Combinatorial microRNA target predictions

Azra Krek, Dominic Grün, Matthew N. Poy, Rachel Wolf, Lauren Rosenberg, Eric J. Epstein, Philip MacMenamin, Isabelle Da Piedade, Kristin C. Gunsalus, Markus Stoffel, Nikolaus Rajewsky

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)495-500
Number of pages6
JournalNature genetics
Volume37
Issue number5
DOIs
StatePublished - May 2005
Externally publishedYes

ASJC Scopus subject areas

  • Genetics

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