Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
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Master Thesis Mining the Toxoplasma Gondii Genome for Microrna Regulatory Patterns(Izmir Institute of Technology, 2017) Acar, İlhan Erkin; Allmer, Jens; Acar, İlhan Erkin; Allmer, Jens; 01. Izmir Institute of Technology; 04.03. Department of Molecular Biology and Genetics; 04. Faculty of ScienceToxoplasma gondii is a parasite that causes mental retardation, blindness or nearblindness, and decreased psycho-motor performance if the patient is congenitally infected. There have been efforts to vaccinate humans against this parasite, yet it was not achieved. Therefore, a better understanding of Toxoplasma gondii can be provided by examining its microRNA regulation. MicroRNAs are known to regulate messenger RNAs and prevent translation. This results in different effects in different biological pathways. In this study, the Toxoplasma gondii genome was used to predict precursor and mature microRNAs, while experimentally validated microRNAs were taken into consideration. This was further explored in terms of microRNA targeting, with the known genes of Toxoplasma gondii. Furthermore, RNA Sequencing data of this organism was obtained and analysed in terms of gene expression and possible microRNA expression outcomes. Combining gene expression analyses with targeting predictions, it was possible to create a microRNA - gene interaction network. Gene expression analyses showed that there was no differentially expressed genes, microRNAs or interactions between two developmental stages of Toxoplasma gondii, tachyzoite and bradyzoite. This result was added to interactions to determine up and down regulations. Then, all of these interactions were connected where they intersect, to create a regulation network of microRNAs. This network was further explored and compared to random networks of the same size. It was seen that the biological network contains many larger sized cliques. This knowledge can be further analysed in future work, to create drug leads that will target vital pathways of Toxoplasma gondii.Master Thesis Cost and Benefit Analysis of Features Used in Machine Learning Based Pre-Mirna Detection(Izmir Institute of Technology, 2016) Suluyayla, Rabia; Allmaer, Jens; 01. Izmir Institute of TechnologyMicroRNAs (miRNAs) are short RNA molecules which play important roles in the post-trancriptional regulation of gene expression. Their transcription is followed by two RNA III endonuclease processing steps leading to mature miRNA formation. They are then incorporated into the RISC-complex which mediates mRNA targeting. Experimental miRNA prediction is difficult since detection relies on many factors therefore, computational methods have become indispensable. Therefore, machine learning methods rely on features describing precursor-miRNAs (pre-miRNAs) to be able to differentiate them from other hairpins in a genome. It is important to define feature groups which are informative, not highly correlated, and don’t incur a large computational cost in order to facilitate accurate miRNA detection. In this study for more than 800 pre-miRNA features the computational cost and benefit was analyzed. From these analyses five features (assl, lsr(%bp), lscm, asal and hpmfe rf I3), (four structural and one structuralthermodynamic one), which aren’t correlated, informative and are not computationally expensive are noticeable. Analyses are done with human hairpins, pseudo data; and a case study using the measles virus and the measles KEGG pathway genes. Overall calculation of human hairpins and measles virus took approximately 2 USD (United States Dollar) on Amazon web services. Supervised learning and random forest machine learning for miRNA prediction was applied and to two genes (TAB2 and BCC3) within the measles KEGG pathway and three hairpins were predicted. They were found to have human mature miRNA sequences embedded in them and their already annotated targets helped enlarge the KEGG measles pathway.