Multiplexed •OHCleavage Analysis with paired-end sequencing) with mutate-and-map secondary structure inference guides Rosetta 3D modeling to consistent 1-nm accuracy for intricately folded ncRNAs with lengths up to 188 nucleotides, including a blind RNA-puzzle challenge, the lariat-capping ribozyme. This multidimensional chemical mapping (MCM) pipeline resolves unexpected tertiary proximities for cyclic-di-GMP, glycine, and adenosylcobalamin riboswitch aptamers without their ligands and a loose structure for the recently discovered human HoxA9D internal ribosome entry site regulon. MCM offers a sequencing-based route to uncovering ncRNA 3D structure, applicable to functionally important but potentially heterogeneous states.Accelerating discoveries of non-coding RNA (ncRNA) in myriad biological processes pose major challenges to structural and functional analysis. Despite progress in secondary structure modeling, high-throughput methods have generally failed to determine ncRNA tertiary structures, even at the 1-nm resolution that enables visualization of how helices and functional motifs are positioned in three dimensions. We report that integrating a new method called MOHCA-seq . Some of these RNA molecules contain instructions to make proteins, but others—known as non-coding RNAs—regulate the activity of genes in cells.The genetic information within RNA is encoded by the sequence of four different chemical parts called ‘nucleotides’. RNA can exist as a single strand of nucleotides, but the nucleotides can also pair up in specific combinations to form sections of double-stranded RNA. Therefore, a single strand of non-coding RNA can fold into a complex three-dimensional shape that contains loops, twists, and bulges.The three-dimensional structures of non-coding RNAs are crucial for their roles in cells, but the variety and complexity of shapes that they can form makes it technically difficult to study them. In 2008, researchers developed a new method called MOHCA that can map the positions of nucleotides that are close together in the three-dimensional structure. Highly reactive chemicals are attached to the nucleotides and these can react with, and damage, other nearby nucleotides. By detecting which nucleotides have been damaged, it is possible to map the positions of these nucleotides and decipher the structure of the RNA molecule using computer algorithms.MOHCA is a promising approach, but the initial methods to find the damaged nucleotides were tedious and required specialized equipment. Now, Cheng, Das et al.—including some of the researchers involved in the 2008 work—have developed an improved version of MOHCA that uses readily available RNA sequencing techniques to find the damaged nucleotides. The RNA sequencing data are then analyzed by a new algorithm in the Rosetta computer modeling software.Cheng, Das et al. used this newly developed ‘MOHCA-seq’ and Rosetta to reveal the structures of a human non-coding RNA and several other non-coding RNA molecules to a much higher level of detail than before. Together, MOHCA-seq and Rosetta provide a rapid method for researchers to decipher the three-dimensional structure of non-coding RNAs. This method is likely to speed up the analysis of the complex structures of non-coding RNAs. It will be useful in future efforts to work out what roles these RNAs play in cells, including their activity in cancer, neurodegeneration, and other diseases.DOI:http://dx.doi.org/10.7554/eLife.07600.002 RNAs fold into and interconvert between specific secondary and tertiary structures to perform a wealth of essential biological functions . In part2), analogous to multidimensional NMR spectroscopy but at nucleotide length scales  algorithm in Rosetta  NMR spectroscopy, which detects ‘through-space’ proximities between atoms that are not necessarily bonded. This problem prompted us to revisit a technique for discovering RNA tertiary proximities termed Multiplexed •OH  Cleavage Analysis (MOHCA)  , which rto-noise . These f2 and Rosetta to give a multidimensional chemical mapping (MCM) pipeline for routine use in ncRNA 3D structure characterization. Here, we present the development, benchmarks, and a blind test of the MCM methodology, followed by applications to detect preformed structure in ligand-free riboswitch aptamer states and a recently discovered human ncRNA regulon.We hypothesized that the recent availability of tabletop deep sequencing would enable an accelerated and higher-precision ‘MOHCA-seq’ protocol and that these data would complement M2 method for secondary structure inference (2-2′-dATP during transcription so that each RNA contained on average one or fewer modifications and then coupled these sites to isothiocyanobenzyl-Fe(III)•EDTA. We chose these reagents because they are commercially available and therefore can be readily acquired by other laboratories. These RNAs were purified and then folded in buffer containing 50 mM Na-HEPES pH 8.0, 10 mM MgCl2, and any ligands appropriate for the system . After folding, RNAs were fragmented by activating the Fenton reaction at the backbone-tethered Fe(III) atoms using ascorbate as a reducing agent (2 modifications), before electrophoresis in the second, perpendicular direction . To reaAfter reverse transcription, the MOHCA-seq workflow is completed by ligating a second Illumina adapter to the cDNA library and then determining the 5′ and 3′ ends of each cDNA by paired-end sequencing on a standard Illumina platform . MAPseek2/Rosetta pipeline by applying it to a well-studied model system with known crystallographic structure, the 158-nucleotide P4–P6 domain of the Tetrahymena ribozyme  the radical source incorporation rate, and (3) the fragmentation reaction time. We observed MOHCA-seq features at similar positions (but with variable intensities) whether radical sources were attached at A, C, G, or U  the 2′-NH G, or U ; whether, or 5:4 ; or whet, or 5:4 . In part, or 5:4 . A few w, or 5:4  may be c2 and 1D SHAPE mapping for modeling. Second, we performed global modeling in Rosetta in two stages. We generated an initial set of over 10,000 low-resolution models using FARNA in parallel, independent modeling runs; then, we minimized the 1/6 of the models with the lowest Rosetta energy score using full-atom refinement. In both stages, we included MOHCA-seq data to constrain sampling.Based on previous MOHCA-gel studies, we expected that our MOHCA-seq data would enable 3D computational modeling of the P4–P6 domain to helical resolution or better without crystallographic information. We used the FARNA with full-atom refinement algorithm in Rosetta  for mode2  distinguishable from the local background signal and (2) not attributable to secondary structure, based on secondary structures inferred by M2   riboswitch aptamer bound to its ligand at 10 µM c-di-GMP (Fusobacterium nucleatum double glycine riboswitch (consisting of two tandem glycine aptamers) bound to two glycines at 10 mM glycine  riboswitch aptamer bound to its ligand at 70 µM AdoCbl . Just as for the P4–P6 domain, these runs produced dramatically less accurate models, as can be visually assessed  to a final RMSD of 11.2 Å . The global accuracy of the models also improved, with a range of RMSDs over the full ribozyme from 7.6 to 8.9 Å, compared to our original submission, which turned out to have an overall RMSD accuracy of 9.6 Å. Taken together, these results rigorously demonstrated the ability of the full MCM pipeline to detect errors and to consistently and blindly refine ncRNA structures to 1-nm resolution or better throughout complex structures.As a rigorous test, we applied MCM to model another RNA-puzzle before the release of its crystal structure, a 188-nucleotide lariat-capping ribozyme from  M2 data  that sup2 and MOHCA-seq data themselves, with Rosetta modeling carried out for initial visualization only .Riboswitches form a major class of ncRNAs, and understanding their structures in the absence of ligands is critical for dissecting mechanisms of ligand capture and modulation of gene expression. While crystal structures and solution scattering of a few ligand-free riboswitch aptamers have suggested the possibility of extensive preformed tertiary structure in special cases , whether2 measurements gave P1a, P1b, and P2 helices in both ligand-free and ligand-bound states, up to possible rearrangements in edge base pairs  elements in the 5′-UTRs of the Hox mRNAs has suggested new roles of structured ncRNAs in mammalian development . To furtelements   spectroscopy, NOE measurements, and computational modeling allows routine NMR-based structure determination of small RNA motifs, we have shown that the M2/MOHCA-seq/Rosetta pipeline achieves models for larger ncRNA structures, including blind test cases, with accuracies of 1 nm.Despite rapid advances in secondary structure inference from chemical mapping and computational modeling, inference of three-dimensional ncRNA structures has been bottlenecked by an inability to experimentally determine how a molecule's helices and non-canonical motifs assemble into complex global structures. The MCM methods described herein leverage tabletop sequencing platforms and commercially available reagents to provide a straightforward route to such proximity information. In the MCM pipeline, both MBeyond resolving the global structures of well-structured ncRNA states, MCM provides global structural frameworks to guide structure-function analyses of ncRNAs that adopt multiple conformations. MCM data corroborate and refine models of ligand-independent tertiary structure in a c-di-GMP riboswitch aptamer and reveal similarly preformed tertiary structure in glycine and AdoCbl riboswitch aptamers. Mutational analysis confirms the importance of junctions in defining these ligand-free global tertiary preferences. Such preformed structure may be needed for rapid kinetic capture of cognate ligands in vivo. We propose that current exceptions to this rule, such as the thiamine pyrophosphate riboswitch aptamer , may alsAs an example of rapid modeling of a complex and biologically important ncRNA, MCM has revealed a global structure for a HoxA9 IRES domain almost immediately after its discovery as a regulator of vertebrate body plan. The structure is less well-defined than the other ncRNAs but exhibits a pseudoknot that is a common feature of viral IRESes. Other macromolecules that recognize this IRES domain, including the 80S ribosome, may further stabilize or remodel this ncRNA's ‘naked’ tertiary structure, and MCM will provide a rapid approach to characterizing such structural effects.In addition to establishing MCM as a routine approach to achieve medium-resolution structures of ncRNA domains, our results suggest both experimental and computational extensions to expand the technology. Cell-permeable nucleotide analogs or chemical modifiers may enable in vivo 3D structure determination. The experimental cost of MCM grows quadratically with the length of the transcript, limiting current analysis to domains of hundreds of nucleotides; however, >1000-nt transcripts may become feasible as sequencing costs continue to decrease and if new strategies to bypass reverse transcription stops are implemented . A fract2 experiments were used as inputs to computational modeling. We previously collected M2 data on the AdoCbl riboswitch aptamer in the presence of 60 µM AdoCbl and separately performed M2 on the class I ligase using experimental methods and data analysis that have been described in detail (2 data are available on the RNA Mapping Database (RMDB) with accession IDs RNAPZ6_1M7_0002 (AdoCbl riboswitch aptamer) and CL1LIG_1M7_0001 (class I ligase). The solution conditions were 50 mM Na-HEPES, pH 8.0, and 10 mM MgCl2, as used previously and for MOHCA-seq experiments below. 