PDF(Pubmed)
Abstract：
Copy number variation (CNV) is a key genetic characteristic for cancer diagnostics and can be used as a biomarker for the selection of therapeutic treatments. Using data sets established in our previous study, we benchmark the performance of cancer CNV calling by six most recent and commonly used software tools on their detection accuracy, sensitivity, and reproducibility. In comparison to other orthogonal methods, such as microarray and Bionano, we also explore the consistency of CNV calling across different technologies on a challenging genome.
While consistent results are observed for copy gain, loss, and loss of heterozygosity (LOH) calls across sequencing centers, CNV callers, and different technologies, variation of CNV calls are mostly affected by the determination of genome ploidy. Using consensus results from six CNV callers and confirmation from three orthogonal methods, we establish a high confident CNV call set for the reference cancer cell line (HCC1395).
NGS technologies and current bioinformatics tools can offer reliable results for detection of copy gain, loss, and LOH. However, when working with a hyper-diploid genome, some software tools can call excessive copy gain or loss due to inaccurate assessment of genome ploidy. With performance matrices on various experimental conditions, this study raises awareness within the cancer research community for the selection of sequencing platforms, sample preparation, sequencing coverage, and the choice of CNV detection tools.
While consistent results are observed for copy gain, loss, and loss of heterozygosity (LOH) calls across sequencing centers, CNV callers, and different technologies, variation of CNV calls are mostly affected by the determination of genome ploidy. Using consensus results from six CNV callers and confirmation from three orthogonal methods, we establish a high confident CNV call set for the reference cancer cell line (HCC1395).
NGS technologies and current bioinformatics tools can offer reliable results for detection of copy gain, loss, and LOH. However, when working with a hyper-diploid genome, some software tools can call excessive copy gain or loss due to inaccurate assessment of genome ploidy. With performance matrices on various experimental conditions, this study raises awareness within the cancer research community for the selection of sequencing platforms, sample preparation, sequencing coverage, and the choice of CNV detection tools.
摘要：
背景：拷贝数变异（CNV）是癌症诊断的关键遗传特征，可以用作选择治疗性治疗的生物标志物。使用我们先前研究中建立的数据集，我们通过六个最新和常用的软件工具对癌症CNV的检测准确性进行基准测试，灵敏度,和再现性。与其他正交方法相比，如微阵列和Bionano，我们还探索了不同技术对具有挑战性的基因组的CNV调用的一致性。
结果：虽然在复制增益方面观察到一致的结果，损失,以及跨测序中心的杂合性(LOH)调用丢失，CNV来电者,和不同的技术，CNV变异主要受基因组倍性测定的影响。使用来自六个CNV呼叫者的共识结果和来自三种正交方法的确认，我们为参考癌细胞系（HCC1395）建立了一个高置信度的CNV调用集。
结论：NGS技术和当前的生物信息学工具可以为检测拷贝增益提供可靠的结果，损失,还有LOH.然而,当使用超二倍体基因组时，由于基因组倍性评估的不准确，一些软件工具可以调用过度的拷贝增益或损失。在各种实验条件下的性能矩阵，这项研究提高了癌症研究界对测序平台选择的认识，样品制备,测序覆盖率，CNV检测工具的选择。
结果：虽然在复制增益方面观察到一致的结果，损失,以及跨测序中心的杂合性(LOH)调用丢失，CNV来电者,和不同的技术，CNV变异主要受基因组倍性测定的影响。使用来自六个CNV呼叫者的共识结果和来自三种正交方法的确认，我们为参考癌细胞系（HCC1395）建立了一个高置信度的CNV调用集。
结论：NGS技术和当前的生物信息学工具可以为检测拷贝增益提供可靠的结果，损失,还有LOH.然而,当使用超二倍体基因组时，由于基因组倍性评估的不准确，一些软件工具可以调用过度的拷贝增益或损失。在各种实验条件下的性能矩阵，这项研究提高了癌症研究界对测序平台选择的认识，样品制备,测序覆盖率，CNV检测工具的选择。
