RNA, like DNA and proteins, can undergo modifications. To date, over 170 RNA modifications have been identified, leading to the emergence of a new research area known as epitranscriptomics. RNA editing is the most frequent RNA modification in mammalian transcriptomes, and two types have been identified: (1) the most frequent, adenosine to inosine (A-to-I); and (2) the less frequent, cysteine to uracil (C-to-U) RNA editing. Unlike other epitranscriptomic marks, RNA editing can be readily detected from RNA sequencing (RNA-seq) data without any chemical conversions of RNA before sequencing library preparation. Furthermore, analyzing RNA editing patterns from transcriptomic data provides an additional layer of information about the epitranscriptome. As the significance of epitranscriptomics, particularly RNA editing, gains recognition in various fields of biology and medicine, there is a growing interest in detecting RNA editing sites (RES) by analyzing RNA-seq data. To cope with this increased interest, several bioinformatic tools are available. However, each tool has its advantages and disadvantages, which makes the choice of the most appropriate tool for bench scientists and clinicians difficult. Here, we have benchmarked bioinformatic tools to detect RES from RNA-seq data. We provide a comprehensive view of each tool and its performance using previously published RNA-seq data to suggest recommendations on the most appropriate for utilization in future studies. RNA editing is a widespread post-transcriptional modification in eukaryotes that includes several types of biochemical changes such as deletions, insertions, and base conversions . AdenosiAmong the RNA modifications that have been identified to date , A-to-I With over one million predicted A-to-I RNA editing sites in human cells , the bioThis study was conducted on a multi-user Linux server with 64 central processing units (CPU) and 528 gigabytes (GB) of random-access memory (RAM). For each tool tested, the run time, percentage of CPU usage, and maximum resident set size  were clocked and measured.The RNA-seq data used for the assessment of the five benchmarked tools were obtained from the Gene Expression Omnibus (GEO) databases, accession number GSE99249 . The RNAhttps://www.gencodegenes.org/human/ (accessed on 7 February 2023)) [http://hgdownload.soe.ucsc.edu/goldenPath/hg19/database (accessed on 15 February 2023)), the browser extensible data (BED) file of simple repeats (http://hgdownload.soe.ucsc.edu/goldenPath/hg19/database (accessed on 15 February 2023)) from the human assembly release 19 (GRCh37), and the BED file with Alu sequences (http://hgdownload.soe.ucsc.edu/goldenPath/hg19/database (accessed on 15 February 2023)) from the same human assembly. SPRINT provides a Python script to adapt a Repeat Masker (rmsk) file into a BED file format. The rmsk file was downloaded from UCSC Table Browser (http://genome.ucsc.edu/cgi-bin/hgTables (accessed on 17 February 2023)) using the human assembly release GRCh38.Due to the requirements of the five benchmarked tools ) . Additiohttps://github.com/ncbi/sra-tools/wiki/01.-Downloading-SRA-Toolkit (accessed on 3 February 2023)). Before mapping sequencing reads, the last 20 bases of FASTQ files were trimmed using fastx_trimmer (FASTX Toolkit 0.0.13) [FASTQ files were downloaded using the SRA toolkit (v3.0.1) ( 0.0.13)  to remov 0.0.13) , and the 0.0.13)  and STAR 0.0.13) . For the 0.0.13) . To comp 0.0.13)  was empl 0.0.13)  and Pica 0.0.13) , the genhttps://github.com/davidrm-bio/Benchmark-of-RNA-Editing-Detection-Tools/tree/main/Tools (accessed on 12 July 2023). Some of the benchmarked tools require a pre-processing step. For example, SPRINT requires changing the MAPing Quality (MAPQ) values of Sequence Alignment MAP (SAM) files for non-BWA generated BAM files, which can be performed with a Python script provided by the authors. JACUSA2 requires the MD tags  of BAM files to be populated, which can be undertaken with SAMtools.To benchmark the performance of RNA editing detection tools, the following tools were tested: RED-ML , SPRINT http://hek293genome.org/v2/about.php (accessed on 2 June 2023) [BAM files were processed separately in a similar way for all five tools tested. Results of BCFtools, RED-ML, SPRINT, and REDItools2 were processed individually for each condition . It should be noted that although SPRINT provides a Python script to process non-BWA generated SAM files, the pre-processing of SAM files generated with STAR was not successful, which was noted by the authors . Thus, tne 2023) . The BAMne 2023) , a databne 2023) .In total, 10 major RNA editing detection tools were compared according to the following criteria: required formats for input files, handling of strandedness and/or replicate samples, and the availability of read mappers or usage of the specific mappers . A detaiREDItools  was the GIREMI  identifiRES-Scanner  performsRNAEditor  is a fulJACUSA  detects SPRINT  identifiRESIC  can clasMachine learning (ML) is increasingly being adopted within computer science and genomics and several ML-based tools for RES detection have been developed in recent years. For example, RDDpred  is the fRED-ML  incorporDeepRed  uses deeIn addition to the above-mentioned stand-alone tools, there are also several online resources for detecting RES, such as DREAM (Detection of RES associated with miRNAs) , AIRlINELastly, there are databases for RES. The two previously mentioned databases, RADAR (last updated in 2014) and DARNED (last updated in 2012), include RES for humans, mice, and fruit flies. Other databases are (1) REDIdb (last updated in 2018)  for planAs listed in the previous section and summarized in As discussed in the previous section, each tool has different input requirements, although most of the benchmarked tools work directly on BAM files. While there is little restriction on the aligner used for generating these BAM files, some tools have specific recommendations and require pre-processing steps of BAM files, as they are optimized for specific aligners and parameters. For example, RED-ML is optimized for BWA and TopHat2  and if BFor the assessment of the four selected tools , the most recent version of each tool was installed by cloning the GitHub repository  usage and physical memory (random-access memory (RAM)) usage are important considerations when analyzing RNA-seq data, especially if there are multiple samples per condition . As expeSince BCFtools, RED-ML, SPRINT, and REDItools2 do not accept replicate samples, the replicates for each condition  were analyzed and processed individually, including removing variants present the HEK 293T cell line. Each output file generated by the different benchmark tools provides different parameters that can be used in the downstream processing to identify true RES. For example, RED-ML provides a detection threshold that provides information about the confidence in the prediction. REDItools2 and SPRINT provide the number of reads that support RES, while the minor allele frequency (MAF) can be used to remove variants with low frequency for BCFtools. We chose different values for each of the following parameters to assess how they impact the prediction of RES: (1) 2, 4, 6, 8, and 10 for the number of supporting reads; (2) 0.5, 0.6, 0.7, 0.8, and 0.9 for the detection threshold; and (3) none and 0.1 for the MAF. In general, increasing the threshold values of these parameters led to a decrease in the number of RES detected for BCFtools , RED-ML Since, as discussed above, higher parameter thresholds can improve the quality of RES predictions, a post-filtering step is recommended to exclude possible false positives. However, since the effects of adjusting these parameters vary according to the specific tools, as a means to compare them the lowest threshold value for each tool was used as a filter .SPRINT showed the highest prediction of RES supported by the REDIportal database, although the lowest number of predicted RES . The reaIn accordance with the previous report , BWA andContrary to the four tools discussed above, the analysis with JACUSA2 was performed by combining the three replicates into one sample, due to the requirement of using replicate samples if RNA-seq data alone are analyzed with JACUSA2. RES present in the HEK 293T database were excluded from the analysis. Thus, to compare the five benchmarked tools, we re-analyzed the results of the tools by keeping only RES present in the three replicates. JACUSA2, like the other tools discussed before, provided several parameters in the output files which could be used for downstream processing, such as tags indicating if variants were artefacts. Like REDItools2 and SPRINT, JACUSA2 output files also include the number of supporting reads for the variants. The same threshold values employed with REDItools2 and SPRINT for this parameter  were used with JACUSA2. In contrast to what was observed before, increasing the number of supporting reads did not result in a decrease in the total number of RES and the fraction of RES present in REDIportal . As in FTo effectively identify RES, it is recommended to use DNA-seq and RNA-seq data from the same samples, as this allows the exclusion of SNPs. However, studies re-analyzing publicly available RNA-seq data may encounter issues with this approach, as most published datasets do not include DNA-seq data, as they can be costly to generate. In this case, publicly available SNPs  can be used to overcome this issue. However, this approach has some limitations as it can lead to the exclusion of SNPs that are not present in the sample. In this study, no DNA-seq data was available in the original study generating the RNA-seq data. To overcome this issue and incorporate genomic information, we removed SNPs present in the HEK293T cell type, based on an available database. As shown in Although a similar comparative study was published previously , no updaThe five benchmarked tools were assessed using two different approaches: (1) individual analysis and (2) combining replicate samples into one. If replicate samples are available, an additional layer of information can be added during the detection of RES as artefacts generated during library preparation can be excluded. However, most of the tools do not accept replicate samples and individual analysis is mandatory. Only JACUSA2 accepts replicates, and therefore a downstream processing step to keep RES present in all or some of the replicates would be recommended to improve confidence. After the identification of RES, we recommend employing additional filters in the downstream processing of the output files, such as the number of supporting reads, to minimize the amount of false positive RES.Based on the re-analysis of the published RNA-seq data of genetic deletion of the ADAR1 gene in human cells, we recommend STAR as the read aligner and REDItools2 as the RNA editing detection tool for the identification of RES, if the analysis time is not a constraint, as this combination identifies the second-largest number of RES in WT samples in both high and low stringencies of threshold cut-off levels in each analysis pipeline . AlthougRNA editing events are known to increase in cellular stress and in disease conditions ,74,75, e


Adenosine-to-inosine (A-to-I) RNA editing is a prevalent type of RNA modification in all domains of lives ranging from bacteria  protein family  rather than making a crosstalk between current RNA vs. ancestral DNA, then it is apparent that the editing mechanism still increases the fitness of the uneditable allele  is no fitter than the ancestral genomic G-allele . This ise allele . Under te allele .E. coli that all change Tyr (TAC) to Cys (TGC), among which the recoding site in hokB gene affects the toxicity of the protein product and could improve the adaptation of bacteria population under antibiotic stress  were shown  rather than 12/0 . Even taken the editing-prone TACG motif into account, roughly half of the A-to-G events in the TACG motifs (in CDS) should be synonymous mutations (ACG context: frame1: NTA-to-NTG (3 synonymous & 1 non-synonymous); frame2: TAC-to-TGC (1 non-synonymous); frame3: ACG-to-GCG (1 non-synonymous)  . Indeed,E. coli contains excessive non-synonymous editing and thus exhibit signal of adaptation, a more fundamental question is, what is the nature of adaptation in bacteria? What is the advantage of recoding? Does non-synonymous editing conform with diversifying hypothesis or restorative hypothesis?Given that the A-to-I RNA editome in Fusarium graminearum while the post-edited allele is beneficial in the sexual stage. Overall, the editable status is more advantageous than uneditable status (agreeing with the model in The diversifying hypothesis stresses the flexibility (condition-specificity) of RNA editing  but in the E. coli genome this site is already fixed as Tyr  is highly complicated  more systematic identification of RNA editing sites, (2) higher resolution of editing levels, and (3) more accurate inference of the ancestral state of each editing site. Nevertheless, we do not exclude the possibility that in bacteria, some of the RNA editing sites play a diversifying role while some other editing sites restore the ancestral sequence.Regarding the nature of adaptive editing in bacteria, the case study of Tyr-to-Cys (Y29C & Y46C) recoding sites in bacteria ing site . MoreoveGiven the ambiguity of the nature of adaptive RNA editing in bacteria, we try to figure out the potential factors shaping the function, natural selection, and evolution of bacterial RNA editing. Firstly, the strength of natural selection is associated with the ploidy of the genome. Haploid genomes lack the recombination mechanism and are less tolerant to the deleterious mutations compared to diploid and polyploid genomes. Bacteria are mostly haploid. Fungi are either haploid, diploid, or polyploid according to different stages. Animals are mostly diploid except Hymenoptera (bees and ants) that has haploid males. Therefore, compared to fungi and animals, bacterial genomes are subjected to stronger purifying selection. Although A-to-I RNA editing occurs in RNA rather than the genome, the editing efficiency (probability) is determined by the sequence context, which is affected by genomic mutations. The consequence is, the evolution of RNA editing is tightly connected to genome evolution  is designed for achieving the wobble pairing between I-C, I-U, and I-A (anticodon-codon), enabling a tRNA to be potentially decoded by multiple synonymous codons  to explain the nature of adaptive non-synonymous RNA editing. Through years of studies, the scenario of genome-wide adaptive editing is relatively clear in fungi, animals, and even plants. In bacteria, however, due to the limited number of discovered editing sites, the adaptive signals are elusive. By re-analyzing the A-to-I RNA editing sites in Conceptualization and supervision: WC and HL. Writing—original draft: YD. Writing—review and editing: YD, WC, and HL. All authors contributed to the article and approved the submitted version.


