| miRNApath is a database that uses miRNA target genes to link miRNAs to metabolic pathways. Currently, databases about miRNA target genes (DIANA miRGen), genomic maps (miRNAMap) and sequences (miRBase) do not provide such correlations. Additionally, miRNApath offers five search services and a download area. For each search, there is a specific type of input, which can be a list of target genes, miRNAs, or metabolic pathways, which results in different views, depending upon the input data, concerning relationships between the target genes, miRNAs and metabolic pathways. |
| Search by gene, miRNA or pathway name Based on target gene prediction |
| DIANA-miRPath v3.0 is an online software suite dedicated to the assessment of miRNA regulatory roles and the identification of controlled pathways. DIANA-miRPath v3.0 database and functionality have been significantly extended to support all analyses for KEGG molecular pathways, as well as multiple slices of Gene Ontology (GO) in seven species. Importantly, more than 600 000 experimentally supported miRNA targets from DIANA-TarBase v7.0 have been incorporated into the new schema. Users of DIANA-miRPath v3.0 can harness this wealth of information and substitute or combine the available in silico predicted targets from DIANA-microT-CDS and/or TargetScan v6.2 with high quality experimentally supported interactions. A unique feature of DIANA-miRPath v3.0 is its redesigned Reverse Search module, which enables users to identify and visualize miRNAs significantly controlling selected pathways or belonging to specific GO categories based on in silico or experimental data. |
| Showing targeted genes on pathways Based on target prediction or experimentally validated results Using microT-CDS-v5.0, Tarbase-v7.0 or TargetScan algorithm Reverse Search, from pathway to miRNA |
| miRror-Suite platform developed to yield a robust and concise explanation for miRNA regulation from a large collection of differentially expressed transcripts and miRNAs. Researchers who performed large-scale transcriptomics or miRNA profiling experiments from cells and tissues will benefit from miRror-Suite. miRror-Suite provides a concise, plausible explanation for the regulation of miRNAs in such complex settings. |
| Integrating 11 miRNA target prediction results Gene to miR or miR to Gene search Forward the results to external analysis |
| Composite target prediction miRNA interactions in networks |
| miRGator v3.0 compiled the deep sequencing miRNA data available in public and implemented several novel tools to facilitate exploration of massive data. miRNA–target relation is essential for understanding miRNA function and role in pathways. Coexpression analysis of miRNA and target mRNAs is visualized in the heat-map and network views where users can investigate the inverse correlation of gene expression and target relations, compiled from various databases of predicted and validated targets. |
| Using different algorithms for predicted effects Comparison a list of genes against defined gene sets such as KEGG pathway, Gene Ontology, the validated/predicted miRNA target databases and inversely coexpressed gene sets |
| Composite target prediction miRNA expression miRNA function |
| miRTar adopts various analyzing scenarios to identify putative miRNA target sites of the gene transcripts and elucidates the biological functions of miRNAs toward their targets in biological pathways. The system has these features. Tthe prediction system is able to consider various analyzing scenarios. miRTar can analyze and highlight a group of miRNA-regulated genes that participate in particular KEGG pathways to elucidate the biological roles of miRNAs in biological pathways. |
| Based on integrating 4 miRNA prediction algorithms TargetScan, miRanda, PITA, and RNAHybrid Multiple scenarios for search |
| Composite target prediction |
| "miRWalk2.0 is supplying the biggest available collection of predicted and experimentally verified miRNA-target interactions with various novel and unique features. It provides possible interactions between miRNAs and genes associated with 597 KEGG, 456 Panther and 522 Wiki pathways. users can also obtain miRNAs which are significantly enriched for their binding sites within the genes associated with pathways, ontologies and classes. |
| Based on predicted and validated miRNA targets Covering 375 pathways from three organisms |
| Composite target prediction Experimentally validated miRNA targets miRNA expression miRNA roles in diseases |
| miTALOS v2 is a tool that provides insights into tissue specific miRNA regulation of biological pathways. miTALOS v2 developed a novel methodology for tissue specific pathway analysis of miRNAs. This database incorporated the most recent and highest quality miRNA targeting data (TargetScan and StarBase), RNA-seq based gene expression data (EBI Expression Atlas) and multiple new pathway data sources to increase the biological relevance of the predicted miRNA-pathway associations. |
| Analyzing the tissue-specific regulation of signaling pathways |
| miRNA function |
| MAGIA2 is dedicated to the integrated analysis of in silico target prediction, microRNA (miRNA) and gene expression data for the reconstruction of post-transcriptional regulatory networks. MAGIA2 performs this integrative approach supporting different association measures, multiple organisms and almost all target predictions algorithms. MAGIA2 tries to dissect this complexity by reconstructing mixed regulatory circuits involving either miRNA or transcription factor (TF) as regulators. |
| Integrate analysis of target prediction, miRNA and gene expression data to rebuild post-transcriptional regulatory networks |
| MAGIA (miRNA and genes integrated analysis) is a novel web tool for the integrative analysis of target predictions, miRNA and gene expression data. MAGIA is divided into two parts: the query section allows the user to retrieve and browse updated miRNA target predictions computed with a number of different algorithms (PITA, miRanda and Target Scan) and Boolean combinations thereof. The analysis section comprises a multistep procedure for (i) direct integration through different functional measures of mRNA and miRNA expression data, (ii) construction of bipartite regulatory network of the best miRNA and mRNA putative interactions and (iii) retrieval of information available in several public databases of genes, miRNAs and diseases and via scientific literature text-mining. |
