Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
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Master Thesis Importance of Database Normalization for Reliable Protein Identification in Mass Spectrometry-Based Proteomics(Izmir Institute of Technology, 2016) Mungan, Mehmet Direnç; Allmer, Jens; Yalçın, TalatOne of the revolutionary steps towards proteomics, was introducing mass spectrometry to protein inference analysis. Its powerful aspects such as speed, and accuracy towards identifying and quantifying proteins have made it the first choice to obtain highthroughput data. Due to development of a variety of fragmentation techniques, mass spectrometry-based analysis even made it possible to acquire knowledge about single polymorphisms and modifications of amino acids of a peptide. Although this technology provides enormous amounts of data, identification of the proteins is still a hard challenge to overcome due to the shortcomings of computational methods. Herein a novel methodology is offered to better analyze mass spectrometry data and overcome the deficiency of protein identification algorithms in terms of speed and accuracy. When the spectral data is acquired from an organism by mass spectrometry, database search algorithms are used for protein identification if the protein sequences of the organism are known. These algorithms compare the experimental data from mass spectrometry analysis to theoretical data gathered from known databases of organism to try and find the best match by ranking the PSMs via scoring functions. Since the databases can be too large to search and multiple databases with different sizes can contain the peptides of experimental data, database search algorithms may fail to produce fair, fast or complete results. In this work a methodology is presented to overcome unfair scoring of peptides in different size databases and enable database search algorithms to utilize relatively big sized entries such as human chromosome six frame translations. In terms of speed and accuracy the method is found to be better than some of the existing methods.Master Thesis Ray: a Profile-Based Approach for Homology Matching of Tandem-Ms Spectra To Sequence Databases(Izmir Institute of Technology, 2012) Yılmaz, Şule; Allmer, Jens; Karaçalı, BilgeMass spectrometry is a tool that is commonly used in proteomics to identify and quantify proteins. Thousands of spectra can be obtained in just few hours. Computational methods enable the analysis of high-throughput studies. There are mainly two strategies: database search and de novo sequencing. Most of the researchers prefer database search as a first choice but any slight changes on protein can prevent identification. In such cases, de novo sequencing can be used. However, this approach highly depends on spectral quality and it is difficult to achieve predictions with full length sequence. Peptide sequence tags (PST) allows some flexibility on database searches. A PST is a short amino acid sequence with certain mass information but obtaining accurate PST is still arduous. In case a sequence is missing in database, homology searches can be useful. There are some homology search algorithms such as MS-BLAST, MS-Shotgun, FASTS. But, they are altered versions of existing algorithms, for example BLAST has been modified for mass spectrometric data and became MS-BLAST. Besides, they are usually coupled with de novo sequencing which still possess limitations. Therefore, there is a need for novel algorithms in order to increase the scope of homology searches. For this purpose, a novel approach that is based on sequence profiles has been implemented. A sequence profile is like a table that contains frequencies of all possible amino acids on a given MS/MS spectrum. Then, they are aligned to sequences in database. Profiles are more specific than PSTs and the requirement for precursor mass restrictions or enzyme information can be removed.Master Thesis Exploiting Fragment-Ion Complementarity for Peptide De Novo Sequencing From Collision Induced Dissociation Tandem Mass Spectra(Izmir Institute of Technology, 2011) Aytun, Belgin; Allmer, JensPeptide identification from mass spectrometric data is a key step in proteomics because this field provides sequence, quantitative, and modification data of actually expressed proteins. Two approaches are generally deployed to interpret experimental MS/MS data, database searching and de novo sequencing. Database search method has been used successfully in proteomics projects for organisms with well-studied genomes. However, it is not applicable in situations where a target sequence is not in the protein database. This can happen for a number of reasons, including novel proteins, protein mutations and post-translational modifications. Because of the disadvantages of database searching method, a lot of research has focused on de novo sequencing method which assigns amino acid sequences to MS/MS spectra without the need for a database. The aim of this study is to enhance the accuracy of de novo sequencing tools. One step commonly employed in all de novo sequencing tools is naming of fragment ions. It is essential to know which peak represents which ion type in order to traverse a spectrum graph to find an amino acid sequence that best explains the MS/MS spectrum. Different approaches have been tried to name ions and some success has been achieved in naming b-type ions and y-type ions. We have presented a new approach which enables the naming of not only b- and y-type ions but other arbitrary ion types as well. This enabled the detection of b-ion ladder. In the latter case, missing fragments were determined by using other named ion types. Furthermore, unexplained data in tandem mass spectra were reduced as much as possible. Therefore, a complete sequence will be derived by the new approach.