Terminal nucleotidyltransferases (TENTs) could generate a ‘mixed tail’ or ‘U-rich tail’ consisting of different nucleotides at the 3′ end of RNA by non-templated nucleotide addition to protect or degrade cellular messenger RNA. Recently, there has been increasing evidence that the decoration of virus RNA terminus with a mixed tail or U-rich tail is a critical way to affect viral RNA stability in virus-infected cells. This paper first briefly introduces the cellular function of the TENT family and non-canonical tails, then comprehensively reviews their roles in virus invasion and antiviral immunity, as well as the significance of the TENT family in antiviral therapy. This review will contribute to understanding the role and mechanism of non-canonical RNA tailing in survival competition between the virus and host. Arabidopsis, and certain virus-infected cells [Almost all kinds of RNA in eukaryotes undergo 3′ end processing. RNA 3′ end changes dynamically in composition and length, which determine the fate of modified RNA ,2. The 3ed cells ,31,32,33Eukaryotic cells have evolved the RNA-based antiviral immunity to escape from viral infection. One well-known mechanism is that long double-stranded RNAs (dsRNAs) derived from the virus in infected cells induce RNA interference (RNAi) to specifically remove viral RNAs ,35. DiceViral RNA 3′ tailing has emerged as an important target pathway for antiviral therapy and also the controlled mRNA 3′ tailing has great potential for improving RNA vaccine efficiency. Therefore, the comprehensive understanding is needed regarding the significance and action mechanism of RNA 3′ tailing in various organisms and virus infection. Here, we review biogenesis and function of mixed tail and U-rich tail particularly in the process of virus infection, and describe the impact of RNA 3′ tailing on virus–host survival competition and relevant antiviral therapy. Arabidopsis, viral infection, and embryos from several organisms including mouse, frog, Drosophila, and zebrafish [Arabidopsis, and viral infection. The experimental data have suggested that host TENT4 is employed by hepatitis A virus (HAV), hepatitis B virus (HBV), and human cytomegalovirus (HCMV) to control viral RNA stability, and inhibitors targeting TENT4 have been rapidly developing as antiviral medicine.To date, guanosine residues in poly(A) tails have been found in human cells, ebrafish ,32,45. ETENT4A and TENT4B are predominantly responsible for generating mixed tail in mammals, decorating poly(A) tail with non-adenosine nucleotides, of which guanosine is the most common ,42. BecaArabidopsis poly(A) tails regulates translation efficiency but not mRNA stability of plant genes. Poly(A)-seq analysis of Arabidopsis has revealed that over 10% of poly(A) tails carry G-content, taking up 0.8–28% of each tail. Surprisingly, the data support that G-content in A-tail is negatively correlated with the binding efficiency of PABP on A-tail and further the gene with higher G-content has lower translation efficiency [Arabidopsis genes  DNA viruses with the relaxed circular dsDNA and linear dsDNA as genomes, respectively. It had been assumed that mRNA maturation in the dsDNA viruses like HBV and HCMV might follow the processes similar to host, including 5′ capping, splicing and 3′ polyadenylation. Out of expectation, HCMV and HBV cleverly hijack host TENT4A/4B to produce mixed tail at the 3′ end of viral mRNA to facilitate their infection . RemarkaHAV is a single-stranded (ss) RNA virus with a small positive-sense RNA as a genome. Viral genome RNA 3′ end is polyadenylated, but instead of 5′ capping, the 5′ end is covalently linked to a small viral protein VPg, the putative protein primer for minus-strand RNA synthesis. Although HAV replication cycle still remains unclear, its genome replication is expected to use RNA as template to directly synthesize complementary RNA . HAV infIn order to identify viral cis-acting elements playing roles in its RNA stability, translation and localization, high-throughput screening was conducted based on luciferase reporter system . The 130The finding of TENT4A/4B as target proteins of antiviral small chemical RG7834 has greatly advanced the knowledge about the mechanism of viral gene expression . IntriguHBV infection is one of the most significant public health issues, with ~350 million chronic HBV patients worldwide ,59,60. ANucleotide analogues and immune modulators are widely-used antiviral agents that are effective in preventing the spread of infectious viruses, but these medicines have several disadvantages such as strong side effects and the development of drug resistance ,66,67. CRG7834, a small chemical in DHQ class, was developed by Roche as a HBsAg inhibitor, and could target HBV and reduce viral gene expression ,74. RG78Roche has also released a series of THP class HBsAg inhibitors, among which, the representative compound 3 inhibits HBsAg and HBV DNA synthesis in HepG2.2.15 cells . Li ZhanViruses have evolved several ways in the RNA level to escape from host immune system whereas hosts have also developed multiple immune responses to resist virus invasion. Most organisms possess innate immune responses to recognize and eliminate viruses. Interferon, RNAi and RNA uridylation pathways function as natural antiviral defense mechanisms in various living creatures.In mammals, cells detect viral infection by pattern recognition receptors (PRRs), and induce an interferon type I response in both cell-autonomous and non-autonomous manners . Virus-sIn contrast, plants and invertebrates rely on powerful RNA interference (RNAi) to combat viral infection ,85. DiceC. elegans identified an essential role of CDE-1 in resisting viral infestation. CDE-1, a homolog of mammalian TUT4/7, uridylates the 3′ end of the OrV RNA genome and drives its degradation independent on the RNAi pathway -infected Host TUT4/7-mediated uridylation was also recently reported to delay mouse hepatitis virus (MHV) replication in mouse 17-CL1 cells . MHV, a Arabidopsis, both TUTases of HESO1 and URT1 participate in the uridylation of viral RNAs from turnip crinkle virus (TCV) and turnip mosaic virus (TuMV). However, the double knockout of TUTases did not affect viral infection [18 primer [The 3′ uridylation was also detected in viral RNAs from varying plant viruses. The extensive profiling of uridylation was carried out using 3′ RACE-seq for the representative ~20 plant viruses with positive-sense ssRNA genomes and uridylation was present in all 47 viral RNAs investigated. According to knockout experiments in nfection . Another8 primer . Additio8 primer ,94.Taken together, uridylation appears to be wide-spread in eukaryotic viruses. Although it is not fully clear, TUTases are more likely to attack viral RNA that lacks or with short A-tail, which may not be protected and is critical for host recognition of pathogens ,95,96,97Arabidopsis. Further research needs to answer whether mixed A/G tailing is conserved in varying organisms, whether its substrates are ubiquitous or specific, as well as the underlying mechanism of its substrate selection.In eukaryotes, RNA tailing is often associated with RNA trimming or decay. In the last 30 years, there have been remarkable breakthroughs in understanding RNA uridylation, a conserved post-transcriptional gene regulation mechanism with a wide range of RNA substrates in living organisms, as an essential tool for intracellular RNA monitoring. New attention has been paid to mixed A/G tailing to date. Recent studies have been focused more on the substrate selectivity and biochemical function of TENT4. TENT4A and TENT4B are two human homologues of the yeast Trf4p protein. In yeast, the Trf4p–Air2p–Mtr4p polyadenylation (TRAMP) complex promotes nuclear surveillance of aberrant mRNAs, rRNAs, snRNAs, snoRNAs and tRNAs ,101,102.Flaviviridae with highly structured 3′ ends [C. elegans poly(UG) polymerase MUT-2 by adding p(UG) tails. With over 16 nt perfectly alternating U and G nucleotides, RNA fragments attached to the p(UG) tail can act as gene-silencing agents that suppress gene expression [6A) has been recently discovered to have a role in the life cycles of many viruses as well as in cellular response to viral infection [The study on RNA non-A tailing illustrates the novel mechanism of virus–host interaction. Modification in viral RNA bypasses or stimulates the host machinery for RNA degradation and thus influencing infection success. The widespread presence of 3′ uridylation in eukaryotic RNA viruses suggests the uridylation-directed RNA decay pathway as a universal defense system against viruses. Perhaps in response to this threat, some viruses have evolved to modify the 3′ ends of their RNAs, which protects them against host degradation, like the mRNAs of HBV and HCMV with mixed tails and single-stranded RNAs of  3′ ends ,106,107.pression ,109. In nfection ,111,112.nfection . The facThe involvement of TENT and its cofactor in viral life cycles may provide mechanistic insights into the development of a new category of antiviral drugs. In spite of requiring further investigation, the role of RNA tailing as an essential regulatory instrument will provide an unexpected opportunity for antiviral treatment and chemoprevention.


