SeqBBS:一种基于变化点模型的基于序列读取比的CNV区域搜索算法和R包

Hua Li, J. Vallandingham, Jing Chen
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引用次数: 4

摘要

继微阵列技术的突破之后,下一代测序(NGS)技术进一步推进了现代生物医学研究的方法。高通量NGS技术现在经常用于分析肿瘤和对照样本,以研究DNA拷贝数变异(CNVs)。特别是,肿瘤样本的读取计数与对照样本的读取计数之比通常用于识别CNV区域。我们说明了变化点(或断点)检测方法,以及贝叶斯方法,特别适合识别读比数据中的cnv。我们将算法写入用户友好的r包SeqBBS(代表测序数据的贝叶斯断点搜索),并将我们的方法应用于乳腺肿瘤细胞系HCC1954与其匹配的正常细胞系BL1954之间的reads比率的测序数据。分离不同CNV区域的断点被成功识别。
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SeqBBS: A change-point model based algorithm and R package for searching CNV regions via the ratio of sequencing reads
Following the breakthrough of the microarray technology, the next generation sequencing (NGS) technology further advanced approaches in modern biomedical research. The high-throughput NGS technology is now frequently used in profiling tumor and control samples for the study of DNA copy number variants (CNVs). In particular, the ratio of read count of the tumor sample to that of the control sample is popularly used for identifying CNV regions. We illustrate that a change-point (or a breakpoint) detection method, along with a Bayesian approach, is particularly suitable for identifying CNVs in the reads ratio data. We have written our algorithm into a user friendly R-package, SeqBBS (stands for Bayesian breakpoints search for sequencing data) and applied our method to the sequencing data of reads ratio between the breast tumor cell lines HCC1954 and its matched normal cell line BL1954. Breakpoints that separate different CNV regions are successfully identified.
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