| miRWalk2.0 is supplying the biggest available collection of predicted and experimentally verified miRNA-target interactions with various novel and unique features. miRWalk2.0 not only documents miRNA binding sites within the complete sequence of a gene, but also combines this information with a comparison of binding sites resulting from 12 existing miRNA-target prediction programs to build novel comparative platforms of binding sites for the promoter, cds, 5’- and 3’-UTR regions. It also documents experimentally verified miRNA-target interaction information collected via an automated text-mining search. |
| Combining miRNA target prediction results from DIANA-microT, miRanda, MirTarget2, RNAhybrid, PicTar4, PicTar5, PITA, RNA22, TargetScan and miRWalk Search of miRNA binding sites within 5´ UTR, CDS and 3´UTR Search in all or longer transcripts Predicted targets are diagrammed to pathways and diseases |
| Experimentally validated miRNA targets miRNA expression miRNA roles in diseases miRNA interactions in pathways |
| The HOCTAR (Host gene Opposite Correlated TARgets) tool is a new 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 is the first and unique database to use transcriptomic data to score putative miRNA targets looking at the expression behavior of their host genes, and it includes and re-analyzes all miRNA target predictions generated by softwares such as miRanda, TargetScan and PicTar. The HOCTAR contains the prediction target lists for 290 human intragenic miRNAs and also provides tentative assignments of miRNA function based on Gene Ontology analyses of their predicted targets. |
| Integrating miRNA target predictions of miRanda, TargetScan and PicTar |
| Target prediction correlated with expression data |
| mirDIP integrates prediction databases to elucidate accurate microRNA:target relationships. Pathway-based analysis of mirDIP data suggests microRNAs are involved in intra-pathway signalling. We identify two distinct classes of microRNAs, suggesting a hierarchical organization of microRNAs co-targeting genes both within and between pathways, and implying differential involvement of universe and intra-pathway microRNAs at the disease level. |
| Merging microRNA predictions twelve microRNA prediction datasets from DIANA-microT, microcosm, miRanda, PicTar, PITA, RNA22 and TargetScan algorithms Finding signaling pathway-associated microRNAS |
| miRNA interactions in networks |
| miRecords is a resource for animal miRNA-target interactions. The Predicted Targets component of miRecords is an integration of predicted miRNA targets produced by 11 established miRNA target prediction programs. The Predicted Targets component of mIRecords integrates the predicted targets of DIANA-microT, MicroInspector, miRanda, MirTarget2, miTarget, NBmiRTar, PicTar, PITA, RNA22, RNAhybrid, and TargetScan/TargertScanS tools. |
| Combining of predicted miRNA targets produced by 11 algorithms DIANA-microT, MicroInspector, miRanda, MirTarget2, miTarget, NBmiRTar, PicTar, PITA, RNA22, RNAhybrid and TargetScan Target predictions can be pooled with the validated interactions |
| Experimentally validated miRNA targets |
| miRGator aims to be the microRNA (miRNA) portal encompassing microRNA diversity, expression profiles, target relationships, and various supporting tools.miRNA-mRNA target relations and expression correlations integrated for identifying reliable targets. We have merged 3 databases of validated targets and 6 databases of predicted targets. Inverse correlation of gene expression is a strong evidence of genuine targetedness, and we have specifically collected studies with miRNA and mRNA expression data from the same samples. |
| Joining the results of PicTar, TargetScan, miRanda, microcosm, PITA and miRDB algorithms Containing microRNA diversity, expression profiles and target relationships |
| miRNA expression miRNA functions miRNA interactions in pathways |
| microRNA body map is an innovative approach to elucidate tissue-specific miRNA functions that goes beyond miRNA target prediction and expression correlation. This approach is based on a multi-level integration of corresponding miRNA and mRNA gene expression levels, miRNA target prediction, transcription factor target prediction and mechanistic models of gene network regulation. Predicted miRNA functions were either validated experimentally or compared to published data. |
| Integration of predicted miRNA targets produced by eight miRNA target algorithms PITA, DIANA-microT, Microcosm, RNA22, TargetScan, miRDB, TarBase and mirecords miRNA function and their effect on pathways |
| miRNA expression miRNA function |
| miRNAMap 2.0 collect experimental verified microRNAs and experimental verified miRNA target genes in human, mouse, rat, and other metazoan genomes. In addition to known miRNA targets, three computational tools previously developed, such as miRanda, RNAhybrid and TargetScan, were applied for identifying miRNA targets in 3' -UTR of genes. In order to reduce the false positive prediction of miRNA targets, several criteria are supported for filtering the putative miRNA targets. |
| Combining miRNA targeting results of Miranda, RNAHybrid and TargetScan algorithms |
