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Perspective

Computational Strategies for Scalable Genomics Analysis

by 1 and 2,3,4,*
1
Department of Computer Science, Florida State University, Tallahassee, FL 32304, USA
2
US Department of Energy, Joint Genome Institute, Walnut Creek, CA 94598, USA
3
Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
4
School of Natural Sciences, University of California at Merced, Merced, CA 95343, USA
*
Author to whom correspondence should be addressed.
Genes 2019, 10(12), 1017; https://doi.org/10.3390/genes10121017
Received: 10 October 2019 / Revised: 1 December 2019 / Accepted: 3 December 2019 / Published: 6 December 2019
(This article belongs to the Special Issue Impact of Parallel and High-Performance Computing in Genomics)
The revolution in next-generation DNA sequencing technologies is leading to explosive data growth in genomics, posing a significant challenge to the computing infrastructure and software algorithms for genomics analysis. Various big data technologies have been explored to scale up/out current bioinformatics solutions to mine the big genomics data. In this review, we survey some of these exciting developments in the applications of parallel distributed computing and special hardware to genomics. We comment on the pros and cons of each strategy in the context of ease of development, robustness, scalability, and efficiency. Although this review is written for an audience from the genomics and bioinformatics fields, it may also be informative for the audience of computer science with interests in genomics applications. View Full-Text
Keywords: scalable genomics analysis; big data; high performance computing; cloud computing scalable genomics analysis; big data; high performance computing; cloud computing
MDPI and ACS Style

Shi, L.; Wang, Z. Computational Strategies for Scalable Genomics Analysis. Genes 2019, 10, 1017. https://doi.org/10.3390/genes10121017

AMA Style

Shi L, Wang Z. Computational Strategies for Scalable Genomics Analysis. Genes. 2019; 10(12):1017. https://doi.org/10.3390/genes10121017

Chicago/Turabian Style

Shi, Lizhen, and Zhong Wang. 2019. "Computational Strategies for Scalable Genomics Analysis" Genes 10, no. 12: 1017. https://doi.org/10.3390/genes10121017

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