R-loops are features of chromatin consisting of a strand of DNA hybridized to RNA, as well as the expelled complementary DNA strand. R-loops are enriched at promoters where they have recently been shown to have important roles in modifying gene expression. However, the location of promoter-associated R-loops and the genomic domains they perturb to modify gene expression remain unclear. To resolve this issue, we developed a bisulfite-based approach, bisDRIP-seq, to map R-loops across the genome at near-nucleotide resolution in MCF-7 cells. We found the location of promoter-associated R-loops is dependent on the presence of introns. In intron-containing genes, R-loops are bounded between the transcription start site and the first exon-intron junction. In intronless genes, the 3' boundary displays gene-specific heterogeneity. Moreover, intronless genes are often associated with promoter-associated R-loop formation. Together, these studies provide a high-resolution map of R-loops and identify gene structure as a critical determinant of R-loop formation. Genes contain coded instructions for making proteins. When the cell needs to use a gene, molecular machinery assembles near the start of the gene in regions called promoters. Part of this machinery then reads along the gene, making a copy of the code in the form of a DNA-like molecule called RNA. These RNAs typically contain regions called exons, which carry the instructions, interspersed with spacer regions called introns. As RNAs are made they are 'spliced' to chop out the introns, leaving behind the final instructions.Most DNA exists in a double helix shape with two connected DNA strands, but the regions near the start of genes often contain structures called R-loops. In these structures, one strand of the DNA partners up with a single strand of RNA, forcing the other strand to bulge out on its own. Their location at gene promoters indicates that R-loops could change the cell's use of genes by impacting the machines that assemble near the start of genes. However, R-loops are not well understood. A major barrier to understanding the role of R-loops is that we do not know exactly where they are with respect to the start of genes.Dumelie and Jaffrey now report a new method to map R-loops almost to the resolution of single letters of the DNA code – a method which they called bisDRIP-seq. The approach extends an existing technique called DRIP-seq, which uses antibodies to capture DNA sequences stuck to strands of RNA. It can find R-loops, but it cannot tell the difference between the loop itself and the DNA surrounding it. The new technique uses a chemical called bisulfite to alter the DNA letters. It only affects the loop of the R-loop because the RNA shields the other strand. Sequencing then pinpoints the modified letters, revealing the exact location of the loop.For human cells grown in the laboratory, the technique found that R-loops form between the start of the gene and its first intron. Some genes do not have any introns, and in these cases, the R-loops extended deep into the code. Most human genes have only a small amount of DNA between the start site and the first intron, which may act to limit the effect of R-loops in these genes.This new technique allows the high-resolution study of R-loops, and could help to reveal their role in regulating genes. Abnormal R-loops have already been linked to a small set of human diseases like fragile-X syndrome. As the tools to study R-loops improve, it is possible that scientists will make connections to other diseases. In time, improved understanding of these structures could lead to better diagnosis, and eventually treatment, for these conditions. R-loops are nucleic acid structures in which a strand of RNA is hybridized to a strand of DNA, while the other strand of DNA is looped out. Recent techniques for genome-wide mapping of R-loops revealed that promoter regions are enriched in R-loops . The preA major unanswered question is the precise location of these R-loops. The location of an R-loop in a gene is likely to dictate how that R-loop can impact promoter function. This is because eukaryotic promoter regions contain multiple functional domains that have distinct roles in transcription, including transcription start sites, transcription factor-binding sites, exon-intron junctions, CpG islands, and nucleosome-associated DNA . The locA major barrier to discovering the exact location of R-loops is the low resolution of current genome-wide R-loop mapping methods like DRIP-seq (DNA-RNA immunoprecipitation sequencing) . This meBased on DRIP-seq and similar R-loop mapping methods, promoter-associated R-loops are thought to form within a few kilobases downstream of the transcription start site . From thMALAT1, NEAT1, and the replication-dependent histone genes. In some of these genes, the R-loops are associated with well-defined 3’ boundaries that are located within the gene body. Thus our high-resolution map of promoter-associated R-loops defines the boundaries of R-loops and suggests a role for first exon length in regulating the formation of R-loops.To understand where R-loops are positioned in genomic promoter regions, we developed bisDRIP-seq (bisulfite-DNA-RNA immunoprecipitation sequencing). bisDRIP-seq is an approach to map R-loops at near-nucleotide resolution throughout the genome. In this approach, we use bisulfite to selectively convert cytosine residues into uracil residues within genomic DNA regions that contain single-stranded DNA. We then identify single-stranded regions likely to be in R-loops based on preferential labeling of one strand of DNA and the requirement that the labeling be transcription dependent. Remarkably, we find that promoter-associated R-loops are typically bounded by the transcription start site and the first exon-intron junction in intron-containing genes. Thus, we find that the maximum size of promoter-associated R-loops is controlled by the location of the first exon-intron junction in intron-containing genes. We also identify prominent promoter-associated R-loop forming regions in intronless genes, including It is not yet possible to define the exact size and location of R-loop-forming regions on a genome-wide scale. In contrast, it is possible to determine the exact location of a specific R-loop-forming region in a specific gene using a previously developed bisulfite mapping approach . EssentiThis use of bisulfite to detect single-stranded regions of DNA contrasts with the use of bisulfite in mapping 5-methylcytosine in DNA. For 5-methylcytosine mapping, DNA is denatured into single strands and then all cytosines are converted to uracils, while 5-methylcytosine is poorly converted . Thus, wThe use of bisulfite to map R-loops on a genome-wide scale poses several significant challenges. First, non-physiological R-loop formation, removal, expansion, or contraction can occur between the time of lysis and the time when bisulfite is used to mark single-stranded DNA . Second,To map R-loops at near-nucleotide resolution, we developed a genome-wide bisulfite-based approach called bisDRIP-seq . This meFinally, to overcome the problem of stochastic cytosine conversions, a computational pipeline was developed to identify single-stranded regions using bisDRIP-seq data. This pipeline was developed to identify regions with high concentrations of cytosine-to-uracil conversions. This computational pipeline also identifies single-stranded regions that are likely to contain R-loops as opposed to other single-stranded DNA structures in the genome. Additionally, the pipeline was designed to reveal the specific strand orientation of R-loops.Thus, bisDRIP-seq provides an approach to map R-loops at near-nucleotide resolution on a genome-wide scale.We used bisDRIP-seq to map single-stranded DNA throughout the genome. Thirteen bisDRIP-seq experiments were performed on different samples of MCF-7 cells. After performing bisDRIP-seq on these samples, the DNA fragments were sequenced using a traditional post-bisulfite library preparation method .We next aligned the sequenced reads to the genome using Bismark, an alignment approach typically used for 5-methylcytosine mapping . This waWe next applied our bioinformatic pipeline to distinguish the multiple conversions seen in single-stranded DNA regions from the stochastic conversions due to background noise.In the first part of this pipeline, the stochastic rate of conversions was estimated. This was estimated based on the overall cytosine-to-uracil conversion rate in a given sample. Reads with a high percentage of conversions were excluded from this calculation since it was assumed that those conversions were not stochastic.Next, we generated a ‘bisDRIP-seq score’ for each read. This score was calculated based on the number of cytosines converted in the read, relative to the likelihood of observing that number of conversions by stochastic noise . Next, the score was normalized so that the sum of bisDRIP-seq scores within each sample was the same.Next, we calculated bisDRIP-seq scores for individual nucleotide positions in the genome. The bisDRIP-seq score for each nucleotide position was calculated as the sum of the bisDRIP-seq scores of all of the reads that overlapped with that nucleotide position. These scores provide a read-length resolution map of single-stranded DNA that filters out stochastic conversions and that is more comparable between genomic regions .Importantly, bisDRIP-seq scores were substantially reduced at specific gene loci in samples treated with RNase H, which degrades RNA in RNA-DNA hybrids . This suWe next performed two basic tests of the quality of data produced by bisDRIP-seq:First, we asked whether reads with high bisDRIP-seq scores are randomly distributed across the genome or whether they are clustered in specific regions. To simulate a random distribution, Monte Carlo simulations were applied to our data. Relative to these Monte Carlo simulations, bisDRIP-seq scores were found to be clustered in specific regions .−16, Spearman's rank-correlation test, Second, we performed correlation tests between the bisDRIP-seq scores obtained in each of our samples. In all cases, there was significant correlation between bisDRIP-seq samples . Thus, bisDRIP-seq scores are enriched in active promoter regions.We next asked if the bisDRIP-seq scores in promoter regions are correlated with promoter activity. Promoter activity was assessed using existing GRO-seq datasets from MCF-7 cells cultured in a similar manner to our cells . GRO-seqNotably, the enrichment of bisDRIP-seq scores in active promoter regions was reduced when RNA synthesis was blocked by triptolide treatment .Together, these results confirm that bisDRIP-seq scores, like DRIP-seq reads, are enriched in active promoter regions.We next wanted to determine if the bisDRIP-seq score enrichment in promoter regions is due to co-transcriptional R-loops. Conceivably, cytosine conversions could occur if single-stranded DNA is exposed as a result of other single-stranded DNA structures near transcription start sites, including unwound DNA due to supercoiling , G-quadrRelative to other single-stranded DNA structures, R-loops are known to produce a specific cytosine conversion signature upon bisulfite treatment. Specifically, cytosine conversions are limited to one strand of DNA in an R-loop. In other types of genomic structures that expose single-stranded DNA, cytosines on both strands of DNA can be converted. Thus efforts to map R-loops with bisulfite must demonstrate preferential labeling of one strand of DNA .Cytosine conversions can also occur as a result of two types of R-loops: ‘sense-strand R-loops’ and ‘antisense-strand R-loops.’ Sense-strand R-loops refer to R-loops in which the RNA component of the R-loop is transcribed from the annotated promoter in the expected direction. In these R-loops, the non-template strand of DNA is exposed for bisulfite-mediated conversion. Antisense-strand R-loops, on the other hand, contain RNA that is transcribed from the opposite DNA strand. In this case, the template strand of DNA for the canonical gene transcript would be modified by bisulfite. Thus, the specific strand of DNA that exhibits bisulfite-mediated cytosine conversions indicates if a sense-strand R-loop or antisense-strand R-loop is present.In order to distinguish between R-loops and other single-stranded DNA structures, we took advantage of the cytosine conversion strand asymmetry that is expected to result from R-loops. In other types of genomic structures that expose single-stranded DNA, cytosines on both strands of DNA can be converted.To determine if the observed bisDRIP-seq enrichment downstream of the transcription start site in promoter regions is caused by sense-strand R-loops, we repeated our comparison of bisDRIP-seq score with promoter activity. However, here bisDRIP-seq scores were plotted separately for the template and non-template strands of DNA . With inNotably, the higher bisDRIP-seq scores on the non-template strand than the template strand were largely eliminated in triptolide-treated samples . This suThe exact starting position and ending positions of promoter-associated R-loops remains unclear. This is due to the low resolution of conventional R-loop mapping methods . We wantWe first asked where R-loops are located in relation to transcription start sites. In many promoters, transcription initiates from multiple nearby transcription start sites . This crWe first mapped R-loops relative to all transcription start sites using metaplots of bisDRIP-seq scores. First, bisDRIP-seq scores were calculated for each nucleotide position surrounding the transcription start site in all individual promoter regions. Promoter regions were defined using the GENCODE database described above. Then, the bisDRIP-seq score at a given nucleotide position relative to the transcription start site was summed across all promoter regions. These scores were then plotted separately for the non-template strand and the template strand .The resulting metaplot suggests that a mix of R-loops and other single-stranded structures surround the transcription start site. The presence of single-stranded DNA at transcription start sites is suggested by the peak of bisDRIP-seq scores near the transcription start site . The locWe next asked whether the R-loops bounded by the transcription start site are dependent on transcription. We repeated our metaplot analysis using the samples treated with triptolide. In these samples, there was minimal difference between the template and non-template strand bisDRIP-seq scores . The smaThe transcription start site boundary was even more apparent after applying a background correction. To do this, we defined an ‘R-loop signal,’ which reflects the difference in bisDRIP-seq labeling of the non-template strand from the template strand after using the triptolide-treated samples for background correction. Thus, the template strand metaplot from the triptolide-treated sample was subtracted from the template strand metaplot from the control sample. The same background correction was used for the non-template strand metaplot. This background correction further enhances the demarcation of the transcription start site as the 5’ boundary of promoter-associated R-loops . Next, wWe repeated this analysis on promoters that have either high promoter activity or low promoter activity . A peak Notably, the observed difference in strand bisDRIP-seq scores 3' of the transcription start site in active promoters is lost after RNase H treatment . This coTaken together, these data indicate that the transcription start site demarcates the 5’ boundary of promoter-associated R-loops. Additionally, since only noise was observed from promoter regions with low promoter activity, these regions were removed from future analysis unless otherwise noted.We next wanted to know if there is a 3’ boundary to R-loops. Based on the metaplot analysis in We therefore asked where R-loops are located relative to the first exon-intron junction. We used the 5' end of the first intron as the reference point for a metaplot of bisDRIP-seq scores. Intronless genes were not considered in this analysis. In these metaplots, we observed that the bisDRIP-seq scores on the non-template strand are significantly higher than on the template strand immediately 5' of the first exon-intron junction . This stWe further tested the idea that the first exon-intron junction is the 3’ boundary of R-loops. To do this, we asked if the size of the R-loop-forming region in promoter regions increases with the length of the first exon. To test this question, metaplot analysis was repeated on groups of promoter regions with different sized first exons. First, promoter regions were binned into five groups based on the annotated size of the first exon. Then, metaplots were created of the R-loop signal centered around either the transcription start site or the first exon-intron junction . StrikinWe next asked if we could map the location of the 3' R-loop boundary at near-nucleotide resolution. bisDRIP-seq scores are shared across entire reads, which limits the resolution of using bisDRIP-seq scores to read length resolution. On the other hand, cytosine-to-uracil conversions should map R-loops at near-nucleotide resolution. We therefore generated a metaplot of the strand asymmetry of cytosine conversions relative to the first exon-intron junction. First, the number of conversions on either strand were background corrected by subtracting the number of conversions observed in our triptolide bisDRIP-seq data. Next, the log ratio of conversions on the non-template strand relative to the template strand was plotted at each site relative to the exon-intron junction . In thisWe next asked whether other exon-intron junctions also act as R-loop boundaries. In particular, we focused on the junctions between the first intron and the second exon, as well as the junction between the second exon and the second intron. We repeated our metaplot analysis by plotting R-loop signal at each position relative to the given exon-intron or intron-exon junction . No cleaTogether, these results suggest that there is a boundary to R-loop formation located within base pairs of the first exon-intron junction. We noticed that there is negative R-loop signal upstream of the transcription start site . This coWe considered that antisense transcription could lead to antisense-strand R-loops that generate these negative R-loop signals. To test this, we calculated the antisense-transcription activity of each promoter region upstream of the transcription start site using the previously described GRO-seq dataset . In promWe next used the promoters that showed the highest level of antisense-transcription promoter activity to generate a metaplot of R-loop signal. In this metaplot, the negative R-loop signal was prominent upstream of the transcription start site . Taken tWe next wanted to identify the promoters that show the strongest association with transcription-dependent R-loops. Any unique features associated with these promoters may be directly related to the R-loops forming at these promoter regions. We searched for promoter regions with two major features: First, we searched for promoter regions that showed disproportionately high bisDRIP-seq score on the non-template strand compared to the template strand. Second, we searched for promoter regions where the majority of the bisDRIP-seq score on the non-template strand was lost upon triptolide treatment. In this analysis, we noticed a set of promoter regions that exhibited both of these features . We therMALAT1, NEAT1, RPPH1, and CTB-58E17.1. We noticed that two other top hits, the protein-coding genes RHOB and JUNB, are also intronless genes.Two important classes of genes were identified in this analysis and both lack introns. First, six of the top 25 genes (24%) and nine of the top 50 genes (18%) are replication-dependent histone genes. The second class of genes encode intronless noncoding RNAs, including In general, intronless genes appear to be enriched among the promoters that show strong association with R-loop structures. In total, 44% of the top 25 genes in our list are intronless, compared to approximately 2% of long non-coding RNAs (lncRNAs) and 3% of protein-coding genes . The preThe presence of R-loops in the replication-dependent histone genes is potentially interesting given how these genes are regulated. As their name suggests, these histone genes are regulated in a cell-cycle dependent manner . They alWe first considered the possibility that the prominent R-loop signal detected in histone promoter regions simply reflects high promoter activity in these genes. However, histone genes consistently had higher R-loop-associated bisDRIP-seq scores than the majority of genes with similar promoter activity . This suSimilar analysis indicated that nuclear RNA levels and recruitment of RNA polymerase II also do not explain the R-loop signal observed in histone genes .We next examined the prevalence of R-loops and other single-stranded DNA structures in the promoter regions of histone genes. We repeated our metaplot analysis using the bisDRIP-seq scores from the entire class of replication-dependent histone genes . In the HIST1H1E, R-loops seemed to cover nearly the entire gene  were cultured for three days at 37°C in 50 ml of Gibco's phenol red-free Dulbecco's Modified Eagle's Medium  containing 5% charcoal-stripped fetal bovine serum . Media was replaced after two days. In these experiments, we included MCF-7 cells treated for different amounts of time with 100 µM estrogen  prior to lysis to ensure transcriptional activation at a broad set of genes . The useEach of the thirteen control-treated bisDRIP-seq experiments and each of the two triptolide-treated bisDRIP-seq experiments were performed using a separate dish of MCF-7 cells. Thus, each bisDRIP-seq replicate used a different sample of MCF-7 cells that had, at a minimum, been cultured on a separate dish for three days.In the bisDRIP-seq protocol, R-loops are treated with bisulfite during cell lysis under non-denaturing conditions. The basic concept underlying this approach is that bisulfite can interact with single-stranded DNA, but cannot interact with DNA in a double helix . This coPrior to performing bisDRIP-seq, lysis buffer was prepared containing 55.5 mg of ammonium sulfite monohydrate , 760 µl of 45% ammonium bisulfite , 85.5 µl of 20 mM hydroquinone  and 62.7 µl of 0.5 M ethylenediaminetetraacetic acid (EDTA) in a final volume of 2.85 ml. Lysis buffer was