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

Andrew T. Magis, John C. Earls, Youn-Hee H. Ko, James A. Eddy, Nathan D. Price
Center for Biophysics and Computational Biology, University of Illinois, Urbana, IL 61801, USA
Bioinformatics (Oxford, England), Vol. 27, No. 6. (15 March 2011), pp. 872-873.

@article{magis2011graphics,

   title={Graphics processing unit implementations of relative expression analysis algorithms enable dramatic computational speedup},

   author={Magis, A.T. and Earls, J.C. and Ko, Y.H. and Eddy, J.A. and Price, N.D.},

   journal={Bioinformatics},

   volume={27},

   number={6},

   pages={872},

   year={2011},

   publisher={Oxford Univ Press}

}

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/.
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