svaNUMT Quick Overview
Ruining Dong
2024-10-29
Introduction
This vignette outlines a workflow of detecting nuclear-mitochondrial
DNA fusions from Variant Call Format (VCF) using the
svaNUMT package.
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").
library(StructuralVariantAnnotation)
library(VariantAnnotation)
library(svaNUMT)
vcf <- readVcf(system.file("extdata", "chr1_numt_pe_HS25.sv.vcf", package = "svaNUMT"))
gr <- breakpointRanges(vcf)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 Nuclear-mitochondrial Genome Fusion Events
Function svaNUMT searches for NUMT events by identifying
breakends supporting the fusion of nuclear chromosome and mitochondrial
genome. svaNUMT returns identified breakends supporting
candidate NUMTs in 2 lists of list of GRanges, grouped by chromosome and
insertion sites.
library(readr)
numtS <- read_table(system.file("extdata", "numtS.txt", package = "svaNUMT"),
col_names = FALSE)
colnames(numtS) <- c("bin", "seqnames", "start", "end", "name", "score", "strand")
numtS <- `seqlevelsStyle<-`(GRanges(numtS), "NCBI")
library(BSgenome.Hsapiens.UCSC.hg19)
genome <- BSgenome.Hsapiens.UCSC.hg19
genomeMT <- genome$chrMTNUMT <- numtDetect(gr, numtS, genomeMT, max_ins_dist = 20)
#> There is no MT sequence from known NUMT events detected.The breakends supporting the insertion sites and the MT sequence are
arranged by the order of events. Below is an example of a detected NUMT
event, where MT sequence MT:15737-15836 followed by
polyadenylation is inserted between
chr1:1688363-1688364.
GRangesList(NU=NUMT$MT$NU$`1`[[1]], MT=NUMT$MT$MT$`1`[[1]])
#> GRangesList object of length 2:
#> $NU
#> GRanges object with 2 ranges and 13 metadata columns:
#> seqnames ranges strand | paramRangeID REF
#> <Rle> <IRanges> <Rle> | <factor> <character>
#> gridss1fb_4o 1 1688363 + | NA C
#> gridss1bf_1o 1 1688364 - | NA C
#> ALT QUAL FILTER sourceId
#> <character> <numeric> <character> <character>
#> gridss1fb_4o C[MT:15737[ 3928.49 PASS gridss1fb_4o
#> gridss1bf_1o ]MT:15836]AAAAAAAAAA.. 3581.13 PASS gridss1bf_1o
#> partner svtype svLen insSeq insLen
#> <character> <character> <numeric> <character> <integer>
#> gridss1fb_4o gridss1fb_4h BND NA 0
#> gridss1bf_1o gridss1bf_1h BND NA AAAAAAAAAAAAA 13
#> event HOMLEN
#> <character> <numeric>
#> gridss1fb_4o gridss1fb_4 0
#> gridss1bf_1o gridss1bf_1 0
#> -------
#> seqinfo: 86 sequences from an unspecified genome
#>
#> $MT
#> GRanges object with 2 ranges and 13 metadata columns:
#> seqnames ranges strand | paramRangeID REF
#> <Rle> <IRanges> <Rle> | <factor> <character>
#> gridss1fb_4h MT 15737 - | NA G
#> gridss1bf_1h MT 15836 + | NA A
#> ALT QUAL FILTER sourceId
#> <character> <numeric> <character> <character>
#> gridss1fb_4h ]1:1688363]G 3928.49 PASS gridss1fb_4h
#> gridss1bf_1h AAAAAAAAAAAAAA[1:168.. 3581.13 PASS gridss1bf_1h
#> partner svtype svLen insSeq insLen
#> <character> <character> <numeric> <character> <integer>
#> gridss1fb_4h gridss1fb_4o BND NA 0
#> gridss1bf_1h gridss1bf_1o BND NA AAAAAAAAAAAAA 13
#> event HOMLEN
#> <character> <numeric>
#> gridss1fb_4h gridss1fb_4 0
#> gridss1bf_1h gridss1bf_1 0
#> -------
#> seqinfo: 86 sequences from an unspecified genomeBelow is an example to subset the detected NUMTs by a genomic region
given seqnames, start, and end.
