In PLOS One, Case Western Reserve University's Jing Li and colleagues from that center's electrical engineering and computer science department describe a method for predicting the presence of structural variation from paired-end, high-throughput sequence data. The approach, dubbed SVMiner, uses a model-based clustering approach to define a range of candidate structural variants from paired-end sequence data given structural variant features.