Messenger RNA vaccines against SARS-CoV-2 hold great promise for the treatment of a wide range of diseases by using mRNA as a tool for generating vaccination antigens as well as therapeutic proteins in vivo. Increasing interest in mRNA preparation warrants reliable methods for in vitro transcription (IVT) of mRNA, which must entail the elimination of surplus side products such as immunogenic double-stranded RNA (dsRNA). We developed a facile method for the removal of dsRNA from in vitro transcribed RNA with mesoporous silica particles as RNA adsorbents. Various polyamines were tested for the facilitation of RNA adsorption onto mesoporous silica particles in the chromatography. Among the polyamines tested for RNA adsorption, spermidine showed a superior capability of RNA binding to the silica matrix. Mesoporous silica-adsorbed RNA was readily desorbed with elution buffer containing either salt, EDTA, or urea, possibly by disrupting electrostatic interaction and hydrogen bonding between RNA and the silica matrix. Purification of IVT RNA was enabled with the adsorption of RNA to mesoporous silica in a spermidine-containing buffer and subsequent elution with EDTA. By differing EDTA concentration in the eluting buffer, we demonstrated that at least 80% of the dsRNA can be removed from the mesoporous silica-adsorbed RNA. When compared with the cellulose-based removal of dsRNA from IVT RNA, the mesoporous silica-based purification of IVT RNA using spermidine and EDTA in binding and elution, respectively, exhibited more effective removal of dsRNA contaminants from IVT RNA. Thus, mRNA purification with mesoporous silica particles as RNA adsorbents is applicable for the facile preparation of nonimmunogenic RNA suitable for in vivo uses. The success of mRNA-based vaccines, particularly during the COVID-19 pandemic, has demonstrated their potential to address other infectious diseases and cancers . The verThere are two main approaches for removing dsRNA: minimizing the generation of dsRNA during the IVT process and eliminating dsRNA after the IVT process. To decrease dsRNA synthesis, previous studies have utilized various techniques such as competing oligonucleotides ,15, chaoSilica is widely used for nucleic acid purification because its negatively charged surface contains silanol groups . Among tHerein, we developed a facile chromatography method for RNA purification using mesoporous silica as an adsorbent and polyamines in the mobile phase, avoiding the use of hazardous chaotropic salts or functionalized silica for RNA purification. We observed that spermidine, a positively charged polyamine present in the IVT reaction buffer, could lead to the adsorption of RNA onto mesoporous silica. We explored not only the capability of spermidine-based RNA purification but also the removal of dsRNA content in the purified IVT RNA. The spermidine-based RNA purification method provides a straightforward and nonhazardous approach for isolating nonimmunogenic RNA.To investigate whether IVT RNA binds to mesoporous silica, we first mixed IVT RNA with mesoporous silica dispersed in a buffer containing sodium, magnesium, or calcium salts in a microcentrifuge tube A. These Unexpectedly, when IVT RNAs were mixed with the IVT buffer, we observed that IVT RNA remained in the pellet after the centrifugation. This intriguing result led us to hypothesize that the substance(s) present in the transcription IVT buffer may facilitate the binding of RNA to mesoporous silica. To find out which substance in the IVT buffer induces the binding of RNA to mesoporous silica, various combinations of MgCl2, dithiothreitol, and spermidine in the IVT buffer were tested for facilitation of IVT RNA binding to the mesoporous silica C. When sNext, we investigated the amount of spermidine required to induce the binding of RNA to the mesoporous silica. To determine the amount of spermidine necessary to bind IVT RNA adsorption onto mesoporous silica, we examined the N/P ratio between the cationic amine groups (N) and negatively charged phosphate groups (P) in RNA. Starting with an N/P ratio of 20, we systematically reduced the N/P ratio by half to determine the minimum N/P ratio needed for IVT RNA to bind to the silica surface D. IVT RNHaving established that spermidine can facilitate the adsorption of IVT RNA to silica particles in a batch mode of chromatography, we tested the feasibility of applying silica particles to fractionate IVT RNA in a simple separation chromatography using spin columns filled with mesoporous silica particles A. We firNucleic acid adsorption to silica is likely attributed to diverse driving forces, such as electrostatic interaction, free electrolytes in solution, and hydrogen bonding between single-stranded RNA and the silica surface . To explNext, we examined the effects of three linear polyamines  on RNA binding to mesoporous silica D. Most i2/g [To examine the effect of silica structure on RNA adsorption and desorption with electrolyte elution buffer, binding and elution efficiencies of RNA with mesoporous silica and spherical silica were compared A. We emp2/g . The amoNext, to enhance the recovery rate of RNA bound to the silica, we compared salt  and chelating reagent  for elution of the silica-bound RNA during the RNA purification process A. StepwiAfter confirming the effectiveness of EDTA as an elution solution, we examined the amount of EDTA required for the elution of a given amount of RNA adsorbed to the mesoporous silica in the presence of spermidine C. VariouNext, we tested whether the spermidine-mediated, RNA-mesoporous silica system is compatible with unprecipitated IVT RNA by directly loading a post-IVT reaction mixture onto the mesoporous silica chromatography. Purification of RNA directly from a post-IVT reaction solution could be challenging because various substances such as enzymes and free nucleotides in the IVT reaction mixture can interfere with the binding of RNA to the matrix and subsequent elution steps, thus impacting purification efficiency and RNA integrity . The anav/v) ethanol.To test whether the spermidine-mediated mesoporous silica spin column chromatography is applicable for the removal of dsRNA contaminants from IVT RNA, we performed the mesoporous silica-filled spin column chromatography with stepwise elution by increasing the EDTA concentration . We evalv/v) ethanol from the cellulose demonstrated that the efficacy of dsRNA removal by both purification methods was almost comparable .To further confirm that the RNA eluted with 5 mM EDTA had a lower dsRNA content than the input RNA, the dot blotting analysis was performed with J2 dsRNA-specific antibody B. Equal v/v) ethanol elution  GelRed nucleic acid gel stain . RNA bands in the agarose gel were visualized using a UV transilluminator. Alternatively, for analysis of RNA using the capillary electrophoresis system, RNA was analyzed using an Agilent 4150 TapeStation  with RNA ScreenTape (Agilent Technologies) according to the manufacturer’s instructions.The plasmid DNA encoding EGFP  harboring the 3’ UTR, 5’ UTR, and poly(A) tail sequences was linearized by digestion with EcoRI restriction enzyme . Some 2 μg of the linearized plasmid was mixed in 50 μL reaction solution containing 2 mM rants mixture , 100 U T7 RNA polymerase , and 40 U recombinant RNase inhibitor (Takara) in 1 × T7 RNA polymerase buffer containing 50 mM Tris-HCl (pH 7.5), 15 mM MgCl2, 2 mM spermidine , and 5 mM DTT (Sigma-Aldrich). The in vitro transcription reaction mixture was incubated at 37 °C for 2 h, and 5 U DNase I (Takara) was added for DNA degradation after 2 h incubation. After incubation at 37 °C for 30 min, 20 mM EDTA was added to the reaction mixture to stop enzymatic reactions. Following the addition of EDTA, the synthesized RNA was isolated by precipitation with 8 M LiCl solution (Sigma-Aldrich) and subsequently dissolved in nuclease-free water. The concentration of the LiCl purified IVT RNA was measured using a UV–Vis spectrophotometer  at a wavelength of 260 nm. Aliquots of the LiCl purified IVT RNAs were heat-denatured and then separated by electrophoresis on 1% agarose gels containing 0.01%  in spermidine relative to the number of bases in the phosphate backbone (P) of RNA was calculated by determining the moles of RNA and spermidine. The mass of the in vitro transcribed RNA (996 nts) was divided by the molecular weight of RNA (320 kDa) to calculate the number of moles of RNA in the sample. Multiplying the number of moles of RNA by the number of nucleotides in the RNA backbone (996 nts) gives the total number of phosphate backbones (P) present in the RNA sample. The number of amine groups (N) in spermidine was obtained by multiplying the moles of spermidine by three (three amine groups per mole of spermidine). The N/P ratio was then obtained by dividing the number of amine groups (N) by the number of phosphates in the RNA (P).g for 3 s to separate the supernatant from the pellet. The collected supernatant was analyzed using urea-PAGE (6%) and visualized using a UV transilluminator after staining with SYBR Gold .Mesoporous silica  slurry was prepared in 10 mM HEPES (pH 7.2) at a concentration of 30 mg/mL. Some 0.6 mg of mesoporous silica, the LiCl purified IVT RNA (2 μg) with 20 mM of various salts , or 1 × T7 RNA polymerase buffer (50 mM Tris-HCl (pH 7.5), 15 mM MgCl2, 2 mM spermidine, and 5 mM DTT) were mixed in a volume of 20 μL in a 1.5 mL microcentrifuge tube. The tube was incubated for 10 min and centrifuged at 3000× g for 3 s. The LiCl purified IVT RNA (2 μg) or unpurified IVT RNA in the post-IVT reaction solution were mixed with spermidine at the N/P ratio of 20, 