scholarly journals Noncanonical targeting contributes significantly to miRNA-mediated regulation

2020 ◽  
Author(s):  
Jennifer Y. Tan ◽  
Baroj Abdulkarim ◽  
Ana C. Marques
Keyword(s):  
Previous Analysis ◽  
Target Prediction ◽  
Seed Region ◽  
Biological Processes ◽  
Mirna Target Prediction ◽  
Mirna Targets ◽  
Sequence Complementarity ◽  
Health And Disease ◽  
The Impact ◽  
Mirna Seed

ABSTRACTDetermining which genes are targeted by miRNAs is crucial to elucidate their contributions to diverse biological processes in health and disease. Most miRNA target prediction tools rely on the identification of complementary regions between transcripts and miRNAs. Whereas important for target recognition, the presence of complementary sites is not sufficient to identify transcripts targeted by miRNAs.Here, we describe an unbiased statistical genomics approach that explores genetically driven changes in gene expression between human individuals. Using this approach, we identified transcripts that respond to physiological changes in miRNA levels. We found that a much smaller fraction of mRNAs expressed in lymphoblastoid cell lines (LCLs) than what is predicted by other tools is targeted by miRNAs. We estimate that each miRNA has a relatively small number of targets. The transcripts we predict to be miRNA targets are enriched in AGO-binding and previously validated miRNAs target interactions, supporting the reliability of our predictions. Consistent with previous analysis, these targets are also enriched among dosage sensitive and highly controlled genes.Almost a third of genes we predict to be miRNA targets lack sequence complementarity to the miRNA seed region (noncanonical targets). These noncanonical targets have higher complementary with the miRNA 3’ end. The impact of miRNAs on the levels of their canonical or noncanonical targets is identical supporting the relevance of this poorly explored mechanism of targeting.

scholarly journals Comprehensive Overview and Assessment of microRNA Target Prediction Tools in Homo sapiens and Drosophila melanogaster

2019 ◽  
Vol 14 (5) ◽  
pp. 432-445 ◽  
Author(s):  
Muniba Faiza ◽  
Khushnuma Tanveer ◽  
Saman Fatihi ◽  
Yonghua Wang ◽  
Khalid Raza
Keyword(s):  
Drosophila Melanogaster ◽  
Mirna Target ◽  
Homo Sapiens ◽  
Target Prediction ◽  
Computational Prediction ◽  
Transcriptional Level ◽  
Mirna Target Prediction ◽  
Microrna Target ◽  
Prediction Tools ◽  
Mirna Targets

Background: MicroRNAs (miRNAs) are small non-coding RNAs that control gene expression at the post-transcriptional level through complementary base pairing with the target mRNA, leading to mRNA degradation and blocking translation process. Many dysfunctions of these small regulatory molecules have been linked to the development and progression of several diseases. Therefore, it is necessary to reliably predict potential miRNA targets. Objective: A large number of computational prediction tools have been developed which provide a faster way to find putative miRNA targets, but at the same time, their results are often inconsistent. Hence, finding a reliable, functional miRNA target is still a challenging task. Also, each tool is equipped with different algorithms, and it is difficult for the biologists to know which tool is the best choice for their study. Methods: We analyzed eleven miRNA target predictors on Drosophila melanogaster and Homo sapiens by applying significant empirical methods to evaluate and assess their accuracy and performance using experimentally validated high confident mature miRNAs and their targets. In addition, this paper also describes miRNA target prediction algorithms, and discusses common features of frequently used target prediction tools. Results: The results show that MicroT, microRNA and CoMir are the best performing tool on Drosopihla melanogaster; while TargetScan and miRmap perform well for Homo sapiens. The predicted results of each tool were combined in order to improve the performance in both the datasets, but any significant improvement is not observed in terms of true positives. Conclusion: The currently available miRNA target prediction tools greatly suffer from a large number of false positives. Therefore, computational prediction of significant targets with high statistical confidence is still an open challenge.

scholarly journals miTAR: a hybrid deep learning-based approach for predicting miRNA targets

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tongjun Gu ◽  
Xiwu Zhao ◽  
William Bradley Barbazuk ◽  
Ji-Hyun Lee
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Mirna Target ◽  
Target Prediction ◽  
Hybrid Models ◽  
Mirna Target Prediction ◽  
Max Pooling ◽  
Mirna Targets ◽  
Spatial Features ◽  
Wide Range

