iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC

CodLncScape Provides a Self-Enriching Framework for the Systematic Collection and Exploration of Coding LncRNAs

Recent studies have revealed that numerous lncRNAs can translate proteins under specific conditions, performing diverse biological functions, thus termed coding lncRNAs. Their comprehensive landscape, however, remains elusive due to this field's preliminary and dispersed nature. This study introduces codLncScape, a framework for coding lncRNA exploration consisting of codLncDB, codLncFlow, codLncWeb, and codLncNLP. Specifically, it contains a manually compiled knowledge base, codLncDB, encompassing 353 coding lncRNA entries validated by experiments. Building upon codLncDB, codLncFlow investigates the expression characteristics of these lncRNAs and their diagnostic potential in the pan-cancer context, alongside their association with spermatogenesis. Furthermore, codLncWeb emerges as a platform for storing, browsing, and accessing knowledge concerning coding lncRNAs within various programming environments. Finally, codLncNLP serves as a knowledge-mining tool to enhance the timely content inclusion and updates within codLncDB. In summary, this study offers a well-functioning, content-rich ecosystem for coding lncRNA research, aiming to accelerate systematic studies in this field.

mRNA-lncRNA gene expression signature in HPV-associated neoplasia and cervical cancer

Cervical cancer is one of the most frequent cancers in women and is associated with human papillomavirus (HPV) in 70 % of cases. Cervical cancer occurs because of progression of low-differentiated cervical intraepithelial neoplasia through grade 2 and 3 lesions. Along with the protein-coding genes, long noncoding RNAs (lncRNAs) play an important role in the development of malignant cell transformation. Although human papillomavirus is widespread, there is currently no well-characterized transcriptomic signature to predict whether this tumor will develop in the presence of HPV-associated neoplastic changes in the cervical epithelium. Changes in gene activity in tumors reflect the biological diversity of cellular phenotype and physiological functions and can be an important diagnostic marker. We performed comparative transcriptome analysis using open RNA sequencing data to assess differentially expressed genes between normal tissue, neoplastic epithelium, and cervical cancer. Raw data were preprocessed using the Galaxy platform. Batch effect correction, identification of differentially expressed genes, and gene set enrichment analysis (GSEA) were performed using R programming language packages. Subcellular localization of lncRNA was analyzed using Locate-R and iLoc-LncRNA 2.0 web services. 1,572 differentially expressed genes (DEGs) were recorded in the "cancer vs. control" comparison, and 1,260 DEGs were recorded in the "cancer vs. neoplasia" comparison. Only two genes were observed to be differentially expressed in the "neoplasia vs. control" comparison. The search for common genes among the most strongly differentially expressed genes among all comparison groups resulted in the identification of an expression signature consisting of the CCL20, CDKN2A, CTCFL, piR-55219, TRH, SLC27A6 and EPHA5 genes. The transcription level of the CCL20 and CDKN2A genes becomes increased at the stage of neoplastic epithelial changes and stays so in cervical cancer. Validation on an independent microarray dataset showed that the differential expression patterns of the CDKN2A and SLC27A6 genes were conserved in the respective gene expression comparisons between groups.

A BERT-based model for the prediction of lncRNA subcellular localization in Homo sapiens

Understanding the subcellular localization of lncRNAs is crucial for comprehending their regulation activities. The conventional detection of lncRNA subcellular location usually uses in situ detection techniques, which are resource intensive. Some machine learning-based algorithms have been proposed for lncRNA subcellular location prediction in mammals. However, due to the low level of conservation of lncRNA sequence, the performance of cross-species models remains unsatisfactory. In this study, we curated a novel dataset containing subcellular location information of lncRNAs in Homo sapiens. Subsequently, based on the BERT pre-trained language algorithm, we developed a model for lncRNA subcellular location prediction. Our model achieved a micro-average area under the receiver operating characteristic (AUROC) of 0.791 on the training set and an AUROC of 0.700 on the testing nucleus set. Additionally, we conducted cross-species validation and motif discovery to further investigate underlying patterns. In summary, our study provides valuable guidance and computational analysis tools for exploring the mechanisms of lncRNA subcellular localization and the dynamic spatial changes of RNA in abnormal physiological states.

DeepLocRNA: an interpretable deep learning model for predicting RNA subcellular localization with domain-specific transfer-learning

MOTIVATION

Accurate prediction of RNA subcellular localization plays an important role in understanding cellular processes and functions. Although post-transcriptional processes are governed by trans-acting RNA binding proteins (RBPs) through interaction with cis-regulatory RNA motifs, current methods do not incorporate RBP-binding information.

RESULTS

In this article, we propose DeepLocRNA, an interpretable deep-learning model that leverages a pre-trained multi-task RBP-binding prediction model to predict the subcellular localization of RNA molecules via fine-tuning. We constructed DeepLocRNA using a comprehensive dataset with variant RNA types and evaluated it on the held-out dataset. Our model achieved state-of-the-art performance in predicting RNA subcellular localization in mRNA and miRNA. It has also demonstrated great generalization capabilities, performing well on both human and mouse RNA. Additionally, a motif analysis was performed to enhance the interpretability of the model, highlighting signal factors that contributed to the predictions. The proposed model provides general and powerful prediction abilities for different RNA types and species, offering valuable insights into the localization patterns of RNA molecules and contributing to our understanding of cellular processes at the molecular level. A user-friendly web server is available at: https://biolib.com/KU/DeepLocRNA/.

Comprehensive non-coding RNA analysis reveals specific lncRNA/circRNA-miRNA-mRNA regulatory networks in the cotton response to drought stress

Root and leaf are essential organs of plants in sensing and responding to drought stress. However, comparative knowledge of non-coding RNAs (ncRNAs) of root and leaf tissues in the regulation of drought response in cotton is limited. Here, we used deep sequencing data of leaf and root tissues of drought-resistant and drought-sensitive cotton varieties for identifying miRNAs, lncRNAs and circRNAs. A total of 1531 differentially expressed (DE) ncRNAs was identified, including 77 DE miRNAs, 1393 DE lncRNAs and 61 DE circRNAs. The tissue-specific and variety-specific competing endogenous RNA (ceRNA) networks of DE lncRNA-miRNA-mRNA response to drought were constructed. Furthermore, the novel drought-responsive lncRNA 1 (DRL1), specifically and differentially expressed in root, was verified to positively affect phenotypes of cotton seedlings under drought stress, competitively binding to miR477b with GhNAC1 and GhSCL3. In addition, we also constructed another ceRNA network consisting of 18 DE circRNAs, 26 DE miRNAs and 368 DE mRNAs. Fourteen circRNA were characterized, and a novel molecular regulatory system of circ125- miR7484b/miR7450b was proposed under drought stress. Our findings revealed the specificity of ncRNA expression in tissue- and variety-specific patterns involved in the response to drought stress, and uncovered novel regulatory pathways and potentially effective molecules in genetic improvement for crop drought resistance.

LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism

MOTIVATION

There is mounting evidence that the subcellular localization of lncRNAs can provide valuable insights into their biological functions. In the real world of transcriptomes, lncRNAs are usually localized in multiple subcellular localizations. Furthermore, lncRNAs have specific localization patterns for different subcellular localizations. Although several computational methods have been developed to predict the subcellular localization of lncRNAs, few of them are designed for lncRNAs that have multiple subcellular localizations, and none of them take motif specificity into consideration.

RESULTS

In this study, we proposed a novel deep learning model, called LncLocFormer, which uses only lncRNA sequences to predict multi-label lncRNA subcellular localization. LncLocFormer utilizes eight Transformer blocks to model long-range dependencies within the lncRNA sequence and shares information across the lncRNA sequence. To exploit the relationship between different subcellular localizations and find distinct localization patterns for different subcellular localizations, LncLocFormer employs a localization-specific attention mechanism. The results demonstrate that LncLocFormer outperforms existing state-of-the-art predictors on the hold-out test set. Furthermore, we conducted a motif analysis and found LncLocFormer can capture known motifs. Ablation studies confirmed the contribution of the localization-specific attention mechanism in improving the prediction performance.

AVAILABILITY AND IMPLEMENTATION

The LncLocFormer web server is available at http://csuligroup.com:9000/LncLocFormer. The source code can be obtained from https://github.com/CSUBioGroup/LncLocFormer.

