PLncPRO for prediction of long non-coding RNAs (lncRNAs) in plants and its application for discovery of abiotic stress-responsive lncRNAs in rice and chickpea

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.

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.

A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions

Plants intricately deploy defense systems to counter diverse biotic and abiotic stresses. Omics technologies, spanning genomics, transcriptomics, proteomics, and metabolomics, have revolutionized the exploration of plant defense mechanisms, unraveling molecular intricacies in response to various stressors. However, the complexity and scale of omics data necessitate sophisticated analytical tools for meaningful insights. This review delves into the application of artificial intelligence algorithms, particularly machine learning and deep learning, as promising approaches for deciphering complex omics data in plant defense research. The overview encompasses key omics techniques and addresses the challenges and limitations inherent in current AI-assisted omics approaches. Moreover, it contemplates potential future directions in this dynamic field. In summary, AI-assisted omics techniques present a robust toolkit, enabling a profound understanding of the molecular foundations of plant defense and paving the way for more effective crop protection strategies amidst climate change and emerging diseases.

The Emerging Role of Non-Coding RNAs (ncRNAs) in Plant Growth, Development, and Stress Response Signaling

Plant species utilize a variety of regulatory mechanisms to ensure sustainable productivity. Within this intricate framework, numerous non-coding RNAs (ncRNAs) play a crucial regulatory role in plant biology, surpassing the essential functions of RNA molecules as messengers, ribosomal, and transfer RNAs. ncRNAs represent an emerging class of regulators, operating directly in the form of small interfering RNAs (siRNAs), microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs). These ncRNAs exert control at various levels, including transcription, post-transcription, translation, and epigenetic. Furthermore, they interact with each other, contributing to a variety of biological processes and mechanisms associated with stress resilience. This review primarily concentrates on the recent advancements in plant ncRNAs, delineating their functions in growth and development across various organs such as root, leaf, seed/endosperm, and seed nutrient development. Additionally, this review broadens its scope by examining the role of ncRNAs in response to environmental stresses such as drought, salt, flood, heat, and cold in plants. This compilation offers updated information and insights to guide the characterization of the potential functions of ncRNAs in plant growth, development, and stress resilience in future research.

CcNFYB3-CcMATE35 and LncRNA CcLTCS-CcCS modules jointly regulate the efflux and synthesis of citrate to enhance aluminium tolerance in pigeon pea

Aluminium (Al) toxicity decreases crop production in acid soils in general, but many crops have evolved complex mechanisms to resist it. However, our current understanding of how plants cope with Al stress and perform Al resistance is still at the initial stage. In this study, the citrate transporter CcMATE35 was identified to be involved in Al stress response. The release of citrate was increased substantially in CcMATE35 over-expression (OE) lines under Al stress, indicating enhanced Al resistance. It was demonstrated that transcription factor CcNFYB3 regulated the expression of CcMATE35, promoting the release of citrate from roots to increase Al resistance in pigeon pea. We also found that a Long noncoding RNA Targeting Citrate Synthase (CcLTCS) is involved in Al resistance in pigeon pea. Compared with controls, overexpression of CcLTCS elevated the expression level of the Citrate Synthase gene (CcCS), leading to increases in root citrate level and citrate release, which forms another module to regulate Al resistance in pigeon pea. Simultaneous overexpression of CcNFYB3 and CcLTCS further increased Al resistance. Taken together, these findings suggest that the two modules, CcNFYB3-CcMATE35 and CcLTCS-CcCS, jointly regulate the efflux and synthesis of citrate and may play an important role in enhancing the resistance of pigeon pea under Al stress.

Long non-coding RNA-mediated epigenetic response for abiotic stress tolerance in plants

Plants perceive environmental fluctuations as stress and confront several stresses throughout their life cycle individually or in combination. Plants have evolved their sensing and signaling mechanisms to perceive and respond to a variety of stresses. Epigenetic regulation plays a critical role in the regulation of genes, spatiotemporal expression of genes under stress conditions and imparts a stress memory to encounter future stress responses. It is quintessential to integrate our understanding of genetics and epigenetics to maintain plant fitness, achieve desired genetic gains with no trade-offs, and durable long-term stress tolerance. The long non-coding RNA >200 nts having no coding potential (or very low) play several roles in epigenetic memory, contributing to the regulation of gene expression and the maintenance of cellular identity which include chromatin remodeling, imprinting (dosage compensation), stable silencing, facilitating nuclear organization, regulation of enhancer-promoter interactions, response to environmental signals and epigenetic switching. The lncRNAs are involved in a myriad of stress responses by activation or repression of target genes and hence are potential candidates for deploying in climate-resilient breeding programs. This review puts forward the significant roles of long non-coding RNA as an epigenetic response during abiotic stresses in plants and the prospects of deploying lncRNAs for designing climate-resilient plants.

Changes in epigenetic features in legumes under abiotic stresses

Legume crops are rich in nutritional value for human and livestock consumption. With global climate change, developing stress-resilient crops is crucial for ensuring global food security. Because of their nitrogen-fixing ability, legumes are also important for sustainable agriculture. Various abiotic stresses, such as salt, drought, and elevated temperatures, are known to adversely affect legume production. The responses of plants to abiotic stresses involve complicated cellular processes including stress hormone signaling, metabolic adjustments, and transcriptional regulations. Epigenetic mechanisms play a key role in regulating gene expressions at both transcriptional and posttranscriptional levels. Increasing evidence suggests the importance of epigenetic regulations of abiotic stress responses in legumes, and recent investigations have extended the scope to the epigenomic level using next-generation sequencing technologies. In this review, the current knowledge on the involvement of epigenetic features, including DNA methylation, histone modification, and noncoding RNAs, in abiotic stress responses in legumes is summarized and discussed. Since most of the available information focuses on a single aspect of these epigenetic features, integrative analyses involving omics data in multiple layers are needed for a better understanding of the dynamic chromatin statuses and their roles in transcriptional regulation. The inheritability of epigenetic modifications should also be assessed in future studies for their applications in improving stress tolerance in legumes through the stable epigenetic optimization of gene expressions.

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.