| Integrative analysis of target predictions, miRNA and gene expression data Using PITA, miRanda and Target Scan algorithms |
| EdgeExpressDB is a novel database and set of interfaces for interpreting biological networks and comparing large high-throughput expression datasets that requires minimal development for new data types and search patterns. The FANTOM4 EdgeExpress database http://fantom.gsc.riken.jp/4/edgeexpress summarizes gene expression patterns in the context of alternative promoter structures and regulatory transcription factors and microRNAs using intuitive gene-centric and sub-network views. This is an important resource for gene regulation in acute myeloid leukemia, monocyte/macrophage differentiation and human transcriptional networks. |
| Covering miRNAs, genes, promoters and expression profiles |
| mirConnX is a web interface for inferring, displaying and parsing mRNA and microRNA gene regulatory networks. mirConnX combines sequence information with gene expression data analysis to create a disease-specific, genome-wide regulatory network. A prior, static network has been constructed for all human and mouse genes. It consists of computationally predicted transcription factor (TF)-gene associations and miRNA target predictions. The prior network is supplemented with known interactions from the literature. Dynamic TF- and miRNA-gene associations are inferred from user-provided expression data using an association measure of choice. The static and dynamic networks are then combined using an integration function with user-specified weights. |
| Based on target predictions and computationally predicted TF-gene associations |
| "mirDIP integrates prediction databases to elucidate accurate microRNA:target relationships. Using NAViGaTOR to produce interaction networks implicating microRNAs in literature-based, KEGG-based and Reactome-based pathways, we find these signalling pathway networks have significantly more microRNA involvement compared to chance (p<0.05), suggesting microRNAs co-target many genes in a given pathway. Further examination of the micronome shows two distinct classes of microRNAs; universe microRNAs, which are involved in many signalling pathways; and intra-pathway microRNAs, which target multiple genes within one signalling pathway. 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. |
| Integrating six miRNA prediction algorithms |
| miRror-Suite platform developed to yield a robust and concise explanation for miRNA regulation from a large collection of differentially expressed transcripts and miRNAs. Researchers who performed large-scale transcriptomics or miRNA profiling experiments from cells and tissues will benefit from miRror-Suite. miRror-Suite provides a concise, plausible explanation for the regulation of miRNAs in such complex settings. |
| Integrating 11 miRNA target prediction results Gene to miR or miR to Gene search Forward the results to external analysis |
| Composite target prediction miRNA interactions in pathways |
| miRTrail is an integrative tool that allows for performing comprehensive analyses of interactions of genes and miRNAs based on expression profiles. The integrated analysis of mRNA and miRNA data should generate more robust and reliable results on deregulated pathogenic processes and may also offer novel insights into the regulatory interactions between miRNAs and genes. Our web-server excels in carrying out gene sets analysis, analysis of miRNA sets as well as the combination of both in a systems biology approach. To this end, miRTrail integrates information on 20.000 genes, almost 1.000 miRNAs, and roughly 280.000 putative interactions. For interactively visualizing obtained results, it relies on the network analyzers and viewers BiNA or Cytoscape-web, also enabling direct access to relevant literature. |
| Assess possible important implications of the miRNAs on the given disease |
| DIANA-miRPath v2.0 utilize miRNA targets predicted with high accuracy based on DIANA-microT-CDS and/or experimentally verified targets from TarBase v6; combine results with merging and meta-analysis algorithms; perform hierarchical clustering of miRNAs and pathways based on their interaction levels; as well as elaborate sophisticated visualizations, such as dendrograms or miRNA versus pathway heat maps, from an intuitive and easy to use web interface. New modules enable DIANA-miRPath server to provide information regarding pathogenic single nucleotide polymorphisms (SNPs) in miRNA target sites (SNPs module) or to annotate all the predicted and experimentally validated miRNA targets in a selected molecular pathway (Reverse Search module). |
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Illustrating targeted genes on pathways |
| miRSystem is a database which integrates seven well known miRNA target gene prediction programs and allows querying multiple miRNAs in one step for the associations between the miRNAs and their target genes. Two algorithms are incorporated to characterize the enriched biological functions/pathways among the genes targeting by queried miRNAs. |
| Target prediction based on DIANA, miRanda, miRBridge, PicTar, PITA, rna22 and TargetScan algorithms Enriched pathways of target genes are characterized by five pathway databases |
| Co-expression Meta-analysis of miRNA Targets (CoMeTa) procedure is based on the assumption that the targets of a given miRNA are likely to be co-expressed and therefore to belong to the same miRNA gene network. The CoMeTa tool aims at the inference of miRNA targets and miRNA-regulated gene networks by integrating expression data from hundreds of cellular and tissue conditions. |
| Inferring miRNA targets and miRNA-regulated gene networks by integrating expression data from hundreds of cell and tissue conditions functional roles of the predicted miRNA regulated network |
