Abstract

Target predictions and validations are major obstacles facing microRNA (miRNA) researchers. Animal miRNA target prediction is challenging because of limited miRNA sequence complementarity to the targets. In addition, only a small number of predicted targets have been experimentally validated and the miRNA mechanism is poorly understood. Here we present a novel algorithm for animal miRNA target prediction. The algorithm combines relevant parameters for miRNA target recognition and heuristically assigns different weights to these parameters according to their relative importance. A score calculation scheme is introduced to reflect the strength of each parameter. We also performed microarray time course experiments to identify downregulated genes due to miRNA overexpression. The computational target prediction is combined with the miRNA transfection experiment to systematically identify the gene targets of human miR-124. miR-124 overexpression led to a significant downregulation of many cell cycle related genes. This may be the result of direct suppression of a few cell growth inhibitors at the early stage of miRNA overexpression, and these targeted genes were continuously suppressed over a long period of time. Our high-throughput approach can be generalized to globally identify the targets and functions of other miRNAs.