Discrete choice experiments (DCEs) are frequently used to estimate and forecast the behavior of an individual's choice. DCEs are based on stated preference; therefore, underlying experimental designs are required for this type of study. According to psychologists, DCE designs consist of a small number of choice sets with a limited size in the number of alternatives within a choice set to increase the response efficiency in the questionnaire. Even though algorithmic constructions (known as efficient designs) become quite common for practitioners, optimal designs  continue to be used in choice experiment studies, particularly in the case that prior information about the extent of the population preference is not available. Various approaches have been developed to construct DCE designs with fewer choice sets. However, the question in many practitioners' minds is which techniques perform better  in a given circumstance. In this paper and to address these concerns, we conducted an overview of the constructions of discrete choice experiments in the literature for models with only main effects. The various ways of constructing optimal and near-optimal designs were compared in terms of their ability to minimize the number of choice sets in the survey. Our findings shed light on the optimal sample sizes needed for efficient experimentation which then can help the researchers to design more effective experiments in this area. A Discrete Choice Experiment has commonly been conducted using the abbreviation DCE. It is often used when revealed preference data is not available or is not suitable for the research question at hand. DCE is based on a long-standing and well-tested theory of choice behavior, known as the random utility theory (RUT), which was proposed in DCEs study mimic situations where people have to select from a variety of competing options based on their perceived utility to assess the influence of the attributes involved in choice options. Practically, each respondent is asked to repeatedly pick out the preferred scenario from a set of different competing options presented on each choice set. In your experiment, participants might be asked to answer all the questions that are included in the experiment, or they might be randomly assigned to answer only a particular subset of them, so-called blocking. Note that designs with a large number of choice sets can lead to poorer decision-making and a decrease in overall satisfaction with the decision. D-optimal in case the prior parameters are zero and all alternatives have the same probability of being chosen continue to be used in choice experiment studies, as these priors are usually not readily available Over the past years, there has been great interest from statisticians in researching the most effective methods for constructing optimal designs that somehow maximize the amount of information that can be obtained from the experiment while minimizing the number of runs required. One way to achieve this is by using fractional factorial designs, which are a type of design that allows practitioners to generate a subset of all possible combinations. There are different types of fractional factorial designs that can be used in constructing DCEs, The literature on obtaining high-efficiency designs of DCEs with optimal reduced choice sets has received a lot of attention from researchers in this field since the late 20th and early 21st centuries. While previous research has covered this topic to some extent, we provide an extensive review in the area of DCE designs for models with only the main effect. Practitioners who specifically want to test for differences between respondents or samples need to use the same choice sets for all respondents, so they are drawn to designs with fewer choice sets to ensure that respondents complete them rationally. Although a lot of recent work on designs used in practice is based on algorithmic constructions, the focus of this paper will be on reviewing known theoretical approaches to designing optimal and near-optimal choice sets from various perspectives and insights. The various ways of constructing optimal and near-optimal designs were compared in terms of their ability to minimize the number of choice sets in the survey. Our findings shed light on the optimal sample sizes needed for efficient experimentation which then aid researchers in designing more effective experiments in this area. Additionally, we suggested directions for developing potentially new theoretical results on designs for DCEs.SB approach including how to define a contrast matrix for the main effect only, and how to compare between different designs of choice sets using the D-optimality criterion. Then, in Section The remainder of this article is laid out as follows. In Section 2N, and each choice set has two or more alternatives , each attribute takes one of the ℓ levels, and these levels are represented numerically by x, m different alternatives ith alternative in the αth choice set, r will choose the ith alternative in a choice set j represents the other alternatives and I extreme value) distributed (IID) with zero mode. Throughout this section, we discuss several concepts that will be helpful in constructing optimal designs for the multinomial logit (MNL) model. Starting with some notation, we say that a DCE consists of a number of choice sets, denoted as α, μ  model for estimating the main effects since recent studies, such as 2.1D-optimal, A-optimal, or E-optimal designs, the focus will be under the D-optimality criterion using the SB approach. D-optimality criterion is commonly used in practice and in literature due to its robustness against reparameterizations In this paper, we offer a condensed overview of the construction of optimal and near-optimal choice sets in the literature. As is known, the properties of DCE studies may vary from one design of choice experiment to another; thus, it would be useful to include studies that have similar aims in order to draw broad conclusions. Therefore, we assume in this paper the following; (i) a multinomial logit (MNL) model will be considered as a defining model used to analyze choice data. We have chosen the MNL model because it is the most commonly used model to analyze choice data SB approach, we will review how the SB approach works to know how to select and group attributes into choice sets and how to evaluate between different choice experiments.Only studies that met or can be applied to these assumptions were included in this comparison. While some of the studies that will be discussed in this paper may not be well-known outside of the statistical community, they are considered by many experts to be superior to more commonly known methods. Thus, it is important to acknowledge the contributions of these researchers in this field and compared them to highlight the optimal reduction in the number of choice sets. Since a comparison of these designs will take place under the 2.2k-tuple of attribute levels per task that optimize a stated criterion of interest for a certain total number of choice sets. This criterion is generally related to the information matrix of the design. Although Street and Burgess (SB) assume the MNL model for their designs, unlike other researchers, they use the previous approach, as it is described in C-matrix, is derived under the orthonormal coding with zero local priors and generic parameters across alternatives and is defined to B is the contrast matrix for the effects to be estimated (i.e. main effects or main effects plus two-factor interactions), and Λ is the matrix of the