Novel hard substratum, introduced through offshore developments, can provide habitat for marine species and thereby function as an artificial reef. To predict the ecological consequences of deploying offshore infrastructure, and sustainably manage the installation of new structures, interactions between artificial reefs and marine ecosystem functions and services must be understood. This requires quantitative data on the relationships between secondary productivity and artificial reef design, across all trophic levels. Benthic secondary productivity is, however, one of the least studied processes on artificial reefs.Flustra foliacea (Linnaeus 1758), were 2.4 times higher on artificial reefs constructed from “complex” blocks than on reefs constructed from “simple” blocks, which had a smaller surface area.In this study, we show that productivity rates of a common suspension feeder, Productivity rates were highest on external areas of reefs. Productivity rates decreased by 1.56%, per cm distance into the reef on complex reefs and 2.93% per cm into the reef on simple block reefs. The differences in productivity rates between reefs constructed from simple and complex blocks are assumed to reflect different current regimes and food supply between the external and internal reef areas, according to reef type.Synthesis and applications. Our results show that artificial reef design can affect secondary productivity at low trophic levels. We demonstrate that the incorporation of voids into reef blocks can lead to a greater proportion of the structure serving as functional habitat for benthic species. By including such modifications into the design of artificial reefs, it may be possible to increase the overall productivity capacity of artificial structures. Artificial reef design can affect secondary productivity of organism low trophic levels. We demonstrate that the incorporation of voids into reef blocks can lead to a greater proportion of the structure serving as functional habitat for benthic species, and a higher overall productive output for the reef. The ecological consequences of the wide‐scale deployment of artificial structures in marine environments, termed “ocean sprawl,” has been identified as a priority for research   to quantify secondary productivity on two different designs of artificial reef and use this information to compare how different reef designs affect the provision of functional habitat for epifauna.The aim of this study was to use transplants of the common epifaunal suspension feeder 22.1F. foliacea to internal and external areas of six artificial reef modules with differing designs. The growth rates of transplanted fronds were measured to quantify the relationship between secondary productivity and distance into the reef as a function of reef design, and to compare the availability of potentially functional habitat for suspension feeders between reef designs.This study used a SCUBA diving approach to transplant fronds of 2.2The Loch Linnhe Artificial Reef complex, on the west coast of Scotland Figure a,b, reprWithin each reef group, three of the modules are constructed from “simple” blocks, which are solid concrete blocks , measuThis study was based on the simple and complex block modules of the “C Group” reefs, which are located in 18–21 m of water. In each module, there are approximately 4,000 blocks that form a roughly conical shape Figure . Within F. foliacea was transplanted.Thirty experimental sites were assigned on each reef module at (predetermined) random heights on the reef, as measured by a digital depth gauges, and (predetermined) random distances around the reef, as measured by the number of times a diver kicked while swimming (“fin kicks”). At each site, a frond of 2.3Flustra foliacea is a marine bryozoan that commonly occurs on a variety of substrata throughout northeast Atlantic subtidal waters. It was selected for this study to quantify the effects of reef design on productivity because of it large colonial form , which can be split into genetically identical segments, thereby reducing the effects of individual differences in growth rates. Furthermore, colonies are robust and able to withstand a range of flow conditions making them amenable to transplantation and manipulative experiments. F. foliacea undergoes a single sexual reproduction event, following the growing season, in late autumn, meaning that assessments of productivity could be measured via growth rates and were not confounded by reproduction  were secured to the poles at positions of either 0, 0.1, 0.2, 0.3, or 0.4 m from the end of the pole. At each experimental site on the reef, the pole was inserted into the reef, between the reef blocks. A 1.5 m measuring stick was then held perpendicular to the F. foliacea pole, and the distance of the pole into the reef was estimated using the marked 50 mm intervals. The position of the frond on the pole  was then subtracted from the measured distance into the reef to determine the penetration of the F. foliacea frond into the reef. This approach allowed frond to be transplanted to a range of depths into the reefs, given that is was not possible to determine how far poles could be inserted before undertaking the transplantation. A schematic of the experimental design is shown in Figure The plates . The relative growth rate was then divided by the number of weeks that each frond was deployed for to give a metric of mg.g2.4F. foliacea fronds (log transformed) as the response variable. There were two fixed predictor variables in the model: block type (simple and complex) and distance into the reef (0–1.2 m). There were two random sources of variance in the experimental design: module (six levels) and colony (twelve levels).Initial data exploration was carried out prior to analysis to check for outliers, homogeneity of variance, colinearity of variables, and obvious trends in the response variable using a visual assessment of exploratory plots, model diagnostic plots, and draftsman plots  to be calculated. The maximum likelihood parameter estimates were used to describe the joint posterior distribution, which specifies combinations of parameter values that are plausible, given the data. From the joint posterior distribution, 2000 possible model parameters were drawn (simulated) using the function “sim” in the R package “arm”  were obtained by calculating the number of simulations that met each condition. All statistical analysis was done using R version 3.0.0; mixed‐effect models were developed using the R “nlme” and “lme4” libraries .Of the 180 fronds deployed to the Loch Linnhe Artificial Reef, 62% were recovered undamaged. Fronds were observed to be actively feeding (lophophores extended and visible to the naked eye) prior to recovery, and epibiont growth was not visible on any of the recovered fronds. An example of a frond before and after deployment is shown in Figure L = 0.431, p = .153; Module: ΔAIC = 1.57, L = 0.428, p = .513). The final model, therefore, did not include any random effects. The optimal model for F. foliacea productivity rates on the Loch Linnhe Artificial Reef included the following fixed effects: block type, distance into the reef and the interaction of block type and distance into the reef. Inclusion of the interaction term significantly improved model fit .Both of the random factors “colony” and “module” only explained a small portion (<1%) of the total variance, and including them did not substantially improve the model fit  higher on complex modules compared with simple modules  are necessary to specify empirical models of the productive capacity of structures in the marine environment , trophic interactions play an important role in the structure and function of artificial reef communities. Langhamer  and EkluGenerating data on the spatial variation in epifaunal productivity rates on artificial reefs (such as the data provided by this study) is the first stage in efforts to incorporate biological knowledge of functional habitat into reef productivity models. The results obtained in this study suggest that total surface area of reef should not be used to model the productive capacity of artificial reefs, and instead, the effective productive area must be calculated. With such information, it also becomes possible to suggest modifications to the design of infrastructure  to achieve particular management goals. This could improve the positive environmental impacts of structures and potentially increase their capacity to serve as mitigation, for example for loss of downstream habitat.None declared.SR, TW and, JP coconceived the research questions, approach, and methodology. SR undertook the majority of the data acquisitions, with contributions from TW and JP. SR undertook data analysis and initial drafting of the manuscript. JP and TW critically reviewed the manuscript and gave final approval for publication. Click here for additional data file.


