This microRNA database and microRNA targets databases is a compilation of databases and web portals and servers used for microRNAs and their targets. MicroRNAs (miRNAs) represent an important class of small non-coding RNAs (ncRNAs) that regulate gene expression by targeting messenger RNAs.[1]
starBase is designed for decoding miRNA-lncRNA, miRNA-mRNA, miRNA-circRNA, miRNA-pseudogene, miRNA-sncRNA, protein-lncRNA, protein-sncRNA, protein-mRNA and protein-pseudogene interactions and ceRNA networks from 108 CLIP-Seq (HITS-CLIP, PAR-CLIP, iCLIP, CLASH) datasets. It also provides Pan-Cancer Analysis for microRNAs, lncRNAs, circRNAs and protein-coding genes from 6000 tumor samples.
StarScan is developed for scanning small RNA (miRNA, piRNA, siRNA) mediated RNA cleavage events in lncRNA, circRNA, mRNA and pseudo genes from degradome sequencing data.
Cupid is a method for simultaneous prediction of miRNA-target interactions and their mediated competing endogenous RNA (ceRNA) interactions. It is an integrative approach significantly improves on miRNA-target prediction accuracy as assessed by both mRNA and protein level measurements in breast cancer cell lines. Cupid is implemented in 3 steps: Step 1: re-evaluate candidate miRNA binding sites in 3' UTRs. Step2: interactions are predicted by integrating information about selected sites and the statistical dependency between the expression profiles of miRNA and putative targets. Step 3: Cupid assesses whether inferred targets compete for predicted miRNA regulators. * Only the source code for step 3 is provided.
Predicts biological targets of miRNAs by searching for the presence of sites that match the seed region of each miRNA. In flies and nematodes, predictions are ranked based on the probability of their evolutionary conservation. In zebrafish, predictions are ranked based on site number, site type, and site context, which includes factors that influence target-site accessibility. In mammals, the user can choose whether the predictions should be ranked based on the probability of their conservation or on site number, type, and context. In mammals and nematodes, the user can choose to extend the predictions beyond conserved sites and consider all sites.
DIANA-microT 3.0 is an algorithm based on several parameters calculated individually for each microRNA and it combines conserved and non-conserved microRNA recognition elements into a final prediction score.
A database of inverse miRNA target predictions, based on the RepTar algorithm that is independent of evolutionary conservation considerations and is not limited to seed pairing sites.
The first link (predictions) provides RNA22 predictions for all protein coding transcripts in human, mouse, roundworm, and fruit fly. It allows you to visualize the predictions within a cDNA map and also find transcripts where multiple miR's of interest target. The second web-site link (custom) first finds putative microRNA binding sites in the sequence of interest, then identifies the targeted microRNA.
The experimentally validated microRNA-target interactions database. As a database, miRTarBase has accumulated more than three hundred and sixty thousand miRNA-target interactions (MTIs), which are collected by manually surveying pertinent literature after NLP of the text systematically to filter research articles related to functional studies of miRNAs. Generally, the collected MTIs are validated experimentally by reporter assay, western blot, microarray and next-generation sequencing experiments. While containing the largest amount of validated MTIs, the miRTarBase provides the most updated collection by comparing with other similar, previously developed databases.
MirGeneDB is a database of manually curated microRNA genes that have been validated and annotated. MirGeneDB 2.1 includes more than 16,000 microRNA gene entries representing more than 1,500 miRNA families from 75 metazoan species. All microRNAs can be browsed, searched and downloaded.
TargetScan7.0 classifies microRNAs according to their level of conservation (i.e., species-specific, conserved among mammals, or broadly conserved among vertebrates) and aggregates them into families based upon their seed sequence. It also annotates conserved isomiRs using small RNA sequencing datasets.[10]
VIRmiRNA is the first dedicated resource on experimental viral miRNA and their targets. This resource also provides inclusive knowledge about anti-viral miRNAs known to play role in antiviral immunity of host.
^Krek A, Grün D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M, Rajewsky N (2005). "Combinatorial microRNA target predictions". Nat Genet. 37 (5): 495–500. doi:10.1038/ng1536. PMID15806104. S2CID22672750.
^Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E (2007). "The role of site accessibility in microRNA target recognition". Nat Genet. 39 (10): 1278–84. doi:10.1038/ng2135. PMID17893677. S2CID1721807.