Computational prediction of submergence responsive microRNA and their binding position within the genome of Oryza sativa.

Background: MicroRNAs (miRNAs) are small noncoding RNAs which play crucial role in response to the adverse biotic and abiotic stress conditions at the post transcriptional level. The functions of the miRNAs are generally based on complementarity to their target region.

Results: We used the online tool psRNA Target for the identification of submergence responsive miRNA using the gene expression profile related to the submergence condition. We wrote a perl script for the prediction of miRNA target gene. The position based feature of the script increases the overall specificity of the program. Our perl script performed well on the genomic data of Oryza sativa and produced significant results with their positions. These results were analyzed on the basis of complementarity and the statistical scores are used to find out the most probable binding regions. These predicted binding regions are aligned with their respective miRNAs to find out the consensus sequence. We scored the alignment using a position dependent, mismatch penalty system. We also identified the rate of conservation of bases at a particular position for all the predicted binding regions and it was found that all the predicted binding regions maintain above 70% rate of conservation of bases.

Conclusion: Our approach provides a novel framework for screening the genome of Oryza sativa. It can be broadly applied to identify complementarity specific miRNA targets computationally by doing a little modification of the script depending on the type of the miRNA.