miREE: miRNA recognition elements ensemble

Small RNA Targets: Advances in Prediction Tools and High-Throughput Profiling

MicroRNAs (miRNAs) are an abundant class of small non-coding RNAs that regulate gene expression at the post-transcriptional level. They are suggested to be involved in most biological processes of the cell primarily by targeting messenger RNAs (mRNAs) for cleavage or translational repression. Their binding to their target sites is mediated by the Argonaute (AGO) family of proteins. Thus, miRNA target prediction is pivotal for research and clinical applications. Moreover, transfer-RNA-derived fragments (tRFs) and other types of small RNAs have been found to be potent regulators of Ago-mediated gene expression. Their role in mRNA regulation is still to be fully elucidated, and advancements in the computational prediction of their targets are in their infancy. To shed light on these complex RNA-RNA interactions, the availability of good quality high-throughput data and reliable computational methods is of utmost importance. Even though the arsenal of computational approaches in the field has been enriched in the last decade, there is still a degree of discrepancy between the results they yield. This review offers an overview of the relevant advancements in the field of bioinformatics and machine learning and summarizes the key strategies utilized for small RNA target prediction. Furthermore, we report the recent development of high-throughput sequencing technologies, and explore the role of non-miRNA AGO driver sequences.

Metapath-Based Deep Convolutional Neural Network for Predicting miRNA-Target Association on Heterogeneous Network

Predicting the interactions between microRNAs (miRNAs) and target genes is of great significance for understanding the regulatory mechanism of miRNA and treating complex diseases. The emergence of large-scale, heterogeneous biological networks has offered unprecedented opportunities for revealing miRNA-associated target genes. However, there are still some limitations about automatically learn the feature information of the network in the existing methods. Since network representation learning can self-adaptively capture structure information of the network, we propose a framework based on heterogeneous network representation, MDCNN (Metapath-Based Deep Convolutional Neural Network), to predict the associations between miRNAs and target genes. MDCNN samples the paths between the node pairs in the form of meta-path based on the heterogeneous information network (HIN) about miRNAs and target genes. Then the node feature and the path feature which is learned by the Deep Convolutional Neural Network (DCNN) are spliced together as the representation of the miRNA-target gene, to predict the miRNA-target gene interactions. The experiment results indicate that the performance of MDCNN outperforms other methods in multiple validation metrics by fivefold cross validation. We set an ablation study to identify the necessity of miRNA similarity and target gene similarity for improving the prediction ability of MDCNN. The case studies on hsa-miR-26b-5p and CDKN1A further demonstrates that MDCNN can successfully predict potential miRNA-target gene interactions.

Prediction methods for microRNA targets in bilaterian animals: Toward a better understanding by biologists

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression at the posttranscriptional level. Because of their wide network of interactions, miRNAs have become the focus of many studies over the past decade, particularly in animal species. To streamline the number of potential wet lab experiments, the use of miRNA target prediction tools is currently the first step undertaken. However, the predictions made may vary considerably depending on the tool used, which is mostly due to the complex and still not fully understood mechanism of action of miRNAs. The discrepancies complicate the choice of the tool for miRNA target prediction. To provide a comprehensive view of this issue, we highlight in this review the main characteristics of miRNA-target interactions in bilaterian animals, describe the prediction models currently used, and provide some insights for the evaluation of predictor performance.

Vps34 and TOR Kinases Coordinate HAC1 mRNA Translation in the Presence or Absence of Ire1-Dependent Splicing

In the budding yeast Saccharomyces cerevisiae, an mRNA, called HAC1, exists in a translationally repressed form in the cytoplasm. Under conditions of cellular stress, such as when unfolded proteins accumulate inside the endoplasmic reticulum (ER), an RNase Ire1 removes an intervening sequence (intron) from the HAC1 mRNA by nonconventional cytosolic splicing. Removal of the intron results in translational derepression of HAC1 mRNA and production of a transcription factor that activates expression of many enzymes and chaperones to increase the protein-folding capacity of the cell. Here, we show that Ire1-mediated RNA cleavage requires Watson-Crick base pairs in two RNA hairpins, which are located at the HAC1 mRNA exon-intron junctions. Then, we show that the translational derepression of HAC1 mRNA can occur independent of cytosolic splicing. These results are obtained from HAC1 variants that translated an active Hac1 protein from the unspliced mRNA. Additionally, we show that the phosphatidylinositol-3-kinase Vps34 and the nutrient-sensing kinases TOR and GCN2 are key regulators of HAC1 mRNA translation and consequently the ER stress responses. Collectively, our data suggest that the cytosolic splicing and the translational derepression of HAC1 mRNA are coordinated by unique and parallel networks of signaling pathways.

