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PMID: 15806104 Published · ppublish English Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.

Combinatorial microRNA target predictions.

Nature genetics ·Vol. 37 ·No. 5 ·2005-05-00 ·Pages 495-500

Krek A, Grün D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M, 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.

MeSH Terms
Algorithms Animals Computational Biology MicroRNAs/metabolism
Chemicals
MicroRNAs
Authors & Affiliations
11 authors, click to expand affiliations / ORCID
Krek Azra
Center for Comparative Functional Genomics, Department of Biology, New York University, 100 Washington Square East, New York, New York 10003, USA.
Grün Dominic
Poy Matthew N
Wolf Rachel
Rosenberg Lauren
Epstein Eric J
MacMenamin Philip
da Piedade Isabelle
Gunsalus Kristin C
Stoffel Markus
Rajewsky Nikolaus
Article Info
Journal
Nature genetics
Abbr.
Nat Genet
ISSN
1061-4036
Published
2005-05-00
Epub
2005-00-03
Pages
495-500
Language
English
Region
United States
NLM ID
9216904
Subset
IM
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