| TargetScan predicts biological targets of miRNAs by searching for the presence of conserved 8mer, 7mer, and 6mer sites that match the seed region of each miRNA. As an option, predictions with only poorly conserved sites are also provided. Also identified are sites with mismatches in the seed region that are compensated by conserved 3' pairing and centered sites. |
| Finding broadly conserved, conserved and poorly conserved microRNA families Showing miRNA position on gene Alignment of miRNA target site in different organisms |
| "BiTargeting presents a method to identify groups of viral and host miRNAs that cooperate in post-transcriptional gene regulation, and their target genes that are involved in similar biological processes. We call these groups of genes and miRNAs of human and viral origin modules. BiTargeting is demonstrated to be an efficient approach for finding bi-targeting modules of viral and human miRNAs. |
| Viral and host miRNAs that cooperate in gene regulation Integrate miRNA-target binding information, miRNA expression profiles and GO annotations |
| DIANA-microT 3.0 provides extensive information for predicted miRNA:target gene interactions providing extensive connectivity to online biological resources. The target prediction algorithm supports parameters calculated individually for each miRNA:target gene interaction and provides a signal-to-noise ratio and a precision score that helps in the evaluation of the significance of the predicted results. |
| Finding targets based on miRNAs, genes or miRNA and gene Link to KEGG, UniProt and iHOP present predictions of TargetScan or Pictar or verification of TarBase Conservation of target sites in different species |
| DIANA-microT v5.0 has been significantly enhanced with an improved target prediction algorithm, DIANA-microT-CDS. microT-CDS is the only algorithm available online, specifically designed to identify miRNA targets both in 3′ untranslated region (3′UTR) and in coding sequences (CDS). |
| Finding targets of a miRNAs based on miRNAs, genes or KEGG descriptions Present predictions of TargetScan, Miranda or verification of TarBase Conservation of target sites in different species |
| The ElMMo miRNA target prediction method is based on a Bayesian model that only uses comparative genomics information. it has as high an accuracy as other widely used target prediction programs that incorporate additional constraints. |
| Target prediction for mRNAs in subsets of transcripts or different tissue or cell type Alignment of multiple genomes in region of predated target site Link to smiRNAdb samples that expression of selected miRNA is high |
| HomoloMTI constructed to extent the concept of finding miRNA target sites in conserved sequences among multiple orthologous species, integrated three databases, miRTarBase, miRBase and HomoloGene, to reveals the miRNA target interactions might be shared in homologous genes. miRNA target predicts on homologous genes that based on experimental proved microRNA target interaction. |
| Identifying miRNA target sites on conserved sequences among several species Integrated three resources, miRTarBase, miRBase and HomoloGene |
| MAMI is a meta mir:target inference for meta prediction of human microRNA targets. Users can tune their desired specificity and sensitivity for target prediction |
| Specificity and sensitivity tuning for prediction |
| A web resource developed by the Enright Lab at the EMBL-EBI containing computationally predicted targets for microRNAs across many species. The miRNA sequences are obtained from the miRBase Sequence database and most genomic sequence from EnsEMBL |
| Microcosm is basically derived from miRBase resource targets Search by gene or miRNA name, GO term or keyword Alignment of multiple genomes in region of predated target site |
| MicroInspector analyses a user-defined RNA sequence, which is typically an mRNA or a part of an mRNA, for the occurrence of binding sites for known and registered miRNAs. The program allows variation of temperature, the setting of energy values as well as the selection of different miRNA databases to identify miRNA-binding sites of different strength. |
| Finding miRNA binding sites in a target sequence Analysis a user-defined RNA sequence |
| MicroRNA.org is a comprehensive resource of microRNA target predictions and expression profiles. Target predictions are based on a development of the miRanda algorithm which incorporates current biological knowledge on target rules and on the use of an up-to-date compendium of mammalian microRNAs. A user can explore the set of genes that are potentially regulated by a particular microRNA and the implied cooperativity of multiple microRNAs on a particular mRNA. |
| miRNA target prediction using miRanda algorithm Search available for mRNA or miRNA Showing miRNA-mRNA interaction |
| miRNA expression |
| miRDB is miRNA target prediction and functional annotations database. In this release, web server allows submission of user-provided sequences for miRNA target prediction. Another major update of miRDB is related to functional miRNA annotations. Although thousands of miRNAs have been identified, many of the reported miRNAs are not likely to play active functional roles. To address this issue, we have performed combined computational analyses and literature mining. |
| miRNA target prediction using miRanda algorithm Search for multiple miRNAs or genes |
| miREE is an ensemble of two parts, the Ab-Initio module to generate a set of candidate sites on the basis of their microRNA-mRNA duplex stability properties and a Support Vector Machine learning module evaluates the impact of microRNA recognition elements on the target gene. As a result, the prediction takes into account information regarding both miRNA-target structural stability and accessibility. |
| miRNA target prediction using an Ab-initio module followed by a SVM module |
