Testing for differentially-expressed microRNAs with errors-in-variables nonparametric regression.
Testing for differentially-expressed microRNAs with errors-in-variables nonparametric regression.
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DOI：
10.1371/journal.pone.0037537
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Xi Y
中科院分区：
文献类型：
--
作者：
Wang B;Zhang SG;Wang XF;Tan M;Xi Y
MicroRNA是一组在转录后/翻译水平上调节基因表达的小RNA分子。大多数成熟的高通量发现平台，如微阵列、实时定量聚合酶链式反应和测序，已被用于研究各种人类疾病的microRNA。人类体内的microRNA总数约为1800个，这对一些需要大量条目的分析方法提出了挑战。与信使RNA不同，大多数microRNA(60%)在细胞中保持相对较低的丰度。当使用基因芯片分析时，这些低表达的microRNAs的信号会受到包括背景噪声在内的其他非特异性信号的影响。在microRNA阵列数据分析中，区分真实的microRNA信号和测量误差是至关重要的。在这项研究中，我们提出了一种新的基于测量误差模型的归一化方法和差异表达的microRNA检测方法，用于从锁定核酸(LNA)microRNA阵列获取的microRNA图谱数据。与现有的一些方法相比，该方法显著提高了实时定量聚合酶链式反应检测低表达microRNAs的效率。
MicroRNA is a set of small RNA molecules mediating gene expression at post-transcriptional/translational levels. Most of well-established high throughput discovery platforms, such as microarray, real time quantitative PCR, and sequencing, have been adapted to study microRNA in various human diseases. The total number of microRNAs in humans is approximately 1,800, which challenges some analytical methodologies requiring a large number of entries. Unlike messenger RNA, the majority of microRNA (60%) maintains relatively low abundance in the cells. When analyzed using microarray, the signals of these low-expressed microRNAs are influenced by other non-specific signals including the background noise. It is crucial to distinguish the true microRNA signals from measurement errors in microRNA array data analysis. In this study, we propose a novel measurement error model-based normalization method and differentially-expressed microRNA detection method for microRNA profiling data acquired from locked nucleic acids (LNA) microRNA array. Compared with some existing methods, the proposed method significantly improves the detection among low-expressed microRNAs when assessed by quantitative real-time PCR assay.
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