Abstract

Biological research has benefitted greatly from the advent of omic methods. For many biomolecules, mass spectrometry (MS) methods are most widely employed due to the sensitivity which allows low quantities of sample and the speed which allows analysis of complex samples. Improvements in instrument and sample preparation techniques create opportunities for large scale experimentation. The complexity and volume of data produced by modern MS-omic instrumentation challenges biological interpretation, while the complexity of the instrumentation, sample noise, and complexity of data analysis present difficulties in maintaining and ensuring data quality, validity, and relevance. We present a corpus of tools which improves quality assurance capabilities of instruments, provides comparison abilities for evaluating data analysis tool performance, distills ideas pertinent in MS analysis into a consistent nomenclature, enhances all lipid analysis by automatic structural classification, implements a rigorous and chemically derived lipid fragmentation prediction tool, introduces custom structural analysis approaches and validation techniques, simplifies protein analysis form SDS-PAGE sample excisions, and implements a robust peak detection algorithm. These contributions provide improved identification of biomolecules, improved quantitation, and improve data quality and algorithm clarity to the MS-omic field.

Degree

PhD

College and Department

Physical and Mathematical Sciences; Chemistry and Biochemistry

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2014-07-01

Document Type

Dissertation

Handle

http://hdl.lib.byu.edu/1877/etd7179

Keywords

bioinformatics, quality assurance, mass spectrometry, lipidomics, proteomics, machine learning, lipid fragmentation, simulated dataset

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