Evaluation of efficacy of non-coding RNA in abiotic stress management of field crops: Current status and future prospective

Abiotic stresses are responsible for the major losses in crop yield all over the world. Stresses generate harmful ROS which can impair cellular processes in plants. Therefore, plants have evolved antioxidant systems in defence against the stress-induced damages. The frequency of occurrence of abiotic stressors has increased several-fold due to the climate change experienced in recent times and projected for the future. This had particularly aggravated the risk of yield losses and threatened global food security. Non-coding RNAs are the part of eukaryotic genome that does not code for any proteins. However, they have been recently found to have a crucial role in the responses of plants to both abiotic and biotic stresses. There are different types of ncRNAs, for example, miRNAs and lncRNAs, which have the potential to regulate the expression of stress-related genes at the levels of transcription, post-transcription, and translation of proteins. The lncRNAs are also able to impart their epigenetic effects on the target genes through the alteration of the status of histone modification and organization of the chromatins. The current review attempts to deliver a comprehensive account of the role of ncRNAs in the regulation of plants' abiotic stress responses through ROS homeostasis. The potential applications ncRNAs in amelioration of abiotic stresses in field crops also have been evaluated.

Phyto-microbiome to mitigate abiotic stress in crop plants

Plant-associated microbes include taxonomically diverse communities of bacteria, archaebacteria, fungi, and viruses, which establish integral ecological relationships with the host plant and constitute the phyto-microbiome. The phyto-microbiome not only contributes in normal growth and development of plants but also plays a vital role in the maintenance of plant homeostasis during abiotic stress conditions. Owing to its immense metabolic potential, the phyto-microbiome provides the host plant with the capability to mitigate the abiotic stress through various mechanisms like production of antioxidants, plant growth hormones, bioactive compounds, detoxification of harmful chemicals and toxins, sequestration of reactive oxygen species and other free radicals. A deeper understanding of the structure and functions of the phyto-microbiome and the complex mechanisms of phyto-microbiome mediated abiotic stress mitigation would enable its utilization for abiotic stress alleviation of crop plants and development of stress-resistant crops. This review aims at exploring the potential of phyto-microbiome to alleviate drought, heat, salinity and heavy metal stress in crop plants and finding sustainable solutions to enhance the agricultural productivity. The mechanistic insights into the role of phytomicrobiome in imparting abiotic stress tolerance to plants have been summarized, that would be helpful in the development of novel bioinoculants. The high-throughput modern approaches involving candidate gene identification and target gene modification such as genomics, metagenomics, transcriptomics, metabolomics, and phyto-microbiome based genetic engineering have been discussed in wake of the ever-increasing demand of climate resilient crop plants.

Pokkali: A Naturally Evolved Salt-Tolerant Rice Shows a Distinguished Set of lncRNAs Possibly Contributing to the Tolerant Phenotype

Pokkali is a strong representation of how stress-tolerant genotypes have evolved due to natural selection pressure. Numerous omics-based investigations have indicated different categories of stress-related genes and proteins, possibly contributing to salinity tolerance in this wild rice. However, a comprehensive study towards understanding the role of long-noncoding RNAs (lncRNAs) in the salinity response of Pokkali has not been done to date. We have identified salt-responsive lncRNAs from contrasting rice genotypes IR64 and Pokkali. A total of 63 and 81 salinity-responsive lncRNAs were differentially expressed in IR64 and Pokkali, respectively. Molecular characterization of lncRNAs and lncRNA-miRNA-mRNA interaction networks helps to explore the role of lncRNAs in the stress response. Functional annotation revealed that identified lncRNAs modulate various cellular processes, including transcriptional regulation, ion homeostasis, and secondary metabolite production. Additionally, lncRNAs were predicted to bind stress-responsive transcription factors, namely ERF, DOF, and WRKY. In addition to salinity, expression profiling was also performed under other abiotic stresses and phytohormone treatments. A positive modulation in TCONS_00035411, TCONS_00059828, and TCONS_00096512 under both abiotic stress and phytohormone treatments could be considered as being of potential interest for the further functional characterization of IncRNA. Thus, extensive analysis of lncRNAs under various treatments helps to delineate stress tolerance mechanisms and possible cross-talk.

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/.

Genome-wide analysis of long non-coding RNAs in sugar beet (Beta vulgaris L.) under drought stress

Drought stress is one of the most severe abiotic stresses that restrict global crop production. Long non-coding RNAs (lncRNAs) have been proved to play a key role in response to drought stress. However, genome-wide identification and characterization of drought-responsive lncRNAs in sugar beet is still lacking. Thus, the present study focused on analyzing lncRNAs in sugar beet under drought stress. We identified 32017 reliable lncRNAs in sugar beet by strand-specific high-throughput sequencing. A total of 386 differentially expressed lncRNAs (DElncRNAs) were found under drought stress. The most significantly upregulated and downregulated lncRNAs were TCONS_00055787 (upregulated by more than 6000 fold) and TCONS_00038334 (downregulated by more than 18000 fold), respectively. Quantitative real-time PCR results exhibited a high concordance with RNA sequencing data, which conformed that the expression patterns of lncRNAs based on RNA sequencing were highly reliable. In addition, we predicted 2353 and 9041 transcripts that were estimated to be the cis- and trans-target genes of the drought-responsive lncRNAs. As revealed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, the target genes of DElncRNAs were significantly enriched in organelle subcompartment, thylakoid, endopeptidase activity, catalytic activity, developmental process, lipid metabolic process, RNA polymerase activity, transferase activity, flavonoid biosynthesis and several other terms associated with abiotic stress tolerance. Moreover, 42 DElncRNAs were predicted as potential miRNA target mimics. LncRNAs have important effects on plant adaptation to drought conditions through the interaction with protein-encoding genes. The present study leads to greater insights into lncRNA biology and offers candidate regulators for improving the drought tolerance of sugar beet cultivars at the genetic level.

Genome-wide screening and characterization of long noncoding RNAs involved in flowering/bolting of Lactuca sativa

BACKGROUND

Lettuce (Lactuca sativa L.) is considered the most important vegetable in the leafy vegetable group. However, bolting affects quality, gives it a bitter taste, and as a result makes it inedible. Bolting is an event induced by the coordinated effects of various environmental factors and endogenous genetic components. Although bolting/flowering responsive genes have been identified in most sensitive and non-sensitive species, non-coding RNA molecules like long non-coding RNAs (lncRNAs) have not been investigated in lettuce. Hence, in this study, potential long non-coding RNAs that regulate flowering /bolting were investigated in two lettuce strains S24 (resistant strain) and S39 (susceptible strain) in different flowering times to better understand the regulation of lettuce bolting mechanism. For this purpose, we used two RNA-seq datasets to discover the lncRNA transcriptome profile during the transition from vegetative to reproductive phase.

