Copy-number variants (CNVs) play a substantial role in the molecular pathogenesis of hereditary disease and cancer, as well as in normal human interindividual variation. However, they are still rather difficult to identify in mainstream sequencing projects, especially involving exome sequencing, because they often occur in DNA regions that are not targeted for analysis. To overcome this problem, we developed OFF-PEAK, a user-friendly CNV detection tool that builds on a denoising approach and the use of “off-target” DNA reads, which are usually discarded by sequencing pipelines. We benchmarked OFF-PEAK on data from targeted sequencing of 96 cancer samples, as well as 130 exomes of individuals with inherited retinal disease from three different populations. For both sets of data, OFF-PEAK demonstrated excellent performance (>95% sensitivity and >80% specificity vs. experimental validation) in detecting CNVs from data alone, indicating its immediate applicability to molecular diagnosis and genetic research.

中文翻译：

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通过使用非编码和脱靶测序读数来检测遗传性疾病和癌症中难以捉摸的 DNA 拷贝数变异


拷贝数变异 (CNV) 在遗传性疾病和癌症的分子发病机制以及正常人类个体间变异中发挥着重要作用。然而，在主流测序项目中，尤其是外显子组测序，它们仍然很难识别，因为它们经常出现在非分析目标的DNA区域。为了克服这个问题，我们开发了 OFF-PEAK，这是一种用户友好的 CNV 检测工具，它建立在去噪方法和使用“脱靶”DNA 读数的基础上，这些读数通常会被测序流程丢弃。我们对 OFF-PEAK 进行了基准测试，该数据来自 96 个癌症样本的靶向测序数据，以及来自三个不同人群的遗传性视网膜疾病个体的 130 个外显子组。对于这两组数据，OFF-PEAK 在仅从数据检测 CNV 方面表现出了优异的性能（与实验验证相比，>95% 的灵敏度和 >80% 的特异性），表明其可直接应用于分子诊断和遗传研究。