second derivatives of the likelihood function. The Λ matrix is a symmetric square matrix of order L with rows and columns indexed by The purpose here is to generate optimal or near-optimal choice sets, which give choices of nted see  and the 2.2.1I is the identity matrix of size In the case of a quantitative variable, with 2.2.2D-, A-, or G-criterion. Often, these criteria, are related to the information matrix of the design, previously mentioned as the D-efficiency, with respect to the estimation of the main effects. The notation D-optimal DCE.When comparing designs for DCE, there are several ways to evaluate their performance. One common approach is to compare the efficiency of the designs, which can be measured by various criteria such as the D-optimal if “it minimizes the generalized variance of the parameter estimates, that is, D-optimal designs” D-error of a design is D-efficiency of a design, denoted by ω is the number of parameters that are to be estimated in the model. For the main effects model we have that, A DCE design is said to be D-efficiency can be used as a guide so that any possible designs with different numbers of choice set N can be compared to a common reference scale D-optimal DCE from amongst the various designs under the assumption of equal choice probabilities. They provided an upper bound for m is given in equation x and y satisfy the equation q in each choice set. It should be noted that D-optimal designs are not necessarily unique and, in particular cases, may not exist. In the next section, we provide some of the efficient techniques for constructing optimal and near-optimal DCE designs and models with only main effects.The 3D-optimal designs for every combination of attributes and runs, the methods we discuss here cover nearly all the practical cases for symmetric attributes  and asymmetric attributes (which have a different number of levels). Some of these methods are very specific and others are more broad-ranging. Under the assumptions included in Section Many researchers sought to find techniques that can produce optimal and/or near-optimal designs of choice experiments that are easier for practitioners to apply and, at the same time, easier for subjects (respondents) to complete them. While it is not yet possible to provide direct constructions of globally Usually, the main concern of practitioners is to find the most appropriate design with a manageable number of choice sets that can fit their experiments. However, the design construction involves several aspects and issues that may be related either to the appropriate size, e.g. the size and/or the number of choice sets that should be included, or to how the options are selected and grouped into choice sets, e.g. randomly or in order. In this section, we describe nearly all the methods used to construct these designs with a sufficiently small number of choice sets, some of which are specific to symmetric attributes, while others can handle asymmetric attributes. 3.1In DCEs, symmetric attributes play a crucial role in designing efficient and balanced choice sets. Explicit constructions of optimal or highly efficient designs with practical numbers of choice sets have been developed. Whilst it is possible to construct certain approaches utilizing designs that exceed four alternatives in size , would greatly benefit in obtaining designs that have the optimal reduction in the number of choice sets and maintain favorable efficiency.N were suggested in p and/or ℓ increase. In their approach to the construction of DCE designs, they used Hadamard matrices and orthogonal arrays, denoted by D-efficient paired choice designs with ℓ levels, are D-efficient design, where j correspond to the jth row. Hence, the total number of choice sets is defined as D-efficiencies, for large attribute levels. i.e. Several constructions in which practitioners can construct optimal two-level paired choice designs for any number of choice sets 3.1.2D-optimal choice sets into the following three categories based on the size of the choice set m in relation to the number of levels ℓ: Case 1:• Case 2:• Case 3:• In the real world, choice problems usually consist of more than two choices, which makes the use of multi-alternatives . Bunch et al. D-efficiency of 100% for the designs of p symmetric attributes when only testing the main effects Shifted approach, contrary to “Foldover”, uses the mathematical idea of modulo arithmetic, i.e., it adds a constant to all attribute levels of the initial D-optimal symmetric choice sets were given in OA. An orthogonal array ℓ levels such that any t-tuple appearing as a row an equal number of times, where R refers to the number of runs, p refers to the number of attributes (columns), ℓ refers to the number of levels, and t is the strength of the array.In addition, four different approaches for constructing N simple parallel classes of x-resolvable xℓ rows, this design is said to be an x-parallel class, which means that each of the ℓ levels occurs in each column of the subarray x times, whereas if its rows can be partitioned into x-parallel classes, this design is said to be an x-resolvable class The first approach of Demirkale et al. p is the number of attributes, k is the number of design generators for an FFD, q is the number of blocking variables , 1) each of size four  that gives a total of m on p attributes each having ℓ levels. The next example will illustrate the above concepts. Case 3• For the case D-optimal or near-optimal designs for DCEs in the case we have OA, is used as the starting design in F, which was proposed by http://neilsloane.com/oadir/. The website uses a file N runs, k attributes (or factors), s levels (for each of the attributes), and strength t  and another of either 1 or 3 (mod 4).Researchers are advised to be cautious in the case of constructing a DCE design, and at least one of its attributes has no prime number of levels, i.e. has a composite number, that may cause some difficulties in constructing an efficient design. For example, constructing paired choice sets with asymmetric attributes while a particular attribute has four levels, hence, at maximum, only four out of the six possible pairs can be generated for this particular attribute. Thus, using several sets of generators across the same starting designs to generate the final design would be the only efficient solution available to date h generators;A general approach, was recently presented in Theorem 3.1The number of generators for the optimal paired choice design with p attributes iswhereforeven andforodd,. The generators are then given by, wheretakes each of the values from the setwith frequency,,.Note that 4p symmetric attributes. However, some techniques have the ability to be applied to more than one case with very high efficiency, and most of the time equal to 100% using an SP approach, but usually with a different number of sets of choices in a given circumstance. In this section, a comparison of the sample sizes of the mentioned techniques in relation to the size of the choice set, the number of attributes, and their levels are attempted to estimate the main effects only. This is applicable under the assumption that all the respondents should face the same choice tasks, in order to highlight the optimal reduction in the number of choice sets. Also, we will delve into the aspects where certain approaches exhibit limitations, shedding light on their constraints and potential challenges.In order to move the study forwards and open up new areas of inquiry, it is essential to conduct a comparison of approaches that have been