Towards computational prediction of microRNA function and activity in turmeric (Curcuma longa L.)

Abstract

MicroRNAs (miRNAs) are recently discovered class of highly conserved, non-coding small RNAs that regulate gene expression in plants. High conservation of miRNAs in plants provides the basis for identification of new miRNAs in other plant species through homology alignment. Expressed sequence tags (ESTs) provide an alternative resource to facilitate identification of miRNAs and their targets. We have identified 8 conserved miRNAs representing two miRNA families from turmeric by in silico analysis of ESTs. The computational prediction was based on the conservation of miRNA sequences, the stem-loop hairpin secondary structures of miRNAs and a series, of filtering criteria. Parameters like length of mature miRNA and precursor miRNA, nucleotide composition and free energy values were well within the range of other plant miRNAs. Multiple sequence alignment of miR167 precursors revealed high conservation of mature miRNA sequences. It was observed that though miRNAs are highly conserved, some specific sites are more likely to mutate. Most of the predicted targets appeared conserved and were classified as proteins involved in stress response, development and metabolism.