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miRNAFold

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A web server dedicated for miRNA precursors identification at a large scale in genomes. It is based on an algorithm called miRNAFold that allows predicting miRNA hairpin structures quickly with high sensitivity. miRNAFold is implemented as a web server with an intuitive and user-friendly interface, as well as a standalone version. miRNAFold was about five times faster than Mirinho for a sequence of 30 500 pb and almost nine times faster on a sequence of ∼1 Mb.

SMIRP

A framework for creating species-specific miRNA prediction systems, leveraging sequence conservation and phylogenetic distance information. Significant improvements in specificity and precision are obtained for non-human hold-out test species when SMIRP is applied to existing prediction systems. To make a prediction using SMIRP, enter an RNA sequence into the Sequence field, choose the model which best matches the species you are working with, and hit "Make a prediction". DNA sequences entered into the sequence field will be converted into RNA sequences by replacing all T characters with U. Due to the computational requirements of generating feature data, the webserver only allows one prediction at a time using pre-computed models. If you wish to predict large datasets, the SMIRP source code is freely available.

iMcRNA

A web-server developed for identifying the real microRNA precursors and false microRNA precursors. iMcRNA contains two predictors: iMcRNA-PseSSC and iMcRNA-ExPseSSC based on the concept of pseudo amino acid composition or Chou’s PseAAC. They were proposed for identifying the human pre-microRNAs by incorporating the global or long-range structure-order information. Rigorous cross-validations on a much larger and more stringent newly constructed benchmark dataset showed that the two new predictors outperformed or were highly comparable with the best existing predictors in this area.

iMiRNA-SSF / Identification of MicroRNA based on Structure and Sequence Features

A computational method for pre-miRNA identification. iMiRNA-SSF employs the sequence and structure features trained with an updated benchmark dataset. This dataset was constructed with the positive samples extracted from the miRBase35–37, and the negative samples selected from existing datasets with different data distributions. Experimental results showed that iMiRNA-SSF outperforms three state-of-the-art computational methods.

miRNA-dis

A high throughput tool for large-scale analysis of microRNA precursors. miRNA-dis is a method proposed in order to 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". Rigorous cross-validations on a much larger and more stringent newly constructed benchmark dataset showed that the miRNA-dis outperformed some state-of-the-art predictors in this area. Remarkably, miRNA-dis trained with human data can correctly predict 87.02% of the 4022 pre-miRNAs from 11 different species ranging from animals, plants and viruses. In addition, the learnt model can be easily analyzed in terms of discriminative features, and some interesting patterns were discovered, which could reflect the characteristics of microRNAs.

CoMeTa / Co-expression Meta-analysis of miRNA Targets

Aims at the inference of miRNA targets and miRNA-regulated gene networks by integrating expression data from hundreds of cellular and tissue conditions. The website includes the CoMeTa corank lists and additional targets for all of the human miRNAs, their associated pathways resulting from COOL analysis, and miRNA communities with their corresponding enriched functional categories. The CoMeTa website is searchable by miRNA, target gene, or biological function of interest, and represents a unique resource to gain insight into miRNA-controlled gene networks and functions.

miRBoost

Uses a boosting technique with support vector machine components to deal with imbalanced training data. Classification is performed following a feature selection on 187 novel and existing features. miRBoost performed better in comparison with state-of-the-art methods on imbalanced human and cross-species data. It also showed the highest ability among the tested methods for discovering novel miRNA precursors. In addition, miRBoost was over 1400 times faster than the second most accurate tool tested and was significantly faster than most of the other tools. miRBoost thus provides a good compromise between prediction efficiency and execution time, making it highly suitable for use in genome-wide miRNA precursor prediction.

miPlantPreMat

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.

miRQuest

A middleware available in a Web server that allows the end user to do the miRNA research in a user-friendly way. It is known that there are many prediction tools for microRNA (miRNA) identification that use different programming languages and methods to realize this task. It is difficult to understand each tool and apply it to diverse datasets and organisms available for miRNA analysis. miRQuest can easily be used by biologists and researchers with limited experience with bioinformatics. We built it using the middleware architecture on a Web platform for miRNA research that performs two main functions: i) integration of different miRNA prediction tools for miRNA identification in a user-friendly environment; and ii) comparison of these prediction tools. In both cases, the user provides sequences (in FASTA format) as an input set for the analysis and comparisons. All the tools were selected on the basis of a survey of the literature on the available tools for miRNA prediction.

