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dc.contributor.authorShen, Jianqiao
dc.date.accessioned2017-12-07 15:22:21 (GMT)
dc.date.available2017-12-07 15:22:21 (GMT)
dc.date.issued2017-12-07
dc.date.submitted2017-11-30
dc.identifier.urihttp://hdl.handle.net/10012/12680
dc.description.abstractTandem mass spectrometry (MS/MS) is widely used nowadays for bioinformaticians to identify peptides and proteins. Three major peptide identification approaches and tools have been developed over the past two decades: database searching, de novo sequencing and spectral library searching. Recently, spectral library searching approach gains increasing popularity because of high-sensitivity and searching speed. Another popular technique is tandem mass tag (TMT). The advantage of this approach is able to simultaneously quantify multiple samples. However, TMT labeling also increases the complexity of spectrum compared to label free spectrum. Currently, there is no TMT-labeled library available and most of the software tools are also optimized for label free data. In this thesis, we will study the differences between TMT-labeled spectra and label free spectra. We find the intensities of fragmentation ions are changed which proves that the algorithm only considers the mass shift of fragmentation ions and reporter ions is insufficient when searching TMT-labeled query spectra against a label free spectral library. It is necessary to build a spectral library from real TMT-labeled spectra. We develop a TMT-labeled spectral library. The target library is constructed with JUMP database search tool and generate the decoy library by swapping precursor mass. We then evaluate the library with a small TMT-labeled dataset and compare the performance of spectral library searching with traditional database searching. The test result shows the identification rate is increased using spectral library searching.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.titleA TMT-labeled Spectral Library for Peptide Sequencingen
dc.typeMaster Thesisen
dc.pendingfalse
uws-etd.degree.departmentDavid R. Cheriton School of Computer Scienceen
uws-etd.degree.disciplineComputer Scienceen
uws-etd.degree.grantorUniversity of Waterlooen
uws-etd.degreeMaster of Mathematicsen
uws.contributor.advisorMa, Bin
uws.contributor.affiliation1Faculty of Mathematicsen
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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