10 mM HEPES buffer (pH 7.2), and 10 mM NaCl in a volume of 20 μL. The RNA samples were transferred to a spin column containing mesoporous silica and incubated at 25 °C with shaking (20 rpm) for 10 min. The spin column was then centrifuged at 3000× g for 3 s to separate the unbound RNA from the mesoporous silica. Elution buffer containing designated concentrations of NaCl , EDTA (Bio Basic), or urea (Bio Basic) in 10 mM HEPES (pH 7.2) was added to the spin column in a volume of 20 μL. The spin column was then shaken for 10 min and centrifuged at 3000× g for 3 s to collect the eluant. For the stepwise elution with increasing the concentration of elution solution, 10 μg of the unpurified IVT RNA in the post-IVT reaction solution was mixed with spermidine at the N/P ratio of 20 in 10 mM HEPES (pH 7.2) and 10 mM NaCl. Next, 100 μL of 5 mM EDTA solution was added to the spin column and incubated for 5 min. Stepwise elution was carried out by consecutively applying an elution solution of EDTA with gradually increased concentration (5 to 10 mM). All eluants obtained from the spin column were loaded onto a 1% agarose gel containing 0.01% (v/v) GelRed and further analyzed with a UV transilluminator.To perform RNA binding and elution experiments using a spin column, 20 μL of the mesoporous silica slurry (30 mg/mL) or spherical silica  slurry (30 mg/mL) in 10 mM HEPES (pH 7.2) was transferred to a Pierce™ spin column (Thermo Fisher Scientific) and centrifuged at 3000× Spermidine-conjugated mesoporous silica particles were prepared as follows: 50 mg of mesoporous silica particles  were amine-functionalized with 80 μL of 3-aminopropyl triethoxysilane  and 50 μL of saturated ammonium hydroxide solution (Sigma-Aldrich) in 2 mL of ethanol. After overnight shaking at 25 °C, the silica particles were first washed with 2 mL of ethanol, followed by two washes with 2 mL of methanol, and then dried in vacuo. Next, the amine-functionalized mesoporous silica particles were dispersed with 1.2 mmol of succinic anhydride (Sigma-Aldrich) and 1.2 mmol of N, N-diisopropylethylamine  in 3 mL of dimethyl formamide . After succinylation for 3 h with shaking at 25 °C, the silica particles were washed with 2 mL of DMF, methylene chloride , methanol, and dried in vacuo. Lastly, to conjugate spermidine with the silica particles, 0.4 mmol of diisopropylcarbodiimide , 0.4 mmol of hydroxy benzotriazole , 0.48 mmol of DIEA, and 0.48 mmol of spermidine solved in 1.5 mL of DMF were added to succinylate mesoporous silica particles dispersed in 1.5 mL of DMF. The reaction proceeded with shaking at 25 °C overnight, and the silica particles were washed with 2 mL of DMF, MC, methanol, and dried in vacuo. The loaded amount of spermidine to mesoporous silica was determined to be 0.69 mmol/g by measuring the weight increment of silica particles.g for 3 s, and the eluants were loaded onto a 1% agarose gel containing 0.01% (v/v) GelRed. The relative amount of RNA was quantified using the ImageJ software (version 1.53e) based on the image of agarose gel under UV visualization.The slurry of spermidine-conjugated mesoporous was prepared at a concentration of 30 mg/mL in 10 mM HEPES (pH 7.2). The LiCl purified IVT RNA (0.2 μg) was mixed with the spermidine-conjugated mesoporous silica slurry in a volume of 20 μL. The same amount of spermidine as that in the stationary phase was added to the mobile phase of the comparison group. RNA samples were incubated with shaking (rpm 20) at 25 °C for 10 min. The spin column was centrifuged at 3000× v/v) ethanol. After mixing for 10 min, 140 μL of the cellulose suspension was transferred to a Pierce™ spin column and centrifuged for 3 s at 3000× g. The flowthrough was discarded, and 100 μL of chromatography buffer was added to the cellulose fibers, followed by shaking for 10 min. Subsequently, the spin column was centrifuged (3000× g) for 3 s at 25 °C. The LiCl purified IVT RNA (10 μg) in 100 μL of chromatography buffer was added to the cellulose-filled spin column and agitated for 10 min to enable the binding of dsRNA to cellulose. Separation of unbound RNA from cellulose was achieved by centrifugation for 3 s at 3000× g.Cellulose fibers  at a concentration of 0.2 g/mL were suspended in the chromatography buffer containing 10 mM HEPES (pH 7.2), 0.1 mM EDTA, 125 mM NaCl, and 16% (Quantification of fractionated RNA was performed using a Quant-it™ RiboGreen RNA assay kit (Invitrogen). The RNA samples were diluted 25-fold in Tris-EDTA buffer (10 mM Tris-HCl (pH 7.5), 1 mM EDTA), and the RiboGreen dye was diluted 300 times in the Tris-EDTA buffer. The 25 μL of diluted RNA samples were mixed with an equal volume of diluted RiboGreen dye in a 96-well microplate and incubated for 5 min at 25 °C in the dark. Fluorescence measurements were performed using a VICTOR X3 Multilabel Plate Reader  at excitation and emission wavelengths of 485 and 535 nm, respectively. The RNA concentrations in the samples were determined by comparing their fluorescence intensities with a standard curve, according to the manufacturer’s instructions. The assay was performed in triplicate for each RNA sample, and the average values were calculated.w/v) BSA in PBS for 2 h at 37 °C. The plate was then washed with PBS containing 0.5% (v/v) Tween 20 (Sigma-Aldrich). The 10 μL of RNA samples mixed with 90 μL of STE buffer (10 mM Tris-HCl (pH 7.5), 1 mM EDTA, 100 mM NaCl) were added to the plates and incubated for 1 h at 37 °C. After washing with PBS containing 0.5% (v/v) Tween 20 for four times, K2 mouse monoclonal antibody (included in the dsRNA ELISA kit) was added to the plate and incubated for 1 h at 37 °C. After washing four times, the plates were incubated with horseradish peroxidase-conjugated goat antimouse antibody (included in the dsRNA ELISA kit) for 1 h at 37 °C. After the washing, TMB substrate solution was added and incubated for 1 h at 25 °C in the dark. Some 2 M H2SO4 was added to stop the reaction, and the signal was read at 450 nm using a VICTOR X3 Multilabel Plate Reader. The amount of dsRNA in the test sample was determined from a standard curve generated using known amounts of dsRNA standard.dsRNA was quantified by sandwich ELISA using J2 dsRNA-specific, antibody-based dsRNA ELISA kit . J2 mouse monoclonal antibodies (included in the dsRNA ELISA kit) were immobilized on 96-well Nunc Immunoplates (Thermo Fisher Scientific) overnight at 4 °C and blocked with 1% (w/v) non-fat dry milk in TBS-T buffer (25 mM Tris-HCl (pH 7.5), 150 mM NaCl, 2 mM KCl, and 0.1% (v/v) Tween 20) at 25 °C for 1 h. The membrane was incubated with dsRNA-specific J2 monoclonal antibody (Sigma-Aldrich) diluted to a ratio of 1:5000. The antibody was diluted in 1% (w/v) non-fat dried milk and TBS-T buffer and incubated with the membranes on a rocker shaker at 4 °C overnight. The membranes were washed three times with TBS-T buffer for 10 min and then incubated with horseradish peroxidase (HRP)-conjugated antimouse immunoglobulin  diluted 1:10,000 in 1% (w/v) non-fat dried milk and TBS-T buffer at 25 °C for 1 h. After washing the membranes three times with TBS-T buffer for 10 min, the membranes were visualized using an Immobilon Western Chemiluminescent HRP Substrate , and signals were detected using a G: BOX Chemi XL .For dot blotting assay using J2 dsRNA-specific antibodies, the nylon membrane  was placed on a sheet of 3M paper, and the silicone mask was tightly pressed onto the membrane to ensure that all wells were sealed to avoid sample leakage before loading the samples. A total of 100 ng of RNA samples were blotted onto nylon membranes using a 96-well biodot silicon gasket . After loading the samples, the membranes were air-dried and blocked in 5% (In summary, our study highlights the significance of spermidine in facilitating the binding of IVT RNA to mesoporous silica. Both electrostatic interaction and hydrogen bonding between secondary amine groups of spermidine and silica surface are involved in the binding of RNA to mesoporous silica. Desorption of the silica-bound RNA in the chromatography setup was accomplished by elution with solutions containing salt, EDTA, or urea, among which EDTA was the most efficient reagent to recover the silica-adsorbed RNA. The spermidine-mediated adsorption of RNA to mesoporous silica and subsequent elution with EDTA was applicable for RNA purification from the post-in vitro transcription reaction solution without the need for a prior salt precipitation step. Importantly, a higher RNA recovery rate was observed with the mesoporous silica-based RNA purification with EDTA elution than with the ethanol elution of RNA in the cellulose-based spin column chromatography. Moreover, the RNA purification with EDTA elution in the spermidine-based mesoporous silica chromatography resulted in the efficient removal of at least 80% of dsRNA contaminants from the input IVT RNA. It would be valuable to conduct further research on optimizing the specificity of the method for different RNA sequences or structures, enhancing sample recovery, and minimizing potential interference from contaminants to ensure high-quality IVT RNA purification. Taken together, the mesoporous silica-based RNA adsorption/desorption provides a facile IVT RNA purification method with a higher recovery rate as well as a successful reduction of dsRNA content. Thus, our RNA purification chromatography system comprising mesoporous silica as an RNA adsorbent, spermidine as the linkage between RNA and silica, and EDTA as an elution reagent provides a promising approach for purification of intact IVT RNA, which can be used for various downstream applications in the mRNA production process.