DlncRNALoc: A discrete wavelet transform-based model for predicting lncRNA subcellular localization

The prediction of long non-coding RNA (lncRNA) subcellular localization is essential to the understanding of its function and involvement in cellular regulation. Traditional biological experimental methods are costly and time-consuming, making computational methods the preferred approach for predicting lncRNA subcellular localization (LSL). However, existing computational methods have limitations due to the structural characteristics of lncRNAs and the uneven distribution of data across subcellular compartments. We propose a discrete wavelet transform (DWT)-based model for predicting LSL, called DlncRNALoc. We construct a physicochemical property matrix of a 2-tuple bases based on lncRNA sequences, and we introduce a DWT lncRNA feature extraction method. We use the Synthetic Minority Over-sampling Technique (SMOTE) for oversampling and the local fisher discriminant analysis (LFDA) algorithm to optimize feature information. The optimized feature vectors are fed into support vector machine (SVM) to construct a predictive model. DlncRNALoc has been applied for a five-fold cross-validation on the three sets of benchmark datasets. Extensive experiments have demonstrated the superiority and effectiveness of the DlncRNALoc model in predicting LSL.

Long non-coding RNA LOC103222771 promotes infection of porcine reproductive and respiratory syndrome virus in Marc-145 cells by downregulating Claudin-4

Porcine reproductive and respiratory syndrome (PRRS) is an important swine disease caused by infection of porcine reproductive and respiratory syndrome virus (PRRSV), which leads to huge loss in swine industry. How to effectively control PRRS is challenging. Long non-coding RNA (lncRNA) are key regulator of viral infections and anti-virus immunological responses, therefore, further understanding of lncRNAs will aid to identification of novel regulators of viral infections and better design of prevention and control strategies to viral infection related diseases and immune disorders. We demonstrated that PRRSV infection upregulated the expression of lncRNA LOC103222771 in Marc-145 cells and porcine alveolar macrophage cells (PAMs) and that LOC103222771 is mainly located in cytoplasm. Knockdown of LOC103222771 could inhibit the PRRSV infection in Marc-145 cells. RNA-seq analysis and subsequent validation revealed increased expression of Claudin-4 (CLDN4) in Marc-145 when LOC103222771 was specifically downregulated，suggesting that LOC103222771 might be an upstream regulator of CLDN4, an important component of tight junctions for establishment of the paracellular barrier that controls the flow of molecules in the intercellular space between epithelial cells. We and others showed that Downregulation of CLDN4 could boost the infection of PRRSV. Collectively, LOC103222771/CLDN4 signal axis might be a novel mechanism of PRRSV pathogenesis, implying a potential therapeutic target against PRRSV infection.

RNA trafficking and subcellular localization-a review of mechanisms, experimental and predictive methodologies

RNA localization is essential for regulating spatial translation, where RNAs are trafficked to their target locations via various biological mechanisms. In this review, we discuss RNA localization in the context of molecular mechanisms, experimental techniques and machine learning-based prediction tools. Three main types of molecular mechanisms that control the localization of RNA to distinct cellular compartments are reviewed, including directed transport, protection from mRNA degradation, as well as diffusion and local entrapment. Advances in experimental methods, both image and sequence based, provide substantial data resources, which allow for the design of powerful machine learning models to predict RNA localizations. We review the publicly available predictive tools to serve as a guide for users and inspire developers to build more effective prediction models. Finally, we provide an overview of multimodal learning, which may provide a new avenue for the prediction of RNA localization.

StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens

BACKGROUND

The identification of tumor T cell antigens (TTCAs) is crucial for providing insights into their functional mechanisms and utilizing their potential in anticancer vaccines development. In this context, TTCAs are highly promising. Meanwhile, experimental technologies for discovering and characterizing new TTCAs are expensive and time-consuming. Although many machine learning (ML)-based models have been proposed for identifying new TTCAs, there is still a need to develop a robust model that can achieve higher rates of accuracy and precision.

RESULTS

In this study, we propose a new stacking ensemble learning-based framework, termed StackTTCA, for accurate and large-scale identification of TTCAs. Firstly, we constructed 156 different baseline models by using 12 different feature encoding schemes and 13 popular ML algorithms. Secondly, these baseline models were trained and employed to create a new probabilistic feature vector. Finally, the optimal probabilistic feature vector was determined based the feature selection strategy and then used for the construction of our stacked model. Comparative benchmarking experiments indicated that StackTTCA clearly outperformed several ML classifiers and the existing methods in terms of the independent test, with an accuracy of 0.932 and Matthew's correlation coefficient of 0.866.

CONCLUSIONS

In summary, the proposed stacking ensemble learning-based framework of StackTTCA could help to precisely and rapidly identify true TTCAs for follow-up experimental verification. In addition, we developed an online web server ( http://2pmlab.camt.cmu.ac.th/StackTTCA ) to maximize user convenience for high-throughput screening of novel TTCAs.

DeepASDPred: a CNN-LSTM-based deep learning method for Autism spectrum disorders risk RNA identification

BACKGROUND

Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders characterized by difficulty communicating with society and others, behavioral difficulties, and a brain that processes information differently than normal. Genetics has a strong impact on ASD associated with early onset and distinctive signs. Currently, all known ASD risk genes are able to encode proteins, and some de novo mutations disrupting protein-coding genes have been demonstrated to cause ASD. Next-generation sequencing technology enables high-throughput identification of ASD risk RNAs. However, these efforts are time-consuming and expensive, so an efficient computational model for ASD risk gene prediction is necessary.

RESULTS

In this study, we propose DeepASDPerd, a predictor for ASD risk RNA based on deep learning. Firstly, we use K-mer to feature encode the RNA transcript sequences, and then fuse them with corresponding gene expression values to construct a feature matrix. After combining chi-square test and logistic regression to select the best feature subset, we input them into a binary classification prediction model constructed by convolutional neural network and long short-term memory for training and classification. The results of the tenfold cross-validation proved our method outperformed the state-of-the-art methods. Dataset and source code are available at https://github.com/Onebear-X/DeepASDPred is freely available.

CONCLUSIONS

Our experimental results show that DeepASDPred has outstanding performance in identifying ASD risk RNA genes.

DAmiRLocGNet: miRNA subcellular localization prediction by combining miRNA-disease associations and graph convolutional networks

MicroRNAs (miRNAs) are human post-transcriptional regulators in humans, which are involved in regulating various physiological processes by regulating the gene expression. The subcellular localization of miRNAs plays a crucial role in the discovery of their biological functions. Although several computational methods based on miRNA functional similarity networks have been presented to identify the subcellular localization of miRNAs, it remains difficult for these approaches to effectively extract well-referenced miRNA functional representations due to insufficient miRNA-disease association representation and disease semantic representation. Currently, there has been a significant amount of research on miRNA-disease associations, making it possible to address the issue of insufficient miRNA functional representation. In this work, a novel model is established, named DAmiRLocGNet, based on graph convolutional network (GCN) and autoencoder (AE) for identifying the subcellular localizations of miRNA. The DAmiRLocGNet constructs the features based on miRNA sequence information, miRNA-disease association information and disease semantic information. GCN is utilized to gather the information of neighboring nodes and capture the implicit information of network structures from miRNA-disease association information and disease semantic information. AE is employed to capture sequence semantics from sequence similarity networks. The evaluation demonstrates that the performance of DAmiRLocGNet is superior to other competing computational approaches, benefiting from implicit features captured by using GCNs. The DAmiRLocGNet has the potential to be applied to the identification of subcellular localization of other non-coding RNAs. Moreover, it can facilitate further investigation into the functional mechanisms underlying miRNA localization. The source code and datasets are accessed at http://bliulab.net/DAmiRLocGNet.

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.

The lncRNA MALAT1 is upregulated in urine of type 1 diabetes mellitus patients with diabetic kidney disease

Long non-coding RNAs (lncRNAs) are RNAs with >200 nucleotides that are unable to encode proteins and are involved in gene expression regulation. LncRNAs have a key role in many physiological and pathological processes and, consequently, they have been associated with several human diseases, including diabetes chronic complications, such as diabetes kidney disease (DKD). In this context, some studies have identified the dysregulation of the lncRNAs MALAT1 and TUG1 in patients with DKD; nevertheless, available data are still contradictory. Thus, the objective of this study was to compare MALAT1 and TUG1 expressions in urine of patients with type 1 diabetes mellitus (T1DM) categorized according to DKD presence. This study comprised 18 T1DM patients with DKD (cases) and 9 long-duration T1DM patients without DKD (controls). MALAT1 and TUG1 were analyzed using qPCR. Bioinformatics analyses were done to identify both lncRNA target genes and the signaling pathways under their regulation. The lncRNA MALAT1 was upregulated in urine of T1DM patients with DKD vs. T1DM controls (P = 0.007). The expression of lncRNA TUG1 did not differ between groups (P = 0.815). Bioinformatics analysis showed these two lncRNAs take part in metabolism-related pathways. The present study shows that the lncRNA MALAT1 is upregulated in T1DM patients presenting DKD.

