Predicting the Spectrum Quality and Digestive Enzyme for Shotgun Proteomics
| dc.contributor.author | Gholamizoj, Soroosh | |
| dc.date.accessioned | 2022-05-03T17:57:00Z | |
| dc.date.available | 2022-05-03T17:57:00Z | |
| dc.date.issued | 2022-05-03 | |
| dc.date.submitted | 2022-04-29 | |
| dc.description.abstract | In proteomics, database search programs are routinely used for peptide identification from tandem mass spectrometry data. However, many low-quality spectra cannot be interpreted by any programs. Meanwhile, certain high-quality spectra may not be identified due to incompleteness of the database, failure of the software, or sub-optimal search parameters. Thus, spectrum quality assessment tools are helpful programs that can eliminate poor-quality spectra before the database search and highlight the high-quality spectra that are not identified in the initial search. These spectra may be valuable candidates for further analyses. We propose SPEQ: a spectrum quality assessment tool that uses a deep neural network to classify spectra into high-quality, which are worthy candidates for interpretation, and low-quality, which lack sufficient information for identification. SPEQ was compared with a few other prediction models and demonstrated improved prediction accuracy. Furthermore, we propose a statistical model to automatically detect the enzyme used for digestion in a proteomics experiment, by analyzing the distribution of amino acids in peptides de novo sequenced with a nonspecific enzyme setting. Results demonstrate that this algorithm can accurately identify correct enzymes. | en |
| dc.identifier.uri | http://hdl.handle.net/10012/18223 | |
| dc.language.iso | en | en |
| dc.pending | false | |
| dc.publisher | University of Waterloo | en |
| dc.title | Predicting the Spectrum Quality and Digestive Enzyme for Shotgun Proteomics | en |
| dc.type | Master Thesis | en |
| uws-etd.degree | Master of Mathematics | en |
| uws-etd.degree.department | David R. Cheriton School of Computer Science | en |
| uws-etd.degree.discipline | Computer Science | en |
| uws-etd.degree.grantor | University of Waterloo | en |
| uws-etd.embargo.terms | 0 | en |
| uws.contributor.advisor | Ma, Bin | |
| uws.contributor.affiliation1 | Faculty of Mathematics | en |
| uws.peerReviewStatus | Unreviewed | en |
| uws.published.city | Waterloo | en |
| uws.published.country | Canada | en |
| uws.published.province | Ontario | en |
| uws.scholarLevel | Graduate | en |
| uws.typeOfResource | Text | en |