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

Biao Liu,Lei Wei,Candace S Johnson,Jianmin Wang,Donald L Trump,Carl D Morrison,Song Liu,Maochun Qin,Adekunle O Odunsi,Jeffrey M Conroy

doi:10.18632/oncotarget.3491

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.

Highlights

The emerging Generation Sequencing (NGS) technology provides unprecedented opportunities to systematically screen Structural Variations (SVs) in the cancer genomes [13]
We describe the primary types of Next Generation Sequencing (NGS) signatures that can be used in SV detections, followed by categorizing the existing computational programs into different groups based on the NGS signatures they require
The revolutionary advances of NGS technologies and their growing adoption in cancer research have made it possible to screen for somatic variations in cancer genomes on an unprecedented scale

Summary

Introduction

The emerging Generation Sequencing (NGS) technology provides unprecedented opportunities to systematically screen SVs in the cancer genomes [13]. The NGS technology was only emerging during the past several years, a number of SV detection programs for NGS data have been developed [4, 16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46], with several capable of detecting somatic SVs in cancer genome studies These programs focus on different subsets of SV types, and use various strategies to detect sequencing signatures or diagnostic patterns indicative of different SV types. An insertion is an event that occurs when the sequence of one or more nucleotides is added between two adjacent nucleotides in the genome

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Conclusion