ParaSAM: A parallelized version of the significance analysis of microarrays
Authors Ashok Sharma, Jieping Zhao, Robert Podolsky, and Richard A. McIndoe
Submitted By Richard McIndoe on 3/23/2010
Status Published
Journal Bioinformatics (Oxford, England)
Year 2010
Date Published 6/1/2010
Volume : Pages Not Specified : Not Specified
PubMed Reference 20400455
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
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 ParaSAM is not only faster than the serial version, but can analyze
extremely large datasets that cannot be performed using existing
Availability: A Web version open to the public is available at For local installations, both the
windows and web implementations of ParaSAM are available for free at

Investigators with authorship
Richard McIndoeAugusta University