Ebolavirus species. This identifies 39SGP1A as an excellent option for integration on a sensor platform to detect ebolavirus infections.With properties such as stability to long-term storage and amenability to repetitive use, nucleic acid aptamers are compatible with many sensing/transducing platforms intended for use in remote locations. Sensors with these properties are important for quickly identifying ebolavirus outbreaks, which frequently start in locations that lack sophisticated equipment. Soluble glycoprotein (sGP), an excellent biomarker for ebolaviruses, is produced from the same gene as the ebolavirus glycoprotein GP1,2 that decorates the surface of the viral particle and is secreted in abundance into the blood stream even during the early stages of infection. Here, we report the selection and properties of a 2′fluoro pyrimidine (2′FY)-modified RNA aptamer, 39SGP1A, that specifically binds sGP. We demonstrate by computational and biochemical analysis that the recognition motif of 39SGP1A is a novel polypyrimidine-rich sequence. Replacement of -F by -OH in the 2′ position of the ribose resulted in complete loss of affinity for sGP. The protein motif to which the aptamer binds requires an intact sGP dimer and binds to an epitope conserved between Ebola virus (EBOV) and Sudan virus (SUDV) sGP, the most divergent  Zaire ebolavirus, in the family of Filoviridae, is a highly virulent enveloped negative strand RNA virus that causes hemorrhagic fever with 40–90% fatality. Early detection is the key to controlling outbreaks and epidemics such as those that have occurred in the recent past. The GP gene of all Ebolavirus species  encodes three glycoproteins. The surface exposed trimeric glycoprotein  is the minor product, generated as a result of occasional transcriptional slippage. The major translated product is soluble glycoprotein (sGP), which is conserved amongst the Ebolavirus species2. sGP shares its first 295 amino acids with GP1,2, but has a unique species-specific 28 amino acid carboxy terminus, and is secreted as disulphide-linked, glycosylated homodimers. sGP and GP1,2 share the same 3D structure in the region of their sequence identity3. Although, the role of sGP in pathogenesis is not yet established, some antibodies elicited during ebolavirus infection cross react with sGP and GP1,2. This observation suggests a role for sGP as a decoy for antibodies against GP1,2 on the virus particle4. Consistent with this hypothesis is the observation that sGP is secreted in abundance in the early stages of infection and its production is selected for in vivo4. Although an excellent choice as an ebolavirus biomarker, sGP has so far been overlooked for this purpose.The species in vitro iterative selection protocol called Systematic Evolution of Ligands through Exponential Enrichment (SELEX)7. They can be chemically synthesized at relatively low cost and have been incorporated into a number of diagnostic sensor platforms8. For increased stability to nucleases, RNA aptamers are often chemically modified at the 2′ position with fluorine, amino, or methyl groups10. Here we describe the application of SELEX combined with Next Generation Sequencing (NGS) and analysis by AptaCluster11 and AptaGUI12 to select a 2′FY-RNA aptamer (39SGP1A) with high affinity and specificity for sGP. 39SGP1A binds EBOV and SUDV sGP tightly (Kd ~30 nM and 250 nM respectively) and does not bind human serum albumin or other proteins present at relatively high concentrations in serum. The requirement for structure in the binding of 39SGP1A to sGP is evidenced by the lack of affinity for sGP of the RNA (2′ OH) version of this 2′FY-RNA aptamer. The 2′F modification promotes the C3′ endo conformation in the ribose sugar, stabilizes the 2′FY-RNA structure and increases molecular hydrophobicity13.Aptamers are short nucleic acids (RNA or DNA) that are selected by an Ebolavirus species and has potential as a bio sensing element for ebolavirus detection.In summary, we describe the selection and characterization of an aptamer with a novel 2′FY-RNA binding domain consisting of a poly2′F-U loop with GAGC in the stem. This aptamer (39SGP1A) binds tightly to sGP from two divergent TAAAAAA) and the DNA templates used for preparing 2′FY and 2′OH RNAs for this study (listed in section “Preparation of 2′FY-RNAs”). 39SGP1A 2′FY RNA (GGGCGCUCAAUUUUUUAUUGCAUUUUUCUUUGAGCGCCC) was purchased from IDT.Oligonucleotides used in the study were synthesized by Integrated DNA Technologies  or the Iowa State University DNA facility. A DNA single stranded oligonucleotide library (487D) was synthesized by IDT with the following sequence: GCCTGTTGTGAGCCTCCTGTCGAA (53 N) TTGAGCGTTTATTCTTGTCTCCC. N symbolizes that an equimolar mixture of A, C, G and T was used during synthesis of the library. The following primers were used for PCR amplification: 484 (TAATACGACTCACTATAGGGAGACAAGAATAAACGCTCAA) and 485 (GCCTGTTGTGAGCCTCCTGTCGAA) to create dsDNAs that included a 5′T7 polymerase promoter for reverse transcription reactions. Other purchased DNAs were 5196  was incubated at 94 °C for 5 min, 65 °C for 15 min then 72 °C for 99 min using the Multi GENE II PCR minicycler. The generated dsDNA was resolved through a 2% agarose gel, purified using a Qiagen PCR purification kit, and quantified using a Nanodrop spectrophotometer ND1000 Spectrophotometer (Nanodrop Technologies).To generate the starting 2′FY-RNA pool, an extension reaction was performed using primer 484 and the starting ssDNA pool. 10 reactions, each of 100 μL and containing 2 μM 487D pool, 3.3 μM 484 primer, 0.5 mM dNTP mix, 0.03 U/μL DNA Taq Polymerase (GenScript) in reaction buffer , 1.5 mM MgClSUDV sGP  was purchased from IBT Bioservices . EBOV sGP protein was produced in 15-cm plates of HEK 293 T cells by PEI transfection with 30 µg of the expression vector. The next day, media was replaced with Opti-MEM low-serum media containing 1 mM HEPES and 1% non-essential amino acids. Supernatant was collected at 48 and 72 hours following transfection and filtered through a 0.45 µm filter and frozen at −80 °C until purified.Supernatants were thawed and concentrated by centrifugation using Amicon Ultra-15 Centrifugal Filter Units with 10 kDa molecular weight cut off (MWCO). sGP-6xHis present in the concentrated supernatants was bound to and eluted from cobalt-bound Dynabeads using the Dynabead His-Tag Isolation & Pulldown Kit (#10103D) protocol. Eluted samples were passed through Zeba Spin Desalting Columns (#89882). The desalted material was washed 5–10 times with 400 µL of DPBS  in an Amicon Ultra-0.5 mL Centrifugal Filter Unit. Final purified samples were diluted to desired concentration in DPBS. Purified sGP was analyzed by Coomassie Blue staining and western blotting using anti-GP monoclonal antibody, 21D10 (IBT Bioservices).in-vitro transcription using a DurascribeTM T7 transcription kit . About 2 nmoles of dsDNA from the pool generated by extension was incubated with 5 mM ATP, 5 mM GTP, 5 mM 2′F-UTP, 5 mM 2′F-CTP, 5 mM DTT, 5% DMSO, 0.2 U/μL T7 polymerase in a total volume of 2.8 mL and incubated at 37 °C for 4 h. The DNA mixture was then digested with 1 MBU DNAse I. The 2′FY-RNA was resolved through a 7 M urea 8% (19:1 acrylamide:bisacrylamide) gel in TBE buffer  to separate the residual NTPs and abortive transcripts. The transcribed RNA was eluted into TE buffer  by incubating for 18 h at 37 °C then ethanol precipitated.2′FY-RNA was prepared by in-vitro transcription using dsDNA (487D) pool as template. The 2′FY-RNA pool  was incubated with 1 nmole sGP in Buffer A  at room temperature  for 1 h and the non-binding 2′FY-RNA sequences were removed by passing the mixture through a nitrocellulose membrane on which the protein and bound RNAs were captured. With each subsequent positive selection the ratio of 2′FY-RNA pool:sGP was increased to increase the stringency of selection. Increased stringency was achieved by maintaining a constant amount of 2 nmoles RNA (10 µM) in each round and decreasing the amount of protein to incrementally increase the ratio of RNA/protein from 2 to 3, 4, 6.25, 10 and 20, respectively in subsequent positive selection rounds. To remove nonspecific membrane binders, negative selections were performed by passing the pool through the nitrocellulose membrane in the absence of protein. Reverse transcription and polymerase chain reaction amplification (PCR) was used to generate DNA for subsequent selection rounds. Eight rounds of selection were performed, which included two negative and six positive selections. PCR (15 cycles) was performed with the reverse transcribed RNA product from the selected pool, 0.05 U/ul Taq polymerase, 1.5 mM MgCl2, 0.2 mM of each of the four deoxynucleotide triphosphates, 50 mM KCl, 10 mM Tris-Cl (pH 9.0), 0.01% Triton X-100 and 2 µM of each primer (oligonucleotides 484 and 485). The enriched pool from the 8th SELEX round was cloned into the TOPO XL PCR cloning plasmid for sequencing, in addition PCR-amplified libraries were prepared for NGS using Illumina sequencing primers and appropriate bar coding.Selection of aptamers that bind sGP was carried out by SELEX . The starting 2′FY-RNA pool was generated by 14 with barcodes ATCACG, TAGCTT, GGCTAC, ATGTCA for rounds 1, 4, 6, and 8 respectively. Quality control was performed by allowing a maximum of one nucleotide mismatch between the reference and matched barcodes, and a maximum of two mismatches between the reference and matched primers (see SELEX library construction in the section “Selection of 2′FY-RNA aptamers against sGP (soluble glycoprotein)”). Any reads not conforming to these standards were excluded from downstream analysis. Aptamer families (clusters) were consequently extracted from each round using AptaCLUSTER. We performed a total of 10 iterations of locality sensitive hashing, sampling 40% of the indices in the randomized regions and allowing no more than 10 nucleotide mismatches between the seed sequence of a cluster to each remaining member. The resulting clusters and additional information were visualized and analyzed through the graphical user interface AptaGUI.Raw sequences were demultiplexed and assigned to their respective rounds using the preprocessing tool AptaPLEXMEME was run with version 4.12.0 using default parameters whereas secondary structures were predicted using Mfold version 2.3 with standard parameters in the RNA mode.2′FY-RNA was prepared using a Durascribe transcription kit (Epicentre Technologies) with PCR-amplified dsDNA, which was prepared from DNA templates containing the same sequences as the resulting 2′FY RNAs. Templates used to make each of the 2′FY oligonucleotides used in this work are listed in parentheses after the number with the core T7 promoter sequence bolded and italicized. The resulting RNA sequences (with T and C in the DNA becoming 2′FU and 2′FC in 2′FY RNAs or U and C in 2′OH RNAs) are underlined. Single stranded DNAs 4789, 4797 and 5189 were amplified with primer 484 complementary to the 5′ end, resulting in RNAs with sequences complementary to the underlined sequences.4789 ;5011 (TAATACGACTCACTATAGGGCGCTCAATTTTTTATTGAGCGCCC)5012 (GGGCGCTCAATAAAAAATTGAGCGCCCTATAGTGAGTCGTATTA)RNA 