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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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Haplotype block partitioning and selection based on the MapReduce framework.
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ijms-16-01096-f008: Haplotype block partitioning and selection based on the MapReduce framework.

Mentions: Figure 8 illustrates the use of the MapReduce framework in the block partitioning and selection scheme. Assuming N map operations and a pattern length of L, we split the input N × L haplotype matrix into L/N chunks. Each map calculates the diversity scores of each block within its allocated data chunk. Thus, the <key, value> pairs of each map are output as <(block start number, block end number), diversity score> pairs. Further, mapi calculates the diversity scores of the blocks {δ(i∙N/L, i∙N/L), δ(i∙N/L, i∙N/L + 1), …, δ(i∙N/L + N/L, i∙N/L + N/L)}.


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)

Haplotype block partitioning and selection based on the MapReduce framework.
© Copyright Policy
Related In: Results  -  Collection

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

ijms-16-01096-f008: Haplotype block partitioning and selection based on the MapReduce framework.
Mentions: Figure 8 illustrates the use of the MapReduce framework in the block partitioning and selection scheme. Assuming N map operations and a pattern length of L, we split the input N × L haplotype matrix into L/N chunks. Each map calculates the diversity scores of each block within its allocated data chunk. Thus, the <key, value> pairs of each map are output as <(block start number, block end number), diversity score> pairs. Further, mapi calculates the diversity scores of the blocks {δ(i∙N/L, i∙N/L), δ(i∙N/L, i∙N/L + 1), …, δ(i∙N/L + N/L, i∙N/L + N/L)}.

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