MicroRNA Target Prediction: Problems and Possible Solutions

MicroRNAs (miRNA) are small non-coding RNA molecules involved in the posttranscriptional regulation of gene expression. miRNAs bind specifically to the 3' untranslated region of messenger RNA (mRNA) molecules and induce translational repression or mRNA degradation. Potential miRNA targets can be predicted by various computational algorithms that take several parameters into consideration and calculate probability scores for each miRNA-mRNA interaction. In this review, three of the most frequently used algorithms (TargetScan, PicTar and miRBase) are compared, and their strengths and weaknesses are highlighted. These algorithms use different input databases and mathematical models that may lead to discrepancies of the outputs. As currently there is no unambiguous evidence for the preference of any of these algorithms, simultaneous analysis by all can be an effective approach. For this purpose a novel software was developed which is capable of collecting all available data about each miRNA-mRNA interaction retrieved by these databases and identifying common putative targets. Another major problem is related to difficulties of experimental validation, therefore only a minority of in silico predicted targets could be validated to date. Following a brief description of the available experimental target validation methods, the authors attempt to provide suggestions for designing in vitro validation approaches.