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Using peak intensity and fragmentati...

Ji, Li.

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Using peak intensity and fragmentation patterns in peptide sequence identification (SQID): A Bayesian learning algorithm for tandem mass spectra.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Using peak intensity and fragmentation patterns in peptide sequence identification (SQID): A Bayesian learning algorithm for tandem mass spectra./
作者:	Ji, Li.
面頁冊數:	573 p.
附註:	Source: Dissertation Abstracts International, Volume: 67-10, Section: B, page: 5715.
Contained By:	Dissertation Abstracts International67-10B.
標題:	Chemistry, Analytical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3238268
ISBN:	9780542922466

Using peak intensity and fragmentation patterns in peptide sequence identification (SQID): A Bayesian learning algorithm for tandem mass spectra.
Ji, Li.

Using peak intensity and fragmentation patterns in peptide sequence identification (SQID): A Bayesian learning algorithm for tandem mass spectra. - 573 p.

Source: Dissertation Abstracts International, Volume: 67-10, Section: B, page: 5715.

Thesis (Ph.D.)--The University of Arizona, 2006.

As DNA sequence information becomes increasingly available, researchers are now tackling the great challenge of characterizing and identifying peptides and proteins from complex mixtures. Automatic database searching algorithms have been developed to meet this challenge. This dissertation is aimed at improving these algorithms to achieve more accurate and efficient peptide and protein identification with greater confidence by incorporating peak intensity information and peptide cleavage patterns obtained in gas-phase ion dissociation research. The underlying hypothesis is that these algorithms can benefit from knowledge about molecular level fragmentation behavior of particular amino acid residues or residue combinations.

ISBN: 9780542922466Subjects--Topical Terms:

586156
Chemistry, Analytical.

Using peak intensity and fragmentation patterns in peptide sequence identification (SQID): A Bayesian learning algorithm for tandem mass spectra.
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245 1 0 $a Using peak intensity and fragmentation patterns in peptide sequence identification (SQID): A Bayesian learning algorithm for tandem mass spectra.
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500 $a Adviser: Vicki H. Wysocki.
502 $a Thesis (Ph.D.)--The University of Arizona, 2006.
520 $a As DNA sequence information becomes increasingly available, researchers are now tackling the great challenge of characterizing and identifying peptides and proteins from complex mixtures. Automatic database searching algorithms have been developed to meet this challenge. This dissertation is aimed at improving these algorithms to achieve more accurate and efficient peptide and protein identification with greater confidence by incorporating peak intensity information and peptide cleavage patterns obtained in gas-phase ion dissociation research. The underlying hypothesis is that these algorithms can benefit from knowledge about molecular level fragmentation behavior of particular amino acid residues or residue combinations.
520 $a SeQuence IDentification (SQID), developed in this dissertation research, is a novel Bayesian learning-based method that attempts to incorporate intensity information from peptide cleavage patterns in a database searching algorithm. It directly makes use of the estimated peak intensity distributions for cleavage at amino acid pairs, derived from probability histograms generated from experimental MS/MS spectra. Rather than assuming amino acid cleavage patterns artificially or disregarding intensity information, SQID aims to take advantage of knowledge of observed fragmentation intensity behavior. In addition, SQID avoids the generation of a theoretical spectrum predication for each candidate sequence, needed by other sequencing methods including SEQUEST. As a result, computational efficiency is significantly improved.
520 $a Extensive testing has been performed to evaluate SQID, by using datasets from the Pacific Northwest National Laboratory, University of Colorado, and the Institute for Systems Biology. The computational results show that by incorporating peak intensity distribution information, the program's ability to distinguish the correct peptides from incorrect matches is greatly enhanced. This observation is consistent with experiments involving various peptides and searches against larger databases with distraction proteins, which indirectly verifies that peptide dissociation behaviors determine the peptide sequencing and protein identification in MS/MS. Furthermore, testing SQID by using previously identified clusters of spectra associated with unique chemical structure motifs leads to the following conclusions: (1) the improvement in identification confidence is observed with a range of peptides displaying different fragmentation behaviors; (2) the magnitude of improvement is in agreement with the peptide cleavage selectivity, that is, more significant improvements are observed with more selective peptide cleavages.
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