Quality-controlled R-loop meta-analysis reveals the characteristics of R-Loop consensus regions

Abstract

R-loops are three-stranded nucleic acid structures formed from the hybridization of RNA and DNA during transcription. While the pathological consequences of R-loops have been well-studied to date, the locations, classes, and dynamics of physiological R-loops remain poorly understood. R-loop mapping studies provide insight into R-loop dynamics, but their findings are challenging to generalize. This is due to the narrow biological scope of individual studies, the limitations of each mapping modality, and, in some cases, poor data quality. In this study, we reprocessed 693 R-loop mapping datasets from a wide array of biological conditions and mapping modalities. From this data resource, we developed an accurate method for R-loop data quality control, and we reveal the extent of poor-quality data within previously published studies. We then identified a set of high-confidence R-loop mapping samples and used them to define consensus R-loop sites called “R-loop regions” (RL regions). In the process, we revealed the stark divergence between S9.6 and dRNH-based R-loop mapping methods and identified biologically meaningful subtypes of both constitutive and variable R-loops. Taken together, this work provides a much-needed method to assess R-loop data quality and reveals intriguing aspects of R-loop biology.

Exploration and analysis of R-loop mapping data with RLBase

“…Following generation of processed data files (peaks, coverage, expression quantification, and quality statistics), downstream data processing was initiated to generate the final RLBase data. This involved (1) R-loop forming sequences (RLFS) analysis, (2) quality model building, (3) sample classification, (4) R-loop consensus analysis, (5) peak annotation and enrichment testing, (6) Expression matrix generation, (7) R-loop region annotation, (8) sample-level correlation analysis, (9) R-loop region abundance matrix generation, (10) calculating Rloop/expression correlation, (11) updating RLHub, (12) updating the RLBase genome browser trackhub, (13) RLSeq analysis of every sample, (14) upload of all data to the RLBase AWS S3 bucket.…”

“…Analyze R-loop forming sequences (analyzeRLFS) R-loop forming sequences are regions of the genome with sequences that are favorable for R-loop formation (41). They are computationally predicted with the QmRLFS-finder.py software program (42) and serve as a test of whether a sample has mapped R-loops (11). The analyzeRLFS function provides a simple permutation testing method for analyzing the enrichment of RLFS within a provided peakset.…”

“…The analyzeRLFS function provides a simple permutation testing method for analyzing the enrichment of RLFS within a provided peakset. The full analysis procedure is described in detail in our recent work (11).…”

“…The "R-Loop Regions" tab (Figure S7) provides the tools necessary to explore consensus sites of R-loop formation (RL regions) derived from high-confidence RLBase samples, as described in our recent work (11).…”

“…R-loop mapping studies apply high-throughput sequencing (HTS) to examine the causes and consequences of R-loops under differing biological conditions. However, each study is limited by the narrow definition of the biological system which it examines and by the limitations of the techniques used in R-loop mapping (11). The mining of public datasets can reveal insights into the fundamental biology of a molecular system, as was previously demonstrated in the transcriptomic (12) and epigenomics (13) fields.…”