constructed in this field. Given the above cases, each time we have included at least one technique to generate optimal or near-optimal choice sets for the designs of D-optimal paired choice designs, DCEs, which were derived using construction techniques based on: reduction of the shifting approach from OMEP as starting designs, given by p and/or ℓ increase even moderately. Because such large designs are not really appealing to a researcher, designers such as D-efficiencies for the estimation of the main effects. Attributes with up to four levels D-efficiency, but still with higher D-efficiencies against the existing orthogonal designs, such as the designs in ℓ varying from 4 to 7 as mentioned in The number of choices in the available optimal paired choice designs increases rapidly as D-optimal choice designs of triple and quadruples size for DCEs, respectively, which derived using the construction techniques based on: reduction of the shifting approach from OMEP as starting designs, given by In light of the information presented in the previous Upon conducting a critical review, it becomes apparent that there exist certain inherent constraints and challenges associated with these approaches. The present text enumerates certain constraints and challenges. A drawback of the foldover technique, as considered in the study conducted in r multiple of 4 up to Turning to another aspect of comparison, there are notable differences between the approaches in terms of their ability to minimize the number of choice sets in each given circumstance. For example, when ℓ= or <m. Conversely, the The tables were obtained by collecting and combining the information about the existing designs from various scholarly sources, with the aim of trying to put the parameters and the properties of the existing designs together. This will provide a direct access to what designs are available in the literature. By identifying the parameters of the needed designs  model was assumed because it could be used to estimate all sophisticated models with relatively high efficiency. Future work in the discrete choice experiments could be to either modify some different initial designs or construct a new initial design to generate improved designs, as new work related to these suggestions by the authors of this paper will be finalized and published soon. Also, a future proposal may be to find alternative experimental designs to construct DCEs with optimal or near-optimal choice sets, and that would be a useful practical contribution.In conclusion, the purpose of this paper was to provide the most efficient techniques for constructing optimal and near-optimal choice sets of main effects only for academics and practitioners to use in their work. These techniques were compared using the No additional information is available for this paper.All authors listed have significantly contributed to the development and the writing of this article.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Nucleotide composition plays a crucial role in the structure, function and recognition of RNA molecules. During infection, virus RNA is exposed to multiple endogenous proteins that detect local or global compositional biases and interfere with virus replication. Recent advancements in RNA:protein mapping technologies have enabled the identification of general RNA-binding preferences in the human proteome at basal level and in the context of virus infection. In this review, we explore how cellular proteins recognise nucleotide composition in virus RNA and the impact these interactions have on virus replication. Protein-binding G-rich and C-rich sequences are common examples of how host factors detect and limit infection, and, in contrast, viruses may have evolved to purge their genomes from such motifs. We also give examples of how human RNA-binding proteins inhibit virus replication, not only by destabilising virus RNA, but also by interfering with viral protein translation and genome encapsidation. Understanding the interplay between cellular proteins and virus RNA composition can provide insights into host–virus interactions and uncover potential targets for antiviral strategies. A fundamental property of nucleic acids is the ability to store and transmit information via the sequence of nucleotides they contain. Coding sequences are composed of an array of codons that are translated by ribosomes to produce polypeptides. This flow of information is universal and can be found both in living organisms and the viruses that infect them. Due to the degeneracy of the genetic code, with 61 codons coding for 20 amino acids, organisms can develop and sustain coding biases. These biases can manifest as an overrepresentation or scarcity of specific codons, codon pairs or nucleotide combinations in protein-coding sequences [Nucleotide composition refers to the relative proportions of the four nucleotides that make up DNA or RNA molecules, i.e. adenine, guanine, cytosine and thymine/uridine. Additionally, composition of nucleic acids may also refer to the overall GC content – which is the percentage of guanine and cytosine bases in a given sequence – or to the frequency of certain dinucleotides. At the most basic level, nucleotide composition affects the physical and chemical properties of the nucleic acid molecule, such as its melting temperature , stabiliViruses, as obligate intracellular pathogens, are exposed to thousands of proteins in cells that recognise nucleotide composition. Viruses are under increased selective pressure since they (1) must hijack cellular machinery to replicate while (2) avoiding recognition by the immune system but (3) maintaining recognition of their own nucleic acids to ensure genome packaging. In this review, we focused on how various cellular proteins recognise nucleotide composition in virus RNA, and how advancements in RNA:protein mapping technologies have enabled the identification of RNA-binding profiles for many RNA-binding proteins (RBPs). Mostly we focused on proteins whose activity leads to reduced virus replication, however, as discussed, many proteins inhibit infection of one virus but aid the replication of others. Canonical virus RNA sensors, such as retinoic acid-inducible gene I (RIG-I)-like receptors (RLRs) and some Toll-like receptors (TLRs) display certain compositional biases in the types of molecules they recognise; e.g., RIG-I preferentially binds U-rich sequences with interspersed cytosine nucleotides , while TOver the past decades, the development of novel techniques to determine with high precision what sequence motifs RBPs recognise have significantly change our understanding of molecular biology. One such approach is crosslinking and immunoprecipitation (CLIP) coupled with RNA sequencing . CLIP meLarge scale RBPs mapping experiments have highlighted the distribution of RNA-binding preferences of the human proteome. While the discovery of RNA-binding motifs is inherently difficult and may be dependent on sequence context and RNA structure , many huStretches of the same mononucleotide or overall representation of that base along an RNA molecule may have the propensity to form certain types of RNA structures. G-rich sequences, for instance, have the tendency to form G-quadruplexes which in turn can be detected by RBPs . TherefoThis bias against A-rich motifs may also impact the evolution of RNA viruses. Indeed, a recent study that looked at the overall nucleotide composition of both positive-sense and negative-sense single-stranded RNA viruses showed that the majority of viral coding sequences tend to have high