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https://scholarbank.nus.edu.sg/handle/10635/14825
DC Field | Value | |
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dc.title | Protein modification and peptide identification from mass spectrum | |
dc.contributor.author | SHEN WEI | |
dc.date.accessioned | 2010-04-08T10:47:12Z | |
dc.date.available | 2010-04-08T10:47:12Z | |
dc.date.issued | 2005-07-08 | |
dc.identifier.citation | SHEN WEI (2005-07-08). Protein modification and peptide identification from mass spectrum. ScholarBank@NUS Repository. | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/14825 | |
dc.description.abstract | Protein sequencing is an important problem in the post-genome era. The thesis considers two problems related to protein sequencing. In the first problem, we proposed a dynamic programming algorithm to identify the post translational modifications (PTMs) in protein with a a??top-downa?? strategy using mass spectrometry. The new method can work without a protein database and can identify modifications in polynomial time. Besides, no prior knowledge about PTMs sites is needed in the method. Our second problem is related to de novo peptide sequencing using tandem mass spectrometer. In the past, little work has been done to utilize the intensities of the peaks in the mass spectrum to improve the accuracy of the peptide sequencing. In this thesis, we propose to model fragment ion intensities using a decision tree which estimates the likelihood of certain observed intensity given the local chemical and physical attributes of the fragment. Besides, a random model is used to estimate the likelihood of certain peak treated as noise. Using these two probabilistic models, we propose a new de novo peptide sequencing algorithm DTSeq. The experiment shows that DTSeq obtains the best results compared with PEAKs and PepNovo.( PEAKs and PepNovo are the two best de novo peptide sequencing algorithm in literature). | |
dc.language.iso | en | |
dc.subject | peptide sequencing, post translational modifications, scoring function, tandem mass spectrum, top-down mass spectrometry, proteomics | |
dc.type | Thesis | |
dc.contributor.department | COMPUTER SCIENCE | |
dc.contributor.supervisor | SUNG WING KIN | |
dc.description.degree | Master's | |
dc.description.degreeconferred | MASTER OF SCIENCE | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Master's Theses (Open) |
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ShenW.pdf | 820.72 kB | Adobe PDF | OPEN | None | View/Download |
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