lncSLdb: a resource for long non-coding RNA subcellular localization

BiGM-lncLoc: Bi-level Multi-Graph Meta-Learning for Predicting Cell-Specific Long Noncoding RNAs Subcellular Localization

The precise spatiotemporal expression of long noncoding RNAs (lncRNAs) plays a pivotal role in biological regulation, and aberrant expression of lncRNAs in different subcellular localizations has been intricately linked to the onset and progression of a variety of cancers. Computational methods provide effective means for predicting lncRNA subcellular localization, but current studies either ignore cell line and tissue specificity or the correlation and shared information among cell lines. In this study, we propose a novel approach, BiGM-lncLoc, treating the prediction of lncRNA subcellular localization across cell lines as a multi-graph meta-learning task. Our investigation involves two categories of data: the localization data of nucleotide sequences in different cell lines and cell line expression data. BiGM-lncLoc comprises a cell line-specific optimization network learning specific knowledge from cell line expression data and a graph neural network optimized across cell lines. Subsequently, the specific and shared knowledge acquired through bi-level optimization is applied to a new cell-line prediction task without the need for re-training or fine-tuning. Additionally, through key feature analysis of the impact of different nucleotide combinations on the model, we confirm the necessity of cell line-specific studies based on correlation analysis. Finally, experiments conducted on various cell lines with different data sizes indicate that BiGM-lncLoc outperforms other methods in terms of prediction accuracy, with an average accuracy of 97.7%. After removing overlapping samples to ensure data independence for each cell line, the accuracy ranged from 82.4% to 94.7%, still surpassing existing models. Our code can be found at https://github.com/BioCL1/BiGM-lncLoc .

A Dataset Curated for the Assessment of G4s in the LncRNAs Dysregulated in Various Human Cancers

Dysregulated expression of long non-coding RNAs (lncRNAs) in cancer contributes to various hallmarks of the disease, presenting novel opportunities for diagnosis and therapy. G-quadruplexes (G4s) within lncRNAs have gained attention recently; however, their systematic evaluation in cancer biology is yet to be performed. In this work, we have formulated a comprehensive dataset integrating experimentally-validated associations between lncRNAs and cancer, and detailed predictions of their G4-forming potential. The dataset categorizes predicted G4-motifs into anticipated G4 types (2 G, 3 G, and 4 G) and provides information about the subcellular localization of the corresponding lncRNAs. It describes lncRNA-RNA and lncRNA-protein interactions, together with the RNA G4-binding capabilities of these proteins. The dataset facilitates the investigation of G4-mediated lncRNA functions in diverse human cancers and provides distinctive leads about G4-mediated lncRNA-protein interactions.

Computational Prediction of Gene Regulation by lncRNAs

High-throughput sequencing technologies and innovative bioinformatics tools discovered that most of the genome is transcribed into RNA. However, only a fraction of the RNAs in cell translates into proteins, while the majority of them are categorized as noncoding RNAs (ncRNAs). The ncRNAs with more than 200 nt without protein-coding ability are termed long noncoding RNAs (lncRNAs). Hundreds of studies established that lncRNAs are a crucial RNA family regulating gene expression. Regulatory RNAs, including lncRNAs, modulate gene expression by interacting with RNA, DNA, and proteins. Several databases and computational tools have been developed to explore the functions of lncRNAs in cellular physiology. This chapter discusses the tools available for lncRNA functional analysis and provides a detailed workflow for the computational analysis of lncRNAs.

GP-HTNLoc: A graph prototype head-tail network-based model for multi-label subcellular localization prediction of ncRNAs

Numerous research results demonstrated that understanding the subcellular localization of non-coding RNAs (ncRNAs) is pivotal in elucidating their roles and regulatory mechanisms in cells. Despite the existence of over ten computational models dedicated to predicting the subcellular localization of ncRNAs, a majority of these models are designed solely for single-label prediction. In reality, ncRNAs often exhibit localization across multiple subcellular compartments. Furthermore, the existing multi-label localization prediction models are insufficient in addressing the challenges posed by the scarcity of training samples and class imbalance in ncRNA dataset. To address these limitations, this study proposes a novel multi-label localization prediction model for ncRNAs, named GP-HTNLoc. To mitigate class imbalance, GP-HTNLoc adopts separate training approaches for head and tail location labels. Additionally, GP-HTNLoc introduces a pioneering graph prototype module to enhance its performance in small-sample, multi-label scenarios. The experimental results based on 10-fold cross-validation on benchmark datasets demonstrate that GP-HTNLoc achieves competitive predictive performance. The average results from 10 rounds of testing on an independent dataset show that GP-HTNLoc outperforms the best existing models on the human lncRNA, human snoRNA, and human miRNA subsets, with average precision improvements of 31.5%, 14.2%, and 5.6%, respectively, reaching 0.685, 0.632, and 0.704. A user-friendly online GP-HTNLoc server is accessible at https://56s8y85390.goho.co.

