Degree Name
MS (Master of Science)
Program
Mathematical Sciences
Date of Award
5-2017
Committee Chair or Co-Chairs
Christina Nicole Holder Lewis
Committee Members
Christina Nicole Holder Lewis, Robert M. Price, JeanMarie Hendrickson
Abstract
Protein identification using tandem mass spectrometry (MS/MS) has proven to be an effective way to identify proteins in a biological sample. An observed spectrum is constructed from the data produced by the tandem mass spectrometer. A protein can be identified if the observed spectrum aligns with the theoretical spectrum. However, data generated by the tandem mass spectrometer are affected by errors thus making protein identification challenging in the field of proteomics. Some of these errors include wrong calibration of the instrument, instrument distortion and noise. In this thesis, we present a pre-processing method, which focuses on the removal of noisy data with the hope of aiding in better identification of proteins. We employ the method of binning to reduce the number of noise peaks in the data without sacrificing the alignment of the observed spectrum with the theoretical spectrum. In some cases, the alignment of the two spectra improved.
Document Type
Thesis - unrestricted
Recommended Citation
Offei, Felix, "Denoising Tandem Mass Spectrometry Data" (2017). Electronic Theses and Dissertations. Paper 3218. https://dc.etsu.edu/etd/3218
Copyright
Copyright by the authors.
Included in
Applied Statistics Commons, Clinical Trials Commons, Genomics Commons, Laboratory and Basic Science Research Commons, Statistical Methodology Commons