2 data and analysis for the AdoCbl riboswitch aptamer, class I ligase, and lariat-capping ribozyme, respectively.High-confidence secondary structures derived from Mn detail . The M2 2, 3.5 mM spermidine, 0.1% Triton X-100, 40 mM DTT, 4% PEG 8000, 20 U T7 RNA Polymerase , 1 mM NTPs, and 0.5 mM 2′-NH2-2′-deoxy-ATP . The yield of transcription was 1–1.5 nmol after purification using RNA Clean & Concentrator columns . Purified RNA was then ethanol precipitated to concentrate it for 5′-end labeling using a 5′ EndTag kit and fluorescein maleimide  to enable gel visualization in subsequent steps; the RNA was then purified again using RNA Clean & Concentrator columns. The hydroxyl radical source, isothiocyanobenzyl-EDTA chelating Fe(III) (ITCB-Fe(III)•EDTA) , was covalently attached to the 2′-NH2 groups on the RNA backbone using a two-step process. First, to couple ITCB-EDTA to the RNA, 0.5 mg ITCB-EDTA was dissolved in 5.7 µl 0.4 M KPO4, pH 8.5 for a concentration of 200 mM, then mixed with 15 µl of RNA and 15 µl 0.8 M KPO4, pH 8.5. The coupling reactions were incubated at 37°C for 12–16 hr. Then, 1/3 vol of 200 mM FeCl3 was added and the coupling reactions were incubated at room temperature for 15 min, after which 10 µl 500 mM Na-EDTA, pH 8.0 was added to chelate excess Fe(III). After purifying with RNA Clean & Concentrator columns to remove excess reagents, the RNA was PAGE-purified using denaturing 8% polyacrylamide/7 M urea gels. Bands were located by scanning with a Typhoon imager  for fluorescein fluorescence, and excised gel slices were immersed in approximately 500 µl RNase-free H2O in non-stick tubes overnight at 4°C to elute the RNA. The RNA was purified from the eluate using RNA Clean & Concentrator columns and stored at −20°C. The yield of PAGE purification was 10–30% of the amount loaded on the gel, which was sufficient for multiple MOHCA-seq experiments.Double-stranded DNA templates for the RNAs of interest were constructed using PCR assembly with primers purchased from IDT  . RNAs we2, as follows. First, the RNA was heated to 65°C in HEPES buffer in 8 µl for 3 min, then cooled to room temperature for 10 min; then 1 µl of 100 mM MgCl2 was added and the RNA was heated to 50°C for 5 min, then cooled to room temperature for 10 min.  After folding, the radical source was activated by adding 1 µl 100 mM sodium ascorbate stock for a final concentration of 10 mM. A control reaction was also prepared with 1 µl RNase-free H2O added instead of ascorbate; this reaction was carried through all subsequent steps in parallel. After 5 to 30 min of incubation at room temperature (10 min was standard), 1 µl 100 mM thiourea was added to quench the Fenton reaction. The RNA fragments were ethanol precipitated as follows: First, 1 µl GlycoBlue  and 1/10 vol (1.2 µl) of 3 M sodium acetate, pH 5.2 were added to the quenched reaction and mixed. Then, 3 vol (40 µl) of 100% ethanol chilled on dry ice were added, and the sample was mixed. Samples were spun down immediately in a tabletop microcentrifuge at maximum speed for 20 min, washed with 50 µl ice-cold 70% ethanol, then spun down again at max speed for 10 min. The supernatant was removed by pipetting and the pellets were allowed to dry, after which 15 µl RNase-free H2O was added and the pellet was allowed to dissolve for 10 min at room temperature.Before activating the radical source to produce spatially localized hydroxyl radicals, 3–4 pmol of folded RNA was prepared in 50 mM Na-HEPES, pH 8.0, and 10 mM MgCl2, 5 mM DTT, 5 µM ATP, and 10 units T4 PNK (NEB), in a 20 µl reaction volume and was incubated at 37°C for 30 min. After end-repair, RNAs were ethanol precipitated as above, except with 0.5 µl GlycoBlue, 2.05 µl 3 M sodium acetate, pH 5.2, and 68 µl 100% ethanol chilled on dry ice. The dried pellets were resuspended in 5.2 µl RNase-free H2O. The T4 PNK reaction was necessary and sufficient for ligation of a pre-adenylated ssDNA universal adapter sequence to the radical-cleaved fragments (see next section), based on experiments with PAGE-purified RNA fragments cleaved by hydroxyl radicals from FeEDTA in solution. The ligation efficiency after T4 PNK treatment was ∼50%, suggesting that non-phosphate end products of strand scission, such as 3′-phosphoglycolates, may not be removed by T4 PNK; however, the yield of ligated product was still sufficient for reverse transcription using limiting amounts of sequencing primers.To remove 3′-phosphates left by hydroxyl radical strand scission events in the RNA backbone , the pur2O, and RNAs were purified from the reaction using RNA Clean & Concentrator columns, which also removed excess ssDNA adapter. Ligation was assayed using 5% of the purified sample by observing the mobility shift of the full-length RNA on CE. Mobility shifts of radical-cleaved fragments were not directly observed in this quality check but were observed in pilot experiments, as described above.To prepare the fragmented RNA for reverse transcription, a pre-adenylated and 3′-blocked ssDNA universal adapter sequence  was ligated to the 3′-end of the end-repaired fragments. Each ligation reaction contained 4.9 µl of the RNA from the previous step , as well as 1× T4 RNA ligase buffer (NEB), 15% PEG 8000, 4–5 pmol ssDNA adapter, and 200 U T4 RNA ligase 2 truncated, K227Q or KQ mutant (NEB), in a 10 µl reaction volume. The reactions were incubated at 4°C for 12 hr, followed by heat inactivation at 65°C for 20 min. After ligation, reaction volumes were supplemented with 50 µl RNase-free HAfter ligation of the ssDNA adapter, RNAs were reverse transcribed with sequencing primers containing, in 5′ to 3′ order, a 5′-fluorescein modification, the Illumina TruSeq Universal adapter, 12-nucleotide barcodes , and a primer for the ssDNA adapter sequence on the 3′-end  and was incubated at 68°C for 2 hr, followed by heat inactivation at 80°C for 10 min. The samples were purified by DynaBeads as above with magnetic separation, and a fraction of the sample was run on a capillary electrophoresis machine  with co-loaded fluorescein-labeled standards and analyzed using HiTRACE . For runs using MiSeq v2 kits, the following protocol was used to prepare the library for sequencing: beads harboring 25 fmol of single-stranded library fragments, usually about 1/5 of the final library volume after ligation of the second adapter, were mixed with 2.5 fmol of PhiX dsDNA control  and EB buffer  to 5 µl total. Then, 5 µl of 0.2 N NaOH was added and the fragments were eluted for 10 min at room temperature. The supernatant of the magnetic beads was diluted into chilled HT1 buffer (10 µl added to 990 µl HT1), and then diluted again (375 µl added to 225 µl HT1) before loading all 600 µl onto MiSeq kits following manufacturer instructions. Paired-end sequencing involved 51 and 25 sequencing cycles for the first and second reads, respectively. For runs using MiSeq v3 kits, the following protocol was used to prepare the library for sequencing: beads harboring 32 fmol of single-stranded library fragments were mixed with 4.2 µl of 95% formamide and 10 mM EDTA and heated at 90°C for 2 min and 40 s, then cooled at room temperature for several minutes. The beads were magnetically separated, and 4 fmol of PhiX dsDNA control (Illumina) was added to the isolated supernatant. Then, 5 µl of 0.2 N NaOH was added and the samples were denatured for 10 min at room temperature. This sample was diluted into chilled HT1 buffer (10 µl added to 990 µl HT1) and 600 µl of this dilution was loaded onto MiSeq kits following manufacturer instructions. Paired-end sequencing involved 51 and 51 sequencing cycles for the first and second reads, respectively.http://rmdb.stanford.edu/tools/), giving final text files in RDAT format corresponding to the number of fragments observed for each pair of possible cleavage and oxidation position (corresponding to 5′ and 3′ positions of cDNA fragments). Our sequencing runs generally produced around 15–20 million total reads, of which around 60–80% contained the ssDNA adapter RT priming site and aligned to the RNA sequence in read 1, and 25–35% aligned to the RNA sequence in both read 1 and read 2. Approximately, 25% of aligned reads corresponded to control reactions with radical sources but without ascorbate treatment, while 75% of aligned reads corresponded to the ascorbate-treated, fragmented RNA, as expected from our method of dividing the ascorbate-treated sample into three fractions for reverse transcription. 25–35% of reads containing the RT priming site were null ligations of unextended reverse transcription primer to the second sequencing adapter.The FASTQ data generated by sequencing were processed with the MAPseeker v1.2 software  libraries were combined as follows: the sum of the signal at each sequence separation  was calculated for each data set. For sequence separations between 50 and 100, the signal was averaged across consecutive bins of 2; above 100, the signal was averaged across bins of 10. Then, to account for size-selection by AMPure XP purification, the ratio of AMPure XP signal to DynaBead signal at each sequence separation was calculated and the AMPure XP signal was ‘rebalanced’ by multiplying it by the maximum AMPure XP/DynaBead ratio and then dividing by AMPure XP/DynaBead ratio at that sequence separation. Finally, the rebalanced AMPure XP data set was combined with the DynaBead data set by taking the mean (weighted by the inverse error squared) between the data sets. The raw data have been deposited in the RMDB with the following accession IDs:P4-P6: TRP4P6_MCA_0001-0004c-di-GMP riboswitch aptamer with 10 µM c-di-GMP: CDIGMP_MCA_0002c-di-GMP riboswitch aptamer with 0 µM c-di-GMP: CDIGMP_MCA_00032+: CDIGMP_MCA_0006c-di-GMP riboswitch aptamer with 10 µM c-di-GMP and 0 mM Mg2+: CDIGMP_MCA_0007c-di-GMP riboswitch aptamer with 0 µM c-di-GMP and 0 mM MgGlycine riboswitch ligand-binding domain with 10 mM glycine: GLYCFN_MCA_0002Glycine riboswitch ligand-binding domain with 0 mM glycine: GLYCFN_MCA_0003Glycine riboswitch ligand-binding domain including leader, with 10 mM glycine: GLYCFN_KNK_0005Glycine riboswitch ligand-binding domain including leader, with 0 mM glycine: GLYCFN_KNK_00062+: GLYCFN_KNK_0009Glycine riboswitch ligand-binding domain including leader, with 10 mM glycine and 0 mM Mg2+: GLYCFN_KNK_0010Glycine riboswitch ligand-binding domain including leader, with 0 mM glycine and 0 mM MgAdoCbl riboswitch aptamer with 70 µM AdoCbl: RNAPZ6_MCA_0002AdoCbl riboswitch aptamer with 0 µM AdoCbl: RNAPZ6_MCA_0003AdoCbl riboswitch aptamer (A124U/A125U mutant) with 70 µM AdoCbl: RNAPZ6_MCA_0006AdoCbl riboswitch aptamer (A124U/A125U mutant) with 0 µM AdoCbl: RNAPZ6_MCA_0007Class I ligase: CL1LIG_MCA_0001-0003Lariat-capping ribozyme: RNAPZ5_MCA_0001-0002HoxA9 5′-UTR 957–1132 domain: HOXA9D_MCA_0001To evaluate the rates of strand scission and reverse transcription-terminating modifications, we quantified data from both paired-end sequencing and CE for the model hairpin RNA shown in To quantify strand scission events from sequencing data, we calculated the sum of all counts for fragments that shared the same ssDNA adapter ligation site, giving a projection of the raw counts after MAPseeker analysis  Figure , top. OnTo quantify reverse transcription stops from sequencing data, we calculated the sum of all raw counts for fragments with stops at the same position after MAPseeker