Integrated Biochemical and Computational Methods for Deciphering RNA-Processing Codes

RNA processing involves steps such as capping, splicing, polyadenylation, modification, and nuclear export. These steps are essential for transforming genetic information in DNA into proteins and contribute to RNA diversity and complexity. Many biochemical methods have been developed to profile and quantify RNAs, as well as to identify the interactions between RNAs and RNA-binding proteins (RBPs), especially when coupled with high-throughput sequencing technologies. With the rapid accumulation of diverse data, it is crucial to develop computational methods to convert the big data into biological knowledge. In particular, machine learning and deep learning models are commonly utilized to learn the rules or codes governing the transformation from DNA sequences to intriguing RNAs based on manually designed or automatically extracted features. When precise enough, the RNA codes can be incredibly useful for predicting RNA products, decoding the molecular mechanisms, forecasting the impact of disease variants on RNA processing events, and identifying driver mutations. In this review, we systematically summarize the biochemical and computational methods for deciphering five important RNA codes related to alternative splicing, alternative polyadenylation, RNA localization, RNA modifications, and RBP binding. For each code, we review the main types of experimental methods used to generate training data, as well as the key features, strategic model structures, and advantages of representative tools. We also discuss the challenges encountered in developing predictive models using large language models and extensive domain knowledge. Additionally, we highlight useful resources and propose ways to improve computational tools for studying RNA codes.

Decoding the Non-coding: Tools and Databases Unveiling the Hidden World of "Junk" RNAs for Innovative Therapeutic Exploration

Non-coding RNAs are pivotal regulators of gene and protein expression, exerting crucial influences on diverse biological processes. Their dysregulation is frequently implicated in the onset and progression of diseases, notably cancer. A profound comprehension of the intricate mechanisms governing ncRNAs is imperative for devising innovative therapeutic interventions against these debilitating conditions. Significantly, nearly 80% of our genome comprises ncRNAs, underscoring their centrality in cellular processes. The elucidation of ncRNA functions is pivotal for grasping the complexities of gene regulation and its implications for human health. Modern genome sequencing techniques yield vast datasets, stored in specialized databases. To harness this wealth of information and to understand the crosstalk of non-coding RNAs, knowledge of available databases is required, and many new sophisticated computational tools have emerged. These tools play a pivotal role in the identification, prediction, and annotation of ncRNAs, thereby facilitating their experimental validation. This Review succinctly outlines the current understanding of ncRNAs, emphasizing their involvement in disease development. It also highlights the databases and tools instrumental in classifying, annotating, and evaluating ncRNAs. By extracting meaningful biological insights from seemingly "junk" data, these tools empower scientists to unravel the intricate roles of ncRNAs in shaping human health.

MRSLpred-a hybrid approach for predicting multi-label subcellular localization of mRNA at the genome scale

In the past, several methods have been developed for predicting the single-label subcellular localization of messenger RNA (mRNA). However, only limited methods are designed to predict the multi-label subcellular localization of mRNA. Furthermore, the existing methods are slow and cannot be implemented at a transcriptome scale. In this study, a fast and reliable method has been developed for predicting the multi-label subcellular localization of mRNA that can be implemented at a genome scale. Machine learning-based methods have been developed using mRNA sequence composition, where the XGBoost-based classifier achieved an average area under the receiver operator characteristic (AUROC) of 0.709 (0.668-0.732). In addition to alignment-free methods, we developed alignment-based methods using motif search techniques. Finally, a hybrid technique that combines the XGBoost model and the motif-based approach has been developed, achieving an average AUROC of 0.742 (0.708-0.816). Our method-MRSLpred-outperforms the existing state-of-the-art classifier in terms of performance and computation efficiency. A publicly accessible webserver and a standalone tool have been developed to facilitate researchers (webserver: https://webs.iiitd.edu.in/raghava/mrslpred/).

Long noncoding RNA study: Genome-wide approaches

Long noncoding RNAs (lncRNAs) have been confirmed to play a crucial role in various biological processes across several species. Though many efforts have been devoted to the expansion of the lncRNAs landscape, much about lncRNAs is still unknown due to their great complexity. The development of high-throughput technologies and the constantly improved bioinformatic methods have resulted in a rapid expansion of lncRNA research and relevant databases. In this review, we introduced genome-wide research of lncRNAs in three parts: (i) novel lncRNA identification by high-throughput sequencing and computational pipelines; (ii) functional characterization of lncRNAs by expression atlas profiling, genome-scale screening, and the research of cancer-related lncRNAs; (iii) mechanism research by large-scale experimental technologies and computational analysis. Besides, primary experimental methods and bioinformatic pipelines related to these three parts are summarized. This review aimed to provide a comprehensive and systemic overview of lncRNA genome-wide research strategies and indicate a genome-wide lncRNA research system.

Exploring the landscape of tools and resources for the analysis of long non-coding RNAs

In recent years, research on long non-coding RNAs (lncRNAs) has gained considerable attention due to the increasing number of newly identified transcripts. Several characteristics make their functional evaluation challenging, which called for the urgent need to combine molecular biology with other disciplines, including bioinformatics. Indeed, the recent development of computational pipelines and resources has greatly facilitated both the discovery and the mechanisms of action of lncRNAs. In this review, we present a curated collection of the most recent computational resources, which have been categorized into distinct groups: databases and annotation, identification and classification, interaction prediction, and structure prediction. As the repertoire of lncRNAs and their analysis tools continues to expand over the years, standardizing the computational pipelines and improving the existing annotation of lncRNAs will be crucial to facilitate functional genomics studies.

A review from biological mapping to computation-based subcellular localization

Subcellular localization is crucial to the study of virus and diseases. Specifically, research on protein subcellular localization can help identify clues between virus and host cells that can aid in the design of targeted drugs. Research on RNA subcellular localization is significant for human diseases (such as Alzheimer's disease, colon cancer, etc.). To date, only reviews addressing subcellular localization of proteins have been published, which are outdated for reference, and reviews of RNA subcellular localization are not comprehensive. Therefore, we collated (the most up-to-date) literature on protein and RNA subcellular localization to help researchers understand changes in the field of protein and RNA subcellular localization. Extensive and complete methods for constructing subcellular localization models have also been summarized, which can help readers understand the changes in application of biotechnology and computer science in subcellular localization research and explore how to use biological data to construct improved subcellular localization models. This paper is the first review to cover both protein subcellular localization and RNA subcellular localization. We urge researchers from biology and computational biology to jointly pay attention to transformation patterns, interrelationships, differences, and causality of protein subcellular localization and RNA subcellular localization.

