Remarkable reductions in cardiovascular morbidity and mortality have been achieved in recent decades through the widespread use of ‘small-molecule’ hypolipidaemic drugs such as statins and ezetimibe. An alternative approach is to perturb the production of proteins through ribonucleic acid (RNA) silencing, leading to long-lasting knock-down of specific biological molecules. This review describes the scientific basis of RNA silencing, and critically evaluates the evidence relating to inclisiran, a small interfering RNA against proprotein convertase subtilisin kexin 9 (PCSK9).Pooled analysis of three recent ORION trials has demonstrated that twice-yearly administration of inclisiran reduces LDL-C by 50% in a range of patient groups, with only mild adverse effects.Inclisiran provides safe, effective and long-lasting reductions in PCSK9 and LDL-C. The results of the phase-3 ORION-4 outcomes study are eagerly awaited. Further promising RNA silencing technologies have the potential to improve the management of dyslipidaemia. Substantial reductions in cardiovascular morbidity and mortality have been achieved in recent decades through the widespread use of hypolipidaemic drugs, which act as enzyme inhibitors (statins) or modulate transporters (ezetimibe) or receptor proteins (fibrates) . Whilst Cardiovascular disease (CVD) prevention, and hypolipidaemic drugs, in particular, is a great successes story of modern medicine. Statins (which inhibit 3-hydroxy-3-methyl-glutaryl-coenzyme A (HMG-CoA) reductase, the rate-limiting step in the mevalonate pathway of hepatic cholesterol synthesis) are safe, well-tolerated, and reduce the risk of major cardiovascular events by about one quarter, for each mmol/l reduction in low-density lipoprotein cholesterol (LDL-C) . AdditioSmall molecules often have ‘off-target’ interactions resulting in adverse effects. In the case of statin therapy, muscle pain is associated with ‘statin intolerance’ whereby patients cannot take the drug at all or cannot escalate the dose sufficiently to reach treatment targets . Whilst Furthermore, it is increasingly apparent that even optimal management of LDL-C does not eliminate the risk of CV events. This residual risk results in part from inflammation , 15 and Finally, many small-molecule approaches have a ‘ceiling effect’, with biological redundancy or compensatory mechanisms limiting the maximum effect of the drug. For example, statin therapy results in upregulation of PCSK9 (the functions of which are described in greater detail below), limiting the extent of achievable LDL-C lowering ••. This Alternative approaches to targeting molecules important in the pathophysiology of atherosclerosis include monoclonal antibodies mAbs and RNA silencing techniques, which have the potential to overcome many of the limitations of ‘small-molecule’ approaches. Both approaches require injectable therapies and have the potential to result in longer lasting and more specific biological activities compared with orally active small molecules but differ in various respects. mAbs act predominantly by binding to extracellular proteins, whereas RNA-based therapies must enter cells to prevent the translation of mRNA. All examples of mAbs and RNA therapeutics used in humans to date require subcutaneous injections for administration and have the potential for injection site reactions. The effectiveness of monoclonal antibody therapies can be reduced by the development of autoantibodies, although such an effect has not yet been observed with RNA-based drugs. mAbs are comparatively complex and expensive to produce in comparison to RNA-based drugs .PCSK9 has emerged in recent years as an important target in the management of atherosclerosis and has been targeted by monoclonal antibody drugs  •• and inInclisiran  is the first example of an approved synthetic small interfering RNA (siRNA) drug for treating FH . The siRRNA interference (RNAi), as the biological basis for a subcategory of gene therapy, is a highly precise approach to suppressing the expression of specific genes . This paRNAi can be achieved by two types of small double-stranded RNA molecule, namely small interfering RNA (siRNA) and microRNA (miRNA), both of which suppress gene expression according to their specific sequence albeit in slightly different ways: miRNA mediates the repression of mRNA translation, whereas siRNA mediates mRNA degradation. These differences provide a range of therapeutic possibilities, with the potential for one miRNA drug to target a number of different selected genes together, whereas an siRNA drug is able to target a single specific gene of interest. The focus of this review is on siRNA technology.Once in the cytoplasm of a hepatocyte, the double-stranded siRNA drug inclisiran is uncoiled into two single, short RNA strands, one of which acts as the guide strand whilst the other becomes the passenger, which has no further role to play and is degraded , 27. TheAs an alternative to double-stranded siRNA, RNA silencing can be achieved with single-stranded antisense oligonucleotides. These molecules ate typically 15–30 nucleotides in length and bind to mRNA, and prevent its translation, or cause its degradation .Whilst siRNA therapeutics has been in development for around 20 years  for a nuDrug delivery technology has generally been improved, partly to protect the otherwise naked siRNA drug and partly to overcome obstacles in the way of reaching the intended target. In an early siRNA drug known as ALN-PCS, which targeted the PCSK9 gene by encapsulating the drug within lipid nanoparticles, it was possible to increase the drug half-life and increase endocytosis into target cells . RNA druChemical conjugation is another method that has been employed to improve the stability and delivery of siRNAs. A covalently bound conjugate allows the siRNA drug to be targeted to specific tissues or cells by binding to a cell surface receptor, followed by entry through receptor-mediated endocytosis. Following on from ALN-PCS, an siRNA for PCSK9, known as ALN-PCSsc, was conjugated with N-acetylgalactosamine , which allowed the drug to target hepatocytes via the asialoglycoprotein receptor , 38. TheInclisiran has been evaluated in a range of clinical trials Table , includiA patient-level pooled analysis including 3,660 patients from three ORION trials has recently been published. The analysis included data from patients with familial hypercholesterolaemia (ORION-9), ASCVD (ORION-10) and ASCVD risk equivalents (ORION-11); 92% of subjects were taking statin therapy and 14% were using ezetimibe. Patients were injected with inclisiran or placebo at baseline, after 3 months and then every 6 months. At 510 days, LDL-C was found to be reduced by 50.7%  in the inclisiran group, after correction for placebo. Adverse effects included bronchitis and mild injection site reactions.A pre-specified safety analysis of the ORION-1 trial (501 patients) focused on haematological variables during a year of follow-up. No evidence was found that inclisiran affected counts of leucocytes, monocytes, platelets or neutrophils , 46. InfOutcomes data available to date are extremely promising. A meta-analysis including data from ORION-10, ORION-11 and studies investigating MoAb-based PCSK9 inhibitors found that the relationship between LDL-C lowering and reduction in MACE followed the same trend for inclisiran and the MoAb agents . A separOn the basis of these data, inclisiran is likely to play an increasing role in guideline-directed management of dyslipidaemias. Polish guidelines recommend that inclisiran may be considered in patients with ASCFD or FH who do not achieve lipid targets on statin and ezetimibe, in statin intolerance and in very high-risk primary prevention patients who do not adhere to, or consent to, other lipid-lowering therapies . In the Whilst inclisiran is the first drug in its class for dyslipidaemia, it is unlikely to be the only one since RNA inhibition offers, at least theoretically, the opportunity to target any gene of interest. The siRNA templates are already established, and therefore, it is a simple step to modify the sequence of nucleic acids to a specific target mRNA. Other proteins associated with plasma lipid homeostasis, particularly if they are functional in the liver, are therefore likely to be included in research and development programmes using this technology. An orally available antisense oligonucleotide for PCSK9 has recently demonstrated promise in preclinical studies , and RNAPooled analysis of three recent ORION trials has demonstrated that twice-yearly administration of inclisiran reduces LDL-C by 50% in a range of patient groups, with only mild adverse effects. Inclisiran, therefore, is a safe, effective approach to the reduction of LDL-C. The results of the phase-3 ORION-4 outcomes study are eagerly awaited and will demonstrate the effectiveness of inclisiran against hard clinical outcomes. Further promising RNA silencing technologies directed at a variety of biological targets have the potential to improve the management of dyslipidaemias.


