Analysis of Genetic Expression with Microarrays using GPU Implemented Algorithms
Centro de Investigaciones Biologicas del Noroeste S.C., Instituto Politecnico Nacional, La Paz, B.C.S., Mexico
Computacion y Sistemas, Vol. 17, No.3, pp. 357-364, 2013
@article{romero2013analysis,
title={Analysis of Genetic Expression with Microarrays using GPU Implemented Algorithms},
author={Romero-Vivas, Eduardo and Von Borstel, Fernando D and Villa-Medina, Isaac},
journal={Computaci{‘o}n y Sistemas},
volume={17},
number={3},
pages={357–364},
year={2013},
publisher={Instituto Polit{‘e}cnico Nacional}
}
DNA microarrays are used to simultaneously analyze the expression level of thousands of genes under multiple conditions; however, massive amount of data is generated making its analysis a challenge and an ideal candidate for massive parallel processing. Among the available technologies, the use of General Purpose computation on Graphics Processing Units (GPGPU) is an efficient cost-effective alternative, compared to a Central Processing Unit (CPU). This paper presents an implementation of algorithms using Compute Unified Device Architecture (CUDA) to determine statistical significance in the evaluation of gene expression levels for a microarray hybridization experiment designed and carried out at the Centro de Investigaciones Biologicas del Noroeste S.C. (CIBNOR). The obtained results are compared to traditional implementations.
October 24, 2013 by hgpu
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