Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, E3527, Baltimore, MD 21205, USA.
Genome Med. 2010 Dec 10;2(12):87. doi: 10.1186/gm208.
Considerable time and effort has been spent in developing analysis and quality assessment methods to allow the use of microarrays in a clinical setting. As is the case for microarrays and other high-throughput technologies, data from new high-throughput sequencing technologies are subject to technological and biological biases and systematic errors that can impact downstream analyses. Only when these issues can be readily identified and reliably adjusted for will clinical applications of these new technologies be feasible. Although much work remains to be done in this area, we describe consistently observed biases that should be taken into account when analyzing high-throughput sequencing data. In this article, we review current knowledge about these biases, discuss their impact on analysis results, and propose solutions.
在开发分析和质量评估方法以允许将微阵列用于临床环境方面已经投入了相当多的时间和精力。与微阵列和其他高通量技术一样,来自新的高通量测序技术的数据受到技术和生物偏见以及系统误差的影响,这些偏差和误差可能会影响下游分析。只有当这些问题能够轻易识别并可靠地调整时,这些新技术的临床应用才是可行的。尽管在这一领域还有很多工作要做,但我们描述了在分析高通量测序数据时应考虑的一致观察到的偏差。在本文中,我们回顾了当前关于这些偏差的知识,讨论了它们对分析结果的影响,并提出了解决方案。