For region chr1:1000000-3000000, there are 3 NUMTs
detected.
seqnames = 1
start = 1000000
end = 3000000
i <- sapply(NUMT$MT$NU[[seqnames]], function(x)
sum(countOverlaps(x, GRanges(seqnames = seqnames, IRanges(start, end))))>0)
list(NU=NUMT$MT$NU[[seqnames]][i], MT=NUMT$MT$MT[[seqnames]][i])
#> $NU
#> $NU[[1]]
#> GRanges object with 2 ranges and 13 metadata columns:
#> seqnames ranges strand | paramRangeID REF
#> <Rle> <IRanges> <Rle> | <factor> <character>
#> gridss1fb_4o 1 1688363 + | NA C
#> gridss1bf_1o 1 1688364 - | NA C
#> ALT QUAL FILTER sourceId
#> <character> <numeric> <character> <character>
#> gridss1fb_4o C[MT:15737[ 3928.49 PASS gridss1fb_4o
#> gridss1bf_1o ]MT:15836]AAAAAAAAAA.. 3581.13 PASS gridss1bf_1o
#> partner svtype svLen insSeq insLen
#> <character> <character> <numeric> <character> <integer>
#> gridss1fb_4o gridss1fb_4h BND NA 0
#> gridss1bf_1o gridss1bf_1h BND NA AAAAAAAAAAAAA 13
#> event HOMLEN
#> <character> <numeric>
#> gridss1fb_4o gridss1fb_4 0
#> gridss1bf_1o gridss1bf_1 0
#> -------
#> seqinfo: 86 sequences from an unspecified genome
#>
#> $NU[[2]]
#> GRanges object with 2 ranges and 13 metadata columns:
#> seqnames ranges strand | paramRangeID REF
#> <Rle> <IRanges> <Rle> | <factor> <character>
#> gridss1fb_5o 1 1791082-1791083 + | NA G
#> gridss1bf_2o 1 1791084 - | NA A
#> ALT QUAL FILTER sourceId
#> <character> <numeric> <character> <character>
#> gridss1fb_5o G[MT:2592[ 1929.85 PASS gridss1fb_5o
#> gridss1bf_2o ]MT:3592]AAAAAAAAAAAA 2894.91 PASS gridss1bf_2o
#> partner svtype svLen insSeq insLen
#> <character> <character> <numeric> <character> <integer>
#> gridss1fb_5o gridss1fb_5h BND NA 0
#> gridss1bf_2o gridss1bf_2h BND NA AAAAAAAAAAA 11
#> event HOMLEN
#> <character> <numeric>
#> gridss1fb_5o gridss1fb_5 1
#> gridss1bf_2o gridss1bf_2 0
#> -------
#> seqinfo: 86 sequences from an unspecified genome
#>
#> $NU[[3]]
#> GRanges object with 2 ranges and 13 metadata columns:
#> seqnames ranges strand | paramRangeID REF
#> <Rle> <IRanges> <Rle> | <factor> <character>
#> gridss2fb_3o 1 2869079 + | NA G
#> gridss2bf_2o 1 2869080 - | NA A
#> ALT QUAL FILTER sourceId
#> <character> <numeric> <character> <character>
#> gridss2fb_3o G[MT:2786[ 2472.12 PASS gridss2fb_3o
#> gridss2bf_2o ]MT:2985]AAAAAAAAAAA.. 2456.81 PASS gridss2bf_2o
#> partner svtype svLen insSeq insLen
#> <character> <character> <numeric> <character> <integer>
#> gridss2fb_3o gridss2fb_3h BND NA 0
#> gridss2bf_2o gridss2bf_2h BND NA AAAAAAAAAAAAAAA 15
#> event HOMLEN
#> <character> <numeric>
#> gridss2fb_3o gridss2fb_3 0
#> gridss2bf_2o gridss2bf_2 0
#> -------
#> seqinfo: 86 sequences from an unspecified genome
#>
#>
#> $MT
#> $MT[[1]]
#> GRanges object with 2 ranges and 13 metadata columns:
#> seqnames ranges strand | paramRangeID REF
#> <Rle> <IRanges> <Rle> | <factor> <character>
#> gridss1fb_4h MT 15737 - | NA G
#> gridss1bf_1h MT 15836 + | NA A
#> ALT QUAL FILTER sourceId
#> <character> <numeric> <character> <character>
#> gridss1fb_4h ]1:1688363]G 3928.49 PASS gridss1fb_4h
#> gridss1bf_1h AAAAAAAAAAAAAA[1:168.. 3581.13 PASS gridss1bf_1h