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.

Functional inference of long non-coding RNAs through exploration of highly conserved regions

Background: Long non-coding RNAs (lncRNAs), which are generally less functionally characterized or less annotated, evolve more rapidly than mRNAs and substantially possess fewer sequence conservation patterns than protein-coding genes across divergent species. People assume that the functional inference could be conducted on the evolutionarily conserved long non-coding RNAs as they are most likely to be functional. In the past decades, substantial progress has been made in discussions on the evolutionary conservation of non-coding genomic regions from multiple perspectives. However, understanding their conservation and the functions associated with sequence conservation in relation to further corresponding phenotypic variability or disorders still remains incomplete. Results: Accordingly, we determined a highly conserved region (HCR) to verify the sequence conservation among long non-coding RNAs and systematically profiled homologous long non-coding RNA clusters in humans and mice based on the detection of highly conserved regions. Moreover, according to homolog clustering, we explored the potential function inference via highly conserved regions on representative long non-coding RNAs. On lncRNA XACT, we investigated the potential functional competence between XACT and lncRNA XIST by recruiting miRNA-29a, regulating the downstream target genes. In addition, on lncRNA LINC00461, we examined the interaction relationship between LINC00461 and SND1. This interaction or association may be perturbed during the progression of glioma. In addition, we have constructed a website with user-friendly web interfaces for searching, analyzing, and downloading to present the homologous clusters of humans and mice. Conclusion: Collectively, homolog clustering via the highly conserved region definition and detection on long non-coding RNAs, as well as the functional explorations on representative sequences in our research, would provide new evidence for the potential function of long non-coding RNAs. Our results on the remarkable roles of long non-coding RNAs would presumably provide a new theoretical basis and candidate diagnostic indicators for tumors.

Application of sequence semantic and integrated cellular geography approach to study alternative biogenesis of exonic circular RNA

BACKGROUND

Concurrent existence of lncRNA and circular RNA at both nucleus and cytosol within a cell at different proportions is well reported. Previous studies showed that circular RNAs are synthesized in nucleus followed by transportation across the nuclear membrane and the export is primarily defined by their length. lncRNAs primarily originated through inefficient splicing and seem to use NXF1 for cytoplasm export. However, it is not clear whether circularization of lncRNA happens only in nucleus or it also occurs in cytoplasm. Studies indicate that circular RNAs arise when the splicing apparatus undergoes a phenomenon of back splicing. Minor spliceosome (U12 type) mediated splicing occurs in cytoplasm and is responsible for the splicing of 0.5% of introns of human cells. Therefore, possibility of cRNA biogenesis mediated by minor spliceosome at cytoplasm cannot be ruled out. Secondly, information on genes transcribing both circular and lncRNAs along with total number of RBP binding sites for both of these RNA types is extractable from databases. This study showed how these apparently unconnected pieces of reports could be put together to build a model for exploring biogenesis of circular RNA.

RESULTS

As a result of this study, a model was built under the premises that, sequences with special semantics were molecular precursors in biogenesis of circular RNA which occurred through catalytic role of some specific RBPs. The model outcome was further strengthened by fulfillment of three logical lemmas which were extracted and assimilated in this work using a novel data analytic approach, Integrated Cellular Geography. Result of the study was found to be in well agreement with proposed model. Furthermore this study also indicated that biogenesis of circular RNA was a post-transcriptional event.

CONCLUSIONS

Overall, this study provides a novel systems biology based model under the paradigm of Integrated Cellular Geography which can assimilate independently performed experimental results and data published by global researchers on RNA biology to provide important information on biogenesis of circular RNAs considering lncRNAs as precursor molecule. This study also suggests the possible RBP-mediated circularization of RNA in the cytoplasm through back-splicing using minor spliceosome.

Screening and identification of cyprinid herpesvirus 2 (CyHV-2) ORF55-interacting proteins by phage display

BACKGROUND

Cyprinid herpesvirus 2 (CyHV-2) is a pathogenic fish virus belonging to family Alloherpesviridae. The CyHV-2 gene encoding thymidine kinase (TK) is an important virulence-associated factor. Therefore, we aimed to investigate the biological function of open reading frame 55 (ORF55) in viral replication.

METHODS

Purified CyHV-2 ORF55 protein was obtained by prokaryotic expression, and the interacting peptide was screened out using phage display. Host interacting proteins were then predicted and validated.

RESULTS

ORF55 was efficiently expressed in the prokaryotic expression system. Protein and peptide interaction prediction and dot-blot overlay assay confirmed that peptides identified by phage display could interact with the ORF55 protein. Comparing the peptides to the National Center for Biotechnology Information database revealed four potential interacting proteins. Reverse transcription quantitative PCR results demonstrated high expression of an actin-binding Rho-activating protein in the latter stages of virus-infected cells, and molecular docking, cell transfection and coimmunoprecipitation experiments confirmed that it interacted with the ORF55 protein.

CONCLUSION

During viral infection, the ORF55 protein exerts its biological function through interactions with host proteins. The specific mechanisms remain to be further explored.

Intestinal cancer development in response to oral infection with high-fat diet-induced Type 2 diabetes (T2D) in collaborative cross mice under different host genetic background effects

Type 2 diabetes (T2D) is a metabolic disease with an imbalance in blood glucose concentration. There are significant studies currently showing association between T2D and intestinal cancer developments. High-fat diet (HFD) plays part in the disease development of T2D, intestinal cancer and infectious diseases through many biological mechanisms, including but not limited to inflammation. Understanding the system genetics of the multimorbidity of these diseases will provide an important knowledge and platform for dissecting the complexity of these diseases. Furthermore, in this study we used some machine learning (ML) models to explore more aspects of diabetes mellitus. The ultimate aim of this project is to study the genetic factors, which underline T2D development, associated with intestinal cancer in response to a HFD consumption and oral coinfection, jointly or separately, on the same host genetic background. A cohort of 307 mice of eight different CC mouse lines in the four experimental groups was assessed. The mice were maintained on either HFD or chow diet (CHD) for 12-week period, while half of each dietary group was either coinfected with oral bacteria or uninfected. Host response to a glucose load and clearance was assessed using intraperitoneal glucose tolerance test (IPGTT) at two time points (weeks 6 and 12) during the experiment period and, subsequently, was translated to area under curve (AUC) values. At week 5 of the experiment, mice of group two and four were coinfected with Porphyromonas gingivalis (Pg) and Fusobacterium nucleatum (Fn) strains, three times a week, while keeping the other uninfected mice as a control group. At week 12, mice were killed, small intestines and colon were extracted, and subsequently, the polyp counts were assessed; as well, the intestine lengths and size were measured. Our results have shown that there is a significant variation in polyp's number in different CC lines, with a spectrum between 2.5 and 12.8 total polyps on average. There was a significant correlation between area under curve (AUC) and intestine measurements, including polyp counts, length and size. In addition, our results have shown a significant sex effect on polyp development and glucose tolerance ability with males more susceptible to HFD than females by showing higher AUC in the glucose tolerance test. The ML results showed that classification with random forest could reach the highest accuracy when all the attributes were used. These results provide an excellent platform for proceeding toward understanding the nature of the genes involved in resistance and rate of development of intestinal cancer and T2D induced by HFD and oral coinfection. Once obtained, such data can be used to predict individual risk for developing these diseases and to establish the genetically based strategy for their prevention and treatment.