prepared using degassed water.Additionally, ammonium bisulfite solution was prepared containing 0.67 g ammonium sulfite monohydrate, 2.08 g of sodium bisulfite , 5 ml of 45% ammonium bisulfite in a final volume of 6 ml. This ammonium bisulfite solution was prepared using degassed water, following the methodology described by Immediately prior to cell lysis, media was removed from cells. Cells were washed three times in ice-cold PBS. Cells were then scraped off of plates into ice-cold PBS. These cells were then transferred to a 15 ml conical tube. The conical tube containing cells was centrifuged for 3 min at 300 g . PBS was then removed from cells.Next, cells were lysed in the presence of bisulfite. Bisulfite was included in the lysis buffer to achieve rapid single-stranded DNA modification that would minimize R-loop expansion or contraction. First, lysis buffer (1.5 ml) was added to the cells. Cells in lysis buffer (475 µl) were added to each of two microcentrifuge tubes, each of which contained 25 µl of lithium dodecyl sulfate. Samples were then incubated in a shaking incubator for 30 min at 37°C at 1100 rpm, while being protected from light.Next, the concentration of bisulfite in samples was increased in order to increase the rate of the irreversible second hydrolytic deamination step in the bisulfite conversion process . The conNext, bisulfite was removed from the samples through dialysis. First, samples were added to 2.5 ml of dialysis buffer  in a 15 ml conical centrifuge tube. Samples were then added to an Amicon Ultra-4 Centrifugal Filter Unit with a 30,000 nominal molecular weight limit . Next, the samples were centrifuged following the instructions of the centrifugal filter unit manufacturer. After centrifugation, fresh dialysis buffer was added to the centrifugal filter unit and centrifugation was repeated. This dialysis process was repeated until the sample was nominally dialyzed at least 200 fold. Proteinase K buffer  was then added to the sample and centrifugation was repeated. This dialysis process was repeated until the sample was nominally dialyzed at least 5000 fold in Proteinase K buffer. At this point, the samples were resuspended in 1 ml of Proteinase K buffer and transferred to a microcentrifuge tube.Next, we used Proteinase K  to degrade the proteins in our samples. In order to treat our samples with Proteinase K, 50 µl of 20% SDS and 200 µl of 20 µg/µl Proteinase K was added to each sample. Samples were then incubated overnight at 37°C while being rotated end over end.Nucleic acids were purified from any remaining proteins present in the sample using phenol-chloroform. Samples were split into two microcentrifuge tubes. An equal volume of Phenol-chloroform  was added to each sample. Samples were mixed and then centrifuged for five minutes at 18400 g. The aqueous phase of each sample was transferred to a new microcentrifuge tube. Each sample was then combined with an equal volume of chloroform. Samples were mixed and centrifuged for two minutes at 18400 g. The aqueous phase of each sample was transferred to a new microcentrifuge tube. Samples were then ethanol precipitated for 2 hr at 4°C. The precipitated DNA was then transferred and combined in a new tube by swirling the DNA around a pipette tip. Finally, the DNA was washed with 70% ethanol.2, 100 µg/ml bovine serum albumin). The samples were then digested overnight at 37°C by a cocktail of restriction enzymes, which included: 200 U/ml HindIII, 200 U/ml EcoRI, 100 U/ml BsrGI, 200 U/ml XbaI and 50 U/ml SspI  in a total volume of 900 µl.Next, the DNA was fragmented using restriction enzymes. The precipitated DNA was resuspended in 850 µl of NEB buffer 3.1  and a SpectraMax M2 Microplate Reader  following the instructions provided by the Quant-iT dsDNA Assay kit manufacturers.Next, RNA-DNA hybrids were enriched using an immunoprecipitation protocol modeled after the DRIP-seq methodology described by Next, the Dynabeads Protein G and immunoprecipitated DNA were washed. First, the supernatant was removed after applying the sample to a magnet for 3 min. Next, beads were washed three times in 750 µl of immunoprecipitation buffer. Each wash lasted 10 min and during the wash the samples were mixed end over end at 4°C. On each occasion, the supernatant was removed after applying the sample to a magnet for 3 min.Next, the bisulfite reaction was completed. First, we added 150 µl of 0.3 N NaOH to the samples. The samples were then incubated for 20 min at 37°C. This step should complete the bisulfite reaction. We then neutralized the NaOH by adding 150 µl of 0.3 N HCl and 17.5 µl of 1 M Tris pH 8.0.2 and 3 µl of 5 U/µl RNase H  to each sample. The samples were then incubated for 20 min at 37°C. To ensure that all DNA fragments were eluted, we added 2 µl of 20 µg/µl of Proteinase K to each sample. The samples were then incubated for 1 hr at 50°C. Finally, a magnet was added to each sample for 3 min and the supernatant containing the eluted DNA was transferred to a new microcentrifuge tube.Next, we eluted the immunoprecipitated DNA. First, 0.5 µl of 100 U/µl RNase I was added to each sample. Samples were then incubated for 20 min at 50°C. RNAs still bound to DNA were degraded by adding 1 µl of 0.5 M MgClNext, DNA was extracted from the supernatant using phenol/chloroform. Prior to adding phenol/chloroform, 150 µl of water was added to each sample. Samples were then combined with 500 µl of phenol/chloroform. Samples were mixed thoroughly and then centrifuged at 18400 g for 5 min. 400 µl of the aqueous phase was transferred to a new microcentrifuge tube and then 400 µl of chloroform was added to this aqueous phase. These samples were mixed and then centrifuged at 18400 g for 2 min. The supernatant was transferred to a new microcentrifuge tube. Finally, the DNA in the sample was ethanol precipitated in the presence of 20 µg of glycogen for 48 hr.In order to treat cells with triptolide , 20 mM triptolide was prepared in dimethyl sulfoxide.Triptolide was added to the media covering the MCF-7 cells at a concentration of 1 µM. The plates containing the cells were gently swirled to evenly distribute the triptolide. The plates were then incubated for 2 hr at 37°C. Following this triptolide treatment, bisDRIP-seq was performed as described above.Each of the two triptolide-treatment biological replicates was performed on a separate plate of cells.Two input bisDRIP-seq samples were prepared using the bisDRIP-seq protocol until the S9.6 antibody immunoprecipitation step. Instead of adding the digested DNA to S9.6 antibody, 15 µl of 3 N NaOH was added to 0.2 µg of digested DNA in 135 µl of water. The samples were then incubated for 20 min at 37°C. This step should complete the bisulfite reaction. We then neutralized the NaOH by adding 150 µl of 0.3 N HCl and 17.5 µl of 1 M Tris pH 8.0.2 and 3 µl of 5 U/µl RNase H  to each sample. The samples were then incubated for 20 min at 37°C. Next, we added 2 µl of 20 µg/µl of Proteinase K to each sample. The samples were then incubated for 1 hr at 50°C. Finally, the DNA in the sample was extracted using phenol-chloroform. After this point, the bisDRIP-seq protocol was followed as described above.Next, we repeated the elution treatment that was applied to the bisDRIP-seq samples. First, 0.5 µl of 100 U/µl RNase I was added to each sample. Samples were then incubated for 20 min at 50°C. RNAs still bound to DNA were degraded by adding 1 µl of 0.5 M MgClBoth an RNase H-treated sample and matched control sample were prepared using a single plate of MCF-7 cells. These cells were treated as described in the initial steps of the bisDRIP-seq protocol described above. These initial steps included all steps until and including fragment ion of the DNA with restriction enzymes and confirmation of the digest with an agarose gel,After those initial steps and immediately prior to immunoprecipitating RNA-DNA hybrids with S9.6 antibody, our sample was split into two samples. RNase H was added to one sample to a final concentration of 0.05 U/µl. We now call this sample the ‘RNase H-treated sample’. Dithiothreitol (at the same concentration as in the RNase H enzyme solution) was added to the other sample to a final concentration of 10 µM. RNase I was also added to both samples to a final concentration of 0.1 U/µl in order to prevent non-specific interactions between RNAs in solution and the S9.6 antibody . Samples2. In each of these final two wash steps, RNase H was added to a final concentration of 0.5 U/µl to the ‘RNase H’ treated sample, while dithiothreitol was added to a final concentration of 50 µM to the matched control sample. Each wash lasted 20 min and during each wash the samples were mixed end over end at 37°C. On each occasion, the supernatant was removed after applying the sample to a magnet for 3 min.Next, we adjusted the immunoprecipitation wash steps to allow for further RNase H treatment. First, three wash steps were performed in immunoprecipitation buffer as described in the standard bisDRIP-seq protocol. Next, we added two additional wash steps using 200 µl of immunoprecipitation buffer supplemented with 3 mM MgClAfter these wash steps, the samples were treated with NaOH and eluted as described in the bisDRIP-seq protocol above.The delayed bisDRIP-seq sample was prepared largely following the bisDRIP-seq protocol, with a few modification.After cells were washed in PBS as described in the bisDRIP-seq protocol, 5 ml of lysis buffer was added. This lysis buffer did not include bisulfite and was instead composed of 1% SDS, 50 mM Tris pH 8, and 10 mM EDTA. Next, 1 ml of 20 µg/µl Proteinase K was added to the sample. Samples were then incubated overnight at 37°C while being rotated end over end.Next, nucleic acids were extracted using phenol-chloroform and ethanol precipitated as described in the bisDRIP-seq protocol.At this point, we treated the nucleic acids with bisulfite. First, the nucleic acids were dissolved in 3.3 mM EDTA. Next, 900 µl of ammonium bisulfite solution and 26.8 µl hydroquinone were added to the sample. The sample was then incubated in a shaking incubator for 2.5 hr at 37°C at 1100 rpm, while being protected from light.2) in a 15 ml conical centrifuge tube. Samples were then added to an Amicon Ultra-4 Centrifugal Filter Unit with a 30,000 nominal molecular weight limit . Next, the samples were centrifuged following the instructions of the centrifugal filter unit manufacturer. After centrifugation, fresh buffer 3.1 lacking BSA was added to the centrifugal filter unit and centrifugation was repeated. This dialysis process was repeated until the sample was nominally dialyzed at least 1000000 fold. At this point, the samples were resuspended in 850 µl of buffer 3.1 and the remaining steps of the bisDRIP-seq protocol were followed as described above.Next, the bisulfite was removed from the samples through dialysis. First, samples were added to 2.5 ml of buffer 3.1 lacking BSA . Samples were then fragmented using an S2-series Covaris ultrasonicator . Sonication was performed at 4°C using the following conditions: intensity 5, 10% Duty Intensity Factor, 200 cycles per burst and 140 s total treatment time.We next used a Pico-Methyl Seq Library Prep Kit  to create the DNA sequencing library. This kit is primarily designed for mapping 5-methylcytosine in