Inferring microRNA activities by combining gene expression with microRNA target prediction.
Inferring microRNA activities by combining gene expression with microRNA target prediction.
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通过将基因表达与microRNA靶向预测相结合来推断microRNA活性。
DOI：
10.1371/journal.pone.0001989
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发表时间：
2008-04-23
期刊：
影响因子：
3.7
通讯作者：
Li, Lei M.
中科院分区：
文献类型：
--
作者：
Cheng, Chao;Li, Lei M.
MicroRNA (miRNA) 通过在 mRNA 水平调节其靶基因的表达，在多种生物过程中发挥着至关重要的作用。已经提出了许多关于 miRNA 的计算方法，但大多数都集中在 miRNA 基因发现或目标预测上。很少有计算工作来研究 miRNA 的有效调控。我们提出了一种通过将微阵列表达数据与 miRNA 靶标预测相结合来推断 miRNA 有效调控活性的方法。该方法基于这样的想法：miRNA的调节活性变化可以通过微阵列测量的其靶转录本的表达变化来反映。为了验证该方法，我们将其应用于微阵列数据集，以测量转染或抑制几种特定 miRNA 后细胞系中基因表达的变化。结果表明，我们的方法可以高灵敏度和特异性地检测转染的 miRNA 的活性增强以及受抑制的 miRNA 的活性降低。此外，我们表明我们的推断对于目标预测的误报是稳健的。文献中提供了大量的基因表达数据集，但这些数据集背后的 miRNA 调控很大程度上是未知的。该方法易于实施，可用于研究微阵列实验获得的表达变化谱背后的miRNA有效调控。
MicroRNAs (miRNAs) play crucial roles in a variety of biological processes via regulating expression of their target genes at the mRNA level. A number of computational approaches regarding miRNAs have been proposed, but most of them focus on miRNA gene finding or target predictions. Little computational work has been done to investigate the effective regulation of miRNAs. We propose a method to infer the effective regulatory activities of miRNAs by integrating microarray expression data with miRNA target predictions. The method is based on the idea that regulatory activity changes of miRNAs could be reflected by the expression changes of their target transcripts measured by microarray. To validate this method, we apply it to the microarray data sets that measure gene expression changes in cell lines after transfection or inhibition of several specific miRNAs. The results indicate that our method can detect activity enhancement of the transfected miRNAs as well as activity reduction of the inhibited miRNAs with high sensitivity and specificity. Furthermore, we show that our inference is robust with respect to false positives of target prediction. A huge amount of gene expression data sets are available in the literature, but miRNA regulation underlying these data sets is largely unknown. The method is easy to be implemented and can be used to investigate the miRNA effective regulation underlying the expression change profiles obtained from microarray experiments.
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