Moreover, the EE-GMFCC method is capable of providing an accurate prediction of the relative conformational excited-state energies for different configurations of the Mango-II RNA aptamer system extracted from the molecular dynamics (MD) simulations. The fragmentation method further provides a straightforward approach to decompose the excitation energy contribution per ribonucleotide around the fluorophore and then reveals the influence of the local chemical environment on the fluorophore. The applications of EE-GMFCC in calculations of excitation energies for other RNA aptamer–fluorophore complexes demonstrate that the EE-GMFCC method is a general approach for accurate and efficient calculations of excited-state properties of fluorescent RNAs.Fluorescent RNA aptamers have been successfully applied to track and tag RNA in a biological system. However, it is still challenging to predict the excited-state properties of the RNA aptamer–fluorophore complex with the traditional electronic structure methods due to expensive computational costs. In this study, an accurate and efficient fragmentation quantum mechanical (QM) approach of the electrostatically embedded generalized molecular fractionation with conjugate caps (EE-GMFCC) scheme was applied for calculations of excited-state properties of the RNA aptamer–fluorophore complex. In this method, the excited-state properties were first calculated with one-body fragment quantum mechanics/molecular mechanics (QM/MM) calculation (the excited-state properties of the fluorophore) and then corrected with a series of two-body fragment QM calculations for accounting for the QM effects from the RNA on the excited-state properties of the fluorophore. The performance of the EE-GMFCC on prediction of the absolute excitation energies, the corresponding transition electric dipole moment (TEDM), and atomic forces at both the TD-HF and TD-DFT levels was tested using the Mango-II RNA aptamer system as a model system. The results demonstrate that the calculated excited-state properties by EE-GMFCC are in excellent agreement with the traditional full-system time-dependent  RNA directly regulates a large number of cellular processes, and effective methods are desirable to fluorescently label and track RNA in living cells . BecauseMango II is an RNA aptamer that can accurately image the subcellular localization of three small noncoding RNAs in fixed and live mammalian cells . These aMany QM methods were proposed for excited-state calculations, such as the approximate coupled cluster singles and double (CCSD) model (CC2) , symmetrIn order to reduce the computational cost of the excited-state calculations, a series of approaches were proposed for the systems with a localized electronic excitation . The simSome fragmentation QM approaches have been proposed for calculations of the excited-state properties of large systems, including the generalized energy-based fragmentation (GEBF) approach developed by Li and coworkers , the divThis paper is organized as follows. First, the convergence of the EE-GMFCC excitation energy as a function of the distance threshold for two-body QM interactions was tested for fluorescent RNA–aptamer systems. Second, the accuracy of the EE-GMFCC method on prediction of the excitation energy was investigated at both TD-HF and TD-DFT levels by comparison with the traditional full-system calculations. Third, the accuracy of calculated transition electric dipole moments (TEDM) and atomic forces by the EE-GMFCC method was demonstrated. Furthermore, a 100-ns molecular dynamics (MD) simulation of Mango II RNA in explicit water solvent was performed, and the relative excited-state energies of 10 different configurations extracted from the MD simulation were calculated by EE-GMFCC and compared with the results obtained from corresponding full-system QM calculations. Finally, the performance of the EE-GMFCC approach was extensively assessed on some other fluorescent RNA–aptamer systems by direct comparison with the full-system QM calculations.Our previous work showed that the QM effects from the protein environment played a significant role in the calculations of the excited-state properties of green fluorescent protein (GFP) . TherefoN residues is divided into N-2 fragments with each residue capped by its neighboring residues (conjugate caps) , the corresponding two-body interactions are also treated at the QM level. Generally, higher-order interactions within the EE-GMFCC scheme are small and can be neglected due to the electrostatic embedding treatment.The EE-GMFCC method was initially developed for calculations of the ground-state total energies of proteins . In the te caps) , and theith ribonucleic acid unit (Ai) capped with a left-side cap of ith and jth nucleotides. Similar to the treatment of the protein, EE-GMFCC can be utilized in the calculation of the ground-state energy of a given RNA system. The ground-state energy of a RNA system is calculated using the EE-GMFCC method as follows ,EEE−GMFous work .ω of the system could be obtained using mth residue.m-1)th, mth, and (m+1)th fragments, which can be obtained as follows:ω for a fragment of The EE-GMFCC method has been applied in the calculation of excitation energies of GFP in our previous work . The treous work . Differem and jth ribonucleotide, which was obtained by QM calculation in the field of the rest of the system represented by the MM point charges taken from the ff99OL3 force field th ribonucleotide and the (N+1)th ribonucleotide at the bonds of C3(N-1)-O3(N-1) and C3(N)-O3(N), where C3(N) and O3(N) represent the C3 and O3 atoms of the Nth ribonucleotide in the RNA chain, respectively. The H atom would be utilized to saturate the dangling bond, as shown in The RNA was cut at the bond between C3 atom and O3 atom as shown in  package .x, y, or z direction, and mth fragment (which is the fluorophore in this study) in the x, y, or z direction and it corresponds to the one-body (1B) term in the EE-GMFCC calculation. N is the total number of ribonucleotides in the RNA.The TEDM between two states under the EE-GMFCC method could be obtained as follows:x, y, and z directions, respectively.The total TEDM of the system between two states with the EE-GMFCC method could be utilized to calculate the oscillator strength as follows:kth atom in the fluorophore molecule at the excited state could also be calculated using the EE-GMFCC method by replacing the TEDM of F.The atomic forces of the The superscripts and subscripts in The initial structure of the Mango-II RNA aptamer system was taken from the X-ray crystal structure (PDB id: 6C63). The generalized Amber force field (GAFF)  and AM1-The residue name of the fluorophore in this study is called EKJ (in PDB id: 6C63), which consists of a thiazole orange1 (TO1) and part of the polyethylene glycol linker. The EKJ binds to the Mango II RNA with a high affinity, and the high brightness of the EKJ in the RNA allows its application in live-cell imaging and also in conventional fixed cell methodologies . The aptThe computational cost of the full-system QM calculation of Mango II RNA containing 1,243 atoms was very expensive. Considering the localization of the QM effect on the excited-state property, we constructed several smaller model systems for the Mango II RNA, which contains the fluorophore and its neighboring ribonucleotides within a predefined distance threshold The accuracy of the EE-GMFCC method for prediction of the excitation energies of the RNA aptamer–fluorophore complex was investigated by comparison with the truncated full-system QM/MM calculations. In the framework of the EE-GMFCC method, a two-body QM calculation was utilized to account for the QM effect from the adjacent ribonucleotides on the calculations of excited-state properties of the fluorescent aptamer. A predefined distance threshold  as a function of the number of atoms in the system, while the computational cost for the traditional full-system TD-HF and TD-DFT calculations exhibits O  is shown in Previous theoretical and experimental studies  on the GThe results of QM/MM calculations are usually affected by the MM parameters for mimicking the atomic point charges. For more accurately reproducing the classical electrostatic effect of the adjacent ribonucleotide on the excited-state properties of the fluorescent molecule in the full 2-body QM calculation, the excited-state calculations were first performed with the full-system QM method (the given two-body fragment including the corresponding adjacent ribonucleotide and the fluorescent molecule was treated by the QM method), and then the obtained ESP charges were utilized in the QM/MM calculations to serve as the background charges (the results were labeled as QM/ESP). For investigating the parameter dependence of the QM/MM calculations, the excited-state calculations were also performed with the adjacent ribonucleotide represented by the ff99OL3 force field.As shown in Accordingly, the opposite effects (the blue and red shifts) of the same kind of ribonucleotides  on the excitation energy see  might beIn order to test the performance of the EE-GMFCC method on different fluorescent RNA–aptamer systems, the calculations of the excitation energies for seven other fluorescent RNA–aptamer systems taken from the PDB shown in  were per2R  method and truncated full-system calculations at the TD-HF/6-31G* level are plotted in R2 ) of the R2 (R2 ) of the In this study, the electrostatically embedded generalized molecular fractionation with conjugate caps (EE-GMFCC) method was applied to calculations of the excited-state properties of the fluorescent RNA aptamer systems. The two-body fragment QM calculations were utilized to account for the QM effect from the local RNA chemical environment on the excited-state properties of the fluorescent molecule. The benchmark study on the Mango-II RNA aptamer system demonstrated that EE-GMFCC could give good agreement with traditional full-system QM calculations of the absolute and relative excitation energies, and the 4 Å distance threshold for the two-body QM calculations could strike a good balance between the attained accuracy and the computational expense incurred for the EE-GMFCC method. Furthermore, the EE-GMFCC method could provide an accurate prediction of other excited-state properties, namely, TEDM and atomic forces. This work demonstrated that incorporating the QM effects of a local RNA chemical environment was essential for an accurate prediction of the excited-state properties of the fluorescent molecule in RNAs, and hundreds of atoms were usually required to be treated with electronic structure theories. It is challenging for the traditional full-system QM calculation to handle such large systems due to the expensive computational cost. In contrast, the computational cost of the EE-GMFCC method is linear-scaling with a low prefactor, and thus the EE-GMFCC approach is computationally efficient, which could be applied for tackling the macromolecular systems. The applications of the EE-GMFCC method in calculations of the excitation energies for different fluorescent RNA aptamer systems demonstrate that the EE-GMFCC is a general approach for the excited-state property calculations of large complex molecular systems.Illustration of the EE-GMFCC fragmentation scheme; Calculated excitation energies as a function of the distance threshold; Calculated TEDM at the TD-ωB97X/6-31G* level for different model systems using the truncated full-system and EE-GMFCC method; The relative excitation energies for different configurations of the RNA system (pdb id: 6C63) predicted by the EE-GMFCC method and truncated full-system calculations at the TD-ωB97X/6-31G* level; The relative excitation energies for different RNA systems predicted by the EE-GMFCC method and truncated full-system calculations at the TD-ωB97X/6-31G* level.


