1 - 50 of 166 results


A plant small RNA target analysis server, which features two important analysis functions: (i) reverse complementary matching between small RNA and target transcript using a proven scoring schema, and (ii) target-site accessibility evaluation by calculating unpaired energy (UPE) required to 'open' secondary structure around small RNA's target site on mRNA. The psRNATarget incorporates recent discoveries in plant miRNA target recognition, e.g. it distinguishes translational and post-transcriptional inhibition, and it reports the number of small RNA/target site pairs that may affect small RNA binding activity to target transcript.


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.


Improves the miRNA target gene (MTG) predictions made with existing tools by making use of interspecies comparison while taking into account miRNA target binding sites (MTBS) turnover. MirAncesTar uses computationally reconstructed ancestral mRNA sequences, rather than relying on pure conservation scores such as phastCons or PhyloP. This approach is not a predictor in itself, but rather an accuracy booster that can be applied to any existing predictor. Applied to three of the most commonly used MTBS predictors, MirAncesTar results in a large improvement in accuracy and compares favorably with three of the recent MTG predictors making use of sequence conservation, mirMark, Diana-microT and TargetScan.

TarPmiR / Target Prediction for miRNAs

An approach for miRNA target site prediction. TarPmiR applies a random-forest-based approach to integrate six conventional features and seven new features to predict miRNA target sites. These features were learned from the only CLASH dataset in mammal. Testing on two human and one mouse non-CLASH datasets, we showed that TarPmiR predicted more than 74.2 % of true miRNA target sites in each dataset. Compared with three existing approaches, we demonstrated that TarPmiR is superior to these existing approaches in terms of better recall and better precision.

MiRSEA / MicroRNA Set Enrichment Analysis

A computational method to identify the pathways regulated by dysfunctional miRNAs. MiRSEA constructs a correlation profile of microRNA and pathways by the hypergeometric statistic test. The gene sets of pathways derived from the three public databases (Kyoto Encyclopedia of Genes and Genomes ('KEGG'); 'Reactome'; 'Biocarta') and the target gene sets of microRNA are provided by four databases ('TarBaseV6.0'; 'mir2Disease'; 'miRecords'; 'miRTarBase';). This package can quantify the change of correlation between microRNA for each pathway (or prior gene set) based on a microRNA expression data with cases and controls. MiRSEA uses the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score (ES) of a microRNA set that co-regulate to a pathway, which reflects the degree to which a given pathway is associated with the specific phenotype. This package can provide the visualization of the results.


An easy-to-use web-based tool that offers statistical, visual and network-based approaches to help researchers understand miRNAs functions and regulatory mechanisms. The key features of miRNet include: (i) a comprehensive knowledge base integrating high-quality miRNA-target interaction data from 11 databases; (ii) support for differential expression analysis of data from microarray, RNA-seq and quantitative PCR; (iii) implementation of a flexible interface for data filtering, refinement and customization during network creation; (iv) a powerful fully featured network visualization system coupled with enrichment analysis. miRNet offers a comprehensive tool suite to enable statistical analysis and functional interpretation of various data generated from current miRNA studies.


An R package able to combine miRNA and mRNA expression data with hybridization information, in order to find potential miRNA-mRNA targets that are more reliable to occur in a specific physiological or disease context. MiRComb generates a list of results that can be the basis for developing multiple hypotheses to be experimentally tested in a wet lab. We have used as examples publicly available data from The Cancer Genome Atlas for different digestive cancers. The results highlight potential miRNA-mRNA interactomes of five digestive cancers and offer an unbiased view of miRComb functions.

CCmiR / Competitive and Cooperative miRNA target prediction

Predicts competitive and cooperative miRNA binding. CCmiR is a hidden Markov model (HMM) based method that considers the miRNA expression levels and the competition and cooperation of multiple miRNAs. This application also visualizes the binding affinity at each position of each mRNA. Even if users can input all miRNAs and all mRNAs in a species to run CCmiR, it is recommended to input a proper set of miRNAs and a proper set of mRNAs in order to have the best performance.

DPMIND / Degradome-based Plant MiRNA-Target Interaction and Network Database

Verifies the predicted relationships between miRNA and their targets via degradome data. DPMIND is a plant miRNA repository and compiles plant degradome data. This database offers to users several query interfaces and graphical visualization pages to ease the access of verified miR-Tar interactions (MTIs) and degradome-based miRNA regulatory networks (MRNs). It provides assistance to study the conservation and specificity of miR-Tar interactions and sub-networks of plant tissues or species.

MIRZA-G / MIRZA-Genome-wide

A suite of algorithms for the prediction of miRNA targets and siRNA off-targets on a genome-wide scale. The MIRZA-G variant that uses evolutionary conservation performs better than currently available methods in predicting canonical miRNA target sites and in addition, it predicts non-canonical miRNA target sites with similarly high accuracy. Furthermore, MIRZA-G variants predict siRNA off-target sites with an accuracy unmatched by currently available programs. Thus, MIRZA-G may prove instrumental in the analysis of data resulting from large-scale siRNA screens.

PEA / a versatile R package for Plants Epitranscriptome Analysis

Offers a set of methods dedicated to plant epitranscriptome analysis. PEA is an R package composed of three modules: (i) CMR Calling that detects chemical modifications of RNA (CMRs) from epitranscriptome sequencing data, (ii) CMR Prediction for determining CMRs, at the transcriptome scale, thanks to a machine-learning approach, and (iii) CMR Annotation that supplies insights into spatial and functional associations of CMRs and permits users to perform motif scanning and discovery.


