Here, we examined effects of acclimation temperature  on editing patterns in six tissues of zebrafish (Danio rerio).RNA editing by Sites and total amounts of editing differed among tissues. Brain showed the highest levels, followed by gill and skin. In these highly edited tissues, decreases in temperatures led to large increases in total amounts of editing and changes in specific edited sites. Gene ontology analysis showed both similarities  and differences in editing among tissues. The majority of edited sites were in transcripts of transposable elements and the 3′UTR regions of protein coding genes. By experimental validation, translation efficiency was directly related to extent of editing of the 3′UTR region of an mRNA.RNA editing increases 3′UTR polymorphism and affects efficiency of translation. Such editing may lead to temperature-adaptive changes in the proteome through altering relative amounts of synthesis of different proteins.The online version contains supplementary material available at 10.1186/s12915-023-01738-4. The mtionally –7. Becautionally  in recogtionally  and cephtionally , 10. Howtionally , and suptionally , 12. RNAtionally .Whatever the diverse functional consequences of RNA editing are, changes in cell temperature would be expected to significantly alter the amounts and sites of editing due to direct temperature effects on RNA higher-order structures and on the activity and turnover of ADAR , 14–17. Danio rerio). This genomically well-characterized species provides a powerful study system for examining the interactions of temperature and RNA editing across a wide range of temperatures and cell types. Our study examined six tissues  that play diverse roles in the fish’s physiology. We characterize the sites of RNA editing in different genomic regions and show that editing is relatively rare in CDS, but abundant in intergenic and 3′UTR regions at all temperatures. Tissue-specific editing patterns are present at all temperatures and changes in temperature are shown to have a strong effect on amounts and types of editing. Importantly, the efficiency with which a mRNA is translated is directly related to the extent of ADAR-driven editing in the 3′UTR. Editing is proposed to play an important role in the metabolic re-organization that commonly occurs in ectotherms like fishes during temperature acclimation.Despite the fact that RNA editing is likely to be highly sensitive to changes in temperature, the occurrence and functional roles of editing in response to changes in temperature and other abiotic environmental factors remain largely unexplored. Here, to elucidate the patterns and potential functional importance of RNA editing in teleost fish under cold stress, we report the results of a study of temperature acclimation  on RNA editing patterns in the eurythermal zebrafish  and to temperatures that are either cool 18 °C) or cold 13 °C) relative to the species’ thermal tolerance range , 24. Ana8 °C or c7 °C and  °C relatRNA edited sites) and hyper-edited sites (RHES indicates RNA hyper-edited sites). This entailed comparing the sequence context surrounding RES and RHES. The upstream nucleotide strongly favored adenosine in both RES and RHES. For the downstream nucleotide, the RES exhibited a preference for guanosine, whereas RHES favored adenosine , 5′UTR (0.66%), CDS (1.95%), intergenic regions (51.13%), and introns 27.16%)  to better characterize the influences of these two factors Fig. . PC1 is To further examine inter-tissue differences in response to acclimation temperature, we used PCA to compare gene expression and RNA editing differentiation across tissues from differently acclimated zebrafish. Based on the gene expression data , principal components analysis of transcriptome data was performed  increased under cold stress  by Sanger sequencing. In the Sanger sequencing chromatograms, RNA editing sites appeared as mixed A and G peaks Fig. A–D. CompRenilla luciferase activity indicates the efficiency of protein synthesis, with a high ratio indicating high production of protein. Among the 5 mRNAs selected, four  showed higher RNA editing rates at colder temperature and one (srsf5b) showed the opposite trend. Interestingly, in all of the 5 mRNA pairs tested, higher protein translation efficiencies (higher firefly: Renilla luminescence ratios) were found in the one that possessed the higher number of edited sites. Furthermore, in HEK-293 T cells  far exceeded the ratio of QPCR (1.46).Because of the high fraction of ADAR editing found in the 3′UTR, a component of mRNA that can be important in governing protein synthesis, we examined the functional significance of editing in the 3′UTR by using the dual luciferase activity assay to measure protein synthetic activity for 5 selected mRNAs exhibiting different temperature-related editing responses in their 3′UTR regions Fig. . The ratDrosophila, which has only a single isoform of ADAR, editing activity at 35 °C was substantially reduced relative to activities at 15 °C [Drosophila [adar gene and temperature-dependent auto-editing of the enzyme that led to a reduced level of activity [Our study of temperature effects on editing in six tissues of zebrafish shows that a high level of tissue specificity occurs in total amounts of editing and in the sites that are edited. Both features of editing exhibited strong dependence on acclimation temperature, with reductions in temperature generally but not invariably leading to increased editing of a message, as shown in Fig. at 15 °C . A similactivity . TemperaDrosophila also have provided evidence for adaptive change in brain function mediated by ADAR-driven editing [The functional consequences of tissue-specific temperature-dependent editing are largely unknown. However, the categories of mRNAs edited in different tissues reflect in some measure effects related to tissue-related functions. Thus, in the highly edited brain, editing of mRNAs associated with synaptic assembly, axonal transport, and regulation of circadian rhythm are consistent with adaptive modification of neural function during acclimation. Studies of temperature acclimation in fish have revealed substantial changes in the biophysical properties of brain synaptic membranes that are correlated strongly with adaptive changes in behaviors like hyperexcitability and maintenance of equilibrium . Studies editing , 15, 17.In muscle, a considerable amount of editing was in mRNAs encoding proteins involved in muscle cell differentiation, including striated muscle differentiation. Temperature acclimation in fish commonly leads to alterations in capacities for swimming and muscle phenotype, as noted in a study of zebrafish . Thus, sWhereas there was marked variation among tissues in editing patterns and temperature sensitivity of editing, there were also cases where similarities among tissues were striking. The editing of mRNAs related with “ER to Golgi vesicle–mediated transport” and/or “response to endoplasmic reticulum stress” occurred in all tissues. The results are consistent with previous studies that showed that cold enhanced endoplasmic reticulum stress . Such stThere are a number of reasons to expect that temperature will have a pronounced effect on RNA editing processes. First, the secondary and tertiary structures of RNA are thermally labile, such that even small changes in temperature are likely to perturb these structures , 39. SecThe most common pattern of temperature dependence of editing was the increase in edited sites that accompanied acclimation to reduced temperature. For example, in brain the number of edited sites rose from 24,678 at 27 °C to 32,471 at 18 °C and to 34,400 at 13 °C. The enhanced level of editing at reduced temperature is consistent with expectations based on the need of ADAR activity for dsRNA. As noted above, the levels of dsRNA would be expected to rise as temperature falls due to more stable base pairing at low temperature. However, this direct effect of temperature on levels of dsRNA, while likely to play some role in determining the temperature dependence of editing, cannot fully account for inter-tissue differences in editing levels or for the variation in editing sites among tissues  exhibited greater editing at high (18 °C) than low (13 °C) temperatures illustrates the potential versatility of ADAR editing in orchestrating re-organization of the proteome, whether acclimation temperature is increased or decreased. The role of editing on the 5′UTR sequence could also be important in governing translation, even though much less editing occurred in this region relative to the 3′UTR. Because the 5′ region is key for initiation of translation, even small changes in base composition that lead to alterations in conformational stability could be instrumental in adjusting rates of initiation [The discovery that editing affects the efficiency of translation and does so in ways that differ among types of mRNAs Fig.  has majoitiation .Drosophila have shown that high fractions of edited sites (at 18 °C) are in regions of secondary structure [The mechanisms by which editing of the 3′UTR altered the efficiency of translation of these five mRNAs were not examined in our study, but there is evidence to support the conjecture that editing of UTRs that alters their secondary structures might account for these effects. Studies in tructure . Moreovetructure . When sutructure . Our finThe time courses of editing-mediated changes in the transcriptome and proteome merit further consideration. Rapid changes in the transcriptome in response to an acute change in temperature may involve differential effects on the 3′UTRs of different classes of mRNAs. For example, a rapid and adaptive re-organization of the transcriptome in response to acute change in temperature was demonstrated by Su et al. . Their wAlthough the work performed with the dual luciferase system involved only five types of mRNAs, the consistency in the findings—translation was stronger in the edited mRNAs in all cases—points to the possibility of a general role of mRNA editing in altering translational patterns in the face of changes in temperature. These ADAR-mediated effects could be important in responses to both decreases and increases in temperature, as shown by the data in Fig. A final point to consider in the context of how mRNA editing might lead to metabolic re-organization during temperature acclimation is the finding that acclimation temperature has an effect on mRNA splicing. Healy and Schulte  discoverWhereas the importance of recoding editing