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Application of high-throughput computing in bioinformatics.

Mark Swindells1, Mark Rae, Martyn Pearce

  • 1Inpharmatica Ltd, 60 Charlotte Street, London W1T 2NU, UK.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|June 14, 2003
PubMed
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Developing large Linux/PC clusters for genomics data analysis presents significant data throughput challenges. High-performance computing requires more than just raw processing power for effective bioinformatics.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • The exponential growth of raw genomics data necessitates advanced computational infrastructure.
  • Existing bioinformatics algorithms require substantial processing power for analysis.

Purpose of the Study:

  • To detail the challenges encountered when building a Linux/PC cluster for large-scale genomics data analysis.
  • To highlight the complexities beyond raw computing power in high-throughput data processing.

Main Methods:

  • Development of a 1314-node Linux/PC cluster with 1734 processors.
  • Application of bioinformatics algorithms to large raw genomics datasets.
  • Analysis of system performance focusing on data throughput.

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Main Results:

  • The cluster processing takes approximately two months to complete.
  • Significant challenges were identified in achieving high data throughput.
  • Raw computational power alone is insufficient for efficient data mining.

Conclusions:

  • Building effective clusters for genomics data analysis involves overcoming substantial data throughput hurdles.
  • Optimizing bioinformatics workflows requires a holistic approach addressing infrastructure and processing bottlenecks.
  • The developed cluster serves as a powerful resource for genomics data mining.