small quantities of genomic DNA. We omitted the bisulfite-treatment steps. Instead, we specifically followed the library preparation steps as instructed by the manufacturer. These steps amplified our DNA and used random priming to add indexes to our DNA library that are compatible with Illumina's TruSeq technology.In preparation for sequencing, the quality of each DNA library was assessed using a 2100 Bioanalyzer  by the Weill Cornell Epigenomics Core . The concentration of DNA was measured using a Quant-iT dsDNA Assay Kit and a SpectraMax M2 Microplate Reader following the instructions provided in the Quant-iT dsDNA Assay kit.DNA libraries were sequenced by the Weill Cornell Epigenomics Core using a HiSeq 2500 System . Either five or six samples were loaded per lane. DNA libraries were sequenced following the manufacturer's instructions for single-index 100 bp paired-end read clustering.Sequencing data was first processed using CASAVA 1.8.2  to obtain the nucleotide sequence of reads in FASTQ format.We next aligned reads to the GRCh38 reference genome. Reads were aligned to the genome using the Bismark software library version 0.14.3 Bismark tool . The BisRead alignment was done in three phases. If we were unable to map a read in one phase, then we attempted to map the read in the following phase.In the first alignment phase, full paired-end sequencing reads were aligned to the genome. First, reads were trimmed to remove both low confidence sequences and the very ends of reads. This was done using Flexbar version 2.5  with theNext, the Bismark alignment tool was repeated on all reads that failed to map in the first alignment phase. In this second phase, the two ends of paired-end reads were treated as single-end reads. The settings employed for alignment did not allow mismatched nucleotides.In the final alignment phase, we attempted to align all reads that were not aligned to the genome in the first two phases after additional preprocessing. In this phase, we continued to treat both ends of paired-end reads as single-end reads. However, we began this phase by using Flexbar version 2.5 to remove all nucleotides 55 nt or further 3' of the start of the read. Reads were then aligned with the Bismark tool using settings that did not allow for mismatched nucleotides.Having aligned reads to the genome, we next removed putative read duplicates. To remove read duplicates, we ran the Bismark deduplicate_bismark tool.Finally, we determined if cytosines within aligned reads had been converted by bisulfite. To determine whether individual cytosines had been converted to uracils, we used the Bismark bismark_methylation_extractor tool. In the case of paired-end alignments, we used the –no_overlap setting. With single-end alignments, we used the --ignore_3prime 10 setting. This setting removed the final ten nucleotides of each alignment. These nucleotides were removed from aligned reads because we observed bias in the fraction of cytosines converted in this region of reads.Next, we developed a method to score reads based on the likelihood that a given read was single-stranded in our samples. This measure was intended to filter out conversions that occur due to spontaneous breathing of DNA. Additionally, we wanted to ensure that single-stranded reads containing large numbers of cytosines were not given greater value than single-stranded reads containing small numbers of cytosines. This method was applied to on each sample separately.For the purposes of calculating bisDRIP-seq scores, we define the ‘original number of cytosines’ as the sum of converted cytosines and unconverted cytosines.The goal of this method was to estimate the fraction of reads that had a set number of conversions due to chance alone. The inverse fraction was then defined as the bisDRIP-seq score for all reads with that number of conversions. Therefore, all of the reads from the same sample, with the same original number of cytosines, and with the same number of converted cytosines were given the same bisDRIP-seq score.First, we made a rough estimate of the background conversion rate for each sample. Initially, we calculated the total fraction of cytosines that were converted in our aligned reads. Next, we repeated this calculation after removing reads that had greater than 2.5 fold more conversions than the average across all reads. This value was used as an estimate of the background conversion rate.Next, we calculated the probability of observing a specified number of converted cytosines in a given read by chance. This probability was calculated using the binomial distribution. In this binomial distribution calculation, the size of the sample was the original number of cytosines in the read and the probability of a conversion was the estimated background conversion rate.Next, we calculated the number of reads expected to have a given original number of cytosines and a given number of converted cytosines. First, we calculated the total number of aligned reads with a given original number of cytosines. Next, we multiplied that number with the probability of observing the specified number of converted cytosines in a read with that original number of cytosines by chance.Next, a bisDRIP-seq score was calculated for each read. First, reads were binned together based on the original number of cytosines in the read and the number of converted cytosines in the read. Next, bins were grouped together if they had the same original number of cytosines. If a group of reads with a given original number of cytosines per read consisted of more than a thousand reads in total and the expected number of reads for each bin was above five, then we calculated the bisDRIP-seq for each bin as:This value was calculated first for reads with one original number of cytosines and then was calculated for reads with progressively higher numbers of original cytosines. If the expected number of reads for a bin was below five, then the bisDRIP-seq score was calculated for all reads with x number of converted cytosines using the equation:In this equation, the original number of cytosines (n) is held constant.If there were fewer than a thousand reads in a group, reads were scored differently. bisDRIP-seq scores with a specific number of conversions were given the same score as reads with the same number of conversions, but one fewer original number of cytosines. Given the total number of aligned reads per sample, the number of reads assigned scores on this basis was relatively small.https://github.com/champben2002/bisDRIPseq/Source code for calculation of bisDRIP-seq scores from read sequence is in bisDRIP-seq scores were normalized to ensure that the sum of the bisDRIP-seq scores were the same across samples after normalization. This normalization procedure assumes that there are no global differences in the amount of single-stranded structure between samples. First, the sum of all read bisDRIP-seq scores was calculated for each sample. Next, the normalized bisDRIP-seq score for each read was calculated for each read in a given sample using the formula:This normalization procedure was repeated for each sample separately.All bisDRIP-seq scores in the manuscript used this standard normalization procedure, unless otherwise noted.Promoter-associated R-loops have previously been associated with promoter regions enriched in guanines on the non-template strand . We did To test for whether our conclusions were affected by the cytosine content of reads, we applied a ‘cytosine normalization’ procedure to the raw bisDRIP-seq scores. First, reads within a sample were grouped based on the original number of cytosines in each read. Next, we normalized the reads within each group so that the average bisDRIP-seq score for each group would be identical. This normalization was done by multiplying the bisDRIP-seq score of each read within a group by a group-specific variable. Once this cytosine normalization was complete, the standard normalization procedure was followed as described above.We repeated our analysis using these cytosine-content normalized scores . Our conhttps://github.com/champben2002/bisDRIPseq/Source code for bisDRIP-seq normalization is included in processingbisDRIPseqreads.py, which has been deposited in NEAT1, we used the gene model from GENCODE version 24  with the GRCh38 human genome . The autrsion 24  of the lrsion 24  of the ibisDRIP-seq scores were calculated for each nucleotide in the genome. The score of a given nucleotide in the genome was calculated as the sum of the bisDRIP-seq scores of reads that aligned to that nucleotide. Notably, this score was strand specific. The bisDRIP-seq score of reads was associated with a given nucleotide only if that nucleotide was on the same strand as the cytosines converted in the read. These bisDRIP-seq scores were then plotted for specific genomic loci.HIST1H1E and HIST1H2BG genes. At each site, the mean number of adenines, cytosines, guanines and thymines in the surrounding region were plotted. The surrounding region included the nucleotide at the site, the twenty-five nucleotides 5' of the site and twenty-five nucleotides 3' of the site on the template strand.Local sequence composition was plotted for sites in the bisDRIP-seq scores were calculated for genomic regions using the following procedure:First, we summed the bisDRIP-seq scores of reads that completely aligned to the specified region.Next, reads partially in the specified region were partially added to the specified region's score. The fraction of the read's score added to the score of the specified region's score was determined by the fraction of the read's length that aligned to the region. Thus the fraction of the read's length contained within the region was multiplied by the read's score. The product of this calculation was added to the specified region's score. This was repeated for each read that partially aligned in the specified region. This procedure provided the region's final bisDRIP-seq score.In some of our analysis, bisDRIP-seq scores were calculated for only one strand within a specified region. In this case, the bisDRIP-seq scores from individual reads was only added to the specified region's bisDRIP-seq score if the read had the correct strand orientation. A read had the correct strand orientation if any converted cytosines in the read mapped to the correct strand.https://github.com/champben2002/bisDRIPseq/.Source code for calculating region scores is incorporated into regionbisDRIPseqscores.py, which has been deposited in Monte Carlo simulations were used to determine the expected distribution of bisDRIP-seq scores if they distributed randomly across the genome. Two simulations were performed.First, a simulation was performed in which reads were mapped to random regions across the genome. This simulates the possibility that both the RNA-DNA hybrid immunoprecipitation and the bisulfite mapping in bisDRIP-seq are non-specific. First, we split the genome into 1 kb regions. Next, we determined if any bisDRIP-seq reads aligned to a given 1 kb region. If no reads aligned to a given region, then that region was removed from further simulation steps. Reads were then associated with a random 1 kb region that was in the same chromosome to which they originally mapped. The region that each read was associated