We confirm our results with orthogonal methods and provide novel insights on the co-occurrence of multiple modified residues on individual RNA molecules.RNA molecules undergo a vast array of chemical post-transcriptional modifications (PTMs) that can affect their structure and interaction properties. In recent years, a growing number of PTMs have been successfully mapped to the transcriptome using experimental approaches relying on high-throughput sequencing. Oxford Nanopore direct-RNA sequencing has been shown to be sensitive to RNA modifications. We developed and validated Nanocompore, a robust analytical framework that identifies modifications from these data. Our strategy compares an RNA sample of interest against a non-modified control sample, not requiring a training set and allowing the use of replicates. We show that Nanocompore can detect different RNA modifications with position accuracy in vitro, and we apply it to profile m Nanopore direct RNA Sequencing data contain information about the presence of RNA modifications, but their detection poses substantial challenges. Here the authors introduce Nanocompore, a new methodology for modification detection from Nanopore data. PTMs are deposited and catalytically removed by specific enzymes and can be recognized by specific ‘reader’ proteins. Overall, PTMs influence fundamental properties and functions of RNAs, including their stability, structure, intermolecular interactions and cellular localization3.RNA post-transcriptional modifications (PTMs) are a pervasive feature common to all domains of life. They arise from covalent alteration or isomerisation of nucleotides, typically involving the addition of chemical groups to different positions of the nitrogenous bases or the ribose cycle. To date, over 150 modifications have been found throughout all classes of RNAs, with the most common modification being methylation5. Other modifications, including Inosine (I), 5-methylcytosine (m5C), pseudouridine (Ψ) N6,N6-dimethyladenosine , 1-methylguanosine (m1G), 2′-O methyladenosine (2′-OMeA), and 7-methylguanosine (m7G), are increasingly recognized as important for the regulation of different RNAs in physiological and pathological contexts, including cancer7.N6-Methyladenosine (m6A) is the best characterised PTM and the most abundant in mRNAs and long non-coding RNAs (lncRNAs). It is deposited mainly by the METTL3/METTL14/WTAP complex and has a variety of functions such as regulation of nuclear export, translation, and degradation of RNAs9.The majority of current methods for mapping PTMs rely on RNA immunoprecipitation, chemoselective alteration, or specific signatures resulting from reverse transcription (RT), and despite being the current gold standard have certain limitations, such as (1) the need to develop ad hoc protocols for each PTM, (2) cross reactivity or low sensitivity of antibodies or chemical reactions, and (3) biases induced by the complex multi-step experimental protocols12. In Nanopore DRS, a single RNA molecule is ratcheted by a molecular motor through a protein pore embedded in a synthetic membrane. The passage of nucleobases through the narrowest section of the pore (reader-head) alters the flow of ions across the membrane, depending on the chemical composition of the bases. At any given point in time, approximately 5 nucleotides (commonly referred to as a kmer) reside within the reader-head of R9 pores, leading to a strong kmer specific signal alteration. Crucially, the presence of nucleotide modifications can induce discernible shifts in current intensity and in the time the nucleic acid sequence resides inside the pore (dwell time)10.The recent advances in Nanopore direct RNA sequencing (DRS) have allowed, for the first time, direct sequencing of full-length native RNA molecules without the need for RT or amplification. Importantly, a number of studies have shown that DRS data intrinsically contain information about RNA modifications13. The current approaches for modification detection based on Nanopore data can be divided into two categories: those based on the detection of modification-induced basecalling errors and those based on the analysis of the electrical signal. The first strategy, which is implemented in tools such as Epinano14, DiffErr15, Eligos16, and Drummer17, has shown interesting results despite not considering the effects of RNA modification on the raw electrical signal; however, modern basecalling models tend to become more insensitive to common PTM, with the risk that methods of this group could quickly become ineffective at detecting modifications. On the other hand, methods based on raw signal space analyses  can lead to richer comparative analyses, but are more complicated and come with steeper computational costs. The methods described above can be further classified into two groups: de novo detection methods, that use a trained model to identify modifications, and comparative methods, where differences between two samples are evaluated to infer the presence of a modification. At present, de novo strategies are often hindered by the difficulty to generate a training set containing all kmer contexts with and without modifications. For this reason, the majority of existing methods instead undertake a comparative approach, where the sample of interest is compared to a reference sample devoid of modifications. Here we introduce Nanocompore, a flexible and versatile analysis method dedicated to the detection of RNA modifications from DRS datasets in signal space. To identify potential modification sites, Nanocompore uses a model-free comparative approach based on a 2 components Gaussian mixture model, where an experimental RNA sample is compared against a sample with fewer or no modifications. Potentially, this can be applied to any modification, provided that an appropriate control depleted of the modification is available, and that the modification significantly alters the current signal. We demonstrate this for seven different RNA modifications in synthetic oligonucleotides, as well as extensively for m6A in coding and noncoding native RNAs in yeast and mammalian cells. Nanocompore includes several unique features: (1) robust signal realignment based on Nanopolish, (2) modelling of the biological variability, (3) ability to run multiple statistical tests, (4) prediction of RNA modifications using both signal intensity and duration (dwell time), and (5) availability of an automated pipeline that runs all the preprocessing steps. Finally, the results generated by Nanocompore can also be leveraged to infer RNA modifications at single molecule resolution.In recent years, the scientific community has devoted substantial resources toward the development of experimental and analytical strategies for the detection of RNA modifications. These efforts have generated a number of algorithms and software packages, which have been extensively reviewed elsewherehttps://github.com/tleonardi/nanocompore_pipeline) that automatically runs the entire analysis from preprocessing of raw Nanopore data 26. The p-values are then corrected for multiple tests using Benjamini-Hochberg’s procedure27 and the results are stored in a lightweight database. Users can obtain a tabular text dump of the database or use the extensive Nanocompore API to explore the results and generate ready-to-publish plots.Nanocompore detects potential RNA modifications by comparing DRS datasets from one experimental test condition containing specific RNA modifications to one control condition containing significantly fewer or no modifications. Ideally, the control RNA is isolated from a cell harbouring either a knock-down (KD) or a knock-out (KO) of a gene encoding an RNA modifying enzyme. Alternatively, for small scale comparison, it is also possible to use either an in vitro transcribed or synthetic RNA containing canonical RNA bases only. We have developed an automated Nextflow pipeline . The data generated from the modified oligos was then analysed with Nanocompore using an unmodified oligo as the reference condition. These results show that Nanocompore can detect all modifications tested Figs. , includidue Fig. . This shp-value cutoff of 0.05 and 512 reads coverage, the GMM-logit test had a mean accuracy of 94.48% at detecting m6A and 89.8% at detecting other modifications.To better gauge the accuracy of Nanocompore at coverage levels representative of real experiments, we generated 100 subsampled datasets containing random samples of 32 to 4096 reads, doubling at each step. By analysing such datasets with Nanocompore, we observed that the GMM-logit method had lower sensitivity but higher specificity than the non-parametric tests on intensity or dwell time Fig. . This waWe then reasoned that the results obtained with these modified oligos are only representative of the extreme situation where 100% of the RNA is modified in the condition under study whereas the modification is completely absent from the control condition. In order to evaluate the performance of our method under conditions more representative of real experimental scenarios, we generated in silico datasets by mixing known proportions of modified and unmodified reads. Such datasets where generated for each intersection of 3 possible factors: (1) % of modified reads in experimental condition ; (2) % of modification reduction in control condition , and (3) read coverage (from 16 to 4096 reads per dataset). For each combination of these three factors we generated 100 independent datasets that were then analysed with Nanocompore, for a total of 80,000 runs Fig. . By knowThese simulations also allowed us to better investigate the performance of the different tests implemented in Nanocompore. We observed that the KS tests on current intensity or dwell time achieved the highest sensitivity at the cost of lower specificity, in particular at high levels of coverage. On the other hand, the GMM logit test has the lowest False Positive Rate overall and the best balance between precision and sensitivity (i.e. highest F1 score) at high coverage Fig.  and S7. Escherichia coli strain MRE600 knock-out for RsmG or RsuA, which are responsible for an m7G residue at position G527 and Ψ at position 516 respectively12. In both cases, Nanocompore was able to detect the modified