Cells must coordinate the activation of thousands of replication origins dispersed throughout their genome. Active transcription is known to favor the formation of mammalian origins, although the role that RNA plays in this process remains unclear. We show that the ORC1 subunit of the human Origin Recognition Complex interacts with RNAs transcribed from genes with origins in their transcription start sites (TSSs), displaying a positive correlation between RNA binding and origin activity. RNA depletion, or the use of ORC1 RNA-binding mutant, result in inefficient activation of proximal origins, linked to impaired ORC1 chromatin release. ORC1 RNA binding activity resides in its intrinsically disordered region, involved in intra- and inter-molecular interactions, regulation by phosphorylation, and phase-separation. We show that RNA binding favors ORC1 chromatin release, by regulating its phosphorylation and subsequent degradation. Our results unveil a non-coding function of RNA as a dynamic component of the chromatin, orchestrating the activation of replication origins. Here the authors describe that the human origin recognition complex subunit 1 (ORC1) binds to RNAs transcribed from genes with origins of replication at their TSS impacting origin activation. Linked to the firing in the S phase, some components of the initiation complex are disassembled and targeted for degradation, which is followed by the activation of DNA helicases and loading of replication factors, avoiding DNA re-duplication events in the same cycle3. ORC1 stands out among ORC components in its distinct regulation throughout the cell cycle, which is consistent with its crucial function in the initiation of replication and the maintenance of undamaged cell propagation4.The initiation of DNA replication involves a sequential assembly and disassembly of protein complexes on genomic DNA, which is tightly controlled along the cell cycle. Initiation occurs at specific sites throughout the genome where the Origin Recognition Complex (ORC) associates in M/G1. ORC is composed of six subunits (ORC1-6), of which ORC1 is the pioneering subunit in the binding to the chromatin. This binding recruits other members of the complex, followed by additional initiation factors  in a sequential manner, leading to, (i) licensing and (ii) firing of replication originsS. cerevisiae, the position of replication origins is defined by ORC recognition of DNA sequence-dependent elements5. In contrast, how origins are positioned in mammalian genomes is still an outstanding question, since ORC does not recognize a known consensus DNA sequence6. Furthermore, only ~20% of licensed origins in a given cell fire in the S phase7, but how this activation is regulated is not fully understood. Multiple chromatin features are known to influence the flexible selection and activation of origins, which, in combination, dictate the probability of stochastic origin activation. These include chromatin accessibility, specific histone marks such as H4K20me29 and H2AZ10, and the presence of DNA sequences prone to form G-quadruplexes (G4)13. While the simultaneous replication and transcription of a precise DNA position are strictly incompatible, the most active origins are localized at transcription start sites (TSSs), with origin activity correlating with the level of gene expression20. Thus, in the entry of the S phase, when early replication origins are fired, RNA is produced in close proximity. This raises the possibility that RNAs could influence origin selection or activation. While previous reports have pointed to specific roles for RNA in replication initiation in X. laevis shortly after fertilization21 and at Epstein Barr virus OriP22, whether transcribed RNA plays a general role in the activation of mammalian origins yet remains unknown.In Here, we show that ORC1 interacts with RNAs transcribed at active origins, which is linked to ORC1 dynamic association with the chromatin and has an impact on replication origin activity.24, part of an Intrinsically Disordered Region (IDR) and separated from other domains mediating, among other functions, its binding to nucleosomes25 , while the control IgG did not recover detectable amounts of RNA. To confirm the capacity of immunoprecipitated ORC1 to retrieve RNA, we also performed anti-Flag RIP-seq from nuclear extracts of HCT116 cells transiently expressing ORC1-3xFlag. Most of the identified transcripts (68%) overlapped with the ones interacting with the endogenous ORC1  . Moreover, it showed a correlation between theoretical binding score and experimental fold changes in RIP-seq , which only recovered neglectable RNA amounts   was found with the RNAs identified by RIP-seq , and interact with each other in the 3D nucleus with higher frequency , and RNA expression levels, while an opposed trend was observed when considering marks of silent chromatin (H3K27me3 and H3K9me3). Interestingly, the ORC1 iCLIP signal also correlated with H4K20 methylation, recognized by ORC19, as well as with ORC1 chromatin binding determined by ChIP-seq   compared to a scramble control ASO  showed a decrease of origin activation at TSSs upon knockdown of GAA-RNAs   did not cause changes in origin activity  inside ORC1 IDR have been implicated in ORC1 self-interaction as well as interaction with Protein Phosphatase 1α (PP1α) and CDC6on4 Fig. . Thus, t38, where the balance between positive and negative charges determines whether RNA promotes the formation or dissolution of IDR-containing protein condensates, RNA could be playing a sequential role in the initiation of replication  were cultured in RPMI-1640 (GIBCO), and U2OS cells (HTB-96) in DMEM (GIBCO), all mediums supplemented with 10% fetal bovine serum (GIBCO) and 1x penicillin/streptomycin (Lonza), and maintained at 37 ˚C and 5% CO47 were cProteasome inhibition was achieved with short pulses (4 and 6 h) of MG-132 (MilliporeSigma) 50 µM. Cycloheximide (Sigma) was incubated in a culture medium at 100 µg/mL for 4 or 6 h for translation inhibition.For RNA in vivo labeling, synchronized U2OS cells were incubated with 5-ethynyl uridine (EU- Thermo Fisher) at 0.2 mM final concentration in complete culture medium, in short (20 minutes) or long (16 and 3 h release) pulses.For fiber assays, exponentially growing HCT116 cells were first pulsed with 50 µM CldU (Sigma) for 20 minutes, washed, and subjected to a second 20 min pulse with 250 µM IdU (Sigma).42.In experiments with RNase A treatment, synchronized cells at different points of the S phase (see cellular synchronization section) were trypsinized, permeabilized with 0.05% Tween-20 in PBS for 10 min on ice, and mock-treated or treated with 1 mg/mL RNase A (Sigma) for 30 min at RT, as previously describedCellular transfections were performed with Lipofectamine 2000 (Invitrogen) in Serum-free Opti-MEM (GIBCO), following manufacturer instructions.31  covering the entire S phase.For cellular synchronization at G1/S, U2OS cells were subjected to serum starvation for 48 h, while HCT116 cells were blocked by double thymidine shock as previously described in ref. 2B4O7 incubation, cells were treated with RNAse A (Promega), resuspended in PBS, and the DNA stained with propidium iodide 1 mg/mL (Sigma). In flow cytometer FACSCalibur, DNA staining was recorded by the BD CellQuest program. Cell cycle profiles were determined by considering the amount of labeled DNA (FL2-H) per cell.For cell cycle analysis, cells were fixed in PBS 2% PFA, and incubated in 2 N HCl 0.5% Triton for 30 min. Followed by 0.1 M NaCell preparations were fixed with TRIzol (Sigma), and RNA precipitated with isopropanol.In bulk RNA-seq experiments of HCT116 cells treated with control or anti-GAA ASOs, total RNA extraction was followed by Turbo DNAse (Invitrogen) digestion and library preparation with TruSeq Stranded mRNA kit from Illumina. Duplicate experiments were sequenced with Illumina NextSeq 500.HPRT1 or GAPDH RNA levels were used for the normalization of total and cytoplasmic cellular extracts, while MALAT1 was used to normalize RNA levels in nuclear extracts. For RIP and UV-RIP validations, RNA levels were normalized by their levels in a 10% experimental input of nuclear RNA. Statistical differences between relative RNA levels or relative enrichments were calculated by unpaired two-tailed Student’s t-test. RT-qPCR primers were self-designed or designed with the Universal Probe Library Assay Design Center (Roche), and purchased from Metabion  and reverse-transcribed using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystem) with random hexamer primers, following manufacturer instructions. The obtained cDNA was analyzed by quantitative PCR (qPCR) using iTaq Universal SYBR Green supermix (Bio-Rad) in a ViiA™ 7 Real-Time PCR System machine (Thermo Fisher), all reactions performed in quadruplicate. 44, while subcellular fractionation in cytoplasm and nucleus, and nucleoplasm if indicated, was performed as described elsewhere45.Soluble-chromatin cellular fractionation was performed as previously described in ref. t-test.Proteins from cell preparations were quantified with Pierce BCA Protein Assay Kit (Thermo Fisher). Samples were run in denaturing polyacrylamide gels by electrophoresis, and then transferred to nitrocellulose membranes (Bio-Rad). Membranes were blocked with 5% milk in PBS-Tween, and incubated overnight with primary antibodies  for ten cycles, pre-cleared with protein A/G Dynabeads, and incubated with 5 µg of control IgG (sc-2025) or antibody of interest . Protein A/G Dynabeads were added to sequester the antibody, and washed five times. For protein analysis, 10% of beads and inputs were resuspended in 2X Laemmli sample loading buffer, and run in acrylamide gels for western blot. RNA from beads and inputs was obtained following the RNA extraction protocol, to perform RT-qPCR  or library preparation. For sequencing, samples were first treated with Turbo DNAse (Invitrogen), and libraries were prepared with the TruSeq Stranded Total RNA kit from Illumina. Sequencing of triplicate (anti-ORC1) or duplicate (anti-Flag) experiments was done with Illumina NextSeq 500.Native RNA immunoprecipitation was performed as previously described6 cells, as previously described in ref. 3, with minor modifications. In brief, after cellular lysis , nuclei were resuspended and incubated for 30 min in high salt buffer , supplemented with phosphatase and protease inhibitors 1x (Roche), as well as benzonase to digest DNA. After sonication (Bioruptor Diagenode) for 30 cycles, NaCl and Igepal concentrations were brought down to 200 mM and 0.2%, respectively, by adding an equal volume of dilution buffer. Clear nuclear lysates were then pre-cleared with protein G Dynabeads, and incubated with 5 µg of anti-FLAG antibody , untransfected cells being the negative control. Protein G Dynabeads were added to sequester the antibody, and washed five times with complete washing buffer . Beads and inputs were resuspended in 2X Laemmli sample loading buffer, and run in acrylamide gels for Coomassie blue staining.Transfected WT-ORC1-3xFlag and MUT-ORC1-3xFlag proteins in HCT116 cells  at 4 °C. Samples were then placed in a 37 °C room overnight. Peptides were later extracted by removing the ammonium bicarbonate solution, followed by one wash with