Shared more. Cited more. Safe forever.
    • advanced search
    • submit works
    • about
    • help
    • contact us
    • login
    View Item 
    •   MOspace Home
    • University of Missouri-Columbia
    • Graduate School - MU Theses and Dissertations (MU)
    • Theses and Dissertations (MU)
    • Dissertations (MU)
    • 2009 Dissertations (MU)
    • 2009 MU dissertations - Freely available online
    • View Item
    •   MOspace Home
    • University of Missouri-Columbia
    • Graduate School - MU Theses and Dissertations (MU)
    • Theses and Dissertations (MU)
    • Dissertations (MU)
    • 2009 Dissertations (MU)
    • 2009 MU dissertations - Freely available online
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.
    advanced searchsubmit worksabouthelpcontact us

    Browse

    All of MOspaceCommunities & CollectionsDate IssuedAuthor/ContributorTitleIdentifierThesis DepartmentThesis AdvisorThesis SemesterThis CollectionDate IssuedAuthor/ContributorTitleIdentifierThesis DepartmentThesis AdvisorThesis Semester

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular AuthorsStatistics by Referrer

    Bioinformatics methods for protein identification using peptide mass fingerprinting data

    Song, Zhao, 1978-
    View/Open
    [PDF] public.pdf (1.987Kb)
    [PDF] short.pdf (9.092Kb)
    [PDF] research.pdf (1.184Mb)
    Date
    2009
    Format
    Thesis
    Metadata
    [+] Show full item record
    Abstract
    Protein identification using mass spectrometry is an important yet partially solved problem in the study of proteomics during the post-genomic era. The major techniques used in mass spectrometry are Peptide Mass Fingerprinting (PMF) and Tandem mass spectrometry (MS/MS). PMF is faster and economical compared with MS/MS and widely applicable in many fields. Our work focus on the method development for protein identification using PMF data and this work covers three subjects: (1) Protein Identification scoring function development: we developed the Probability Based Scoring Function (PBSF) which is used to quantify the degree of match between PMF data and candidate protein. The derived score is used to rank the protein and predict the identification. (2) Confidence Assessment development: scoring function may lead to false positive identification since the top hit from a database search may not be the target protein. In addition, the identification scores assigned singly by a scoring function (raw scores) are not normalized. Therefore, the ranking based on raw scores may be biased. To address the above issue, we have developed a statistical model to evaluate the confidence of the raw score and to improve the ranking of proteins for identification. (3) Software development: we implemented our computational methods in an open source package "ProteinDecision" which is freely available upon request. .
    URI
    https://hdl.handle.net/10355/6125
    https://doi.org/10.32469/10355/6125
    Degree
    Ph. D.
    Thesis Department
    Computer science (MU)
    Rights
    OpenAccess.
    This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
    Collections
    • Computer Science electronic theses and dissertations (MU)
    • 2009 MU dissertations - Freely available online

    Send Feedback
    hosted by University of Missouri Library Systems
     

     


    Send Feedback
    hosted by University of Missouri Library Systems