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A service-based BLAST command tool supported by cloud infrastructures.

Abel Carrión1, Ignacio Blanquer, Vicente Hernández

  • 1Institute of Instrumentation for Molecular Imaging, Universitat Politècnica de València.

Studies in Health Technology and Informatics
|September 4, 2012
PubMed
Summary
This summary is machine-generated.

This study simplifies using distributed computing for bioinformatics by porting BLAST. The new service offers a familiar interface, reducing learning curves for researchers and enabling efficient, cost-effective analysis.

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Area of Science:

  • Bioinformatics and Computational Biology
  • Distributed Computing
  • High-Performance Computing

Background:

  • Distributed computing offers significant potential for bioinformatics analysis tools, providing necessary computing and storage capabilities.
  • However, widespread adoption is hindered by steep learning curves and deployment complexities, limiting its impact on the research community.

Purpose of the Study:

  • To present a strategy for porting the Basic Local Alignment Search Tool (BLAST) to a distributed computing environment.
  • To minimize the learning curve and integration challenges for researchers by maintaining a familiar interface.

Main Methods:

  • Developed a multiplatform client and a service mirroring the sequential BLAST interface.
  • Utilized execution and data access components from the EC project Venus-C.
  • Leveraged the Windows Azure infrastructure for deployment and execution.

Main Results:

  • The ported BLAST service demonstrated low overhead within the global execution framework.
  • Achieved reasonable speed-up compared to the sequential BLAST version.
  • Showcased cost-efficiency for bioinformatics analyses on the distributed infrastructure.

Conclusions:

  • The developed porting strategy effectively addresses the challenges of adopting distributed computing in bioinformatics.
  • The service facilitates easier integration of powerful computational resources into existing research workflows.
  • This approach enhances the accessibility and efficiency of bioinformatics analyses, promoting wider research community engagement.