| miRMaid is a software framework, with the goal of integrating miRNA data resources in a uniform web service interface that can be accessed and queried by researchers and by computers. miRMaid is built around data from miRBase and is designed to follow the official miRBase data releases. |
| Integrating miRNA data sources and developing an interface for computer programming |
| Target prediction using miRanda and RNAhybrid algorithms on mRNA 5´ UTR's , coding regions and 3´ UTR's |
| MiRTif (miRNA:target interaction filter) is a machine learning algorithm based on support vector machine that serves as a post-processing filter for the miRNA:target duplexes predicted by softwares such as miRanda, PicTar and TargetScanS. The SVM is trained with validated positive and negative miRNA:target interactions, obtained from TarBase. |
| A post-processing filter for the miRNA: gene hybrids prediction of various algorithms |
| MultiMiTar, a Support Vector Machine based classifier integrated with a multiobjective metaheuristic based feature selection technique. The robust performance of the proposed method is mainly the result of using high quality negative examples and selection of biologically relevant miRNA-targeting site context specific features. True positive predictions are distributed preferentially at the top of the ranked list that makes MultiMiTar reliable for the biologists. |
| Target prediction with multi-objective feature selection |
| NBmiRTar does not require sequence conservation, using instead, machine learning by a naïve Bayes classifier. It generates a model from sequence and miRNA:mRNA duplex information from validated targets and artificially generated negative examples. Both the ‘seed’ and ‘out-seed’ segments of the miRNA:mRNA duplex are used for target identification. |
| Target prediction based on NbmiRTar algorithm |
| The Patrocles database compiles DNA sequence polymorphisms that are predicted to perturb miRNA-mediated gene regulation. Distinctive features include: the coverage of seven vertebrate species in its present release, the coverage of the three compartments involved in the silencing, contextual information that enables users to prioritize candidate and a tool (Patrocles finder) that allows the user to determine whether her favorite DSP may perturb miRNA-mediated gene regulation of custom target sequences. |
| DNA polymorphisms predicted to affect miRNA-gene control |
| PicTar is a computational method for identifying common targets of microRNAs. Statistical tests using genome-wide alignments of eight vertebrate genomes, PicTar's ability to specifically recover published microRNA targets, and experimental validation of seven predicted targets suggest that PicTar has an excellent success rate in predicting targets for single microRNAs and for combinations of microRNAs. |
| Target prediction based on PicTar algorithm Multiple sequence alignment in region of predated target site |
| RepTar is based on identification of repetitive elements in 3'-UTRs and is independent of both evolutionary conservation and conventional binding patterns. The modularity of RepTar enables the prediction of targets with conventional seed sites as well as rarer targets with non-conventional sites, such as sites with seed wobbles, 3'-compensatory sites and the newly discovered centered sites. Furthermore, RepTar's independence of conservation enables the prediction of cellular targets of the less evolutionarily conserved viral miRNAs. |
| Target prediction in human, mouse and several cellular targets of viral miRNAs |
| STarMir web server predicts microRNA binding sites on a target ribonucleic acid. STarMir is an implementation of logistic prediction models developed with miRNA binding data from crosslinking immunoprecipitation (CLIP) studies. In both intra-dataset and inter-dataset validations, the models showed major improvements over established algorithms in predictions of both seed and seedless sites. |
| Finding miRNA binding sites in a target sequence Analysis a user-defined RNA sequence |
| TargetMiner is a support vector machine based classifier which is assessing the prediction accuracy on cross-validation experiments and has been validated with a completely independent experimental test dataset. TargetMiner outperforms 10 existing target prediction algorithms and provides a good balance between sensitivity and specificity that is not reflected in the existing methods. |
| Target prediction with systematic identification of negative examples |
| TargetRank scores the seed matches in a UTR relative to a given siRNA or miRNA, and then calculates an overall score for the mRNA as a whole by summing the scores for all seed matches present in the 3' UTR.The score for each seed match is based on its seed match type, the base composition at position t9, flanking AU content, and flanking conservation. |
| Targeting by endogenous and exogenous microRNAs and siRNAs |
| TargetSpy is a novel computational approach for predicting target sites regardless of the presence of a seed match. It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. This model does not rely on evolutionary conservation, which allows the detection of species-specific interactions and makes it suitable for analyzing un conserved genomic sequences. |
| Target prediction based on scores without demanding a seed match |
| ViTa investigate regulatory relationships between host miRNAs and related viruses which curate the known virus miRNA genes and the known/putative target sites of human, mice, rat and chicken miRNAs. ViTa also provides the virus annotations, virus-infected tissues and tissue specificity of host miRNAs. This work also facilitates the comparisons between subtypes of viruses. |
| Prediction of host miRNA targets in viruses |
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comTAR is a web tool for the prediction of miRNA targets that is mainly based on the conservation of the potential regulation in different species. The database contains information describing each miRNA-target pair, their function and evolutionary conservation. The tool also allows the search using new miRNAs. |
