RLSeq (part of RLSuite) is used for downstream analysis of R-loop datasets. It provides methods for data quality control and exploratory analysis within the context of the hundreds of publicly-available R-loop mapping data sets provided by RLBase and accessed via RLHub. Finally, RLSeq provides a user-friendly HTML report that summarizes the analysis results.
NOTE: To run RLSeq in your browser, please see RLBase.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(version = "devel")
BiocManager::install("RLSeq")
devel
version of bioconductor. BiocManager::install(version = "devel")
remotes::install_github("Bishop-Laboratory/RLHub")
remotes::install_github("Bishop-Laboratory/RLSeq")
This is an example workflow using a publicly-available R-loop mapping data set that was reprocessed and standardized in RLBase.
# Peaks and coverage can be found in RLBase
rlbase <- "https://rlbase-data.s3.amazonaws.com"
pks <- file.path(rlbase, "peaks", "SRX1025890_hg38.broadPeak")
cvg <- file.path(rlbase, "coverage", "SRX1025890_hg38.bw")
# Initialize data in the RLRanges object.
# Metadata is optional, but improves the interpretability of results
rlr <- RLRanges(
peaks = pks,
coverage = cvg,
genome = "hg38",
mode = "DRIP",
label = "POS",
sampleName = "TC32 DRIP-Seq"
)
# The RLSeq command performs all analyses
rlr <- RLSeq(rlr)
# Generate an html report
report(rlr)
The code above performs a typical analysis. It builds the RLRanges
object, an extension of GRanges
for
use with RLSeq. Then, it runs all core analyses using RLSeq()
. Finally, it generates an
HTML report with report()
(see the report
here).
For more information, see the package website here.
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