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Atlas - a data warehouse for integrative bioinformatics.

Sohrab P Shah1, Yong Huang, Tao Xu

  • 1UBC Bioinformatics Centre, University of British Columbia, Vancouver, BC, Canada. sohrab@bioinformatics.ubc.ca <sohrab@bioinformatics.ubc.ca>

BMC Bioinformatics
|February 23, 2005
PubMed
Summary
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Atlas is a biological data warehouse that integrates diverse biological data, providing a software infrastructure for bioinformatics research and development. This system facilitates data integration and enables new scientific inferences through flexible access to comprehensive biological datasets.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Presents Atlas, a biological data warehouse for local storage and integration of diverse biological data.
  • Includes biological sequences, molecular interactions, homology, gene annotations, and ontologies.
  • Aims to provide data and software infrastructure for bioinformatics research and development.

Purpose of the Study:

  • To develop a comprehensive biological data warehouse for integrated access to heterogeneous biological data.
  • To create a software infrastructure supporting bioinformatics research and development.
  • To enable new scientific inferences through integrated biological data analysis.

Main Methods:

  • Utilizes relational data models for different data types.

Related Experiment Videos

  • Manages data via SQL calls implemented in C++, Java, and Perl APIs.
  • Employs loader applications for data parsing and toolbox applications for data retrieval.

    • Main Results:

    • Integrates local instances of major biological databases like GenBank, UniProt, GO, and OMIM.
    • Provides flexible and integrated data access through retrieval APIs and toolbox applications.
    • Demonstrates use cases in genome annotation, cross-species interaction inference, and gene-disease associations.

    Conclusions:

    • Atlas serves as essential data infrastructure for bioinformatics research.
    • Facilitates integration of disparate biological data sources for novel scientific insights.
    • Achieves data integration through common data models, APIs, ontologies, and tools.