Biomedical Quality Assurance Research Unit, Department of Public Health and Primary Care, University of Leuven, Leuven, Belgium.
Center for Human Genetics, University of Leuven, Leuven, Belgium.
J Mol Diagn. 2018 Nov;20(6):743-753. doi: 10.1016/j.jmoldx.2018.06.006. Epub 2018 Jul 26.
Because interpretation of next-generation sequencing (NGS) data remains challenging, optimization of the NGS process is needed to obtain correct sequencing results. Therefore, extensive validation and continuous monitoring of the quality is essential. NGS performance was compared with traditional detection methods and technical quality of nine NGS technologies was assessed. First, nine formalin-fixed, paraffin-embedded patient samples were analyzed by 114 laboratories by using different detection methods. No significant differences in performance were observed between analyses with NGS and traditional techniques. Second, two DNA control samples were analyzed for a selected number of variants by 26 participants with the use of nine different NGS technologies. Quality control metrics were analyzed from raw data files and a survey about routine procedures. Results showed large differences in coverages, but observed variant allele frequencies in raw data files were in line with predefined variant allele frequencies. Many false negative results were found because of low-quality regions, which were not reported as such. It is recommended to disclose the reportable range, the fraction of targeted genomic regions for which calls of acceptable quality can be generated, to avoid any errors in therapy decisions. NGS can be a reliable technique, only if essential quality control during analysis is applied and reported.
由于下一代测序(NGS)数据的解释仍然具有挑战性,因此需要优化 NGS 过程以获得正确的测序结果。因此,广泛的验证和持续的质量监测是必不可少的。比较了 NGS 的性能,并评估了九种 NGS 技术的技术质量。首先,有 114 个实验室使用不同的检测方法对 9 个福尔马林固定、石蜡包埋的患者样本进行了分析。使用 NGS 和传统技术进行分析时,未观察到性能有显著差异。其次,有 26 个参与者使用九种不同的 NGS 技术对两个 DNA 对照样本进行了选定变异的分析。从原始数据文件和常规程序调查中分析了质量控制指标。结果表明,覆盖范围存在很大差异,但原始数据文件中观察到的变异等位基因频率与预设的变异等位基因频率一致。由于低质量区域,发现了许多假阴性结果,但没有报告为低质量区域。建议公开可报告范围,即可以生成可接受质量的目标基因组区域的分数,以避免治疗决策中的任何错误。只有在分析过程中应用和报告必要的质量控制时,NGS 才能成为一种可靠的技术。