Identification of a Novel lncRNA LNC_001186 and Its Effects on CPB2 Toxin-Induced Apoptosis of IPEC-J2 Cells

The Clostridium perfringens (C. perfringen) beta2 (CPB2) toxin produced by C. perfringens type C (CpC) can cause necrotizing enteritis in piglets. Immune system activation in response to inflammation and pathogen infection is aided by long non-coding RNAs (lncRNAs). In our previous work, we revealed the differential expression of the novel lncRNA LNC_001186 in CpC-infected ileum versus healthy piglets. This implied that LNC_001186 may be a regulatory factor essential for CpC infection in piglets. Herein, we analyzed the coding ability, chromosomal location and subcellular localization of LNC_001186 and explored its regulatory role in CPB2 toxin-induced apoptosis of porcine small intestinal epithelial (IPEC-J2) cells. RT-qPCR results indicated that LNC_001186 expression was highly enriched in the intestines of healthy piglets and significantly increased in CpC-infected piglets' ileum tissue and CPB2 toxin-treated IPEC-J2 cells. The total sequence length of LNC_001186 was 1323 bp through RACE assay. CPC and CPAT, two online databases, both confirmed that LNC_001186 had a low coding ability. It was present on pig chromosome 3. Cytoplasmic and nuclear RNA isolation and RNA-FISH assays showed that LNC_001186 was present in the nucleus and cytoplasm of IPEC-J2 cells. Furthermore, six target genes of LNC_001186 were predicted using cis and trans approaches. Meanwhile, we constructed ceRNA regulatory networks with LNC_001186 as the center. Finally, LNC_001186 overexpression inhibited IPEC-J2 cells' apoptosis caused by CPB2 toxin and promoted cell viability. In summary, we determined the role of LNC_001186 in IPEC-J2 cells' apoptosis caused by CPB2 toxin, which assisted us in exploring the molecular mechanism of LNC_001186 in CpC-induced diarrhea in piglets.

RNA out of the mist

RNA has long been regarded primarily as the intermediate between genes and proteins. It was a surprise then to discover that eukaryotic genes are mosaics of mRNA sequences interrupted by large tracts of transcribed but untranslated sequences, and that multicellular organisms also express many long 'intergenic' and antisense noncoding RNAs (lncRNAs). The identification of small RNAs that regulate mRNA translation and half-life did not disturb the prevailing view that animals and plant genomes are full of evolutionary debris and that their development is mainly supervised by transcription factors. Gathering evidence to the contrary involved addressing the low conservation, expression, and genetic visibility of lncRNAs, demonstrating their cell-specific roles in cell and developmental biology, and their association with chromatin-modifying complexes and phase-separated domains. The emerging picture is that most lncRNAs are the products of genetic loci termed 'enhancers', which marshal generic effector proteins to their sites of action to control cell fate decisions during development.

GM-lncLoc: LncRNAs subcellular localization prediction based on graph neural network with meta-learning

In recent years, a large number of studies have shown that the subcellular localization of long non-coding RNAs (lncRNAs) can bring crucial information to the recognition of lncRNAs function. Therefore, it is of great significance to establish a computational method to accurately predict the subcellular localization of lncRNA. Previous prediction models are based on low-level sequences information and are troubled by the few samples problem. In this study, we propose a new prediction model, GM-lncLoc, which is based on the initial information extracted from the lncRNA sequence, and also combines the graph structure information to extract high level features of lncRNA. In addition, the training mode of meta-learning is introduced to obtain meta-parameters by training a series of tasks. With the meta-parameters, the final parameters of other similar tasks can be learned quickly, so as to solve the problem of few samples in lncRNA subcellular localization. Compared with the previous methods, GM-lncLoc achieved the best results with an accuracy of 93.4 and 94.2% in the benchmark datasets of 5 and 4 subcellular compartments, respectively. Furthermore, the prediction performance of GM-lncLoc was also better on the independent dataset. It shows the effectiveness and great potential of our proposed method for lncRNA subcellular localization prediction. The datasets and source code are freely available at https://github.com/JunzheCai/GM-lncLoc .

Non-coding RNAs in human health and disease: potential function as biomarkers and therapeutic targets

Human diseases have been a critical threat from the beginning of human history. Knowing the origin, course of action and treatment of any disease state is essential. A microscopic approach to the molecular field is a more coherent and accurate way to explore the mechanism, progression, and therapy with the introduction and evolution of technology than a macroscopic approach. Non-coding RNAs (ncRNAs) play increasingly important roles in detecting, developing, and treating all abnormalities related to physiology, pathology, genetics, epigenetics, cancer, and developmental diseases. Noncoding RNAs are becoming increasingly crucial as powerful, multipurpose regulators of all biological processes. Parallel to this, a rising amount of scientific information has revealed links between abnormal noncoding RNA expression and human disorders. Numerous non-coding transcripts with unknown functions have been found in addition to advancements in RNA-sequencing methods. Non-coding linear RNAs come in a variety of forms, including circular RNAs with a continuous closed loop (circRNA), long non-coding RNAs (lncRNA), and microRNAs (miRNA). This comprises specific information on their biogenesis, mode of action, physiological function, and significance concerning disease (such as cancer or cardiovascular diseases and others). This study review focuses on non-coding RNA as specific biomarkers and novel therapeutic targets.

Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations

Subcellular localization of messenger RNAs (mRNAs) plays a key role in the spatial regulation of gene activity. The functions of mRNAs have been shown to be closely linked with their localizations. As such, understanding of the subcellular localizations of mRNAs can help elucidate gene regulatory networks. Despite several computational methods that have been developed to predict mRNA localizations within cells, there is still much room for improvement in predictive performance, especially for the multiple-location prediction. In this study, we proposed a novel multi-label multi-class predictor, termed Clarion, for mRNA subcellular localization prediction. Clarion was developed based on a manually curated benchmark dataset and leveraged the weighted series method for multi-label transformation. Extensive benchmarking tests demonstrated Clarion achieved competitive predictive performance and the weighted series method plays a crucial role in securing superior performance of Clarion. In addition, the independent test results indicate that Clarion outperformed the state-of-the-art methods and can secure accuracy of 81.47, 91.29, 79.77, 92.10, 89.15, 83.74, 80.74, 79.23 and 84.74% for chromatin, cytoplasm, cytosol, exosome, membrane, nucleolus, nucleoplasm, nucleus and ribosome, respectively. The webserver and local stand-alone tool of Clarion is freely available at http://monash.bioweb.cloud.edu.au/Clarion/.

Global characterization of megakaryocytes in bone marrow, peripheral blood, and cord blood by single-cell RNA sequencing

Megakaryocytes (MK) are mainly derived from bone marrow and are mainly involved in platelet production. Studies have shown that MK derived from bone marrow may have immune function, and that MK from peripheral blood are associated with prostate cancer. Single-cell transcriptome sequencing can help us better understand the heterogeneity and potential function of MK cell populations in bone marrow (BM), peripheral Blood (PB), and cord blood (CB) of healthy and diseased people.We integrated more than 1.2 million single-cell transcriptome data from 132 samples of PB, BM, and CB from healthy individuals and patients from different dataset. We examined the MK (including MK and product of MK) by single-cell RNA sequencing data analysis methods and identification of MK-related protein expression by the Human Protein atlas. We investigate the relationship between the MK subtype and Non-Small Cell Lung Cancer (NSCLC) in 77 non-cancer and 402 NSCLC. We found that MK were widely distributed and the amount of MK in peripheral blood was more than that in bone marrow and there were specificity MK subtypes in peripheral blood. We found classical MK1 with typical MK characteristics and non-classical MK2 closely related to immunity which was the most common subtype in bone marrow and cord blood. Classical MK1 was closely related to Non-Small Cell Lung Cancer (NSCLC) and can be used as a diagnostic marker. MK2 may have potential adaptive immune function and play a role in tumor NSCLC and autoimmune diseases Systemic Lupus Erythematosus. MK have 14 subtypes and are widely distributed in PB, CB, and BM. MK subtypes are closely related to immunity and have potential to be a diagnostic indicator of NSCLC.

A single natural RNA modification can destabilize a U•A-T-rich RNA•DNA-DNA triple helix

Recent studies suggest noncoding RNAs interact with genomic DNA, forming RNA•DNA-DNA triple helices, as a mechanism to regulate transcription. One way cells could regulate the formation of these triple helices is through RNA modifications. With over 140 naturally occurring RNA modifications, we hypothesize that some modifications stabilize RNA•DNA-DNA triple helices while others destabilize them. Here, we focus on a pyrimidine-motif triple helix composed of canonical U•A-T and C•G-C base triples. We employed electrophoretic mobility shift assays and microscale thermophoresis to examine how 11 different RNA modifications at a single position in an RNA•DNA-DNA triple helix affect stability: 5-methylcytidine (m⁵C), 5-methyluridine (m⁵U or rT), 3-methyluridine (m³U), pseudouridine (Ψ), 4-thiouridine (s⁴U), N ⁶-methyladenosine (m⁶A), inosine (I), and each nucleobase with 2'-O-methylation (Nm). Compared to the unmodified U•A-T base triple, some modifications have no significant change in stability (Um•A-T), some have ∼2.5-fold decreases in stability (m⁵U•A-T, Ψ•A-T, and s⁴U•A-T), and some completely disrupt triple helix formation (m³U•A-T). To identify potential biological examples of RNA•DNA-DNA triple helices controlled by an RNA modification, we searched RMVar, a database for RNA modifications mapped at single-nucleotide resolution, for lncRNAs containing an RNA modification within a pyrimidine-rich sequence. Using electrophoretic mobility shift assays, the binding of DNA-DNA to a 22-mer segment of human lncRNA Al157886.1 was destabilized by ∼1.7-fold with the substitution of m⁵C at known m⁵C sites. Therefore, the formation and stability of cellular RNA•DNA-DNA triple helices could be influenced by RNA modifications.

Identification of LOC101927355 as a Novel Biomarker for Preeclampsia

Preeclampsia, a disorder with a heterogeneous physiopathology, can be attributed to maternal, fetal, and/or placental factors. Long non-coding RNAs (lncRNAs) refer to a class of non-coding RNAs, the essential regulators of biological processes; their differential expression has been associated with the pathogenesis of multiple diseases. The study aimed to identify lncRNAs, expressed in the placentas and plasma of patients who presented with preeclampsia, as potential putative biomarkers of the disease. In silico analysis was performed to determine lncRNAs differentially expressed in the placentas of patients with preeclampsia, using a previously published RNA-Seq dataset. Seven placentas and maternal plasma samples collected at delivery from preterm preeclamptic patients (≤37 gestational weeks of gestation), and controls were used to validate the expression of lncRNAs by qRT-PCR. Six lncRNAs were validated and differentially expressed (p < 0.05) in the preeclampsia and control placentas: UCA1 and HCG4 were found upregulated, and LOC101927355, LINC00551, PART1, and NRAD1 downregulated. Two of these lncRNAs, HCG4 and LOC101927355, were also detected in maternal plasma, the latter showing a significant decrease (p = 0.03) in preeclamptic patients compared to the control group. In silico analyses showed the cytoplasmic location of LOC101927355, which suggests a role in post-transcriptional gene regulation. The detection of LOC101927355 in the placenta and plasma opens new possibilities for understanding the pathogenesis of preeclampsia and for its potential use as a biomarker.

