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A computational tool that incorporates a Naive Bayes classifier to identify mature miRNA candidates based on sequence and secondary structure information of their miRNA precursors. We take into account both positive (true mature miRNAs) and negative (same-size non-mature miRNA sequences) examples to optimize sensitivity as well as specificity. Our method can accurately predict the start position of experimentally verified mature miRNAs for both human and mouse, achieving a significantly larger (often double) performance accuracy compared with two existing methods. Moreover, the method exhibits a very high generalization performance on miRNAs from two other organisms. More importantly, our method provides direct evidence about the features of miRNA precursors which may determine the location of the mature miRNA.
A tool for identifying the sequence of mature microRNA from the stem-loop structure that exists in the pri-microRNA. MiRmat is based on the principle of free energy in molecule interaction and the process of mature microRNA biogenesis. Firstly, the free energy distribution pattern of the stem-loop structure derived from the pri-microRNA is introduced for the prediction of Drosha processing sites, i.e., the prediction of precise pre-microRNA. Then, the structural features of pre-microRNA are applied for the prediction of Dicer processing site, so that the mature microRNA sequence is produced. We prove that MiRmat has better performance than the existing tools and is applicable among vertebrates.
Identifies real/pseudo plant pre-miRNAs and the corresponding miRNAs. miPlantPreMat is a classifier developed by analyzing existing miRNA prediction methods, combining the characteristics of plant pre-miRNAs, extracting features, selecting features and training samples to achieve efficient and effective classification. It achieves high accuracy on plant datasets from nine plant species, including Arabidopsis thaliana, Glycine max, Oryza sativa, Physcomitrella patens, Medicago truncatula, Sorghum bicolor, Arabidopsis lyrata, Zea mays and Solanum lycopersicum.
SplamiR / Spliced plant miRNAs
Identifies candidate pre-miRNAs in whole genomes whose mature miRNAs likely regulate an mRNA of interest. SplamiR is a method that consists of two phases: (i) sequence pairs with a high degree of complementarity are identified from whole genome or chromosome sequences and (ii) sequences with complementarity to a given potential target mRNA are searched among those sequence pairs, possible splicing events are considered. Secondary-structure characteristics are than evaluated to identify candidate pre-miRNAs.
Performs intragenomic matching followed by classification of miRNA candidates using support vector machines (SVMs). MiMatcher predicts miRNA candidates and their targets independently in each plant genome. It employs intragenomic matching in a single genome followed by hairpin classification. This tool was applied to three plant genomes A. thaliana, O. sativa, and P. trichocarpa, and finds species-specific miRNA-like hairpins with almost perfect complementarity to mRNA targets.
Provides a computational program for plant prediction. miRNAPrediction accurately predicts when a sequence is not a miRNA at the expense of missing a few true miRNAs, which limits the number of false positives. This predictor can be applied to any short sequence that aligns to a precursor candidate in a genome or transcriptome. The source of the sequence for testing can be short reads from deep sequencing, or short segments taken from chromosomes or expressed sequence tags (ESTs) sliding windows.
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