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