adenosine frequency at the cost of cytosine representation . This biin vitro and that CNBP promotes the translation of mRNA containing G4 structures in human cells [G-rich motifs seem to be overrepresented in RNA:RBP mapping experiments ,22,36, aan cells . The curan cells . Intriguan cells . To our an cells . Many G4an cells ,58, and an cells . Crucialan cells . FurtherIFNB promoter region [Furthermore, a recent study showed that CNBP binds to SARS-CoV-2 RNA . This har region . Consequr region ,67. NeveAnother antiviral protein that recognises G-rich RNA sequences is the Fused in Sarcoma (FUS) protein. FUS expression is up-regulated upon treatment with type I interferon, hinting to a role in controlling viral infections . During in vitro [Poly-cytosine binding proteins (PCBPs) are RBPs that recognise C-rich nucleic acids. This family of proteins includes PCBP1, PCBP2 and hnRNP-K, and they all have functions in mRNA stability, RNA nuclear traffic and splicing. Some of these proteins are also induced upon interferon stimulation and in response to virus infection, suggesting a potential role in controlling virus replication. PCBP1 and PCBP2 have been implicated in the replication of numerous viruses, and while they share nearly 90% of protein sequence identity, their effects on virus replication are distinct . PCBP2 cin vitro . More rein vitro . The fatin vitro ,82, theyin vitro . Comparain vitro . Conversin vitro . While kin vitro . Attemptin vitro . While tin vitro . PCBP2 iin vitro .Another member of the poly-cytosine binding protein family is the heterogeneous nuclear ribonucleoprotein K (hnRNPK), which was originally identified as a component of the heterogeneous nuclear ribonucleoprotein complex . This poenv gene. Alternative splicing at this site results in the production of two main classes of mRNA molecules: partially spliced and fully spliced RNAs. While the unspliced mRNA serves as the genomic RNA that is packaged into virions and contains genetic information for the production of structural proteins (Gag and Pol) and the enzymes required for viral replication , spliced RNAs encodes for the envelope and multiple regulatory viral proteins, including Rev and Tat [hnRNPK has also been shown to directly bind to the HIV-1 RNA genome, where it binds to a C-rich region of a stem-loop structure located in the Env gene region . Immunop and Tat . These p and Tat . How hnR and Tat . hnRNPK  and Tat . At leas and Tat . Additio and Tat . Depleti and Tat . hnRNPK  and Tat . ConsideU-rich RNA sequences are crucial for RNA metabolism, primarily through uridylation of mRNA molecules. Uridylation refers to the addition of uridine monophosphate residues to RNA molecules. It is a post-transcriptional modification that mostly occurs at the 3′ end of RNA molecules, however, internal uridylation sites have also been identified in trypanosomes ,108. TheThe protein hnRNPL is also known to bind U-rich RNA sequences ,19. hnRNAside from mononucleotide biases, virus genomes also maintain biased dinucleotide composition. In vertebrate RNA viruses, one of the most pervasive dinucleotide bias is the low frequency of CpG dinucleotides, which resembles the CG-suppressed genome of the human. The paucity of CG dinucleotides in the human genome is thought to be a consequence of the activity of DNA methyltransferases that catalyse the reaction of cytosine to 5-methylcytosine in a CpG context; methylated cytosines are naturally prone to spontaneous deamination, resulting in a C-to-T mutation . While vThere are two main mechanisms of ZAP that required recruitment of different cofactors: upon interaction with viral RNA, ZAP can inhibit viral protein translation and target viral RNA for degradation . To blocNotably, TRIM25 is an RNA-binding protein itself as well. Previous studies have shown that TRIM25 may bind to RNA through two potential motifs: the 7K sequence in the L2 linker region located between the coiled-coil and the PRY/SPRY domains, and also through residues located within the SPRY domain ,154. HowEmerging evidence suggests that some human proteins detect local or global compositional biases in virus RNA. These proteins seem to regulate not only virus replication but also multiple aspect of RNA metabolism. Indeed, mutations found in many of the genes encoding for such proteins contribute to several human diseases ,125. The


Sjögren’s syndrome is an autoimmune rheumatic disease characterized by inflammation of the salivary and lacrimal glands, often manifesting as dry mouth and dry eyes. To simplify diagnostics of primary Sjögren’s syndrome (pSS), a non-invasive marker is needed. The aim of the study was to compare the RNA content of salivary extracellular vesicles (EVs) between patients with pSS and healthy controls using microarray technology. Stimulated whole saliva was collected from 11 pSS patients and 11 age-matched controls. EV-RNA was isolated from the saliva samples using a Qiagen exoRNeasy Midi Kit and analyzed using Affymetrix Clariom D™ microarrays. A one-way ANOVA test was used to compare the mean signal values of each transcript between the two groups. A total of 9307 transcripts, coding and non-coding RNA, were detected in all samples. Of these transcripts, 1475 showed statistically significant differential abundance between the pSS and the control groups, generating two distinct EV-RNA patterns. In particular, tRNAs were downregulated in pSS patients, with the transcript tRNA-Ile-AAT-2-1 showing a 2-fold difference, and a promise as a potential biomarker candidate. This study therein demonstrates the potential for using salivary EV-RNA in pSS diagnostics. Sjögren’s syndrome (SS) is a chronic autoimmune disease characterized by inflammatory destruction of the salivary and lacrimal exocrine glands with resultant hypofunction . ConsequWhile the exact etiology of pSS is still undetermined, a combination of environmental factors together with a genetic predisposition is presumed . Viral i2 gives 3 points. The presence of autoantibodies (anti-Ro/anti-SSA) also gives 3 points. An unstimulated whole saliva flow rate ≤ 0.1 mL/min, a Schirmer test ≤ 5 mm/5 min in at least one eye, and an ocular staining score ≥ 5 in at least one eye give 1 point each.The most recent set of classification criteria for pSS was developed based on an international collaborative effort by the American College of Rheumatology and the European League Against Rheumatism (ACR-EULAR) in 2016 [With no curative option presently available, treatment of pSS is generally a combination of targeted symptomatic therapy and broad-spectrum immunosuppressive therapy . Early dEVs are a heterogeneous group of membrane-bound, nano-sized particles released by cells into the extracellular space. They have been shown to participate in intercellular communication by means of either ligand-receptor binding or cellular uptake of EV bioactive components . The maiInterest in EV-RNA analysis has increased with the knowledge that RNA transcripts can be transferred from cell to cell via EVs. Once delivered, the RNA transcripts are hypothesized to contribute to regulation within the recipient cell, thereby introducing the possibility of intercellular genetic communication. Advances in both next-generation sequencing (NGS) and microarray analysis, as well as improvements in bioinformatic data processing capabilities, have greatly facilitated the accumulation of EV-RNA data such that comparative studies are now possible. Comparing the EV-RNA profiles between pathological and healthy states may thereby uncover RNA transcripts as potential biomarker candidates. To date, there is