Bioinformatics Methods for Studying MicroRNA and ARE-Mediated Regulation of Post-Transcriptional Gene Expression

2010 ◽  
Vol 1 (3) ◽  
pp. 97-112 ◽  
Author(s):  
Richipal Singh Bindra ◽  
Jason T. L. Wang ◽  
Paramjeet Singh Bagga
Keyword(s):  
Mirna Target ◽  
Target Prediction ◽  
Untranslated Regions ◽  
Regulatory Rna ◽  
Rna Motifs ◽  
Protein Coding ◽  
Rna Molecules ◽  
Cellular Processes ◽  
Target Sites ◽  
Transcriptional Gene Regulation

MicroRNAs (miRNAs) are short single-stranded RNA molecules with 21-22 nucleotides known to regulate post-transcriptional expression of protein-coding genes involved in most of the cellular processes. Prediction of miRNA targets is a challenging bioinformatics problem. AU-rich elements (AREs) are regulatory RNA motifs found in the 3’ untranslated regions (UTRs) of mRNAs, and they play dominant roles in the regulated decay of short-lived human mRNAs via specific interactions with proteins. In this paper, the authors review several miRNA target prediction tools and data sources, as well as computational methods used for the prediction of AREs. The authors discuss the connection between miRNA and ARE-mediated post-transcriptional gene regulation. Finally, a data mining method for identifying the co-occurrences of miRNA target sites in ARE containing genes is presented.

Bioinformatics Methods for Studying MicroRNA and ARE-Mediated Regulation of Post-Transcriptional Gene Expression

2013 ◽  
pp. 156-173
Author(s):  
Richipal Singh Bindra ◽  
Jason T. L. Wang ◽  
Paramjeet Singh Bagga
Keyword(s):  
Mirna Target ◽  
Target Prediction ◽  
Specific Interactions ◽  
Rna Motifs ◽  
Protein Coding ◽  
Rna Molecules ◽  
Cellular Processes ◽  
Protein Coding Genes ◽  
Prediction Tools ◽  
Transcriptional Gene Regulation

MicroRNAs (miRNAs) are short single-stranded RNA molecules with 21-22 nucleotides known to regulate post-transcriptional expression of protein-coding genes involved in most of the cellular processes. Prediction of miRNA targets is a challenging bioinformatics problem. AU-rich elements (AREs) are regulatory RNA motifs found in the 3’ untranslated regions (UTRs) of mRNAs, and they play dominant roles in the regulated decay of short-lived human mRNAs via specific interactions with proteins. In this paper, the authors review several miRNA target prediction tools and data sources, as well as computational methods used for the prediction of AREs. The authors discuss the connection between miRNA and ARE-mediated post-transcriptional gene regulation. Finally, a data mining method for identifying the co-occurrences of miRNA target sites in ARE containing genes is presented.

scholarly journals PUMA: PANDA Using MicroRNA Associations

2020 ◽  
Vol 36 (18) ◽  
pp. 4765-4773
Author(s):  
Marieke L Kuijjer ◽  
Maud Fagny ◽  
Alessandro Marin ◽  
John Quackenbush ◽  
Kimberly Glass
Keyword(s):  
Messenger Rna ◽  
Message Passing ◽  
Mirna Target ◽  
Target Prediction ◽  
Network Models ◽  
Tissue Expression ◽  
Supplementary Information ◽  
Tissue Specific ◽  
Cellular Processes ◽  
Regulatory Processes

Abstract Motivation Conventional methods to analyze genomic data do not make use of the interplay between multiple factors, such as between microRNAs (miRNAs) and the messenger RNA (mRNA) transcripts they regulate, and thereby often fail to identify the cellular processes that are unique to specific tissues. We developed PUMA (PANDA Using MicroRNA Associations), a computational tool that uses message passing to integrate a prior network of miRNA target predictions with target gene co-expression information to model genome-wide gene regulation by miRNAs. We applied PUMA to 38 tissues from the Genotype-Tissue Expression project, integrating RNA-Seq data with two different miRNA target predictions priors, built on predictions from TargetScan and miRanda, respectively. We found that while target predictions obtained from these two different resources are considerably different, PUMA captures similar tissue-specific miRNA–target regulatory interactions in the different network models. Furthermore, the tissue-specific functions of miRNAs we identified based on regulatory profiles (available at: https://kuijjer.shinyapps.io/puma_gtex/) are highly similar between networks modeled on the two target prediction resources. This indicates that PUMA consistently captures important tissue-specific miRNA regulatory processes. In addition, using PUMA we identified miRNAs regulating important tissue-specific processes that, when mutated, may result in disease development in the same tissue. Availability and implementation PUMA is available in C++, MATLAB and Python on GitHub (https://github.com/kuijjerlab and https://netzoo.github.io/). Supplementary information Supplementary data are available at Bioinformatics online.

