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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >SeeSite: Characterizing Relationships between Splice Junctions and Splicing Enhancers
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SeeSite: Characterizing Relationships between Splice Junctions and Splicing Enhancers

机译：SeeSite：表征熔接点和熔接增强剂之间的关系

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摘要

RNA splicing is a cellular process driven by the interaction between numerous regulatory sequences and binding sites, however, such interactions have been primarily explored by laboratory methods since computational tools largely ignore the relationship between different splicing elements. Current computational methods identify either splice sites or other regulatory sequences, such as enhancers and silencers. We present a novel approach for characterizing co-occurring relationships between splice site motifs and splicing enhancers. Our approach relies on an efficient algorithm for approximately solving Consensus Sequence with Outliers , an NP-complete string clustering problem. In particular, we give an algorithm for this problem that outputs near-optimal solutions in polynomial time. To our knowledge, this is the first formulation and computational attempt for detecting co-occurring sequence elements in RNA sequence data. Further, we demonstrate that SeeSite is capable of showing that certain ESEs are preferentially associated with weaker splice sites, and that there exists a co-occurrence relationship with splice site motifs.
机译：RNA剪接是由许多调控序列和结合位点之间的相互作用驱动的细胞过程，然而，由于计算工具很大程度上忽略了不同剪接元件之间的关系，因此这种相互作用主要是通过实验室方法进行的。当前的计算方法识别剪接位点或其他调控序列，例如增强子和沉默子。我们提出了一种新颖的方法来表征剪接位点图案和剪接增强子之间的共现关系。我们的方法依靠一种有效的算法来近似解决带有离群值的共识序列（NP完整字符串聚类问题）。特别是，我们针对此问题给出了一种在多项式时间内输出接近最优解的算法。就我们所知，这是检测RNA序列数据中同时出现的序列元素的第一个公式化和计算尝试。此外，我们证明SeeSite能够显示某些ESE优先与较弱的剪接位点相关，并且与剪接位点基序存在共存关系。

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