OD) and C-terminal domain (PCTD) of a tetramer of P. The density of the methyltransferase (MT) domain of L and the N-terminal domain of P (PNTD) is missing. Further analysis and comparison with other RNA polymerases at different stages suggest the structure we obtained is likely to be at an elongation-compatible stage. Together, these data provide enriched insights into the interrelationship, the inhibitors, and the evolutionary implications of the RSV polymerase.The respiratory syncytial virus (RSV) RNA polymerase, constituted of a 250 kDa large (L) protein and tetrameric phosphoprotein (P), catalyzes three distinct enzymatic activities — nucleotide polymerization, cap addition, and cap methylation. How RSV L and P coordinate these activities is poorly understood. Here, we present a 3.67 Å cryo-EM structure of the RSV polymerase (L:P) complex. The structure reveals that the RNA dependent RNA polymerase (RdRp) and capping (Cap) domains of L interact with the oligomerization domain  is a pathogenic non-segmented negative-sense RNA virus and active RSV polymerase is composed of a 250 kDa large (L) protein and tetrameric phosphoprotein (P). Here, the authors present the 3.67 Å cryo-EM structure of the RSV polymerase (L:P) complex. RSV infection is the leading cause of severe lower respiratory tract diseases in young children, older adults, and immunocompromised patients worldwide3. RSV initiates viral infection by delivering into the host cell a virus-specific RNA synthesis machine required for both genome replication and gene transcription5. This machine comprises the nucleoprotein (N) coated genomic RNA (N:RNA) and the RNA polymerase6. The catalytic core is a 250 kDa large (L) protein that catalyzes the RNA polymerization in both replication and transcription, the cap addition, and cap methylation of nascent viral mRNAs. A tetrameric phosphoprotein (P) is essential to modulate and constitute an active RNA polymerase with L5.Nonsegmented negative-sense (NNS) RNA viruses are a class of pathogenic and sometimes deadly viruses that include rabies, Ebola, and respiratory syncytial virus (RSV)9. Like all NNS RNA viruses, the RSV RNA template is N:RNA, not RNA alone. The leader (Le) or trailer complementary (TrC) sequences from the terminus of the RNA genome or antigenome serve as the promoters for the RSV RNA synthesis13. To copy the N:RNA template, L requires the tetrameric P to displace N14. Interestingly, the RSV polymerase not only synthesizes mRNAs but also co-transcriptionally adds a cap and a poly-A tail to each transcript. The mRNA caps are synthesized using unconventional chemical reactions: (a) the cap is formed by a polyribonucleotide transferase but not a guanylyltransferase through generating a covalent L:RNA intermediate, distinct from eukaryotes and all other virus families18; and (b) the cap is methylated at the 2′-O position first, followed by the N-7 position, the opposite order of mammalian mRNAs21. To date, the in vitro RNA polymerization assays of RSV and several other NNS RNA viruses have been established23. The RSV cap addition and cap methylation assays have not been described yet and are speculated to share similar mechanisms with vesicular stomatitis virus (VSV), of which the assays were reported25.RSV RNA synthesis is believed to follow the “start-stop model” of sequential and polar transcription27. The domain boundaries and the active sites of the three functional domains, RNA dependent RNA polymerization (RdRp), capping (Cap), and cap methyltransferase (MT), are highlighted as GDN, HR, and GXGXG (X denotes any residues) with numbers, respectively31 , N, and M2-1  using cryo-electron microscopy (cryo-EM). Our studies reveal that the RdRp and Cap domains of the RSV L shares similar architectures of that of the VSV L40, uncover a previously unknown basis of how P interacts with L, and provide molecular insights into RNA synthesis by the RSV polymerase.There are six conserved regions and three functional domains shared within L of NNS RNA virusesy31 Fig. . P is pr2-1 Fig. . The pre41, we demonstrated the characteristic RNA products of both de novo initiation (≤25 nt) and back primer (>25 nt) activities by the wt polymerase L:P (lane 6), but not the catalytically inactive polymerase L(D811A):P (lane 3) using a short trailer complementary 25 (TrC25) RNA template. The Le or TrC sequences of the genome serve as the promoters for the RSV polymerase. The trailer 25 (Tr25) sequence, which is the transcription product of TrC25, is not a natural template and was included as a negative control. As expected, we did not observe RNA products when using Tr25 as a template (lane 5)  from Sf21 insect cells. Gel filtration and SDS-PAGE indicated pure and full-length wild-type (wt) L:P and mutant L(D811A):P complexes Fig. . Using a 5) Fig. . TherefoCryo-EM analysis was conducted using a 200 kV Talos Arctica microscope with a BioQuantum/K2 direct electron detector. An initial dataset of 1349 movies resulted in 4.3 Å reconstruction, and one additional dataset of 1251 movies was collected. A total of 2600 movies gave rise to the final 3.67 Å map, refined with 253,372 particles selected from multiple rounds of 2D and 3D classifications . We refined the model coordinates with PHENIX. The cryo-EM map revealed the characteristic ring-like core RdRp domain and an unconventional Cap domain of the L protein that only exists in NNS RNA viruses. The map also showed the typical helix bundles of the oligomerization domain of P  and C-terminal domain  of tetrameric P proteins  and the Cap domain (green) of the RSV L protein, and the oligomerization domain  among NNS RNA viruses, while the P protein is flexible and contains many disordered regionsOD and PCTD. The interactions between L and a tetrameric P can be divided into three parts: (1) two of four-helix bundles of POD and the RdRp; (2) two flexible PCTD chains wrap around the surface of RdRp and stabilize the base of the tetrameric POD; (3) one flexible PCTD chain extends to the positively charged “palm” motif of RdRp and the edge of the putative NTP entrance channel , the CD, MT, and CTD domains (gray) extends from the top part of the complex   There are additional motifs at the N-terminal regions of the RSV RdRp; (2) The RSV PdRp has a missing connecting helix (residues 660–691)  adjacent to the active site Fig. ; (3) The52) Fig. .Filoviridae) share a common feature and exist as an oligomer  in solution60 . Further 3D classification revealed an almost equal number  of the particles that show less P density and yielded a cryo-EM map at 4.86 Å resolution. Compared with a 4.54 Å map, the RdRp and Cap domains of L are virtually identical, but the density for P is almost depleted , but P exists as diverse oligomeric protein  of RSV, a significant NNS RNA virus pathogen, and offers a framework for understanding the coordination of the enzymatic activities of L within structurally distinct but functionally coordinated domains. Further investigations are required to discover whether the RSV L alone exhibits the same way as VSV L. Interestingly, all RNA polymerases of NNS RNA viruses require P  revealed that this priming-loop-like element of the VSV L occupied the same location as the priming loop in the reovirus polymerase50. In addition, the comparisons of the VSV L and the initiation/elongation complex of the reovirus polymerase suggest that if the RNA product extends, there will be not enough space to accommodate additional newly synthesized RNA products, and this priming-loop-like element is likely to move away from this initiation stage50  and influenza polymerase , we modeled the RNA template and the transcript into our RSV polymerase structure63  of ALS-8112 are in approximate close location to the active site , and the QUAD mutations can potentially alter the microenvironment of RNA synthesis and the conformation of the RdRp active sites  in the RdRp domain of L, while a nonnucleoside inhibitor AZ-27 that inhibited the RdRp activity could be escaped by a Y1631H resistance mutationFurther, our study has evolutionary implications: How did three distinct enzymatic activities  for RNA synthesis integrate within a single polypeptide (L)? How are those functional domains evolved/related to multiple much complex counterparts in the eukaryotic cell and many other viruses? Further studies will reveal whether this “compact” mechanism illustrates the evolutionary pressure applied for the RNA synthesis machinery in general.apo state was published65. We superposed the published structure (PDB: 6PZK) with our structure (PDB: 6UEN) described here, and they share similar overall fold with high similarities (RMSD = 1.450 Å). Interestingly, the superpositions of the individual RdRp domain, Cap domain, and the P tetramers yield lower RMSD values of 1.240, 1.021, and 0.991 Å, respectively. The structural comparisons suggest that the individual domains are mostly the same, but the inter-domain arrangements of the two structures have slight differences. In the closer dissection of both structures, we identified minor shifts between the interface of the L:P complex, in particular, the P tetramers and the two helixes of L that interact with P (dock A and dock B) if fixed the position of the RdRp domain. It appears that the P tetramers slide closer to L, and the docks A and B shift towards the RdRp domain and adopt more compact packing to accommodate closer interactions with P (6PZK) than that of this work (6UEN)  complex in an NTD (1–127) but are not visible in this study38. (2) How other co-factors such as M2-1, N0, or N:RNA influence (or stabilize) the polymerase conformations? To determine the structures of L:P:RNA or L:P:M2-1 complexes may be a future research focus.Outstanding questions remain: (1) It is known that the phosphorylation of P regulates the activities of L. Previous studies suggested many potential phosphorylation sites reside in the P4, 10% glycerol, 0.2% NP-40, and EDTA-free protease inhibitor), followed by Co2+-NTA agarose beads (GoldBio), washed with wash buffer , and eluted with elution buffer . The eluted sample was treated with TEV enzyme in TEV cleavage buffer  overnight at 16 °C and further applied to Co-NTA. The flow-through sample was applied to the Heparin column  and followed by size-exclusion chromatography using the gel filtration buffer  with a Superose 6 Increase 10/300 GL . The quality of purified proteins was analyzed by SDS-PAGE gel. The bands migrating ~250 and ~35 kDa were confirmed to be RSV L and P polypeptides by liquid chromatography-mass spectrometry  L and P genes (the DNA sequences and primers are listed in Supplementary Table  L and P 13). RNA oligonucleotides were chemically synthesized from IDT. Radioactive isotope-labeled nucleotides [α-32P] GTP was purchased from Perkin Elmer. The reaction mixtures contained 2 µM RNA template, RSV L:P complexes (~300 ng RSV L), NTPs , and reaction buffer  in a final volume of 20 µl. The reactions were incubated at 30 °C for 5 h, heated to 90 °C for 5 min, and then stopped by adding 5 µl stop buffer . The RNA products were analyzed using a 20% polyacrylamide gel containing 7 M urea in a TBE buffer, followed by autoradiography. The RNA ladder used was generated by incubating [ɣ-32P] ATP and 7-nt, 14-nt, 21-nt, and 25-nt RNA trailer sequences  with T4 PNK (NEB). The sequences of the RNA oligos used to generate the ladders are as follows: Tr7 (5′-ACGAGAA-3′), Tr14 (5′-ACGAGAAAAAAAGU-3′), Tr21 (5′-ACGAGAAAAAAAGUGUCAAAA-3′), and Tr25 (5′-ACGAGAAAAAAAGUGUCAAAAACUA-3′).The terminal trailer complementary (TrC25: 5′-UAGUUUUUGACACUUUUUUUCUCGU-3′) template and trailer (Tr25: 5′-ACGAGAAAAAAAGUGUCAAAAACUA-3′) product sequences of the RSV genome were used in the in vitro RdRp assay .Protein samples (~3 μg) were treated with 1 mM dithiothreitol (DTT) at room temperature (RT) for 30 min, followed by 5 mM iodoacetamide