HSCHR6_MHC_MCF_CTG1, HSCHR6_MHC_QBL_CTG1, HSCHR6_MHC_SSTO_CTG1, HSCHR7_RANDOM_CTG1, HSCHR8_RANDOM_CTG1, HSCHR8_RANDOM_CTG4, HSCHR9_RANDOM_CTG1, HSCHR9_RANDOM_CTG2, HSCHR9_RANDOM_CTG4, HSCHR9_RANDOM_CTG5, HSCHR11_RANDOM_CTG2, HSCHR17_1_CTG5, HSCHR17_RANDOM_CTG1, HSCHR17_RANDOM_CTG2, HSCHR17_RANDOM_CTG3, HSCHR17_RANDOM_CTG4, HSCHR18_RANDOM_CTG1, HSCHR19_RANDOM_CTG1, HSCHR19_RANDOM_CTG2, HSCHR21_RANDOM_CTG9, HSCHRUN_RANDOM_CTG1, HSCHRUN_RANDOM_CTG2, HSCHRUN_RANDOM_CTG3, HSCHRUN_RANDOM_CTG4, HSCHRUN_RANDOM_CTG5, HSCHRUN_RANDOM_CTG6, HSCHRUN_RANDOM_CTG7, HSCHRUN_RANDOM_CTG9, HSCHRUN_RANDOM_CTG10, HSCHRUN_RANDOM_CTG11, HSCHRUN_RANDOM_CTG13, HSCHRUN_RANDOM_CTG14, HSCHRUN_RANDOM_CTG15, HSCHRUN_RANDOM_CTG16, HSCHRUN_RANDOM_CTG17, HSCHRUN_RANDOM_CTG19, HSCHRUN_RANDOM_CTG20, HSCHRUN_RANDOM_CTG21, HSCHRUN_RANDOM_CTG22, HSCHRUN_RANDOM_CTG23, HSCHRUN_RANDOM_CTG24, HSCHRUN_RANDOM_CTG25, HSCHRUN_RANDOM_CTG26, HSCHRUN_RANDOM_CTG27, HSCHRUN_RANDOM_CTG28, HSCHRUN_RANDOM_CTG29, HSCHRUN_RANDOM_CTG30, HSCHRUN_RANDOM_CTG31, HSCHRUN_RANDOM_CTG32, HSCHRUN_RANDOM_CTG33, HSCHRUN_RANDOM_CTG34, HSCHRUN_RANDOM_CTG35, HSCHRUN_RANDOM_CTG36, HSCHRUN_RANDOM_CTG37, HSCHRUN_RANDOM_CTG38, HSCHRUN_RANDOM_CTG39, HSCHRUN_RANDOM_CTG40, HSCHRUN_RANDOM_CTG41, HSCHRUN_RANDOM_CTG42
#> Make sure to always combine/compare objects based on the same reference
#> genome (use suppressWarnings() to suppress this warning).
#> Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
#> - in 'x': MT
#> - in 'y': chrM, HSCHR1_RANDOM_CTG5, HSCHR1_RANDOM_CTG12, HSCHR4_1_CTG9, HSCHR4_RANDOM_CTG2, HSCHR4_RANDOM_CTG3, HSCHR6_MHC_APD_CTG1, HSCHR6_MHC_COX_CTG1, HSCHR6_MHC_DBB_CTG1, HSCHR6_MHC_MANN_CTG1, HSCHR6_MHC_MCF_CTG1, HSCHR6_MHC_QBL_CTG1, HSCHR6_MHC_SSTO_CTG1, HSCHR7_RANDOM_CTG1, HSCHR8_RANDOM_CTG1, HSCHR8_RANDOM_CTG4, HSCHR9_RANDOM_CTG1, HSCHR9_RANDOM_CTG2, HSCHR9_RANDOM_CTG4, HSCHR9_RANDOM_CTG5, HSCHR11_RANDOM_CTG2, HSCHR17_1_CTG5, HSCHR17_RANDOM_CTG1, HSCHR17_RANDOM_CTG2, HSCHR17_RANDOM_CTG3, HSCHR17_RANDOM_CTG4, HSCHR18_RANDOM_CTG1, HSCHR19_RANDOM_CTG1, HSCHR19_RANDOM_CTG2, HSCHR21_RANDOM_CTG9, HSCHRUN_RANDOM_CTG1, HSCHRUN_RANDOM_CTG2, HSCHRUN_RANDOM_CTG3, HSCHRUN_RANDOM_CTG4, HSCHRUN_RANDOM_CTG5, HSCHRUN_RANDOM_CTG6, HSCHRUN_RANDOM_CTG7, HSCHRUN_RANDOM_CTG9, HSCHRUN_RANDOM_CTG10, HSCHRUN_RANDOM_CTG11, HSCHRUN_RANDOM_CTG13, HSCHRUN_RANDOM_CTG14, HSCHRUN_RANDOM_CTG15, HSCHRUN_RANDOM_CTG16, HSCHRUN_RANDOM_CTG17, HSCHRUN_RANDOM_CTG19, HSCHRUN_RANDOM_CTG20, HSCHRUN_RANDOM_CTG21, HSCHRUN_RANDOM_CTG22, HSCHRUN_RANDOM_CTG23, HSCHRUN_RANDOM_CTG24, HSCHRUN_RANDOM_CTG25, HSCHRUN_RANDOM_CTG26, HSCHRUN_RANDOM_CTG27, HSCHRUN_RANDOM_CTG28, HSCHRUN_RANDOM_CTG29, HSCHRUN_RANDOM_CTG30, HSCHRUN_RANDOM_CTG31, HSCHRUN_RANDOM_CTG32, HSCHRUN_RANDOM_CTG33, HSCHRUN_RANDOM_CTG34, HSCHRUN_RANDOM_CTG35, HSCHRUN_RANDOM_CTG36, HSCHRUN_RANDOM_CTG37, HSCHRUN_RANDOM_CTG38, HSCHRUN_RANDOM_CTG39, HSCHRUN_RANDOM_CTG40, HSCHRUN_RANDOM_CTG41, HSCHRUN_RANDOM_CTG42
#> Make sure to always combine/compare objects based on the same reference
#> genome (use suppressWarnings() to suppress this warning).
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,uc001unu.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,uc001unu.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,uc001unu.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,uc001unu.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,uc001unu.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,uc001unu.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,uc001unu.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,uc001unu.3,uc001unv.3 176917 SKA3
#> -------
#> seqinfo: 25 sequences from an unspecified genome
#>
#> $insSite
#> GRanges object with 4 ranges and 18 metadata columns:
#> seqnames ranges strand | paramRangeID REF ALT QUAL FILTER sourceId partner svtype svLen insSeq insLen event HOMLEN exons txs rtFound rtFoundSum gene_symbol
#> <Rle> <IRanges> <Rle> | <factor> <character> <character> <numeric> <character> <character> <character> <character> <numeric> <character> <numeric> <character> <numeric> <list> <list> <list> <logical> <list>
#> MantaBND:245251:0:3:0:0:0:0 13 21746762 + | NA T T[11:108585702[ 49 PASS MantaBND:245251:0:3:.. MantaBND:245251:0:3:.. BND NA 0 <NA> 0 176918 uc001unt.3,uc001unu.3 TRUE,TRUE TRUE SKA3
#> MantaDEL:245251:5:6:0:0:0_bp2 13 21731995 - | NA T <DEL> 283 PASS MantaDEL:245251:5:6:.. MantaDEL:245251:5:6:.. DEL -2734 <NA> 0 <NA> 0 176911 uc001unt.3,uc001unu.3,uc001unv.3 TRUE,TRUE,TRUE TRUE SKA3
#> MantaBND:245251:0:3:0:0:0:1 11 108585702 - | NA T ]13:21746762]T 49 PASS MantaBND:245251:0:3:.. MantaBND:245251:0:3:.. BND NA 0 <NA> 0 <NA> <NA> <NA> <NA> <NA>
#> MantaDEL:245251:5:6:0:0:0_bp1 13 21729260 + | NA T <DEL> 283 PASS MantaDEL:245251:5:6:.. MantaDEL:245251:5:6:.. DEL -2734 <NA> 0 <NA> 0 <NA> <NA> <NA> <NA> <NA>
#> -------
#> seqinfo: 25 sequences from an unspecified genome
Visualising breakpoint pairs via circos plots
One way of visualising RT is by circos plots. Here we use the package
circlize to
demonstrate the visualisation of insertion site and exon-exon junctions.