Ashbya gossypii, the RNA-binding protein Whi3 forms distinct condensates with different RNA molecules. Using criteria derived from a physical framework for explaining how compositionally distinct condensates can form spontaneously via thermodynamic considerations, we find that condensates in vitro form mainly via heterotypic interactions in binary mixtures of Whi3 and RNA. However, within these condensates, RNA molecules become dynamically arrested. As a result, in ternary systems, simultaneous additions of Whi3 and pairs of distinct RNA molecules lead to well-mixed condensates, whereas delayed addition of an RNA component results in compositional distinctness. Therefore, compositional identities of condensates can be achieved via dynamical control, being driven, at least partially, by the dynamical arrest of RNA molecules. Finally, we show that synchronizing the production of different RNAs leads to more well-mixed, as opposed to compositionally distinct condensates in vivo.Cellular matter can be organized into compositionally distinct biomolecular condensates. For example, in  In this work, the authors report that protein-RNA condensates with shared proteins and distinct RNAs can form and persist in vitro and in cells as distinct entities if the nonshared RNA molecules are dynamically arrested, but the shared protein components are dynamically exchangeable. A key challenge is understanding how condensates with shared and distinct macromolecular compositions can form and coexist as distinct functional entities7  with varying types of interactions17. Here, valency refers to the numbers of cohesive motifs, which we refer to as stickers in accord with the nomenclature used in the field of associative polymers20. Stickers can engage in complementary homotypic or heterotypic interactions21. Note that the models we use are phenomenological as opposed to being faithful representations of the Whi3 protein and RNA molecules.We used computations to ascertain the types of interactions in ternary systems that can lead to the spontaneous formation of two distinct coexisting dense phases with one shared component. We used explicit lattice-based simulations as opposed to mean-field models because they allow for proof-of-concept titrations of different variables and require fewer simplifying assumptions. We performed lattice-based coarse-grained simulationsRNA1 and RNA2 mimics with the Whi3 mimic are the same in strength and purely heterotypic in nature, the dense phase comprises all three macromolecules  and yP = , and m points along the upper boundary of the one-phase regime xN =  and yN = . Here, the subscript N and P represent points in the one- (No phase separation) and two-phase (Phase separation) regimes, respectively. As our null hypothesis, we assume that the system is dominated by heterotypic interactions. Using this assumption, we rescaled the RNA sticker concentrations (yP and yN) by varying a scaling factor, s, to find the highest degree of symmetry. The optimal s should generate the lowest overlap between the area defined by  and . If heterotypic interactions dominate, then there should be minimal overlap between the area defined by the upper boundary of the one-phase regime and the area defined by the lower boundary of the two-phase regime plus its mirrored data. We determined the overlap area as described in “Methods”. The apparent sticker valence of the RNA was taken to be the value of s that yields the minimum overlap area. Lastly, for the value of s that corresponds to the minimum overlap we fit a closed loop to  using the fitting method of Szpak et al.32, and extracted the R2 of the fit.Given the importance of the apparent sticker valence, we describe the method by which this parameter was extracted for each binary Whi3-RNA system. First, we extracted n points along the lower boundary of the two-phase regime R2 values of 0.88-0.92  in which the cognate-binding sites were disrupted by scrambling the consensus sequence. Disruption of cognate sites does not abrogate phase separation. However, there are two prominent changes to the shape of the phase boundary. Heterotypic interactions are likely to be a blend of favorable and unfavorable contributions. Scrambling the cognate sites alters the shape of the dilute arm. Specifically, we note a switch from being locally convex to locally concave, implying a more prominent contribution from unfavorable heterotypic interactions at low concentrations of Whi3 and the mutant RNA when compared to the wild-type BNI1  of Whi3 and RNA in each of the Whi3-RNA condensates. Although up to ~20% of the Whi3 fluorescence can be recovered after photobleaching Fig. a, c, the42. These dynamical processes will be influenced by the stoichiometry of stickers that enable complementary homotypic and heterotypic interactions  and different RNA molecules (5 nM) were chosen to ensure that these lie within the two-phase regimes in binary mixtures Fig. . The conRNA Fig. . The sechi3 Fig. . To quanCLN3 to pre-formed Whi3-BNI1 condensates was low when condensates in ternary mixtures were prepared using the delayed approach  and long-lived physical crosslinks influenced by interactions among RNA molecules within condensates.Our findings complement the recent work of Seim et al.65. The large cytoplasm of the filamentous fungus A. gossypii, and quite likely other mammalian cells such as axons66, are likely candidates to leverage dynamical control to enable the formation of coexisting, demixed ribonucleoprotein condensates comprising the same protein and different RNA molecules.Overall, our findings bolster theoretical predictions of the thermodynamics of phase separation while also highlighting the relevance of long-lived crosslinks as a likely passive mechanism for demixing that generates compositional identity in vitro and in cellulo15. To generate plots and figures we used the common Python packages NumPy, Matplotlib and Seaborn. Adobe Illustrator was used to make the final figures.To investigate minimal models for associative polymers that can lead to equilibrium demixed condensates, we used a customized version of the lattice simulation engine LaSSIRNA1 and RNA2 respectively. The beads are connected via implicit linkers with a length of two lattice sites. Using a lattice size of For investigating the emergence of demixed condensates given two different RNA species with Whi3 protein, we simulated three different finite-sizes polymers. The Whi3 protein is modeled as a polymer containing three beads, RNA1 + RNA2 systems, we used multiple interaction models to assess how different interactions among the components affect the overall phase behavior.To study scenarios that give rise to thermodynamical control of demixing of condensates generated by three-component systems that mimic the Whi3 protein + Base Case. Stickers interact via pairwise interactions, that are equivalent to the binding between the stickers. Given Base Case are defined asTo set baseline expectations regarding the phase behaviors of three-component systems containing the Whi3 protein and two RNA species, we included heterotypic interactions between the Whi3 protein and the RNAs. This is referred to as the RNA1 and RNA2, respectively. Lastly, with this notation, Here, the Base Case mentioned above, where no interactions occur between the RNAs.To test the effects of homotypic interactions between the RNAs, we added a scaling factor, Base Case:RNA1. The case where Base Case where there are no differences between RNA species.To test whether asymmetric interactions between the Whi3 protein and an RNA species could lead to demixing, we applied a scaling factor, Base Case.To set expectations about the phase behavior of a model that does lead to demixing, we added an additional isotropic repulsive interaction between the RNA species. The isotropic repulsion is a contact potential with a radius of one lattice site or All simulations start with random initial conditions. For 67. All anisotropic/binding interactions are also turned off during this phase of the simulation. After T0 and the biasing potential is turned off. The simulations are run for mentclass2pt{minimFor generating the two-component phase boundaries, we sampled a large set of concentrations and stoichiometries and explicitly quantify the coexisting densities, when condensates are formed in the simulations. Since we have two explicit components, the system composition is determined by the numbers of each molecule and the overall concentration of the system. We fixed the total number of molecules to 5000 and changed the stoichiometry by changing the ratio between the two components. This results in 22 different stoichiometries. The simulation box size is then used to set the total concentration of the system. For each pair of molecule numbers, we have five different box sizes. Lastly, for each pair of molecule numbers, All simulations start with random initial conditions. For Given each simulation, we explicitly calculated the radial density profiles of each component from the center-of-mass (COM) of the system, and from the COM of the largest cluster of each component. The density profiles are generated by first computing a number histogram of beads, To calculate the coexisting densities for the two-component systems, given a component Let component 68, we can define a measure for demixing:Then, using the Hellinger DistanceCombining all definitions,Thus, x,y) was then mirrored by also plotting . The points that defined the dilute phase arm and the mirrored data  were then fit to an ellipse using the guaranteed ellipse fitting method of Szpak et al.32, and the mean square of the residuals R2 of the fit was extracted.Phase diagrams were plotted in terms of sticker concentration. The dilute phase arm of the phase diagram  containing 10 mg of lysozyme (Sigma 9001-63-2), one Roche cOmpleteTM protease inhibitor cocktail tablet (Sigma 5056489001), and PMSF (Sigma 10837091001) at a final concentration of 200 µM. The resulting lysate was sonicated on ice with a Branson SFX550 at 30% strength alternating 1 s on, 2 s off for 1 min. This was repeated five times, swirling the lysate gently between each sonication. The lysate was spun down and the supernatant passed over a HisTRAP FF column (Cytiva) on an ÄKTA pure 25 L (GE). The bound protein was eluted in 150 mM KCl, 50 mM HEPES pH 8.0, 200 mM Imidazole, and 5 mM BME. The eluate was then cleaved with 200 µg of TEV [pRK793 was a gift from David Waugh ]. The cleaved supernatant was concentrated in 3 kDa Amicon® centrifugal filter units , and then injected onto a HiLoad 16/600 Superdex 200 pg column (Cytiva). Untagged Whi3 was then dialyzed into storage buffer  and concentrated using 3 kDa Amicon® centrifugal filter units. For tagged Whi3, Alexa Fluor 488 NHS Ester  in DMSO (ThermoFisher D12345) was added at a ratio of 4:1 and incubated at room temperature with continuous mixing for 1 h in the absence of light. The tagged Whi3 was then loaded onto a HiLoad 16/600 Superdex 200 pg column with storage buffer, and the fractions were concentrated with 3 kDa Amicon® centrifugal filter units.Full-length Whi3, with a N-terminal 6x His tag and TEV cleavage site, in BL21 cells from NEB (#C2527H) in TB media  was induced with IPTG (Santa Cruz Biotechnology SC-202185B) to a final concentration of 1 mM at an ODCLN3, BNI1, and SPA2 were linearized with restriction enzymes to obtain a linearized template. CLN3 RNA was transcribed with HiScribeTM T7 Quick High Yield RNA Synthesis Kit (NEB). BNI1 and SPA2 RNA were transcribed using Hi-T7® RNA Polymerase and Reaction Buffer (NEB) in lieu of T7 polymerase and buffer. For labeled RNA, 0.1 µL of 5 mM Cy3-UTP or Cy5-UTP  was added to each transcription reaction. After transcription, each reaction was treated with DNase before being purified with Monarch® RNA Cleanup Kit (NEB).Plasmids in which the T7 promoter sequence was placed upstream of the coding regions for 384-well plates with #1.5 polymer coverslip bottoms (Ibidi) were passivated for 15 min with 0.1% Tween-20 before being rinsed thrice with droplet buffer . Untagged Whi3 protein and tagged RNA were diluted in droplet buffer and mixed to obtain the desired final concentration of protein and RNA. After incubation at room temperature for 1 h, the samples were visualized on a Zeiss Axiovert 200 M with a C-Apochromat 40×1.2NA water objective.boundary was used to define the boundaries of  and . Here, xN and yN define the x and y vectors of points along the upper boundary of the one-phase regime, xP and yP define the x and y vectors of points along the lower boundary of the two-phase regime, and s is the scaling factor for the RNA sticker concentration. Then, the MATLAB function polyshape was used to create polygons defined by the boundaries. Next, we extracted the intersection of the two polygons using the MATLAB function intersect and calculated the area of this overlap region using the MATLAB function area. Then, the apparent sticker valence of the RNA was taken to be the s value that yields the minimum overlap area. The boxplots in Fig. To determine the overlap area, the MATLAB function 384-well plates with #1.5 glass coverslip bottoms (Cellvis) were passivated for 15 min with 0.1% Tween-20 before being rinsed thrice with droplet buffer . For each sample, protein and RNA were diluted to 5 µM and 5 nM, respectively, in droplet buffer. For simultaneous samples, tagged Whi3 and tagged RNAs were added in quick succession to a low protein binding microcentrifuge tube (ThermoFisher) and allowed to incubate in darkness at room temperature for 4 h before being transferred to a passivated well and imaged on a Nikon Eclipse Ti2 equipped with a Yokogawa CSU-X1 Spinning Disk, 60X oil objective using Nikon Type F oil, and a Hamamatsu ORCA-Flash4.0 V3 camera. For delayed samples, tagged Whi3 and one of two tagged RNAs were mixed in a low protein binding microcentrifuge tube and allowed