5015 (GGGCGCUCAAUUUUUUAUUGAGCGCCC) was synthesized from a template that was created by annealing oligonucleotides 5011 and 5012.5179 (TAATACGACTCACTATAGGGAGACAAGAATAAACGCTCAATCTTTTTATTTTACTTTTTGTTGTAGAGCGCTTGTTTCTCTGAGTTGTAGCCATTCGACAGGAGGCTCACAACAGGC);5183 (TAATACGACTCACTATAGGGAGACAAGAATAAACGCTCAATT TTTTATTGCATTTTTCTTTGAGCGTCATTGATACCCTGTTTCCTTGAGCACTTCGACAGGAGGCTCACAACAGGC); 5189 (AGGAAACAGGGTATCAATGACGCTCAAAGAAAAATGCAATAAAAAATTGAGCGTTTATTCTTGTCTCCC);5192 (TAATACGACTCACTATAGGGAGACATAAACGCTCAATTTTTTATTGCATTTTTCTTTGAGCGTCATTGTGTTTCCTTGAGCACTTCGACAGGAGGCTCACAACAGGC);5193 (GCCTGTTGTGAGCCTCCTGTCGAATAGCTCAAGGAAACAGGGTATCATTCTTGTCTCCCTATAGTGAGTCGTATTA);5197 (TAATACGACTCACTATAGGGCGCTCAATTTTTTATTGCATTTTTCTTTGAGCGCCC); 5198 (GGGCGCTCAAAGAAAAATGCAATAAAAAATTGAGCGCCCTATAGTGAGTCGTATTA)RNA 5199 (GGGCGCTCAAUUUUUUATTGCAUUUUUCUUUGAGCGCCC) was synthesized from a template that was created by annealing oligonucleotides 5197 and 5198.2, 0.1 mM ZnCl2, 1 mM spermidine, 0.02 U/μL calf intestine phosphatase  then extracted with phenol-chloroform, and precipitated with ethanol. The 2′FY-RNA was end-labeled by incubating with [γ32P] ATP and 1U/ul T4 polynucleotide kinase , in 7 mM Tris-Cl,1 mM MgCl2, 0.5 mM DTT pH 7.6 at 25 °C. In preparation for the filter capture assay, the 2′FY-RNAs (2 nM) were denatured at 95 °C for 5 min. The 100 nt 2′FY RNAs such as 5183 were refolded in Buffer A at room temperature (RT) for 30 min. The 2′FY-RNA aptamer, (39SGP1A) was refolded by rapid cooling in ice water for 5 min then left on the bench for 10 min at RT.The transcribed 2′FY-RNA was resolved by electrophoresis through an 8% polyacrylamide–7 M urea gel, and eluted from the gel. The 2′FY-RNA was dephosphorylated by incubating for 1 h at 37 °C in 50 mM Tris-Cl pH 9.3, 1 mM MgClThe most stable MFold-predicted structures for all oligonucleotides investigated in this work are shown in Fig. 32P-2′FY-RNAs were incubated at RT for 30 min with varying concentrations of sGP (10 nM to 1 μM) then the samples were passed through nitrocellulose membranes (HAWP 02500). The filters were immediately washed with 3 mL Buffer A, and the 32P quantified using a liquid scintillation counter . The data was fit to the formula: F = Fmin + (Fmax*L^n)/(L^n + Kd^n) to determine the Kd, where Fmax is the maximum bound fraction, L is the ligand concentration, Fmin is the starting minimum bound fraction, n is the Hill coefficient and F is the corrected fraction bound. The concentration of sGP is reported throughout as the monomer concentration and the Kd is calculated on this basis. Thus, the calculated Kd’s reported here should be halved when referring to the sGP dimer.Refolded 32P-2′FY-RNAs were incubated with sGP in Buffer A for 30 min at RT. The mixtures were analyzed for complex formation with sGP by resolution through a 6% native polyacrylamide gel run in THE buffer  at 200 V. The 32P was visualized using a phosphorimager.Refolded in vitro transcribed in the presence of 2′F-UTP and 4-thio-uridine 5′triphosphate  at a molar ratio of 1:1 and 5′end-labeled with 32P as described previously. Three pmol of RNA was denatured at 95 °C for 5 min followed by immediately cooling on ice for 5 min. The RNA was folded in Buffer A for 10 min at RT. To link sGP to the 2′FY-RNA aptamer 5183, 3 pmol 2′FY-RNA was incubated with 15 pmol sGP in 15 μL Buffer A for 20 min at 23 °C for complex formation. This mixture was irradiated for 6 min and 12 min at 4 °C under a UV lamp (365 nm) with the samples placed 3 cm from the lamps. The UV-irradiated samples were then treated with proteinase K for 30 min and resolved through a 10% reducing SDS PAGE gel to separate the 2′FY-RNA-protein complex, free 2′FY-RNA and free protein. The 32P on the gel was visualized using a phosphorimager.RNA was 32P-labeled 39SGP1A 2′FY-RNA was added to the reaction mix to a final concentration of 150 nM and the samples were incubated for 15 min. The 2′FY-RNA-protein complexes were then UV crosslinked at 365 nm for 15 min at 4 °C. Crosslinked products were analyzed by resolving through a 10% SDS PAGE reducing gel, the gel was dried and imaged by Typhoon scanner. Quantification was performed using Image J software.Competition binding reactions were performed by incubating 3 μM sGP with 9 μM and 18 μM competitor 2′FY-RNA sequences for 30 min at RT in Buffer A. Following the pre-incubation period, 32P-39SGP1A 2′FY-RNA aptamer was incubated in the presence and absence of a 10-fold excess of unlabeled DNA oligonucleotide 5196 for 20 min. sGP was then added and the samples incubated for another 30 min followed by irradiation for 15 min (UV at 365 nm). The samples were resolved through a 10% reducing SDS PAGE gel to separate the 2′FY-RNA aptamer-protein complex from the free 2′FY-RNA and protein. The 32P on the gel was visualized using a phosphorimager.To establish if the poly2′F-U loop is important for sGP binding, When two values were subtracted one from the other or divided one by the other, the standard deviations were calculated to take into account both errors by calculating the square root of the sum of the squares of the two errors. Statistical comparisons were performed by a two-tailed t-test, or a one-way analysis of variance (ANOVA) with appropriate post-hoc analysis as indicated. A p value of < 0.05 was considered significant.15 molecules of 2′FY-RNA. The SELEX protocol followed a mathematically defined approach of starting with a relatively high molar ratio of 2:1 (2′FY-RNA: sGP) followed by harmonic reductions in the sGP concentration15 to increase the stringency of selections  through the selectionsate Fig. . Based oate Fig.  were preate Fig. . These r32P-2′FY-5199 RNA contained protein as it was lost after proteinase K treatment  was taken as the sGP aptamer and named 39SGP1A Fig. . This apnt Fig., . Involvent Fig., . An olignt Fig., . These rEbolavirus species.We also determined that 39SGP1A binds SUDV sGP, albeit with a 10-fold lower affinity, but still in the nanomolar range Fig. . Thus, 319. Thus, the aptamer affinity for sGP must be more than 30-fold higher than for HSA to avoid HSA interference in detection of sGP by the aptamer. The original aptamer (oligo 5183) bound HSA with an estimated Kd of 400 nM, which is only about 10-fold higher than its Kd for sGP  is highly abundant in blood, reaching concentrations up to 300 μM, whereas the concentration of sGP in serum is likely to be about 2 μM or lowersGP Fig. . HoweversGP Fig. . 39SGP1AsGP Fig. .Figure 532P-39SGP1A for binding to sGP as did 5179  did not compete with 39SGP1A, but shifted the mobility of the sGP:39SGP1A complex to a higher position on the gel, suggesting that this oligonucleotide binds sGP in a different site from that bound by 39SGP1A.2. sGP is a 82 KDa dimeric glycoprotein secreted by ebolavirus infected cells and is believed to be responsible for immune modulation during infection4. An aptamer that binds sGP could be incorporated onto a biosensor platform to detect early stages of ebolavirus infection.In this study, we identified and characterized a 2′FY-RNA aptamer that binds the EBOV sGP dimer with high affinity and specificity and also binds SUDV sGP with 10-fold lower affinity. 39SGP1A is the first 2′FY-RNA or RNA aptamer reported to be specific for ebolavirus sGP. The choice of sGP as an aptamer target was motivated by the fact that it is secreted in abundance in blood early during infection, which makes it an excellent biomarker for ebolavirus infection15 2′FY modified RNA oligonucleotides our results support the conclusion that the SELEX protocol resulted in the selection of two populations of oligonucleotides with high and low affinity for sGP, respectively. Some of the selected sequence clusters contained a conserved consensus sequence of poly U and GAGC, for which MFold-predicted secondary structures showed the poly U stretch of 18 nt in a loop region followed by GAGC’s in a stem. Oligonucleotides containing the polyU/GAGC motif bound sGP with high affinity (Kds = 20–54 nM). The 39 nucleotide sequence near the 5′ end of the oligonucleotide 5183 containing this motif was identified as the aptamer, 39SGP1A. Validation of this sequence as the aptamer included demonstrating the capture by UV-crosslinking of 39SGP1A and sGP in a single complex resolved by EMSA, its high affinity for the selection target, EBOV sGP, and the ability of a DNA oligonucleotide (5196), complementary to the polyU loop, to reduce 39SGP1A binding to sGP.The 2′F ribose modification was chosen for aptamer selection as it provides nuclease resistance and has been widely used in aptamer selections. After six rounds of positive selection and two rounds of negative selection from a starting pool of ~10Aptamers from other clusters in the top 10 sequence populations that lacked the polyU/GAGC motif bound sGP with lower affinity. High and low affinity oligonucleotides competed for the same site in binding sGP but at least one oligonucleotide (4797) appears to bind to a second site on the protein. Thus, there are likely to be at least two epitopes on sGP that provide suitable targets for binding by 2′FY RNA aptamers.22, by examining if 5183 RNA (prepared with 2′OH uracil and cytosine) bound sGP. The lack of affinity of 5183 RNA for sGP showed that the poly2′F-U is important for aptamer binding. This result and the observed differential affinity for EBOV and SUDV sGP is consistent with the hypothesis that the 2′F-U loop folds into a structure complementary to a sGP surface epitope.The bias for T in the starting pool increased the probability that aptamers with high U content would be recovered. We investigated if sGP contained a region with high affinity for poly(U), which is present in some proteins23, which is usually favored in RNA duplexes, increasing their helical stability. In a comparison of aptamers selected from libraries of RNA molecules containing 2′ amino- and 2′ fluoro-pyrimidines, the 2′FY-RNAs were found to have higher affinities than the 2′ NH2 modified aptamers13. The authors speculated that the increased affinity of 2′FY modified RNA is due to formation of thermodynamically stable helices and rigid structures in 2′FY-RNA compared with the 2′amino modified RNA13. However, X ray crystallography studies with 2′F modified and unmodified duplexes demonstrated that the presence of the 2′F modification does not alter the overall helical structure of the duplex24. The increased thermodynamic stability of the helices is due to the highly electronegative fluorine polarizing the nucleobases, which enhances Watson-Crick pairing. Osmotic stress and X ray crystallography studies showed that the presence of 2′fluorine reduced hydration of a duplex, increasing its hydrophobicity25. The additional helical stability and hydrophobicity of the 2′FY residues in 39SGP1A might promote a more stable structure, stronger intermolecular hydrogen bonding interactions and π-π stacking between the available 2′F uracils and hydrophobic amino acid side chains in sGP, thereby increasing aptamer affinity for the protein. In our study also, we observed higher retention to the hydrophobic nitrocellulose membranes of oligonucleotides containing poly2′F-U compared with those containing uridine.The presence of 2′F-modified pyrimidines alters RNA structure by promoting the C3′ endo conformation of the ribose39SGP1A binds dimeric, but not monomeric sGP and its binding site does not overlap with the epitopes recognized of any of three monoclonal antibodies antibodies (21D10, FVM-09, FVM-04; Fig. 26 or other body fluids, the 39SGP1A aptamer should not bind other serum proteins and should detect sGP in the presence of serum. Albumin, the most abundant serum protein is present in blood at concentrations as high as 300 μM. Although the selected oligonucleotide, 5183, from which 39SGP1A was derived, bound to HSA with a Kd of 400 nM, 39SGP1A had very low affinity for HSA. Thus, in this instance, truncating the oligonucleotide eliminated the options for 2′FY-RNA structures that could bind HSA. 39SGP1A also did not bind α2 macroglobulin and fibrinogen, two other serum proteins that could contribute to the background noise.To be useful for detecting early ebolavirus infections, which would be performed with samples of patient bloodIn summary, we report the selection of a 2′FY-RNA aptamer that binds EBOV and SUDV sGP with high affinity and specificity and demonstrate that the 2′F modification and poly2′FU loop is required for binding to sGP. This aptamer is an excellent candidate sensor for incorporating into a device for detecting evidence of an Ebola virus infection in biological matrices such as blood and serum.Supplementary information