in modifying the thermal responses of proteins has been demonstrated , the rolLastly, we wish to consider issues that reflect major differences between editing in homeothermic endotherms like mammals and ectothermic species like fishes. We conjecture that ADAR editing may play quite different roles in these two broad groups of animals. To approach these differences, we first consider another perspective on the significance of temperature-dependent stability of the base pairing that establishes RNA secondary and tertiary structures. This analysis is based on considerations of the need to conserve the abilities of RNAs to undergo reversible conformational changes that are essential for function at different body temperatures. Reversible formation of secondary structural elements is key to several aspects of mRNA function, so the ability to retain a capacity for undergoing these reversible changes in conformation at different temperatures seems critical. This type of conservation of conformational stability is found in orthologous proteins of species adapted to different temperatures , 49, 50 We conjecture that a similar temperature-adaptive pattern in RNA stability could arise from RNA editing and could potentially conserve the right balance between stability and flexibility in all components of mRNA . Increased strength of base pairing as temperature is reduced could lead to perturbation of RNA function if, for example, secondary structures become too rigid to allow needed changes in mRNA conformation during translation, splicing reactions, or RNA turnover. Editing might lead to reduced amounts of base pairing at low temperature, an effect that could compensate for the strengthening of base pairing due to reduced thermal energy. This type of adaptive change in RNA structural stability across different genomic regions during acclimation may be the most general role of temperature-dependent RNA editing in ectotherms. Conversely, the fact that the preponderance of RNA editing in mammals is focused on repetitive sequences, e.g., Alu elements, and is less common in other genomic regions, could be a sign of a lack of need for genome-wide temperature-compensating changes in RNA stability in homeotherms. In this context, it merits mentioning recent work on hibernating (heterothermic) ground squirrels in which RNA editing increased during periods of low body temperature . Few chaIn summary, we conjecture that A-to-I editing plays a number of roles in acclimation to temperature in ectotherms and endotherms, and that these roles may differ in important respects between the two groups. Temperature-compensatory adjustment in RNA stability could be important in ectotherms, to ensure that reversible changes in RNA secondary and tertiary structure can occur. These compensatory changes in stability may be important components of a broader function for ADAR-catalyzed editing in orchestrating adaptive re-organization of metabolic pathways during temperature acclimation.Danio rerio AB strain, 6 months) held at 27 °C were divided randomly into three groups of three fish and transferred into three equivalent recirculating aquaculture systems set at 27 °C, 18 °C, and 13 °C. Acclimation lasted 30 days. Only females were used to control for possible sex-specific differences. The epaxial skeletal muscles, gills, brains, skins, livers, and ovaries of all nine specimens were dissected from the bodies with scissors and tweezers for RNA extraction. The residual tissue was used for DNA extraction. Total RNA was extracted using Trizol reagent (Sigma). Using the method of protease K-phenol chloroform, the DNA was extracted from the nine specimens.To identify the temperature-dependent editing events, nine adult wild type zebrafish  and subjected to 150 bp paired-end sequencing on a Novaseq 6000 Illumina platform. To decrease the cost of constructing libraries and sequencing, we pooled equivalent amounts of the genomic DNA extracted from the nine specimens. The DNA-seq library was constructed using an Annoroad Universal DNA library Prep Kit V2.0  and was subjected to 150 bp paired-end sequencing on a Novaseq 6000 Illumina platform.We then performed RNA editing analysis using previously published methods (RES-scanner)  for normIn total, editing levels measured from 54 samples spanning 6 tissues and 3 acclimation temperatures were used for PCA. We averaged the editing levels for samples of the same tissue type from the same temperature. We imputed missing values for the sites using the missForest  packagesFor multiple samples per group, we calculated the differential RNA editing sites using the logistic regression method integrated into methylKit software . In brieMale and female fish were placed at an even ratio in breeding traps, where they were held overnight, and eggs were collected 0.5 h after the onset of light and were cultured in water containing 0.5 mg/L methylene blue. The injection mixture contained a final concentration of 25 ng/μL of EGFP-Tol2 plasmid DNA and 25 ng/μL of plasmid DNA. Each egg was injected with 1nL of the mixture. At least 200 embryos were injected for each plasmid. After 24 h development of embryos, we selected eggs with green fluorescence using a fluorescence microscope (Zeiss). We homogenized 90 eggs with each plasmid, adding 150μL DPBS with 1% PMSF for dual luciferase reporter assay.Renilla): forward, 5′-TCTGATGCCGCCGTGTTCC-3′; reverse, 5′-CCCAATAGCAGCCAGTCCCT-3′.Each sample included about 15 injected eggs for total RNA extraction. The total RNA extraction method was the same as above. Reverse transcription used the PrimeScript™ RT reagent Kit with gDNA Eraser  from Takara. Primers were designed by Primer Premier 5. RT-PCR used ChamQ Universal SYBR qPCR Master Mix from Vazyme and was conducted in a BIO-RAD CFX96. The expression level was normalized to hRluc-neo fusion using the 2 − ΔΔCt method. luc2 reporter gene (firefly): forward, 5′-CACCGTCGTATTCGTGAGCAA-3′; reverse: 5′-GGGTGGCAAATGGGAAGTCA-3′. hRluc-neo fusion protein coding region  were added to the PCR product. The PCR products were inserted into the MCS located downstream of the luc2 reporter gene in the dual luciferase reporter vector pmirGLO Vector containing the Green and Renilla florescence reporting system (Promega) by In-Fusion® HD Cloning Kit (Takara Bio).The 6 cells/well until reaching 70–90% confluence. After that, 500 ng of constructed plasmid per well were transfected into cells using a Lipofectamine6000 transfection kit (Invitrogen). We replaced the previous medium in each well with new DMEM after 6 h. The values of dual luciferase were measured after 24 h post-transfection.The HEK-293 T cells were plated in 24-well plates with a density of 10Renilla luminescence was quantified in a luminometer.The dual luciferase reporter assay used the Dual-Glo® Luciferase Assay System (Promega) and was measured with a BioTek Synergy2 spectrofluorophotometer. To each well of the 96-well flat-bottom plate (Beyotime) was added 75μL egg homogenate and an equal volume of Dual-Glo® Reagent. At a time of at least 10 min after mixing of the egg homogenate and reagent, the firefly luminescence was quantified in a luminometer. Then, a volume of Dual-Glo® Stop & Glo® Reagent equal to the original culture medium volume was added to each well and mixed. After at least 10 min post-mixing, the Additional file 1:Figure S1. Relative abundance of each editing type in hyper-editing mode and normal mode. Figure S2. The relative abundance of editing in TE and non-TE regions. Figure S3. The percentage of editing in various genomic regions. Syn indicates synonymous CDS sites. Figure S4. Characteristics of the editing level of each tissue based on the total editing sites combined from all tissues .Figure S5. Global repeats editing index. Figure S6. Principal component analysis (PCA) of FPKM profiles across tissues from zebrafish acclimated to different temperatures. Figure S7. Expression level of adar, adarb1a, adarb1b and adarb2 genes across tissues and temperatures, as measured by FPKM. Figure S8. The relationship between the number of edited sites and efficiency of protein synthesis in 293T cells. Figure S9. Quantitative real-time PCR for 24h zebrafish embryos which were injected with the structured plasmids.Additional file 2: The editing levels of all the A-to-I sites in each of the tissues at the various temperatures.Additional file 3:Table S1. The types of amino acid substitution induced by RNA editing.Additional file 4: The individual data values for Figure.


Although long-read RNA-seq is increasingly applied to characterize full-length transcripts it can also enable detection of nucleotide variants, such as genetic mutations or RNA editing sites, which is significantly under-explored. Here, we present an in-depth study to detect and analyze RNA editing sites in long-read RNA-seq. Our new method, L-GIREMI, effectively handles sequencing errors and read biases. Applied to PacBio RNA-seq data, L-GIREMI affords a high accuracy in RNA editing identification. Additionally, our analysis uncovered novel insights about RNA editing occurrences in single molecules and double-stranded RNA structures. L-GIREMI provides a valuable means to study nucleotide variants in long-read RNA-seq.The online version contains supplementary material available at 10.1186/s13059-023-03012-w. ADAR) gene family in Metazoans [Alu repeats [Adenosine-to-inosine (A-to-I) RNA editing is one of the most common RNA modification types in human cells, which greatly diversifies the transcriptome . A-to-I etazoans –5. ADAR etazoans –9. Most  repeats –13, the A-to-I RNA editing occurs in both coding and non-coding regions, with diverse functional roles in human cells , 14, 15.Next-generation sequencing technologies, especially RNA-sequencing (RNA-seq), have greatly facilitated the discovery of RNA editing events –34. To dWith the development of third-generation sequencing (TGS) technologies, long-read RNA-seq methods recently emerged as powerful tools to study RNA biology. Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) are the two main representatives of the TGS platforms. Different from short-read RNA-seq methods, long-read RNA-seq interrogates full-length transcripts without breaking the RNAs into small fragments, thus preserving transcript structures . As a reA number of methods have been developed to analyze long-read RNA-seq data, primarily focusing on transcript isoform identification and their abundance analysis –41. AnotWe present L-GIREMI (long-read GIREMI), a method to identify RNA editing sites in long-read RNA-seq (without the need of genome information). L-GIREMI effectively handles sequencing errors and biases in the reads and uses a model-based approach to score RNA editing sites. L-GIREMI allows investigation of RNA editing patterns of single RNA molecules, co-occurrence of multiple RNA editing events, and detection of allele-specific RNA editing. This method provides new opportunities to study RNA nucleotide variants in long-read RNA-seq.Linkage patterns between alternative alleles of RNA variants in the mRNA differ for different types of RNA variants. For example, a pair of SNPs within the mRNA are generally expected to possess perfect allelic linkage. In contrast, non-genetic RNA variants, such as RNA editing sites, do not generally show significant allelic linkage with each other nor with SNPs . In our previous work, we showed that these properties can be employed to distinguish RNA editing sites from genetic variants using short-read RNA-seq data . Long-reAfter a typical read mapping procedure , the L-bona fide nucleotide variants.We first tested the performance of L-GIREMI using a dataset derived from the brain sample of an Alzheimer’s disease (AD) patient . As expected , the majp value for each mismatch site and identified those with p < 0.05 as candidate RNA editing sites. This step detected a total of 13,442 editing sites, with 83.3% of them being the A-to-G type . We examined the %A-to-G among all predicted sites by both methods and separately for different types of regions  via the PacBio Sequel II platform. The 3 datasets had similar error profiles. However, ENCFF417VHJ, which was built using the MaximaH- reverse transcriptase, showed relatively lower levels of indels and mismatches than the other two datasets , L-GIRESince short-read RNA-seq has been frequently used in RNA editing analyses, we compared the results identified in short- and long-read RNA-seq of GM12878. PolyA-selected cytosolic RNA-seq data generated by the ENCODE project (ID: ENCSR000COR (ENCLB555ANM)) was used for this purpose. The data was randomly down-sampled to retain a total of 30 million short reads, which is approximately equivalent to the base coverage afforded by the long-read data (ENCFF417VHJ), considering read length differences. We used the same “genome-aware” method for short reads as in our previous work . As showAlu elements where the majority of editing sites reside. To conduct further comparisons, we obtained the union of mismatch sites that were testable  in both datasets. A total of 190 editing sites were identified in both, with 339 unique to the long reads and 148 unique to the short reads  and AD datasets were located in non-coding regions and nts Fig. a, b [31,Alu was approximately equally distributed among the reads corresponding to the Alu. To this end, we calculated the Gini index for the number of editing sites observed in each read of an Alu. Gini index is a measure of inequality among a given set of values (Methods). As background controls, we shuffled the occurrence of the observed editing sites among all reads of the Alu. As shown in Fig. Alu.We first asked whether editing sites in the same As an alternative method, we examined the MI of pairs of editing sites in a gene and asked whether their values were larger than that of randomly shuffled editing sites. Consistent with the above finding Fig. c, the MILong-read RNA-seq allows examination of linkage patterns between any type of RNA variants in the same read. One type of linkage event, allele-specific RNA editing, reflects the existence of genetic determinants of RNA editing, which has been shown in human and mouse tissues . HoweverThe above results suggest that allele-specific editing may only affect a minority of editing sites. To exclude the possibility that apparent allele-specific RNA editing may be largely due to the existence of genetic variants among REDIportal-defined editing sites , we tested 6 likely allele-specific editing sites  can generate deletion artifacts in cDNAs, which is caused by intramolecular template switching, an event where RT skips the hairpin structure of the template RNA  among the editing sites predicted by MI alone  pairs. Region-skipping was rare in IRAlu regions with low RNA editing levels  . The “--The L-GIREMI algorithm consists of four main steps: (1) correction of read orientation, (2) collection of mismatches, (3) calculation of mutual information among mismatch sites, and (4) scoring of mismatches with a generalized linear model (GLM). The details of each step are provided below.Although most reads generated by PacBio had the correct read strand, a minority of reads had wrong orientations, which can affect the accuracy of nucleotide variant analysis. We adopted the following strategy to check and, if necessary, correct the read strand. First, we obtained 3 types of strand information: the strand of the read from minimap2 , the strand of the gene annotated (Gencode v34) at the read location , and the strand from which the majority of the splice site sequences of the read were consistent with the known motifs  . For most reads, all 3 sources gave the same strand. Otherwise, the read strand was set to be the dominant one among the three. Furthermore, in rare cases, only two types of strand information were available due to the read mapping to intergenic regions or the lack of known splicing motif. For these cases, the annotated strand was given the highest priority, followed by splice site strand and mapped strand, respectively. If the read was mapped to a region with sense and antisense gene pairs, the gene that had a larger overlap with the read sequence was used. It should be noted that reads harboring no spliced junctions were removed from the analysis to avoid contamination by DNA-derived reads.In this step, we obtain a catalog of all mismatches in the mapped reads, detected via the cs tags generated by minimap2. Their genomic coordinates, reference nucleotides, and alternative nucleotides were stored. In order to remove likely sequencing errors, several filters were implemented according to common practice in RNA editing identification via RNA-seq . Specifimi) and a SNP (sj), the MI (I) was calculated as:N1 = {the two most frequent nucleotides for site 1}, N2 = {the two most frequent nucleotides for site 2}, and ni and nj denote the nucleotides of the sites in the pair. p represents the probability of observing the ni and nj nucleotides in the same read, calculated using the maximum likelihood method. Natural logarithm was used for the calculation. Only the two most frequent alleles at each site were used. The MI of a pair of SNPs was calculated similarly.L-GIREMI calculates the MI for each mismatch site relative to heterozygous SNPs, and for pairs of heterozygous SNPs, respectively. For a mismatch  and not included in the dbSNP database were selected as predicted RNA editing sites, which were used for GLM training described below.For every mismatch or SNP site, there might exist multiple other SNPs harbored within the same reads. Thus, the overall MI for the mismatch or SNP was calculated as the average of all the pairwise MI values. Empirical p values larger than the threshold (0.05 by default) in the MI calculation step were used as negative training data. The training and testing of the GLM was carried out similarly as in GIREMI [GLM was used as the scoring model of L-GIREMI. Features included in the model are the allelic ratio of the mismatch, the mismatch type, and the sequences of the nearest neighbor nucleotides before and after the mismatch site. RNA editing sites identified in the above MI calculation step were used as positive training data. dbSNPs with n GIREMI . The scoAlu using the editing ratio of each read. For each Alu, all possible editing sites were identified using all the reads that covered the Alu. Then, for each read, the fraction of possible editing sites that were edited in this read was calculated, referred to as the editing ratio of the read. The editing ratios of all reads for each Alu were then used to calculate the Gini index (G). In order to speed up the calculation, the Gini index was calculated as half of the mean absolute difference normalized by the mean of editing ratios [xi and xj are the relative editing ratios of the read i and j, respectively, and n is the total number of reads for the Alu. This calculation was also carried out for shuffled data (based on randomization of As and Gs at possible editing sites across reads).The Gini index was calculated for each g ratios :\documenWe obtained polyA-selected cytosolic short-read RNA-seq data for GM12878 from the ENCODE project (ID: ENCSR000COR (ENCLB555ANM)). The RNA-seq reads were aligned to the human reference genome (hg38) with HISAT2 . We thenWe applied a previously developed method to identify long dsRNAs harboring editing-enriched regions (EERs) . This meGenomic DNA (gDNA) and total RNA were extracted from GM12878 cells using the Quick-DNA™ Miniprep kit (Zymo Research) and the Direct-zol™ RNA Miniprep Plus kit (Zymo Research), respectively, following the manufacturer’s protocols. Two micrograms of the total RNA was used to generate cDNA using the SuperScript™ IV First-Strand Synthesis System (Invitrogen). Sequences + / − 100 bp flanking the mismatch site were amplified using the DreamTaq PCR Master Mix (2X) (Thermo Scientific). The primers used for each amplicon are provided in Additional file Additional file 1: Fig. S1. Overview of the Alzheimer’s disease (AD) data. Fig. S2. Summary of mismatches observed in the AD dataset. Fig. S3. The data quality and RNA editing sites in the GM12878 long-read RNA-seq datasets generated by the Sequel II platform. Fig. S4. The data quality and RNA editing sites in the GM12878 long-read RNA-seq datasets generated by the Sequel platform. Fig. S5. Comparison of RNA editing sites identified in the short- and long-read data of GM12878. Fig. S6. Cumulative distribution of mutual information of pairs of REDIportal editing sites or pairs of SNPs in the same gene. Fig. S7. Histogram of the MI for the editing site. Fig. S8. Histograms of the read coverage of detected dsRNAs in two datasets. Fig. S9. Pattern of region-skipping and editing index of inverted Alu repeats.Additional file 2: Table S1. Performance of L-GIREMI in different types of regions of the Alzheimer's Disease Brain dataset. Table S2. Performance of L-GIREMI in different types of regions of the GM12878 dataset. Table S3. Primer sequences of validated sites.Additional file 3: Peer review history.