to incubate in darkness at room temperature for 4 h. Then the second RNA was added and allowed to incubate for 1 h before being transferred to a passivated well and imaged on the system described above.2 (approximately 469 µm2) in Fiji69 The images were then split into separate channels and analyzed with the Coloc2 plugin, which assesses colocalization between two channels on a pixel-by-pixel basis, to obtain the Pearson correlation coefficient (Pearson’s r). We employed a bisection threshold regression and 1000 randomizations with a PSF of 3.0. The Pearson’s r was taken for each of the three fields of view and plotted with standard error.Images from three random fields of view for each sample were collected and cropped to 40,000 px384-well plates with #1.5 glass coverslip bottoms (Cellvis) were passivated for 15 min with 0.1% Tween-20 before being rinsed thrice with droplet buffer . Either tagged Whi3 or RNA is mixed with untagged RNA or Whi3 respectively and allowed to incubate for 1 h at room temperature. Samples were imaged on a Nikon A1Rsi with a 60X oil objective using Cargille Type B oil. Laser strength for photobleaching was 50% of the maximum intensity of the wavelength corresponding to the fluorophore used. Photobleaching occurred for 5 s with recovery measured for 3 min utilizing the laser corresponding to the fluorophore used.n of acquisitions). FRAP traces were generated in matplotlib.The Region of Interest (ROI) and Time Analysis tools in Nikon Imagine Software (NIS) were used to trace the fluorescence intensity of three regions—the photobleached spot, an unbleached condensate, and a region with no condensates . Intensity changes over time were evaluated for all three regions and only acquisitions wherein the intensity of the unbleached condensate deviated less than 5% were used for analysis. Raw intensity values of the photobleached spots from three acquisitions were averaged, subtracted from the background, and normalized to the maximum average value. The “max” recovery value corresponds to the maximum value among all the recovery values . The standard error of the mean (SEM) was calculated for individual time points across the three acquisitions and for the maximum intensity values across three acquisitions. SEM was calculated by taking the standard deviation of time-point-matched intensity values or maximum intensity values and dividing this value by the square root of three  was utilized to identify complementary sites between pairs of RNAs. Here, exact matches of length 11 or longer were identified using G-C, A-U, and G-U base pairing. Additionally, for the CLN3-BNI1, BNI1-SPA2, CLN3-CLN3, and BNI1-BNI1 pairs the mean SHAPE value was calculated over all nucleotides in the complementary sequences using the data from Langdon et al.8.GUUGle , for wild type or AFM supplemented with G418 (200 µg/mL) for the strain overexpressing CLN3 and BNI1 in a 500 mL baffled flask. Cells were grown shaking at 110 rpm at 30 °C for 16 h. After formaldehyde fixation and ethanol permeabilization, cells were probed with custom FISH probes from Stellaris. TAMRA-labeled probes against agCLN3 and Quasar-670-labeled probes against agBNI1 were both used at a final concentration of 2.5 nM and hybridized simultaneously at 37 °C overnight. Nuclei were stained with 5 µg/mL Hoechst in Wash Buffer . Then, 20 µL of cells were then mounted in a 20 µL Vectashield mounting medium , sealed with a coverslip and imaged.RNA smFISH was performed as previously describedImaging was performed on a Nikon Ti-E stand using a Yokogawa CSU-W1 spinning disk confocal unit. Images were acquired on a Plan-Apochromat 60x/1.49NA oil-immersion objective using a Zyla sCMOS camera (Andor) on Nikon NIS-Elements software v.4.60.CLN3 and BNI1 spots were identified using the BIGFish Spot detection algorithm70 that treats puncta as local maxima in the smFISH channels given a specified object radius. Puncta (spot) detection can be performed using two methods: (1) a single function call that enumerates the spots directly, or (2) a series of intermediate detection steps that yields the same results as in method 1, but with more debugging information. The latter option was chosen as it provided more data for troubleshooting the development of the spot detection pipeline. Using the latter method for detecting spots, a spot radius, r, of 150 nm (1.389 pixels) was used for detection. Maximum intensity projections were used, and images with uneven illumination and a few regions of non-overlapping hyphae were discarded. For each image in which the number of CLN3 and BNI1 spots were within an order of magnitude, the centers of identified RNA spots were collected. Then, a CLN3 spot was determined to be colocalized with a BNI1 spot if any BNI1 center was a distance less than 2r away from the CLN3 center. Fraction colocalized then refers to the number of all CLN3 spots that had at least one BNI1 spot less than 2r away divided by the total number of CLN3 spots. For the pixel shift analysis, the centers of CLN3 spots were shifted simultaneously by 2r in both the x and y directions before determining overlap. Then, using the shifted CLN3 spot centers we determined if any BNI1 spot was less than 2r away.71. Specifically, we ascribed a minimum object diameter of 10 voxels or 1.08 μm for the detection of regions of interest and performed segmentation. Larger object diameters perform poorly and detect false positives. Then, the centroids and areas of the nuclei masks were extracted using the Scikit-Image Python package72. Using the collected centroids and areas, we then determined whether the center of a CLN3 spot was less than r + R away from the center of any nucleus. Here, R is the nucleus radius in pixels and was estimated to be √(A/π), where A is the area of the nucleus. CLN3 spots less than r + R away from the center of any nucleus were defined to be nuclear proximal and CLN3 spots greater than or equal to r + R were defined to be not nuclear proximal. Using these classifications, we then determined the fraction of CLN3 spots colocalized with BNI1 split by nuclear proximal and not nuclear proximal where colocalization was defined as any BNI1 spot center less than 2r away from the CLN3 center. The same analyses were also performed using BNI1 as the reference RNA.Proximity to the nucleus was determined by first identifying nuclei using the Cellpose 2.0 Python packageFurther information on research design is available in the Supplementary informationPeer review fileReporting SummarySource data


Nonetheless, various challenges remain before the full potential of machine learning for epitranscriptomics can be leveraged. In this review, we provide a comprehensive survey of machine learning methods to detect RNA modifications using diverse input data sources. We describe strategies to train and test machine learning methods and to encode and interpret features that are relevant for epitranscriptomics. Finally, we identify some of the current challenges and open questions about RNA modification analysis, including the ambiguity in predicting RNA modifications in transcript isoforms or in single nucleotides, or the lack of complete ground truth sets to test RNA modifications. We believe this review will inspire and benefit the rapidly developing field of epitranscriptomics in addressing the current limitations through the effective use of machine learning.The expanding field of epitranscriptomics might rival the epigenome in the diversity of biological processes impacted. In recent years, the development of new high-throughput experimental and computational techniques has been a key driving force in discovering the properties of RNA modifications. Machine learning applications, such as for classification, clustering or  The dev6A) , 4, 5-me5C) [5C)  and inos5C) [5C) , with di5C) [5C) . Some mo5C) [5C) , suggest5C) [5C) . One of 5C) [5C) , 4. The 5C) [5C) . Through5C) [5C) , 19. EveA critical advance in epitranscriptomics has been enabled by the development of transcriptome-wide experimental methods to detect modifications at single-nucleotide and/or single-molecule resolution in a quantitative manner. These range from a targeted detection of RNA modifications by specific antibodies or enzymes to directly reading RNA modifications in RNA molecules using direct RNA sequencing (DRS). Computational tools have been essential in processing and analyzing the experimental outputs to identify RNA modifications . ParticuML has increased our ability to perform complex prediction tasks that are difficult to manually or formally define because of the large number of parameters or the many special cases or exceptions . ML algoPrevious reviews describing ML approaches for RNA modification detection have focused either on technology-specific methods, like those based on long-read sequencing , 27, or In this review, we make the distinction between two types of ML algorithms: deep learning (DL), which employs neural networks (NNs) with multiple layers, and classical ML, which includes methods such as linear models, random forests (RFs) and support vector machines (SVMs). One of the main differences between the two types is feature extraction, i.e. the identification and transformation of the input data for the ML task. In classical ML, feature extraction is usually performed separately prior to using the algorithm. In contrast, DL algorithms can perform ‘end-to-end learning’, creating an internal representation of the input data in their latent space and performing feature extraction automatically. One of the advantages of DL methods is that they can be specifically adapted to suit initial assumptions about the nature of the input data distribution, also called inductive bias. For instance, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) incorporate properties that make them especially suitable for modeling data with a strong spatial component, such as images or sequential data. Similarly, geometric DL methods can take inputs in the form of a graph, which makes them suitable for RNA secondary structure or gene co-expression modeling . AlthougMultiple relevant biological and computational elements must be considered for the identification of RNA modifications with ML methods. From the epitranscriptomics perspective, algorithms should ideally be capable of identifying RNA modifications at single-nucleotide resolution, associating RNA modifications with transcript isoforms, and accurately estimating their stoichiometry, i.e. the fraction of copies of a given RNA molecule that harbor an RNA modification at a given site. On the other hand, from the ML perspective, algorithmic expertise must be combined with domain knowledge to guide training, testing, feature extraction, feature selection and interpretability. We will discuss these topics in the next sections for the two major strategies for detecting RNA modifications with ML, which we refer to as experiment-independent and experiment-based methods. Experiment-based methods use data from high-throughput experiments during ML inference that are either specifically designed to identify or enrich a specific modification (reviewed in ), or thaExperiment-independent ML methods for the prediction of RNA modifications are based on the observation that many RNA modifications show a certain sequence and positional preference in their deposition, as exemplified by the preferred occurrence of m6A modifications within DRACH motifs and close to stop codons , 4, 35 oCorrect labeling of the training and testing examples is an essential—albeit not trivial—step for RNA modification modeling. Most experiment-independent tools surveyed in this review take the union of detected modification sites from several experiments as positive examples for training and testing . The excIn contrast to the positive examples, the strategies for collecting negative examples, i.e. sequences not containing the RNA modification, are more heterogeneous across tools. The most common strategy for m6A prediction extracts DRACH/GAC/RRAC/A sites either from the complete transcriptome or only from those transcripts that harbor the positive examples  Figure . In contk-mer frequencies  in the surrounding sequences, RNA secondary structure-related features or even physicochemical properties of the nucleotides. For example, M6A-HPCS [k-nearest neighbor (kNN) algorithm. The proportion of positive samples in the kNNs is then used as the kNN similarity score to make the prediction. Although some tools compare the performance among certain encoding schemes [Experiment-independent ML models follow two main approaches regarding the selection of features: they either use only features based on the nucleotide sequence, referred to as sequence-only features, or incorporate other genomic features, referred to as genome-derived features . The majM6A-HPCS , RAM-ESVM6A-HPCS , iRNA-MeM6A-HPCS  and iRNAM6A-HPCS  implemenM6A-HPCS , which eM6A-HPCS  and SRAMM6A-HPCS  compare  schemes , 51, 55,Methods based on genome-derived features  use posiIn most ML scenarios , the exaML performance metrics are essential to assess how the predictions of a given algorithm resemble the ground truth when tested on an independent testing set prepared as described above. There is no single universal performance metric, as different metrics can describe different advantages and limitations of an algorithm. Metrics like the area under the receiver operating characteristic curve (AUROC) and overall accuracy (ACC) are popular to measure the model’s performance. However, the training and testing data in experiment-independent methods are often unbalanced, as they tend to contain more negative than positive examples, which can lead to an underestimation of the false positive rate (FPR). The AUROC uses the FPR, which may not be realistic on an unbalanced testing set. Therefore, choosing the appropriate metric is essential to avoid over- or underestimation