DeviCNV: detection and visualization of exon-level copy number variants in targeted next-generation sequencing data.
DeviCNV: detection and visualization of exon-level copy number variants in targeted next-generation sequencing data.
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DOI：
10.1186/s12859-018-2409-6
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发表时间：
2018-10-16
影响因子：
3
通讯作者：
Park I
中科院分区：
文献类型：
--
作者：
Kang Y;Nam SH;Park KS;Kim Y;Kim JW;Lee E;Ko JM;Lee KA;Park I
靶向下一代测序（NGS）越来越多地被临床实验室用于基因组诊断测试。我们开发了一种新的计算方法DeviCNV，用于检测靶向NGS数据中的外显子水平拷贝数变异（CNV）。DeviCNV为每个探针构建具有自举的线性回归模型，以捕获单个探针的读取深度与样品中所有探针的读取深度值的中值之间的关系。从回归模型中，它估计了每个探针的观察和预测读取深度的读取深度比以及置信区间，其应用于循环二进制分割（CBS）算法以获得CNV候选物。然后，基于从其中探针的读取深度比推断的CNV信号的可靠性和强度，将置信度分数分配给那些候选者。最后，它还提供了以基因为中心的图，具有CNV候选物的置信水平，用于目视检查。我们将DeviCNV应用于新生儿筛查产生的靶向NGS数据，并证明了其从临床样本中检测新型致病性CNV的能力。我们提出了一种新的实用的方法来检测CNVs在有针对性的NGS数据与直观的可视化和系统的方法来分配候选CNVs的置信度得分。由于DeviCNV被开发用于临床诊断，因此通过检测外显子水平的CNV来提高灵敏度。本文的在线版本（10.1186/s12859-018-2409-6）包含补充材料，可供授权用户使用。
Targeted next-generation sequencing (NGS) is increasingly being adopted in clinical laboratories for genomic diagnostic tests. We developed a new computational method, DeviCNV, intended for the detection of exon-level copy number variants (CNVs) in targeted NGS data. DeviCNV builds linear regression models with bootstrapping for every probe to capture the relationship between read depth of an individual probe and the median of read depth values of all probes in the sample. From the regression models, it estimates the read depth ratio of the observed and predicted read depth with confidence interval for each probe which is applied to a circular binary segmentation (CBS) algorithm to obtain CNV candidates. Then, it assigns confidence scores to those candidates based on the reliability and strength of the CNV signals inferred from the read depth ratios of the probes within them. Finally, it also provides gene-centric plots with confidence levels of CNV candidates for visual inspection. We applied DeviCNV to targeted NGS data generated for newborn screening and demonstrated its ability to detect novel pathogenic CNVs from clinical samples. We propose a new pragmatic method for detecting CNVs in targeted NGS data with an intuitive visualization and a systematic method to assign confidence scores for candidate CNVs. Since DeviCNV was developed for use in clinical diagnosis, sensitivity is increased by the detection of exon-level CNVs. The online version of this article (10.1186/s12859-018-2409-6) contains supplementary material, which is available to authorized users.
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