Yueviridae is a family of negative-sense RNA viruses with genomes of 7.8–8.2 kb that have been associated with crustaceans, insects, stramenopiles and plants. The yuevirid genome consist of two segments, each with at least one ORF. The large (L) segment ORF encodes a large protein containing an RNA-directed RNA polymerase domain. The small (S) segment ORF encodes a nucleocapsid protein. This is a summary of the International Committee on Taxonomy of Viruses (ICTV) Report on the family Yueviridae, which is available at http://www.ictv.global/report/yueviridae. L, large; N, nucleocapsid; RdRP, RNA-directed RNA polymerase; S, small.Unknown.L) encodes an RNA-directed RNA polymerase (RdRP) with a GDP : polyribonucleotidyltransferase (PRNTase) domain that is related to that of viruses in the order Mononegavirales, suggesting a similar capping mechanism. The S segment ORF (N) encodes a nucleocapsid protein structurally related to the homologues encoded by mononegavirals, in particular paramyxovirids.The yuevirid genome comprises two segments of linear negative-sense RNA with a total length of 7.8–8.2 kb (large (L) segment, 6.6–6.9 kb; small (S) segment, 1.2–1.3 kb)  Table 1. The L sUnknown.ictv.global/taxonomy. The family Yueviridae includes at least one genus and two species for the viruses Běihǎi sesarmid crab virus 3 and Shāhé yuevirus-like virus 1, found in unspecified sesarmid crabs (Crustacea: Decapoda) and freshwater isopods (Crustacea: Isopoda), respectively, in China [Current taxonomy: in China , stramenin China , tracheoin China . Thus, tin China , 4.Yueviridae is available at: ictv.global/report/yueviridae.The full ICTV Report on the family


Many of the bioinformatics tools currently used to analyze EV cargo rely on third-party annotations. Recently, analysis of unannotated expressed RNAs has become of interest, since these may provide complementary information to traditional annotated biomarkers or may help refine biological signatures used in machine learning by including unknown regions. Here we perform a comparative analysis of annotation-free and classical read-summarization tools for the analysis of RNA sequencing data generated for EVs isolated from persons with amyotrophic lateral sclerosis (ALS) and healthy donors. Differential expression analysis and digital-droplet PCR validation of unannotated RNAs also confirmed their existence and demonstrates the usefulness of including such potential biomarkers in transcriptome analysis. We show that find-then-annotate methods perform similarly to standard tools for the analysis of known features, and can also identify unannotated expressed RNAs, two of which were validated as overexpressed in ALS samples. We demonstrate that these tools can therefore be used for a stand-alone analysis or easily integrated into current workflows and may be useful for re-analysis as annotations can be integrated  Liquid biopsy is a broad term used to describe the collection of biological fluids, such as blood, urine, and saliva, to identify biomarkers associated with a specific disease . This apEVs can be categorized into two different classes: exosomes and ectosomes . These sde novo RNA discovery; however, these tools frequently require some form of annotation for differential expression analysis. Most of these tools apply some form of successive alignment steps to various annotations, similar to exceRpt. Alignment-free methods have recently gained significant popularity in genomics and have been adapted to sRNA-Seq with the development of DEUS, an R package for small RNA profiling that is based on the Differential Expression of Unique Sequences  was used for these analyses . Plasma https://github.com/acri-nb/derfinder-pipe.We performed the alignment of our small RNA seq data  with bowA 12-sample small RNA-seq experiment was simulated using the polyester package for R (version 1.20.0) . Reads wAmplification and validation of unannotated RNA sequences were performed similarly to other small RNA (miRNA) as previously described  using reAll relevant sequencing data used in these experiments were deposited to the Gene Expression Omnibus (GEO) with the accession number GSE183942.We first chose to test the applicability of annotation-agnostic tools for RNA sequencing (RNA-seq) data using a simulated dataset. RNA-seq reads were simulated using the polyester package for R before analysis using derfinder, srnadiff, ShortStack or two popular annotation-based quantification methods: HTSeq and featureCounts. Count matrices for specific regions detected with derfinder were generated using the regionMatrix function. Regions in this matrix were then assigned to known features using the GenomicRanges package. Results from derfinder, ShortStack and srnadiff were then compared to those from featureCounts and HTSeq. Feature-wise counts generated by annotation-agnostic methods were highly correlated to those from featureCounts and those from HTSeq for all 12 simulated samples . Out of We were interested in testing the applicability of annotation-agnostic tools for the analysis of small RNA-seq data obtained from liquid biopsy material, namely, EVs. We therefore sought to compare results from annotation-free methods to those from other standard tools that summarize reads to feature-level using real RNA-seq data from EVs, which was previously reported in Furthermore, when quantifying total RNA, the relative abundances of different RNA species such as mRNA, miRNA and lncRNA did not differ substantially between both strategies, with most reads aligning to miRNA, mRNA or lncRNA, which is typical of extracellular RNA sequencing data  Figure . miRNA qAnother frequently used method for analyzing sequencing data in EV genomics consists of aligning reads directly to known transcript annotations, such as miRbase. This was the original approach used for miRNA analysis of the published sample data . We compp-value and rank-correlated. This analysis showed a good (>0.6) correlation of gene ontology (GO) enrichment terms among all tools  common features as significantly differentially expressed, while 163  features were detected by two or more tools . Afterwall tools . The samll tools . Gene-seWe then generated an R script that outputs expressed regions detected by derfinder and Shortstack with several supplementary columns describing overlaps with known features in the provided annotation. When run agnostically and considering only expressed regions rather than genes, 2,672, 2,737, and 2,473 expressed regions were identified by derfinder, ShortStack, and srnadiff after low-count filtering, respectively. Regions identified by all three tools were chosen for subsequent validation by qPCR. For this analysis, alpha was set to 0.1 to maximize the number of candidate biomarker regions.Unannotated expressed regions were mapped to human and exogenous genomes to omit possible confounding sequences, as small RNAs often also map to exogenous organisms. Using BLAST, 56% of unannotated expressed regions could also be mapped to exogenous genomes (not shown). These regions were excluded from downstream validation. Following these analyses, three targets were chosen for subsequent validation by reverse transcription digital droplet PCR, two of which could be successfully amplified. Both RNAs were highly abundant, more than 20 nucleotides in length, and significantly differentially expressed in ALS patient EVs compared to EVs from healthy donors with all three tools . Fifteende novo biomarker discovery and machine learning, especially for liquid biopsies. Thus, in some cases, it may be worthwhile to include expressed RNAs from loci usually considered uninformative, especially with supervised learning algorithms, where the end goal may not necessarily be maximizing interpretability. The field of liquid biopsy research shares many of the challenges of sRNA-Seq analyses since small RNA species are often the most abundantly reported class of RNAs packaged into EVs. In addition, the majority of mRNA and lncRNA sequences detected in EVs are present as fragments of these large RNA species. These challenges are further complicated for EV analyses due to the complex and variable nature of sequencing data for EVs that is caused by multiple cell types contributing to the overall circulating EV pool in most biofluids. It is therefore of prime importance that the bioinformatic methods available for these types of analyses be as accurate, accessible, and complete as possible. Several tools designed specifically for miRNA analysis have been developed (The use of annotation-based approaches, such as direct alignment to an annotation database or application of read-summarization software, for RNA-sequencing data analysis, has many advantages. These approaches quantify read counts within the scope of known high-quality annotations, which increases the interpretability of the data generated by RNA-sequencing experiments. It may also help in avoiding the interpretation of repetitive or otherwise problematic loci; however, some information is lost when restricting analyses to known regions. Post-transcriptional processing of RNA species and incomplete annotations may lead to expressed regions that may not be identified with annotation-based approaches. Reads mapping to regions outside of known annotations are perhaps not as interpretable as those aligning to genomic features; however, they could be valuable in eveloped , but feweveloped . Herein,Using biological data from p-value rank correlation among approaches, indicating that enriched GO terms are likely consistent, regardless of the choice of method. Pathway analysis is fairly correlated among derfinder, HTSeq, featureCounts, and srnadiff; however, pathway enrichment results from ShortStack do not correlate with those from other tools. This indicates that the conclusions drawn from results generated by annotation-agnostic methods generally resemble those from other approaches if using GO annotations, but may show substantial differences depending on which database is queried, especially for pathway enrichment databases. We also observed that srnadiff identifies more significantly differentially expressed regions than other tools when using built-in statistical methods. Therefore, we suggest that anyone considering the analysis of EV data with annotation-agnostic approaches make use of the output of multiple tools, and take great care in the choice of alignment and quantification parameters.Gene-ontology analysis of results from each tool indicates good de novo expressed regions is advantageous for small RNA, as these RNAs tend to be challenging to annotate due to ambiguity in feature start and end coordinates (Here we assigned annotations using a single annotation file from GENCODE, which contains protein coding, miRNAs, lncRNAs, tRNAs, and other non-coding RNAs; however, it is possible to provide a custom annotation file as input for the method, such as piRNAs at pirBase  or tRNA-rdinates . Neverthad hoc basis for the targets chosen in the context of this comparative study, this should be done systematically, as is currently implemented in the exeRpt pipeline (Validation of our data confirmed two unannotated regions as potential biomarkers for ALS, as these were highly over-expressed in ALS samples compared to healthy donors. Caution must be taken when choosing candidate targets for validation, as these may align to recent annotation releases and also to exogenous species, especially for short sequences. Bacterial RNA may be detected in circulation, and while we have verified this on an pipeline . In addi


