Reticulitermes flavipes is one of the most common termite species in the world, and has been an intriguing research model due to its ecological and biological and economic significance. The fundamental biological question addressed by this study is to elucidate the role of miRNAs in termite development and how miRNA can influence labor division. miRNAs are short non-coding RNAs that have an important role in gene regulation at post-transcriptional level, and can potentially be involved in the regulation of caste polyphenism. Using a computational approach, I identified 167 conserved and 33 novel miRNAs in the dataset. miR-iab-4 and 19 other miRNAs showed highly differential expression between worker and soldier, and their possible roles in termite biology are discussed. To reliably quantify miRNA expression in experiments, I tested the stability of 10 miRNAs as reference gene using quantitative real-time PCR. miR-8_3, bantam and miR-276a-3p are the most stable miRNAs in different castes, pre-soldier formation, and different tissues, respectively. Lastly, the predicted miRNA expression is verified by the qRT-PCR for 8 miRNAs. Overall, this study shows that miRNA plays a role in mediating the work-soldier transition in R. flavipes.

<p>In summary there currently exist techniques to discover miRNA however both require many calculations to be performed during the identification limiting their use at a genomic level. Machine learning techniques are currently providing the best results by combining a number of calculated and statistically derived features to identify miRNA candidates, however almost all of these still include computationally intensive secondary-structure calculations. It is the aim of this project to produce a miRNA identification process that minimises and simplifies the number of computational elements required during the identification process.</p>

<p>In summary there currently exist techniques to discover miRNA however both require many calculations to be performed during the identification limiting their use at a genomic level. Machine learning techniques are currently providing the best results by combining a number of calculated and statistically derived features to identify miRNA candidates, however almost all of these still include computationally intensive secondary-structure calculations. It is the aim of this project to produce a miRNA identification process that minimises and simplifies the number of computational elements required during the identification process.</p>

Magister Scientiae - MSc / In summary there currently exist techniques to discover miRNA however both require many calculations to be performed during the identification limiting their use at a genomic level. Machine learning techniques are currently providing the best results by combining a number of calculated and statistically derived features to identify miRNA candidates, however almost all of these still include computationally intensive secondary-structure calculations. It is the aim of this project to produce a miRNA identification process that minimises and simplifies the number of computational elements required during the identification process. / South Africa