In silico identification and target prediction of micrornas in Sesame (sesamum indicum L.) expressed sequence tags
Sesame (Sesamum indicum L.), a member of the Pedaliaceae family, is one of the oldest oilseed crops. For its high oil content, it is known as the “queen of oilseeds”. MicroRNAs (miRNAs) represent a class of endogenous non-coding small RNAs that play important roles in multiple biological processes by degrading targeted mRNAs or repressing mRNA translation. Thousands of miRNAs have been identified in many plant species by computational methods, whereas there is no report of miRNAs in S. indicum till date. In present study, previously known plant miRNAs were BLASTed against the Expressed Sequence Tag (EST) database of Sesame genes. The aligned miRNA hits were further aligned to protein database and BLASTX was carried out to remove protein coding primary miRNAs. The non-coding precursor miRNAs were subjected to online MFold server in order to predict their secondary structures. After applying the filtering criteria, a total of 12 potential miRNAs belonging to 6 miRNAs families were detected. 203 unique miRNAs: target pairs were predicted online by psRNATarget web server. Most of the targets were found to encode transcription factors or enzymes that participate in the regulation of development, growth, metabolism, and other physiological processes and stress response.