Degradome Assisted MicroRNA Prediction in Plants

Ribonucleic acid (RNA) is a polymeric molecule essential in various biological processes. In the past two decades, extensive research effort has been devoted to short non-coding regulatory RNAs called small RNAs (sRNAs). In particular, micro RNAs (miRNAs), 20–22 nucleotide in length, have emerged as an important class of gene regulators. In plants, miRNAs function at post-transcriptional level by suppressing the translation of their target messenger RNAs (mRNAs) through cleavage and degradation, leading to their participation in larger regulatory networks. In recent years, developments in next generation sequencing (NGS) technologies have enabled the large-scale sequencing of sRNAs and cleaved mRNA fragments, called the degradome. Consequently, multiple computational methods have been developed for the identification of miRNAs and their targets. The advance in regulatory miRNA discoveries relies on understanding their biogenesis and function. Recently, a newly updated plant miRNA biogenesis criteria has been reported, which benefited in identifying more validated miRNAs compared to the old criteria. The new criteria bring the possibility of recommending a further update to the miRNA annotation rules. Moreover, the function of miRNAs is interpreted through their targets that could be determined and validated using degradome. The interactions between miRNAs and their target mRNAs contribute to biological regulatory networks. In this thesis, we demonstrate a degradome-assisted approach that employs a hill-climbing algorithm to explore miRNAs with extreme biogenesis features in a controlled manner. We apply this approach on Arabidopsis thaliana, evaluate its performance using differential expression analysis, and identify a potentially novel miRNA that has been previously missed by the existing miRNA prediction tools. The approach is presented within PAREfirst tool. Furthermore, we present PAREnet tool that utilises a degradome analysis tool to assist the simplifying, construction, and visualisation of sRNA-mediated regulatory networks on a genome-wide scale. Analysing the constructed simplified sRNA-mRNA network shows the possibility of unraveling the implications of sRNA-mediated regulation in biological processes. In conclusion, the research focuses on identifying miRNAs, particularly condition specific miRNAs, with unique biogenesis, predicting their targets using degradome analysis, and presenting their interactions by constructing simplified sRNA-mRNA networks with retrievable biological reality. Through these efforts, the study could contributes towards enhancing our understanding of the biogenesis and function of plant miRNA, and the complexity of genes networks in plants