Computational analysis of structural variation in cancer genomes

Abstract

Cancer onset and progression is often triggered by the accumulation of structural abnormalities in the genome. Somatically acquired large structural variants (SV) are one class of abnormalities that can lead to cancer onset by, for example, deactivating tumor suppressor genes and by upregulating oncogenes. Detecting and classifying these variants can lead to improved therapies and diagnostics for cancer patients. This chapter provides an overview of the problem of computational genomic SV detection using next-generation sequencing (NGS) platforms, along with a brief overview of typical approaches for addressing this problem. It also discusses the general protocol that should be followed to analyze a cancer genome for SV detection in NGS data.