RESULTS

For identifying unannotated transcripts in these datasets, a 7-step pipeline was employed to filter out these transcripts and terminate with 293 novel lncRNAs predicted by PLncPRO and CREMA. These transcripts were then utilized to predict cis and trans flowering-associated targets and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Computational predictions of target gene function showed the involvement of putative flowering-related genes and enrichment of the floral regulators FLC, CO, FT, and SOC1 in both datasets. Finally, 17 and 18 lncRNAs were proposed as competing endogenous target mimics (eTMs) for novel and known lncRNA miRNAs, respectively.

CONCLUSION

Overall, this study provides new insights into lncRNAs that control the flowering time of plants known for bolting, such as lettuce, and opens new windows for further study.

CicerSpTEdb2.0: An Upgrade of Cicer Species Transposable Elements Database

To meet the critical demand of LTR-RTs data-driven research, we updated the CicerSpTEdb database to version 2.0, which includes more accurate intact LTR-RT elements with annotation of internal domains. We also added the ability to BLAST against TEs of Cicer species. As a result, 3701 intact LTR-RTs were detected in the studied genomes, including 2840 Copia and 861 Gypsy elements. Of the 3701 intact LTR-RTs, 588 were in C. arietinum, including 475 Copia and 113 Gypsy. While 1373 were detected in C. reticulatum, including 1041 Copia and 332 Gypsy. Furthermore, 1740 were found in C. echinospermum, including 1324 Copia and 416 Gypsy. Based on LTR-RT clades, the analysis classified the 3701 identified intact LTR-RTs in the studied genomes as Ale (850), SIRE (740), unknown (455), Ikeros (323), Reina (290), Tork (290), Ivana (282), Tekay (197), Athila (128), TAR (99), CRM (31), and Ogre (16) elements. The newly updated CicerSpTEdb2.0 will be a valuable resource for TEs of Cicer species and their comparative genomics.Database URL: http://cicersptedb.easyomics.org/index.php.

Role of long non coding RNA in plants under abiotic and biotic stresses

Evolutionary processes have evolved plants to cope with several different natural stresses. Basic physiological activities of crop plants are significantly harmed by these stresses, reducing productivity and eventually leading to death. The recent advancements in high-throughput sequencing of transcriptome and expression profiling with NGS techniques lead to the innovation of various RNAs which do not code for proteins, more specifically long non-coding RNAs (lncRNAs), undergirding regulate growth, development, and the plant defence mechanism transcriptionally under stress situations. LncRNAs are a diverse set of RNAs that play key roles in various biological processes at the level of transcription, post-transcription, and epigenetics. These are thought to serve crucial functions in plant immunity and response to changes in the environment. In plants, however, just a few lncRNAs have been functionally identified. In this review, we will address recent advancements in comprehending lncRNA regulatory functions, focusing on the expanding involvement of lncRNAs in modulating environmental stress responsiveness in plants.

Long non-coding RNAs as the regulatory hubs in rice response to salt stress

Salinity seriously constrains growth and fertility of rice worldwide. Long non-coding RNAs (lncRNAs) play crucial roles in plant abiotic stress response. However, salt responsive lncRNAs are poorly understood in rice. Herein, salt responsive lncRNAs (DE-lncRNAs) were identified in FL478 (salt tolerant) compared to its susceptible parent (IR29) using RNA-seq in root tissues at seedling stage. In FL478 and IR29, 8724 and 9235 transcripts with length of > 200 bp were nominated as potential lncRNAs, respectively. Rigorous filtering left four (in FL478) and nine (in IR29) DE-lncRNAs with only 2 DE-lncRNAs in common. ATAC-seq data showed that the genomic regions of all four lncRNAs in FL478 and 6/9 in IR29 are significantly accessible for transcription. Weighted correlation network analysis (WGCNA) revealed that lncRNA.2-FL was highly correlated with 173 mRNAs as trans-targets and a gene encoding pentatricopeptide repeat (PPR) protein was predicted as cis-target of lncRNA.2-FL. In silico mutagenesis analysis proposed the same transcription factor binding sites (TFBSs) in vicinity of the trans- and cis-regulatory target genes of lncRNA.2-FL, which significantly affect their transcription start site (TSS). This study provides new insights into involvement of the DE-lncRNAs in rice response to salt stress. Among them, lncRNA.2-FL may play a significant regulatory role in the salt stress tolerance of FL478.

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.

From single- to multi-omics: future research trends in medicinal plants

Medicinal plants are the main source of natural metabolites with specialised pharmacological activities and have been widely examined by plant researchers. Numerous omics studies of medicinal plants have been performed to identify molecular markers of species and functional genes controlling key biological traits, as well as to understand biosynthetic pathways of bioactive metabolites and the regulatory mechanisms of environmental responses. Omics technologies have been widely applied to medicinal plants, including as taxonomics, transcriptomics, metabolomics, proteomics, genomics, pangenomics, epigenomics and mutagenomics. However, because of the complex biological regulation network, single omics usually fail to explain the specific biological phenomena. In recent years, reports of integrated multi-omics studies of medicinal plants have increased. Until now, there have few assessments of recent developments and upcoming trends in omics studies of medicinal plants. We highlight recent developments in omics research of medicinal plants, summarise the typical bioinformatics resources available for analysing omics datasets, and discuss related future directions and challenges. This information facilitates further studies of medicinal plants, refinement of current approaches and leads to new ideas.

An integrated transcriptome mapping the regulatory network of coding and long non-coding RNAs provides a genomics resource in chickpea

Large-scale transcriptome analysis can provide a systems-level understanding of biological processes. To accelerate functional genomic studies in chickpea, we perform a comprehensive transcriptome analysis to generate full-length transcriptome and expression atlas of protein-coding genes (PCGs) and long non-coding RNAs (lncRNAs) from 32 different tissues/organs via deep sequencing. The high-depth RNA-seq dataset reveal expression dynamics and tissue-specificity along with associated biological functions of PCGs and lncRNAs during development. The coexpression network analysis reveal modules associated with a particular tissue or a set of related tissues. The components of transcriptional regulatory networks (TRNs), including transcription factors, their cognate cis-regulatory motifs, and target PCGs/lncRNAs that determine developmental programs of different tissues/organs, are identified. Several candidate tissue-specific and abiotic stress-responsive transcripts associated with quantitative trait loci that determine important agronomic traits are also identified. These results provide an important resource to advance functional/translational genomic and genetic studies during chickpea development and environmental conditions.

A full-length transcriptome and expression atlas of protein-coding genes and long non-coding RNAs is generated in chickpea. Components of transcriptional regulatory networks and candidate tissue-specific transcripts associated with quantitative trait loci are identified.

