scholarly journals A comparison of performance of plant miRNA target prediction tools and the characterization of features for genome-wide target prediction

BMC Genomics ◽  
2014 ◽  
Vol 15 (1) ◽  
pp. 348 ◽  
Author(s):  
Prashant K Srivastava ◽  
Taraka Moturu ◽  
Priyanka Pandey ◽  
Ian T Baldwin ◽  
Shree P Pandey
Keyword(s):  
Mirna Target ◽  
Target Prediction ◽  
Mirna Target Prediction ◽  
Prediction Tools ◽  
Genome Wide ◽  
Plant Mirna

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 A novel computational approach to the silencing of Sugarcane Bacilliform Guadeloupe A Virus determines potential host-derived MicroRNAs in sugarcane (Saccharum officinarum L.)

2019 ◽  
Author(s):  
Fakiha Ashraf ◽  
Muhammad Aleem Ashraf ◽  
Xiaowen Hu ◽  
Shuzhen Zhang
Keyword(s):  
Mirna Target ◽  
Target Prediction ◽  
Saccharum Officinarum ◽  
Minimum Free Energy ◽  
Open Reading Frames ◽  
Mirna Target Prediction ◽  
Folding Energy ◽  
Multiple Target ◽  
Genome Wide ◽  
Reading Frames

Sugarcane Bacilliform Guadeloupe A Virus (SCBGAV, genus Badnavirus, family Caulimoviridae) is an emerging, deleterious pathogen of sugarcane which presents a substantial barrier to producing high sugarcane earnings. The circular, double-stranded (ds) DNA genome of SCBGAV (7.4 Kb) is composed of three open reading frames (ORF) that replicate by a reverse transcriptase. In the current study, we used miRNA target prediction algorithms to identify and comprehensively analyze the genome-wide sugarcane (Saccharum officinarum L.)-encoded microRNA (miRNA) targets against the SCBGAV. A total of 28 potential mature target miRNAs were retrieved from the miRBase database and were further analyzed for hybridization to the SCBGAV genome. Multiple computational approaches—including miRNA-target seed pairing, multiple target positions, minimum free energy, target site accessibility, maximum complementarity, pattern recognition and minimum folding energy for attachments— were considered by all algorithms. Only 4 sugarcane miRNAs are selected for SCBGAV silencing. Among those 4, sof-miR396 was identified as the top effective candidate, capable of targeting the vital ORF3 which encodes polyprotein of the SCBGAV genome. miRanda, RNA22 and RNAhybrid algorithms predicted hybridization of sof-miR396 at common locus position 3394. A Circos plot was created to study the network visualization of sugarcane-encoded miRNAs with SCBGAV genes determines detailed evidence for any ideal targets of SCBGAV ORFs by precise miRNAs. The present study concludes a comprehensive report towards the creation of SCBGAV-resistant sugarcane through the expression analysis of the identified miRNAs.

scholarly journals A novel computational approach to the silencing of Sugarcane Bacilliform Guadeloupe A Virus determines potential host-derived MicroRNAs in sugarcane (Saccharum officinarum L.)

2019 ◽  
Author(s):  
Fakiha Ashraf ◽  
Muhammad Aleem Ashraf ◽  
Xiaowen Hu ◽  
Shuzhen Zhang
Keyword(s):  
Mirna Target ◽  
Target Prediction ◽  
Saccharum Officinarum ◽  
Minimum Free Energy ◽  
Open Reading Frames ◽  
Mirna Target Prediction ◽  
Folding Energy ◽  
Multiple Target ◽  
Genome Wide ◽  
Reading Frames

Sugarcane Bacilliform Guadeloupe A Virus (SCBGAV, genus Badnavirus, family Caulimoviridae) is an emerging, deleterious pathogen of sugarcane which presents a substantial barrier to producing high sugarcane earnings. The circular, double-stranded (ds) DNA genome of SCBGAV (7.4 Kb) is composed of three open reading frames (ORF) that replicate by a reverse transcriptase. In the current study, we used miRNA target prediction algorithms to identify and comprehensively analyze the genome-wide sugarcane (Saccharum officinarum L.)-encoded microRNA (miRNA) targets against the SCBGAV. A total of 28 potential mature target miRNAs were retrieved from the miRBase database and were further analyzed for hybridization to the SCBGAV genome. Multiple computational approaches—including miRNA-target seed pairing, multiple target positions, minimum free energy, target site accessibility, maximum complementarity, pattern recognition and minimum folding energy for attachments— were considered by all algorithms. Only 4 sugarcane miRNAs are selected for SCBGAV silencing. Among those 4, sof-miR396 was identified as the top effective candidate, capable of targeting the vital ORF3 which encodes polyprotein of the SCBGAV genome. miRanda, RNA22 and RNAhybrid algorithms predicted hybridization of sof-miR396 at common locus position 3394. A Circos plot was created to study the network visualization of sugarcane-encoded miRNAs with SCBGAV genes determines detailed evidence for any ideal targets of SCBGAV ORFs by precise miRNAs. The present study concludes a comprehensive report towards the creation of SCBGAV-resistant sugarcane through the expression analysis of the identified miRNAs.

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 A Scalable Genetic Programming Approach to Integrate miRNA-Target Predictions: Comparing Different Parallel Implementations of M3GP

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Stefano Beretta ◽  
Mauro Castelli ◽  
Luis Muñoz ◽  
Leonardo Trujillo ◽  
Yuliana Martínez ◽  
...  
Keyword(s):  
Genetic Programming ◽  
Mirna Target ◽  
Target Genes ◽  
Parallel Implementation ◽  
Target Prediction ◽  
Programming Approach ◽  
Learning Approaches ◽  
Mirna Target Prediction ◽  
Parallel Implementations ◽  
Genome Wide

There are many molecular biology approaches to the analysis of microRNA (miRNA) and target interactions, but the experiments are complex and expensive. For this reason, in silico computational approaches able to model these molecular interactions are highly desirable. Although several computational methods have been developed for predicting the interactions between miRNA and target genes, there are substantial differences in the results achieved since most algorithms provide a large number of false positives. Accordingly, machine learning approaches are widely used to integrate predictions obtained from different tools. In this work, we adopt a method called multidimensional multiclass GP with multidimensional populations (M3GP), which relies on a genetic programming approach, to integrate and classify results from different miRNA-target prediction tools. The results are compared with those obtained with other classifiers, showing competitive accuracy. Since we aim to provide genome-wide predictions with M3GP and, considering the high number of miRNA-target interactions to test (also in different species), a parallel implementation of this algorithm is recommended. In this paper, we discuss the theoretical aspects of this algorithm and propose three different parallel implementations. We show that M3GP is highly parallelizable, it can be used to achieve genome-wide predictions, and its adoption provides great advantages when handling big datasets.

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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