However, due to the complexity of biotic environment, a true RNA structure always keeps the balance of biological potential energy status, rather than the optimal folding status that meets the minimum energy. For short sequence RNA its equilibrium energy status for the RNA folding organism is close to the minimum free energy status; therefore, the minimum free energy algorithm for predicting RNA secondary structure has higher accuracy. Nevertheless, in a longer sequence RNA, constant folding causes its biopotential energy balance to deviate far from the minimum free energy status. This deviation is because of its complex structure and results in a serious decline in the prediction accuracy of its secondary structure. In this paper, we propose a novel RNA secondary structure prediction algorithm using a convolutional neural network model combined with a dynamic programming method to improve the accuracy with large-scale RNA sequence and structure data. We analyze current experimental RNA sequences and structure data to construct a deep convolutional network model, and then we extract implicit features of an effective classification from large-scale data to predict the pairing probability of each base in an RNA sequence. For the obtained probabilities of RNA sequence base pairing, an enhanced dynamic programming method is applied to obtain the optimal RNA secondary structure. Results indicate that our proposed method is superior to the common RNA secondary structure prediction algorithms in predicting three benchmark RNA families. Based on the characteristics of deep learning algorithm, it can be inferred that the method proposed in this paper has a 30% higher prediction success rate when compared with other algorithms, which will be needed as the amount of real RNA structure data increases in the future.In recent years, obtaining RNA secondary structure information has played an important role in RNA and gene function research. Although some RNA secondary structures can be gained experimentally, in most cases, efficient, and accurate computational methods are still needed to predict RNA secondary structure. Current RNA secondary structure prediction methods are mainly based on the minimum free energy algorithm, which finds the optimal folding state of RNA  RNA is an important basic substance in living organisms. It plays an important role in encoding, decoding, regulating, and expressing genes. The function of RNA in an organism depends mainly on its tertiary structure. However, the tertiary structure of RNA molecules is complex and lacks an effective representation to describe it; thus, it is very difficult to directly predict the tertiary structure from the primary structure of RNA molecules. Therefore, predicting the secondary structure of RNA from the primary structure of RNA becomes the main process for studying RNA structure.At present, the identified RNA secondary structure can be obtained mainly by means of biological experiments such as X-ray diffraction and NMR. However, biological experimental methods are inefficient, expensive, and arduous when measuring structures on large scales , which leads to:According to the number of hydrogen bonds between the paired bases, the pairing weight between A and U is set to 2, and the pairing weight between G and C is set to 3. Since the U-G pair is a wobble base pair, the pairing weight between U and G is set to i, j on the RNA sequence, this article must not only consider the pairing of these two bases, but also whether these two positions can form paired bases on the stem. Therefore, we had to take into account the pairing of the bases on the left (right) side of i with the right (left) side of j.For any two positions, such as i and j, on an RNA sequence. This article refers to the idea of the local weighted linear regression, adding a Gaussian function as a weight. The closer i and j were, the higher the weight of the paired bases and the greater their effect.For one stem, the paired bases in the middle of the stem are relatively stable, and the paired bases on both sides are relatively unstable. Therefore, the calculation offers the possibility of pairing the two positions, We built the matrix Wi×i, as shown in Combining these points of view, this paper introduces the following algorithm flow to calculate the specific values of each position of the coding matrix Base pairing according to RNA sequence coding matrix can be obtained by calculation. Through the analysis of the matrix, we can know that the position of the stem region in the real structure of the RNA is represented by a sub-diagonal line with a large intermediate value and a small value on both sides in the coding matrix. The advantage of the convolutional neural networks in deep learning methods is that they can effectively extract the regional features of the blocks in the matrix. Therefore, we used the convolutional neural networks instead of other machine learning models to predict the pairing of bases in RNA sequences.n into a matrix of size n× n. We use the sliding window method to divide the matrix into n matrices of size d × n. Where d is the size of the sliding window. Thus, the bases on each RNA sequence can be represented by a matrix of size d × n. The size of the sliding window, using the sliding window method, has a great influence on the experiment. If the sliding window is set too small, the extracted features will be incomplete. Too large a window setting will result in more redundant information in the matrix, which leads to a longer training model and may affect the accuracy of the final prediction model prediction. After analysis, the value of the sliding window should be related to the length of the stem region in the RNA. Therefore, we had to count the stem region information of the experimental object to determine the size of the sliding window.Our goal is to predict the pairing of each base on an RNA sequence; so, we had to split the RNA sequence encoding matrix. The RNA representation method converts a sequence of length n into n matrices of the same size, which satisfies the requirements of the convolutional neural network for input data.The convolutional neural network requires that the data input into the model be of a uniform size, and the size of the RNA sequence corresponding to each RNA sequence is different due to the length of the RNA sequence. Therefore, during the experiment, we need to calculate the mean value of the RNA sequence length in the experimental data set and use that mean value to normalize the data. The sliding window method and normalization of the RNA coding matrix can convert the RNA sequence of length This article uses the dot bracket representation to represent the RNA secondary structure. The dot-bracket indicates that the RNA secondary structure is represented as a combination of sequences of “” and “.”. Therefore, the output layer of the convolutional neural network designed in this paper is composed of three nodes, and the output of each base corresponds to the matrix corresponding to the probability of the three labels “"and “.”.The deep learning method has a high accuracy rate for classification problems. However, RNA secondary structure prediction is not a simple classification problem. We can consider RNA secondary structure prediction as a combination of multiple classification problems under certain restrictions.Pleft, Pright, and Ppoint. Which are the probabilities of the three labels “” and “.” in the secondary structure of each base in the RNA sequence. However, if the label with the highest probability of prediction is used as the prediction result for each base, this combination does not guarantee that such a result will satisfy the definition of the secondary structure defined for RNA: It may appear that the number of left brackets is not equal to the number of right brackets or a prediction could be made in which the matched brackets cannot pair with the corresponding bases. So we need to modify the prediction results to meet the requirements of the definition of an RNA secondary structure.From the result of the previous step, we can obtain Sequence Bracket Matching.Matching brackets in the sequence that pair the bases in the corresponding positions .To satisfy 1 and 2, the sum of the probabilities is maximized based on the output of each tag within its convolutional neural network.Based on the probabilistic results obtained in the previous step of the convolutional neural network, the goal of this paper is to find a compatible bracketed sequence that represents the secondary structure of the RNA. To achieve this, the process requires:To find a sequence that meets these requirements, this article enhances the Nussinov algorithm in the dynamic programming method. This requires changing the number of iteratively accumulated paired bases in the Nussinov algorithm to the sum of the cumulative probability of the iterative cumulative bases. Thus, a maximum probability sum algorithm was proposed. This algorithm makes use of the dynamic programming method. Through multiple iterations, the secondary structure of RNA that satisfies the requirements can be obtained. The specific iteration formula follows:N  is the maximum probability of the i-th base to the j-th base in the RNA sequence. Pleft, Pright , and Ppointrepresent the probability of the ith base of the RNA sequence being outputted by the convolutional neural network for three labels.where The data used in our experiment are derived from Turner and Mathews . The datAmong the various RNA families included in the dataset, we first selected the 5sRNA with the largest number and the most concentrated distribution without