RLSuite: An Integrative R-Loop Bioinformatics Framework

We recently described the development of a database of 810 R-loop mapping datasets and used this data to conduct a meta-analysis of R-loops. R-loops are three-stranded nucleic acid structures containing RNA:DNA hybrids and we were able to verify that 30% of expressed genes have an associated R-loop in a location conserved manner.. Moreover, intergenic R-loops map to enhancers, super enhancers and with TAD domain boundaries. This work demonstrated that R-loop mapping via high-throughput sequencing can reveal novel insight into R-loop biology, however the analysis and quality control of these data is a non-trivial task for which few bioinformatic tools exist. Herein we describe RLSuite, an integrative R-loop bioinformatics framework for pre-processing, quality control, and downstream analysis of R-loop mapping data. RLSuite enables users to compare their data to hundreds of public datasets and generate a user-friendly analysis report for sharing with non-bioinformatician colleagues. Taken together, RLSuite is a novel analysis framework that should greatly benefit the emerging R-loop bioinformatics community in a rapidly expanding aspect of epigenetic control that is still poorly understood.