#> partner svtype svLen insSeq insLen
#> <character> <character> <numeric> <character> <integer>
#> gridss1fb_4h gridss1fb_4o BND NA 0
#> gridss1bf_1h gridss1bf_1o BND NA AAAAAAAAAAAAA 13
#> event HOMLEN
#> <character> <numeric>
#> gridss1fb_4h gridss1fb_4 0
#> gridss1bf_1h gridss1bf_1 0
#> -------
#> seqinfo: 86 sequences from an unspecified genome
#>
#> $MT[[2]]
#> GRanges object with 2 ranges and 13 metadata columns:
#> seqnames ranges strand | paramRangeID REF
#> <Rle> <IRanges> <Rle> | <factor> <character>
#> gridss1fb_5h MT 2592-2593 - | NA G
#> gridss1bf_2h MT 3592 + | NA G
#> ALT QUAL FILTER sourceId
#> <character> <numeric> <character> <character>
#> gridss1fb_5h ]1:1791082]G 1929.85 PASS gridss1fb_5h
#> gridss1bf_2h GAAAAAAAAAAA[1:17910.. 2894.91 PASS gridss1bf_2h
#> partner svtype svLen insSeq insLen
#> <character> <character> <numeric> <character> <integer>
#> gridss1fb_5h gridss1fb_5o BND NA 0
#> gridss1bf_2h gridss1bf_2o BND NA AAAAAAAAAAA 11
#> event HOMLEN
#> <character> <numeric>
#> gridss1fb_5h gridss1fb_5 1
#> gridss1bf_2h gridss1bf_2 0
#> -------
#> seqinfo: 86 sequences from an unspecified genome
#>
#> $MT[[3]]
#> GRanges object with 2 ranges and 13 metadata columns:
#> seqnames ranges strand | paramRangeID REF
#> <Rle> <IRanges> <Rle> | <factor> <character>
#> gridss2fb_3h MT 2786 - | NA T
#> gridss2bf_2h MT 2985 + | NA C
#> ALT QUAL FILTER sourceId
#> <character> <numeric> <character> <character>
#> gridss2fb_3h ]1:2869079]T 2472.12 PASS gridss2fb_3h
#> gridss2bf_2h CAAAAAAAAAAAAAAA[1:2.. 2456.81 PASS gridss2bf_2h
#> partner svtype svLen insSeq insLen
#> <character> <character> <numeric> <character> <integer>
#> gridss2fb_3h gridss2fb_3o BND NA 0
#> gridss2bf_2h gridss2bf_2o BND NA AAAAAAAAAAAAAAA 15
#> event HOMLEN
#> <character> <numeric>
#> gridss2fb_3h gridss2fb_3 0
#> gridss2bf_2h gridss2bf_2 0
#> -------
#> seqinfo: 86 sequences from an unspecified genomeVisualising breakpoint pairs via circos plots
One way of visualising paired breakpoints is by circos plots. Here we
use the package circlize
to demonstrate breakpoint visualisation. The bedpe2circos
function takes BEDPE-formatted dataframes (see
breakpointgr2bedpe()) and plotting parameters for the
circos.initializeWithIdeogram() and
circos.genomicLink() functions from
circlize.
To generate a simple circos plot of one candidate NUMT event:
library(circlize)
numt_chr_prefix <- c(NUMT$MT$NU$`1`[[2]], NUMT$MT$MT$`1`[[2]])
GenomeInfoDb::seqlevelsStyle(numt_chr_prefix) <- "UCSC"
pairs <- breakpointgr2pairs(numt_chr_prefix)
pairsTo see supporting breakpoints clearly, we generate the circos plot according to the loci of event.
circos.initializeWithIdeogram(
data.frame(V1=c("chr1", "chrM"),
V2=c(1791073,1),
V3=c(1791093,16571),
V4=c("p15.4",NA),
V5=c("gpos50",NA)), sector.width = c(0.2, 0.8))
#circos.initializeWithIdeogram()
circos.genomicLink(as.data.frame(S4Vectors::first(pairs)),
as.data.frame(S4Vectors::second(pairs)))
circos.clear()SessionInfo
sessionInfo()
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