In silico prediction and characterization of microRNAs from red flour beetle (Tribolium castaneum)

MicroRNAs (miRNAs), are endogenous, similar to 22-nucleotide-long RNA molecules. They bind to the complementary sites on target mRNAs and regulate protein production of the target transcript by unknown mechanisms. Since the discovery of first miRNA in Caenorhabditis elegans, different approaches have been pursued for the prediction of miRNAs and their target(s). Because of many difficulties and limitations involved in the experimental identification of spatially and temporally expressed miRNAs, many computational approaches have been successfully employed for prediction of miRNAs and their target(s). In the present study, we demonstrate a genome-wide computational approach to predict miRNAs and their target(s) in the red flour beetle, Tribolium castaneum. We have predicted and characterized 45 miRNAs by genome-wide homology search against all the reported miRNAs. These miRNAs were further validated by statistical and phylogenetic analyses. In addition, we have also attempted to predict the putative targets of these miRNAs, by making use of 3' untranslated regions of mRNAs from T. castaneum. These miRNAs and their targets in T. castaneum will serve as useful resources for initiating studies on their experimental validation and functional analyses of miRNA-regulated phenotypes in T. castaneum through gene knockdown and transgenesis.