Sequence features around cleavage sites are highly conserved among different species and a critical determinant for RNA cleavage position across eukaryotes

RNA degradation is one of the critical steps for control of gene expression, and endonucleolytic cleavage-dependent RNA degradation is conserved among eukaryotes. Some cleavage sites are secondarily capped in the cytoplasm and identified using the Cap analysis of gene expression (CAGE) method. Although uncapped cleavage sites are widespread in eukaryotes, comparatively little information has been obtained about these sites using CAGE-based degradome analysis. Previously, we developed the truncated RNA-end sequencing (TREseq) method in plant species and used it to acquire comprehensive information about uncapped cleavage sites; we observed G-rich sequences near cleavage sites. However, it remains unclear whether this finding is general to other eukaryotes. In this study, we conducted TREseq analyses in fruit flies (Drosophila melanogaster) and budding yeast (Saccharomyces cerevisiae). The results revealed specific sequence features related to RNA cleavage in D. melanogaster and S. cerevisiae that were similar to sequence patterns in Arabidopsis thaliana. Although previous studies suggest that ribosome movements are important for determining cleavage position, feature selection using a random forest classifier showed that sequences around cleavage sites were major determinant for cleaved or uncleaved sites. Together, our results suggest that sequence features around cleavage sites are critical for determining cleavage position, and that sequence-specific endonucleolytic cleavage-dependent RNA degradation is highly conserved across eukaryotes.

Genome-wide analysis suggests the potential role of lncRNAs during seed development and seed size/weight determination in chickpea

The integrated transcriptome data analyses suggested the plausible roles of lncRNAs during seed development in chickpea. The candidate lncRNAs associated with QTLs and those involved in miRNA-mediated seed size/weight determination in chickpea have been identified. Long non-coding RNAs (lncRNAs) are important regulators of various biological processes. Here, we identified lncRNAs at seven successive stages of seed development in small-seeded and large-seeded chickpea cultivars. In total, 4751 lncRNAs implicated in diverse biological processes were identified. Most of lncRNAs were conserved between the two cultivars, whereas only a few of them were conserved in other plants, suggesting their species-specificity. A large number of lncRNAs differentially expressed between the two chickpea cultivars associated with seed development-related processes were identified. The lncRNAs acting as precursors of miRNAs and those mimicking target protein-coding genes of miRNAs involved in seed size/weight determination, including HAIKU1, BIG SEEDS1, and SHB1, were also revealed. Further, lncRNAs located within seed size/weight associated quantitative trait loci were also detected. Overall, we present a comprehensive resource and identified candidate lncRNAs that may play important roles during seed development and seed size/weight determination in chickpea.

Exploring Chickpea Germplasm Diversity for Broadening the Genetic Base Utilizing Genomic Resourses

Legume crops provide significant nutrition to humans as a source of protein, omega-3 fatty acids as well as specific macro and micronutrients. Additionally, legumes improve the cropping environment by replenishing the soil nitrogen content. Chickpeas are the second most significant staple legume food crop worldwide behind dry bean which contains 17%–24% protein, 41%–51% carbohydrate, and other important essential minerals, vitamins, dietary fiber, folate, β-carotene, anti-oxidants, micronutrients (phosphorus, calcium, magnesium, iron, and zinc) as well as linoleic and oleic unsaturated fatty acids. Despite these advantages, legumes are far behind cereals in terms of genetic improvement mainly due to far less effort, the bottlenecks of the narrow genetic base, and several biotic and abiotic factors in the scenario of changing climatic conditions. Measures are now called for beyond conventional breeding practices to strategically broadening of narrow genetic base utilizing chickpea wild relatives and improvement of cultivars through advanced breeding approaches with a focus on high yield productivity, biotic and abiotic stresses including climate resilience, and enhanced nutritional values. Desirable donors having such multiple traits have been identified using core and mini core collections from the cultivated gene pool and wild relatives of Chickpea. Several methods have been developed to address cross-species fertilization obstacles and to aid in inter-specific hybridization and introgression of the target gene sequences from wild Cicer species. Additionally, recent advances in “Omics” sciences along with high-throughput and precise phenotyping tools have made it easier to identify genes that regulate traits of interest. Next-generation sequencing technologies, whole-genome sequencing, transcriptomics, and differential genes expression profiling along with a plethora of novel techniques like single nucleotide polymorphism exploiting high-density genotyping by sequencing assays, simple sequence repeat markers, diversity array technology platform, and whole-genome re-sequencing technique led to the identification and development of QTLs and high-density trait mapping of the global chickpea germplasm. These altogether have helped in broadening the narrow genetic base of chickpeas.

DeepPlnc: Bi-modal deep learning for highly accurate plant lncRNA discovery

We present here a bi-modal CNN based deep-learning system, DeepPlnc, to identify plant lncRNAs with high accuracy while using sequence and structural properties. Unlike most of the existing software, it works accurately even in conditions with ambiguity of boundaries and incomplete sequences. It scored consistently high for performance metrics while breaching accuracy of >98% when tested across a large number of validated instances. During multiple benchmarkings it consistently outperformed all the compared tools and maintained a highly significant lead in the range of 2.5%- 4.6% from the second best performing tool (p-value << 0.01). DeepPlnc was used to annotate a de novo assembled transcriptome of a himalayan species where again it suggested its much better suitability for genome and transcriptome annotation purposes than the existing tools. DeepPlnc has been made freely available as a web-server and stand-alone program at https://scbb.ihbt.res.in/DeepPlnc/.

Understanding of the various aspects of gene regulatory networks related to crop improvement

The hierarchical relationship between transcription factors, associated proteins, and their target genes is defined by a gene regulatory network (GRN). GRNs allow us to understand how the genotype and environment of a plant are incorporated to control the downstream physiological responses. During plant growth or environmental acclimatization, GRNs are diverse and can be differently regulated across tissue types and organs. An overview of recent advances in the development of GRN that speed up basic and applied plant research is given here. Furthermore, the overview of genome and transcriptome involving GRN research along with the exciting advancement and application are discussed. In addition, different approaches to GRN predictions were elucidated. In this review, we also describe the role of GRN in crop improvement, crop plant manipulation, stress responses, speed breeding and identifying genetic variations/locus. Finally, the challenges and prospects of GRN in plant biology are discussed.

