Copy number analysis of whole-genome data using BIC-seq2 and its application to detection of cancer susceptibility variants.

Copy number analysis of whole-genome data using BIC-seq2 and its application to detection of cancer susceptibility variants.
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使用 BIC-seq2 对全基因组数据进行拷贝数分析及其在癌症易感性变异检测中的应用

DOI:
10.1093/nar/gkw491
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发表时间:
2016-07-27
影响因子:
14.9
通讯作者:
Park PJ
Park PJ
中科院分区:
生物学2区
文献类型:
--
作者:
Xi R;Lee S;Xia Y;Kim TM;Park PJ

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全基因组测序数据能够以高分辨率检测拷贝数变异(CNV)。然而,由于GC含量和其他因素的影响,基于基因组阅读覆盖率的估计受到偏差。在这里,我们开发了一种称为BIC-seq2的算法,该算法结合了核苷酸水平上的数据标准化和基于贝叶斯信息准则的分割,以准确检测体细胞和生殖系CNV。对仿真数据的分析表明,该方法的性能优于现有方法。我们将该算法应用于来自癌症基因组图谱(TCGA)中11种癌症类型的近千名患者外周血的低覆盖率全基因组测序数据,以识别癌症易感CNV区域。我们确认了已知的区域,并发现了新的区域,包括覆盖KMT2C、GOLPH3、ERBB2和PLAG1的区域。对结直肠癌基因组的分析尤其揭示了新的复发CNV,包括两个染色质重塑基因Rere和NPM2的缺失。这种方法将对许多有兴趣从全基因组测序数据中分析CNV的研究人员有用。
Whole-genome sequencing data allow detection of copy number variation (CNV) at high resolution. However, estimation based on read coverage along the genome suffers from bias due to GC content and other factors. Here, we develop an algorithm called BIC-seq2 that combines normalization of the data at the nucleotide level and Bayesian information criterion-based segmentation to detect both somatic and germline CNVs accurately. Analysis of simulation data showed that this method outperforms existing methods. We apply this algorithm to low coverage whole-genome sequencing data from peripheral blood of nearly a thousand patients across eleven cancer types in The Cancer Genome Atlas (TCGA) to identify cancer-predisposing CNV regions. We confirm known regions and discover new ones including those covering KMT2C, GOLPH3, ERBB2 and PLAG1. Analysis of colorectal cancer genomes in particular reveals novel recurrent CNVs including deletions at two chromatin-remodeling genes RERE and NPM2. This method will be useful to many researchers interested in profiling CNVs from whole-genome sequencing data.
DOI: 10.1038/ng2046
发表时间: 2007-06
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