analysis. To account for reverse transcriptase attenuation, we normalized the signal at each position by the total counts for all fragments that were ligated 3′ of that position and were terminated at that position or 5′ of that position, analogous to the exact correction applied for CE data , bottom Analysis of MOHCA-seq data requires modeling backgrounds, modulation from reverse transcription attenuation, and sources of error. Here, we outline a general analysis framework for MOHCA-seq experiments, with the following section describing a statistical procedure developed to reach a numerical solution, that gave consistent proximity maps. The resulting processed proximity maps have been deposited in the RMDB with the following accession IDs:P4–P6: TRP4P6_MCA_0000c-di-GMP riboswitch aptamer with 10 µM c-di-GMP: CDIGMP_MCA_0000c-di-GMP riboswitch aptamer with 0 µM c-di-GMP: CDIGMP_MCA_00012+: CDIGMP_MCA_0004c-di-GMP riboswitch aptamer with 10 µM c-di-GMP and 0 mM Mg2+: CDIGMP_MCA_0005c-di-GMP riboswitch aptamer with 0 µM c-di-GMP and 0 mM MgGlycine riboswitch ligand-binding domain with 10 mM glycine: GLYCFN_MCA_0000Glycine riboswitch ligand-binding domain with 0 mM glycine: GLYCFN_MCA_0001Glycine riboswitch ligand-binding domain including leader, with 10 mM glycine: GLYCFN_KNK_0003Glycine riboswitch ligand-binding domain including leader, with 0 mM glycine: GLYCFN_KNK_00042+: GLYCFN_KNK_0007Glycine riboswitch ligand-binding domain including leader, with 10 mM glycine and 0 mM Mg2+: GLYCFN_KNK_0008Glycine riboswitch ligand-binding domain including leader, with 0 mM glycine and 0 mM MgAdoCbl riboswitch aptamer with 70 µM AdoCbl: RNAPZ6_MCA_0000AdoCbl riboswitch aptamer with 0 µM AdoCbl: RNAPZ6_MCA_0001AdoCbl riboswitch aptamer (A124U/A125U mutant) with 70 µM AdoCbl: RNAPZ6_MCA_0004AdoCbl riboswitch aptamer (A124U/A125U mutant) with 0 µM AdoCbl: RNAPZ6_MCA_0005Class I ligase: CL1LIG_MCA_0000Lariat-capping ribozyme: RNAPZ5_MCA_0000HoxA9 5′-UTR 957–1132 domain: HOXA9D_MCA_0000j and a radical source at nucleotide i corresponds to a sequence i + 1 to j − 1 ligated between the two adapters necessary for paired-end Illumina sequencing. The frequency Fij of such products is related to the proximity of i and j but is modulated by the actual distribution of radical sources, for example, primarily at adenosines for 2′-NH2-dATP-incorporating transcripts. The frequency is also suppressed by signal attenuation for long sequence separations j − i due to the possibility of reverse transcription termination between i and j. A master expression for these MOHCA-seq frequencies is:s indexing the possible source positions  and ε(s) the fraction of transcripts containing a source at s. Values i for a transcript with source at s, and values j − 1 and j for a transcript with source at s. This expression simplifies in the limit of no background processes and low-oxidative damage rates  /2, whereas the total number of model parameters ε(s), 2), leading to an ill-posed problem. However, basic chemical considerations reduce the number of parameters. First, we assume that the cleavage fraction that allows ssDNA adapter ligation, bj that is independent of source s but can vary with nucleotide j , and additional, correlated cleavage due to hydroxyl radical-induced strand scission events , parameterized by In the general case, the number of observed frequencies ri of uncorrelated oxidative damage  and (2) additional, correlated oxidative damage due to hydroxyl radicals generated at source s, which is tethered to the RNA , Second, we assume that the stopping events i from source s, and the partitioning of these radicals into events that lead to cleavage /2 for N for bj, ri, and ε(s). By further enforcing positivity of each of these parameters and assuming that N, the number of parameters is reduced to below the number of observables, and the problem becomes well-posed. Requiring sparsity of the proximity map is similar to assumptions used in solvent flattening and other density modification approaches in crystallography  and cleavage profile Bj and a two-point correlation function Qij , which encodes the desired proximity map:A closure-based •OH correlation analysis (COHCOA) estimates a solution to i) within the general ‘background’ stopping rate Ri (not shown). In general, all processes that lead to stops or cleavage across all transcripts are subsumed into Ri and Bj. As a corollary to this simplification, however, this framework does not seek or enable deconvolution of the separate contributions to each of these background rates.In the derivation of (8), we have dropped higher order terms corresponding to neglecting higher cumulants of the damage function  and the process is iterated until convergence. This procedure does not require assuming symmetry of the two-point correlation function Qij, but in the end returns an estimate of this matrix only for (i < j), where there are data Fij. Propagating Poisson counting errors on Fij in Aij, and combining these errors in quadrature with the errors on Fij in Qij. Empirically, 20 or fewer iteration cycles lead to convergence for all data sets tested; 40 iterations have been used in this study to ensure convergence of final Qij values within 1% (taking less than 1 min on a MacBook Pro 2.8 GHz Intel Core i7 running MATLAB 2012B). A comparison of raw counts and COHCOA-analyzed data is shown in The For visualization of the COHCOA-analyzed data as 2D proximity maps, we first applied a filter to remove points with signal-to-noise ratio < 1, then applied a 2D smoothing algorithm to aid visualization of the strongest features. Finally, we calculated the mean of the filtered and smoothed data with all negative values set to 0, then scaled the data by dividing by 5 times this value.2O was added to a no-modification control reaction. Reactions were quenched and RNA was purified and reverse-transcribed as described previously  chemical mapping of wild-type AdoCbl riboswitch aptamer and the A124U/A125U double mutant using dimethyl sulfate (DMS), 1-cyclohexyl-[2-morpholinoethyl] carbodiimide metho-p-toluene sulfonate (CMCT), and 1-methyl-7-nitroisatoic anhydride , as described previously . Brieflyeviously , then cDeviously . Sequenceviously . The HiT/hitrace  and web race.org ) was usehttps://www.rosettacommons.org/software) (We used the Rosetta software (version r56277) to model all RNAs of interest in three steps (software available at oftware) . Modelinrna_denovo -nstruct 100 -params_file helix.params -fasta helix.fasta -out:file:silent helix0.out -include_neighbor_base_stacks -minimize_rna true -rna::corrected_geo -score:rna_torsion_potential RNA11_based_new -geom_sol_correct_acceptor_base -chemical::enlarge_H_lj -score:weights rna/rna_helix -cycles 1000 -output_res_num 136-142 221-227For small RNAs, such as the c-di-GMP riboswitch aptamer, we allowed conformational sampling of helix elements by omitting helix pre-assembly and instead including pseudo-energy constraints (as described below) for secondary structure elements.2  distinguishable from the local background signal by unbiased inspection and (2) not attributable to secondary structure, based on secondary structures inferred by M2 . This pr2 . We sort2 :S(x)=3x2S is the smoothstep function, d is the distance between the atom pair, and E is the constraint potential in Rosetta energy units. For residue pairs with a weak MOHCA-seq signal, we applied a constraint potential of the same shape but weaker amplitude .Here, 2 on the P4–P6 domain) were available, these constraints were also included in the modeling. To assess the importance of MOHCA-seq constraints for accurate modeling, we also performed modeling without MOHCA-seq constraints (M2/Rosetta modeling) using the same command lines as above but without the flags to input constraint files and stage constraints . A sample Rosetta command line is shown below:With the constraints and the pre-assembled helices, we performed an FARNA simulation to generate a large number of low-resolution Rosetta models . For cases in which other experimental constraints  using the high-resolution Rosetta score to obtain final minimized models. This was achieved using the rna_minimize Rosetta application:rna_minimize -native rna_native.pdb -cst_fa_file constraint -params_file rna_params.params -skip_coord_constraints -in:file:silent 0.silent -out:file:silent rna_min.outTo find representative models, we clustered the lowest energy models , with a threshold  chosen so as to give 1/6 of the clustered models in the most populous cluster, as in prior work . The clucluster -in:file:silent silent.out -in:file:fullatom -out:file:silent_struct_type binary_rna -export_only_low false -out:file:silent cluster.out -cluster:radius 7The RMSD threshold described above represents an estimate of the intra-cluster RMSD of the structure ensemble from the modeling. We used this intra-cluster RMSD to calculate an in situ RMSD estimate (‘precision’) of the ensemble to the ‘true’ structure. The in situ RMSD of the cluster center model to the ‘true’ structure can be stated as the distance in structure space between the ‘true’ structure and the cluster center. This can be conservatively estimated as the sum in quadrature of the RMSDs between (1) the ‘true’ structure and the most accurate member of the cluster and (2) this cluster member and the cluster center. The intra-cluster RMSD represents an upper bound on distance (2), and assuming that distance (1) is no greater than distance (2), the in situ RMSD of the cluster center to the ‘true’ structure is estimated as the intra-cluster RMSD multiplied by the square root of 2. This precision estimate is reported in 2/Rosetta model using a previously described empirical formula that relates mean pairwise phosphate backbone RMSD to RNA length to allow for calculation of a prediction significance . For the AdoCbl riboswitch aptamer, the pre-built fragments consisted of three models each of the P1 through P6, P7 through P8, and P10 through P11 regions. We modeled the AdoCbl riboswitch aptamer with three distinct combinations of these fragments, each containing one fragment of each region. To estimate the precision of MCM modeling for the AdoCbl riboswitch aptamer in the ligand-bound and ligand-free states, we calculated the average pairwise all-heavy-atom RMSD between the cluster centers of the largest clusters for the three modeling setups and used this as the intra-cluster RMSD for calculating the in situ precision estimate.For the AdoCbl riboswitch aptamer (RNA-puzzle 6) and the lariat-capping ribozyme (RNA-puzzle 5), we did not pre-assemble helices and instead used prebuilt RNA fragments that we previously generated during the RNA-puzzles challenges, as described in . The priFor the lariat-capping ribozyme, we used MCM to guide rebuilding of the peripheral regions of the structure, and RMSD accuracies to the crystal structure and constraint satisfaction percentages are reported separately for these peripheral regions and the full structure . RMSD acFor the class I ligase, we calculated RMSD accuracies to the crystal structure and constraint satisfaction percentages separately for the ribozyme core (excluding the substrate helix) and the full structure. RMSD accuracies of the core by itself were calculated by aligning the core residues, including the active site (residues 28–114), of the models to the crystal structure using MAMMOTH with the distance threshold set to 20 Å, then computing the RMSD of the refined residues . To estiFor the ligand-free states of the c-di-GMP, glycine, and AdoCbl riboswitch