B lymphocytes change antibody heavy chain (IgH) isotypes by a recombination/deletion process called IgH class switch recombination (CSR). CSR involves introduction of DNA breaks into a donor switch (S) region and also into one of six downstream S regions, with joining of the breaks changing antibody isotype. A chromatin super-anchor, of unknown function, is located just downstream of the IgH locus. We show that complete deletion of this super-anchor variably decreases CSR to most S regions and creates an ectopic S region downstream of IgH locus that undergoes aberrant CSR-driven chromosomal rearrangements. Based on these and other findings, we conclude that the super-anchor downstream of IgH is a critical insulator for focusing potentially dangerous CSR rearrangements to the IgH locus. H) exons with one of six downstream CHs by joining transcription-targeted double-strand breaks (DSBs) in the Cμ switch (S) region to DSBs in a downstream S region. Chromatin loop extrusion underlies fundamental CSR mechanisms including 3′IgH regulatory region (3′IgHRR)-mediated S region transcription, CSR center formation, and deletional CSR joining. There are 10 consecutive CTCF-binding elements (CBEs) downstream of the 3′IgHRR, termed the “3′IgH CBEs.” Prior studies showed that deletion of eight 3′IgH CBEs did not detectably affect CSR. Here, we report that deletion of all 3′IgH CBEs impacts, to varying degrees, germline transcription and CSR of upstream S regions, except that of Sγ1. Moreover, deletion of all 3′IgH CBEs rendered the 6-kb region just downstream highly transcribed and caused sequences within to be aligned with Sμ, broken, and joined to form aberrant CSR rearrangements. These findings implicate the 3′IgH CBEs as critical insulators for focusing loop extrusion-mediated 3′IgHRR transcriptional and CSR activities on upstream CH locus targets.IgH class switch recombination (CSR) replaces Cμ constant region (C Hs) to produce other antibody isotypes  (H transcription in a cytokine and activation-specific manner (H locus sites inhibits I-promoter activation and CSR in upstream promoters (except Iγ1) but not in downstream promoters, suggesting that linear competition over 100-kb distances of I promoters for 3′IgHRR activation contributes to CSR regulation (Mature B cells undergo immunoglobulin (Ig) heavy chain (IgH) class switch recombination (CSR) to change the constant region of IgH chains and modulate antibody effector functions , 2. Tran or IgA) . Both th or IgA) –9. The 3c manner , 11. Thec manner  initiategulation .H region also are synapsed with Sμ in the CSRC, after which their infrequent AID-initiated DSBs are joined in deletional orientation to AID-initiated Sμ DSBs  and used our RDBC system . Approximately 75% of splenic B cells activated with αCD40/IL4 switched to Sγ1, and ∼10% switched to Sε . While complete 3′IgH CBEs deletion had no significant effect on CSR to Sγ1, it decreased CSR to Sε to about 30% of normal levels (from 10.4 to 2.8%) . In LPS/αIgD-dextran–treated splenic B cells, the complete 3′IgH CBE deletion modestly decreased CSR to Sγ3 to about 60% of WT B-cell levels (from 14.1 to 8.1%) and CSR to Sγ2b to about 75% of WT B-cell levels (from 21.3 to 16.3%) . However, CSR to Sγ2a substantially decreased to about 15% (from 22.9 to 3.4%) of WT B-cell levels . IgG1 surface staining of 4-d αCD40/IL4-stimulated B cells revealed no effect on IgG1 expression by the 3′IgH CBEs deletion , consistent with CSR-HTGTS-seq data. Moreover, IgG3 and IgG2b surface staining of LPS/αIgD-dextran–stimulated B cells revealed decreased IgG3- and IgG2b-positive B-cell frequencies upon 3′IgH CBEs deletion  similar to findings of CSR-HTGTS-seq analyses.While previous work indicated that deletion of 8 of the 10 CTCF-binding sites of the 3′IgH CBEs had little effect on class switching , it remaC system  to generC system . SubsequC system , 19. . In approximate correspondence to CSR effects, the 3′IgH CBEs deletion had no significant effects on Iγ1-Sγ1 transcription but reduced Iε-Sε transcription to about 40% of WT B-cell levels . Activation of splenic B cells with LPS/αIgD-dextran, as expected . Compared to WT B-cell levels for this treatment, the 3′IgH CBEs deletion decreased Iγ3-Cγ3 transcription to about 15%, Iγ2b-Cγ2b transcription to about 30%, and Iγ2a-Cγ2a transcription to about 13% . While the reduction in transcription levels across Iγ3-Cγ3, Iγ2b-Cγ2b, and Iγ2a-Cγ2a does not absolutely reflect the reduction in CSR levels to these CH units in the LPS/aIgD-dextran–treated WT and 3′IgH CBEs-deleted B cells, general trends are similar. In this regard, we do not know the threshold of transcription for each S region required to promote given levels of CSR, which also are influenced by S-region sequence composition or length, among other potential factors . In this regard, the 3′IgH CBEs complete deletion had no significant effects on Sμ-Sγ1 synapsis while it modestly reduced Sμ-Sε synapsis to about 75% of WT B-cell levels . In LPS/αIgD-dextran–stimulated B cells, the iEμ-Sμ locale had relatively less interaction with Sγ3, Sγ2b, and Sγ2a ; however, the 3′IgH CBEs deletion significantly decreased Sμ-Sγ3 synapsis to about 45%, Sμ-Sγ3 synapsis to about 60%, and Sμ-Sγ3 synapsis to about 65% of WT B-cell levels . Again, the trend of these reductions is in the same direction as CSR, but also is subject, beyond mapping issues with some core S-region sequences, to the same comparison issues mentioned for correlation of S-region transcription levels for CSR levels above.Our prior studies, which employed the highly sensitive 3C-HTGTS chromatin interaction method , indicatSI Appendix, Fig. S5), suggesting that the 3′IgHRR may activate transcription of this downstream IgH region in the absence of the 3′IgH CBEs. Indeed, in contrast to the reduction transcription of various S regions (excluding Sγ1) in αCD40/IL4- or LPS/αIgD-dextran–stimulated splenic B cells, transcription of this immediately downstream IgH region was substantially increased in the absence of the 3′IgH CBEs . Moreover, our 3C-HTGTS data showed that the iEμ-Sμ locale in the CSRC had greatly increased interactions with this transcriptionally activated region just downstream of the IgH locus upon deletion of the 3′IgH CBEs , indicating that, in the absence of the 3′IgH CBEs, this highly transcribed downstream region participates in substantial synapsis with Sμ within the CSRC .To further address the potential mechanism of the reduction of CSR to various S regions upon the complete deletion the 3′IgH CBEs, we employed GRO-seq to analyze transcription of sequences downstream of the 3′IgH CBEs in WT and complete 3′IgH CBEs-deleted nonstimulated splenic B cells. This analysis revealed that the 30-kb region just downstream of the 3′IgH CBEs was highly activated transcriptionally in nonstimulated, complete 3′IgH CBEs-deleted splenic B cells , creating convergent transcription known to facilitate AID targeting . In addition, LPS/αIgD-dextran treatment of complete 3′IgH CBEs-deleted splenic B cells also induced aberrant translocations between Sμ and sequences within this 6-kb sequence downstream of the normal 3′IgH CBEs location that accounted for nearly 3% of all CSR-related junctions . Moreover, examination of junctions across a 600-bp “core” region of this 6-kb sequence downstream of the normal 3′IgH CBE location, which has the highest rearrangement frequency across this region, revealed that about 80% of the junctions within occurred within AID-targeting motifs under both aCD40/IL4 and LPS/aIgD-dextran stimulation conditions , indicating that factors beyond AID-targeting motif density  can influence overall CSR-junction frequency. Thus, these studies indicate that the 3′IgH CBEs prevent the region just downstream from them from becoming transcriptionally activated, synapsing with Sμ in the CSRC and serving as an ectopically induced S region (“eS”) for CSR and aberrant chromosomal deletions.Notably, αCD40/IL4 treatment of complete 3′IgH CBEs-deleted splenic B cells induced the aberrant translocations across the first 6 kb of this 30-kb transcribed sequence just downstream of the normal 3′IgH CBEs location, as indicated by junctions between Sμ and sequences across this region that accounted for nearly 1% of all CSR-related junctions . Subsequently, we deleted both copies of the Aicda (AID) gene from the homozygous 3′IgH CBEs-deleted ES cells via the Cas9/guide RNA (gRNA) approach. The gRNA oligonucleotides for the CRISPR/Cas9 used were cloned into the pX330 vector (Addgene plasmid ID 42230) (All 10 3′IgH CBEs were deleted by a standard gene-targeted deletion approach  on both D 42230) . For CSRD 42230) . For GROPrimary splenic B cells were isolated by a CD43-negative selection kit from chimeric mice and cultured in medium R15 . Primary splenic B-cell stimulation was performed with αCD40  plus IL4  or with LPS  plus αIgD-dextran (3 ng/mL) for 96 h. CSR-HTGTS-seq was performed in AID-proficient cells stimulated for 96 h . GRO-seqFlow cytometric analysis was used for measuring IgH class switching in splenic B cells stimulated with either αCD40/IL4 or LPS/αIgD-dextran for 96 h. After 96 h of stimulation, cells were collected and washed once with PBS. Then, the cells were stained for the surface makers with the indicated antibodies . The APC-IgM and APC-IgG1 antibodies were diluted 100 times from stock concentration, while the PE-IgG3 and PE-IgG2b antibodies were diluted 200 times from stock concentration at room temperature for 10 min. The stained cells were washed once with PBS and resuspended in PBS for flow cytometric analysis with a BDFACSCalibur (BD Bioscience). CellQuest Pro alias software was used for collecting the data, and FlowJo software (10.0.6) was used for analyzing the data.CSR-HTGTS-seq libraries generated with a 5′Sμ bait , 19 wereH-containing portion of the IgH, are listed in SI Appendix, Tables S1 and S2. Primers used for CSR-HTGTS-seq are listed in SI Appendix, Table S3.Libraries were processed via our published pipeline  and mapp−/− mature splenic B cells stimulated with αCD40/IL4 or LPS/αIgD-dextran for 96 h as previously described . Primers used for 3C-HTGTS are listed in SI Appendix, Table S3. Each experiment was repeated three times for statistical analyses.The 3C-HTGTS analyses  were perescribed . Ten milescribed , 31. All−/− mature splenic B cells stimulated with αCD40/IL4 or LPS/αIgD-dextran for 96 h as described . The nuclear run-on reaction was performed by adding 100 μL of 2X run-on mix  at 37 °C for 5 min. RNA was extracted by TRIzol. Hydrolysation was performed by adding NaOH at a final concentration of 0.2 N on ice for 18 min and followed by quenching with ice-cold Tris⋅HCl, pH 6.8, and exchanging buffer via Bio-Rad P30 columns. Then the purified RNA was incubated with BrdU antibody-conjugated beads  for 1 h. The enriched run-on samples were incubated with RppH  for 1 h and hydroxyl repair with T4 PNK  for another 1 h. RT-PCR was performed after the 5′ and 3′ RNA adaptor ligation. The complementary DNA template was subjected to making GRO-seq libraries by two rounds of PCR. PCR products of 200 to 500 bp from the first round of PCR were purified by separation on 2.5% TAE gel and subjected to the second round of PCR. The final PCR products were further purified by size-selection with SPRIselect beads . GRO-seq libraries were sequenced via paired-end 150-bp sequencing on a Next-Seq550 and normalized to a coverage of 10 million 100-nucleotide reads for display. Transcriptional activity of specific regions was calculated as Reads Per Million Reads (RPM) . Each experiment was repeated three times for statistical analyses.GRO-seq libraries were prepared from AIDescribed . Ten milt test was used to examine the significant difference between samples. At least three repeats were done for each statistical analysis. Quantitative data are mean ± SD; n.s. indicates P > 0.05, *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001.An unpaired two-tailed Student Supplementary File