at RT for 30 min in the dark. Proteins were digested overnight with 0.5 μg lysyl endopeptidase (Wako) at RT and further digested overnight with 1 μg trypsin (Promega) at RT. Resulting peptides were desalted with an HLB column (Waters). Peptides were analyzed with a Q Exactive™ Plus Hybrid Quadrupole-Orbitrap™ Mass Spectrometer (Thermo Fisher Scientific). Mass spectrometry data were analyzed using Proteome Discoverer 2.1 against the UniProt database of human respiratory syncytial virus A (strain A2) and -/Å2/frame. The total exposure time of each movie was 10 s, leading to a total accumulated dose of 55 e−/Å2, fractionated into 40 frames (250 ms per frame). All movies were recorded in a defocus range between −1.25 and −2.5 μm. A total of 2600 micrographs were collected from two separate data collection sessions.A total of 3.0 μl of the purified RSV polymerase (L:P) complex at a concentration of 0.33 mg/ml were applied to a glow-discharged Quantifoil holey carbon grid  (SPI). Grids were blotted for 3 s at ~90% humidity at RT and plunge-frozen in liquid ethane using a Cryoplunge 3 System (Gatan). Cryo-EM data were recorded on a Talos Arctica 200 kV (TEM) with BioQuantum/K2 direct electron detector (Thermo Fisher) at Emory University. All cryo-EM movies were recorded in counting mode using EPU (Thermo Fisher). The nominal magnification of ×130,000 corresponds to a calibrated pixel size of 1.045 Å on the specimen. The dose rate was set to 1.365 e66 with a 5 × 5 patch, resulting in corrected movies and summed images. All 40 frames in each movie were summed with the dose-weighted scheme. The summed images were used in all image processing steps. The contrast transfer function (CTF) was estimated using the program CTFFIND467. To generate RSV polymerase complex templates for automatic picking and the initial model, around 50,000 particles were manually picked and classified. The data were initially processed in two datasets, one for each data collection session (1349 and 1251 micrographs) and then merged into a total of 2600 micrographs. A total of 792,070 particles were picked, and the box size of 200 pixels was used to extract the particles. Particle picking and screening, 2D classification, as well as the initial 3D model building, 3D classification, 3D refinement, CTF refinement, and polishing were performed using RELION 3.0.768. The final refinement was validated using cisTEM69, using the best class as the initial model. The global search was performed once without the mask followed by another global search using a soft mask (six-pixel soft edge) that was generated in RELION. All reported resolutions were based on gold-standard refinement procedures and the Fourier shell correlation (FSC) = 0.143 criterion. Local resolution was estimated using ResMap70.Drift correction, beam-induced motion, and dose-weighting were performed for dose-fractionated movies by the program MotionCor25A22) and the homology models of the RSV L protein by Phyre271 and I-TASSER72 were positioned into the map as the initial guides using UCSF Chimera73 and COOT74. The structure model was manually built by COOT. Structure factors were calculated by PHENIX76, and the full structure was subjected to multiple cycles of global real-space refinement with rotamer, Ramachandran plot restraints enabled in PHENIX. FSCs were calculated between the two half maps, the model against the working map and the other (free) half map and full (sum) map. The confidence maps and locally sharpened maps were calculated to facilitate the model building78. MolProbity79 was used to validate the geometries of the model.The 3.67 Å resolution map was used for model building and refinement. To obtain better side-chain densities for model building, B-factor was used for map sharpening. The coordinates of the VSV L protein (PDB: 74, UCSF Chimera73, and PyMOL80. Multiple sequence alignments were performed using Multalin81 and ESPript82.All the figures representing model and electron density maps were generated using COOTFurther information on research design is available in the Supplementary InformationReporting Summary


RNA profiling has emerged as a powerful tool to investigate the biomarker potential of human biofluids. However, despite enormous interest in extracellular nucleic acids, RNA sequencing methods to quantify the total RNA content outside cells are rare. Here, we evaluate the performance of the SMARTer Stranded Total RNA-Seq method in human platelet-rich plasma, platelet-free plasma, urine, conditioned medium, and extracellular vesicles (EVs) from these biofluids. We found the method to be accurate, precise, compatible with low-input volumes and able to quantify a few thousand genes. We picked up distinct classes of RNA molecules, including mRNA, lncRNA, circRNA, miscRNA and pseudogenes. Notably, the read distribution and gene content drastically differ among biofluids. In conclusion, we are the first to show that the SMARTer method can be used for unbiased unraveling of the complete transcriptome of a wide range of biofluids and their extracellular vesicles. Although in the past most attention was given to circulating DNA, its more dynamic derivate extracellular RNA may provide additional layers of information. However, RNA sequencing in biofluids is technically challenging. Low input amounts, large dynamic range, and  degradation of RNA hamper straightforward quantification. While sequencing of small RNAs3 and targeted or capture sequencing of longer RNAs4 proved to be successful, studies using total RNA sequencing on biofluids are rare. To date, only a few whole transcriptome profiling attempts were made on urine, plasma or extracellular vesicles9, quantifying both polyadenylated and non-polyadenylated RNA transcripts  polyadenylated RNAs. Next to polyadenylated mRNAs, various other RNA biotypes including circular RNAs, histone RNAs, and a sizable fraction of long non-coding RNAs can be distinguished. In addition, the study of posttranscriptional regulation is possible by comparing exonic and intronic readsin vitro. More specifically, the SMARTer Stranded Total RNA-Seq Kit – Pico Input Mammalian, including a ribosomal RNA depletion step at the cDNA level, was extensively evaluated. We found the method to be accurate and precise. Low-input volumes are technically feasible and the method allows the detection of several thousand genes of different classes.Here we aimed to assess the performance of a strand-specific total RNA library preparation method for different types of biofluids and derived extracellular vesicles (EVs). We applied the method on platelet-rich plasma, platelet-free plasma, urine and conditioned medium from human healthy donors, cancer patients or cancer cells grown In a first experiment Fig. , we sequIn the second experiment Fig. , we sequWe subsequently investigated two other technical characteristics of our biofluid total RNA seq method: the level of strandedness and the inner distance between paired-end reads. In general, the method generates strand-specific sequencing reads in all the biofluids we assessed Sup Fig. . The cDNIn order to assess the quantitative aspect of the total RNA sequencing method, we added an ERCC RNA spike-in mix to all RNA samples prior to library preparation in the experiments above. The addition of spike-in RNA is effective as processing control when working with challenging and low input material, and can be used to normalize sequencing reads or calculate input RNA amounts. In addition, the correlation values between the expected and observed relative quantities of the spikes can be calculated. The high correlation in our experiments indicate excellent recovery of the ERCC spike-in mix during the entire library preparation and sequencing workflow in all samples but the conditioned medium Sup Fig. .As there is an inverse relationship between the number of spike-in RNA reads and the number of endogenous RNA reads, the ratio between the sum of the reads consumed by the endogenous transcripts and the total number of spike-in reads is a relative measure for the RNA concentration of the various samples. When adding the same amount of ERCC RNA to all samples, a higher ratio is indicative of more endogenous RNA. We found the highest RNA extraction concentration in conditioned medium, and the lowest in plasma EVs Sup Fig. . Of noteth highest abundant endogenous gene. Only by aiming for coverage in the biofluid abundance range, one is able to assess the accuracy of biologically relevant differences. The results indicate how reliably fold changes can be detected using our total RNA seq method. Overall, there is a strong correlation between the expected and observed fold changes, with ERCC spikes  behaving slightly better than Sequin spikes  since the slope is expected to be ‘1’  and a maximum of 91.4% (for a difference of 2) of the spikes show a deviation from the expected value of maximum 0.5  and ERCC spikes (n = 92) were diluted in opposite order by a factor 1.41 in the five derivative samples. In this way, a biologically relevant 4-fold dynamic range for both Sequin and ERCC spikes was covered Fig. . The aim‘1’ Fig. . Notably‘1’ Fig. . In orde0.5 Fig. . This in2 fold change of 0.403, indicated in Fig. As indicated above, technical replicates of the e PRP and ePFP samples were prepared at the level of RNA isolation. Scatter plots of the read counts clearly show that gene counts are reproducible between independent RNA extractions of the same plasma sample Fig. . In addiTo assess the inherent variation of the various transcriptomes, we clustered all plasma, urine, conditioned medium and EV samples in a t-SNE plots Sup Fig. . This plWe further investigated five different gene biotypes in all samples, according to their annotation in Ensembl . The percentage of counts assigned to these five gene types differs among the biofluids. ePRP for instance contains high number of pseudogene reads, resulting from mitochondrial genes as illustrated above, whereas ePFP mainly consists of reads mapping to protein coding genes Fig. . The dif11, the most comprehensive database of human long non-coding RNAs  the SOLiD total RNA sequening method13, (3) TGIRT-sequencing using thermostable group II intron reverse transcriptases5 and (4) SILVER-seq.14. The SMARTer method assessed in our study adds a fifth promising method to the sequencing armory. In addition, the SMARTer method avoids limitations seen in other methods such as short fragments only, low number of quantified genes, high level of ribosomal RNA contamination, or lack of strandedness. Compared to most other existing technologies on biofluids, the SMARTer method is not purposed to quantify very short length RNA molecules such as microRNAs. A proper and extensive benchmarking study may be required to assess the relative merits and limitations of the various whole transcriptome profiling methods to study biofluid and EV associated RNA cargo.Extracellular RNA content analysis of human biofluids and extracellular vesicles may provide insights into their biogenesis and reveal biomarkers for health and disease. There are currently four types of sequencing-based total RNA profiling of such challenging clinical samples: (1) the recent modified small RNA sequencing methods15. We recommend, in concordance with the ISEV guidelines, to treat EV samples with an RNase if one is interested in RNA contained inside the vesicle.While not marketed for this application, extensive technical performance assessment demonstrated that the SMARTer Stranded Total RNA-Seq method to be an accurate, precise and sensitive method to quantify total RNA in human biofluids. The tested biomaterials are purposely diverse and belong to donors with varying health status. Notable differences among plasma, urine, conditioned medium and their EVs could be related to the biology of each fluid and should be taken