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Cloud computing-based TagSNP selection algorithm for human genome data.

Hung CL, Chen WP, Hua GJ, Zheng H, Tsai SJ, Lin YL - Int J Mol Sci (2015)

Bottom Line: They provide the highest-resolution genetic fingerprint for identifying disease associations and human features.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Communication Engineering, Providence University, Taichung 43301, Taiwan. clhung@pu.edu.tw.

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.

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Speed-up comparison between sequential and MapReduce haplotype block selection: (a) block size of 300 bp and (b) block size of 500 bp.
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ijms-16-01096-f006: Speed-up comparison between sequential and MapReduce haplotype block selection: (a) block size of 300 bp and (b) block size of 500 bp.

Mentions: Deploying more map operations effectively reduces the computational time. Deployment of 8 and 16 map operations improves the computation time by more than sixfold and tenfold, respectively, with respect to implementation on a single CPU. When the number of map operations is increased to 24, moderate enhancements are observed for smaller sequence lengths (10,000–40,000 bp), since 16 and 24 operations split the dataset into similar sizes. As evident in Figure 4, Figure 5 and Figure 6, the computation efficiency of our algorithm is proportional to the number of processors employed.


Cloud computing-based TagSNP selection algorithm for human genome data.

Hung CL, Chen WP, Hua GJ, Zheng H, Tsai SJ, Lin YL - Int J Mol Sci (2015)

Speed-up comparison between sequential and MapReduce haplotype block selection: (a) block size of 300 bp and (b) block size of 500 bp.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4307292&req=5

ijms-16-01096-f006: Speed-up comparison between sequential and MapReduce haplotype block selection: (a) block size of 300 bp and (b) block size of 500 bp.
Mentions: Deploying more map operations effectively reduces the computational time. Deployment of 8 and 16 map operations improves the computation time by more than sixfold and tenfold, respectively, with respect to implementation on a single CPU. When the number of map operations is increased to 24, moderate enhancements are observed for smaller sequence lengths (10,000–40,000 bp), since 16 and 24 operations split the dataset into similar sizes. As evident in Figure 4, Figure 5 and Figure 6, the computation efficiency of our algorithm is proportional to the number of processors employed.

Bottom Line: They provide the highest-resolution genetic fingerprint for identifying disease associations and human features.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Communication Engineering, Providence University, Taichung 43301, Taiwan. clhung@pu.edu.tw.

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.

Show MeSH