Plant lncRNAs Research Articles

A multispecies study reveals the diversity and potential regulatory role of long noncoding RNAs in cucurbits.

Plant long noncoding RNAs (lncRNAs) exhibit features such as tissue-specific expression, spatiotemporal regulation, and stress responsiveness. Although diverse studies support the regulatory role of lncRNAs in model plants, our knowledge about lncRNAs in crops is limited. We employ a custom pipeline on a dataset of over 1000 RNA-seq samples across nine representative species of the family Cucurbitaceae to predict 91 209 nonredundant lncRNAs. The lncRNAs were characterized according to three confidence levels and classified by their genomic context into intergenic, natural antisense, intronic, and sense-overlapping. Compared with protein-coding genes, lncRNAs were, on average, expressed at low levels and displayed significantly higher specificity when considering tissue, developmental stages, and stress responsiveness. The evolutionary analysis indicates higher positional conservation than sequence conservation, probably linked to the conserved modular motifs within syntenic lncRNAs. Moreover, a positive correlation between the expression of intergenic/natural antisense lncRNAs and their closest/parental gene was observed. For those intergenic, the correlation decreases with the distance to the neighboring gene, supporting that their potential cis-regulatory effect is within a short-range. Furthermore, the analysis of developmental studies showed that a conserved NAT-lncRNA family is differentially expressed in a coordinated way with their cognate sense protein-coding genes. These genes code for proteins associated with phloem development, thus providing insights about the potential involvement of some of the identified lncRNAs in a developmental process. We expect that this extensive inventory will constitute a valuable resource for further research lines focused on elucidating the regulatory mechanisms mediated by lncRNAs in cucurbits.

CANTATAdb 3.0: An Updated Repository of Plant Long Non-Coding RNAs.

CANTATAdb 3.0 is an updated database of plant long non-coding RNAs (lncRNAs), containing 571,688 lncRNAs identified across 108 species, including 100 Magnoliopsida (flowering plants), a significant expansion from the previous version. A notable feature is the inclusion of 112,980 lncRNAs that are expressed specifically in certain plant organs or embryos, indicating their potential role in development and organ-specific processes. In addition, CANTATAdb 3.0 includes 74,886 pairs of evolutionarily conserved lncRNAs found across 47 species and inferred from genome-genome alignments as well as conserved lncRNAs obtained using a similarity search approach in 5,479 species pairs, which would further aid in the selection of lncRNAs for functional studies. Interestingly, we find that conserved lncRNAs with tissue-specific expression patterns tend to occupy the same plant organ across different species, pointing toward conserved biological roles. The database now offers extended search capabilities and downloadable data in popular formats, further facilitating research on plant lncRNAs.

Transcriptomic profiling reveals salt-responsive long non-coding RNAs and their putative target genes for improving salt tolerance in upland cotton (Gossypium hirsutum)

Salt stress continues to exert notable impacts on the growth and development of cotton plants. In various biological processes of plants and in response to abiotic stress, long noncoding RNAs (lncRNAs) play a crucial regulatory role. In this study, we conducted transcriptome sequencing of cotton plants grown under salt stress and normal water conditions. A total of 519 lncRNAs and 75,376 messenger RNAs (mRNAs) were identified. Compared with mRNAs, lncRNAs have shorter transcript lengths and open reading frames, fewer exons, and lower expression levels. By comparing and analyzing the differentially expressed lncRNAs (DELs) and differentially expressed coding genes (DEGs) after salt treatment, we identified a total of 60 DELs and 217 DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that these target genes are enriched primarily in intracellular organelles and participate in metabolic regulation pathways. Ultimately, lncRNA XLOC_043671, along with its target gene Gohir.D02G125200, and the lncRNA XLOC_034831, along with its target gene Gohir.A12G114800 was identified as a participant in salt tolerance regulation in cotton. RNA-seq and RTqPCR revealed that both these lncRNAs and their target genes exhibited significantly enhanced expression under salt stress. Subsequently, utilizing virus-induced gene silencing (VIGS) technology, we successfully silenced the two lncRNAs in cotton plants, which resulted in reduced expression levels of the corresponding target genes, rendering the plants less salt tolerant than did the control plants. Similarly, we successfully silenced these two target genes in cotton seedlings, and the silenced plants demonstrated a pronounced salt-sensitive phenotype. In summary, our transcriptome analysis revealed a significant number of differentially expressed lncRNAs and their corresponding coding genes. These findings lay the foundation for further investigating the mechanisms of lncRNAs and their target genes in the salt stress response of cotton.

