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MP-LAMP: parallel detection of statistically significant multi-loci markers on cloud platforms
SUMMARY: 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 e...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6129301/ https://www.ncbi.nlm.nih.gov/pubmed/29659720 http://dx.doi.org/10.1093/bioinformatics/bty219 |
Sumario: | SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: MP-LAMP is available at https://github.com/tsudalab/mp-lamp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
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