Multi-feature Fusion Method Based on Linear Neighborhood Propagation Predict Plant LncRNA-Protein Interactions

Long non-coding RNAs (lncRNAs) have attracted extensive attention due to their important roles in various biological processes, among which lncRNA-protein interaction plays an important regulatory role in plant immunity and life activities. Laboratory methods are time consuming and labor-intensive, so that many computational methods have gradually emerged as auxiliary tools to assist relevant research. However, there are relatively few methods to predict lncRNA-protein interaction of plant. Due to the lack of experimentally verified interactions data, there is an imbalance between known and unknown interaction samples in plant data sets. In this study, a multi-feature fusion method based on linear neighborhood propagation is developed to predict plant unobserved lncRNA-protein interaction pairs through known interaction pairs, called MPLPLNP. The linear neighborhood similarity of the feature space is calculated and the results are predicted by label propagation. Meanwhile, multiple feature training is integrated to better explore the potential interaction information in the data. The experimental results show that the proposed multi-feature fusion method can improve the performance of the model, and is superior to other state-of-the-art approaches. Moreover, the proposed approach has better performance and generalization ability on various plant datasets, which is expected to facilitate the related research of plant molecular biology.

The Rice ILI2 Locus Is a Bidirectional Target of the African Xanthomonas oryzae pv. oryzae Major Transcription Activator-like Effector TalC but Does Not Contribute to Disease Susceptibility

Xanthomonas oryzae pv. oryzae (Xoo) strains that cause bacterial leaf blight (BLB) limit rice (Oryza sativa) production and require breeding more resistant varieties. Transcription activator-like effectors (TALEs) activate transcription to promote leaf colonization by binding to specific plant host DNA sequences termed effector binding elements (EBEs). Xoo major TALEs universally target susceptibility genes of the SWEET transporter family. TALE-unresponsive alleles of clade III OsSWEET susceptibility gene promoter created with genome editing confer broad resistance on Asian Xoo strains. African Xoo strains rely primarily on the major TALE TalC, which targets OsSWEET14. Although the virulence of a talC mutant strain is severely impaired, abrogating OsSWEET14 induction with genome editing does not confer equivalent resistance on African Xoo. To address this contradiction, we postulated the existence of a TalC target susceptibility gene redundant with OsSWEET14. Bioinformatics analysis identified a rice locus named ATAC composed of the INCREASED LEAF INCLINATION 2 (ILI2) gene and a putative lncRNA that are shown to be bidirectionally upregulated in a TalC-dependent fashion. Gain-of-function approaches with designer TALEs inducing ATAC sequences did not complement the virulence of a Xoo strain defective for SWEET gene activation. While editing the TalC EBE at the ATAC loci compromised TalC-mediated induction, multiplex edited lines with mutations at the OsSWEET14 and ATAC loci remained essentially susceptible to African Xoo strains. Overall, this work indicates that ATAC is a probable TalC off-target locus but nonetheless documents the first example of divergent transcription activation by a native TALE during infection.

Role of lncRNAs in cis- and trans-regulatory responses to salt in Populus trichocarpa

Long non-coding RNAs (lncRNAs) are emerging as versatile regulators in diverse biological processes. However, little is known about their cis- and trans-regulatory contributions in gene expression under salt stress. Using 27 RNA-seq data sets from Populus trichocarpa leaves, stems and roots, we identified 2988 high-confidence lncRNAs, including 1183 salt-induced differentially expressed lncRNAs. Among them, 301 lncRNAs have potential for positively affecting their neighboring genes, predominantly in a cis-regulatory manner rather than by co-transcription. Additionally, a co-expression network identified six striking salt-associated modules with a total of 5639 genes, including 426 lncRNAs, and in these lncRNA sequences, the DNA/RNA binding motifs are enriched. This suggests that lncRNAs might contribute to distant gene expression of the salt-associated modules in a trans-regulatory manner. Moreover, we found 30 lncRNAs that have potential to simultaneously cis- and trans-regulate salt-responsive homologous genes, and Ptlinc-NAC72, significantly induced under long-term salt stress, was selected for validating its regulation of the expression and functional roles of the homologs PtNAC72.A and PtNAC72.B (PtNAC72.A/B). The transient transformation of Ptlinc-NAC72 and a dual-luciferase assay of Ptlinc-NAC72 and PtNAC72.A/B promoters confirmed that Ptlinc-NAC72 can directly upregulate PtNAC72.A/B expression, and a presence/absence assay was further conducted to show that the regulation is probably mediated by Ptlinc-NAC72 recognizing the tandem elements (GAAAAA) in the PtNAC72.A/B 5' untranslated region (5'-UTR). Finally, the overexpression of Ptlinc-NAC72 produces a hypersensitive phenotype under salt stress. Altogether, our results shed light on the cis- and trans-regulation of gene expression by lncRNAs in Populus and provides an example of long-term salt-induced Ptlinc-NAC72 that could be used to mitigate growth costs by conferring plant resilience to salt stress.

Transcriptome landscape of early inflorescence developmental stages identifies key flowering time regulators in chickpea

Transcriptome landscape during early inflorescence developmental stages identified candidate flowering time regulators including Early Flowering 3a. Further genomics approaches validated the role of this gene in flowering time regulation. The early stages of inflorescence development in plants are as crucial as the later floral developmental stages. Several traits, such as inflorescence architecture and flower developmental timings, are determined during those early stages. In chickpea, diverse forms of inflorescence architectures regarding meristem determinacy and the number of flowers per node are observed within the germplasm. Transcriptome analysis in four desi chickpea accessions with such unique inflorescence characteristics identifies the underlying shared regulatory events leading to inflorescence development. The vegetative to reproductive stage transition brings about major changes in the transcriptome landscape. The inflorescence development progression associated genes identified through co-expression network analysis includes both protein-coding genes and long non-coding RNAs (lncRNAs). Few lncRNAs identified in our study positively regulate flowering-related mRNA stability by acting competitively with miRNAs. Bulk segregrant analysis and association mapping narrowed down an InDel marker regulating flowering time in chickpea. Deletion of 11 bp in first exon of a negative flowering time regulator, Early Flowering 3a gene, leads to early flowering phenotype in chickpea. Understanding the key players involved in vegetative to reproductive stage transition and floral meristem development will be useful in manipulating flowering time and inflorescence architecture in chickpea and other legumes.

