rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Statistical methods for the analysis of mass spectrometry-based proteomics data
  name:
    name part: Wang, Xuan.
    role:
      role term: creator
  name:
    name part: Dabney, Alan R.
    role:
      role term: thesis advisor.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2012
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: Proteomics serves an important role at the systems-level in understanding of biological functioning. Mass spectrometry proteomics has become the tool of choice for identifying and quantifying the proteome of an organism. In the most widely used bottom-up approach to MS-based high-throughput quantitative proteomics, complex mixtures of proteins are first subjected to enzymatic cleavage, the resulting peptide products are separated based on chemical or physical properties and then analyzed using a mass spectrometer. The three fundamental challenges in the analysis of bottom-up MS-based proteomics are as follows: (i) Identifying the proteins that are present in a sample, (ii) Aligning different samples on elution (retention) time, mass, peak area (intensity) and etc, (iii) Quantifying the abundance levels of the identified proteins after alignment. Each of these challenges requires knowledge of the biological and technological context that give rise to the observed data, as well as the application of sound statistical principles for estimation and inference. In this dissertation, we present a set of statistical methods in bottom-up proteomics towards protein identification, alignment and quantification. We describe a fully Bayesian hierarchical modeling approach to peptide and protein identification on the basis of MS/MS fragmentation patterns in a unified framework. Our major contribution is to allow for dependence among the list of top candidate PSMs, which we accomplish with a Bayesian multiple component mixture model incorporating decoy search results and joint estimation of the accuracy of a list of peptide identifications for each MS/MS fragmentation spectrum. We also propose an objective criteria for the evaluation of the False Discovery Rate (FDR) associated with a list of identifications at both peptide level, which results in more accurate FDR estimates than existing methods like PeptideProphet. Several alignment algorithms have been developed using different…
  note: by Xuan Wang.
  note: "Major Subject: Statistics"
  note: Electronic resource.
  note: Doctor of Philosophy Texas A&M University 2012 http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-10777
  note: Includes bibliographical references.
  note: Text (Dissertation)
  subject:
    topic: Major Statistics
  subject:
    topic: LC-MS
  subject:
    topic: Missingness
  subject:
    topic: Identification
  subject:
    topic: PSM
  subject:
    topic: FDR
  subject:
    topic: Bottom-up
  subject:
    topic: Bayesian Hierarchical Model
  subject:
    topic: Mass spectrometry
  subject:
    topic: Alignment
  subject:
    topic: Proteomics
  subject:
    topic: Anchor Points
  subject:
    topic: Quantitation
  identifier: http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-10777
  record info:
    record content source: TXA
    record creation date: 121022
    record change date: 20150922131529.0
    record identifier: in00002764580