Ye K, Hall G, Ning Z
Structural variations (SVs) are the genetic variations in the structure of chromosome with different types of rearrangements. They comprise millions of nucleotides of heterogeneity within every genome, and are likely to make an important contribution to genetic diversity and disease susceptibility. In the genomics community, substantial efforts have been devoted to improving understanding of the roles of SVs in genome functions relating to diseases and researchers are working actively to develop effective algorithms to reliably identify various types of SVs such as deletions, insertions, duplications and inversions. Structural variant detection using Next-generation sequencing (NGS) data is difficult, and identification of large and complex structural variations is extremely challenging. In this short review, we mainly discuss various algorithms and computational tools for identifying SVs of different types and sizes with a brief introduction to complex SVs. At the end, we highlight the impact and potential applications of the 3rd generation sequencing data, generated from PacBio and Oxford Nanopore long read sequencing platforms.
