Abstract
BackgroundMicroRNAs (miRNAs) are single-stranded non-coding RNAs known to regulate a wide range of cellular processes by silencing the gene expression at the protein and/or mRNA levels. Computational prediction of miRNA targets is essential for elucidating the detailed functions of miRNA. However, the prediction specificity and sensitivity of the existing algorithms are still poor to generate meaningful, workable hypotheses for subsequent experimental testing. Constructing a richer and more reliable training data set and developing an algorithm that properly exploits this data set would be the key to improve the performance current prediction algorithms.ResultsA comprehensive training data set is constructed for mammalian miRNAs with its positive targets obtained from the most up-to-date miRNA target depository called miRecords and its negative targets derived from 20 microarray data. A new algorithm SVMicrO is developed, which assumes a 2-stage structure including a site support vector machine (SVM) followed by a UTR-SVM. SVMicrO makes prediction based on 21 optimal site features and 18 optimal UTR features, selected by training from a comprehensive collection of 113 site and 30 UTR features. Comprehensive evaluation of SVMicrO performance has been carried out on the training data, proteomics data, and immunoprecipitation (IP) pull-down data. Comparisons with some popular algorithms demonstrate consistent improvements in prediction specificity, sensitivity and precision in all tested cases. All the related materials including source code and genome-wide prediction of human targets are available at http://compgenomics.utsa.edu/svmicro.html.ConclusionsA 2-stage SVM based new miRNA target prediction algorithm called SVMicrO is developed. SVMicrO is shown to be able to achieve robust performance. It holds the promise to achieve continuing improvement whenever better training data that contain additional verified or high confidence positive targets and properly selected negative targets are available.
Highlights
MicroRNAs are single-stranded non-coding RNAs known to regulate a wide range of cellular processes by silencing the gene expression at the protein and/or mRNA levels
We investigate the length of 3′ untranslated region (UTR) in our training data set and the result shows that the positive targets on average have longer length than the negative targets
Almost no negative target sites possess these seed type match features; this implies that using these features on top of 6mer seed match reduces the false positive rate, they are not as nearly sensitive as 6mer seed match
Summary
MicroRNAs (miRNAs) are single-stranded non-coding RNAs known to regulate a wide range of cellular processes by silencing the gene expression at the protein and/or mRNA levels. An Considerable advances have been made in computational target prediction [4] and many algorithms have been proposed including TargetScan [5], PicTar [6], miRanda [7], PITA [8], DIANA-microT [9], RNAhybrid [10], microInspector [11], MovingTargets [12], rna22 [13], NBmiRTar [14] and Nucleus [15] These algorithms make predictions mainly based on various important features of miRNA-target nucleotide sequence interaction. The data driven algorithms hold the promise to provide accurate prediction, since they have the ability to uncover important features from data that cannot be observed otherwise
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