of the performance of a model. The area under a precision-recall curve (AUPRC) measures the ability to predict positive examples considering the false positives and thereby represents a more realistic measure of an algorithm’s performance. Another summary metric that is robust to unbalanced datasets is the Matthews correlation coefficient (MCC), which generates a high score only if the prediction obtained good results in all four categories of the confusion matrix . In our survey, AUROC, ACC and MCC are the most commonly used performance metrics by experiment-independent predictors . DespiteInterpretability refers to the degree to which we can understand the decisions made by an ML method and gain biological insights from it . ClassicModel-specific interpretability methods take advantage of certain properties of the ML algorithm to interrogate its predictions. One of the most common techniques for epitranscriptomics algorithms is feature importance from decision tree-based algorithms such as RF and XGBoost . The feaNNs trained to classify modified and unmodified RNA sequences can also be interrogated for interpretability. CNNs use kernels, i.e. a fixed-size weight matrix, to process the input from one layer and extract important features for classification. In the first CNN layer, kernels scan through all the positions of the input vector and calculate an activation value for each position. Activation values increase with the relevance of a combination of input features on the classification score. Tools like TDm6A , Gene2veDL models with attention have also been used to identify RNA sequence motifs important for model classification. MultiRM  is the fModel-agnostic interpretability methods can be used with any ML model, as their applicability does not depend on the specific properties of the ML algorithm. For instance, to understand the relative contribution of each feature for identifying m6A modifications, M6A-HPCS  uses an in silico saturation mutagenesis to understand how individual nucleotides influence the ML-based predictions of m6A . m6Aboost is thereby able to detect both non-DRACH and DRACH m6A sites with very high performance on an independent testing set.To date, two approaches have been presented that use classical ML methods to leverage information from targeted experiments for m6A . The firde novo.Classical ML has also been employed to investigate m6A sites detected by the antibody-independent experimental method MAZTER-seq . MAZTER-Nanopore DRS is an emerging technology that allows the sequencing of native RNA molecules at a transcriptomic scale . In DRS,in vitro transcripts (IVTs) or RNA from a sample after a KO or knockdown (KD) of an RNA-modifying enzyme. ELIGOS [Several algorithms detect RNA modification by comparing features of the nanopore sequencing reads between two conditions—most often in an unsupervised manner—where one of them is expected to contain lower levels of one or more RNA modifications. Many such methods use error patterns to predict modifications. The underlying idea is that RNA basecalling algorithms, which predict the RNA sequence from the nanopore sequencing signal, have not been trained with this modification information. It is thus expected that they will present a low prediction probability and a higher error rate on and around modified nucleotides. Based on this principle, samples with and without modifications are expected to have a non-symmetric set of errors that can be leveraged to detect RNA modifications. This strategy is used by EpiNano (comparative mode) , ELIGOS . ELIGOS  also use. ELIGOS . Moreovelevel_sample_compare. Nanocompore [Error profiles are not the only features that can be leveraged in a comparison between two conditions. Other features such as average signal intensity values and dwell time can be used to compare a sample and a reference to detect modifications. Non-error features improve the detection of RNA modifications and correlate better with stoichiometry than error-based features , 64. Forocompore  clustersocompore . xPore [ocompore  also impocompore . xPore [ocompore  also shoocompore . Yanocomocompore  uses a socompore  is the oIn contrast to the previously described tools, nanoDoc  uses a sOne of the advantages of the comparative and unsupervised approaches is that, in principle, they can be used to detect the modification status of RNA without requiring a ground truth. Their main limitation is that, since these approaches require a control or KO/KD sample, the prediction is inherently indirect. Moreover, the necessity for KO/KD samples increases the complexity and costs of the experiment and may not always be feasible. An additional problem is that the removal of an RNA modification may generate secondary effects like the depletion of other RNA modifications. For instance, it has been shown that the depletion of NSUN2 to remove m5C leads to a reduction of hm5C . FurtherIME4, a yeast gene encoding a METTL3 homolog.Supervised learning algorithms can be trained to recognize patterns linked with modified and unmodified nucleotides in future unseen samples. In contrast to the comparative methods described above, supervised methods directly predict the specific modification, rather than inferring it from a comparison between two conditions. Several tools have been developed using this approach. Besides the comparative mode described above, EpiNano  can alsode novo mode to classify DRACH motifs into modified or unmodified [In a different approach to predict m6A sites, MINES  uses an modified . MINES wmodified  implemenin vivo HEK293 dataset, it only identifies m6A sites in DRACH motifs and has a low precision. Another method that uses previously identified m6A sites to train an NN is DENA [Arabidopsis thaliana at various RRACH motifs. The mean and median values of sites with significant error differences are used as predictive features. Other similar DL methods have been developed to detect A-to-I editing sites from nanopore DRS data [Several other methods apply DL to model DRS data. For instance, m6Anet  is a fee is DENA . DENA’s DRS data .in vivo samples. It was shown to outperform xPore [In parallel, CHEUI was developed as a two-stage prediction model that detects both m6A and m5C from the DRS signals . In the rm xPore , Nanocomrm xPore , Tombo [rm xPore  and Nanorm xPore  in stoicML models have also been developed for other RNA modifications, such as pseudouridine (Ψ) . Using aIdentifying RNA modifications at isoform-specific resolution is one of the major open challenges in epitranscriptomics. Experimental methods based on short-read sequencing have the inherent limitation that most reads cannot be confidently assigned to a specific isoform. As a consequence, when RNA modifications are predicted in transcript regions that occur in multiple isoforms, it is challenging to identify whether all isoforms, or only a subset of them, are modified. Long reads from nanopore DRS can span the full length of a transcript isoform, circumventing the ambiguity of short reads. In principle, experimental-based methods based on DRS data could take this advantage to assign modifications to specific transcript isoforms . In pracTo quantitatively characterize and fully understand the functional roles of RNA modifications, it will be crucial not only to accurately predict their positions but also their stoichiometry, i.e. the fraction of copies of a given RNA molecule that harbor an RNA modification at a given site. Current evidence suggests that stoichiometry can be variable across sites and modification types in the same condition or even in the same transcript . MoreoveAn important question in epitranscriptomics is whether RNA modifications vary across cell types, tissues and other conditions and whether these differences are related to regulatory mechanisms or phenotypic states. As experiment-independent methods do not incorporate condition-specific information, they will detect the same RNA modifications in different cell types and conditions, hence providing a ‘static’ description of the epitranscriptome. Even if experiment-independent ML methods were trained using cell type-dependent or condition-specific training data, they would only discover modifications in those specifically pre-trained conditions. In contrast, experiment-based ML methods can detect changes in modifications between conditions and can be used to discover new patterns and functions for RNA modifications. However, since RNA modification detection in such approaches is performed on a specific dataset, the technical and biological variations in the experimental measurements must be considered. ML methods that can work on any sample and are robust to the variability of the experimental techniques will provide new opportunities to uncover the role of RNA modifications across cell types, tissues and other conditions.A limiting factor for training and testing ML methods to detect RNA modifications is the scarcity and variable quality of the available experimental data. For m6A, there are datasets with single-nucleotide resolution from multiple cell types and orthogonally validated sites using several experimental techniques. This has facilitated the development of ML algorithms to detect m6A sites at single-nucleotide resolution. In contrast, other RNA modifications have fewer or no datasets available, and often not at single-nucleotide resolution, which prevents the effective development of ML methods. Fewer experimentally validated sites also result in higher uncertainty about the expected accuracy of the algorithms. To understand the full extent of the epitranscriptome and create more reliable and comprehensive ML models, extensive data for all RNA modifications need to be generated. Furthermore, limited by the experimental techniques, modified sites with low stoichiometry or located in lowly expressed transcripts are still hard to detect. This also increases the difficulty of identifying the appropriate negative examples. For DRS-based ML tools, an alternative to biological sources for obtaining data to train models are IVTs. With IVTs, molecules can be made without modifications or substituting unmodified bases for their modified counterparts. DRS of these molecules yields a complete ground truth of signals for modified and unmodified nucleotides in the same sequence contexts, which can be used for training and testing new methods. As with any training strategy, biases contained in the dataset, and artifacts from experimental procedures and other sources will be passed on to the model. An alternative way to evaluate the performance of such models is to use data obtained using orthogonal techniques, but these present also their own biases and limitations. The coordinated efforts between computational and experimental researchers will therefore continue to be crucial to generate appropriate training and testing datasets.Well-studied RNAs, such as ribosomal and transfer RNAs, are known to harbor multiple modifications, and it is likely that this is common in mRNA too. DRS provides for the first time the theoretical possibility of measuring all RNA modifications present in each mRNA copy. However, it is not yet known how different nearby modifications in the same molecule affect the nanopore signal current. Specific training strategies incorporating combinations of modifications will be necessary to train and test model performance in realistic scenarios. These training and testing configurations are challenging to generate, and so far, only combinations of IVTs and cell line datasets have been used. Other challenges include the 3′ end bias and frequent 5′ end truncation in DRS. This results in a significant under-sampling of modifications in the 5′ untranslated regions, which are critical to understanding translation regulation. Another open question is whether there is a limitation in the type of modification and the resolution of the modifications that DRS can detect. Several current methods failed to separate m1A and m6A, two chemical isomers, based on their nanopore signals . This maThe last few years have made patent that more computing power, larger training data and more complex architectures with more variables most likely increase the accuracy of DL models. Case studies in the computer vision  and natuML methods play a central role in detecting RNA modifications. In this review, we outlined two main approaches used to detect RNA modifications with ML, experiment-independent and experiment-based methods. Their applicability mainly depends on the research question at hand. Experiment-independent methods are useful to investigate RNA modification sites in a similar condition as the training data or to obtain a general characterization of the epitranscriptome. Moreover, these algorithms can be easily interrogated to understand the relevant features of the modified sites. On the other hand, experiment-based tools will be more suitable to identify RNA modifications in new conditions that differ from the training data or to discover functional and regulatory aspects of the RNA modifications, guided by the experimental features. Both together provide an exciting framework for new discoveries and innovation in the field of epitranscriptomics.This review highlights how machine learning provides effective strategies to identify and characterize RNA modifications at the transcriptome-wide level.Machine learning strategies are broadly categorized into two types based on the input data used for training and prediction, experiment-independent and experiment-based, and the advantages and applicability of each approach are discussed.The main strategies to train, test and interpret machine learning methods for RNA modifications are described.Current challenges and open questions about RNA modification analysis are presented, and the opportunities of machine learning to address these questions are discussed.