a solution containing 50% acetonitrile and 1% formic acid. The extracts were dried in a speed-vac (~1 h). For the analysis, samples were reconstituted in 5–10 µl of HPLC solvent A . A nano-scale reverse-phase HPLC capillary column was created by packing 2.6 µm C18 spherical silica beads into a fused silica capillary (100 µm inner diameter × ~30 cm length) with a flame-drawn tip48. After equilibrating the column, each sample was loaded via a Famos autosampler  onto the column. A gradient was formed and peptides were eluted with increasing concentrations of solvent B (97.5% acetonitrile and 0.1% formic acid). As each peptides were eluted, they were subjected to electrospray ionization and then they entered into an LTQ Orbitrap Velos Pro ion-trap mass spectrometer . Eluting peptides were detected, isolated, and fragmented to produce a tandem mass spectrum of specific fragment ions for each peptide. Peptide sequences (and hence protein identity) were determined by matching protein or translated nucleotide databases with the acquired fragmentation pattern by the software program, Sequest 49. The modification of 79.9663 mass units to serine, threonine, and tyrosine was included in the database searches to determine phosphopeptides. Phosphorylation assignments were determined by the Ascore algorithm50. All databases include a reversed version of all the sequences and the data were filtered to between a one and two percent peptide false discovery rate. The position and amount of detected phosphopeptides is presented in Supplementary Data Bands at the expected molecular weight were cut, and containing proteins were subjected to mass spectrometry. The samples were reduced with 1 mM DTT for 30 min at 60 °C and then alkylated with 5 mM iodoacetamide for 15 min in the dark at room temperature. Gel pieces were then subjected to a modified in-gel trypsin digestion procedure6 asynchronous HCT116 cells were rinsed in cold PBS and irradiated with 150 mJ/cm2 in a Stratalinker 2400 at 254 nm. To isolate cellular nuclei, the first steps of fractionation iCLIP protocol were performed51, followed by UV-RIP or iCLIP protocols.About 40 × 10For UV-RIP, fixed nuclear pellets of HCT116 cells transiently expressing Flag-tagged WT-ORC1 were resuspended in RIPA buffer and sonicated (Bioruptor diagenode) for 15 cycles. Solubilized nuclear extracts were pre-cleared with protein G Dynabeads, and 5 µg of control IgG (sc-2025) or anti-FLAG  antibodies incubated overnight at 4 °C. Protein G Dynabeads were then added to sequester the antibody, and washed six times. Immunoprecipitates and inputs were eluted, and Proteinase K (NEB) was incubated for 45 min at 45 °C for protein digestion, followed by RNA extraction and RT-qPCR .52, using 106 HCT116 UV-fixed nuclei previously transfected with ORC1-WT-3xFlag (n = 5), ORC1-MUT-3xFlag (n = 2) or untransfected cells as control (n = 1) (Supplementary Table n = 3) or 1 U (n = 2) of RNase  in the presence of 4U of Turbo DNase (Invitrogen), to avoid DNA contamination. Cleared supernatant was immunoprecipitated overnight at 4 °C with 5 µg of anti-Flag antibody . Protein–RNA complexes were visualized using pre-adenylated, infrared dye-labeled L3 adapter with the following sequence: /5rApp/AG ATCGGAAGAGCGGTTCAGAAAAAAAAAAAA /iAzideN/AAAAAAAAAAAA/3Bio/. Reverse transcription was performed using RNA-dependent retrotranscriptase Superscript IV (Invitrogen) and barcoded primers (XXXXX) containing UMIs (NNNN): /5Phos/ WWW XXXXX NNNN AGATCGGAAGAGCGTCGTGAT /iSp18/ GGATCC /iSp18/ TACTGAACCGC. Purification of cDNAs following reverse transcription and circularization was performed using AMPure XP beads  and isopropanol. Libraries were sequenced as single-end 100 bp reads on Illumina HiSeq 4000.iCLIP data of ORC1 was generated by following the iCLIP method described elsewhere6 asynchronous HCT116 cells, stably expressing Flag-tagged wild type and mutated ORC1 (RNA-binding mutant) , with self-designed primers at genomic DNA replication origins or control regions , precipitated with ethanol, and solubilized in TE buffer pH 8 supplemented with RNase OUT (Invitrogen) for 48 h at 4 °C. Denatured DNA was ultracentrifuged in a seven-step 5 to 20% discontinuous sucrose gradient, with SW40Ti rotor at 24,000 rpm for 20 h at 20 °C. About 1 mL fractions were collected, and the DNA precipitated with ethanol. 10% volume of DNA in each fraction was denatured with 0.2 M NaOH and run onto a 1% alkaline agarose gel (50 mM NaOH and 1 mM EDTA). Followed by neutralization with 1x TAE, Syber Gold (Thermo Fisher) staining was used to visualize the fractionation profile with a UV-Biorad camera. Fractions of interest (0.5–2 kb) were treated twice with PNK (Thermo Fisher) and λ-exonuclease (Thermo Fisher) to remove short-cut DNA and keep newly synthetized DNA, which has a 5’ RNA segment. The efficiency of λ-exonuclease digestion was evaluated by incubation of 10% reaction volume with a digested control plasmid.Briefly, genomic DNA of 1–2 × 10t-test. The associated p values were used for heatmap representations.qPCR of purified nascent strands was done as cDNA samples (see RNA processing section), with self-designed primers at genomic DNA replication origins or control regions having SNS-seq data in wild-type HCT116 cells as reference  to eliminate both mRNAs and RNA primers, and facilitate adapters ligation for sequencing. Samples were mixed with random hexamer primer phosphate (Roche), and a reaction with the Klenow exo- polymerase (NEB) was used to extend the primers and synthetize the complementary DNA strand. Taq DNA ligase (NEB) was used to ligate the synthetized fragments, and the dsDNA was extracted and precipitated with ethanol. For library preparation, a protocol primarily designed for High-throughput chromatin immunoprecipitation (HT-ChIP) was followed53. Duplicate or triplicate experiments were sequenced with Illumina NextSeq 2000.For nascent strand sequencing (SNS-seq), ssDNA fragments were converted to dsDNA as previously described in ref. Cultured HCT116 cells on coverslips, and transfected with control or anti-GAA ASOs , and incubated with α-FAM-POD antibody (Roche). After washing with 4x SSC, preparations were incubated with TSA-Cy3 (PerkinElmer) diluted in Amplification Diluent (PerkinElmer). Secondary antibodies were washed with 4x SSC, followed by a wash with 4x SSC 0.1% Triton, and a last wash with 4x SSC. Cells were mounted on microscope glass slides with the mounting solution with DAPI (Vectashield) and imaged with confocal fluorescence microscope Zeiss LSM 880 NLO 63x objective, and images were captured with the ZEN microscopy software (Zeiss). Fiji software was employed for stacks deconvolution and signal quantifications. Probe fluorescence per cell (15 cells each) in 4 independent biological replicates were compared between control and anti-GAA treated cells by unpaired two-tailed Student’s 55, with anti-CDC45  and anti-PCNA (sc-56) antibodies. After immunofluorescence staining with secondary antibodies, coverslips were mounted on microscope glass slides with a mounting solution with DAPI (Vectashield). Preparations were imaged using the 63x objective of the automated microscope Zeiss Axio Imager M1, and images were captured with the ZEN microscopy software (Zeiss). Fiji software was employed for stacks deconvolution and signal quantifications. After delimiting cellular nuclei, fluorescence intensities were measured and normalized by nuclei area. Comparisons between cells (>100) in different experimental conditions were done by unpaired two-tailed Mann–Whitney t-test. Results were validated in two independent biological replicates.Protein imaging at chromatin of HCT116 cells, after removing soluble cellular fractions, was performed as previously described in ref. n section), untreated, mock-transfected, or transiently expressing Halo-tagged wild type and mutated ORC1 (Supplementary Table 56. Incorporated EU (see culture treatments section) was detected with the Click-it Plus Kit (Thermo Fisher) AF647 (Thermo Fisher), and ORC1 with anti-ORC1 antibody (F-10) (sc-398734) or Janelia Fluor® 549 HaloTag® Ligand . After immunofluorescence staining with secondary antibodies, coverslips were mounted on microscope glass slides with freshly prepared super-resolution imaging buffer  flowed through. All raw images were acquired using a custom-built inverse microscope platform (Applied Scientific Instrumentation). Briefly, 639 nm  and 561 nm  laser lines were adjusted to 1.5 and 0.8 kW/cm2, respectively. Fluorescence emission was expanded with a 1.67× achromatic lens tube and was collected on a sCMOS camera . Fluorescence signals were collected sequentially using the AF647  and AF568  single-band pass filters in a filter wheel . A 405 nm laser line  was introduced to enhance recovery of dark state fluorophores when required. About 2000 Frames at 33 Hz were acquired for each color.In STORM experiments, synchronized U2OS cells  is defined asρi (R) denotes the local density of Im1 at location R and R; R. As the correlation is not orientation-specific, the 2D r.Localization of single molecules and the mapping of the two different channels were carried out in algorithms written in MATLAB as previously described in ref. 56. An unpaired two-sample t-test between experimental and randomized data was done to determine the significance of the correlation. The same analyses on cells with no EU incubation were used as the experimental negative control. Results were validated in two independent biological replicates.In brief, each nucleus was first manually outlined to generate a ROI for independent analysis. EU and ORC1 signals from the same ROI were submitted for cross-correlation analysis to obtain their association magnitudes, whilst the cross-correlation between the two species from different ROIs serve as a control describing random distributions500 HCT116 cells transfected with siRNAs  of the IdU track and dividing it by the 20 min of the duration of the pulse; inter-origin distance, measuring the distance between adjacent origins (recognized as IdU-CldU-IdU tracks). Comparisons of fork rate and inter-origin distances between experimental conditions were analyzed by unpaired two-tailed Mann–Whitney 24. Protein concentrations were estimated by Coomassie blue or silver staining, compared to BSA known concentrations.Purified wildtype and mutated  ORC1 RNA-binding regions (amino acids 413–511), fused to GST, were produced in Shou Waga laboratory4. Briefly, E.coli BL21 cells were transformed with fusion plasmids (tagged full-length ORC1) . After centrifugation, the clarified supernatant was incubated with pre-washed Ni-NTA beads for 3 h at 4 °C. Bead-bound proteins were washed with lysis buffer, and eluted with 300 mM imidazole. WT and MUT MBP-PP-GFP-ORC1-6xHis proteins were further purified with amylose beads and eluted with 20 mM maltose. Protein concentrations were estimated by Coomassie blue or silver staining, compared to BSA known concentrations.Wildtype and mutated full-length MBP-PP-GFP-ORC1-6xHis were expressed and submitted to Ni-NTA beads purification, followed by a second purification with amylose beads, as previously described in ref. 2H3O2)2, 0.1 mM EDTA, 5% Glycerol), supplemented with BSA 2 mg/mL, 3 mM ATP (NEB), and RNAs in ribonuclease inhibitors (Promega). The binding reaction was incubated at 30 °C for 30 min, and samples were immediately loaded into a pre-run non-denaturing gel for electrophoresis at 4 °C. RNA was stained in Sybr Gold (Thermo Fisher), visualized with a UV-Biorad camera, and quantified with Fiji software. In experiments with total RNA, RNA fragments were obtained by sonicating (Bioruptor diagenode) total extracts of HCT116 cells, and purifying short RNA fragments with miRNA columns (PureLink). Electrophoresis was done in 7.5% polyacrylamide gels. In EMSAs with CEP95 RNA, RNA fragments were in vitro synthetized with T7 RNA polymerase (Promega), using as a template the PCR products amplified from the pCDNA3-CEP95 vector (Supplementary Table In electrophoretic mobility shift assays (EMSA), wildtype and mutated GST-ORC1 413–511) (concentrations indicated in figure captions) or control buffer , were incubated with RNA (concentration indicated in figure captions) in 20 µL binding buffer , and incubated at 4 °C for 16 h. Following incubation, the reactions were spotted on microscope slides with coverslips and observed immediately with 63x oil immersion objective of the automated microscope Zeiss Axio Imager M1. Images were captured with the ZEN microscopy software (Zeiss). GFP-droplet size was measured with Fiji software.In liquid-liquid phase separation (LLPS) assays, full-length wild type and mutated MBP-PP-GFP-ORC1-6xHis (4 µM) were resuspended in LLPS reaction buffer  supplemented with Prescission protease, in presence or absence of 4 µM of RNA probes previously used (same sequence) in DNA phase separation experimentscatRAPID algorithm estimates the binding potential of a protein–RNA pair through van der Waals, hydrogen bonding, and secondary structure propensities, allowing the identification of binding partners with an accuracy of 0.78 or higher58.The catRAPID analysis to predict ORC1 direct interactions with ORC1 RIP-RNAs was performed following standard pipelines59. Briefly, we used the major RNA isoform for each gene reported in ORC1 RIP-seq experiments, retrieved the ORC1 interaction scores from RNAct60, and all transcripts with p value >0.01 were filtered out. Two classes were analyzed: depleted RNA, when log2 fold change <0, and enriched RNA, if log2 fold change >0, and the difference in the predicted catRAPID scores of enriched and depleted was represented (z-score) and computed with unpaired two-tailed Student’s t-test. We also used the experimental log2 fold change to rank the two groups of transcripts, enriched and depleted. Equal fractions of enriched  and depleted  RNAs were compared. The discriminative power, measured as the Area under the ROC curve (AUC), increases proportionally to the experimental signal and reaches a value of 0.80, indicating strong enrichment of predicted physical interactions.The catRAPID omics v2 was used to predict RNA interactions of the wild type and mutated ORC1 protein sequences 61. We divided the Interaction Propensity score in bins of width 20 (a.u.). For each of the Interaction Propensity Bins, we calculated the fraction of RNAs that obtained decreased Interaction Propensity score against the mutated ORC1 sequence. We found that the RNAs exhibiting “High Interaction Propensity” scores were enriched targets of WT-ORC1, while those exhibiting ‘Low Interaction Propensity’ scores were enriched targets of MUT-ORC1. This finding reinforces the notion that the identified RNAs by RIP-seq are bona-fide ORC1 direct interactors.CleverSuite approach62, which uses two protein sequence sets (Positive and Control/Negative) to build a model able to separate them based on physicochemical features . The model can be reused to predict the classification of other sets. We trained 2 models: RNA and DNA binding ability. For the RNA model we used RNA binding proteins63 and, as control, a set of proteins that were found in the lysate from the same study. For the DNA model, we used Proteins annotated as “DNA binding” from UniProt and, as control, a sample set of similar size as the DNA binding proteins that were not annotated as “DNA binding” nor “RNA binding”. The two models can be found at the links (http://crg-webservice.s3.amazonaws.com/submissions/2021-09/393018/output/index.html?unlock=8bc230ac56 and http://crg-webservice.s3.amazonaws.com/submissions/2013-12/17868/output/index.html?unlock=f3a7ffa08f). We used the predictive ability of the CleverSuite to assess if the ORC1 mutant  belongs to the positive or control set. Specifically, we tested the hypothesis of whether the ORC1 region, including the mutations, affected its ability to interact with RNA (positive) and/or DNA (control). The results unequivocally showed that mutant ORC1 has decreased RNA binding activity, while its DNA binding activity remains unaffected. In the analysis, we considered different ORC1 fragments centered around the mutations to control the signal-to-noise ratio of CleverSuite scores. Specifically, in the RNA-Binding model, all ORC1 WT fragments are predicted as “Positive” (RNA binding) and all the Mutated ORC1 fragments were predicted as “Control”. Importantly, the same regions were predicted as “Positive” for both WT and mutant (DNA binding) when using the second (Control) model. Thus, the DNA binding ability is predicted to be unaffected by RNA-binding mutations.To investigate the effect of amino acid mutations on ORC1, we used the Experimental data were plotted and analyzed using the GraphPad statistical software, following the statistical analysis for each type of data, specified in each method section and/or figure captions. Most experimental data are represented as the average of at least three biological replicates, indicated at figure captions. Imaging data is presented as a representative experiment with multiple measurements, which was validated in additional biological replicates. The number of replicates in sequencing experiments is specified in each method section.https://cran.r-project.org/web/packages/ggplot2/index.html). Significance was obtained using the statistical test corresponding to each type of data analyzed, as explained in each analysis section. In all cases, p values were given using the following thresholds: ns for p value > 0.05; * for p value ≤ 0.05; ** for p value ≤ 0.01; *** for p value ≤ 0.001.R software was used for bioinformatic analysis, using the R package ggplot2 to generate different types of plots (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Fastq files were trimmed with Trimmomatic (v 0.38)64, aligned with STAR65, reads aligning to GL contigs were removed, and FeatureCounts66 was used to quantify the number of reads falling in annotated genes in hg19 human reference genome, downloaded from Ensembl67. DESeq268 was used to measure differential expression, being ASO control, and inputs the reference conditions for RNA-seq and RIP-seq analyses, respectively.In ASO control and anti-GAA (Supplementary Table 69 (GSE118051) were trimmed with Trimmomatic (v 0.38)64, aligned to the hg38 reference genome, downloaded from GENCODE70, using STAR65 with parameters: ‘winAnchorMultimapNmax20 -outFilterMultimapNmax 20 -twopassMode Basic’.RNA-seq public sequences from untreated HCT116 cells71 with the golden standard of the NF-Core consortia (https://nf-co.re/). From ENCODE72: H3K27me3 (ENCFF457PEW), H3K9me3 (ENCFF020CHJ), H3K4me1 (ENCFF531IUP), H3K27ac (ENCFF227RRY), H3K4me3 (ENCFF213WKK), and H3K36me3 (ENCFF059WYR). From Sequence Read Archive (SRA) the following published data10: H2A.Z , H4K20me1 , and H4K20me2 (SRR9850582 and SRR9850583), ORC1 ChIP input (SRR9850586) and IP (SRR9850594 and SRR9850595). Raw sequencing reads were trimmed and quality control was performed to remove poor quality sequences. Adapter-trimmed reads were then aligned to hg38 human reference genome, downloaded from GENCODE70, with BWA73 under default parameters. Replicates were merged and deduplicated to remove optical reads74. To obtain robust estimates of the results, we respected the best practices as suggested by ENCODE consortia75. Then, aligned reads passing all ChIP-seq QC metrics were submitted to MACS2 peak calling76 comparing no antibody input and corresponding samples using the function “callpeak -g hs -B -q 0.05–fe-cutoff 1.5 –broad”.Public ChIP-seq sequencing reads from duplicate experiments  were analyzed using a Nextflow pipeline64. Bowtie277 was used to align the reads to the hg19 human reference genome downloaded from ENSEMBL67. Coverage tracks were generated with bamCoverage78, and the average signal at TSS positions (±5 kb) was divided in bins of 10 bp length, with computeMatrix from deeptools, to be analyzed by paired t-test . Coverage differences  between WT and MUT-ORC1 cells were also measured at TSSs of genes in individual iCLIP-defined quantiles.Self-generated CDC45-treated ChIP-seq samples were trimmed to remove adapted and low-quality sequenced reads with Trimmomatic (v 0.38)79 (https://github.com/tomazc/iCount). Briefly, experimental barcodes were removed and sequencing reads aligned with STAR65 to hg38 human reference genome downloaded from GENCODE70, allowing two mismatches and ten secondary alignments. DNA or chromatin contamination was excluded by aligned data interrogation with infer_experiment.py package from RSeQC software80. Unique Molecular Identifiers (UMIs), were used to distinguish and remove PCR duplicates. To determine protein–RNA contact sites, the sequencing read preceding nucleotide was allocated as the crosslink site event.ORC1 iCLIP sequencing reads were analyzed on the iMaps server  using the iCount softwarehttps://github.com/tomazc/iCount/blob/master/iCount/genomes/segment.py).The presented data refers to analysis from the three experimental replicates of ORC1-3xFlag transfected cells and low (0.4 U) RNase treatment, while non-transfected control was used to corroborate data specificity  and the transcriptional termination site (TTS) of genes, defined by the GENCODE70 annotation from hg38 human reference genome, using 100 nucleotide bins. Normalized iCLIP data was also plotted in metagenes, in ±10 kb windows around genomic TSS positions.For data representation, the iCLIP signal was normalized by sequencing deep and millions of tags (CPM) and binned per nucleotide. Coverage tracks were generated using deepToolshttps://github.com/tomazc/iCount/blob/master/iCount/analysis/peaks.py).The significant crosslink signal was normalized by sequencing deep and millions of tags (CPM). Significant contact sites were identified as iCLIP peaks, using the iCount peak function, based on a false discovery rate (FDR) <0.05 comparing specific sites within a window of three nucleotides with randomized data (100 permutations) and within co-transcribed regions 82, using pre-set motif configuration  and search “+” strand only. Statistical significance was analyzed by two-proportions z-test.To identify RNA motifs mediating ORC1 binding, iCLIP peaks with more than five crosslinks per nucleotide were slop 100 nucleotides both sides and submitted to MEME motif finding algorithm70, were separately and further assessed in experimental snoDB database v.1.2.1 (http://scottgroup.med.usherbrooke.ca/snoDB/ downloaded in December 2020).ORC1 binding preferences for small nuclear RNAs (snoRNAs) annotated hg38 reference