Insight Into the Long Noncoding RNA and mRNA Coexpression Profile in the Human Blood Transcriptome Upon Leishmania infantum Infection

Visceral leishmaniasis (VL) is a vector-borne infectious disease that can be potentially fatal if left untreated. In Brazil, it is caused by Leishmania infantum parasites. Blood transcriptomics allows us to assess the molecular mechanisms involved in the immunopathological processes of several clinical conditions, namely, parasitic diseases. Here, we performed mRNA sequencing of peripheral blood from patients with visceral leishmaniasis during the active phase of the disease and six months after successful treatment, when the patients were considered clinically cured. To strengthen the study, the RNA-seq data analysis included two other non-diseased groups composed of healthy uninfected volunteers and asymptomatic individuals. We identified thousands of differentially expressed genes between VL patients and non-diseased groups. Overall, pathway analysis corroborated the importance of signaling involving interferons, chemokines, Toll-like receptors and the neutrophil response. Cellular deconvolution of gene expression profiles was able to discriminate cellular subtypes, highlighting the contribution of plasma cells and NK cells in the course of the disease. Beyond the biological processes involved in the immunopathology of VL revealed by the expression of protein coding genes (PCGs), we observed a significant participation of long noncoding RNAs (lncRNAs) in our blood transcriptome dataset. Genome-wide analysis of lncRNAs expression in VL has never been performed. lncRNAs have been considered key regulators of disease progression, mainly in cancers; however, their pattern regulation may also help to understand the complexity and heterogeneity of host immune responses elicited by L. infantum infections in humans. Among our findings, we identified lncRNAs such as IL21-AS1, MIR4435-2HG and LINC01501 and coexpressed lncRNA/mRNA pairs such as CA3-AS1/CA1, GASAL1/IFNG and LINC01127/IL1R1-IL1R2. Thus, for the first time, we present an integrated analysis of PCGs and lncRNAs by exploring the lncRNA–mRNA coexpression profile of VL to provide insights into the regulatory gene network involved in the development of this inflammatory and infectious disease.

Comprehensive Analysis of the PROSER2-AS1-Related ceRNA Network and Immune Cell Infiltration in Papillary Thyroid Carcinoma

Background

Papillary thyroid carcinoma (PTC) is a malignant tumor of the endocrine system, and distant metastasis leads to poor prognosis for patients with PTC. The competitive endogenous RNA (ceRNA) network and tumor-infiltrating immune cells might participate in tumor prognosis and distant metastasis. However, few studies have focused on ceRNAs and immune cells in PTC.

Methods

We identified differentially expressed lncRNAs (DELs) using the GEO2R tool of the GEO database. Through comprehensive analysis, we selected lncRNA PROSER2-AS1 and constructed a PROSER2-AS1-mediated ceRNA network. Survival was analyzed with a Kaplan-Meier (KM) curve. Gene set enrichment analysis (GSEA) was performed to determine the function of PROSER2-AS1 in the ceRNA network using TCGA database. Moreover, the relationship between PROSER2-AS1 and immune cell infiltration was analyzed with ssGSEA using the “GSVA” package in R.

Results

Comprehensive analysis of the GSE66783 dataset revealed 105 significantly differentially expressed lncRNAs. Univariate and multivariate Cox regression analyses were performed to assess the prognostic significance of the DELs, and we identified lncRNA PROSER2-AS1 as an independent factor for prognosis in PTC (p < 0.05). Considering the online tools LncRNASNP2 and miRWalk3.0, we constructed a PROSER2-AS1-related ceRNA network. Furthermore, the GSEA results suggested that PROSER2-AS1 may be involved in immune cell infiltration and that PROSER2-AS1 was correlated with 14 types of tumor-infiltrating immune cells. PROSER2-AS1 might function through TGFBR3.

Conclusion

lncRNA PROSER2-AS1 and related mRNAs (TGFBR3) may be potential prognostic biomarkers in PTC and may correlate with immune infiltrates.

Illuminating lncRNA Function Through Target Prediction

Most of the transcribed human genome codes for noncoding RNAs (ncRNAs), and long noncoding RNAs (lncRNAs) make for the lion's share of the human ncRNA space. Despite growing interest in lncRNAs, because there are so many of them, and because of their tissue specialization and, often, lower abundance, their catalog remains incomplete and there are multiple ongoing efforts to improve it. Consequently, the number of human lncRNA genes may be lower than 10,000 or higher than 200,000. A key open challenge for lncRNA research, now that so many lncRNA species have been identified, is the characterization of lncRNA function and the interpretation of the roles of genetic and epigenetic alterations at their loci. After all, the most important human genes to catalog and study are those that contribute to important cellular functions-that affect development or cell differentiation and whose dysregulation may play a role in the genesis and progression of human diseases. Multiple efforts have used screens based on RNA-mediated interference (RNAi), antisense oligonucleotide (ASO), and CRISPR screens to identify the consequences of lncRNA dysregulation and predict lncRNA function in select contexts, but these approaches have unresolved scalability and accuracy challenges. Instead-as was the case for better-studied ncRNAs in the past-researchers often focus on characterizing lncRNA interactions and investigating their effects on genes and pathways with known functions. Here, we focus most of our review on computational methods to identify lncRNA interactions and to predict the effects of their alterations and dysregulation on human disease pathways.

