论文标题
多个序列比对的小耦合扩展
Small Coupling Expansion for Multiple Sequence Alignment
论文作者
论文摘要
生物学序列（例如DNA，RNA和蛋白）的比对是允许检测不同生物体中同源序列之间的进化模式以及功能/结构特性的基本工具之一。通常，最先进的生物信息学工具基于剖面模型，该模型假定序列不同位点的统计独立性。在过去的几年中，越来越清楚的是，同源序列由于自然进化过程的结果表现出比初级序列相关的复杂模式，该过程在保留序列的功能/结构确定性的约束下选择了遗传变异。在这里，我们基于消息传递技术来介绍一种新的对齐算法，该算法克服了配置文件模型的局限性。我们的方法基于模型的自由能的新扰动小耦合扩展，该扩展将线性链近似为$ 0^\ mathrm {th} $ - 扩展的顺序。我们测试了算法对几个生物学序列的标准竞争策略的潜力。
The alignment of biological sequences such as DNA, RNA, and proteins, is one of the basic tools that allow to detect evolutionary patterns, as well as functional/structural characterizations between homologous sequences in different organisms. Typically, state-of-the-art bioinformatics tools are based on profile models that assume the statistical independence of the different sites of the sequences. Over the last years, it has become increasingly clear that homologous sequences show complex patterns of long-range correlations over the primary sequence as a consequence of the natural evolution process that selects genetic variants under the constraint of preserving the functional/structural determinants of the sequence. Here, we present a new alignment algorithm based on message passing techniques that overcomes the limitations of profile models. Our method is based on a new perturbative small-coupling expansion of the free energy of the model that assumes a linear chain approximation as the $0^\mathrm{th}$-order of the expansion. We test the potentiality of the algorithm against standard competing strategies on several biological sequences.
