Computational methods for detecting copy number variations in cancer genome using next generation sequencing: principles and challenges

Biao Liu, Carl D. Morrison _, Candace S. Johnson, Donald L. Trump, Maochun Qin, Jeffrey C. Conroy, Jianmin Wang and Song Liu

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Oncotarget. 2013; 4:1868-1881. https://doi.org/10.18632/oncotarget.1537

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Biao Liu1,2, Carl D. Morrison1,3, Candace S. Johnson1,4, Donald L. Trump1,5, Maochun Qin1,2, Jeffrey C. Conroy1,6, Jianmin Wang1,2, and Song Liu1,2

1 Center for Personalized Medicine, Roswell Park Cancer Institute, Buffalo, NY

2 Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, NY

3 Department of Pathology, Roswell Park Cancer Institute, Buffalo, NY

4 Department of Pharmacology and Therapeutics, Roswell Park Cancer Institute, Buffalo, NY

5 Department of Medicine, Roswell Park Cancer Institute, Buffalo, NY

6 Department of Cancer Genetics, Roswell Park Cancer Institute, Buffalo, NY


Biao Liu, email:

Jianmin Wang, email:

Song Liu, email:

Keywords: copy number variation, next generation sequencing, cancer genome analysis, somatic mutations

Received: October 24, 2013 Accepted: November 14, 2013 Published: November 16, 2013


Accurate detection of somatic copy number variations (CNVs) is an essential part of cancer genome analysis, and plays an important role in oncotarget identifications. Next generation sequencing (NGS) holds the promise to revolutionize somatic CNV detection. In this review, we provide an overview of current analytic tools used for CNV detection in NGS-based cancer studies. We summarize the NGS data types used for CNV detection, decipher the principles for data preprocessing, segmentation, and interpretation, and discuss the challenges in somatic CNV detection. This review aims to provide a guide to the analytic tools used in NGS-based cancer CNV studies, and to discuss the important factors that researchers need to consider when analyzing NGS data for somatic CNV detections.

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