aptamers, we performed modeling as above with pseudo-energy constraints from MOHCA-seq proximity maps collected in ligand-free conditions. Because the ligand-free states are expected to exhibit greater conformational heterogeneity, we omitted the clustering step and compared the minimized models with the lowest Rosetta energy scores for visualization. We found that for constructs with weak residual tertiary MOHCA-seq hits, such as the c-di-GMP riboswitch aptamer and glycine riboswitch ligand-binding domain with leader sequence, these lowest energy models included both compact or native-like conformations as well as extended and incorrectly folded conformations. For the AdoCbl riboswitch aptamer, we compared the cluster centers of the three independent modeling setups as a similar way of assessing heterogeneity between modeling runs. These comparisons are illustrated in 2 and mutate-and-rescue, and one without the pseudoknot, predicted by only M2 data. When the pseudoknot was not specified in the secondary structure, a MOHCA-seq pseudo-energy constraint was included during modeling to reflect the proximity observed by the MOHCA-seq experiment , the first nucleotide and last nine nucleotides of the tRNA in the co–crystal structure did not match the RNA-puzzle sequence, so the RMSD was calculated over the regions of matching sequence, residues 6–66; the RMSD calculations included all T-box residues.where the native PDB was the crystal structure . For the review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent , the member of the Board of Reviewing Editors who oversaw the original review, and the three original peer reviewers. As you will see, all thought that the paper was significantly improved; thank you for the very conscientious revision. Please address the remaining concerns raised by referee 1 before uploading your final files.Thank you for resubmitting your work entitled “Consistent global structures of complex RNA states through multidimensional chemical mapping” for further consideration at Reviewer #1:The revised manuscript by Cheng et al. on modeling of RNA structures is greatly improved over the original submission. In particular, it is now possible to understand the method from the main text, and the controls and assumptions are explained more clearly. These explanations also better demonstrate the usefulness and potential of this method for probing and modeling RNA structures. Overall, this is very interesting and significant work.I still have a few questions that were not fully addressed by the authors in their revisions.Specific comments:In In response to the reviewer comments, the authors provide better comparisons of models with and without the MOHCA constraints, but they didn't address how sensitive the models are to the number  of the constraints. If you leave out the “false” constraints, are the models better? If you include only half the number of “true” constraints, are the models significantly worse? This is certainly worth discussing and testing .The comparison of liganded and free riboswitches in eLife. Your full submission has been evaluated by James Manley (Senior editor), a Reviewing editor, and three peer reviewers, and the decision was reached after discussions between the reviewers. Based on our discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife.Thank you for choosing to send your work entitled “Consistent and blind inference of RNA tertiary folds through multidimensional chemical mapping” for consideration at The following individuals responsible for the peer review of your submission have agreed to reveal their identity: Timothy Nilsen (Reviewing editor); Hashim Al-Hashim (reviewer). Two reviewers remain anonymous.We have received comments on your manuscript from three experts in the specific area of RNA structure analyses. As you will see one reviewer was enthusiastic about the work and recommended publication; however, the other two reviewers, while agreeing that the work was potentially important, raise significant concerns regarding the method you describe. These concerns include technical points as well as uncertainty regarding validation of the method. Given the substantive nature of the issues raised, we must decline your manuscript.Reviewer #1:The authors describe an improved method for using high throughput sequencing and tethered hydroxyl radical cleavage to obtain distance constraints for RNA modeling. They offer an impressive array of successful examples, and also describe how the method is designed to work in tandem with their previously described mutate and map strategy for obtaining secondary structures. A large amount of work is presented in this paper. Although the pipeline is complicated and beyond the reach of most labs, it would represent a significant step forward, if it proves reliable over the long term. Below I outline some concerns about the presentation of the work and the basic premise of MOHCA-Seq.The body of the manuscript is more of an advertisement than a description of what was done. The substance of the paper is in the Methods section, and the manuscript should be reorganized so that much more of the modeling strategy and data analysis is brought forward into the “Results” section. Although the authors present many success stories, they do not describe the statistics on how many models are correct, how dependent the final models are on individual constraints (what happens when one leaves some out?), and so on. To help the reader judge the importance of the 3D MOHCA constraints, comparisons in the supplemental Tables 3 and 4 should be brought into the main text and possibly illustrated. A better discussion of the effects of noise, false distance constraints that arise from background cleavage of the RNA or other artifacts is needed.My main concerns have to do with the distribution of the MOHCA-Seq reads and their interpretation as “distance constraints”. In the main text (and supplement), the authors note that they observe additional sequence reads from arrest of RT at damaged (oxidized) nucleotides that lie between the radical source and the site of cleavage. First, the authors assume these “extra” stops arise from non-scissile base damage, but of course, they could arise from strand scission events. The authors write as if these are in different categories, but this distinction is meaningless as the reason for the RT stop can't be ascertained after the fact.In On the other hand, as the total dose of Fe(II)EDTA-dependent hydroxyl radical cleavage increases, the frequency of stops at a cleaved residue will equal or exceed RT stops at the site of the tether. In this limit, any information about the pairwise correlation between the site of the tethered reagent and the site of cleavage will be lost, undermining the premise of the experiment. At what limit does this occur, and does this tendency already account for the very uniform map before COHCOA analysis?Even when cleavage events are sparse, I had trouble understanding how the authors turn the distribution of RT stops into reliable distance constraints. Backbone cleavage arising from a tethered source of hydroxyl radical can be interpreted as a (blurred) vector between two residues. By contrast, sequence reads that start and end at two damaged or cleaved sites may arise when both residues are close to the radical source. However, one cannot be sure how close the sites are to each other, especially as one does not know where the Fe-EDTA was tethered. Presumably the inconsistencies arising from such ambiguities in the distance constraints are sorted out during the modeling, but what evidence do the authors have that this is the case? In the modeling section, it is stated that 60%-80% of constraints were satisfied by the best models; to what degree does this reflect the level of cleavage or specific types of non-random noise (see below)?bj. Of course, the background cleavage is almost certainly non-uniform. It is well known that certain residues will be more prone to hydrolysis (or RNase digestion) than others, and the resulting distribution of reads will appear similar to a genuine signal from a tethered Fe(II)EDTA. Is this type of non-random background addressed by using a mock-treated control? How is the information from the control incorporated into the data analysis? It would be great to see examples of data sets in which the iron was left out, or data sets on a long double-stranded RNA, and so on, to get an idea of how well the method works in these idealized or control examples.Finally, a drawback of the original MOHCA was that the experimentally observed constraints were all short range with respect to the RNA sequence. A cutoff was applied so that these short-range constraints did not overwhelm the model building. A similar strategy is presumably used here: how sensitive are the models to this cutoff, and what is the practical limit on the size of the RNA that can be modeled by this method?Reviewer #2:This manuscript describes important advances in high throughout structure modeling of RNA. The authors show that integrating distance contact information that can readily be obtained from multiplexed hydroxyl radical cleavage analysis with powerful methods developed by the group , the tertiary fold of large and complex RNAs can be determined with an estimated accuracy of 1 nm. The authors demonstrate their approach on a variety of RNAs including a blind RNA-puzzle. The results are convincing and suggest that the improvements achieved by this new method are both significant and robust.Reviewer #3:The determination of RNA structures has been challenging. While RNA molecules have evaded easy crystallization at a larger scale for a long time due to their highly charged backbone, structure predictions based sequence has gained large interest. Chemical probing of their structures has complemented these approaches. RNA molecules are comparably simple at the primary sequence and easily accessible to enzymatic and chemical manipulation followed by sequencing.Cheng et al. present an experimental-computational multi-step pipeline that combines a mutate-and-map approach (M2) with a deep-sequencing based version of multiplexed hydroxyl radical cleavage analysis. The authors use this updated version in what they call a multidimensional chemical mapping (MCM) pipeline to determine a number of RNA structures. Some were known structures in the context of the RNA-puzzle challenge, in part done blindly. Using their pipeline Cheng et al. propose the structures of three riboswitches with and without their ligands and find that those two states are structurally much more similar than previously thought. Attempts to crystallize those switches without ligands may force the molecules into an undesired stable conformation and be non-physiological or these conformations might never result in well diffracting crystals. Lastly they propose a first structure for the human HoxA9D IRES, a cellular IRES implicated in the translational regulation of developmental patterning.The authors describe an interesting experimental pipeline that may move the determination of RNA structures out of the hands of few specialized RNA biophysics labs into the hands of molecular biologists with access to deep sequencing equipment. The experimental method is relatively straightforward for anybody who has generated deep-sequencing libraries. Even though far from trivial, the adaptation of the protocol can provide a handle to address structural features at a larger scale which is very desirable in different fields of RNA biology.The method