a pseudoknot. Sequence analysis of the 5sRNA dataset reveals that some identical or similar sequence data exists in RNA dataset. In order to avoid the effect of the experiments by the same or similar sequence data, it is necessary to preprocess the data in the dataset. That is, the 5sRNA data set is programmed to remove the same or similar sequences in the data. After the duplication removal operation, the number of 5s RNAs used in the experiment is 1,059. To train the model and accurately evaluate the entire model, we divide the number of removed 5sRNA datasets into a training set, consisting of a validation set and a test set. The ratio of RNAs in the training sets, validation sets and test set is 7:2:1. The experiment uses the training set to train the network model and determine the model parameters; then, the verification set is used to make the model selection. Therefore, in the final optimization and determination of the model, the final test set is used to measure the generalization ability of the whole prediction method.Several parameters in the CDPfold can affect the results of the experiment, and the problematic parameters must be fixed before the experiment. The first problem parameter is the size of the sliding window. We calculated the length of the largest stem region of all RNAs in the 5sRNA data set used in the experiment. The results obtained are shown in the The framework used in the convolutional neural network model constructed in this paper is Tensorflow. The convolutional neural network model consists of an input layer, three convolutional layers, three pooling layers, two fully connected layers, and a final output layer. In the test phase, the tf.nn.top_k function of the output layer is removed to obtain the probability that each base will correspond to three tags. The convolutional neural network model used in this paper is shown in The data input from the input layer of the convolutional neural network is represented by the sliding window algorithm and the normalized base matrix. The parameter optimization method using a batch random gradient descent uses 256 data for each iteration. The convolutional neural network consists of three convolutional layers, three pooling layers, and two fully connected layers, where each convolutional layer uses 16 3 × 3 convolution kernels, and each pool layer also uses 3 × 3 convolution kernels. The largest pool, and each full connection layer uses 32 nodes. The output layer of the model maps the data to the three labels of the point bracket representation, and the probability that the base belongs to three labels can be verified. The initialization method of each parameter of the model is the Xavier initialization method, and the error function of the output layer adopts the maximum entropy function. When the model parameters are trained, the model is iterated 400 times.Through the sliding window method and normalization, a matrix representation corresponding to each base in the 5s RNA sequence in training set can be obtained, wherein each base has a corresponding structural label. Analysis of the data shows that because the number of unpaired bases in each 5s RNA sequence is slightly larger than the number of paired base pairs, this will result in an imbalance of the three types of data samples in the data set, so the data needs to be processed with unbalanced data. Since the amount of experimental data is sufficient, the upsampling data processing method will be adopted to balance the various sample data in the data set.The processed data is used to train the convolutional neural network model. The performance of the convolutional neural network model we built on the training set and test set is shown in After determining the model used in the experiment, we need to select an appropriate value for the weight x of G-U pairing (Formula 1). The matching weight of the swing pair should not be too large or too small. Unfavorable weights will result in a decrease in prediction accuracy. In order to select the appropriate weights, we conducted a number of experiments. The results are shown in The test set data are input into the trained CDPfold, and the pairing probability of each base on each RNA obtained by the convolutional neural network is used as an intermediate result. These intermediate results are used in our probability and maximum correction algorithm. The optimal secondary structure that satisfies the definition of RNA secondary structure is obtained, and compared with the corresponding real structure, thereby validating our complete model design.F-score can be used to measure the precision and recall.For the prediction of an RNA secondary structure obtained by the CDPfold, we used two indicators, sensitivity and specificity. Sensitivity refers to the predicted percentage of all base pairs in the real structure, corresponding to the recall-rate in machine learning. Specificity refers to the correct percentage of all predicted base pairs, corresponding to the precision-rate in machine learning. The RNA secondary structure prediction algorithm is difficult to achieve in general since it is always biased to one side. The Based on the above metrics, we obtained the predicted effects of the designed algorithm model on the 5sRNA dataset. We used the same data to perform experiments under other published algorithms. Based on the above studies, we used the 5sRNA dataset trained model to predict the secondary structure of tRNA. The results have a sensitivity of 0.2 and a specificity of 0.15.This is quite different from the 5sRNA dataset trained model effect on the 5sRNA data set. We analyzed this result and found the function of 5sRNA to quite different from that of tRNA. Models trained using the 5sRNA dataset only extract features that favor the classification of 5sRNA. The lack of these features in tRNA resulted in a greatly reduced prediction accuracy. Therefore, without determining the RNA function or family, it is not possible to directly predict those characteristics using the established model. Thus, the entire sample data must be obtained to build a general model.First, we analyzed all the data in the dataset and found pseudoknots in some RNA structure data. Since the pseudoknot belongs to RNA tertiary structure category, all data with pseudoknots were deleted in the pre-processing operation. We chose the 5sRNA, srpRNA, and tRNA using a number >100 after the pseudoknot, and first performed removal of redundant operations on these three types of RNA to delete the same or similar sequence data in the RNA data set. We also calculated the maximum stem length of each RNA sequence in the data set and the average length of the RNA sequence, which are shown in As it can be seen from After our convolutional neural network model is trained, the test set data is input into the trained general model to obtain the pairing probability of each base on each RNA, and the maximum probability and base correction algorithm are used for pairing probability and RNA sequence. In this manner, the optimal secondary structure of the RNA sequence is obtained.Using the F-score, we can get the predicted effect of the designed generic model on the three types of RNA datasets. We use the same test data to perform experiments under other published algorithms. The comparison results are shown in This paper proposes a CDPfold prediction method based on a convolutional neural network for RNA secondary structure. This method uses the convolutional neural network to extract the hidden features of RNA sequence data and applies it to the field of structural prediction. The results are corrected using a dynamic programming-based correction algorithm to obtain an optimal RNA secondary structure. Experimentally, our method has had good performance in predicting the accuracy of a RNA secondary structure.Although CDPfold has achieved good results in RNA secondary structure prediction, some problems encountered during the experiment process are summarized below, and suggestions for solving the problem follow.First, the reason why RNA can form stem regions depends on the hydrogen bonds formed by the complementary pairing of bases. The secondary structure of DNA mainly exists in the form of a double helix. Due to the limitation of double helix structure, base pairing in DNA can only be composed of pyrimidines and hydrazine pairs. Therefore, DNA molecules only have two pairing modes: A-T and G-C.RNA molecules are different. RNA molecules mainly exist in single-stranded forms. Their double-stranded regions are composed of different regions of the same chain. They do not have a long structural regular double-helix structure. Therefore, in addition to standard A-U and G-C base pairs, there are also G-U swing pairs. The hydrogen bonds formed by the rocking pair are unstable, and not all G and U elements can form paired base pairs. The current handling of G-U swing pairings will be fixed, either as pairable or as unpairable. In this paper, a smaller number is selected as the pairing weight on the G-U swing pair, but the G-U pairing problem is not well-explained. We believe that it is necessary to dynamically determine whether G and U can form paired base pairs according to different states during the process of RNA folding, but this dynamic method is extremely difficult, and there is no research to propose a corresponding solution.The results predicted by the CDPfold method proposed in this paper still need to be further corrected in the results predicted by the convolutional neural network. This is because all machine learning algorithms have generalization errors, and the convolutional nerves are caused by the existence of generalization errors. The results obtained by the network did not form a satisfactory RNA secondary structure. A similar situation has emerged in other studies that use machine learning algorithms to solve RNA secondary structure predictions. There are two main solutions to obtaining a satisfactory RNA secondary structure for more accurate predictions. One is to directly optimize the results of the machine learning model. This paper adopts this approach. The second is to use the results as conditional constraints, and use these constraints to optimize other algorithms. In essence, both approaches are an optimization process for intermediate results. In this problem, it may be an effective solution to generate an anti-network model. The generator that generates the anti-network is used to generate the RNA secondary structure, and the discriminator is used to determine whether the results satisfy the definition of the RNA secondary structure. The optimal RNA secondary structure is obtained by the confrontation between the generator and the discriminator. The difficulty of this method is how to design a good training method. Otherwise, the output may be unsatisfactory due to the freedom of generating the model.In the selection method of an optimization algorithm, the authors of this paper used group intelligence optimization algorithms such as genetic algorithm. These intelligent algorithms can solve complex non-linear problems by simulating biological evolution. In this