minimal data available from salivary EV-RNA analysis in pSS patients ,12,13.The aim of this study was therefore to characterize the RNA content of salivary EVs using microarray technology and then compare the EV-RNA profiles between pSS patients and healthy controls.9 particles/mL, with a mean particle size of 154 nm, and the pooled control sample had a concentration of 3.7 × 108 particles/mL, with a mean particle size of 157 nm.EVs were characterized in joint fractions 8–10 following SEC EV-isolation of a pooled pSS patient sample and a pooled control sample. NTA was used to determine the concentration and size distribution of EVs in the patient and control samples A,B. The The presence of the EV surface protein marker tetraspanin CD9 was assessed in the patient sample using flow cytometry C. The meWestern blotting to detect the EV tetraspanins CD9 and CD63, and the internal EV-marker protein heat shock 70 protein (Hsc70/Hsp70), was performed on EVs isolated from patient and control samples by both SEC and a modified Qiagen exoRNeasy column technique D. CD9 anWhen viewed with TEM, EVs in the pooled pSS patient sample could be identified by their cup-shaped morphology and size (<220 nm). Some EVs were observed as aggregates E.® 2.0 Fluorometer analysis. The average EV-RNA concentration of the control samples was 1.9 ng/μL, with a range of 1.0 ng/μL to 4.0 ng/L. Finally, the size distribution of the salivary EV-RNA was between 25 to 200 nucleotides in all samples, as demonstrated by Agilent 2100 Bioanalyzer analysis  and non-coding RNA (ncRNA) were detected in all saliva samples. A total of 9307 transcripts were identified from the microarrays’ total of approximately 540,000 transcripts (1.7% transcripts identified). Transcript signal values ranged from under 10 to over 11,000.Most known RNA subtypes were identified in all the patient and control saliva samples A. Close p-value < 0.05, and approximately 60% of these were ncRNA. After controlling statistically for the false discovery rate (FDR), seven of these transcripts showed q-values < 0.05, where five were lncRNAs, one a tRNA (tRNA-Ile-AAT-2-1), and one a miRNA (MIR6870). The percentage of transcripts showing statistically significant differential abundance within each RNA subtype is displayed in A one-way ANOVA was used to compare the mean fluorescence signal values, representing the mean transcript levels, of the pSS patient and control transcripts. Of the 9307 transcripts detected, 1475 had a statistically significant differential abundance between the two groups, q-value < 0.05. LINC00673 was identified as the transcript with the greatest negative fold change (−3.2), however, the mean signal values were <20. Of the six miRNAs included, MIR6870 had a q-value < 0.05, with a fold change of −1.4.A hierarchical cluster heatmap was produced, using the 100 transcripts with the most statistically significant differences between the patient and the control groups A. Of theIn order to further distinguish transcripts with a difference in abundance between the patient and the control groups, transcripts with statistically significant differential abundance were further filtered for fold change (≥2) and mean signal values (≥20) . The mRNRT-qPCR was used to validate two of the RNA transcripts, B2M and RPL9, in three pSS patients and three control samples. The RT-qPCR results confirmed transcript levels consistent with the different signal values detected by the microarrays.q-value < 0.05.The Affymetrix microarray analysis of salivary EV-RNA unveiled an extensive repertoire of coding and non-coding RNAs in all samples. Approximately half of the RNA transcripts identified were mRNAs, while the other half were ncRNAs, of which most known ncRNA subtypes were detected. These included lncRNA, miRNA, snRNA, circRNA, rRNA, tRNA, yRNA, lincRNA, snoRNA, pRNA, scaRNA, and vRNA. A comparison of the RNA profiles between patients with pSS and controls revealed statistically significant differential abundance in the levels of approximately 16% of transcripts. The most remarkable difference was seen in tRNA transcripts, with close to 50% showing a significant difference between the patient and control groups. Several mRNA and miRNA transcripts also deserve further consideration. Furthermore, an abundance of lncRNAs was detected, five with a To our knowledge, Ogawa et al., (2008) were the first to show that human whole saliva contains EVs . SubsequmRNAs represented approximately half of the RNA transcripts detected by the microarrays, with almost 12% showing statistically significant differential abundance between the pSS and control groups. Of these, two stood out as transcripts of interest: seysnoy and AC087392.1. These transcripts had positive fold changes of 6 times and close to 4 times, respectively, with higher levels of the transcripts in the pSS patient group compared with the control group.The official symbol for the seysnoy gene is MTRNR2L2 and it is located on chromosome 5. While there is uncertainty as to whether it is a transcribed protein-coding gene or a paralog of the mitochondrial gene MT-RNR2, several recent studies have identified its potential involvement in coronary artery disease, tumorigenesis, Huntington’s disease, and diabetic kidney disease ,22,23,24There are presently no disease or trait phenotypes found in connection with the protein-coding gene AC087392.1. Further investigation of this transcript is warranted.In this study, immature miRNAs represented 9% of the ncRNA transcripts detected in salivary EVs, with 21% of the identified miRNAs showing a significant difference in abundance between the two groups. MiRNAs have a well-established role in translational and transcriptional regulation, and their dysfunction has been linked to many pathological conditions, including neurodegenerative disorders, cardiovascular disease and obesity , autoimmq-value < 0.05. Follow-up validation is required using either RT-qPCR or NGS.Close to 50% of the 55 different tRNA transcripts detected in the salivary EV-RNA samples showed statistically significant differential abundance between the pSS patient and control groups. Five showed a fold change over two and when correcting for the false discovery rate, and tRNA-Ile-AAT-2-1 had a There are over 600 tRNA loci ,30 posittRNA fragments are proposed to have roles in various cellular processes, including cell proliferation ,33, RNA A total of 53 yRNA transcripts were identified in the salivary EV-RNA samples. Four of the transcripts were annotated as Ro-associated Y1, Y3, Y4, and Y5, originating from chromosome 7. Interestingly, none of these four transcripts showed a statistically significant difference in abundance between the patients and controls. The remaining yRNA transcripts were categorized as pseudogenes, originating from diverse chromosomes, with three showing a statistically significant difference in abundance between the patient and control groups, but with fold changes < 1.5.Y-RNAs were originally discovered as the RNA component of ribonucleoprotein (RNP) complexes in the serum of patients with pSS and SLE. They were found bound to the RNA-binding proteins Ro60 and La, which are now considered the main autoantigenic targets in both pSS and SLE . Y-RNAs Considering the occurrence of autoantigens to Ro60 and La in the majority of pSS patients, and the association these antigens have with Y-RNA, one could conceivably expect to see a discrepancy in the EV-associated Y-RNAs between pSS patients and controls. Interestingly, the present study found that the levels of all four Y-RNA types were present at near equal