is a further step in structure determination, but has only been verified on a very limited set of RNA structures. This makes some of the predictions difficult to assess. As an example: The manuscript is to a large extent the description of a new method. It is absolutely essential that the protocol is immediately applicable to different questions by different labs. In their Material and methods the authors are not consistent with the information they provide. It would be important if they provide information on how much material they recover at each step of the protocol  as well as the reaction volumes of all steps (Results).For the description of the riboswitch structures, I prefer to see the apo and the bound structure overlaid to form a better opinion on the extent of structural rearrangement of the two.I do not feel competent to judge the computational assumptions that have been made by the authors to refine and translate the experimental results to steer the computational structure prediction (Methods). Specific comments:: The authors now explain the effects of oxidative damage far more clearly. Nevertheless, the very high frequency of additional RT stops could have a negative effect on the modeling, which the authors should discuss. First, by generating reads that are shorter than those arising from RT stops at site of the tether, they inflate the number of short pairwise constraints. It is still not clear to me that these do not bias the modeling procedure.The reviewer is correct that the high frequency of RT stops may cause attenuation of signal for pairs of residues that are far apart in the RNA sequence. Our analysis accounts for this type of attenuation, as described in the Results and Methods sections. We also present in the Methods section an experimental method to enrich long cDNA products and to rigorously combine sequencing data for these samples with non-enriched sequencing data (in the subsection headed ‘Reverse transcription with sequencing primers (first adapter)’).For the lengths of RNAs probed in our study (up to 188 nucleotides in the region of interest), we were able to observe long-range hits throughout the proximity map, suggesting that our protocol was sufficient to correct for RT attenuation  reported a similar frequency of RT stops and strand cleavage, and not the 6-fold difference reported here. Could the authors comment on the reason for this difference? Is this because the authors use only ascorbate, rather than a combination of hydrogen peroxide and ascorbate? I am still puzzled by the claim that these additional RT stops arise from common base oxidation products, such as 8-oxoG or 5'-HOC. Reverse transcriptases (and other DNA polymerases) are reported in the literature to bypass such lesions fairly easily.The exact oxidative lesions present in MOHCA-seq samples that result in RT stops are uncertain. Prior studies indicate that many oxidative modifications to RNA exist that have not been specifically demonstrated to permit or terminate reverse transcription, and the distribution of these modifications between ascorbate- and peroxide/ascorbate-mediated Fenton reactions has also not been characterized. Further experiments may help to identify the particular oxidative lesions that result in RT stops in MOHCA-seq experiments. We have edited the text regarding the reverse transcription-terminating modifications to indicate this (Results section):“However, we were surprised to find additional stop sites beyond these source attachment positions , right eLife. We received strong positive comments  and detailed, valuable suggestions from three reviewers.This is a major reworking of an article previously submitted to The reviewers’ comments explicitly suggested moving several supporting sections with ‘the substance of the paper’ to the main text, which we have done. This has notably improved the clarity of the manuscript. Second, comments asking for more methodological details made us realize that we should have included several control experiments in the paper, especially on detailed quantitation of chemical modification events and on the accuracies of the 3D models without data. In fact, we had these measurements and calculations in hand but had previously left them out due to length concerns; they are now in the revised paper as main text figures. Third, comments from reviewers 2 and 3 on not feeling ‘competent to judge the computational assumptions’ made us realize that the original manuscript did not appropriately define our work’s place in the lineage of hybrid computational/experimental approaches for RNA structure. We have now made major changes to the Title, Abstract, and Introduction to set our work in the context of classic and ongoing research in modelling molecules such as the group I ribozyme, for which global structures at 1-nm resolution have been powerful for fueling new experiments but not consistently attained for general RNAs. Along these lines, we should have suggested more reviewers with computational expertise.Reviewer #1:The authors describe an improved method for using high throughput sequencing and tethered hydroxyl radical cleavage to obtain distance constraints for RNA modeling. They offer an impressive array of successful examples, and also describe how the method is designed to work in tandem with their previously described mutate and map strategy for obtaining secondary structures. A large amount of work is presented in this paper. Although the pipeline is complicated and beyond the reach of most labs, it would represent a significant step forward, if it proves reliable over the long term.We thank the reviewer for their detailed assessment of our work. While we agree that our pipeline involves several steps for library preparation, we believe (as does reviewer #3 below) that the method is not more complicated than RNA-seq and related protocols that are entering wide use, such as ribosome profiling (Ribo-Seq), crosslinking and immunoprecipitation sequencing (CLIP-seq), or crosslinking ligation and sequencing of hybrids (CLASH), and should be accessible to most labs.Below I outline some concerns about the presentation of the work and the basic premise of MOHCA-Seq.The body of the manuscript is more of an advertisement than a description of what was done. The substance of the paper is in the Methods section, and the manuscript should be reorganized so that much more of the modeling strategy and data analysis is brought forward into the “Results” section.and its supplements could be (ought to be) a substantial fraction of the “main” manuscript—this assessment is both a testament to the amount of work described in this paper, and a problem in that critical tests and examples of the method are buried.eLife does not have an oppressive length limit), we have now reorganized the manuscript so that all methodological steps of data analysis and modeling—including newly presented controls (see below)—are now given in the main text. These moves include the following: The previous version of the manuscript did not sufficiently describe the methods for data analysis and modeling, mainly because we were under the wrong impression that we needed to limit the length of the main text. Based on the reviewer suggestions , and so on. To help the reader judge the importance of the 3D MOHCA constraints, comparisons in the supplemental Tables 3 and 4 should be brought into the main text and possibly illustrated.We agree that the importance of MOHCA-seq constraints could not previously be assessed upon reading the main text. To better illustrate the importance of the MOHCA-seq constraints, we now: (i) have incorporated modeling statistics (with and without MOHCA-seq data) from Supplementary files 3 and 4 into 2 secondary structure for Rosetta modeling of P4-P6”: “Performing the same Rosetta modeling protocol without MOHCA-seq pairwise constraints led to a strikingly worse model with 38.3 Å accuracy to the crystal structure ; just as for the P4-P6 domain, these runs produced dramatically less accurate models, as can be visually assessed  were reported previously in our tables, which were provided as supplementary files, but they were not discussed thoroughly in the main text. We have added a better description of how noisy data are treated in signal processing:2 secondary structure for Rosetta modeling of P4-P6”: “Additionally, the digital form of MOHCA-seq data enabled rigorous analysis to resolve further features. […] we apply a two-dimensional smoothing algorithm to produce the final proximity map for visualization  as well as conformational flexibility (see responses to reviewer 2 below).My main concerns have to do with the distribution of the MOHCA-Seq reads and their interpretation as “distance constraints”. In the main text (and supplement), the authors note that they observe additional sequence reads from arrest of RT at damaged (oxidized) nucleotides that lie between the radical source and the site of cleavage. First, the authors assume these “extra” stops arise from non-scissile base damage, but of course, they could arise from strand scission events. The authors write as if these are in different categories, but this distinction is meaningless as the reason for the RT stop can't be ascertained after the fact.We agree that our language and experimental assessment of the types and extent of oxidative damage were unclear, but are very important for establishing the proposed method. The reviewer’s comments have led us to include additional experimental controls that quantitated the extent and sequence dependence of damage (we had these in hand from our pilot work but did not include them previously). For example, we observed similar results from both capillary electrophoresis and sequencing analysis that the total amount of RT- stopping damage exceeds scissile backbone damage by at least 6-fold. These results are presented in the new Subsection “Deep proximity maps of ncRNA 3D structure from single experiments”: “We expected reverse transcription to then stop at the nucleotide tethered to the bulky radical source at its C2´ position […] the 5´ and 3´ ends of the resulting cDNAs still reported pairs of sites that were proximal in the RNA structure, albeit at somewhat longer distances than source-to-cleavage pairs.”In, are the authors simply attempting to model the distribution of their data as arising from a component that depends on the frequency of tethered Fe(II)-EDTA complexes and a component that is independent of having tethered complexes? I presume this is the idea behind the COHCOA correlation analysis, which rightly ignores the distinction between “oxidative damage” and “cleavage”. This could be explained more forthrightly.The reviewer is correct on this point, and we have amended our language to make this clearer:Ri (corresponding to uncorrelated oxidative damage) and cleavage profile Bj and a two-point correlation function Qij  which encodes the desired proximity map”Subsection “Closure-based •OH COrrelation Analysis (COHCOA)”: “The effects of the radical cleavage result in a one-dimensional damage profile On the other hand, as the total dose of Fe(II)EDTA-dependent hydroxyl radical cleavage increases, the frequency of stops at a cleaved residue will equal or exceed RT stops at the site of the tether. In this limit, any information about the pairwise correlation between the site of the tethered reagent and the site of cleavage will be lost, undermining the premise of the experiment. At what limit does this occur, and does this tendency already account for the very uniform