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Project management system for structural and functional proteomics: Sesame.

Zsolt Zolnai1, Peter T Lee, Jing Li

  • 1Center for Eukaryotic Structural Genomics, Department of Biochemistry, University of Wisconsin-Madison, Madison Wisconsin 53706, USA. zsolt@nmrfam.wisc.edu

Journal of Structural and Functional Genomics
|August 29, 2003
PubMed
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The Sesame computing infrastructure streamlines complex projects, particularly in structural proteomics. It manages all stages from target selection to data deposition, enhancing collaboration and data accessibility.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Structural Proteomics

Background:

  • Managing complex, multi-step projects in scientific research, such as structural proteomics, presents significant organizational and data integration challenges.
  • Existing systems often lack the flexibility and scalability required for large-scale, collaborative research endeavors.

Purpose of the Study:

  • To present the design criteria and successful implementation of the Sesame computing infrastructure.
  • To demonstrate Sesame's capability in managing all phases of a large-scale structural proteomics project.
  • To highlight Sesame's role in facilitating secure, remote collaboration and data management.

Main Methods:

  • Developed a client/server architecture utilizing Java2 for both client and server tiers, ensuring cross-platform compatibility and internet accessibility.

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  • Implemented a three-tier system (client, server, database) with a secure framework supporting industry standards.
  • Designed application modules with user interfaces, server-side objects, and centralized databases, controlled by access privileges.

    • Main Results:

    • The Sesame system architecture successfully meets its basic design goals, demonstrating robust management of complex project workflows.
    • Developed and routinely utilized several Sesame modules supporting key steps in structural and functional proteomics.
    • The system facilitates secure local and remote collaborations, accessible via the internet using Java Web Start or web browsers.

    Conclusions:

    • The Sesame computing infrastructure provides a scalable and secure solution for managing complex scientific projects, particularly in structural proteomics.
    • Sesame's design promotes efficient data handling, collaboration, and accessibility, addressing key challenges in large-scale biological research.
    • The software is available for use and further development by interested parties, fostering community-driven advancement.