Introduction: UNC-6/Netrin is a conserved bi-functional guidance cue which regulates dorsal-ventral axon guidance in C. elegans. In the Polarity/Protrusion model of UNC-6/Netrin mediated dorsal growth away from UNC-6/Netrin, The UNC-5 receptor first polarizes the VD growth cone such that filopodial protrusions are biased dorsally. Based on this polarity, the UNC-40/DCC receptor stimulates growth cone lamellipodial and filopodial protrusion dorsally. The UNC-5 receptor maintains dorsal polarity of protrusion, and inhibits growth cone protrusion ventrally, resulting in net dorsal growth cone advance.Methods: Growth cone imaging in mutants, combined with Cas9 genome editing and genetic analysis, were used to analyze the role of a novel short isoform on unc-5 in growth cone polarity and protrusion.Results: Work presented here demonstrates a novel role of a previously undescribed, conserved short isoform of UNC-5 (UNC-5B). UNC-5B lacks the cytoplasmic domains of UNC-5 long, including the DEATH domain, the UPA/DB domain, and most of the ZU5 domain. Mutations that specifically affect only the unc-5 long isoforms were hypomorphic, suggesting a role of unc-5B short. A mutation specifically affecting unc-5B caused loss of dorsal polarity of protrusion and reduced growth cone filopodial protrusion, the opposite of unc-5 long mutations. Transgenic expression of unc-5B partially rescued unc-5 axon guidance defects, and resulted in large growth cones. Tyrosine 482 (Y482) in the cytoplasmic juxtamembrane region has been shown to be important for UNC-5 function, and is present in both UNC-5 long and UNC-5B short. Results reported here show that Y482 is required for the function of UNC-5 long and for some functions of UNC-5B short. Finally, genetic interactions with unc-40 and unc-6 suggest that UNC-5B short acts in parallel to UNC-6/Netrin to ensure robust growth cone lamellipodial protrusion.Discussion: These results demonstrate a previously-undescribed role for the UNC-5B short isoform, which is required for dorsal polarity of growth cone filopodial protrusion and to stimulate growth cone protrusion, in contrast to the previously-described role of UNC-5 long in inhibiting growth cone protrusion. Caenorhabditis elegans, UNC-6/Netrin is a conserved, extracellular laminin-like guidance cue, and is expressed in ventral cells and axonal processes   . In any The 19 GABAergic DD and VD motor neuron cell bodies reside along the ventral nerve cord  [from G]. The VDC. elegans VD growth cones during their outgrowth in UNC-6/Netrin signaling mutants led to the Polarity/Protrusion model of growth cone migration away from UNC-6  from being delivered to the growth cone plasma membrane.Previous studies implicate three distinct pathways utilized by UNC-5 to inhibit growth cone protrusion. The FMO flavin monooxygenases act downstream of UNC-5 to inhibit protrusion , possiblunc-5 locus is predicted to encode for multiple isoforms (The isoforms .unc-5B) differs at the 3’ end of unc-5 gene, and is predicted to encode a molecule lacking most of the cytoplasmic domains, including the DEATH domain, the UPA/DB domain, and most of the ZU5 domain. ZU5 domains interact with several molecules including ankyrin. The ZU5 domain in Unc5A binds to the melanoma-associated antigen D1 (MAGE-D1/NRAGE) and participates in apoptosis (C. elegans (unc-5 isoforms (unc-5 strong loss-of-function mutants)  and Thrombospondin (TSP) repeats. One isoform  .unc-5B short isoform in axon guidance and growth cone morphology. unc-5 mutant alleles that affect only the long isoforms and not the short isoform were found to be hypomorphic for unc-5 function compared to unc-5 strong loss-of-function mutants: they displayed less severely uncoordinated locomotion and weaker VD/DD axon guidance defects. However, growth cone polarity and protrusion largely resembled strong unc-5 mutants, with loss of dorsal polarization of filopodial protrusion and a larger growth cone area and longer filopodia compared to wild-type. Using CRISPR/Cas9 genome editing, an unc-5 allele was generated that was predicted to affect only the unc-5B short isoform. This unc-5B mutant displayed weakly uncoordinated locomotion and weak VD/DD axon defects. unc-5B mutant growth cones lacked dorsal polarity of filopodial protrusion, indicating that UNC-5B is required for VD growth cone polarity. unc-5B mutant growth cones were also smaller and less protrusive than wild-type, indicating a possible pro-protrusive role for UNC-5B. unc-5B failed to complement a strong loss-of-function unc-5 mutant for these phenotypes, but complemented unc-5 hypomorphic mutants, consistent with the idea that unc-5B short encodes a distinct genetic function unc-5 long isoforms. Transgenic expression of unc-5B rescued uncoordinated locomotion and axon defects of unc-5 mutants, and perturbed VD growth cone polarity and protrusion consistent with a pro-protrusive role.Work reported here is an analysis of the role of the unc-5B short isoform in VD/DD axon guidance that is genetically separable from the function of the unc-5 long isoforms. Both are required for VD growth cone dorsal polarity of protrusion, UNC-5 long isoforms are required to inhibit growth cone protrusion, and UNC-5B short isoform might have a pro-protrusive role. Vertebrate genomes encode four Unc5 genes, A, B, C, and D, with Unc5A having a predominant role in axon guidance   (unc-5B. These results indicate that unc-5B constituted 3.6% (9/251) of unc-5 transcripts in mixed-stage animals.The e WS245) . Six of d 3′ end . The sho intron) . The ope intron) , resulti intron) . NeighboThe UNC-5A long isoform encodes a molecule with two extracellular immunoglobulin (Ig) domains, two extracellular thrombospondin type I repeats (TSPI), a transmembrane domain, and cytoplasmic zona occludens I/UNC-5 domain (ZU5), UNC- 5/PIDD/Ankyrin (UPA), and DEATH domains . A tyrosunc5 genes, A-D. The human Unc5A locus encodes a truncated isoform similar to C. elegans UNC-5B, called Unc5A X4  .Vertebrates, including humans, have four 32989.1) . Unc5A X32989.1) . unc5A Xunc-5 isoforms A, C, D, E and F are collectively referred to as unc-5 long isoforms, and unc-5B the short isoform. The unc-5 alleles e791, e53, e553, and e152 introduce premature stop codons that affect all unc-5 isoforms  phenotype  from the end of the predicted unc-5B reading frame. This is downstream of the transmembrane domain and Y482. Any truncated molecule made by translation of e152 would include both  displayed growth cones that were excessively protrusive compared to wild-type, with larger growth cone area and longer filopodial protrusions (unc-5(e53) (unc-5(e791) showed a similar loss of filopodial dorsal polarity (unc-5(e152) hypomorphic allele also displayed increased growth cone area and filopodial length, and loss of dorsal bias of filopodial protrusion (unc-5(ev480) hypomorphic mutant  . Strong polarity  and excepolarity  and incrpolarity . The uncotrusion    . PreviouMethods) . MicrotuMethods) . UNC-5 wMethods) . These sunc-5(e152) and unc-5(ev480) also displayed loss of dorsal F-actin accumulation  was produced by precise removal of intron 7, which is predicted to preclude the formation of the unc-5B short isoform by fusing together exons seven and eight  mutants displayed wild-type locomotion and weak but significant total axon defects (3%) including axon wandering (unc- 5B(lq190) did not significantly affect VD growth cone area (unc-5B(lq190)  . MicrotuB(lq190) .unc-5B(lq190) was due to the unc-5 genome edit and not to a background mutation, we analyzed trans-heterozygotes of unc-5B(lq190) and the strong loss-of-function allele unc-5(tmIs1241) found on the balancer chromosome tmC25 (. tmIs1241 is an insertion of a myo- 2::gfp transgene in the unc-5 exon seven shared by long and short isoforms (unc-5(e791) (data not shown). Thus, unc-5(tmIs1241) a strong loss-of-function unc-5 mutant. VD growth cones of lq190/tmC25 trans-heterozygotes resembled unc- 5B(lq190) alone, with significantly reduced filopodial length compared to wild-type and loss of dorsal polarity of filopodial protrusion (unc- 5(tm1241) failed to complement unc-5B(lq190) as expected.To ensure that the phenotype observed in me tmC25 . tmIs124isoforms , and hasotrusion . This inlq190 trans-heterozygote with the hypomorphic ev480 allele displayed filopodial length similar to wild-type and dorsally-polarized filopodial protrusion (unc-5(ev480) complemented unc-5B(lq190), consistent with the idea that these isoforms encode distinct functions. In sum, these results indicate that unc-5 long and unc-5B short encode genetically distinct and separable functions. unc-5B short isoform is required for dorsal polarity of filopodial protrusion, as well as robust filopodial protrusion, suggesting a pro-protrusive role.An otrusion . Thus, uunc-5::gfp minigene is composed of the unc-5 promoter driving the unc-5A cDNA fused to gfp  short isoform having a distinct role from the long isoforms, which inhibit protrusion.A MYR::UNC-5B short isoform transgene was constructed with the 3’ end of unc- 5B . Expresspolarity . This isunc-40(n324) null mutant displayed a slight but not significant reduction in growth cone area, and significantly shorter filopodial protrusions (unc-40(n324) did not significantly affect dorsal polarity of filopodial protrusions  (unc-5(lq190); unc-40(n324) double mutant growth cones resembled the additive effect of each mutant alone: slight but not significant reduction in growth cone area; significantly reduced filopodial length, and loss of dorsal polarity of filopodial protrusion (unc-5B(lq190) synergistically increased unc-40(n324) VD/DD axon guidance defects  . unc-5 strong loss-of-function resulted in unpolarized growth cones with no significant effect on growth cone and filopodial protrusion (unc-6(ev400); unc-5(lq190) growth cones showed significantly reduced growth cone area compared to either single mutant alone. Filopodial length resembled the short filopodia of unc- 5(lq190) alone. This suggests that unc-6 and unc-5B short might act in parallel to promote growth cone protrusion and is consistent with a pro-protrusive role of unc-5B short isoform.otrusion . unc-6 did not significantly reduce increased growth cone area or filopodial length in unc-5(ev480) hypomorphic mutant (unc-5(ev480) hypomorph is not solely due to overactivity of UNC-40. Possibly, the UNC-5B short isoform has pro-protrusive activity that is increased in the absence