A Supervised Ensemble Approach for Sensitive microRNA Target Prediction

MicroRNAs, a class of small non-coding RNAs, regulate important biological functions via post-transcriptional regulation of messenger RNAs (mRNAs). Despite rapid development in miRNA research, precise experimental methods to determine miRNA target interactions are still lacking. This motivated us to explore the in silico target interaction features and incorporate them in predictive modeling. We propose a systematic approach towards developing a sensitive miRNA target prediction model to explore the interplay of target recognition features. In the first step, we have employed a supervised ensemble under-sampling approach to address the problem of imbalance in the training dataset due to a larger number of negative instances. Various feature selection techniques were evaluated to obtain the optimal feature subset that best recognizes the true miRNA-mRNA targets. In the second step, we have built our optimal model, miRTPred, a novel blending ensemble-based approach that combines the predictions of the best performing traditional and classical ensemble models, through a weighted voting classifier, achieving a sensitivity of 87 percent and F1-score of 0.88 for 3'UTR region of the mRNA transcript. miRTPred outperforms popular machine learning (ML) and non-ML approaches to target prediction algorithms. miRTPred is freely available at http://bicresources.jcbose.ac.in/zhumur/mirtpred.

mirDIP 4.1-integrative database of human microRNA target predictions

MicroRNAs are important regulators of gene expression, achieved by binding to the gene to be regulated. Even with modern high-throughput technologies, it is laborious and expensive to detect all possible microRNA targets. For this reason, several computational microRNA-target prediction tools have been developed, each with its own strengths and limitations. Integration of different tools has been a successful approach to minimize the shortcomings of individual databases. Here, we present mirDIP v4.1, providing nearly 152 million human microRNA-target predictions, which were collected across 30 different resources. We also introduce an integrative score, which was statistically inferred from the obtained predictions, and was assigned to each unique microRNA-target interaction to provide a unified measure of confidence. We demonstrate that integrating predictions across multiple resources does not cumulate prediction bias toward biological processes or pathways. mirDIP v4.1 is freely available at http://ophid.utoronto.ca/mirDIP/.

In silico identification of conserved miRNAs and their selective target gene prediction in indicine (Bos indicus) cattle

The modern cattle was domesticated from aurochs, sharing its physiological traits into two subspecies Bos taurus and Bos indicus. MicroRNAs (miRNAs) are a class of non-coding short RNAs of ~22nt which have a key role in the regulation of many cellular and physiological processes in the animal. The current study was aimed to predict and annotate the potential mutations in indicine miRNAs throughout the genome using de novo and homology-based in silico approaches. Genome-wide mapping was performed in available indicine assembly by the homology-based approach and 768 miRNAs were recovered out of 808 reported taurine miRNAs belonging to 521 unique mature miRNA families. While 42 precursors were dropped due to lack of secondary miRNA structure, increasing stringency or decreasing similarity between the two genomes' miRNA. Increasing tendency of miRNAs incidence was observed on chr5, chr7, chr8, chr12 and chr21 with 19 polycistronic miRNA within 1-kilobase distance throughout the indicine genome. Notably, 12 miRNAs showed copy number variation. Eighteen miRNAs showed a mutation in their mature sequences in which eight were found in their seed region. Whilst in de novo based approach, 12 novel potential miRNAs on Y chromosome in indicine cattle along with a new miRNA (bind-miR-1264) on chrX were found. The final data set is annotated and explains the impending target genes that are responsible for enhanced immunity, heat tolerance and disease tolerance regulation in indicine. The study conforms to better understanding and perceptive approach towards indicine genome.

