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Detecting Protein Variants within Ma...
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Alfaro, Javier Antonio.
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Detecting Protein Variants within Mass Spectrometry Datasets.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Detecting Protein Variants within Mass Spectrometry Datasets./
作者:
Alfaro, Javier Antonio.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:
113 p.
附註:
Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
Contained By:
Dissertation Abstracts International79-08B(E).
標題:
Bioinformatics. -
電子資源:
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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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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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10681585
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