levels in the two groups, with the highest levels found for Y3, followed by Y1, Y4, and then Y5.While it is beyond the scope of this article to make any predictions regarding the involvement of Y-RNA-RNP complexes and autoimmunity in SS, it is noteworthy that Y-RNA, as well as signal recognition peptide- (SRP) RNA, are also selectively incorporated into the capsids of certain viruses . Moreoveq-value < 0.05. While the function of lncRNAs has not been definitively determined, they are believed to be involved in the regulation of gene expression [Of the ncRNA transcripts identified by the microarray analysis, over 70% were lncRNA. Of these, 17% showed statistically significant differential abundance between the patient group and the control group. However, most of the lncRNA transcripts detected by the microarrays were transcripts with an uncharacterized function and with no gene symbol. At least 42 of the transcripts included in the heatmap were lncRNAs, five with a pression .Comparisons between EV studies are difficult due to the lack of standardization of methods. Both the EV isolation method and the EV-RNA isolation method, as well as the type of RNA analysis, greatly impact the RNA transcripts identified. In the future, improved and standardized EV-RNA isolation techniques may provide more comparable RNA samples. Increasing the depth of EV-RNA sequencing will similarly broaden the probe options available for microarray production.While NGS techniques enable the analysis of the entire genetic material of a sample, microarrays provide a method for profiling previously identified nucleic acid sequences. This presents the obvious disadvantage that novel sequences will not be identified . HoweverThe Clariom™ D microarrays used in this study were designed for use with total cellular RNA. Only 1.7% of the approximate 540,000 transcripts represented on the chips hybridized with the RNA transcripts from the salivary EVs. There are several apparent explanations for this, but they are yet unsubstantiated. Firstly, it supports the notion that EV-RNA is a selection of total cellular RNA. Secondly, the concentration of EV-RNA is substantially less than cellular RNA. Thirdly, the Affymetrix microarrays are designed for, and the hybridization steps are optimized for, total cellular RNA, not EV-RNA. Taking this into consideration, the EV transcripts identified should only be considered a representation of the total.Most of the RNAs in EVs are under 200 nt in length ,55. The An overview of the study workflow is illustrated in Saliva samples were provided by the Dry Mouth Clinic at the Department of Clinical Dentistry, Faculty of Dentistry, University of Oslo. Stimulated whole saliva (SWS) was collected from 11 pSS patients and 11 age-matched healthy controls. All participants were women aged between 39 to 72 years, with a mean age of 55. All pSS patients fulfilled the ACR criteria; all were ANA- and anti-SSA-positive and had dry eyes, dry mouth, and reduced saliva secretion . Most of®-GV, Merck Millipore, Cork, Ireland) was subsequently added to each sample at a ratio of 1:1. Two centrifugation steps were performed: 300 g for 10 min, 4 °C, to remove cells, bacteria, and food residue, and 10,000× g for 20 min, 4 °C, to remove cellular debris and other particulate matter. Next, 500 μL of each sample was mixed with an equal volume of filtered PBS and then filtered with a 0.22 μm filter .Saliva samples were thawed on ice and then, to inhibit enzymatic activity, 1 μL of RiboLock RNase Inhibitor (40 U/μL) , together with 5 μL of cOmpleteTM Mini Protease Inhibitor (Roche), were added per 1 mL of saliva and mixed well. Filtered phosphate-buffered saline (PBS)  and pooled control saliva samples (n = 11) using size-exclusion chromatography (SEC) with IZON qEV original 70 nm  isolation columns. Pooled samples (500 μL) were diluted 1:1 with filtered PBS (0.1 μm), applied to pre-washed SEC columns, and EVs eluted into 10 fractions of 500 μL. Combined joint fractions 8–10 were further used for EV characterization.Due to limited patient material, EV characterization was performed on pooled samples. EVs were isolated from pooled patient saliva samples (n = 4) and 200 μL of pooled control saliva (n = 4) were used in the first spin column steps of the exoRNeasy protocol to isolate and capture the EVs in the column filters; see ® Ultra-2, 100 K .Due to the relatively low EV yield following SEC-EV isolation, EVs were also isolated using a modified Qiagen exoRNeasy EV isolation technique for use in Western blotting. Briefly, 200 μL of pooled patient saliva  using a Nanosight NS500 Instrument  , with de2O, incubated with 2% methylcellulose containing 0.3% uranyl acetate for 10 min on ice, and then air-dried. A Tecnai G2 Spirit transmission electron microscope  was used together with a Morada digital camera and RADIUS imaging software (version 2.1) to create images which were further processed using Adobe Photoshop.Transmission electron microscopy (TEM) was performed on the patient samples. Formvar-carbon coated copper grids were placed on top of 5 μL drops of sample, incubated for 5 min at RT, washed 3 times with distilled HImmunoaffinity capture and detection of EV-membrane protein CD9 were performed in conjunction with flow cytometry to identify EVs in the patient sample, as previously described . EVs wer® Plus 2 Pre-Stained Protein Standard  was used as a ladder.Western blotting was performed as previously described . In addiEV-RNA isolation was performed on all individual saliva samples using a Qiagen exoRNeasy Midi Kit , according to the manufacturer’s guidelines. Sample volumes of 500 μL were applied to the Qiagen exoRNeasy isolation columns and 200 μL of the aqueous phase was transferred. Additionally, a DNase step was included following the 100% ethanol-centrifugation step. RNA was recovered using RNAse-free water, producing elutes of approximately 12 μL, and subsequently quantified and characterized. The samples were frozen at −80 °C until further use.® 2.0 Fluorometer  was used in conjunction with a Qubit® microRNA Assay Kit  to determine the RNA concentration of all salivary EV-RNA samples. A Nanodrop™ One Spectrophotometer  was used to assess the presence of potential contaminants (guanidine or phenol) and a Bioanalyzer  analysis was performed on all samples using an Agilent 6000 Pico Assay Kit  to assess the size and quality of the RNA. All analyses were performed according to the manufacturer’s guidelines and using 1 μL of sample.A QubitTM WT Pico Reagent Kit , in accordance with the manufacturer’s guidelines. The input quantity of total RNA was 3 ng and pre-IVT amplification was performed using 9 PCR cycles. Hela RNA  was used as a positive control to verify that the reagents worked as expected, and RNA-free water was used as a negative control to monitor for RNA and DNA contamination. Loaded ClariomTM D microarrays were incubated in an Affymetrix GeneChip™ 645 hybridization oven for 17 h at 45 °C with a rotation of 60 rpm, then washed and stained in a GeneChip™ Fluidics Station 450 using a GeneChip™ Hybridization, Wash, and Stain Kit . A GeneChip™ Scanner 3000 System was used to read the generated fluorescence signal values, corresponding to RNA transcript levels, and scanned data were processed with Affymetrix Command Console® Software version 4.0 (AGCC), producing DAT files ready for bioinformatic processing .Clariom™ D microarray chips  were used to analyze the salivary EV-RNA samples. The samples were prepared using a GeneChip® Gene Expression Assay, Hs01552541_g1 and B2M Taqman® Gene Expression Assay, Hs99999907_m1). A