Using Peak Intensity and Fragmentation Patterns in Peptide SeQuence IDentification (SQID) - A Bayesian Learning Algorithm for Tandem Mass Spectra

Persistent Link:
http://hdl.handle.net/10150/193559
Title:
Using Peak Intensity and Fragmentation Patterns in Peptide SeQuence IDentification (SQID) - A Bayesian Learning Algorithm for Tandem Mass Spectra
Author:
Ji, Li
Issue Date:
2006
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
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.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.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.
Type:
text; Electronic Dissertation
Keywords:
tandem mass spectrometry; peptide and protein identificaiton; bayesian learning; sequencing algorithm; machine learning algorithm; probability-based algorithm
Degree Name:
PhD
Degree Level:
doctoral
Degree Program:
Chemistry; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Wysocki, Vicki H.
Committee Chair:
Wysocki, Vicki H.

Full metadata record

DC FieldValue Language
dc.language.isoENen_US
dc.titleUsing Peak Intensity and Fragmentation Patterns in Peptide SeQuence IDentification (SQID) - A Bayesian Learning Algorithm for Tandem Mass Spectraen_US
dc.creatorJi, Lien_US
dc.contributor.authorJi, Lien_US
dc.date.issued2006en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractAs 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.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.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.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjecttandem mass spectrometryen_US
dc.subjectpeptide and protein identificaitonen_US
dc.subjectbayesian learningen_US
dc.subjectsequencing algorithmen_US
dc.subjectmachine learning algorithmen_US
dc.subjectprobability-based algorithmen_US
thesis.degree.namePhDen_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineChemistryen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorWysocki, Vicki H.en_US
dc.contributor.chairWysocki, Vicki H.en_US
dc.contributor.committeememberAspinwall, Craig A.en_US
dc.contributor.committeememberPemberton, Jeanne E.en_US
dc.identifier.proquest1973en_US
dc.identifier.oclc659746549en_US
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