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Conserved Expression Patterns Predict microRNA Targets

Overview of attention for article published in PLoS Computational Biology, September 2009
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99 Mendeley
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16 CiteULike
Title
Conserved Expression Patterns Predict microRNA Targets
Published in
PLoS Computational Biology, September 2009
DOI 10.1371/journal.pcbi.1000513
Pubmed ID
Authors

William Ritchie, Megha Rajasekhar, Stephane Flamant, John E. J. Rasko

Abstract

microRNAs (miRNAs) are major regulators of gene expression and thereby modulate many biological processes. Computational methods have been instrumental in understanding how miRNAs bind to mRNAs to induce their repression but have proven inaccurate. Here we describe a novel method that combines expression data from human and mouse to discover conserved patterns of expression between orthologous miRNAs and mRNA genes. This method allowed us to predict thousands of putative miRNA targets. Using the luciferase reporter assay, we confirmed 4 out of 6 of our predictions. In addition, this method predicted many miRNAs that act as expression enhancers. We show that many miRNA enhancer effects are mediated through the repression of negative transcriptional regulators and that this effect could be as common as the widely reported repression activity of miRNAs. Our findings suggest that the indirect enhancement of gene expression by miRNAs could be an important component of miRNA regulation that has been widely neglected to date.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 99 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Germany 2 2%
Norway 1 1%
Australia 1 1%
Turkey 1 1%
United Kingdom 1 1%
Brazil 1 1%
China 1 1%
Mexico 1 1%
Other 0 0%
Unknown 87 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 33 33%
Researcher 27 27%
Professor 8 8%
Student > Master 7 7%
Professor > Associate Professor 5 5%
Other 16 16%
Unknown 3 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 47 47%
Biochemistry, Genetics and Molecular Biology 18 18%
Medicine and Dentistry 11 11%
Computer Science 8 8%
Pharmacology, Toxicology and Pharmaceutical Science 2 2%
Other 7 7%
Unknown 6 6%