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Related Experiment Videos

Molecular Genetics Information System (MOLGENIS): alternatives in developing local experimental genomics databases.

Morris A Swertz1, E O De Brock, Sacha A F T Van Hijum

  • 1Groningen Bioinformatics Center, Faculty of Medical Sciences and Faculty of Mathematics and Natural Sciences, University of Groningen, Groningen, The Netherlands. m.a.swertz@cs.rug.nl

Bioinformatics (Oxford, England)
|April 3, 2004
PubMed
Summary

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Genomic research labs need clear criteria for selecting data management systems. A new system, MOLGENIS, was developed using code generation to meet key requirements for microarray data.

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Genetics

Background:

  • Genomic research laboratories require robust data management infrastructure.
  • Establishing criteria for adequate infrastructure is challenging.
  • A molecular genetics group faced decisions in setting up a microarray laboratory.

Purpose of the Study:

  • To define criteria for adequate genomic data management infrastructure.
  • To document the decision-making process for selecting or developing a system.
  • To provide insights for large-scale genomic projects.

Main Methods:

  • Identified five key requirements for experimental genomics database systems: evolution ability, suitable data model, data file storage, software exchange, and low maintenance costs.
  • Evaluated alternative solutions for each requirement.

Related Experiment Videos

  • Developed a new system, Molecular Genetics Information System (MOLGENIS), using automatic code generation.
  • Implemented a web-based user interface for low maintenance.

    • Main Results:

    • A customized data model based on standards was chosen.
    • Datasets were stored as black boxes.
    • The MOLGENIS system was built in three months.
    • The system offers flexibility and low maintenance.

    Conclusions:

    • The MOLGENIS system addresses critical needs for genomic data management.
    • The development process offers valuable lessons for software developers and researchers.
    • Adequate infrastructure is crucial for efficient genomic research workflows.