Two Distinct Types of RNA:DNA Hybrids Revealed by Evaluating Divergent “R-loop” Profiles Detected with Different Mapping Strategies

R-loop, a three-stranded nucleic acid structure, has been recognized to play pivotal roles in critical physiological and pathological processes. Multiple technologies have been developed to profile R-loops genome-wide, but the existing data suffer from major discrepancies on determining genuine R-loop localization and its biological functions. Here, we experimentally and computationally evaluate eight representative R-loop mapping technologies, and reveal inherent biases and artifacts of individual technologies as key sources of discrepancies. Analyzing signals detected with different R-loop mapping strategies, we note that genuine R-loops predominately form at gene promoter regions, whereas most signals in gene body likely result from structured RNAs as part of repeat-containing transcripts. Interestingly, our analysis also uncovers two classes of R-loops: The first class consists of typical R-loops where the single-stranded DNA binding protein RPA binds both the template and non-template strands. By contrast, the second class appears independent of Pol II-mediated transcription and is characterized by RPA binding only in the template strand. These two different classes of RNA:DNA hybrids in the genome suggest distinct biochemical activities involved in their formation and regulation. In sum, our findings will guide future use of suitable technology for specific experimental purposes and the interpretation of R-loop functions. To evaluate DRIP-seq, we implemented an RNase H treatment step after (rather than before, as with DRIP-seq) S9.6 IP and before sonication to deduce RNase H sentative DRIP-seq peak regions. We also sought for independent approaches to evaluate the performance of different technologies. Replication protein A (RPA) is a primary candidate that has been implicated in DNA replication/repair pathways and reported as part of R-loops. Therefore, we performed strand-specific RPA ChIP-seq and correlated its binding profile with peaks detected by R-loop mapping technologies.