Combining Supervised and Unsupervised Learning for Improved miRNA Target Prediction

MicroRNAs (miRNAs) are short non-coding RNAs which bind to mRNAs and regulate their expression. MiRNAs have been found to be associated with initiation and progression of many complex diseases. Investigating miRNAs and their targets can thus help develop new therapies by designing anti-miRNA oligonucleotides. While existing computational approaches can predict miRNA targets, these predictions have low accuracy. In this paper, we propose a two-step approach to refine the results of sequence-based prediction algorithms. The first step, which is based on our previous work, uses an ensemble learning approach that combines multiple existing methods. The second step utilizes support vector machine (SVM) classifiers in one- and two-class modes to infer miRNA-mRNA interactions based on both binding features, as well as network features extracted from gene regulatory network. Experimental results using two real data sets from TCGA indicate that the use of two-class SVM classification significantly improves the precision of miRNA-mRNA prediction.

Inferring MicroRNA Targets Based on Restricted Boltzmann Machines

Predicting the miRNA-target interactions (MTIs) is a critical task for elucidating mechanistic roles of miRNAs in pathophysiology. However, most existing techniques have a higher false positive because the precise miRNA target mechanisms are poorly known. Considering that ensemble methods can take advantage of the complementary knowledge in different methods, we propose an alternative optimization framework, Inferring MiRNA Targets based on Restricted Boltzmann Machines (IMTRBM), to enhance the accuracy of previous prediction results. First, the proposed method directly constructs a weighted MTI network though the results predicted by individual methods and each miRNA target pair is weighted based on the frequency appearing in these results. Second, we transform the miRNA-target prediction problem into a complete bipartite graph model, named restricted Boltzmann machine, and utilize a practical learning procedure to train our model and make predictions. Our results show that the algorithm outperforms individual miRNA-target prediction approach in the number of validated miRNA targets at cutoffs of top list. Moreover, our framework can tolerate the decrease and increase of predicted MTIs and even discover new miRNA targets, which have been a challenge to predict for any individual methods. Finally, for the miRNAs that are not appearing in IMTRBM, we design a new method to supplement IMTRBM based on the intuition that similar miRNAs have similar functions, which also achieves a comparable result. The source code of IMTRBM is available at https://github.com/liuying201705/IMTRBM.

miRNAs target databases: developmental methods and target identification techniques with functional annotations

PURPOSE

microRNA (miRNA) regulates diverse biological mechanisms and metabolisms in plants and animals. Thus, the discoveries of miRNA has revolutionized the life sciences and medical research.The miRNA represses and cleaves the targeted mRNA by binding perfect or near perfect or imperfect complementary base pairs by RNA-induced silencing complex (RISC) formation during biogenesis process. One miRNA interacts with one or more mRNA genes and vice versa, hence takes part in causing various diseases. In this paper, the different microRNA target databases and their functional annotations developed by various researchers have been reviewed. The concurrent research review aims at comprehending the significance of miRNA and presenting the existing status of annotated miRNA target resources built by researchers henceforth discovering the knowledge for diagnosis and prognosis.

METHODS AND RESULTS

This review discusses the applications and developmental methodologies for constructing target database as well as the utility of user interface design. An integrated architecture is drawn and a graphically comparative study of present status of miRNA targets in diverse diseases and various biological processes is performed. These databases comprise of information such as miRNA target-associated disease, transcription factor binding sites (TFBSs) in miRNA genomic locations, polymorphism in miRNA target, A-to-I edited target, Gene Ontology (GO), genome annotations, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, target expression analysis, TF-miRNA and miRNA-mRNA interaction networks, drugs-targets interactions, etc.

CONCLUSION

miRNA target databases contain diverse experimentally and computationally predicted target through various algorithms. The comparison of various miRNA target database has been performed on various parameters. The computationally predicted target databases suffer from false positive information as there is no common theory for prediction of miRNA targets. The review conclusion emphasizes the need of more intelligent computational improvement for the miRNA target identification, their functional annotations and datasbase development.

