scholarly journals FilTar: Using RNA-Seq data to improve microRNA target prediction accuracy in animals

2019 ◽  
Author(s):  
Thomas Bradley ◽  
Simon Moxon
Keyword(s):  
Prediction Accuracy ◽  
Target Prediction ◽  
Mirna Target Prediction ◽  
Rna Seq ◽  
Microrna Target ◽  
Microrna Target Prediction ◽  
Non Coding Rna ◽  
Mrna Transcripts ◽  
Cellular Context ◽  
Mirna Targeting

AbstractMicroRNAs (miRNAs) are a class of small non-coding RNA molecule, approximately 22nt in length, which guide the repression of mRNA transcripts. A number of tools have been developed to predict miRNA targets in animals which do not account for the effects of a specific cellular context on miRNA targeting. We present FilTar (Filtering of predicted miRNATargets), a method which utilises available RNA-Seq information to filter non- or lowly expressed transcripts and refine existing 3’UTR annotations for a given cellular context, to increase miRNA target prediction accuracy in animals.The FilTar tool is available athttps://github.com/TBradley27/FilTar.

scholarly journals FilTar: using RNA-Seq data to improve microRNA target prediction accuracy in animals

2020 ◽  
Vol 36 (8) ◽  
pp. 2410-2416
Author(s):  
Thomas Bradley ◽  
Simon Moxon
Keyword(s):  
Programming Languages ◽  
Mirna Target ◽  
Target Prediction ◽  
Supplementary Information ◽  
Mirna Target Prediction ◽  
Rna Seq ◽  
Microrna Target ◽  
R Programming ◽  
High Level ◽  
Transcript Expression Level

Abstract Motivation MicroRNA (miRNA) target prediction algorithms do not generally consider biological context and therefore generic target prediction based on seed binding can lead to a high level of false-positive predictions. Here, we present FilTar, a method that incorporates RNA-Seq data to make miRNA target prediction specific to a given cell type or tissue of interest. Results We demonstrate that FilTar can be used to: (i) provide sample specific 3′-UTR reannotation; extending or truncating default annotations based on RNA-Seq read evidence and (ii) filter putative miRNA target predictions by transcript expression level, thus removing putative interactions where the target transcript is not expressed in the tissue or cell line of interest. We test the method on a variety of miRNA transfection datasets and demonstrate increased accuracy versus generic miRNA target prediction methods. Availability and implementation FilTar is freely available and can be downloaded from https://github.com/TBradley27/FilTar. The tool is implemented using the Python and R programming languages, and is supported on GNU/Linux operating systems. Supplementary information Supplementary data are available at Bioinformatics online.

ANIMAL MICRORNA TARGET PREDICTION USING DIVERSE SEQUENCE-SPECIFIC DETERMINANTS

2010 ◽  
Vol 08 (04) ◽  
pp. 763-788 ◽  
Author(s):  
YUN ZHENG ◽  
WEIXIONG ZHANG
Keyword(s):  
Drosophila Melanogaster ◽  
Protein Level ◽  
Mirna Target ◽  
Homo Sapiens ◽  
Target Prediction ◽  
Prediction Algorithm ◽  
Mirna Target Prediction ◽  
Microrna Target ◽  
Microrna Target Prediction ◽  
Target Prediction Algorithm

Many recent studies have shown that access of animal microRNAs (miRNAs) to their complementary sites in target mRNAs is determined by several sequence-specific determinants beyond the seed regions in the 5′ end of miRNAs. These factors have been related to the repressive power of miRNAs and used in some programs to predict the efficacy of miRNA complementary sites. However, these factors have not been systematically examined regarding their capacities for improving miRNA target prediction. We develop a new miRNA target prediction algorithm, called Hitsensor, by incorporating many sequence-specific features that determine complementarities between miRNAs and their targets, in addition to the canonical seed regions in the 5′ ends of miRNAs. We evaluate the performance of our algorithm on 720 known animal miRNA:target pairs in four species, Homo sapiens, Mus musculus, Drosophila melanogaster and Caenorhabditis elegans. Our experimental results show that Hitsensor outperforms five popular existing algorithms, indicating that our unique scheme for quantifying the determinants of complementary sites is effective in improving the performance of a miRNA target prediction algorithm. We also examine the effectiveness of miRNA-mediated repression for the predicted targets by using a published quantitative protein expression dataset of miR-223 knockout in mouse neutrophils. Hitsensor identifies more targets than the existing algorithms, and the predicted targets of Hitsensor show comparable protein level changes to those of the existing algorithms.

scholarly journals Accurate microRNA Target Prediction Using Detailed Binding Site Accessibility and Machine Learning on Proteomics Data

2012 ◽  
Vol 2 ◽  
Author(s):  
Martin Reczko ◽  
Manolis Maragkakis ◽  
Panagiotis Alexiou ◽  
Giorgio L. Papadopoulos ◽  
Artemis G. Hatzigeorgiou
Keyword(s):  
Machine Learning ◽  
Binding Site ◽  
Target Prediction ◽  
Proteomics Data ◽  
Microrna Target ◽  
Microrna Target Prediction

scholarly journals Practical Aspects of microRNA Target Prediction

2011 ◽  
Vol 11 (2) ◽  
pp. 93-109 ◽  
Author(s):  
T. M. Witkos ◽  
E. Koscianska ◽  
W. J. Krzyzosiak
Keyword(s):  
Target Prediction ◽  
Microrna Target ◽  
Microrna Target Prediction

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.

The RNAhybrid approach to microRNA target prediction

MicroRNAs ◽  
2009 ◽  
pp. 199-209
Author(s):  
Marc Rehmsmeier ◽  
Sidney Altman ◽  
Victor R. Ambros
Keyword(s):  
Target Prediction ◽  
Microrna Target ◽  
Microrna Target Prediction
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