into account when setting up biomarker studies. Possible improvements to profile platelet-rich plasma from EDTA tubes could be made by designing probes that remove mitochondrial ribosomal RNA, shown to be highly abundant (and unwanted) in this type of plasma. In this way, read diversity should increase and more genes at lower abundance will be identified. Quite striking was the observation that EVs from platelet-free citrate plasma contain substantially fewer genes. Whether the workflow can be optimized for plasma EVs definitely is a subject for further research. Besides, treatment of EVs with RNases to remove any non-encapsulated RNA may also prove useful16. In our study, we also observed differences between ePFP and cPFP, which are identical biofluids collected in different blood tubes and prepared with a slightly different centrifugation protocol. In general, differences in pre-analytical variables such as blood collection tubes, processing time, centrifugation speeds, RNA isolation kit, and freeze-thaw cycles could well be responsible for great variation in RNA sequencing results. Systematic evaluation of the impact of pre-analytical variables would definitely be of huge added value to progress the fields of extracellular RNA research and liquid biopsies. In order to obtain biologically meaningful results in future studies, uniform processing of all samples according to standard operating procedures will be of paramount importance.It has been shown that pre-analytical variables may have an effect on the resulting RNA profiles17 during RNA extraction and ERCC spikes before library preparation are added in all RNA sequencing experiments to control for different types of technical variation. As data interpretation is often complex in experiments involving different biofluids and input volumes, spike-in RNA could help with normalization, clarification and assimilation of raw data.In our study we included synthetic spike-in RNA mixes to control for variation during RNA isolation and/or library preparation. Of note, we did not include spikes during RNA isolation of EVs and their biofluids or origin because we did not include replicates at the RNA level. Ideally however, both Sequin spikesFinally, nuclear acids present in all sorts of biofluids and their EVs are promising biomarkers for diagnosis, prognosis, therapy response and monitoring of disease. The advantage of the SMARTer Stranded Total RNA-Seq method is its potential to process low amounts of input material. Indeed, collecting samples is often the bottleneck of fundamental, (pre)clinical and translational research projects and being able to disseminate large amounts of information from only 200 μL (or less) can substantially impact research progress.rd time , and transferred to a 15 ml tube, leaving approximately 0.5 cm above the separation. The resulting platelet-free plasma (ePFP) was gently inverted, snap-frozen in five aliquots of 220 µl and stored at −80 °C. The entire plasma preparation protocol was finished in less than two hours. 200 µl ePRP and ePFP was used for each RNA isolation. For the spike-in RNA titration experiment, the protocol was identical except for the fact that 4 EDTA tubes of 10 ml were drawn and that the second centrifugation step was different .For the first experiment, venous blood was drawn from an elbow vein of two healthy donors in 3 EDTA tubes  using the BD Vacutainer Push blood collection set (21 G needle). Collection of blood samples was according to the Ethical Committee of Ghent University Hospital approval EC/2017/1207, following the ICH Good Clinical Practice rules, and written informed consents of all donors were obtained. The tubes were inverted 5 times and centrifuged within 15 minutes after blood draw . Per donor, the upper plasma fractions were pipetted (leaving approximately 0.5 cm plasma above the buffy coat) and pooled in a 15 ml tube. After gently inverting, five aliquots of 220 µl platelet-rich plasma (ePRP) were snap-frozen in 1.5 ml LoBind tubes (Eppendorf Protein LoBind microcentrifuge tubes Z666548 - DNA/RNA) in liquid nitrogen and stored at −80 °C. The remaining plasma was centrifuged  and transferred to a new 15 ml tube, leaving approximately 0.5 cm plasma above the separation. This plasma was centrifuged a 3Venous blood was collected using a 21 G needle in 3.2% (w/v) sodium citrate tubes  from an elbow vein of a healthy donor. Collection of blood samples was according to the Ethical Committee of Ghent University Hospital approval EC/2014/0655 following the ICH Good Clinical Practice rules. The participant had given written informed consent. Absence of hemolysis was confirmed by the lack of a spectrophotometric absorbance peak of free hemoglobin at 414 nm using a BioDrop DUO spectrophotometer . The blood tubes were inverted 5 times and plasma was prepared by centrifugation . The upper plasma fraction was collected (leaving approximately 0.5 cm plasma above the buffy coat layer) and transferred to a new 15 ml tube. Platelet-depleted plasma was prepared by centrifugation . Platelet-depleted plasma was collected (leaving approximately 0.5 cm plasma above the bottom of the tube), aliquoted per 1.5 ml in 2 ml cryo-vials and stored at −80 °C. To ensure the depletion of platelets in plasma we used the XP-300 Hematology Analyzer . The blood sample was processed within 120 min after blood collection. 200 µl plasma was used for RNA isolation.18. For preparation of the SEC column, nylon filter with 20 µm pore size  was placed on bottom of a 10 ml syringe , followed by stacking of 10 ml Sepharose CL-2B. On top of three SEC columns, 6 ml plasma was loaded (2 ml per column) and fractions of 1 ml eluate were collected. SEC fractions 4, 5 and 6 were pooled and concentrated to 1 ml using 10 kDa centrifugal filter . The resulting 1 ml sample was loaded on top of a DG, as previously described19. This discontinuous iodixanol gradient was prepared by layering 4 ml of 40%, 4 ml of 20%, 4 ml of 10% and 3.5 ml of 5% iodixanol in a 17 ml Thinwall Polypropylene Tube . The DG was centrifuged 18 h at 100,000 g and 4 °C using SW 32.1 Ti rotor . Density fractions of 1 ml were collected and fractions 9–10 pooled. An additional SEC was performed on the pooled density fraction to remove iodixanol20. SEC fractions 4–7 were pooled and concentrated to 100 µl and stored at −80 °C until further use. Samples were further diluted to 200 µl in PBS prior to RNA isolation.A combination of size exclusion chromatography (SEC) and OptiPrep density gradient (DG) centrifugation was used to isolate EV from plasma. Sepharose CL-2B  was washed 3 times with PBS  containing 0.32% (w/v) trisodiumcitrate dihydrate One whole urine sample was collected from a prostate cancer patient prior to local treatment. Sample collection was according to the Ethical Committee of Ghent University Hospital approval EC/2015/0260 following the ICH Good Clinical Practice rules. The participant had given written informed consent. The urine sample was collected immediately following digital rectal examination (DRE). DRE was performed as 3 finger strokes per prostate lobe. The urine sample was centrifuged for 10 minutes at 1000 g and 4 °C in accordance with the Eurokup/HKUPP Guidelines. Cell-free urine supernatants were collected (leaving approximately 0.5 cm urine above the cell pellet) and stored at −80 °C in 1.7 ml SafeSeal Microcentrifuge Tubes (Sorenson Bioscience) until further use. 200 µl urine was used for RNA isolation.The cell-free urine sample (45 ml) was thawed at room temperature and vortexed extensively before being concentrated to 800 µl using a 10 kDa centrifugal filter device . The concentrated urine sample was resuspended in 3.2 ml of a 50% iodixanol solution and layered on the bottom of a 17 ml Thinwall Polypropylene Tube . A discontinuous DG was prepared by additional layering of 4 ml of 20%, 4 ml of 10% and 3.5 ml of 5% iodixanol, and 1 ml PBS on top of the urine suspension. The DG was centrifuged 18 h at 100,000 g and 4 °C using SW 32.1 Ti rotor . Density fractions of 1 ml were collected and fractions 9–10 pooled. An additional SEC was performed on the pooled density fraction to remove iodixanol. SEC fractions 4–7 were pooled and concentrated to 100 µl and stored at −80 °C until further use. Samples were further diluted in PBS to 200 µl for RNA isolation.21. MCF-7 GFP-Rab27b cells were cultured in Dulbecco’s Modified Eagle Medium supplemented (DMEM) with 10% fetal bovine serum, 100 U/ml penicillin, 100 µg/ml streptomycin and 1 mg/ml G418. Presence of mycoplasma was routinely tested using MycoAlert Mycoplasma Detection Kit . To prepare conditioned medium (CM), 4 × 108 MCF-7 GFP-Rab27b cells  were washed once with DMEM, followed by two washing steps with DMEM supplemented with 0.5% EV-depleted fetal bovine serum (EDS). EDS was obtained after 18 h ultracentrifugation at 100,000 g and 4 °C , followed by 0.22 µm filtration. Flasks were incubated at 37 °C and 10% CO2 with DMEM containing 0.5% EDS. After 24 h, CM was collected and centrifuged for 10 min at 200 g and 4 °C. Cell counting was performed with trypan blue staining to assess cell viability . The supernatant was passed through a 0.45 µm cellulose acetate filter  and CM was concentrated to 1 ml at 4 °C using a 10 kDa Centricon Plus-70 centrifugal unit . 200 µl was used for RNA isolation. After filtering through a 0.22 µm filter , 1 ml concentrated conditioned medium (CCM) was used for DG ultracentrifugation. Fractions of 1 ml were collected and fractions 9–10 pooled. Pooled fractions were diluted to 15 ml with phosphate-buffered saline (PBS), followed by 3 h ultracentrifugation at 100,000 g and 4 °C using SW 32.1 Ti rotor . Resulting pellets were resuspended in 100 µl PBS and stored at −80 °C until further use. Samples were further diluted in PBS to 200 µl for RNA isolation.The MCF-7 cell line  was stably transfected with peGFP-C1 vector  containing the GFP-Rab27b fusion protein, as previously described (MCF-7 GFP-Rab27b)22 (EV-TRACK ID: EV190039).We have submitted all relevant data of our experiments to the EV-TRACK knowledgebaseThe following antibodies were used for immunostaining: anti-Alix , anti-TSG101 , anti-CD9 , anti-THP , anti-Flot-1 , anti-Ago2 , anti-ApoA-1 , sheep anti-mouse horseradish peroxidase-linked antibody , donkey anti-rabbit horseradish peroxidase-linked antibody .EV protein concentrations were measured using the fluorometric Qubit Protein Assay . Sample preparation was done by 1:1 dilution with SDS 0.4%. Protein measurements were performed using the Qubit Fluorometer 3.0  according to the manufacturer’s instructions.ODG fractions were dissolved in reducing sample buffer (0.5 M Tris-HCl (pH 6.8), 40% glycerol, 9.2% SDS, 3% 2-mercaptoethanol, 0.005% bromophenol blue) and boiled at 95 °C for 5 min. Proteins were separated by SDS-PAGE (SDS-polyacrylamide gel electrophoresis), transferred to nitrocellulose membranes , blocked in 5% non-fat milk in PBS with 0.5% Tween-20 and immunostained. Chemiluminescence substrate  was added and imaging was performed using the Proxima 2850 Imager .8–1 × 109).EV samples were analyzed by Nanoparticle tracking analysis (NTA) using a NanoSight LM10 microscope  equipped with a 405 nm laser. For each sample, three 60 second videos were recorded at camera level 13. Temperature was monitored during recording. Recorded videos were analyzed at detection threshold 3 with NTA Software version 3.2 to determine the concentration and size distribution of measured particles with corresponding standard error. For optimal measurements, samples were diluted with PBS until particle concentration was within the optimal concentration range for particle analysis . Samples were deposited on Formvar carbon-coated, glow discharged grids, stained with uranylacetate and embedded in methylcellulose/uranylacetate. These grids were examined using a Tecnai Spirit transmission electron microscope  and images were captured with a Quemasa charge-coupled device camera .17 were added to the lysate at a dilution of 1/3000 for PFP and 1/250 for PRP, to control for variation in RNA isolation. After isolation, 2 µl of ERCC RNA spikes (ThermoFisher) were added to the eluate at a dilution of 1/25 000 for PFP and 1/5000 for PRP. This allows to estimate the relative concentration of the eluate. For the ePFP RNA of the healthy donor, used for the spike-in RNA titration experiment (see 4.4), we used 6 aliquots of 200 µl plasma and pooled the RNA after isolation. We did not add Sequin spikes during RNA isolation. ERCC spikes were added following a titration series, as described in the next paragraph. Finally, RNA from EVs and their respective biofluids was isolated with the same kit, using 200 µl sample input . No duplicates were included at the level of RNA isolation, no Sequin spikes were added, and the standard spin columns were replaced by Ultra-Clean Production (UCP) columns (Qiagen). ERCC spikes were added to the RNA isolation eluate at a dilution of 1/30 000 for plasma and urine and 1/50 for conditioned medium.RNA isolation was performed using the miRNeasy Serum/Plasma Kit (Qiagen). In experiment 1, ePRP and ePFP RNA was isolated from 200 µl of platelet-rich and platelet-free plasma from two healthy donors. Two RNA replicates were included. 2 µl of Sequin RNA spikesPooled ePFP RNA (prepared without Sequin spike-in RNA addition) was distributed in five separate tubes, each containing 12 µl RNA. Then, we added 1 µl DNase, 1.6 µl reaction buffer, 2 µl Sequin spikes and 2 µl ERCC spikes to each tube. Both spike-in RNA types were added in a 5-point 1.414-fold dilution series, in opposing order. For Sequin: 1/15,000, 1/21,277, 1/30,000, 1/42,433 and 1/60,000. For ERCC: 1/100,000, 1/70,721, 1/50,000, 1/35,461 and 1/25,000.On the total amount of 12 µl eluate, gDNA heat-and-run removal was performed by adding 1 µl of HL-dsDNase  and 1 µl reaction buffer (ArcticZymes 66001). Of the resulting volume, 4 µl was used as input for the total RNA library preparation protocol. Sequencing libraries were generated using SMARTer Stranded Total RNA-Seq Kit v2 - Pico Input Mammalian . Compared to the manufacturer’s protocol, the fragmentation step was set to 4 min at 94 °C, hereafter the option to start from highly degraded RNA was followed. Library quality control was performed with the Fragment Analyzer high sense small fragment kit . Based on Qubit concentration measurements or KAPA qPCR, samples were pooled and loaded on the NextSeq. 500 (Illumina) with a loading concentration of 1.1 or 1.2 pM. Note that the 1.2 pM resulted in lower quality reads as the run was slightly overloaded. Paired end sequencing was performed (2 × 75 bp) with median depth of 15.3 million reads per sample. The fastq data is deposited in GEO (GSE131689).23 (www.primerxl.org) and efficiencies tested using a 6-point 10-fold synthetic template dilution series from 1,000,000 down to 10 molecules. For cDNA synthesis, the iScript cDNA Synthesis Kit (Bio-Rad) was used according to the manufacturer; 2 µl of input RNA was added to 1 µl of enzyme, 4 µl of 5x reaction mix, and 13 µl of nuclease free water. The samples were incubated for 30 minutes at 42 °C and 5 min at 85 °C. Subsequently, cDNA was diluted by adding 60 µl nuclease free water. RT-qPCR was performed in a 5 µl reaction in duplicate in 384-multiwell plates (Bio-Rad) using 2.5 µl 2x SsoAdvanced SYBR Green Supermix (Bio-Rad), 0.25 µl of each primer (5 µM) and 2 µl cDNA. The thermal cycling protocol comprises a polymerase activation step at 95 °C for 30 seconds, followed by 40 cycles of denaturation at 98 °C for 15 seconds, annealing/extension and read out at 60 °C and ends with melt curve analysis during 5 second 0.5 °C increment steps from 65 °C to 95 °C on a CFX384 (Bio-Rad). Cq values were exported from the CFX Manager software and imported in qbase + v3.124 (www.qbaseplus.com) for data-analysis using multigene normalization , error propagation, and rescaling to the sample with the highest abundance.For the validation of 12 selected genes by RT-qPCR, assays were carefully designed using primerXL25 to remove 3 nucleotides of the 5′ end of read 2. To enable a fair comparison, we started data-analysis from an equal number of reads by subsampling to 1 million trimmed and deduplicated reads. To assess the quality of the data, the reads were mapped using STAR (v.2.6.0)26 on the hg38 genome including the spikes, full ribosomal DNA  and mitochondrial DNA sequences. The parameters of STAR were according to the ENCODE project . Using SAMtools (v1.6)27,reads mapping to the different nuclear chromosomes, mitochondrial DNA and rRNA were extracted and annotated as exonic, intronic or intergenic. The SMARTer total RNA sequencing data is stranded and processed accordingly, so strandedness was considered for each analysis step. Gene body coverage was calculated using the full Ensembl (v91)28 transcriptome. The coverage per percentile was calculated.The reads with a low quality score (Q30) were discarded, hereafter read duplicates were removed with Clumpify . The libraries were trimmed using cutadapt (v.1.16)29 using both Ensembl (v.91)28 extended with the ERCC spike and Sequin spike sequences and LNCipedia (v.5.0)11. The strandedness of the total RNA-seq reads was considered by running the –rf-stranded mode. Further processing was done with R (v.3.5.1) making use of tidyverse (v.1.2.1). A cut-off for filtering noisy genes was set based on an analysis of single positive and double positive genes. For a cut-off of 4 counts, at least 95% of the single positive values are filtered out. To measure the biological signal, we first performed differential expression analysis between the treatment groups using DESeq. 2 (v.1.20.0)30. To identify enriched gene sets a fsgea (v.1.6.0) analysis was performed, calculating enrichment for the gene sets retrieved from MSigDB (v.6.2).Genes were quantified by Kallisto (v.0.43.1)26 and CIRCexplorer2 (v2.3.3)31. The settings of STAR  are slightly different compared to linear mapping4. Human genome hg38 was used for circRNA analysis. CircRNAs were annotated with host gene names from RefSeq.CircRNAs were annotated by using the combination of STAR (v.2.6.0)Supplemental FiguresSupplemental Table


Cellular RNA molecules contain phosphate or hydroxyl ends. A 2′,3′-cyclic phosphate (cP) is one of the 3′-terminal forms of RNAs mainly generated from RNA cleavage by ribonucleases. Although transcriptome profiling using RNA-seq has become a ubiquitous tool in biological and medical research, cP-containing RNAs (cP-RNAs) form a hidden transcriptome layer, which is infrequently recognized and characterized, because standard RNA-seq is unable to capture them. Despite cP-RNAs’ invisibility in RNA-seq data, increasing evidence indicates that they are not accumulated simply as non-functional degradation products; rather, they have physiological roles in various biological processes, designating them as noteworthy functional molecules. This review summarizes our current knowledge of cP-RNA biogenesis pathways and their catalytic enzymatic activities, discusses how the cP-RNA generation affects biological processes, and explores future directions to further investigate cP-RNA biology. After transcription, newly synthesized RNA molecules must undergo maturation steps to become functional molecules, and unnecessary RNAs are subjected to turnover. In both the RNA maturation and turnover mechanisms, enzymatic cleavage of RNA molecules plays a crucial role. When cleaved, RNAs can generally possess a hydroxyl group (OH), a phosphate (P), or a 2′,3′-cyclic phosphate (cP) at their termini. While OH and P can be found at both the 5′- and 3′-ends of RNAs, a cP presents only at the 3′-end of RNAs in which the 2′- and 3′-positions of ribose is bridged by the phosphate Figure . Catalyt1, and RNase T2], which eventually generate RNAs with 3′-P/5′-OH ends , RNase T-OH ends . RNA cle-OH ends  -mediated nucleophilic attack of ATP α-phosphorus, followed by covalent bond formation and pyrophosphate (PPi) release. Second, the holoenzyme then transfers the AMP to 3′-P of the substrate RNA to form an RNA with 3′-PP-5′A. Third, the energetically unstable phosphoanhydride bond between the two phosphates is cleaved by 2′-OH-mediated attack, resulting in cP formation and releasing AMP.While cP end is predominantly formed by ribonuclease-catalyzed transesterification, RNA 3′-terminal phosphate cyclase (RtcA) can catalyze ee steps . First, LeuCAA, tRNAIleUAU, and tRNATyrGUA, contain an intronic region within their anticodon-loop  . In yeasHAC1 mRNA  by cleaving both 5′- and 3′-splice sites, in which a cP is formed at the conserved 3′-terminal G of 5′-cleavage products  pathway upon endoplasmic reticulum (ER) stress. Inositol-requiring enzyme 1 (Ire1), a cP-forming endonuclease, is associated with the ER membrane with its C-terminal domain exposed to the cytosol . While aproducts   and U35  systems. TA systems involve bacterial stress responses, often considered “suicidal programs,” comprising a stable toxin and an unstable antitoxin that neutralizes the cognate toxin in cells , which is considered to be another bacterial suicide program , a specific modified RNA nucleotide present at np 34 of tRNAGluUUC, tRNALysUUU, and tRNAGlnUUG, and cleaves between U34 and U35 of those tRNAs  and cleaves between U34 and U35 of tRNAGlnUUG, leaving a cP at the ribose of mcm5U in the cleavage product   Figure . 5′-tiRNANG gene mutations have been identified in patients with amyotrophic lateral sclerosis (ALS) and Parkinson’s disease   Figure . 5′-SHOT disease , implyin disease . Indeed, disease .B. mori germ cells, some abundant species of Piwi-interacting RNAs (piRNAs), a germline-specific class of small regulatory RNAs, are produced directly from cP-containing 5′-tRNA halves (cP-RNAs can also function as direct precursors for shorter functional RNAs. In A halves  (Figure A halves . AlthougA halves , whetherO-methyl ribose modification (B. mori BmN4 cells (To further expand cP-RNA research, it is imperative to capture cP-RNA expression profiles accurately, which is not possible using standard RNA-seq methods. Specific cP-RNA sequencing can be achieved by cP-RNA-seq , 2016 whfication , can alsN4 cells , 2017. AArabidopsis tRNA ligase AtRNL can be used for specific cP-RNA sequencing (Saccharomyces cerevisiae 2′-phosphotransferase Tpt1, were purified and used in the method (As an alternative method, quencing . Becausee method . Applicae method . BecauseAfter cP-RNA sequencing, amplification and quantification of the representative identified cP-RNA species are necessary to validate their expression and analyze whether a cP end is the major 3′-end form of the identified sequences. Standard RT-qPCR, amplifying internal sequences of targeted RNAs, is inappropriate for specific amplification of cP-RNAs because it cannot distinguish between cP-RNAs and RNAs with other terminal states. To specifically analyze cP-RNAs, RNAs treated with T4 PNK or CIP can be subjected to 3′-adapter ligation, followed by TaqMan RT-qPCR targeting 3′-adapter-RNA ligation products  Figure . The depDespite the findings described in this review, current information regarding cellular expression profiles of cP-RNAs is very limited and fragmented. Although increasing accumulation of RNA-seq data has accelerated the comparative analyses of transcriptomes and, therefore, been critical to identifying significant RNA species in biological phenomena and diseases, the “invisibility” of cP-RNA expression in RNA-seq data makes cP-RNA research be still at an initial stage. The immediate future focus should be on capturing the comprehensive repertoire of cP-RNAs expressed in different tissues and cells by using the above described specific sequencing methods. Given that