with was determined using the R 'random' package. This was repeated for each of our thirteen samples.Second, a simulation was performed in which bisDRIP-seq scores were randomly shuffled between reads. This simulates the possibility that the bisDRIP-seq score associated with each read was stochastic. For each sample, we randomly shuffled all of the scores associated with reads aligned to a given chromosome. The random shuffle of read scores was performed using the R 'random' package. This simulation was repeated for each chromosome and for each of the thirteen bisDRIP-seq samples.Next, bisDRIP-seq scores were calculated for each 1 kb region using either real bisDRIP-seq scores or using the products of one of the two simulations. This analysis used the same 1 kb regions discussed in the first Monte Carlo simulation.https://github.com/champben2002/bisDRIPseq/Source code for Monte Carlo simulations are included in Monte_Carlo_random_assign_reads_to_regions.py and Monte_Carlo_for_shuffling_bisDRIPseq_scores.py, which have been deposited in GENCODE's annotated list of transcription start sites  was usedFirst, the GENCODE comprehensive gene annotation release 25 was downloaded from GENCODE's website.Next, transcription start sites were removed from the transcription start site list if they were within a thousand base pairs of a higher priority transcription start site. The priority of transcription start sites was determined as follows:The highest priority transcription start sites were transcription start sites within 25 bp of a site identified by CAGE-seq. Transcription start sites near sites that had higher CAGE-seq signal were given a higher priority than transcription start sites near sites with lower CAGE-seq signal.Priority was next determined by the confidence annotation scores associated with each transcription start site in the GENCODE annotations.Finally, priority was established using a random number generated using the Python ‘random’ package.After removing lower priority transcription start sites, we had a final reference GENCODE transcription start site list. Promoter regions were then defined as the region within a thousand base pairs of each transcription start site in our reference GENCODE transcription start site list. See Source_data_file_1.xls for all transcription start sites in this final listIn later analysis, inactive promoter regions were removed. Unless noted otherwise, inactive promoter regions were defined as promoter regions with sense-strand promoter activity in the bottom eighty percentile between the transcription start site and + 1000 bp.In order to calculate the promoter activity of each promoter, we used publicly available GRO-seq data obtained from MCF-7 cells by Unless noted otherwise, promoter activity was defined by the mean number of sense-strand GRO-seq reads measured in the region between the transcription start site and + 1000 bp. The method for calculating the number of reads in this region of each promoter was similar to method used to calculate bisDRIP-seq scores for regions.For each promoter region, we first summed the number of GRO-seq reads that completely aligned to the region between the transcription start site and + 1000 bp.Next, reads partially in the specified region were partially added to the specified region's score. The fraction of the read's length contained within the region between the transcription start site and + 1000 bp was added to the promoter region's score. This was repeated for each read that partially aligned to the promoter region. This procedure provided the promoter region's final promoter activity.A similar procedure was followed to determine the antisense promoter activity and the promoter activity between the transcription start site and + 250 bp.Exon-intron junctions were extracted from the GENCODE comprehensive gene annotation release 25 . Exon-inNext, all exon-intron junctions were removed that were associated with inactive promoter regions. Inactive promoter regions did not contain positive R-loop signal in MALAT1 and NEAT1, were also removed.Exon-intron junctions associated with the intronless genes, This procedure provided a final reference set of exon-intron junctions. See First-exon length was calculated for each first exon-intron junction in the final reference set of first exon-intron junctions. The first-exon length was calculated as the distance between the transcription start site and the first exon-intron junction. This list of first-exon lengths was used to calculate the median length of all first exons for active promoter regions.In order to compare some external dataset to our data, it was necessary to convert the coordinates of sites or regions to the GRCh38 genome from an earlier version of the reference human genome. In all cases, this was accomplished using the UCSC utility liftOver .Cap analysis gene expression sequencing (CAGE-seq) data came from the publicly-available ENCODE MCF-7 dataset ENCFF207DXM .In order to compare bisDRIP-seq datasets with DRIPc-seq datasets, we used the DRIPc-seq data of In order to compare bisDRIP-seq datasets with DRIP-seq datasets, we used the DRIP-seq data of Total cell RNA-seq data displayed for individual genes came from ENCODE files ENCFF426WXY and ENCFF866OVQ . The RNANucleus RNA-seq levels of active genes in RNA Polymerase II levels were calculated using the ENCODE files ENCFF496YAE and ENCFF881YOO . These dFor DRIP-seq and bisDRIP-seq promoter region enrichment tests, only promoter regions in genes larger than 2 kb were considered. For each promoter region, a matched region was selected from the same gene. This matched region was centered on an exonic site more than 2 kb from the transcription start site. Promoter enrichment was then tested using the non-parametric Wilcoxon signed-rank test described below.Promoter region ranking was only performed on active promoter regions. Promoter activity was defined here based on the number of sense-strand GRO-seq reads that aligned to the promoter region between the transcription start site and +250 bp. Active promoter regions were promoter regions in the top twenty percentile of promoter regions in terms of promoter activity. This filter was intended to filter out inactive promoter regions, which appeared to only contribute noise to our analysis.Next, promoter regions were filtered on the basis of sense-strand bisDRIP-seq scores. The non-template strand bisDRIP-seq score had to be positive in the region between the transcription start site and + 250 bp in all control-treated biological replicates. Otherwise, the promoter region was removed from out list.The remaining promoter regions were scored based on the value:2 x (log2(control-treated non-template bisDRIP-seq score + 1) - log2(triptolide-treated non-template bisDRIP-seq score + 1)) - log2(control-treated template bisDRIP-seq score + 1))Loess smoothed plots were generated using the r fANCOVA package's loess.as algorithm with parameters ‘criterion = aicc’ and ‘family = gaussian’.Scatterplots were generated with the ggplot2 package geom_points algorithm, using the lm option to plot a linear regression line of the displayed points.Violin plots and jitter plots were created using the ggplot2 package. Jitter plots were created using geom_jitter with height = 0, width = 0.725. Violin plots were created using geom_violin with alpha = 0.5.bisDRIP-seq score metaplots were created in relation to reference points. These reference points were either transcription start sites or exon-intron junctions, depending on the analysis. The final metaplot associated a bisDRIP-seq score to each ‘location’ relative to a given reference point. Location refers to the distance from the reference point and whether the site is upstream or downstream of the reference site. For a given read, we determined the location of each nucleotide in the read relative to the proximal reference point. Next, the location of each nucleotide was associated with the bisDRIP-seq score of the read. After repeating this process for all reads, all of the scores associated with a given location were summed unless otherwise noted. This sum was then used as the bisDRIP-seq score for that location in the metaplot. Finally, each location was plotted against the bisDRIP-seq score associated with that location.https://github.com/champben2002/bisDRIPseq/.Source code for metaplot analysis is incorporated into bisDRIPseqmetaplotanalysis.py, which has been deposited in The conversion asymmetry metaplot was generated in relation to transcription start sites. The total number of conversions was summed at each site relative to the transcription start site across all active promoter regions. This was repeated for each sample, strand and sample type. Next, we calculated the mean number of conversions across samples for each position relative to the transcription start site. Next, we subtracted the mean number of conversions for each position relative to the transcription start site in triptolide-treated samples from the number of conversions observed in control-treated samples. Finally, we plotted the log ratio of the tripolide-corrected number of conversions on the non-template strand to the tripolide-corrected number of conversions on the template strand.To create a metaplot of the percentage of cytosines converted on the template strand, we calculated the number of cytosines and converted cytosines that aligned to a given site relative to the transcription start site. Next, the number of converted cytosines was divided by the total number of original cytosines that aligned to that site.DRIPc-seq metaplots were generated using essentially the same process as was used for bisDRIP-seq score. Instead of using the scores of reads, we used the read-count associated with regions in the wig file.In all plots of R-loop signal, R-loop signal =  - .In all plots where a log2 transformation was applied to a dataset, each value in the dataset was added to one prior to the log2 transformation.MALAT1 R-loop forming region, we selected all reads that: (1) aligned to the non-template strand of MALAT1, (2) start and end between the transcription start site and + 1600 bp, and (3) are larger than 75 bp long. Next, we randomly selected twenty-five reads. Reads were then plotted using ggplot2's geom_point function.To sample the reads from the Since we are not confident that our data follows a Gaussian distribution, we typically used non-parametric tests of significance. In particular, we applied the Wilcoxon signed-rank test whether various datasets were significantly different. Wilcoxon signed-rank tests were performed using the two-sided R wilcox.test algorithm using default parameters with the exception of the ‘paired’ setting which was set to ‘FALSE’.In the case of comparisons between metaplots, a conservative multiple-hypothesis correction was performed on the p-values derived from Wilcoxon signed-rank tests. This multiple-hypothesis correction involved multiplying the p-value of an individual nucleotide position by 2001, since the metaplots involved examining 2001 nucleotide positions simultaneously.The significance of correlations between datasets was examined using the Spearman's rank-correlation test with asymptotic t approximation. Spearman's rank-correlation tests were used since we are not confident