Comprehensive Transcriptome Analysis Uncovers Hub Long Non-coding RNAs Regulating Potassium Use Efficiency in Nicotiana tabacum

Potassium (K) is the essential element for plant growth. It is one of the critical factors that determine crop yield, quality, and especially leaf development in tobacco. However, the molecular mechanism of potassium use efficiency (KUE), especially non-coding RNA, is still unknown. In this study, tobacco seedlings were employed, and their hydro-cultivation with K treatments of low and sufficient concentrations was engaged. Physiological analysis showed that low potassium treatment could promote malondialdehyde (MDA) accumulation and antioxidant enzyme activities such as peroxidase (POD), ascorbate-peroxidase (APX). After transcriptomic analysis, a total of 10,585 LncRNA transcripts were identified, and 242 of them were significantly differently expressed under potassium starvation. Furthermore, co-expression networks were constructed and generated 78 potential regulation modules in which coding gene and LncRNAs are involved and functional jointly. By further module-trait analysis and module membership (MM) ranking, nine modules, including 616 coding RNAs and 146 LncRNAs, showed a high correlation with K treatments, and 20 hub K-responsive LncRNAs were finally predicted. Following gene ontology (GO) analysis, the results showed potassium starvation inducing the pathway of antioxidative stress which is consistent with the physiology result mentioned above. Simultaneously, a part of detected LncRNAs, such as MSTRG.6626.1, MSTRG.11330.1, and MSTRG.16041.1, were co-relating with a bench of MYB, C3H, and NFYC transcript factors in response to the stress. Overall, this research provided a set of LncRNAs that respond to K concentration from starvation and sufficient supply. Simultaneously, the regulation network and potential co-functioning genes were listed as well. This massive dataset would serve as an outstanding clue for further study in tobacco and other plant species for nutrient physiology and molecular regulation mechanism.

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.

Global identification and integrated analysis of heat-responsive long non-coding RNAs in contrasting rice cultivars

Identified 2743 rice lncRNAs LncRNAs in response to heat stress Function prediction of HRLs Network among HRLs, genes and miRNAs co-localization of HRLs with QTLs Significant motifs in HRL sequences Long non-coding RNAs (lncRNAs) play vital roles in plant responses to environmental challenges. A better understanding of the gene regulation mediated by lncRNAs and their systematic identification would provide great benefits for modern agriculture. In this study, we performed strand-specific RNA sequencing for two rice varieties, heat-tolerant ZS97B and heat-susceptible SYD2 under heat stress. In total, 2743 putative lncRNAs were identified, and their expression profiles in response to heat treatments were established. We identified 231 differentially expressed lncRNAs (DELs) under heat stress, including 31 DELs common to both varieties and 103 and 97 specific to ZS97B and SYD2, respectively, all defined as heat-responsive lncRNAs (HRLs). The target-coding genes of HRLs were predicted, and GO and KEGG annotations of HRL targets revealed functions in which HRLs might be involved. The interaction network between HRLs, target genes and relevant miRNAs was constructed. The HRLs and their targets were compared with publicly available QTLs for rice seedling growth under heat stimulus. Ten HRLs and twelve target genes were linked with five heat stress-relevant QTLs. Sequence analysis revealed several motifs significantly enriched within the 231 HRL sequences. Our findings provide a valuable resource for further characterization of lncRNAs in terms of heat response and plant heat tolerance improvement.

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.

Common Features in lncRNA Annotation and Classification: A Survey

Long non-coding RNAs (lncRNAs) are widely recognized as important regulators of gene expression. Their molecular functions range from miRNA sponging to chromatin-associated mechanisms, leading to effects in disease progression and establishing them as diagnostic and therapeutic targets. Still, only a few representatives of this diverse class of RNAs are well studied, while the vast majority is poorly described beyond the existence of their transcripts. In this review we survey common in silico approaches for lncRNA annotation. We focus on the well-established sets of features used for classification and discuss their specific advantages and weaknesses. While the available tools perform very well for the task of distinguishing coding sequence from other RNAs, we find that current methods are not well suited to distinguish lncRNAs or parts thereof from other non-protein-coding input sequences. We conclude that the distinction of lncRNAs from intronic sequences and untranslated regions of coding mRNAs remains a pressing research gap.

Draft genome and transcriptome analyses of halophyte rice Oryza coarctata provide resources for salinity and submergence stress response factors

Oryza coarctata is a wild relative of rice that has adapted to diverse ecological environments, including high salinity and submergence. Thus, it can provide an important resource for discovering candidate genes/factors involved in tolerance to these stresses. Here, we report a draft genome assembly of 573 Mb comprised of 8877 scaffolds with N50 length of 205 kb. We predicted a total of 50,562 protein-coding genes, of which a significant fraction was found to be involved in secondary metabolite biosynthesis and hormone signal transduction pathways. Several salinity and submergence stress-responsive protein-coding and long noncoding RNAs involved in diverse biological processes were identified using RNA-sequencing data. Based on small RNA sequencing, we identified 168 unique miRNAs and 3219 target transcripts (coding and noncoding) involved in several biological processes, including abiotic stress responses. Further, whole genome bisulphite sequencing data analysis revealed at least 19%-48% methylcytosines in different sequence contexts and the influence of methylation status on gene expression. The genome assembly along with other datasets have been made publicly available at http://ccbb.jnu.ac.in/ory-coar. Altogether, we provide a comprehensive genomic resource for understanding the regulation of salinity and submergence stress responses and identification of candidate genes/factors involved for functional genomics studies.

Non-Coding RNAs in Response to Drought Stress

Drought stress causes changes in the morphological, physiological, biochemical and molecular characteristics of plants. The response to drought in different plants may vary from avoidance, tolerance and escape to recovery from stress. This response is genetically programmed and regulated in a very complex yet synchronized manner. The crucial genetic regulations mediated by non-coding RNAs (ncRNAs) have emerged as game-changers in modulating the plant responses to drought and other abiotic stresses. The ncRNAs interact with their targets to form potentially subtle regulatory networks that control multiple genes to determine the overall response of plants. Many long and small drought-responsive ncRNAs have been identified and characterized in different plant varieties. The miRNA-based research is better documented, while lncRNA and transposon-derived RNAs are relatively new, and their cellular role is beginning to be understood. In this review, we have compiled the information on the categorization of non-coding RNAs based on their biogenesis and function. We also discuss the available literature on the role of long and small non-coding RNAs in mitigating drought stress in plants.