paper, the probabilistic results provided by the convolutional neural network are used as the probability of selection, mutation, hybridization, etc. in the genetic algorithm, and the number of mismatches in the simulated RNA structural species is used as the optimization target. Although this method can also obtain the RNA secondary structure that meets the requirements, the randomness of each link in the group intelligent optimization algorithm and the discreteness of the data prevent the algorithm from having a fixed number of optimization iterations. In addition, since the goal is to find the secondary structure of the RNA that does not mismatch, and the number of such results is large, the result of each optimization is uncertain, so the group intelligence algorithm cannot be used as the optimization algorithm of this paper. Therefore, the dynamic programming algorithm was chosen as the optimization algorithm, and the probability and maximum correction method are proposed based on the Nussinov algorithm.In the current prediction of the RNA secondary structure, the prediction of pseudoknots is still a difficult point. In this study, it was found that 5sRNA, srpRNA, and tRNA are free of pseudoknots, while most of RNasePRNA and tmRNA have pseudoknots. In these RNAs containing pseudoknots, the number of pseudoknots in each RNA is relatively small, but their existence cannot be ignored. Not only pseudoknots plays an important role in the function of the RNA, but also the prediction of the pseudoknot effect is wrong, it will cause a mistake in the normal stem area. The RNA structure representation method used in this paper uses the dot bracket representation. However, the dot bracket representation does not reflect the false knots present in the RNA structure. Therefore, the data containing the pseudoknots are deleted in the experiment. If a secondary structure representation of RNA can be found that can represent a pseudoknot, the CDPfold proposed in this paper can be modified accordingly to predict the secondary structure of the RNA with a pseudoknot.The experimental data used in this paper focuses on 5sRNA, srpRNA, and tRNA. The length of these three types of RNA sequences is mostly between 50 and 350 nt. In this part of the length range, the effect of CDPfold is due to the existing RNA prediction software. The prediction of the secondary structure of longer RNA sequences is not reflected. This is because the current experimental methods are not perfect enough. The secondary structure data of long-sequence RNAs measured by experiments are not enough. The data set was provided by Turner and Mathews  used in in vitro environment, leading to structural damage; thus, in vivo structural prediction experiments are not perfect, which means the current RNA secondary structure is not necessarily a real structure.The last point is the instability of the RNA structure. The structure of the RNA molecule is highly susceptible to environmental factors. Studies have shown that RNA molecules can damage their natural structures when they are exposed to an In addition, unlike proteins, which function differently, not all RNA molecules can function in the body; furthermore, RNA that encodes proteins accounts for only 2% of the total RNA. Thus, RNA structures that do not have an actual function may not be as fixed as functional RNA structures. These problems all have an impact on the prediction of RNA secondary structure prediction.In general, the CDP-Fold algorithm based on the convolutional neural network for RNA secondary structure prediction achieved good results in data sets without pseudo-knots. Many difficulties remain in the research of RNA secondary structure prediction, and many parts still need to be improved. Our research provides new ideas for the study of the RNA secondary structure and serves as a very good source of structural prediction problems and solutions for other researchers.https://github.com/zhangch994/CDPfold.Publicly available datasets were analyzed in this study. This data can be found here: YL, HZ, and ZL conceived and directed the project. CZ, CL, and XW obtained the raw data and interpreted the data. HZ, CZ, and ZL conducted the data analysis and interpreted the results. HZ, CZ, XW, and BZ helped to design the study and reviewed the data. YL, HZ, CZ, and BZ wrote and edited the manuscript. All the authors helped with the draft and reviewed the manuscript before approving for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


High-throughput sequencing (HTS) of virus-infected cells can be used to study in great detail the patterns of virus transcription and replication. For paramyxoviruses, and by analogy for all other negative-strand RNA viruses, we show that directional sequencing must be used to distinguish between genomic RNA and mRNA/antigenomic RNA because significant amounts of genomic RNA copurify with poly(A)-selected mRNA. We found that the best method is directional sequencing of total cell RNA, after the physical removal of rRNA , because quantitative information on the abundance of both genomic RNA and mRNA/antigenomes can be simultaneously derived. Using this approach, we revealed new details of the kinetics of virus transcription and replication for parainfluenza virus (PIV) type 2, PIV3, PIV5, and mumps virus, as well as on the relative abundance of the individual viral mRNAs. We have developed a high-throughput sequencing (HTS) workflow for investigating paramyxovirus transcription and replication. We show that sequencing of oligo(dT)-selected polyadenylated mRNAs, without considering the orientation of the RNAs from which they had been generated, cannot accurately be used to analyze the abundance of viral mRNAs because genomic RNA copurifies with the viral mRNAs. The best method is directional sequencing of infected cell RNA that has physically been depleted of ribosomal and mitochondrial RNA followed by bioinformatic steps to differentiate data originating from genomes from viral mRNAs and antigenomes. This approach has the advantage that the abundance of viral mRNA (and antigenomes) and genomes can be analyzed and quantified from the same data. We investigated the kinetics of viral transcription and replication during infection of A549 cells with parainfluenza virus type 2 (PIV2), PIV3, PIV5, or mumps virus and determined the abundances of individual viral mRNAs and readthrough mRNAs. We found that the mRNA abundance gradients differed significantly between all four viruses but that for each virus the pattern remained relatively stable throughout infection. We suggest that rapid degradation of non-poly(A) mRNAs may be primarily responsible for the shape of the mRNA abundance gradient in parainfluenza virus 3, whereas a combination of this factor and disengagement of RNA polymerase at intergenic sequences, particularly those at the NP:P and P:M gene boundaries, may be responsible in the other viruses.IMPORTANCE High-throughput sequencing (HTS) of virus-infected cells can be used to study in great detail the patterns of virus transcription and replication. For paramyxoviruses, and by analogy for all other negative-strand RNA viruses, we show that directional sequencing must be used to distinguish between genomic RNA and mRNA/antigenomic RNA because significant amounts of genomic RNA copurify with poly(A)-selected mRNA. We found that the best method is directional sequencing of total cell RNA, after the physical removal of rRNA , because quantitative information on the abundance of both genomic RNA and mRNA/antigenomes can be simultaneously derived. Using this approach, we revealed new details of the kinetics of virus transcription and replication for parainfluenza virus (PIV) type 2, PIV3, PIV5, and mumps virus, as well as on the relative abundance of the individual viral mRNAs. Paramyxoviridae belongs to the order Mononegavirales and is populated by a large number of vertebrate viruses, some of which cause diseases in humans, including mumps, measles, and respiratory infections . Parainfluenza virus 2 (PIV2), parainfluenza virus 5 (PIV5) and mumps virus (MuV) are members of the species Human orthorubulavirus 2, Mammalian orthorubulavirus 5, and Mumps orthorubulavirus, respectively, in genus Orthorubulavirus of subfamily Rubulavirinae. Parainfluenza virus 3 (PIV3) is a member of the species Human respirovirus 3 in genus Respirovirus of subfamily Orthoparamyxovirinae; measles virus is a member of the species Measles morbillivirus in genus Morbillivirus of the same subfamily.The family cis-acting elements in the genome. These elements include the gene start (GS) and gene end (GE) sites that flank the individual genes. Immediately downstream of the GE site is a poly(U) tract of variable length, which forms the site of mRNA polyadenylation. Between each pair of genes there is an additional cis-acting element known as the intergenic (IG) region, which consists of a short sequence (1 to 56 nt) that is not generally transcribed into mRNA. IG regions vary in sequence and length among paramyxovirus genera. Respiroviruses and morbilliviruses have IG regions that are conserved in length and sequence within the genome, whereas rubulaviruses possess IG regions that vary in length and sequence throughout the genome  in size. The genomes of different paramyxoviruses encode comparable, but not identical, cohorts of genes that exhibit largely analogous functions , which is kept soluble by its interaction with the N-terminal common domain of P and V  tract, in a manner similar to that occurring during polyadenylation , 21. In de novo genomes. The newly synthesized genomes and antigenomes are concurrently encapsidated by NP0 to form the nucleocapsid structure. It is thought that concurrent replication and encapsidation allow vRdRP to ignore GS and GE sites  elements required for RdRp polymerase recognition and initiation of the production of GE sites , 23.Mononegavirales.Despite this general understanding of the general patterns of paramyxovirus transcription and replication, detailed descriptions are lacking for most individual paramyxoviruses. In the present study, we exploited high-throughput sequencing (HTS) to analyze simultaneously the kinetics of transcription and replication of several paramyxoviruses, thus potentially also shedding light on these processes in all members of the order In preliminary studies, untransformed human skin fibroblasts (HSFs) (that had undergone only limited passage in tissue culture cells) were infected with PIV5-W3 at a multiplicity of infection (MOI) of 50 PFU/cell. RNA was extracted at 18 h postinfection (p.i.), and mRNA was isolated by poly(A) selection prior to HTS on the MiSeq platform. The resulting R1 and R2 files contained a total of 6,523,498 reads, which were trimmed and mapped to the PIV5-W3 genome sequence without considering the orientation of the RNAs from which they had been generated. Viral reads accounted for 4.7% of the total. Coverage depth of the NP and V/P genes was greater than that of other genes, reflecting the anticipated mRNA abundance gradient , top. HoAlthough viral genomes copurified with mRNA during poly(A) selection most