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

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

doi:10.1093/bioinformatics/btq161

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 journal › Article › peer-review

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 language	English (US)
Article number	btq161
Pages (from-to)	1465-1467
Number of pages	3
Journal	Bioinformatics
Volume	26
Issue number	11
DOIs	https://doi.org/10.1093/bioinformatics/btq161
State	Published - Apr 15 2010

ASJC Scopus subject areas

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

Access to Document

10.1093/bioinformatics/btq161

Cite this

@article{9ac46c1422a24357836e0e6c594c8ff6,

title = "Para SAM: A parallelized version of the significance analysis of microarrays algorithm",

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.",

author = "Sharma, {Ashok Kumar} and Jieping Zhao and Robert Podolsky and McIndoe, {Richard A}",

note = "Funding Information: Funding: National Institute of Diabetes Digestive and Kidney Diseases (Grant U24DK076169) to R.A.M.",

year = "2010",

month = apr,

day = "15",

doi = "10.1093/bioinformatics/btq161",

language = "English (US)",

volume = "26",

pages = "1465--1467",

journal = "Bioinformatics",

issn = "1367-4803",

publisher = "Oxford University Press",

number = "11",

}

TY - JOUR

T1 - Para SAM

T2 - A parallelized version of the significance analysis of microarrays algorithm

AU - Sharma, Ashok Kumar

AU - Zhao, Jieping

AU - Podolsky, Robert

AU - McIndoe, Richard A

N1 - Funding Information: Funding: National Institute of Diabetes Digestive and Kidney Diseases (Grant U24DK076169) to R.A.M.

PY - 2010/4/15

Y1 - 2010/4/15

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=77953906873&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77953906873&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/btq161

DO - 10.1093/bioinformatics/btq161

M3 - Article

C2 - 20400455

AN - SCOPUS:77953906873

SN - 1367-4803

VL - 26

SP - 1465

EP - 1467

JO - Bioinformatics

JF - Bioinformatics

IS - 11

M1 - btq161

ER -

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

Abstract

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this