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ParaSAM: a parallelized version of the significance analysis of microarrays algorithm
Ashok Sharma1, Jieping Zhao, Robert Podolsky
1Center for Biotechnology and Genomic Medicine, School of Medicine, Medical College of Georgia, Augusta, GA 30912, USA.
ParaSAM is a new parallelized tool that overcomes memory limitations for Significance Analysis of Microarrays (SAM) on large datasets. This high-performance application enables faster analysis and handles datasets previously too large for existing implementations.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Significance Analysis of Microarrays (SAM) is a common method for identifying differentially expressed genes.
- Existing SAM implementations have memory limitations, restricting analysis of large microarray datasets.
Purpose of the Study:
- To develop a parallelized version of SAM to address memory constraints.
- To create a user-friendly application for analyzing large-scale gene expression data.
Main Methods:
- Developed ParaSAM, a parallelized, multithreaded version of the SAM algorithm.
- Utilized web services for performing permutations to enable parallel processing.
- Created a client-server Windows application with a graphical user interface.
Main Results:
- ParaSAM demonstrates faster performance compared to the serial SAM version.
- ParaSAM successfully analyzes extremely large datasets that are intractable for existing implementations.
- The application is accessible without requiring programming expertise.
Conclusions:
- ParaSAM effectively overcomes memory limitations associated with large microarray datasets.
- This tool enhances the capability for analyzing complex gene expression data.
- ParaSAM is available as a public web version and for local installation.
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