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Related Experiment Videos

Cruella: developing a scalable tissue microarray data management system.

James D Cowan1, David L Rimm, David P Tuck

  • 1Department of Pathology, Yale University School of Medicine, New Haven, CT 06520-8023, USA. james.cowan@yale.edu

Archives of Pathology & Laboratory Medicine
|June 3, 2006
PubMed
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A new data system, Cruella, manages complex tissue microarray data, integrating diverse experimental and clinical information. This flexible system supports various research needs and accommodates new data types effectively.

Area of Science:

  • Biomedical Informatics
  • Genomics
  • Pathology

Background:

  • Tissue microarray (TMA) data systems require specialized design considerations distinct from DNA microarrays.
  • Integrating diverse data elements (clinical, biological, specimen) and accommodating new project-specific data are key challenges.
  • Existing systems often lack flexibility for evolving research questions and data types.

Purpose of the Study:

  • To develop a flexible and scalable data storage and management system for tissue microarray research.
  • To accommodate varied disease types, investigators, and research questions, enabling dynamic data integration.
  • To address the need for interoperability with pre-existing clinical and biological data models.

Main Methods:

  • A custom Java-based persistence layer was developed to interact with an object-relational database.

Related Experiment Videos

  • A novel database schema was designed to handle unpredictable data elements.
  • A Java Servlet-based Web interface was implemented for user interaction.

    • Main Results:

    • The Cruella system is a widely used resource, supporting daily research activities for numerous scientists.
    • It stores millions of experimental values across over 300 biological markers and 30 disease types.
    • Experimental data is integrated with aggregated clinical data, facilitating management, analysis, and export.

    Conclusions:

    • Cruella effectively addresses the unique challenges of managing tissue microarray experimental and clinical data.
    • A metadata-driven approach ensures practical solutions and facilitates interoperability with new data models.
    • The system's flexibility allows for straightforward accommodation of evolving research data requirements.