Challenges for MicroRNA Microarray Data Analysis.
Challenges for MicroRNA Microarray Data Analysis.
复制标题
DOI：
10.3390/microarrays2020034
复制
发表时间：
2013-06
期刊：
影响因子：
--
通讯作者：
Xi Y
中科院分区：
文献类型：
--
作者：
Wang B;Xi Y
微阵列是一种高通量的发现工具，已被广泛用于基因组研究。探针-靶杂交是该技术的核心概念，通过基于荧光的检测来确定核酸序列的相对丰度。在微阵列实验中，表达测量的变化可以归因于影响微阵列平台的稳定性和再现性的许多不同来源。标准化是减少非生物学错误并将来自多个阵列（通道）的原始图像数据转换为高质量数据以供进一步分析的重要步骤。通常，对于传统的微阵列分析，大多数建立的标准化方法基于两个假设：（1）靶基因的总数足够大（> 10，000）;以及（2）大多数基因的表达水平保持恒定。然而，microRNA（miRNA）阵列通常以低密度点样，这是由于miRNA的总数小于2，000个并且大多数miRNA弱表达或不表达的事实。因此，基于上述两个假设的标准化方法不适用于miRNA谱研究。在这篇综述中，我们讨论了市场上几个代表性的微阵列平台的miRNA谱，并比较了传统的方法与一些新的策略，具体的miRNA微阵列。
Microarray is a high throughput discovery tool that has been broadly used for genomic research. Probe-target hybridization is the central concept of this technology to determine the relative abundance of nucleic acid sequences through fluorescence-based detection. In microarray experiments, variations of expression measurements can be attributed to many different sources that influence the stability and reproducibility of microarray platforms. Normalization is an essential step to reduce non-biological errors and to convert raw image data from multiple arrays (channels) to quality data for further analysis. In general, for the traditional microarray analysis, most established normalization methods are based on two assumptions: (1) the total number of target genes is large enough (>10,000); and (2) the expression level of the majority of genes is kept constant. However, microRNA (miRNA) arrays are usually spotted in low density, due to the fact that the total number of miRNAs is less than 2,000 and the majority of miRNAs are weakly or not expressed. As a result, normalization methods based on the above two assumptions are not applicable to miRNA profiling studies. In this review, we discuss a few representative microarray platforms on the market for miRNA profiling and compare the traditional methods with a few novel strategies specific for miRNA microarrays.