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用于构建大型Burrows-Wheeler变换的递归无前缀解析

Recursive Prefix-Free Parsing for Building Big BWTs.

作者信息

Oliva Marco, Gagie Travis, Boucher Christina

出版信息

bioRxiv. 2023 Jan 20:2023.01.18.524557. doi: 10.1101/2023.01.18.524557.

Abstract

Prefix-free parsing is useful for a wide variety of purposes including building the BWT, constructing the suffix array, and supporting compressed suffix tree operations. This linear-time algorithm uses a rolling hash to break an input string into substrings, where the resulting set of unique substrings has the property that none of the substrings' suffixes (of more than a certain length) is a proper prefix of any of the other substrings' suffixes. Hence, the name prefix-free parsing. This set of unique substrings is referred to as the . The is the ordered list of dictionary strings that defines the input string. Prior empirical results demonstrated the size of the parse is more burdensome than the size of the dictionary for large, repetitive inputs. Hence, the question arises as to how the size of the parse can scale satisfactorily with the input. Here, we describe our algorithm, , which accomplishes this by computing the prefix-free parse of the parse produced by prefix-free parsing an input string. Although conceptually simple, building the BWT from the parse-of-the-parse and the dictionaries is significantly more challenging. We solve and implement this problem. Our experimental results show that recursive prefix-free parsing is extremely effective in reducing the memory needed to build the run-length encoded BWT of the input. Our implementation is open source and available at https://github.com/marco-oliva/r-pfbwt .

摘要

无前缀解析对于多种目的都很有用,包括构建Burrows-Wheeler变换(BWT)、构造后缀数组以及支持压缩后缀树操作。这种线性时间算法使用滚动哈希将输入字符串分解为子串,其中得到的唯一子串集合具有这样的特性:没有任何子串(超过一定长度)的后缀是其他任何子串后缀的真前缀。因此,得名无前缀解析。这组唯一子串被称为 。 是定义输入字符串的字典字符串的有序列表。先前的实证结果表明,对于大型重复输入,解析的大小比字典的大小更繁重。因此,就产生了一个问题,即解析的大小如何能令人满意地随输入进行扩展。在这里,我们描述我们的算法 ,它通过计算对输入字符串进行无前缀解析所产生的解析的无前缀解析来实现这一点。虽然概念上很简单,但从解析的解析和字典构建BWT要困难得多。我们解决并实现了这个问题。我们的实验结果表明,递归无前缀解析在减少构建输入的游程编码BWT所需的内存方面极其有效。我们的实现是开源的,可在https://github.com/marco-oliva/r-pfbwt获取。

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