Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
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Master Thesis Systematic Computational Analysis of Potential Rna Interference Regulation in Toxoplasma Gondii(Izmir Institute of Technology, 2009) Çakır, Mehmet Volkan; Allmer, JensRNA-mediated silencing was first described in plants and became famous by studies in Caenorhabditis elegans. RNA interference (RNAi) is the mechanism through which an RNA interferes with the production of other RNAs in a sequence specific manner. MiRNAs are a type of RNA which originate from the genome with their active form being ss-RNAs of 21-23 nucleotides in length. They are being transcribed as primiRNAs then processed in the nucleus by Drosha to pre-miRNAs with a stem-loop structure and 70 nucleotides in length. This stem-loop containing pre-miRNAs is then processed in the cytoplasm to ds-RNA one strand of which will serve as interfering RNA. Toxoplasma gondii is a species of parasitic protozoa which causes several diseases. T.gondii emerges as a good candidate for computational efforts with its small genome size, publicly available genome files and extensive information about its gene structure, either based on experimental data or the prediction with several gene finders in parallel. Therefore, it seems important to establish the regulatory network composed of RNAi which may be beneficial for the Toxoplasma community. Within this context the pool of possible stem-loop constitutive transcripts are produced, further analysis of this pool for desired 2D structure is integrated and mapping of possible RNAi regulation to T.gondii.s genome is established. In connection with computational assessment and mapping, the derived information is provided as a database for quick lookup using a convenient web interface for experimental studies of RNAi regulation in Toxoplasma, thus reduce time and money costs in such studies.Master Thesis An Integrative Data Mining Approach for Microrna Detection in Human(Izmir Institute of Technology, 2013) Saçar, Müşerref Duygu; Allmer, JensMicroRNAs (miRNAs) are single-stranded, small, usually non-coding RNAs of about 22 nucleotides in length, that control gene expression at the posttranscriptional level through translational inhibition, degradation, adenylation, or destabilization of their target mRNAs. Although hundreds of miRNAs have been identified in various species, many more may still remain unknown. Therefore, the discovery of new miRNA genes is an important step for understanding miRNA mediated post transcriptional regulation mechanisms. First attempts for the identification of novel miRNA genes were almost exclusively based on directional cloning of endogenous small RNAs and high-throughput sequencing of large numbers of cDNA clones. However, conventional forward genetic screening is known to be biased towards abundantly and/or ubiquitously expressed miRNAs that can dominate the cloned products. Hence, such biological approaches might be limited in their ability to detect rare miRNAs, and restricted to the tissues and the developmental stage of the organism under examination. These limitations have led to the development of sophisticated computational approaches attempting to identify possible miRNAs in silico. Nevertheless, the programs designed to predict possible miRNAs in a genome are not sensitive or accurate enough to warrant sufficient confidence for validating all their predictions experimentally. With this study, we aim to solve these problems by developing a new and sensitive machine learning based approach to predict potential miRNAs in the human genome.