Crosstalk between long non-coding RNAs and miRNAs regulates transferase activities in response to salt stress in Pistachio (Pistacia vera L.)

Long non-coding RNA (lncRNA) molecules are important regulators of a broad spectrum of biological processes, including adaptation to diverse stresses in plants. However, how plant lncRNAs function during salt stress conditions is largely unknown. In our study, we identified 48 conserved mature Pistacia vera L. (P. vera) miRNAs among pistachio miRNA precursors and used RNA sequencing data to predict and validate the interaction relationship between lncRNAs and miRNAs. We indicated that the regulatory networks of pve-miRNA-lncRNA-mRNAs were more complex in the salt-tolerant (Ghazvini) compared to the salt-sensitive (Sarakhs) pistachio cultivar under NaCl treatment. We also found that 7 and 8 salt-responsive lncRNAs play a role as competing endogenous target mimics (eTMs) for 7 pve-miRNA families in Ghazvini (mir164, mir169, mir397, and mir827) and Sarakhs (mir156, mir159, mir169, and mir482), respectively. The GO-term enrichment analysis showed that protein-coding target genes of pve-miRNAs that regulated with target mimic relationship mainly enriched in transferase activity include: 7-deoxyloganetin glucosyltransferase, Arginine N-methyltransferase, Glutathione transferase, Flavonol sulfotransferase, and Beta-glucuronosyltransferase. Their activity regulates alkaloids and polysulfated flavonols biosynthesis, sequesters sodium, improves antioxidant capacity and controls epigenetic elements in Ghazvini under salt treatment. In Sarakhs, the analysis of GO-term enrichment in pve-miRNAs protein-coding target genes of lncRNA-related mimic relationship enriched in response to wounding, cell death, and jasmonic acid biosynthetic process. Compared to Sarakhs, Ghazvini had a more effective ceRNA network for regulating transferase enzymes and specific transcription factors including MYB, WRKY, and ABR1 in response to salt stress, which led to more salt tolerance in the Ghazvini cultivar. The present study provides new information about the complex lncRNA-miRNA-mRNA regulatory networks in response to plant stress conditions in pistachio. Furthermore, our findings can be used for different breeding programs and the introduction of salt-tolerant pistachio rootstocks.

Plant-LncPipe: a computational pipeline providing significant improvement in plant lncRNA identification.

Long non-coding RNAs (lncRNAs) play essential roles in various biological processes, such as chromatin remodeling, post-transcriptional regulation, and epigenetic modifications. Despite their critical functions in regulating plant growth, root development, and seed dormancy, the identification of plant lncRNAs remains a challenge due to the scarcity of specific and extensively tested identification methods. Most mainstream machine learning-based methods used for plant lncRNA identification were initially developed using human or other animal datasets, and their accuracy and effectiveness in predicting plant lncRNAs have not been fully evaluated or exploited. To overcome this limitation, we retrained several models, including CPAT, PLEK, and LncFinder, using plant datasets and compared their performance with mainstream lncRNA prediction tools such as CPC2, CNCI, RNAplonc, and LncADeep. Retraining these models significantly improved their performance, and two of the retrained models, LncFinder-plant and CPAT-plant, alongside their ensemble, emerged as the most suitable tools for plant lncRNA identification. This underscores the importance of model retraining in tackling the challenges associated with plant lncRNA identification. Finally, we developed a pipeline (Plant-LncPipe) that incorporates an ensemble of the two best-performing models and covers the entire data analysis process, including reads mapping, transcript assembly, lncRNA identification, classification, and origin, for the efficient identification of lncRNAs in plants. The pipeline, Plant-LncPipe, is available at: https://github.com/xuechantian/Plant-LncRNA-pipline.

From environmental responses to adaptation: the roles of plant lncRNAs.

