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The detection of genomic structural variations (SV) remains a difficultchallenge in analyzing sequencing data, and the growing size and number ofsequenced genomes have rendered SV detection a bona fide big data problem.MapReduce is a proven, scalable solution for distributed computing on huge datasets. We describe a conceptual framework for SV detection algorithms inMapReduce based on computing local genomic features, and use it to develop adeletion and insertion detection algorithm, Cloudbreak. On simulated and realdata sets, Cloudbreak achieves accuracy improvements over popular SV detectionalgorithms, and genotypes variants from diploid samples. It providesdramatically shorter runtimes and the ability to scale to big data volumes onlarge compute clusters. Cloudbreak includes tools to set up and configureMapReduce (Hadoop) clusters on cloud services, enabling on-demand clustercomputing. Our implementation and source code are available athttp://github.com/cwhelan/cloudbreak .

Cloudbreak: Accurate and Scalable Genomic Structural Variation Detection in the Cloud with MapReduce