LncRNAWiki 2.0: a knowledgebase of human long non-coding RNAs with enhanced curation model and database system

LncRNAWiki, a knowledgebase of human long non-coding RNAs (lncRNAs), has been rapidly expanded by incorporating more experimentally validated lncRNAs. Since it was built based on MediaWiki as its database system, it fails to manage data in a structured way and is ineffective to support systematic exploration of lncRNAs. Here we present LncRNAWiki 2.0 (https://ngdc.cncb.ac.cn/lncrnawiki), which is significantly improved with enhanced database system and curation model. In LncRNAWiki 2.0, all contents are organized in a structured manner powered by MySQL/Java and curators are able to submit/edit annotations based on the curation model that includes a wider range of annotation items. Moreover, it is equipped with popular online tools to help users identify lncRNAs with potentially important functions, and provides more user-friendly web interfaces to facilitate data curation, retrieval and visualization. Consequently, LncRNAWiki 2.0 incorporates a total of 2512 lncRNAs and 106 242 associations for disease, function, drug, interacting partner, molecular signature, experimental sample, CRISPR design, etc., thus providing a comprehensive and up-to-date resource of functionally annotated lncRNAs in human.

High frequency of intron retention and clustered H3K4me3-marked nucleosomes in short first introns of human long non-coding RNAs

BACKGROUND

It is established that protein-coding exons are preferentially localized in nucleosomes. To examine whether the same is true for non-coding exons, we analysed nucleosome occupancy in and adjacent to internal exons in genes encoding long non-coding RNAs (lncRNAs) in human CD4+ T cells and K562 cells.

RESULTS

We confirmed that internal exons in lncRNAs are preferentially associated with nucleosomes, but also observed an elevated signal from H3K4me3-marked nucleosomes in the sequences upstream of these exons. Examination of 200 genomic lncRNA loci chosen at random across all chromosomes showed that high-density regions of H3K4me3-marked nucleosomes, which we term 'slabs', are associated with genomic regions exhibiting intron retention. These retained introns occur in over 50% of lncRNAs examined and are mostly first introns with an average length of just 354 bp, compared to the average length of all human introns of 6355 and 7987 bp in mRNAs and lncRNAs, respectively. Removal of short introns from the dataset abrogated the high upstream H3K4me3 signal, confirming that the association of slabs and short lncRNA introns with intron retention holds genome-wide. The high upstream H3K4me3 signal is also associated with alternatively spliced exons, known to be prominent in lncRNAs. This phenomenon was not observed with mRNAs.

CONCLUSIONS

There is widespread intron retention and clustered H3K4me3-marked nucleosomes in short first introns of human long non-coding RNAs, which raises intriguing questions about the relationship of IR to lncRNA function and chromatin organization.

RNALocate v2.0: an updated resource for RNA subcellular localization with increased coverage and annotation

Resolving the spatial distribution of the transcriptome at a subcellular level can increase our understanding of biology and diseases. To facilitate studies of biological functions and molecular mechanisms in the transcriptome, we updated RNALocate, a resource for RNA subcellular localization analysis that is freely accessible at http://www.rnalocate.org/ or http://www.rna-society.org/rnalocate/. Compared to RNALocate v1.0, the new features in version 2.0 include (i) expansion of the data sources and the coverage of species; (ii) incorporation and integration of RNA-seq datasets containing information about subcellular localization; (iii) addition and reorganization of RNA information (RNA subcellular localization conditions and descriptive figures for method, RNA homology information, RNA interaction and ncRNA disease information) and (iv) three additional prediction tools: DM3Loc, iLoc-lncRNA and iLoc-mRNA. Overall, RNALocate v2.0 provides a comprehensive RNA subcellular localization resource for researchers to deconvolute the highly complex architecture of the cell.

DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding

Long non-coding RNAs (lncRNAs) are a class of RNA molecules with more than 200 nucleotides. A growing amount of evidence reveals that subcellular localization of lncRNAs can provide valuable insights into their biological functions. Existing computational methods for predicting lncRNA subcellular localization use k-mer features to encode lncRNA sequences. However, the sequence order information is lost by using only k-mer features. We proposed a deep learning framework, DeepLncLoc, to predict lncRNA subcellular localization. In DeepLncLoc, we introduced a new subsequence embedding method that keeps the order information of lncRNA sequences. The subsequence embedding method first divides a sequence into some consecutive subsequences and then extracts the patterns of each subsequence, last combines these patterns to obtain a complete representation of the lncRNA sequence. After that, a text convolutional neural network is employed to learn high-level features and perform the prediction task. Compared with traditional machine learning models, popular representation methods and existing predictors, DeepLncLoc achieved better performance, which shows that DeepLncLoc could effectively predict lncRNA subcellular localization. Our study not only presented a novel computational model for predicting lncRNA subcellular localization but also introduced a new subsequence embedding method which is expected to be applied in other sequence-based prediction tasks. The DeepLncLoc web server is freely accessible at http://bioinformatics.csu.edu.cn/DeepLncLoc/, and source code and datasets can be downloaded from https://github.com/CSUBioGroup/DeepLncLoc.