cP-RNAs are expressed as functional molecules, capturing the entire cP-RNA repertoire would broaden the catalog of functional non-coding RNAs and could reveal significant biological events that have been eluding standard RNA-seq. Besides cP-RNA expression, molecular mechanisms behind cP-RNA biogenesis and function still remain elusive. Presumably, not all cP-RNA-producing enzymes have been identified and characterized to date. Because determining cP-RNA-generating enzymes only by their amino-acid sequences and protein motifs is impossible, discovering novel cP-RNA-generating enzymes will rely on detailed structural and biochemical characterizations of each enzyme. Given the already-proven biological roles of cP formation and cP-RNA expression, it is not surprising that cP-RNAs are involved in a wide range of biological processes. Considering the “hidden” nature of cP-RNAs in conventional RNA-seq data, further research efforts to characterize cP-RNAs would likely clarify substantially greater biological significance of cP-RNAs, which will advance our understanding of the expanding realm of non-coding RNA molecules.MS and YK conceptualized the theme and wrote the review with substantial help by TK in compiling reference papers. All authors reviewed and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Next-generation sequencing (NGS) techniques have been generating various molecular maps, including transcriptomes via RNA-seq. Although the primary purpose of RNA-seq is to quantify the expression level of known genes, RNA variants are also identifiable. However, care must be taken to account for RNA’s dynamic nature. In this study, we evaluated the following popular splice-aware alignment algorithms in the context of RNA variant-calling analysis: HISAT2, STAR, STAR (two-pass mode), Subread, and Subjunc. For this, we performed RNA-seq with ten pieces of invasive ductal carcinoma from breast tissue and three pieces of adjacent normal tissue from a single patient. These RNA-seq data were used to evaluate the performance of splice-aware aligners. Surprisingly, the number of common potential RNA editing sites (pRESs) identified by all alignment algorithms was less than 2% of the total. The main cause of this difference was the mapped reads on the splice junctions. In addition, the RNA quality significantly affected the outcome. Therefore, researchers must consider these experimental and bioinformatic features during RNA variant analysis. Further investigations of common pRESs discovered that BDH1, CCDC137, and TBC1D10A transcripts contained a single non-synonymous RNA variant that was unique to breast cancer tissue compared to adjacent normal tissue; thus, further clinical validation is required. RNA editing is a post-transcriptional process mediated by specific enzymes that induce changes in RNA sequences compared to the original DNA sequence . It was Recent advances in DNA sequencing technology that have been led by next-generation sequencing have opened up a new era of genomics by providing a vast number of genome-wide molecular maps regarding genomes, transcriptomes, and epigenomes –15. ReceOne tissue of invasive ductal carcinoma  from breast tissue and a corresponding adjacent normal tissue were biopsied from a single Korean woman with informed consent. Then, we cut the tumor and adjacent normal tissues into ten and three pieces, respectively. Poly(A) RNA was purified from 1 g total RNA from each sample, and cDNA was synthesized using SuperScript II (Invitrogen). Sequencing libraries were prepared using the TruSeq RNA library preparation kit (Illumina) and sequenced using HiSeq 2500 (Illumina). The RNA-seq data was deposited in GEO under accession number (GSE110114). The study was approved by the institutional review board of Catholic Medical Center . The patient signed an informed consent form before study-related procedures were conducted.https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) with cutadapt (version 1.1.2) [Low-quality portions of paired-end reads were trimmed using Trim galore  . Sequencn 1.1.2) ; HITSAT2n 1.1.2) ; and STAn 1.1.2) . The PCRn 1.1.2) . The mapn 1.1.2) . Finallyn 1.1.2) . pRESs wThe quality of RNAs in each sample was assessed using RSeQC   with eachttps://www.gencodegenes.org/releases/current.html) were estimated using Cufflink (version 2.2.1) [The abundances of known genes  .In this study, “RNA variant” is defined as a single nucleotide variant observed in RNA-seq data, while “potential RNA editing site (pRES)” is defined as an RNA variant that did not overlap with the known 325,083,445 DNA variants.Unlike DNA variants, the identification of RNA variants can be affected by several factors due to RNA’s dynamic and unstable nature. Due to the lack of a gold-standard RNA-seq set for evaluation, we performed RNA-seq with ten pieces (C0–C9) of invasive ductal carcinoma tissue and three pieces (N1–N3) of adjacent normal tissue from a single Korean breast cancer patient  . The canSeveral analysis steps should be considered for accurately identifying RNA variants. We followed an analysis scheme in this study . We atteThe performance of alignment methods was assessed by independently merging ten cancer and three normal RNA-seq reads that were mapped by each alignment tool into cancer and normal merged sets, respectively. pRESs identified in each merged set were defined as a true set and used to calculate the precision and recall of each individual sample (C0–C9 or N1–N3) for a given alignment method. The read depth (DP) cutoffs for pRES calling in cancer and normal groups were scaled up to 100 and 30, respectively, according to the number of samples merged. The results showed that STAR and Subread identified the largest numbers of pRESs  and thatSRSF11 gene, but not the other aligners . Therefore, BDH1, CCDC137, and TBC1D10A were finally examined. Based on the pRESs, the following amino acid changes were predicted in cancer tissue compared to adjacent normal tissue: K275R in BDH1, Q288E in CCDC137, and D44H in TBC1D10A  of the ten pieces from the cancer tissue seemed to be degraded even though an expert performed all experiments at the same time. The number of RNA variants was positively correlated with RNA quality, which was measured by the TIN score regardless of aligners Figs  and 3B. S1 FigRecall was defined as the number of overlapped pRESs between merged RNA-seq and individual RNA-seq divided by the total number of pRESs identified in the given merged RNA-seq data (bottom).(TIF)Click here for additional data file.S2 Fig(A) The bar graph shows the number of pRESs identified in the six samples from a human RNA-seq data set (GSE75688) . (B) Pie(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.


Adenosine-to-inosine RNA editing can markedly diversify the transcriptome, leading to a variety of critical molecular and biological processes in mammals. Over the past several years, researchers have developed several new pipelines and software packages to identify RNA editing sites with a focus on downstream statistical analysis and functional interpretation.Here, we developed a user-friendly public webserver named MIRIA that integrates statistics and visualization techniques to facilitate the comprehensive analysis of RNA editing sites data identified by the pipelines and software packages. MIRIA is unique in that provides several analytical functions, including RNA editing type statistics, genomic feature annotations, editing level statistics, genome-wide distribution of RNA editing sites, tissue-specific analysis and conservation analysis. We collected high-throughput RNA sequencing (RNA-seq) data from eight tissues across seven species as the experimental data for MIRIA and constructed an example result page.https://mammal.deepomics.org.MIRIA provides both visualization and analysis of mammal RNA editing data for experimental biologists who are interested in revealing the functions of RNA editing sites. MIRIA is freely available at  RNA editing is defined as a critical post-transcriptional regulatory RNA-processing event (excluding RNA splicing) that generates an RNA transcript with a primary nucleotide sequence different from its gene. In mammals, the most common form of RNA editing, A-to-I RNA editing, is catalysed by the ADAR family of enzymes (Adenosine Deaminase that Acts on RNA) , 2, and Recently, increasing number of mammalian RNA editing databases have been published –22, therMIRIA is a new tool that was designed for RNA editing analysis in mammals. Users need to provide a compressed file (.zip) containing all RNA editing files from different mammals. The architecture of the zip file has two layers. The first layer contains all species folders , and the second layer, which is inside each species folder, contains all RNA editing files of the species . The format of the single RNA editing file (.res) is listed below (Table Adenosine-to-inosine (A-to-I) editing is the most common form of editing in mammals. As such, the percentage of A-to-G editing, an indicator of A-to-I editing, is an important measurement to indirectly assess the detection accuracy of the RNA editing sites. Before calculating the percentage of A-to-G editing, we classified all editing sites into one of three categories, namely, those in Alu regions, those in repetitive non-Alu regions and those in non-repetitive regions. We performed the repeat region annotation using the RepeatMasker file downloaded from the UCSC Table Browser  . The peMIRIA can annotate RNA editing sites with a variety of useful genomic features. First, we used SnpEff , a genomTo examine the editing level statistics of each tissue in the uploaded data, we determined the editing level of each RNA editing site as the ratio of the number of reads supporting this site to the number of reads covering this site. The overall editing level statistics of each tissue were displayed using a boxplot Fig. e.For the statistical analysis of RNA editing sites on a genome-wide level, each chromosome was partitioned into contiguous 1-Mb windows, and the total number of RNA editing sites was calculated within each window. Thereafter, an interactive Circos graph was generated to compare the different RNA editing numbers between the different tissues on a genome-wide level  from the overview page of the job results interface.The MIRIA website was built using the Django Python Web framework  coupled https://mammal.deepomics.org/demo/.To evaluate the MIRIA webserver, we collected 55 RNA-seq datasets from seven mammals  as the test data. For each mammal, seven or eight tissue samples  were used. The RNA-seq data were downloaded from the National Center for Biotechnology Information (NCBI) SRA database. The genomes and annotation files of the mammals were downloaded from the ENSEMBL database . RepeatMWe developed the MIRIA to provide both visualization and analysis of mammal RNA editing data. MIRIA enables experimental biologists without any computational programming skills to perform a diverse range of analyses including RNA editing type statistics, genomic feature annotations, editing level statistics, genome-wide distribution of RNA editing sites, tissue-specific analysis and conservation analysis. For every analysis, the result is presented with a visualized graph and can be downloaded as a publication-ready format. In general, with the functions of the MIRIA designed for mammal RNA editing data, we believe that this webserver will be a valuable resource for experimental biologists who are interested in revealing the functions of RNA editing sites.Project name: MIRIAProject home page:https://mammal.deepomics.org/Operating system(s): Platform independentProgramming language: PythonOther requirements: Chrome, Safari, Firefox or IELicense: GNU GPLAny restrictions to use by non-academics: NoneAdditional file 1: Table S1. The data source from NCBI.