Arabidopsis and Escherichia coli thiamine pyrophosphate (TPP) riboswitches, and another called DUF-3268 was tentatively identified from sequence information. In this work we show that the Arabidopsis and E. coli riboswitch k-junctions fold in response to the addition of magnesium or sodium ions, and that atomic mutations that should disrupt key hydrogen bonding interactions greatly impair folding. Using X-ray crystallography, we have determined the structure of the DUF-3268 RNA and thus confirmed that it is a k-junction. It also folds upon the addition of metal ions, though requiring a 40-fold lower concentration of either divalent or monovalent ions. The key difference between the DUF-3268 and riboswitch k-junctions is the lack of nucleotides inserted between G1b and A2b in the former. We show that this insertion is primarily responsible for the difference in folding properties. Finally, we show that the DUF-3268 can functionally substitute for the k-junction in the E. coli TPP riboswitch such that the chimera can bind the TPP ligand, although less avidly.k-Junctions are elaborated forms of kink turns with an additional helix on the nonbulged strand, thus forming a three-way helical junction. Two were originally identified in the structures of  Although RNA structure determination and analysis techniques have advanced greatly, understanding and predicting the tertiary structure of RNA from the primary and secondary structure is still not fully solved. There are two reasons for this. First, only about 200 unique RNA structures have been solved. Second, too few RNA motifs have been studied in detail. RNA structures can be usefully analyzed as a collection of helical duplex sections connected by junctions that determine the trajectory and mediate long-range interactions. One quite widespread junction is the kink-turn (k-turn); a motif commonly found in duplex RNA comprising a short  bulge-loop followed by conserved G:A and A:G sheared base pairs . k-turnsn (nonbulged) strand opposite the loop on the b (bulged) strand  strand B,C. Thesswitches  using a switches . In the switches . The k-jtructure .The application of bioinformatics has proved to be a powerful approach to identifying potentially functional structured regions of RNA, and the Breaker laboratory has developed comparative genomic analysis to seek out conserved secondary structured regions within intergenic regions . Using tIn this work we have asked two questions. First, do the k-junctions found in the TPP riboswitches undergo folding in solution into the kinked geometry that is observed in the crystal driven by the addition of metal ions? This has not been examined experimentally for any k-junction to date. Second, can we demonstrate that the DUF-3268 sequence adopts the k-junction conformation experimentally? We have determined the crystal structure of this motif and shown that it does indeed adopt the conformation of a k-junction. Furthermore, like the TPP riboswitch k-junctions, folding is promoted by the addition of both divalent and monovalent metal ions in a two-state conformational change.Arabidopsis and E. coli TPP riboswitch junctions and converted them into species similar to those used previously for analysis of the folding of simple k-turns. To achieve this, we extended the C- and NC-helices to 12 and 13 bp, respectively, and converted the T-helices into 4 bp stem–loops . Fluorescein and cyanine-3 fluorophores were attached to the 5′ termini of the C- and NC-helices, respectively, in order to study folding by fluorescence resonance energy transfer (FRET). By analogy with standard k-turns, folding would be expected to kink the structure at the junction, so bringing the fluorophores at the helical termini closer together, and thus increase the efficiency of energy transfer (EFRET). FRET efficiency was measured in the steady-state, using the acceptor normalization method . The −1b:−1n base pair then followed L1:c1 as the next base pair in the C-helix. Thus the −1n nucleotide that donates its O2′ to A2b is immediately 3′ to c1.In a standard k-turn there are two important cross-strand k-turns . One is Arabidopsis and E. coli k-junctions and repeated the Mg2+ ion titrations by measuring FRET efficiency . The ion-induced folding was impaired, though to a lesser degree than for the L1 O2′H variant. EFRET reached 0.48, and [Mg2+]1/2 = 1.2 mM  analogously to the riboswitch k-junctions above.While the switches , DUF-326switches  within oswitches . No inve2+ or Na+ ions to the DUF-3268 k-junction leads to an increase in FRET efficiency   A. This cSupplemental Fig. S3). This crystallized in space group C121, and diffracted to a resolution of 1.94 Å. The structure was obtained by molecular replacement using three separate models, corresponding to two 5 bp stem–loops and a 5 bp helix corresponding to the NC-, T-, and C-helices, respectively, as the search model. The nucleotides of the k-junction in the crystal have been numbered sequentially in the PDB file (ID 8ITS). Crystallographic statistics are tabulated in Supplemental Table S1, and the correspondence between the numbering in the PDB file and our nomenclature for the k-junction . The structure of the DUF-3268 k-junction is compared with those of Arabidopsis and E. coli plus the Kt-7 k-turn are shown in Supplemental Figure S6. They superimpose well, except for a reorientation of the T-helix in DUF-3268 compared to the other k-junctions. trans G(sugar):A(Hoogsteen), as in standard k-turns. These participate in the standard cross-strand hydrogen bonds . Equivalent base pairs are made in the Arabidopsis and E. coli k-junctions. However, the interaction made by the second connecting nucleotide c2 adopts a different conformation from that of the riboswitch k-junctions. Instead of base pairing with L1.1 it is extended out, and UL1.1 forms an in-plane triple base interaction with the major groove edge of the junction-proximal base pair of the T-helix . The Watson–Crick edge of the nucleobase of UL1.1 forms two hydrogen bonds to the Hoogsteen edge of the adenine, UL1.1 N3 to A N7 and A N6 to UL1.1 O4. Lastly, the structure is buttressed by two ribose–ribose interactions where the backbones approach relatively closely. One is between the O2′ atoms of A1n and c1, and the other between the O2′ atoms of A2b and A-1b. Thus, in the DUF-3268 k-junction structure, both adenine nucleotides of the sheared G:A base pairs make two cross-strand interactions using the extreme ends of the nucleotide.The nucleobases of the loop  lie on the major groove side of the junction. The nucleobases are stacked in a manner equivalent to a standard k-turn, so that L1 is stacked on the C-helix, L1.1 is stacked on L1, L2 is stacked on the NC-helix, while L3 is directed away from the RNA. The first connecting nucleotide c1 forms a Arabidopsis and E. coli is the bulged nucleotide(s) between G1b and A2b. This is a single adenine in the Arabidopsis k-junction and the dinucleotide UC in that of E. coli. In the DUF-3268 k-junction there is no nucleotide inserted between G1b and A2b. We asked the question whether or not the difference between the folding characteristics of the Arabidopsis and DUF-3268 k-junctions might arise from the bulged A of the former. We therefore made a version of the Arabidopsis k-junction lacking the A-bulge, and a version of the DUF-3268 k-junction in which an A was inserted between G1b and A2b. These were analyzed by measuring FRET efficiency as a function of Mg2+ ion concentration as previously . We further made an A1nU point mutation in the DUF k-junction sequence; mutation in that position is known to impair folding , 100 mM KCl, 10 mM MgCl2 at 25°C. The native E. coli riboswitch binds one molecule of TPP in an exothermic reaction, from which we calculate a dissociation constant Kd = 23.9 ± 2.6 nM  and the calorimetry (affinity of ligand binding) indicate that the DUF-3268 can functionally substitute for the E. coli TPP riboswitch.In these studies we have shown that k-junctions fold into a tightly kinked geometry on the addition of metal ions. We have further shown that the DUF-3268 sequence that was thought likely to adopt a k-junction conformation  does ind2+ or Na+ ions, where the half-metal ion concentration for the monovalent ions is three orders of magnitude higher than that for the divalent ions. This strongly suggests that folding responds to the neutralization of phosphate charge by atmospheric (or diffuse) binding, not site-specific binding that involves the formation of an inner sphere complex with the metal ion :G(sugar) base pairs, in common with standard k-turns and the k-junctions. The two adenine nucleobases accept hydrogen bonds from the L1 and −1n 2′-hydroxyl groups, explaining their ≥99% identity conservation in the Arabidopsis TPP riboswitches  than for the Arabidopsis k-junction (EFRET = 0.57)  . The mosEFRET with and without the adenine bulge must reflect a conformational change in the global structure of the RNA. EFRET is related to r, the distance between the donor and acceptor fluorophores  by (R0 is the Förster length (in Å) for a given pair of fluorophores, defined byQD is the fluorescent quantum yield of the donor, n is the refractive index of the medium, and J is the spectral overlap between the emission of the donor and the excitation of the acceptor. The parameter κ depends on the relative orientation of the transition moment vectors of the two fluorophores. If both fluorophores adopt a constrained position on the nucleic acid this can vary significantly . The observed change from EFRET = 0.57 to 0.77 would correspond to a shortening of the inter-fluorophore distance of 7.5 Å (assuming R0 = 56 Å for fluorescein-cyanine 3). Using molecular graphics, we have taken the crystal structures of the Arabidopsis . There is a small reduction of the angle included between the helical axes from 36° in the Arabidopsis k-junction to 31° in the DUF-3268 k-junction. The bulged adenine of the Arabidopsis k-junction also results in a local unwinding, with the rotation about the helical axis between the G1b:A1n and A2b:G2n base pairs increasing by 10° from the DUF-3268 to the Arabidopsis k-junction . As a result of these conformational changes, the distance between the phosphates that are the points of attachment of the fluorophores increases by 6 Å . This is not a big change in conformation, but this simple analysis of the conformational change accounts well for the observed change in EFRET between the two k-junctions.The difference in the maximum value of here) by :(1)EFREficantly . Howevere