StackPR is a new computational approach for large-scale identification of progesterone receptor antagonists using the stacking strategy

Progesterone receptors (PRs) are implicated in various cancers since their presence/absence can determine clinical outcomes. The overstimulation of progesterone can facilitate oncogenesis and thus, its modulation through PR inhibition is urgently needed. To address this issue, a novel stacked ensemble learning approach (termed StackPR) is presented for fast, accurate, and large-scale identification of PR antagonists using only SMILES notation without the need for 3D structural information. We employed six popular machine learning (ML) algorithms (i.e., logistic regression, partial least squares, k-nearest neighbor, support vector machine, extremely randomized trees, and random forest) coupled with twelve conventional molecular descriptors to create 72 baseline models. Then, a genetic algorithm in conjunction with the self-assessment-report approach was utilized to determine m out of the 72 baseline models as means of developing the final meta-predictor using the stacking strategy and tenfold cross-validation test. Experimental results on the independent test dataset show that StackPR achieved impressive predictive performance with an accuracy of 0.966 and Matthew’s coefficient correlation of 0.925. In addition, analysis based on the SHapley Additive exPlanation algorithm and molecular docking indicates that aliphatic hydrocarbons and nitrogen-containing substructures were the most important features for having PR antagonist activity. Finally, we implemented an online webserver using StackPR, which is freely accessible at http://pmlabstack.pythonanywhere.com/StackPR. StackPR is anticipated to be a powerful computational tool for the large-scale identification of unknown PR antagonist candidates for follow-up experimental validation.

Integrative analyses of prognosis, tumor immunity, and ceRNA network of the ferroptosis-associated gene FANCD2 in hepatocellular carcinoma

Extensive evidence has revealed that ferroptosis plays a vital role in HCC development and progression. Fanconi anemia complementation group D2 (FANCD2) has been reported to serve as a ferroptosis-associated gene and has a close relationship with tumorigenesis and drug resistance. However, the impact of the FANCD2-related immune response and its mechanisms in HCC remains incompletely understood. In the current research, we evaluated the prognostic significance and immune-associated mechanism of FANCD2 based on multiple bioinformatics methods and databases. The results demonstrated that FANCD2 was commonly upregulated in 15/33 tumors, and only the high expression of FANCD2 in HCC was closely correlated with worse clinical outcomes by OS and DFS analyses. Moreover, ncRNAs, including two major types, miRNAs and lncRNAs, were closely involved in mediating FANCD2 upregulation in HCC and were established in a ceRNA network by performing various in silico analyses. The DUXAP8-miR-29c-FANCD2 and LINC00511-miR-29c-FANCD2 axes were identified as the most likely ncRNA-associated upstream regulatory axis of FANCD2 in HCC. Finally, FANCD2 expression was confirmed to be positively related to HCC immune cell infiltration, immune checkpoints, and IPS analysis, and GSEA results also revealed that this ferroptosis-associated gene was primarily involved in cancer-associated pathways in HCC. In conclusion, our investigations indicate that ncRNA-related modulatory overexpression of FANCD2 might act as a promising prognostic and immunotherapeutic target against HCC.

DeepCPPred: A Deep Learning Framework for the Discrimination of Cell-Penetrating Peptides and Their Uptake Efficiencies

Cell-penetrating peptides (CPPs) are special peptides capable of carrying a variety of bioactive molecules, such as genetic materials, short interfering RNAs and nanoparticles, into cells. Recently, research on CPP has gained substantial interest from researchers, and the biological mechanisms of CPPS have been assessed in the context of safe drug delivery agents and therapeutic applications. Correct identification and synthesis of CPPs using traditional biochemical methods is an extremely slow, expensive and laborious task particularly due to the large volume of unannotated peptide sequences accumulating in the World Bank repository. Hence, a powerful bioinformatics predictor that rapidly identifies CPPs with a high recognition rate is urgently needed. To date, numerous computational methods have been developed for CPP prediction. However, the available machine-learning (ML) tools are unable to distinguish both the CPPs and their uptake efficiencies. This study aimed to develop a two-layer deep learning framework named DeepCPPred to identify both CPPs in the first phase and peptide uptake efficiency in the second phase. The DeepCPPred predictor first uses four types of descriptors that cover evolutionary, energy estimation, reduced sequence and amino-acid contact information. Then, the extracted features are optimized through the elastic net algorithm and fed into a cascade deep forest algorithm to build the final CPP model. The proposed method achieved 99.45 percent overall accuracy with the CPP924 benchmark dataset in the first layer and 95.43 percent accuracy in the second layer with the CPPSite3 dataset using a 5-fold cross-validation test. Thus, our proposed bioinformatics tool surpassed all the existing state-of-the-art sequence-based CPP approaches.

Prediction of Synaptically Localized RNAs in Human Neurons Using Developmental Brain Gene Expression Data

In the nervous system, synapses are special and pervasive structures between axonal and dendritic terminals, which facilitate electrical and chemical communications among neurons. Extensive studies have been conducted in mice and rats to explore the RNA pool at synapses and investigate RNA transport, local protein synthesis, and synaptic plasticity. However, owing to the experimental difficulties of studying human synaptic transcriptomes, the full pool of human synaptic RNAs remains largely unclear. We developed a new machine learning method, called PredSynRNA, to predict the synaptic localization of human RNAs. Training instances of dendritically localized RNAs were compiled from previous rodent studies, overcoming the shortage of empirical instances of human synaptic RNAs. Using RNA sequence and gene expression data as features, various models with different learning algorithms were constructed and evaluated. Strikingly, the models using the developmental brain gene expression features achieved superior performance for predicting synaptically localized RNAs. We examined the relevant expression features learned by PredSynRNA and used an independent test dataset to further validate the model performance. PredSynRNA models were then applied to the prediction and prioritization of candidate RNAs localized to human synapses, providing valuable targets for experimental investigations into neuronal mechanisms and brain disorders.

Machine learning for the micropeptide encoded by LINC02381 regulates ferroptosis through the glucose transporter SLC2A10 in glioblastoma

Glioblastoma (GBM) is the most common primary intracranial tumor in the central nervous system, and resistance to temozolomide is an important reason for the failure of GBM treatment. We screened out that Solute Carrier Family 2 Member 10 (SLC2A10) is significantly highly expressed in GBM with a poor prognosis, which is also enriched in the NF-E2 p45-related factor 2 (NRF2) signalling pathway. The NRF2 signalling pathway is an important defence mechanism against ferroptosis. SLC2A10 related LINC02381 is highly expressed in GBM, which is localized in the cytoplasm/exosomes, and LINC02381 encoded micropeptides are localized in the exosomes. The micropeptide encoded by LINC02381 may be a potential treatment strategy for GBM, but the underlying mechanism of its function is not precise yet. We put forward the hypothesis: “The micropeptide encoded by LINC02381 regulates ferroptosis through the glucose transporter SLC2A10 in GBM.” This study innovatively used machine learning for micropeptide to provide personalized diagnosis and treatment plans for precise treatment of GBM, thereby promoting the development of translational medicine. The study aimed to help find new disease diagnoses and prognostic biomarkers and provide a new strategy for experimental scientists to design the downstream validation experiments.

Circ-LocNet: A Computational Framework for Circular RNA Sub-Cellular Localization Prediction

Circular ribonucleic acids (circRNAs) are novel non-coding RNAs that emanate from alternative splicing of precursor mRNA in reversed order across exons. Despite the abundant presence of circRNAs in human genes and their involvement in diverse physiological processes, the functionality of most circRNAs remains a mystery. Like other non-coding RNAs, sub-cellular localization knowledge of circRNAs has the aptitude to demystify the influence of circRNAs on protein synthesis, degradation, destination, their association with different diseases, and potential for drug development. To date, wet experimental approaches are being used to detect sub-cellular locations of circular RNAs. These approaches help to elucidate the role of circRNAs as protein scaffolds, RNA-binding protein (RBP) sponges, micro-RNA (miRNA) sponges, parental gene expression modifiers, alternative splicing regulators, and transcription regulators. To complement wet-lab experiments, considering the progress made by machine learning approaches for the determination of sub-cellular localization of other non-coding RNAs, the paper in hand develops a computational framework, Circ-LocNet, to precisely detect circRNA sub-cellular localization. Circ-LocNet performs comprehensive extrinsic evaluation of 7 residue frequency-based, residue order and frequency-based, and physio-chemical property-based sequence descriptors using the five most widely used machine learning classifiers. Further, it explores the performance impact of K-order sequence descriptor fusion where it ensembles similar as well dissimilar genres of statistical representation learning approaches to reap the combined benefits. Considering the diversity of statistical representation learning schemes, it assesses the performance of second-order, third-order, and going all the way up to seventh-order sequence descriptor fusion. A comprehensive empirical evaluation of Circ-LocNet over a newly developed benchmark dataset using different settings reveals that standalone residue frequency-based sequence descriptors and tree-based classifiers are more suitable to predict sub-cellular localization of circular RNAs. Further, K-order heterogeneous sequence descriptors fusion in combination with tree-based classifiers most accurately predict sub-cellular localization of circular RNAs. We anticipate this study will act as a rich baseline and push the development of robust computational methodologies for the accurate sub-cellular localization determination of novel circRNAs.