of the long isoforms. This is consistent with unc-5B(lq190) short isoform mutants having reduced filopodial length.otrusion . unc-40(c mutant . This suunc-40 mutation suppressed the VD/DD axon guidance defects of unc-5(e53) strong loss-of-function and of unc-5(e152) hypomorphic mutants (unc-5(ev480) hypomorph was not suppressed by unc-40(n324) and in fact showed synergistic enhancement for failure of lateral midline crossing and total axon guidance defects (unc-5(ev480) hypomorphic mutants driving excess protrusion, possibly involving UNC-5B.Previous results showed that  mutants . In cont defects . This isunc-5(e791) mutants  mutants  mutants  VD/DD axon guidance defects by unc- 5A(Y482F) was significantly reduced compared to unc-5A(+) (Tyrosine 482 was previously shown to be required for the ability of an  mutants . Tyrosinnc-5A(+) .unc-5A(Y482F) caused a significant increase in growth cone area and filopodial length similar to unc-5(e791) mutants (unc-5(e791) mutants  failed to rescue VD/DD axon guidance defects in unc-5 mutants, indicating that Y482 is important in UNC-5B function  caused severely uncoordinated locomotion and severe VD/DD axon guidance defects (unc-5 strong loss-of-function). Very few axons were observed emanating from the ventral nerve cord, and those that did failed to extend dorsally past the lateral midline   . Howeverunc-5B short isoform was generated by CRISPR/Cas9 genome editing. The unc-5B 3’ end results from failure to splice intron 7 from the unc-5 transcript, resulting in a stop codon after 16 additional amino acid coding codons (unc-5B reads in intron 7 (unc-5B(lq190) was a precise removal of intron 7, resulting in the fusion of exons 7 and 8, leaving unc-5 long isoform coding potential unchanged. unc-5b(lq190) mutants were only slightly uncoordinated with weak VD/DD axon guidance defects  failed to complement an unc-5 strong loss-of-function mutation for these phenotypes, but did complement an unc-5 hypomorphic long isoform-specific mutant  resulted in unpolarized F-actin, but did not affect microtubule + end abundance  double mutants with unc-6 had severely reduced VD growth cone area compared to wild-type and single mutants alone, and transgenic expression of unc-5B resulted in significantly increased growth cone area compared to wild type  does not rescue the uncoordinated locomotion or VD/DD axon defects of unc- 5 mutants as well as the wild-type transgene (unc-5 mutants (Previous studies showed that tyrosine 482 (Y482) is phosphorylated and that it is required for the function of UNC-5 in axon guidance  expression alone still resulted in increased growth cone area and loss of filopodial protrusion. Thus, in UNC-5B short, Y482 is required for some functions but not others. While it is unclear which tyrosine kinase might phosphorylate Y482, the SH2 domain of SRC-1 binds to phosphorylated Y482, SRC-1 is required for robust UNC-5 tyrosine phosphorylation, and SRC-1 functions with UNC-5 in gonadal distal tip cell migration and axon guidance ; unc-40 displayed an additive phenotype, and no significant genetic interaction. This suggests that UNC-5B short might act independently of UNC-40, or in the same pathway. UNC- 5B short is lacking the cytoplasmic UPA/DB domain that mediates physical interaction with the UNC-40 P1 cytoplasmic domain. This suggests that UNC-5B short might act independently of UNC-40.These results indicate that the UNC-5B short isoform is required for VD growth cone dorsal polarity of filopodial protrusion and for robust growth cone lamellipodial and filopodial protrusion. This is in contrast to the UNC-5A long isoform, which inhibits growth cone protrusion. Double mutants of unc-5B(lq190); unc-6 double mutants displayed growth cones that were smaller than wild-type and either single mutant alone. This suggests that UNC-5B short might act in parallel to UNC-6 to promote growth cone lamellipodial protrusion. In the Polarity/Protrusion model, UNC-6 acts through UNC-40 to stimulate growth cone protrusion. However, this effect is independent of UNC-40 as unc-5B(lq190); unc-40 double mutants did not display small growth cones similar to unc-5B(lq190); unc-6 mutants. This suggests that an unidentified ligand might interact with UNC-5B short to stimulate protrusion in parallel to UNC-6, and that an unidentified receptor for UNC-6 might act in parallel to UNC-5B short ], lqIs387 [Punc-5::unc-5B(Y482F)], lqIs326 [Punc-5::myr::unc- 5A], lqIs315 [Punc-5::myr::unc-5B].Experiments were performed at 20°C using standard chniques . The prePunc-5::unc-5B short isoform transgene was constructed by removing the coding sequence of exons eight and nine, and replacing with the first 45 bases of intron 7 .  as previously described   . Resultin 2.1.0)   . Sequencunc-5(lq190) was generated by CRISPR/Cas9 genome editing which precisely deleted the entire intron 7 of unc-5A, between exon 7 and 8  co-CRISPR reagents . Deletion of unc-5 intron 7 was confirmed by PCR and sequencing and outcrossed to N2 three times. Genome editing reagents were produced by In Vivo Biosystems : 7 and 8 . This resgRNA1: 5′ CTCTCCCCAGTCATCGTCAT 3’sgRNA2: 5′ TAACAGATCAACCTCACCTC 3’lq190 mutation): 5′GAAAATGCTTTATCTGGCTGTTTCGGACACCTTAACCGACCAGCCACATCTTAAGCCAATTGAGTCTGCCCTTTCTCCTGTTATTGTCATCGGACAATGTGATGTTTCAATGTCAGCTCATG3’ (recoded exon sequence is underlined).ssODN repair template , with an ⍺ false positive value of 0.05.Proportional data were analyzed using Fisher’s Exact test. Continuous data were analyzed with Student’s Punc-25::gfp transgene, juIs76 , which is expressed in 13 VD and 6DD GABAergic motor neurons. Axon guidance defects were scored as previously described  is much lower than 0.2. Growth cones were imaged with Qimaging Retiga EXi camera on a Leica DM5500 microscope at ×100 magnification. Projections less than 0.5 um in width were scored as filopodia. Growth cone area and filopodial length were quantified using ImageJ software as previously described (t-test with unequal variance.Growth cones of VD axons were imaged as previously described . Late L1escribed . SignifiPolarity of growth filopodial protrusions was determined as previously described . Growth t-test with unequal variance on these average normalized ratios of multiple growth cones of each genotype.Growth cone F-actin was analyzed as previously described , using tt-test with unequal variance.Growth cone EBP-2::GFP puncta  was anal


Long non-coding RNAs (lncRNAs) have emerged as auxiliary regulators of gene expression influencing tumor microenvironment, metastasis and radio-resistance in cancer. The presence of lncRNA in extracellular fluids makes them promising diagnostic markers. LncRNAs deploy higher-order structures to facilitate a complex range of functions. Among such structures, G-quadruplexes (G4s) can be detected or targeted by small molecular probes to drive theranostic applications. The in vitro identification of G4 formation in lncRNAs can be a tedious and expensive proposition. Bioinformatics-driven strategies can provide comprehensive and economic alternatives in conjunction with suitable experimental validation. We propose a pipeline to identify G4-forming sequences, protein partners and biological functions associated with dysregulated lncRNAs in cervical cancer. We identified 17 lncRNA clusters which possess transcripts that can fold into a G4 structure. We confirmed in vitro G4 formation in the four biologically active isoforms of SNHG20, MEG3, CRNDE and LINP1 by Circular Dichroism spectroscopy and Thioflavin-T-assisted fluorescence spectroscopy and reverse-transcriptase stop assay. Gene expression data demonstrated that these four lncRNAs can be potential prognostic biomarkers of cervical cancer. Two approaches were employed for identifying G4 specific protein partners for these lncRNAs and FMR2 was a potential interacting partner for all four clusters. We report a detailed investigation of G4 formation in lncRNAs that are dysregulated in cervical cancer. LncRNAs MEG3, CRNDE, LINP1 and SNHG20 are shown to influence cervical cancer progression and we report G4 specific protein partners for these lncRNAs. The protein partners and G4s predicted in lncRNAs can be exploited for theranostic objectives. The abundance of K+ ions inside human cells likely facilitates the adoption of G4 structures by RNA in relation to other RNA secondary structures [+ concentrations and the presence of helicases capable of resolving the structures. The G4RP-seq technique developed by Yang et al. supports the existence of transient G4-RNA in the human transcriptome. The study reported that lncRNAs avoid G4 formation under normal conditions in the absence of G4-binding ligands and when a lncRNA such as Metastasis Associated Lung Adenocarcinoma Transcript 1 (MALAT1) folds spontaneously into G4, then it is immediately countered or resolved by helicases and RNA-binding proteins (RBPs) [Non-coding RNA (ncRNA), as the name suggests, are the RNAs that do not code for any protein. These sequences outnumber protein-coding sequences in the human genome. These nucleic acids were once considered dark matter and rendered unimportant due to their perceived disconnect from the central dogma . Howeverructures ,12. Neves (RBPs) . Many res (RBPs) ,12. G-Qus (RBPs) . LINC002s (RBPs) ,16,17,18The in silico identification of G4s in lncRNAs is challenging because RNA folding/structure-prediction algorithms do not explicitly account for putative G4 sequences. The Vienna RNA folding suite estimates RNA G4 folding energy and assesses the competition between G4 folded and alternative RNA secondary structures . HoweverWe identified a total of 785 lncRNA transcript sequences as being mis-regulated in cervical cancer. After multi-sequence alignment, 622 unique lncRNA transcript sequences were shortlisted as input for QGRS mapper analysis. We obtained 47 transcript sequences after validation of G4-forming potential using non-B database. These were then segregated into lncRNA clusters. We obtained 14 lncRNA clusters at the end of our in silico screening methodology. The distribution of data and filtering of lncRNA can be seen in The previously identified 14 lncRNA clusters possess 45 PQS tracts. While we intend to comprehensively scrutinize all these eventually, in the present study, we have restricted our examination to four lncRNA clusters, MEG3, CRNDE, LINP1 and SNHG20. These four lncRNA clusters have G-scores ranging from 69 to 71 and possess different PQS-containing transcripts and expression patterns, thereby representing a varied sample set. The selected RNA sequences are listed in + and Li+. + and Li+. While parallel topology of G4s is evident in the presence of ions, these are not found to exert a pronounced effect on change in G4 topology.CD spectra of the lncRNA sequences A suggest+ lowers the ThT emission across all the RNA sequences studied, the presence of K+ negatively affects the ThT emission in the case of MEG3, LINP1 and CRNDE-R1. The results of ThT emission assay for SNHG20 are closest to the expected behavior of G4s, with respect to superior enhancement in presence of K+ versus Li+ as observed in the positive control TERRA. To a modest extent, the results of ThT emission obtained with CRNDE-R2 also follow the expected behavior of greater G4 stabilization in the presence of K+ as opposed to Li+. The ThT excitation spectra with various RNA G4s display a similar pattern of fluorescence in comparing the presence versus absence of K+ and Li+ (We next investigated the ability of these RNA G4 structures to respond to Thioflavin T (ThT).  