likely due to hybridization of cRNA during RNA extraction, the number of viral genomes in infected cells could not be quantified because the efficiency of selection was not known. Therefore, we investigated whether directional sequencing following depletion of rRNA, rather than poly(A) selection, could achieve the quantification of both genome and mRNA/antigenome RNA from the same data set. A549 cells were infected with PIV5-W3 at an MOI of 10 PFU/cell. RNA was extracted at 6, 12, and 18 h p.i. and subjected to rRNA reduction or poly(A) selection prior to HTS on the MiSeq platform. The resulting R1 and R2 files were processed into genome and mRNA/antigenome files and mapped to the PIV5-W3 sequence. Since neither poly(A) selection nor depletion of rRNA was capable of completely removing rRNA from the samples, and also did not remove mitochondrial RNA, residual rRNA and mitochondrial reads were removed bioinformatically from this point . The abuNo significant differences were observed between poly(A) selection and rRNA reduction in terms of either relative mRNA abundance or the shape of the mRNA abundance gradient  indicateThe analysis described above involved physical reduction of rRNA. However, a significant proportion of reads originated from mitochondrial RNA . All subThe workflow described above was used to investigate and compare the rates of viral mRNA and genome accumulation of PIV2-Co, PIV3-Wash, PIV5-W3, and MuV-Enders. Triplicate cultures of A549 cells were infected with the individual viruses at an MOI of 10 to 20 PFU/cell. Total infected cell RNA was isolated at 0, 6, 12, 18, and 24 h p.i. and processed for sequencing and subsequent bioinformatic analysis . Since wPIV3-Wash exhibited significantly faster transcriptional kinetics than the other viruses, with mRNA contributing approximately 10% of total RNA at 6 h p.i. and reaching maximal levels (approximately 18%) by 12 h p.i. In contrast, the levels of PIV2-Co, PIV5-W3, and MuV-Enders transcripts were <2% of total RNA at 6 h p.i. The greatest increase in the rate of viral transcription for PIV2-Co, PIV5-W3, and MuV-Enders was observed between 6 and 12 h p.i. However, the pattern of PIV5-W3 transcription differed significantly at later times from that of MuV-Enders and PIV2-Co, with mRNA levels peaking at 16 to 19% of total RNA at 18 and 24 h p.i., respectively. In contrast, the levels of PIV5-W3 mRNA peaked between 12 and 18 h p.i., contributing 4 to 5% of total RNA, after which the abundance decreased to 2 to 3% by 24 h p.i. This reflects an almost 4-fold difference in peak mRNA abundance between PIV5-W3 and PIV2-Co and MuV-Enders (discussed further below). Despite differences in the kinetics of transcription and relative abundances of the PIV2-Co, PIV5-W3, and MuV-Enders mRNAs, the abundance of viral genomes gradually increased for all three viruses between 6 and 24 h p.i. from approximately 0.03 to 1 to 2% of total RNA. As would be expected from the higher rate of transcription in PIV3-Wash, replication was also slightly faster, with a significant increase in viral genome numbers being observed between 6 and 12 h p.i., reaching maximal levels by 18 h p.i.The viral mRNA abundance gradients were analyzed in the above-named samples by determining the relative abundance of individual viral mRNAs using values for fragments per kilobase of transcript per million mapped reads (FPKM), which take into account gene length in order to allow the relative amounts of mRNA transcribed from individual genes to be compared. These values were then used to determine the percent contribution of each viral mRNA to the total .There were significant differences between the transcriptional profiles of the four viruses. For PIV2-Co and PIV5-W3, the NP mRNAs were clearly the most abundant, contributing >45% of total mRNA in the case of PIV2-Co. There was then a relatively steep reduction in the abundance of the V/P mRNAs and then a more gradual decline until the HN mRNA, followed by a sharp decline in the abundance of L mRNA, particularly for PIV3 and PIV5. In contrast, the relative levels of the NP and V/P mRNAs were similar for MuV-Enders, with a relatively steep reduction to the M mRNA. For PIV3-Wash, there was a more gradual decline until the sharp decrease in the abundance of the L mRNA. Unexpectedly, although not open to meaningful statistical analysis, the relative abundance of the PIV3-Wash P/V/D mRNAs in most samples appeared to be slightly less than that of the M mRNA. Assuming that there is no internal entry site for vRdRP, this may reflect differences in mRNA stability. This may also explain the slight apparent differences observed in the mRNA abundance gradients for each virus at different time points. However, the fact that the transcriptional profiles at later time points were similar to those at 6 h p.i., a time when the relative stability of different viral mRNAs is unlike to significantly affect the mRNA abundance gradients, suggests that there is no significant temporal control of the levels of viral transcription of individual genes.The distribution of additional G residues inserted at the editing site into the relevant mRNAs is shown in The generation of readthrough mRNAs has been proposed as a secondary mechanism by which paramyxoviruses control the level of production of viral proteins because translation of genes beyond the first represented in the mRNA will not occur. Readthrough mRNAs are generated when vRdRP fails to terminate transcription at a GE site and continues transcribing the IG region and a subsequent gene(s) to produce a bicistronic (or polycistronic) mRNA. The generation of readthrough mRNAs was analyzed by calculating the average coverage depth of reads overlapping each IG region and comparing it to the average coverage depth of reads of the gene immediately upstream . This meSingle strains of PIV2, PIV3, PIV5, and MuV were used in the analysis described above. To investigate whether strain differences influence the patterns of paramyxovirus transcription and replication, we analyzed the mRNA abundance gradient, RNA editing, and readthrough mRNA profiles of PIV5-CPI+ . In compRecently there have been several studies that quantified viral mRNAs using HTS for negative-strand viruses such as Ebola, respiratory syncytial, and Hendra viruses  replication and that of PIV2, PIV5, and MuV (orthorubulavirus) replication. It will be interesting to determine whether this holds for other viruses in these groups.The maximal amount of PIV5-W3 mRNA in infected cells was significantly lower than that of the other viruses examined. As discussed above, this is because PIV5-W3 transcription and replication are repressed at late times in infection due to phosphorylation of a serine residue at position 157 on the P protein. PIV5 transcription is not repressed following infection with strains of PIV5, including PIV5-CPI+ and rPIV5-W3:P(F157), that have a phenylalanine residue at position 157, and this is reflected in higher levels of viral mRNA at late times p.i. . InteresIn the context of the mRNA abundance gradient, PIV3-Wash exhibited a relatively small decline in the relative abundance of the P/D/C, M, F, and HN mRNAs. However, there was a dramatic decrease in the abundance of L mRNA compared to HN mRNA. In comparison, PIV2-Co, PIV5-W3, and PIV5-CPI+ exhibited a relatively large decrease in the relative abundance of P/V mRNA compared to NP mRNA and then a gradual decline until the HN mRNA, before again showing a marked decrease in the abundance of L mRNA. MuV-Enders was similar to PIV2-Co and PIV5-W3, except that the first obvious decrease in abundance occurred between the P/V and M mRNAs. Although the reasons for the decrease in the relative abundance of L mRNA compared to HN mRNA is unclear, it may be that the much greater length of the former is a contributing factor. The generally accepted model for the stepwise reduction in mRNA abundance across the genome is that the vRdRP may disengage from the genome at a GE site, rather than continuing to transcribe downstream genes, but if it does so it must reinitiate at the Le promoter to continue transcribing. An alternative explanation is that vRdRP can disengage at any nucleotide with equal probability, with the aborted, non-poly(A) RNAs being very rapidly degraded , 34. SucBecause eukaryotic ribosomes do not generally recognize internal AUG initiation sites, viral protein expression can be further controlled by the generation of readthrough mRNAs, as downstream genes transcribed as polycistronic mRNAs would not be translated. In agreement with published work –37, PIV5–To initiate RNA synthesis at the Le promoter, the vRdRP recognizes a conserved sequence at the 3′ end of the genome and a set of tandem repeats in the untranslated region of the NP gene that must be in the correct hexamer phase  in Dulbecco’s modified Eagle’s medium (Invitrogen) supplemented with 10% (vol/vol) heat-inactivated fetal bovine serum (Biowest) and incubated in 5% (vol/vol) CO2 at 37°C. The viruses used were PIV2 strain Colindale (PIV2-Co), PIV3 strain Washington/47885/57 (PIV3-Wash [2 at 37°C until harvested.Human skin fibroblasts (HSFs) and A549 cells  were maintained as monolayers in 25-cmIV3-Wash ), MuV stIV3-Wash ), and PIhttps://support.illumina.com). Quality control and quantification of DNA libraries were monitored using a 2100 Bioanalyzer with 1,000 or 5,000 DNA-specific chips (Agilent) and a Qubit fluorometer (Invitrogen). The libraries were normalized to 10 nM, pooled in equal volumes, and subjected to HTS on an MiSeq or NextSeq instruments (Illumina) to produce paired-end reads in two files (R1 and R2) that contained data obtained with the forward and reverse primers.Cells were scraped into the medium and transferred into 15-ml tubes which were centrifuged at 4,700 rpm for 5 min. The pellet was resuspended in 1 ml of TRIzol (Invitrogen), and an equal volume of ethanol was added. RNA was isolated using a Direct-zol RNA miniprep kit (Zymo) and sequenced directionally, either by selection of poly(A) mRNA using a TruSeq stranded mRNA library preparation kit LS (Illumina) or by reduction of rRNA or rRNA plus mitochondrial RNA using a TruSeq stranded total RNA library preparation kit with a Ribo-Zero human/mouse/rat kit (Illumina) or a Ribo-Zero Gold kit LS (Illumina), respectively. Identical steps for library preparation were then followed . Read quality (Q score) was restricted to >30.The sequencing data were demultiplexed, and the reads were trimmed to remove adapter sequences and filtered to remove low-quality reads using TrimGalore . The PIV3-Wash sequence was obtained by de novo assembly of the read data. The aligned reads were then binned from the R1 and R2 assemblies on the basis of their orientation in relation to the genome sequence and combined to produce two files exclusively containing genome or mRNA/antigenome reads. The reads in these files were then mapped independently to the reference sequence using BWA. The number of reads mapping to the genome and their coverage depth across the genome were ascertained by visualizing these alignments using Tablet version 1.15.09.01 .A bioinformatic pipeline was developed for analyzing viral transcription and replication. The reads contained in the R1 and R2 files were mapped independently to the appropriate reference genome sequence using BWA version 0.7.5a-r405 . The refRelative mRNA abundances were calculated from values for fragments per kilobase of transcript per million mapped reads (FPKM) obtained using RSEM version 1.3.0 . FPKM vahttps://github.com/vbsreenu/Sam2Consensus). The proportion of readthrough mRNAs was calculated by comparing the number of reads that cross the IG region to the average coverage depth of the gene immediately upstream.To quantify reads that cross IG regions, the average coverage depth of reads that align to specific genes or that cross the IG region was calculated using SAM2CONSENSUS version 2.0 (available at