Abstract Background microRNAs (miRNAs) have been shown to play essential roles in a wide range of biological processes. Many computational methods have been developed to identify targets of miRNAs. However, the majority of these methods depend on pre-defined features that require considerable efforts and resources to compute and often prove suboptimal at predicting miRNA targets. Results We developed a novel hybrid deep learning-based (DL-based) approach that is capable of predicting miRNA targets at a higher accuracy. This approach integrates convolutional neural networks (CNNs) that excel in learning spatial features and recurrent neural networks (RNNs) that discern sequential features. Therefore, our approach has the advantages of learning both the intrinsic spatial and sequential features of miRNA:target. The inputs for our approach are raw sequences of miRNAs and genes that can be obtained effortlessly. We applied our approach on two human datasets from recently miRNA target prediction studies and trained two models. We demonstrated that the two models consistently outperform the previous methods according to evaluation metrics on test datasets. Comparing our approach with currently available alternatives on independent datasets shows that our approach delivers substantial improvements in performance. We also show with multiple evidences that our approach is more robust than other methods on small datasets. Our study is the first study to perform comparisons across multiple existing DL-based approaches on miRNA target prediction. Furthermore, we examined the contribution of a Max pooling layer in between the CNN and RNN and demonstrated that it improves the performance of all our models. Finally, a unified model was developed that is robust on fitting different input datasets. Conclusions We present a new DL-based approach for predicting miRNA targets and demonstrate that our approach outperforms the current alternatives. We supplied an easy-to-use tool, miTAR, at https://github.com/tjgu/miTAR. Furthermore, our analysis results support that Max Pooling generally benefits the hybrid models and potentially prevents overfitting for hybrid models.

scholarly journals miTAR: a hybrid deep learning-based approach for predicting miRNA targets

2020 ◽  
Author(s):  
Tongjun Gu ◽  
Xiwu Zhao ◽  
William Bradley Barbazuk ◽  
Ji-Hyun Lee
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Mirna Target ◽  
Target Prediction ◽  
Unified Model ◽  
Brier Score ◽  
Mirna Target Prediction ◽  
Learning Methods ◽  
Mirna Targets ◽  
Wide Range

AbstractmicroRNAs (miRNAs) are a major type of small RNA that alter gene expression at the post-transcriptional or translational level. They have been shown to play important roles in a wide range of biological processes. Many computational methods have been developed to predict targets of miRNAs in order to understand miRNAs’ function. However, the majority of the methods depend on a set of pre-defined features that require considerable effort and resources to compute, and these methods often do not effectively on the prediction of miRNA targets. Therefore, we developed a novel hybrid deep learning-based approach that is capable to predict miRNA targets at a higher accuracy. Our approach integrates two deep learning methods: convolutional neural networks (CNNs) that excel in learning spatial features, and recurrent neural networks (RNNs) that discern sequential features. By combining CNNs and RNNs, our approach has the advantages of learning both the intrinsic spatial and sequential features of miRNA:target. The inputs for the approach are raw sequences of miRNA and gene sequences. Data from two latest miRNA target prediction studies were used in our study: the DeepMirTar dataset and the miRAW dataset. Two models were obtained by training on the two datasets separately. The models achieved a higher accuracy than the methods developed in the previous studies: 0.9787 vs. 0.9348 for the DeepMirTar dataset; 0.9649 vs. 0.935 for the miRAW dataset. We also calculated a series of model evaluation metrics including sensitivity, specificity, F-score and Brier Score. Our approach consistently outperformed the current methods. In addition, we compared our approach with earlier developed deep learning methods, resulting in an overall better performance. Lastly, a unified model for both datasets was developed with an accuracy higher than the current methods (0.9545). We named the unified model miTAR for miRNA target prediction. The source code and executable are available at https://github.com/tjgu/miTAR.

scholarly journals miRDB: an online database for prediction of functional microRNA targets

2019 ◽  
Vol 48 (D1) ◽  
pp. D127-D131 ◽  
Author(s):  
Yuhao Chen ◽  
Xiaowei Wang
Keyword(s):  
Mirna Target ◽  
Expression Profiles ◽  
Target Prediction ◽  
Prediction Algorithm ◽  
Specific Cell ◽  
Mirna Target Prediction ◽  
Online Database ◽  
Functional Annotations ◽  
Small Noncoding Rnas ◽  
Mirna Targets

