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Development of Data Independent Acquisition Methods to Systematically Analyze the Human Proteome.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Development of Data Independent Acquisition Methods to Systematically Analyze the Human Proteome./
Author:	Searle, Brian C.
Description:	1 online resource (186 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
Contained By:	Dissertation Abstracts International79-09B(E).
Subject:	Biochemistry. -
Online resource:	click for full text (PQDT)
ISBN:	9780355850802

Development of Data Independent Acquisition Methods to Systematically Analyze the Human Proteome.
Searle, Brian C.

Development of Data Independent Acquisition Methods to Systematically Analyze the Human Proteome. - 1 online resource (186 pages)

Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.

Thesis (Ph.D.)--University of Washington, 2018.

Includes bibliographical references

Data independent acquisition (DIA) mass spectrometry is a powerful technique that is improving the reproducibility and throughput of mass spectrometry-based proteomics studies. Here we explore several new approaches to leverage this technology. First we present an overview of modern data independent acquisition techniques, and demonstrate their internal detection rate and quantification consistency relative to targeted parallel reaction monitoring (PRM) and data dependent acquisition (DDA) methods despite large variations in data sampling strategies. Second, we use DIA experiments to construct a prediction model that helps determine optimal peptide selection for targeted experiments. Third, we introduce a new experimental workflow that uses chromatogram libraries to enable sensitive peptide detection in quantitative experiments, while still maintaining the throughput necessary for large scale experiments. Finally, we discuss a new approach to statistically validate phosphopeptide positional isomers and explore their prevalence in studies of Human cells.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355850802Subjects--Topical Terms:

582831
Biochemistry.
Index Terms--Genre/Form:

554714
Electronic books.

Development of Data Independent Acquisition Methods to Systematically Analyze the Human Proteome.
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245 1 0 $a Development of Data Independent Acquisition Methods to Systematically Analyze the Human Proteome.
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300 $a 1 online resource (186 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
500 $a Adviser: Michael J. MacCoss.
502 $a Thesis (Ph.D.)--University of Washington, 2018.
504 $a Includes bibliographical references
520 $a Data independent acquisition (DIA) mass spectrometry is a powerful technique that is improving the reproducibility and throughput of mass spectrometry-based proteomics studies. Here we explore several new approaches to leverage this technology. First we present an overview of modern data independent acquisition techniques, and demonstrate their internal detection rate and quantification consistency relative to targeted parallel reaction monitoring (PRM) and data dependent acquisition (DDA) methods despite large variations in data sampling strategies. Second, we use DIA experiments to construct a prediction model that helps determine optimal peptide selection for targeted experiments. Third, we introduce a new experimental workflow that uses chromatogram libraries to enable sensitive peptide detection in quantitative experiments, while still maintaining the throughput necessary for large scale experiments. Finally, we discuss a new approach to statistically validate phosphopeptide positional isomers and explore their prevalence in studies of Human cells.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Biochemistry. $3 582831
650 4 $a Genetics. $3 578972
650 4 $a Bioinformatics. $3 583857
655 7 $a Electronic books. $2 local $3 554714
690 $a 0487
690 $a 0369
690 $a 0715
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710 2 $a University of Washington. $b Genome Sciences. $3 1190110
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