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Characterize A-to-I RNA editing in bulk and single-cell RNA sequencing experiments
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rnabioco/raer
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raer facilitates analysis of RNA adenosine editing in theBioconductor ecosystem.
raer is available onBioconductor:
if (!require("BiocManager",quietly=TRUE)) { install.packages("BiocManager")}BiocManager::install("raer")
You can install the development version of raer fromGitHub with:
BiocManager::install("rnabioco/raer")
raer provides methods to compute per site read count summaries from BAMalignment files, either for known editing sites, or for all detectedsites.
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 genomiccoordinates are stored in aRangedSummarizedExperiment.
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): sampleassays(rse)#> List of length 7#> names(7): ALT nRef nAlt nA nT nC nGcolData(rse)#> DataFrame with 2 rows and 1 column#> sample#> <character>#> ko ko#> wt wt
assays(rse)$nRef[1:4, ]#> ko wt#> site_SSR3_1_2 13 12#> site_SSR3_2_2 14 12#> site_SSR3_3_2 14 12#> site_SSR3_4_2 15 12assays(rse)$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
TheFilterParam() class holds multiple options for customizing theoutput ofpileup_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 insingle 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)$nRef#> 3 x 3 sparse Matrix of class "dgCMatrix"#> CACCAAACAACAACAA-1 TATTCCACACCCTCTA-1 GACCTTCAGTTGTAAG-1#> site_2_579_2_AG 0 0 1#> site_2_625_2_AG 0 0 0#> site_2_589_2_AG 1 1 2assays(sce)$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 inraer are implemented using thehtslib library andmethods fromsamtools andbcftools.raer builds off of approachesfrom other RNA editing detection tools:
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Characterize A-to-I RNA editing in bulk and single-cell RNA sequencing experiments