RNA molecules are master regulators of cells. They are involved in a variety of molecular processes: they transmit genetic information, sense cellular signals and communicate responses, and even catalyze chemical reactions. As in the case of proteins, RNA function is dictated by its structure and by its ability to adopt different conformations, which in turn is encoded in the sequence. Experimental determination of high-resolution RNA structures is both laborious and difficult, and therefore the majority of known RNAs remain structurally uncharacterized. To address this problem, predictive computational methods were developed based on the accumulated knowledge of RNA structures determined so far, the physical basis of the RNA folding, and taking into account evolutionary considerations, such as conservation of functionally important motifs. However, all theoretical methods suffer from various limitations, and they are generally unable to accurately predict structures for RNA sequences longer than 100-nt residues unless aided by additional experimental data. In this article, we review experimental methods that can generate data usable by computational methods, as well as computational approaches for RNA structure prediction that can utilize data from experimental analyses. We outline methods and data types that can be potentially useful for RNA 3D structure modeling but are not commonly used by the existing software, suggesting directions for future development. RNA molecules play fundamental roles in many biological processes, such as regulation of gene expression , RNA splAs of 20 September 2018, 1345 RNA structures (and 2284 RNA complexes with other molecules) were available in the Protein Data Bank (PDB) [per se is difficult, and biomacromolecules (proteins and nucleic acids likewise) often require some extent of engineering or chemical modification so that their structure can be analyzed with a given method [It will be very challenging to obtain a comprehensive overview of structures for all RNA molecules that have a biochemical function (beyond protein-coding). Such efforts have been undertaken toward elucidating protein structures, e.g., through the Protein Structural Genomics initiative and related efforts around the world ,20. The n method . In any n method ,27. Furtn method . Thus, sGiven the scarcity of experimentally determined high-resolution structures of RNA molecules, theoretical models of RNA 3D structure can assist in understanding and guiding the identification of functionally important regions and can provide a starting point for higher resolution descriptions . HoweverThis review provides necessary information about the use of experimental data that, in combination with computational approaches for the determination of RNA 3D structures, are commonly applied to study structural properties of RNA molecules. We review this issue from two complementary perspectives: experimental methods providing data that can be used in the RNA 3D structure modeling process, and computational methods that can use the experimental data. It is worth emphasizing that various experimental approaches discussed in this article have various interesting applications beyond the generation of data for macromolecular modeling: such applications are out of the scope of this review and are not discussed here. We focus primarily on the state-of-the-art and contemporary techniques while avoiding giving historical overviews. Likewise, in the description of computational methods, we concentrate on the utilization of information derived from experimental data, while ‘purely theoretical’ modeling approaches and historical perspectives are not covered. The discussion describes approaches combining experimental data (both low and high-resolution) on RNA molecules as well as their complexes, with computational modeling to obtain high-resolution models. In summary, we provide a methodological workflow to build structural models of RNA 3D structures for cases, where the use of traditional methods  may encounter difficulties and where the application of purely theoretical modeling is unlikely to be successful.A related important area of research, not covered by this article, is the use of computational modeling methods, in particular various types of simulations, to make predictions about various physicochemical properties of RNA molecules, which can be compared with results of experiments. The reader interested in the use of RNA modeling methods to predict functional properties of RNA molecules beyond the 3D structure is referred to recent publications on this topic, e.g., .Our knowledge of RNA 2D and 3D structures is primarily based on various experimental observations. On the one hand, experiments can be interpreted in terms of specific structural knowledge about a particular RNA molecule, although large sets of experimental data collected systematically for various RNAs can be used to infer principles that apply to RNA structure in general. This review focusses on the use of experimental data to infer individual RNA structures.A number of experimental methods can be used to determine the overall 3D shapes of biological macromolecules. Here we review the main types of experimental approaches applicable to structural studies of RNAs alone or in complex with other molecules. We focus on the type of data generated by these methods, which can be further processed to obtain spatial restraints for building 3D structural models of RNA molecules.Macromolecular X-ray crystallography (MX) is the most widely used technique of structural biology because it provides highly accurate models of large macromolecular complexes . In a crOne of the most significant bottlenecks of MX is the necessity to obtain well-diffracting crystals of the sample of interest. The resolution, at which crystal structures can be determined, relies highly on the size of the crystal and on how well the molecules are ordered in it. It is very difficult to obtain such crystals for RNA molecules due to their overall negatively charged surface, that often leads to non-specific interactions and causes a poor degree of internal order within the crystal .Hydrogen atoms exhibit very low electron density; hence MX experiments cannot define their positions even when the structures are determined at very high resolution. The identification of H atoms can be addressed by neutron diffraction experiments. Although it is in principle the method of choice for the experimental determination of H atoms positions, neutron crystallography is still very challenging, e.g., the technique requires relatively large crystals, which are usually challenging to grow and there are very few facilities worldwide where the experiments can take place . DespiteX-ray (SAXS) and neutron (SANS) scattering, employed to characterize biological macromolecules, including RNAs. In each of these techniques, radiation is elastically scattered by a sample in solution, where molecules are randomly oriented [Small angle scattering (SAS) is the collective name given to various techniques, including oriented . The scaoriented . In SAXSoriented ,52,53. Noriented . In bothoriented . The efforiented ,57. TherElectron microscopy (EM) is a technique used for visualization of biomacromolecules, which relies on the contrast formed by the interaction of incoming electrons with the specimen. In single-particle EM, multiple 2D projection images of randomly oriented molecules are recorded, which can be then computationally combined into a 3D density map. It should be emphasized that electron micrographs are real-space images containing both amplitude and phase information. Therefore, EM techniques do not have the ‘phase problem’ that occurs in X-ray crystallography.In negative stain EM, the sample is immobilized on the surface of a carbon film and covered with a thin layer of stain that yields high contrast images. Even though the stain limits the level of molecular detail of the reconstructed envelopes, negative stain EM has been very useful in the study of RNA assemblies, in particular when higher resolution crystal and NMR structures can be fit in the maps .In cryo-EM, the sample is embedded in a thin layer of amorphous ice, which avoids the artifacts of staining. The high-energy electrons used for imaging cause, however, severe radiation damage of the individual molecules, which requires imaging with low doses of electrons. These imaging conditions yield lower contrast images, requiring the analysis of a very large number of particle images to obtain a satisfactory signal-to-noise ratio. Once a sufficient number of 2D images containing high-resolution information are captured, they are aligned and averaged into 2D classes. Particles from these classes are used to produce a 3D reconstruction. The 3D reconstruction is a Coulomb potential density map that can be interpreted similarly to electron density maps determined by X-ray crystallography. At very high resolution, cryo-EM maps can be used to determine the position of individual atoms. Currently, it is hard to say where the limits of cryo-EM are, as structures of proteins as small as hemoglobin (64 kDa)  have beeThere are several examples of structures of large RNA–protein assemblies solved by cryo-EM, such as the yeast mitochondrial large ribosomal subunit  and the Atomic force microscopy (AFM) is a type of scanning probe microscopy (SPM), which can be used for various applications, including imaging and force-probing biological samples. Unlike optical or electron microscopes that record images, AFM instruments collect data to generate images by physically touching the samples. In AFM, a molecule is immobilized on a solid substrate, such as mica or glass. Subsequently, the surface is raster scanned with sub-nanometer precision by an ultrasharp tip, mounted at the end of a force-sensing cantilever. It can be bent by repulsive and attractive interactions between the tip and the sample. The lever deflection is measured, enabling the detection of height changes with a nanometer spatial resolution, leading to a 3D topography image of the surface . The tapAnother class of methods for 3D structure analysis studies interactions within (or between) molecules rather than their global shapes. These techniques offer a different point of view on the molecular structure, providing spatial restraints from which 3D molecular models can be obtained. We review the main types of experimental approaches applicable to structural studies of RNA and its complexes.1H, 2H, 13C, 15N, 31P) carry magnetic dipoles that can take up various orientations characterized by different energies when the magnetic field is applied. In response to the pulsed magnetic wave at specific (resonance) frequencies, these nuclei can absorb and re-emit electromagnetic radiation, and the obtained NMR signals are the consequence of the occurring energy state transitions [Nuclear magnetic resonance (NMR) spectroscopy is a technique used to observe local magnetic fields around atomic nuclei and measure magnetic interactions between them. The principle behind NMR is that some nuclei isotopes  . TypicalSolution NMR is an effective tool for RNA structure elucidation since its advantage relies on the collection of dynamic information of flexible macromolecules at atomistic or residue resolution. However, NMR probing of biomolecules has been limited by their size  and so it is most successfully applied for determining the structure of small RNA domains, aptamers, and ribozymes . NMR strRecently developed solid-state NMR (ssNMR) is the spectroscopic method applicable to macromolecules of any size in a non-crystalline form and may become a solution to the obstacles in RNA structure determination. Although ssNMR has been rarely used in nucleic acids study and still requires more advancement and methodology preparation, recent reports suggest that it holds great promise for the study of RNAs and RNPs .Electron paramagnetic resonance (EPR) is a spectroscopic technique used to study chemical species with unpaired electrons. The principles behind EPR are analogous to those of NMR, with the difference in electron spins being excited instead of the spins of atomic nuclei . RadicalFörster resonance energy transfer (FRET) is a technique that measures non-radiative energy transfer between two light-sensitive molecules (chromophores). In typical applications relevant to structural biology, both the donor and the acceptor chromophores are fluorescent, and the term ‘fluorescence resonance energy transfer’ is often used instead. Intrinsic fluorescence of RNA is relatively low, thus for FRET analysis, the potentially interacting molecules or their parts have to be labeled at specific positions with donor and the acceptor fluorophores. The energy transfer occurs by means of intermolecular long-range dipole–dipole coupling, which is distance dependent . FRET isChemical cross-linking (XL) is a technique for analyzing macromolecular interactions, which has been widely used in