that our data follows a Gaussian distribution and therefore wished to apply a non-parametric test of correlation. Spearman's rank-correlation tests were performed using the R cor.test algorithm.https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE98886For each sample, we deposited the sequence reads, the conversion frequency at each cytosine nucleotide and a bedGraph of bisDRIP-seq scores in GEO series GSE98886: Private token for reviewers: azyfacgczpertip In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Kevin Struhl as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Defining the location of promoter-associated R-loops at near-nucleotide resolution using bisDRIP-seq" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.As stated by the reviewers the new method to map the position of R-loops at higher resolution than existing techniques is important to the field. The approach entails converting cytosines to uracils in the displaced DNA strand with bisulfite, while the cytosines in the opposite strand are protected by RNA. However all reviewers felt that it is crucial to include an RnaseH control. In addition since this is a methods paper it is necessary to discuss the differences and advantages of the reported method to those that have been previously developed and how the method and/or results represent a significant advance over the literature. We look forward to see an improved manuscript.Reviewer #1:R-loops are three-stranded structures consisting of a DNA-RNA hybrid and a displaced single-stranded DNA. R-loops are involved in multiple cellular processes, including mitochondria DNA replication, immunoglobulin class switch recombination, and transcription regulation. However, when dysregulated, R-loops could challenge genome integrity by introducing mutations, single-strand breaks, stalled replication forks and double-strand breaks. Precise genome-wide mapping of R-loops may provide insights into their functions and dynamics.In this manuscript, the authors developed a new method to map R-loops at near-nucleotide resolution. Their new method, named bisDRIP-seq, takes advantage of the unique three-stranded structures of R-loops. After fragmentation, RNA-DNA hybrids are pulled down using antibody S9.6, and cytosines in the displaced single-stranded DNA are converted to uracils by bisulfite. The cytosine-uracil-conversions can be mapped to the genome at near-nucleotide resolution. While this reviewer finds the approach novel, it does not appear to provide significantly higher resolution over the existing DRIPc-seq method developed recently by Chedin and coworkers (PMID: 27373332). Major concerns are listed below. In particular, baseline cytosine-uracil-conversions without S9.6 pull-down should be performed for the purpose of background elimination. Furthermore, as stated below, RNase H treatment prior to immunoprecipitation should be performed to validate the specificity of the bisDRIP-seq signals.Major concerns:1) It is crucial to confirm signals from bisDRIP-seq are indeed R-loop dependent. Simple bisDRIP-seq and DRIP-seq correlation is not sufficient. BisDRIP-seq with in vitro RNase H treatment should be performed.2) In bisDRIP-seq, signals not only come from R-loops, but also from promoter single-stranded structures. Therefore it is important to include non-denatured cytosine-uracil-conversions (without S9.6 pull-down) as bisDRIP-seq input.3) Template-strand bisDRIP-seq scores were subtracted from non-template-strand bisDRIP-seq scores to artificially recover R-loop signals. However, no assay was used to prove the validity of such a method.4) Since previously reported DRIPc-seq already provides strand-specific, near base-pair resolution (PMID: 27373332), basic comparisons should be performed to test whether bisDRIP-seq outperforms DRIPc-seq in a significantly manner.Reviewer #2:The authors describe a new method to map the position of R-loops at higher resolution than existing techniques. The approach entails converting cytosines to uracils in the displaced DNA strand with bisulfite, while the cytosines in the opposite strand are protected by RNA. To minimize the amount of sequencing they focus on regions immunoprecipitated by the S9.6 antibody specific for RNA-DNA hybrids. After bioinformatic processing the authors obtain a genome-wide "bisDRIP-score" which measures the frequency of single-stranded DNA present at a given locus. Asymmetric single-stranded regions are considered R-loops. R-loops are correlated with transcription and enriched in the promoter region of active genes. The high resolution of bisDRIP-seq allowed the authors to identify the TSS as a 5' boundary and the first exon-intron junction as a 3' boundary. Finally, they report that histone genes and lncRNAs MALAT1 and NEAT1 have a particularly strong R-loop signature.eLife. There are some major issues that need to be addressed, mostly ensuring that the authors are truly measuring R-loops and that no biases are skewing their analyses.R-loops are believed to play important roles in gene regulation and the manuscript by Dumelie et al. describes a creative strategy to better map their genomic localization. The results are well presented and the methods are described clearly and in much detail. Overall I support publication in – BisDRIP score: I find the explanation of how this score was calculated rather convoluted. The choice of using the sum of the bisDRIP-score for cumulative anlayses seems questionable. This makes it impossible to compare axes when different number of regions are analyzed. For example 3C vs. 3E. Why not using the average?– Validations: because bisDRIP-seq is a new genome-wide technique a high level of confidence in its measurements is required before it can be employed for new discoveries. 1) The authors show significant correlation genome-wide between DRIP-seq (the established technique) and their new bisDRIP-seq, but the correlation is obviously not perfect. Can the authors identify examples in which their new technique and the old technique differ and independently verify that the new mapping is more accurate? 2) To validate DRIP-seq typically RNaseH is used to remove RNA-DNA hybrids and prove the specificity of the signal. I believe this would be a more convincing control than triptolide also for bisDRIP-seq.– Boundaries: the authors state that the TSS and the first exon-intron junction form 5' and 3' boundaries for R-loop formation, respectively, yet in several figures e.g.  I see si– Methylcytosines: it seems that the presence of methylated cytosines would make it difficult to detect R-loops in heavily methylated areas of the genome. Is this a problem? It should be discussed.– Genes with strong R-loop signals: the authors conclude that not having introns is a reason why some of the genes make in the top 25 list shown in Reviewer #3:In this manuscript, Dumelie et al. develop a new method, Bis-DRIP Seq, to identify the genomic localization of R loops at near-nucleotide resolution. Given the growing interest in understanding the function of R loops, this new method will benefit research in this area. Using this technique authors show that R loops are associated with the promoter regions of genes with or without introns. However, in genes with introns, 1st exon-intron junction defines the boundary of the R loop. We suggest a few minor experiments before publication of this manuscript.Specific comments:1) The authors highlight that BisDRIP-Seq provides more information about R loop boundaries as compared to previous DRIP-Seq datasets. This new method is an extension of the previously established DRIP-Seq and in theory it is understandable why it is an improvement. But since this is a 'methodology paper', the authors must provide comparative evidence that it is better. A figure showing how their dataset compares with a DRIP-Seq dataset will be beneficial.2) The authors show that the 1st exon-intron junction serves as the 3' R-loop boundary  which th Reviewer #1:R-loops are three-stranded structures consisting of a DNA-RNA hybrid and a displaced single-stranded DNA. R-loops are involved in multiple cellular processes, including mitochondria DNA replication, immunoglobulin class switch recombination, and transcription regulation. However, when dysregulated, R-loops could challenge genome integrity by introducing mutations, single-strand breaks, stalled replication forks and double-strand breaks. Precise genome-wide mapping of R-loops may provide insights into their functions and dynamics.In this manuscript, the authors developed a new method to map R-loops at near-nucleotide resolution. Their new method, named bisDRIP-seq, takes advantage of the unique three-stranded structures of R-loops. After fragmentation, RNA-DNA hybrids are pulled down using antibody S9.6, and cytosines in the displaced single-stranded DNA are converted to uracils by bisulfite. The cytosine-uracil-conversions can be mapped to the genome at near-nucleotide resolution. While this reviewer finds the approach novel, it does not appear to provide significantly higher resolution over the existing DRIPc-seq method developed recently by Chedin and coworkers (PMID: 27373332). Major concerns are listed below. In particular, baseline cytosine-uracil-conversions without S9.6 pull-down should be performed for the purpose of background elimination. Furthermore, as stated below, RNase H treatment prior to immunoprecipitation should be performed to validate the specificity of the bisDRIP-seq signals.The reviewer points out that our method to map R-loops at near-nucleotide resolution is novel. Still, the reviewer points out that the manuscript would be more compelling if we clearly showed how our method improves upon pre-existing DRIP-seq based techniques. Finally, the reviewer suggests several important experiments that could help us improve readers' confidence in the bisDRIP-seq method.Major concerns:1) It is crucial to confirm signals from bisDRIP-seq are indeed R-loop dependent. Simple bisDRIP-seq and DRIP-seq correlation is not sufficient. BisDRIP-seq with in vitro RNase H treatment should be performed.We fully agree with the reviewer's suggestion. We therefore performed an RNase H-control bisDRIP-seq experiment as suggested by the reviewer and as detailed below. We then demonstrated that R-loop signal was lost upon RNase H treatment. Additionally, we performed a matched control experiment to show that, absent RNase H, the same experiment with the same input material recapitulates our earlier results.The RNase H treatment experiment was performed by repeating the initial bisDRIP-seq steps, including lysing the cells in the presence of bisulfite, degrading proteins with Proteinase K treatment and fragmenting the DNA. RNase I was added to deplete the sample of single-stranded RNAs that could bind the S9.6 antibody . After this point, the sample was split into two. One sample received an RNase H treatment. This sample was treated with RNase H both immediately prior to RNA/DNA immunoprecipitation and during the final two wash steps of the immunoprecipitation. The other sample was treated in an identical manner, except that no RNase H was added to the sample. Sequencing libraries were then prepared from both samples using the bisDRIP-seq method.After performing this experiment, we compared