nucleotides as highly significant .As a further control for Nanocompore sensitivity, we re-analysed DRS dataset of 16S rRNA from Saccharomyces cerevisiae strain KO for IME4 (ime4Δ), the only known m6A methyltransferase in yeast. We then used DRS to sequence the polyA+ transcriptome in Wild Type (WT) cells as well as ime4Δ cells. We sequenced three biological replicates per condition in individual flowcells, generating a total of 14,554,547 reads and obtaining a coverage above 30x for 2,523 genes . Nanocompore analysis of such a dataset identified 15,961 significant kmers in 1,510 distinct transcripts . This analysis revealed that 21% (124/602) of known m6A sites overlap with a Nanocompore peak, whereas 8% (124/1549) of the sites identified by Nanocompore were also supported by a peak in the orthogonal reference set  of all methods tested, while Nanocompore’s GMM method and GMM context 2 method had a sensitivity of only 16% and 5.5% respectively Fig. . We thenWe then repeated a similar analysis only considering DRACH kmers but including Epinano and MINES in the comparisons. This time we found that Eligos achieved the best balance of sensitivity and specificity with an F1 Score of 0.287, whereas Nanocompore had the second best score of 0.180 Fig. . HoweverNanocompore, similarly to Eligos and diff_err, also reports the odds ratio of modified sites, which indicates the magnitude of the effect . We therefore also measured the sensitivity and specificity of Nanocompore at a stringent log odds ratio threshold. As expected, we found that more stringent filtering increased specificity at the cost of lower sensitivity, with an overall increase in precision Figure . Finally33, it is of particular importance to accurately map it in leukemia cells. We therefore used DRS to profile the poly-A+ transcriptome of human MOLM13 cells with inducible shRNA-mediated knock-down (KD) of METTL3, as well as control Wild Type (WT) MOLM13 transfected with a scrambled shRNA. We sequenced RNA from two biological replicates per condition on independent Minion flow cells after 4 days of induced KD of METTL3, yielding a total of 3,768,380 reads. After applying a 30× coverage threshold, we obtained data for 751 unique transcripts robustly expressed in all samples  position of specific RNAs. Since m6A is required for development and maintenance of acute myeloid leukemiales Fig. . Overallp-value = 0.4, see Materials and Methods).The identification of RNA modifications outlined so far operates at consensus level, i.e., looking at the distribution of signal across the entire population of reads. However, the information obtained from GMM clustering at the population level can be leveraged to calculate the probability of each read to belong to the modified or unmodified cluster. Hence, it is possible to assign modification probabilities at the single-molecule, single-site level. As a proof of concept, we calculated the single-molecule modification probabilities of the three β-actin high-confidence m6A sites previously described Fig. . We founMaterials and Methods and Supplementary Table p-value<0.01, Fig. 37. Interestingly, the 3’ GAUC palindrome at position 64 is also a significant site  and A65 , supporting the presence of the two m6A sites that we identified above in the double stranded HEXIM1 binding site.We next sought to extend our investigation of 7SK to include other modifications in addition to m6A. To this end we used IVT to generate large amounts of 7SK RNA devoid of all modifications. We then sequenced this IVT 7SK by DRS and analysed the resulting data with Nanocompore, using the dataset from targeted sequencing of WT MOLM13 cells as the reference condition. This approach potentially allows mapping of all RNA modifications in targeted RNAs, albeit without revealing the type of each modification. We identified 68 significant kmers spread across the entire 7SK sequence  expression level, (b) modification stoichiometry, and (c) efficiency of modification reduction in control. These observations strengthen the importance of having good control conditions  and high depth of sequencing. In our experiments, to profile m6A in yeast we achieved a median coverage of 120 reads per transcript. The True Positive Rate for m6A detection at this sequencing depth is ~48%, highlighting the fact that the low throughput of individual MinION flowcells currently does not provide enough coverage to resolve RNA modifications transcriptome wide. However, newer releases of DRS kits provide constant improvements in terms of throughput. Furthermore, when cost and amount of RNA are not limiting factors, users have the option of pooling multiple MinION flowcells or using a PromethION to achieve higher coverage. A further limitation that emerged from our benchmarks, which is intrinsic to the methods that directly use electrical signals to identify modifications, is the spatial resolution of modification calling. Since the reader head of the pore contains ~5 nucleotides, a single modified nucleotide can potentially affect the signal of up to 5 consecutive kmers, making it hard to resolve modification position with single nucleotide precision.45. However, an important caveat to be considered when pursuing this approach—as well as any other method based on loss-of-function of catalytic enzymes—is that compensation between different enzymes or functional interactions between neighbouring modifications could be a confounding factors for Nanocompore analysis and currently cannot be accurately resolved solely with our method, in particular for long periods of inhibition of the RNA modifying enzymes. Because of this intrinsic inability of comparative methods to directly assign modifications, it is currently not possible to study multiple types of modifications at the same time.An additional feature of Nanocompore is that by analysing knock-down or knock-out samples it intrinsically assigns RNA modifications to specific writer enzymes, thus allowing to discern the individual roles of multiple enzymes that catalyse the same modification. It will also be of great interest to assess the effects of pharmacological inhibition of enzymes that regulate or deposit RNA modifications, for example in cancer, viral infections and potentially other diseasesAn intrinsic feature of Nanocompore is its ability to assign modifications to specific isoforms, although this implies that Nanocompore requires either a well-annotated transcriptome or a custom transcriptome annotation generated from the DRS data. In addition, it is becoming increasingly important to obtain information about modification stoichiometry and combinatorics. Although Nanocompore currently does not allow measuring stoichiometry, one of its major advantages is the ability to detect RNA modifications at single molecule resolution. As a proof of concept we applied our analysis to the most significant m6A sites found by Nanocompore in β-actin mRNA and found that multiple methylated residues are present in the same molecule independently of one another at a given time. Although this type of analysis can not currently be applied transcriptome-wide, and although these results are still not quantitative in nature, they suggest the presence of highly site-selective intramolecular deposition and/or removal of m6A. This is the first observation of this kind to date, and it will need to be cross-validated when other methods enabling the same level of resolution become available.13. Here we have benchmarked the performance of Nanocompore at detecting m6A against a small set of representative tools , finding that in most situation Nanocompore achieves very high accuracy at the cost of lower sensitivity. Although this benchmark was done in yeast, we expect similar results for other species. However—as we and others20 have observed—low sequencing coverage negatively impacts modification detection. For this reason, a lower sensitivity can be expected for complex transcriptomes such as the human one. In addition, our experiments with synthetic RNAs also show that performance metrics are heavily influenced by modification stoichiometry and relative reduction of the modification in the control condition. Despite these observations, the field is still lacking a systematic comparison of the performance of all the methods available, of how it is impacted by the factors mentioned above and how it varies between different modifications or model species. For this reason, we recommend users seeking to detect RNA modifications from Nanopore data to test multiple methods that implement different approaches and to carefully assess the impact of coverage and knock-down/knock-out efficiency under their experimental settings.The last few years have seen a remarkable increase in the number of methods available for modification detection from Nanopore data. The majority of these focus on the identification of only one type modification  whereas others, such as Nanocompore, NanoRMS, Epinano, and Eligos have been tested on a larger number of distinct modifications. The tools available also differ greatly in terms of methodology employed: for example, certain tools use machine learning algorithms  whereas others apply clustering techniques and statistical testing . At the same time, these methods also differ in terms of strengths and shortcomings, which have been extensively reviewed in recent worksIn conclusion, Nanocompore offers a versatile, robust, and practical method to readily identify RNA modifications from Nanopore DRS experiments. Its adoption by the scientific community has already benefited a number of studies and should continue shedding light on the distribution and function of RNA modifications at high resolution, helping to reveal the currently hidden life of RNAs.32. Briefly, cells were cultured in RPMI1640 (Invitrogen) supplemented with 10% FBS and 1% penicillin/streptomycin/glutamine. Conditional knock-downs (KD) using METTL3-targeting or scrambled shRNAs were performed as previously described32. For lentivirus production, 293T cells were transfected with PLKO.1 lentiviral vector containing the shRNA sequences , followed by spinfection  and further incubated overnight at 37 °C. The medium was refreshed on the following day and the transduced cells were cultured further. MOLM13 cells (5 × 105) were infected using PLKO-TETon-Puro lentiviral vectors expressing shRNAs. After 24 h of infection, the cells were replated in fresh medium containing 1 μg/ml of puromycin and kept in selection medium for 7 days. shRNA expression was induced by treatment with 200 ng/ml doxycycline for 4 days for METTL3 KD. Near complete loss of METTL3 RNA and protein was confirmed by Western Blot and qPCR by Barbieri et al.32. For METTL3 knock-out (KO) experiments, lentiviruses were produced in HEK293 cells using ViraPower Lentiviral Expression System (Invitrogen) according to manufacturer’s instructions. MOLM13 cells stably expressing Cas9 were transduced with lentiviral gRNA vectors expressing either empty or METTL3 gRNAs  as above.Total RNA was isolated from MOLM13 cells using the RNeasy midi kit (Quiagen) and polyA+ RNA was purified from 30 μg total RNA using the Dynabeads mRNA Purification Kit (Thermo Fisher Scientific) according to the manufacturer’s instructions. For production of unmodified 7SK RNA, synthetic double stranded DNA template for in vitro transcription (IVT) was produced by hybridization of synthetic Megamer® Single-Stranded DNA Fragments (IDT) containing the 7SK sequence downstream of a T7 promoter Table . 