Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
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Master Thesis Analysis of Tnfrsf10b-As Long-Noncoding Rna's Effects on Various Cancer Cell Properties(Izmir Institute of Technology, 2019) Alkan, Ayşe Hale; Akgül, Bünyamin; Akgül, Bünyamin; 04.03. Department of Molecular Biology and Genetics; 04. Faculty of Science; 01. Izmir Institute of TechnologyLong noncoding RNAs (lncRNAs) being longer than 200 nucleotides constitute a different class of RNA molecules. Several studies indicated that they have regulatory role in cellular processes including cancer development. Some of them have exclusively high expression in particular cancer types and regulate certain cancer cell properties. This renders them potential biomarker or therapeutic target in cancer. In this study, effects of a candidate lncRNA TNFRSF10B-AS and lncCAMTA1 on cancer cell properties were investigated. Candidate lncRNAs from Doxorubicin, Fas mAB, TNF-alpha and Cisplatin treated HeLa cell line were chosen and their expression level was measured in different cell lines including healthy (BEAS2B and MCF10A), metastatic (H1299 and MDA-MB- 231) and non-metastatic cell lines (A549 and MCF-7) by qPCR. From a few candidates lncCAMTA1 and TNFRSF10B-AS were selected for further analysis. qPCR results obtained from comparison of different cancer cell lines showed that their expression differs at least in one comparison of cell lines. TNFRSF10B-AS silencing decreased proliferation of HeLa cells. lncCAMTA1 was silenced or overexpressed in HeLa cells but phenotypic effect couldn’t be detected by apoptosis and cell proliferation assay. Additionally, phenotypic effect also couldn’t be observed in other cell lines when TNFRSF10B-AS was silenced.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.