HeteroMirPred

An ensemble classifier approach for microRNA precursor (pre-miRNA) classification was proposed based upon combining a set of heterogeneous algorithms including support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF), then aggregating their prediction through a voting system. Additionally, the proposed algorithm, the classification performance was also improved using discriminative features, self-containment and its derivatives, which have shown unique structural robustness characteristics of pre-miRNAs. These are applicable across different species.

deepMiRGene

Designs hand-crafted features without manual feature engineering. deepMiRGene is able to rediscover intrinsic features in a data-driven fashion. It is based on recurrent neural networks (RNNs) and specifically long short-term memory (LSTM) networks. This tool learns palindromic structures by using structure pre-processing of split and flip. It shows that it is possible to visually inspect the transition of the LSTM cell states on each position in the sequence in order to find existing structural features.

miRNAfe

Extracts features from RNA sequences. miRNAfe is freely available as a web service, allowing a single access point to almost all state-of-the-art feature extraction methods used today in a variety of works from different authors. It has a very simple user interface, where the user only needs to load a file containing the input sequences and select the features to extract. As a result, the user obtains a text file with the features extracted, which can be used to analyze the sequences or as input to a miRNA prediction software. The tool can calculate up to 80 features where many of them are multidimensional arrays. In order to simplify the web interface, the features have been divided into six pre-defined groups, each one providing information about: primary sequence, secondary structure, thermodynamic stability, statistical stability, conservation between genomes of different species and substrings analysis of the sequences. Additionally, pre-trained classifiers are provided for prediction in different species. All algorithms to extract the features have been validated, comparing the results with the ones obtained from other software.

MiRank

A ranking algorithm based on random walks to propagate information of known miRNAs to candidates. The miRank method has the following properties. First, it does not require information of genome annotation. This is particularly important because many sequenced genomes have not been well annotated, and their closely related species are yet to be sequenced. Thus, a large number of false positive candidates with hairpinned secondary structures cannot be filtered out with genome annotation or by phylogenetic conservation. miRank can be applied to such newly sequenced genomes with little annotation. Second, it does not rely on cross-species conservation so that it can identify species-specific miRNAs. Third, miRank is able to accommodate a small number of known miRNAs while enjoys a high-prediction accuracy. Hence, miRank is a useful tool for many species including most viruses that have a very few reported miRNAs.

miR-BAG

Identifies miRNA candidates with high accuracy and stable performance over wide range of species. Biologically relevant novel features like miRNA specific mature miRNA guided structural profile matrices and structural triplet density variation profiles with respect to position have been introduced to derive a superior and stable performance. An ensemble machine learning methodology, bootstrap aggregating (BAGging), has been implemented. It employs complementary classifiers like support vector machine (SVM), naive Bayes (NB) and best first decision trees (BFTree) to build the final classifier models for large number of species, enhancing the performance strongly. An NGS module has been built to find miRNA precursor candidates, using Illumina read data. The process of miRNA candidate detection requires large volume of sequence data scanning, which makes it dependent upon extensive computing.

Semirna

A tool for predicting miRNAs in plant genomes. Semirna takes a putative target sequence such as a messenger RNA (mRNA) as input, and allows users to search for miRNAs that target this sequence. It can also be used to determine whether small RNA sequences from massive sequencing analysis represent true miRNAs and to search for miRNAs in new genomes using homology. Semirna has shown a high level of accuracy using various test sets, and gives users the ability to search for miRNAs with several different adjustable parameters.

YamiPred / Yet another miRNA predictor

Predicts human miRNA sequences. YamiPred is based on a Support Vector Machines (SVM) with Genetic Algorithms (GA) in order to provide feature selection and parameters optimization. It is robust to predict miRNA for organisms for which a very small number of miRNA genes is known. The tool combines data from heterogeneous data sources into a cellular interaction network. The advantages of Yamipred are attributed to the elegant way of dealing with the class imbalance problem, slow convergence and interpretability through a simple mechanism for selecting the ratio of positive and negative samples.

CID-miRNA

A tool and a web server for identification of miRNA precursors in a given DNA sequence, utilising 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.

miRRim

Obsolete
Detects conserved miRNAs. miRRim2 can not only accurately detect miRNA hairpins, but infer the location of a mature miRNA sequence. In miRRim2, each position of a miRNA hairpin is expressed as a multidimensional feature vector to detect position-specific features; therefore, a miRNA hairpin is expressed as a sequence of the feature vectors. miRNA hairpins, expressed by sequences of feature vectors, are modeled using conditional random fields (CRFs, which optimize feature weights so that a trained model can most probably discriminate between miRNA hairpins and background data. The probabilistic model used in miRRim2 has several sub-components, each of which corresponds to a specific component of miRNA hairpins, such as mature miRNA, passenger strand, and terminal loop regions; therefore, the position-specific features of each component are appropriately modeled.