genome, downloaded from GENCODE83 and Bedops84 were used to do different types of operations with genomic data, and the UCSC liftOver tool was used to convert coordinates between different genome versions.SAMtoolsp value < 0.05) based on annotation of hg38 human reference genome, downloaded from GENCODE70, defining ORC1 iCLIP-RNAs, RIP-RNAs, and ORC1-RNAs (union iCLIP and RIP) or HC ORC1-RNAs (overlap iCLIP and RIP)  were analyzed by unpaired two-tailed Student’s t-test.Effective ORC1 protein–RNA contact sites from iCLIP were pooled overlapping Ensembl IDs from iCLIP peaks (>5 crosslink sites and <0.05 FDR) and RIP-seq technique . Negative controls of ORC1-RNAs and HC ORC1-RNAs consisted in an equal number of RNAs with log2 fold change <−0.25 and p adj <0.5, or with −0.16 <log2 fold change <0.16 in ORC1 RIP-seq experiment, to consider genes with no ORC1 RNA-binding, with expression in HCT116 cells. Comparisons of gene length and expression between the different groups of genes were statistically interrogated by unpaired two-tailed Student’s t-test. The same controls were used to determine whether ORC1-RNAs and HC ORC1-RNAs interact with each other in the 3D nucleus with different frequencies than controls, and Hi-C data of HCT116 cells were obtained from GEO series accession number GSE10433387 (untreated synchronized combined MAPQ ≥30). Juicer88 tools command dump was used to extract data from the.hic file and obtain KR-normalized intra-chromosomal contact information at 100 kb resolution. Statistical significance in terms of Hi-C contacts between control or genes of interest was analyzed by a two-proportions z-test.Biotypes and gene length of different groups of genes  were obtained from Ensembl BioMarthttps://www2.replicationdomain.com/index.php)89 database, hg19 human reference genome (Int90617792 and Int97243322). Significant enrichment of ORC1-RNA and HC ORC1-RNA genes for early-replicating regions was assessed by hypergeometric test, considering the universe of early and late replicating regions genome-wide.To establish ORC1-RNAs localization within early or late replicating regions of the genome, Repli-seq data for HCT116 cells were obtained from ReplicationDomain  by using the differential enrichment objective function. Negative controls consisted of the same number of randomly selected transcripts from ORC1 RIP-seq data, thus expressed in HCT116 cells.To detect enriched sequence motifs through entire ORC1-bound RNAs, sequences of the largest transcripts were extracted from the list of candidate genes. Input datasets of RNA sequences were run to find significantly enriched motifs on any length with MEMEhttp://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Bowtie277 was used to align the reads to the human hg19 reference genome, downloaded from Ensembl67, and Picard (http://broadinstitute.github.io/picard) was used to remove duplicate reads.The quality of the sequencing files was assessed with FastQC .To compare SNS-seq in control vs anti-GAA cells, statistical analyses were done for each of the experimental replicates, by considering the normalized raw sequencing signal  at TSS (±7.5 kb) genomic positions. The average signal was divided in bins of 10 bp length, with computeMatrix from deeptools, and analyzed by paired t-test .To compare SNS-seq experiments in WT vs MUT-ORC1 expressing HCT116 cells, we merged raw sequencing signal  at TSS (±5 kb) genomic positions, from the two experimental replicates. The average signal was divided in bins of 10 bp length, with computeMatrix from deeptools, and compared by paired 77, that uses two peak callers: MACS276 with a threshold of q = 0.1 to identify narrow peaks, and EPIC290 with FDR = 0.1, to detect diffuse peaks. To obtain the common peaks detected with both methods, we used intersectBed from BedTools91, with parameters -wa -u . The final set of peaks considered for subsequent analyses includes those common MACS2 + EPIC2 peaks that are present in at least two out of the three SNS-seq replicates in the wild-type HCT116 samples, and in both replicates anti-GAA and control ASOs.Origin peaks were determined following the analysis pipeline in13, since 63% of the HCT116 origins overlapped with those in quantiles Q1 and Q2 of the ten defined by the mentioned study, which represent the most robust origins with the highest conservation among cell types 13. Significant enrichment of SNS-seq peaks at TSSs of ORC1-RNA genes, also visualized in metagenes of normalized reads, was assessed by a hypergeometric test, considering the presence of SNS-seq peaks at TSS of genes in the entire genome. To compare anti-GAA samples against control samples, peaks were divided into different groups: present only in control replicates, present only in anti-GAA replicates, common peaks (those overlapping anti-GAA and control peaks), and peaks differentially bound in anti-GAA or in control. Differentially bound peaks were determined with DiffBind92 (v.2.10.0) and DESeq268 (v.1.22.1) R packages. ChiPseeker93 was used for SNS-seq peak annotation, comparison, and visualization in heatmaps around TSS positions .SNS-seq identified 37725 origins that were consistent with previously published origin mapping in other cell typesDifferences in SNS-seq (ASO control vs ASO anti-GAA and WT-ORC1 vs MUT-ORC1) in combination with other data (RNA-seq ASO control vs ASO anti-GAA or ORC1 iCLIP) were assessed by Gene Set Enrichment analyses .10, were visualized and plotted in the IGV (integrative genomics viewer) browser94. Normalized sequencing data was also presented in coverage tracks and metagenes, using deepTools78, heatmaps, and violin or bar plots.ChIP-seq, SNS-seq, and RNA-seq data were normalized by RPKM, in non-overlapping bins of 10 nucleotides. Genome-wide tracks of normalized ORC1 iCLIP (crosslinks and iCLIP peaks), SNS-seq, ORC1 RIP-seq, and public ChIP-seq dataTo study the correlation between replication origins and chromatin marks at TSSs of genes encoding for ORC1-RNAs, normalized ChIP-seq, SNS-seq, and RNA-seq data were represented in a correlation heatmap, with associated Spearman’s Correlation values.t-test.ORC1 RNA-binding iCLIP data, normalized by sequencing deep and millions of tags and binned per nucleotide (CTPM), was used to define the division of ORC1-RNA genes  in 6 quantiles (Q1 to Q6), defining levels of direct ORC1-RNA binding. Having determined iCLIP-defined groups of genes, normalized sequencing data was represented and subjected to statistical analyses, to study the correlation between replication origins, chromatin marks, and ORC1 RNA-binding. Statistical differences between quantiles of genes in terms of RNA levels (RNA-seq), SNS-seq, and ChIP-seq, represented in profile, heatmap, violin, or bar plots, were analyzed by unpaired two-tailed Student’s 95 (v.1.22.0) R package with 10,000 permutations to calculate statistical significance. Genes were filtered ranked according to their log2 fold change between anti-GAA vs Control RNA-seq samples, or between the signal across the TSS (±5 Kb) in WT-ORC1 against MUT-ORC1 SNS-seq replicates. In both cases, genes with significant  differences between conditions were considered for generating the ranking lists.Gene set enrichment analysis (GSEA) analyses were performed using fgsea92 (v.2.10.0), with a cut-off of p value < 0.05, to select genes with TSS SNS-seq peaks  enriched in the control condition.Ranked genes according to their upregulation or downregulation in RNA-seq experiments (ASO control vs ASO anti-GAA) were crossed with a list of genes with reduced SNS-seq signal in the anti-GAA condition. This second list of genes was defined by DiffBindRanked genes according to their enrichment of SNS-seq signal at TSS (WT vs MUT SNS-seq) were crossed with the list of genes in iCLIP-defined quantiles, individually  or in combination (Q1 + Q2 + Q3 and Q4 + Q5 + Q6).To trace orthologues of human ORC1, sequence datasets for 132 proteomes . All Inparanoid blasts were run using a threshold e-value of 0.01 and different matrices were used in pair-wise comparisons to account for different evolutionary distances: Blossum45 to compare prokaryotes, Blossum62 for eukaryotes, and Blossum80 for comparisons between metazoans.Homologous sequences of human proteins were identified using Inparanoid97 was used to build a multiple sequence alignment (MSA) with the ORC1 orthologous proteins from vertebrates and metazoans 103. Non-overlapping hits with scores above the conditional e-value threshold of 0.05 were considered significant.To analyze the domain repertoire of ORC1 orthologues, we ran the Hmmscan program from HMMER 3.2 (hmmer.org)104, and the MetaDisorder server105 was used to predict intrinsic protein disorder using iPDA106, PrDOS107, Pdisorder (http://www.softberry.com/) and IUPred long108.ORC1 protein secondary structure was predicted with PsiPred109 was used to predict the 3D protein structure of human ORC1 and the model was visualized and colored using the PyMOL Molecular Graphics System .Phyre2Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Reporting Summary


Non-coding RNAs (ncRNAs) are transcribed throughout the genome and provide regulatory inputs to gene expression through their interaction with chromatin. Yet, the genomic targets and functions of most ncRNAs are unknown. Here we use chromatin-associated RNA sequencing (ChAR-seq) to map the global network of ncRNA interactions with chromatin in human embryonic stem cells and the dynamic changes in interactions during differentiation into definitive endoderm. We uncover general principles governing the organization of the RNA-chromatin interactome, demonstrating that nearly all ncRNAs exclusively interact with genes in close three-dimensional proximity to their locus and provide a model predicting the interactome. We uncover RNAs that interact with many loci across the genome and unveil thousands of unannotated RNAs that dynamically interact with chromatin. By relating the dynamics of the interactome to changes in gene expression, we demonstrate that activation or repression of individual genes is unlikely to be controlled by a single ncRNA. Many types of RNAs are associated with chromatin. Here the authors identify chromatin-bound RNAs and their binding sites in human embryonic stem cells suggesting that most chromatin-associated RNAs act proximal to their encoding loci and single RNAs are unlikely to alter gene expression. These programs are controlled by coordinated signals from regulatory DNA sequences, transcription factors, histone modifications and variants, and 3D genome organization. The role of RNAs in modulating these programs is increasingly appreciated3. Many classes of RNAs bind chromatin, collectively termed here, chromatin-associated RNAs (caRNAs). These include long non-coding RNA(lncRNAs)5, heterogeneous nuclear RNAs (hnRNAs)7, enhancer-RNAs (eRNAs)10, transposable element (TE)-derived RNAs14, and other chromatin enriched RNAs (cheRNAs)16. Yet, the function of these RNAs on chromatin remains largely unknown.Cell identity is determined by the precise execution of lineage-specific gene expression programsXIST, which acts as a core regulator of X-chromosome inactivation17, and KCNQ1OT1 that mediates allele-specific silencing of imprinted genes near its locus19. In addition to