Exploration and analysis of R-loop mapping data with RLBase

Abstract

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) explore hundreds of public R-loop mapping datasets, (2) explore consensus R-loop regions, (3) analyze user-supplied datasets to generate an HTML quality report, and (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: https://gccri.bishop-lab.uthscsa.edu/rlsuite/. RLBase is directly accessible via the following URL: https://gccri.bishop-lab.uthscsa.edu/rlbase/.

RLSuite: An integrative R-loop bioinformatics framework

“…In recent work, we reprocessed 810 R-loop mapping datasets, establishing a robust method of quality control (8). We then used these data to build RLBase , the largest R-loop mapping database yet described (15). We also established novel approaches for the analysis of R-loop mapping data, and demonstrated the utility of analysing R-loop mapping data in the context of publicly available R-loop datasets (8).…”

“…RLSuite provides an integrative workflow for R-loop data analysis, including automated pre-processing of R-loop mapping data using a standard pipeline, multiple robust methods for quality control, and a range of tools for the initial exploration of R-loop data. RLSuite also provides the unique capability to compare analysis results with those from the public R-loop datasets that we previously reprocessed (8,15). Moreover, it provides a user-friendly HTML analysis report which should prove useful for both bioinformaticians and biologists alike.…”

“…With this QC approach, we identified high-quality R-loop datasets and performed a large-scale meta-analysis that revealed novel biological insights regarding the differences between in situ and ex vivo mapped R-loops (8). Moreover, we built a database of reprocessed and standardized R-loop mapping datasets, and described novel methods for analysing R-loop data (8,15). With the data and code developed in these studies, we created RLSuite , a collection of software packages for the analysis of R-loop mapping data.…”