Next Article in Journal
Agomelatine beyond Borders: Current Evidences of Its Efficacy in Disorders Other than Major Depression
Next Article in Special Issue
Factor XIII B Subunit Polymorphisms and the Risk of Coronary Artery Disease
Previous Article in Journal
Ca2+-Dependent Regulations and Signaling in Skeletal Muscle: From Electro-Mechanical Coupling to Adaptation
Previous Article in Special Issue
Association between ADIPOQ +45T>G Polymorphism and Type 2 Diabetes: A Systematic Review and Meta-Analysis
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Int. J. Mol. Sci. 2015, 16(1), 1096-1110;

Cloud Computing-Based TagSNP Selection Algorithm for Human Genome Data

Department of Computer Science and Communication Engineering, Providence University, Taichung 43301, Taiwan
Department of Applied Chemistry, Providence University, Taiwan 43301, Taiwan
Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan
School of Computing and Mathematics, University of Ulster, Newtownabbey BT37 0QB, UK
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)
Full-Text   |   PDF [2716 KB, uploaded 5 January 2015]   |  


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

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Int. J. Mol. Sci. EISSN 1422-0067 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top