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Motif-Aware PRALINE: Improving the alignment of motif regions

机译:Motif感知的PRALINE:改善图案区域的对齐方式

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摘要

Protein or DNA motifs are sequence regions which possess biological importance. These regions are often highly conserved among homologous sequences. The generation of multiple sequence alignments (MSAs) with a correct alignment of the conserved sequence motifs is still difficult to achieve, due to the fact that the contribution of these typically short fragments is overshadowed by the rest of the sequence. Here we extended the PRALINE multiple sequence alignment program with a novel motif-aware MSA algorithm in order to address this shortcoming. This method can incorporate explicit information about the presence of externally provided sequence motifs, which is then used in the dynamic programming step by boosting the amino acid substitution matrix towards the motif. The strength of the boost is controlled by a parameter, α. Using a benchmark set of alignments we confirm that a good compromise can be found that improves the matching of motif regions while not significantly reducing the overall alignment quality. By estimating α on an unrelated set of reference alignments we find there is indeed a strong conservation signal for motifs. A number of typical but difficult MSA use cases are explored to exemplify the problems in correctly aligning functional sequence motifs and how the motif-aware alignment method can be employed to alleviate these problems.
机译:蛋白质或DNA基序是具有生物学重要性的序列区域。这些区域在同源序列之间通常是高度保守的。由于这些典型的短片段的贡献被其余序列所掩盖的事实,仍然难以实现具有保守序列基序的正确比对的多重序列比对(MSA)的产生。在这里,我们扩展了PRALINE多序列比对程序,采用了一种新颖的可识别主题的MSA算法,以解决这一缺点。该方法可以并入有关外部提供的序列基序存在的明确信息,然后通过将氨基酸取代矩阵推向基序,将其用于动态编程步骤。增强的强度由参数α控制。使用基准的比对组,我们确认可以找到一个很好的折衷方案,可以改善基序区域的匹配度,而不会显着降低总体比对质量。通过估计一组不相关的参考对齐方式上的α,我们发现确实存在强烈的基序保守信号。探索了许多典型但困难的MSA用例,以举例说明正确比对功能序列基序的问题以及如何使用基序感知比对方法来缓解这些问题。

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