Graphics processing unit implementations of relative expression analysis algorithms enable dramatic computational speedup

Andrew T Magis; John C Earls; Youn-Hee Ko; James A Eddy; Nathan D Price

doi:10.1093/bioinformatics/btr033

Graphics processing unit implementations of relative expression analysis algorithms enable dramatic computational speedup

Bioinformatics. 2011 Mar 15;27(6):872-3. doi: 10.1093/bioinformatics/btr033. Epub 2011 Jan 20.

Authors

Andrew T Magis¹, John C Earls, Youn-Hee Ko, James A Eddy, Nathan D Price

Affiliation

¹ Department of Computer Science, University of Illinois, Urbana, IL 61801, USA.

Abstract

Summary: The top-scoring pair (TSP) and top-scoring triplet (TST) algorithms are powerful methods for classification from expression data, but analysis of all combinations across thousands of human transcriptome samples is computationally intensive, and has not yet been achieved for TST. Implementation of these algorithms for the graphics processing unit results in dramatic speedup of two orders of magnitude, greatly increasing the searchable combinations and accelerating the pace of discovery.

Availability: http://www.igb.illinois.edu/labs/price/downloads/.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Computational Biology / methods*
Computer Graphics*
Gene Expression Profiling / methods*
Humans

Grants and funding

R00 CA126184/CA/NCI NIH HHS/United States