Computational methods for detecting copy number variations in cancer genome using next generation sequencing: principles and challenges.
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.
Center for Personalized Medicine, Roswell Park Cancer Institute, Buffalo, NY.
Biao
B
Liu
Carl D
CD
Morrison
Candace S
CS
Johnson
Donald L
DL
Trump
Maochun
M
Qin
Jeffrey C
JC
Conroy
Jianmin
J
Wang
Song
S
Liu
D056915:DNA Copy Number Variations; D015894:Genome, Human; D023281:Genomics / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000235:genetics*; D017422:Sequence Analysis, DNA / Q000379:methods*