Analysis of Genetic Expression with Microarrays using GPU Implemented Algorithms

Authors

  • Eduardo Romero Vivas Centro de Investigaciones Biológicas del Noroeste, S.C.
  • Fernando Daniel Von Borstel Luna Centro de Investigaciones Biológicas del Noroeste, S.C.
  • Isaac Villa Medina Centro de Investigaciones Biológicas del Noroeste, S.C., instituto Tecnológico de La Paz

DOI:

https://doi.org/10.13053/cys-17-3-1539

Keywords:

GPU, Microarray, CUDA

Abstract

DNA microarrays are used to analyze simultaneously 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 the 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 Biológicas del Noroeste S.C (CIBNOR). The results with respect to traditional implementations are compared. Los microarreglos de ADN permiten analizar simultáneamente el nivel de expresión de miles de genes ante condiciones múltiples; sin embargo, la gran cantidad dedatos generados representa un reto para su análisis y los hace un candidato ideal para el procesamiento masivo paralelo. Dentro de las tecnologías disponibles, el uso de cómputo en tarjetas gráficas de propósito general(GPGPU), es una alternativa eficiente, en términos de costo-efectividad, comparada con respecto a las unidades de procesamiento central (CPU). Este artículo presenta la implementación de algoritmos utilizando la arquitectura de cómputo unificada (CUDA), para determinar la significancia estadística en la evaluación de niveles de expresión génica para un experimento de hibridación de microarreglos, diseñado y llevado a cabo en el Centro de Investigaciones Biológicas del Noroeste, S.C. (CIBNOR). Los resultados obtenidos se comparan con respecto a las implementaciones tradicionales. Palabras clave. GPU,Microarreglos, CUDA.

Author Biographies

Eduardo Romero Vivas, Centro de Investigaciones Biológicas del Noroeste, S.C.

Eduardo Romero Vivas received his PhD from the University of Southampton, UK. Researcher at Centro de Investigaciones Biologicas del Noroeste (CIBNOR), Mexico in the Bioinformatics Group, he is a member of the Signal Processing, and Engineering in Medicine and Biology IEEE Societies.

Fernando Daniel Von Borstel Luna, Centro de Investigaciones Biológicas del Noroeste, S.C.

Fernando D. Von Borstel received his PhD in Artificial Intelligence from the Instituto Tecnológico y de Estudios Superiores de Monterrey, México. He works as a researcher at Centro de Investigaciones Biológicas del Noroeste, México (CIBNOR), and he is member of the Bioinformatics Group.

Isaac Villa Medina, Centro de Investigaciones Biológicas del Noroeste, S.C., instituto Tecnológico de La Paz

Isaac Villa Medina received his MCS from Instituto Tecnológico de La Paz, Mexico (ITLP). Currently he holds an academic position at ITLP.

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Published

2013-09-30

Issue

Vol. 17 No. 3 (2013): 17(3) July-September 2013

Section

Articles

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