COMPUTATIONAL IDENTIFICATION OF MICRORNAS FROM SSDNA VIRUSES

Abstract

MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression and the fact that they are associated with various disease phenotypes is one of the main reasons for their importance. The complexity of experimental detection of miRNAs due to their characteristics led to the development of computational methods. In this work, a machine learning based approach was applied to identify and analyze potential miRNAs that might be originated from 60 single strand DNA (ssDNA) viruses’ genomes. The results suggest that 53 of these viruses may possibly produce proper miRNA precursors. Moreover, the possibility of these candidate miRNA precursors’ ability to generate mature miRNAs that could target human genes and viral genomes has been tested. Overall, the outcomes of this research indicate that there might be another level of host-virus interaction through miRNAs which requires further experimental confirmation.