ArticleBioinformatics (Oxford, England)2018
MP-LAMP: parallel detection of statistically significant multi-loci markers on cloud platforms.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Refining the genetic risk of breast cancer with rare haplotypes and pattern mining.Life science alliance · 2023Article
- Machine learning to reveal hidden risk combinations for the trajectory of posttraumatic stress disorder symptoms.Scientific reports · 2020Article
- A Fast and Scalable Workflow for SNPs Detection in Genome Sequences Using Hadoop Map-Reduce.Genes · 2020Article
- CASMAP: detection of statistically significant combinations of SNPs in association mapping.Bioinformatics (Oxford, England) · 2019Article
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Authors and funding
3 authors.
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Abstract
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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