lncRNAs, other classes of caRNAs have genome regulatory functions. For example, eRNAs can affect the expression of neighboring genes through modulation of RNA polII elongation21 or recruitment of transcriptional coregulators23. Nascent pre-mRNAs can interact with chromatin binding proteins and locally regulate chromatin compaction24, and TE-derived RNAs can silence immune response genes and hamper T-cell effector functions25. Furthermore, many proteins involved in controlling chromatin state30 and topology31 have RNA-binding activity, suggesting additional roles for caRNAs in chromatin regulation. Despite these examples, which caRNAs have gene regulatory roles and their mechanisms of action remain to be determined32.LncRNAs can orchestrate complex regulatory circuits, exemplified by 34 and regulatory elements37 exhibits strong tissue specificity such that the ncRNA-gene interaction network is also likely cell-state dependent, although this remains to be experimentally tested. Characterization of the network of human caRNA-gene interactions at the full transcriptome scale represents an important goal41.With the exception of a small number of caRNAs, we do not know the genomic loci where these RNAs act. As a result, we do not understand the network of interactions between caRNAs and genes or its complexity. Transcription of both lncRNAs44. From these data, we characterize the global architecture of this interactome, present a predictive model for most RNA-DNA chromatin interactions, and identify RNAs deviating from this model. We generate a detailed caRNA-gene interaction network that defines the set of caRNAs that interact with each gene based on physical proximity. These interactions encompass lncRNAs and many unannotated intergenic RNAs that may help prioritize specific caRNAs for future functional validation. Through analysis of the dynamics of the interactome during differentiation, we find that regulation of gene expression by individual caRNAs is very rare.Here, we used chromatin-associated RNA sequencing (ChAR-seq) to map the RNA-chromatin interactome in H9 embryonic stem cells and definitive endoderm44, a proximity-ligation method that captures and sequences RNA-DNA contacts genome-wide . We found that nearly all introns and half of all non-coding RNAs had over 3-fold enrichment on chromatin, in agreement with prior characterizations of caRNA49, indicating that ncRNAs tend to have nuclear or chromatin localization  and other functionally heterogeneous non-coding RNAs (referred to here as ncRNAs) such as small nuclear RNAs (snRNAs) and small nucleolar RNAs  RNAs such as XIST57 localizing across a single chromosome. We confirmed by RNA fluorescence in situ hybridization microscopy that the nuclear localization of select RNAs from these classes was consistent with their classification by ChAR-seq , which we hereafter refer to as unannotated transcribed loci (UTLs)  and were possibly the result of transcriptional readthrough, as reported in prior studies65. (2) Some UTLs overlapped with regulatory signals, such as high ATAC-seq or H3K27ac levels . (3) Some UTLs overlapped with TEs , in agreement with prior studies showing that TEs are a source of RNAs that are associated with chromatin25. (4) Finally, some UTLs did not have any of the above features but had sequence similarity with known transfer RNAs (tRNAs), snRNAs and other small RNAs66. Guided by these observations, we classified the UTLs based on their proximity to the 3’ or 5’ ends of genes, their overlap with transposable elements, snRNAs, or tRNAs, and their overlap with cis-regulatory elements annotated in the Encode Registry of Regulatory Elements67, yielding seven categories of unannotated RNAs  in 14% of all RNA-DNA contacts, a proportion similar to that of exons for annotated RNAs Fig. . To charved Fig. . Over 60ved Fig. . Four peved Fig. . Overallved Fig. .Fig. 2CeAlthough these RNAs were present in the total RNA population, we found that all categories of UTLs were enriched on chromatin Fig.  and wereWe next quantified the dynamics of the RNA-chromatin interactome during ES-DE cell differentiation. To identify cell-state dependent interactions, we binned the DNA contacts of each RNA into 100 kb or 1 Mb intervals and performed a quantitative analysis analogous to differential expression analysis to obtain the fold change of each contact in ES versus DE cells and its associated statistical significance (“Methods”). We filtered the data to only include contacts with at least 10 counts in at least two samples and tested ~100,000 exon-chromatin contacts, ~300,000 UTL-chromatin contacts, and 1.6 million intron-chromatin contacts  for differential representation in ES vs DE cells . These two differential interaction maps were highly similar Fig. . We furtMALAT1, and the XIST RNA are two well-studied lncRNAs that act to regulate gene expression broadly across the genome or throughout the X chromosome68. Yet, it is not known which other RNAs have similar widespread interaction patterns on chromatin.We hypothesized that the dynamic RNA-DNA interactome contains a mixture of (1) functional interactions linked to the regulatory activity of the RNA on chromatin and (2) coincidental interactions due to transient proximity of the RNA to chromatin, for instance, during nascent transcription or diffusion within the nucleus. We thus analyzed the contact patterns of individual RNAs to detect features consistent with functional interaction, beginning with features at the chromosome scale. The nuclear speckle-associated lncRNA, trans-delocalization and a cis-delocalization score  from its locus on its source chromosome. To account for expression, chromosome of origin and sample biases, these scores were calibrated using mRNAs as a reference , we can use these metrics to screen for candidate RNAs with type I and type II patterns. We found that lncRNAs with large trans-delocalization scores  the total abundance of each RNA on chromatin, (2) a DNA-locus bias which models the propensity for an RNA to be captured at this locus, independently of the identity of that RNA, and (3) the distance between each RNA transcription site and its DNA target loci  and no signal at L2. The fold difference signal of the observed data over the model confirmed that the two peaks at L1 and L2 were not explained by simple diffusion of the AC106864 or accessibility biases. Interestingly, Hi-C data showed two corner peaks characteristic of a chromatin loop linking the LARP7 locus with both L1 and L2. This result suggests that AC106864 localization at L1 and L2 might be mediated by the chromatin loop. It is also possible that AC106864 targets these loci through other mechanisms, such as base pairing or association with RBP, that are independent of genome folding. Yet this biochemically targeted interaction is unlikely given that the introns of the overlapping mRNA LARP7 also have contact peaks at L1 and L2. Together, these data suggest that TAD organization influences the contact patterns of RNAs and that chromatin looping enables distal RNA-DNA interactions.To examine how the 3D organization of the genome affects the localization patterns of individual RNAs on chromatin, we focused on a small ~50 kb TAD on chr4q25, which is nested inside a larger 100 kb TAD Fig. . Two geny-axis maps each row to an individual RNA and the x-axis maps each column to a genomic bin. To compare ChAR-seq to Hi-C data at the chromosome scale, we collapsed one dimension of the Hi-C maps into genes while keeping the other dimension as genomic bins. In these transformed Hi-C maps, each pixel represents the contact frequency between the gene and a cognate DNA bin. We detected in the ChAR-seq maps the same plaid pattern found in Hi-C data resulting from the 3D partitioning of the genome into two major compartments, the A and B compartments, also associated with active and inactive chromatin, respectively83. This pattern indicates that any individual caRNA tends to have a specific compartment (either A or B) with which it interacts preferentially. Equivalently, when one caRNA contacts a locus in, say, the A compartment, it has a higher likelihood of contacting other loci in the A compartment rather than in the B compartment. It was not surprising that this pattern was not produced by our generative model since only linear distance is encoded in the model. We concluded that A/B compartments also modulate the long-range interactions of individual RNAs with chromatin.This observation prompted us to ask whether the larger-scale topological organization of the chromosome also influences RNA-DNA contacts Fig. . ChAR-seXIST17, KCNQ1OT118, and HOTAIR84. Thus, we defined a proximal regulatory region (PRR) around each protein-coding gene, encompassing +10 kb upstream and −90 kb downstream of its TSS, and measured the contact density of each caRNA at the PRR of each gene. Using this approach, we mapped all the physical contacts between the chromatin-associated transcriptome and protein-coding genes  the caRNA expression level, (2) the genomic distance from the RNA locus to the DNA target and (3) the 3D chromatin topology. We wanted to determine whether this result is compatible with the hypothesis that ncRNAs participate in the regulation of cell-state-specific protein-coding genes. We reasoned that RNAs with transcriptional regulatory roles are likely to be found near their cognate gene, where they could modulate local chromatin state, TF binding, RNA polymerase, or the activity of gene-proximal regulatory elements. This colocalization hypothesis is consistent with the better-studied ncRNAs with gene regulatory activity, including Consistent with the dynamics of the genome-wide RNA-DNA interactome Fig. , the caRTo identify potential regulatory caRNAs and their putative gene targets, we classified each caRNA and each protein-coding gene as an ES, DE, or stable caRNA or gene based on those cells . We then examined the statistical associations between the class (ES/DE/stable) of a caRNA, its cognate gene, and their interaction. Figure To further understand the relationship between gene expression and the presence of a caRNA in the PRR of a gene, we examined how many cell-state-specific contacts are made at cell-state-specific genes. This analysis revealed that >97% of cell state-specific genes are not contacted by lncRNAs in a cell state-specific manner . We binned differential contacts in three categories: (1) positive edges, where the contact dynamics were positively correlated with the proximal gene dynamics , (2) negative edges , (3) neutral edges (contacts that increased in ES or DE to genes that were neither ES or DE genes).p-value < 0.05 by bootstrap) and up to 1.3 times fewer negative edges  than would be expected for a random interactome under the null model  are known RNA residents of the nucleolus  or nuclear speckles . SPRITE, a Hi-C-like method that probes high-order chromatin interactions, showed that the 3D genome is organized into 2 major hubs around the nucleolus and nuclear speckles, where abundant long-range and interchromosomal DNA-DNA contacts occur86. We hypothesize that the proximity of these RNA loci to the genomic hubs may be important in enabling interactions with dispersed genomic loci. This behavior is reminiscent of XIST, whose