As sessile organisms, plants are continuously exposed to heterogeneous and changing environments and constantly need to adapt their growth strategies. They have evolved complex mechanisms to recognize various stress factors, activate appropriate signaling pathways, and respond accordingly by reprogramming the expression of multiple genes at the transcriptional, post-transcriptional, and even epigenome levels to tolerate stressful conditions such as drought, high temperature, nutrient deficiency, and pathogenic interactions. Apart from protein-coding genes, long non-coding RNAs (lncRNAs) have emerged as key players in plant adaptation to environmental stresses. They are transcripts larger than 200 nucleotides without protein-coding potential. Still, they appear to regulate a wide range of processes, including epigenetic modifications and chromatin reorganization, as well as transcriptional and post-transcriptional modulation of gene expression, allowing plant adaptation to various environmental stresses. LncRNAs can positively or negatively modulate stress responses, affecting processes such as hormone signaling, temperature tolerance, and nutrient deficiency adaptation. Moreover, they also seem to play a role in stress memory, wherein prior exposure to mild stress enhances plant ability to adapt to subsequent stressful conditions. In this review, we summarize the contribution of lncRNAs in plant adaptation to biotic and abiotic stresses, as well as stress memory. The complex evolutionary conservation of lncRNAs is also discussed and provides insights into future research directions in this field.

Update on functional analysis of long non-coding RNAs in common crops.

With the rapid advances in next-generation sequencing technology, numerous non-protein-coding transcripts have been identified, including long noncoding RNAs (lncRNAs), which are functional RNAs comprising more than 200 nucleotides. Although lncRNA-mediated regulatory processes have been extensively investigated in animals, there has been considerably less research on plant lncRNAs. Nevertheless, multiple studies on major crops showed lncRNAs are involved in crucial processes, including growth and development, reproduction, and stress responses. This review summarizes the progress in the research on lncRNA roles in several major crops, presents key strategies for exploring lncRNAs in crops, and discusses current challenges and future prospects. The insights provided in this review will enhance our comprehension of lncRNA functions in crops, with potential implications for improving crop genetics and breeding.

Exploring novel insights: Methyl jasmonate treatment reveals novel lncRNA-mediated regulation of secondary metabolite biosynthesis pathways in Echinacea purpurea

RNA-seq analysis has revolutionized the discovery of long non-coding RNAs (lncRNAs) in medicinal plants like Echinacea purpurea (L.) Moench, commonly known as purple coneflower. These lncRNAs potentially play crucial roles in regulating the biosynthesis of bioactive secondary metabolites. Our study aimed to identify regulatory lncRNAs involved in these pathways by treating 40-day-old E. purpurea seedlings with 100 μM methyl jasmonate (MeJA). Subsequently, RNA-sequencing was performed, identifying 509 differentially expressed transcripts for co-expression network analysis. The correlation between differentially expressed lncRNAs and coding transcripts was calculated, constructing a gene network to identify hub lncRNAs and their associated genes. Gene ontology and KEGG enrichment analyses were conducted to gain insights into the functions of these hub differentially expressed lncRNAs (DElncRNAs). Results revealed that these hub DElncRNAs regulate key genes involved in various pathways. For instance, CSE and BEBT are associated with the conversion of caffeoylshikimate to caffeate and shikimate, respectively. Additionally, SAT and NEC3 contribute to alkaloid biosynthesis, while NES1 and CFAT are involved in the biosynthesis of aromatic compounds, phenylpropanoids, and terpenoids. HST and SILD are associated with lignin biosynthesis. Furthermore, these lncRNAs mediate the acyl group transfer on both shikimate and quinate in the phenylpropanoid and flavonoid biosynthesis pathways. In summary, our findings offer comprehensive insights into the transcriptomic and metabolomic changes induced by methyl jasmonate treatment. We provide novel insights into the regulatory mechanisms of secondary metabolite biosynthesis, specifically highlighting the role of lncRNAs in E. purpurea.

Emphasizing the Role of Long Non-Coding RNAs (lncRNA), Circular RNA (circRNA), and Micropeptides (miPs) in Plant Biotic Stress Tolerance

Biotic stress tolerance in plants is complex as it relies solely on specific innate immune responses from different plant species combating diverse pathogens. Each component of the plant immune system is crucial to comprehend the molecular basis underlying sustainable resistance response. Among many other regulatory components, long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) have recently emerged as novel regulatory control switches in plant development and stress biology. Besides, miPs, the small peptides (100–150 amino acids long) encoded by some of the non-coding portions of the genome also turned out to be paramount regulators of plant stress. Although some studies have been performed in deciphering the role of miPs in abiotic stress tolerance, their function in regulating biotic stress tolerance is still largely elusive. Hence, the present review focuses on the roles of long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) in combating biotic stress in plants. The probable role of miPs in plant–microbe interaction is also comprehensively highlighted. This review enhances our current understanding of plant lncRNAs, circRNAs, and miPs in biotic stress tolerance and raises intriguing questions worth following up.

