• DocumentCode
    472009
  • Title

    Design and Implementation of Probability-Based Scoring Function for Peptide Mass Fingerprinting Protein Identification

  • Author

    Song, Zhao ; Chen, Luonan ; Zhang, Chao ; Xu, Dong

  • Author_Institution
    Comput. Sci. Dept., Missouri Univ., Columbia, MO
  • fYear
    2006
  • fDate
    Aug. 30 2006-Sept. 3 2006
  • Firstpage
    4556
  • Lastpage
    4559
  • Abstract
    Protein identification through high-throughput mass spectrum data is an important domain in proteomics. Peptide mass fingerprinting (PMF) is one of the major methods for protein identification using the mass-spec technology. We developed a software package called "ProteinDecision" for PMF protein identification, together with a user-friendly graphical interface. "ProteinDecision" can handle the issues of selecting peaks from mass spectrum, transforming database format, displaying the top ranks of identification result, and detailed information for each ranking. We used a novel scoring function by considering the distribution of matching a mass-to-charge and peak intensity in a database based on the MOWSE table. Our new scoring function is assessed better than existing ones by comparing the computational results using experimental PMF data. A standalone version of "ProteinDecision" is freely available upon request
  • Keywords
    biological techniques; biology computing; graphical user interfaces; mass spectra; molecular biophysics; probability; proteins; software packages; MOWSE table; ProteinDecision; mass spectrum data; peptide mass fingerprinting; probability-based scoring function; protein identification; proteomics; software package; user-friendly graphical interface; Bonding; Chaos; Computer science; Fingerprint recognition; Frequency; Peptides; Protein engineering; Sequences; Software packages; Spatial databases;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE
  • Conference_Location
    New York, NY
  • ISSN
    1557-170X
  • Print_ISBN
    1-4244-0032-5
  • Electronic_ISBN
    1557-170X
  • Type

    conf

  • DOI
    10.1109/IEMBS.2006.260150
  • Filename
    4462816