The interphase genome is dynamically organized in the nucleus and decorated with chromatin-associated RNA (caRNA). It remains unclear whether the genome architecture modulates the spatial distribution of caRNA and vice versa. Here, we generate a resource of genome-wide RNA-DNA and DNA-DNA contact maps in human cells. These maps reveal the chromosomal domains demarcated by locally transcribed RNA, hereafter termed RNA-defined chromosomal domains. Further, the spreading of caRNA is constrained by the boundaries of topologically associating domains (TADs), demonstrating the role of the 3D genome structure in modulating the spatial distribution of RNA. Conversely, stopping transcription or acute depletion of RNA induces thousands of chromatin loops genome-wide. Activation or suppression of the transcription of specific genes suppresses or creates chromatin loops straddling these genes. Deletion of a specific caRNA-producing genomic sequence promotes chromatin loops that straddle the interchromosomal target sequences of this caRNA. These data suggest a feedback loop where the 3D genome modulates the spatial distribution of RNA, which in turn affects the dynamic 3D genome organization. How chromatin-associated RNAs (caRNAs) are spatially organized in the context of the multiscale genome architecture is unclear. Here the authors suggest how the 3D genome architecture can modulate the spatial distribution of caRNAs at different scales, and how in turn caRNA could affect the dynamic 3D genome organization. The multiscale organizational features of the genome have been characterized, including A/B compartments2, topologically associating domains (TADs)4, and chromatin loops5. This multiscale organization begs the question of what the functions of such an intricate architecture are. Transcriptional regulation is one of the possible functions and the most extensively studied function. In this direction, the genome architecture is shown to regulate the transcription of specific genes7, but it remains debatable whether the genome architecture has a widespread role in modulating the transcription of many genes8. Moreover, it remains unclear if the 3D genome’s regulatory roles are limited to transcriptional regulation. Other possible functions have rarely been tested. Here, we test another possible function, namely regulating spatial localization of chromatin-associated RNA (caRNA)9.The interphase genome is highly organized9, caRNA has been recognized as an integral component of interphase chromosomes rather than passive degradation products14. Growing evidence confirms that caRNA regulates gene transcription and RNA splicing26. These regulatory roles often depend on caRNA’s spatial localization within the nucleus30. Depending on their spatial localizations, caRNAs can orchestrate the organization of nuclear bodies and compartments32 and foster the formation of transcriptionally silent or active chromosomal domains34. However, it remains unclear how the caRNAs are spatially organized in the context of the multiscale genome architecture; whether there is any specificity in the spatial distribution of caRNAs; if there is, how is such specificity regulated; and in turn, whether the spatial localization of caRNA modulates the dynamic organization of the genome.After initial debates38 in human cells using in situ Mapping of RNA-Genome Interaction (iMARGI)36. iMARGI captures RNA-genome associations by jointly sequencing caRNAs and their associated genomic sequences with paired-end sequence reads35. iMARGI can differentiate the sequencing reads originating from RNA (iMARGI RNA-end reads) or genomic DNA (iMARGI DNA-end reads). We also use in situ Hi-C (Hi-C)39 to map genome-wide chromatin interactions. These maps reveal that most caRNAs are associated with the genomic sequences within several megabases of their transcription sites.Guided by these questions, we generate high-resolution genome-wide RNA-DNA contact mapsTo dissect any causal relationships between the 3D genome organization and caRNA, we generate RNA-DNA contact maps in the genetically engineered human cells where the TAD boundaries are deleted or inserted. Comparisons of these maps reveal the ability of TAD boundaries to constrain the spreading of caRNA on the chromosomes. These data demonstrate the 3D genome’s functions in regulating the spatial localization of the caRNA. Moreover, we generate RNA-DNA and DNA-DNA contact maps in human cells undergone either acute RNA depletion or deletion of a specific caRNA-producing sequence. These data reveal a suppressive role of between-anchor caRNA, i.e., the caRNA associated with the genomic region between the loop anchors, on chromatin looping. Thus, the spatial localization of the caRNA, in turn, modulates the dynamic 3D genome organization.35. For example, in H1 ES cells the coding gene Jumonji and AT-Rich Interaction Domain Containing 2 (JARID2) exhibited large RAL in an approximately 5 Mb region containing the JARID2 gene  exhibited large RAL in an approximately 7 Mb region containing the PVT1 gene , foreskin fibroblast (HFF), and chronic myelogenous leukemia (K562) cells in duplicates Table . These dene Fig. . Additioene Fig. . Overallene Fig. .Fig. 1Lo36 , and in each row peak using HOMER to call one strongest peak in the columns (column peak). A pair of row peak and column peak defines a rectangular block. We identified 3217, 2019, and 2468 rectangular blocks from H1, HFF, and K562 iMARGI data 41 exhibited a genome-wide correlation with cRAL , revealing a correlation between 3D genome compartmentalization and RNA-chromatin association.The 3D genome is organized on different scales, including compartments, TADs, and chromatin loopsta Table . We calcJARID2 and PVT1’s RALs with A/B compartments41    Fig. . Convers42. We carried out iMARGI experiments on the KO and WT cells. We counted the numbers of cross-over and non-cross-over iMARGI read pairs in WT and KO. Compared to WT, KO exhibited an increased proportion in the cross-over read pairs   (KO) within a TAD boundary from H9 human ES cells (WT) abolishes this TAD boundaryst) Fig. . Thus, d42  using piggyBac transposon-mediated genomic insertion of this HERV-H sequence and identified seven insertion sites in KIp-value < 1.0e-4, Chi-square test, stars in Fig. p-value = 3.7e-5, Chi-square test, Fig. To test any impact of any insertion site on RNA-DNA contacts, we carried out iMARGI in KI and WT cells. For every insertion site, our null hypothesis is that whether any RNA-DNA contact is a cross-over or a non-cross-over contact is independent of whether this RNA-DNA contact is detected in KI or WT. The three likely-de_novo-boundary sites (delta_DI > 20) all led to some degrees of decrease in the odds ratio (OR) of the cross-over RNA-DNA contacts in KI (OR < 0.90), in which the decreases on two of the three likely-de_novo-boundary sites were significant  led to a detectable decrease in the OR of the cross-over RNA-DNA contacts in KI , in HCT116 cells. RPB1 depletion resulted in increases in loop number and strengths as measured by Hi-C . This result is consistent with the recent report in another cell line (DLD-1), where depletion of RPB1 led to the emergence of chromatin loops44 , and subsequently tested the loops with chromosome conformation capture (3 C)46. Compared to without doxycycline (Dox-), Dox+ weakened the AAVS1 loop , and nearby, there is a non-overlapping loop with a similar size to the AAVS1 loop49, and RNase-treated cells50. If the primary cause is the transcription process, we expect to see a widespread impact in transcription inhibition but not in acute RNase treatment. However, if transcription inhibition and acute RNase treatment lead to overlapping changes in chromatin loops genome-wide, the data would suggest RNA is involved in modulating chromatin looping. Additionally, we included another experimental condition where electrostatic molecular interactions are inhibited to test if any observed impacts are attributable to charge-driven condensates or phase separation52.We asked whether it is the transcription  or the RNA that impacts chromatin looping. We recognized that we could not answer this question by only testing with a specific genomic locus. This is because the answer at one genomic locus cannot necessarily rule out the alternative answer in other genomic regions. Thus, we recognized that the perhaps more important question is whether transcription or RNA has a genome-wide impact on chromatin looping. Furthermore, we recognized that if RNA impacts chromatin looping, then the transcriptional process must be implicated. However, if the transcriptional process is the cause, the causal chain does not necessarily involve RNA. With these considerations, we revised our question to whether RNA can impact chromatin looping genome-wide. To answer this question, we compared chromatin looping in control, transcription-inhibited53, flavopiridol (FL) to suppress transcription elongation without displacing RNAPII from chromatin54, and acute RNase treatment to reduce RNA in the nuclei (10-minute RNase treatment before fixing the cells)50 based on established protocols . NH4OAc disrupts molecular electrostatic interactions in living cells by providing monovalent cations without perturbing intracellular pH52. To check the expected effects of the three treatments, we immunostained nuclear speckle-associated proteins SON55 and SC3556 in control and each treatment. NH4OAc reduced the numbers of SON and SC35’s foci    to disrupt electrostatic molecular interactions (the interactions due to electric charges)61. The loop numbers were similar in Control (2473 loops) and NH4OAc (2437 loops)  and were increased in FL   and RNase (4963 loops)   Fig. . These lst) Fig. . Most ofst) Fig. . For exast) Fig. .5 , whereas NH4OAc’s P2Ms were not different from the control’s   Fig.  and Z-sc5 Fig. . We repest) Fig. . The conOur next question is what RNA has an impact on which chromatin loops. Although we cannot analyze every aspect of a select RNA, we can analyze the chromatin-associated fraction of this RNA, in terms of this RNA’s genomic target regions (target region) and the RNA attachment level (RAL) of this RNA on any target region. We can compare the target region with the genomic location of any chromatin loop. Thus, we asked whether the change of any caRNA, in terms of changes of target regions or RAL, correlates with the change of any chromatin loop. Answering this question can inform us on which RNA could impact which chromatin loop, although we will miss those impacts that are independent of RNA-chromatin association.p-value < 3e-104, Wilcoxon rank-sum test)    in duplicates Table  and analWe analyzed two groups of caRNA, namely those associated with loop anchors (anchor caRNA), and those between loop anchors (between-anchor caRNA). We asked if the changes in the level of between-anchor caRNA correlate with the changes of chromatin loops across our treatment conditions. To answer this question, we analyzed the union of the loops (Union loops) detected in every condition . These Union loops represent all possible loop locations, including those detected as loops in Control (control loop) or in an RNA perturbation experiment (emergent loop). We used the ratio of between-anchor caRNA and anchor caRNA levels (Inside-loop To Anchor ratio (ITA ratio)) to represent the relative level of between-anchor caRNA for any Union loop.62. According to GSEA’s procedure, we sorted the Union loops by increasing levels of between-anchor caRNA, i.e., increasing ITA ratios, creating a ranked list  tend to locate at the genomic locations with a low level of between-anchor caRNA in the control. We carried out this test using Gene Set Enrichment Analysis (GSEA)ist Fig. . We thenist Fig. , where aist Fig. , suggestSecond, RNase reduced caRNA levels in all Union loops Fig.  and nearWe wondered if we could apply the aforementioned correlation to identify which RNA has an impact on what chromatin loops. To this end, we tested whether we could create a particular chromatin loop by reducing a specific RNA, which is the RNA that exhibits a strong level of chromatin attachment to the genomic region between the anchors of this chromatin loop. We chose the ZMYND8 RNA for this test. We chose the ZMYND8 RNA because (1) FL reduced the RAL of the ZMYND8 RNA in an approximately 90 kb genomic region Fig. ; (2) RNa21. Therefore, we do not have a method to effectively remove ZMYND8 caRNA near the ZMYND8 gene without affecting the transcription of the ZMYND8 gene. We employed two