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A two-step site and mRNA-level model for predicting microRNA targets

机译:两步法和mRNA水平模型可预测microRNA靶标

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Background Despite experiments showing that the number of microRNA (miRNA) target sites is critical for miRNA targeting, most existing methods focus on identifying individual miRNA target sites and do not model contributions of multiple target sites to miRNA regulation. To address this possible fault, we developed a miRNA target prediction model that recognizes the individual characteristics of functional binding sites and the global characteristics of miRNA-targeted mRNAs. Results Benchmark experiments showed that this two-step model generally had a higher overall performance than other established miRNA target prediction algorithms and that the model was especially suited to identify true miRNA targets among genes that all contain conserved target sites. Conclusions This improved performance could partly be explained by the model not relying on conservation when predicting targets. The critical factors for the model's performance, however, were mRNA-level features that characterized the number and strength of individual target sites within the mRNA. The model is available for online predictions or as pre-computed predictions on the human genome http://tare.medisin.ntnu.no/mirna_target webcite .
机译:背景技术尽管实验表明microRNA(miRNA)靶位点的数目对于miRNA靶向至关重要,但大多数现有方法都专注于鉴定单个miRNA靶位点,而不是对多个靶位点对miRNA调控的贡献进行建模。为了解决这个可能的错误,我们开发了一个miRNA目标预测模型,该模型可以识别功能性结合位点的各个特征以及以miRNA为目标的mRNA的整体特征。结果基准实验表明,该两步模型通常比其他已建立的miRNA靶标预测算法具有更高的总体性能,并且该模型特别适合在所有包含保守靶标位点的基因中鉴定真正的miRNA靶标。结论这种改进的性能部分可以由模型在预测目标时不依赖保护性来解释。然而,影响模型性能的关键因素是mRNA水平特征,这些特征表征了mRNA中单个靶位的数量和强度。该模型可用于在线预测或作为人类基因组http://tare.medisin.ntnu.no/mirna_target网站上的预先计算的预测。

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