Mammalian genomes encode tens of thousands of noncoding RNAs. Most noncoding transcripts exhibit nuclear localization and several have been shown to play a role in the regulation of gene expression and chromatin remodeling. To investigate the function of such RNAs, methods to massively map the genomic interacting sites of multiple transcripts have been developed; however, these methods have some limitations. Here, we introduce RNA And DNA Interacting Complexes Ligated and sequenced (RADICL-seq), a technology that maps genome-wide RNA–chromatin interactions in intact nuclei. RADICL-seq is a proximity ligation-based methodology that reduces the bias for nascent transcription, while increasing genomic coverage and unique mapping rate efficiency compared with existing methods. RADICL-seq identifies distinct patterns of genome occupancy for different classes of transcripts as well as cell type–specific RNA-chromatin interactions, and highlights the role of transcription in the establishment of chromatin structure. Mammalian genomes encode tens of thousands of ncRNAs that have important roles in regulation of gene expression and chromatin organization. Here, the authors present RADICLseq to map RNA-chromatin interactions in intact nuclei to shed light on these fine-tuned processes. In particular, long ncRNAs (lncRNAs) have emerged as important regulators of various biological processes2. Although most lncRNAs exhibit nuclear localization with enrichment for the chromatin fraction, the genomic-interacting regions for most of these transcripts are still unknown3.The vast majority of mammalian genomes is pervasively transcribed, accounting for a previously unappreciated complexity of the noncoding RNA (ncRNA) fraction6. However, these methodologies rely on the use of antisense probes to target individual transcripts and are not suitable for de novo discovery and high-throughput application in multiple cell types.Several technologies have been developed to map the genomic-interacting sites of lncRNAs9, but each has limitations. Mapping RNA-genome interactions (MARGI) is a proximity ligation-based technology that requires a high number of input cells  and the disruption of the nuclear structure7, which can result in detection of a large number of spurious interactions10; because of this, it has limited applicability to investigations of RNA–chromatin interactions in multiple cell types. Chromatin-associated RNA-sequencing (CHAR-seq) and global RNA interaction with DNA by deep-sequencing (GRID-seq) utilize in situ approaches to detect genome-wide RNA–chromatin contacts9. CHAR-seq requires a large number of cells as starting material and the use of DpnII to digest the chromatin. Because DpnII has a restricted number of sites across the genome, this method has limited coverage of captured RNA–DNA interactions9. In addition, the technology does not size select the molecules containing both interacting RNA and DNA, resulting in a large fraction of uninformative sequences in the final library. GRID-seq preferentially captures nascent RNA–chromatin interactions and, consequently, may overlook the presence of other patterns of genome occupancy by specific classes of transcripts. Furthermore, its reliance on a restriction enzyme for chromatin fragmentation coupled with read length restricted to 20 nucleotides (nt) limits both genome coverage and mappability.A few technologies have emerged to assess genome-wide RNA–chromatin interactionsTo address these limitations, we introduce RNA And DNA-Interacting Complexes Ligated and sequenced (RADICL-seq), a methodology to identify genome-wide RNA–chromatin interactions in crosslinked nuclei that substantially improves on previously published methods. Specifically, RADICL-seq reduces the bias for nascent transcription while increasing genomic coverage and unique mapping rate efficiency. Application of RADICL-seq to mouse embryonic stem cells (mESCs) and mouse oligodendrocyte progenitor cells (mOPCs) reveals distinct genome occupancy patterns for specific classes of transcripts and uncovers cell-type-specific RNA–chromatin interactions. Furthermore, our results highlight the role of transcription in the establishment of the three-dimensional (3D) structure of chromatin.11, to identify genome-wide RNA–chromatin (or RNA–DNA) interactions in preserved nuclei  away from each of its two recognition sites strategically placed within the adapter  datasets. After deep sequencing, RADICL-seq produced an average of 120 and 115 million 150-nt single-end raw reads from 1FA and 2FA libraries, respectively  data), we observed a minor enrichment from genomic regions located in heterochromatic regions, consistent with the role of some lncRNAs as repressors of gene expression13  and assay for transposase-accessible chromatin using sequencing n13 Fig. .14, for 4 h before crosslinking with 1% FA  than MARGI or CHAR-seq, which require 400 and 100 million cells, respectively.RADICL-seq introduces substantial improvements over similar RNA–chromatin proximity ligation approachesRADICL-seq differs from GRID-seq in four main technical aspects Fig. , as desc15.(i) In the fixation step, GRID-seq employs a higher concentration of FA and uses disuccinimidyl glutarate, a strong protein–protein crosslinker, thus capturing RNA and DNA linked together indirectly via multiple protein intermediatesMmeI to trim RNA- and DNA-interacting sequences, generating 20-nt tags as opposed to the 27-nt tags produced by the EcoP15I restriction enzyme, thus resulting in tags that are more difficult to map uniquely to the genome. Indeed, when we compared the percentage of sequencing reads that can be mapped to the mouse genome, RADICL-seq outperformed GRID-seq with a more than a 3-fold increase in uniquely mappable reads (45% vs. 14%)  The GRID-seq protocol employs the type II restriction enzyme 4%) Fig. . To confAluI, whereas the RADICL-seq protocol employs a controlled DNase I digestion that avoids the sequence biases encountered with restriction enzymes, and, therefore, generates a more homogeneous shearing of the chromatin. When we looked at the genomic coverage of DNA regions identified by both technologies, RADICL-seq exhibited higher coverage  GRID-seq technology digests genomic DNA with age Fig. . We addiage Fig. .17. Notably, RADICL-seq uses a similar amount of input cells as GRID-seq, but it achieves higher detection power by yielding a higher number of informative RNA–DNA interactions at a lower cost. The publicly available GRID-seq dataset has higher sequencing depth , but yields less usable reads 9, whereas RADICL-seq was sequenced at a lower depth  and produced 19.9M uniquely mapped reads . Moreover, when comparing the capture rate for different classes of transcripts between the two technologies, we observed that RADICL-seq detects a higher number of RNAs from different biotype groups compared to GRID-seq  Finally, RADICL-seq employs RNase H treatment prior to the RNA ligation step to reduce the number of captured interactions generated by nascent transcription and consequently increases the variety of captured RNA–DNA interactions. When compared with uniquely mapped RNA and DNA reads detected by GRID-seq, the RADICL-seq dataset showed increased detection of intergenic transcripts and ncRNAs  interactions, RADICL-seq recovered ~30% more trans interactions   Fig. , thus pres) Fig. , suggest15 for Malat1 and chromatin isolation by RNA purification (ChIRP-seq)18 for Rn7sk. Malat1 targets detected by RADICL-seq data had a genomic distribution comparable to that of Malat1-targeted RAP-DNA libraries prepared from pSM33 ES cells15 . Furthermore, RADICL-seq detected 56% of the protein-coding genes interacting with Rn7sk, compared with only 13% of targets for GRID-seq  and Rn7sk  detected by RADICL-seq with those observed using RNA antisense purification, followed by DNA-sequencing (RAP-DNA)seq Fig. .Collectively, these analyses suggest that RADICL-seq captures RNA–chromatin interactions more comprehensively and with less genomic bias when compared with GRID-seq.19 has been published. SPRITE has also the capability to map RNA–chromatin interactions, and it has been used to map RNA–DNA contacts in mESCs. However, we noticed that out of 52.5M total DNA–DNA and RNA–DNA complexes identified by SPRITE, only 0.275M (0.5%) contained RNA species, and 80% of these contained rRNA. Hence, this indicates that RNA–DNA interactions detected by SPRITE are only corollary to the overwhelming majority of rRNA-mediated chromatin interactions. Nevertheless, we included SPRITE data in our analysis and compared RNA–DNA interactions captured by existing technologies in mESCs. Compared to other technologies, RADICL-seq exhibited significantly higher resolution for the genomic DNA signal for Malat1 22; this approach assumes that all biases  are reflected in the observed interaction counts. To this end, we partitioned the linear genome into intervals  to represent the RADICL-seq data as a contact matrix between RNA and DNA loci. We then used a one-sided cumulative binomial test to detect significant RNA–chromatin interactions, assuming that the transcript-specific background interaction frequency of a given RNA and a genomic interval depends also on their relative genome-wide coverage23. We employed the Benjamini–Hochberg multiple-testing correction to control for the false discovery rate and used an adjusted P cut-off of 0.05 to define the “significant” set  on oli-neu, a neural cell line derived from mOPCscis from intronic regions increased with the distance to the genomic region bound by the transcript, and on the other hand, we observed a dominant contribution of exonic regions from noncoding transcripts in the trans interactions  data from the nuclear fraction of mESCs and mOPCs, and divided the genic transcripts into expression quartiles . When we plotted the number of interactions as a function of the distance to the gene body in the total datasets of both cell types, we observed a sharper decay rate for transcripts that were expressed at lower levels, suggesting that the expression plays a role in the establishment of RNA–DNA contacts proximal to the transcription locus , we leveraged Hi-C data produced from similar cell types 28 and found a clear signal enrichment at TAD boundaries in both cell types total and NPM datasets  identified in the above Hi-C dataets Fig. . This fiets Fig. . Furtherets Fig. . InteresNext, we asked whether RNAs originating from loci