and Li+ . These r+ ions, the full-length product intensity decreased and stop product intensity increased. The greater amount of stop product in the presence of K+ suggests a stabilizing effect of the same on the G4 structure of the RNA. Notably, a similar pattern was observed in the presence of Li+ ions. However, the decrease in the full-length product intensity in the presence of Li+ was not as prominent as in the case of K+ ions. This suggests that the net stabilizing role of Li+ is lower compared to K+. These results reaffirm the outcomes of CD and ThT fluorescence experiments discussed previously. Nevertheless, the RT stop experiments are more sensitive in detecting the stabilizing effect of K+ on the RNA G4s. Our experiments successfully validate the in silico G4 identification in dysregulated lncRNAs of cervical cancer.While the ThT fluorescence assay on RNA G4s depict an interesting and unexpected effect of monovalent ions, the indirect character of the assay could result in misleading inferences. We performed the reverse-transcriptase (RT) stop assay on the selected RNA sequences to develop another perspective on the role of monovalent ions. The RT stop assay was performed in the presence of monovalent cations, i.e., KCl and LiCl (150 mM). As shown in Based on the dysregulated lncRNAs selected as per our in silico workplan, we decided to computationally predict the corresponding protein-interacting partners. We used two approaches to predict the protein-interacting partners of these lncRNAs. For reference, the FASTA sequence information for all proteins mentioned in the manuscript is provided in In our first, top-to-bottom approach, we begin with information from a database called lnc2catlas. We performed a co-variation analysis to investigate the connectivity of lncRNA expression and TP53/CDKN2A expression. We selected an RNA expression platform for CESC cancer type and analyzed a gene probe/gene probe heatmap for identifying covariation between lncRNA and proteins (TP53 and CDKN2A). In the heatmap shown in We next performed a bottom-to-top approach based on literature reports for the identification of RNA G4-interacting proteins and computational prediction of their interaction with the lncRNAs under study. We identified subcellular localization of lncRNAs using the lncATLAS database . Based oThis work is based on two primary objectives: (1) in silico identification of PQSs present in dysregulated lncRNAs of cervical cancer, and (2) in silico enunciation of G4-specific RNA-binding proteins that are likely to associate with the RNAs obtained from objective (1). The first part of our work highlights the feasibility of deploying appropriate in silico prediction methodologies for identifying G-quadruplex-forming sequences in hitherto-unexplored nucleic acid contexts. Exploration of G4 structures originated from experimental information about the behavior of specific motifs that could also be considered as reference points. The advent of multiple data repositories and structure-prediction algorithms has made it possible to develop ab initio reference points first before prioritizing experimental follow-up. At the end of our in silico pipeline, we identified 14 lncRNA clusters . A few l+ to stabilize G4 formation, in contrast to the supportive role of physiologically relevant Na+ and K+ cations, is widely used to scrutinize the G4-forming behavior of oligonucleotides [+ impairs the G4-folding ability of all the selected RNAs, the presence of K+ is most beneficial for the G4 formed by SNHG20. We experimentally validated in silico predictions by a combination of CD spectroscopy, ThT fluorescence assay and reverse-transcriptase (RT) stop assay. As demonstrated by the in vitro experiments, the predicted putative quadruplex sequences in the four selected lncRNAs form stable G4s. Different RNA G4 topologies exhibit distinctive CD signals ,36. The leotides . Notably+ or Li+, it is possible that the parallel G4s being formed arise from a different number of participating RNA molecules. Moreover, the ThT assay relies on the dye’s ability to bind in end-stacking mode, and G4 topologies that do not provide easy access for end-stacking may be mis-identified as unstable G4s [Interestingly, the fluorescence enhancement of ThT was weakened in the presence of the monovalent ions for specific RNAs. While the effect of monovalent cations on DNA G4s is widely deployed as a canonical assessment of quadruplex stability, similar interpretations of RNA G4 behavior are not straight-forward. The architecture of G-tracts and spacer lengths in the MEG3, LINP1 and CRNDE-R1 sequences being tested suggest potential for polymorphism in the corresponding G4s in the presence of specific monovalent ions ,42. Consable G4s ,44. The able G4s ,46. WhilAs part of our second objective, we tested two approaches to predict G4-specific RBPs that are likely to interact with the lncRNAs under study. LncRNAs are purported to exert distinctive effects via interaction with partners such as proteins, DNA, mRNA or even other lncRNAs . Among tWe employed both top-to-bottom and bottom-to-top approaches. In the top-to-bottom approach, we utilized a database called lnc2catlas, which resulted in four RBPs, TP53, CDKN2A, PTEN and SMAD4, that are ranked and categorized based on a score and their association with specific cancer types. Heatmaps were employed to analyze the co-occurrence patterns between lncRNAs and proteins. Literature mining has revealed that LINP1 does not bear the TP53-binding site to directly regulate its cellular function but p53 regulates the expression and function of LINP1 . We coulThe inability of the top-to-bottom approach to focus exclusively on G4-binding proteins led us to test a converse bottom-to-top approach to identify the proteins that interact with RNA G4 structures. This approach relied on previously reported RNA G4-binding proteins, including FMR2, hnRNP A2, Nucleolin, DHX36, SRSF1, SRSF9, TLS and TRF2. It is intuitive to assume that the probability of binding between a lncRNA and a protein would be higher if they shared the same subcellular location. Therefore, we examined the subcellular locations of the selected lncRNAs and their interacting proteins. The in silico predictions showed colocalization between RNA-protein pairs that had attractive scores in the RPISeq analysis. Consequently, these proteins have a significant likelihood of physically interacting with lncRNAs. LINP1 is the only lncRNA having cytoplasmic presence and is known to translocate to the nucleus in response to DNA damage . It may The value of the results obtained in the second part of our work can also be better appreciated from The in silico predictions in this work do not replace experimental validation. Instead, they support the in silico approach and provide a framework for systematic experimental investigations. In future, experimental validation of protein-interacting partners identified by the approach reported in this work would facilitate further scrutiny of their diagnostic and therapeutic potentials. Notably, alterations in quadruplex structure using synthetic ligands can potentially disrupt or stabilize the tertiary structure of lncRNAs, thereby affecting the lncRNA–protein partner interactions and providing a therapeutic handle. Our laboratory is currently pursuing these G4-mediated activities of dysregulated lncRNAs in cervical cancer.http://bio-bigdata.hrbmu.edu.cn/lnc2cancer/ assessed on 1 June 2020) is a manually curated database which has a list of lncRNAs experimentally supported as bearing association with specific cancers [Lnc2cancer  was used for the prediction of PQS in our work. QGRS mapper is an established algorithm that identifies putative G-quadruplex-forming sequences (PQS) in input nucleotide sequences [https://nonb-abcc.ncifcrf.gov/apps/site/default assessed on 10 December 2022). This database contains comprehensive information on all mammalian genomic regions that are predicted to adopt alternate structures to B-DNA such as Z-DNA, quadruplex-forming motifs, mirror repeats, inverted repeats and direct repeats with subsets of cruciform, triplex and slipped structures [QGRS mapper  provides interactions between 33 different cancers and 27,670 lncRNA transcripts [https://bioinformatics.mdanderson.org/TCGA/NGCHMPortal/, assessed on 10 January 2023. This compendium includes 297 interactive Next-Generation Clustered Heat Maps (NG-CHMs) for exploring cancer bioinformatics data from The Cancer Genome Atlas (TCGA) project. Our choice of proteins as part of our bottom-to-top approach was based on 8 RNA G4-binding proteins reported by Brazda et al. These are FRAXE-associated Mental Retardation Protein (FMR2), Heterogeneous Nuclear Ribonucleoproteins A2 (hnRNPA2), Nucleolin, DEAH Box Protein 36 (DHX36), Serine/Arginine-Rich Splicing Factor 1 (SRSF1), Serine/Arginine-Rich Splicing Factor 9 (SRSF9), protein Translocated in Liposarcoma (TLS) and Telomeric Repeat Binding factor 2 (TRF2) [Lnc2Catlas (nscripts . This da2 (TRF2) .http://pridb.gdcb.iastate.edu/RPISeq/, assessed on 15 January 2023) can predict the probability that a specific protein and RNA interact and is based on a family of machine learning classifiers.We also used the database RPIseq for predicting RNA–protein interactions using only sequence information. The RPIseq server  was used to obtain the localization of the proteins that interact with lncRNAs. To detect the localization of lncRNAs, LncATLAS was used. LncATLAS  is an easy-to-use web-based visualization tool for obtaining useful information about expression localization of lncRNAs [PROTEIN ATLAS ( lncRNAs .