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Detecting Protein Variants within Ma...

Alfaro, Javier Antonio.

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Detecting Protein Variants within Mass Spectrometry Datasets.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Detecting Protein Variants within Mass Spectrometry Datasets./
Author:	Alfaro, Javier Antonio.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	113 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
Contained By:	Dissertation Abstracts International79-08B(E).
Subject:	Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10681585
ISBN:	9780355813449

Detecting Protein Variants within Mass Spectrometry Datasets.
Alfaro, Javier Antonio.

Detecting Protein Variants within Mass Spectrometry Datasets. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 113 p.

Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.

Thesis (Ph.D.)--University of Toronto (Canada), 2018.

An important aim of cancer proteomics is to understand how the cancer proteome, with all its variation in both protein sequence and protein abundance, emerges from aberrations characterized within cancer genomic and transcriptomic datasets. A challenge in reaching this goal is that standard mass-spectrometry-based onco-proteomic strategies typically ignore variations in protein sequences, instead characterizing cancer proteomes in terms of changing abundances of standard reference proteins. In this thesis, I assess strategies for incorporating information about protein sequence variation within standard proteomics analysis strategies. I develop a pipeline for the characterization of variations within protein sequences and assess the best methodology to do so.

ISBN: 9780355813449Subjects--Topical Terms:

553671
Bioinformatics.

Detecting Protein Variants within Mass Spectrometry Datasets.
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300 $a 113 p.
500 $a Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
500 $a Includes supplementary digital materials.
500 $a Advisers: Thomas Kislinger; Paul C. Boutros.
502 $a Thesis (Ph.D.)--University of Toronto (Canada), 2018.
520 $a An important aim of cancer proteomics is to understand how the cancer proteome, with all its variation in both protein sequence and protein abundance, emerges from aberrations characterized within cancer genomic and transcriptomic datasets. A challenge in reaching this goal is that standard mass-spectrometry-based onco-proteomic strategies typically ignore variations in protein sequences, instead characterizing cancer proteomes in terms of changing abundances of standard reference proteins. In this thesis, I assess strategies for incorporating information about protein sequence variation within standard proteomics analysis strategies. I develop a pipeline for the characterization of variations within protein sequences and assess the best methodology to do so.
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