Taqman™ Fast Advanced Master Mix (ref 4444557) was used. Three patient samples and three control samples were used to generate the cDNA library with SuperScript™ Enzyme Mix (Cat no 11754-050) and VILO™ Reaction Mix (Cat no 11754-250). Total cellular RNA and RNA-free water were used as positive and negative controls, respectively. All reagents were provided by Thermo Fisher Scientific, Oslo, Norway.Reverse transcription-quantitative PCR (RT-qPCR) was used to validate the presence of selected RNA transcripts  based on the availability of assays in the laboratory and sufficiently high and even signal values across all the samples applied to the arrays (RPL9 Taqman® Genomics Suite® software (version 6.6) was used for the normalization and statistical analysis of the Affymetrix microarray results. The DAT files were normalized using Robust Multichip Average (RMA) normalization and the resulting CEL files were filtered to remove transcripts with signal values under 5, thereby reducing background interference. A one-way Analysis of Variance (ANOVA) was then performed to determine any statistically significant difference in transcript levels between the patient and control groups, with p-values less than 0.05 considered indicative of statistical significance. q-values and fold changes were also determined. Additionally, a Principal Component Analysis (PCA) and a hierarchical cluster heatmap were produced, with the heatmap incorporating the 100 transcripts with the most statistically significant differences in signal value between the groups.PartekThis present study uncovered several differentially abundant salivary EV-RNA transcripts between pSS patients and controls. These transcripts included both mRNAs and ncRNAs, specifically miRNAs, tRNAs, and lncRNAs. The most striking finding was the differential abundance of tRNA transcripts between the groups, in particular, transcript tRNA-Ile-AAT-2-1, indicating their potential use in biomarker discovery.


Triticum aestivum. HISAT2 showed the best performance for 3′ RNA-seq with the least mapping errors and quick computational time. The number of detected homeologs was further improved by extending 1 kb of the 3′ UTR annotation. Differentially expressed genes in response to mild cold treatment detected by the 3′ RNA-seq were verified with high-coverage conventional RNA-seq, although the latter detected more differentially expressed genes. Finally, downsampling showed that even a 2 million sequencing depth can still detect more than half of expressed homeologs identifiable by the conventional 32 million reads. These data demonstrate that this low-cost 3′ RNA-seq facilitates large-scale transcriptomic studies of allohexaploid wheat and indicate the potential application to other allopolyploid species.Although allopolyploid species are common among natural and crop species, it is not easy to distinguish duplicated genes, known as homeologs, during their genomic analysis. Yet, cost-efficient RNA sequencing (RNA-seq) is to be developed for large-scale transcriptomic studies such as time-series analysis and genome-wide association studies in allopolyploids. In this study, we employed a 3′ RNA-seq utilizing 3′ untranslated regions (UTRs) containing frequent mutations among homeologous genes, compared to coding sequence. Among the 3′ RNA-seq protocols, we examined a low-cost method Lasy-Seq using an allohexaploid bread wheat,  High-thet al.  and Kash , with a genome size of ∼16 Gb , was formed by allopolyploidization among three species, namely Triticum urartu , an unknown species related to Aegilops speltoides  and Aegilops tauschii   . The maj 14; DD) . The tim50 years . Althoug species .The aforementioned studies revealed the difference between the transcriptomic patterns of diploid and polyploid species and premised the importance of large-scale or long-term time-series expression studies of allopolyploid species. To realize such studies, it would be necessary to employ high-throughput and cost-efficient methods that can cope with highly similar homeologous sequences.T. aestivum, we first examined the accuracy of subgenome mapping by using the RNA-seq data of Tetra Chinese Spring (TCS) as a ground truth. In the TCS, the subgenome D was removed from an experimental hexaploid cultivar, Chinese Spring (CS), by repeated backcrossing  and insertions and deletions (indels), are higher in 3′ UTR than in the CDS of homeologous sequences  because crossing ; thus, tcrossing ,22. TherReference sequences of allohexaploid wheat, CS, and its annotations were downloaded from the International Wheat Genome Sequencing Consortium (IWGSC) website . ReferenThe homeolog triad list was prepared following a previous study . We extrlater , and the samples were stored in a biomedical freezer. From these samples, the total RNA was extracted using the RNeasy Plant Mini Kit  according to the manufacturer’s instructions.We grew three individuals of CS for RNA-seq. All CS plants for RNA sampling were grown in a plant growth chamber KCLP-1000II-CT  for 5 weeks at 22°C, and the fourth leaves were sampled. Afterward, the CS plants were kept in a walk-in growth chamber in Kihara Institute of Biological Research  for 1 week at 7°C followed by sampling of the fifth leaves thereafter considered as cold-treated samples. Both the growth chambers were set at 14/10 h light/dark cycle using time-controlled white fluorescent light. The tip of the fully expanded youngest leaf of each individual was sampled and immediately soaked into RNA stabilization solution, RNA2SO4 to adjust pH 5.2). The total RNA was extracted using the Maxwell® RSC Plant RNA Kit with Maxwell® RSC Instrument  according to the manufacturer’s instructions.The TCS plants were grown in the experimental field of Kihara Institute of Biological Research. The seedlings were planted there on 9 December 2018. The leaves were sampled on 30 December 2018 and 26 April 2019 for three samples each. The field climate record showed that the daily lowest, mean and highest air temperatures in the last week were 6.8, 11.5 and 17.2°C for the first sampling, and 14.4, 18.2 and 23.5°C for the second sampling, respectively. TCS leaves were sampled and stored in a similar manner to the above CS leaf samples. The RNA stabilization solution was homemade nucleic acid preservation medium  rThe conventional RNA-seq reads of the CS samples were preprocessed using Trimmomatic v0.36 to trim low-quality bases over the whole reads. Only reads >40 bases were passed for downstream analysis.Homeolog expression of 3′ RNA-seq samples (i.e. the six CS and TCS samples) was quantified using HISAT2 , STAR 2 and EAGLThe preprocessed reads of conventional RNA-seq samples were processed using EAGLE-RC following the standard procedure , similar2 fold change (|log2FC|) >1. GO enrichment analysis was performed against the DEGs using topGO v2.49.0  and classification-based method (EAGLE-RC). HISAT2 and STAR took ∼0.3 and ∼0.8 min to process a million reads with 16 computational threads, respectively. In contrast, EAGLE-RC took ∼35.4 min to process a million reads with 16 threads because it involves multiple processes, including subgenome-independent mapping, SNP calling, homeolog likelihood calculation and read classification.To evaluate the classification error rates of 3′ RNA-seq, we also sequenced six TCS samples using 3′ RNA-seq: ∼2.6 million single-end reads with 151 bases per library were sequenced. Thereafter, the reads were preprocessed and mapped using the same procedures used for analyzing CS 3′ RNA-seq samples. There were 81.8 ± 2.1% reads left after the preprocessing , Sheet 1In