Genome-Wide Analysis of MicroRNA-Regulated Transcripts

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression by either degrading transcripts or repressing translation . Over the past decade the significance of miRNAs has been unraveled by the characterization of their involvement in crucial cellular functions and the development of disease. However, continued progress in understanding the endogenous importance of miRNAs, as well as their potential uses as therapeutic tools, has been hindered by the difficulty of positively identifying miRNA targets. To face this challenge algorithmic approaches have primarily been utilized to date, but strictly mathematical models have thus far failed to produce a generally accurate, widely accepted methodology for accurate miRNA target determination. As such, several laboratory-based, comprehensive strategies for experimentally identifying all cellular miRNA regulations simultaneously have recently been developed. This chapter discusses the advantages and limitations of both classic and comprehensive strategies for miRNA target prediction .

MiRTDL: A Deep Learning Approach for miRNA Target Prediction

MicroRNAs (miRNAs) regulate genes that are associated with various diseases. To better understand miRNAs, the miRNA regulatory mechanism needs to be investigated and the real targets identified. Here, we present miRTDL, a new miRNA target prediction algorithm based on convolutional neural network (CNN). The CNN automatically extracts essential information from the input data rather than completely relying on the input dataset generated artificially when the precise miRNA target mechanisms are poorly known. In this work, the constraint relaxing method is first used to construct a balanced training dataset to avoid inaccurate predictions caused by the existing unbalanced dataset. The miRTDL is then applied to 1,606 experimentally validated miRNA target pairs. Finally, the results show that our miRTDL outperforms the existing target prediction algorithms and achieves significantly higher sensitivity, specificity and accuracy of 88.43, 96.44, and 89.98 percent, respectively. We also investigate the miRNA target mechanism, and the results show that the complementation features are more important than the others.

PlantMirnaT: miRNA and mRNA integrated analysis fully utilizing characteristics of plant sequencing data

miRNA is known to regulate up to several hundreds coding genes, thus the integrated analysis of miRNA and mRNA expression data is an important problem. Unfortunately, the integrated analysis is challenging since it needs to consider expression data of two different types, miRNA and mRNA, and target relationship between miRNA and mRNA is not clear, especially when microarray data is used. Fortunately, due to the low sequencing cost, small RNA and RNA sequencing are routinely processed and we may be able to infer regulation relationships between miRNAs and mRNAs more accurately by using sequencing data. However, no method is developed specifically for sequencing data. Thus we developed PlantMirnaT, a new miRNA-mRNA integrated analysis system. To fully leverage the power of sequencing data, three major features are developed and implemented in PlantMirnaT. First, we implemented a plant-specific short read mapping tool based on recent discoveries on miRNA target relationship in plant. Second, we designed and implemented an algorithm considering miRNA targets in the full intragenic region, not just 3' UTR. Lastly but most importantly, our algorithm is designed to consider quantity of miRNA expression and its distribution on target mRNAs. The new algorithm was used to characterize rice under drought condition using our proprietary data. Our algorithm successfully discovered that two miRNAs, miRNA1425-5p, miRNA 398b, that are involved in suppression of glucose pathway in a naturally drought resistant rice, Vandana. The system can be downloaded at https://sites.google.com/site/biohealthinformaticslab/resources.

Ensemble learning can significantly improve human microRNA target prediction

MicroRNAs (miRNAs) regulate the function of their target genes by down-regulating gene expression, participating in various biological processes. Since the discovery of the first miRNA, computational tools have been essential to predict targets of given miRNAs that can be biologically verified. The precise mechanism underlying miRNA-mRNA interaction has not yet been elucidated completely, and it is still difficult to predict miRNA targets computationally in a robust fashion, despite the large number of in silico prediction methodologies in existence. Because of this limitation, different target prediction tools often report different and (occasionally conflicting) sets of targets. Therefore, we propose a novel target prediction methodology called stacking-based miRNA interaction learner ensemble (SMILE) that employs the concept of stacked generalization (stacking), which is a type of ensemble learning that integrates the outcomes of individual prediction tools with the aim of surpassing the performance of the individual tools. We tested the proposed SMILE method on human miRNA-mRNA interaction data derived from public databases. In our experiments, SMILE improved the accuracy of the target prediction significantly in terms of the area under the receiver operating characteristic curve. Any new target prediction tool can easily be incorporated into the proposed methodology as a component learner, and we anticipate that SMILE will provide a flexible and effective framework for elucidating in vivo miRNA-mRNA interaction.