CicerSpTEdb: A web-based database for high-resolution genome-wide identification of transposable elements in Cicer species

Recently, Cicer species have experienced increased research interest due to their economic importance, especially in genetics, genomics, and crop improvement. The Cicer arietinum, Cicer reticulatum, and Cicer echinospermum genomes have been sequenced and provide valuable resources for trait improvement. Since the publication of the chickpea draft genome, progress has been made in genome assembly, functional annotation, and identification of polymorphic markers. However, work is still needed to identify transposable elements (TEs) and make them available for researchers. In this paper, we present CicerSpTEdb, a comprehensive TE database for Cicer species that aims to improve our understanding of the organization and structural variations of the chickpea genome. Using structure and homology-based methods, 3942 C. echinospermum, 3579 C. reticulatum, and 2240 C. arietinum TEs were identified. Comparisons between Cicer species indicate that C. echinospermum has the highest number of LTR-RT and hAT TEs. C. reticulatum has more Mutator, PIF Harbinger, Tc1 Mariner, and CACTA TEs, while C. arietinum has the highest number of Helitron. CicerSpTEdb enables users to search and visualize TEs by location and download their results. The database will provide a powerful resource that can assist in developing TE target markers for molecular breeding and answer related biological questions. Database URL: http://cicersptedb.easyomics.org/index.php.

Fab Advances in Fabaceae for Abiotic Stress Resilience: From 'Omics' to Artificial Intelligence

Legumes are a better source of proteins and are richer in diverse micronutrients over the nutritional profile of widely consumed cereals. However, when exposed to a diverse range of abiotic stresses, their overall productivity and quality are hugely impacted. Our limited understanding of genetic determinants and novel variants associated with the abiotic stress response in food legume crops restricts its amelioration. Therefore, it is imperative to understand different molecular approaches in food legume crops that can be utilized in crop improvement programs to minimize the economic loss. 'Omics'-based molecular breeding provides better opportunities over conventional breeding for diversifying the natural germplasm together with improving yield and quality parameters. Due to molecular advancements, the technique is now equipped with novel 'omics' approaches such as ionomics, epigenomics, fluxomics, RNomics, glycomics, glycoproteomics, phosphoproteomics, lipidomics, regulomics, and secretomics. Pan-omics-which utilizes the molecular bases of the stress response to identify genes (genomics), mRNAs (transcriptomics), proteins (proteomics), and biomolecules (metabolomics) associated with stress regulation-has been widely used for abiotic stress amelioration in food legume crops. Integration of pan-omics with novel omics approaches will fast-track legume breeding programs. Moreover, artificial intelligence (AI)-based algorithms can be utilized for simulating crop yield under changing environments, which can help in predicting the genetic gain beforehand. Application of machine learning (ML) in quantitative trait loci (QTL) mining will further help in determining the genetic determinants of abiotic stress tolerance in pulses.

LncRNA TCONS_00021861 is functionally associated with drought tolerance in rice (Oryza sativa L.) via competing endogenous RNA regulation

BACKGROUND

Water deficit is an abiotic stress that retards plant growth and destabilizes crop production. Long non coding RNAs (lncRNAs) are a class of non-coding endogenous RNAs that participate in diverse cellular processes and stress responses in plants. lncRNAs could function as competing endogenous RNAs (ceRNA) and represent a novel layer of gene regulation. However, the regulatory mechanism of lncRNAs as ceRNA in drought stress response is yet unclear.

RESULTS

In this study, we performed transcriptome-wide identification of drought-responsive lncRNAs in rice. Thereafter, we constructed a lncRNA-mediated ceRNA network by analyzing competing relationships between mRNAs and lncRNAs based on ceRNA hypothesis. A drought responsive ceRNA network with 40 lncRNAs, 23 miRNAs and 103 mRNAs was obtained. Network analysis revealed TCONS_00021861/miR528-3p/YUCCA7 regulatory axis as a hub involved in drought response. The miRNA-target expression and interaction were validated by RT-qPCR and RLM-5'RACE. TCONS_00021861 showed significant positive correlation (r = 0.7102) with YUCCA7 and negative correlation with miR528-3p (r = -0.7483). Overexpression of TCONS_00021861 attenuated the repression of miR528-3p on YUCCA7, leading to increased IAA (Indole-3-acetic acid) content and auxin overproduction phenotypes.

CONCLUSIONS

TCONS_00021861 could regulate YUCCA7 by sponging miR528-3p, which in turn activates IAA biosynthetic pathway and confer resistance to drought stress. Our findings provide a new perspective of the regulatory roles of lncRNAs as ceRNAs in drought resistance of rice.

Pan-transcriptome identifying master genes and regulation network in response to drought and salt stresses in Alfalfa (Medicago sativa L.)

Alfalfa is an important legume forage grown worldwide and its productivity is affected by environmental stresses such as drought and high salinity. In this work, three alfalfa germplasms with contrasting tolerances to drought and high salinity were used for unraveling the transcriptomic responses to drought and salt stresses. Twenty-one different RNA samples from different germplasm, stress conditions or tissue sources (leaf, stem and root) were extracted and sequenced using the PacBio (Iso-Seq) and the Illumina platforms to obtain full-length transcriptomic profiles. A total of 1,124,275 and 91,378 unique isoforms and genes were obtained, respectively. Comparative analysis of transcriptomes identified differentially expressed genes and isoforms as well as transcriptional and post-transcriptional modifications such as alternative splicing events, fusion genes and nonsense-mediated mRNA decay events and non-coding RNA such as circRNA and lncRNA. This is the first time to identify the diversity of circRNA and lncRNA in response to drought and high salinity in alfalfa. The analysis of weighted gene co-expression network allowed to identify master genes and isoforms that may play important roles on drought and salt stress tolerance in alfalfa. This work provides insight for understanding the mechanisms by which drought and salt stresses affect alfalfa growth at the whole genome level.

Long Non-coding RNA Signatures Associated With Liver Aging in Senescence-Accelerated Mouse Prone 8 Model

The liver is sensitive to aging because the risk of hepatopathy, including fatty liver, hepatitis, fibrosis, cirrhosis, and hepatocellular carcinoma, increases dramatically with age. Long non-coding RNAs (lncRNAs) are >200 nucleotides long and affect many pathological and physiological processes. A potential link was recently discovered between lncRNAs and liver aging; however, comprehensive and systematic research on this topic is still limited. In this study, the mouse liver genome-wide lncRNA profiles of 8-month-old SAMP8 and SAMR1 models were explored through deep RNA sequencing. A total of 605,801,688 clean reads were generated. Among the 2,182 identified lncRNAs, 28 were differentially expressed between SAMP8 and SAMR1 mice. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) surveys showed that these substantially dysregulated lncRNAs participated in liver aging from different aspects, such as lipid catabolic (GO: 0016042) and metabolic pathways. Further assessment was conducted on lncRNAs that are most likely to be involved in liver aging and related diseases, such as LNC_000027, LNC_000204E, NSMUST00000144661.1, and ENSMUST00000181906.1 acted on Ces1g. This study provided the first comprehensive dissection of lncRNA landscape in SAMP8 mouse liver. These lncRNAs could be exploited as potential targets for the molecular-based diagnosis and therapy of age-related liver diseases.

