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README.md
# RLSeq <img src="https://rlbase-data.s3.amazonaws.com/misc/assets/whitebgRLSeq+Logo.png" align="right" alt="logo" width="240" style = "border: none; float: right;"> <!-- badges: start --> [![BiocCheck](https://github.com/Bishop-Laboratory/RLSeq/workflows/BiocCheck/badge.svg)](https://github.com/Bishop-Laboratory/RLSeq/actions) [![Codecov test coverage](https://codecov.io/gh/Bishop-Laboratory/RLSeq/branch/main/graph/badge.svg)](https://codecov.io/gh/Bishop-Laboratory/RLSeq?branch=main) [![BioC status](http://www.bioconductor.org/shields/build/release/bioc/RLSeq.svg)](https://bioconductor.org/checkResults/release/bioc-LATEST/RLSeq) <!-- badges: end --> # Introduction *RLSeq* (part of [*RLSuite*](https://gccri.bishop-lab.uthscsa.edu/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](https://github.com/Bishop-Laboratory/RLBase) and accessed via [RLHub](https://github.com/Bishop-Laboratory/RLHub). Finally, *RLSeq* provides a user-friendly HTML report that summarizes the analysis results. **NOTE**: To run *RLSeq* in your browser, please see [*RLBase*](https://gccri.bishop-lab.uthscsa.edu/rlbase/). ## Installation ### From Bioconductor ```r if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("RLSeq") ``` ### From Github 1. Update to the `devel` version of bioconductor. ```r BiocManager::install(version = "devel") ``` 2. Install **RLHub** and **RLSeq** with remotes. ``` r remotes::install_github("Bishop-Laboratory/RLHub") remotes::install_github("Bishop-Laboratory/RLSeq") ``` ## Quick-start This is an example workflow using a publicly-available R-loop mapping data set that was reprocessed and standardized in [*RLBase*](https://gccri.bishop-lab.uthscsa.edu/rlbase/). ```r # 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](https://rlbase-data.s3.amazonaws.com/misc/rlseq_report_example.html)). ## Detail For more information, see the package website [here](https://bishop-laboratory.github.io/RLSeq/).