500ng o48, but uses total RNA as input and follows a more recent variant of iCLIP protocol49. 4 μg of total RNA were fragmented with RNA fragmentation reagents (ThermoFisher) following the manufacturer’s instructions. Fragmented RNA was then incubated with 2.5 μg anti-m6A antibody  in IP buffer  at 4 °C for 2 h, in rotation. Subsequently, the solution was placed in 6-well plates on ice and irradiated twice with 0.3 J cm−2 UV light (254 nm) in a Stratalinker crosslinker. 30 μl protein G beads (Dynabeads) per sample were washed twice with IP buffer and then incubated with the RNA-antibody solution at 4 °C for 1.5 h, in rotation. After the IP, the RNA-antibody-beads complexes were washed twice with High-Salt Wash buffer , once with IP buffer and once with PNK Wash buffer . The beads then proceeded to 3′ dephosphorylation and the rest of the iCLIP protocol. The 3’ adapters for on-bead ligation carry the sequences found in Table miCLIP was performed in duplicates with RNA isolated from wild type and METTL13 KO MOLM13 cells. The protocol is conceptually related to the original m6A miCLIP protocolRNA sequencing was performed following the instruction provided by Oxford Nanopore Technologies , using R9.4 chemistry flowcells (FLO-MIN106) and direct-RNA chemistry sequencing kits (SQK-RNA001 or SQK-RNA002). For polyA+ transcriptome sequencing, we followed the conventional DRS protocol using the provided polyT (RTA) adapter. For the targeted sequencing, we ordered custom reverse transcription adapters complementary to the 3’ end of 4 selected noncoding RNAs, and followed the sequence-specific DRS protocol Table . For lib2, 10 mM EDTA, 0.5% NP40 and RNase inhibitor  for 30 min on ice followed by centrifugation at 3,000 × g for 3 min. Nuclear RNA fraction was then purified using the RNAeasy midi kit (Qiagen). Successively, 4 μg of nuclear RNA were fragmented for 3 min and 30 second at 70 °C using the RNA fragmentation Reagents . Fragmented nuclear RNA was then purified using the RNA Clean & Concentrator™-5 kit . meRIP qRT-PCR was performed, as previously described50 with some modifications. Briefly, for each immunoprecipitation reaction 1 μg of fragmented nuclear RNA was incubated 2 h at 4 °C in rotation with anti-m6A  or anti-GFP Antibodies  in a final volume of 1ml RIP Buffer 50, and subsequently incubated 2h at 4 °C in rotation with 50 µL of BSA-coated Dynabeads G . A total of 5% of each immunoprecipitation reaction was saved as input control. To elute RIP-RNA, beads were incubated twice 30 min at 37 °C in a thermo-shaker (1100rpm) in 40 μl of elution buffer , RNaseOUT™). Input and RIP samples were finally purified using the RNA Clean & Concentrator™-5 kit . cDNA was obtained using the high-capacity cDNA reverse transcription kit . The levels of 7SK were measured using a QuantStudio 6 Flex real-time PCR machine and PowerUp™ SYBR™ Green PCR master mix  according to the manufacturer’s instructions. Statistical testing for differences between KD and Control was done with the one-tailed Welch’s t-test. qRT-PCR primers: 7sk (22-73): Fwd 5′-GCGACATCTGTCACCCCATT-3′; Rev 5′-CAGCCAGATCAGCCGAATCA-3′. 7sk (50-160): Fwd 5′-GGGTTGATTCGGCTGATCT-3′; Rev 5′-GGGGATGGTCGTCCTCTT-3′ 7sk (258-308): Fwd 5’-CGTAGGGTAGTCAAGCTTCCA-3’; Rev 5’-CAGCGCCTCATTTGGATGTG-3′Cell nuclei were obtained from MOLM13 WT  or METTL3-KD cells  six days after doxycycline administration. Cell lysis was performed in 10 mM TRIS pH = 7.8, 140 mM NaCl, 1.5 mM MgClWestern blot experiments were performed as previously described  using the following antibodies: anti-METTL3  and anti-beta Actin .https://github.com/nanopore-wgs-consortium/NA12878). This dataset contains all possible 5-mers on average 58,307 times. The reads were aligned on gencode release 28 human reference transcriptome with Minimap2 v2.14 and we realigned the signal to the reference sequence using Nanopolish eventalign v0.10.1 followed by NanopolishComp Eventalign_collapse v0.5 . Next, we collected the median intensity and dwell time data for each 5mers and tried to fit 44 different distributions. We selected distributions minimising the sum of square root error for all kmers between the observed and modelled data. In addition, we also based our selection on the possibility to easily change the parameters of the distributions to simulate the presence of modifications. We selected the Wald distribution and the Logistic distribution for dwell time and median intensity, respectively. Finally, we generated a model file containing the parameters of the observed and model distributions for each 5-mer. The up-to-date model file is distributed with Nanocompore. The detailed analysis is available in the following Jupyter notebook: https://github.com/tleonardi/nanocompore_paper_analyses/blob/master/in_silico_dataset/01_IVT_Kmer_Model.ipynb.We used an in vitro transcribed human RNA DRS dataset released by the Nanopore WGS consortium as a ground truth for non-modified RNA bases  bounded by the extreme observed values. This tool can also offset the model mean by a fraction of the distribution standard deviation to simulate the effect of RNA modifications. This can be done for all the reads or only on a subpopulation of reads. SimReads generates files similar to the output of NanopolishComp EventalignCollapse. This means that the datasets can be directly used as input for NanoCompore SampComp. Using Nanocompore v1.0.0rc3 with the previously described simulated reference sequence set we generated 144 in silico datasets with various amplitude of modification of the median signal intensity and the dwell time  as well as different fractions of modified reads . All the datasets were simulated in duplicate with a uniform coverage depth of 100 reads. The detailed analysis is available in the following Jupyter notebook: https://github.com/tleonardi/nanocompore_paper_analyses/blob/master/in_silico_dataset/03_Simulated_dataset_gen.ipynb.Nanocompore using a sequence context of 2 nucleotides . The result database was subsequently parsed and the predicted modified sites were compared with the position of the known simulated positions. A hit was considered true positive (TP) when we found a significant p-value within 3 nucleotides of a known modified position. A significant hit outside of this window was counted as a false positive (FP). Finally, we plotted the Receiver Operating Characteristic (ROC) curves corresponding to the TP rate compared with the FP rate for every Nanocompore comparison performed .We compare the 144 datasets containing simulated modifications against the reference dataset generated from the unmodified model with Nanocompore v1.0.0rc3 (See Nanocompore section after). The analysis was performed with all the statistical methods supported by m6A: GGACU (strong DRACH consensus)m6A: CGACC (Weak NRACH consensus)m6A: CUAGC (Anti DRACH consensus)Inosine: UUAGC (loose motif in editing-enriched regions (EERs) – from Blango and Bass 2016, and Eggington et al. 2011).PseudoU: UGUAG m62A: GUGAACC (from the 18S rRNA modified sequence)m5C: CCCGGG m1G: CAGGTCG (from the tRNA m1G37 position)2’OmeA: GAGAGAA (from rRNA doi: 10.1093/nar/gkw810)The four PAGE-purified, synthetic oligonucleotides of 100nt were ordered through Horizon Discovery LTD at a concentration of 0.2 µmol. Oligo1, 2, and 3 carried 3 modified nucleotides each, whereas Oligo4 was the unmodified control. All the oligonucleotides have the same sequence, but they contain different modifications sufficiently spaced (23 bases) to avoid interactions between modifications. The sequence was chosen in order to combine all the know consensus of the modifications in a single oligo sequence in order to be able to use a single non-modified reference for all oligos:The motifs were all expanded to 7 bases and combined in a sequence separated by a randomly generated buffer of 9 bases. We generated all possible permutations of the blocks and 1000 different versions of the randomly generated buffer sequences , totalling 216,000 candidate sequences. We then computationally folded all of the candidate sequences using RNAfold v2.4.15 from the Vienna package. Finally, we calculated a combined score taking into account the folding score and the base composition balance and picked the best candidate:m6A_strong-Inosine-m62A-m6A_anti-m5C-m1G-m6A_weak-PseudoU-2OmeA|seed=802>controlAUACUCGACAUAGAUAGGACUCUUUAGCUAGUGAACCCUAGCCUCCGGAGACAGGUCGCGACCUGUGUAGAUGAGAGAACUGAGUGCACAAAAAAAAAAA>mod_set_1AUACUCGACAUAGAUAGG(m6A)CUCUUUAGCUAGUGAACCCU(m6A)GCCUCCGGAGACAGGUCGCG(m6A)CCUGUGUAGAUGAGAGAACUGAGUGCACAAAAAAAAAAA>mod_set_2AUACUCGACAUAGAUAGGACUCUUU(I)GCUAGUGAACCCUAGCCUC(m5C)GGAGACAGGUCGCGACCUGUG(PseudoU)AGAUGAGAGAACUGAGUGCACAAAAAAAAAAA>mod_set_3AUACUCGACAUAGAUAGGACUCUUUAGCUAGUG(m62A)ACCCUAGCCUCCGGAGACAG(m1G)UCGCGACCUGUGUAGAUGAG(2’OmeA)GAACUGAGUGCACAAAAAAAAAAAhttps://github.com/tleonardi/nanocompore_paper_analyses/tree/master/control_oligos_designThe full design analysis is now provided in the online companion analysis repository DRS libraries were prepared from 500 ng of each oligo using the SQK-RNA002 kit (ONT) and following the standard protocol. Libraries were then