Exploration and analysis of R-loop mapping data with RLBase

R-loops are three-stranded nucleic acid structures formed from the hybridization of RNA and DNA during nascent transcription. In 2012, Ginno et al. introduced the first R-loop mapping method, DNA:RNA immunoprecipitation (DRIP) sequencing. Since that time, dozens of studies have implemented R-loop mapping and new high-resolution techniques have been developed. The resulting datasets have tremendous potential to reveal the causes and consequences of R-loops genome-wide. However, poor quality and variability between mapping approaches pose serious barriers to the meta-analysis of these data. In our recent work, we reprocessed 693 R-loop mapping samples, devising new quality methods, defining a set of high-confidence mapping samples, and then deriving R-loop regions, consensus sites of R-loop formation. This analysis yielded the largest R-loop data resource to date along with novel computational approaches for R-loop mapping analysis. Now, we introduce RLBase , an innovative web server which builds upon those data and software by providing users with the capability to ([1][1]) explore hundreds of public R-loop mapping datasets, ([2][2]) explore consensus R-loop regions, ([3][3]) analyze user-supplied datasets to generate an HTML quality report, and ([4][4]) download all the processed data for the 693 samples we previously reprocessed and standardized. In addition to RLBase , we also describe the other software which, along with RLBase , provides a computational framework for R-loop bioinformatics. RLBase , and the rest of these software (termed “RLSuite”), are provided freely under an MIT license and made publicly available: . RLBase is directly accessible via the following URL: . ### Competing Interest Statement The authors have declared no competing interest. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4