Predicts microRNA target site. SeedVicious finds microRNA canonical sites plus other, less efficient, target sites. It detects near-target sites, which have one nucleotide different from a canonical site. It predicts targets on alignments and computes evolutionary gains/losses of target sites using maximum parsimony. The tool permits the inference of gains and losses of microRNA target sites by first predicting individual target sites at all transcripts in a given alignment, and then fitting a maximum parsimony (MP) model to a tree provided by the user.

miRNAsong / MicroRNA SpONge Generator and tester

Generates miRNA sponge constructs for specific miRNAs and miRNA families/clusters and tests them for potential binding to miRNAs in selected organisms. miRNAsong is a freely available web-based tool for generation and in silico testing of miRNA sponges. This tool allows the user to generate miRNA sponge sequences specific to a target miRNA, miRNA family and/or cluster. It also has the ability to test sponge sequences in silico for potential off-targets in 219 species covering 35,828 mature miRNA sequences.

ActMiR / activity of miRNAs

A computational approach for identifying active miRNAs and miRNA-mediated regulatory mechanisms. Applying ActMiR to four cancer datasets in The Cancer Genome Atlas (TCGA), we showed that (1) miRNA activity was tumor subtype specific; (2) genes correlated with inferred miRNA activities were more likely to enrich for miRNA binding motifs; (3) expression levels of these genes and inferred miRNA activities were more likely to be negatively correlated. In summary, inferred activity of key miRNA provided a functional link to its mediated regulatory network, and can be used to robustly predict patient's survival.


An integrated resource for deciphering miRNA-target interaction networks, and provides a broad range of analyzing scenarios for miRNA-target interactions, including one miRNA to one gene, one miRNA to multiple genes, and others, to help biologists understand the regulation between the miRNAs and target genes. By integrating several external databases and analyzing tools, miRTar can provide further information for elucidating miRNA regulation affected by alternative splicing. Besides, miRTar can enable biologists to easily identify the biological functions and regulatory relationships between a group of known/putative miRNAs and protein coding genes.


An integrated software platform with a graphical user interface (GUI), to process deep-sequencing data of small RNAs and to analyze miRNA sequence and expression evolution based on the multiple-species whole genome alignments (WGAs). Three major features are provided by miREvo: (i) to identify novel miRNAs in both plants and animals, based on a modified miRDeep algorithm, (ii) to detect miRNA homologs and measure their pairwise evolutionary distances among multiple species based on a WGA, and (iii) to profile miRNA expression abundances and analyze expression divergence across multiple species (small RNA libraries).

PACCMIT/PACCMIT-CDS / Prediction of ACcessible and/or Conserved MIcroRNA Targets

A simple-to-use web server for accurate prediction of targets of both conserved and non-conserved miRNAs both in the 3′ UTR (PACCMIT) and in the coding sequences (PACCMIT-CDS). The web server asks the user for microRNAs and mRNAs to be analyzed, accesses the precomputed P-values for all microRNA–mRNA pairs from a database for all mRNAs and microRNAs in a given species, ranks the predicted microRNA–mRNA pairs, evaluates their significance according to the false discovery rate and finally displays the predictions in a tabular form. The results are also available for download in several standard formats.

HOCTAR / Host gene Opposite Correlated TARgets

A procedure to improve the prediction of miRNA targets. The HOCTAR procedure is based on the integration of expression profiling and sequence-based miRNA target recognition softwares. HOCTAR database (db) is the first and unique database to use transcriptomic data to score putative miRNA targets looking at the expression behaviour of their host genes, and it includes and re-analyzes all miRNA target predictions generated by softwares such as miRanda, TargetScan and PicTar.


A web-based analytical workbench that allows miRs and mRNAs to be co-analyzed via biologically centered approaches in which gene function associated annotations are used to train a machine learning-based analysis engine. ToppMiR learns about biological contexts based on gene associated information from expression data or from a user-specified set of genes that relate to context-relevant knowledge or hypotheses. Within the biological framework established by the genes in the training set, its associated information content is then used to calculate a features association matrix composed of biological functions, protein interactions and other features. This scoring matrix is then used to jointly rank both the test/candidate miRs and mRNAs. Results of these analyses are provided as downloadable tables or network file formats usable in Cytoscape.


Predicts microRNA (miRNA) binding sites on a target ribonucleic acid (RNA). STarMir is an implementation of logistic prediction models developed with miRNA binding data from crosslinking immunoprecipitation (CLIP) studies. The input data for STarMir is processed by the web server to perform prediction of miRNA binding sites, compute comprehensive sequence, thermodynamic and target structure features and a logistic probability as a measure of confidence for each predicted site. For each of seed and seedless sites and for all three regions of a mRNA (3' UTR, CDS and 5' UTR), STarMir output includes the computed binding site features, the logistic probability and a publication-quality diagram of the predicted miRNA:target hybrid. The prediction results are available through both an interactive viewer and downloadable text files.


A miRNA-target interaction R package and database, which includes several novel features not available in existing R packages: (i) compilation of nearly 50 million records in human and mouse from 14 different databases, more than any other collection; (ii) expansion of databases to those based on disease annotation and drug microRNA response, in addition to many experimental and computational databases; and (iii) user-defined cutoffs for predicted binding strength to provide the most confident selection.