alexa Structural Variation Detection from Next Generation Sequencing
ISSN 2469-9853

Journal of Next Generation Sequencing & Applications
Open Access

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Review Article

Structural Variation Detection from Next Generation Sequencing

Kai Ye1*, George Hall2,3 and Zemin Ning2*
1McDonnell Genome Institute, Washington University School of Medicine, Forest Park Avenue, Saint Louis, Missouri, USA
2The Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
3Department of Computer Science, University of York, Heslington, York, YO10 5GH, UK
*Corresponding Authors : Kai Ye
McDonnell Genome Institute
Washington University School of Medicine
4444 Forest Park Avenue, Saint Louis
Missouri 63108, USA
Tel: 1-314-813-0879
  Zemin Ning, The Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK, Tel: 44-1223494705; E-mail:
Received: November 26, 2015 Accepted: February 10, 2016 Published: February 15, 2016
Citation:Ye K, Hall G, Ning Z (2016) Structural Variation Detection from Next Generation Sequencing . Next Generat Sequenc & Applic S1:007. doi:10.4172/2469-9853.S1-007
Copyright: © 2016 Ye K, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


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.


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