To generate a simple circos plot of RT event with SKA3 transcript:
library(circlize)
rt_chr_prefix <- c(RT$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.
circos.initializeWithIdeogram(
data.frame(V1=c("chr13", "chr11"),
V2=c(21720000,108585000),
V3=c(21755000,108586000),
V4=c("q12.11","q24.3"),
V5=c("gneg","gpos50")))
circos.genomicLink(as.data.frame(S4Vectors::first(pairs)), as.data.frame(S4Vectors::second(pairs)))
circos.clear()
PapenfussLab/RTDetect documentation built on June 15, 2022, 1:42 a.m.
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svaRetro: R package for retrotransposed transcript detection from structural variants
svaRetro contains functions for detecting retrotransposed transcripts
(RTs) from structural variant calls. It takes structural variant calls
in GRanges of breakend notation and identifies RTs by exon-exon
junctions and insertion sites. The candidate RTs are reported by events
and annotated with information of the inserted transcripts.
This package uses a breakend-centric event notation adopted from the
StructuralVariantAnnotation
package. 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").
Installation
svaNUMT is currently available for download in Bioconductor (since BioC 3.14 & R 4.1):
# install.packages("BiocManager")
BiocManager::install("svaRetro")
The development version can be installed from GitHub:
BiocManager::install("PapenfussLab/svaRetro")
How to cite
If you use svaRetro, please cite svaRetro
here.
@article {Dong2021.08.18.456578,
author = {Dong, Ruining and Cameron, Daniel and Bedo, Justin and Papenfuss, Anthony T},
title = {svaRetro and svaNUMT: Modular packages for annotation of retrotransposed transcripts and nuclear integration of mitochondrial DNA in genome sequencing data},
elocation-id = {2021.08.18.456578},
year = {2021},
doi = {10.1101/2021.08.18.456578},
publisher = {Cold Spring Harbor Laboratory},
abstract = {Background The biological significance of structural variation is now more widely recognized. However, due to the lack of available tools for downstream analysis, including processing and annotating, interpretation of structural variant calls remains a challenge.Findings Here we present svaRetro and svaNUMT, R packages that provide functions for annotating novel genomic events such as non-reference retro-copied transcripts and nuclear integration of mitochondrial DNA. We evaluate the performance of these packages to detect events using simulations and public benchmarking datasets, and annotate processed transcripts in a public structural variant database.Conclusions svaRetro and svaNUMT provide efficient, modular tools for downstream identification and annotation of structural variant calls.Competing Interest StatementThe authors have declared no competing interest.SVstructural variantNUMTnuclear mitochondrial integrationRTretroposed transcriptTSDtarget site duplicationmtDNAmitochondrial DNA},
URL = {https://www.biorxiv.org/content/early/2021/08/19/2021.08.18.456578},
eprint = {https://www.biorxiv.org/content/early/2021/08/19/2021.08.18.456578.full.pdf},
journal = {bioRxiv}
}
Workflow
Below is a workflow example for detecting RTs from a human SV callset.
This example is taken from the vignette of svaRetro.