location on the X chromosome defines where heterochromatin spreading initiates17. We speculate that a general principle may underlie these observations, where the interactions of an RNA with chromatin are constrained by the position of their transcription locus relative to other loci or to nuclear domains.Second, it is noteworthy that all delocalized lncRNAs but TERC and 2 uncharacterized ncRNAs . The junk mail model is compatible with our observations. Another possibility, which we termed the “democratic RNA model,” is that the distributed activity of multiple, weakly influential ncRNAs, rather than that of a single, strongly influential ncRNA, determines the overall regulatory output of RNA-chromatin interactions at a gene. The lack of a strong correlation between RNA interaction and gene expression indicates that testing the functions of individual RNAs in gene regulation may be challenging, and evaluating RNA regulatory roles will require combinatorial perturbations of RNAs and putative effectors at specific loci.To our surprise, we observed a general lack of correlation between the dynamics of the RNA contacts at a given gene and the dynamics of the expression of that gene. This observation challenges models proposing that the activation or silencing of a gene may be controlled by a single ncRNA93, or (2) recruitment of transcription activators such as the CREB-binding protein30. These two effects would also give rise to a positive correlation between caRNA presence at a gene and the transcriptional output of this gene.We found that an increase in interaction frequency between a specific ncRNA and a target gene is more likely to correlate with an increase in target gene expression than one would expect should the ncRNA-gene contacts and the gene expression be uncorrelated with one another. Three scenarios may explain this result. First, this may merely reflect increased accessibility during chromatin activation and a higher likelihood of crosslinking nearby RNAs. Second, there may be local coregulation of nearby ncRNAs and genes, for instance, through shared regulatory elements. Third, it is possible that the default activities of caRNAs are: (1) a decoying of the silencing machinery, as proposed by the junk mail model, in the context of PRC2 evictiontrans beyond the interactions explained by the expression levels of these lncRNAs and the distance to their targets. This finding will need to be reconciled with the models proposed for a few ncRNAs, such as DIGIT or RMST, which have been reported to broadly colocalize with BRD3 at endoderm differentiation genes, and SOX2 at genes that control pluripotency and neurogenesis, respectively91. Given that lncRNAs and eRNAs are highly cell state-specific34, the architecture of the caRNA-chromatin interactome may be qualitatively different and perhaps contain more trans interactions in further differentiated cells. Additionally, we cannot exclude the possibility that our analysis missed affinity-driven trans interactions due to sequencing-depth limitations, in particular for lowly expressed ncRNAs. Thus, deeper sequencing or more powerful statistical frameworks may reveal weak deviations from the model at more loci. However, the fact that our analysis reveals broad differences in the contactome between ES and DE cells gives us confidence that any undetected deviation from the model must be more subtle than the contactome changes related to cellular differentiation.As mentioned previously, we did not identify lncRNAs localized at defined genomic targets in This work presents a global analysis of caRNA-chromatin interaction and establishes that caRNAs predominately operate locally through diffusion and genome conformation-driven interactions. We anticipate this work will direct the efforts in the non-coding RNA field by providing data-informed priors on the localization of RNAs and a simple model predicting where non-coding RNAs may act. Future studies to identify the proteins mediating these RNA-chromatin interactions will be necessary to inform the interplay between caRNA and RNA-binding proteins in the control of transcription and chromatin state.45. Briefly, 6-well plates were prepared with matrigel by adding 1 mL of matrigel (diluted in serum-free DMEM/F-12 according to lot dilution factor) to each well and polymerized for 1 h at room temperature. DMEM/F-12 was aspirated and replaced with 1.5 mL mTeSR1 warmed to room temperature, and then 2 µM of 10 mM ROCK inhibitor (Y27632-Dihydrochrolride) was added to each well. H9 hESCs  were thawed and immediately diluted by dropwise addition of 10 mL prewarmed mTeSR1, spun at 200×g for 5 min, and gently resuspended in 1.5 mL mTeSR. Then, 0.5 mL of cells were added to each well and placed at 37 °C. Media was replaced daily with 2 mL fresh mTeSR1 per well. When colonies were ~70% confluent and started to touch each other, cells were passaged as colonies. Each well was washed with 1x PBS, 1 mL of Versene-EDTA was added, and cells were incubated at 37 °C for 5 min. Colonies were detached, broken up with gentle pipetting, and resuspended in mTeSR1 at a 1:5 to 1:10 dilution. Then, 0.5 mL of cells was added dropwise to each well containing 1.5 mL mTeSR1 and coated with matrigel prepared as described above (without ROCK inhibitor).H9 hESCs cells (ES cells) were obtained from Wicell (cell line WA09) and cultured on Matrigel hESC qualified matrix (Corning 354277) with mTeSR1 medium (StemCell Technologies 85850) according to manufacturer’s protocols and as described in Loh et al.Colonies were seeded from 1:10 dilution on day 0 into four 15 cm dishes with matrigel, two for maintenance as ES cells and two for differentiation into Definitive Endoderm (DE) cells. ES cells were maintained as above with daily mTeSR1 media replacement. For differentiation, cells were treated with 10 µM ROCK inhibitor on day 0, and their media was replaced on day 1 with DE induction Media A (Gibco Cat# A3062601) and on day 2 with DE induction Media B (Gibco Cat# A3062601). On day 3, cells were harvested for ChAR-seq. In addition to the 15 cm dishes used for ChAR-seq, cells were also seeded and maintained as ES cells or differentiated into DE cells in 6-well plates with poly-L-lysine coated coverslips under matrigel, and collected at the same time for immunofluorescence analysis. Cells were also differentiated in 6-well plates for RNA-seq and ATAC-seq.z-stacks. Maximum intensity projections were processed with CellProfiler (3.1.8) to identify nuclei based on the Hoechst signal and to measure the mean intensity of each channel. Histograms of mean nuclear intensity for each marker were plotted in R.Cells were cultured in 6-well plates on poly-L-lysine coated coverslips under matrigel and maintained as ES cells or differentiated into DE cells as described above. Cells were washed three times with PBS and fixed with 2% PFA in PBS added directly to the wells for 10 min at room temperature. The PFA solution was aspirated, cells were washed three times with PBS, and permeabilized with 0.1% Triton X-100 in PBS for 5 min at room temperature. Coverslips were transferred to parafilm-coated staining chambers, washed with PBS, and blocked with Antibody Dilution Buffer  for 30 min at room temperature. Samples were incubated in primary antibody for 30 min at room temperature  1:500, Goat anti-Sox17 (R&D Systems #AF1924) 1:1000, Rabbit anti-Sox2 ((D696) XR(R), Cell Signaling, 3579 T) 1:500, Rabbit anti-FoxA2,  1:500, diluted in AbDil), washed three times with AbDil, and incubated with secondary antibodies conjugated to Goat anti-Rabbit Alexa-647 (Thermo-Fisher A32733) and Donkey anti-Goat Alexa-568 (Thermo-Fisher A11057) (1:1000 diluted in AbDil) for 30 min at room temperature. Cells were washed with AbDil three times, stained for 5 min with 10 µg/mL Hoechst-33342 in PBS, and washed with PBS with 0.1% Triton X-100 before being mounted  onto slides and sealed with nail polish. Samples were imaged with an IX70 Olympus microscope with a Sedat quad-pass filter set  and monochromatic solid-state illuminators. Cells were imaged using a 40x objective. At least 10 images per coverslip were captured using 0.2-µM 2O and amplified using gene-specific primers that had been tested for amplification efficiencies >90% and to amplify a single product. Real-time PCR was performed using the Powerup SYBR Master Mix (ThermoFisher) for 40 cycles  on an ABI ViiA 7 Real-Time PCR Machine with cycle thresholds (CTs) determined automatically and with all samples in triplicate. Experimental genes were normalized to the PBGD gene, with relative expression levels calculated using the 2CT∆∆ method, and the transcript level fold-change in DE versus ES cells was calculated. If a gene’s expression was too low to detect via qPCR, these undetermined Ct values were assigned a value of 38 to provide a conservative overestimate for use in the calculation of expression change. Oligonucleotide primer sequences are listed in Supplementary Data For qPCR, RNA was extracted from each well of a 6-well plate containing ES or DE cells (~1 million cells per well) using 1 mL Tripure reagent and according to the manufacturer’s protocol. RNA was treated with DNase  for 1 h at room temperature, followed by isolation with a minElute RNA Cleanup Kit (Qiagen). RNA concentrations were measured by Nanodrop, and total RNA integrity was assayed using an Agilent Bioanalyzer. All RNAs had an RNA integrity number (RIN) greater than 9.0. Then, 0.5–1 µg of RNA was reverse transcribed with random hexamer primers using SuperScript III reverse transcriptase  according to the manufacturer’s protocols. First-strand cDNA was diluted 1:10 in nuclease-free HFor RNA-seq, RNA was extracted from each well of a 6-well plate containing ES or DE cells using 1 mL Tripure and the Direct-Zol RNA Extraction kit (Zymo Research) according to the manufacturer’s instructions. RNA concentrations and quality were assayed as described for qPCR. For each sample, 2.5 µg of RNA was treated with DNase  for 1 h at room temperature, followed by isolation with an RNA Clean & Concentrator-25 kit (Zymo Research). Then, 1 µg RNA was converted to ribosomal depleted cDNA libraries ready for sequencing using the TruSeq Stranded Total RNA Library Prep Human/Mouse/Rat kit (Illumina) according to the manufacturer’s instructions. Samples were uniquely dual-indexed using IDT for Illumina TruSeq RNA UD Indices. The four biological replicates from both conditions (ES and DE cells) were pooled and sequenced at low read depth on a MiSeq (2 x PE75) at the Stanford Functional Genomics Facility to assess quality and on 1 lane of the HiSeq4000 (2 x PE150) at NovoGene . All reported analysis was generated using the HiSeq dataset.g for 5 min. The pellet was resuspended in DPBS, and cells were counted and immediately processed. ATAC-seq was performed as previously described using the OMNI-ATAC protocol94 with slight modifications. Briefly, ~100 K cells were resuspended in 50 µL cold ATAC-Resuspension Buffer  and incubated on ice. Cells were washed with 1 mL cold ATAC-RSB (without NP40 and digitonin) and centrifuged at 500×g for 10 min at 4 °C. The pellet was resuspended in a 50 µL transposition mixture (2x TD buffer and 2.5 µL transposase) from the Illumina Nextera DNA Library Prep Kit and incubated