Long Non-coding RNA TMEM220-AS1 Suppressed Hepatocellular Carcinoma by Regulating the miR-484/MAGI1 Axis as a Competing Endogenous RNA

Long non-coding RNAs (lncRNAs) have a considerable regulatory influence on multiple biological processes. Nevertheless, the role of TMEM220-AS1 in hepatocellular carcinoma (HCC) remains unclear. We used The Cancer Genome Atlas (TCGA) database to analyze the differentially expressed lncRNAs. qRT-PCR was used to verify the results for a large population. The in vitro effects of TMEM220-AS1 on HCC cells were determined using Cell Counting Kit-8 (CCK-8), 5-ethynyl-2'-deoxyuridine (EdU), flow cytometry, and Transwell assays in HCC cells. We used qRT-PCR and western blotting to identify the epithelial-mesenchymal transition (EMT). Moreover, we performed bioinformatics analysis, western blotting, dual luciferase reporter gene assay, RNA pull-down, and RNA binding protein immunoprecipitation (RIP) to investigate the underlying molecular mechanisms of TMEM220-AS1 function. Finally, the function of TMEM220-AS1 was verified in vivo. The results showed that TMEM220-AS1 was expressed at considerably low levels in HCC. It was demonstrated that malignant phenotypes and EMT of HCC cells were promoted by the knock down of TMEM220-AS1 both in vivo and in vitro. TMEM220-AS1, which was detected primarily in the cytoplasm, functioned as an miRNA sponge to bind miR-484 and promote the level of membrane-associated guanylate kinase, WW, and PDZ domain containing 1 (MAGI1), thereby curbing the malignant phenotypes of HCC cells. In conclusion, low levels of TMEM220-AS1 promote proliferation and metastasis through the miR-484/MAGI1 axis in HCC.

Long non-coding RNAs and their involvement in bipolar disorders

Non-coding RNAs (nc-RNAs) can be defined as RNA molecules that are not translated into proteins. Although the functional meaning of many nc-RNAs remains still to be verified, several of these molecules have a clear biological importance, which goes from translation of mRNAs to DNA replication. Indeed, regulatory nc-RNAs can be classified into two groups: short non-coding RNAs (sncRNAs) and long-non coding RNAs (lncRNAs). In the last years, lncRNAs have gained increasing importance in the study of gene regulation, helping authors understand the molecular mechanisms underlying cellular physiology and pathology. LncRNAs are greater than 200 bp and accumulate in nucleus, cytoplasm and exosomes with high tissue specificity, acting in cis or in trans in order to exert enhancer or silencer modulation on gene expression. Such regulatory features, which are widespread in human cells and tissues, can be disrupted in several morbid states. Recent evidences may suggest a disruption of lncRNAs in bipolar disorders, a cluster of severe, chronic and disabling psychiatric diseases, which are characterized by major depressive states cyclically alternating with manic episodes. Here, the authors reviewed genes, classification, biogenesis, structures, functions and databases regarding lncRNAs, and also focused on bipolar disorders, in which some lncRNAs, especially those involved in inflammation and neuronal development, has reported to be dysregulated.

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.

mRNALocater: Enhance the prediction accuracy of eukaryotic mRNA subcellular localization by using model fusion strategy

The functions of mRNAs are closely correlated with their locations in cells. Knowledge about the subcellular locations of mRNA is helpful to understand their biological functions. In recent years, it has become a hot topic to develop effective computational models to predict eukaryotic mRNA subcellular localizations. However, existing state-of-the-art models still have certain deficiencies in terms of prediction accuracy and generalization ability. Therefore, it is urgent to develop novel methods to accurately predict mRNA subcellular localizations. In this study, a novel method called mRNALocater was proposed to detect the subcellular localization of eukaryotic mRNA by adopting the model fusion strategy. To fully extract information from mRNA sequences, the electron-ion interaction pseudopotential and pseudo k-tuple nucleotide composition were used to encode the sequences. Moreover, the correlation coefficient filtering algorithm and feature forward search technology were used to mine hidden feature information, which guarantees that mRNALocater can be more effectively applied to new sequences. The results based on the independent dataset tests demonstrate that mRNALocater yields promising performances for predicting eukaryotic mRNA subcellular localizations and is a powerful tool in practical applications. A freely available online web server for mRNALocater has been established at http://bio-bigdata.cn/mRNALocater.

MicroRNAs and long non-coding RNAs as novel regulators of ribosome biogenesis

Ribosome biogenesis is the fine-tuned, essential process that generates mature ribosomal subunits and ultimately enables all protein synthesis within a cell. Novel regulators of ribosome biogenesis continue to be discovered in higher eukaryotes. While many known regulatory factors are proteins or small nucleolar ribonucleoproteins, microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) are emerging as a novel modulatory layer controlling ribosome production. Here, we summarize work uncovering non-coding RNAs (ncRNAs) as novel regulators of ribosome biogenesis and highlight their links to diseases of defective ribosome biogenesis. It is still unclear how many miRNAs or lncRNAs are involved in phenotypic or pathological disease outcomes caused by impaired ribosome production, as in the ribosomopathies, or by increased ribosome production, as in cancer. In time, we hypothesize that many more ncRNA regulators of ribosome biogenesis will be discovered, which will be followed by an effort to establish connections between disease pathologies and the molecular mechanisms of this additional layer of ribosome biogenesis control.

