Genomic Medicine Institute, Medical Research Center, Seoul National University, Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul 110-799, Korea.
Nucleic Acids Res. 2010 Nov;38(20):e190. doi: 10.1093/nar/gkq730. Epub 2010 Aug 27.
Comparative genomic hybridization (CGH) microarrays have been used to determine copy number variations (CNVs) and their effects on complex diseases. Detection of absolute CNVs independent of genomic variants of an arbitrary reference sample has been a critical issue in CGH array experiments. Whole genome analysis using massively parallel sequencing with multiple ultra-high resolution CGH arrays provides an opportunity to catalog highly accurate genomic variants of the reference DNA (NA10851). Using information on variants, we developed a new method, the CGH array reference-free algorithm (CARA), which can determine reference-unbiased absolute CNVs from any CGH array platform. The algorithm enables the removal and rescue of false positive and false negative CNVs, respectively, which appear due to the effects of genomic variants of the reference sample in raw CGH array experiments. We found that the CARA remarkably enhanced the accuracy of CGH array in determining absolute CNVs. Our method thus provides a new approach to interpret CGH array data for personalized medicine.
比较基因组杂交 (CGH) 微阵列已被用于确定拷贝数变异 (CNVs) 及其对复杂疾病的影响。在 CGH 阵列实验中,独立于任意参考样本的基因组变异检测绝对 CNV 一直是一个关键问题。使用具有多个超高分辨率 CGH 阵列的大规模并行测序进行全基因组分析为目录高度准确的参考 DNA (NA10851) 基因组变异提供了机会。利用变异信息,我们开发了一种新方法,即 CGH 阵列无参考算法 (CARA),它可以从任何 CGH 阵列平台确定参考无偏的绝对 CNV。该算法能够分别去除和挽救由于参考样本的基因组变异在原始 CGH 阵列实验中的影响而出现的假阳性和假阴性 CNV。我们发现,CARA 显著提高了 CGH 阵列确定绝对 CNV 的准确性。因此,我们的方法为个性化医疗提供了一种解释 CGH 阵列数据的新方法。
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