Illuminating RNA biology through imaging

RNA processing plays a central role in accurately transmitting genetic information into functional RNA and protein regulators. To fully appreciate the RNA life-cycle, tools to observe RNA with high spatial and temporal resolution are critical. Here we review recent advances in RNA imaging and highlight how they will propel the field of RNA biology. We discuss current trends in RNA imaging and their potential to elucidate unanswered questions in RNA biology.

IDDLncLoc: Subcellular Localization of LncRNAs Based on a Framework for Imbalanced Data Distributions

Long non-coding RNAs play a crucial role in many life processes of cell, such as genetic markers, RNA splicing, signaling, and protein regulation. Considering that identifying lncRNA's localization in the cell through experimental methods is complicated, hard to reproduce, and expensive, we propose a novel method named IDDLncLoc in this paper, which adopts an ensemble model to solve the problem of the subcellular localization. In the proposal model, dinucleotide-based auto-cross covariance features, k-mer nucleotide composition features, and composition, transition, and distribution features are introduced to encode a raw RNA sequence to vector. To screen out reliable features, feature selection through binomial distribution, and recursive feature elimination is employed. Furthermore, strategies of oversampling in mini-batch, random sampling, and stacking ensemble strategies are customized to overcome the problem of data imbalance on the benchmark dataset. Finally, compared with the latest methods, IDDLncLoc achieves an accuracy of 94.96% on the benchmark dataset, which is 2.59% higher than the best method, and the results further demonstrate IDDLncLoc is excellent on the subcellular localization of lncRNA. Besides, a user-friendly web server is available at http://lncloc.club .

Effect of berberine on global modulation of lncRNAs and mRNAs expression profiles in patients with stable coronary heart disease

BACKGROUND

Berberine (BBR) is an isoquinoline alkaloid found in the Berberis species. It was found to have protected effects in cardiovascular diseases. Here, we investigated the effect the regulatory function of long noncoding RNAs (lncRNAs) during the treatment of stable coronary heart disease (CHD) using BBR. We performed microarray analyses to identify differentially expressed (DE) lncRNAs and mRNAs between whole blood samples from 5 patients with stable CHD taking BBR and 5 no BBR volunteers. DE lncRNAs and mRNAs were validated by quantitative real-time PCR.

RESULTS

A total of 1703 DE lncRNAs and 912 DE mRNAs were identified. Kyoto Encyclopedia of Genes and Genomes pathway analysis indicated DE mRNAs might be associated with mammalian target of rapamycin and mitogen-activated protein kinase pathway. These pathways may be involved in the healing process after CHD. To study the relationship between mRNAs encoding transcription factors (DNA damage inducible transcript 3, sal-like protein 4 and estrogen receptor alpha gene) and CHD related de mRNAs, we performed protein and protein interaction analysis on their corresponding proteins. AKT and apoptosis pathway were significant enriched in protein and protein interaction network. BBR may affect downstream apoptosis pathways through DNA damage inducible transcript 3, sal-like protein 4 and estrogen receptor alpha gene. Growth arrest-specific transcript 5 might regulate CHD-related mRNAs through competing endogenous RNA mechanism and may be the downstream target gene regulated by BBR. Verified by the quantitative real-time PCR, we identified 8 DE lncRNAs that may relate to CHD. We performed coding and non-coding co-expression and competing endogenous RNA mechanism analysis of these 8 DE lncRNAs and CHD-related DE mRNA, and predicted their subcellular localization and N6-methyladenosine modification sites.

CONCLUSION

Our research found that BBR may affect mammalian target of rapamycin, mitogen-activated protein kinase, apoptosis pathway and growth arrest-specific transcript 5 in the process of CHD. These pathways may be involved in the healing process after CHD. Our research might provide novel insights for functional research of BBR.

Progress in Expression Pattern and Molecular Regulation Mechanism of LncRNA in Bovine Mastitis

Simple Summary

Bovine mastitis is an inflammatory disease of the mammary glands that causes serious harm to cow health and huge economic losses. Susceptibility or resistance to mastitis in individual cows is mainly determined by genetic factors, including coding genes and non-coding genes. Long non-coding RNAs (lncRNAs) are non-coding RNA molecules with a length of more than 200 nucleotides (nt) that have recently been discovered. They can regulate a variety of diseases of humans and animals, especially the immune response and inflammatory disease process. This paper reviews the role of long non-coding RNA (lncRNA) in inflammatory diseases, emphasizes on the latest research progress of lncRNA expression and the molecular regulatory mechanism in bovine mastitis, and looks forward to the research and application prospect of lncRNA in bovine mastitis, intending to provide a reference for scientific researchers to systematically understand this research field.

Abstract

Bovine mastitis is an inflammatory disease caused by pathogenic microbial infection, trauma, or other factors. Its morbidity is high, and it is difficult to cure, causing great harm to the health of cows and the safety of dairy products. Susceptibility or resistance to mastitis in individual cows is mainly determined by genetic factors, including coding genes and non-coding genes. Long non-coding RNAs (lncRNAs) are a class of endogenous non-coding RNA molecules with a length of more than 200 nucleotides (nt) that have recently been discovered. They can regulate the immune response of humans and animals on three levels (transcription, epigenetic modification, and post-transcription), and are widely involved in the pathological process of inflammatory diseases. Over the past few years, extensive findings revealed basic roles of lncRNAs in inflammation, especially bovine mastitis. This paper reviews the expression pattern and mechanism of long non-coding RNA (lncRNA) in inflammatory diseases, emphasizes on the latest research progress of the lncRNA expression pattern and molecular regulatory mechanism in bovine mastitis, analyzes the molecular regulatory network of differentially expressed lncRNAs, and looks forward to the research and application prospect of lncRNA in bovine mastitis, laying a foundation for molecular breeding and the biological therapy of bovine mastitis.

m5CRegpred: Epitranscriptome Target Prediction of 5-Methylcytosine (m5C) Regulators Based on Sequencing Features

5-methylcytosine (m5C) is a common post-transcriptional modification observed in a variety of RNAs. m5C has been demonstrated to be important in a variety of biological processes, including RNA structural stability and metabolism. Driven by the importance of m5C modification, many projects focused on the m5C sites prediction were reported before. To better understand the upstream and downstream regulation of m5C, we present a bioinformatics framework, m5CRegpred, to predict the substrate of m5C writer NSUN2 and m5C readers YBX1 and ALYREF for the first time. After features comparison, window lengths selection and algorism comparison on the mature mRNA model, our model achieved AUROC scores 0.869, 0.724 and 0.889 for NSUN2, YBX1 and ALYREF, respectively in an independent test. Our work suggests the substrate of m5C regulators can be distinguished and may help the research of m5C regulators in a special condition, such as substrates prediction of hyper- or hypo-expressed m5C regulators in human disease.

Emerging Potential of Exosomal Non-coding RNA in Parkinson’s Disease: A Review

Exosomes are extracellular vesicles that are released by cells and circulate freely in body fluids. Under physiological and pathological conditions, they serve as cargo for various biological substances such as nucleotides (DNA, RNA, ncRNA), lipids, and proteins. Recently, exosomes have been revealed to have an important role in the pathophysiology of several neurodegenerative illnesses, including Parkinson’s disease (PD). When secreted from damaged neurons, these exosomes are enriched in non-coding RNAs (e.g., miRNAs, lncRNAs, and circRNAs) and display wide distribution characteristics in the brain and periphery, bridging the gap between normal neuronal function and disease pathology. However, the current status of ncRNAs carried in exosomes regulating neuroprotection and PD pathogenesis lacks a systematic summary. Therefore, this review discussed the significance of ncRNAs exosomes in maintaining the normal neuron function and their pathogenic role in PD progression. Additionally, we have emphasized the importance of ncRNAs exosomes as potential non-invasive diagnostic and screening agents for the early detection of PD. Moreover, bioengineered exosomes are proposed to be used as drug carriers for targeted delivery of RNA interference molecules across the blood-brain barrier without immune system interference. Overall, this review highlighted the diverse characteristics of ncRNA exosomes, which may aid researchers in characterizing future exosome-based biomarkers for early PD diagnosis and tailored PD medicines.