MicroRNAs as Haematopoiesis Regulators

The production of different types of blood cells including their formation, development, and differentiation is collectively known as haematopoiesis. Blood cells are divided into three lineages erythriod (erythrocytes), lymphoid (B and T cells), and myeloid (granulocytes, megakaryocytes, and macrophages). Haematopoiesis is a complex process regulated by several mechanisms including microRNAs (miRNAs). miRNAs are small RNAs which regulate the expression of a number of genes involved in commitment and differentiation of hematopoietic stem cells. Evidence shows that miRNAs play an important role in haematopoiesis; for example, myeloid and erythroid differentiation is blocked by the overexpression of miR-15a. miR-221, miR-222, and miR-24 inhibit the erythropoiesis, whereas miR-150 plays a role in B and T cell differentiation. miR-146 and miR-10a are downregulated in megakaryopoiesis. Aberrant expression of miRNAs was observed in hematological malignancies including chronic myelogenous leukemia, chronic lymphocytic leukemia, multiple myelomas, and B cell lymphomas. In this review we have focused on discussing the role of miRNA in haematopoiesis.

Big effects of small RNAs: a review of microRNAs in anxiety

Epigenetic and regulatory elements provide an additional layer of complexity to the heterogeneity of anxiety disorders. MicroRNAs (miRNAs) are a class of small, noncoding RNAs that have recently drawn interest as epigenetic modulators of gene expression in psychiatric disorders. miRNAs elicit their effects by binding to target messenger RNAs (mRNAs) and hindering translation or accelerating degradation. Considering their role in neuronal differentiation and synaptic plasticity, miRNAs have opened up new investigative avenues in the aetiology and treatment of anxiety disorders. In this review, we provide a thorough analysis of miRNAs, their targets and their functions in the central nervous system (CNS), focusing on their role in anxiety disorders. The involvement of miRNAs in CNS functions (such as neurogenesis, neurite outgrowth, synaptogenesis and synaptic and neural plasticity) and their intricate regulatory role under stressful conditions strongly support their importance in the aetiology of anxiety disorders. Furthermore, miRNAs could provide new avenues for the development of therapeutic targets in anxiety disorders.

HomoTarget: a new algorithm for prediction of microRNA targets in Homo sapiens

MiRNAs play an essential role in the networks of gene regulation by inhibiting the translation of target mRNAs. Several computational approaches have been proposed for the prediction of miRNA target-genes. Reports reveal a large fraction of under-predicted or falsely predicted target genes. Thus, there is an imperative need to develop a computational method by which the target mRNAs of existing miRNAs can be correctly identified. In this study, combined pattern recognition neural network (PRNN) and principle component analysis (PCA) architecture has been proposed in order to model the complicated relationship between miRNAs and their target mRNAs in humans. The results of several types of intelligent classifiers and our proposed model were compared, showing that our algorithm outperformed them with higher sensitivity and specificity. Using the recent release of the mirBase database to find potential targets of miRNAs, this model incorporated twelve structural, thermodynamic and positional features of miRNA:mRNA binding sites to select target candidates.

One decade of development and evolution of microRNA target prediction algorithms

Nearly two decades have passed since the publication of the first study reporting the discovery of microRNAs (miRNAs). The key role of miRNAs in post-transcriptional gene regulation led to the performance of an increasing number of studies focusing on origins, mechanisms of action and functionality of miRNAs. In order to associate each miRNA to a specific functionality it is essential to unveil the rules that govern miRNA action. Despite the fact that there has been significant improvement exposing structural characteristics of the miRNA–mRNA interaction, the entire physical mechanism is not yet fully understood. In this respect, the development of computational algorithms for miRNA target prediction becomes increasingly important. This manuscript summarizes the research done on miRNA target prediction. It describes the experimental data currently available and used in the field and presents three lines of computational approaches for target prediction. Finally, the authors put forward a number of considerations regarding current challenges and future directions.