studies of proteins and nucleic acids. It involves the formation of chemical bonds between proximal elements of the macromolecule analyzed. One variant of this approach uses photo-activatable reactive chemical groups incorporated into the molecule before the experiment. For studies on RNA structure and interactions, photosensitive nucleotides have been used, such as 4-thiouridine (4SU). Upon irradiation with far-UV (<300 nm) or near-UV (320–365 nm) light, photo-activated compounds generate zero-length cross-links between residues that interact with each other or at least form transient contacts in the folded RNA structure. An alternative is to employ bifunctional cross-linking reagents, such as cisplatin (CPT), nitrogen mustard (NM), chlorambucil (CHB), cyanuric acid (sTT), bikethoxal (BKT), or phenyl-diglyoxal (PDG) that can react with specific nucleophilic groups found on the surface of protein and nucleic acid substrates. These reagents bridge across chemical groups present in the native molecule, if they are positioned within the cross-linking range specified by the size of the cross-linker . As the The sites of cross-linking can be identified by alkaline hydrolysis, primer extension, PAGE, or RNA sequencing techniques . Cell-baLigation-based techniques exemplified by RNA proximity ligation (RPL) are a family of methods for studying interactions within or between RNA molecules by reconnecting the ribonucleotide backbone. Typically, RNA molecules under study are initially fragmented to introduce breaks in the RNA chain and to generate chemically reactive 5′ and 3′ termini. Then, chemical ligation of spatially proximal termini of RNA fragments is induced, followed by sequencing to identify ligation junctions in chimeric reads. Some variants of this approach include PARIS , LIGR-seEnzymatic or chemical structure probing methods are often used in footprinting experiments, to obtain information about local structural features of ribonucleotide residues, such as solvent accessibility, paired/unpaired state, or rigidity/flexibility. Such 1D features do not indicate which ribonucleotide residues are mutually interacting, and hence they cannot be used to determine the secondary or tertiary structure. However, they can be used to inform computational methods for higher order structure prediction, e.g., to find the best match between experimental results and possible patterns of interactions, especially when correlations between structural features of different residues are considered.Probing techniques involve enzymatic cleavage and/or chemical modification of the RNA, followed by the detection of modified residues and/or sites of cleavage via reverse-transcribing the modified RNA to cDNA. The cDNA synthesis starts with a primer  and the reverse transcriptase halts at cleavage sites as well as may halt at modifications, leading to cDNA truncations (RT-stop). Some techniques employ the ability of the RT to misincorporate non-complementary residues for the modification sites (RT-map). Sequenced cDNA reads are computationally aligned to map the positions affected by probing and to calculate average reactivities for all residues in the RNA under study. Reactivities can be then used to represent the relative strength or prevalence of particular structural features, e.g., as restraints or constraints in various types of computational structure prediction. Primer extension by the reverse transcriptase is problematic for small RNA molecules that may not allow for the stable annealing of short primers and for RNA molecules with very stable secondary structures that may cause the RT to stop regardless of any chemical modifications. In these cases, the implementation of MS analysis allows one to dispense with the cDNA synthesis and to directly characterize the chemical adducts that exhibit a characteristic change in molecular mass . The mosEnzymatic probes are nucleases that discriminate between paired and unpaired regions . NucleasChemical probes may be used as footprinting reagents to interrogate various structural features, including base pairing, local nucleotide dynamics, and solvent accessibility. Chemical reagents typically used for RNA structure probing can be divided into ones that modify bases and are sequence-specific, and those that act on backbone in a sequence-nonspecific manner.Base-specific probes that modify the Watson–Crick (WC) edge of specific ribonucleotides include dimethyl sulphate (DMS), 1-cyclohexyl-3-(2-morpholinoethyl)carbodiimide metho-p-toluene sulfonate (CMCT), and 3-ethoxy-a-ketobutyraldehyde . They can be used to discriminate between bases that have the WC edge involved in pairing (and hence protected from modification) from those that have the WC edge exposed and are therefore more reactive. It should be emphasized that having the WC edge involved in pairing does not necessarily indicate the involvement of a given residue in secondary structure, as the WC edge can be used for the formation of various non-canonical interactions with bases, backbone, and with ligands bound to RNA.Light-activated structural examination of RNA (LASER) technique is used to obtain solvent accessibility information of purine nucleobases in RNA molecules [in vitro to monitor RNA structural changes, e.g., resulting from ligand binding, and it can also be applied in vivo for studying RNA structures and interactions.olecules . This teHydroxyl radical footprinting (HRF) technique employs sequence-nonspecific cleavage of the nucleic acid backbone in the presence of hydroxyl radicals that are generated by decomposition of hydrogen peroxide into a hydroxyl radical and a hydroxide ion in the presence of Fe(II)-EDTA [II)-EDTA ,110. RegII)-EDTA .Selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) [3 [ (SHAPE) ,113 tech (SHAPE) , 1M6 [11 (SHAPE) , BzCN [1 (SHAPE) , NAI [11 (SHAPE) , NAI-N3 HAPE) [3 , and FAIHAPE) [3 , to formHAPE) [3 ,112,113.HAPE) [3 .in vitro and/or in vivo. In particular, a number of methods were developed that use RT-stop as reactivity detection strategy, which differ in the probing reagents and in the priming strategy (defined or random primer) used to prepare a library for high-throughput sequencing. High-throughput methods for RNA enzymatic probing include PARS with RNase S1 vs RNase V1 [High-throughput probing involves the application of structure-selective reagents to many RNA molecules at the same time, coupled with high-throughput sequencing. This method allows for the investigation of very large RNA molecules, different mixtures of RNAs (even whole transcriptomes), for analyzing different conformational states of RNAs , and for detecting correlated changes at different positions in the sequence, which may be indicative of long-range interactions. There are many varieties of high-throughput RNA probing techniques developed to probe RNA structure RNase V1 , FragSeqRNase V1 , PARTE wRNase V1 , and dsRRNase V1 . HRF-seqRNase V1 . MOHCA-sRNase V1 . Also, mRNase V1 .Mutational profiling (MaP) is a high-throughput chemical probing approach that employs detection of multiple chemical modification positions within a single RNA by the RT-map method. The advantage of the MaP method over other sequencing-based probing methods is that a single RNA molecule with the misincorporated nucleotide is analyzed, which eliminates the multiple ligation steps required during sequencing. The sequencing readout is then processed to obtain information about the reactivity of each nucleotide, which in turn can be used to infer structural features. In SHAPE-MaP, the reactivities are used primarily for discriminating between paired and unpaired regions, and they can also be used for identifying competing and alternative RNA conformations or quantitate any process or function that modulates local RNA dynamics [in vivo with 1M7 as a probing reagent [dynamics ,128. Thi reagent ,130. RNA reagent .2)Mutate-and-map . For thThe experimentally determined macromolecular structures have been widely used as a source of ‘spare parts’ in the process of model building, from structures and conformations of chemical groups comprising several atoms, to fragments comprising several residues, to local 3D motifs, and to whole molecules. The most successful programs available for molecular modelers often combine multiple approaches, however for the purpose of this review we define the key modeling approaches separately from the actual implementations.Comparative modeling  technique is based on the observation that evolutionarily related (homologous) molecules adopt very similar structures, despite divergent mutations accumulated in their sequences [equences . Thus, aTypically, comparative modeling can produce highly accurate models for target molecules that exhibit high sequence similarity to the templates, indicative of a close evolutionary relationship. Evolutionarily related RNAs with divergent sequences often retain the ancestral tertiary folds, very much like proteins do . HoweverIn the absence of closely related molecules in the database of experimentally determined structures, the detection of templates becomes challenging. This issue is addressed by fold-recognition (FR) methods, which aim at predicting which of the known 3D structures is likely to exhibit a common architecture with the target sequence. FR has been developed for proteins and while it is applicable to RNA, to our knowledge it remains to be implemented in this context. FR can involve sequence similarity searches  or threading (finding structures that can accommodate the target sequence in such a way that favorable contacts are formed in 3D) or the combination of these approaches.Comparative modeling methods often use atomic coordinates of experimentally determined structures in one of two ways. One group of methods, exemplified by ModeRNA ,142, dirFragment assembly is another approach for structure prediction, which can use atomic coordinates of experimentally determined structures. As opposed to comparative modeling, it does not require a match of the template sequence to any particular structure, so it is essentially a template-free approach. Instead, it builds the structure de novo by using segments extracted from known 3D structures as building blocks. Often, the modeling procedure involves the testing of various combinations of fragments, which are assessed by a scoring function that can be based on statistics, physics, or both. Typically, secondary structure information is used in the form of constraints in the process of fragment selection. Additional spatial restraints may also be used. Both in comparative modeling based on restraints and in fragment assembly, additional information may also be encoded: pairwise distance restraints are relatively straightforward to encode and are enabled by most methods, and some methods  also allow 3D shape restraints in the form of density maps. There exist methods for manual (semi-automated) modeling with the use of fragment assembly such as RNA2D3D [ RNA2D3D  and Asse RNA2D3D ,147. The RNA2D3D  and Rose RNA2D3D ,150.De novo modeling is an approach for structure prediction, which by definition does not use templates. ‘Ab initio’ modeling methods that rely solely on first principles , even with various simplifications, are extremely costly and are incapable of predicting 3D structures for objects as large as typical RNA structures starting from sequence information alone [de novo folding methods enable simulating the RNA structure formation process based on sequence alone or supplemented only with secondary structure information. Therefore they introduce various simplifications and biases to make the prediction process plausible, by trading off accuracy for speed, given limited computing resources. Many of these methods use a coarse-grained representation and simplify or ignore various aspects of RNA structure and dynamics that are not essential for predicting the 3D structure that is as close to the real one, as possible and feasible [de novo modeling of RNA structure, do utilize prior information on experimentally determined structures, very often in the form of statistics that guide the folding toward conformations that are similar to those observed earlier. It has been shown, e.g., in the series of community-wide RNA Puzzles experiments, that the use of additional information in the form of various additional restraints, contributes greatly to the accuracy of predictions [on alone . On the feasible ,152. Alldictions . In factde novo folding, as well as fragment assembly methods, actually require a secondary structure to be included as one of the elements of the input, so they either take the secondary structure supplied by the user or they predict it using one or more of the external tools. Many programs can also use additional pairwise restraints derived from different types of experiments, beyond the information about the secondary structure. Multidimensional Chemical Mapping (MCM) is a computational workflow for RNA 3D structure modeling that utilizes Rosetta/FARFAR with restraints based on experimental probing with the M2 and MOHCA-seq approaches, which provide secondary structure, and tertiary proximity information, respectively [Essentially all methods for RNA 3D structure prediction can use restraints or constraints on secondary structure. Many of the ectively . Exampleectively , iFoldRNectively , and Simectively .1H chemical shift data to restrain MD simulation and suggested the prospects of using chemical shift data as restraints in RNA structural modeling as well as in the qualitative evaluation of RNA dynamic ensembles [1H chemical shift data obtained from NMR experiments can also be used in an extension of the Rosetta method, Chemical-shift-ROSETTA for RNA (CS-ROSETTA-RNA), to model non-canonical RNA motifs using [1H–1H NMR NOESY and NMR JN-COSY experiments, respectively [A few modeling methods specialize in the use of restraints from NMR experiments. The Al-Hashimi group used nsembles . 