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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...

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Detalles Bibliográficos
Autores principales: Yoshizoe, Kazuki, Terada, Aika, Tsuda, Koji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
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
Descripción
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.