Peptide identification using tandem mass spectrometry depends on matching the observed spectrum with the theoretical spectrum. The raw data from tandem mass spectrometry, however, is often not optimal because it may contain noise or measurement errors. Denoising this data can improve alignment between observed and theoretical spectra and reduce the number of peaks. The method used by Lewis et. al (2018) uses a combined constant and moving threshold to denoise spectra. We compare the effects of using the standard preprocessing methods baseline removal, wavelet smoothing, and binning on spectra with Lewis et. al’s threshold method. We consider individual methods and combinations, using measures of distance from Lewis et. al's scoring function for comparison. Our findings showed that no single method provided better results than Lewis et. al's, but combining techniques with that of Lewis et. al's reduced the distance measurements and size of the data set for many peptides.
| Identifer | oai:union.ndltd.org:ETSU/oai:dc.etsu.edu:etd-5035 |
| Date | 01 May 2019 |
| Creators | Carpenter, Skylar |
| Publisher | Digital Commons @ East Tennessee State University |
| Source Sets | East Tennessee State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
| Rights | Copyright by the authors. |
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