---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
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# raer <a href="https://rnabioco.github.io/raer"><img src="man/figures/logo.png" align="right" height="138" /></a>
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raer facilitates analysis of RNA adenosine editing in the
[Bioconductor](https://bioconductor.org/) ecosystem.
## Installation
`raer` is available on [Bioconductor](https://bioconductor.org/packages/release/bioc/html/raer.html):
```{r, eval = FALSE}
if (!require("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("raer")
```
You can install the development version of raer from [GitHub](https://github.com/rnabioco/raer) with:
```{r, eval = FALSE}
BiocManager::install("rnabioco/raer")
```
## Quick start
raer provides methods to compute per site read count summaries from BAM
alignment files, either for known editing sites, or for all detected sites.
```{r pileup_sites}
library(raer)
bam1fn <- raer_example("SRR5564269_Aligned.sortedByCoord.out.md.bam")
bam2fn <- raer_example("SRR5564277_Aligned.sortedByCoord.out.md.bam")
fafn <- raer_example("human.fasta")
bams <- c("ko" = bam1fn, "wt" = bam2fn)
rse <- pileup_sites(bams, fafn)
```
To facilitate comparisons across groups, base count data and genomic coordinates
are stored in a `RangedSummarizedExperiment`.
```{r}
suppressMessages(library(SummarizedExperiment))
rse
assays(rse)
colData(rse)
```
```{r}
assays(rse)$nRef[1:4, ]
assays(rse)$nAlt[1:4, ]
```
The `FilterParam()` class holds multiple options for customizing the output of `pileup_sites()`.
```{r params}
fp <- FilterParam(
only_keep_variants = TRUE,
library_type = "fr-first-strand",
min_depth = 2
)
rse <- pileup_sites(bams, fafn, param = fp)
rse
```
`pileup_cells()` provides support for quantifying editing sites in single cell libraries.
```{r scrnaseq}
scbam_fn <- raer_example("5k_neuron_mouse_possort.bam")
outdir <- tempdir()
editing_sites <- GRanges(
c(
"2:579:-",
"2:625:-",
"2:589:-"
),
REF = "A",
ALT = "G"
)
cbs <- c(
"CACCAAACAACAACAA-1",
"TATTCCACACCCTCTA-1",
"GACCTTCAGTTGTAAG-1"
)
sce <- pileup_cells(scbam_fn,
sites = editing_sites,
cell_barcodes = cbs,
param = fp,
output_directory = outdir
)
sce
```
```{r}
assays(sce)$nRef
assays(sce)$nAlt
```
## Related work
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:
- [REDItools](https://github.com/BioinfoUNIBA/REDItools) from [Picardi E, Pesole G](https://doi.org/10.1093/bioinformatics/btt287)
- [JACUSA2](https://github.com/dieterich-lab/JACUSA2) from [Piechotta M et al ](https://doi.org/10.1186/s12859-016-1432-8)
- [deNovo-Detect](https://github.com/a2iEditing/deNovo-Detect) from [Gabay O et al ](https://doi.org/10.1038/s41467-022-28841-4)
- [RNAEditingIndexer](https://github.com/a2iEditing/RNAEditingIndexer) from [Roth SH et al](https://doi.org/10.1038/s41592-019-0610-9)
- [SAILOR](https://github.com/YeoLab/sailor) from [Washburn MC et al](https://10.1016/j.celrep.2014.01.011)
