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首页> 外文期刊>Chinese science bulletin >A new method for splice site prediction based on the sequence patterns of splicing signals and regulatory elements
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A new method for splice site prediction based on the sequence patterns of splicing signals and regulatory elements

机译：基于剪接信号和调控元件序列模式的剪接位点预测新方法

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It is of significance for splice site prediction to develop novel algorithms that combine the sequence patterns of regulatory elements such as enhancers and silencers with the patterns of splicing signals. In this paper, a statistical model of splicing signals was built based on the entropy density profile (EDP) method, weight array method (WAM) and kappa test; moreover, the model of splicing regulatory elements was developed by an unsupervised self-learning method to detect motifs associated with regulatory elements. With two models incorporated, a multi-level support vector machine (SVM) system was devised to perform ab initio prediction for splice sites originating from DNA sequence in eukaryotic genome. Results of large scale tests on human genomic splice sites show that the new method achieves a comparative high performance in splice site prediction. The method is demonstrated to be with at least the same level of performance and usually better performance than the existing SpliceScan method based on modeling regulatory elements, and shown to have higher accuracies than the traditional methods with modeling splicing signals such as the GeneSplicer. In particular, the method has evident advantage over splice site prediction for the genes with lower GC content.
机译：开发新的算法，将调控元件（例如增强子和沉默子）的序列模式与剪接信号的模式相结合，对于剪接位点预测非常重要。本文基于熵密度分布图（EDP），权重数组法（WAM）和kappa检验建立了拼接信号的统计模型。此外，通过无监督的自学习方法开发了剪接调控元件的模型，以检测与调控元件相关的基序。通过整合两个模型，设计了一种多级支持向量机（SVM）系统，可以对真核生物基因组中DNA序列的剪接位点进行从头算。在人类基因组剪接位点上的大规模测试结果表明，该新方法在剪接位点预测中实现了相对较高的性能。与基于建模调控元件建模的现有SpliceScan方法相比，该方法具有至少相同的性能水平，并且通常具有更好的性能，并且显示出比传统的对拼接信号进行建模的传统方法（如GeneSplicer）具有更高的准确性。特别地，对于具有较低GC含量的基因，该方法相对于剪接位点预测具有明显的优势。

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