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MP-LAMP: Parallel Detection of Statistically Significant Multi-Loci Markers on Cloud Platforms.


ABSTRACT: Exhaustive detection of multi-loci markers from genome-wide association study datasets is a computationally challenging problem. This paper presents a massively parallel algorithm for finding all significant combinations of alleles and introduces a software tool termed MP-LAMP that can be easily deployed in a cloud platform, such as Amazon Web Service, as well as in an in-house computer cluster. Multi-loci marker detection is an unbalanced tree search problem that cannot be parallelized by simple tree-splitting using generic parallel programming frameworks, such as Map-Reduce. We employ work stealing and periodic reduce-broadcast to decrease the running time almost linearly to the number of cores.MP-LAMP is available at https://github.com/tsudalab/mp-lamp.tsuda@k.u-tokyo.ac.jp.Supplementary data are available at Bioinformatics online.

SUBMITTER: Yoshizoe K 

PROVIDER: S-EPMC6129301 | biostudies-other | 2018 Apr

REPOSITORIES: biostudies-other

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MP-LAMP: parallel detection of statistically significant multi-loci markers on cloud platforms.

Yoshizoe Kazuki K   Terada Aika A   Tsuda Koji K  

Bioinformatics (Oxford, England) 20180901 17


<h4>Summary</h4>Exhaustive detection of multi-loci markers from genome-wide association study datasets is a computationally challenging problem. This paper presents a massively parallel algorithm for finding all significant combinations of alleles and introduces a software tool termed MP-LAMP that can be easily deployed in a cloud platform, such as Amazon Web Service, as well as in an in-house computer cluster. Multi-loci marker detection is an unbalanced tree search problem that cannot be paral  ...[more]

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