MicroRNAs in Development and Disease

2011 ◽  
Vol 91 (3) ◽  
pp. 827-887 ◽  
Author(s):  
Danish Sayed ◽  
Maha Abdellatif
Keyword(s):  
Gene Regulation ◽  
Mammalian Cells ◽  
Untranslated Region ◽  
Mirna Target ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Target Sites ◽  
Health And Disease ◽  
Additional Level ◽  
Made In

MicroRNAs (miRNAs) are a class of posttranscriptional regulators that have recently introduced an additional level of intricacy to our understanding of gene regulation. There are currently over 10,000 miRNAs that have been identified in a range of species including metazoa, mycetozoa, viridiplantae, and viruses, of which 940, to date, are found in humans. It is estimated that more than 60% of human protein-coding genes harbor miRNA target sites in their 3′ untranslated region and, thus, are potentially regulated by these molecules in health and disease. This review will first briefly describe the discovery, structure, and mode of function of miRNAs in mammalian cells, before elaborating on their roles and significance during development and pathogenesis in the various mammalian organs, while attempting to reconcile their functions with our existing knowledge of their targets. Finally, we will summarize some of the advances made in utilizing miRNAs in therapeutics.

scholarly journals Position-wise binding preference is important for miRNA target site prediction

2020 ◽  
Vol 36 (12) ◽  
pp. 3680-3686
Author(s):  
Amlan Talukder ◽  
Xiaoman Li ◽  
Haiyan Hu
Keyword(s):  
Dynamic Programming ◽  
Markov Model ◽  
Mirna Target ◽  
Dynamic Programming Algorithm ◽  
Target Prediction ◽  
Target Site ◽  
Mirna Target Site ◽  
Supplementary Information ◽  
Target Sites ◽  
Target Binding

Abstract Motivation It is a fundamental task to identify microRNAs (miRNAs) targets and accurately locate their target sites. Genome-scale experiments for miRNA target site detection are still costly. The prediction accuracies of existing computational algorithms and tools are often not up to the expectation due to a large number of false positives. One major obstacle to achieve a higher accuracy is the lack of knowledge of the target binding features of miRNAs. The published high-throughput experimental data provide an opportunity to analyze position-wise preference of miRNAs in terms of target binding, which can be an important feature in miRNA target prediction algorithms. Results We developed a Markov model to characterize position-wise pairing patterns of miRNA–target interactions. We further integrated this model as a scoring method and developed a dynamic programming (DP) algorithm, MDPS (Markov model-scored Dynamic Programming algorithm for miRNA target site Selection) that can screen putative target sites of miRNA-target binding. The MDPS algorithm thus can take into account both the dependency of neighboring pairing positions and the global pairing information. Based on the trained Markov models from both miRNA-specific and general datasets, we discovered that the position-wise binding information specific to a given miRNA would benefit its target prediction. We also found that miRNAs maintain region-wise similarity in their target binding patterns. Combining MDPS with existing methods significantly improves their precision while only slightly reduces their recall. Therefore, position-wise pairing patterns have the promise to improve target prediction if incorporated into existing software tools. Availability and implementation The source code and tool to calculate MDPS score is available at http://hulab.ucf.edu/research/projects/MDPS/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Circular and long non-coding RNAs and their role in ophthalmologic diseases

2018 ◽  
Author(s):  
Olga Wawrzyniak ◽  
Żaneta Zarębska ◽  
Katarzyna Rolle ◽  
Anna Gotz-Więckowska
Keyword(s):  
Current Knowledge ◽  
Critical Role ◽  
Circular Rna ◽  
Age Related Macular Degeneration ◽  
Circular Rnas ◽  
Protein Coding ◽  
Rna Molecules ◽  
Age Related ◽  
Non Coding Rnas ◽  
Transcriptional Gene Regulation

Long non-coding RNAs are >200-nucleotide-long RNA molecules which lack or have limited protein-coding potential. They can regulate protein formation through several different mechanisms. Similarly, circular RNAs are reported to play a critical role in post-transcriptional gene regulation. Changes in the expression pattern of these molecules are known to underlie various diseases, including cancer, cardiovascular, neurological and immunological disorders (Rinn & Chang, 2012; Sun & Kraus, 2015). Recent studies suggest that they are differentially expressed both in healthy ocular tissues as well as in eye pathologies, such as neovascularization, proliferative vitreoretinopathy, glaucoma, cataract, ocular malignancy or even strabismus (Li et al., 2016). Aetiology of ocular diseases is multifactorial and combines genetic and environmental factors, including epigenetic and non-coding RNAs. In addition, disorders like diabetic retinopathy or age-related macular degeneration lack biomarkers for early detection as well as effective treatment methods that would allow controlling the disease progression at its early stages. The newly discovered non-coding RNAs seem to be the ideal candidates for novel molecular markers and therapeutic strategies. In this review, we summarized the current knowledge about gene expression regulators – long non-coding and circular RNA molecules in eye diseases.

