MicroRNA target recognition from compositional features of aligned microRNA-mRNA duplexes

The discovery of microRNAs (miRNAs), small non-coding RNAs that regulate gene expression at the post-transcriptional level, has led to a shift in our understanding of the complexity in gene regulatory networks. One key question in understanding the effect of an individual miRNA is to identify its potential targets in a local or genome-wide regulation process. We define here a novel algorithm for computational prediction of miRNA target genes using solely the sequence information for mature miRNA and potential target mRNAs. Our method considers the aligned miRNA-mRNA duplex as a new sequence and extracts compositional features from newly constructed sequence to be fed into a Naive Bayes Classifier. A rigorous analysis in a common benchmark set reveals that, in terms of prediction accuracy, the new algorithm outperforms most of the available methods, while being competitive with more complex and relatively inefficient methods.