N6-methyladenosine (m6A) RNA modifications can be detected with high accuracy, in the form of systematic errors and decreased base-calling qualities. Specifically, we find that our algorithm, trained with m6A-modified and unmodified synthetic sequences, can predict m6A RNA modifications with ~90% accuracy. We then extend our findings to yeast data sets, finding that our method can identify m6A RNA modifications in vivo with an accuracy of 87%. Moreover, we further validate our method by showing that these ‘errors’ are typically not observed in yeast ime4-knockout strains, which lack m6A modifications. Our results open avenues to investigate the biological roles of RNA modifications in their native RNA context.The epitranscriptomics field has undergone an enormous expansion in the last few years; however, a major limitation is the lack of generic methods to map RNA modifications transcriptome-wide. Here, we show that using direct RNA sequencing,  6A RNA modifications can be detected with high accuracy using nanopore direct RNA sequencing.We currently lack generic methods to map RNA modifications across the entire transcriptome. Here, the authors demonstrate that m Systematic efforts to characterize this regulatory layer have revealed that RNA modifications are far more widespread than previously thought, can be subjected to dynamic regulation, and can profoundly impact RNA processing stability and translation10. A fundamental challenge in the field, however, is the lack of a generic approach for mapping and quantifying RNA modifications, as well as, the lack of single molecule resolution11.In the last few years, our ability to map RNA modifications transcriptome-wide has revolutionized our understanding of how these chemical entities shape cellular processes, modulate cancer risk, and govern cellular fate14; and (ii) chemical-based detection, using chemical compounds that selectively react with the modified ribonucleotide of interest, followed by reverse-transcription of the RNA fragment, which leads to accumulation of reads that have the same identical ends15. Although these methods have provided highly valuable information, they are limited by the available repertoire of commercial antibodies and the lack of selective chemical reactivities towards a particular RNA modification16, often lack single nucleotide resolution7 or require complex protocols to achieve it17, cannot provide quantitative estimates of the stoichiometry of the modification at a given site, and are often unable to identify the underlying RNA molecule that is modified.Current technologies to map the epitranscriptome rely on next-generation sequencing and, as such, they are typically blind to nucleotide modifications. Consequently, indirect methods are required to identify RNA modifications transcriptome-wide, which has been mainly approached using two different strategies: (i) antibody immunoprecipitation, which specifically recognizes the modified ribonucleotide18 and Pacific Biosciences (PacBio)3, have been proposed as a new means to detect RNA modifications in native RNA sequences. RNA modifications can be detected using PacBio in the form of kinetic changes of reverse transcriptases, which occur when the enzyme encounters a modified RNA nucleotide19. On the other hand, RNA modifications can be identified in its native RNA context using ONT, by pulling the RNA molecules through the nanopores that are embedded in a membrane. The ONT platform relies on the measurement of disruptions in the current intensity as the RNA or DNA molecule passes through the pore, which can be used to identify the transiting nucleotides. Although ONT direct RNA sequencing is already a reality21, extracting RNA modification information from ONT reads is still an unsolved challenge. RNA modifications are known to cause disruptions in the pore current that can be detected upon comparison of raw current intensities — also known as “squiggles”20. However, current efforts have not yet yielded an efficient and accurate RNA modification detection algorithm, largely due to the challenges in the alignment and re-squiggling of RNA current intensities23.To overcome these limitations, third-generation sequencing technologies, such as the platforms provided by Oxford Nanopore Technologies (ONT)N6-methyladenosine (m6A) RNA modifications in native RNA sequences, and propose a novel algorithm, EpiNano (github.com/enovoa/EpiNano), which can be used to identify m6A RNA modifications from RNA reads with an overall accuracy of ~90%. Our results provide a proof of concept for the use of base-called features to identify RNA modifications using direct RNA sequencing, and open avenues to explore additional RNA modifications in the future.As an alternative strategy, we hypothesized that the current intensity changes caused by the presence of RNA modifications may lead to increased “errors” and decreased qualities from the output of base-calling algorithms that do not model base modifications Fig. . Indeed,27. Therefore, to systematically identify the current intensity changes caused by the presence of a given RNA modification, perturbations of the current intensity signals should be measured and analyzed for each possible 5-mer (n = 1024).Previous work has shown that ONT raw current intensity signals, known as “squiggles”, can be subdivided into “events”, which correspond to consecutive 5-mer sequences shifted one nucleotide at a time 6A” instead of adenine, or unmodified ribonucleotides , while minimizing the RNA secondary structure  for each position of the k-mer   were largely nonoverlapping  using as input the base-called features from m6A-containing RRACH k-mers and unmodified RRACH k-mers (see the Methods section). We first tested whether each individual feature at position 0 (the modified site) was able to classify a given RRACH k-mer into m6A-modified or unmodified. Our results show that base quality, deletion frequency and mismatch frequency alone were able to accurately predict the modification status with reasonable accuracy   increased the accuracy of the model (88–91%) Fig. . We then6A modification status in individual sites have estimated that m6A methylation in mRNAs occurs only partially, with methylation ratios ranging from 6 to 80%28. Therefore, we wondered whether our algorithm would be able to detect m6A modifications on mixtures of methylated and unmethylated reads. To test this, we sampled reads from both m6A-modified and unmodified data sets and mixed them in different proportions, to achieve partial methylation ratios of 0 (unmodified), 5, 10, 25, 50, 75, 90, 95, and 100% (fully modified). We find that the algorithm performance is dependent on the proportion of methylated reads  of 0.72  and ime4∆ knockout strains -selected RNA from both wt and ime4∆ strains were sequenced in independent flow cells (see the Methods section), producing more than five million sequenced reads -selected RNA from EpiNano to extract base-called features for all six samples. We first analyzed the features corresponding to ~1300 known m6A-modified RRACH sites, previously identified using antibody immunoprecipitation coupled to next-generation sequencing (m6A-Seq)29. We found that base-called features at m6A-modified RRACH sites were distinct across yeast strains (wt and ime4∆), for all three metrics analyzed   and average per-read quality scores  Fig. . These rcy) Fig. . By conttes Fig. .ime4∆ strains, which also lack m6A modifications  showed intermediate mismatch frequencies between fully modified (CC 100%) and unmodified  sequences. Using linear regression, we estimated that the median stoichiometry of m6A modifications in wt strains was 12–30%  in yeast samples30. Altogether, our results reveal that nonrandom base-called “errors” present in in vivo data sets are replicable, are in agreement with in vitro results, and are correlated with the presence of m6A RNA modifications in a given site.To determine whether our trained SVM could be applied to in vivo data sets, we first investigated whether the global in vivo base-called features were consistent with those observed in vitro. We found that unmodified in vitro sequences (CC 0%) displayed similar mismatch frequencies to those observed in ons Fig. . By cont30% Fig. , which i6A-modified and unmodified in vitro constructs, to predict the transcriptome-wide m6A modification status of yeast RRACH sites, both in wt and ime4∆ data sets. A site was kept for downstream analyses if there was at least 1 read per site in each of the six samples. This criterion was met by 61,163 RRACH sites, from which 363 had been reported as “m6A-modified”, based on Illumina sequencing29. Per-site SVM predictions for each biological replicate were then merged into a single “m6A modification score” (see the Methods section). We should note that low read coverage leads to decreased accuracy  in wt and ime4∆, finding that modification scores in wt were significantly higher than those observed in ime4∆ (p = 8e–14), for the same set of sites  and unknown sites in the same strain, which do not contain m6A modifications, were relatively similar   of known m6A-modified sites  Fig. . InteresTombo22, a software originally developed for the detection of DNA modifications in nanopore sequencing data, which has recently been extended to detect RNA modifications. Identification of modifications from raw signal typically requires a two-step process: (i) re-squiggling of the raw signal to “align” all reads mapping to the same genomic location and (ii) comparison of raw current intensities across reads or samples. First, we find that the re-squiggling step used by Tombo discards ~50% of the reads . Thus, we find that, for the same set of 61,163 sites, Tombo correctly predicts known m6A sites with an accuracy of 69% and a recovery of 59%, whereas EpiNano predicts them with an accuracy of 87% and recovery of 32% . Future work will be needed to decipher whether different types of RNA modifications can be associated to distinct “error signatures”, which could be potentially used to identify the underlying RNA modification type. Thirdly, although m6A-modified RRACH k-mers globally display altered base qualities, mismatch frequencies and deletion frequencies, we should note that the contribution of each feature varies across different k-mers. For example, we find that the presence of m6A in