Application of Integrated Computational Approaches in Prediction of Plant Virus Encoded miRNAs and Their Targeted Plant Genes.

Abstract

This chapter presents a comprehensive approach to predict novel miRNAs encoded by plant viruses and identify their target plant genes, through integration of various ab initio computational approaches. The predictive process begins with the analysis of plant viral sequences using the VMir Analyzer software. VMir Viewer software is then used to extract primary hairpins from these sequences. To distinguish real miRNA precursors from pseudo miRNA precursors, MiPred web-based software is employed. Verified real pre-miRNA sequences with a minimum free energy of< -20 Kcal/mol, arefurther analyzedusingthe RNAshapes software. Validation of predictions involves comparing them with available Expressed Sequence Tags (ESTs) from the relevant plant using BlastN. Short sequences with lengths ranging from 19 to 25 nucleotides and exhibiting <5 mismatches are prioritized for miRNA prediction. The precise locations of these short sequences within pre-miRNA structures generated using RNAshapes are meticulously identified, with a focus on those situated on the 5' and 3' arms of the structures, indicating potential miRNAs. Sequences within the arms of pre-miRNA structures are used to predict target sites within the ESTs of the specific plant, facilitated by psRNA Target software, revealing genes with potential regulatory roles in the plant. To confirm the outcome of target prediction, results are individually submitted to the RNAhybrid web-based software. For practical demonstration, this approach is applied to analyze African cassava mosaic virus (ACMV) and East African cassava mosaic virus-Uganda (EACMV-UG) viruses, as well as the ESTs of Jatropha and cassava.