Plant long non-coding RNAs: identification and analysis to unveil their physiological functions.

Eukaryotic genomes encode thousands of RNA molecules; however, only a minimal fraction is translated into proteins. Among the non-coding elements, long non-coding RNAs (lncRNAs) play important roles in diverse biological processes. LncRNAs are associated mainly with the regulation of the expression of the genome; nonetheless, their study has just scratched the surface. This is somewhat due to the lack of widespread conservation at the sequence level, in addition to their relatively low and highly tissue-specific expression patterns, which makes their exploration challenging, especially in plant genomes where only a few of these molecules have been described completely. Recently published high-quality genomes of crop plants, along with new computational tools, are considered promising resources for studying these molecules in plants. This review briefly summarizes the characteristics of plant lncRNAs, their presence and conservation, the different protocols to find these elements, and the limitations of these protocols. Likewise, it describes their roles in different plant physiological phenomena. We believe that the study of lncRNAs can help to design strategies to reduce the negative effect of biotic and abiotic stresses on the yield of crop plants and, in the future, help create fruits and vegetables with improved nutritional content, higher amounts of compounds with positive effects on human health, better organoleptic characteristics, and fruits with a longer postharvest shelf life.

Single-cell transcriptome analysis dissects lncRNA-associated gene networks in Arabidopsis

The plant genome produces an extremely large collection of long noncoding RNAs (lncRNAs) that are generally expressed in a context-specific manner and have pivotal roles in regulation of diverse biological processes. Here, we mapped the transcriptional heterogeneity of lncRNAs and their associated gene regulatory networks at single-cell resolution. We generated a comprehensive cell atlas at the whole-organism level by integrative analysis of 28 published single-cell RNA sequencing (scRNA-seq) datasets from juvenile Arabidopsis seedlings. We then provided an in-depth analysis of cell-type-related lncRNA signatures that show expression patterns consistent with canonical protein-coding gene markers. We further demonstrated that the cell-type-specific expression of lncRNAs largely explains their tissue specificity. In addition, we predicted gene regulatory networks on the basis of motif enrichment and co-expression analysis of lncRNAs and mRNAs, and we identified putative transcription factors orchestrating cell-type-specific expression of lncRNAs. The analysis results are available at the single-cell-based plant lncRNA atlas database (scPLAD; https://biobigdata.nju.edu.cn/scPLAD/). Overall, this work demonstrates the power of integrative single-cell data analysis applied to plant lncRNA biology and provides fundamental insights into lncRNA expression specificity and associated gene regulation.

Deciphering shared attributes of plant long non-coding RNAs through a comparative computational approach

Over the past decade, long non-coding RNA (lncRNA), which lacks protein-coding potential, has emerged as an essential regulator of the genome. The present study examined 13,599 lncRNAs in Arabidopsis thaliana, 11,565 in Oryza sativa, and 32,397 in Zea mays for their characteristic features and explored the associated genomic and epigenomic features. We found lncRNAs were distributed throughout the chromosomes and the Helitron family of transposable elements (TEs) enriched, while the terminal inverted repeat depleted in lncRNA transcribing regions. Our analyses determined that lncRNA transcribing regions show rare or weak signals for most epigenetic marks except for H3K9me2 and cytosine methylation in all three plant species. LncRNAs showed preferential localization in the nucleus and cytoplasm; however, the distribution ratio in the cytoplasm and nucleus varies among the studied plant species. We identified several conserved endogenous target mimic sites in the lncRNAs among the studied plants. We found 233, 301, and 273 unique miRNAs, potentially targeting the lncRNAs of A. thaliana, O. sativa, and Z. mays, respectively. Our study has revealed that miRNAs, which interact with lncRNAs, target genes that are involved in a diverse array of biological and molecular processes. The miRNA-targeted lncRNAs displayed a strong affinity for several transcription factors, including ERF and BBR-BPC, mutually present in all three plants, advocating their conserved functions. Overall, the present study showed that plant lncRNAs exhibit conserved genomic and epigenomic characteristics and potentially govern the growth and development of plants.

