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Cloud Computing-Based TagSNP Selection Algorithm for Human Genome Data

Hung, Che-Lun, Chen, Wen-Pei, Hua, Guan-Jie, Zheng, Huiru, Tsai, Suh-Jen and Lin, Yaw-Ling (2015) Cloud Computing-Based TagSNP Selection Algorithm for Human Genome Data. International Journal of Molecular Sciences, 16 (1). pp. 1096-1110. [Journal article]

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URL: http://dx.doi.org/10.3390/ijms16011096

DOI: doi:10.3390/ijms16011096


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.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute > Smart Environments
Computer Science Research Institute
ID Code:30932
Deposited By: Dr Huiru Zheng
Deposited On:26 Jan 2015 11:35
Last Modified:17 Oct 2017 16:17

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