Laboratory of Pharmaceutical Biotechnology, Ghent University, Ottergemsesteenweg 460, 9000, Ghent, Belgium.
Sci Rep. 2017 Jun 13;7(1):3422. doi: 10.1038/s41598-017-03711-y.
Whole genome amplification (WGA) has become an invaluable tool to perform copy number variation (CNV) detection in single, or a limited number of cells. Unfortunately, current WGA methods introduce representation bias that limits the detection of small CNVs. New WGA methods have been introduced that might have the potential to reduce this bias. We compared the performance of PicoPLEX DNA-Seq (Picoseq), DOPlify, REPLI-g and Ampli-1 WGA for aneuploidy screening and copy number analysis using shallow whole genome massively parallel sequencing (MPS), starting from single or a limited number of cells. Although the four WGA methods perform differently, they are all suited for this application.
全基因组扩增 (WGA) 已成为在单个或有限数量的细胞中进行拷贝数变异 (CNV) 检测的一项不可或缺的工具。不幸的是，目前的 WGA 方法会引入代表性偏差，从而限制了对小 CNV 的检测。已经引入了新的 WGA 方法，这些方法有可能减少这种偏差。我们使用浅层全基因组大规模平行测序 (MPS) ，比较了 PicoPLEX DNA-Seq (Picoseq)、DOPlify、REPLI-g 和 Ampli-1 WGA 在进行非整倍体筛选和拷贝数分析时，从单个或有限数量的细胞开始的性能。尽管这四种 WGA 方法的性能不同，但它们都适用于这种应用。
