Detecting and analyzing genomic structural variation using distributed computing

Genomic structural variations are an important class of genetic variants with a wide va- riety of functional impacts. The detection of structural variations using high-throughput short-read sequencing data is a difficult problem, and published algorithms do not pro- vide the sensitivity and specificity required in research and clinical settings. Meanwhile, high-throughput sequencing is rapidly generating ever-larger data sets, necessitating the development of algorithms that can provide results rapidly and scale to use cloud and cluster infrastructures. MapReduce and Hadoop are becoming a standard for managing the distributed processing of large data sets, but existing structural variation detection approaches are difficult to translate into the MapReduce framework. We have formulated a general framework for structural variation detection in MapReduce, and implemented a software package called Cloudbreak, which detects genomic deletions and insertions with very high accuracy compared to existing popular tools.