Combinatorial microRNA target predictions

Item Type:	Article
Title:	Combinatorial microRNA target predictions
Creators Name:	Krek, A. and Grün, D. and Poy, M.N. and Wolf, R. and Rosenberg, L. and Epstein, E.J. and MacMenamin, P. and da Piedade, I. and Gunsalus, K.C. and Stoffel, M. and Rajewsky, N.
Abstract:	MicroRNAs are small noncoding RNAs that recognize and bind to partially complementary sites in the 3' untranslated regions of target genes in animals and, by unknown mechanisms, regulate protein production of the target transcript. Different combinations of microRNAs are expressed in different cell types and may coordinately regulate cell-specific target genes. Here, we present PicTar, a computational method for identifying common targets of microRNAs. Statistical tests using genome-wide alignments of eight vertebrate genomes, PicTar's ability to specifically recover published microRNA targets, and experimental validation of seven predicted targets suggest that PicTar has an excellent success rate in predicting targets for single microRNAs and for combinations of microRNAs. We find that vertebrate microRNAs target, on average, roughly 200 transcripts each. Furthermore, our results suggest widespread coordinate control executed by microRNAs. In particular, we experimentally validate common regulation of Mtpn by miR-375, miR-124 and let-7b and thus provide evidence for coordinate microRNA control in mammals.
Keywords:	Algorithms, Computational Biology, MicroRNAs, Animals
Source:	Nature Genetics
ISSN:	1061-4036
Publisher:	Nature Publishing Group
Volume:	37
Number:	5
Page Range:	495-500
Date:	May 2005
Official Publication:	https://doi.org/10.1038/ng1536
PubMed:	View item in PubMed

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