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Predicting effective microRNA target sites in mammalian mRNAs

机译:预测哺乳动物mRNa中的有效microRNa靶位点

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摘要

MicroRNA targets are often recognized through pairing between the miRNA seed region and complementary sites within target mRNAs, but not all of these canonical sites are equally effective, and both computational and in vivo UV-crosslinking approaches suggest that many mRNAs are targeted through non-canonical interactions. Here, we show that recently reported non-canonical sites do not mediate repression despite binding the miRNA, which indicates that the vast majority of functional sites are canonical. Accordingly, we developed an improved quantitative model of canonical targeting, using a compendium of experimental datasets that we pre-processed to minimize confounding biases. This model, which considers site type and another 14 features to predict the most effectively targeted mRNAs, performed significantly better than existing models and was as informative as the best high-throughput in vivo crosslinking approaches. It drives the latest version of TargetScan (v7.0; targetscan.org), thereby providing a valuable resource for placing miRNAs into gene-regulatory networks.
机译:MicroRNA靶标通常是通过miRNA种子区域与靶标mRNA中互补位点之间的配对来识别的,但并非所有这些规范位点都具有同等效力,并且计算和体内UV交联方法都表明,许多mRNA都是通过非规范性靶标靶向的互动。在这里,我们显示,尽管结合了miRNA,最近报道的非规范位点也不会介导阻抑作用,这表明绝大多数功能位点是规范的。因此,我们使用经过预处理的实验数据集来开发一种改进的规范化靶向目标定量模型,以最大程度地减少混淆偏差。该模型考虑了位点类型和其他14个特征来预测最有效靶向的mRNA,其性能明显优于现有模型,并且与最佳的高通量体内交联方法一样具有参考价值。它驱动了最新版本的TargetScan(v7.0; targetscan.org),从而为将miRNA置于基因调控网络中提供了宝贵的资源。

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