approaches to address this issue. First, we asked whether suppression of ZMYND8 transcription has the same effect as RNase in creating the “straddling loop”. Second, we will describe in subsequent sections the analysis of inter-chromosomal RNA-chromatin interactions, where we can better distinguish between impacts of the RNA from the transcriptional process.RNA knockdown without affecting transcription can reduce nucleoplasmic RNA and suppress long-range RNA-chromatin interactions, however, it cannot effectively remove nascent RNA that are associated with the chromatin near the transcription locusZMYND8 by CRISPR interference (CRISPRi) in an H1 ES cell line with doxycycline-inducible dCas9-KRAB63. Compared to scrambled gRNA control, our gRNA targeting ZMYND8’s promoter reduced ZMYND8’s transcription level to approximately 25%  a negative control “loop” (Negative Ctrl) that is located 200 kb upstream of the emerged loop and has approximately the same size as the emerged loop, which is not detected as a loop in any Hi-C experiment, (2) a positive control loop (Positive Ctrl) detected by Hi-C in both Control and FL, which is not on the same chromosome as ZMYND8, and (3) the straddling loop . We carried out 3 C after treating the cells with doxycycline without supplying gRNA (gRNA:None Ctrl), supplying with a scrambled gRNA (gRNA:Scramble Ctrl), and with gRNA targeting ZMYND8’s promoter (gRNA:ZMYND8). The Negative Ctrl primers did not yield any product in any experiment (the first 3 lanes), and the Positive Ctrl primers yielded products at the expected sizes in all three experiments  can be re-created by suppression of the expression of a specific gene.We suppressed 25% Fig. . We desiOur next question is whether an RNA can impact the chromatin loops located far from the transcription locus of this RNA . We recognize that our previously mentioned correlation is not sufficient to connect a specific RNA to specific distal loops. This is because an RNA can associate with many distal genomic regions, often at low levels. Thus, we proceeded to identify additional correlational rule(s) between RNA and chromatin loops.60. We tested whether the convergent CBS are enriched in the anchors of the loops with increased loop strengths in RNase. To this end, we categorized the Union Loops (the union of the loops detected in any treatment condition) into three groups based on the orientations of the CTCF binding sites at their anchors, namely the loops with convergent CBS, non-convergent CBS, or no CBS. We used Peak to Lower Left (P2LL) to quantify the strength of each loop5. Compared to Control, RNase treatment increased P2LL in the Union Loops with convergent CBS  in the loop anchors is a characteristic of the loops created by loop extrusionp-value = 5.621e-5, Chi-square test, Fig. candidate HERV-H caRNA insulated loops” (CHRI-loops). CHRI-loops are enriched with convergent CBS in their loop anchors as compared to (1) control loops striding across HERV-H caRNA-attached genomic sequences , and to (2) the other control loops not striding across HERV-H caRNA-attached genomic sequences , and to (3) the RNase emergent loops not striding across any HERV-H caRNA-attached genomic sequences in Control  in Control and compared them with the locations of the loops emerged in RNase (RNase emergent loops). The RNase emergent loops are enriched at the locations that exhibit between-anchor HERV-H caRNA in Control  in the WT. We call the loops that stride across any Chr13:55.5MB_HERV targets as “target-crossing loops”. We compared the loop strength changes of all target-crossing loops between Chr13:55.5MB_HERV KO and WT based on Hi-C data. No target-crossing loop exhibited detectable decrease in loop strength in Chr13:55.5MB_HERV KO, whereas two target-crossing loops exhibited increased loop strengths in the Chr13:55.5MB_HERV KO  human ES cells KO Fig. . Both Ch KO Fig. . Neither KO Fig. . Thus, rhttps://www.4dnucleome.org/protocols/). The three human cell lines for data generation were nominated by the 4DN Joint Analysis Working Group and cultured under the 4DN Cell Working Group approved protocols (https://www.4dnucleome.org/cell-lines/). All the data are accessible through the 4DN Data Portal  Consortium Omics Standards Working Group and the 4DN Steering Committee ”64 . These data confirm that the impacts of RNA to the 3D genome are subtle, at least at the scales of A/B compartments and TADs.It remains unclear how RNA may affect the 3D genome. Because several 3D features of the genome can be reproduced by computational models without considering RNAIt has not been tested whether an acute reduction of RNA exerts systematic impacts to chromatin loops. Our data reveal either transcription inhibition or acute RNA reduction induced chromatin loops. Most induced loops are shared between transcription inhibition and acute RNA reduction, indicating that the impact on chromatin looping cannot be completely attributed to transcription or the presence of RNAPII on chromatin. Indeed, suppressing a specific caRNA created a chromatin loop, with the loop anchors striding across the genomic sequence associated with this caRNA Fig. . Further66. When two caRNAs, that are attached to two genomic sequences, have complementary sequences, they tend to bring their associated genomic sequences to spatial proximity, thus promoting loop formation65. Following the same idea, any sequence complementarity between any between-anchor caRNA and any other caRNA associated with any outside-of-loop genomic sequences could promote spatial proximity of this between-anchor genomic sequence and those outside-of-loop genomic sequences, thus reducing the spatial proximity of the loop anchors. Thus, a unified model can explain the suppressive role of between-anchor caRNA and the promotive role of the caRNAs with sequence complementarity on chromatin looping.What remains to be addressed is whether there is any rule that links specific RNA with specific loops that this RNA can modulate. Disrupting electrostatic interactions by NH4OAc did not lead to significant changes in chromatin loops, withholding us from exploring possible rules based on charge-mediated condensates or phase separation. Instead, we investigated RNA’s target regions, because the genomic locations of the target regions can be compared with the genomic locations of loops. We found the caRNA located between the two anchors of a chromatin loop often weakens this loop. This suppressive role of between-anchor caRNAs is compatible with the recently reported promotive role of the caRNAs with complementary sequences to chromatin looping43. Without underestimating RNAPII’s role, our experiments were designed to test if there are any effects of the RNA as well. FL treatment does not displace RNAPII from chromatin54, making the FL emergent loops unlikely due to a change of RNAPII’s presence on chromatin. Furthermore, our analysis focused on the shared loops that are created by both FL or RNase treatment. These shared loops are even more unlikely attributable to the loading of RNAPII on chromatin. Our data suggest that in addition to RNAPII, RNA should be considered toward obtaining a complete picture on the interplay between transcription and genome organization.Recent work revealed a suppressive role of RNAPII’s presence on chromatin to chromatin loopinghttps://www.4dnucleome.org/cell-lines.html). The cell lines in the 4DN Cell Repository were established by the 4DN Consortium in collaboration with WiCell and ATCC for providing quality-controlled cells from the identical batch to minimize cell source and culture condition variations. The cell culture protocols were developed by the 4DN Cell Line Working Group and approved by the 4DN Steering Committee.Human embryonic stem cells (H1), hTert-immortalized human foreskin fibroblasts (HFF), and chronic myelogenous leukemia lymphoblasts (K562) were obtained from the 4D Nucleome (4DN) Cell Repository and cultured following the 4DN Consortium’s approved culture protocol for each cell line  was dissolved in nuclease-free water and further diluted in cell medium. Aspirate medium in each well and H1 cells were treated with 0.1 M NH4OAc in medium for 10 min at RT.H1 cells were treated with 0.1 M NH48. Specifically, a crystalline flavopiridol (hydrochloride)  was dissolved in DMSO to prepare 1 mM flavopiridol (FL) stock solution. 1 mM FL stock solution was further diluted with complete mTeSR medium. Aspirate cell medium in each well and H1 cells were either treated with 1 µM FL in medium or an equivalent amount of DMSO in the medium in an incubator at 37 °C for 1 h.H1 cells were treated with 1 µM flavopiridol in complete mTeSR medium for 1 h in an incubator as described previously50  on rotator for 10 min at RT. The treated cells were fixed with 4% formaldehyde  for immunofluorescence imaging. For Hi-C and iMARGI library generation, the treated cells were fixed with 1 mL 1% formaldehyde on rotator for 10 min at RT. Then, the reactions were terminated with 250 µL 1 M glycine on rotator for 10 min at RT. The treated sample was centrifuged at 2000 X g for 5 min at 4 °C and washed with 1 mL cold 1X PBS.H1 cells were harvested from cell culture plate and aliquoted cell suspension to 10 million H1 cells per 1.5 mL tube. Wash the cells with 1 mL 1X PBS and centrifuge at 500 X g for 3 min at RT. Then, cells were gently permeabilized by resuspending cell pallets with 0.01% PBST (TritonX-100 in PBS) and treated for 5 min at RT. After permeabilization, cells were treated with 200 µg/mL RNase A as described previouslyhttps://4dnucleome.org), with TRE-dCas9-KRAB and CAGGS-M2rtTA targeted into the AAVS1 locus.The doxycycline-inducible dCas9-KRAB H1 ES cell line is generated and karyotyped by the 4D Nucleome Consortium (Danwei Huangfu Laboratory) (42.The control H9 human ES cells (H9 MLC2v:H2B), HERV-H deletion cell line (H9 MLC2v:H2B HERV2-KO), and HERV-H insertion cell line (H9 MLC2v:H2B HERV2-ins-clone2) were generated by Bing Ren lab and described in ref. 67.The RPB1 auxin-inducible degron 2 cells (HCT116 RPB1-Dox-OsTIR1-mClover-mAID) were generated by Masato Kanemaki lab and described in ref. 61 with default parameters. A TAD boundary called in KI but not in WT is regarded as a de novo TAD boundary.TAD boundaries in WT (H9 MLC2v:H2B) and KI (H9 MLC2v:H2B HERV2-ins-clone2) were separately called based on their respective Hi-C data using The Arrowhead tool in the Juicer ToolsThe cells on coverslip  were fixed with 4% formaldehyde at RT for 30 min. The fixed cells were washed with 1X PBS once and permeabilized with 0.1% TritonX-100 in PBS (PBST) at RT for 15 min on shaker. Afterwards, cells were blocked with 5% BSA  in PBST at RT for 30 min with gentle shaking. For SC35 staining, H1 cells were incubated with 1 mL diluted mouse monoclonal anti-SC35 primary antibody (1:250)  in 5% BSA at 37 °C for 1 h, and subsequently washed three times with PBST on shaker for 10 min. Cells were further incubated with 1 mL diluted goat anti-mouse secondary antibody with Alexa Fluor 568 (1:500 dilution)  in 5% BSA at 37 °C for 30 min. For SON staining, the cells were incubated with 1 mL diluted rabbit anti-SON primary antibody (1:2000 dilution)  in 5% BSA at 37 °C for 1 h, and subsequently washed three times with PBST on shaker for 10 min. The cells were incubated with 1 mL diluted goat anti-rabbit secondary antibody with Alexa Fluor 488 (1:500 dilution)  in 5% BSA at 37 in 5% BSA at 37 °C for 30 min. After staining, the cells were washed three times with PBST on shaker for 10 min. The cells on coverslips were mounted on slides  with 10 µL ProLong antifade glass mountant with NucBlue stain , placed in dark room for air-dry overnight. Images in the size of 512 × 512 pixels were acquired on Applied Precision OMX Super Resolution Microscope using a 100X/1.518 oil objective  (pixel size = 0.079 μm). Z-stack images were acquired with a 0.3 μm sample thickness.48. Briefly, the nuclei were manually segmented and the mean fluorescence intensity in nuclei was measured with FIJI. The nuclear speckle foci were identified by FIJI 3D Object Counter plugin, with an appropriate intensity threshold of the mean fluorescence intensity in the cell nuclei and a size cut-off of more than 50 adjoining pixels .Nuclear speckle foci were identified by a previously described methodhttps://www.4dnucleome.org/protocols.html). Next, the Hi-C data were processed using the 4D Nucleome (4DN)’s Hi-C data Processing Pipeline (v0.2.5) , with MAPQ > 30 to filter out multiple mappings.The Hi-C libraries were generated with