Machine learning approaches for the investigation of features beyond seed matches affecting miRNA binding

Abstract

MicroRNAs are one type of noncoding RNA that regulate their target mRNAs before mRNAs are translated into proteins. Although it has been demonstrated that the regulation is through partial binding of the seed region of a miRNA and its targets, the mechanism of this process is not fully discovered. Some biological experiments have shown that even perfect base pairing in the seed region does not always guarantee the down-regulation of the targets. It has been suspected that some other characteristics of mRNAs may facilitate the regulation. An earlier study (1) has identified five additional features beyond seed matching that seem to significantly affect repressions. However, the observation that evolutionally conserved targets have shown significantly more destabilization comparing to nonconserved targets with the same score using these five features leads to the suspicion that additional features remain to be discovered. This motivates our study to identify additional features that may differentiate down-regulated mRNAs (positive set) from those not down-regulated ones (negative set) provided both sets have perfect seed matches with miRNAs. Our first attempt to search for different sequence motifs around seed site regions in the two different sets is not successful. We further construct a set of 18 sequence/structure features based on domain knowledge and evaluate them individually and jointly. By employing feature selection techniques in combination with several classification methods, we have been able to identify a subset of features that may facilitate the down-regulation of mRNAs. Our results can be incorporated into target prediction algorithms to further improve target specificities.