library(StructuralVariantAnnotation)
library(VariantAnnotation)
library(svaRetro)
RT_vcf <- readVcf(system.file("extdata", "diploidSV.vcf", package = "svaRetro"))
RT_gr <- StructuralVariantAnnotation::breakpointRanges(RT_vcf, nominalPosition=TRUE)
head(RT_gr)
#> GRanges object with 6 ranges and 13 metadata columns:
#> seqnames ranges strand | paramRangeID REF ALT QUAL FILTER sourceId partner svtype svLen insSeq insLen event HOMLEN
#> <Rle> <IRanges> <Rle> | <factor> <character> <character> <numeric> <character> <character> <character> <character> <numeric> <character> <numeric> <character> <numeric>
#> MantaINS:0:775:775:0:1:0_bp1 1 66365 + | NA AATATAATATATAA ATATATATATTATTATATAA.. 999 MaxDepth MantaINS:0:775:775:0.. MantaINS:0:775:775:0.. INS 129 TATATATATTATTATATAAT.. 142 <NA> 0
#> MantaINS:35:0:0:0:0:0_bp1 1 1004204 + | NA G GGCCACGCGGGCTGTGCAGA.. 999 PASS MantaINS:35:0:0:0:0:0 MantaINS:35:0:0:0:0:.. INS 78 GCCACGCGGGCTGTGCAGAT.. 78 <NA> 10
#> MantaDEL:92:0:0:0:0:0_bp1 1 1161716 + | NA CCTGTACGGTCAGGAGGAAA.. CT 999 PASS MantaDEL:92:0:0:0:0:0 MantaDEL:92:0:0:0:0:.. DEL -63 T 1 <NA> 0
#> MantaDEL:127:0:0:0:0:0_bp1 1 1162672 + | NA GGCGGGAAGGCGAGCTCGTG.. G 440 PASS MantaDEL:127:0:0:0:0:0 MantaDEL:127:0:0:0:0.. DEL -174 0 <NA> 9
#> MantaDEL:130:0:0:0:0:0_bp1 1 1183434 + | NA CAGGCTGGATCTCCAACTCT.. C 643 PASS MantaDEL:130:0:0:0:0:0 MantaDEL:130:0:0:0:0.. DEL -263 0 <NA> 7
#> MantaDEL:107:0:0:0:1:0_bp1 1 1302326 + | NA GAATGAGTGGATTGGTGAGT.. GCAGTGTGAA 999 PASS MantaDEL:107:0:0:0:1:0 MantaDEL:107:0:0:0:1.. DEL -197 CAGTGTGAA 9 <NA> 0
#> -------
#> seqinfo: 25 sequences from an unspecified genome
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.
Identifying Retrotransposed Transcripts
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)
#> Loading required package: GenomicFeatures
#> Loading required package: AnnotationDbi
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following object is masked from 'package:AnnotationDbi':
#>
#> select
#> The following object is masked from 'package:VariantAnnotation':
#>
#> select
#> The following objects are masked from 'package:Biostrings':
#>
#> collapse, intersect, setdiff, setequal, union
#> The following object is masked from 'package:XVector':
#>
#> slice
#> The following object is masked from 'package:Biobase':
#>
#> combine
#> The following object is masked from 'package:matrixStats':
#>
#> count
#> The following objects are masked from 'package:GenomicRanges':
#>
#> intersect, setdiff, union
#> The following object is masked from 'package:GenomeInfoDb':
#>
#> intersect
#> The following objects are masked from 'package:IRanges':
#>
#> collapse, desc, intersect, setdiff, slice, union
#> The following objects are masked from 'package:S4Vectors':
#>
#> first, intersect, rename, setdiff, setequal, union
#> The following objects are masked from 'package:BiocGenerics':
#>
#> combine, intersect, setdiff, union
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
hg19.genes <- TxDb.Hsapiens.UCSC.hg19.knownGene
RT_vcf <- readVcf(system.file("extdata", "diploidSV.vcf", package = "svaRetro"))