Abstract MicroRNAs (miRNAs) are small noncoding RNAs that act as master regulators in many biological processes. miRNAs function mainly by downregulating the expression of their gene targets. Thus, accurate prediction of miRNA targets is critical for characterization of miRNA functions. To this end, we have developed an online database, miRDB, for miRNA target prediction and functional annotations. Recently, we have performed major updates for miRDB. Specifically, by employing an improved algorithm for miRNA target prediction, we now present updated transcriptome-wide target prediction data in miRDB, including 3.5 million predicted targets regulated by 7000 miRNAs in five species. Further, we have implemented the new prediction algorithm into a web server, allowing custom target prediction with user-provided sequences. Another new database feature is the prediction of cell-specific miRNA targets. miRDB now hosts the expression profiles of over 1000 cell lines and presents target prediction data that are tailored for specific cell models. At last, a new web query interface has been added to miRDB for prediction of miRNA functions by integrative analysis of target prediction and Gene Ontology data. All data in miRDB are freely accessible at http://mirdb.org.

scholarly journals TarDB: an online database for plant miRNA targets and miRNA-triggered phased siRNAs

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jing Liu ◽  
Xiaonan Liu ◽  
Siju Zhang ◽  
Shanshan Liang ◽  
Weijiang Luan ◽  
...  
Keyword(s):  
Stress Responses ◽  
Mirna Target ◽  
Target Prediction ◽  
Mirna Target Prediction ◽  
Online Database ◽  
Small Interfering Rnas ◽  
High Confidence ◽  
Web Resource ◽  
Mirna Targets ◽  
Plant Mirna

Abstract Background In plants, microRNAs (miRNAs) are pivotal regulators of plant development and stress responses. Different computational tools and web servers have been developed for plant miRNA target prediction; however, in silico prediction normally contains false positive results. In addition, many plant miRNA target prediction servers lack information for miRNA-triggered phased small interfering RNAs (phasiRNAs). Creating a comprehensive and relatively high-confidence plant miRNA target database is much needed. Results Here, we report TarDB, an online database that collects three categories of relatively high-confidence plant miRNA targets: (i) cross-species conserved miRNA targets; (ii) degradome/PARE (Parallel Analysis of RNA Ends) sequencing supported miRNA targets; (iii) miRNA-triggered phasiRNA loci. TarDB provides a user-friendly interface that enables users to easily search, browse and retrieve miRNA targets and miRNA initiated phasiRNAs in a broad variety of plants. TarDB has a comprehensive collection of reliable plant miRNA targets containing previously unreported miRNA targets and miRNA-triggered phasiRNAs even in the well-studied model species. Most of these novel miRNA targets are relevant to lineage-specific or species-specific miRNAs. TarDB data is freely available at http://www.biosequencing.cn/TarDB. Conclusions In summary, TarDB serves as a useful web resource for exploring relatively high-confidence miRNA targets and miRNA-triggered phasiRNAs in plants.

scholarly journals MicroRNAs: Promising New Antiangiogenic Targets in Cancer

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Sandra Gallach ◽  
Silvia Calabuig-Fariñas ◽  
Eloisa Jantus-Lewintre ◽  
Carlos Camps
Keyword(s):  
Cell Types ◽  
Biological Processes ◽  
Tumour Angiogenesis ◽  
Proliferation And Apoptosis ◽  
Sequence Complementarity ◽  
Specific Proteins ◽  
Health And Disease ◽  
Multiple Cell ◽  
Non Coding Rnas

MicroRNAs are one class of small, endogenous, non-coding RNAs that are approximately 22 nucleotides in length; they are very numerous, have been phylogenetically conserved, and involved in biological processes such as development, differentiation, cell proliferation, and apoptosis. MicroRNAs contribute to modulating the expression levels of specific proteins based on sequence complementarity with their target mRNA molecules and so they play a key role in both health and disease. Angiogenesis is the process of new blood vessel formation from preexisting ones, which is particularly relevant to cancer and its progression. Over the last few years, microRNAs have emerged as critical regulators of signalling pathways in multiple cell types including endothelial and perivascular cells. This review summarises the role of miRNAs in tumour angiogenesis and their potential implications as therapeutic targets in cancer.