map before COHCOA analysis?The described effect of overmodification by radicals does not actually occur; we did not explain this well in the original paper. In our experiments, ascorbate-induced, spatially localized oxidative damage appears to be ‘self-limiting’; the correlated signal in MOHCA-seq datasets remains visible even after 30 minutes of incubation with ascorbate, which is three times our standard reaction time of 10 minutes, without a rising background. Based on separate electrophoresis experiments on the ascorbate-activated RNA and model constructs, the self-limitation appears to be due to the radical source cleaving itself off the RNA after some time. Although we previously described this mechanism in our MOHCA-gel paper, we neglected to discuss this in the original submission. We have added discussion of this point to the main text:2 secondary structure for Rosetta modeling of P4-P6”: “To test the robustness of the protocol, we varied several experimental parameters […]. Capillary electrophoresis of ascorbate-treated end-labeled RNA supported this mechanism of self-limiting the reaction; see also ref .”Subsection headed “MOHCA-seq tertiary proximity map complements MEven when cleavage events are sparse, I had trouble understanding how the authors turn the distribution of RT stops into reliable distance constraints. Backbone cleavage arising from a tethered source of hydroxyl radical can be interpreted as a (blurred) vector between two residues. By contrast, sequence reads that start and end at two damaged or cleaved sites may arise when both residues are close to the radical source. However, one cannot be sure how close the sites are to each other, especially as one does not know where the Fe-EDTA was tethered. Presumably the inconsistencies arising from such ambiguities in the distance constraints are sorted out during the modeling, but what evidence do the authors have that this is the case?The reviewer is correct: the location of a radical source that causes two correlated oxidative damage events that are both 3´ of the radical source’s location, one of which terminates a reverse transcription product that started at the other position, is not reported by the resulting cDNA. Such pairs of oxidative damage events are nevertheless still correlated by virtue of each being correlated to the radical source location, but their spatial distances will be blurred compared to direct source-scission events. It was a working hypothesis that these distances would still allow accurate global structure modeling; our original language did not make this clear. We have added discussion of this point to the text:Subsection entitled “Deep proximity maps of ncRNA 3D structure from single experiments“: “Since these sites of reverse transcription termination marked locations that were also nearby in three dimensions to the radical source […] this significantly larger number of pairwise proximities would permit rapid ncRNA modeling with comparable accuracy to the prior method .”As the reviewer indicates, we suspected that the inconsistencies in a set of many pairwise MOHCA-seq constraints would be sorted out by highly-sampled Rosetta modeling, as it was in the previous MOHCA-gel study. Here, we found that we were able to recover the 3D structures of our benchmark RNAs even though some MOHCA-seq constraints were not satisfied by our final models. We have added discussion of this point in the subsection “Multidimensional chemical mapping allows rapid modeling of diverse non-coding RNAs, including blind prediction challenges”, as quoted in our response to an earlier point.In the modeling section, it is stated that 60%-80% of constraints were satisfied by the best models; to what degree does this reflect the level of cleavage or specific types of non-random noise (see below)?As described in an above response, we observed that the level of correlated cleavage in the MOHCA-seq reaction is self-limiting, and as noted in our next responses, the non-random two-dimensional background arising from uncorrelated oxidative damage is removed by the COHCOA analysis and, for a few noise features, by averaging experimental replicates. So we believe the percentage of constraints satisfied is likely not related to this uncorrelated background but instead to alternative conformational states or inconsistent pairwise constraints arising from signal due to double oxidative damage events, as discussed in the previous response and in our revised text in “Multidimensional chemical mapping allows rapid modeling of diverse non-coding RNAs, including blind prediction challenges”.assumes a uniform rate of background cleavage bj. Of course, the background cleavage is almost certainly non-uniform. It is well known that certain residues will be more prone to hydrolysis (or RNase digestion) than others, and the resulting distribution of reads will appear similar to a genuine signal from a tethered Fe(II)EDTA.bj is not uniform along the sequence of an RNA molecule but is independent of and uncorrelated with any specific radical source:Our previous language describing a “uniform rate of background cleavage” was unclear. We have updated the language to clarify that the background cleavage bj that is independent of source s but can vary with nucleotide j , and additional, correlated cleavage due to hydroxyl radical-induced strand scission events , parameterized by In the subsection headed “General MOHCA-seq analysis framework”: “First, we assume that the cleavage fraction that allows ssDNA adapter ligation, ”Is this type of non-random background addressed by using a mock-treated control? How is the information from the control incorporated into the data analysis? It would be great to see examples of data sets in which the iron was left out, or data sets on a long double-stranded RNA, and so on, to get an idea of how well the method works in these idealized or control examples.We hope that our updated language for the previous response clarifies that the COHCOA analysis is explicitly formulated to account for this type of non-random background.In terms of controls, a standard part of the MOHCA- seq experiment is to carry out the full protocol on a sample that is not treated with ascorbate, in parallel with the ascorbate-treated (fragmented) sample; this is similar to a mock-treated control where Fe(III) is not loaded on the tethered ITCB-EDTA. This control is to ensure that signal that we observe in the ascorbate-treated samples does not occur independent of radical source activation, but we do not directly use the control data in our analysis because COHCOA accounts for such non-random background. A control sample treated with ascorbate but without tethered radical sources is shown in Finally, a drawback of the original MOHCA was that the experimentally observed constraints were all short range with respect to the RNA sequence. A cutoff was applied so that these short-range constraints did not overwhelm the model building. A similar strategy is presumably used here: how sensitive are the models to this cutoff?The cutoffs for determining MOHCA constraints were not clear in the original manuscript. Briefly, the large background for short sequence separations  prevented use of MOHCA-seq constraints for short sequence separations. Further, given the accuracy of the newly available mutate-and-map protocol for inferring secondary structure, we sought to only use MOHCA-seq to determine tertiary proximities for nucleotide pairs that are far in secondary structure. The discussion of this constraint determination has been significantly expanded in the main text:2 secondary structure for Rosetta modeling of P4-P6”: “First, to reduce the computational expense of sampling helices […] Therefore, we used all data, including the few weak features not explained by the crystal structure  with a deep-sequencing based version of multiplexed hydroxyl radical cleavage analysis. The authors use this updated version in what they call a multidimensional chemical mapping (MCM) pipeline to determine a number of RNA structures. Some were known structures in the context of the RNA-puzzle challenge, in part done blindly. Using their pipeline Cheng et al. propose the structures of three riboswitches with and without their ligands and find that those two states are structurally much more similar than previously thought. Attempts to crystallize those switches without ligands may force the molecules into an undesired stable conformation and be non-physiological or these conformations might never result in well diffracting crystals. Lastly they propose a first structure for the human HoxA9D IRES, a cellular IRES implicated in the translational regulation of developmental patterning.The authors describe an interesting experimental pipeline that may move the determination of RNA structures out of the hands of few specialized RNA biophysics labs into the hands of molecular biologists with access to deep sequencing equipment. The experimental method is relatively straightforward for anybody who has generated deep-sequencing libraries. Even though far from trivial, the adaptation of the protocol can provide a handle to address structural features at a larger scale which is very desirable in different fields of RNA biology.The method is a further step in structure determination, but has only been verified on a very limited set of RNA structures. This makes some of the predictions difficult to assess.We wish to note that the presented benchmark, with 6 ‘benchmark’ RNAs of known structure and 4 cases with unknown (but interesting) structure, is at a larger scale than prior studies introducing new experimental methods (including our own MOHCA-gel paper and recent 3D approaches based on NMR). It also includes a blind test, which provides rigorous assessment, and is unusual in modeling.As an example:shows the refinement of P2.1 towards P4-P6. Other structural elements are not refined at all (purple loop). Why is that? Is a global RMSD score the best measure to describe the refinement of the structure? A wrong prediction of functionally critical residues may hamper the use of the method significantly. Can the authors use their data and compare whether the structural refinements are meaningful by focusing on the critical elements for their direct comparison?2/Rosetta modeling to the figure legend  from MCM and Me legend , in addiDue to the linearity of their sequencing approach the method can only detect cleavage that occurs 3' of the •OH source. The authors themselves suggest, that circularization of the RNA might circumvent this issue but argue against it. This assumption may have major consequences on the quality of the prediction.We have removed this text, as it was confusing. Circularization will not be generally applicable to any RNA domain, but may enhance the information content for cases that can be circularized; we are testing this procedure now but the results are beyond the scope of this study.The manuscript is to a large extent the description of a new method. It is absolutely essential that the protocol is immediately applicable to different questions by different labs. In their Material and methods the authors are not consistent with the information they provide. It would be important if they provide information on how much material they recover at each step of the protocol  as well as the reaction volumes of all steps (Results).We thank the reviewer for pointing out these omissions. We have added these details to the Materials and methods section, including estimated yields for each step.For the description of the riboswitch structures, I prefer to see the apo and the bound structure overlaid to form a better opinion on the extent of structural rearrangement of the two.We now show the ligand-bound crystallographic structures next to the MCM models of unbound riboswitches, in the same orientation, in the figures depicting these comparisons . We trie