positioned within or outside TADs showed specific DNA-binding patterns, possibly dictated by genomic structural constraints. When we looked at the distribution of the signal for RNAs transcribed within TADs in the mESC and mOPC total datasets, we found it to dramatically drop outside the domain regions, whereas the signal from transcripts transcribed outside TADs showed the opposite trend Fig. , thus su29. In the mESC and mOPC total datasets we observed that ~12% and ~8%, respectively, of the uniquely mapped genic RNA tags intersected with REs as defined by RepeatMasker30  have emerged in recent years as key contributors to genomic regulation and organizationpes Fig. . In termpes Fig.  even in pes Fig. . AlthougTo better understand the involvement of RNA in genome organization and the fine-tuning of cell-specific gene expression, we compared genome-wide RNA–DNA binding profiles and capture rates between mESCs and mOPCs. For each transcript, we calculated the Jaccard distance between the RNA–DNA binding profiles as a function of the difference in RADICL-seq capture rate, here considered a proxy for gene expression Fig. . As expeNext, we assessed whether RADICL-seq can be used to discriminate biologically relevant differences between cell types. To this end, we collected from the literature a list of marker genes that are specifically expressed in mESCs or mOPCs  Chromatin shearing is achieved by a controlled DNase I digestion, which results in a greater resolution compared with digestion with restriction enzymes. Indeed, the distribution of cut sites for restriction enzymes is often uneven and may result in the inability to detect important interaction chromatin regions. The sequence-independent digestion of chromatin by DNase I enables RADICL-seq to overcome such resolution limitations. (ii) The use of paramagnetic carboxylated beads as carriers for the nuclei allows additional washes to remove small genomic fragments (upon DNase digestion) and the excess of biotinylated bridge adapter, thus reducing noise. Furthermore, beads improve visualization of the nuclear pellet when using fewer cells and can potentially decrease the number of input cells for future applications. (iii) Digestion of RNA–DNA hybrids with RNase H reduces the fraction of nascent RNA–chromatin interactions captured by RADICL-seq, thereby increasing the capture rate of other types of interaction. (iv) Use of cis36. However, one surprising finding was the dominant contribution of intronic RNA sequences from protein-coding genes in cis interactions. These binding events seem to be of stable nature, as we observe interactions mediated by intronic RNAs following inhibition of transcription elongation for several hours. Excised introns have been reported to exert a biological function on growth phenotype in yeast37. We speculate the existence of a similar mechanism in higher eukaryotes where specific intronic RNA sequences might escape degradation and interact with the chromatin in cis. Additionally, a subset of interactions mediated by intronic RNAs from protein-coding genes might be involved in the transcriptional regulation of neighboring genes mediated by protein-coding transcripts as previously reported38.Our results confirm previous observations regarding modality of interaction for lncRNAs, with most noncoding transcripts binding genomic targets locally or over short- and medium-range distances in Drosophila melanogaster, suggesting an evolutionary conservation of this phenomenon. Intriguingly, Heinz et al.39 have reported that transcription elongation remodels 3D chromatin architecture by displacing TAD boundaries. Moreover, SINE sequences have previously been found to be enriched at TAD boundaries40. With RADICL-seq we have uncovered specific patterns of interactions for transcripts overlapping REs, thus suggesting that transcripts from REs might facilitate the formation of 3D structures in cells, especially after cell division when TADs are temporarily dismantled.To test our hypothesis of the involvement of genome structure in transcriptional regulation mediated by RNA interactions, we used RADICL-seq to assess the frequency of RNA–DNA interactions at TAD boundaries. The widespread enrichment of RNA–chromatin interactions at these boundaries indicates a possible role for transcription or for its products in influencing 3D genome structure. CHAR-seq technology identified enrichment of transcription-associated RNAs at TAD boundaries in 41.The observed enrichment of CHARs at TAD boundaries even in the ActD dataset—where RNA pol II elongation is inhibited—potentially suggests that TADs might generate a barrier effect, which is consistent with the transcripts preferentially binding to DNA within or outside, but not across, TADs. This observation confirms recent evidence of a TAD-restricted genome occupancy mediated by immune gene-priming lncRNAs42. Although the RADICL-seq protocol includes an RNase H step to remove RNAs paired with DNA in Watson–Crick fashion while the sample is still crosslinked, proteins located nearby the complementary RNA–DNA binding could hinder enzyme accessibility, thus preventing the complete digestion of these hybrids. Nevertheless, we were still able to observe a consistent number of interactions that can be explained by triple helix formation. Future extensions of the protocol could include an RNase H step after reversal of crosslinks to enrich for triplexes structures. Although a protein-free method that separately identifies RNA and DNA involved in triple helix formation has been published43, to our knowledge RADICL-seq performed in the NPM condition is the only approach that can simultaneously link interacting RNA and DNA to triplex structures.The NPM datasets identified reproducible RNA–chromatin interactions that are not mediated by the presence of proteins. Such interactions can be explained either by direct RNA–DNA pairing or by binding of RNA to duplex DNA via the formation of triplexes. Increased frequency of RNA–chromatin interactions and enrichment for R-loops at TAD boundaries in NPM datasets indicate the possible role of active transcription in partitioning the genome, as disruption of the cohesin/CTCF complex in mammals does not lead to disappearance of TAD boundariestrans with the chromatin. Recently, Katsel et al.44 have reported down-regulation of Neat1 expression in schizophrenia patients, which is associated with reduction in the number of cells of the oligodendrocyte lineage.Our results further show that RADICL-seq can pinpoint cell-type-specific RNA–chromatin interactions. We uncovered an mOPC-specific genome occupancy pattern for Neat1 RNA where the long isoform interacts in cis and trans interactions, and the identification of the cell type specificity of such associations. We anticipate that the RADICL-seq technology will pave the way for a deeper understanding of the fine regulatory network governing gene expression and ultimately cell identity.In summary, we have developed a technology to map genome-wide RNA–chromatin interactions that significantly improves upon other existing technologies by reducing nascent transcription bias and increasing genomic coverage and unique mapping rate efficiency. Application of RADICL-seq in mESC- and mOPC-derived cells allowed the unveiling of principles of RNA–chromatin l-glutamine (Invitrogen), 0.1 mM non-essential amino acids (Invitrogen), 0.1 mM 2-mercaptoethanol (Gibco), 50 U/ml penicillin, and 50 μg/ml streptomycin (Gibco). Culture media were changed daily, and cells were passaged every 2–3 days. For the ActD-treated RADICL-seq libraries, R08 mESCs were treated with ActD (Sigma) at a final concentration of 5 µg/ml for 4 h before crosslinking, as described below.R08 mESCs (CIRA) were grown under feeder-free conditions in mouse ESC medium consisting of Dulbecco’s modified Eagle’s medium  supplemented with 1000 U/ml leukemia inhibitory factor (Millipore), 15% fetal bovine serum (Gibco), 2.4 mM l-lysine-coated dishes and expanded in proliferation media consisting of DMEM (Lifetech), N2 supplement, penicillin–streptomycin–glutamine (Lifetech), T3  340 ng/ml, l-thyroxine (Sigma-Aldrich) 400 ng/ml, fibroblast growth factor-basic 10 ng/ml, and platelet-derived growth factor-BB 1 ng/ml. Culture media were changed on alternate days and the cells were passaged every 5–6 days.Oli-neu cells were grown on poly-The origins, authentications, and mycoplasma-testing methods of the cell lines used in the current study are listed in the Reporting Summary.Confluent cells were rinsed with pre-warmed phosphate-buffered saline (PBS) and trypsinized. Detached cells were pelleted, resuspended in PBS, counted, and pelleted again. Cell pellets were then crosslinked by resuspension in freshly prepared 1% or 2% FA (Thermo Fisher Scientific) solution using 1 ml for every one million cells. Cells were incubated at room temperature for 10 min with rotation, followed by quenching with 125 mM glycine (Sigma). Cells were pelleted at 4 °C, washed with ice-cold PBS, pelleted again, and snap frozen in liquid nitrogen.We use the term biological replicates to refer to batches of cells with different passage numbers.Adenylation of the adapter. The adapter is a partially double-stranded DNA molecule containing chemical modifications . The upper strand sequence is 5ʹ-/5Phos/CTGCTGCTCCTTCCCTTTCCCCTTTTGGTCCGACGGTCCAAGTCAGCAGT-3ʹ. The lower strand sequence is 5ʹ-/5Phos/CTGCTGACT/ibiodT/GGACCGTCGGACC-3ʹ. The upper strand was pre-adenylated by using a DNA 5′ Adenylation Kit (New England BioLabs). The pre-adenylated upper strand was mixed with an equimolar quantity of the lower strand and subsequently incubated at 95 °C for 2 min, followed by 71 cycles of 20 s, with a reduction of 1 °C every cycle. Annealed pre-adenylated adapter was then purified using a Nucleotide Removal Kit (Qiagen).45. Briefly, cell pellets containing approximately two million crosslinked cells were resuspended in cold lysis buffer  and incubated on ice for 10 min. Nuclei were pelleted at 2500 × g for 60 s, resuspended in 100 μl of 0.5× DNase I digestion buffer (Thermo Fisher Scientific), containing 0.5 mM MnCl2 and 0.2% sodium dodecyl sulfate (SDS), and incubated at 37 °C for 30 min. An equal volume of 0.5× DNase I digestion buffer containing 2% Triton X-100 was added, and then incubation at 37 °C was continued for 10 min. Then, 1.5 U DNase I (Thermo Fisher Scientific) was added and digestion was carried out at room temperature for 4 or 6 