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Related Experiment Videos

A rapid retrieval tool for operating on large, flat archive files

O White1, W FitzHugh

  • 1Institute for Genomic Research, Gaithersburg, MD 20878, USA.

Computer Applications in the Biosciences : CABIOS
|February 1, 1995
PubMed
Summary
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This study introduces a new retrieval system for flat-file databases, improving access to large biological archives. The system offers efficient data searching for computer-aided sequencing and analysis facilities.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomic Data Management

Background:

  • Sequencing facilities require efficient access to large flat-file archives for computer-aided analysis.
  • Network-based retrieval methods are often impractical for accessing numerous selected entries.
  • Local installation of public archives via FTP is a viable alternative but necessitates a rapid access scheme.

Purpose of the Study:

  • To develop an efficient retrieval system for entries within flat-file databases.
  • To provide a practical solution for accessing large biological data archives locally.
  • To ensure the retrieval system is compatible with the operational needs of sequencing facilities.

Main Methods:

  • Development of a novel retrieval system designed for flat-file database structures.

Related Experiment Videos

  • Implementation of a scheme for rapid data access after local archive installation.
  • Testing the system's compatibility with various flat-file database formats.

    • Main Results:

    • The developed system enables efficient searching and retrieval of entries from flat-file databases.
    • The system overcomes the limitations of network-based retrieval for large datasets.
    • Demonstrated applicability to public DNA and protein archives.

    Conclusions:

    • The new retrieval system significantly enhances data accessibility for bioinformatics and computational biology.
    • Local archive management is optimized, supporting efficient sequencing and analysis workflows.
    • The system provides a scalable solution for managing and accessing biological sequence data.