Human MicroRNA Targets

By Bino John, Anton J. Enright, Alexei Aravin, Thomas Tuschl, Christopher Sander, Debora Marks. PLoS Biology, 2004

3,900 citations recorded by the source corpus.

MicroRNAs (miRNAs) interact with target mRNAs at specific sites to induce cleavage of the message or inhibit translation. The specific function of most mammalian miRNAs is unknown. We have predicted target sites on the 3′ untranslated regions of human gene transcripts for all currently known 218 mammalian miRNAs to facilitate focused experiments. We report about 2,000 human genes with miRNA target sites conserved in mammals and about 250 human genes conserved as targets between mammals and fish. The prediction algorithm optimizes sequence complementarity using position-specific rules and relies on strict requirements of interspecies conservation. Experimental support for the validity of the method comes from known targets and from strong enrichment of predicted targets in mRNAs associated with the fragile X mental retardation protein in mammals. This is consistent with the hypothesis that miRNAs act as sequence-specific adaptors in the interaction of ribonuclear particles with translationally regulated messages. Overrepresented groups of targets include mRNAs coding for transcription factors, components of the miRNA machinery, and other proteins involved in translational regulation, as well as components of the ubiquitin machinery, representing novel feedback loops in gene regulation. Detailed information about target genes, target processes, and open-source software for target prediction (miRanda) is available at http://www.microrna.org. Our analysis suggests that miRNA genes, which are about 1% of all human genes, regulate protein production for 10% or more of all human genes.

Subfields: Cancer Research, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/eab6ab732d7951e804769646a42a261665ed7297

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Citations in the corpus

MicroRNA sponges: competitive inhibitors of small RNAs in mammalian cells (2007, background): “0 and miRanda algorithms, respectively, these UTRs are each regulated by a single miR-20 sit”
Expression profiling of microRNAs by deep sequencing (2009, neutral): “org) [27].”
Epigenetic regulation of miR-184 by MBD1 governs neural stem cell proliferation and differentiation (2010, neutral): “…mRNA targets of miR184 by cross-referencing three widely used programs, TargetScan (Lewis et al., 2003), PicTar (Krek et al., 2005), and miRanda (John et al., 2004), and they respectively predicted 18, 10, and 852 target genes for miR-184; however, no common target was predicted by all three…”
An overview of microRNAs (2015, methodology): “A number of algorithms have been developed, with TargetScan, miRanda, and PicTar perhaps the most popular[69, 99-101].”
Impaired microRNA processing enhances cellular transformation and tumorigenesis (2007, methodology): “To examine potential miRNA-mediated regulation, we used the miRNA target prediction program miRand”
A guide through present computational approaches for the identification of mammalian microRNA targets (2006, methodology): “MiRanda uses a modified dynamic programming approach that recognizes the importance of seed binding, but does not require perfect seed complementarit”
Principles of MicroRNA–Target Recognition (2005, background): “Although this result obviously depends on the evolutionary distance of the species under consideration (see [43] for a comparison of mammalian sites), it shows that conclusions about the contribution of miRNA 39 pairing to target site function cannot be drawn solely from the degree of sequence conse”
Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. (2008, background): “Common target prediction algorithms assess complementarity of miRNAs to their targets (John et al. 2004), many enforcing strong pairing within the seed region of the miRNA (positions 2–8) (Lewis et al. 2005; Grimson et al. 2007).”