Long Non-coding RNA LINC01969 Promotes Ovarian Cancer by Regulating the miR-144-5p/LARP1 Axis as a Competing Endogenous RNA

Accumulating evidence has shown that long non-coding RNAs (lncRNAs) can be used as biological markers and treatment targets in cancer and play various roles in cancer-related biological processes. However, the lncRNA expression profiles and their roles and action mechanisms in ovarian cancer (OC) are largely unknown. Here, we assessed the lncRNA expression profiles in OC tissues from The Cancer Genome Atlas (TCGA) database, and one upregulated lncRNA, LINC01969, was selected for further study. LINC01969 expression levels in 41 patients were verified using quantitative real-time polymerase chain reaction (qRT-PCR). The in vitro effects of LINC01969 on OC cell migration, invasion, and proliferation were determined by the CCK-8, ethynyl-2-deoxyuridine (EdU), wound healing, and Transwell assays. Epithelial–mesenchymal transition (EMT) was evaluated using qRT-PCR and Western blotting. The molecular mechanisms of LINC01969 in OC were assessed through bioinformatics analysis, RNA-binding protein immunoprecipitation (RIP), dual luciferase reporter gene assays, and a rescue experiment. Finally, in vivo experiments were conducted to evaluate the functions of LINC01969. The results of the current study showed that LINC01969 was dramatically upregulated in OC, and patients with lower LINC01969 expression levels tended to have better overall survival. Further experiments demonstrated that LINC01969 promoted the migration, invasion, and proliferation of OC cells in vitro and sped up tumor growth in vivo. Additionally, LINC01969, which primarily exists in the cytoplasm, boosted LARP1 expression by sponging miR-144-5p and promoted the malignant phenotypes of OC cells. In conclusion, the LINC01969/miR-144-5p/LARP1 axis is a newly identified regulatory signaling pathway involved in OC progression.

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.

Predicting LncRNA Subcellular Localization Using Unbalanced Pseudo-k Nucleotide Compositions

Background:

Long non-coding RNAs (lncRNAs) are transcripts with a length more than 200 nucleotides, functioning in the regulation of gene expression. More evidence has shown that the biological functions of lncRNAs are intimately related to their subcellular localizations. Therefore, it is very important to confirm the lncRNA subcellular localization.

Methods:

In this paper, we proposed a novel method to predict the subcellular localization of lncRNAs. To more comprehensively utilize lncRNA sequence information, we exploited both kmer nucleotide composition and sequence order correlated factors of lncRNA to formulate lncRNA sequences. Meanwhile, a feature selection technique which was based on the Analysis Of Variance (ANOVA) was applied to obtain the optimal feature subset. Finally, we used the support vector machine (SVM) to perform the prediction.

Results:

The AUC value of the proposed method can reach 0.9695, which indicated the proposed predictor is an efficient and reliable tool for determining lncRNA subcellular localization. Furthermore, the predictor can reach the maximum overall accuracy of 90.37% in leave-one-out cross validation, which clearly outperforms the existing state-of- the-art method.

Conclusion:

It is demonstrated that the proposed predictor is feasible and powerful for the prediction of lncRNA subcellular. To facilitate subsequent genetic sequence research, we shared the source code at https://github.com/NicoleYXF/lncRNA.

Design powerful predictor for mRNA subcellular location prediction in Homo sapiens

Messenger RNAs (mRNAs) shoulder special responsibilities that transmit genetic code from DNA to discrete locations in the cytoplasm. The locating process of mRNA might provide spatial and temporal regulation of mRNA and protein functions. The situ hybridization and quantitative transcriptomics analysis could provide detail information about mRNA subcellular localization; however, they are time consuming and expensive. It is highly desired to develop computational tools for timely and effectively predicting mRNA subcellular location. In this work, by using binomial distribution and one-way analysis of variance, the optimal nonamer composition was obtained to represent mRNA sequences. Subsequently, a predictor based on support vector machine was developed to identify the mRNA subcellular localization. In 5-fold cross-validation, results showed that the accuracy is 90.12% for Homo sapiens (H. sapiens). The predictor may provide a reference for the study of mRNA localization mechanisms and mRNA translocation strategies. An online web server was established based on our models, which is available at http://lin-group.cn/server/iLoc-mRNA/.

A Novel lnc-RNA, Named lnc-ORA, Is Identified by RNA-Seq Analysis, and Its Knockdown Inhibits Adipogenesis by Regulating the PI3K/AKT/mTOR Signaling Pathway

Obesity is closely associated with numerous adipogenic regulatory factors, including coding and non-coding genes. Long noncoding RNAs (lncRNAs) play a major role in adipogenesis. However, differential expression profiles of lncRNAs in inguinal white adipose tissue (iWAT) between wild-type (WT) and ob/ob mice, as well as their roles in adipogenesis, are not well understood. Here, a total of 2809 lncRNAs were detected in the iWAT of WT and ob/ob mice by RNA-Sequencing (RNA-Seq), including 248 novel lncRNAs. Of them, 46 lncRNAs were expressed differentially in WT and ob/ob mice and were enriched in adipogenesis signaling pathways as determined by KEGG enrichment analysis, including the PI3K/AKT/mTOR and cytokine-cytokine receptor interaction signaling pathways. Furthermore, we focused on one novel lncRNA, which we named lnc-ORA (obesity-related lncRNA), which had a seven-fold higher expression in ob/ob mice than in WT mice. Knockdown of lnc-ORA inhibited preadipocyte proliferation by decreasing the mRNA and protein expression levels of cell cycle markers. Interestingly, lnc-ORA knockdown inhibited adipocyte differentiation by regulating the PI3K/AKT/mTOR signaling pathway. In summary, these findings contribute to a better understanding of adipogenesis in relation to lncRNAs and provide novel potential therapeutic targets for obesity-related metabolic diseases.