1H chemfs using . iFoldNMectively .The integration of XL-MS with computational techniques for molecular 3D structure determination has been initially developed for proteins and termed as MS3D . The feaDe novo RNP modeling in Real-space through Assembly of Fragments Together with Electron density in Rosetta), which can be used to build RNA coordinates into cryo-EM maps of RNAs and RNPs [While the information about molecular shapes is the basis of several methods for macromolecular structure determination, it is rarely used as a restraint by modeling methods that are largely predictive. However, a number of computational methods were developed that blur the line between data-driven structure determination and data-driven predictive modeling. Such methods perform flexible fitting of molecular structures into density maps obtained from EM or MX experiments. They were developed to improve the model building in particular for maps that are of too low resolution for traditional structure determination methods to produce reliable atomic coordinates, e.g., at the resolution > 3.5 Å for MX. This type of modeling usually does not construct the whole structure from scratch and should be typically initiated with a 3D model that is relatively close to the expected final conformation, and only small to medium structural changes are introduced. For instance, the MD Flexible Fitting (MDFF) method incorporates the EM data as an external potential added to the MD force field, allowing the internal features present in the EM map to be used in the fitting process. In this approach, the expected conformational changes must be within the range of the MD method . A similand RNPs .Given the scarcity of experimentally determined high-resolution structures of RNA molecules and their complexes and the wide range of heterogeneous biochemical data, computational techniques can be used to integrate existing data, guide structure elucidation, and subsequently determine the mechanisms of action and interactions between components. This approach is commonly called hybrid or integrative modeling. It has been developed and used mainly for protein complexes, but it has also been applied to systems involving RNA. Typically, the result of integrative modeling is not just a single structure, but rather a series of models that are maximally consistent with the input data. Hence, in the case of insufficient data, a user obtains multiple alternative solutions that can be utilized to plan additional experiments. Theoretical models of macromolecular structures can assist in understanding and guiding the identification of important interaction surfaces and, more importantly, can provide a starting point for higher resolution descriptions.http://genesilico.pl/pyry3d/), which aimed at the relative simplicity of the input while retaining the ability to use various restraints. This method is particularly useful for data-driven modeling of complexes where the users have only limited knowledge about the system analyzed and wish to test alternative hypotheses, e.g., by using restraints that may be ambiguous or mutually incompatible. PyRy3D enables the construction of models for individual molecules as well as for large macromolecular complexes, and it combines the modeling of rigid bodies  with flexible shapes . PyRy3D has been used to model RNA and RNA–protein complexes such as the complex between the 2′,5′-oligoadenylate synthetase OAS1 and the 3′-terminal region of the WNV RNA genome [Integrative Modeling Platform (IMP) is an example of a very comprehensive computational toolbox for hybrid modeling, which encompasses various applications for data-driven modeling. It supports the use of multiple types of restraints that are applicable for RNA modeling, including SAXS profiles, EM images and density maps, and sparse NMR data . There aA genome . M3 is yA genome . The M3 A genome , and acca posteriori assessment [a priori model assessment attempts to answer questions such as ‘is a given structural model likely to be correct or is it likely to be wrong’, ‘which parts of the model are likely to be correct, and which parts are uncertain’ and ‘for a given set of models, which are likely to be the better than others’.The ability to discriminate correct models from incorrect models is of great importance for RNA 3D structure prediction. When the experimentally determined structure is available as a reference, a number of computational methods can be used to make sessment  about thThe most obvious way to assess structural models is to compare them with data that were not used in the process of structure prediction and can be considered independent. In crystallography, for instance the model is assessed with the R-work and R-free values. The R-work measures the agreement between the model and the actual experimental data, by calculating a simulated diffraction pattern from the model. The R-work value can be, however, biased as the model can be refined to better agree with the experimental data. A less biased measure is the R-free, which measures how well the model can predict a test set of reflections (5–10% of the dataset) that were not used during refinement . For othhttp://genesilico.pl/mqapRNA/, Marcin Magnus and Janusz M. Bujnicki, unpublished) to facilitate the comparison of different approaches.What can be done if no additional experimental data exist? A 3D structural model can be assessed statistically: first, if the modeled structure exhibits physico-chemical and geometrical features that are generally expected from a well-folded molecule and second, if a particular model is similar to other models, predicted independently by other computational methods. Many methods for the theoretical prediction of 3D structural model accuracy based on these principles have been developed for proteins, and they have been evaluated regularly in the CASP experiment . Thus faResults of RNA Puzzles experiments conducted so far indicate that the assessment of RNA 3D structure model quality is a significant bottleneck in RNA structure prediction, as even the best modelers are unable to predict with confidence which of their own models are likely to be better than others .Large-scale structural studies of RNA gained momentum much later than efforts to systematically determine protein structures such as ‘structural genomics’. In the last decade, many of the strategies used to model 3D structures of proteins have been adapted for RNA structural modeling . Nonethede novo structure determination in order to solve the so-called phase problem. Both anomalous dispersion and isomorphous replacement approaches require the incorporation of heavy atoms within the crystals, and the structure determination process relies on the determination of their location (substructure determination) through Patterson or direct methods. The diffraction data collected in the presence of heavy atoms is usually of lower resolution than the data collected from unmodified crystals, and thus the electron density maps obtained after phasing suffer from a lack of features that can make the manual model building very challenging or even impossible. The situation can drastically improve if native data at better resolution and a partial model are available for phase extension. Obtaining the latter could be used by modeling programs that, taking advantage of low-resolution electron density maps, would be able to place helices, known 3D structural motifs or other distinguishable structural features to yield a partial model for phase extension and subsequent all-atom model building.In MX, experimental phasing methods are required for As mentioned earlier, electron density maps from MX and EM have been used as restraints for modeling of RNA and RNP 3D structures. However, this has been largely limited to data at medium resolution, where individual residues can be distinguished from each other. On the other hand, the EM technique can generate low  and very low (>>10 Å) resolution maps for macromolecules of unknown structure, which could be used as potentially useful restraints by the modeling methods. In principle, the restriction of the conformational space by any shape restraints is very helpful for modeling, as it saves the time that could be otherwise wasted on analyzing completely wrong molecular architectures. Instead, conformational sampling can be focussed on testing models that are more likely to be similar to the true structure.2D class averages calculated from cryo-EM micrographs are used in predicting 3D structures of protein assemblies ,187. ThiAnother type of data that could be very useful for RNA modeling is offered by AFM. With the recent advances in this technique, RNA molecules can be visualized to reveal features such as double- and single-stranded regions, and their interactions ,188. AFMRestraints from XL-MS and MS-MS experiments are used in modeling of proteins and their complexes with other macromolecules including RNA–protein complexes ,189,190.Last, but not the least, computational methods for RNA 3D structure prediction could be improved in the way how the footprinting data are used. Reactivity profiles, e.g., from SHAPE and related experiments, are usually first used as an input for specialized secondary structure prediction methods, and only then the predicted secondary structures are used as constraints or restraints in the RNA 3D structure modeling process. However, for some RNA modeling methods, it should be possible, and potentially advantageous, to use the probing data directly at the stage of model building. For example, probing data that indicate the likelihood of individual residues to be paired compared with unpaired could be used as an element of the scoring function without the necessity to specify the binding partners. This could be particularly advantageous for predicting complex RNA structures with pseudoknots that are notoriously difficult to infer for secondary structure prediction tools. On the other hand, modeling tools such as our own program SimRNA, which make and break base pairs and entire secondary structures as a part of the conformational sampling process, do not require any special treatment for pseudoknots, and could be guided to maximize the agreement of the 3D model directly with the probing pattern .Ultimately, we propose that methods for RNA 3D structure modeling that enable the use of experimental data as restraints could be significantly improved. First, there exists a number of experimental techniques that are incapable of unambiguous structure determination, but they generate data that can be potentially used as restraints in molecular modeling. Many of these data are low-resolution and ambiguous, hence modeling methods would have to be adapted to treat them accordingly, and use appropriate statistical frameworks to allow selection amongst multiple alternatives and to tolerate a relatively high level of restraints that could be completely wrong. Second, modeling methods often use highly processed (interpreted) experimental data, which may be misleading. For example, MX and EM data are usually used at the level of 3D density maps and not at the level of diffraction patterns or 2D images. Likewise, footprinting data are not used directly, but in the form of secondary structure predictions. Therefore, we suggest that significant advancement in 3D structure prediction could be made by adapting the existing methods to use experimental data at the possibly early stage of processing, to avoid unnecessary biases. This may require the development of novel ways of treating the experimental data in the prediction process, and as a result, would bring the theoretical approaches closer to what kind of information is actually recorded in the experiments. Such a direction of development may also prompt the theoreticians to collaborate with experimentalists more closely. Computational approaches that deal with the original data may better capture details of biological processes studied experimentally, and could potentially lead to discoveries of new biological phenomena.