sequenced in individual FLO-MIN106 flowcells on a GridION instrument. The data was then basecalled with Guppy (v3.2.10) with default parameters. A known limitation of DRS is the poor data normalisation for short reads. To overcome this limitation and reduce noise, we only retained for further analysis the Guppy pass reads of at least 100nt in length . Filtered reads were then mapped to the reference unmodified sequence using minimap2 (-k 9 -m 5), the signal data was then resquiggled with Nanopolish and the aligned events table was collapsed with NanopolishComp as outlined before. The filtered datasets for Oligo1, 2, and 3 were then analysed with Nanocompore . The Nanocompore signal peaks were generated as described in Peak Calling section using a p-value threshold of 0.01.f, the fraction of modified reads in experimental condition, ranging from 0 to 1 in 0.1 incrementsr, the fraction of modification reduction in control condition. Values 1, 0.8 or 0.5n, the read coverage ranging from 16 to 4096 and doubling at each step.We then generated artificial datasets containing variable fractions of unmodified and modified reads, covering all possible combinations of 3 factors:A file referencing a random sample of n*f reads from the dataset containing the modificationA file referencing a random sample of n*(1-f) reads from the unmodified datasetA file referencing a random sample of n*f*r reads from the dataset containing the modificationA file referencing a random sample of n*(1-f*r) reads from the unmodified datasetFor each dataset to be generated, we created 4 NanopolishComp index files:This procedure was repeated 100 times for each combination of n, f, and r and analysed in 81.000 distinct Nanocompore runs using the combined files 1 and 2 as the experimental sample and the combined files 3 and 4 as the reference sample. We then analysed the results of Nanocompore in order to calculate, for each combination of n, f, and r, the mean number of True Positives, False Positives, True Negatives, and False Negatives identified. For this purpose, True positives were defined as the number of known modification sites with at least 1 significant kmer; False positives were defined as the number of significant kmers outside of the known modification sites; True negatives as the number of known unmodified sites that didn’t have any significant kmer and False negatives as the number of known modification sites not supported by any significant kmer.Human Genome reference: Human genome assembly GRCh38.p12Human Annotation reference: Ensembl Gene build release-97http://cbio.mskcc.org/public/SK1_MvO/Yeast SK1 Genome Reference: For this study we used the following Human reference files all obtained from Ensembl:https://github.com/tleonardi/nanocompore_pipeline). Raw reads FAST5 files were basecalled with ONT Guppy v3.1.5 and the basecalled reads were saved in FASTQ format. A post-basecalling quality control was performed with pycoQC (v2.2.4)51 to verify the consistency of the sequencing runs. A transcriptome reference FASTA file was created from the annotation BED file and genome FASTA file with Bedparse (v0.2.2)52. Reads were then aligned on the transcriptome reference with Minimap2 (v2.16)53 in unspliced mode (-x map-ont). The resulting aligned reads were filtered with samtools (v1.9)54 to keep only primary alignments mapped on the forward strand (-F 2324) and the raw signal was realigned on reads using Nanopolish eventalign (v0.11.1)55. Finally, the data was processed by NanopolishComp Eventalign_collapse (v0.6.2)56 to generate a random access indexed tabulated file containing realigned median intensity and dwell time values for each kmer of each read.All the datasets were preprocessed using an automated analysis NextFlow pipeline, before running Nanocompore  white-listing of transcripts with sufficient coverage, (2) parallel processing and statistical testing of transcripts position per position, (3) post-processing and saving.In order to reduce the computational burden, Nanocompore first filters out transcripts with insufficient coverage. This is achieved by a rapid tally of reads mapped per transcripts followed by selection of transcripts having at least 30 reads mapped in all of the samples provided. Users can modify the threshold but the default value allows to get reproducible results. Optionally, one can provide a custom list of transcripts to include or exclude.predicted_cluster~1+sample_label and report the coefficient’s p-value. 2) As an optional alternative we do a one-way ANOVA test comparing the log odds of data points belonging to cluster one between the two conditions. After testing, it’s optionally also possible to aggregate the p-values of neighbouring kmers to account for the fact that modified bases affect the signal of multiple kmers. To this end, and due to the fact that neighbouring p-values are non-independent, we implemented in python a method that extends the Fisher’s statistic X=-2log(P1w1 P2w2 … Pkwk) to approximate the distribution of the weighted combination of non-independent probabilities26. The combined p-values are computed all along the sequence using a sliding window of a given length. This method greatly reduces the prediction noise  at the expense of spatial resolution, while giving more weight to sites for which the effect of RNA modifications on the signal is spread over several kmers.White-listed transcripts are processed in parallel to take advantage of multi-threaded architecture. First, the data corresponding to the reads mapped on each transcript is loaded in memory and transposed in the transcript space in a position-wise fashion. The current implementation of Nanocompore only uses the median signal intensity and the scaled log10 transformed dwell time, but the framework is flexible enough to aggregate more variables, such as the error rate or additional Nanopolish HMM states. The 2 experimental conditions are compared positions per position using a range of statistical tests. We included the Kolmogorov-Smirnov (KS) test as a robust univariate pairwise statistical test on current intensity and dwell time. These tests are performed independently on the median intensity and the dwell time. We also implemented a Gaussian mixture model (GMM) clustering-based method. For a given position we fit a bivariate 2 components GMM to all the data points observed , irrespective of the sample label. We then assign each data point to one of the two clusters and test for differences in the distribution of reads between clusters across conditions. To this purpose, testing is implemented in two ways: 1) by default, we fit a Logit model to the data using the formula https://nanocompore.rna.rocks/demo/SampCompDB_usage/). The wrapper was initially developed for Jupyter but can essentially work with any python IDE. At the time of publication the wrapper allows to generate 6 different types of publication ready plots for a given transcript including (1) the distribution of p-values, (2) the distribution of signal intensity and dwell time, (3) the overall coverage per sample, (4) the nanopolish HMM states, (5) the kernel density of the signal and dwell time for a specific position and (6) the sharkfin plot of the p-values compared with Log Odds Ratio (for the GMM method).Results generated by the statistical module are collected and written in a simple key/value GDBM database. Although this data structure has limitations in terms of portability and concurrent access, it is natively supported by python and allows storing complicated data structures. For each test previously performed p-values are temporarily loaded in memory and corrected for multiple tests with the Benjamini-Hochberg procedure. Users can then obtain a tabulated text dump of the database containing all the statistical results for all the positions in the transcripts space or a BED file with the positions of significant hits found by Nanocompore converted in the genome space. Finally, we provide a convenient python wrapper over the GDBM database, allowing users to interactively access simple high level functions to plot and export the results . Examples can be found in Figure Given that a single modification can affect the signal for multiple overlapping kmers, we developed a peak calling method to refine our predictions. Briefly, we first converted p-values in -log10 so that peaks correspond to positions with higher probability of being modified. We then defined a dynamic threshold per transcripts corresponding to the median of all the values above 2 . In the case where no significant p-values were found, the threshold was set to 2. Peaks were called using The metagene m6A coverage analysis was done considering all nanocompore kmers with GMM logit p-value<0.01 and a log odds ratio >0.5. The plot was produced in R/Bioconductor with the Guitar package using the TxDb.Hsapiens.UCSC.hg38.knownGene package for the human transcriptome annotation and the SK1 reference transcriptome GFF for the yeast annotation.For the motif enrichment analysis of m6A sites identified by Nanocompore analysis of METTL3 KD, we extracted the sequence of all kmers tested by Nanocompore and having a p-value<0.5 (GMM-logit). The sequences were then sorted by p-value and analysed with Sylamer for the identification of over-represented words, using a word size of 5 and a growth parameter of 100. The Sylamer results were then imported in R for plotting. To produce a combined profile of the DRACH motif, the per-window p-values of all DRACH kmers were combined using Fisher’s method. For visualisation purposes, the final plot only reports the lines for the top 100 motifs with the greatest area under the sylamer curve, with the top one represented in colour.https://github.com/tleonardi/nanocompore_paper_analyses/m6acode/parse_sampcomdb.py. Briefly, for each of the three positions of interest, we extract the GMM model saved in sampCompDB, and for each read we then predict the probability that it belongs to each of the two clusters. To define which of the two clusters corresponds to m6A modified reads, we consider which of the two clusters has negative log odds of data points belonging to it in the KD condition . To test the independence of the methylation events at these three sites, we performed a chi-squared test of independence comparing the