Plasmodium falciparum causes the most severe form of human malaria and is responsible for a significant public health burden in the developing world. These protozoan parasites invade circulating red blood cells (RBCs) and maintain prolonged infections through an intricate gene-expression switching mechanism that enables immune evasion through antigenic variation . Thi. Thi9]. Trichomonas display traces of the standard CTD heptads but not the canonical repeat structure [Plasmodium, on the other hand, has a bona fide RNA pol II CTD; however, members of this genus display an unprecedented plasticity in the length and composition of the CTD, something not observed in any other eukaryotic lineage .Comparison of RNA pol II CTD sequences from different evolutionary lineages shows that CTD length is loosely correlated with genome size . Moreovetructure –14. Plasetail in ). Malariariation . This obP. falciparum is PfSET2, a histone methyltransferase that deposits a trimethylation mark on H3K36. The H3K36me3 mark is tightly coupled to transcription by RNA pol II in both yeast and mammalian cells; therefore, it is found throughout transcribed regions of the genome of these organisms [Plasmodium genus, orthologues of SET2 are found only within the genomes of parasites that infect primates, thus mirroring the RNA pol II CTD expansion [var genes [Plasmodium when compared to higher eukaryotes [Similar to its counterparts in higher eukaryotes, recent work indicates that the RNA pol II CTD of malaria parasites serves as a platform for the recruitment of nuclear factors for gene regulation and cross talk between chromatin and RNA pol II . The firrganisms ,18. Howexpansion . Furtherar genes . Intereskaryotes .var gene expression and antigenic variation [var gene family is transcribed at a time, with switching of the expressed copy resulting in antigenic variation. A truncated version of PfSET2 consisting solely of the CTD binding domain competes with the endogenous PfSET2 for binding to the CTD, thus functioning as a “dominant-negative” when overexpressed in transgenic parasites . Numeroarasites –33. The arasites ,35, and arasites . These oP. falciparum genome [var gene [Plasmodium RNA pol II CTD and the peculiar nature of its phosphorylation code will undoubtedly cast considerable light on this subject, potentially unifying previous observations regarding specific deposition of histone modifications, noncoding RNAs, gene activation and silencing, and mutually exclusive expression.In addition to PfSET2, there are eight additional SET-domain-containing proteins encoded in the m genome  and anotvar gene . These p


MEF35  to be required for RNA editing at three sites in mitochondria of Arabidopsis thaliana. The MEF35 protein contains eleven PPR repeats and E and DYW extensions at the C-terminus. Two T-DNA insertion mutants, one inserted just upstream and the other inside the reading frame encoding the DYW domain, show loss of editing at a site in each of the mRNAs for protein 16 in the large ribosomal subunit (site rpl16-209), for cytochrome b (cob-286) and for subunit 4 of complex I (nad4-1373), respectively. Editing is restored upon introduction of the wild type MEF35 gene in the reading frame mutant. The MEF35 protein interacts in Y2H assays with the mitochondrial MORF1 and MORF8 proteins, mutation of the latter also influences editing at two of the three MEF35 target sites. Homozygous mutant plants develop indistinguishably from wild type plants, although the RPL16 and COB/CYTB proteins are essential and the amino acids encoded after the editing events are conserved in most plant species. These results demonstrate the feasibility of the computational target prediction to screen for target RNA editing sites of E domain containing PLS-class PPR proteins.RNA editing in plant mitochondria and plastids alters specific nucleotides from cytidine (C) to uridine (U) mostly in mRNAs. A number of PLS-class PPR proteins have been characterized as RNA recognition factors for specific RNA editing sites, all containing a C-terminal extension, the E domain, and some an additional DYW domain, named after the characteristic C-terminal amino acid triplet of this domain. Presently the recognition factors for more than 300 mitochondrial editing sites are still unidentified. In order to characterize these missing factors, the recently proposed computational prediction tool could be of use to assign target RNA editing sites to PPR proteins of yet unknown function. Using this target prediction approach we identified the nuclear gene  RNA editing sites in mitochondria and in plastids in flowering plants are recognized by PPR proteins (Pentatrico-Peptide-Repeat Proteins), a family of about 450 nuclear encoded proteins –4. The cThe RNA editing PPR proteins are also characterized by additional C-terminal domains, all containing at least one extension domain (E domain) and about half of them being further extended by the so-called DYW domain –21. The rpl16, nad4 and cob mRNAs in mitochondria of Arabidopsis thaliana.In flowering plants, about 90 E subgroup and 100 DYW subgroup PLS class PPR proteins are encoded, which contain only E or E and DYW domains, respectively. Most of the characterized E or DYW subgroup PPR proteins are involved in RNA editing, the rest of them play a role in other RNA processing steps. For example, the proteins OTP70 and PpPPR_43 are involved in intron splicing and CRR2 is involved in intercistronic RNA cleavage in plant organelles –25. DireArabidopsis thaliana ..Fig 2). ectively . The oth. The gene encoded by At4g14050 was thus confirmed as the specific RNA editing factor for these sites and we renamed this gene to code for mitochondrial editing factor 35, MEF35.To confirm the direct involvement of the mef35-2 young mutant plantlets develop somewhat slower than the wild type  and in the apocytochrome b proteins (F>L). Whether the function of the ribosomal complex is affected by the amino acid alteration in subunit 16 of the large subunit is discussed later.The wild type-like growth pattern of MORF mutants. Among the three target sites of MEF35, rpl16-209 and cob-286 sites are reduced to about 70% in a morf8/rip1 mutant while no significant reduction was detected in knock-down and T-DNA knock-out lines of other MORF genes  has very strong effects [Since there is little detectable growth and developmental phenotype in the s plants , none of effects .Cycas and Gingko  in maize [Arabidopsis, a genomic rearrangement resulting in a disruption of the rpl16 transcription unit by deletion of upstream sequences from the cotranscribed rps3 gene leads to a severe phenotype [In the ethality . This muin maize . In Arabhenotype . It is trpl16-209 RNA editing site is located just before the main contact site with the tRNA during the translocation step when it is handed over to RPL5. The contacting fragments have the highest degree of conservation within the RPL16 protein and are especially sensitive to changes in the amino acid charges [Proteins L16 and L27 are crucial components of the peptidyltransferase center in the 50S subunit of RPL16 and directly contact the A-site and the P-site, respectively . Several charges . The MEFrpl16-209 and cob-286 MEF35 target sites but is not affected at the nad4-1373 site [Indirect evidence for a potential cooperation between a given PPR protein such as MEF35 and one of the MORF proteins can be provided by a comparison of editing at the target sites between mutants of the PPR protein and any or all of the nine full-sized MORF proteins. Such an analysis reveals that in mutants of MORF8, RNA editing is reduced to 70% at the 373 site ,28.)Arabidopsis thaliana cells. The other two proteins, MORF1 and MORF8 are as MEF35 targeted to plant mitochondria. The relevance of the MEF35 –MORF1 interaction is not clear, but the MEF35 –MORF8 contact is potentially a functional connection since both proteins influence RNA editing at the same nucleotides.In the experimental analysis by yeast two-hybrid assays, the MEF35 PPR protein interacts with MORF1, MORF2 and MORF8 proteins. MORF2 is plastid located and therefore in a different organelle in rpl16-209 and cob-286 sites is affected in the MORF8 mutant. Similar distinct impact of the MORF8 mutation on the target sites of respective PPR RNA editing proteins has been observed also for CRR22, OTP81 MEF11 and COD1 targets. While one or few sites show little effect in a morf8 mutant, other target sites of these RNA editing factors are strongly affected. For these RNA editing factors, MORF8 may support binding to RNA or to other co-factors such as other MORF, ORRM [It is unclear why only the editing at the RF, ORRM ,43 or OZRF, ORRM  proteinsRF, ORRM . The PPRRF, ORRM .These analyses now contribute to a better understanding of the plant organellar editosomes and will allow further investigations. MEF35 offers a good platform to further analyse e.g. the MEF-MORF protein-protein interactions and their functional relevance.Arabidopsis thaliana, SAIL_101_B01 (mef35-1) and SALK_060464 (mef35-2), were obtained from the TAIR resources. Arabidopsis thaliana plants were grown in growth chambers under 21°C, 65% humidity, 16 h light and 8 h dark. Green leaves of about 1.5 cm length were harvested from the rosettes of about three week old plantlets. Preparation of DNA or RNA from the leaves was done as described [Two T-DNA insertion lines of escribed . In the Arabidopsis by the FIMO program [Prediction of At4g14050 target sites was performed as described in Takenaka et al. . The com program .Relevant cDNA fragments covering one or more editing sites were obtained by RT-PCR amplification according to published protocols . At the Sequences were obtained commercially from 4base lab, Reutlingen, Germany or from Macrogen, Seoul, Korea.mef35-2 were transformed with the MEF35 wild type (wt) Col reading frame under control of the 35S promoter in vector pMDC123 [mef35-2 were screened by spraying with Basta®. The respective RNA editing sites were analysed by direct sequencing of cDNA [Plants of the mutant line  pMDC123  with a G pMDC123  and a mu pMDC123  Transgen of cDNA .TM Gold Yeast Two-Hybrid System  for transformation into yeast cells (PJ69-4A) [600nm of 0.1 and in addition 0.3 [The MEF35 and MORF protein coding sequences were cloned with the In-Fusion HD cloning system  and transferred into the bait (pGBKT7) and prey (pGADT7) vectors of the GAL4 two Hybrid System 3 or the MatchmakerPJ69-4A) ,52–54. Ftion 0.3  in SD meS1 Figmef35-2 initially develop a little slower in comparison to the wild type Col, adult plants at the flowering stage after seven weeks on soil look very similar.While seedlings of mutant line (PDF)Click here for additional data file.S2 Figmorf1-1 EMS mutant, morf3-1 (GK-109E12.01), morf4-1 (SAIL_731_D08), morf5-1 (SALK_016801C), morf6-1 (GK-184F04.01).Percentages of RNA editing at the target sites are shown as determined by direct sequence analysis. As this experimental approach does allow to not distinguish between a background of up to 10% and genuine effects, values above 90% have to be considered as full editing. These data are experimentally determined from the mutant lines (PDF)Click here for additional data file.


