A theoretical and experimental analysis of BWT variants for string collections

David Cenzato, Zsuzsanna Lipt'ak
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引用次数: 1

Abstract

The extended Burrows-Wheeler-Transform (eBWT), introduced by Mantaci et al. [Theor. Comput. Sci., 2007], is a generalization of the Burrows-Wheeler-Transform (BWT) to multisets of strings. While the original BWT is based on the lexicographic order, the eBWT uses the omega-order, which differs from the lexicographic order in important ways. A number of tools are available that compute the BWT of string collections; however, the data structures they generate in most cases differ from the one originally defined, as well as from each other. In this paper, we review the differences between these BWT variants, both from a theoretical and from a practical point of view, comparing them on several real-life datasets with different characteristics. We find that the differences can be extensive, depending on the dataset characteristics, and are largest on collections of many highly similar short sequences. The widely-used parameter $r$, the number of runs of the BWT, also shows notable variation between the different BWT variants; on our datasets, it varied by a multiplicative factor of up to $4.2$.
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字符串集合中BWT变量的理论和实验分析
扩展Burrows-Wheeler-Transform (eBWT),由Mantaci等人提出。第一版。科学。[j], 2007],是burrows - wheeler变换(BWT)对多组字符串的推广。最初的BWT是基于字典顺序的,而eBWT使用omega- 3顺序,它在许多重要方面与字典顺序不同。有许多工具可用于计算字符串集合的BWT;但是,在大多数情况下,它们生成的数据结构与最初定义的数据结构不同,而且彼此之间也不同。在本文中,我们从理论和实践的角度回顾了这些BWT变体之间的差异,并在具有不同特征的几个现实数据集上进行了比较。我们发现差异可以是广泛的,这取决于数据集的特征,并且在许多高度相似的短序列的集合中最大。广泛使用的参数r,即BWT的运行次数,也显示出不同BWT变体之间的显著差异;在我们的数据集上,它以高达4.2美元的倍数因子变化。
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