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Int. J. Mol. Sci. 2015, 16(1), 1096-1110; doi:10.3390/ijms16011096

Cloud Computing-Based TagSNP Selection Algorithm for Human Genome Data

1
Department of Computer Science and Communication Engineering, Providence University, Taichung 43301, Taiwan
2
Department of Applied Chemistry, Providence University, Taiwan 43301, Taiwan
3
Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan
4
School of Computing and Mathematics, University of Ulster, Newtownabbey BT37 0QB, UK
5
Department of Computer Science and Information Engineering, Providence University, Taichung 43301, Taiwan
*
Authors to whom correspondence should be addressed.
Academic Editor: Emil Alexov
Received: 16 September 2014 / Accepted: 4 December 2014 / Published: 5 January 2015
(This article belongs to the Collection Human Single Nucleotide Polymorphisms and Disease Diagnostics)
View Full-Text   |   Download PDF [2716 KB, uploaded 5 January 2015]   |  

Abstract

Single nucleotide polymorphisms (SNPs) play a fundamental role in human genetic variation and are used in medical diagnostics, phylogeny construction, and drug design. They provide the highest-resolution genetic fingerprint for identifying disease associations and human features. Haplotypes are regions of linked genetic variants that are closely spaced on the genome and tend to be inherited together. Genetics research has revealed SNPs within certain haplotype blocks that introduce few distinct common haplotypes into most of the population. Haplotype block structures are used in association-based methods to map disease genes. In this paper, we propose an efficient algorithm for identifying haplotype blocks in the genome. In chromosomal haplotype data retrieved from the HapMap project website, the proposed algorithm identified longer haplotype blocks than an existing algorithm. To enhance its performance, we extended the proposed algorithm into a parallel algorithm that copies data in parallel via the Hadoop MapReduce framework. The proposed MapReduce-paralleled combinatorial algorithm performed well on real-world data obtained from the HapMap dataset; the improvement in computational efficiency was proportional to the number of processors used. View Full-Text
Keywords: SNPs; haplotype; cloud computing; parallel processing; MapReduce SNPs; haplotype; cloud computing; parallel processing; MapReduce
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Hung, C.-L.; Chen, W.-P.; Hua, G.-J.; Zheng, H.; Tsai, S.-J.J.; Lin, Y.-L. Cloud Computing-Based TagSNP Selection Algorithm for Human Genome Data. Int. J. Mol. Sci. 2015, 16, 1096-1110.

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