GGACA and GGACT k-mers mainly affects the mismatch and deletion frequency, whereas in the case of GGACC, base quality and deletion frequency are the most altered features by the presence of m6A modifications , and thus lead to improved m6A RNA modification base-callers for the whole community.Here we report that RNA modifications can be identified in the form of systematic and reproducible base-calling “errors” in direct RNA sequencing data sets. These “errors” can be detected in the form of altered per-base qualities, mismatch frequencies and deletion frequencies at the modified site. We find that the method accurately detects modifications both in vitro (90% accuracy) and in vivo (87% accuracy), with an overall recovery of 32% of known sites. Despite the promising results, it is important to note, however, that the current method presents several limitations as well as ample room for improvement. Firstly, our current algorithm does not predict RNA modifications in individual RNA molecules, but rather employs information from all the reads mapping to a specific site to determine whether a given position is modified or unmodified. Secondly, our algorithm does not distinguish between different types of RNA modifications , which internally uses RNAshapes version 2.1.6 (http://bibiserv.techfak.uni-bielefeld.de/rnashapes) to predict RNA secondary structure. The final output sequence given by the software was ~10 kb long. For synthesis purposes, a total of four sequences were designed by splitting the 10 kb sequence into smaller sequences of slightly different size . Each sequence was designed with an internal strong T7 polymerase promoter, an additional BamHI site at the end of the sequence, and with all EcoRV and BamHI sites removed from the sequence   for the IVT reaction of m6A-modified RNA. IVT reaction was incubated for 4 h at 42 °C. IVT RNA was then incubated with DNAse I (Lucigen), followed by purification using the RNeasy Mini Kit (Qiagen-74104). Integrity and quality of the RNA was determined using Agilent 4200 Tapestation, to ensure that a single product band of the correct size had been produced for each IVT product  Polymerase (NEB-M0276S), following the manufacturer’s recommendations. Poly(A)-tailed RNAs were purified using RNAClean XP beads, and the addition of poly(A)-tail was confirmed using Agilent 4200 Tapestation  sequences were produced using the Ampliscribe™ T7-Flash™ Transcription Kit (Lucigen-ASF3507), using 1 µg of purified digestion product as starting material, following the manufacturer’s recommendations. ATP was replaced by ime4∆”). These strains are characterized in Agarwala et al.43. To induce synchronous meiotic entry, cells were grown for 24 h in 1% yeast extract, 2% peptone, 4% dextrose at 30 °C, diluted in BYTA  to OD600 = 0.2 and grown for another 16 h at 30 °C, 200 rpm. Cells were then washed twice with water and resuspended in SPO (0.3% potassium acetate) at OD600 = 2.0 and incubated at 30 °C at 190 rpm. Cells were isolated from SPO following 5 h and collected by 2 min centrifugation at 3000 × g. Pellets were snap frozen and stored at −80 °C for RNA extraction. Three independent biological replicates for each strain were collected.Sk1 strains used in this study were SAy841 comprising a deletion of NDT80 , and SAy966, in which both NDT80 and IME4 were deleted  dynabeads mRNA-DIRECT kit  for small mRNA amounts.Yeast total RNA samples were prepared using a modified protocol of nucleospin® 50RNA kit . Specifically, cells lysis was done in a 1.5 ml tube by adding 450 µl of lysis buffer containing 1 M sorbitol (SIGMA-ALDRICH), 100 mM EDTA and 0.45 µl lyticase (10 U/µl). The sample was incubated in 30 °C for 30 min to break the cell wall, centrifuged for 10 min at 800 × 6A-modified RNA, total of four flow cells). Each biological replicate and condition was sequenced independently in a different flow cell. For the in vivo analysis in S. cerevisiae, three biological replicates for each yeast strain (wild type and ime4∆) were sequenced, and each biological condition and replicate was sequenced in an independent flow cell .RNA library for direct RNA Sequencing (SQK-RNA001) was prepared following the ONT Direct RNA Sequencing protocol version DRS_9026_v1_revP_15Dec2016. Briefly, 800 ng of Poly(A)-tailed and capped IVT RNA — in the case of curlcakes — or 500 ng of yeast polyA + RNA were ligated to ONT RT Adapter using concentrated T4 DNA Ligase (NEB-M0202T), and was reverse transcribed using SuperScript III Reverse Transcriptase (Thermo Fisher Scientific-18080044). The products were purified using 1.8X Agencourt RNAClean XP beads (Fisher Scientific-NC0068576), washing with 70% freshly prepared ethanol. RNA Adapter (RMX) was ligated onto the RNA:DNA hybrid, and the mix was purified using 1X Agencourt RNAClean XP beads, washing with Wash buffer twice. The sample was then eluted in elution buffer and mixed with RNA running buffer (RRB) prior to loading onto a primed R9.4.1 flow cell, and ran on a GridION (MinION for the second replicate) sequencer with MinKNOW acquisition software version v1.14.1 (v.1.15.1 for the second replicate in the curlcake experiment). The sequencing was performed in independent days and machines, with two biological replicates for each “curlcake” experiment condition (nonmodified and m6A-modified data sets (with the largest increased in A positions) and decreased qualities . Base-called reads were filtered using NanoFilt, a component from Nanopack with settings “-q 0 -headcrop 5 -tailcrop 3”, and mapped to the 4 synthetic sequences using minimap2 with the settings -ax map-ont. Mapped reads were then converted into mpileup format using Samtools version 1.4. Read basecalling and mapping metrics can be found in Supplementary Table EpiNano (github.com/enovoa/EpiNano).To extract per-site features , BAM alignment files were converted to tab delimited format using sam2tsv from jvarkit. For each individual reference site, the mean quality of the aligned bases, the mismatch, insertion, and deletion frequency was computed using in-house scripts (available on github). To extract current intensity information from individual reads, the h5py (version 2.7.0) module in python was used to parse each individual fast5 file. Reference sequences were slided with a window size of 5 bp, and mean and standard deviation of current intensities was computed for each sliding window. All in-house python scripts used to extract the features described above are publicly available as part of 6A-modified and unmodified “curlcakes” was used as input to train a SVM. Initial training (75% of the sites) and testing (25%) of the SVM was performed with m6A-modified and unmodified curlcake reads from one replicate (rep1). Multiple kernels  were compared, and the best performing kernel was retained. The model was validated on new sequencing runs of IVT m6A-modified and unmodified sequences (rep2), which had not been used for initial training or testing of the SVM. The reported accuracy values refer to the predictions on replicate 2. The code to extract the set of features for machine learning from fastq and fast5 reads, the code for building the SVM models, as well as the trained SVM models, are publicly available in github (github.com/enovoa/EpiNano). We should note that a limitation in utilizing IVT to generate all possible 5-mers is that 5-mers that contain more than one “A” will contain more than one modification in the k-mer, e.g., AGACC will in fact be m6AGm6ACC, which are unlikely to occur in a biological context. Therefore, 5-mers that contained more than one A have been excluded from the analyses, as well as from the training set. Accuracy of the model has been computed as the sum of correct m6A modification predictions — correctly predicted m6A-modified k-mers (TP) and correctly predicted unmodified k-mers  — divided by the total number of k-mers tested.The set of extracted features of both mEpiNano was used to extract per-site features  from the mapped BAM files of the six samples sequenced . m6A-modified RRACH sites with minimum coverage of 5 reads/site were kept and scored using the previously trained SVM model. 5-mers containing more than one “A” in the motif were discarded from downstream analyses, as these k-mers had not been included in the training sets (see above). A total of 61,163 sites were analyzed for each sample and replicate, from which 363 corresponded to “known” m6A-modified sites, which had been identified using Illumina sequencing44.6A modification scores for each site were computed by merging the SVM predicted probabilities across replicates. Specifically, if the probability being modified was >0.5 in all three biological replicates , the modification score (M) was set to 1. Otherwise, the modification score was determined by computing the mean of the probabilities (pseudocode 1). Modification scores were obtained for each site, both for wild-type and ime4∆ strains.MPseudocode 1:if (s1 ≥ 0.5 and s2 ≥ 0.5 and s3 ≥ 0.5):M = 1else:M = (s1 + s2 + s3)/36A-modified” or “unmodified”, we compared the modification scores of each site, obtained for each of the two strains. Specifically, modification ratio was calculated by dividing the modification score of the wild-type strains (Mwt) and the modification score of the ime4∆ strains (Mko). A site was considered to be modified if the modification ratio was >1.5 and the modification score in wild-type strains (Mwt) was greater than 0.5 (pseudocode 2).To classify a site as “mPseudocode 2:Mwt/Mko) > 1.5 and Mwt > 0.5:if (status = modifiedelse:status = unmodified6A modification predictions — correctly predicted m6A-modified k-mers (TP) and correctly predicted unmodified k-mers (TN) — divided by the total number of k-mers tested . Positive predictive value was computed by dividing the number correctly predicted m6A-modified k-mers (TP) by the total number of m6A-modified k-mers included in the analysis (n = 363). The performance of EpiNano on yeast wt and ime4∆ samples can be found in Supplementary Table Accuracy of the predictions was computed as the sum of correct mTombo version 1.522 to align the raw signal and the base-called reads sequences (tombo resquiggle), both for wild type and ime4∆ samples. We then used the Tombo “canonical sample comparison” method (tombo model_sample_compare) to identify significant shifts in raw signals in paired data sets (wt and ime4∆) using the parameter -num-most-significant-stored 14,000,000 and -minimum-test-reads 1. M6A modification scores for each site were computed by merging the Tombo predicted probabilities across replicates. Specifically, if the probability being modified was >0.5 in all three biological replicates , the modification score (M) was set to 1, as previously done for EpiNano. Otherwise, the modification score was determined by computing the mean of the probabilities (pseudocode 1). A site was considered as modified if the modification score was >0.5. The performance of Tombo on yeast wt and ime4∆ samples can be found in Supplementary Table We first ran Further information on research design is available in the Supplementary Information NewPeer Review FileReporting SummarySource Data