Removal of AU Bias from Microarray mRNA Expression Data Enhances Computational Identification of Active MicroRNAs
Removal of AU Bias from Microarray mRNA Expression Data Enhances Computational Identification of Active MicroRNAs
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DOI：
10.1371/journal.pcbi.1000189
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发表时间：
2008-10-01
影响因子：
4.3
通讯作者：
Agami, Reuven
中科院分区：
文献类型：
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作者：
Elkon, Ran;Agami, Reuven
阐明 microRNA (miR) 在各种生物网络中发挥的调节作用是当前分子和计算生物学面临的最大挑战之一。基因表达数据和 3'-UTR 序列的综合分析有望成为系统描述不同生物过程中活性 miR 的有效手段。应用这样的综合分析，我们在众多微阵列数据集中发现了 3'-UTR AU 含量与基因反应之间的惊人关系。我们表明，这种关系是连接基因反应和探针 AU 内容的一般偏差的次要因素，并反映了当前大多数阵列探针选自目标转录本 3'-UTR 的事实。因此，在整合表达数据和 3'-UTR 序列以识别嵌入该区域的调控元件时，消除这种偏差对于微阵列数据集的任何分析都是至关重要的。我们开发了可视化和标准化方案来检测和消除此类 AU 偏差，并证明它们在微阵列数据中的应用显着增强了活性 miR 的计算识别。我们的结果证实，在消除 AU 偏差后，mRNA 表达谱包含充足的信息，允许在计算机上检测在生理条件下活跃的 miR。
Elucidation of regulatory roles played by microRNAs (miRs) in various biological networks is one of the greatest challenges of present molecular and computational biology. The integrated analysis of gene expression data and 3'-UTR sequences holds great promise for being an effective means to systematically delineate active miRs in different biological processes. Applying such an integrated analysis, we uncovered a striking relationship between 3'-UTR AU content and gene response in numerous microarray datasets. We show that this relationship is secondary to a general bias that links gene response and probe AU content and reflects the fact that in the majority of current arrays probes are selected from target transcript 3'-UTRs. Therefore, removal of this bias, which is in order in any analysis of microarray datasets, is of crucial importance when integrating expression data and 3'-UTR sequences to identify regulatory elements embedded in this region. We developed visualization and normalization schemes for the detection and removal of such AU biases and demonstrate that their application to microarray data significantly enhances the computational identification of active miRs. Our results substantiate that, after removal of AU biases, mRNA expression profiles contain ample information which allows in silico detection of miRs that are active in physiological conditions.