The research described in this thesis can be broadly described by term "statistical analysis of mass spectrometry data". Bioinformatics is a new science which attempts to amalgamate statistical methodology with bring statistical thinking and the biological understanding to area which have previously been void of such. Mass spectrometry which is used to study proteins and their functions, is a relatively new field of bioinformatics research. In this thesis we explore three main themes, all of which tackle a different statistical learning method which arises in mass spectrometry. The main focus of the first theme of the research is on using statistical methods to study fragmentation patterns of mass spectrometry experiments. The analysis contained in this theme has been loosely split into parts: firstly, we calculate a probability of a process called cleavage as part of our preliminary analysis to determine which combination of fragmentation site residues were likely to break. In part two, we apply statistical models to investigate factors influencing the relative intensity of fragment ions formed in tandem mass spectrometry experiments. Separate models were formulated for different types of ions as it was thought that different factors may influence the formation of each type of fragment ion. Statistical regression methods are applied to two types of datasets of mass spectra data: tryptic and nontryptic peptide sequences. We find that several factors have a highly significant influence on the relative intensity of fragment ions formed in the experiment.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:659026 |
| Date | January 2013 |
| Creators | Ben-Farag, Suaad Omran S. |
| Publisher | University of Leeds |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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