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YPED: a web-accessible database system for protein expression analysis.

Mark A Shifman1, Yuli Li, Christopher M Colangelo

  • 1Center for Medical Informatics, Department of Anesthesiology, Yale University, New Haven, Connecticut 06520, USA. mark.shifman@yale.edu

Journal of Proteome Research
|September 18, 2007
PubMed
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The Yale Protein Expression Database (YPED) is an open-source system for managing high-throughput proteomic data. It integrates various experimental results, aiding researchers in protein identification and biological interpretation.

Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput proteomic technologies generate vast amounts of complex data.
  • Efficient storage, retrieval, and integrated analysis are crucial for interpreting these datasets.
  • Existing systems may lack comprehensive integration of diverse proteomic data types.

Purpose of the Study:

  • To develop an integrated, web-accessible software system for managing high-throughput proteomic data.
  • To provide tools for data storage, retrieval, and analysis.
  • To facilitate biological interpretation of proteomic results.

Main Methods:

  • Development of the Yale Protein Expression Database (YPED) as an open-source system.
  • Integration of data from DIGE, MudPIT, iTRAQ, and ICAT experiments.

Related Experiment Videos

  • Association of gel images and spots with mass spectrometric protein identifications.
  • Validation of tandem MS/MS results using the Trans-Proteomic Pipeline.
  • Implementation of a secure web interface for data access and analysis.

    • Main Results:

    • YPED successfully integrates gel analysis (DeCyder) with mass spectrometry-based protein identifications (Mascot, AB GPS Explorer).
    • The system supports multiple proteomic workflows including DIGE, MudPIT, iTRAQ, and ICAT.
    • Sample descriptions adhere to Minimum Information About a Proteomics Experiment (MIAPE) standards.
    • Researchers can securely view, subset, and download integrated proteomic data.
    • Tools for sample comparison, phosphoprotein viewing, and biological interpretation (PANTHER) are available.

    Conclusions:

    • YPED provides a comprehensive solution for managing and analyzing large-scale proteomic data.
    • The integrated approach facilitates deeper biological insights from diverse high-throughput experiments.
    • The open-source nature of YPED promotes accessibility and further development within the research community.