| CID-miRNA identify miRNA precursors in a given DNA sequence, based on secondary structure-based filtering systems and an algorithm based on stochastic context free grammar trained on human miRNAs. CID-miRNA analyses a given sequence using a web interface, for presence of putative miRNA precursors and the generated output lists all the potential regions that can form miRNA-like structures. It can also scan large genomic sequences for the presence of potential miRNA precursors in its stand-alone form. |
| Identification is based on secondary structure filtering systems |
| MapMi, a computational system for automated miRNA mapping across and within species. Given the sequence of a known miRNA in one species it is relatively straightforward to determine likely loci of that miRNA in other species. This method has a sensitivity of 92.20% and a specificity of 97.73%. MapMi obtained 10,944 unannotated potential miRNAs when was applied to all 21 species in Ensembl Metazoa release 2 and 46 species from Ensembl release 55. |
| Recognition is based on mapping validated miRNAs in one species to their most likely orthologous in other species |
| MatureBayes is a Naive Bayes classifier to identify mature miRNA candidates based on sequence and secondary structure information of their miRNA precursors. This method can accurately predict the start position of experimentally verified mature miRNAs for both human and mouse. |
| Finding mature miRNA within a miRNA precursor Identification is based on the sequence and secondary structures |
| microPred includes the introduction of more representative datasets, extraction of new biologically relevant features, feature selection, handling of class imbalance problem in the datasets and extensive classifier performance evaluation via systematic cross-validation methods. |
| Human pre-miRNA prediction with high sensitivity and specificity |
| Micro-pro SVM is a classifier that predicts 5' Drosha processing sites in hairpins that are candidate miRNAs. This classifier correctly predicts the processing site for 50% of known human 5' miRNAs, and 90% of its predictions are within two nucleotides of the true site. |
| Recognition of miRNA based on Drosha cleavage sites on a sequence |
| Mir-abela focus on genomic regions around already known miRNAs, in order to exploit the property that miRNAs are occasionally found in clusters. Starting with the known human, mouse and rat miRNAs this database analyze 20 kb of flanking genomic regions for the presence of putative precursor miRNAs. This ab initio method show that although the overall miRNA content in the observed clusters is very similar across the three considered species. |
| Discovery of novel miRNAs based on the theory that many miRNAs occur in clusters |
| Miralign uses a novel genome-wide computational approach to detect miRNAs in animals based on both sequence and structure alignment. Experiments show this approach has higher sensitivity and comparable specificity than other reported homologue searching methods. |
| Finding new miRNAs using structural similarity and sequence conservations |
| miREval 2.0 is an online tool that can search up to 100 sequences for novel microRNAs in multiple organisms. This tool uses multiple published in silico approaches to detect miRNAs in sequences of interest and can be used to discover miRNAs from DNA sequences or to validate candidates from sequencing data. |
| Finding new miRNAs based on secondary structure and conservation analysis |
| miRNAminer is used for homologous conserved miRNA gene search in several animal species. Given a search query, candidate homologs from different species are tested for their known miRNA properties, such as secondary structure, energy and alignment and conservation, in order to assess their fidelity. |
| Identifying conserved homolog miRNA genes |
| Microprocessor SVM is a classifier that predicts 5′ Drosha processing sites in hairpins that are candidate miRNAs. This tool suggests that expressed hairpins should not be annotated as miRNAs until they are verified to be Drosha and Dicer substrates. |
| suggesting microRNAs by a classifier that predicts 5´ Drosha processing sites in hairpins that are candidate miRNAs |
| Web-based Application to find miRNA candidater and display target expression |
| finding microRNA candidates in a sequence display target expression |
| MiRscan is a computational procedure to identify miRNA genes conserved in more than one genome. Applying this program together with molecular identification and validation methods, MiRscan have identified most of the miRNA genes in the nematode Caenorhabditis elegans. |
