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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Detection Copy Number Variants from NGS with Sparse and Smooth Constraints
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Detection Copy Number Variants from NGS with Sparse and Smooth Constraints

机译：具有稀疏和平滑约束的NGS中的检测拷贝数变体

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摘要

It is known that copy number variations (CNVs) are associated with complex diseases and particular tumor types, thus reliable identification of CNVs is of great potential value. Recent advances in next generation sequencing (NGS) data analysis have helped manifest the richness of CNV information. However, the performances of these methods are not consistent. Reliably finding CNVs in NGS data in an efficient way remains a challenging topic, worthy of further investigation. Accordingly, we tackle the problem by formulating CNVs identification into a quadratic optimization problem involving two constraints. By imposing the constraints of sparsity and smoothness, the reconstructed read depth signal from NGS is anticipated to fit the CNVs patterns more accurately. An efficient numerical solution tailored from alternating direction minimization (ADM) framework is elaborated. We demonstrate the advantages of the proposed method, namely ADM-CNV, by comparing it with six popular CNV detection methods using synthetic, simulated, and empirical sequencing data. It is shown that the proposed approach can successfully reconstruct CNV patterns from raw data, and achieve superior or comparable performance in detection of the CNVs compared to the existing counterparts.
机译：已知拷贝数变异（CNV）与复杂的疾病和特定的肿瘤类型相关，因此可靠鉴定CNV具有巨大的潜在价值。下一代测序（NGS）数据分析的最新进展有助于证明CNV信息的丰富性。但是，这些方法的性能不一致。有效地在NGS数据中可靠地找到CNV仍然是一个充满挑战的话题，值得进一步研究。因此，我们通过将CNV识别公式化为涉及两个约束的二次优化问题来解决该问题。通过施加稀疏性和平滑性的约束，可以预期从NGS重建的读取深度信号将更准确地拟合CNV模式。阐述了一种根据交替方向最小化（ADM）框架量身定制的有效数值解决方案。通过与使用合成，模拟和经验测序数据的六种流行CNV检测方法进行比较，我们证明了所提出方法的优势，即ADM-CNV。结果表明，所提出的方法可以成功地从原始数据重构CNV模式，并且与现有的CNV模式相比，在检测CNV方面具有优异或相当的性能。

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