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

Author(s):  
Richipal Singh Bindra ◽  
Jason T. L. Wang ◽  
Paramjeet Singh Bagga

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.

2010 ◽  
Vol 1 (3) ◽  
pp. 97-112 ◽  
Author(s):  
Richipal Singh Bindra ◽  
Jason T. L. Wang ◽  
Paramjeet Singh Bagga

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.


2021 ◽  
Author(s):  
Reza K Hammond ◽  
Parth Patel ◽  
Pallavi Gupta ◽  
Blake C. Meyers

Plant microRNAs (miRNAs) are short, non-coding RNA molecules that restrict gene expression via post-transcriptional regulation and function in several essential pathways including development, growth, and stress responses. Accurately identifying miRNAs in populations of small RNA (sRNA) sequencing libraries is a computationally intensive process which has resulted in the misidentification of inaccurately annotated miRNA sequences. In recent years, criteria for miRNA annotation have been refined to reduce these misannotations. Here, we describe miRador, a novel miRNA identification tool that utilizes the most up-to-date, community-established criteria for accurate identification of miRNAs in plants. We combine target prediction and Parallel Analysis of RNA Ends (PARE) data to assess the precision of the miRNAs identified by miRador. We compare miRador to other commonly used miRNA prediction tools and we find that miRador is at least as precise as other prediction tools while being significantly faster than other tools.


2019 ◽  
Vol 14 (5) ◽  
pp. 432-445 ◽  
Author(s):  
Muniba Faiza ◽  
Khushnuma Tanveer ◽  
Saman Fatihi ◽  
Yonghua Wang ◽  
Khalid Raza

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.


2020 ◽  
Vol 36 (18) ◽  
pp. 4765-4773
Author(s):  
Marieke L Kuijjer ◽  
Maud Fagny ◽  
Alessandro Marin ◽  
John Quackenbush ◽  
Kimberly Glass

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.


Genes ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1527
Author(s):  
Kun Li ◽  
Juanjuan Liu ◽  
Zhibo Zeng ◽  
Muhammad Fakhar-e-Alam Kulyar ◽  
Yaping Wang ◽  
...  

Probiotic bacteria are receiving increased attention due to the potential benefits to their hosts. Plateau yaks have resistance against diseases and stress, which is potentially related to their inner probiotics. To uncover the potential functional genes of yak probiotics, we sequenced the whole genome of Lactobacillus sakei (L. sakei). The results showed that the genome length of L. sakei was 1.99 Mbp, with 1943 protein coding genes (21 rRNA, 65 tRNA, and 1 tmRNA). There were three plasmids found in this bacteria, with 88 protein coding genes. EggNOG annotation uncovered that the L. sakei genes were found to belong to J (translation, ribosomal structure, and biogenesis), L (replication, recombination, and repair), G (carbohydrate transport and metabolism), and K (transcription). GO annotation showed that most of the L. sakei genes were related to cellular processes, metabolic processes, biological regulation, localization, response to stimulus, and organization or biogenesis of cellular components. CAZy annotation found that there were 123 CAZys in the L. sakei genome, with glycosyl transferases and glycoside hydrolases. Our results revealed the genome characteristics of L. sakei, which may give insight into the future employment of this probiotic bacterium for its functional benefits.


2011 ◽  
Vol 91 (3) ◽  
pp. 827-887 ◽  
Author(s):  
Danish Sayed ◽  
Maha Abdellatif

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.


BMC Genomics ◽  
2014 ◽  
Vol 15 (1) ◽  
pp. 348 ◽  
Author(s):  
Prashant K Srivastava ◽  
Taraka Moturu ◽  
Priyanka Pandey ◽  
Ian T Baldwin ◽  
Shree P Pandey

Author(s):  
Olga Wawrzyniak ◽  
Żaneta Zarębska ◽  
Katarzyna Rolle ◽  
Anna Gotz-Więckowska

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.


2020 ◽  
Author(s):  
Silke Jensen ◽  
Emilie Brasset ◽  
Elise Parey ◽  
Hugues Roest-Crollius ◽  
Igor V. Sharakhov ◽  
...  

ABSTRACTPIWI-interacting RNAs (piRNAs) target transcripts by sequence complementarity serving as guides for RNA slicing in animal germ cells. The piRNA pathway is increasingly recognized as critical for essential cellular functions such as germline development and reproduction. In the Anopheles gambiae ovary, as much as 11% of piRNAs map to protein-coding genes. Here we show that ovarian mRNAs and long non-coding RNAs (lncRNAs) are processed into piRNAs that can direct other transcripts into the piRNA biogenesis pathway. Targeting piRNAs fuel transcripts either into the ping-pong cycle of piRNA amplification or into the machinery of phased piRNA biogenesis, thereby creating networks of inter-regulating transcripts. RNAs of the same network share related genomic repeats. These repeats give rise to piRNAs, which target other transcripts and lead to a cascade of concerted RNA slicing. While ping-pong networks are based on repeats of several hundred nucleotides, networks that rely on phased piRNA biogenesis operate through short ∼40-nucleotides long repeats, which we named snetDNAs. Interestingly, snetDNAs are recurring in evolution from insects to mammals. Our study brings to light a new type of a conserved regulatory pathway, the snetDNA-pathway, by which short sequences can include independent genes and lncRNAs in the same biological pathway.AUTHOR SUMMARYSmall RNA molecules are essential actors in silencing mobile genetic elements in animal germ cells. The 24-29-nucleotide-long Piwi-interacting RNAs (piRNAs) target transcripts by sequence complementarity serving as guides for RNA slicing. Mosquitoes of the Anopheles gambiae species complex are the principal vectors of malaria, and research on their germline is essential to develop new strategies of vector control by acting on reproduction. In the Anopheles gambiae ovary as much as 11% of piRNAs originate from protein-coding genes. We identified piRNAs which are able to target transcripts from several distinct genes or long non-coding RNAs (lncRNAs), bringing together genic transcripts and lncRNAs in a same regulation network. piRNA targeting induces transcript slicing and production of novel piRNAs, which then target other mRNAs and lncRNAs leading again to piRNA processing, thus resulting in a cascade of RNA slicing and piRNA production. Each network relies on piRNAs originating from repeated genetic elements, present in all transcripts of the same network. Some of these repeats are very short, only ∼40-nucleotides long. We identified similar repeats in all 43 animal species that we analysed, including mosquitoes, flies, arachnidae, snail, mouse, rat and human, suggesting that such regulation networks are recurrent, possibly conserved, in evolutionary history.


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