Population-based discovery of large-scale structural genomic changes relevant to disease

Here, we present 1-2-3-SV, a clustering algorithm for detection of inter- and intra-chromosomal structural variants across multiple samples based on next-generation sequencing read pair data. The method efficiently detects de novo structural changes in a trio-based setup and we show that the population-based filtering strategy of 1-2-3-SV can be used to eliminate common structural variants or mapping and sequencing artifacts in any read pair data set even in the absence of related control samples