Skip to contents

raer

raer facilitates analysis of RNA adenosine editing in the Bioconductor ecosystem.

Installation

raer is available on Bioconductor:

if (!require("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}

BiocManager::install("raer")

You can install the development version of raer from GitHub with:

BiocManager::install("rnabioco/raer")

Quick start

raer workflow overview

raer provides methods to compute per site read count summaries from BAM alignment files, either for known editing sites, or for all detected sites.

library(raer)
bam1fn <- raer_example("SRR5564269_Aligned.sortedByCoord.out.md.bam")
bam2fn <- raer_example("SRR5564277_Aligned.sortedByCoord.out.md.bam")
fafn <- raer_example("human.fasta")

bams <- c("ko" = bam1fn, "wt" = bam2fn)

rse <- pileup_sites(bams, fafn)

To facilitate comparisons across groups, base count data and genomic coordinates are stored in a RangedSummarizedExperiment.

suppressMessages(library(SummarizedExperiment))
rse
#> class: RangedSummarizedExperiment 
#> dim: 1695 2 
#> metadata(0):
#> assays(7): ALT nRef ... nC nG
#> rownames(1695): site_SSR3_1_2 site_SSR3_2_2 ... site_DHFR_517_2
#>   site_DHFR_518_2
#> rowData names(4): REF rpbz vdb sor
#> colnames(2): ko wt
#> colData names(1): sample
assays(rse)
#> List of length 7
#> names(7): ALT nRef nAlt nA nT nC nG
colData(rse)
#> DataFrame with 2 rows and 1 column
#>         sample
#>    <character>
#> ko          ko
#> wt          wt
assays(rse)$nRef[1:4, ]
#>               ko wt
#> site_SSR3_1_2 13 12
#> site_SSR3_2_2 14 12
#> site_SSR3_3_2 14 12
#> site_SSR3_4_2 15 12
assays(rse)$nAlt[1:4, ]
#>               ko wt
#> site_SSR3_1_2  0  0
#> site_SSR3_2_2  0  0
#> site_SSR3_3_2  0  0
#> site_SSR3_4_2  0  0

The FilterParam() class holds multiple options for customizing the output of pileup_sites().

fp <- FilterParam(
    only_keep_variants = TRUE,
    library_type = "fr-first-strand",
    min_depth = 2
)

rse <- pileup_sites(bams, fafn, param = fp)
rse
#> class: RangedSummarizedExperiment 
#> dim: 74 2 
#> metadata(0):
#> assays(7): ALT nRef ... nC nG
#> rownames(74): site_SSR3_102_2 site_SSR3_125_2 ... site_DHFR_430_2
#>   site_DHFR_513_2
#> rowData names(4): REF rpbz vdb sor
#> colnames(2): ko wt
#> colData names(1): sample

pileup_cells() provides support for quantifying editing sites in single cell libraries.

scbam_fn <- raer_example("5k_neuron_mouse_possort.bam")
outdir <- tempdir()

editing_sites <- GRanges(
    c(
        "2:579:-",
        "2:625:-",
        "2:589:-"
    ),
    REF = "A",
    ALT = "G"
)

cbs <- c(
    "CACCAAACAACAACAA-1",
    "TATTCCACACCCTCTA-1",
    "GACCTTCAGTTGTAAG-1"
)

sce <- pileup_cells(scbam_fn,
    sites = editing_sites,
    cell_barcodes = cbs,
    param = fp,
    output_directory = outdir
)
sce
#> class: SingleCellExperiment 
#> dim: 3 3 
#> metadata(0):
#> assays(2): nRef nAlt
#> rownames(3): site_2_579_2_AG site_2_625_2_AG site_2_589_2_AG
#> rowData names(2): REF ALT
#> colnames(3): CACCAAACAACAACAA-1 TATTCCACACCCTCTA-1 GACCTTCAGTTGTAAG-1
#> colData names(0):
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
assays(sce)$nRef
#> 3 x 3 sparse Matrix of class "dgCMatrix"
#>                 CACCAAACAACAACAA-1 TATTCCACACCCTCTA-1 GACCTTCAGTTGTAAG-1
#> site_2_579_2_AG                  0                  0                  1
#> site_2_625_2_AG                  0                  0                  0
#> site_2_589_2_AG                  1                  1                  2
assays(sce)$nAlt
#> 3 x 3 sparse Matrix of class "dgCMatrix"
#>                 CACCAAACAACAACAA-1 TATTCCACACCCTCTA-1 GACCTTCAGTTGTAAG-1
#> site_2_579_2_AG                  1                  1                  1
#> site_2_625_2_AG                  1                  1                  1
#> site_2_589_2_AG                  0                  0                  0

Core routines in raer are implemented using the htslib library and methods from samtools and bcftools. raer builds off of approaches from other RNA editing detection tools: