CANTATAdb 2.0: Expanding the Collection of Plant Long Noncoding RNAs

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.

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.

Challenges in LncRNA Biology: Views and Opinions

This is a mini-review capturing the views and opinions of selected participants at the 2021 IEEE BIBM 3rd Annual LncRNA Workshop, held in Dubai, UAE. The views and opinions are expressed on five broad themes related to problems in lncRNA, namely, challenges in the computational analysis of lncRNAs, lncRNAs and cancer, lncRNAs in sports, lncRNAs and COVID-19, and lncRNAs in human brain activity.

MFPINC: prediction of plant ncRNAs based on multi-source feature fusion

Non-coding RNAs (ncRNAs) are recognized as pivotal players in the regulation of essential physiological processes such as nutrient homeostasis, development, and stress responses in plants. Common methods for predicting ncRNAs are susceptible to significant effects of experimental conditions and computational methods, resulting in the need for significant investment of time and resources. Therefore, we constructed an ncRNA predictor(MFPINC), to predict potential ncRNA in plants which is based on the PINC tool proposed by our previous studies. Specifically, sequence features were carefully refined using variance thresholding and F-test methods, while deep features were extracted and feature fusion were performed by applying the GRU model. The comprehensive evaluation of multiple standard datasets shows that MFPINC not only achieves more comprehensive and accurate identification of gene sequences, but also significantly improves the expressive and generalization performance of the model, and MFPINC significantly outperforms the existing competing methods in ncRNA identification. In addition, it is worth mentioning that our tool can also be found on Github ( https://github.com/Zhenj-Nie/MFPINC ) the data and source code can also be downloaded for free.

Pan-transcriptomic analysis reveals alternative splicing control of cold tolerance in rice

Plants have evolved complex mechanisms to adapt to harsh environmental conditions. Rice (Oryza sativa) is a staple food crop that is sensitive to low temperatures. However, its cold stress responses remain poorly understood, thus limiting possibilities for crop engineering to achieve greater cold tolerance. In this study, we constructed a rice pan-transcriptome and characterized its transcriptional regulatory landscape in response to cold stress. We performed Iso-Seq and RNA-Seq of 11 rice cultivars subjected to a time-course cold treatment. Our analyses revealed that alternative splicing-regulated gene expression plays a significant role in the cold stress response. Moreover, we identified CATALASE C (OsCATC) and Os03g0701200 as candidate genes for engineering enhanced cold tolerance. Importantly, we uncovered central roles for the 2 serine-arginine-rich proteins OsRS33 and OsRS2Z38 in cold tolerance. Our analysis of cold tolerance and resequencing data from a diverse collection of 165 rice cultivars suggested that OsRS2Z38 may be a key selection gene in japonica domestication for cold adaptation, associated with the adaptive evolution of rice. This study systematically investigated the distribution, dynamic changes, and regulatory mechanisms of alternative splicing in rice under cold stress. Overall, our work generates a rich resource with broad implications for understanding the genetic basis of cold response mechanisms in plants.

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.

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.

Population-level annotation of lncRNAs in Arabidopsis reveals extensive expression variation associated with transposable element-like silencing

Long noncoding RNAs (lncRNAs) are understudied and underannotated in plants. In mammals, lncRNA loci are nearly as ubiquitous as protein-coding genes, and their expression is highly variable between individuals of the same species. Using Arabidopsis thaliana as a model, we aimed to elucidate the true scope of lncRNA transcription across plants from different regions and study its natural variation. We used transcriptome deep sequencing data sets spanning hundreds of natural accessions and several developmental stages to create a population-wide annotation of lncRNAs, revealing thousands of previously unannotated lncRNA loci. While lncRNA transcription is ubiquitous in the genome, most loci appear to be actively silenced and their expression is extremely variable between natural accessions. This high expression variability is largely caused by the high variability of repressive chromatin levels at lncRNA loci. High variability was particularly common for intergenic lncRNAs (lincRNAs), where pieces of transposable elements (TEs) present in 50% of these lincRNA loci are associated with increased silencing and variation, and such lncRNAs tend to be targeted by the TE silencing machinery. We created a population-wide lncRNA annotation in Arabidopsis and improve our understanding of plant lncRNA genome biology, raising fundamental questions about what causes transcription and silencing across the genome.

Non-coding RNAs (ncRNAs) in plant: Master regulators for adapting to extreme temperature conditions

Unusual daily temperature fluctuations caused by climate change and climate variability adversely impact agricultural crop production. Since plants are immobile and constantly receive external environmental signals, such as extreme high (heat) and low (cold) temperatures, they have developed complex molecular regulatory mechanisms to cope with stressful situations to sustain their natural growth and development. Among these mechanisms, non-coding RNAs (ncRNAs), particularly microRNAs (miRNAs), small-interfering RNAs (siRNAs), and long-non-coding RNAs (lncRNAs), play a significant role in enhancing heat and cold stress tolerance. This review explores the pivotal findings related to miRNAs, siRNAs, and lncRNAs, elucidating how they functionally regulate plant adaptation to extreme temperatures. In addition, this review addresses the challenges associated with uncovering these non-coding RNAs and understanding their roles in orchestrating heat and cold tolerance in plants.

DNABERT-based explainable lncRNA identification in plant genome assemblies

Long non-coding ribonucleic acids (lncRNAs) have been shown to play an important role in plant gene regulation, involving both epigenetic and transcript regulation. LncRNAs are transcripts longer than 200 nucleotides that are not translated into functional proteins but can be translated into small peptides. Machine learning models have predominantly used transcriptome data with manually defined features to detect lncRNAs, however, they often underrepresent the abundance of lncRNAs and can be biased in their detection. Here we present a study using Natural Language Processing (NLP) models to identify plant lncRNAs from genomic sequences rather than transcriptomic data. The NLP models were trained to predict lncRNAs for seven model and crop species (Zea mays, Arabidopsis thaliana, Brassica napus, Brassica oleracea, Brassica rapa, Glycine max and Oryza sativa) using publicly available genomic references. We demonstrated that lncRNAs can be accurately predicted from genomic sequences with the highest accuracy of 83.4% for Z. mays and the lowest accuracy of 57.9% for B. rapa, revealing that genome assembly quality might affect the accuracy of lncRNA identification. Furthermore, we demonstrated the potential of using NLP models for cross-species prediction with an average of 63.1% accuracy using target species not previously seen by the model. As more species are incorporated into the training datasets, we expect the accuracy to increase, becoming a more reliable tool for uncovering novel lncRNAs. Finally, we show that the models can be interpreted using explainable artificial intelligence to identify motifs important to lncRNA prediction and that these motifs frequently flanked the lncRNA sequence.

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.

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.

A Survey of Computational Methods and Databases for lncRNA-MiRNA Interaction Prediction

Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are two prevalent non-coding RNAs in current research. They play critical regulatory roles in the life processes of animals and plants. Studies have shown that lncRNAs can interact with miRNAs to participate in post-transcriptional regulatory processes, mainly involved in regulating cancer development, metastatic progression, and drug resistance. Additionally, these interactions have significant effects on plant growth, development, and responses to biotic and abiotic stresses. Deciphering the potential relationships between lncRNAs and miRNAs may provide new insights into our understanding of the biological functions of lncRNAs and miRNAs, and the pathogenesis of complex diseases. In contrast, gathering information on lncRNA-miRNA interactions (LMIs) through biological experiments is expensive and time-consuming. With the accumulation of multi-omics data, computational models are extremely attractive in systematically exploring potential LMIs. To the best of our knowledge, this is the first comprehensive review of computational methods for identifying LMIs. Specifically, we first summarized the available public databases for predicting animal and plant LMIs. Second, we comprehensively reviewed the computational methods for predicting LMIs and classified them into two categories, including network-based methods and sequence-based methods. Third, we analyzed the standard evaluation methods and metrics used in LMI prediction. Finally, we pointed out some problems in the current study and discuss future research directions. Relevant databases and the latest advances in LMI prediction are summarized in a GitHub repository https://github.com/sheng-n/lncRNA-miRNA-interaction-methods, and we'll keep it updated.

Linking discoveries, mechanisms, and technologies to develop a clearer perspective on plant long noncoding RNAs

Long noncoding RNAs (lncRNAs) are a large and diverse class of genes in eukaryotic genomes that contribute to a variety of regulatory processes. Functionally characterized lncRNAs play critical roles in plants, ranging from regulating flowering to controlling lateral root formation. However, findings from the past decade have revealed that thousands of lncRNAs are present in plant transcriptomes, and characterization has lagged far behind identification. In this setting, distinguishing function from noise is challenging. However, the plant community has been at the forefront of discovery in lncRNA biology, providing many functional and mechanistic insights that have increased our understanding of this gene class. In this review, we examine the key discoveries and insights made in plant lncRNA biology over the past two and a half decades. We describe how discoveries made in the pregenomics era have informed efforts to identify and functionally characterize lncRNAs in the subsequent decades. We provide an overview of the functional archetypes into which characterized plant lncRNAs fit and speculate on new avenues of research that may uncover yet more archetypes. Finally, this review discusses the challenges facing the field and some exciting new molecular and computational approaches that may help inform lncRNA comparative and functional analyses.

Long non-coding RNA and microRNA landscape of two major domesticated cotton species

Allotetraploid cotton plants Gossypium hirsutum and Gossypium barbadense have been widely cultivated for their natural, renewable textile fibres. Even though ncRNAs in domesticated cotton species have been extensively studied, systematic identification and annotation of lncRNAs and miRNAs expressed in various tissues and developmental stages under various biological contexts are limited. This influences the comprehension of their functions and future research on these cotton species. Here, we report high confidence lncRNAs and miRNA collection from G. hirsutum accession and G. barbadense accession using large-scale RNA-seq and small RNA-seq datasets incorporated into a user-friendly database, CoNCRAtlas. This database provides a wide range and depth of lncRNA and miRNA annotation based on the systematic integration of extensive annotations such as expression patterns derived from transcriptome data analysis in thousands of samples, as well as multi-omics annotations. We assume this comprehensive resource will accelerate evolutionary and functional studies in ncRNAs and inform future breeding programs for cotton improvement. CoNCRAtlas is accessible at http://www.nipgr.ac.in/CoNCRAtlas/.

Mining of long non-coding RNAs with target genes in response to rust based on full-length transcriptome in Kentucky bluegrass

Kentucky bluegrass (Poa pratensis L.) is an eminent turfgrass species with a complex genome, but it is sensitive to rust (Puccinia striiformis). The molecular mechanisms of Kentucky bluegrass in response to rust still remain unclear. This study aimed to elucidate differentially expressed lncRNAs (DELs) and genes (DEGs) for rust resistance based on the full-length transcriptome. First, we used single-molecule real-time sequencing technology to generate the full-length transcriptome of Kentucky bluegrass. A total of 33,541 unigenes with an average read length of 2,233 bp were obtained, which contained 220 lncRNAs and 1,604 transcription factors. Then, the comparative transcriptome between the mock-inoculated leaves and rust-infected leaves was analyzed using the full-length transcriptome as a reference genome. A total of 105 DELs were identified in response to rust infection. A total of 15,711 DEGs were detected (8,278 upregulated genes, 7,433 downregulated genes) and were enriched in plant hormone signal transduction and plant-pathogen interaction pathways. Additionally, through co-location and expression analysis, it was found that lncRNA56517, lncRNA53468, and lncRNA40596 were highly expressed in infected plants and upregulated the expression of target genes AUX/IAA, RPM1, and RPS2, respectively; meanwhile, lncRNA25980 decreased the expression level of target gene EIN3 after infection. The results suggest that these DEGs and DELs are important candidates for potentially breeding the rust-resistant Kentucky bluegrass.

CircPCBL: Identification of Plant CircRNAs with a CNN-BiGRU-GLT Model

Circular RNAs (circRNAs), which are produced post-splicing of pre-mRNAs, are strongly linked to the emergence of several tumor types. The initial stage in conducting follow-up studies involves identifying circRNAs. Currently, animals are the primary target of most established circRNA recognition technologies. However, the sequence features of plant circRNAs differ from those of animal circRNAs, making it impossible to detect plant circRNAs. For example, there are non-GT/AG splicing signals at circRNA junction sites and few reverse complementary sequences and repetitive elements in the flanking intron sequences of plant circRNAs. In addition, there have been few studies on circRNAs in plants, and thus it is urgent to create a plant-specific method for identifying circRNAs. In this study, we propose CircPCBL, a deep-learning approach that only uses raw sequences to distinguish between circRNAs found in plants and other lncRNAs. CircPCBL comprises two separate detectors: a CNN-BiGRU detector and a GLT detector. The CNN-BiGRU detector takes in the one-hot encoding of the RNA sequence as the input, while the GLT detector uses k-mer (k = 1 - 4) features. The output matrices of the two submodels are then concatenated and ultimately pass through a fully connected layer to produce the final output. To verify the generalization performance of the model, we evaluated CircPCBL using several datasets, and the results revealed that it had an F1 of 85.40% on the validation dataset composed of six different plants species and 85.88%, 75.87%, and 86.83% on the three cross-species independent test sets composed of Cucumis sativus, Populus trichocarpa, and Gossypium raimondii, respectively. With an accuracy of 90.9% and 90%, respectively, CircPCBL successfully predicted ten of the eleven circRNAs of experimentally reported Poncirus trifoliata and nine of the ten lncRNAs of rice on the real set. CircPCBL could potentially contribute to the identification of circRNAs in plants. In addition, it is remarkable that CircPCBL also achieved an average accuracy of 94.08% on the human datasets, which is also an excellent result, implying its potential application in animal datasets. Ultimately, CircPCBL is available as a web server, from which the data and source code can also be downloaded free of charge.

Genome-wide identification and characterization of tissue-specific non-coding RNAs in black pepper (Piper nigrum L.)

Long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) are the two classes of non-coding RNAs (ncRNAs) present predominantly in plant cells and have various gene regulatory functions at pre- and post-transcriptional levels. Previously deemed as "junk", these ncRNAs have now been reported to be an important player in gene expression regulation, especially in stress conditions in many plant species. Black pepper, scientifically known as Piper nigrum L., despite being one of the most economically important spice crops, lacks studies related to these ncRNAs. From a panel of 53 RNA-Seq datasets of black pepper from six tissues, namely, flower, fruit, leaf, panicle, root, and stem of six black pepper cultivars, covering eight BioProjects across four countries, we identified and characterized a total of 6406 lncRNAs. Further downstream analysis inferred that these lncRNAs regulated 781 black pepper genes/gene products via miRNA-lncRNA-mRNA network interactions, thus working as competitive endogenous RNAs (ceRNAs). The interactions may be various mechanisms like miRNA-mediated gene silencing or lncRNAs acting as endogenous target mimics (eTMs) of the miRNAs. A total of 35 lncRNAs were also identified to be potential precursors of 94 miRNAs after being acted upon by endonucleases like Drosha and Dicer. Tissue-wise transcriptome analysis revealed 4621 circRNAs. Further, miRNA-circRNA-mRNA network analysis showed 432 circRNAs combining with 619 miRNAs and competing for the binding sites on 744 mRNAs in different black pepper tissues. These findings can help researchers to get a better insight to the yield regulation and responses to stress in black pepper in endeavor of higher production and improved breeding programs in black pepper varieties.

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.

Long Non-Coding RNAs of Plants in Response to Abiotic Stresses and Their Regulating Roles in Promoting Environmental Adaption

Abiotic stresses triggered by climate change and human activity cause substantial agricultural and environmental problems which hamper plant growth. Plants have evolved sophisticated mechanisms in response to abiotic stresses, such as stress perception, epigenetic modification, and regulation of transcription and translation. Over the past decade, a large body of literature has revealed the various regulatory roles of long non-coding RNAs (lncRNAs) in the plant response to abiotic stresses and their irreplaceable functions in environmental adaptation. LncRNAs are recognized as a class of ncRNAs that are longer than 200 nucleotides, influencing a variety of biological processes. In this review, we mainly focused on the recent progress of plant lncRNAs, outlining their features, evolution, and functions of plant lncRNAs in response to drought, low or high temperature, salt, and heavy metal stress. The approaches to characterize the function of lncRNAs and the mechanisms of how they regulate plant responses to abiotic stresses were further reviewed. Moreover, we discuss the accumulating discoveries regarding the biological functions of lncRNAs on plant stress memory as well. The present review provides updated information and directions for us to characterize the potential functions of lncRNAs in abiotic stresses in the future.

The RNAome landscape of tomato during arbuscular mycorrhizal symbiosis reveals an evolving RNA layer symbiotic regulatory network

Arbuscular mycorrhizal symbiosis (AMS) is an ancient plant-fungus relationship that is widely distributed in terrestrial plants. The formation of symbiotic structures and bidirectional nutrient exchange requires the regulation of numerous genes. However, the landscape of RNAome during plant AMS involving different types of regulatory RNA is poorly understood. In this study, a combinatorial strategy utilizing multiple sequencing approaches was used to decipher the landscape of RNAome in tomato, an emerging AMS model. The annotation of the tomato genome was improved by a multiple-platform sequencing strategy. A total of 3,174 protein-coding genes were upregulated during AMS, 42% of which were alternatively spliced. Comparative-transcriptome analysis revealed that genes from 24 orthogroups were consistently induced by AMS in eight phylogenetically distant angiosperms. Seven additional orthogroups were specifically induced by AMS in all surveyed dicot AMS host plants. However, these orthogroups were absent or not induced in monocots and/or non-AMS hosts, suggesting a continuously evolving AMS-responsive network in addition to a conserved core regulatory module. Additionally, we detected 587 lncRNAs, ten miRNAs, and 146 circRNAs that responded to AMS, which were incorporated to establish a tomato AMS-responsive, competing RNA-responsive endogenous RNA (ceRNA) network. Finally, a tomato symbiotic transcriptome database (TSTD, https://efg.nju.edu.cn/TSTD) was constructed to serve as a resource for deep deciphering of the AMS regulatory network. These results help elucidate the reconfiguration of the tomato RNAome during AMS and suggest a sophisticated and evolving RNA layer responsive network during AMS processes.

Peptidomics Methods Applied to the Study of Flower Development

Understanding the global and dynamic nature of plant developmental processes requires not only the study of the transcriptome, but also of the proteome, including its largely uncharacterized peptidome fraction. Recent advances in proteomics and high-throughput analyses of translating RNAs (ribosome profiling) have begun to address this issue, evidencing the existence of novel, uncharacterized, and possibly functional peptides. To validate the accumulation in tissues of sORF-encoded polypeptides (SEPs), the basic setup of proteomic analyses (i.e., LC-MS/MS) can be followed. However, the detection of peptides that are small (up to ~100 aa, 6-7 kDa) and novel (i.e., not annotated in reference databases) presents specific challenges that need to be addressed both experimentally and with computational biology resources. Several methods have been developed in recent years to isolate and identify peptides from plant tissues. In this chapter, we outline two different peptide extraction protocols and the subsequent peptide identification by mass spectrometry using the database search or the de novo identification methods.

A large-scale benchmark study of tools for the classification of protein-coding and non-coding RNAs

Identification of protein-coding and non-coding transcripts is paramount for understanding their biological roles. Computational approaches have been addressing this task for over a decade; however, generalized and high-performance models are still unreliable. This benchmark study assessed the performance of 24 tools producing >55 models on the datasets covering a wide range of species. We have collected 135 small and large transcriptomic datasets from existing studies for comparison and identified the potential bottlenecks hampering the performance of current tools. The key insights of this study include lack of standardized training sets, reliance on homogeneous training data, gradual changes in annotated data, lack of augmentation with homology searches, the presence of false positives and negatives in datasets and the lower performance of end-to-end deep learning models. We also derived a new dataset, RNAChallenge, from the benchmark considering hard instances that may include potential false alarms. The best and least well performing models under- and overfit the dataset, respectively, thereby serving a dual purpose. For computational approaches, it will be valuable to develop accurate and unbiased models. The identification of false alarms will be of interest for genome annotators, and experimental study of hard RNAs will help to untangle the complexity of the RNA world.

LncRNA NORAD accelerates the progression of non-small cell lung cancer via targeting miRNA-455/CDK14 axis

BACKGROUND

To explore the potential involvement of long non-coding RNA (lncRNA) NORAD in regulating the progression of Non-small cell lung cancer (NSCLC), and its possible mechanism.

METHODS

Relative level of NORAD in NSCLC tissues and cell lines was determined. Its level in NSCLC patients with different tumor staging (T1-T2, T3-T4) and either with lymphatic metastasis or not was examined as well. Kaplan-Meier curves were depicted for assessing the prognostic value of NORAD in NSCLC. Regulatory effects of NORAD on the proliferative ability of NCI-H1650 and HCC827 cells were evaluated. Dual-luciferase reporter gene assay was conducted to identify the binding between NORAD and miRNA-455, as well as between miRNA-455 and CDK14. At last, the role of NORAD/miRNA-455/CDK14 regulatory loop in influencing the progression of NSCLC was determined.

RESULTS

NORAD was upregulated in NSCLC tissues and cells. Its level was higher in NSCLC patients with advanced stage or accompanied with lymphatic metastasis. Worse prognosis was observed in NSCLC patients presenting high level of NORAD. Silence of NORAD attenuated the proliferative ability of NCI-H1650 and HCC827 cells. MiRNA-455 was the downstream target binding to NORAD. Its level was negatively regulated by NORAD. Knockdown of miRNA-455 could reverse the role of NORAD in regulating the proliferative ability of NSCLC. Moreover, CDK14 was the target gene of miRNA-455. CDK14 level was negatively regulated by miRNA-455.

CONCLUSIONS

LncRNA NORAD is upregulated in NSCLC, which enhances the proliferative ability of tumor cells by targeting miRNA-455/CDK14 axis and thereby accelerates the progression of NSCLC.

LncPheDB: a genome-wide lncRNAs regulated phenotypes database in plants

LncPheDB (https://www.lncphedb.com/) is a systematic resource of genome-wide long non-coding RNAs (lncRNAs)-phenotypes associations for multiple species. It was established to display the genome-wide lncRNA annotations, target genes prediction, variant-trait associations, gene-phenotype correlations, lncRNA-phenotype correlations, and the similar non-coding regions of the queried sequence in multiple species. LncPheDB sorted out a total of 203,391 lncRNA sequences, 2000 phenotypes, and 120,271 variants of nine species (Zea mays L., Gossypium barbadense L., Triticum aestivum L., Lycopersicon esculentum Mille, Oryza sativa L., Hordeum vulgare L., Sorghum bicolor L., Glycine max L., and Cucumis sativus L.). By exploring the relationship between lncRNAs and the genomic position of variants in genome-wide association analysis, a total of 68,862 lncRNAs were found to be related to the diversity of agronomic traits. More importantly, to facilitate the study of the functions of lncRNAs, we analyzed the possible target genes of lncRNAs, constructed a blast tool for performing similar fragmentation studies in all species, linked the pages of phenotypic studies related to lncRNAs that possess similar fragments and constructed their regulatory networks. In addition, LncPheDB also provides a user-friendly interface, a genome visualization platform, and multi-level and multi-modal convenient data search engine. We believe that LncPheDB plays a crucial role in mining lncRNA-related plant data.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42994-022-00084-3.

Transcriptome-guided annotation and functional classification of long non-coding RNAs in Arabidopsis thaliana

Long non-coding RNAs (lncRNAs) are a prominent class of eukaryotic regulatory genes. Despite the numerous available transcriptomic datasets, the annotation of plant lncRNAs remains based on dated annotations that have been historically carried over. We present a substantially improved annotation of Arabidopsis thaliana lncRNAs, generated by integrating 224 transcriptomes in multiple tissues, conditions, and developmental stages. We annotate 6764 lncRNA genes, including 3772 that are novel. We characterize their tissue expression patterns and find 1425 lncRNAs are co-expressed with coding genes, with enriched functional categories such as chloroplast organization, photosynthesis, RNA regulation, transcription, and root development. This improved transcription-guided annotation constitutes a valuable resource for studying lncRNAs and the biological processes they may regulate.

Biogenesis, Functions, Interactions, and Resources of Non-Coding RNAs in Plants

Plant transcriptomes encompass a large number of functional non-coding RNAs (ncRNAs), only some of which have protein-coding capacity. Since their initial discovery, ncRNAs have been classified into two broad categories based on their biogenesis and mechanisms of action, housekeeping ncRNAs and regulatory ncRNAs. With advances in RNA sequencing technology and computational methods, bioinformatics resources continue to emerge and update rapidly, including workflow for in silico ncRNA analysis, up-to-date platforms, databases, and tools dedicated to ncRNA identification and functional annotation. In this review, we aim to describe the biogenesis, biological functions, and interactions with DNA, RNA, protein, and microorganism of five major regulatory ncRNAs (miRNA, siRNA, tsRNA, circRNA, lncRNA) in plants. Then, we systematically summarize tools for analysis and prediction of plant ncRNAs, as well as databases. Furthermore, we discuss the silico analysis process of these ncRNAs and present a protocol for step-by-step computational analysis of ncRNAs. In general, this review will help researchers better understand the world of ncRNAs at multiple levels.

MILNP: Plant lncRNA-miRNA Interaction Prediction Based on Improved Linear Neighborhood Similarity and Label Propagation

Knowledge of the interactions between long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) is the basis of understanding various biological activities and designing new drugs. Previous computational methods for predicting lncRNA-miRNA interactions lacked for plants, and they suffer from various limitations that affect the prediction accuracy and their applicability. Research on plant lncRNA-miRNA interactions is still in its infancy. In this paper, we propose an accurate predictor, MILNP, for predicting plant lncRNA-miRNA interactions based on improved linear neighborhood similarity measurement and linear neighborhood propagation algorithm. Specifically, we propose a novel similarity measure based on linear neighborhood similarity from multiple similarity profiles of lncRNAs and miRNAs and derive more precise neighborhood ranges so as to escape the limits of the existing methods. We then simultaneously update the lncRNA-miRNA interactions predicted from both similarity matrices based on label propagation. We comprehensively evaluate MILNP on the latest plant lncRNA-miRNA interaction benchmark datasets. The results demonstrate the superior performance of MILNP than the most up-to-date methods. What's more, MILNP can be leveraged for isolated plant lncRNAs (or miRNAs). Case studies suggest that MILNP can identify novel plant lncRNA-miRNA interactions, which are confirmed by classical tools. The implementation is available on https://github.com/HerSwain/gra/tree/MILNP.

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

Long noncoding RNAs (lncRNAs) are a class of RNAs longer than 200 nt and cannot encode the protein. Studies have shown that lncRNAs can regulate gene expression at the epigenetic, transcriptional, and posttranscriptional levels, which are not only closely related to the occurrence, development, and prevention of human diseases, but also can regulate plant flowering and participate in plant abiotic stress responses such as drought and salt. Therefore, how to accurately and efficiently identify lncRNAs is still an essential job of relevant researches. There have been a large number of identification tools based on machine-learning and deep learning algorithms, mostly using human and mouse gene sequences as training sets, seldom plants, and only using one or one class of feature selection methods after feature extraction. We developed an identification model containing dicot, monocot, algae, moss, and fern. After comparing 20 feature selection methods (seven filter and thirteen wrapper methods) combined with seven classifiers, respectively, considering the correlation between features and model redundancy at the same time, we found that the WOA-XGBoost-based model had better performance with 91.55%, 96.78%, and 91.68% of accuracy, AUC, and F1_score. Meanwhile, the number of elements in the feature subset was reduced to 23, which effectively improved the prediction accuracy and modeling efficiency.

preMLI: a pre-trained method to uncover microRNA-lncRNA potential interactions

The interaction between microribonucleic acid and long non-coding ribonucleic acid plays a very important role in biological processes, and the prediction of the one is of great significance to the study of its mechanism of action. Due to the limitations of traditional biological experiment methods, more and more computational methods are applied to this field. However, the existing methods often have problems, such as inadequate acquisition of potential features of the sequence due to simple coding and the need to manually extract features as input. We propose a deep learning model, preMLI, based on rna2vec pre-training and deep feature mining mechanism. We use rna2vec to train the ribonucleic acid (RNA) dataset and to obtain the RNA word vector representation and then mine the RNA sequence features separately and finally concatenate the two feature vectors as the input of the prediction task. The preMLI performs better than existing methods on benchmark datasets and has cross-species prediction capabilities. Experiments show that both pre-training and deep feature mining mechanisms have a positive impact on the prediction performance of the model. To be more specific, pre-training can provide more accurate word vector representations. The deep feature mining mechanism also improves the prediction performance of the model. Meanwhile, The preMLI only needs RNA sequence as the input of the model and has better cross-species prediction performance than the most advanced prediction models, which have reference value for related research.

Data integration and evolutionary analysis of long non-coding RNAs in 25 flowering plants

Background

Long non-coding RNAs (lncRNAs) play vital roles in many important biological processes in plants. Currently, a large fraction of plant lncRNA studies center at lncRNA identification and functional analysis. Only a few plant lncRNA studies focus on understanding their evolutionary history, which is crucial for an in-depth understanding of lncRNAs. Therefore, the integration of large volumes of plant lncRNA data is required to deeply investigate the evolution of lncRNAs.

Results

We present a large-scale evolutionary analysis of lncRNAs in 25 flowering plants. In total, we identified 199,796 high-confidence lncRNAs through data integration analysis, and grouped them into 5497 lncRNA orthologous families. Then, we divided the lncRNAs into groups based on the degree of sequence conservation, and quantified the various characteristics of 756 conserved Arabidopsis thaliana lncRNAs. We found that compared with non-conserved lncRNAs, conserved lncRNAs might have more exons, longer sequence length, higher expression levels, and lower tissue specificities. Functional annotation based on the A. thaliana coding-lncRNA gene co-expression network suggested potential functions of conserved lncRNAs including autophagy, locomotion, and cell cycle. Enrichment analysis revealed that the functions of conserved lncRNAs were closely related to the growth and development of the tissues in which they were specifically expressed.

Conclusions

Comprehensive integration of large-scale lncRNA data and construction of a phylogenetic tree with orthologous lncRNA families from 25 flowering plants was used to provide an oversight of the evolutionary history of plant lncRNAs including origin, conservation, and orthologous relationships. Further analysis revealed a differential characteristic profile for conserved lncRNAs in A. thaliana when compared with non-conserved lncRNAs. We also examined tissue specific expression and the potential functional roles of conserved lncRNAs. The results presented here will further our understanding of plant lncRNA evolution, and provide the basis for further in-depth studies of their functions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08047-6.

A vast pool of lineage-specific microproteins encoded by long non-coding RNAs in plants

Pervasive transcription of eukaryotic genomes results in expression of long non-coding RNAs (lncRNAs) most of which are poorly conserved in evolution and appear to be non-functional. However, some lncRNAs have been shown to perform specific functions, in particular, transcription regulation. Thousands of small open reading frames (smORFs, <100 codons) located on lncRNAs potentially might be translated into peptides or microproteins. We report a comprehensive analysis of the conservation and evolutionary trajectories of lncRNAs-smORFs from the moss Physcomitrium patens across transcriptomes of 479 plant species. Although thousands of smORFs are subject to substantial purifying selection, the majority of the smORFs appear to be evolutionary young and could represent a major pool for functional innovation. Using nanopore RNA sequencing, we show that, on average, the transcriptional level of conserved smORFs is higher than that of non-conserved smORFs. Proteomic analysis confirmed translation of 82 novel species-specific smORFs. Numerous conserved smORFs containing low complexity regions (LCRs) or transmembrane domains were identified, the biological functions of a selected LCR-smORF were demonstrated experimentally. Thus, microproteins encoded by smORFs are a major, functionally diverse component of the plant proteome.

Global identification of long non-coding RNAs involved in the induction of spinach flowering

Background

Spinach is a beneficial annual vegetable species and sensitive to the bolting or early flowering, which causes a large reduction in quality and productivity. Indeed, bolting is an event induced by the coordinated effects of various environmental factors and endogenous genetic components. Although some key flowering responsive genes have been identified in spinach, non-coding RNA molecules like long non-coding RNAs (lncRNAs) were not investigated yet. Herein, we used bioinformatic approaches to analyze the transcriptome datasets from two different accessions Viroflay and Kashan at two vegetative and reproductive stages to reveal novel lncRNAs and the construction of the lncRNA-mRNA co-expression network. Additionally, correlations among gene expression modules and phenotypic traits were investigated; day to flowering was chosen as our interesting trait.

Results

In the present study, we identified a total of 1141 lncRNAs, of which 111 were differentially expressed between vegetative and reproductive stages. The GO and KEGG analyses carried out on the cis target gene of lncRNAs showed that the lncRNAs play an important role in the regulation of flowering spinach. Network analysis pinpointed several well-known flowering-related genes such as ELF, COL1, FLT, and FPF1 and also some putative TFs like MYB, WRKY, GATA, and MADS-box that are important regulators of flowering in spinach and could be potential targets for lncRNAs.

Conclusions

This study is the first report on identifying bolting and flowering-related lncRNAs based on transcriptome sequencing in spinach, which provides a useful resource for future functional genomics studies, genes expression researches, evaluating genes regulatory networks and molecular breeding programs in the regulation of the genetic mechanisms related to bolting in spinach.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07989-1.

Decoding LncRNAs

Non-coding RNAs (ncRNAs) have been considered as unimportant additions to the transcriptome. Yet, in light of numerous studies, it has become clear that ncRNAs play important roles in development, health and disease. Long-ignored, long non-coding RNAs (lncRNAs), ncRNAs made of more than 200 nucleotides have gained attention due to their involvement as drivers or suppressors of a myriad of tumours. The detailed understanding of some of their functions, structures and interactomes has been the result of interdisciplinary efforts, as in many cases, new methods need to be created or adapted to characterise these molecules. Unlike most reviews on lncRNAs, we summarize the achievements on lncRNA studies by taking into consideration the approaches for identification of lncRNA functions, interactomes, and structural arrangements. We also provide information about the recent data on the involvement of lncRNAs in diseases and present applications of these molecules, especially in medicine.

AlnC: An extensive database of long non-coding RNAs in angiosperms

Long non-coding RNAs (lncRNAs) are defined as transcripts of greater than 200 nucleotides that play a crucial role in various cellular processes such as the development, differentiation and gene regulation across all eukaryotes, including plant cells. Since the last decade, there has been a significant rise in our understanding of lncRNA molecular functions in plants, resulting in an exponential increase in lncRNA transcripts, while these went unannounced from the major Angiosperm plant species despite the availability of large-scale high throughput sequencing data in public repositories. We, therefore, developed a user-friendly, open-access web interface, AlnC (Angiosperm lncRNA Catalogue) for the exploration of lncRNAs in diverse Angiosperm plant species using recent 1000 plant (1KP) trancriptomes data. The current version of AlnC offers 10,855,598 annotated lncRNA transcripts across 682 Angiosperm plant species encompassing 809 tissues. To improve the user interface, we added features for browsing, searching, and downloading lncRNA data, interactive graphs, and an online BLAST service. Additionally, each lncRNA record is annotated with possible small open reading frames (sORFs) to facilitate the study of peptides encoded within lncRNAs. With this user-friendly interface, we anticipate that AlnC will provide a rich source of lncRNAs for small-and large-scale studies in a variety of flowering plants, as well as aid in the improvement of key characteristics in relevance to their economic importance. Database URL: http://www.nipgr.ac.in/AlnC

Comparative analysis of long noncoding RNAs in angiosperms and characterization of long noncoding RNAs in response to heat stress in Chinese cabbage

Long noncoding RNAs (lncRNAs) are widely present in different species and play critical roles in response to abiotic stresses. However, the functions of lncRNAs in Chinese cabbage under heat stress remain unknown. Here, we first conducted a global comparative analysis of 247,242 lncRNAs among 37 species. The results indicated that lncRNAs were poorly conserved among different species, and only 960 lncRNAs were homologous to 524 miRNA precursors. We then carried out lncRNA sequencing for a genome-wide analysis of lncRNAs and their target genes in Chinese cabbage at different stages of heat treatment. In total, 18,253 lncRNAs were identified, of which 1229 differentially expressed (DE) lncRNAs were characterized as being heat-responsive. The ceRNA network revealed that 38 lncRNAs, 16 miRNAs, and 167 mRNAs were involved in the heat response in Chinese cabbage. Combined analysis of the cis- and trans-regulated genes indicated that the targets of DE lncRNAs were significantly enriched in the "protein processing in endoplasmic reticulum" and "plant hormone signal transduction" pathways. Furthermore, the majority of HSP and PYL genes involved in these two pathways exhibited similar expression patterns and responded to heat stress rapidly. Based on the networks of DE lncRNA-mRNAs, 29 and 22 lncRNAs were found to interact with HSP and PYL genes, respectively. Finally, the expression of several critical lncRNAs and their targets was verified by qRT-PCR. Overall, we conducted a comparative analysis of lncRNAs among 37 species and performed a comprehensive analysis of lncRNAs in Chinese cabbage. Our findings expand the knowledge of lncRNAs involved in the heat stress response in Chinese cabbage, and the identified lncRNAs provide an abundance of resources for future comparative and functional studies.

Characterization of novel pollen-expressed transcripts reveals their potential roles in pollen heat stress response in Arabidopsis thaliana

Arabidopsis pollen transcriptome analysis revealed new intergenic transcripts of unknown function, many of which are long non-coding RNAs, that may function in pollen-specific processes, including the heat stress response. The male gametophyte is the most heat sensitive of all plant tissues. In recent years, long noncoding RNAs (lncRNAs) have emerged as important components of cellular regulatory networks involved in most biological processes, including response to stress. While examining RNAseq datasets of developing and germinating Arabidopsis thaliana pollen exposed to heat stress (HS), we identified 66 novel and 246 recently annotated intergenic expressed loci (XLOCs) of unknown function, with the majority encoding lncRNAs. Comparison with HS in cauline leaves and other RNAseq experiments indicated that 74% of the 312 XLOCs are pollen-specific, and at least 42% are HS-responsive. Phylogenetic analysis revealed that 96% of the genes evolved recently in Brassicaceae. We found that 50 genes are putative targets of microRNAs and that 30% of the XLOCs contain small open reading frames (ORFs) with homology to protein sequences. Finally, RNAseq of ribosome-protected RNA fragments together with predictions of periodic footprint of the ribosome P-sites indicated that 23 of these ORFs are likely to be translated. Our findings indicate that many of the 312 unknown genes might be functional and play a significant role in pollen biology, including the HS response.

LncMachine: a machine learning algorithm for long noncoding RNA annotation in plants

Following the elucidation of the critical roles they play in numerous important biological processes, long noncoding RNAs (lncRNAs) have gained vast attention in recent years. Manual annotation of lncRNAs is restricted by known gene annotations and is prone to false prediction due to the incompleteness of available data. However, with the advent of high-throughput sequencing technologies, a magnitude of high-quality data has become available for annotation, especially for plant species such as wheat. Here, we compared prediction accuracies of several machine learning algorithms using a 10-fold cross-validation. This study includes a comprehensive feature selection step to refine irrelevant and repeated features. We present a crop-specific, alignment-free coding potential prediction tool, LncMachine, that performs at higher prediction accuracies than the currently available popular tools (CPC2, CPAT, and CNIT) when used with the Random Forest algorithm. Further, LncMachine with Random Forest performed well on human and mouse data, with an average accuracy of 92.67%. LncMachine only requires either a FASTA file or a TAB separated CSV file containing features as input files. LncMachine can deploy several user-provided algorithms in real time and therefore be effortlessly applied to a wide range of studies.

PLncDB V2.0: a comprehensive encyclopedia of plant long noncoding RNAs

Long noncoding RNAs (lncRNAs) are transcripts longer than 200 nucleotides with little or no protein coding potential. The expanding list of lncRNAs and accumulating evidence of their functions in plants have necessitated the creation of a comprehensive database for lncRNA research. However, currently available plant lncRNA databases have some deficiencies, including the lack of lncRNA data from some model plants, uneven annotation standards, a lack of visualization for expression patterns, and the absence of epigenetic information. To overcome these problems, we upgraded our Plant Long noncoding RNA Database (PLncDB, http://plncdb.tobaccodb.org/), which was based on a uniform annotation pipeline. PLncDB V2.0 currently contains 1 246 372 lncRNAs for 80 plant species based on 13 834 RNA-Seq datasets, integrating lncRNA information from four other resources including EVLncRNAs, RNAcentral and etc. Expression patterns and epigenetic signals can be visualized using multiple tools (JBrowse, eFP Browser and EPexplorer). Targets and regulatory networks for lncRNAs are also provided for function exploration. In addition, PLncDB V2.0 is hierarchical and user-friendly and has five built-in search engines. We believe PLncDB V2.0 is useful for the plant lncRNA community and data mining studies and provides a comprehensive resource for data-driven lncRNA research in plants.

NONCODEV6: an updated database dedicated to long non-coding RNA annotation in both animals and plants

NONCODE (http://www.noncode.org/) is a comprehensive database of collection and annotation of noncoding RNAs, especially long non-coding RNAs (lncRNAs) in animals. NONCODEV6 is dedicated to providing the full scope of lncRNAs across plants and animals. The number of lncRNAs in NONCODEV6 has increased from 548 640 to 644 510 since the last update in 2017. The number of human lncRNAs has increased from 172 216 to 173 112. The number of mouse lncRNAs increased from 131 697 to 131 974. The number of plant lncRNAs is 94 697. The relationship between lncRNAs in human and cancer were updated with transcriptome sequencing profiles. Three important new features were also introduced in NONCODEV6: (i) updated human lncRNA-disease relationships, especially cancer; (ii) lncRNA annotations with tissue expression profiles and predicted function in five common plants; iii) lncRNAs conservation annotation at transcript level for 23 plant species. NONCODEV6 is accessible through http://www.noncode.org/.

Barley Anther and Meiocyte Transcriptome Dynamics in Meiotic Prophase I

In flowering plants, successful germinal cell development and meiotic recombination depend upon a combination of environmental and genetic factors. To gain insights into this specialized reproductive development program we used short- and long-read RNA-sequencing (RNA-seq) to study the temporal dynamics of transcript abundance in immuno-cytologically staged barley (Hordeum vulgare) anthers and meiocytes. We show that the most significant transcriptional changes in anthers occur at the transition from pre-meiosis to leptotene-zygotene, which is followed by increasingly stable transcript abundance throughout prophase I into metaphase I-tetrad. Our analysis reveals that the pre-meiotic anthers are enriched in long non-coding RNAs (lncRNAs) and that entry to meiosis is characterized by their robust and significant down regulation. Intriguingly, only 24% of a collection of putative meiotic gene orthologs showed differential transcript abundance in at least one stage or tissue comparison. Argonautes, E3 ubiquitin ligases, and lys48 specific de-ubiquitinating enzymes were enriched in prophase I meiocyte samples. These developmental, time-resolved transcriptomes demonstrate remarkable stability in transcript abundance in meiocytes throughout prophase I after the initial and substantial reprogramming at meiosis entry and the complexity of the regulatory networks involved in early meiotic processes.

Web tools to perform long non-coding RNAs analysis in oncology research

Accumulated evidence suggests that the widely expressed long-non-coding RNAs (lncRNAs) are involved in biogenesis. Some aberrant lncRNAs are closely related to pathological changes, for instance, in cancer. Both in tumorigenesis and cancer progression, depending on the interplay with cellular molecules, lncRNAs can modulate transcriptional interference, chromatin remodeling, post-translational regulation and protein modification, and further interfere with signaling pathways. Aiming to the diagnosis/ prognosis markers or potential therapeutical targets, it is important to figure out the specific mechanism and the tissue-specific expressing patterns of lncRNAs. Generally, the bioinformatics analysis is the first step. More and more in silico databases are increasing. But the existing integrative online platforms’ functions are not only having their unique features but also share some common features, which may lead to a waste of time for researchers. Here, we reviewed these web tools according to the functions. For each database, we clarified the data source, analysis method and the evidence that the analysis result is derived from. This review also illustrated examples in practical use for a specific lncRNA by these web tools. It will provide convenience for researchers to quickly choose the appropriate bioinformatics web tools in oncology studies.

