Para SAM: A parallelized version of the significance analysis of microarrays algorithm

Ashok Kumar Sharma, Jieping Zhao, Robert Podolsky, Richard A McIndoe

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Motivation: Significance analysis of microarrays (SAM) is a widely used permutation-based approach to identifying differentially expressed genes in microarray datasets. While SAM is freely available as an Excel plug-in and as an R-package, analyses are often limited for large datasets due to very high memory requirements. Summary: We have developed a parallelized version of the SAM algorithm called ParaSAM to overcome the memory limitations. This high performance multithreaded application provides the scientific community with an easy and manageable client-server Windows application with graphical user interface and does not require programming experience to run. The parallel nature of the application comes from the use of web services to perform the permutations. Our results indicate that ParaSAM is not only faster than the serial version, but also can analyze extremely large datasets that cannot be performed using existing implementations.

Original languageEnglish (US)
Article numberbtq161
Pages (from-to)1465-1467
Number of pages3
JournalBioinformatics
Volume26
Issue number11
DOIs
Publication statusPublished - Apr 15 2010

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ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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