of 2/3 . Becausebidopsis  and DUF-E. coli TPP riboswitch, and the chimeric riboswitch can bind ligand indicating that the k-junctions can be exchanged with preservation of function to some degree. However, the E. coli: DUF-3268 chimeric riboswitch binds TPP an order of magnitude more weakly than the natural E. coli riboswitch , despite the established stability of the DUF-3268 k-junction. The likely reason for this lies in the differences in conformation between the k-junctions. The TPP ligand is bound between the two helices that radiate from the k-junction, held at its two ends. The pyrimidine ring and the pyrophosphate moieties of TPP bind in internal loops contained in the extensions of the C- and NC-helices, respectively, of the k-junction   and Cy3  were attached to the 5′ termini of the oligonucleotides as phosphoramidites in the final synthesis cycle as required.RNA oligonucleotides were synthesized using phosphoramidite chemistry  implement-BDMS)  (Link Te18 column using an 0.1 M TEAA/acetonitrile gradient. Strands were hybridized in 50 mM Tris-HCl (pH 7.5), 25 mM NaCl by slowly cooling an equimolar mixture of strands from 80°C to 4°C, and purified by gel electrophoresis in 12% polyacrylamide in 45 mM Tris-borate (pH 8.3), 25 mM NaCl buffer at 4°C. The hybridized species were recovered by excising the bands and elution from the crushed fragments followed by precipitation in ethanol. The sequences are listed in full in Supplemental Figure S1.RNA was purified by gel electrophoresis in 16% polyacrylamide in 90 mM Tris-borate (pH 8.3), 10 mM EDTA, 7 M urea at 23 W constant power at room temperature. RNA-containing bands were excised and recovered by elution from the crushed fragments. The eluted RNA was precipitated with ethanol and dissolved in 0.1 M TEAA. Fluorescent RNA strands were purified by reversed-phase chromatography on a CArcheoglobus fulgidus L7Ae was expressed as a hexahistidine fusion protein from a modified pET-Duet1 plasmid (Novagen) (E. coli BL21-Gold (DE3) pLysS cells (Stratagene), and purified as described previously (Novagen)  in E. coeviously .EFRET) were measured using the acceptor normalization method , n is the Hill coefficient and KA is the apparent association constant for metal ion binding. Data were fitted by nonlinear regression in Kaleidagraph (Synergy Software). Half-metal ion concentration was calculated as [M]1/2 = (1/KA)n1/.FRET efficiency was measured from an RNA k-junction species terminally 5′-labeled with fluorescein on the bulged strand and Cy3 on the nonbulged strand A. Fluoren method  implemenSupplemental Fig. 3). The DNA sequences were amplified by PCR to generate DNA templates. In vitro transcription was performed with T7 RNA polymerase at 37°C for 6 h, followed by purification by electrophoresis in 10% polyacrylamide gel electrophoresis under denaturing conditions. The RNA transcript was visualized by UV-shadowing at a wavelength of 365 nm, the bands excised and soaked in 0.25× TBE buffer. The eluted RNA was precipitated with isopropanol, washed with 70% ethanol, and the air-dried RNA was dissolved in double-distilled water.To facilitate crystallization, we introduced GAAA tetra-loop motifs into the variable loops of stem NC and stem T of the DUF-3268 k-junction . The RNA was diluted to a concentration of 0.6 mM and annealed at 95°C for 1 min in a buffer containing 5 mM HEPES (pH 7.0), 100 mM KCl, 5 mM MgCl2 before cooling at room temperature for 10 min. An amount of 0.2 µL RNA samples were mixed with 0.2 µL reservoir solution for crystallization screening using sitting drop vapor diffusion at 18°C. Crystals of RNA diffracting to high resolution grew after 4 d from the condition 0.1 M Mg acetate, 0.05 M MES (pH 6.0), 30% v/v 2-methyl-2,4-pentanediol and 0.05 M NaF. The crystals were mounted in nylon loops and flash-frozen by plunging into liquid nitrogen. X-ray diffraction data were collected at beamline BL19U1 at the National Facility for Protein Science in Shanghai (NFPS).Crystals were obtained with the 46 nt RNA (https://rnacomposer.cs.put.poznan.pl/). The translation function Z score (TFZ) was 12.9 and log-likelihood gain (LLG) was 289.4. The model was further adjusted using Coot and subjected to several rounds of adjustment and optimization using Coot, phenix.refine, and PDB_REDO. Model geometry and the fit to the electron-density maps were monitored with MOLPROBITY and the validation tools in Coot. Simulated annealing omit maps were calculated by Composite omit map in PHENIX suite using the method anneal.Structures at 1.94 Å resolution were determined by molecular replacement using PHASER with three models; these were 5 bp stem–loops corresponding to the NC and T stem–loops, and a 5 bp helix corresponding to the C-helix with sequences as present in the complex crystallized. All the models were produced by the fully automated RNA structure modeling server RNAComposer , 1 mM MgCl2 following preincubation in the same buffer plus 0, 100 µM, or 1 mM TPP for 5 min. at 20°C followed by the addition of 5% glycerol and 0.025% (w/v) bromophenol blue and xylene cyanol FF. Electrophoresis was performed at 4 W for 2 h, during which the temperature remained below 20°C. After electrophoresis, RNA was visualized by UV-shadowing.The E. coli TPP riboswitch species were dissolved to a final concentration of 30 µM in a buffer containing 40 mM Hepes (pH 7.5), 100 mM KCl, 10 mM MgCl2 followed by incubation on ice. TPP was dissolved in the same buffer and diluted to a concentration of 250 µM before titration into the RNA in the sample cell. Titration experiments were performed at 25°C, starting with an injection of 0.4 µL, followed by 18 consecutive injections of 2 µL TPP with 120 s intervals between injections, and a reference power of 41.9 µW. Titration data were integrated and analyzed using MicroCal PEAQ-ITC analysis software. The apparent dissociation constant (Kd) was calculated from a “one set of sites” binding model. All binding constants and thermodynamic values are listed in Supplemental Table 2.Isothermal titration calorimetry experiments were performed on a Microcal PEAQ-ITC microcalorimeter at Sun Yat-sen University Cancer Center. Wild-type and variant The coordinates of the DUF-3268 k-junction have been deposited with the PDB with ID 8ITS.Supplemental material is available for this article.


In this case, the elastic energy is transferred more efficiently to the welding zone through the upper adherend.The optimal mode for ultrasonic welding (USW) of the “PEEK–ED (PEEK)–prepreg (PEI impregnated CF fabric)–ED (PEEK)–PEEK” lap joint was determined by artificial neural network (ANN) simulation, based on the sample of the experimental data expanded with the expert data set. The experimental verification of the simulation results showed that mode 10  ensured the high strength properties and preservation of the structural integrity of the carbon fiber fabric (CFF). Additionally, it showed that the “PEEK–CFF prepreg–PEEK” USW lap joint could be fabricated by the “multi-spot” USW method with the optimal mode 10, which can resist the load per cycle of 50 MPa (the bottom HCF level). The USW mode, determined by ANN simulation for the neat PEEK adherends, did not provide joining both particulate and laminated composite adherends with the CFF prepreg reinforcement. The USW lap joints could be formed when the USW durations ( Laminated polymer composites reinforced with continuous carbon fibers (CFs), so- called laminates, are widely applied in the aerospace and other high-tech industries. Zhang et al.  reviewedTo this end, one of the important challenges is joining the CF-reinforced composites ,10,11,12When developing the USW procedures for laminates, the influence of various technological parameters on the structure and functional properties of such joints is investigated. The research studies on such phenomena and the issues concerning optimization of the USW parameters include examining both heat generation and its transfer ,18,19, tEven though few knowledge gaps remain in the USW of laminates, great interest in this topic still exists among researchers. In particular, two important areas should be noted: (i) the development of USW procedures for the particulate composites rather than the layered ones , and  between two polyetheretherketone (PEEK)/CF adherends, in addition to the PEI-based ED . ConsideIt should also be noted that there is a distinction between continuous and multi-spot USW methods  since thBased on the above, optimization of the joint formation conditions is a vector approximation task due to the variety of possible USW modes and their numerous parameters  was used as a feedstock for manufacturing plates (adherends) with dimensions of 100 mm × 20 mm × 2.2 mm using the “RR/TSMP” injection molding machine . The mold heating temperature range was 200–205 °C; the powder feeder (hopper) was heated to 395 °C.The USW lap joints were formed with the ED from a commercially available PEEK film Aptiv 2000 250 µm thick  cut into 22 × 22 mm square pieces. Before the USW process, the EDs were placed between the PEEK adherends while the prepreg was located in the center of the joints . A descrIt should be noted that the USW machine used for this study provided the possibility of varying the USW duration and the clamping pressure. However, the US oscillation amplitude was constant at 10 μm. The US vibration frequency of 20 kHz was also not varied.t) range was set as 600, 850, and 1150 ms since it was not possible to join the PEEK plates at lower t values (since the heat input was low to initiate the melting of the PEEK). However, the prepreg could be seriously damaged at higher t levels, resulting in highly faulty USW lap joints.The USW duration (P) and its (holding) duration after applying US vibrations (τ) were determined based on the technical characteristics of the USW machine and by visual control of the USW lap joints.Ranges of the clamping pressure  and their possible variation ranges were preset :The USW The tensile tests of the USW lap joints were performed according to ASTM D5868. The tests were carried out with an “Instron 5582” electromechanical tensile testing machine . The cross-head speed was 13 mm/min. To minimize misalignment during the tests, gaskets made of identical-thickness PEEK plates were installed in the wedge grips.The structure of cross-sections of the USW lap joints was analyzed with “Neophot 2” optical microscope  after the tensile tests.As mentioned above, both continuous and multi-spot USW modes could be applied. For a qualitative assessment of suitability of the optimal USW parameters, the samples for the fatigue tests were fabricated by using the PEEK plates, the prepreg from the PEI-impregnated CFF, and the PEEK film EDs  and “Sony® ICX625 2/3” CCD matrix 2448 × 2048 . The required surface illumination level was provided with “Jinbei EF-100 LEDSun Light” LED illuminator . The analysis of the sample surface images and a drawing of the strain fields were conducted using VIC 2D 2009 software .The strain values were sampled at a frequency of 5 Hz and