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Suboptimal Local Alignments Across Multiple Scoring Schemes

机译:跨多个评分方案的次优局部比对

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摘要

Sequence alignment algorithms have a long standing tradition in bioinformatics. In this paper, we formulate an extension to existing local alignment algorithms: local alignments across multiple scoring functions. For this purpose, we use the Waterman-Eggert algorithm for suboptimal local alignments as template and introduce two new features therein: 1) an alignment of two strings over a set of score functions and 2) a switch cost function δ for penalizing jumps into a different scoring scheme within an alignment. Phylogenetic footprinting, as one potential application of this algorithm, was studied in greater detail. In this context, the right evolutionary distance and thus the scoring scheme is often not known a priori. We measured sensitivity and specificity on a test set of 21 human-rodent promoter pairs. Ultimately, we could attain a 4.5-fold enrichment of verified binding sites in our alignments.
机译:序列比对算法在生物信息学中具有悠久的传统。在本文中,我们制定了对现有局部比对算法的扩展:跨多个评分函数的局部比对。为此,我们使用用于次优局部比对的Waterman-Eggert算法作为模板,并在其中引入两个新功能:1)在一组计分函数上对两个字符串进行比对;以及2)转换成本函数δ,用于将跳变惩罚为a路线中的不同计分方案。系统发育足迹,作为该算法的一种潜在应用,已进行了更详细的研究。在这种情况下,正确的进化距离以及因此的评分方案通常不是先验的。我们在21对人鼠启动子对的测试集上测量了敏感性和特异性。最终,我们可以在比对中获得经过验证的结合位点的4.5倍富集。

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