Identification of regulatory networks from predictions of miRNAs and their targets

Background: Eucalyptus is the world's most widely planted hardwood tree. Its outstanding adaptability and growth have made them a global renewable resource of fibre and energy. Their commercial value motivates studies over their genome and genetic regulation aiming to improve the wood quality. MiRNAs are important molecules in the genetic regulation. Their importance has been studied in the last two decades. The recent and important advances in the quality in silico miRNA predictions by computational tools motivate their application in the recently published Eucalyptus grandis genome. Results: In this work we used the results from CRAVELA framework that predicts a set of miRNA precursor and mature miRNA candidates.. The program psRNAtarget was used to predict the possible targets of the miRNA enabling to draw several regulatory networks. The networks related to the wood formation process were identified using two di↵erent methods. In the first, the regulated genes were compared using BLASTn against a database assembled by the research team. For the formulation of the database there were used scientific articles from the last decade, which identify important genes in wood formation in a several plant species. In the second method, the genes from the networks were compared with the EUCAWOOD database. From these two approaches, two new regulatory networks involved in wood formation have been identified. This networks have 3 genes regulated by miRNAs. Conclusions: The two regulatory networks found from this research are supported with statistical data that assess their high reliability. However, the statistical data is not enough to ensure that these regulatory networks are real. Thus, the next step of this work will be focused on the biological validation. Trnascriptional data was added and the expresson levels of the genes and miRNAs were not sucient to validate the regulatory network.