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Analyze the variations of known miRNA targets during evolution |
| RNA22, a method for identifying microRNA binding sites and their corresponding heteroduplexes. RNA22 does not rely upon cross-species conservation, is resilient to noise, and, unlike previous methods, it first finds putative microRNA binding sites in the sequence of interest, then identifies the targeting microRNA. |
| Identifying microRNA binding sites and their corresponding heteroduplexes Access pre-computed rna22 target predictions for multiple species |
| User defined sequence target prediction |
| LncBase v2 is an extensive collection of miRNA:lncRNA (Long non-coding RNAs ) interactions which includes more than 70 000 direct miRNA:lncRNA experimentally supported interactions, derived from manually curated publications and the analysis of AGO CLIP-Seq libraries. LncBase v2 hosts in silico predicted miRNA targets on lncRNAs, identified with the DIANA-microT algorithm. |
| Computationally predicted and experimentally validated results Genomic locations |
| GUUGle efficiently locates potential helical regions under RNA base pairing rules, which include Watson–Crick as well as G–U pairs. It accepts a positive and a negative set of sequences, and determines all exact matches under RNA rules between positive and negative sequences that exceed a specified length. |
| Locates potential helical regions under RNA base pairing rules, which include Watson-Crick as well as G-U pairs |
| mBISON calculates the significance of over-representation of miRNA targets in a given non-ranked gene set. The gene set can be specified either by a list of genes or by one or more ChIP-seq datasets followed by a user-defined peak-gene association procedure. mBISON is based on predictions from TargetScan and uses a randomization step to calculate False-Discovery-Rates for each miRNA, including a correction for gene set specific properties such as 3'UTR length. |
| Calculates the significance of over-representation of miRNA targets in a given non-ranked gene set |
| miRcode is a comprehensive searchable map of putative microRNA target sites across lncRNA that facilitate the study of microRNA–lncRNA interactions. The miRcode interface provides basic search functionality for finding putative microRNA–target sites in lncRNAs of interest or predicted targets of specific microRNAs. |
| Target prediction for mRNA, LncRNA and pseudogene Including 5´ UTR's , coding regions and 3´ UTR's |
| miRmap for the first time comprehensively covers all four approaches of thermodynamic, evolutionary, probabilistic, or sequence-based features using eleven predictor features, three of which are novel. miRmap combined all the features into an integrated model that almost doubles the predictive power of TargetScan. |
| Prediction of microRNA target repression strength Including 5´ UTR's , coding regions and 3´ UTR's |
| miRNA_Targets implemented well establised miRNA target prediction algorithms miRanda and RNAhybrid on mRNA 5 prime UTR's , coding regions and 3 prime UTR's. MicroRNA target predictions using 2 algorithms on 7 genomes. Versatile search capabilities using miRNA or mRNA individually or in groups. Gene ontology classification of sets of target genes. Visualization of miRNA and target gene location network on chromosomes. |
| Target prediction using miRanda and RNAhybrid algorithms on mRNA 5´ UTR's , coding regions and 3´ UTR's |
| MtiBase (MiRNA-target interactions database) is a database that identify CDS-located and 5'UTR-located miRNA binding sites and systematically evaluate miRNA regulatory effects on mRNA stability and translation by integrating multiple high-throughput experimental datasets such as Ago CLIP-Seq (HITS-CLIP, PAR-CLIP, iCLIP, CLASH), mRNA profiles, ribisome-protected fragment sequencing (RPF) and pulsed stable isotope labeling with amino acids in culture (pSILAC). |
| Identify CDS-located and 5'UTR-located miRNA binding sites Systematically evaluate miRNA regulatory effects on mRNA stability and translation |
| p-TAREF (plant-Target Refiner) is a Support Vector Regression approach has been implemented for plant miRNA target identification, utilizing position specific dinucleotide density variation information around the target sites, to yield highly reliable result. Performance comparison for p-TAREF was done with other prediction tools for plants with utmost rigor and where p-TAREF was found better performing in several aspects. |
| Refines the process of microRNA target identification through incorporation of local interaction information |
| TargetRNA2 is a web server that identifies mRNA targets of sRNA regulatory action in bacteria. TargetRNA2 takes the sequence of an sRNA and the name of a sequenced bacterial replicon. TargetRNA2 for searching uses a variety of features, including conservation of the sRNA in other bacteria, the secondary structure of the sRNA, the secondary structure of each candidate mRNA target and the hybridization energy between the sRNA and each candidate. |
| Identifies mRNA targets of sRNA regulatory action in bacteria |
| VIRmiRNA is the first specialized resource of experimentally proven virus-encoded miRNAs and their associated targets. It includes VIRMIRNA, experimentally validated miRNA sequences with their isomiRs encoded by 44 viruses in viral miRNA, VIRMIRTAR, viral miRNA target genes and AVIRMIR antiviral miRNAs encoded by the host against viruses. This database would enhance the understanding of viral/host gene regulation and may also prove beneficial in the development of antiviral therapeutics. |
| Including VIRmiRNA, VIRmiRtar and AVIRmir subdatabases VIRmiRtar contains 7283 target genes of viral miRNAs AVIRmir includes 542 antiviral miRNAs encoded by the host against 24 viruses |
| miRNA sequence and annotation |
