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MP-LAMP: parallel detection of statistically significant multi-loci markers on cloud platforms
Kazuki Yoshizoe1,2, Aika Terada2,3, Koji Tsuda1,2
1Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan.
Bioinformatics (Oxford, England)
|April 17, 2018
Summary
This study introduces MP-LAMP, a parallel algorithm and software tool for efficiently detecting multi-loci markers in genome-wide association studies. It overcomes computational challenges for faster genetic analysis.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- Detecting multi-loci markers in genome-wide association studies (GWAS) is computationally intensive.
- Existing parallel frameworks like Map-Reduce are insufficient for this unbalanced tree search problem.
Purpose of the Study:
- To present a massively parallel algorithm for exhaustive detection of significant multi-loci marker combinations.
- To introduce MP-LAMP, a software tool for efficient multi-locus analysis.
Main Methods:
- Developed a novel massively parallel algorithm specifically designed for unbalanced tree search.
- Implemented work stealing and periodic reduce-broadcast techniques for efficient parallelization.
- Designed MP-LAMP for easy deployment on cloud platforms (AWS) and in-house clusters.
Main Results:
- The MP-LAMP algorithm significantly reduces computation time for multi-loci marker detection.
- Achieved near-linear speedup with respect to the number of processing cores.
- Demonstrated efficient parallelization overcoming limitations of standard frameworks.
Conclusions:
- MP-LAMP provides an effective solution for the computationally demanding task of multi-loci marker detection in GWAS.
- The tool enhances the feasibility of exhaustive analysis of genetic datasets.
- Facilitates large-scale genetic association studies through efficient computation.
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