min for 1 and 2% FA, respectively. DNase I digestion was stopped by adding 40 μl of 6× Stop Solution , followed by centrifugation at 2500 × g for 60 s. Nuclei were resuspended in 150 μl nuclease-free H2O and purified with two volumes (300 μl) of AMPure XP magnetic beads (Beckman Coulter). After 5 min incubation at room temperature, beads were separated using a magnetic rack, washed twice with 80% ethanol, and air dried for 2 min.Chromatin digestion. Chromatin preparation was performed using a modified protocolg for 60 s; resuspended in 200 μl 1× NEBuffer 2 (New England Biolabs) containing 0.5 mM dATP, 1% Triton X-100, and 0.375 U/μl Klenow (exo-) (Thermo Fisher Scientific), and then incubated at 37 °C for 1 h. After that, 0.122 U/μl RNase H (New England Biolabs) was added, and the reaction was incubated at 37 °C for a further 40 min. The dA-tailing and RNase H reactions were stopped by adding 5 μl of 10% SDS.Chromatin end-repair, dA-tailing, and RNase H treatment. The purified bead–nuclei pellet was resuspended in 200 μl 1× T4 DNA ligase buffer (New England Biolabs) containing 0.25 mM dNTPs, 0.075 U/μl T4 DNA polymerase (Thermo Fisher Scientific), and 0.15 U/μl Klenow fragment (Thermo Fisher Scientific), and then incubated at room temperature for 1 h. The end-repair reaction was stopped by adding 5 μl of 10% SDS. The bead–nuclei mixture was pelleted at 2500 × g for 60 s and then resuspended in 200 μl H2O. To remove soluble RNA, 165 μl of 20% polyethylene glycol (PEG) in 2.5 M NaCl was added to the mixture, followed by a 5-min incubation at room temperature. Beads were collected with a magnetic rack, washed once with 80% ethanol, and resuspended in 200 μl H2O. This purification step was repeated once. After the second ethanol wash, the air-dried bead–nuclei mixture was resuspended in 23 μl H2O, 3 μl 10× T4 RNA ligase buffer, 1 μl pre-adenylated and biotinylated bridge adaptor (20 μM), 1 μl RNaseOut (Thermo Fisher Scientific), and 13.3 U/μl T4 RNA ligase 2, truncated KQ (New England Biolabs). The mixture was incubated at 20 °C overnight to ligate the pre-adenylated bridge adapter to the 3ʹ-OH of the RNA molecules. The reaction was stopped by adding 5 μl 10% SDS, and the bead–nuclei mixture was then pelleted at 2,500 × g for 60 s and resuspended in 200 μl H2O. To remove excess unligated adapter, 165 μl of 20% PEG in 2.5 M NaCl was added to the mixture, and then the reaction was incubated at room temperature for 5 min. Beads were then collected with a magnetic rack, washed once with 80% ethanol, and then resuspended in 200 μl H2O. This purification was repeated once.Bridge adapter RNA ligation. The bead–nuclei mixture was pelleted at 2500 × g for 60 s and resuspended in 200 μl H2O. To remove remaining unligated and DNA-only ligated adapter, 165 μl of 20% PEG in 2.5 M NaCl was added to the mixture, and then purified as previously described. Beads were then resuspended in 200 μl H2O.Proximity ligation. The in situ proximity ligation was carried out by resuspending the air-dried bead–nuclei mixture in 500 μl 1× T4 DNA ligase buffer with ATP containing 4 U/μl T4 DNA ligase (New England Biolabs) and incubating it at room temperature for 4 h. After the incubation, bead–nuclei complexes were pelleted at 2500 × g centrifugation at 4 °C for 30 min. The resulting bead–nucleic acids pellet was resuspended in 100 μl H2O and further purified using 100 μl AMPure XP beads. After 5 min incubation at room temperature, beads were separated using a magnetic rack, washed twice with 80% ethanol, and then air dried for 2 min. DNA was eluted using 130 μl H2O and quantified with a Qubit High Sensitivity Kit (Thermo Fisher Scientific).Reversal of crosslinking and purification of RNA–DNA complexes. Reversal of crosslinks was performed by adding 50 μl of proteinase K solution  and 1.6 U/μl proteinase K (Ambion) to the resuspended beads. The mixture was incubated overnight at 65 °C and the RNA–DNA complexes were then precipitated with 3 μl GlycoBlue (Ambion), 28 μl 3 M sodium acetate, pH 5.2, and 303 μl isopropanol for 1 h on ice followed by 20,000 × Reverse transcription and second-strand synthesis of the RNA–DNA complexes. Since reverse transcriptase can use DNA sequences as primers for the polymerization, the double-stranded region of the bridge adapter acts as the primer for the reaction. The RNA ligated to the bridge adapter was reverse transcribed after the sample was concentrated to a final volume of 12 μl. First, 1 μl of 10 mM dNTPs was added to the sample, and the mixture was incubated at 65 °C for 5 min. Subsequently, 4 μl of 5× first-strand buffer, 1 μl of 0.1 M dithiothreitol, and 1 μl each of RNaseOut and SuperScript IV (both from Thermo Fisher Scientific) were added, and the reaction was incubated at 56 °C for 10 min and 80 °C for 10 min.Escherichia coli DNA polymerase I (New England Biolabs), 1 μl of E. coli ligase (New England Biolabs), and H2O to the reverse transcription sample, in a final volume of 150 μl. The mixture was incubated at 16 °C for 2 h and the reaction was stopped by adding 10 μl of 0.5 M EDTA. The sample was purified using a Nucleotide Removal Kit (Qiagen) by adding 1.6 ml of the buffer PNI to the sample, with a final elution in 50 μl H2O. Sample volume was reduced to 8 μl using a Speedvac concentrator (Tomy).Next, the generated complementary DNA (cDNA)–RNA hybrid was converted to double-stranded DNA through a second-strand synthesis reaction by the addition of 30 μl of 5× second-strand buffer (Thermo Fisher Scientific), 3 μl of 10 mM dNTPs (Thermo Fisher Scientific), 3 μl of RNase H , 4 μl of EcoP15I digestion of the cDNA–DNA complexes. The sample was then subjected to hairpin linker  ligation to selectively ligate the bridge adapter that was covalently bound only to RNA and therefore prevent subsequent ligation of sequencing adapters. A total of 100 pmol of hairpin linker was mixed with 10 μl of 2× Quick ligase buffer (New England Biolabs), 8 μl of sample, and 1 μl of Quick ligase (New England Biolabs). The reaction was carried out for 15 min at room temperature and was then purified with DNA Clean & Concentrator-5 Kit (Zymo) according to the manufacturer’s instructions. Elution was performed in 50 μl of H2O and final volume was reduced to 30 μl.Hairpin ligation and EcoP15I digestion of the double-stranded cDNA–DNA complexes was performed by using 10 U of enzyme for 1.5 μg of DNA in the presence of 5 μl NEBuffer 3.1 (New England Biolabs), 5 μl 10 × ATP, 0.5 μl 10 mM sinefungin , and H2O in a final reaction volume of 50 μl. The sample was incubated at 37 °C overnight.The sample concentration was measured by using a Qubit dsDNA High Sensitivity Kit (Invitrogen). EcoP15I-digested sample was purified using a Nucleotide Removal Kit (Qiagen) by adding 1.3 ml of PNI buffer. The sample was eluted in 50 μl and the volume was further reduced to 20 μl. To prepare the sample for the sequencing linkers ligation, 6.5 μl of 10× reaction buffer, 3 μl of End Prep Enzyme Mix from NEB Next Ultra End-Repair/dA-Tailing Module (New England Biolabs), and H2O in a final volume of 65 µl were added to the concentrated sample, and then the reaction was incubated at 20 °C for 30 min and 65 °C for 30 min.End preparation and sequencing linkers ligation. Each Next, Y-shaped sequencing linkers were prepared. The upper strand (5ʹ-/5Phos/GATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT-3ʹ) and lower strand (5ʹ-CTCGGCATTCCTGCTGAACCGCTCTTCCGATCT-3ʹ) were annealed in 1× NEBuffer 2 (New England Biolabs) at 95 °C for 2 min, followed by 71 cycles of 20 s, with a reduction of 1 °C every cycle.Sequencing linkers ligation was performed with NEB Next Ultra Ligation Module and 20 pmol of annealed Y-shaped sequencing linkers at 20 °C for 15 min. After ligation, the sample volume was reduced to 40 μl.Pull-down of the RNA–DNA ligated complexes and PCR titration. The RNA–DNA ligated complexes were pulled-down with MyOne C1 Streptavidin magnetic beads (Invitrogen). A total of 20 μl of beads were washed twice with 1× western blot (WB) buffer , once with 2× WB buffer, and finally resuspended in 40 μl of 2× WB buffer. An equal volume of sample was added to the beads, and the mixture was incubated at room temperature for 20 min with rotation. Isolated RNA–DNA ligated complexes were extensively washed three times with 1× WB buffer, washed once with elution (EB) buffer (Qiagen), and finally resuspended in 30 μl of EB buffer.PCR cycle check was performed by using Phusion High Fidelity PCR Kit (Thermo Fisher Scientific) with Universal FW primer , Index RV primer , and 4 µl of isolated libraries. PCR was carried out at 98  °C for 30 s, followed by 14 cycles of 98 °C for 10 s, 65 °C for 15 s, and 72 °C for 15 s. After respectively 8, 11, and 14 cycles, 10 μl aliquots were collected and run on a pre-cast 6% polyacrylamide gel (Invitrogen) at 145 V for 60 min. The lowest PCR cycle where the 225-bp band representing the RNA–DNA ligated complexes could be visualized was chosen for the final library amplification.Library amplification and sequencing. A total of four PCR reactions, each using a different barcoded primer, were prepared for each library. After amplification, the four reactions were pooled and run on a pre-cast 6% polyacrylamide gel (Invitrogen) at 145 V for 60 min. The 225-bp band was excised and purified.Library size was assessed using a High Sensitivity DNA Bioanalyzer Kit (Agilent) and quantified by quantitative PCR using the Library Quantification Kit for Illumina sequencing platforms (KAPA Biosystems) and StepOne Real-Time PCR System (Applied Biosystems). Sequencing was performed with a Single-End 150-bp Kit on the Illumina HiSeq 2500 platform using the sequencing primer 5ʹ-ACACTCTTTCCCTACACGACGCTCTTCCGATCT-3ʹ.2O. DNA ligation was carried out by adding 450 μl of 1× T4 DNA ligase buffer with ATP containing 4 U/μl T4 DNA ligase (New England Biolabs) and incubating the solution at room temperature for 4 h. The RNA–DNA complexes were purified with DNA Clean & Concentrator-5 Kit (Zymo) as described above, and then subjected to reverse transcription and subsequent steps of RADICL-seq library preparation and sequencing.Generation of libraries capturing NPM interactions. NPM libraries were produced following the above protocol for the generation of RADICL-seq libraries with the modifications below. After the purification steps that followed dA-tailing and RNase H reactions, reversal of crosslinks was performed as described above. Samples were concentrated to a