Integrated Analysis of lncRNA and mRNA Expression Profiles Indicates Age-Related Changes in Meniscus

Little has been known about the role of long non-coding RNA (lncRNA) involves in change of aged meniscus. Microarray analyses were performed to identify lncRNAs and mRNAs expression profiles of meniscus in young and aging adults and apple bioinformatics methods to analyse their potential roles. The differentially expressed (DE) lncRNAs and mRNAs were confirmed by qRT-PCR. A total of 1608 DE lncRNAs and 1809 DE mRNAs were identified. Functional and pathway enrichment analyses of all DE mRNAs showed that DE mRNAs were mainly involved in the TGF-beta, Wnt, Hippo, PI3K-Akt signaling pathway. The expressions of TNFRSF11B and BMP2 were significantly upregulated in aging group. LASSO logistic regression analysis of the DE lncRNAs revealed four lncRNAs (AC124312.5, HCG11, POC1B-AS1, and AP001011.1) that were associated with meniscus degradation. CNC analysis demonstrated that AP001011 inhibited the expression of TNFRSF11B and AC1243125 upregulated the expression of TNFRSF11B. CeRNA analysis suggested that POC1B-AS1 regulates the expression of BMP2 by sponging miR 130a-3p, miR136-5p, miR 18a-3p, and miR 608. Furthermore, subcellular localization and m⁶A modification sites prediction analysis of these four lncRNAs was performed. These data lay a foundation for extensive studies on the role of lncRNAs in change of aged meniscus.

SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins

Fast and accurate identification of phage virion proteins (PVPs) would greatly aid facilitation of antibacterial drug discovery and development. Although, several research efforts based on machine learning (ML) methods have been made for in silico identification of PVPs, these methods have certain limitations. Therefore, in this study, we propose a new computational approach, termed SCORPION, (StaCking-based Predictior fOR Phage VIrion PrOteiNs), to accurately identify PVPs using only protein primary sequences. Specifically, we explored comprehensive 13 different feature descriptors from different aspects (i.e., compositional information, composition-transition-distribution information, position-specific information and physicochemical properties) with 10 popular ML algorithms to construct a pool of optimal baseline models. These optimal baseline models were then used to generate probabilistic features (PFs) and considered as a new feature vector. Finally, we utilized a two-step feature selection strategy to determine the optimal PF feature vector and used this feature vector to develop a stacked model (SCORPION). Both tenfold cross-validation and independent test results indicate that SCORPION achieves superior predictive performance than its constitute baseline models and existing methods. We anticipate SCORPION will serve as a useful tool for the cost-effective and large-scale screening of new PVPs. The source codes and datasets for this work are available for downloading in the GitHub repository (https://github.com/saeed344/SCORPION).

Large-scale comparative review and assessment of computational methods for phage virion proteins identification

Phage virion proteins (PVPs) are effective at recognizing and binding to host cell receptors while having no deleterious effects on human or animal cells. Understanding their functional mechanisms is regarded as a critical goal that will aid in rational antibacterial drug discovery and development. Although high-throughput experimental methods for identifying PVPs are considered the gold standard for exploring crucial PVP features, these procedures are frequently time-consuming and labor-intensive. Thusfar, more than ten sequence-based predictors have been established for the in silico identification of PVPs in conjunction with traditional experimental approaches. As a result, a revised and more thorough assessment is extremely desirable. With this purpose in mind, we first conduct a thorough survey and evaluation of a vast array of 13 state-of-the-art PVP predictors. Among these PVP predictors, they can be classified into three groups according to the types of machine learning (ML) algorithms employed (i.e. traditional ML-based methods, ensemble-based methods and deep learning-based methods). Subsequently, we explored which factors are important for building more accurate and stable predictors and this included training/independent datasets, feature encoding algorithms, feature selection methods, core algorithms, performance evaluation metrics/strategies and web servers. Finally, we provide insights and future perspectives for the design and development of new and more effective computational approaches for the detection and characterization of PVPs.

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.

Long Noncoding RNAs: Recent Insights into Their Role in Male Infertility and Their Potential as Biomarkers and Therapeutic Targets

Long noncoding RNAs (lncRNAs) are composed of nucleotides located in the nucleus and cytoplasm; these are transcribed by RNA polymerase II and are greater than 200 nt in length. LncRNAs fulfill important functions in a variety of biological processes, including genome imprinting, cell differentiation, apoptosis, stem cell pluripotency, X chromosome inactivation and nuclear transport. As high throughput sequencing technology develops, a substantial number of lncRNAs have been found to be related to a variety of biological processes, such as development of the testes, maintaining the self-renewal and differentiation of spermatogonial stem cells, and regulating spermatocyte meiosis. These indicate that lncRNAs can be used as biomarkers and potential therapeutic targets for male infertility. However, only a few comprehensive reviews have described the role of lncRNAs in male reproduction. In this paper, we summarize recent findings relating to the role of lncRNAs in spermatogenesis, their potential as biomarkers for male infertility and the relationship between reproductive arrest and transgenerational effects. Finally, we suggest specific targets for the treatment of male infertility from the perspective of lncRNAs.

Prediction of RNA subcellular localization: Learning from heterogeneous data sources

RNA subcellular localization has recently emerged as a widespread phenomenon, which may apply to the majority of RNAs. The two main sources of data for characterization of RNA localization are sequence features and microscopy images, such as obtained from single-molecule fluorescent in situ hybridization-based techniques. Although such imaging data are ideal for characterization of RNA distribution, these techniques remain costly, time-consuming, and technically challenging. Given these limitations, imaging data exist only for a limited number of RNAs. We argue that the field of RNA localization would greatly benefit from complementary techniques able to characterize location of RNA. Here we discuss the importance of RNA localization and the current methodology in the field, followed by an introduction on prediction of location of molecules. We then suggest a machine learning approach based on the integration between imaging localization data and sequence-based data to assist in characterization of RNA localization on a transcriptome level.

Integrated Analysis Reveals a lncRNA-miRNA-mRNA Network Associated with Pigeon Skeletal Muscle Development

Growing evidence has demonstrated the emerging role of long non-coding RNA as competitive endogenous RNA (ceRNA) in regulating skeletal muscle development. However, the mechanism of ceRNA regulated by lncRNA in pigeon skeletal muscle development remains unclear. To reveal the function and regulatory mechanisms of lncRNA, we first analyzed the expression profiles of lncRNA, microRNA (miRNA), and mRNA during the development of pigeon skeletal muscle using high-throughput sequencing. We then constructed a lncRNA-miRNA-mRNA ceRNA network based on differentially expressed (DE) lncRNAs, miRNAs, and mRNAs according to the ceRNA hypothesis. Functional enrichment and short time-series expression miner (STEM) analysis were performed to explore the function of the ceRNA network. Hub lncRNA-miRNA-mRNA interactions were identified by connectivity degree and validated using dual-luciferase activity assay. The results showed that a total of 1625 DE lncRNAs, 11,311 DE mRNAs, and 573 DE miRNAs were identified. A ceRNA network containing 9120 lncRNA-miRNA-mRNA interactions was constructed. STEM analysis indicated that the function of the lncRNA-associated ceRNA network might be developmental specific. Functional enrichment analysis identified potential pathways regulating pigeon skeletal muscle development, such as cell cycle and MAPK signaling. Based on the connectivity degree, lncRNAs TCONS_00066712, TCONS_00026594, TCONS_00001557, TCONS_00001553, and TCONS_00003307 were identified as hub genes in the ceRNA network. lncRNA TCONS_00026594 might regulate the FSHD region gene 1 (FRG1)/ SRC proto-oncogene, non-receptor tyrosine kinase (SRC) by sponge adsorption of cli-miR-1a-3p to affect the development of pigeon skeletal muscle. Our findings provide a data basis for in-depth elucidation of the lncRNA-associated ceRNA mechanism underlying pigeon skeletal muscle development.