at 37 °C for 30 min in a thermomixer at 1000 RPM. Libraries were purified with the DNA Clean & Concentrator-5 Kit (Zymo Research) and PCR amplified with barcoded primers. The amplification cycle number for each sample was monitored by qPCR to minimize PCR bias. PCR amplified libraries were purified with the MinElute purification kit (Qiagen) and excess primers and large (>1000 bp). DNA fragments were removed by AMPure XP bead selection (Beckman Coulter). Four biological replicates from each cell type (ES and DE) were pooled and sequenced at low read depth on a MiSeq (2 x PE75) at the Stanford Functional Genomics Facility to assess quality and on 1 lane of the HiSeq4000 (2 x PE150) at NovoGene . All reported analysis was generated using the HiSeq dataset.Cells for ATAC-seq were differentiated as described above and collected by dissociating in Versene, followed by resuspension in warm mTeSR media. Cells were transferred to 15 mL conical tubes and centrifuged at 200×42, as briefly described below. All reagents used were RNAse-free.ChAR-seq libraries were prepared according to the published protocolg at 4 °C, washed with 10 mL ice-cold PBS, and resuspended in ~5–10 mL PBS. Cell concentration was measured, and cells were aliquoted in batches of 10 million cells in 1.5 mL tubes. Aliquots were spun for 5 min at 500×g at 4 °C, the supernatant was removed, and pellets were flash-frozen in liquid nitrogen and stored at −80 °C until library preparation.About 10 million cells were harvested from a 15 cm dish with Versene and fixed in 3% formaldehyde for 10 min at room temperature. Formaldehyde was quenched with the addition of 0.6 M glycine for 5 min at room temperature, then 15 min on ice. Cells were pelleted for 5 min at 500×g, discarding of supernatant, resuspension and mixing in the indicated wash buffer, spinning for 4 min at 2500×g, and aspiration of the wash buffer) with 500 µL of lysis buffer without Igepal, RNaseOUT, or Protease Inhibitor, then resuspended in 400 µL of 0.5% SDS , and incubated for 10 min at 37 °C. SDS was then quenched by adding Triton X-100 to 1.4% final concentration and incubating for 15 min at 37 °C.Frozen pellets were resuspended in 500 µL ice-cold lysis buffer  and incubated for 15 min on ice. Nuclei were washed , and resuspended in 250 µL of second strand synthesis mix  for 1.5 h at 37 °C.To fragment RNAs, nuclei were pelleted and resuspended in 150 µL fragmentation buffer  and exposed to heat for 4 min at 70 °C.To dephosphorylate RNA 5’ ends, nuclei were washed twice , resuspended in 150 µL dephosphorylation mix , and incubated for 30 min at 37 °C. To perform RNA-bridge ligation, nuclei were washed twice as above and resuspended in 200 µL RNA-bridge ligation mixture  and incubated overnight at 23 °C on a thermomixer at 900 RPM. To perform first-strand synthesis, nuclei were washed twice as above and resuspended in 250 µL of first-strand synthesis mixture , and incubated for 15 min at 23 °C, 10 min at 37 °C, and 20 min at 50 °C. Bst3.0 was inactivated by adding 8 µL of 0.5 mM EDTA , 14 µL of 1% SDS , and incubating for 10 min at 37 °C. SDS was then quenched with 43 µL of 10% Triton X-100  for 15 min at 37 °C. Next, to perform genomic digestion, nuclei were washed twice and resuspended in 250 µL of DpnII reaction mixture  overnight at 37 °C on a thermomixer at 900 RPM. DpnII was inactivated in the same manner as Bst3.0 inactivation. SDS was quenched as above. Next, to perform bridge-DNA ligation, nuclei were washed twice and resuspended in 250 µL of ligation mixture  for 4 h at 23 °C. T4 was inactivated by adding 8 µL of 0.5 M EDTA . Finally, to perform second strand synthesis, nuclei were washed twice Reverse crosslinking was carried out by adding 31.25 µL of 10% SDS, 31.25 µL 0.5 M NaCl, 9 µL of 20 mg/mL proteinase K and incubating overnight at 68 °C. DNA was purified by phenol chloroform extraction, ethanol precipitated, and resuspended in 130 µL TE  buffer. DNA was sheared with a Covaris S220 to target a mean fragment size of ~200 bp . Fragment size distribution was quality controlled on an Agilent High Sensitivity DNA Bioanalyzer.2O). The PCR reaction was performed using the following program . Beads were magnetically collected, and the supernatant containing amplified DNA was transferred to a clean 1.5 mL microcentrifuge tube. The amplified libraries were purified using magnetic SPRI beads at a ratio of 1:1 and eluted with 31 µL 10 mM Tris-HCl, pH 8.Molecules containing the biotinylated bridge sequence were isolated using 150 µL of MyOne Streptavidin T1 dynabeads. To bind bridge-containing molecules, beads were washed with 750 µL tween wash buffer  and resuspended in 130 µL 2x bead binding buffer  and 130 µL sheared DNA sample, then incubated at room temperature for 15 min with agitation. To remove unbound DNA, beads were washed twice with 750 µL TWB (with incubation at 50 °C for 2 min with agitation during the first wash), then resuspended in 40 µL TE buffer. DNA ends were prepared for ligation by adding 7 µL of NEBNext End Prep Buffer and 3 µL NEXext End Prep enzyme mix and incubating for 20 min at room temperature and 30 min at 65 °C. Adapters were ligated using the NEBNext Ultra II Ligation module according to the manufacturer’s protocols. Beads were washed twice as above and resuspended in 50 µL PCR amplification mix . The number of off-bead PCR cycles to perform was determined by finding the number of cycles such that the fluorescence intensity is about one-third of the plateau intensity at the PCR saturation. The remaining 25 µL of the library was combined with 30 µL 2x NEBNext High Fidelity master mix, 2.5 µL 10 µM universal Primer, and 2.5 µL 10 µM indexing primer. Each sample was then cycled as above for the number of cycles determined by the side qPCR.To purify the amplified library, high molecular weight fragments were bound to Ampure beads by adding 0.6x volume of the PCR reaction of Ampure beads and collecting the supernatant. Low molecular weight fragments were purified by adding 0.1875x the volume of the supernatant transferred to obtain a final ratio of 0.9x beads:slurry. DNA was eluted in 33 µL 10 mM Tris pH 8. Library concentration was assessed using a Qubit dsDNA High Sensitivity kit, and size distributions were determined using an Agilent High Sensitivity DNA bioanalyzer. Samples were pooled and sequenced on 1 lane of an Illumina HiSeq4000 platform (2x PE150) to assess library quality, then later deeply sequenced on 2 lanes of an Illumina NovaSeq platform at NovoGene . All reported analysis was generated using the NovaSeq dataset. Replicates 1 and 2 of ES and DE ChAR-seq libraries were prepared at different times and each sequenced separately on 1 NovaSeq lane.https://github.com/straightlab/charseq-pipelines), outputting pairs files containing the RNA and DNA coordinates of each RNA(cDNA)-DNA chimeric read and relevant annotations for each RNA-DNA contact. A summary of the pipeline workflow is depicted in Supplementary Fig. Q < 30) were removed, and sequencing adapters were trimmed using Trimmomatic v0.38. Paired-end reads were merged using Pear v0.9.6 when possible, and reads containing a single instance of the ChAR-seq bridge sequence were identified using chartools v0.1, a custom ChAR-seq reads preprocessing package released as part of this study (https://github.com/straightlab/chartools). Reads were split into a rna.fastq and dna.fastq file corresponding to the sequences of the RNA (cDNA) and DNA side of the chimeric molecule using chartools. Reads with either the RNA or DNA side shorter than 15 bp were removed using chartools and reads whose RNA side aligned to a rRNA sequence by Bowtie2 were filtered out using Picard. DNA reads were aligned to hg38 using Bowtie2, and RNA reads were aligned to hg38 using STAR and Gencode v29 annotations. RNA reads were assigned specific genes using tagtools (https://github.com/straightlab/tagtools), a package released as part of this study. Genes were assigned an RNA type  based on the GencodeV29 gene type field and the lookup table in Supplementary Data Q < 255) or DNA (Bowtie2 Q < 40) side. Reads whose RNA overlapped with the hg38 ENCODE blacklist or that could not be attributed to a single known gene or genomic locus were also removed.Demultiplexed fastq files from the ChAR-seq data were processed using a custom Snakemake pipeline (Q < 255), reads that could not be attributed to a single known gene or a single locus, and reads that overlapped with a locus on the ENCODE blacklist were discarded.RNA-seq reads were processed using a Snakemake pipeline mirroring the ChAR-seq pipeline, but all of the operations related to the DNA-side of the reads were skipped. In brief, demultiplexed fastq files were deduplicated, sequencing adapters were removed, paired mates were merged as described for ChAR-seq reads. Reads that aligned to a rRNA sequence by Bowtie2 were filtered out using Picard. Reads were aligned to hg38 using STAR and were annotated with tagtools using the Gencode V29 gene models. Reads with low mapping scores  and produce a single bigwig file per cell type used to display the ATAC-seq tracks and a single bam file to determine ATAC-seq peaks. ATAC-seq peaks were identified in each cell line using HMMRATAC v1.2.10.Illumina Nextera Adapters were removed using a custom Python script. Reads were aligned to the hg38 using Bowtie2. Duplicates were removed with Picard. Mitochondrial reads or reads with Bowtie2 MAPQ score i as the log fold difference between the level of RNA i in the chromatin-associated RNA transcriptome (measured with the RNA-side of the ChAR-seq reads) and its level in the total RNA transcriptome .To estimate the chromatin association score in a way that was robust to small counts and obtain p-values to detect RNAs with meaningful chromatin enrichment, we used DEseq2 with a design formula ~cell + sequencing + cell:sequencing. In this design matrix, the cell covariate represented the cell type and the sequencing covariate indicated whether the sample originated from RNA-seq or ChAR-seq. The interaction term cell:sequencing captured differences in the chromatin association of a given RNA between ES and DE cells. We used the shrunken estimate of the regression coefficient associated with the sequencing covariate as the estimate of the chromatin association score. We computed the chromatin association score in ES and DE cells separately by setting the reference level for the cell covariate to ES and DE, respectively, before running DEseq2. The apeglm method was used to compute the shrunken estimates. We ran DEseq2 using an input count matrix with 16 samples: 2 ES and 2 DE replicates from ChAR-seq and 4 ES and 4 DE replicates from RNA-seq. Gene counts for all Gencode V29 genes and all UTLs identified in this study were included in the input matrix, except those with fewer than 10 counts combined across all 16 samples. Counts from exons and introns of a given gene and from UTLs were input as separate entries (rows) in the matrix. All DESeq2 parameters were set to their default value, except for the sample depth normalization step. For sample depth normalization, we ran the estimateSizeFactors command on a subset of the rows of the count matrix that included only exons of annotated genes with at least 50 counts combined across