With continuous advances in high-throughput sequencing technologies, the amount of omic data is increasing exponentially, offering us a great opportunity to boost our understandings of complex biological systems.

Structural variations (SVs) are large-scale alterations that change the DNA structure. They include deletions, duplications, insertions, and other forms that are accompanied by copy number changes as well as inversions, translocations, and other copy-neutral forms. They are an important type of variations, affecting an order of magnitude more base pairs than single nucleotide variations (SNVs) in normal human population. In cancer, several chromosomal translocations have been identified and subsequently became targets of successful treatments. However, the causes and functional impacts of somatic SVs and their roles in treatment response are largely unexplored. We develop new computational methods and design experimental approaches to study the mutational mechanisms leading to these alterations, identify disease-driving events, and design better treatment strategies.