Linc-ROR facilitates progression and angiogenesis of hepatocellular carcinoma by modulating DEPDC1 expression

Linc-ROR have been well-demonstrated to play important roles in cancer progression and angiogenesis. However, the underlying oncogenic mechanism of Linc-ROR in hepatocellular carcinoma is poorly understood. In this study, we demonstrate that Linc-ROR plays an oncogenic role in part through its positive regulation of DEPDC1 expression. Mechanistically, Linc-ROR acts as competing endogenous RNA to stabilize DEPDC1 mRNA and regulates DEPDC1 mRNA stability by binding HNRNPK. Thus, these findings suggest that function of Linc-ROR-mediated DEPDC1 could predispose hepatocellular carcinoma patients to progression and angiogenesis, and may serve as a potential target for anticancer therapies.

StackHCV: a web-based integrative machine-learning framework for large-scale identification of hepatitis C virus NS5B inhibitors

Fast and accurate identification of inhibitors with potency against HCV NS5B polymerase is currently a challenging task. As conventional experimental methods is the gold standard method for the design and development of new HCV inhibitors, they often require costly investment of time and resources. In this study, we develop a novel machine learning-based meta-predictor (termed StackHCV) for accurate and large-scale identification of HCV inhibitors. Unlike the existing method, which is based on single-feature-based approach, we first constructed a pool of various baseline models by employing a wide range of heterogeneous molecular fingerprints with five popular machine learning algorithms (k-nearest neighbor, multi-layer perceptron, partial least squares, random forest and support vectors machine). Secondly, we integrated these baseline models in order to develop the final meta-based model by means of the stacking strategy. Extensive benchmarking experiments showed that StackHCV achieved a more accurate and stable performance as compared to its constituent baseline models on the training dataset and also outperformed the existing predictor on the independent test dataset. To facilitate the high-throughput identification of HCV inhibitors, we built a web server that can be freely accessed at http://camt.pythonanywhere.com/StackHCV . It is expected that StackHCV could be a useful tool for fast and precise identification of potential drugs against HCV NS5B particularly for liver cancer therapy and other clinical applications.

iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features

Accurate identification of bitter peptides is of great importance for better understanding their biochemical and biophysical properties. To date, machine learning-based methods have become effective approaches for providing a good avenue for identifying potential bitter peptides from large-scale protein datasets. Although few machine learning-based predictors have been developed for identifying the bitterness of peptides, their prediction performances could be improved. In this study, we developed a new predictor (named iBitter-Fuse) for achieving more accurate identification of bitter peptides. In the proposed iBitter-Fuse, we have integrated a variety of feature encoding schemes for providing sufficient information from different aspects, namely consisting of compositional information and physicochemical properties. To enhance the predictive performance, the customized genetic algorithm utilizing self-assessment-report (GA-SAR) was employed for identifying informative features followed by inputting optimal ones into a support vector machine (SVM)-based classifier for developing the final model (iBitter-Fuse). Benchmarking experiments based on both 10-fold cross-validation and independent tests indicated that the iBitter-Fuse was able to achieve more accurate performance as compared to state-of-the-art methods. To facilitate the high-throughput identification of bitter peptides, the iBitter-Fuse web server was established and made freely available online. It is anticipated that the iBitter-Fuse will be a useful tool for aiding the discovery and de novo design of bitter peptides.

Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs

Apart from protein-coding Ribonucleic acids (RNAs), there exists a variety of non-coding RNAs (ncRNAs) which regulate complex cellular and molecular processes. High-throughput sequencing technologies and bioinformatics approaches have largely promoted the exploration of ncRNAs which revealed their crucial roles in gene regulation, miRNA binding, protein interactions, and splicing. Furthermore, ncRNAs are involved in the development of complicated diseases like cancer. Categorization of ncRNAs is essential to understand the mechanisms of diseases and to develop effective treatments. Sub-cellular localization information of ncRNAs demystifies diverse functionalities of ncRNAs. To date, several computational methodologies have been proposed to precisely identify the class as well as sub-cellular localization patterns of RNAs). This paper discusses different types of ncRNAs, reviews computational approaches proposed in the last 10 years to distinguish coding-RNA from ncRNA, to identify sub-types of ncRNAs such as piwi-associated RNA, micro RNA, long ncRNA, and circular RNA, and to determine sub-cellular localization of distinct ncRNAs and RNAs. Furthermore, it summarizes diverse ncRNA classification and sub-cellular localization determination datasets along with benchmark performance to aid the development and evaluation of novel computational methodologies. It identifies research gaps, heterogeneity, and challenges in the development of computational approaches for RNA sequence analysis. We consider that our expert analysis will assist Artificial Intelligence researchers with knowing state-of-the-art performance, model selection for various tasks on one platform, dominantly used sequence descriptors, neural architectures, and interpreting inter-species and intra-species performance deviation.

Review and comparative analysis of machine learning-based predictors for predicting and analyzing of anti-angiogenic peptides

Cancer is one of the leading causes of death worldwide and underlying this is angiogenesis that represents one of the hallmarks of cancer. Ongoing effort is already under way in the discovery of anti-angiogenic peptides (AAPs) as a promising therapeutic route by tackling the formation of new blood vessels. As such, the identification of AAPs constitutes a viable path for understanding their mechanistic properties pertinent for the discovery of new anti-cancer drugs. In spite of the abundance of peptide sequences in public databases, experimental efforts in the identification of anti-angiogenic peptides have progressed very slowly owing to its high expenditures and laborious nature. Owing to its inherent ability to make sense of large volumes of data, machine learning (ML) represents a lucrative technique that can be harnessed for peptide-based drug discovery. In this review, we conducted a comprehensive and comparative analysis of ML-based AAP predictors in terms of their employed feature descriptors, ML algorithms, cross-validation methods and prediction performance. Moreover, the common framework of these AAP predictors and their inherent weaknesses are also discussed. Particularly, we explore future perspectives for improving the prediction accuracy and model interpretability, which represents an interesting avenue for overcoming some of the inherent weaknesses of existing AAP predictors. We anticipate that this review would assist researchers in the rapid screening and identification of promising AAPs for clinical use.

In Silico Approaches for the Prediction and Analysis of Antiviral Peptides: A Review

In light of the growing resistance toward current antiviral drugs, efforts to discover novel and effective antiviral therapeutic agents remain a pressing scientific effort. Antiviral peptides (AVPs) represent promising therapeutic agents due to their extraordinary advantages in terms of potency, efficacy and pharmacokinetic properties. The growing volume of newly discovered peptide sequences in the post-genomic era requires computational approaches for timely and accurate identification of AVPs. Machine learning (ML) methods such as random forest and support vector machine represent robust learning algorithms that are instrumental in successful peptide-based drug discovery. Therefore, this review summarizes the current state-of-the-art application of ML methods for identifying AVPs directly from the sequence information. We compare the efficiency of these methods in terms of the underlying characteristics of the dataset used along with feature encoding methods, ML algorithms, cross-validation methods and prediction performance. Finally, guidelines for the development of robust AVP models are also discussed. It is anticipated that this review will serve as a useful guide for the design and development of robust AVP and related therapeutic peptide predictors in the future.

Comparison and Analysis of Computational Methods for Identifying N6-Methyladenosine Sites in Saccharomyces cerevisiae

BACKGROUND

N⁶-methyladenosine (m⁶A) plays critical roles in a broad range of biological processes. Knowledge about the precise location of m⁶A site in the transcriptome is vital for deciphering its biological functions. Although experimental techniques have made substantial contributions to identify m⁶A, they are still labor intensive and time consuming. As complement to experimental methods, in the past few years, a series of computational approaches have been proposed to identify m⁶A sites.

METHODS

In order to facilitate researchers to select appropriate methods for identifying m⁶A sites, it is necessary to conduct a comprehensive review and comparison of existing methods.

RESULTS

Since research works on m⁶A in Saccharomyces cerevisiae are relatively clear, in this review, we summarized recent progress of computational prediction of m⁶A sites in S. cerevisiae and assessed the performance of existing computational methods. Finally, future directions of computationally identifying m⁶A sites are presented.

CONCLUSION

Taken together, we anticipate that this review will serve as an important guide for computational analysis of m⁶A modifications.

mLoc-mRNA: predicting multiple sub-cellular localization of mRNAs using random forest algorithm coupled with feature selection via elastic net

BACKGROUND

Localization of messenger RNAs (mRNAs) plays a crucial role in the growth and development of cells. Particularly, it plays a major role in regulating spatio-temporal gene expression. The in situ hybridization is a promising experimental technique used to determine the localization of mRNAs but it is costly and laborious. It is also a known fact that a single mRNA can be present in more than one location, whereas the existing computational tools are capable of predicting only a single location for such mRNAs. Thus, the development of high-end computational tool is required for reliable and timely prediction of multiple subcellular locations of mRNAs. Hence, we develop the present computational model to predict the multiple localizations of mRNAs.