RT_gr <- StructuralVariantAnnotation::breakpointRanges(RT_vcf, nominalPosition=TRUE)
RT <- rtDetect(RT_gr, hg19.genes, maxgap=50, minscore=0.3)
#> Warning in (function (seqlevels, genome, new_style) : cannot switch some of hg19's seqlevels from UCSC to NCBI style
#> Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
#> - in 'x': MT
#> - in 'y': chrM, HSCHR1_RANDOM_CTG5, HSCHR1_RANDOM_CTG12, HSCHR4_1_CTG9, HSCHR4_RANDOM_CTG2, HSCHR4_RANDOM_CTG3, HSCHR6_MHC_APD_CTG1, HSCHR6_MHC_COX_CTG1, HSCHR6_MHC_DBB_CTG1, HSCHR6_MHC_MANN_CTG1, HSCHR6_MHC_MCF_CTG1, HSCHR6_MHC_QBL_CTG1, HSCHR6_MHC_SSTO_CTG1, HSCHR7_RANDOM_CTG1, HSCHR8_RANDOM_CTG1, HSCHR8_RANDOM_CTG4, HSCHR9_RANDOM_CTG1, HSCHR9_RANDOM_CTG2, HSCHR9_RANDOM_CTG4, HSCHR9_RANDOM_CTG5, HSCHR11_RANDOM_CTG2, HSCHR17_1_CTG5, HSCHR17_RANDOM_CTG1, HSCHR17_RANDOM_CTG2, HSCHR17_RANDOM_CTG3, HSCHR17_RANDOM_CTG4, HSCHR18_RANDOM_CTG1, HSCHR19_RANDOM_CTG1, HSCHR19_RANDOM_CTG2, HSCHR21_RANDOM_CTG9, HSCHRUN_RANDOM_CTG1, HSCHRUN_RANDOM_CTG2, HSCHRUN_RANDOM_CTG3, HSCHRUN_RANDOM_CTG4, HSCHRUN_RANDOM_CTG5, HSCHRUN_RANDOM_CTG6, HSCHRUN_RANDOM_CTG7, HSCHRUN_RANDOM_CTG9, HSCHRUN_RANDOM_CTG10, HSCHRUN_RANDOM_CTG11, HSCHRUN_RANDOM_CTG13, HSCHRUN_RANDOM_CTG14, HSCHRUN_RANDOM_CTG15, HSCHRUN_RANDOM_CTG16, HSCHRUN_RANDOM_CTG17, HSCHRUN_RANDOM_CTG19, HSCHRUN_RANDOM_CTG20, HSCHRUN_RANDOM_CTG21, HSCHRUN_RANDOM_CTG22, HSCHRUN_RANDOM_CTG23, HSCHRUN_RANDOM_CTG24, HSCHRUN_RANDOM_CTG25, HSCHRUN_RANDOM_CTG26, HSCHRUN_RANDOM_CTG27, HSCHRUN_RANDOM_CTG28, HSCHRUN_RANDOM_CTG29, HSCHRUN_RANDOM_CTG30, HSCHRUN_RANDOM_CTG31, HSCHRUN_RANDOM_CTG32, HSCHRUN_RANDOM_CTG33, HSCHRUN_RANDOM_CTG34, HSCHRUN_RANDOM_CTG35, HSCHRUN_RANDOM_CTG36, HSCHRUN_RANDOM_CTG37, HSCHRUN_RANDOM_CTG38, HSCHRUN_RANDOM_CTG39, HSCHRUN_RANDOM_CTG40, HSCHRUN_RANDOM_CTG41, HSCHRUN_RANDOM_CTG42
#> Make sure to always combine/compare objects based on the same reference
#> genome (use suppressWarnings() to suppress this warning).
#> Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
#> - in 'x': MT
#> - in 'y': chrM, HSCHR1_RANDOM_CTG5, HSCHR1_RANDOM_CTG12, HSCHR4_1_CTG9, HSCHR4_RANDOM_CTG2, HSCHR4_RANDOM_CTG3, HSCHR6_MHC_APD_CTG1, HSCHR6_MHC_COX_CTG1, HSCHR6_MHC_DBB_CTG1, HSCHR6_MHC_MANN_CTG1, HSCHR6_MHC_MCF_CTG1, HSCHR6_MHC_QBL_CTG1, HSCHR6_MHC_SSTO_CTG1, HSCHR7_RANDOM_CTG1, HSCHR8_RANDOM_CTG1, HSCHR8_RANDOM_CTG4, HSCHR9_RANDOM_CTG1, HSCHR9_RANDOM_CTG2, HSCHR9_RANDOM_CTG4, HSCHR9_RANDOM_CTG5, HSCHR11_RANDOM_CTG2, HSCHR17_1_CTG5, HSCHR17_RANDOM_CTG1, HSCHR17_RANDOM_CTG2, HSCHR17_RANDOM_CTG3, HSCHR17_RANDOM_CTG4, HSCHR18_RANDOM_CTG1, HSCHR19_RANDOM_CTG1, HSCHR19_RANDOM_CTG2, HSCHR21_RANDOM_CTG9, HSCHRUN_RANDOM_CTG1, HSCHRUN_RANDOM_CTG2, HSCHRUN_RANDOM_CTG3, HSCHRUN_RANDOM_CTG4, HSCHRUN_RANDOM_CTG5, HSCHRUN_RANDOM_CTG6, HSCHRUN_RANDOM_CTG7, HSCHRUN_RANDOM_CTG9, HSCHRUN_RANDOM_CTG10, HSCHRUN_RANDOM_CTG11, HSCHRUN_RANDOM_CTG13, HSCHRUN_RANDOM_CTG14, HSCHRUN_RANDOM_CTG15, HSCHRUN_RANDOM_CTG16, HSCHRUN_RANDOM_CTG17, HSCHRUN_RANDOM_CTG19, HSCHRUN_RANDOM_CTG20, HSCHRUN_RANDOM_CTG21, HSCHRUN_RANDOM_CTG22, HSCHRUN_RANDOM_CTG23, HSCHRUN_RANDOM_CTG24, HSCHRUN_RANDOM_CTG25, HSCHRUN_RANDOM_CTG26, HSCHRUN_RANDOM_CTG27, HSCHRUN_RANDOM_CTG28, HSCHRUN_RANDOM_CTG29, HSCHRUN_RANDOM_CTG30, HSCHRUN_RANDOM_CTG31, HSCHRUN_RANDOM_CTG32, HSCHRUN_RANDOM_CTG33, HSCHRUN_RANDOM_CTG34, HSCHRUN_RANDOM_CTG35, HSCHRUN_RANDOM_CTG36, HSCHRUN_RANDOM_CTG37, HSCHRUN_RANDOM_CTG38, HSCHRUN_RANDOM_CTG39, HSCHRUN_RANDOM_CTG40, HSCHRUN_RANDOM_CTG41, HSCHRUN_RANDOM_CTG42
