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.
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.
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 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.
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 machine learning tool, with the purpose to distinguish miRNA precursor sequences from other (putative) non-coding RNAs. Based on multiple sequence alignments a support vector machine, trained with all known metazoan miRNA precursor alignments and other non-coding RNAs, returns the probability how likely the input may be a miRNA precursor.
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.
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.