RESULTS

The mRNA sequences from 9 different localizations were considered. Each sequence was first transformed to a numeric feature vector of size 5460, based on the k-mer features of sizes 1-6. Out of 5460 k-mer features, 1812 important features were selected by the Elastic Net statistical model. The Random Forest supervised learning algorithm was then employed for predicting the localizations with the selected features. Five-fold cross-validation accuracies of 70.87, 68.32, 68.36, 68.79, 96.46, 73.44, 70.94, 97.42 and 71.77% were obtained for the cytoplasm, cytosol, endoplasmic reticulum, exosome, mitochondrion, nucleus, pseudopodium, posterior and ribosome respectively. With an independent test set, accuracies of 65.33, 73.37, 75.86, 72.99, 94.26, 70.91, 65.53, 93.60 and 73.45% were obtained for the respective localizations. The developed approach also achieved higher accuracies than the existing localization prediction tools.

CONCLUSIONS

This study presents a novel computational tool for predicting the multiple localization of mRNAs. Based on the proposed approach, an online prediction server "mLoc-mRNA" is accessible at http://cabgrid.res.in:8080/mlocmrna/ . The developed approach is believed to supplement the existing tools and techniques for the localization prediction of mRNAs.

Lantern: an integrative repository of functional annotations for lncRNAs in the human genome

BACKGROUND

With advancements in omics technologies, the range of biological processes where long non-coding RNAs (lncRNAs) are involved, is expanding extensively, thereby generating the need to develop lncRNA annotation resources. Although, there are a plethora of resources for annotating genes, despite the extensive corpus of lncRNA literature, the available resources with lncRNA ontology annotations are rare.

RESULTS

We present a lncRNA annotation extractor and repository (Lantern), developed using PubMed's abstract retrieval engine and NCBO's recommender annotation system. Lantern's annotations were benchmarked against lncRNAdb's manually curated free text. Benchmarking analysis suggested that Lantern has a recall of 0.62 against lncRNAdb for 182 lncRNAs and precision of 0.8. Additionally, we also annotated lncRNAs with multiple omics annotations, including predicted cis-regulatory TFs, interactions with RBPs, tissue-specific expression profiles, protein co-expression networks, coding potential, sub-cellular localization, and SNPs for ~ 11,000 lncRNAs in the human genome, providing a one-stop dynamic visualization platform.

CONCLUSIONS

Lantern integrates a novel, accurate semi-automatic ontology annotation engine derived annotations combined with a variety of multi-omics annotations for lncRNAs, to provide a central web resource for dissecting the functional dynamics of long non-coding RNAs and to facilitate future hypothesis-driven experiments. The annotation pipeline and a web resource with current annotations for human lncRNAs are freely available on sysbio.lab.iupui.edu/lantern.

Identification and Functional Prediction of Long Non-Coding RNAs in Dilated Cardiomyopathy by Bioinformatics Analysis

Dilated cardiomyopathy (DCM) is a relatively common cause of heart failure and the leading cause of heart transplantation. Aberrant changes in long non-coding RNAs (lncRNAs) are involved in DCM disorder; however, the detailed mechanisms underlying DCM initiation and progression require further investigation, and new molecular targets are needed. Here, we obtained lncRNA-expression profiles associated with DCM and non-failing hearts through microarray probe-sequence re-annotation. Weighted gene co-expression network analysis revealed a module highly associated with DCM status. Then eight hub lncRNAs in this module (FGD5-AS1, AC009113.1, WDFY3-AS2, NIFK-AS1, ZNF571-AS1, MIR100HG, AC079089.1, and EIF3J-AS1) were identified. All hub lncRNAs except ZNF571-AS1 were predicted as localizing to the cytoplasm. As a possible mechanism of DCM pathogenesis, we predicted that these hub lncRNAs might exert functions by acting as competing endogenous RNAs (ceRNAs). Furthermore, we found that the above results can be essentially reproduced in an independent external dataset. We observed the localization of hub lncRNAs by RNA-FISH in human aortic smooth muscle cells and confirmed the upregulation of the hub lncRNAs in DCM patients through quantitative RT-PCR. In conclusion, these findings identified eight candidate lncRNAs associated with DCM disease and revealed their potential involvement in DCM partly through ceRNA crosstalk. Our results facilitate the discovery of therapeutic targets and enhance the understanding of DCM pathogenesis.

i6mA-VC: A Multi-Classifier Voting Method for the Computational Identification of DNA N6-methyladenine Sites

DNA N6-methyladenine (6 mA), as an essential component of epigenetic modification, cannot be neglected in genetic regulation mechanism. The efficient and accurate prediction of 6 mA sites is beneficial to the development of biological genetics. Biochemical experimental methods are considered to be time-consuming and laborious. Most of the established machine learning methods have a single dataset. Although some of them have achieved cross-species prediction, their results are not satisfactory. Therefore, we designed a novel statistical model called i6mA-VC to improve the accuracy for 6 mA sites. On the one hand, kmer and binary encoding are applied to extract features, and then gradient boosting decision tree (GBDT) embedded method is applied as the feature selection strategy. On the other hand, DNA sequences are represented by vectors through the feature extraction method of ring-function-hydrogen-chemical properties (RFHCP) and the feature selection strategy of ExtraTree. After fusing the two optimal features, a voting classifier based on gradient boosting decision tree (GBDT), light gradient boosting machine (LightGBM) and multilayer perceptron classifier (MLPC) is constructed for final classification and prediction. The accuracy of Rice dataset and M.musculus dataset with five-fold cross-validation are 0.888 and 0.967, respectively. The cross-species dataset is selected as independent testing dataset, and the accuracy reaches 0.848. Through rigorous experiments, it is demonstrated that the proposed predictor is convincing and applicable. The development of i6mA-VC predictor will become an effective way for the recognition of N6-methyladenine sites, and it will also be beneficial for biological geneticists to further study gene expression and DNA modification. In addition, an accessible web-server for i6mA-VC is available from http://www.zhanglab.site/ .

Integrated analysis of lncRNA, miRNA and mRNA profiles reveals potential lncRNA functions during early HIV infection

Background

Long noncoding RNAs (lncRNAs) can regulate gene expression in a cis-regulatory fashion or as “microRNA sponges”. However, the expression and functions of lncRNAs during early human immunodeficiency virus (HIV) infection (EHI) remain unclear.

Methods

3 HAART-naive EHI patients and 3 healthy controls (HCs) were recruited in this study to perform RNA sequencing and microRNA (miRNA) sequencing. The expression profiles of lncRNAs, mRNAs and miRNAs were obtained, and the potential roles of lncRNAs were analysed based on discovering lncRNA cis-regulatory target mRNAs and constructing lncRNA–miRNA–mRNA competing endogenous RNA (ceRNA) networks. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on 175 lncRNA-associated differentially expressed (DE) mRNAs to investigate the potential functions of DE lncRNAs in ceRNA networks.

Results

A total of 242 lncRNAs, 1240 mRNAs and 21 mature known miRNAs were determined as differentially expressed genes in HAART-naive EHI patients compared to HCs. Among DE lncRNAs, 44 lncRNAs were predicted to overlap with 41 target mRNAs, and 107 lncRNAs might regulate their nearby DE mRNAs. Two DE lncRNAs might regulate their cis-regulatory target mRNAs BTLA and ZAP70, respectively, which were associated with immune activation. In addition, the ceRNA networks comprised 160 DE lncRNAs, 21 DE miRNAs and 175 DE mRNAs. Seventeen DE lncRNAs were predicted to regulate HIF1A and TCF7L2, which are involved in the process of HIV-1 replication. Twenty DE lncRNAs might share miRNA response elements (MREs) with FOS, FOSB and JUN, which are associated with both immune activation and HIV-1 replication.

Conclusions

This study revealed that lncRNAs might play a critical role in HIV-1 replication and immune activation during EHI. These novel findings are helpful for understanding of the pathogenesis of HIV infection and provide new insights into antiviral therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02802-9.