® RNA Cleanup Kit (500 μg) , following the manufacturer’s protocol. The concentration and purity of eluted RNAs were quantified using a NanoDrop™ 2000 spectrophotometer  and were stored at −80 °C until further use.The oligonucleotide sequences used for the experimental studies are listed in Circular Dichroism (CD) spectroscopy was used to evaluate potential G4 formation in RNA sequences. CD spectra were recorded on a JASCO J-815 spectrophotometer and all measurements were carried out at 16 °C in the wavelength range of 220–350 nm, using a response time of 1 s, a step size of 1 nm and a 2 nm bandwidth. The scanning speed of the instrument was set at 100 nm/min, with an average of three scans. A 10 mm path length quartz cuvette was used in all experiments. Samples containing 5 μM RNA were folded in a buffer containing 10 mM Tris-Cl (pH 7.5) and 10 mM Tris-Cl (pH 7.5), 0.01 mM EDTA (pH 8.0) by incubating at 95 °C for 5 min and cooled to room temperature before CD analysis.ThT has been suggested as an efficient reporter for distinguishing between G4 and non-G4 RNA structures . FluoresAs per the design of the RT stop assay, the RNA template is translated by the reverse transcriptase enzyme, up until it encounters a stable RNA G4 structure. Truncated complement DNA products are created and can be visualized by denaturing PAGE assay . Texas Rt-test was carried out. Statistical significance is shown with asterisks: * p ≤ 0.05; ** p ≤ 0.001, *** p ≤ 0.0001.For statistical analysis, an unpaired In this study, we have outlined an in silico method to predict and analyze cervical cancer specific G-quadruplex-bearing lncRNAs. From among 14 different lncRNAs that are considered to possess G4 motifs, we have experimentally characterized the G4 formation by 4 lncRNAs, namely, SNHG20, MEG3, CRNDE and LINP1. As part of this study, we have profiled the RNA-binding proteins that are likely to interact with these lncRNAs, playing important roles in the progression of cervical cancer. Based on the outcome of this work, we suggest G4 motifs as an attractive structural element that could be used to identify dysregulated lncRNAs in cervical cancer and their interacting proteins as potential biomarkers. While this study does not purport to sacrifice experimentation, it offers a workable plan for identifying and prioritizing dysregulated lncRNA-based experiments that seek to shed light on their mechanistic or functional links in cervical cancer progression. Identification of lncRNA–protein axes using the approach presented here could be valuable from diagnostic and therapeutic perspectives, and researchers in relevant domains are thus likely to find value in the in silico approach.


We present a detailed protocol for sequencing full-length mRNA isoforms using the Oxford nanopore long-read sequencing technology. We describe steps for poly(A) RNA isolation, library preparation, and cDNA size selection. We then detail procedures for sequencing and processing and a computational framework to identify exon couplings and assign mRNA 5′ ends and 3′ ends to each other. Our approach enables the identification of links between transcription initiation and co-transcriptional RNA processing events.For complete details on the use and execution of this protocol, please refer to Alfonso-Gonzalez et al.  •Optimization of nanopore long-read sequencing samples for very long mRNAs•Computational pipeline for the quantification of mRNA isoforms transcriptome wide•Methods LASER and LATER identify couplings between transcription and RNA processing Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. We present a detailed protocol for sequencing full-length mRNA isoforms using the Oxford nanopore long-read sequencing technology. We describe steps for poly(A) RNA isolation, library preparation, and cDNA size selection. We then detail procedures for sequencing and processing and a computational framework to identify exon couplings and assign mRNA 5′ ends and 3′ ends to each other. Our approach enables the identification of links between transcription initiation and co-transcriptional RNA processing events. The protocol below describes the specific steps for sequence analysis of long RNA fragments, including instructions for both sample preparation and computational analysis. We focus our analysis on the polyadenylated fraction of the transcriptome (Poly(A)+ RNA fragments).Analysis of ultra-long RNA fragments absolutely depends on the preservation of long molecules throughout sampling and preparation steps. RNA is very susceptible to degradation by ribonucleases (RNases). Typical sources of RNase contamination are exposure to lab surfaces, aerosols from pipetting, ungloved hands or usage of contaminated reagents. Therefore, always wear gloves and exchange them frequently. Use RNase-free certified solutions and disposable plasticware, including filter tips. Use a separate, decontaminated area for RNA work and include RNase inhibitors into enzymatic reactions containing RNA fragments. Furthermore, keep RNA and thawed reagents on ice until needed and aliquot RNA samples to avoid multiple freeze-thaw cycles. Store RNA in RNase-free water or TE buffer at −85°C to −65°C for up to one year. Process only RNA of highest quality with no fragmentation detectable during initial quality assessment.This protocol relies on a number of reagent kits and equipment for sample preparation, quality controls and sequencing. Make sure all reagent kits are available and devices including a sequencing platform from Oxford Nanopore Technologies (ONT) are installed and have been thoroughly tested. Depending on the sequencing platform in use, a high volume of data will be generated during sequencing runs. Platform-specific IT-requirements must be considered and trained bioinformaticians are needed for quality control, basecalling and data analysis.•2O to 8 mL EtOH.80% EtOH solution (10 mL stock): add 2 mL ddHNote: Always prepare fresh and use within a day.•2O to 7 mL EtOH.70% EtOH solution (10 mL stock): add 3 mL ddHNote: Always prepare fresh and use within a day.•2O.Elution Buffer : Add 1 mL 1 M Tris-HCl (pH 8.5) to 99.0 mL ddHNote: Store at room temperature (20°C–25°C) for up to one year.Alternatives: Elution Buffer can be purchased at Buffer EB from Qiagen, Cat. No. 19086•2O.0.1% Tween (10 mL): Add 0.1 mL 10% Tween 20 to 9.90 mL ddHNote: Aliquot (1 mL) and store at 4°C for up to 6 month.Alternatives: Use 0.1% Tween solution provided in Kit BLF7510 from Sage ScienceTiming: 2 h1.a.Note: The starting material is expected to have a relatively high RNA concentration, and using the Qubit broad range kit (0.5–2000 ng/μL) eliminates the need for sample dilution steps. In later stages of the protocol (following Poly(A) + RNA isolation and clean-up), the RNA concentration is anticipated to fall within the range of the high sensitivity RNA Qubit kit (0.2–200 ng/μL).Dilute RNA samples with nuclease-free water to the assay-specific detection range.b.Prepare the Qubit working solution by diluting the Qubit RNA Reagent 1:200 in Qubit RNA Buffer.c.To prepare the standards, add 10 μL of each Qubit standard to 190 μL Qubit working solution in Qubit assay tubes, mix by vortexing.d.To prepare RNA samples, add 1 μL of RNA sample to 199 μL Qubit working solution in Qubit assay tubes, mix by vortexing.e.Measure concentration in ng/μL using the Qubit fluorometer.Fluorometric RNA quantification using the Qubit RNA HS  or BR  assay Kits. 2.a.Use the Agilent DNF-471 RNA Kit  or the Agilent DNF-472 HS (High Sensitivity) RNA  kit to perform automated capillary electrophoresis.b.Depending on the RNA concentration, dilute the samples using nuclease-free water to the detection range of the fragment analysis kit.c.https://www.agilent.com/cs/library/usermanuals/public/Fragment_Analyzer_system_manual_D0002110.pdf; https://www.agilent.com/cs/library/usermanuals/public/ProSize%20data%20analysis%20software%20user%20manual_D0002111_Rev_B.pdf.Analyze RNA integrity by following the operating instructions of the Fragment Analyzer System and the ProSize data analysis software. Manuals are available online: Fragment analysis using the Fragment Analyzer System from Agilent Total RNA, isolated from the source of interest, must undergo rigorous quality checking including fluorometric quantification and fragment analysis .CRITICAL: Use only total RNA preparations of very high quality with no RNA fragmentation visible (flat baseline of electropherogram). RQN/RIN should be > 9. Use RNA Quality Numbers (RQN) for data interpretation. Timing: 2 h3.Prepare several DNA-free total RNA aliquots in PCR tube strips containing 4 μg of high-quality total RNA each. According to the Poly(A)+ selection module, starting material for Poly(A)+ isolation can vary from 1 to 5 μg of total RNA. The RNA should be diluted in a final volume of 50 μL nuclease-free water.Note: To generate sufficient material for a single experiment, we found eight 4-μg total RNA aliquots sufficient for an experiment run using a MinION/GridION or even a PromethION sequencing platform.4.https://international.neb.com/-/media/nebus/files/manuals/manuale7490.pdf?rev=8fc35eb8aee94d0b90f0b671aa5a46ac&hash=C8CCC69263E3FEA33B00126133F6A118.Perform Poly(A)+ RNA isolation by following instructions of the NEBNext Poly(A)+ mRNA Magnetic Isolation module. 