| prediction based on sequence conservation and structural conformation |
| miRNA finding based on structure and sequence of precursors Indicate position of mature miRNAs based on cleavage site by Drosha |
| SSCprofiler utilizing a probabilistic method based on Profile Hidden Markov Models to predict novel miRNA precursors. Via the simultaneous integration of biological features such as sequence, structure and conservation, SSCprofiler achieves a performance accuracy of high sensitivity and specificity on a large set of human miRNA genes. |
| predicting putative miRNA genes based on sequence, structure and conservation Scanning by entering chromosome number and start and end nucleotide |
| Vir-Mir db established an interface for users to query the predicted viral miRNA hairpins based on taxonomic classification, and a host target gene prediction service based on the RNAhybrid program and the 3′-UTR gene sequences of human, mouse, rat, zebrafish, rice and Arabidopsis. |
| Prediction of viral miRNA candidate hairpins |
| deepSOM propose a novel and effective way of approaching this problem using machine learning, without the definition of negative examples. The proposal is based on clustering unlabeled sequences of a genome together with well-known miRNA precursors for the organism under study, which allows for the quick identification of the best candidates to miRNA as those sequences clustered with known precursors. |
|
Machine learning tool for novel miRNA precursor prediction in genome-wide data |
| MiPred distinguish the real pre-miRNAs from other hairpin sequences with similar stem-loops (pseudo pre-miRNAs), a hybrid feature which consists of local contiguous structure-sequence composition, minimum of free energy (MFE) of the secondary structure and P-value of randomization test is used. Given a sequence, MiPred decides whether it is a pre-miRNA-like hairpin sequence or not. If the sequence is a pre-miRNA-like hairpin, the RF classifier will predict whether it is a real pre-miRNA or a pseudo one. |
|
Distinguish the real pre-miRNAs from other hairpin sequences with similar stem-loops |
| miRClassify introduce a novel machine learning-based web server which can rapidly identify miRNA from the primary sequence and classify it into a miRNA family regardless of similarity in sequence and structure. Additionally, the medical implication of the miRNA family is also provided when it is available in PubMed. |
|
A machine learning-based web server that can rapidly identify miRNA from the primary sequence |
| miRNA-dis incorporate the structure-order information into the prediction in which the feature vector was constructed by the occurrence frequency of the distance structure status pair or just the distance-pair. miRNA-dis trained with human data can correctly predict pre-miRNAs from 11 different species ranging from animals, plants and viruses. |
|
MicroRNA precursor identification based on distance structure status pairs |
| miRNAFold is a web server dedicated for miRNA precursors identification based on ab initio method at a large scale in genomes. It allows predicting miRNA hairpin structures quickly with high sensitivity. |
|
Ab initio miRNA precursor prediction in genomes |
| miRQuest is a novel middleware that allows the end user to do the miRNA research in a user-friendly way. miRQuest performs two main functions integration of different miRNA prediction tools for miRNA identification in a user-friendly environment; and comparison of these prediction tools. |
|
miRNA prediction interface which integrates four main tools |
| PlantMiRNAPred is a classification method based on support vector machine (SVM) is proposed specifically for predicting plant pre-miRNAs. |
|
Classify real plant pre-miRNAs and pseudo hairpins |
| ProMiR is a web-based service for the prediction of potential microRNAs in a query sequence of 60–150 nt, using a probabilistic co learning model. ProMiR identify new clusters near known or unknown miRNAs and integrates additional evidence, such as free energy data, G/C ratio, conservation score and entropy of candidate sequences. |
|
Conserved, non-conserved, clustered, non-clustered miRNA prediction |
| PsRobot is a web-based tool dedicated to the identification of miRNAs with stem-loop shaped precursors and their target genes/transcripts. It performs fast analysis to identify smRNAs with stem-loop shaped precursors among batch input data and predicts their targets using a modified Smith-Waterman algorithm. |
|
Stem-loop small RNA prediction |