final volume of 13.9 µl, followed by the addition of 3 μl of 10× T4 RNA ligase buffer; 1 μl of 20 μM pre-adenylated and biotinylated bridge adaptor; 1 μl of RNaseOut (Thermo Fisher Scientific); 13.3 U/μl T4 RNA ligase 2, truncated KQ (New England Biolabs); and 9 µl of 50% PEG. The mixture was incubated at 20 °C overnight to ligate the pre-adenylated bridge adapter to the 3ʹ-OH of the RNA molecules. Next, bridge adapter-ligated molecules were purified with DNA Clean & Concentrator-5 Kit (Zymo) according to the manufacturer’s instructions and eluted in 50 μl of H4, and 10 U nuclease S1 (Thermo Fisher Scientific), and then incubated at room temperature for 30 min. The reaction was stopped by adding 5 μl of 10% SDS, and then the RADICL-seq protocol was followed. (iii) For the library generated with RNase V1 treatment, after the chromatin dA-tailing step, each sample was digested with 0.01 U of RNase V1 (Ambion) at room temperature for 30 min. The reaction was stopped by adding 5 μl of 10% SDS, and then the RADICL-seq protocol was followed.Generation of libraries treated with different enzymes targeting the RNA. Libraries for treatment with different enzymes were produced following the above protocol for the generation of RADICL-seq libraries with the following exceptions: (i) For the library with no enzymatic treatment, the RNase H digestion after chromatin dA-tailing was omitted. (ii) For the library generated with nuclease S1 treatment, prior to the chromatin end-repair step, the purified bead–nuclei pellet was resuspended in a 200 μl solution containing 40 mM sodium acetate (pH 4.5 at 25 °C), 300 mM NaCl, 2 mM ZnSO11. CAGE libraries were prepared according to Takahashi et al. 46. Briefly, 3 μg of nuclear RNA was used for reverse transcription with random primers. The 5ʹ end of each cDNA–RNA hybrid was biotinylated and captured using magnetic streptavidin-coated beads. After capture, cDNAs from cap-trapped RNAs were released, ligated to 5ʹ-barcoded linkers and digested with EcoP15I. The cDNA tags were then ligated with a 3ʹ linker and amplified with nine PCR cycles. Libraries were sequenced on an Illumina HiSeq 2500 system, with 50-bp single-end reads.CAGE library preparation. RNAs from nuclear fractions of mESCs and mOPCs were extracted as previously publishedl47 (ver. 2.31). Multiplexed reads were split by six nucleotide barcodes embedded in the 3ʹ-linker sequence. Artificial sequences were removed using TagDust48 (ver. 1.1.3). We identified and removed rRNAs from the extracted RNA tags using RNAdust49 (ver. 1.0.6) with a ribosomal DNA repeating unit (GenBank: BK000964.2). PCR duplicates were removed from paired RNA–DNA tags using FastUniq50. We aligned RNA and DNA tags separately to the mouse genome (mm10 assembly) using BWA51 (ver. 0.7.15-r1140) with aln and samse functions. Mapped RNA and DNA tags were paired with unique sequencing read IDs using samtools52 (ver. 1.3.1) and bedtools53 (ver. 2.17.0). These processes were run on the MOIRAI pipeline platform54.RADICL-seq mapping and processing. RNA and DNA tags at both ends of the adapter were extracted from raw sequencing reads by using TagDust212 (GENCODE release M14) was downloaded and genic transcripts  were divided into four major groups of biotypes: protein_coding, long_ncRNA (defined according to GENCODE “Long noncoding RNA gene annotation”), ncRNA (defined according to GENCODE “Noncoding RNA predicted using sequences from Rfam and miRBase”), and other . The mouse genome binning at 25-kb resolution was performed using bedtools53 (ver. 2.26.0) with parameter -w 25000, and 1 Mb bins for the genomic heatmap were generated using -w 1000000.Reference annotation and genome binning. The comprehensive gene annotation for mouseAnnotated feature overlaps. RNA and DNA tags were associated unambiguously with the corresponding annotated features. To achieve that, we identified each fragment by the single-nucleotide position at its center to reduce the possibility of fragments overlapping with multiple genes or bins. The strand information was considered for RNA tags, but discarded for DNA tags. Both RNA and DNA tags were required to map uniquely  = 37) to the genome.55.Reproducibility. The summary of interactions by condition, replicate, and RNA–DNA pairs; the calculation of pairwise Pearson’s correlation coefficients on the counts of complete sets of observations; and the visualization of the results were performed using the CAGEr package56 (ver. 2.5.0a) with default parameters, and low-quality reads (MAPQ <10) were removed. The files were similarly intersected with GENCODE ver. M14 to generate counts and then normalized to transcripts per million (TPM). A lower threshold of 1 was applied to both RPK and TPM to select interacting and expressed transcripts, respectively. The square of the Pearson’s correlation coefficient was used to determine the dependency between variables.Correlation with RNA-seq. RADICL-seq interactions were summarized per genic transcripts and normalized to reads per kilobase (RPK) to account for over-representation of longer transcripts. Nuclear and cytosolic RNA-seq (and CAGE-seq) reads were mapped to the mm10 genome using STARP value calculations were performed in R using the binom.test function with parameters N, P, and alternative = “greater.” For any given interaction, N is the total number of interactions of the RNA involved and P is the reciprocal of the total number of unique genomic bins the RNA has been observed to interact with. The p value correction was performed using the p.adjust function with method = “BH.” Interactions with an adjusted p value < 0.05 were deemed significant. The significant datasets were then filtered to remove any RNA–DNA contacts intersecting blacklisted regions for the mm10 genome.RADICL-seq and GRID-seq robust interactions calculation. 57 where each value is colored by the most-represented RNA region or RNA class if more than one individual interaction was assigned to these coordinates. All interactions were then divided into six classes of genomic distance between the RNA and DNA end of the tag, and the proportion of interactions derived from specific RNA regions and RNA classes were visualized in barplots.Genome-wide RNA–DNA interaction plots. For the genome-wide interaction plots, the genome was divided into 25-kb bins, and each interaction was assigned to these bins for both the RNA and DNA end of the tag. For each unique combination of the two bins (RNA and DNA coordinates), all interactions were collapsed into one value to avoid overplotting. The plot was produced in R using the ggplot2 package28. For all cell types and conditions, the coverage of RNA and DNA tags was visualized at the boundaries of TADs in the form of a metaplot.RNA–DNA interactions with respect to TAD boundaries. To investigate any relationship between RNA–DNA binding and TAD boundaries, published mESC and mouse neural progenitor cell TADs were retrievedTo further investigate the effects of TAD boundaries on the spread of RNA–DNA interactions, interactions were split into those that originated within a TAD and those that did not. This was performed on a TAD-by-TAD basis, because interactions that originate outside one TAD may originate within a neighboring TAD. The coverage of DNA-binding locations for each set of interactions was visualized in windows around TADs as metaplots.58. This function divides the genomic regions of interest into an equal number of bins, and then it calculates the mean scores for each bin across regions. In our case, each TAD region was extended by the same size both upstream and downstream, and the resulting region was chopped into 300 bins and averaged per position by the ScoreMatrixBin function to generate the average profile of tags coverage.The metadata profiles were generated using the ScoreMatrixBin function from the genomation R package25. As with RADICL-seq RNA and DNA tags, the average coverage of DRIP-seq reads in bins around TAD boundaries was visualized in the form of a metaplot. To investigate whether there was enrichment for RADICL-seq interactions binding within R-loops, the average coverage of RADICL-seq DNA tags within DRIP-seq peaks was visualized in the form of a metaplot for the total and NPM datasets.Comparison of RADICL-seq data with DRIP-seq data. To compare DRIP-seq and RADICL-seq DNA-binding patterns in the significant datasets, mESC DRIP-seq data were retrieved from the Gene Expression Omnibus (GEO) database (accession no. GSM1720620)Correlation of differences in gene expression and genome-wide DNA binding profiles between mESCs and mOPCs. To explore the relationship between gene expression and RNA–DNA binding, the binding pattern and expression level of all genes were compared between the mESC and mOPC total datasets. To compare gene expression between the cell types, the difference in normalized counts between cell types was calculated as a percentage of the maximum expression in either condition. To compare the DNA-binding profiles between the cell types, the genome was divided into 5-kb bins, and each bin was assigned a value of 1 or 0 depending on whether there was a DNA tag present in that bin. This was performed for all genes. The binary vectors produced in this way were then compared by calculating the Jaccard distance between the vectors derived from each cell type. The resulting data from each analysis was visualized as a scatterplot.59 (ver. 0.4.5) with parameters --paired --trim1 and aligned to the mm10 genome by using Bowtie260 (ver. 2.3.3.1) with parameters --no-mixed --no-discordant. Samtools52 (ver. 1.5) was employed to select uniquely mapping fragments and Picard MarkDuplicates  was used to remove PCR duplicates. Peak calling was performed using macs261 with parameters --broad --nolambda –nomodel. We employed featureCounts62 to summarize the gene reads from RAP-DNA and Malat1-DNA tags from GRID-seq and RADICL-seq. Gene counts were normalized to RPK and sorted according to the normalized values. The top 10,000 genes from each dataset were used to generate the Venn diagram with the VennDiagram package63 in R. Linear distance to nearest peak was calculated using the peaks output file obtained from macs2 and the distanceToNearest function from the GenomicRanges package64 in R. SPRITE RNA and DNA clusters were retrieved from GEO (accession no. GSE114242), and only DNA coordinates present within the same cluster as Malat1 RNA were selected for the analysis.Comparison of RADICL-seq