MicroRNA and microRNA target database

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]

microRNA target gene databases
Name DescriptiontypeLinkReferences
StarBase 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.databasewebsite[2][3]
StarScan 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.web-based softwarewebsite[4]
Cupid 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.software (MATLAB)website[5]
TargetScan 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.database, webserverwebsite[6][7][8][9][10][11]
TarBase A comprehensive database of experimentally supported animal microRNA targetsdatabasewebsite[12]
Diana-microT 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.webserverwebserver[13]
miRecords an integrated resource for microRNA-target interactions.databasewebsite[14]
PicTar PicTar is Combinatorial microRNA target predictions.database, webserver, predictionswebsite[15]
PITA PITA, incorporates the role of target-site accessibility, as determined by base-pairing interactions within the mRNA, in microRNA target recognition.webserver, predictionspredictions[16]
RepTar 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.databasewebsite[17]
RNA22 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.webserver, predictionspredictions custom[18]
miRTarBase 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.databasewebsite[19][20][21][22]
miRwalk Aggregates and compare results from other miRNA-to-mRNA databasesdatabase, webserver[23]
MBSTAR Multiple Instance approach for finding out true or functional microRNA binding sites.webserver, predictionspredictions[24]
.

microRNA databases
Name DescriptiontypeLinkReferences
deepBase deepBase is a database for annotating and discovering small and long ncRNAs (microRNAs, siRNAs, piRNAs...) from high-throughput deep sequencing data.databasewebsite[25]
miRBase miRBase database is a searchable database of published miRNA sequences and annotation.databasewebsite[26]
microRNA.org microRNA.org is a database for Experimentally observed microRNA expression patterns and predicted microRNA targets & target downregulation scores.databasewebsite[27]
miRGen 2.0 miRGen 2.0: a database of microRNA genomic information and regulationdatabasewebsite[28]
miRNAMap miRNAMap: genomic maps of microRNA genes and their target genes in mammalian genomesdatabasewebsite[29]
PMRD PMRD: plant microRNA databasedatabasewebsite[30]
TargetScan 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] database website [10]
VIRmiRNA 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. Database website [31]

References
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  2. Yang, J. -H.; Li, J. -H.; Shao, P.; Zhou, H.; Chen, Y. -Q.; Qu, L. -H. (2010). "StarBase: A database for exploring microRNA-mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data". Nucleic Acids Research. 39 (Database issue): D202–D209. doi:10.1093/nar/gkq1056. PMC 3013664. PMID 21037263.
  3. Li, JH; Liu, S; Zhou, H; Qu, LH; Yang, JH (Jan 1, 2014). "starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data". Nucleic Acids Research. 42 (1): D92-7. doi:10.1093/nar/gkt1248. PMC 3964941. PMID 24297251.
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  5. Chiu, Hua-Sheng; Llobet-Navas, David; Yang, Xuerui; Chung, Wei-Jen; Ambesi-Impiombato, Alberto; Iyer, Archana; Kim, Hyunjae "Ryan"; Seviour, Elena G.; Luo, Zijun; Sehgal, Vasudha; Moss, Tyler; Lu, Yiling; Ram, Prahlad; Silva, José; Mills, Gordon B.; Califano, Andrea; Sumazin, Pavel (February 2015). "Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks". Genome Research. 25 (2): 257–67. doi:10.1101/gr.178194.114. PMC 4315299. PMID 25378249.
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  13. Maragkakis M, Alexiou P, Papadopoulos GL, Reczko M, Dalamagas T, Giannopoulos G, Goumas G, Koukis E, Kourtis K, Simossis VA, Sethupathy P, Vergoulis T, Koziris N, Sellis T, Tsanakas P, Hatzigeorgiou AG (2009). "Accurate microRNA target prediction correlates with protein repression levels". BMC Bioinformatics. 10: 295. doi:10.1186/1471-2105-10-295. PMC 2752464. PMID 19765283.
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Further reading
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