Retracted: PLncWX: A Machine-Learning Algorithm for Plant lncRNA Identification Based on WOA-XGBoost

Retracted: PLncWX: A Machine-Learning Algorithm for Plant lncRNA Identification Based on WOA-XGBoost

ICAnnoLncRNA: A Snakemake Pipeline for a Long Non-Coding-RNA Search and Annotation in Transcriptomic Sequences

Long non-coding RNAs (lncRNAs) are RNA molecules longer than 200 nucleotides that do not encode proteins. Experimental studies have shown the diversity and importance of lncRNA functions in plants. To expand knowledge about lncRNAs in other species, computational pipelines that allow for standardised data-processing steps in a mode that does not require user control up until the final result were actively developed recently. These advancements enable wider functionality for lncRNA data identification and analysis. In the present work, we propose the ICAnnoLncRNA pipeline for the automatic identification, classification and annotation of plant lncRNAs in assembled transcriptomic sequences. It uses the LncFinder software for the identification of lncRNAs and allows the adjustment of recognition parameters using genomic data for which lncRNA annotation is available. The pipeline allows the prediction of lncRNA candidates, alignment of lncRNA sequences to the reference genome, filtering of erroneous/noise transcripts and probable transposable elements, lncRNA classification by genome location, comparison with sequences from external databases and analysis of lncRNA structural features and expression. We used transcriptomic sequences from 15 maize libraries assembled by Trinity and Hisat2/StringTie to demonstrate the application of the ICAnnoLncRNA pipeline.

Plant long noncoding RNAs: Recent progress in understanding their roles in growth, development, and stress responses

Long noncoding RNA (lncRNA) transcripts are longer than 200 nt and are not translated into proteins. LncRNAs function in a wide variety of processes in plants and animals, but, perhaps because of their lower expression and conservation levels, plant lncRNAs had attracted less attention than protein-coding mRNAs. Now, recent studies have made remarkable progress in identifying lncRNAs and understanding their functions. In this review, we discuss a number of lncRNAs that have important functions in growth, development, reproduction, responses to abiotic stresses, and regulation of disease and insect resistance in plants. Additionally, we describe the known mechanisms of action of plant lncRNAs according to their origins within the genome. This review thus provides a guide for identifying and functionally characterizing new lncRNAs in plants.

JustRNA: a database of plant long noncoding RNA expression profiles and functional network.

Long noncoding RNAs (lncRNAs) are regulatory RNAs involved in numerous biological processes. Many plant lncRNAs have been identified, but their regulatory mechanisms remain largely unknown. A resource that enables the investigation of lncRNA activity under various conditions is required because the co-expression between lncRNAs and protein-coding genes may reveal the effects of lncRNAs. This study developed JustRNA, an expression profiling resource for plant lncRNAs. The platform currently contains 1 088 565 lncRNA annotations for 80 plant species. In addition, it includes 3692 RNA-seq samples derived from 825 conditions in six model plants. Functional network reconstruction provides insight into the regulatory roles of lncRNAs. Genomic association analysis and microRNA target prediction can be employed to depict potential interactions with nearby genes and microRNAs, respectively. Subsequent co-expression analysis can be employed to strengthen confidence in the interactions among genes. Chromatin immunoprecipitation sequencing data of transcription factors and histone modifications were integrated into the JustRNA platform to identify the transcriptional regulation of lncRNAs in several plant species. The JustRNA platform provides researchers with valuable insight into the regulatory mechanisms of plant lncRNAs. JustRNA is a free platform that can be accessed at http://JustRNA.itps.ncku.edu.tw.

Roles of long non-coding RNAs in plant immunity.

Robust plant immune systems are fine-tuned by both protein-coding genes and non-coding RNAs. Long non-coding RNAs (lncRNAs) refer to RNAs with a length of more than 200 nt and usually do not have protein-coding function and do not belong to any other well-known non-coding RNA types. The non-protein-coding, low expression, and non-conservative characteristics of lncRNAs restrict their recognition. Although studies of lncRNAs in plants are in the early stage, emerging studies have shown that plants employ lncRNAs to regulate plant immunity. Moreover, in response to stresses, numerous lncRNAs are differentially expressed, which manifests the actions of low-expressed lncRNAs and makes plant-microbe/insect interactions a convenient system to study the functions of lncRNAs. Here, we summarize the current advances in plant lncRNAs, discuss their regulatory effects in different stages of plant immunity, and highlight their roles in diverse plant-microbe/insect interactions. These insights will not only strengthen our understanding of the roles and actions of lncRNAs in plant-microbe/insect interactions but also provide novel insight into plant immune responses and a basis for further research in this field.

ASLncR: a novel computational tool for prediction of abiotic stress-responsive long non-coding RNAs in plants.

Abiotic stresses are detrimental to plant growth and development and have a major negative impact on crop yields. A growing body of evidence indicates that a large number of long non-coding RNAs (lncRNAs) are key to many abiotic stress responses. Thus, identifying abiotic stress-responsive lncRNAs is essential in crop breeding programs in order to develop cropcultivars resistant to abiotic stresses. In this study, we have developed the first machine learning-based computational model for predicting abiotic stress-responsive lncRNAs. The lncRNA sequences which were responsive and non-responsive to abiotic stresses served as the two classes of the dataset for binary classification using the machine learning algorithms. The training dataset was created using 263 stress-responsive and 263 non-stress-responsive sequences, whereas the independent test set consists of 101 sequences from both classes. As the machine learning model can adopt only the numeric data, the Kmer features ranging from sizes 1 to 6 were utilized to represent lncRNAs in numeric form. To select important features, four different feature selection strategies were utilized. Among the seven learning algorithms, the support vector machine (SVM) achieved the highest cross-validation accuracy with the selected feature sets. The observed 5-fold cross-validation accuracy, AU-ROC, and AU-PRC were found to be 68.84, 72.78, and 75.86%, respectively. Furthermore, the robustness of the developed model (SVM with the selected feature) was evaluated using an independent test dataset, where the overall accuracy, AU-ROC, and AU-PRC were found to be 76.23, 87.71, and 88.49%, respectively. The developed computational approach was also implemented in an online prediction tool ASLncR accessible at https://iasri-sg.icar.gov.in/aslncr/ . The proposed computational model and the developed prediction tool are believed to supplement the existing effort for the identification of abiotic stress-responsive lncRNAs in plants.

Conjoint Analysis of Genome-Wide lncRNA and mRNA Expression during the Salicylic Acid Response in Populus × euramericana.

Long noncoding RNAs (lncRNAs) participate in a wide range of biological processes, but lncRNAs in plants remain largely unknown; in particular, we lack a systematic identification of plant lncRNAs involved in hormone responses. To explore the molecular mechanism of the response of poplar to salicylic acid (SA), the changes in protective enzymes, which are closely related to plant resistance induced by exogenous SA, were studied, and the expression of mRNA and lncRNA were determined by high-throughput RNA sequencing. The results showed that the activities of phenylalanine ammonia lyase (PAL) and polyphenol oxidase (PPO), in the leaves of Populus × euramericana, were significantly increased by exogenous SA application. High-throughput RNA sequencing showed that 26,366 genes and 5690 lncRNAs were detected under the different treatment conditions: SA and H2O application. Among these, 606 genes and 49 lncRNAs were differentially expressed. According to target prediction, lncRNAs and target genes involved in light response, stress response, plant disease resistance, and growth and development, were differentially expressed in SA-treated leaves. Interaction analysis showed that lncRNA-mRNA interactions, following exogenous SA, were involved in the response of poplar leaves to the external environment. Our study provides a comprehensive view of Populus × euramericana lncRNAs and offers insights into the potential functions and regulatory interactions of SA-responsive lncRNAs, thus forming the foundation for future functional analysis of SA-responsive lncRNAs in Populus × euramericana.

Identification of small open reading frames in plant lncRNA using class-imbalance learning

Recently, small open reading frames (sORFs) in long noncoding RNA (lncRNA) have been demonstrated to encode small peptides that can help study the mechanisms of growth and development in organisms. Since machine learning-based computational methods are less costly compared with biological experiments, they can be used to identify sORFs and provide a basis for biological experiments. However, few computational methods and data resources have been exploited for identifying sORFs in plant lncRNA. Besides, machine learning models produce underperforming classifiers when faced with a class-imbalance problem. In this study, an alternative method called SMOTE based on weighted cosine distance (WCDSMOTE) which enables interaction with feature selection is put forward to synthesize minority class samples and weighted edited nearest neighbor (WENN) is applied to clean up majority class samples, thus, hybrid sampling WCDSMOTE-ENN is proposed to deal with imbalanced datasets with the multi-angle feature. A heterogeneous classifier ensemble is introduced to complete the classification task. Therefore, a novel computational method that is based on class-imbalance learning to identify the sORFs with coding potential in plant lncRNA (sORFplnc) is presented. Experimental results manifest that sORFplnc outperforms existing computational methods in identifying sORFs with coding potential. We anticipate that the proposed work can be a reference for relevant research and contribute to agriculture and biomedicine.