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This article has not been peer reviewed.

Deposited research article

A statistical approach predicts human microRNA targets

Neil R Smalheiser* and Vetle I Torvik

Author Affiliations

University of Illinois at Chicago, UIC Psychiatric Institute, MC 912, 1601 W. Taylor Street, room 285 Chicago, IL 60612, USA

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Genome Biology 2004, 5:P4  doi:10.1186/gb-2004-5-2-p4


This was the first version of this article to be made available publicly. This article was submitted to Genome Biology for peer review.

Published: 14 January 2004

Abstract

Background

MicroRNAs are approximately 18-24 nt. noncoding RNAs found in all eukaryotes that degrade messenger RNAs via RNA interference (if they bind in a perfect or near-perfect complementarity to the target mRNA), or arrest translation (if the binding is imperfect). Several microRNA targets have been identified in lower organisms, but no mammalian microRNA targets have yet been validated experimentally.

Results

We carried out a population-wide statistical analysis of how human microRNAs interact complementarily with human mRNAs present in the RefSeq database, looking for characteristics that differ significantly as compared with scrambled control sequences. These characteristics were used to predict a list of 72 candidate mRNA targets with 81% confidence. Unlike the case in C. elegans and Drosophila, many human microRNAs exhibited long exact matches (10 or more bases in a row), up to and including perfect target complementarity. Human microRNAs hit putative mRNA targets within the protein coding region about 2/3 of the time. And, microRNA hits in the candidate list did not have better complementarity near their 5'-end than expected by chance. In several cases, an individual microRNA hit multiple mRNAs that belonged to the same functional class.

Conclusions

The candidate list predicts a significant number of well-known and novel human genes that warrant experimental validation as mRNA targets, including several that may be regulated by RNA interference. The list also provides a training set and suggests an unified model to assist prediction of mRNA targets that do not have especially long regions of target complementarity.