Predicting siRNA efficacy based on multiple selective siRNA representations and their combination at score level.
Predicting siRNA efficacy based on multiple selective siRNA representations and their combination at score level.
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基于多个选择性 siRNA 表示及其在评分水平上的组合来预测 siRNA 功效
DOI：
10.1038/srep44836
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发表时间：
2017-03-20
影响因子：
4.6
通讯作者：
Li Y
中科院分区：
文献类型：
--
作者：
He F;Han Y;Gong J;Song J;Wang H;Li Y
小干扰RNA（siRNA）可以诱导靶基因的敲除，而基因沉默的效果依赖于siRNA的功效。因此，构建一种有效的siRNA预测方法是本文的任务。在我们的工作中，我们试图从定量和定性两个方面来描述siRNA。在定量分析方面，我们将siRNA-mRNA相互作用的热力学特征、核苷酸频率特征、热力学稳定性特征和mRNA相关特征作为一种新的混合表征，首次将siRNA-mRNA相互作用的热力学特征引入到siRNA效能预测中。然后采用基于F值的特征选择方法，考察各特征的贡献，去除弱相关特征。同时，我们编码的siRNA序列和现有的经验设计规则作为一个定性的siRNA表示。将这两种siRNA表示组合以在评分水平上通过支持向量回归（SVR）预测siRNA功效。实验结果表明，该方法可以选择具有较强区分能力的特征，使两种siRNA表达都能充分发挥作用。预测结果还表明，我们的方法可以优于其他流行的siRNA功效预测算法。
Small interfering RNAs (siRNAs) may induce to targeted gene knockdown, and the gene silencing effectiveness relies on the efficacy of the siRNA. Therefore, the task of this paper is to construct an effective siRNA prediction method. In our work, we try to describe siRNA from both quantitative and qualitative aspects. For quantitative analyses, we form four groups of effective features, including nucleotide frequencies, thermodynamic stability profile, thermodynamic of siRNA-mRNA interaction, and mRNA related features, as a new mixed representation, in which thermodynamic of siRNA-mRNA interaction is introduced to siRNA efficacy prediction for the first time to our best knowledge. And then anF-score based feature selection is employed to investigate the contribution of each feature and remove the weak relevant features. Meanwhile, we encode the siRNA sequence and existed empirical design rules as a qualitative siRNA representation. These two kinds of siRNA representations are combined to predict siRNA efficacy by supported Vector Regression (SVR) at score level. The experimental results indicate that our method may select the features with powerful discriminative ability and make the two kinds of siRNA representations work at full capacity. The prediction results also demonstrate that our method can outperform other popular siRNA efficacy prediction algorithms.