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dc.contributor.authorSharma, Ashok
dc.contributor.authorZhao, Jieping
dc.contributor.authorPodolsky, Robert H.
dc.contributor.authorMcIndoe, Richard A
dc.date.accessioned2010-09-24T22:03:25Z
dc.date.available2010-09-24T22:03:25Z
dc.date.issued2010-05-20en_US
dc.identifier.citationBioinformatics. 2010 Jun 1; 26(11):1465-1467en_US
dc.identifier.issn1367-4811en_US
dc.identifier.pmid20400455en_US
dc.identifier.doi10.1093/bioinformatics/btq161en_US
dc.identifier.urihttp://hdl.handle.net/10675.2/135
dc.description.abstractMOTIVATION: 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. AVAILABILITY: A web version open to the public is available at http://bioanalysis.genomics.mcg.edu/parasam. For local installations, both the windows and web implementations of ParaSAM are available for free at http://www.amdcc.org/bioinformatics/software/parasam.aspx.
dc.rightsThe PMC Open Access Subset is a relatively small part of the total collection of articles in PMC. Articles in the PMC Open Access Subset are still protected by copyright, but are made available under a Creative Commons or similar license that generally allows more liberal redistribution and reuse than a traditional copyrighted work. Please refer to the license statement in each article for specific terms of use. The license terms are not identical for all articles in this subset.en_US
dc.subject.meshAlgorithmsen_US
dc.subject.meshDatabases, Factualen_US
dc.subject.meshOligonucleotide Array Sequence Analysis / methodsen_US
dc.subject.meshSoftwareen_US
dc.titleParaSAM: a parallelized version of the significance analysis of microarrays algorithm.en_US
dc.typeJournal Articleen_US
dc.typeResearch Support, N.I.H., Extramuralen_US
dc.identifier.pmcidPMC2872005en_US
dc.contributor.corporatenameDepartment of Pathologyen_US
refterms.dateFOA2019-04-09T16:26:07Z
html.description.abstractMOTIVATION: 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. AVAILABILITY: A web version open to the public is available at http://bioanalysis.genomics.mcg.edu/parasam. For local installations, both the windows and web implementations of ParaSAM are available for free at http://www.amdcc.org/bioinformatics/software/parasam.aspx.


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