ParaSAM: a parallelized version of the significance analysis of microarrays algorithm
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 hi...
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| Published in | Bioinformatics Vol. 26; no. 11; pp. 1465 - 1467 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Oxford University Press
01.06.2010
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1367-4803 1367-4811 1460-2059 1367-4811 |
| DOI | 10.1093/bioinformatics/btq161 |
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| 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. 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 Contact: rmcindoe@mail.mcg.edu Supplementary information: Supplementary Data is available at Bioinformatics online. |
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| AbstractList | 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.
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.
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. 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.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.aspxCont act: rmcindoeail.mcg.eduSupplementary information: Supplementary Data is available at Bioinformatics online. 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. 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 Contact: rmcindoe@mail.mcg.edu Supplementary information: Supplementary Data is available at Bioinformatics online. 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. 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 Contact: rmcindoe@mail.mcg.edu Supplementary information: Supplementary Data is available at Bioinformatics online. 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.MOTIVATIONSignificance 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.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.SUMMARYWe 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.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.AVAILABILITYA 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. |
| Author | Zhao, Jieping Sharma, Ashok Podolsky, Robert McIndoe, Richard A. |
| AuthorAffiliation | 1 Center for Biotechnology and Genomic Medicine, 2 Department of Medicine and 3 Department of Pathology, School of Medicine, Medical College of Georgia, Augusta, GA 30912, USA |
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| Keywords | Microarray Algorithm Analysis |
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| References | Kerr (2023012507510614000_B3) 2009; 25 Kaizer (2023012507510614000_B2) 2007; 92 Tusher (2023012507510614000_B5) 2001; 98 Kraj (2023012507510614000_B4) 2008; 9 Benjamini (2023012507510614000_B1) 1995; 57 18416829 - BMC Bioinformatics. 2008;9:200 17595242 - J Clin Endocrinol Metab. 2007 Sep;92(9):3705-11 11309499 - Proc Natl Acad Sci U S A. 2001 Apr 24;98(9):5116-21 19528084 - Bioinformatics. 2009 Aug 15;25(16):2035-41 |
| References_xml | – volume: 25 start-page: 2035 year: 2009 ident: 2023012507510614000_B3 article-title: Comments on the analysis of unbalanced microarray data publication-title: Bioinformatics doi: 10.1093/bioinformatics/btp363 – volume: 92 start-page: 3705 year: 2007 ident: 2023012507510614000_B2 article-title: Gene expression in peripheral blood mononuclear cells from children with diabetes publication-title: J. Clin. Endocrinol. Metab. doi: 10.1210/jc.2007-0979 – volume: 98 start-page: 5116 year: 2001 ident: 2023012507510614000_B5 article-title: Significance analysis of microarrays applied to the ionizing radiation response publication-title: Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.091062498 – volume: 9 start-page: 200 year: 2008 ident: 2023012507510614000_B4 article-title: ParaKMeans: implementation of a parallelized K-means algorithm suitable for general laboratory use publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-9-200 – volume: 57 start-page: 289 year: 1995 ident: 2023012507510614000_B1 article-title: Controlling the false discovery rate - a practical and powerful approach to multiple testing publication-title: J. Royal Stat. Soc. Series B Methodol. doi: 10.1111/j.2517-6161.1995.tb02031.x – reference: 19528084 - Bioinformatics. 2009 Aug 15;25(16):2035-41 – reference: 17595242 - J Clin Endocrinol Metab. 2007 Sep;92(9):3705-11 – reference: 18416829 - BMC Bioinformatics. 2008;9:200 – reference: 11309499 - Proc Natl Acad Sci U S A. 2001 Apr 24;98(9):5116-21 |
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| SubjectTerms | Acoustic microscopes Algorithms Applications Note Bioinformatics Biological and medical sciences Communities Computation Databases, Factual Fundamental and applied biological sciences. Psychology General aspects Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Oligonucleotide Array Sequence Analysis - methods On-line systems Parallel processing Serials Software |
| Title | ParaSAM: a parallelized version of the significance analysis of microarrays algorithm |
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