--- title: "svaRetro Quick Overview" author: "Ruining Dong" date: "`r Sys.Date()`" output: BiocStyle::html_document # html_document: # toc: yes # toc_float: # collapsed: yes # smooth_scroll: yes vignette: > %\VignetteIndexEntry{svaRetro Package} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE, echo=FALSE} options(width=1000) knitr::opts_chunk$set( collapse = TRUE, comment = "#>") ``` # Introduction This vignette outlines a workflow of detecting retrotransposed transcripts (RTs) from Variant Call Format (VCF) using the `svaRetro` package. # Installation The `svaRetro` package can be installed from *Bioconductor* as follows: ```{r, eval=FALSE} if(!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("svaRetro") ``` # Using GRanges for structural variants: a breakend-centric data structure 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. # Workflow ## Loading data from VCF 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")`. ```{r input, include=TRUE,results="hide",message=FALSE,warning=FALSE} library(StructuralVariantAnnotation) library(VariantAnnotation) library(svaRetro) RT_vcf <- readVcf(system.file("extdata", "diploidSV.vcf", package = "svaRetro")) ``` ```{r, include=TRUE,results="hide",message=FALSE,warning=FALSE} RT_gr <- StructuralVariantAnnotation::breakpointRanges(RT_vcf, nominalPosition=TRUE) head(RT_gr) ``` 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. ```{r, include=TRUE,results="hide",message=FALSE,warning=FALSE} 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. ```{r} RT$SKA3 ``` # Visualising breakpoint pairs via circos plots One way of visualising RT is by circos plots. Here we use the package [`circlize`](https://doi.org/10.1093/bioinformatics/btu393) to demonstrate the visualisation of insertion site and exon-exon junctions. To generate a simple circos plot of RT event with SKA3 transcript: ```{r, include=TRUE,results="hide",message=FALSE,warning=FALSE} 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. ```{r} 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() ``` # SessionInfo ```{r} sessionInfo() ```