5.Measure Poly(A)+ RNA concentration and analyze RNA fragment size. Perform quality control by following the instructions detailed under step 1 “Note: Software assisted assessment of RNA fragmentation level (RQN/RIN) is not possible at this step, since ribosomal RNA signals are removed post Poly(A)+ enrichment. Hence, only a visual inspection of the traces is possible. After Poly(A)+ enrichment, typically RNA fragments are visible beyond 500 nt. Enrichment of fragments smaller than 500 nucleotides can be interpreted as sample fragmentation. Poly(A)+ RNA extracted from drosophila typically shows a prominent signal around 2000 nt  isolation, except the NEBNext Oligo d(T)25 beads, on ice when not in use.Poly(A)+ RNA transcripts are selected from DNA-free total RNA preparations using Oligo d(T)-coupled paramagnetic beads. Use the NEBNext Poly(A) mRNA Magnetic Isolation Module from NEB (E7490). All reagents within the module should be stored at 4°C. Reagent expiry dates can be found on each tube. The module provides a step-by-step protocol for isolating Poly(A)+ RNA from total RNA preparations, which should be followed in detail as the selection of Poly(A)+ RNA increases library preparation efficiency and enables sequencing of full-length RNAs at a later stage.Timing: 2 h6..a.Bring Agencourt RNAClean XP beads to room temperature (30 min at 20°C–25°C).b.Prior to usage, vortex Agencourt RNAClean XP beads vigorously.c.If multiple Poly(A)+ RNA fractions were prepared, pool fractions prior to bead clean-up.d.Use a bead-to-sample ratio of 0.4. For example, starting with a Poly(A)+ RNA sample volume of 50 μL, add 20 μL beads.e.Mix Poly(A)+ RNA sample and Agencourt RNAClean XP beads and incubate for 5 min at room temperature to bind RNA to magnetic beads.f.Place on a magnetic stand, wait until supernatant is clear Remove and discard supernatant.g.While keeping tubes on a magnetic rack, add 200 μL of 70% EtOH (freshly prepared) without disturbing the beads.h.Remove the ethanol and repeat the wash step while keeping the tubes on the magnet.i.Remove any residual Ethanol and dry beads for maximum 30 s while keeping the cap of the tubes open.j.Remove tubes from the magnet and add 20 μL elution buffer  to elute Poly(A)+ RNA from the beads.k.Mix well by pipetting and incubate at room temperature for 2 min.l.Place tubes on a magnetic stand and wait until supernatant is clear.m.Transfer supernatant, containing the sample, into clean tubes. Keep tubes on ice until cDNA generation.Exclude short RNA fragments using Agencourt RNAClean XP beads 7.Measure RNA concentration using Qubit HS RNA assay following manufacturer’s instructions.Optional: assess size distribution by capillary electrophoresis to visualize small molecule-depleted Poly(A)+ RNA. This control is informative to check whether any RNA degradation occurred in the process. At this point, the loss of small RNA fragments is not yet visible and can be only visualized after cDNA synthesis + RNA in 9 μL nuclease-free water (input recommendation of the library preparation kit). Perform multiple preparations in parallel to collect enough material for gel-based size selection.9.Split each 20 μL reaction into 4 PCR reactions using 5 μL reverse-transcribed RNA as input.10.Amplify each reaction using the cycling conditions detailed in SQK-PCB111.24 but change the extension time to 16 min (60 s/kb).Note: The number of PCR cycles can vary between 10 and 18. We recommend using 16 cycles as a starting point, since enough material for gel-based size selection must be prepared. The number of PCR cycles can negatively impact read length, which results in a decrease of N50 values. In our experiments, we have not reduced PCR cycles below 16, so amplification down to 14 or even 10 cycles should be tested case by case.Note: Due to long extension times (16 min/cycle), the PCR will last up to 5 h. We typically set up the PCR overnight and leave the reaction at 4°C prior to AMPure XP clean up.11. , with bead-to-sample ratio of 0.4.Note: after pooling, the volume of the resulting sample to clean up can be relatively high. For instance, if library preparation has been started with four poly(A)+ RNA size selected replicates and each replicate split into four for the PCR step, a volume of 800 μL should be expected . In this example 320 μL of AMPure XP beads have to be used to clean this pool.a.Bring AMPure XP beads to room temperature (30 min at 20°C–25°C).b.Vortex AMPure XP beads vigorously.c.Add 20 μL of beads per every 50 μL of sample volume.d.Mix sample and AMPure XP beads by vortexing and incubate for 5 min at room temperature to bind cDNA to magnetic beads.e.Place on a magnetic stand, wait until supernatant is clear.f.Remove and discard supernatant.g.While keeping tubes on a magnetic rack, add 200 μL of 80% EtOH (freshly prepared) without disturbing the beads.h.Remove the ethanol and repeat the wash step while keeping the tubes on the magnet.i.Remove any residual Ethanol and dry beads for max. 30 s while keeping the cap of the tubes open. Remove tubes from the magnet and elute amplified cDNA in not more than 30 μL TE buffer.j.Mix well by pipetting and incubate at room temperature for 2 min.k.Place tubes on a magnetic stand and wait until supernatant is clear.l.Transfer supernatant containing the size-selected cDNA, into clean tubes. Keep tubes on ice until cDNA generation.Pool all the processed fractions into a 1.5 mL tube. Clean and size select the sample using AMPure XP beads 12.Elute amplified cDNA in not more than 30 μL TE buffer.13.Asses yield (fluorometer) and size distribution (capillary electrophoresis) of amplified, full-length cDNA .14.DNA quality control: for quantification, use the Qubit dsDNA HS Assay. For fragment analysis, use the HS NGS Fragment assay  or the HS Genomic DNA 50 kb Kit .Pause point: After library preparation, samples can be stored overnight at 4°C or for up to 4 weeks at –20°C.Depending on the scale of the experiment and the sequencing platform, choose the workflow with or without sample barcoding and follow the step-by-step protocol (PCR-cDNA Barcoding Kit (SQL-PCB 111.24) or PCR-cDNA Kit (SQK-PCS111). Store reagents of the library preparation kit at −20°C. Expiry dates are printed on each tube. To maximize read length, include the optimized steps detailed below:Timing: 4 h15.Note: Pre-cast gels are stored at room temperature, provided reagents (marker and electrophoresis buffer) are stored at 4°C. Expiry dates are given on each gel cassette and reagent tube.a.Bring DNA sample up to 30 μL in TE buffer (max. 5 μg DNA).b.Add 10 μL loading solution to each sample and mix samples thoroughly, centrifuge to collect sample.c.Program a protocol as described in the reference guide of the BluePippin software.d.Enter a base pair threshold (BP Start) for DNA size selection: 3 kb.e.Note: BP end values do not represent the true upper limit of collection. All fragments > 3 Kb will be collected. The value (BP end) is required for the software to function properly.Enter a base pair threshold (BP End) for DNA size selection: 15 kb.f.Determine the lane to which the external marker S1 (3–10 Kb) will be added.g.After run completion, wait for 45 min with closed lid to collect 40 μL of the sample out of the cassette and transfer into a 1.5 mL tube (fraction 1).h.CRITICAL: Samples are either in a Tris-TAPS buffer (fraction 1) or in 0.1% Tween solution (fraction 2). Both Tris-TAPS buffer and 0.1% Tween are not suitable for nanopore sequencing, therefore the buffer must be exchanged through an AMPure XP cleanup (see step below).Perform a second elution with a 0.1% Tween solution . Before pipetting out the second eluate, wait for 10 min and transfer 40 μL of sample into a fresh 1.5 mL tube (fraction 2).Perform DNA size selection using a 0.75% Agarose Gel Cassette with the external marker DF 3–10 kb S1.16.a.Combine cDNA fractions of the same sample and mix by vortexing, with equal volume of AMPure XP beads.b.Incubate for 5 min at room temperature to bind cDNA to magnetic beads.c.Place on a magnetic stand, wait until supernatant is clear.d.Remove and discard supernatant. Elute the cDNA in 12 μL Oxford Nanopore elution buffer (EB).Perform an additional AMPure XP cleanup.17.For each sample, analyze 1 μL of the amplified cDNA for size, quantity and quality using a Qubit fluorometer and a Fragment Analyzer .Figure 4The BluePippin instrument is an automated DNA size selection system. Using pre-cast gel cassettes with different agarose concentrations, selection of DNA fragments between 100 bp – 50 kb is supported. The section below describes settings for selection of cDNA fragments larger than 3 kb.Note: Oxford Nanopore offers sequencing platforms scalable in size. The number of nanopores per flow cell vary and define the maximal output measured in number of reads or number of sequenced bases. SpotON flow cells (compatible with MinION and GridION devices) hold 512 active nanopores and, on average, 10 GB data can be produced per run. PromethION flow cells (compatible with PromethION devices) hold 5000 nanopores and can produce an average of 100 GB of data per run. Based on our experience, one million reads with an average size of 3 Kb (3 GB of data) is sufficient to detect a significant number of genes and couplings  amplified cDNA in 23 μL Elution Buffer from Nanopore.b.For SpotON flow cells (GridION and MinION) load 15–25 fmols of (barcoded) amplified cDNA in 11 μL Elution Buffer from Nanopore.c.https://nebiocalculator.neb.com/-!/dsdnaamt.For Mass to Moles conversion of dsDNA, the following webpage is useful: Load samples. Instructions differ for GridION/MinION or PromethION sequencing platforms:Note: Oxford Nanopore released R10.4 flow cells on 01/2023, improving read accuracy. Up to now, not all kits are compatible with the newly released flow cells. SQK-PCB111.24/SQK-PCS111 library preparation kits are currently only compatible with previously used R9.4.1 flow cells.Follow instructions of the PCR-cDNA library preparation kit for adapter ligation, flow cell priming and loading, as well as for data acquisition parameters and basecalling.After sequencing, the mapped reads should show a clearly shifted size distribution compared to the non-size selected samples .2.https://github.com/lh3/minimap2) using the dm6 genome assembly (https://nov2020.archive.ensembl.org/Drosophila_melanogaster). Sorted and indexed using samtools (https://github.com/samtools/samtools).FASTQ files were aligned with minimap2  is used for links between 5′ ends and 3′ ends, while LASER (https://github.com/hilgers-lab/LASER) is used for links between 5′ ends with alternative exons and 3′ ends with alternative exons.We developed two computational approaches for processing the alignment files to study transcriptional couplings. LATER  > devtools::install_github("hilgers-lab/LATER") > devtools::install_github("hilgers-lab/LASER") > library(LATER) > library(LASER)4.Download the reference annotation for analysis. LATER and LASER build a database that identifies 5′–3′ isoforms that will allow the read to isoform assignment: > Annotation_url <- "https://ftp.ensembl.org/pub/release-109/gtf/drosophila_melanogaster/Drosophila_melanogaster.BDGP6.32.109.gtf.gz" > destfile <- "Drosophila_melanogaster.BDGP6.32.109.gtf.gz" > download.fileNote: Utilizing de novo assemblies derived from long-read