Phd Degree / Doktora
Permanent URI for this collectionhttps://hdl.handle.net/11147/2869
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Doctoral Thesis Enhancement and Validation of Current Human Genome Annotation Via Novel Proteogenomics Algorithms(Izmir Institute of Technology, 2016) Has, Canan; Allmer, JensProteogenomics includes the transfer of knowledge from proteomics to genomics and vice versa. To have high confidence in the information transferred it is essential that it be based on experimental results. Genomics is currently fueled by high throughput techniques involving next generation sequencing. Proteomics is based on mass spectrometry (MS) which is also a high throughput approach. Both fields are generating a wealth of data which needs to be correlated and annotated to generate knowledge. Publicly available human blood plasma mass spectrometric data exist for samples in data repositories such as PeptideAtlas, PRIDE. We acquired high-quality collections from this data and stored it in a custom database developed by us. First, we aimed to amend this data by employing a proteogenomic pipeline PGMiner developed in this study against a custom sequence database which includes all predicted alternative open reading frames as well as the six-frame translation of the human genome and exosome. Then, we correlated the existing annotations with the available mass spectrometric measurements. The human genome in tandem with currently available genome annotations from HAVANA and ENSEMBL enabled us to validate and enhance current gene annotations.Doctoral Thesis Computational Establishment of Microrna Metabolic Networks(Izmir Institute of Technology, 2017) Saçar Demirci, Müşerref Duygu; Allmer, JensMicroRNAs (miRNAs) are single-stranded, small, non-coding RNAs, that control gene expression at the post transcriptional level through various mechanisms such as translational inhibition, degradation and destabilisation of their target mRNAs. Despite the fact that thousands of miRNAs have been reported in various species, most still remain unknown. Due to this, the identification of new miRNAs is an essential process for analysing miRNA mediated post transcriptional regulation mechanisms. Moreover, many biological approaches suffer from limitations in their capacity to reveal rare miRNAs, and are further restricted to the state of the organism under examination. Such limitations have resulted in the construction of sophisticated computational tools for identification of possible miRNAs in silico. However, these programs suffer from low sensitivity and/or accuracy and as a result they do not provide enough confidence for validating all their predictions experimentally. In this study, the aim is overcoming these challenges by creating a new and adaptable machine learning based method to predict potential miRNAs in any given sequence. The efficiency of proposed method is shown by comparison with available tools on various data sets. By using this approach, miRNAs from the genomes of various organisms like human (Homo sapiens), fly (Drosophila melanogaster) and tomato (Solanum lycopersicum) are identified. Moreover, networks between the possible miRNAs of virus and human genes as well as the communications among nuclear and organelle genomes of Solanum lycopersicum through miRNAs are investigated.