the RNase H and matched control libraries to the libraries generated by our other bisDRIP-seq experiments. Importantly, we show that the R-loops in the MALAT1, NEAT1, RPPH1 and the histone genes lack signal in the RNase H-treated sample .To further demonstrate that RNase H depletes metaplot R-loop signal, the revised manuscript now includes metaplots of the bisDRIP-seq signal relative to transcription start sites from the RNase H and matched-control experiments . In our We thank the reviewer for this suggestion since this important control experiment strengthens our manuscript by validating that the observed bisDRIP-seq strand asymmetry is due to R-loops.2) In bisDRIP-seq, signals not only come from R-loops, but also from promoter single-stranded structures. Therefore it is important to include non-denatured cytosine-uracil-conversions (without S9.6 pull-down) as bisDRIP-seq input.We agree that sequencing the bisulfite-treated DNA used as input for the RNA-DNA hybrid immunoprecipitation helps ensure that artifacts are not contributing to our results.To address this concern, we prepared sequencing libraries from two separate bisDRIP-seq “input” samples. These samples were treated identically to our bisDRIP-seq experiments, except that no bisDRIP-seq immunoprecipitation steps were performed on these samples. We then sequenced these two libraries using the bisDRIP-seq methodology.We examined the amount of input bisDRIP-seq signal at individual R-loop forming regions. These regions included the RPPH1 gene, the histone gene loci, MALAT1 and NEAT1 promoter regions . In thesAs suggested by the reviewer, we also used the results of the input bisDRIP-seq experiments to perform background correction. In general, there was very little signal in promoter regions. As a result, the input background correction  did not We agree with the reviewer that this important validation of our method should provide readers with additional confidence in our methodology.3) Template-strand bisDRIP-seq scores were subtracted from non-template-strand bisDRIP-seq scores to artificially recover R-loop signals. However, no assay was used to prove the validity of such a method.We apologize that our manuscript did not sufficiently discuss the basis for using bisDRIP-seq score asymmetry as evidence for the presence of R-loops. We agree that providing additional evidence that R-loops lead to preferential bisDRIP-seq strand asymmetry adds additional clarity to the manuscript.We have taken two main steps to address the reviewer's suggestion.First, as discussed above, we now specifically show that strand asymmetry is lost when the sample is treated with RNase H . This prSecond, we have improved the wording regarding the discussion of the theoretical and experimental basis for using strand asymmetry to map R-loops. Non-denaturing bisulfite mapping of R-loops is one of the best-established methods for mapping R-loops and is used to map R-loops at individual gene loci . A critical aspect of this type of R-loop measurement is to show that bisulfite preferentially labels one strand relative to the other strand . We have included more of this discussion in the Results sections entitled “bisDRIP-seq concept” and “Transcription-dependent R-loops form downstream of transcription start sites” to give readers a better understanding of the historical basis for this approach.We hope these changes will provide further assurance to readers that comparing the reactivity of each strand to bisulfite provides a method to predict the location of R-loops.4) Since previously reported DRIPc-seq already provides strand-specific, near base-pair resolution (PMID: 27373332), basic comparisons should be performed to test whether bisDRIP-seq outperforms DRIPc-seq in a significantly manner.In our initial manuscript, we largely focused on comparing our data to the more established DRIP-seq technique. However, we agree with the reviewer that there is considerable value to comparing our technique to DRIPc-seq. The revised manuscript now has a figure  specificIn our new Results section, we discuss why DRIPc-seq, as performed on human cells in (PMID:27373332) by Sanz et al., is not expected to provide near-nucleotide resolution of R-loops. Most significantly, as described by Sanz et al., 2016, the technique is essentially a simple RNA immunoprecipitation sequencing (RIP-seq) experiment. Thus, both the RNA incorporated in the RNA-DNA hybrid and the surrounding RNA is immunoprecipitated by the S9.6 antibody and sequenced. This should reduce the resolution of DRIPc-seq to the full length of the RNA. The evidence that Sanz et al. provide for the method's improved resolution derives from the fact that they observe smaller peak sizes than they had observed with DRIP-seq. However, the strategy employed by Sanz et al., should theoretically map to the entire RNA and not just the portion of the RNA in an RNA-DNA hybrid. We believe this is important context prior to actually examining the data from each method.In order to directly compare our data to the data from DRIPc-seq, we plotted a metaplot of DRIPc-seq signal overlapped with bisDRIP-seq R-loop signal . Our mapWe also compared the bisDRIP-seq R-loop map to the DRIPc-seq map in individual genes. In We also address whether the time at which the R-loop is mapped in the two protocols influences the resolution of R-loop mapping. In bisDRIP-seq, R-loop labeling commences at the cell lysis step since the lysis buffer contains bisulfite. In all other protocols, the location of R-loops is measured by immunoprecipitation several hours or days after cell lysis. It therefore remains possible that R-loops could expand, contract, or disappear ex vivo, which could affect the results of conventional R-loop mapping methods. While this issue is not discussed extensively in the modern literature, it was shown in the 1970s intron-mapping R-loop literature that R-loops can be unstable in solution . Thus delaying the measurement of R-loops could allow R-loops to change their structure to be more thermodynamically favorable. Therefore, we performed a bisDRIP-seq protocol in which the bisulfite step is delayed until the second day of the protocol. In this protocol, we observed several examples where the borders of the R-loop were less well demarcated  and in sIn addition to directly comparing our results with DRIPc-seq, we have added new figures that highlight how our method provides results that are unobtainable by DRIPc-seq. In We wish to thank the reviewer for suggesting that we compare our data with the data from DRIPc-seq. We believe that this comparison will help readers better understand how our method is different and, in many ways an improvement to existing methods.Reviewer #2:The authors describe a new method to map the position of R-loops at higher resolution than existing techniques. The approach entails converting cytosines to uracils in the displaced DNA strand with bisulfite, while the cytosines in the opposite strand are protected by RNA. To minimize the amount of sequencing they focus on regions immunoprecipitated by the S9.6 antibody specific for RNA-DNA hybrids. After bioinformatic processing the authors obtain a genome-wide "bisDRIP-score" which measures the frequency of single-stranded DNA present at a given locus. Asymmetric single-stranded regions are considered R-loops. R-loops are correlated with transcription and enriched in the promoter region of active genes. The high resolution of bisDRIP-seq allowed the authors to identify the TSS as a 5' boundary and the first exon-intron junction as a 3' boundary. Finally, they report that histone genes and lncRNAs MALAT1 and NEAT1 have a particularly strong R-loop signature.R-loops are believed to play important roles in gene regulation and the manuscript by Dumelie et al. describes a creative strategy to better map their genomic localization. The results are well presented and the methods are described clearly and in much detail. Overall I support publication in eLife. There are some major issues that need to be addressed, mostly ensuring that the authors are truly measuring R-loops and that no biases are skewing their analyses.eLife”. The reviewer then suggests a few issues are important for the revision to address.The reviewer notes that “R-loops are believed to play important roles in gene regulation and the manuscript by Dumelie et al. describes a creative strategy to better map their genomic localization”. Additionally, the reviewer feels that “the results are well presented and the methods are described clearly and in much detail”. The reviewer therefore states that they “support publication in – BisDRIP score: I find the explanation of how this score was calculated rather convoluted. The choice of using the sum of the bisDRIP-score for cumulative anlayses seems questionable. This makes it impossible to compare axes when different number of regions are analyzed. For example 3C vs. 3E. Why not using the average?First, we thank the reviewer for their positive comments regarding the overall clarity of our manuscript. However, we fully agree with the reviewer that we could have provided a less convoluted explanation for how we calculated bisDRIP-seq scores. We have updated our explanation of this technique in the Results section to make it more concise and clear . We have removed some of the details and moved them to the Materials and methods section, which now contains a more detailed explanation for how we calculated bisDRIP-seq scores. The Materials and methods section entitled “Calculation of raw bisDRIP-seq read scores” has also been edited in an effort to make it easier to understand. We hope these textual changes will assist readers in fully understanding our method.The reviewer asks why we used sums rather than averages in the metaplot of genes. We feel that this is a valid concern and recognize that using the cumulative sum of genes' bisDRIP-seq scores makes some comparisons more challenging. To address the reviewer’s concern, we have now added an additional figure supplement  where meOur main rationale for presenting the cumulative sum of bisDRIP-seq scores in the panels of – Validations: because bisDRIP-seq is a new genome-wide technique a high level of confidence in its measurements is required before it can be employed for new discoveries. 1) The authors show significant correlation genome-wide between DRIP-seq (the established technique) and their new bisDRIP-seq, but the correlation is obviously not perfect. Can the authors identify examples in which their new technique and the old technique differ and independently verify that the new mapping is more accurate? 2) To validate DRIP-seq typically RNaseH is used to remove RNA-DNA hybrids and prove the specificity of the signal. I believe this would be a more convincing control than triptolide also for bisDRIP-seq.We appreciate these suggestions, which were also raised by reviewer #1. Please see our response to reviewer #1, point 4, for our detailed comparison of bisDRIP-Seq to DRIPc-seq. Also, please see our response to reviewer #1, point 1 for our discussion of how our new experiments using RNase H support the specificity of our method.– Boundaries: the authors state that the TSS and the first exon-intron junction form 5' and 3' boundaries for R-loop formation, respectively, yet in several figures e.g.  