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Using Grid technology for computationally intensive applied bioinformatics analyses.

Jorge Andrade1, Lisa Berglund, Mathias Uhlén

  • 1Department of Biotechnology, Royal Institute of Technology (KTH), Stockholm, Sweden.

In Silico Biology
|May 24, 2007
PubMed
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Grid computing accelerates computationally intensive bioinformatics analyses by enabling parallel execution of algorithms. This approach, demonstrated with BLAST, offers a flexible and efficient solution for large-scale data processing without requiring pre-installed software.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Grid Computing

Background:

  • Large-scale bioinformatics analyses face computational time bottlenecks on single workstations.
  • Existing solutions involve algorithm modification or reduced accuracy, limiting scalability.
  • Grid computing presents a viable alternative for tackling massive computational challenges.

Purpose of the Study:

  • To implement and evaluate a Grid-aware model for computationally intensive bioinformatics tasks.
  • To demonstrate the model's flexibility using a blastp sliding window algorithm for proteome analysis.
  • To assess performance compared to local clusters and single workstations.

Main Methods:

  • Developed a Grid-aware model for parallel execution of bioinformatics algorithms.

Related Experiment Videos

  • Implemented temporary installations of BLAST executables and databases on remote Grid nodes.
  • Ensured dynamic environment compatibility, avoiding the need for pre-installed software.

    • Main Results:

    • The Grid-aware model successfully facilitated whole proteome sequence similarity analysis.
    • Performance evaluation demonstrated the model's efficiency compared to traditional setups.
    • The implementation proved generic, adaptable for other parallelizable bioinformatics tools.

    Conclusions:

    • Grid computing offers a powerful and flexible solution for overcoming computational limitations in bioinformatics.
    • The presented model enhances scalability and efficiency for large dataset analyses.
    • This approach is broadly applicable to various computationally demanding bioinformatics applications.