then evaluated by the DIC method. For drawing the strain fields, a speckle pattern was deposited on the sample surfaces, which was captured with “Point Gray Grasshopper 50S5M” digital camera (Point Gray ResearchR ratio of 0). The loading frequency was 1 Hz with a periodic deceleration down to 0.05 Hz (measuring cycle) for photographing the sample surfaces with the DIC. For implementing the high-cycle fatigue (HCF) mode, the maximum stress per cycle σmax was chosen, ~50 MPa, which was below the yield point (0.8 · σ0.2). For the low-cycle fatigue (LCF) mode, a σmax value of 55 MPa was preset, which was 0.85 of the ultimate tensile strength level.To assess the fatigue properties of the USW lap joints, cyclic loading of the samples was carried out in the “tensile–tensile” mode with the load control, a sinusoidal shape of the loading signal, and a zero-asymmetry cycle (the Our previous study was conducted on the variation of the PEEK binder contents in a CFF-based prepreg . It was 3H7NO). Then, the fragments of the CFF were soaked in the solution for one hour, followed by evaporation of the organic solvent on the “IKA C-MAG HP 4” heater . The latter was conducted under ventilation conditions to guarantee the removal of volatiles.Since the prepreg was fabricated under laboratory conditions, additional studies were carried out on impregnating the CFF with thermoplastic solutions without using strong acids. For this reason, the option of dissolving PEEK was not considered. However, assuming that a chemical bond was formed between the adherends and the PEEK film ED from the USW process, polyetherimide (PEI) and polyethersulfone (PES) were taken as options. First, the PEI and PES were dissolved in N,N-Dimethylformamide  sizing agent from the surfaces, the CFF was annealed at 500 °C for 30 min. Then, it was impregnated with the dissolved PES. Finally, the solvent was evaporated in an oven at 120 °C.As a result, the impregnated CFF thickness was ~500 µm. After that, it was subjected to compression molding in order to reduce the thickness to that of the initial CFF of 230–250 µm. In the prepreg, the ratio of the components was 66 wt.% CFF/34 wt.% PES. Finally, the thickness of the prepreg (the CFF impregnated with the polymer) was ~250 µm a,b. The prepreg from the PEI-impregnated CFF was made in a similar way. As a result, the component ratio and the final prepreg thickness were also identical . Its general view from the surface and SEM micrographs of the cross-section is shown in Next, the prepregs were compared by their effect on the strength properties of the USW lap joints, obtained with the following parameters preset based on our previous results : a USW dAdditionally, the USW joint thinning was evaluated with a micrometer. In the case of the PES-impregnated prepreg, it was ~410 µm, while it was reduced to ~290 µm for the PEI one . AccordiThe strain–stress diagrams for the USW lap joints are shown in Additionally, the USW joint thinning values, measured with the micrometer, are reported. The primary data processing was carried out by the Taguchi method. The results are presented in the After the tensile tests, the failure patterns of the USW lap joints were analyzed . Table 4UTS parameter was used as a strength characteristic of the lap joints rather than the lap shear strength (LSS) one. Its impact was minimized by ANN simulation and optimization of the USW parameters . As a result, the USW lap joint thinning did not exceed 160 and 120 µm, respectively,  and mode 3 . At the same time, the CFF thickness changed slightly relative to its initial value of ~250 µm . The “excessive” polymer melted in the US-welding zone and was squeezed out beyond its boundaries . However, its thickness variation slightly increased to 90–100 µm  and mode 4 failed similarly. Therefore, it is highly likely, the maximum implemented clamping pressure hindered the frictional heating development, which was also facilitated by the minimum clamping duration after US vibrations of 2000 ms. In this case, the distance between the joined PEEK adherends was ~190 µm, and the CFF thickness was ~200 µm at the center of the specimen after the USW process, according to the optical observation . Probably, the USW lap joint thinning down to 700 µm contributed to this phenomenon for modes 7 and 8.By way of summarizing, the following remark on frictional heating is to be added. The USW duration was shown to mostly affect the frictional heating since mechanical vibrations are transformed into a mutual displacement of the US-weld components. The clamping pressure initially suppresses the mutual displacement and frictional heating. However, after melting the EDs and the prepreg, the former stimulates mass transfer and mutual mixing. Thus, the effect of the USW parameters is complex and cannot be characterized in an isolated way.Determining the optimal USW conditions (combinations of the process parameters) from a limited number of experiments might be reduced to the problem of vector approximation of the mechanical properties of the USW lap joints in the multidimensional space of the USW modes . Among tMany papers reported some ANN applications and their advantages clearly ,57. Theyt ≤ 1000 ms). Previously, we proposed a solution based on increasing the sample size by adding a random noise and synthesizing several models . HoweverANN simulation was carried out with the MathLab software package, which has the standard tools for the synthesis, training, and analysis of ANN. In addition, feedforward neural networks (FFNs) and radial basis function networks (RBFs) were implemented. The number of neurons in the hidden layer varied from four to eight, and the activation functions  were used.By analogy with the technique described in , two-sidAmong many developed models based on experimental and a priori data, the two were chosen. They provided the minimum acceptable root mean square approximation error (the bottom limit) and excluded excessive nonlinearity of approximation (due to overtraining of networks). The latter condition was the top limit.Model 1. The FNNs were implemented with six neurons in the hidden layer and the activation functions: linear in the hidden layer and hyperbolic tangent in the output one. The training was carried out on a sample of the normalized data (with zero mean and unit variance). The sample was added with a synthesized data set obtained by a random spread of the normalized experimental data with the uniform distribution law .Model 2. The RBN parameters were determined based on the analysis of the initial data: a spread of radial basis functions of 0.35 and the mean squared error goal of 0.0001. Then, training was done on a sample of the normalized data (with zero mean and unit variance).The areas with the determined optimal parameters for both models are shown in t = 900 ms, P = 1.7 atm, τ = 2.000 ms . The point in the bottom segment (mode 10) was between modes 4 and 5, while another (mode 11) in the top segment was between modes 5 and 6.However, the top segment of the “region of interest” in this setting remained uncovered. For this reason, we chose a point from this segment for subsequent verification, shown in After the implementation of mode 11, both ultimate tensile strength and elongation at break values were lower, while the USW joint thinning was slightly greater , causing the brittle fracture pattern. In addition, a significant difference in the elastic modulus values of the CFF and the PEEK plates (adherends) contributed to the development of both shear stresses and strains at the interlayer boundary. Thus, the strength properties of the USW lap joints depended on a level of adhesion between the prepreg and the PEEK adherends. Respectively, the fracture was induced by discontinuities at the interlayer boundaries.YY strain fields under analysis . Up to 1000 cycles , a change in the hysteresis loop slope was observed when the operating time reached 2000 and then 6000 cycles, reflecting a decrease in the sample stiffness (3 to 104 cycles. The endurance limit could not be identified in the framework of the tests carried out in this research. The graphs in For the LCF mode (σtiffness a. In contiffness . Howevermax = 55 MPa), the strain fields are shown in For the LCF mode , and the residual strains were drawn for both LCF and HCF modes. According to re point a. The scThis study was a further development of our previous research , both in®-A, Teijin Carbon Europe GMBH) in the “PEEK770 MCF” matrix . They possessed a characteristic length of 200 μm at an aspect ratio of ~30. In addition, the “CF/PEEK770” laminated composites were made, consisting of 10 layers of bidirectional “ACM C285S” CFF at a ratio of 40 wt.% PEEK/60 wt.% CF. They were used as the adherends, while the ED films and the PEI-impregnated prepreg with the CFF were inserted in the same way as in the previous sections.Further, it was important to estimate the applicability of the results of ANN simulation for the development of the USW procedures for particulate or laminated composites. In response, the particulate composite samples were fabricated containing 30 wt.% CFs , the USW lap joints were obtained for the particulate (mode 12) and laminate PEEK/CF (mode 13) composites. Their mechanical properties are presented in It should be noted that all attempts to form a USW lap joint at the “optimal” parameters established for the neat PEEK adherends (modes 10 and 11) failed for the CF-enforced PEEK ones . Nevertheless, experimentally, by increasing the USW duration  at t = 1600 ms. At the same time, the CFF was noticeably thinned . However, when USW the “PEEK–ED (PEEK)–prepreg (PEI impregnated CFF)–ED (PEEK)–PEEK” composites, the lower melting point and the higher melting flow index caused an extrusion of the molten PEI, which could be accompanied by damage to the CFF.t = 900 ms, P = 1.7 atm, τ = 2000 ms) ensured the high strength properties and preservation of the structural integrity of the CFF.By ANN simulation, based on the sample of the experimental data expanded with the expert data set, the optimal modes were determined for the USW lap joint of the “PEEK–ED (PEEK)–prepreg (PEI impregnated CFF)–ED (PEEK)–PEEK” composites. Furthermore, the experimental verification of the simulation results was carried out, which showed that mode 10 .t up to 1200 and 1600 ms, respectively. This occurred since the elastic energy transferred to the welding zone through the upper adherend.The USW mode, determined by ANN simulation for the neat adherends, did not provide joining both the particulate and laminated composite adherends with the CFF prepreg reinforcement. As a result, the USW lap joints were formed only by significantly increasing the USW durations The proposed approach for ANN simulation of the USW process can be implemented to determine the optimal USW modes for the particulate and PEEK/CF laminates, including those containing the CFF (prepreg) layer. However, a new training sample should be formed for this purpose, based on the experimental data and augmented with a priori results.


