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Parallel computing in genomic research: advances and applications

Authors Ocaña K, de Oliveira D

Received 1 July 2015

Accepted for publication 21 September 2015

Published 13 November 2015 Volume 2015:8 Pages 23—35


Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Professor Diego Bonatto

Peer reviewer comments 3

Editor who approved publication: Dr Juan Fernandez-Recio

Kary Ocaña,1 Daniel de Oliveira2

1National Laboratory of Scientific Computing, Petrópolis, Rio de Janeiro, 2Institute of Computing, Fluminense Federal University, Niterói, Brazil

Abstract: Today's genomic experiments have to process the so-called "biological big data" that is now reaching the size of Terabytes and Petabytes. To process this huge amount of data, scientists may require weeks or months if they use their own workstations. Parallelism techniques and high-performance computing (HPC) environments can be applied for reducing the total processing time and to ease the management, treatment, and analyses of this data. However, running bioinformatics experiments in HPC environments such as clouds, grids, clusters, and graphics processing unit requires the expertise from scientists to integrate computational, biological, and mathematical techniques and technologies. Several solutions have already been proposed to allow scientists for processing their genomic experiments using HPC capabilities and parallelism techniques. This article brings a systematic review of literature that surveys the most recently published research involving genomics and parallel computing. Our objective is to gather the main characteristics, benefits, and challenges that can be considered by scientists when running their genomic experiments to benefit from parallelism techniques and HPC capabilities.

Keywords: high-performance computing, genomic research, cloud computing, grid computing, cluster computing, parallel computing

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