data has the potential to significantly enhance the outcomes, as it enables the identification of novel 5′–3′ isoforms and increases the detection of isoforms that are not currently annotated.Optional: A similar procedure can be used for other species available in ensembl (http://www.ensembl.org). # Mouse reference annotation > Annotation_url <- "https://ftp.ensembl.org/pub/release-109/gtf/mus_musculus/Mus_musculus.GRCm39.109.gtf.gz" > destfile <- "Mus_musculus.GRCm39.109.gtf.gz " > download.file # Human reference annotation > Annotation_url <- " https://ftp.ensembl.org/pub/release-109/gtf/homo_sapiens/Homo_sapiens.GRCh38.109.gtf.gz" > destfile <- " Homo_sapiens.GRCh38.109.gtf.gz" > download.fileLATER leverages long-read sequencing data to estimate the couplings between transcription start sites (TSSs) and transcription end sites (TESs). It focuses exclusively on full-length reads and identifies genes that exhibit "promoter dominance", whereby a particular TSS is significantly linked to the expression of a specific 3ʹ end.Timing: 5 min (for step 5)Timing: 10 min (for step 6)5.Use reference annotation to create a 5′–3′ isoform database, this will contain all TSS and TES combinations found in the annotation. # load annotation to R > refAnnotation <- rtracklayer::import.gff(annot_path) # keep only protein coding exons > refExons <- refAnnotation[refAnnotation $type == "exon" &refAnnotation$gene_biotype == "protein_coding"] # Prepare isoform database. > isoformData <- prepareIsoformDatabaseNote: Following parameters have to be tuned depending on the organism or the transcripts of interest. The windows for 5′–3′ isoform definition can substantially affect the couplings detected. tss.window: distance in nucleotides to cluster TSSs into a single transcription unit. Depending on the dataset, the parameters for tss.window and tes.window can be optimized. For instance if the resolution of TSS definition is important, tss.window can be reduced to fewer nucleotides and allow quantification of smaller differences. Similarly, for 3′ ends, if windows of less than 150 nt are relevant for the study, the window can be reduced. tes.window: distance in nucleotides to cluster TESs into a single TES.Optional: Many transcriptome annotations, even when up to date, are missing 5′ ends or 3′ ends. This substantially affects the analysis since potential 5′–3′ links will not be considered. To overcome this challenge, it is possible to use already published, highly accurate 3′ end databases as well as TSS/5′ CAGE curated data. Databases like the Eukaryotic Promoter Database (EPD) > url <-"ftp://ccg.epfl.ch/epdnew/D_melanogaster/005//Dm_EPDnew_005_dm6.bed" > file_name <- " dmel_tss_annotation.gtf.gz " > download.file > ref_tss_annot <- rtracklayer::import.gff("dmel_tss_annotation.gtf.gz")Optional: Continuing from the previous optional step, add TSSs databases to the reference annotation. > isoformData <- addPromoterDatabaseOptional: A similar approach can be followed to download the 3′ end database. For this protocol, we use the 3′ end database generated in Alfonso-Gonzalez et al., 2023. > url <- " https://github.com/hilgers-lab/CIAtranscriptome_assembly/blob/dk/db/combined.rds.clusters.new.gff" > download.file > ref_tes_annot <- rtracklayer::import.gff("dmel_tss_annotation.gtf.gz") > isoformData <- add3pEndDatabaseTiming: 1–2 hTiming: 2–4 h 6. > bamPath <- system.file > countData <- countLinksNote: In this step, only reads overlapping a TSS and a TES are counted. The countData object contains all additional information for downstream processing steps.Optional: Write the full-length read id as a tsv file and then use it to subset the mapped bam files using samtools.Optional: In R. > readr::write_tsv(readAssignments(countData), "read_assignments.txt")Optional: In Linux terminal: > samtools view -N read_assignments.txt -o filtered_output.bam output.bamAfter mapping and creating the isoform database, quantify long-reads data.7.Long reads can then be visualized using IGV 8.Perform multinomial testing using 5′–3′ isoform counts. > gene_bias_estimates <- estimatePromoterDominance # Access resuls from the statistical testing at gene level > result(gene_bias_estimates) # Access resuls from the statistical testing at isoform level > stats(gene_bias_estimates) > dominance(gene_bias_estimates)Note: The assessment of TSS-TES couplings is limited to genes that exhibit multiple transcription start sites (TSS) and transcription end sites (TES). Consequently, the outcomes of this analysis are influenced by both the coverage of the data and the complexity of the transcriptome within the evaluated sample.9.The overall distribution genome-wide and at individual genes can be visualized with the following functions  # Plot gene with promoter dominance > plotGeneBias # Plot gene with no significant TSS bias > plotGeneBiasNote: To obtain accurate estimates for transcriptional couplings, high coverage is necessary (ecessary . Since mOur method aims to identify associations between transcription start sites (TSS) and 3′ end sites. By utilizing 5′–3′ isoform counts, our approach determines whether there exists a significant association between the respective expressed TSSs and 3′ ends, within a gene. This analysis involves comparing the observed frequencies of these variables with their expected frequencies, assuming independence between the variables A. For stIn long-read sequencing data, each read contains all the information, starting from the transcription start site (TSS), spanning all exon junctions, and ultimately the 3′ end site. This enables the quantification of co-occurrence frequencies within individual RNA molecules, specifically capturing the relationships between exon junctions, TSS, and TES features .Timing: 45 min (for step 10)Timing: 2–4 h (for step 11)Timing: 40 min–1.5 h (for step 12)10.Create reference junction database using short-read sequencing data and reference annotation. > annot_path <- system.file > ref_annot <- rtracklayer::import.gff(annot_path) > junction_path <- system.file > reference_junctions <- create_reference_junctionsNote: We recommend the use of short-read sequencing data to increase the detection of junctions not found in reference annotation.Note: Depending on the type of data, the following parameters have to be adjusted:LASER utilizes a reference junction set. This reference is constructed by combining information from a reference annotation and junctions derived from short-read sequencing data obtained through STAR A. The LAmin.jcounts: refers to the minimum number of junction reads from short-read sequencing data for a junction to be considered part of the reference. This parameter is important since lowly expressed or rare variants can substantially affect the analysis.11.Create a full-length read database. Assign reads to a TSS, 3′ end site and junctions found in the database. > bamPath <- system.file > exonlinks.counts<- read_to_junctionsNote: During this step, reads are filtered to retain only full-length read isoforms that span the transcript from the 5′ to the 3′ end.Note: Long-read sequencing data are susceptible to a high rate of mismatches, which may result in noisy estimations of exon boundaries. To address this, LASER leverages all the junctions identified in the reference annotation, as well as the short-read data, to correct the long reads and mitigate the impact of mismatches.12.Perform multinomial testing to identify exon couplings. Additionally, LASER contains gene-level visualization functions C. > couplings <- calculate_exon_couplings # Plot stai gene couplings # Plot couplings exon with TSS > plotExonCouplings # Plot couplings exon with TES > plotExonCouplingsNote: Similar to LATER, LASER conducts multinomial testing for every combination of exon junction-TES or exon junction-TES in each full-length molecule. This approach enables the identification of associations between features and to predict meaningful regulatory relationships.Note: LASER exhibits a high sensitivity to coverage as it relies on an adequate level of splice-junction detection for accurate assessment. Sufficient coverage is crucial to ensure the reliable detection and analysis of splice junctions in order to obtain good results.type: the "short" flag will consider both the reference annotation and short-read sequencing found junctions to build a reference that will be used to correct the long-read sequencing data.The identification of transcriptional couplings is heavily reliant on the coverage and quality of the data. Therefore, it is crucial to have high-quality data to obtain representative and reliable global results. Samples with low RNA quality or yield may not accurately reflect the transcriptome. Furthermore, even with size selection techniques in place, it is not always possible to fully recover transcripts that exceed 10 kb in length.Low input total RNA samples.•If possible, repeat RNA extractions on biological replicates and pool total RNA fractions.•For the initial step of the protocol (Isolation of poly(A)+ RNA), 5 μg of total RNA in 50 μL nuclease-free water were used.•We found that eight poly(A)+ RNA selection reactions  is a good number to start the experiment (including backup).Low-quality total RNA samples.•If RQN is < 9, the RNA should not be used for full-length analysis.•A new RNA purification should be performed under RNase-free conditions, with minimal times during which the RNA stays at room temperature.•Avoid vortexing during the RNA extraction, mix by tube flicking and inverting to avoid RNA damage.Computing time too long, more than 7 h.•https://snakemake.readthedocs.io/en/stable/):Use run LASER/LATER as batch script. We provide an example using snakemake print(SAMPLE)rule all: input:  expandrule LATER:  input: "workdir/bams_folders/{sample}.bam",  ref: "workdir/reference_annotation.gtf"  output:   "LATER_output/{sample}.countedPairs.tsv"  params:   refAnnot="workdir/reference_annotation.gtf",   windowTSS=50,   windowTES=150,   outfFilePrefix="workdir/countfiles"  threads: 4  shell: """  Rscript LATER.R -b {input[0]} -r {params.refAnnot} -t {params.windowTSS} -e {params.windowTES} -o {params.outfFilePrefix}"""•Filter genome for full-length reads using samtools.>samtools view -b -L tss_regions.bed input.bam | samtools view -b -L pas_regions.bed - > output.bamhilgers@ie-freiburg.mpg.de.Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Valérie Hilgers: This study did not generate new unique reagents.