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MiRDB: an Online Database for Prediction of Functional MicroRNA Targets

Overview
Specialty Biochemistry
Date 2019 Sep 11
PMID 31504780
Citations 1383
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Abstract

MicroRNAs (miRNAs) are small noncoding RNAs that act as master regulators in many biological processes. miRNAs function mainly by downregulating the expression of their gene targets. Thus, accurate prediction of miRNA targets is critical for characterization of miRNA functions. To this end, we have developed an online database, miRDB, for miRNA target prediction and functional annotations. Recently, we have performed major updates for miRDB. Specifically, by employing an improved algorithm for miRNA target prediction, we now present updated transcriptome-wide target prediction data in miRDB, including 3.5 million predicted targets regulated by 7000 miRNAs in five species. Further, we have implemented the new prediction algorithm into a web server, allowing custom target prediction with user-provided sequences. Another new database feature is the prediction of cell-specific miRNA targets. miRDB now hosts the expression profiles of over 1000 cell lines and presents target prediction data that are tailored for specific cell models. At last, a new web query interface has been added to miRDB for prediction of miRNA functions by integrative analysis of target prediction and Gene Ontology data. All data in miRDB are freely accessible at http://mirdb.org.

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References
1.
Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin A, Kim S . The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012; 483(7391):603-7. PMC: 3320027. DOI: 10.1038/nature11003. View

2.
Wong N, Wang X . miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Res. 2014; 43(Database issue):D146-52. PMC: 4383922. DOI: 10.1093/nar/gku1104. View

3.
Helwak A, Kudla G, Dudnakova T, Tollervey D . Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell. 2013; 153(3):654-65. PMC: 3650559. DOI: 10.1016/j.cell.2013.03.043. View

4.
Klijn C, Durinck S, Stawiski E, Haverty P, Jiang Z, Liu H . A comprehensive transcriptional portrait of human cancer cell lines. Nat Biotechnol. 2014; 33(3):306-12. DOI: 10.1038/nbt.3080. View

5.
Miska E . How microRNAs control cell division, differentiation and death. Curr Opin Genet Dev. 2005; 15(5):563-8. DOI: 10.1016/j.gde.2005.08.005. View