MicroRNAs are known to be generated from primary transcripts mainly through the sequential cleavages by two enzymes, Drosha and Dicer. The sequence of a mature microRNA, especially the 'seeding sequence', largely determines its binding ability and specificity to target mRNAs. Therefore, methods that predict mature microRNA sequences with high accuracy will benefit the identification and characterization of novel microRNAs and their targets, and contribute to inferring the post-transcriptional regulation network at a genome scale.
A computational predictor for the identification of the most likely miRNA location within a given pre-miRNA or the validation of a candidate miRNA. MiRdup is based on a random forest classifier trained with experimentally validated miRNAs from miRbase, with features that characterize the miRNA-miRNA* duplex.
Recognizes 20mers with the potential to encode plant miRNAs. MIRcheck can restrict the total number of unpaired nucleotides, the number of bulged or asymmetrically unpaired nucleotides, the number of consecutive unpaired nucleotides, and the length of the hairpin. Users can relax its parameters to allow up to two asymmetric bulges, shorter hairpins and an additional mismatch did not identify any additional verifiable miRNAs.
A collection of small RNA analysis tools. sRNAtoolbox is aimed to provide small RNA researchers with several useful tools including sRNA expression profiling from deep sequencing experiments and several downstream analysis tools. The center piece of sRNAtoolbox is sRNAbench, which allows the expression profiling and prediction of novel microRNAs in deep sequencing experiments. The other tools can be either launched on sRNAbench results, or independently using the appropriate file formats.
Screens candidate miRNA sets derived from comparative approaches to identify representatives of new miRNA families. microHARVESTER takes advantage of the conservation pattern typical for miRNA genes. It can generate a set of candidates which is then rigorously refined by a series of filters - exploiting structural features specific to plant miRNAs to achieve specificity. This tool is based on animal a miRNA homolog identification approach.
Recognizes mature microRNAs from small RNA sequencing reads with several structurally related features of miRNA duplex, such as minimum free energy, triplet elements, and the read count difference between pairing strands. microRPM provides three miRNA prediction models: not-reference-required, dicotreference-required, and monocot-reference-required. It was tested on an Arabidopsis dataset based on the reference-not-required model of this tool.
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.
Predicts miRNA duplexes and achieves high performance for mammalian hairpins. miRduplexSVM is a methodology for the computational identification of the mature molecules within novel miRNA hairpins. This application performs equally well on plant hairpins, without any particular customization. It includes the identification of opposite strand miRNAs and the evaluation of potential miRNAs detected experimentally through scoring of their computationally identified duplexes.
Aims users to detect primary miRNAs and precursor miRNAs (pre-miRNAs). miRLocator was constructed based on 440 sequence and structural features extracted from miRNA duplexes. This tool characterizes miRNAs with more sequence and structural features and applies an improved post-processing approach to locate mature miRNAs within the pre-miRNAs by considering the prediction score of all possible miRNA duplexes.
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.
Discovers mature microRNA. miRNAgFree is based on biogenesis features and duplex features. It employs sRNAseq, miRNAseq and sncRNAseq to realize its prediction task. This tool is useful for non-model species with genomes yet to be sequenced or for those lacking an appropriate quality. It was evaluated on its capability to recover well annotated genomes and on an unfinished genome assembly.
Generates a miRNA sequence according to the precursor and the mature sequences. StarSeeker extracts all potential miRNA sequences with respect to the two overhang nucleotide rules. It can delete duplicate entries to make final dataset non-redundant. This software utilizes a simple algorithm based on precursor-mature miRNA matching and the secondary structure of the pre-miRNA to produce a list of all possible miRNA sequences that remain in the input hairpins.
Detects accurately miRNAs in human genome. Mi-Discoverer is a computational approach relies on a multiple sequence alignment to predict human miRNAs and successfully applied the program to identify miRNAs. This application provides also a comprehensive database for human genome miRNAs, including all available latest information about pre-miRNA sequence and length of the stem loop region and function.
Predicts mature miRNA from known precursors. InSilicoDicer suggests the most probable position of the mature sequence within the precursor for every pre-miRNA input. This software allows users to visualize the output results with multiple graphical representations to facilitate the assessment of the predicted position. It enables the possibility to match the input sequence data against chosen database sequences.
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.
Predicts plant miRNAs and achieve higher prediction accuracy compared with the existing methods. MaturePred is a prediction model based on SVM was developed for predicting the starting position of plant miRNAs.
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.