Structural variation discovery in the cancer genome using next generation sequencing: Computational solutions and perspectives

// Biao Liu 1 , Jeffrey M. Conroy 1 , Carl D. Morrison 1 , Adekunle O. Odunsi 2 , Maochun Qin 3 , Lei Wei 3 , Donald L. Trump 4 , Candace S. Johnson 5 , Song Liu 3 and Jianmin Wang 3 1 Center for Personalized Medicine, Roswell Park Cancer Institute, Buffalo, NY, USA 2 Department of Gynecologic Oncology, Roswell Park Cancer Institute, Buffalo, NY, USA 3 Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, USA 4 Department of Medicine, Roswell Park Cancer Institute, Buffalo, NY, USA 5 Department of Pharmacology and Therapeutics, Roswell Park Cancer Institute, Buffalo, NY, USA Correspondence to: Biao Liu, email: // Song Liu, email: // Jianmin Wang, email: // Keywords : structural variation, next generation sequencing, cancer genome analysis, somatic mutation Received : December 04, 2014 Accepted : February 04, 2015 Published : March 08, 2015 Abstract Somatic Structural Variations (SVs) are a complex collection of chromosomal mutations that could directly contribute to carcinogenesis. Next Generation Sequencing (NGS) technology has emerged as the primary means of interrogating the SVs of the cancer genome in recent investigations. Sophisticated computational methods are required to accurately identify the SV events and delineate their breakpoints from the massive amounts of reads generated by a NGS experiment. In this review, we provide an overview of current analytic tools used for SV detection in NGS-based cancer studies. We summarize the features of common SV groups and the primary types of NGS signatures that can be used in SV detection methods. We discuss the principles and key similarities and differences of existing computational programs and comment on unresolved issues related to this research field. The aim of this article is to provide a practical guide of relevant concepts, computational methods, software tools and important factors for analyzing and interpreting NGS data for the detection of SVs in the cancer genome.