The lncRNA Toolkit: Databases and In Silico Tools for lncRNA Analysis

Long non-coding RNAs (lncRNAs) are a rapidly expanding field of research, with many new transcripts identified each year. However, only a small subset of lncRNAs has been characterized functionally thus far. To aid investigating the mechanisms of action by which new lncRNAs act, bioinformatic tools and databases are invaluable. Here, we review a selection of computational tools and databases for the in silico analysis of lncRNAs, including tissue-specific expression, protein coding potential, subcellular localization, structural conformation, and interaction partners. The assembled lncRNA toolkit is aimed primarily at experimental researchers as a useful starting point to guide wet-lab experiments, mainly containing multi-functional, user-friendly interfaces. With more and more new lncRNA analysis tools available, it will be essential to provide continuous updates and maintain the availability of key software in the future.

Genome-wide analysis of long non-coding RNAs responsive to multiple nutrient stresses in Arabidopsis thaliana

Nutrient stress is the most important environmental stress that limits plant growth and development. Although recent evidence highlights the vital functions of long non-coding RNAs (lncRNA) in response to single nutrient stress in some model plants, a comprehensive investigation of the effect of lncRNAs in response to nutrient stress has not been performed in Arabidopsis thaliana. Here, we presented the identification and characterization of lncRNAs under seven nutrient stress conditions. The expression pattern analysis revealed that aberrant expression of lncRNAs is a stress-specific manner under nutrient stress conditions and that lncRNAs are more sensitive to nutrient stress than protein-coding genes (PCGs). Moreover, competing endogenous RNA (ceRNA) network and lncRNA-mRNA co-expression network (CEN) were constructed to explore the potential function of these lncRNAs under nutrient stress conditions. We further combined different expressed lncRNAs with ceRNA network and CEN to select key lncRNAs in response to nutrient stress. Together, our study provides important information for further insights into the role of lncRNAs in response to stress in plants.

Long non-coding RNAs: emerging players regulating plant abiotic stress response and adaptation

BACKGROUND

The immobile nature of plants means that they can be frequently confronted by various biotic and abiotic stresses during their lifecycle. Among the various abiotic stresses, water stress, temperature extremities, salinity, and heavy metal toxicity are the major abiotic stresses challenging overall plant growth. Plants have evolved complex molecular mechanisms to adapt under the given abiotic stresses. Long non-coding RNAs (lncRNAs)-a diverse class of RNAs that contain > 200 nucleotides(nt)-play an essential role in plant adaptation to various abiotic stresses.

RESULTS

LncRNAs play a significant role as 'biological regulators' for various developmental processes and biotic and abiotic stress responses in animals and plants at the transcription, post-transcription, and epigenetic level, targeting various stress-responsive mRNAs, regulatory gene(s) encoding transcription factors, and numerous microRNAs (miRNAs) that regulate the expression of different genes. However, the mechanistic role of lncRNAs at the molecular level, and possible target gene(s) contributing to plant abiotic stress response and adaptation, remain largely unknown. Here, we review various types of lncRNAs found in different plant species, with a focus on understanding the complex molecular mechanisms that contribute to abiotic stress tolerance in plants. We start by discussing the biogenesis, type and function, phylogenetic relationships, and sequence conservation of lncRNAs. Next, we review the role of lncRNAs controlling various abiotic stresses, including drought, heat, cold, heavy metal toxicity, and nutrient deficiency, with relevant examples from various plant species. Lastly, we briefly discuss the various lncRNA databases and the role of bioinformatics for predicting the structural and functional annotation of novel lncRNAs.

CONCLUSIONS

Understanding the intricate molecular mechanisms of stress-responsive lncRNAs is in its infancy. The availability of a comprehensive atlas of lncRNAs across whole genomes in crop plants, coupled with a comprehensive understanding of the complex molecular mechanisms that regulate various abiotic stress responses, will enable us to use lncRNAs as potential biomarkers for tailoring abiotic stress-tolerant plants in the future.

LuluDB-The Database Created Based on Small RNA, Transcriptome, and Degradome Sequencing Shows the Wide Landscape of Non-coding and Coding RNA in Yellow Lupine (Lupinus luteus L.) Flowers and Pods

Yellow lupine (Lupinus luteus L.) belongs to a legume family that benefits from symbiosis with nitrogen-fixing bacteria. Its seeds are rich in protein, which makes it a valuable food source for animals and humans. Yellow lupine is also the model plant for basic research on nodulation or abscission of organs. Nevertheless, the knowledge about the molecular regulatory mechanisms of its generative development is still incomplete. The RNA-Seq technique is becoming more prominent in high-throughput identification and expression profiling of both coding and non-coding RNA sequences. However, the huge amount of data generated with this method may discourage other scientific groups from making full use of them. To overcome this inconvenience, we have created a database containing analysis-ready information about non-coding and coding L. luteus RNA sequences (LuluDB). LuluDB was created on the basis of RNA-Seq analysis of small RNA, transcriptome, and degradome libraries obtained from yellow lupine cv. Taper flowers, pod walls, and seeds in various stages of development, flower pedicels, and pods undergoing abscission or maintained on the plant. It contains sequences of miRNAs and phased siRNAs identified in L. luteus, information about their expression in individual samples, and their target sequences. LuluDB also contains identified lncRNAs and protein-coding RNA sequences with their organ expression and annotations to widely used databases like GO, KEGG, NCBI, Rfam, Pfam, etc. The database also provides sequence homology search by BLAST using, e.g., an unknown sequence as a query. To present the full capabilities offered by our database, we performed a case study concerning transcripts annotated as DCL 1–4 (DICER LIKE 1–4) homologs involved in small non-coding RNA biogenesis and identified miRNAs that most likely regulate DCL1 and DCL2 expression in yellow lupine. LuluDB is available at http://luluseqdb.umk.pl/basic/web/index.php.

Long noncoding RNAs shape transcription in plants

The advent of novel high-throughput sequencing techniques has revealed that eukaryotic genomes are massively transcribed although only a small fraction of RNAs exhibits protein-coding capacity. In the last years, long noncoding RNAs (lncRNAs) have emerged as regulators of eukaryotic gene expression in a wide range of molecular mechanisms. Plant lncRNAs can be transcribed by alternative RNA polymerases, acting directly as long transcripts or can be processed into active small RNAs. Several lncRNAs have been recently shown to interact with chromatin, DNA or nuclear proteins to condition the epigenetic environment of target genes or modulate the activity of transcriptional complexes. In this review, we will summarize the recent discoveries about the actions of plant lncRNAs in the regulation of gene expression at the transcriptional level.