归一化多序列比对算法
Algorithms for normalized multiple sequence alignments
摘要：
序列比对支持生物信息学、自然语言处理、模式识别、社会科学和其他领域中的许多任务。虽然两个序列的比对在许多应用中可以快速进行，但多个序列的同时比对被证明自然更加复杂。虽然大多数多序列比对（MSA）公式是NP难的，但已经开发了几种方法，因为它们可以优于成对比对方法或对于某些应用是必要的。不仅考虑相似性，而且考虑比较序列的长度（即归一化）可以提供比未归一化或后归一化方法更好的比对结果。虽然已经开发了一些标准化方法用于成对序列比对，但没有一种方法被提出用于MSA。这项工作是第一次努力，对MSA的规范化方法的发展。我们讨论了归一化多序列比对（NMSA）的多个方面。我们定义了三个新的标准，用于计算归一化分数时，对齐多个序列，显示NP-硬度和精确的算法，用于解决NMSA使用这些标准。此外，我们提供了近似算法的MSA和NMSA的一些类别的评分矩阵。
英文摘要：
Sequence alignment supports numerous tasks in bioinformatics, natural language processing, pattern recognition, social sciences, and others fields. While the alignment of two sequences may be performed swiftly in many applications, the simultaneous alignment of multiple sequences proved to be naturally more intricate. Although most multiple sequence alignment (MSA) formulations are NP-hard, several approaches have been developed, as they can outperform pairwise alignment methods or are necessary for some applications. Taking into account not only similarities but also the lengths of the compared sequences (i.e. normalization) can provide better alignment results than both unnormalized or post-normalized approaches. While some normalized methods have been developed for pairwise sequence alignment, none have been proposed for MSA. This work is a first effort towards the development of normalized methods for MSA. We discuss multiple aspects of normalized multiple sequence alignment (NMSA). We define three new criteria for computing normalized scores when aligning multiple sequences, showing the NP-hardness and exact algorithms for solving the NMSA using those criteria. In addition, we provide approximation algorithms for MSA and NMSA for some classes of scoring matrices.
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