Global Security, Lawrence Livermore National Laboratory, Livermore, CA 94551 USA.
Brief Funct Genomics. 2011 Nov;10(6):342-53. doi: 10.1093/bfgp/elr027. Epub 2011 Sep 19.
DNA microarrays have emerged as a viable platform for detection of pathogenic organisms in clinical and environmental samples. These microbial detection arrays occupy a middle ground between low cost, narrowly focused assays such as multiplex PCR and more expensive, broad-spectrum technologies like high-throughput sequencing. While pathogen detection arrays have been used primarily in a research context, several groups are aggressively working to develop arrays for clinical diagnostics, food safety testing, environmental monitoring and biodefense. Statistical algorithms that can analyze data from microbial detection arrays and provide easily interpretable results are absolutely required in order for these efforts to succeed. In this article, we will review the most promising array designs and analysis algorithms that have been developed to date, comparing their strengths and weaknesses for pathogen detection and discovery.
DNA 微阵列已成为一种可行的平台，可用于检测临床和环境样本中的病原体。这些微生物检测阵列在低成本、针对性强的检测方法（如多重 PCR）和更昂贵、广谱的技术（如高通量测序）之间占据了一席之地。虽然病原体检测阵列主要用于研究领域，但有几个研究小组正在积极开发用于临床诊断、食品安全检测、环境监测和生物防御的阵列。为了使这些努力取得成功，绝对需要能够分析微生物检测阵列数据并提供易于解释结果的统计算法。在本文中，我们将回顾迄今为止开发的最有前途的阵列设计和分析算法，并比较它们在病原体检测和发现方面的优缺点。