| miRNA expression |
| miRò is a web-based knowledge base that provides users with miRNA-phenotype associations in humans. It integrates data from various online sources, such as databases of miRNAs, ontologies, diseases and targets, into a unified database equipped with an intuitive and flexible query interface and data mining facilities. The main goal of miRò analysis through sophisticated mining techniques and the introduction of a new layer of associations between genes and phenotypes inferred based on miRNAs annotations. |
| Integration miRNA target predictions of miRanda, PicTar, TargetScan and mirecords algorithms |
| miRNA function miRNA roles in diseases |
| miRror-Suite platform developed to yield a robust and concise explanation for miRNA regulation from a large collection of differentially expressed transcripts and miRNAs. Users select the preferred databases from 11 miRNA target prediction resources for predictions and numerous optional filters/parameters that restrict the search to the desired tissues, cell lines, level of expression and predictor scores. |
| Analyzing series of genes or miRNAs by integrating 11 miRNA target prediction resources |
| miRNA interactions in pathways miRNA interactions in networks |
| MicroRNA Target prediction (miRTar) is a tool that enables biologists easily to identify the biological functions and regulatory relationships between a group of known/putative miRNAs and protein coding genes. It uses RNAHybrid, TargetScan, Miranda and PITA algorithms. It also provides perspective of information on the miRNA targets on alternatively spliced transcripts. |
| 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 |
| miRNA interactions in pathways |
| Since different existing algorithms rely on different features and classifiers, there is a poor agreement among the results of different algorithms. To benefit from the advantages of different algorithms, we proposed an algorithm called BCmicrO that combines the prediction of TargetScan, miRanda, PicTar, mirTarget, PITA, and DianamicroT. |
| Combines the prediction of TargetScan, miRanda, PicTar, mirTarget, PITA, and Diana microT |
| ComiR (Combinatorial miRNA targeting) predicts whether a given mRNA is targeted by a set of miRNAs. ComiR uses miRNA expression to improve and combine multiple miRNA targets for each of the four prediction algorithms:miRanda, PITA, TargetScan and mirSVR. The composite scores of the four algorithms are then combined using a support vector machine trained on Drosophila Ago1 IP data. |
| Combining miRNA target prediction results from miRanda, PITA, TargetScan and mirSVR Using miRNA expression to improve and combine multiple miRNA targets |
| miRGate is a curated database of human, mouse and rat miRNAs/mRNAs targets. It is designed to analyze miRNA and gene isoforms lists under a common and consistent space of annotations. Including all existing 3 UTR and the entirely known miRNAs. All Havana biotypes and ENCODE principal isoforms for the three organisms are also included. |
| Using miRanda, Pita, RNAHybrid, Microtar, TargetScan, TarBase, mirTarBase and miRecords Analyze miRNA and gene isoforms lists under a common and consistent space of annotations |
| miRSystem is a database which integrates seven well known miRNA target gene prediction programs: DIANA,miRanda, miRBridge, PicTar, PITA, rna22and TargetScan. This database contains validated data from TarBase and miRecords on interaction between miRNA and its target genes. |
| Combining miRNA targeting of DIANA, miRanda, miRBridge, PicTar, PITA, rna22 and TargetScan algorithms |
| mirTarPri is a web toolkit for prioritising candidate mirRNA targets in the context of functional genomic data. mirTarPri also provides a full prioritized mirRNA target list of six commonly used methods TargetScan, PicTar, PITA, DIANA-microT, miRanda and RNAhybrid |
| Prioritizing candidate mirRNA targets in the context of functional genomic data Using Gene Ontology and protein protein interaction networks Provides mirRNA target list of TargetScan, PicTar, PITA,DIANA-microT, miRanda, RNAhybrid tarBase, mirRecord and mir2Disease |
| multiMiR is a comprehensive collection of predicted and validated miRNA-target interactions and their associations with diseases and drugs. multiMiR collecting nearly 50 million records from 14 different databases. User-defined cutoffs for predicted binding strength to provide the most confident selection. |
| Using predicted and validate miRNA-target Interactions Using DIANA-microT-CDS, ElMMo, MicroCosm, miRanda, miRDB, PicTar, PITA, TargetScan, miRecords, miRTarBase and TarBase algorithms |
| targetHub is a database of miRNA-mRNA interactions. The interaction data is obtained various external data sources and in some cases computed in-house by algorithms implemented for miRNA target prediction |
| Using predicted and validate miRNA-target Interactions Using miRanda, PicTar4, PicTar5, TargetScan, miRecords and miRTarBase algorithms |
| ToppMiR is 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. |
| Ranking targets based on biological functions and context Using PicTar, mirSVR, TargetScan, MSigDB, PITA, miRecords and miRTarbase algorithms |