all 16 samples. Subselecting exonic reads removed length bias due to the low representation of introns in the total RNA-seq data compared to the ChAR-seq data. False Discovery Rate (FDR) adjusted p-values corresponding to the regression coefficient associated with the sequencing covariate were used to identify genes with significant chromatin enrichment. Genes with an adjusted p-value smaller than 0.05 and a chromatin association score either greater than 3 were labeled as chromatin enriched, and those with an adjusted p-value smaller than 0.05 and a chromatin association score less than −3 were labeled as chromatin depleted. To identify genes with statistically significant changes in their chromatin association score in ES versus DE cells  to produce pairs files corresponding to RNAs emanating from UTLs.For each ChAR-seq sample, reads whose RNA did not overlap with any gene body in GenecodeV29 in the sense orientation were classified as intergenic by tagtools and their STAR RNA alignments were extracted in a separate bam file. Only RNA reads with a STAR alignment score of 85 to the RNA-side of the ChAR-reads. CASK annotates each read with a candidate TE family (if any) based on its k-mer composition analyzed against a database of TE-specific k-mers built using the T2Tv1 genome assembly and T2T-CHM13 repeat annotations. Then, for each UTL, we identified the CASK annotation with the highest representation among all the reads (across the 2 ES and 2 DE replicates) mapped to this UTL. We assigned this annotation as the dominant TE family for this UTL and the proportion of reads from this UTL with this specific CASK annotation as its TE-score. (2) If the 5’ end of a UTL was within ±300 bp of a cis-regulatory element (CRE) active in either ES or DE cells, we annotated this UTL with the closest such CRE and its associated 7-group classification based on the Encode Registry of Regulatory Elements67 (file ID GRCh38-cCREs.bed). To determine active CRE in ES or DE cells, we selected, among the Encode Registry of Regulatory Elements , those that overlapped with an ATAC-seq peak in that cell line. (3) UTLs whose 5’ end were within −200 bp to +100 bp of the 3’ end of a GencodeV29 gene body were flagged as candidate readthrough. (4) UTLs with at least 10% overlap with the antistrand of a GenecodeV29 gene body were flagged as candidate antisense. Finally, these 4 metrics and tags were combined to determine the final UTL classification using the following priority rule: (1) UTLs with a dominant TE family of tRNAs and at TE-score greater than 10% were classified as tRNA-derived. (2) Remaining UTLs with a dominant TE family in {snRNA, snoRNA, scaRNA, srpRNA, scRNA, rRNA} and at TE-score greater than 10% were classified as snRNA-derived. (3) Remaining UTLs flagged as candidate readthroughs were classified as readthroughs. (4) Remaining UTLs with a CRE annotation in either ES or DE cells were classified as CRE-derived, and the subtype of CRE was selected from the ES cell annotation if the CRE was active in ES cells and from DE cell annotation otherwise. (5) Remaining UTLs with a TE-score greater than 50% were classified as repeat-derived, with the specific repeat family determined by their dominant TE family. (6) Remaining UTLs flagged as candidate antisense were classified as antisense. (7) All remaining UTLs were classified as intergenic.Each UTL was assigned 4 metrics or tags. (1) We attributed each UTL a dominant Transposable Element (TE) family and a TE-score. For this task, we applied Classification of Ambivalent Sequences using K-mers (CASK)95. We applied DEseq2 in the interactome space  using the number of ChAR-seq reads linking a specific RNA to a specific DNA locus, hereafter referred to as an RNA-DNA interaction, as separate rows in the input count matrix. We defined a DNA locus as either a 100 kb or 1 Mb genomic window  is constant across the columns (representing the 100 kb-wide DNA loci), any information about the localization of individual RNAs is effectively removed, and only the information about the abundance of each RNA is retained. We next applied DEseq2 in the interactome space as described above but with the following modifications. First, the count matrix input to DEseq2 contained 8 samples/columns: the 2 ES and the 2 DE replicates of the observed contact maps and the 4 corresponding expression-only maps. Second, we used a design matrix of the form ~cell + mapType + cell:mapType, where the mapType covariate indicated whether the column corresponded to an observed ChAR-seq map or an expression-only map, and the cell covariate indicated whether the column corresponded to a map in ES or DE cells. Third, the count matrix was prefiltered as above by removing interactions for which fewer than 2 samples had at least 10 reads, except that only the true observed samples (mapType=observed) were considered for the purpose of the filter. The interaction term cell:mapType captured differences in the ES to DE dynamics in the true maps compared to the expression-only maps. All interactions that had an FDR adjusted p-value associated with the cell:mapType covariate smaller than 0.05 were flagged as not explained by expression. Maps shown in Fig. Model 3 in Fig. trans-delocalization scores, please refer to Supplementary Note For full details on the trans-delocalization score for each RNA as the ratio of the contact density of this RNA on trans chromosomes  over the contact density of this RNA on its cis chromosome. The raw delocalization score was difficult to interpret due to sample-specific biases and dependency on the chromosome of origin and expression  were not used directly but further filtered to eliminate any possible remaining multimappers on the RNA side by using a more stringent multimapping threshold than STAR δ for each ChAR-seq read corresponding to a cis-chromosomal contact as the distance between the mapping locus of the RNA and the mapping locus of the DNA. cis-delocalization scores were defined and computed similarly to the trans-delocalization scores, except for the following replacements: the number of cis-chromosomal contacts for RNA i was replaced with the number of contacts Nδ<1Mb,i such that the absolute RNA travel distance was smaller than 1 Mb, and the number of trans-chromosomal contacts was replaced with the number of contacts Nδ>1Mb,i such that the absolute RNA travel distance was greater than 1 Mb. The covariates for the GLM remained unchanged. Detection of RNAs with extreme delocalization scores The analysis described below was used for the trans-delocalization scores and was performed similarly for the cis-delocalization scores. Briefly, for each RNA and each sample, we computed the probability p,idelocalized that a random sample drawn from the posterior distribution of the trans-contact rate θ,ipost was larger than a random sample drawn from the GLM trained on the mRNA population. This probability was used as a p-value for identifying trans-delocalized RNAs. One p-value was obtained per RNA and per sample, and p-values from replicates were combined using Fisher’s method. Multiple hypothesis testing was corrected using the Benjamini–Hochberg procedure. RNAs with an adjusted p-value smaller than 0.05 were declared as trans-delocalized. To identify RNAs on the other side of the distribution tail  1 −p,idelocalized was used, and Fisher’s and BH methods were applied similarly. An RNA was declared ultralocalized if the resulting adjusted p-value was smaller than 0.05. All computations were performed in R. For further details, please refer to Supplementary Note i refers to an RNA associated with the ith gene in the transcriptome), and for each RNA i, a set of Ni reads coming from this RNA whose RNA mapping coordinates are {ri,j}j=1…Ni and DNA mapping coordinates are {di,j}j=1…Ni. We modeled for each RNA i the probability of observing any particular realization of the DNA mapping coordinates, conditional on knowing (1) the set of RNA mapping coordinates and (2) the total number of contacts for this RNA on each chromosome. We modeled the cis- and trans-chromosomal contacts separately. For cis-contacts, we assumed the probability for an RNA emanating from coordinates r to contact locus j with coordinates dj, is proportional to: (1) an RNA-independent and DNA locus-dependent bias bj representing the biological and technical variation of RNA localization and detection along the genome and (2) an interaction frequency dependent on the distance between the RNA and the DNA locus. The latter effect captures diffusion and tethering effects at short distances, whereby an RNA is more likely to interact with loci near its transcription site. Under this model, the probability of observing any specific localization pattern for RNA i in cis is given by a multinomial distribution of the form:i is the set of indices among the reads from RNA i, for which the DNA-side maps to a locus in cis. For trans-contacts, we assumed that the probability for any RNA to contact locus j is only proportional to the DNA bias. Under this model, the probability of observing any specific localization pattern for RNA i on a trans chromosome c is given by a multinomial distribution of the formNi,chri) = c and had an FDR adjusted p-value smaller than 0.05 were flagged as not explained by the model. We computed the regression coefficient associated with the observedORmodel and its p-value in ES and DE cells separately by setting the reference level for the cell covariate to ES and DE, respectively, before running DEseq2. This analysis was performed separately for maps corresponding to exons, introns, and UTLs. Maps shown in Figs. p-value smaller than 0.05 for the cell covariate (reference level for observedORmodel set to observed) were flagged as cell-state-specific.To compare the true observed ChAR-seq RNA-DNA contact maps to those predicted by the generative model, we applied DEseq2 in the interactome space as described in the section “Quantification of the RNA-DNA interactome dynamics” with the following modifications. First, the count matrix input to DEseq2 contained 8 samples/columns: the 2 ES and the 2 DE replicates of the true observed contact maps and the 4 corresponding model maps obtained by a single simulation of the generative model. Second, the design matrix was set to ~ cell + observedORmodel + cell:observedORmodel, where the observedORmodel covariate indicated whether the column corresponded to observed or model ChAR-seq map. Third, the count matrix was prefiltered by removing interactions for which fewer than two samples among the observed samples had at least 10 reads. The interaction term cell: observedORmodel captured differences between the observed and modeled data that were specific to either ES to DE cells. All interactions whose apeglm shrunken estimate of the regression coefficient associated with the observedORmodel covariate was greater than 96, visualized at 2 kb resolution after ICE normalization, and manually aligned with the ChAR-seq, ATAC-seq, H3K27ac and H3K4me3 tracks plotted in IGV based on their genomic coordinates. H3K27ac and K3K4me3 tracks in Fig. 45. PolII localization peaks used for the metagene analysis in Supplementary Fig. 97.Hi-C data in Fig. Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Reporting Summary