expected number of molecules for each of the 8 combinations of modifications to the observed number of molecules. The results reported are obtained using a probability threshold of 0.75 (as predicted by the GMM) to consider a read as methylated. However, to ensure robustness of these results, the chi-squared test was repeated for all thresholds between 0.1 and 1 (0.05 steps) and p-values were adjusted accordingly using the Benjamini–Hochberg procedure. Adjusted p-values were >0.39 for all thresholds used.To assign an m6A probability at A652, A1324 and A1535 for each read covering the β-actin transcript, we developed a dedicated post-processing script available at 59 and a custom python script to annotate p-values as colour shading The 7SK multiple alignments and consensus secondary structure were obtained from Rfam (RF00100). Secondary structure plots were produced with R2Rhttps://imaps.genialis.com/). Briefly, raw reads were demultiplexed and trimmed , before being mapped to a tRNA and rRNA index using STAR (v2.4.0.1)60. Unmapped reads were then mapped to GRCh38 GENCODE primary assembly, using GENCODE annotation v30. STAR parameter -alignEndsType Extend5pOfRead1 was used to ensure no soft clipping of cDNA start sites. PCR duplicates were removed based on unique molecular identifier (UMI) and mapping position. cDNA start -1 positions were taken as crosslink sites. Significant Nanocompore clusters were determined by merging overlapping kmers with a GMM_logit_contex_2 p-value < 0.001 using bedtools merge (v2.28.0). Control sites were selected as those with a context 2 p-value of 1 and split into those that did or did not contain DRACH within a 100nt window around the center of the cluster. Due to large differences in library size, miCLIP crosslinks were first filtered to remove intergenic and ncRNA sites and then subsampled using GNU coreutils shuf, to generate libraries equal in size to the smallest library, totalling 47,012 crosslinks. Crosslink counts were divided by gene TPMs calculated from either WT or KO mock miCLIP samples. BigWig files were generated from the normalised bedgraphs, which were used as the input to deepTools61 (v3.3.0) computeMatrix and plotHeatmap to generate metaprofiles -1000 to +1000bp around the center of Nanocompore clusters with a bin size of 2bp. The resulting tabular output was further analysed in R. Shaded regions on the plot represent the mean +/- the standard deviation at each position in the profile . Both the mean and bounds were smoothed using loess regression with a span of 0.6. In order to test for a significant difference between WT and KO profile, mean values from WT and KO miCLIP between positions -20 to +20 around nanopore sites were subjected to a Mann–Whitney U test.miCLIP data and corresponding input data was analysed using the iMaps web server (https://github.com/a-slide/MetaCompore). For this study, we used MetaCompore v0.1.2, which includes the latest version of following tools : Epinano 1.2.0, Eligos 2.0.0, Tombo 1.5.1, differr_nanopore_drs (latest version), Mines (latest version) and Nanocompore 1.0.3. MetaCompore preprocess the data for all the tools, including Basecalling with ONT-Guppy 4.2.2 (except for Epinano which required the older 3.1.5 version), read alignment to the reference transcriptome with Minimap2 2.17, alignments filtering with pyBiotools 0.2.7 and signal realignment with f5c 0.6. For portability and reproducibility reasons, every module of MetaCompore is provided within its own singularity container and all the options used for a run are tracked in a YAML configuration file. Nanocompore and Epinano are the only tools to support experimental replicates. For all the other tools we merged the data obtained from replicates. Since every tool outputs a different kind of statistics/format, MetaCompore filters the data following the respective authors recommendations and when possible converts the result in a similar format containing the significant site associated with their p-value and Effect size. For Nanocompore and Tombo which both work in signal space, we added a peak calling denoising step to narrow down the results.In order to compare Nanocompore against most of the other tools available for RNA modification detection in a reproducible way, we wrote a snakemake pipeline called MetaCompore (https://www.yeastgenome.org/strain/SK1). Prior to modification detection, we ran an optional pipeline step to filter out any reference transcript with less than 30 reads in all replicates. The command line options used for all the tools are available in the MetaCompore configuration file provided as For the comparison in this paper we used a Yeast SK1 dataset comparing 2 replicates of WT yeast against 2 replicates of an IME4 KO mutant (m6A writer in Yeast). We used the Yeast SK1 reference transcriptome False Positives: the number of significant kmers that do not overlap a ground-truth m6A site. The False Positive Rate was further defined as the number of False Positives divided by the sum of False Positives and True Negatives.False Negatives: the number of ground-truth m6A sites not overlapped by any significant kmerThe comparison of each method in MetaCompore with this orthogonal reference dataset was based on our For the purposes of the calculations above, we used the results tables produced by each method (prior to Metacompore postfiltering) and applied the following criteria to consider a kmer as significant:Eligos2: reported p-value<=0.01 and odds ratio>1.2 (as recommended by the authors)p-value<0.01 Diff_err: reported MINES: all sites (MINES only reports significant sites)Epinano: sites classified as modified (modification probability >0.5)p-value<0.01 after Benjamini-Hochberg adjustmentTombo: reported p-value<0.01 and GMM log odds ratio>0.5 (for GMM method only)Nanocompore: reported For the benchmarks above, the single nucleotide sites identified by each method were extended to 10nt prior to overlapping them with the ground-truth set.Further information on research design is available in the Supplementary InformationReporting Summary


This article belongs to the Special Issue mRNA Therapeutics: A Themed Issue in Honor of Professor Katalin Karikó.Advances in the using in vitro transcribed (IVT) modRNA in the past two decades, especially the tremendous recent success of mRNA vaccines against SARS-CoV-2, have brought increased attention to IVT mRNA technology. Despite its well-known use in infectious disease vaccines, IVT modRNA technology is being investigated mainly in cancer immunotherapy and protein replacement therapy, with ongoing clinical trials in both areas. One of the main barriers to progressing mRNA therapeutics to the clinic is determining how to deliver mRNA to target cells and protect it from degradation. Over the years, many different vehicles have been developed to tackle this issue. Desirable vehicles must be safe, stable and preferably organ specific for successful mRNA delivery to clinically relevant cells and tissues. In this review we discuss various mRNA delivery platforms, with particular focus on attempts to create organ-specific vehicles for therapeutic mRNA delivery. Currentin vivo without degrading the mRNA. In this review, we summarize different vehicles that have been used to deliver mRNA. We will cover the advantages and disadvantages of each vehicle and point out future directions in this exciting and important field.To overcome these barriers, two researchers, Dr. Katalin Karikó and Dr. Drew Weissman, asked the fundamental question: what mechanism is responsible for the immunological response, triggered by TLR7 and TLR8, to mRNA? Their landmark work . showed Since RNA was first discovered, researchers have employed many methods of delivering it to cells. Initial techniques used naked RNA, which, as mentioned above, is prone to RNase degradation and evokes a strong proinflammatory response. More sophisticated methods sought to enable cell entry and, on a systemic level, allow sufficient circulation time for the therapeutic mRNA to reach its destination and be released into target cells.In vivo, PEI was successfully used for aerosol gene delivery into the lungs [in vitro and in vivo, they are also significantly cytotoxic, partly because of their poor degradability, which prevents PEI-based carriers from broader use in pre-clinical and clinical settings [To date, lipidbased nanoparticles (LNPs) are the only RNA therapeutic carriers approved for clinical use ,15,16; the lungs . Althougsettings ,21.in vitro and formulations most efficiently transfected IGROV1 cells with luciferase (Luc) mRNA were subsequently tested in vivo. Adding pluronic F127 decreased the overall charge of the nanoparticle and increased its stability. Further, manipulating F127 content resulted in lung-specific mRNA delivery that potentially could be used to treat pulmonary disease [Polyesters are another group of materials being used for RNA delivery. A library of 480 biodegradable polyesters was screened  disease .TM), was developed for prolonged siRNA delivery into the pancreatic tumor environment and, combined with chemotherapy, was tested in a phase 1/2a clinical trial [A miniature biodegradable polymeric matrix, LOcal Drug EluteR  are spherical vesicles made of lipids. Lipids are organic, water-insoluble lipid compounds that can form defined structures such as cell membranes due to their unique features. Lipids have a hydrophilic head and a hydrophobic tail that allow LNPs to undergo self-assembly into well-defined structures, such as cell membranes . LNP-RNAAdditional modifications tuned the properties of LNPs. For example, in addition to ionizable lipids, LNPs contain phospholipids that serve as helper lipids, cholesterol to improve cell entry and polyethylene glycol (PEG) to improve stability and circulation time by preventing serum protein binding a,b 27,3,32,33.Each LNP component can be altered to adjust the properties of the final vehicles. PEG content is inversely proportional to LNP size; changing PEG content from 1% to 5% produces LNPs 100nm to 20nm in size . SimilarWe will describe other application-specific modifications to LNP formulations in detail below.in vitro transfections have long used cationic lipids including commercially available Lipofectamine, which is widely used for RNA and DNA in