Center for Human Genome Variation, Duke University, Durham, NC 27708, USA.
Am J Hum Genet. 2012 Sep 7;91(3):408-21. doi: 10.1016/j.ajhg.2012.07.004. Epub 2012 Aug 30.
Although there are many methods available for inferring copy-number variants (CNVs) from next-generation sequence data, there remains a need for a system that is computationally efficient but that retains good sensitivity and specificity across all types of CNVs. Here, we introduce a new method, estimation by read depth with single-nucleotide variants (ERDS), and use various approaches to compare its performance to other methods. We found that for common CNVs and high-coverage genomes, ERDS performs as well as the best method currently available (Genome STRiP), whereas for rare CNVs and high-coverage genomes, ERDS performs better than any available method. Importantly, ERDS accommodates both unique and highly amplified regions of the genome and does so without requiring separate alignments for calling CNVs and other variants. These comparisons show that for genomes sequenced at high coverage, ERDS provides a computationally convenient method that calls CNVs as well as or better than any currently available method.
虽然有许多方法可从下一代测序数据中推断拷贝数变异（CNVs），但仍需要一种计算效率高且在所有类型 CNVs 中保持良好灵敏度和特异性的系统。在这里，我们介绍了一种新方法，即基于单核苷酸变异的读深度估计（ERDS），并使用各种方法将其性能与其他方法进行比较。我们发现，对于常见的 CNVs 和高覆盖度基因组，ERDS 的性能与当前可用的最佳方法（Genome STRiP）一样好，而对于罕见的 CNVs 和高覆盖度基因组，ERDS 的性能优于任何可用的方法。重要的是，ERDS 可以适应基因组中独特和高度扩增的区域，并且不需要为调用 CNVs 和其他变体分别进行对齐。这些比较表明，对于高覆盖度测序的基因组，ERDS 提供了一种计算方便的方法，其调用 CNVs 的性能与当前可用的任何方法一样好或更好。