Non-Coding RNAs in Legumes: Their Emerging Roles in Regulating Biotic/Abiotic Stress Responses and Plant Growth and Development

Noncoding RNAs, including microRNAs (miRNAs), small interference RNAs (siRNAs), circular RNA (circRNA), and long noncoding RNAs (lncRNAs), control gene expression at the transcription, post-transcription, and translation levels. Apart from protein-coding genes, accumulating evidence supports ncRNAs playing a critical role in shaping plant growth and development and biotic and abiotic stress responses in various species, including legume crops. Noncoding RNAs (ncRNAs) interact with DNA, RNA, and proteins, modulating their target genes. However, the regulatory mechanisms controlling these cellular processes are not well understood. Here, we discuss the features of various ncRNAs, including their emerging role in contributing to biotic/abiotic stress response and plant growth and development, in addition to the molecular mechanisms involved, focusing on legume crops. Unravelling the underlying molecular mechanisms and functional implications of ncRNAs will enhance our understanding of the coordinated regulation of plant defences against various biotic and abiotic stresses and for key growth and development processes to better design various legume crops for global food security.

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

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.

Feature extraction approaches for biological sequences: a comparative study of mathematical features

As consequence of the various genomic sequencing projects, an increasing volume of biological sequence data is being produced. Although machine learning algorithms have been successfully applied to a large number of genomic sequence-related problems, the results are largely affected by the type and number of features extracted. This effect has motivated new algorithms and pipeline proposals, mainly involving feature extraction problems, in which extracting significant discriminatory information from a biological set is challenging. Considering this, our work proposes a new study of feature extraction approaches based on mathematical features (numerical mapping with Fourier, entropy and complex networks). As a case study, we analyze long non-coding RNA sequences. Moreover, we separated this work into three studies. First, we assessed our proposal with the most addressed problem in our review, e.g. lncRNA and mRNA; second, we also validate the mathematical features in different classification problems, to predict the class of lncRNA, e.g. circular RNAs sequences; third, we analyze its robustness in scenarios with imbalanced data. The experimental results demonstrated three main contributions: first, an in-depth study of several mathematical features; second, a new feature extraction pipeline; and third, its high performance and robustness for distinct RNA sequence classification. Availability:  https://github.com/Bonidia/FeatureExtraction_BiologicalSequences.

Analysis of Rice Transcriptome Reveals the LncRNA/CircRNA Regulation in Tissue Development

BACKGROUND

Long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) can play important roles in many biological processes. However, no study of the influence of epigenetics factors or the 3D structure of the genome in their regulation is available in plants.

RESULTS

In the current analysis, we identified a total of 15,122 lncRNAs and 7902 circRNAs in three tissues (root, leaf and panicle) in the rice varieties Minghui 63, Zhenshan 97 and their hybrid Shanyou 63. More than 73% of these lncRNAs and parental genes of circRNAs (P-circRNAs) are shared among Oryza sativa with high expression specificity. We found that, compared with protein-coding genes, the loci of these lncRNAs have higher methylation levels and the loci of circRNAs tend to locate in the middle of genes with high CG and CHG methylation. Meanwhile, the activated lncRNAs and P-circRNAs are mainly transcribed from demethylated regions containing CHH methylation. In addition, ~ 53% lncRNAs and ~ 15% P-circRNAs are associated with transposable elements (TEs), especially miniature inverted-repeat transposable elements and RC/Helitron. We didn't find correlation between the expression of lncRNAs and histone modifications; however, we found that the binding strength and interaction of RNAPII significantly affects lncRNA expression. Interestingly, P-circRNAs tend to combine active histone modifications. Finally, we found that lncRNAs and circRNAs acting as competing-endogenous RNAs have the potential to regulate the expression of genes, such as osa-156 l-5p (related to yield) and osa-miR444a-3p (related to N/P metabolism) confirmed through dual-luciferase reporter assays, with important roles in the growth and development of rice, laying a foundation for future rice breeding analyses.

CONCLUSIONS

In conclusion, our study comprehensively analyzed the important regulatory roles of lncRNA/circRNA in the tissue development of Indica rice from multiple perspectives.

Machine Learning-Based Annotation of Long Noncoding RNAs Using PLncPRO

Long noncoding RNAs (lncRNAs) are noncoding RNAs with transcript length more than 200 nucleotides. Although poorly conserved, lncRNAs are expressed across diverse species, including plants and animals, and are known to be involved in regulation of various biological processes. To understand their biological significance, we first need to identify the lncRNAs accurately. However, distinguishing lncRNAs from coding transcripts is still a challenging task. Here, we describe a machine learning-based approach to accurately identify the plant lncRNAs. We describe the usage of plant long noncoding RNA prediction by random forests (PLncPRO), which employs machine learning-based random forest algorithm to recognize the lncRNAs from the set of given transcript sequences. Stepwise instructions have been provided to use PLncPRO to annotate the lncRNA sequences.

A systematic evaluation of bioinformatics tools for identification of long noncoding RNAs

High-throughput RNA sequencing unveiled the complexity of transcriptome and significantly increased the records of long noncoding RNAs (lncRNAs), which were reported to participate in a variety of biological processes. Identification of lncRNAs is a key step in lncRNA analysis, and a bunch of bioinformatics tools have been developed for this purpose in recent years. While these tools allow us to identify lncRNA more efficiently and accurately, they may produce inconsistent results, making selection a confusing issue. We compared the performance of 41 analysis models based on 14 software packages and different data sets, including high-quality data and low-quality data from 33 species. In addition, computational efficiency, robustness, and joint prediction of the models were explored. As a practical guidance, key points for lncRNA identification under different situations were summarized. In this investigation, no one of these models could be superior to others under all test conditions. The performance of a model relied to a great extent on the source of transcripts and the quality of assemblies. As general references, FEELnc_all_cl, CPC, and CPAT_mouse work well in most species while COME, CNCI, and lncScore are good choices for model organisms. Since these tools are sensitive to different factors such as the species involved and the quality of assembly, researchers must carefully select the appropriate tool based on the actual data. Alternatively, our test suggests that joint prediction could behave better than any single model if proper models were chosen. All scripts/data used in this research can be accessed at http://bioinfo.ihb.ac.cn/elit.