the Arima-HiC kit  following the manufacturer’s instructions and processed following 4DN consortium’s in situ Hi-C processing protocol (68 (v0.8.10) and Juicer Tools61 (v1.22.01) to generate .mcool and .hic files. The .mcool file was used in HiGlass69 for visualization. The .hic files were inputted in Juicer Tools for A/B compartment, TAD, and loop analyses. A/B compartments were called by Juicer’s “Eigenvector” tool based on KR normalized observed/expected (O/E) contacts at 500 kb resolution. TADs were called by Juicer’s “Arrowhead” tool based on KR-normalized contacts at 10 kb resolution. Loops were called by Juicer’s “CPU HiCCUPS” tool based on KR-normalized contacts simultaneously at 5 kb and 10 kb resolutions. Except for the resolution parameter, all the other parameters were left as the default.The output .pairs files were provided to Cooler70.TAD boundaries were extracted as the genomic regions between TADs in each sample. TAD boundary insulation score was calculated according to the definition in Crane et al. i (in the union) with anchor size s (either 5 or 10 kb) was re-assigned to a sample j if both anchors of loop i were within +/−s flanking regions of a loop in sample j. Aggregate Peak Analysis was performed using the Juicer’s “APA” tool with default parameters. Metrics to define the loop strength such as Peak to Lower Left (P2LL), Z-score Lower Left (ZscoreLL), and Peak to Mean (P2M) were calculated as defined in Juicer’s APA61. The control loop straddling the AAVS1 locus was detected from H1-hESC Micro-C data44. To select RNase emergent loops that stride across HERV-H caRNA-attached genomic sequences in Control, i.e., the candidate HERV-H caRNA insulated loops (CHRI-loops), we used a threshold of at least 2 iMARGI read pairs with their RNA ends overlapping with HERV-H and their DNA ends mapped to the between-loop-anchor sequence. To check if deleting a copy of the HERV-H repeats led to increase of loop strengths of CHRI-loops, we used a threshold of 0.05 for the delta peak (KO peak - control peak), where peak is the normalized Hi-C read count at the loop’s pixel, normalized by the total number of read pairs in each sample.Unique loops and overlapping loops were determined as follows. First, the Juicer called loops from each condition were merged into “unique loops” by taking the union. Then the unique loops in the union were reassigned to each condition by the following rule: a unique loop 71. Briefly, a T7 RNA polymerase substrate template was assembled by PCR from a variable 58–59 nt primer containing T7 promoter, variable gRNA guide sequence, the first 15 nt of the non-variable region of the gRNA , and an 83 nt primer containing the reverse complement of the invariant region of the gRNA , along with amplification primers . The two long primers anneal in the first cycle of PCR and are then amplified in subsequent cycles. Phusion high-fidelity DNA polymerase was used for assembly (New England Biolabs). Assembled template was used without purification as a substrate for in vitro transcription by T7 RNA polymerase, using the HiScribe T7 High Yield RNA Synthesis kit (New England Biolabs-E2040S) following the manufacturer’s instructions. Resulting transcription reactions were treated with DNAse I (New England Biolabs-M0303S), and RNA was purified by Qiagen RNeasy mini spin column (Qiagen-74104) and eluted in RNAse-free water.gRNA was synthesized by assembly PCR and in vitro transcription as previously described in ref. gRNAs were treated with phosphatases as follows: rSAP (New England Biolabs-M0371S) were added per 20 μl in vitro transcription reaction, and samples were incubated at 37 °C for 3 h before proceeding to purification and DNAseI treatment. gRNA was purified using a Qiagen RNeasy Mini Kit (Qiagen-74104)). The detailed protocol and additional notes are available online (10.17504/protocols.io.nghdbt6).dCas9-KRAB hPSCs were treated with doxycycline (2 μg/ml) for 24 h before and during transfection. For transfection, cells were dissociated using Accutase (Stem Cell Technologies), replated onto Matrigel (Corning)-coated plates and transfected in suspension with gRNAs using Lipofectamine RNAiMAX (Invitrogen-13778075) following manufacturer’s instructions. Briefly, gRNA (mix of 3 guides against ZMYND8) were added at a 10 nM and 20 nM final concentrations respectively, unless otherwise indicated. gRNAs and Lipofectamine RNAiMAX were diluted separately in Opti-MEM (Gibco-31985062), mixed together, incubated for 5 min at RT, and added dropwise to cultured hPSCs. A second transfection was performed 24 hours later in some experiments. Cells were harvested after 2 days.73. In brief, 10 million cells were crosslinked with 2% Formaldehyde (Thermo Scientific-28908). Chromatin was digested with DpnII (NEB- R0543T), ligated with T4 DNA ligase (NEB-M0202S) and reverse crosslinked by incubation with Proteinase K (Thermo Scientific-EO0491). DNA libraries were purified by phenol/chloroform (Sigma-77617) and subsequent ammonium acetate precipitation (Invitrogen-AM9740). 3 C libraries generated post CRISPRi are used as templates for downstream PCRs.3 C libraries were generated as previously described in refs. 74 with default parameters for bias correction and subsequently subjected to noise removal using the LoopDenoise function in DeepLoop75. All data processing was done with Hg19 per HiCorr and DeepLoop software’s requirements.H9 Control and HERV-H2 KO Hi-C data were subjected to HiCorr36. According to 4DN’s approved iMARGI’s data processing protocol36, any iMARGI read pair in which the RNA end and the DNA end mapped to within 1000 bp of each other on the genome are removed from the data analysis. The RNA attachment level (RAL) of each genomic segment is the count of the DNA-ends mapped to this genomic segment35. Only the inter-chromosomal and the intra-chromosomal iMARGI read pairs that are separated by at least 200 kb apart were used for calculating RAL in any of the correlation analyses. Repeats of hg38 were downloaded from RepeatMasker . RAL of Alu-containing caRNA (Alu-caRNA) and LINE1-containing caRNA (L1-caRNA) were calculated as the count of the DNA ends mapped to each genomic segment (500 kb size) whose RNA ends mapped to a repeat segment of the Alu or LINE1 family respectively.iMARGI libraries were generated and processed as previously described in ref. 40 function was applied to the RNA ends of iMARGI’ read pairs  to identify the peaks on the RNA ends (RNA peak). For reach RNA peak, all the iMARGI’s read pairs with their RNA ends inside this RNA peak were retrieved. The retrieved read pairs’ DNA ends were subjected to HOMER’s findPeaks  to identify the peaks on the DNA ends (DNA peaks). If multiple DNA peaks were reported, the DNA peak with the highest read number was designated as the corresponding DNA peak.Each rectangular block on iMARGI’s contact matrix was identified as a peak of the iMARGI’s read pairs’ RNA ends (the height of this RNA peak) and a corresponding DNA peak of the DNA ends (the width of this RNA peak). HOMER’s findPeaksAll plotted genome coordinates are based on Hg38.See Supplementary Data Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Reporting SummarySource Data


Structure determination is a key step in the functional characterization of many non-coding RNA molecules. High-resolution RNA 3D structure determination efforts, however, are not keeping up with the pace of discovery of new non-coding RNA sequences. This increases the importance of computational approaches and low-resolution experimental data, such as from the small-angle X-ray scattering experiments. We present RNA Masonry, a computer program and a web service for a fully automated modeling of RNA 3D structures. It assemblies RNA fragments into geometrically plausible models that meet user-provided secondary structure constraints, restraints on tertiary contacts, and small-angle X-ray scattering data. We illustrate the method description with detailed benchmarks and its application to structural studies of viral RNAs with SAXS restraints.http://iimcb.genesilico.pl/rnamasonry. The source code is available at https://gitlab.com/gchojnowski/rnamasonry.The program web server is available at  Non-coding RNAs (ncRNAs) are involved in the regulation of many cellular processes. New families of ncRNAs are being continuously discovered .de novo fragment assembly and (ii) RNA model refinement. The input is either RNA sequence with the secondary structure or a preliminary RNA 3D structural model. The input atomic model is analyzed by the ClaRNA program  RNA Masonry is based on hierarchical organization of RNA molecules, which are composed of 3D motifs defined at a secondary structure level; double-stranded helices, single-stranded fragments, and various types of loops. During the model assembly, the motifs are used as a whole, which strictly preserves the input secondary structure. The program accepts restraints for long-range tertiary interactions. Additionally, the model building can be restrained with a goodness-of-fit to the experimental small-angle X-ray scattering (SAXS) data, which is calculated using FoXS (http://genesilico.pl/rnabricks2).There are other methods available that can assemble RNA 3D structures from fragments of experimentally determined structures like Assemble (de novo modeling.If the input is an RNA atomic model (mmCIF or PDB format), the program annotates its secondary structure with ClaRNA which is further processed analogously to a secondary structure given directly on input for Pseudo-knots are removed from the input secondary structure using the K2N library , and stoAfter building the motif-graph, which is a basic data structure used in RNA Masonry, 3D fragments from RNA Bricks database are assigned to each of its nodes. A fragment assigned to a node must match its secondary structure (stem or a loop with specific topology), but no sequence information is considered. If the number of fragments assigned to a node is fewer than a user-defined threshold (10 by default) additional fragments are generated by circular permutations . If needed, new fragments are also automatically built from smaller ones by introducing insertions using ModeRNA  are closer than 6 Å apart. To reduce the combinatorial complexity of the task, the assembly process is initiated from the terminal elements of the structure (the motif graph ‘leaves’) and proceeds iteratively by adding a single node at a time. At each step, from all possible partial models only statistically relevant representatives are selected as described by 2 value from 1 with a mixture of two Gaussians with variances 1 and 10, where a perfect fit (χ2 of 1) corresponds to a value 1.5.The starting model is optimized in a replica exchange Monte-Carlo simulation with a geometric distribution of temperature levels. We use a scoring function based on the SimRNA statistical potential  model constructed during the entire simulation is returned to the user.2) estimated using CRYSOL of 2.8 (2 = 1.5), but also to the ab initio reconstruction obtained independently using DAMMIF (We used an early version RNA Masonry to model the adenovirus virus-associated RNA (VA-I) structure with SAXS restraints   in the target structure. The small number of degrees of freedom significantly reduces the modeling time (e.g. compared to SimRNA simulations). In the case of modeling with SAXS data it also reduces the risk of overfitting. The SAXS experiments provide a number of unique observations much smaller than the already limited number of experimental data points . This inIt must be stressed, that the presented approach may be unable to model fine details of RNA structures  that are not encoded within the 3D motifs used for assembly. These, however, can be modeled using complementary high-resolution approaches (e.g. using QRNAS (a priori information could be in principle used during model building in RNA Masonry to increase reliability of resulting models. This can be achieved by a more restrictive selection of fragments for model assembly and penalizing the absence of expected stacking interactions by the SimRNA scoring function. We plan such an extension in further releases of the software.The current RNA Masonry version selects RNA fragments for model building based exclusively on canonical secondary structure constraints. It was observed, however, that the presence of certain motif types and coaxial stacking between adjacent helices can be reliably predicted from sequence . Both thbtad527_Supplementary_DataClick here for additional data file.