with RAP-DNA for Malat1. Malat1 RAP-DNA data was obtained from GEO (accession no. GSE55914). The raw reads were processed with TrimGalore!18. Peak coordinates were lifted from the mm9 genome to the mm10 genome by using LiftOver, and genes overlapping these peaks were considered as targeted by Rn7sk. We employed featureCounts62 to summarize by gene the Rn7sk-DNA tags from GRID-seq and RADICL-seq data. Counts were normalized to count per million and averaged across replicates. The Venn diagram was generated using the VennDiagram R package. Linear distance to nearest peak was calculated using the peaks output file obtained from LiftOver and the distanceToNearest function from the GenomicRanges package64 in R. SPRITE RNA and DNA clusters were retrieved from GEO (accession no. GSE114242), and only DNA coordinates present within the same cluster as Rn7sk RNA were selected for the analysis.Comparison of RADICL-seq with ChIRP-seq for Rn7sk. The list of Rn7sk genomic target locations in the mm9 genome was retrieved from Flynn et al. 53 (ver. 2.26.0) and RepeatMasker30 (ver. 4.0.6) to generate the dataset of RADICL-seq RNAs intersecting (with options -s -wb) and not intersecting (with option -v) with REs annotated in the mm10 genome. Self-interactions were removed from the datasets by comparing the GENCODE Gene ID associated with both the RNA and the DNA tags of the same pair. The relative percentage of interactions across different intervals of RNA–DNA distance was calculated by dividing the number of significant interactions in a given RNA–DNA distance interval by the total number of RNA tags intersecting a given repeat family for each experimental condition. P values were calculated by two-sided Student’s t test.Analysis of repetitive elements. The significant RNA–DNA pairs in this dataset were generated by using the same approach described above for the other RADICL-seq significant datasets. RNA and DNA tags with MAPQ ≥37 were processed using bedtoolscirclize package65 in R was used to visualize the results genome wide and in greater detail for cis interactions, and the log2 of counts were used to color the connecting lines of the RNA–DNA pairs.Transcript-specific genomic interactions. The significant interactions for the transcripts under analysis were summarized by DNA bins, and reported as counts associated with genomic coordinates. The 28 and processed using the Hi-C User Pipeline (HiCUP)66 (ver. 0.5.3). Using this pipeline, we mapped Hi-C paired-end reads to the mm10 genome and filtered reads for expected artifacts resulting from the sonication and ligation steps  of the Hi-C protocol. Data from different biological replicates were then pooled together. HOMER67 (ver. 4.7.2) software was used to filter HiCUP-processed Hi-C paired-end reads with MAPQ = 30 (following recommendations described in Yaffe and Tanay20) and other HOMER recommended settings . HOMER-filtered Hi-C-mapped paired-end reads were then binned at a fixed resolution of 25 kb and normalized for restriction fragment bias using HOMER’s simpleNorm algorithm.High-throughput Chromosome Conformation Capture (Hi-C) paired-end reads processing. Publicly available Hi-C sequencing paired-end reads for mESCs and neural progenitor cells were obtained from Bonev et al. random. Peak information for histone modifications and DHS-seq and ATAC-seq data was downloaded from ENCODE. Only H3K4me3 and H3K9ac peaks positioned outside the first exon and intron were used for the analysis. Data of density plots were calculated using makeTagDirectory followed by annotatedPeaks in HOMER67 (ver. 4.9) with parameters -size 10000 -hist 1 -histNorm 100. Finally, data were divided by the mean of values of ±5000 bp to align baselines. Plots were generated by ggplot257 with the geom smooth option.Density plots. Data for density plots were extracted from RADICL-seq RNA and DNA processed data. Random regions were extracted using bedtools trans contacts, we used the spliced transcripts ENSMUST00000146701.7 and ENSMUST00000172812.2, respectively. We assessed the enrichment in triple helices in NPM trans contacts expanded by 1000 bp over genomic average. To do so, the Triplex Domain Finder (TDF) region test68 was used  with 1000 random genomic background sets.Triple helix search. To computationally determine the location of triplex helices in Meg3 and Malat1 Distribution of unique interacting RNAs at gene promoters. DNA tags from the mESC and mOPC total datasets were mapped to de novo CAGE-derived gene promoters by using intersectBed in bedtools, allowing for a 2-kb window around the actual promoter coordinates. The number of RNA tags originating from unique genes was then calculated for each promoter. Promoters were ranked from highest to lowest number of unique interactions, and additional information  was collated for use in further analyses.69 (ver. 1.2.2) with up to two mismatches, only keeping unique alignments with MAPQ = 20. The CAGEr55 (ver. 1.20.0) R package was used to extract TSSs, normalize counts, and define transcriptional clusters. Briefly, the most 5′ position of a CAGE tag represents a TSS; the 5′ coordinates were extracted from the tags, and a genome-wide TSS map was generated at single-nucleotide resolution. The raw counts for the CAGE tag starting site were normalized to expression per million tags using a power-law distribution, and used as proxy for the expression of the corresponding genic transcript. We clustered the tags to define distinct CAGE peaks that occurred within 20 bp and expression was summed per transcriptional cluster.CAGE data mapping and processing. Multiplexed sequencing reads were split by barcode sequences. Reads with ambiguous bases “N” were removed, and linker sequences were trimmed from the 3′ end to create reads of length 21–27 nts. The reads were mapped to the mm10 genome (GENCODE release M14) using bowtieFurther information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Guanine-rich sequences are able to form complex RNA structures termed RNA G-quadruplexes in vitro. Because of their high stability, RNA G-quadruplexes are proposed to exist in vivo and are suggested to be associated with important biological relevance. However, there is a lack of direct evidence for RNA G-quadruplex formation in living eukaryotic cells. Therefore, it is unclear whether any purported functions are associated with the specific sequence content or the formation of an RNA G-quadruplex structure.Arabidopsis transcriptome. We find a global enrichment of RNA G-quadruplexes with two G-quartets whereby the folding potential is strongly influenced by RNA secondary structures. Using in vitro and in vivo RNA chemical structure profiling, we determine that hundreds of RNA G-quadruplex structures are strongly folded in both Arabidopsis and rice, providing direct evidence of RNA G-quadruplex formation in living eukaryotic cells. Subsequent genetic and biochemical analyses show that RNA G-quadruplex folding is able to regulate translation and modulate plant growth.Using rG4-seq, we profile the landscape of those guanine-rich regions with the in vitro folding potential in the Our study reveals the existence of RNA G-quadruplex in vivo and indicates that RNA G-quadruplex structures act as important regulators of plant development and growth. AArabidopsis G3VL1-9: G3 RG4 with guanine vacancy (G3V) in one of the G-quartet layers, with loop length between 1 and 9 nt [G3N1-9 +G3N1-9+G3N1-9+G2 or G2N1-9+G3N1-9+G3N1-9+G3]G3bulge: G3 RG4 with a bulge of loop, with loop length between 1 and 9 nt [G3N1-9+ (G2HGN1-9 or G2HGN1-9) + (G2HGN1-9 or G2HGN1-9)+G3]G2L1-2: G2 RG4s with loop length between 1 and 2 nt [G2N1-2+G2N1-2+G2N1-2+G2]G2L1-4: G2 RG4s with loop length between 1 and 4 nt [G2N1-4+G2N1-4+G2N1-4+G2]G2L1-9: G2 RG4s with loop length between 1 and 9 nt [G2N1-9+G2N1-9+G2N1-9+G2]In definitions, N represents A, C, G or U while H represents A, C or U.+ or K++PDS conditions were listed in Additional file The G-rich region sites identified with KArabidopsis transcriptome were predicted by Quadbase 2 with non-overlapping and non-greedy settings. The predicted regions with an average coverage above 60 of Li+ channel in rG4-seq, not overlapped with the detected regions, and 50-nt flanking sequences were pooled as undetected G-rich regions.G-rich regions of RNAfold from ViennaRNA (version 2.3.3) with options ‘-p -d2 -T 22.0’ for sequences folding at 22 °C and the base-pairing probability corresponding to MFE was extracted.We used n denotes the number of G residues in the G-tracts (continuous runs of guanine in the G-rich region), ri denotes the reads number in SHALiPE profiles at position i, and Gin index was calculated from the SHALiPE-Seq libraries with reads number of G residues in G-tracts as described with modifications .1\docum+)/Gini (in vitro Li+) ≥ 1.1 and G residues with average read count ≥ 50 reads/nt were included.To calculate in vivo folding score of RG4s, the regions with Gini (in vitro Khttp://geneontology.org). The genes with G-rich regions identified by rG4-seq in the presence of K+ for Arabidopsis, or genes with average RT read counts ≥ 50 on predicted G-rich regions for rice, were used. The GO categories with a P value smaller than 0.05 were listed.Enrichment analysis of GO categories was performed online by the Gene Ontology Consortium (http://www.r-project.org). Statistical methods and significance are indicated in the main text or figure legends for individual tests.All analysis was performed using the R programming language (https://www.ncbi.nlm.nih.gov/sra) under BioProject ID number PRJNA561194.Sequence data have been deposited in the Sequence Read Archive (SRA)  analysis reveals enrichment of genes with similar molecular functions in Arabidopsis and rice for the genes containing RG4s. Figure S7. RG4 on HIRD11 modulates plant growth and translation. Figure S8. Dual luciferase reporting assay reveals that the RG4 on the 3’UTR of HIRD11 regulates translation.Additional file 2: Table S1. G-rich regions with folding potential identified by rG4-seq with K+. TableS2. G-rich regions with folding potential identified by rG4-seq with K++PDS. TableS3. Gini index of SHALiPE profiles in vitro in the presence of Li+ and K+. TableS4. In vivo folding scores of Arabidopsis G-rich regions. TableS5. In vivo folding scores of rice G-rich regions. TableS6. Gene pairs of orthologues with RG4s in Arabidopsis and rice. TableS7. Primers used in this study.Additional file 3. Review history.