Errors during transcription may play an important role in determining cellular phenotypes: the RNA polymerase error rate is >4 orders of magnitude higher than that of DNA polymerase and errors are amplified >1000-fold due to translation. However, current methods to measure RNA polymerase fidelity are low-throughout, technically challenging, and organism specific. Here I show that changes in RNA polymerase fidelity can be measured using standard RNA sequencing protocols. I find that RNA polymerase is error-prone, and these errors can result in splicing defects. Furthermore, I find that differential expression of RNA polymerase subunits causes changes in RNA polymerase fidelity, and that coding sequences may have evolved to minimize the effect of these errors. These results suggest that errors caused by RNA polymerase may be a major source of stochastic variability at the level of single cells.DOI:http://dx.doi.org/10.7554/eLife.09945.001 Genes encode instructions to make proteins and other molecules. To issue an instruction, a gene is first used as a template to make molecules of ribonucleic acid  in a process called transcription. An enzyme called RNA polymerase – which comprises several protein subunits that all work together – is responsible for making the mRNA molecules. Occasionally, this enzyme makes mistakes that lead to small changes in the instruction that is produced. These mistakes are rare, but because cells make thousands of mRNAs, a single human cell can make 10-100 transcription errors per second.It has been difficult to study how often RNA polymerase makes mistakes and what effect these mistakes have on organisms because the techniques available for research are labour-intensive and technically challenging. Here, Lucas Carey demonstrates that it is possible to use a technique called RNA sequencing to study the accuracy of RNA polymerase in human and yeast cells.The experiments show that altering the levels of the different subunits of RNA polymerase in cells can change how many mistakes are made during transcription. This suggests that cells may be able regulate number of mistakes by controlling the production of specific subunits. Carey found that the severity of the mistakes made by RNA polymerase depends on where the mistake is in the mRNA. For example, errors in specific parts of the mRNA can alter how the whole instruction is edited later, while others might make only a tiny change to the protein encoded by the gene. Carey also found evidence that the instructions encoded by genes may have evolved in such a way to minimise the effect of any errors on their roles in cells.RNA sequencing is less labour-intensive than other methods used to study the accuracy of RNA polymerase and is already used to address other research questions on a wide variety of different organisms. Therefore, Carey’s findings will make it easier to study what genes or environmental factors influence the number of errors made during transcription. A major challenge for the future is to find out if the mistakes made by RNA polymerase can lead to cancer and other human diseases.DOI:http://dx.doi.org/10.7554/eLife.09945.002 DNA polymerase error rates have been well characterized in a variety of species and environmental conditions, and are low – on the order of one mutation per 10neration . In contTo overcome these obstacles I developed MORPhEUS (Measurement Of RNA Polymerase Errors Using Sequencing), which enables measurement of differential RNA polymerase fidelity using existing RNA-seq data . The inpTechnical errors from reverse transcription and sequencing, and biological errors from RNA polymerase look identical (single-nucleotide differences from the reference genome). Therefore, a major challenge in identifying single-nucleotide polymorphisms (SNPs) and in measuring changes in polymerase fidelity is the reduction of technical errors  Figure . First, I reasoned that RNA polymerase errors that alter positions necessary for splicing should result in intron retention, while sequencing errors should not affect the final structure of the mRNA . HoweverRPB9 is known to be involved in RNA polymerase fidelity in vitro and in vivo . TherefoIn addition, export of mRNAs from the nucleus involves a quality-control mechanism that checks if mRNAs are fully spliced and have properly formed 5’ and 3’ ends . I hypot3EVpr-RPB9 and Z3EVpr-DST1) in different concentrations of β-estradiol and performed RNA-seq. I find that cells expressing low levels of RPB9 have high RNA polymerase error rates  table browser  to downl600 of 0.1 – 0.4. Cellular RNA was extracted using the Epicenter MasterPure RNA Purification Kit, and Illumina sequencing libraries were prepared using the Truseq Stranded mRNA kit, and sequenced on an HiSeq2000 with at least 20,000,000 50 bp sequencing reads per sample.The parental strain DBY12394   are all from the Gingeras lab at CSHL. review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent  and three reviewers, one of whom is a member of our Board of Reviewing Editors.Thank you for submitting your work entitled "RNA polymerase errors cause splicing defects and can be regulated by differential expression of RNA polymerase subunits" for peer review at The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.In this manuscript, Carey describes a computational method called MORPhEUS that allows measuring RNA polymerase error rates using published RNA-seq data. By counting the number of matches and mismatches to the reference genome after technical errors are minimized, MORPhEUS enables the author to estimate the error rate at each position of the genome. Using chromatin-associated RNA from RNA-seq data in K562 cells, the author calculates error rates at splice site junctions and demonstrates that these are higher at 5` splice sites, suggesting that this could affect intron retention. Using ENCODE RNA-seq data, the author further investigates if the expression level of the Pol II subunit Rpb9, known to be required in Pol II fidelity, affects RNA polymerase error rates in different cell lines. Furthermore, the author measures RNA-seq error rates using two yeast strains in which it is possible to modulate the expression of Rpb9 or Dst1, finding that cells with low expression of Rpb9 or Dst1 possess higher error rates, consistent with known biochemical and genetic data. The idea is intriguing and the explanations for observations in terms of Pol II error rates make sense. The proposed method MORPhEUS is appropriate to perform comparative analysis of RNApol induced transcription errors between two or more samples or to identify RNApol errors leading to intron retention. Other than currently available methods this approach is able to identify errors transcriptome-wide and does not "require specialised organism-specific genetic constructs", therefore it seems to be highly useful. The method presented here is interesting and it is a valuable tool for estimating RNA Pol II error rates from RNA-seq data, although several points need to be addressed before publication can be considered.Essential revisions:1) Possible alternative explanations for the observationsA main issue with this manuscript is that alternative explanations could also make sense. The author has to show that his explanations are the only or at least most plausible ones. 2) Choice of null modelrelative error rates on the y-axis. The error rates observed around 5' splice sites are normalized by the error rates seen for the same dinucleotides, GT, at other places in the transcriptome. The 4-fold elevated error rate therefore depends on the null model. It would be important to compute the relative error rate at the uracil with more refined trimer null models to see if the 4-fold increase holds up. Two versions, one with the mutated nucleotide at the first position and another model with the mutated nucleotide at the last of the three trimer positions, should be used. The latter version could model sequence-dependent effects during reverse transcription. For each trimer in the transcriptome one can compute the error rate at the first and third nucleotide. Then, the total mutations for each position around the 5' splice site (and the 3' splice site and branch point) are divided by expected numbers of mutations, which is simply the sum of error rates for each of the trimer contexts for the position.Figure 2b shows 3) Effects of Rpb9The author demonstrates that expression of Rpb9 negatively correlates with error rates in human cell lines, suggesting that the differential expression of Rpb9 affects RNA polymerase fidelity in vivo. The level of mRNA expression does not necessarily correlate with protein level and, more importantly, the author should normalize the expression of Rpb9 with another subunit of Pol II (e.g. Rpb3) in each cell line used for the analysis . An alte4) Possible bias resulting from conservationTo measure the error rates at splicing junctions, the author counts errors at each position relative to 5´ donor sites, using reads spanning intron-exon junctions centered on GT donor sites. As a result, the errors at the T nucleotide are more enriched compared to other positions. It is not clear if the analysis is performed measuring the average GT error rate comparing all the reads at intron-exon junctions or single mRNAs . If the 5) Suggestions for additional controlsA positive control would be to analyse RNA-seq data of an organism with a mutated polymerase known to have an elevated mutation rate and to show that this mutation rate leads to higher relative error rates at conserved splicing motifs. A negative control would be to analyse RNA-seq data of a mutant organism with a known transcription elongation defect and to show that the elongation defect does not affect the putative Pol II error rate in a similar way as Rbp9 and TFIIs overexpression. If possible we encourage the author to conduct these controls.6) Repetitive readsIn paragraph four the alignment quality filter procedure is explained. However it is not mentioned how repetitive reads  are handled and might affect the result. This must be clarified.7) Possible bias from coverageNot counting identical mismatches occurring twice or more at the same position (paragraph four) is problematic, because:– This needs to be adjusted by depth-of-coverage at each position. Positions with high coverage are much more likely to have the same 'real' RNApol error twice, than positions with low coverage. – RNA polymerase errors seem to be biased to e.g. C->T see , making In general the uncertainty of RNApol error estimates at low coverage positions (i.e. lowly expressed genes) should be much worse than for high coverage (highly expressed genes). Is this addressed in the algorithm? (Maybe this problem has been discussed but missed by reviewers.) If not it needs some clarification, how different depth-of-coverage and mutation bias is considered when estimating the errors or removing mismatches of the same type. 1) Possible alternative explanations for the observationsA main issue with this manuscript is that alternative explanations could also make sense. The author has to show that his explanations are the only or at least most plausible ones. I agree that is was strange that only mutations at the U were enriched in reads spanning intron-exon junctions. Using a newer dataset with a far lower overall mismatch frequency, I find that both the G and U in the 5’ site, and the A in the 3’ site, have higher observed mismatch rates in exon-intron spanning reads .2) Choice of null modelFigure 2b shows relative error rates on the y-axis. The error rates observed around 5' splice sites are normalized by the error rates seen for the same dinucleotides, GT, at other places in the transcriptome. The 4-fold elevated error rate therefore depends on the null model. It would be important to compute the relative error rate at the uracil with more refined trimer null models to see if the 4-fold increase holds up. Two versions, one with the mutated nucleotide at the first position and another model with the mutated nucleotide at the last of the three trimer positions, should be used. The latter version could model sequence-dependent effects during reverse transcription. For each trimer in the transcriptome one can compute the error rate at the first and third nucleotide. Then, the total mutations for each position around the 5' splice site (and the 3' splice site and branch point) are divided by expected numbers of mutations, which is simply the sum of error rates for each of the trimer contexts for the position.3) Effects of Rpb9The author demonstrates that expression of Rpb9 negatively correlates with error rates in human cell lines, suggesting that the differential expression of Rpb9 affects RNA polymerase fidelity in vivo. The level of mRNA expression does not necessarily correlate with protein level and, more importantly, the author should normalize the expression of Rpb9 with another subunit of Pol II (e.g. Rpb3) in each cell line used for the analysis .I agree that RNA levels do not necessarily determine protein levels. This is a common caveat with interpreting RNA-seq results. In addition, RNA polymerase complex assembly is highly regulated; knowing the cellular concentration of a specific subunit doesn’t tell you about its phosphorylation status nor how much of that subunit is incorporated into polymerase. I have added text regarding this to the Discussion.The normalization by the expression of other subunits is a good idea. I have added a figure  showing An alternative explanation for I cannot think of a good experiment to determine if the difference in splicing due to RBP9 and DST1 underexpression  are due Further, in 4) Possible bias resulting from conservationTo measure the error rates at splicing junctions, the author counts errors at each position relative to 5´ donor sites, using reads spanning intron-exon junctions centered on GT donor sites. As a result, the errors at the T nucleotide are more enriched compared to other positions. It is not clear if the analysis is performed measuring the average GT error rate comparing all the reads at intron-exon junctions or single mRNAs . If the Unfortunately, because the error rates are lower than the coverage at any one position, the error rate at any particular exon is dominated by sampling bias. We are in the process of developing combining single-molecule unique IDs with targeted sequencing to ask this very question, but cannot do so using standard RNA-sequencing data.5) Suggestions for additional controlsA positive control would be to analyse RNA-seq data of an organism with a mutated polymerase known to have an elevated mutation rate and to show that this mutation rate leads to higher relative error rates at conserved splicing motifs.The error rates at exon-intron junctions  are compI don’t have reason to believe that an RNA polymerase fidelity mutant will have a larger increase in error rates at conserved splicing motifs relative to the increase at other positions. In the mutants, the increase in error rates at splice junctions should be the same as at other similar sequences context. The greater error rate at splice junctions is because these errors can result in intron retention.A negative control would be to analyse RNA-seq data of a mutant organism with a known transcription elongation defect and to show that the elongation defect does not affect the putative Pol II error rate in a similar way as Rbp9 and TFIIs overexpression. If possible we encourage the author to conduct these controls.This was a very nice suggestion. I’ve done it .6) Repetitive readsIn paragraph four the alignment quality filter procedure is explained. However it is not mentioned how repetitive reads  are handled and might affect the result. This must be clarified.To determine if defects in elongation result in increased RNA-seq mismatch frequencies, I analyzed RNA-seq data from spt4 and elc1 strains, which as shown in Lacadie et al., do not have fidelity defects. I see no difference in RNA-seq mismatch frequencies, suggesting that perturbations that affect elongation would not results in an increase in RNA-seq mismatches.7) Possible bias from coverageNot counting identical mismatches occurring twice or more at the same position (paragraph four) is problematic, because:– This needs to be adjusted by depth-of-coverage at each position. Positions with high coverage are much more likely to have the same 'real' RNApol error twice, than positions with low coverage. – RNA polymerase errors seem to be biased to e.g. C->T see , making Reads that map to multiple locations in the genome are discarded. I’ve clarified this in the text.-5, while 90% of positions have a coverage of <102. Therefore, while many positions in the genome exhibit multiple identical errors the likelihood of observing multiple identical errors is very low.I have added a supplementary figure showing both (1) this filtering does not affect the results, and (2) the statistical reasoning for this filter . Briefly