I see siWe really appreciate the reviewer's comment. In the original manuscript, we only displayed bisDRIP-seq scores in metaplots. As a result, we did not fully use the benefits of our technique to display the location of R-loop boundaries. We have added new figures that more clearly show the location of the exon-intron boundary, which we believe will address the reviewer's comment regarding R-loop boundaries.The reviewer is correct to note that there is R-loop signal 5' of the transcription start site. In regard to this comment, it is important to note that the location of transcription start sites are not well defined. This is because genes do not necessarily have a single site at which they begin transcription. Instead, they often begin transcription at numerous sites within a promoter region . We discuss this briefly in subsection “Transcription-dependent R-loops form downstream of transcription start sites”. This uncertainty in the location of the transcription start sites likely contributes to the R-loop signal observed upstream of the transcription start site.The reviewer is also correct to note that there is R-loop signal 3' of the first exon-intron junction. In contrast to transcription start sites, exon-intron junctions are well-defined. As such, our method should be able to observe a sharp R-loop boundary in relation to exon-intron junctions. A likely explanation for the observed R-loop signal 3' of the exon-intron junction is our use of bisDRIP-seq scores in our metaplots. Importantly, the bisDRIP-seq scores have read length resolution. In other words, the entire read is assigned a bisDRIP-seq score, even if the read crosses the border of the R-loop. This will reduce the resolution in the metaplots. This was not described clearly enough in the original manuscript, for which we apologize to the reviewer.In order to allow readers to see the R-loop boundary at near-nucleotide resolution, we have added a metaplot of cytosine-conversion data. In this metaplot, we examine cytosine-conversion strand-asymmetry relative to the first exon-intron junction. Cytosine-conversion strand-asymmetry is defined as the ratio of the number of conversions on the non-template strand relative to the template strand after background correction. Unlike the bisDRIP-seq score metaplot, this plot should provide near-nucleotide resolution. Indeed, we observe with this plot that the strand asymmetry in cytosine conversions is lost within base pairs of the first exon-intron junction .We hope that this result provides additional clarity to readers regarding the location of the 3' R-loop boundary. Additionally, we believe this plot nicely highlights the capability of bisDRIP-seq to map R-loops at near-nucleotide resolution.The reviewer also commented on the first exon bias observed by Halász et al., 2017, in DRIP-seq experiments. Halász et al. found that the sequence composition of first exons is biased so that this region is less likely to be fragmented by the restriction enzymes used in DRIP-seq approaches. As a result, DRIP-seq is biased towards immunoprecipitating first exons. We have added commentary to the Discussion section (first paragraph) regarding this issue. In particular, this first exon bias should result in higher bisDRIP-seq signal on both the template and nontemplate strands of DNA in the first exon. However, this should not lead to strand asymmetry between the signal on either strands of DNA. Since most of our conclusions are derived from strand asymmetry, we do not believe that the conclusions made in this manuscript should be significantly affected by the bias uncovered by Halász et al., 2017. We agree with the reviewer that this discussion is important to include in the manuscript and appreciate the thoughtful advice that they have provided here.– Methylcytosines: it seems that the presence of methylated cytosines would make it difficult to detect R-loops in heavily methylated areas of the genome. Is this a problem? It should be discussed.We agree that this is an important issue to discuss regarding our method. We have added discussion regarding this issue to the bisDRIP-seq protocol portion of the Methods section.– Genes with strong R-loop signals: the authors conclude that not having introns is a reason why some of the genes make in the top 25 list shown in MALAT1, NEAT1 and RPPH1 may contribute to R-loop formation in these genes. Indeed, we think this a very interesting possibility. We have added additional commentary to the Discussion portion of the manuscript to discuss this possibility.We agree with the reviewer that the non-canonical transcription termination observed in histone genes, We also agree with the reviewer that steady state levels of RNAs are a potential explanation for genes having high R-loop signal. As suggested by the reviewer we compared R-loop signal to the RNA-seq levels of nuclear RNA from an MCF-7 RNA-seq dataset generated by ENCODE . PerhapsReviewer #3:In this manuscript, Dumelie et al. develop a new method, Bis-DRIP Seq, to identify the genomic localization of R loops at near-nucleotide resolution. Given the growing interest in understanding the function of R loops, this new method will benefit research in this area. Using this technique authors show that R loops are associated with the promoter regions of genes with or without introns. However, in genes with introns, 1st exon-intron junction defines the boundary of the R loop. We suggest a few minor experiments before publication of this manuscript.The reviewer observes that there is “growing interest in understanding the function of R-loops” and they state that bisDRIP-seq “will benefit research in this area.” The reviewer therefore suggests “a few minor experiments before publication of this manuscript.”Specific comments:1) The authors highlight that BisDRIP-Seq provides more information about R loop boundaries as compared to previous DRIP-Seq datasets. This new method is an extension of the previously established DRIP-Seq and in theory it is understandable why it is an improvement. But since this is a 'methodology paper', the authors must provide comparative evidence that it is better. A figure showing how their dataset compares with a DRIP-Seq dataset will be beneficial.We thank the reviewer for this question. This was raised by the other reviewers and a detailed description of our new comparisons is provided in our response reviewer #1, point 4.2) The authors show that the 1st exon-intron junction serves as the 3' R-loop boundary  which thWe agree with the reviewer that this would be a very interesting possibility. We were not able to identify genes that have alternative splice forms with clear R-loop signal past the first exon. This may be a result of us not having good annotation of which genes have retained first introns in MCF-7 cells. Another possibility is that the cell may target mRNAs with retained first-introns that form R-loops to be degraded by RNase H. In this model, mRNAs with retained introns that are observable by RNA-seq must have mechanisms to prevent the RNA from forming a long R-loop that would be susceptible to RNase H degradation. We hope that we can better answer the reviewer's question in our future studies of R-loops.eLife readers, while also strengthening our original conclusions.We would like to thank all of the reviewers for taking the time and effort to provide ideas for enhancing our manuscript. We believe that these suggestions and experiments have helped make the manuscript more compelling to ReferenceSchwalb, B., Michel, M., Zacher, B., Frühauf, K., Demel, C., Tresch, A., Gagneur, J. and Cramer, P., 2016. TT-seq maps the human transient transcriptome. Science, 352, 1225-1228.


Arabidopsis thaliana, 45S rRNA genes are found in two large ribosomal DNA (rDNA) clusters and little is known about the contribution of each to the overall transcription pattern in the species.Ribosomal RNA (rRNA) accounts for the majority of the RNA in eukaryotic cells, and is encoded by hundreds to thousands of nearly identical gene copies, only a subset of which are active at any given time. In By taking advantage of genome sequencing data from the 1001 Genomes Consortium, we characterize rRNA gene sequence variation within and among accessions. Notably, variation is not restricted to the pre-rRNA sequences removed during processing, but it is also present within the highly conserved ribosomal subunits. Through linkage mapping we assign these variants to a particular rDNA cluster unambiguously and use them as reporters of rDNA cluster-specific expression. We demonstrate that rDNA cluster-usage varies greatly among accessions and that rDNA cluster-specific expression and silencing is controlled via genetic interactions between entire rDNA cluster haplotypes .We show that rRNA gene cluster expression is controlled via complex epistatic and allelic interactions between rDNA haplotypes that apparently regulate the entire rRNA gene cluster. Furthermore, the sequence polymorphism we discovered implies that the pool of rRNA in a cell may be heterogeneous, which could have functional consequences.The online version of this article (doi:10.1186/s13059-017-1209-z) contains supplementary material, which is available to authorized users. The central importance of ribosomal RNA (rRNA) genes for our understanding of biology cannot be overstated: they may well be evolutionarily the oldest genes –4; they Arabidopsis thaliana have multiple rDNA clusters on different autosomes .[reference].[alternative]. (CSV 39979 kb)45S rRNA gene variants analysis for the 1138 genomes. Variable sites along the 45S rRNA gene present in at least 5% of the copies within an individual for 1138 accessions. Nomenclature: X.[reference].[alternative]. (CSV 492 kb)45S rRNA gene variants analysis for the MAGIC population and founders. Variable sites along the 45S rRNA genes at the DNA and RNA level for the founders of the MAGIC population, the MAGIC lines, and the Fruction”  and corrAdditional file 4:Nucleolar association of rDNA-2 and rDNA-4 in five accessions. (PDF 55 kb)Additional file 5:Figure S1. Different accessions express either rDNA-2 or rDNA-4, or both. Figure S2. The pattern of rRNA expression is consistent across replicate lines. Figure S3. Expression of Bur-0 rDNA-4 when used as a mother in an F1 cross. Figure S4. Cvi-0 expresses rDNA-4. Figure S5. Col-0 rDNA-2 is not reactivated in mutants of the RdDM pathway. (PDF 991 kb)Additional file 6:2 population and parental accessions. Variable sites along the 45S rRNA genes at the DNA and RNA level for the parental accessions of the F2 population, and the F2 lines used in this study. Predicted genotypes at rDNA-2 and rDNA-4 for each line and type of sequencing-library source are also indicated. Nomenclature: X[position].[reference].[alternative]. (CSV 109 kb)45S rRNA gene variants analysis for the the FAdditional file 7:position].[reference].[alternative]. (CSV 4 kb)45S rRNA gene variants analysis for accession Cvi-0. Variable sites along the 45S rRNA genes at the DNA and RNA level for the accession Cvi-0. The predicted genotypes at rDNA-2 and rDNA-4 for 8 recombinant inbred lines  and type of sequencing-library source are also indicated. Nomenclature: X[Additional file 8:Data availability. (PDF 69.2 kb)