Recent development of bioinformatics tools for microRNA target prediction

2021 ◽  
Vol 28 ◽  
Author(s):  
Mst Shamima Khatun ◽  
Md Ashad Alam ◽  
Watshara Shoombuatong ◽  
Md Nurul Haque Mollah ◽  
Hiroyuki Kurata ◽  
...  
Keyword(s):  
Mirna Target ◽  
Target Identification ◽  
Target Prediction ◽  
Mirna Target Prediction ◽  
Microrna Target ◽  
Rna Sequences ◽  
Target Mrnas ◽  
Mirna Targets ◽  
Bioinformatics Tools ◽  
Experimental Approaches

: MicroRNAs (miRNAs) are central players that regulate the post-transcriptional processes of gene expression. Binding of miRNAs to target mRNAs can repress their translation by inducing the degradation or by inhibiting the translation of the target mRNAs. High-throughput experimental approaches for miRNA target identification are costly and time-consuming, depending on various factors. It is vitally important to develop the bioinformatics methods for accurately predicting miRNA targets. With the increase of RNA sequences in the post-genomic era, bioinformatics methods are being developed for miRNA studies specially for miRNA target prediction. This review summarizes the current development of state-of-the-art bioinformatics tools for miRNA target prediction, points out the progress and limitations of the available miRNA databases, and their working principles. Finally, we discuss the caveat and perspectives of the next-generation algorithms for the prediction of miRNA targets.

Drug Induced Changes in Cell Organelles

1968 ◽  
Vol 26 ◽  
pp. 110-111
Author(s):  
Sarah A. Luse
Keyword(s):  
Clinical Medicine ◽  
Cell Organelles ◽  
Riboflavin Deficiency ◽  
Drug Induced ◽  
New Era ◽  
Health And Disease ◽  
In The Beginning ◽  
Cellular Pathology ◽  
Induced Changes ◽  
The Impact

In the mid-nineteenth century Virchow revolutionized pathology by introduction of the concept of “cellular pathology”. Today, a century later, this term has increasing significance in health and disease. We now are in the beginning of a new era in pathology, one which might well be termed “organelle pathology” or “subcellular pathology”. The impact of lysosomal diseases on clinical medicine exemplifies this role of pathology of organelles in elucidation of disease today.Another aspect of cell organelles of prime importance is their pathologic alteration by drugs, toxins, hormones and malnutrition. The sensitivity of cell organelles to minute alterations in their environment offers an accurate evaluation of the site of action of drugs in the study of both function and toxicity. Examples of mitochondrial lesions include the effect of DDD on the adrenal cortex, riboflavin deficiency on liver cells, elevated blood ammonia on the neuron and some 8-aminoquinolines on myocardium.

Fluorescent potentiometric dyes for membrane-potential imaging

1994 ◽  
Vol 52 ◽  
pp. 166-167
Author(s):  
Leslie M. Loew
Keyword(s):  
Membrane Potential ◽  
Single Cells ◽  
Quantitative Imaging ◽  
Optical Recording ◽  
Biological Processes ◽  
Major Focus ◽  
The Past ◽  
Excitable Systems ◽  
Major Application ◽  
The Impact

A major application of potentiometric dyes has been the multisite optical recording of electrical activity in excitable systems. After being championed by L.B. Cohen and his colleagues for the past 20 years, the impact of this technology is rapidly being felt and is spreading to an increasing number of neuroscience laboratories. A second class of experiments involves using dyes to image membrane potential distributions in single cells by digital imaging microscopy - a major focus of this lab. These studies usually do not require the temporal resolution of multisite optical recording, being primarily focussed on slow cell biological processes, and therefore can achieve much higher spatial resolution. We have developed 2 methods for quantitative imaging of membrane potential. One method uses dual wavelength imaging of membrane-staining dyes and the other uses quantitative 3D imaging of a fluorescent lipophilic cation; the dyes used in each case were synthesized for this purpose in this laboratory.

The Laryngeal Epithelium in Reflux

2011 ◽  
Vol 21 (3) ◽  
pp. 112-117 ◽  
Author(s):  
Elizabeth Erickson-Levendoski ◽  
Mahalakshmi Sivasankar
Keyword(s):  
Laryngopharyngeal Reflux ◽  
Critical Role ◽  
Structure And Function ◽  
Laryngeal Disease ◽  
Health And Disease ◽  
And Function ◽  
The Impact

The epithelium plays a critical role in the maintenance of laryngeal health. This is evident in that laryngeal disease may result when the integrity of the epithelium is compromised by insults such as laryngopharyngeal reflux. In this article, we will review the structure and function of the laryngeal epithelium and summarize the impact of laryngopharyngeal reflux on the epithelium. Research investigating the ramifications of reflux on the epithelium has improved our understanding of laryngeal disease associated with laryngopharyngeal reflux. It further highlights the need for continued research on the laryngeal epithelium in health and disease.

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