scholarly journals Selection against maternal microRNA target sites in maternal transcripts

2014 ◽  
Author(s):  
Antonio Marco
Keyword(s):  
Small Rnas ◽  
Early Embryo ◽  
Microrna Target ◽  
Protein Coding ◽  
Functional Impact ◽  
Rna Molecules ◽  
Target Sites ◽  
Maternal Transcripts ◽  
Gene Transcripts

In animals, before the zygotic genome is expressed, the egg already contains gene products deposited by the mother. These maternal products are crucial during the initial steps of development. In Drosophila melanogaster a large number of maternal products are found in the oocyte, some of which are indispensable. Many of these products are RNA molecules, such as gene transcripts and ribosomal RNAs. Recently, microRNAs ? small RNA gene regulators ? have been detected early during development and are important in these initial steps. The presence of some microRNAs in unfertilized eggs has been reported, but whether they have a functional impact in the egg or early embryo has not being explored. I have extracted and sequenced small RNAs from Drosophila unfertilized eggs. The unfertilized egg is rich in small RNAs and contains multiple microRNA products. Maternal microRNAs are often encoded within the intron of maternal genes, suggesting that many maternal microRNAs are the product of transcriptional hitch-hiking. Comparative genomics analyses suggest that maternal transcripts tend to avoid target sites for maternal microRNAs. I also developed a microRNA target mutation model to study the functional impact of polymorphisms at microRNA target sites. The analysis of Drosophila populations suggests that there is selection against maternal microRNA target sites in maternal transcripts. A potential role of the maternal microRNA mir-9c in maternal-to-zygotic transition is also discussed. In conclusion, maternal microRNAs in Drosophila have a functional impact in maternal protein-coding transcripts.

scholarly journals The Landscape of RNA-Protein Interactions in Plants: Approaches and Current Status

2021 ◽  
Vol 22 (6) ◽  
pp. 2845
Author(s):  
Vesper Burjoski ◽  
Anireddy S. N. Reddy
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Translational Regulation ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Current Status ◽  
Protein Coding ◽  
Rna Molecules ◽  
Cellular Processes ◽  
Binding Domains

RNAs transmit information from DNA to encode proteins that perform all cellular processes and regulate gene expression in multiple ways. From the time of synthesis to degradation, RNA molecules are associated with proteins called RNA-binding proteins (RBPs). The RBPs play diverse roles in many aspects of gene expression including pre-mRNA processing and post-transcriptional and translational regulation. In the last decade, the application of modern techniques to identify RNA–protein interactions with individual proteins, RNAs, and the whole transcriptome has led to the discovery of a hidden landscape of these interactions in plants. Global approaches such as RNA interactome capture (RIC) to identify proteins that bind protein-coding transcripts have led to the identification of close to 2000 putative RBPs in plants. Interestingly, many of these were found to be metabolic enzymes with no known canonical RNA-binding domains. Here, we review the methods used to analyze RNA–protein interactions in plants thus far and highlight the understanding of plant RNA–protein interactions these techniques have provided us. We also review some recent protein-centric, RNA-centric, and global approaches developed with non-plant systems and discuss their potential application to plants. We also provide an overview of results from classical studies of RNA–protein interaction in plants and discuss the significance of the increasingly evident ubiquity of RNA–protein interactions for the study of gene regulation and RNA biology in plants.

Identification of BAP1-associated MicroRNAs and Implications in Cancer Development

2019 ◽  
pp. 1-4
Author(s):  
Tikam Chand ◽  
Tikam Chand
Keyword(s):  
Gene Regulation ◽  
In Silico ◽  
Mirna Target ◽  
Target Prediction ◽  
Differential Regulation ◽  
Cancer Development ◽  
Mirna Target Prediction ◽  
Cancer Types ◽  
In Silico Tools

Having role in gene regulation and silencing, miRNAs have been implicated in development and progression of a number of diseases, including cancer. Herein, I present potential miRNAs associated with BAP1 gene identified using in-silico tools such as TargetScan and Exiqon miRNA Target Prediction. I identified fifteen highly conserved miRNA (hsa-miR-423-5p, hsa-miR-3184-5p, hsa-miR-4319, hsa-miR125b-5p, hsa-miR-125a-5p, hsa-miR-6893-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-505-3p.1, hsa-miR-429, hsa-miR-370-3p, hsa-miR-125a-5p, hsa-miR-141-3p, hsa-miR-200a-3p, and hsa-miR-429) associated with BAP1 gene. We also predicted the differential regulation of these twelve miRNAs in different cancer types.

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