Credit: Courtesy of Janez Plavec The paper: A. Grimson, et al., "MicroRNA targeting specificity in mammals: determinants beyond seed pairing," Mol Cell, 27:91-105, 2007. (Cited in 109 papers) The bottom line: Massachusetts Institute of Technology biologist David Bartel and colleagues constructed an algorithm to predict miRNA target sites on untranslated" /> Credit: Courtesy of Janez Plavec The paper: A. Grimson, et al., "MicroRNA targeting specificity in mammals: determinants beyond seed pairing," Mol Cell, 27:91-105, 2007. (Cited in 109 papers) The bottom line: Massachusetts Institute of Technology biologist David Bartel and colleagues constructed an algorithm to predict miRNA target sites on untranslated" />
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microRNA: target this

Credit: Courtesy of Janez Plavec" /> Credit: Courtesy of Janez Plavec The paper: A. Grimson, et al., "MicroRNA targeting specificity in mammals: determinants beyond seed pairing," Mol Cell, 27:91-105, 2007. (Cited in 109 papers) The bottom line: Massachusetts Institute of Technology biologist David Bartel and colleagues constructed an algorithm to predict miRNA target sites on untranslated

By | February 1, 2009

<figcaption> Credit: Courtesy of Janez Plavec</figcaption>
Credit: Courtesy of Janez Plavec

The paper:

A. Grimson, et al., "MicroRNA targeting specificity in mammals: determinants beyond seed pairing," Mol Cell, 27:91-105, 2007. (Cited in 109 papers)

The bottom line:

Massachusetts Institute of Technology biologist David Bartel and colleagues constructed an algorithm to predict miRNA target sites on untranslated regions (UTRs) of mRNAs, which affects posttranscriptional repression. In addition to seed pairing, which is the alignment of complimentary sequences between mRNAs and miRNAs, and central to microRNA's function, factors that predict where miRNA binds include adenine and urasil-rich sequences, as well as the distance of target sites away from the center of long UTR's.

The detail:

Prior to this Hot Paper, researchers knew that factors besides seed pairing influenced binding, but Bartel's group considered these other factors in unprecedented detail, according to John Rossi, a molecular geneticist at City of Hope.

The rub:

Oliver Hobert at Columbia University cautions that there are key experimentally-proven, in vivo exceptions to each of the handful of prediction algorithms out there. "I don't trust any one more than the others," he says. Bartel concedes the controversy, but "a lot of people are using our target predictions," he says.

The applications:

Rossi, for example, says that his group has used the "Bartelian method" to indentify several SNPs in seed regions or in sequences that precede miRNA genes that are linked to schizophrenia or autism. Furthermore, Bartel's group recently used the algorithms to successfully predict the target sites and show the affect of miRNA miR-223 on protein output in the cell (Nature, 455:64-71, 2008).

AlgorithmAvg. protein repression of top 29 genes predicted to be miR-223 targets
miRanda 13.1%
PicTar 17.0%
TargetScan (based on this Hot Paper) 26.4%
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