Increasing MicroRNA Target Prediction Confidence by the Relative R-squared Method

Abstract

MicroRNAs (miRNAs) are short, stable noncoding RNAs involved in post-transcriptional gene regulation via binding to mRNAs. Studies show that in a multicellular organism microRNAs (miRNAs) downregulate a large number of target mRNAs. However, predicting the target genes of a miRNA is highly challenging. Microarray expression profiling has been proposed as a complementary method to increase the confidence of miRNA target prediction, but it can become computationally costly or even intractable when many miRNAs and their effects across multiple tissues are to be considered. Here, we propose a statistical method, the relative R2 method, to find high-confidence targets among the set of potential targets predicted by a computational method such as TargetScan or by microarray analysis, when expression data of both miRNAs and mRNAs are available for multiple tissues. Applying this method to existing data, we obtain many high-confidence targets in mouse.