#> Make sure to always combine/compare objects based on the same reference
#> genome (use suppressWarnings() to suppress this warning).
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,uc001unu.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,uc001unu.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,uc001unu.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,uc001unu.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,uc001unu.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,uc001unu.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,uc001unu.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,uc001unu.3,uc001unv.3 176917 SKA3
#> -------
#> seqinfo: 25 sequences from an unspecified genome
#>
#> $insSite
#> GRanges object with 4 ranges and 18 metadata columns:
#> seqnames ranges strand | paramRangeID REF ALT QUAL FILTER sourceId partner svtype svLen insSeq insLen event HOMLEN exons txs rtFound rtFoundSum gene_symbol
#> <Rle> <IRanges> <Rle> | <factor> <character> <character> <numeric> <character> <character> <character> <character> <numeric> <character> <numeric> <character> <numeric> <list> <list> <list> <logical> <list>
#> MantaBND:245251:0:3:0:0:0:0 13 21746762 + | NA T T[11:108585702[ 49 PASS MantaBND:245251:0:3:.. MantaBND:245251:0:3:.. BND NA 0 <NA> 0 176918 uc001unt.3,uc001unu.3 TRUE,TRUE TRUE SKA3
#> MantaDEL:245251:5:6:0:0:0_bp2 13 21731995 - | NA T <DEL> 283 PASS MantaDEL:245251:5:6:.. MantaDEL:245251:5:6:.. DEL -2734 <NA> 0 <NA> 0 176911 uc001unt.3,uc001unu.3,uc001unv.3 TRUE,TRUE,TRUE TRUE SKA3
#> MantaBND:245251:0:3:0:0:0:1 11 108585702 - | NA T ]13:21746762]T 49 PASS MantaBND:245251:0:3:.. MantaBND:245251:0:3:.. BND NA 0 <NA> 0 <NA> <NA> <NA> <NA> <NA>
#> MantaDEL:245251:5:6:0:0:0_bp1 13 21729260 + | NA T <DEL> 283 PASS MantaDEL:245251:5:6:.. MantaDEL:245251:5:6:.. DEL -2734 <NA> 0 <NA> 0 <NA> <NA> <NA> <NA> <NA>
#> -------
#> seqinfo: 25 sequences from an unspecified genome
Visualising breakpoint pairs via circos plots
One way of visualising RT is by circos plots. Here we use the package
circlize to
demonstrate the visualisation of insertion site and exon-exon junctions.
To generate a simple circos plot of RT event with SKA3 transcript:
library(circlize)
rt_chr_prefix <- c(RT$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.
circos.initializeWithIdeogram(
data.frame(V1=c("chr13", "chr11"),
V2=c(21720000,108585000),
V3=c(21755000,108586000),
V4=c("q12.11","q24.3"),
V5=c("gneg","gpos50")))
circos.genomicLink(as.data.frame(S4Vectors::first(pairs)), as.data.frame(S4Vectors::second(pairs)))
circos.clear()
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