E.coli with moderate accuracy from the sequences around the start codon. We also demonstrated that DeepRaccess achieved tens to hundreds of times software speed-up in a GPU environment. The source codes and the trained models of DeepRaccess are freely available at https://github.com/hmdlab/DeepRaccess.RNA accessibility is a useful RNA secondary structural feature for predicting RNA-RNA interactions and translation efficiency in prokaryotes. However, conventional accessibility calculation tools, such as Raccess, are computationally expensive and require considerable computational time to perform transcriptome-scale analysis. In this study, we developed DeepRaccess, which predicts RNA accessibility based on deep learning methods. DeepRaccess was trained to take artificial RNA sequences as input and to predict the accessibility of these sequences as calculated by Raccess. Simulation and empirical dataset analyses showed that the accessibility predicted by DeepRaccess was highly correlated with the accessibility calculated by Raccess. In addition, we confirmed that DeepRaccess could predict protein abundance in  RNA molecules play crucial roles in the regulation of diverse cellular processes, and their regulatory functions are closely linked to their structures . For exaRNA accessibility is one of the secondary structural features and is defined as the energy required for an RNA region not to form a stem structure. The accessibility is used to predict RNA-RNA interactions  and tranIn recent years, machine learning-based software acceleration has attracted attention in computer simulation . Some ofE. coli. Finally, we verified that DeepRaccess was significantly faster than an existing method on various datasets in a GPU environment.In this study, we developed DeepRaccess, a fast accessibility prediction tool based on deep learning-based software acceleration. We confirmed that DeepRaccess could reproduce the results of an existing RNA accessibility calculation method with high accuracy on both simulation and empirical datasets. We also demonstrated that the accessibility calculated by DeepRaccess was moderately correlated with protein abundance in la is fixed in the training step, and the accessibility of all subregions with the length la are the output. When users require the accessibility with a different length la, they have to redo the training of the prediction model. In this study, we used 35 as the default value for la. Note that this value has been used to predict prokaryotic translation efficiency in a previous study  where N is the sequence length. In this study, we used the CONTRAfold model as the score model because of its high accuracy  Fully Convolutional Network (FCN) , 2) U-NeThe input RNA sequences are embedded into numerical vectors and fed into the neural networks. The FCN and U-Net models used token embedding. This embedding first randomly generates six 120-dimensional numerical vectors corresponding to each of the six states: four RNA bases , one undetermined nucleotide (N) and padding. The resulting vectors are then assigned to each state in the input sequences. The BERT and RNABERT models used positional embedding in addition to token embedding. In this embedding, 120-dimensional numerical vectors corresponding to each position in the sequences are randomly generated. Finally, the values of the two embedding results are summed for each base.We briefly review each network architecture. FCN is a type of CNN architecture that is widely used for image segmentation. FCN does not use fully connected layers and is composed only of convolutional layers. We used a network of 40 convolutional layers with constant channel and unit sizes as the FCN model. U-Net is a variant of FCN, and consists of three parts, bottom-up path, bottleneck, and top-down path. The data is downsampled in the bottom-up path, the computation is performed in convolutional layers with the smallest unit sizes in the bottleneck, and the data is upsampled in the top-down path. The essential feature of U-Net is that the layers on the bottom-up and top-down paths have skip connections. For the U-Net model, we used a network consisting of 3, 35, and 3 layers on the bottom-up path, bottleneck, and top-down path, respectively. BERT was originally developed for natural language processing and is a model in which transformer layers are stacked several times. In this study, we stacked six transformer layers. Transformer can incorporate positional information of elements into the model by using the attention mechanism. RNABERT is a BERT model pre-trained on 76,237 human small ncRNAs in the RNAcentral database . Both RNAll sequence data used for training were artificially generated. We generated the sequences using two methods: 1) uniform base sampling to generate RNAs that lack strong stem structures and 2) sampling to generate RNAs with strong stem structures similar to small ncRNAs. In this paper, we refer to these methods as the uniform and the structured RNA sampling methods, respectively.N by sampling from the uniform distribution unif. The bases in the sequences were sampled from the categorical distribution Cat(x|π) for the category , and π was sampled from the Dirichlet distribution Dir. π was sampled once per sequence.In the uniform sampling method, we first determined the sequence length l by sampling from unif. We next selected the length d of the region flanked between two stem regions from unif. We also selected the start position of the first stem region from unif. We then substituted the bases in the second stem regions so that the bases were complementary to the bases of the first region. When the base was G or U, whether the base formed a Watson-Crick base pair or a wobble base pair was determined by the Bernoulli distribution Bern(x|μ). μ was sampled from the Beta distribution Beta. After that, we substituted the bases in the stem region to create internal loops, and whether a base was substituted or not was determined by Bern(x|μ). Here, μ was sampled from Beta, and the base after the substitution was sampled from Cat(x|π) using the uniform sampling method. We also substituted the next base after the substituted base according to Bern(x|μ), and μ was sampled from Beta.In the structured RNA sampling method, after generating a sequence based on the uniform sampling method, we determined the stem length Using these two methods, we created two training datasets that were a uniform RNA dataset and a structured RNA dataset. In the former, all sequences were generated by the uniform sampling method, while the latter contained half of each of the sequences generated by the two sampling methods. We performed the training on each of the two training datasets and created two predictive models for each architecture. We used 10 million as the default number of sequences per the training.E.coli synthetic mRNA datasets. As the test simulation dataset, we used a dataset generated in the same way as the training data used for the trained model. We used 100 thousand as the number of sequences per the test dataset. The Rfam dataset consisted of 3,105,149 sequences from the Rfam 14.9 database  and normalized mean square error (NMSE), which is the MSE divided by the target value.We evaluated the prediction accuracy of DeepRaccess using simulation test datasets and three empirical datasets: Rfam, Gencode, and database , and mosdatabase . We haveic mRNAs . We applE. coli synthetic mRNA dataset for this analysis, and calculated Spearman’s ρ between protein abundance and the accessibility based on the DeepRaccess. For the comparison, we used the accessibility calculated by Raccess, minimum free energy (MFE) calculated by CONTRAfold  Xeon(R) Gold 6,148 2.1 GHz, memory: 8 GB). In addition, DeepRaccess was also run in an environment where both CPU and GPU were available (CPU: Intel(R) Xeon(R) CPU E5-2,698 v4 2.2 GHz, GPU: Tesla V100 DGXS 32GB×4, memory: 257GiB).ρs were greater than 0.97 in all cases, suggesting that deep learning is effective in predicting RNA accessibility. In addition, the scores based on the structured RNA dataset were worse than those based on the uniform RNA dataset in each architecture. The reason for the difficulty in prediction may be that the structured RNA dataset has a large variance in the RNA accessibility. We also verified that the FCN was the best-performing architecture in both datasets and therefore used the FCN in the following analyses.We first evaluated the prediction accuracy of DeepRaccess using simulation test datasets and compared the performances of different neural network architectures. la and W on the performances (la was large. In addition, small W resulted in accurate prediction. This may be because the RNA accessibility has small variances and ranges when W is small.We next investigated the effect of the training data size on the prediction performances using the structured RNA dataset . We conformances . We founρ was greater than 0.88 for all datasets. While the predictive model trained on the uniform RNA dataset outperformed that trained on the structured RNA dataset for the Gencode and E.coli synthetic mRNA datasets, the opposite trend was found for the Rfam dataset. In addition, the prediction results on the Rfam dataset had the highest NMSE of the three datasets. These results are probably due to the fact that the Rfam dataset contains many structured RNAs. Furthermore, for the Rfam and Gencode datasets, we found some data has very low predicted accessibility although the accessibility calculated by Raccess was large. This result means that these regions were predicted to form few stems, even though they actually have strong stem structures. In conclusion, DeepRaccess was also able to predict RNA accessibility with high accuracy for empirical RNA sequences, but its accuracy was insufficient for highly structured RNAs.We then assessed whether DeepRaccess could predict the accessibility of empirical RNA sequences using three datasets . We veriE.coli and the accessibility calculated by DeepRaccess (ρ by DeepRaccess trained with the uniform and the structured dataset were 0.585 and 0.493, respectively, indicating that DeepRaccess could predict the protein abundance with moderate accuracy. The prediction accuracy of DeepRaccess trained with the uniform dataset was lower than that based on the accessibility calculated by Raccess, but comparable to that of the MFE score and higher than those of RBSDesigner and RBSCalculator.We also evaluated the correlation between the protein abundance in pRaccess . We founFinally, we evaluated the runtime of DeepRaccess. First, we looked at the time taken to train the predictive model and found it to be 2 days and 20 h in the GPU environment. This is not short but the prediction model only needs to be trained once, and thus the long time is not a practical bottleneck. Note that this training step is not necessary when users are using trained models of DeepRaccess. We next assessed the time taken to predict the RNA accessibility . In the E.coli mRNA calculated by DeepRaccess can predict the protein abundance.In the current study, we proposed DeepRaccess, a rapid RNA accessibility prediction method based on the deep learning. We evaluated the prediction accuracy and the computational speed of DeepRaccess using the simulation and three empirical datasets. We generated two training datasets, the uniform RNA datasets and the structured RNA datasets. We validated that DeepRaccess had a high level prediction accuracy while exhibiting significantly faster performance on the GPU environment. When calculating the accessibility of RNAs such as mRNAs and long ncRNAs, DeepRaccess trained with the uniform RNA datasets was more effective. On the other hand, when calculating the accessibility of structured RNAs such as short ncRNAs, DeepRaccess trained with the structured RNA datasets was preferable. In addition, we demonstrated that the accessibility of regions around start codons of Although DeepRaccess had high prediction accuracy, further improvement in prediction performance is an essential issue. The simplest approach is to increase the number of training data. In this study, we used 10 million RNA sequences as our training data, but we expect to improve the accuracy by using several billion RNA sequences. While increasing data size is difficult in machine learning for bioinformatics in general, our method allows unlimited data growth by generating data through simulation. In addition, there is scope for improvement in training data generation methods. In this study, we employed two sampling methods: the uniform and structured RNA sampling methods. We investigated how the distribution of accessibility in our training dataset differs from those in the empirical dataset . As a reet al. proposed that embedding in the hyperbolic space improves the accuracy of predicting the edit distance between sequences (Furthermore, the development of neural network architectures is also a promising approach for improving accuracy. In this study, we could not fully optimize the architecture due to time and computational resource constraints. The current study is limited to an initial investigation into identifying the most suitable model from among various architectures, including FCN and BERT. Further optimization of the architecture is an important research topic. Given that we had not yet achieved convergence in prediction accuracy when the data size was increased, the current architecture may be overly complex. The development of lightweight architectures with comparable accuracy to the current study should lead to the faster computation of accessibility. As another example, Corso equences , and thuequences .The computational speedup provided by the deep learning method can be applied to the other secondary structural features such as base pairing probabilities , structu