Comprehensive performance comparison of high-resolution array platforms for genome-wide Copy Number Variation (CNV) analysis in humans.
Comprehensive performance comparison of high-resolution array platforms for genome-wide Copy Number Variation (CNV) analysis in humans.
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DOI：
10.1186/s12864-017-3658-x
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发表时间：
2017-04-24
期刊：
影响因子：
4.4
通讯作者：
Urban AE
中科院分区：
文献类型：
--
作者：
Haraksingh RR;Abyzov A;Urban AE
高分辨率微阵列技术通常用于基础研究和临床实践，以有效地检测整个人类基因组的拷贝数变异（CNVs）。结合高探针密度和优化设计的新一代阵列将在未来几年内成为基因组分析的重要工具。我们通过将1000个基因组计划受试者NA 12878的充分表征的基因组与所有阵列杂交，并使用生物学家推荐的和平台无关的软件进行数据分析，系统地比较了来自Affyphase、Agilent和Illumina平台的所有17种可用阵列设计的全基因组CNV检测能力。我们使用来自1000个基因组计划全基因组测序数据的基因组的CNV金标准集对来自每个阵列的所得CNV调用集进行基准测试。测试的阵列包括具有不同设计的SNP和aCGH平台，并且包含约0.5至约4.6百万个探针。在整个阵列中，CNV检测在CNV调用的数量（4-489）、CNV大小范围（~40 bp至~8 Mbp）和未经验证的CNV的百分比（0-86%）方面变化很大。我们发现了特定阵列设计原则对性能的显著影响。例如，与探针数目有时小一个数量级的设计相比，具有最大数目的探针和广泛的外显子覆盖的一些SNP阵列设计产生了相当数量的不能被验证的CNV调用。使用不同的分析软件和优化数据分析参数仅部分改善了这种效果。高分辨率微阵列将继续作为可靠的，成本和时间效率高的工具用于CNV分析。然而，不同的应用在CNV检测中容忍不同的限制。我们的研究量化了这些阵列在检测到的CNV的总数和大小范围以及灵敏度方面的差异，并确定了每个阵列如何平衡这些属性。该分析将为未来的CNV研究提供适当的阵列选择，并允许更好地评估已发表和正在进行的基于阵列的基因组学研究的CNV分析能力。此外，我们的研究结果强调了在基于阵列的CNV检测研究中同时使用多种分析算法和独立实验验证的重要性。本文的在线版本（doi：10.1186/s12864-017-3658-x）包含补充材料，可供授权用户使用。
High-resolution microarray technology is routinely used in basic research and clinical practice to efficiently detect copy number variants (CNVs) across the entire human genome. A new generation of arrays combining high probe densities with optimized designs will comprise essential tools for genome analysis in the coming years. We systematically compared the genome-wide CNV detection power of all 17 available array designs from the Affymetrix, Agilent, and Illumina platforms by hybridizing the well-characterized genome of 1000 Genomes Project subject NA12878 to all arrays, and performing data analysis using both manufacturer-recommended and platform-independent software. We benchmarked the resulting CNV call sets from each array using a gold standard set of CNVs for this genome derived from 1000 Genomes Project whole genome sequencing data. The arrays tested comprise both SNP and aCGH platforms with varying designs and contain between ~0.5 to ~4.6 million probes. Across the arrays CNV detection varied widely in number of CNV calls (4–489), CNV size range (~40 bp to ~8 Mbp), and percentage of non-validated CNVs (0–86%). We discovered strikingly strong effects of specific array design principles on performance. For example, some SNP array designs with the largest numbers of probes and extensive exonic coverage produced a considerable number of CNV calls that could not be validated, compared to designs with probe numbers that are sometimes an order of magnitude smaller. This effect was only partially ameliorated using different analysis software and optimizing data analysis parameters. High-resolution microarrays will continue to be used as reliable, cost- and time-efficient tools for CNV analysis. However, different applications tolerate different limitations in CNV detection. Our study quantified how these arrays differ in total number and size range of detected CNVs as well as sensitivity, and determined how each array balances these attributes. This analysis will inform appropriate array selection for future CNV studies, and allow better assessment of the CNV-analytical power of both published and ongoing array-based genomics studies. Furthermore, our findings emphasize the importance of concurrent use of multiple analysis algorithms and independent experimental validation in array-based CNV detection studies. The online version of this article (doi:10.1186/s12864-017-3658-x) contains supplementary material, which is available to authorized users.