Many RNA polymerases terminate transcription using allosteric/intrinsic mechanisms, whereby protein alterations or nucleotide sequences promote their release from DNA. RNA polymerase II (Pol II) is somewhat different based on its behavior at protein-coding genes where termination additionally requires endoribonucleolytic cleavage and subsequent 5′→3′ exoribonuclease activity. The Pol-II-transcribed small nuclear RNAs (snRNAs) also undergo endoribonucleolytic cleavage by the Integrator complex, which promotes their transcriptional termination. Here, we confirm the involvement of Integrator but show that Integrator-independent processes can terminate snRNA transcription both in its absence and naturally. This is often associated with exosome degradation of snRNA precursors that long-read sequencing analysis reveals as frequently terminating at T-runs located downstream of some snRNAs. This finding suggests a unifying vulnerability of RNA polymerases to such sequences given their well-known roles in terminating Pol III and bacterial RNA polymerase.  •Integrator-dependent and -independent mechanisms terminate snRNA transcription•Integrator-independent termination is allosteric/intrinsic in nature•Sites of allosteric/intrinsic termination often coincide with T-runs•Some Pol II termination may share features with Pol III/prokaryotic mechanisms Davidson et al. show that termination of RNA polymerase II transcription at human snRNA genes can occur by Integrator-dependent and -independent pathways. The latter uses an allosteric/intrinsic mechanism most efficiently detected where T-runs are present in the non-template strand. This is reminiscent of the way many other RNA polymerases terminate. Termination of RNA polymerase II (Pol II) on protein-coding genes requires a polyadenylation signal (PAS) that is bound and endoribonucleolytically processed by a multi-protein cleavage and polyadenylation (CPA) complex with catalytic activity supplied by CPSF73 , 2016. CThe spliceosomal small nuclear RNAs (snRNAs) are a prominent Pol-II-transcribed gene class. snRNA precursors are endoribonucleolytically cleaved by the Integrator complex, in which a paralog of CPSF73, called INTS11, provides endoribonuclease activity and promotes transcriptional termination . Cleavagin vitro  modality -terminally tag endogenous modality . An intemodality A. A consmodality . HCT116 th SMASh A and S1Bth SMASh B.Figure INTS11-SMASh cells but not in unmodified cells, showing the specificity of the system  with primers spanning the canonical Integrator cleavage sites on RNU4-1 and RNU5A-1. These species accumulate following ASN treatment, confirming the effectiveness of the system C. Next, e system D. The efd snRNAs E. An anad snRNAs .RNU4-2 read-through was assayed following INTS1 and INTS11 co-depletion  for 15 min and unprocessed RNU4-2, RNU5A-1, and RNU1-1 transcripts were monitored by qRT-PCR  sno/snRNAs , which has single-nucleotide resolution , and comDIS3-AID cells treated or not with auxin. Long-read protocols normally detect polyadenylated RNA, but snRNA precursors may not have this modification. RNA was, therefore, GI-tailed to provide a common 3′ end to amplify (in vitro), reverse -transcription was designed to enrich oligoadenylated 3′ ends, which are well-established features of exosome substrates  and upstream processing status of snRNA termination products, we used long-read nanopore sequencing on nuclear RNA from  amplify A. To disbstrates . RNA-derbstrates B.Figure RNU5F-1 where reads extend beyond some T-stretches (Escherichia coli poly(A) polymerase (EPAP) and short-read sequencing gave similar findings to these long-read data  is not implicated. We suggest that Pol II is generally prone to terminate stochastically downstream of snRNAs, with this activity focused by T-runs. T-runs are intrinsic terminators of other RNA polymerases and of purified Pol II in vitro , but we in vitro . Finallyin vitro . In thoss.west@exeter.ac.uk).Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Steven West (New reagents generated in this study are available via the lead contact.The accession number for the new sequencing datasets reported in this paper is Gene Expression Omnibus: GSE150238.2 and grown in dulbeccos modified eagle medium (DMEM) containing 10% fetal bovine serum. INTS11-SMASh cells were obtained by transfecting a subconfluent 6-well dish with 1ug px330, containing the INTS11 guide RNA sequence, and 1ug each of SMASh neomycin and hygromycin repair constructs using Jetprime. Media was refreshed after 16 hr and after a further 48 hr cells were expanded to a 100mm dish containing 30ug/ml hygromycin and 800ug/ml G418. ∼10-14 days later, single colonies were picked and expanded for genomic PCR confirmation of INTS11 modification. DIS3-AID cells are described in detail elsewhere as are the nucleotide sequences of P2A, Neomycin, Hygromycin and the SV40 PAS included in the HDR templates  and derivatives thereof. These were maintained at 37°C, 5% COemplates .INTS11 homology arms were synthesized by Integrated DNA Technologies and inserted into a pUC-based plasmid using Gibson Assembly. The SMASh tag was PCR isolated from pCS6-YFP-SMASh and inserted into our previously described Hygromycin/Neomycin selection vectors  underlayerd with 1ml HLB with 10% sucrose. After spinning for 5 min (500xg) the nuclear pellet was resuspended in 1ml of Tri-reagent and RNA isolated as described for qRT-PCR above. RNA quantity was determined using a Nanodrop 2000 spectrophotometer (Thermo) and its quality was assessed using a Tapestation (Agilent). 1 μg was rRNA-depleted and libraries were generated using TruSeq Stranded Total RNA Library Prep Kit.Nuclei were extracted from a sub-confluent 100mm dish of CPSF73-AID cells and the control siRNA samples -/+IAA were previously analyzed for protein-coding termination defects  and incubated for 5 min on ice before the addition of 1 mL of NUN2 buffer . Cells were incubated on ice for 10 mins and shook at 2-3min intervals. After spinning , nucleoplasmic RNA was phenol chloroform extracted from the supernatant and concentrated by ethanol precipitation. The pellet (containing chromatin-associated RNA) was re-suspended in Trizol and incubated for ∼30 mins at 37 degrees. RNA was then isolated as per the manufacturers guidelines. 1ug was prepared and used for RNA sequencing as described for nuclear RNA. Chromatin-associated RNA-seq  and GI-tailed using the poly(A) tail-length assay kit. The final RNA quantity and average length were determined using an HS Tapestation (Agilent). 50 ng of input RNA was reverse transcribed into cDNA using a modified VNP primer, downstream steps were performed using the cDNA-PCR Sequencing Kit (SQK-PCS109) according to the protocol. For each library, we loaded 100 fmol sequentially on a MK 1 R9 flow cell and sequenced for ∼4 h per library on a MinION device. The flow cell was washed between loading and sequencing of each library using a Wash Kit according to the user manual.Nuclear RNA extracted from 1ug of chromatin-associated or nucleoplasmic RNA was treated or not with EPAP  then depleted of rRNA. Sequencing libraries were made using the QUANT-seq REV kit.The mNET-seq library protocol was performed as described by CPSF73-AID cells were used per condition. ChIP was performed with the Simple ChIP enzymatic chromatin IP kit exactly as described in the product guidelines with one exception. Antibody  was coupled to sheep anti-mouse dynabeads instead of the beads included in the kit. Sequencing libraries were generated with the NEBNext® Ultra II DNA Library Prep Kit.One 100mm The quality of demultiplexed raw 50 bp fastq reads was assessed using a combination of FastQC and MultiQC, before adaptor trimming using Trim_Galore (wrapper for Cutadapt) with default settings. Adaptor trimmed reads passing both length and quality cut-offs were then mapped to GRCh38 (Ensembl) using Hisat2 . UnmappeAdaptor sequences were first removed from raw 50 bp fastq EPAP reads using Trim_Galore with default setting. Trimmed reads were screened using FastQC and MultiQC to confirm adaptor removal before trimming a second time using Trim_Galore, this time to remove long, non-encoded 3′ poly(A) and poly(T) sequences. Following this, reads were mapped to GRCh38 using Hisat2 , discard-a -k 15 -w 5–splice -g 2000 -G 200k -A 1 -B 2 -O 2,32 -E 1,0 -C 9 -z 200 -u f–junc-bonus = 9–splice-flank = yes–no-long-join–secondary = noThe fast5 sequences were base-called and converted to a fastq file format using Guppy before being passed to pychopper to extract full-length reads and orientate them by strand. Adaptor sequences were then removed from full-length reads using porechop and mapped to GRCh38 using minimap2 , with thAligned reads were then filtered to remove unmapped, multi-mapped and low MAPQ (< 30) scored reads using the SAMTools suite .The mNET-seq traces used single-nucleotide resolution BAM files corresponding to the 3′ end of the RNA fragment .For IGV visualization and metagene analysis, libraries were merged with Bamtools . Normalihttps://rnacentral.org/ and a window extending 1 kb downstream of each TES was then calculated and the snRNA gene body was scaled to 200bp. To prevent counting of ambiguous reads, snRNA genes near annotated genes (< 1kb downstream of their TES) were discarded using the BEDtools suite  were selected from 157 ensembl annotated snRNA genes present in ls suite . Pol IIIFor CPSF73-AID RNA Pol II ChIP metagene analysis all annotated snRNA genes were selected and a window of 1 kb downstream of the TES was incorporated. Due to differences in signal coverage between the CPSF73-AID samples, the auxin treated profile was scaled to the control profile using the mean signal difference over the gene body . Further graphical processing of metagene plots was performed in the R environment.qRT-PCR was quantitated using the comparative quantitation function associated with the QIAGEN Rotorgene instrument. Values were first normalized to a loading control (stated in the relevant figure legend) and then samples were compared by quantitating the experimental values relative to the control condition . Bars show the average of at least three replicates (exact n provided in figure legends) and error bars show the standard deviation of the mean.