vitro transfections despite its known cytotoxicity [II phase formation. This hexagonal phase disorganizes bilayer structures and correlates with membrane fusion as well as the disruption that is partially responsible for cationic lipid toxicity [in vitro. [As mentioned above, cationic lipids are used to formulate LNPs containing nucleic acids . Cationitoxicity . While stoxicity . When sytoxicity ,45. Inden vitro. . Addition vitro. . Excessin vitro. ,49. Thoun vitro. .Ka value. The pKa value is supposed to be sufficiently high so at low pH lipids are positively charged which enable binding with negatively charged RNA molecules and formation of LNPs. Thus, at low endosomal pH positive charge of ionizable lipid allow interactions with endogenous anionic lipids hence leads to disruption of endosomal structure and release of LNPs cargo into cytoplasm. Simultaneously, the pKa value of the ionizable lipids should be sufficiently low so at physiological pH the surface charge of LNPs will remain relatively neutral. This allows to modulate toxicity and immunogenicity of resulting LNPs and increases their circulation time [Currently, the LNPs that are most widely used in systemic nucleic acid delivery typically contain ionizable cationic lipids, helper  phospholipids, cholesterol and PEG. Ionizable LNPs were created to avoid the toxicity of the permanently cationic lipids originally used in LNP-RNA systems, in order to enable their therapeutic applications. Patisiran (brand name Onpattro), the only FDA-approved LNP-RNA therapeutic prior to anti-SARS-CoV-2 vaccines, utilizes ionizable lipids in its LNP formulation . Design ion time ,40,52.At the cellular level, the limiting factor in efficiently translating LNP-RNA is the RNA cargo release into target cells’ cytoplasm. One mechanistic explanation for the endosomal escape process is molecular structure hypothesis. According to this hypothesis, cationic ionizable lipids become protonated in increasingly acidic endosomal environments, a process that allows interaction with anionic lipids in the endosomal bilayer. That interaction forms non-bilayer hexagonal structures that disrupt the bilayer, releasing LNP cargo into cytoplasm ,52,53. DIntroducing and evolving ethanol loading procedures facilitated effective production of homogenous, small-size (diameter < 100nm) LNPs with high entrapment efficiency (>80%) ,56,57,58in vivo using six different delivery routes . The LNPs were 70-100nm in size and comprised cationic ionizable lipid, phospholipid, cholesterol and PEG at 50:10:38.5:1.5 mol/mol ratio. Expression kinetics analysis showed different expression patterns depending on the delivery route. Local intramuscular injection produced Luc expression at the injection site as well as diffusion to the liver. Additionally, systemic delivery via intravenous or intraperitoneal injection also resulted in strong protein production in the liver [Prior to developing SARS-CoV-2 vaccines, both BioNTech and Moderna worked on LNP-encapsulated mRNA therapeutics with a range of properties optimized for different aims ,64,65,66he liver . These fKa value defines their behavior at various pH levels. This study tested LNPs containing ionizable lipids with different pKa and found that with regard to protein expression, the best lipids for intramuscular administration have higher pKa than those that are best for IV administration, a result that suggests LNPs should be specifically designed their intended purposes [Ka for immunogenicity was between 6.6 and 6.8; however, an ionizable lipids’s pKa is not the only factor that plays a role in optimization. Another 2019 publication focused on designing ionizable lipids for LNP formulations intended to serve as antigen mRNA delivery vehicles as well as adjuvants. This extensive study prepared a library of over 1000 lipid formulations and tested their ability to cause antigen protein expression and induce immune response for anti-tumor vaccines. The authors concluded that the formulations containing ionizable lipids with cyclic amine head groups, unsaturated lipid tails and dihydroimidazole linkers most efficiently inhibited tumor growth and increased survival in both melanoma and human papillomavirus E7 mouse models [Research published in 2019 compared various LNP formulations for intramuscular administration of mRNA vaccines. As noted above, in LNPs ionizable lipids’ properties depend on pH, and their ppurposes . For exae models .To date, ionizable lipid LNPs are the preferred carriers for clinical therapeutic RNA delivery. The anti-SARS-Cov-2 modRNA vaccines are the most prominent example, with millions of doses already administered worldwide ,15. AddiHaving proved that LNP-RNA therapeutics are safe and efficient, the next step for the field is to focus on designing cell- and organ-specific treatments for minimally invasive clinical delivery routes. Indeed, several trials have been already completed in pursuit of these goals, and many more are ongoing.apoE−/− mice using cationic and ionizable lipid LNPs carrying siRNA demonstrated that the hepatic uptake of ionizable, but not cationic, LNPs is ApoE dependent, suggesting that LNP charge plays a role in LNP tissue tropism [The abovementioned vaccine studies used intramuscular injections of LNP-RNA for systemic immune response, as well as systemic delivery to the liver in the case of Patisiran, but how could we direct LNP-RNA into other organs for protein replacement therapy? Apolipoprotein E (ApoE) in blood serum has been shown to bind to intravenously injected LNPs. Crucial to transporting and metabolizing lipids, ApoE regulates lipoprotein and cholesterol levels in the plasma via high-affinity binding to the family of LDL receptors ,71. The  tropism . While excessive liver homing is a notable disadvantage of intravenously injected ionizable LNPs, a selective organ targeting (SORT) strategy could overcome this issue . In thisIn addition to organ-specific delivery, researchers have pursued targeting specific cell subsets in the liver by engineering ionizable lipid nanoparticles for selective RNA delivery into hepatocytes and liver sinusoidal endothelial cells (LSEC). For hepatocyte-specific delivery, the authors manipulated particle size by adjusting PEG content, whereas incorporating mannose favored targeted LSEC RNA delivery .in vivo deployed theranostic LNPs containing PEGylated BODIPY dyes (PBD) [One of the most therapeutically important targets for gene delivery is tumor tissue. Cancer remains the main cause of death worldwide, accounting for nearly 10 million deaths in 2020  and varies (PBD) , which ses (PBD) ,87. Ther+ cell depletion, the spleen signal was not detectable after Luc-LPX administration, indicating that this formulation preferentially targeted antigen-presenting cells (APC). More detailed analysis showed that dendritic cells showed the highest protein expression even though macrophages internalized more RNA from injected RNA-LPXs. When treated with RNA-LPX bearing influenza virus hemagglutinin, both cell types induced TLR7-dependent overexpression of INFα. Systemic delivery of RNA-LPX vaccines carrying tumor-specific antigens (OVA for B16-OVA melanoma and gp70 for CT26 colon carcinoma), produced complete, long-lasting protection against subcutaneous tumor challenge, and re-challenge in the case of CT26. Additionally, RNA-LPX vaccines protected the mice from lung metastasis when delivered after intravenous tumor implantation and significantly increased their survival. The authors concluded that the observed tumor rejection occurred via INFα-dependent T-cell activation [So far, systemic delivery into tumors remains an unmet challenge, though many researchers are exploring the use of lipoplexes, which are cationic liposomes, to carry nucleic acid as another approach to cancer immunotherapy and cancer vaccines. In a study that used RNA-lipoplexes (RNA-LPX) to convey cancer antigens into dendritic cells , the auttivation . The formulation used in this study was also employed in a project focusing on immune system desensitization in multiple sclerosis . Antigen+ inflammatory leukocytes in a mouse model of inflammatory bowel disease (IBD) [+ leukocytes significantly reduced the severity of intestinal inflammation in treated mice [Another study utilizing modified mRNA LNPs investigated therapeutic delivery of interleukin 10 (IL-10) into Ly6Cse (IBD) . In addited mice .Thoughtfully designing and tuning LNP properties is one approach for tissue-specific delivery of therapeutic RNA. Another possibility would be to create a ‘self-controlling’ RNA expression system that only allows protein translation in specific cell subsets despite RNA uptake by other cell types. The Specific Modified mRNA Translation System (SMRTs) was designed to permit specific expression of therapeutic genes in cardiomyocytes (CM) but not in non-CMs in the heart . This syUntil recently, LNPs were mostly designed for small interference RNAs (siRNAs), which are significantly smaller than mRNA. More tailored LNP formulations may therefore need to be developed to allow such a big molecule to be carried and translated efficiently ,96,97.To date, the only broadly used organ-specific delivery approach is based on the overall charge of the LNPs . PositivTumor targeting can be improved by adding tumor-specific antibodies to the surface of LNPs. However, due to the lack of cancer-specific surface markers, this approach cannot be widely utilized. One attempt to boost tumor-specific RNA-LNP delivery is to increase its circulation time in order to expand its ability to accumulate in tumor tissue. PEGylation is one method that can promote LNP accumulation in tumor tissue after systemic delivery. Adding PEG to the LNP surface prevents protein binding and opsonization which increase circulation time but may also be related to the more favorable nanoparticle size that helps extravasation into tumor tissue . AnotherIn last decade, lipid-based carriers for RNA delivery received significantly increased attention in this rapidly growing field. LNPs used in mRNA vaccines against SARS-Cov2 are a safe and efficient vehicle for therapeutic gene delivery, and their success will certainly support the development of LNPs for other applications, such as protein replacement therapies or gene editing, in the future.