summary, among these analyses, HISAT2 and STAR produced similar proportions of reads attributable to each subgenome, whereas EAGLE-RC was not suitable for 3′ RNA-seq. In addition, both HISAT2 and STAR produced low proportions of misclassified reads; however, HISAT2 had a lower rate. Moreover, HISAT2 is ∼2.7 times faster than STAR in computing the hexaploid data and 118 times faster than EAGLE-RC. Thus, we concluded that HISAT2 is most suitable for the 3′ RNA-seq data and used it for subsequent analysis.Subsequently, we examined the effect of annotation extension in the 3′ regions, since many genes (homeologs) only had annotations of the coding region in the IWGSC annotation. This is presumably because homology to known genes is informative to predict coding sequences but not effective in noncoding regions with higher divergence. Although full-length complementary DNA data can provide strong evidence to annotate 3′ UTRs, the quality of such data may be limited in allopolyploid species.In RNA-seq, relative expression levels can be considered as the number of reads mapped on the gene region. However, due to the insufficiency of the 3′ UTR annotations in the IWGSC annotation, counting the mapped reads in the 3′ RNA-seq samples based on this annotation may result in underestimation of the expression levels. It was reported that the extension of the 3′-end annotation of each gene model can improve RNA-seq analysis in barley, as many predicted gene models lacked 3′ UTR regions in this species (45). To address this issue, we extended the gene region by 0.5–4.0 kb longer than the IWGSC annotation and counted the reads assigned to both the original and extended gene regions.To investigate the effect of annotation extension, we compared the read counts assigned to the original gene regions and the extended gene regions by HISAT2 using CS 3′ RNA-seq samples. We found that the annotation extension of the gene regions increased the number of reads assigned to that region Figure . With anFor further analysis, we used the IWGSC annotation with 1.0 kb extension (IWGSC+1k). By using this annotation, 53.2% reads were mapped on the annotated gene region, which is 5.2% more than the original IWGSC annotation (48.0%) Figure ; in contFT1-A1/VRN-A3 encoding florigen to control heading time, could be better quantified  obtained from the IWGSC and IWGSC+1k annotations for each gene in one of the TCS 3′ RNA-seq samples. We found that 29 282 genes (81.5% of the expressed genes) showed the same expression levels between the two annotations, while 6579 genes (18.3%) had increased read counts and only 44 genes had decreased read counts when using IWGSC+1k annotation Figure , suggestd Figure . Not surd Figure , confirmIn addition, we checked the annotation extending effects on the CS 3′ RNA-seq samples Figure . The treHere, we have shown that 3′ RNA-seq could be rather largely influenced by the quality of gene annotations, as it focuses on the 3′ end of the mRNA. Most gene annotations prioritize CDS regions, so that the quality of UTR annotations is poor. We also demonstrated that gene detection could be improved by extending the insufficient annotation of the 3′ end, similar to a study using barley . The num3′ RNA-seq and conventional RNA-seq methods are based on different experimental procedures of library synthesis and do not necessarily give equivalent expression levels. It is known that Pearson’s correlation coefficient of gene expression levels is relatively high  although low-expression genes tend to show low correlation ,46, impl10-transformed normalized expression of expressed genes  between 3′ RNA-seq (quantified with HISAT2) and conventional RNA-seq (quantified with HISAT2 and EAGLE-RC).To examine the correlation in the polyploid species, we sequenced the same CS RNA samples using conventional RNA-seq as another library and sequencing protocol, in addition to the 3′ RNA-seq data described above. Approximately 32.0 million read pairs with 151 × 2 bases per library were obtained by a conventional RNA-seq protocol, and the reads were preprocessed using the same procedures used for analyzing CS 3′ RNA-seq samples. Then, we quantified the expression of CS conventional RNA-seq samples using two methods, HISAT2 and EAGLE-RC . The forWe found that the average values of correlation coefficients of six samples for A, B and D homeologs between 3′ RNA-seq quantified with HISAT2 and conventional RNA-seq quantified with EAGLE-RC were 0.73 ± 0.05, 0.74 ± 0.05 and 0.74 ± 0.05, respectively Figure . A similIn addition, we made ternary plots to visualize the expression ratio of the A, B and D homeologs  using 3′A. thaliana , dehydrin, ethylene-responsive transcription factors and aquaporin, were identified as DEGs from both 3′ RNA-seq and conventional RNA-seq data. In addition, flowering-promoting factor-1 like protein 1 (FPFL1), which involved vegetative–reproductive transition, was also upregulated by cold treatment. These results suggest that although many more significant DEGs can be identified by deeper coverage of conventional RNA-seq, DEGs are also identifiable by low-coverage 3′ RNA-seq methods.We next compared DEGs detectable by the low-coverage 3′ RNA-seq with those by the conventional RNA-seq data. There were 218 and 4806 genes differentially expressed between mild cold treatment and control CS samples detected from the low-coverage 3′ RNA-seq and conventional RNA-seq data quantified using HISAT2 with the criteria of FDR < 0.05 and |logectively . We founectively , Sheet 5P < 0.05)  , Sheet 2 < 0.05) , Sheet 4 < 0.05) , Sheet 6 < 0.05) . AlthougTaken together, 3′ RNA-seq provides a balanced opportunity to obtain biologically significant implications with low cost, although a higher coverage of 3′ RNA-seq might be more powerful in differential analysis of gene expression.To systematically address the effects of sequencing depth on the detection of expressing genes, we performed a downsampling experiment. We created datasets by downsampling 20%, 40%, 60% and 80% of the reads from each original CS 3′ RNA-seq sample and performed gene expression quantification using the same protocol that was applied to the original dataset. The result indicated that the number of additionally detected genes decreased logarithmically with respect to sequencing depth (Figure T. aestivum, an allohexapolyploid with relatively large subgenomes. In addition to the usage of cost-efficient Lasy-Seq method, our downsampling results suggested that the coverage of 2 million reads per sample allows >100 multiplexing per lane for many existing sequencing systems. HISAT2 showed rapid computation with relatively low error in mapping. Compared with genes or homeolog triads that were detected with 32.0 million reads by using the conventional RNA-seq data, approximately half of them were detected with only 2 million reads by using 3′ RNA-seq methods. Considering both cost-effectiveness and quality, 3′ RNA-seq should be considered for large-scale transcriptomic, time-series or condition transition analyses of allohexaploid wheat, providing useful insights into breeding as well as environmental adaptation. Even if technical adjustments may be required, 3′ RNA-seq is also expected to contribute to the research of other agriculturally or ecologically important allopolyploid species.3′ RNA-seq was developed as a low-cost RNA-seq technology . We applThe RNA-seq data are deposited on DDBJ DRA with the accession number DRA015200. The scripts used in this study are deposited on Zenodo with doi:10.5281/zenodo.7402763.lqad067_Supplemental_FilesClick here for additional data file.